1
|
Zhang S, Wang X, Xu J, Chen Q, Peng M, Hao J. Green manufacturing for achieving carbon neutrality goal requires innovative technologies: A bibliometric analysis from 1991 to 2022. J Environ Sci (China) 2024; 140:255-269. [PMID: 38331506 DOI: 10.1016/j.jes.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 08/09/2023] [Accepted: 08/14/2023] [Indexed: 02/10/2024]
Abstract
Recent years have seen a significant increase in interest in green manufacturing as a key driver of global carbon-neutral efforts and sustainable development. To find the research hotspots of green manufacturing and reveal future research trends, this study reviewed and analyzed research articles from the Web of Science database on green manufacturing from 1991 to 2022 using a bibliometric method. The findings indicate a significant rise in the number of articles related to green manufacturing since the 2010s. Moreover, there has been an increase in the involvement of scholars from developing countries such as China and India in this field. Based on the literature review and bibliometric cluster analysis on green manufacturing, we believed that future research may continue following the lines of intelligent technology integration, adoption of frontier engineering techniques, and industry development in line with carbon reduction targets. A framework for future green manufacturing development is proposed, with a focus on Chinese policies. The framework could provide policy implications for developing countries looking to pursue opportunities for development in green manufacturing.
Collapse
Affiliation(s)
- Sheng Zhang
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Xintian Wang
- School of Environment & Natural Resources, Renmin University of China, Beijing 100872, China.
| | - Jiayu Xu
- School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Qinqin Chen
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Meng Peng
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Jiming Hao
- School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| |
Collapse
|
2
|
Chen S, Xiao Y, Zhang C, Lu X, He K, Hao J. Cost dynamics of onshore wind energy in the context of China's carbon neutrality target. Environ Sci Ecotechnol 2024; 19:100323. [PMID: 38021369 PMCID: PMC10654034 DOI: 10.1016/j.ese.2023.100323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 09/24/2023] [Accepted: 09/24/2023] [Indexed: 12/01/2023]
Abstract
Wind energy has become one of the most important measures for China to achieve its carbon neutrality goal. The spatial and temporal evolvement of economic competitiveness for wind energy becomes an important concern in shaping the decarbonization pathway in China. There has been an urgent need in power system planning to model the future dynamics of cost decline and supply potential for wind power in the context of carbon neutrality until 2060. Existing studies often fail to capture the rapid decline in the cost of wind power generation in recent years, and the prediction of wind power cost decline is more conservative than the reality. This study constructs an integrated model to evaluate the cost-competitiveness and grid parity potential of China's onshore wind electricity at fine spatial resolution with updated parameters. Results indicate that the total onshore wind potential amounts to 54.0 PWh. The average levelized cost of wind power is expected to decline from CNY 0.39 kWh-1 in 2020 to CNY 0.30 and CNY 0.21 kWh-1 in 2030 and 2060. 28.3%, 67.6%, and 97.6% of the technical potentials hold power costs lower than coal power in 2020, 2030, and 2060.
Collapse
Affiliation(s)
- Shi Chen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
| | - Youxuan Xiao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
| | - Chongyu Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
| | - Xi Lu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
- Beijing Laboratory of Environmental Frontier Technologies, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Kebin He
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
| |
Collapse
|
3
|
Yin D, Zhao B, Wang S, Donahue NM, Feng B, Chang X, Chen Q, Cheng X, Liu T, Chan CK, Schervish M, Li Z, He Y, Hao J. Fostering a Holistic Understanding of the Full Volatility Spectrum of Organic Compounds from Benzene Series Precursors through Mechanistic Modeling. Environ Sci Technol 2024. [PMID: 38691504 DOI: 10.1021/acs.est.3c07128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
A comprehensive understanding of the full volatility spectrum of organic oxidation products from the benzene series precursors is important to quantify the air quality and climate effects of secondary organic aerosol (SOA) and new particle formation (NPF). However, current models fail to capture the full volatility spectrum due to the absence of important reaction pathways. Here, we develop a novel unified model framework, the integrated two-dimensional volatility basis set (I2D-VBS), to simulate the full volatility spectrum of products from benzene series precursors by simultaneously representing first-generational oxidation, multigenerational aging, autoxidation, dimerization, nitrate formation, etc. The model successfully reproduces the volatility and O/C distributions of oxygenated organic molecules (OOMs) as well as the concentrations and the O/C of SOA over wide-ranging experimental conditions. In typical urban environments, autoxidation and multigenerational oxidation are the two main pathways for the formation of OOMs and SOA with similar contributions, but autoxidation contributes more to low-volatility products. NOx can reduce about two-thirds of OOMs and SOA, and most of the extremely low-volatility products compared to clean conditions, by suppressing dimerization and autoxidation. The I2D-VBS facilitates a holistic understanding of full volatility product formation, which helps fill the large gap in the predictions of organic NPF, particle growth, and SOA formation.
Collapse
Affiliation(s)
- Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Neil M Donahue
- Center for Atmospheric Particle Studies, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Boyang Feng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xing Chang
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
| | - Qi Chen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, BIC-ESAT and IJRC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Xi Cheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, BIC-ESAT and IJRC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Tengyu Liu
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Chak K Chan
- Division of Physical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Meredith Schervish
- Center for Atmospheric Particle Studies, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
- Department of Chemistry, University of California, Irvine, California 92697, United States
| | - Zeqi Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yicong He
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
4
|
Yu Q, Ge X, Zheng H, Xing J, Duan L, Lv D, Ding D, Dong Z, Sun Y, Maximilian P, Xie D, Zhao Y, Zhao B, Wang S, Mulder J, Larssen T, Hao J. A probe into the acid deposition mitigation path in China over the last four decades and beyond. Natl Sci Rev 2024; 11:nwae007. [PMID: 38495813 PMCID: PMC10941815 DOI: 10.1093/nsr/nwae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 12/25/2023] [Accepted: 01/03/2024] [Indexed: 03/19/2024] Open
Abstract
China currently has the highest acid deposition globally, yet research on its status, impacts, causes and controls is lacking. Here, we compiled data and calculated critical loads regarding acid deposition. The results showed that the abatement measures in China have achieved a sharp decline in the emissions of acidifying pollutants and a continuous recovery of precipitation pH, despite the drastic growth in the economy and energy consumption. However, the risk of ecological acidification and eutrophication showed no significant decrease. With similar emission reductions, the decline in areas at risk of acidification in China (7.0%) lags behind those in Europe (20%) or the USA (15%). This was because, unlike Europe and the USA, China's abatement strategies primarily target air quality improvement rather than mitigating ecological impacts. Given that the area with the risk of eutrophication induced by nitrogen deposition remained at 13% of the country even under the scenario of achieving the dual targets of air quality and carbon dioxide mitigation in 2035, we explored an enhanced ammonia abatement pathway. With a further 27% reduction in ammonia by 2035, China could largely eliminate the impacts of acid deposition. This research serves as a valuable reference for China's future acid deposition control and for other nations facing similar challenges.
Collapse
Affiliation(s)
- Qian Yu
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Pollution Control & Resource Reuse and School of the Environment, Nanjing University, Nanjing 210023, China
| | - Xiaodong Ge
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Haotian Zheng
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jia Xing
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Lei Duan
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Dongwei Lv
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Dian Ding
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Zhaoxin Dong
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yisheng Sun
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Posch Maximilian
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Danni Xie
- International Institute for Applied System Analysis (IIASA), Laxenburg A-2361, Austria
| | - Yu Zhao
- School of Land Engineering, Chang'an University, Xi'an 710064, China
| | - Bin Zhao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Pollution Control & Resource Reuse and School of the Environment, Nanjing University, Nanjing 210023, China
| | - Shuxiao Wang
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Pollution Control & Resource Reuse and School of the Environment, Nanjing University, Nanjing 210023, China
| | - Jan Mulder
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås 5003, Norway
| | | | - Jiming Hao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Pollution Control & Resource Reuse and School of the Environment, Nanjing University, Nanjing 210023, China
| |
Collapse
|
5
|
Li Y, Li X, Cai R, Yan C, Zheng G, Li Y, Chen Y, Zhang Y, Guo Y, Hua C, Kerminen VM, Liu Y, Kulmala M, Hao J, Smith JN, Jiang J. The Significant Role of New Particle Composition and Morphology on the HNO 3-Driven Growth of Particles down to Sub-10 nm. Environ Sci Technol 2024; 58:5442-5452. [PMID: 38478878 DOI: 10.1021/acs.est.3c09454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
New particle formation and growth greatly influence air quality and the global climate. Recent CERN Cosmics Leaving OUtdoor Droplets (CLOUD) chamber experiments proposed that in cold urban atmospheres with highly supersaturated HNO3 and NH3, newly formed sub-10 nm nanoparticles can grow rapidly (up to 1000 nm h-1). Here, we present direct observational evidence that in winter Beijing with persistent highly supersaturated HNO3 and NH3, nitrate contributed less than ∼14% of the 8-40 nm nanoparticle composition, and overall growth rates were only ∼0.8-5 nm h-1. To explain the observed growth rates and particulate nitrate fraction, the effective mass accommodation coefficient of HNO3 (αHNO3) on the nanoparticles in urban Beijing needs to be 2-4 orders of magnitude lower than those in the CLOUD chamber. We propose that the inefficient uptake of HNO3 on nanoparticles is mainly due to the much higher particulate organic fraction and lower relative humidity in urban Beijing. To quantitatively reproduce the observed growth, we show that an inhomogeneous "inorganic core-organic shell" nanoparticle morphology might exist for nanoparticles in Beijing. This study emphasized that growth for nanoparticles down to sub-10 nm was largely influenced by their composition, which was previously ignored and should be considered in future studies on nanoparticle growth.
Collapse
Affiliation(s)
- Yuyang Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Xiaoxiao Li
- School of Resources and Environmental Sciences, Wuhan University, 430072 Wuhan, China
| | - Runlong Cai
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Chao Yan
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023 Nanjing, China
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Guangjie Zheng
- Minerva Research Group, Max Planck Institute for Chemistry, Mainz 55128, Germany
| | - Yiran Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Yijing Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Yusheng Zhang
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Yishuo Guo
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Chenjie Hua
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Veli-Matti Kerminen
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Yongchun Liu
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Markku Kulmala
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - James N Smith
- Chemistry Department, University of California, Irvine, California 92697, United States
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| |
Collapse
|
6
|
He L, Li G, Wu X, Zhang S, Tian M, Li Z, Huang C, Hu Q, Wu Y, Hao J. Characteristics of NO X and NH 3 emissions from in-use heavy-duty diesel vehicles with various aftertreatment technologies in China. J Hazard Mater 2024; 465:133073. [PMID: 38039816 DOI: 10.1016/j.jhazmat.2023.133073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/24/2023] [Accepted: 11/22/2023] [Indexed: 12/03/2023]
Abstract
Some in-use China IV and China V heavy-duty diesel vehicles (HDDVs) with selective catalytic reduction (SCR) systems probably fail to mitigate nitrogen oxide (NOX) emissions as expected. Meanwhile, these SCR-equipped HDDVs might emit excessive ammonia (NH3). To better understand the NOX and NH3 emissions from typical HDDVs in China, seventeen in-use vehicles with various emission-control technologies were tested by using laboratory chassis dynamometers. The results indicated that individual NOX and NH3 emissions from HDDV fleets widely varied owing to differences in aftertreatment performance. China V and VI HDDVs with effectively functioning SCRs could substantially control their NOX emissions to be below the corresponding emission limits (i.e., 4.0 and 0.69 g/kWh for China V and China VI vehicles, respectively) but with a potential risk of high NH3 emissions caused by diesel exhaust fluid (DEF) overdosing. Furthermore, higher vehicle speed and payload resulted in lower NOX emissions and possibly higher NH3 emissions from HDDVs with effectively functioning SCRs, while higher NOX emissions from tampered- and non-SCR HDDVs. NOX emissions from China VI HDDVs were more sensitive to cold starts compared to China V and earlier vehicles, but there was no significant discrepancy in NH3 emissions between cold- and hot-start tests.
Collapse
Affiliation(s)
- Liqiang He
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Gang Li
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Xiaomeng Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China.
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Miao Tian
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Zhenhua Li
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Cause and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Qingyao Hu
- State Environmental Protection Key Laboratory of Cause and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
7
|
Song Q, Zhang N, Zhang Y, Yin D, Hao J, Wang S, Li S, Xu W, Yan W, Meng X, Xu X, Wu X, Xie D, Zhu Y, Qu Q, Hou X, Jiang Y, Dong Z, Zheng H, Sun Y, Li Z, Zhao B. The development of local ambient air quality standards: A case study of Hainan Province, China. Eco Environ Health 2024; 3:11-20. [PMID: 38169841 PMCID: PMC10758711 DOI: 10.1016/j.eehl.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 09/15/2023] [Accepted: 10/02/2023] [Indexed: 01/05/2024]
Abstract
The ambient air quality standard (AAQS) is a vital policy instrument for protecting the environment and human health. Hainan Province is at the forefront of China's efforts to protect its ecological environment, with an official goal to achieve world-leading air quality by 2035. However, neither the national AAQS nor the World Health Organization guideline offers sufficient guidance for improving air quality in Hainan because Hainan has well met the former while the latter is excessively stringent. Consequently, the establishment of Hainan's local AAQS becomes imperative. Nonetheless, research regarding the development of local AAQS is scarce, especially in comparatively more polluted countries such as China. The relatively high background values and significant interannual fluctuations in air pollutant concentrations in Hainan present challenges in the development of local AAQS. Our research proposes a world-class local AAQS of Hainan Province by reviewing the AAQS in major countries or regions worldwide, analyzing the influence of different statistical forms, and carefully evaluating the attainability of the standard. In the proposed AAQS, the annual mean concentration limit for PM2.5, the annual 95th percentile of daily maximum 8-h mean (MDA8) concentration limit for O3, and the peak season concentration limit for O3 are set at 10, 120, and 85 μg/m3, respectively. Our study indicates that, with effective control policies, Hainan is projected to achieve compliance with the new standard by 2035. The implementation of the local AAQS is estimated to avoid 1,526 (1,253-1,789) and 259 (132-501) premature deaths attributable to long-term exposure to PM2.5 and O3 in Hainan in 2035, respectively.
Collapse
Affiliation(s)
- Qian Song
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Nannan Zhang
- Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Yanning Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Wenshuai Xu
- Hainan Research Academy of Environmental Sciences, Haikou 571126, China
- Hainan Provincial Ecological and Environmental Monitoring Center, Haikou 570206, China
| | - Weijun Yan
- Hainan Provincial Ecological and Environmental Monitoring Center, Haikou 570206, China
| | - Xinxin Meng
- Hainan Provincial Ecological and Environmental Monitoring Center, Haikou 570206, China
| | - Xinghong Xu
- Hainan Provincial Ecological and Environmental Monitoring Center, Haikou 570206, China
| | - Xiaochen Wu
- Hainan Research Academy of Environmental Sciences, Haikou 571126, China
| | - Donghai Xie
- Hainan Radiation Environmental Monitoring Station, Haikou 571126, China
| | - Yun Zhu
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Qipeng Qu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xuan Hou
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yisheng Sun
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Zeqi Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
8
|
Qu Q, Wang S, Zhao B, Hu R, Liang C, Zhang H, Li S, Feng B, Hou X, Yin D, Du J, Chu Y, Zhang Y, Wu Q, Wen Y, Wu X, Hu J, Zhang S, Hao J. Response of organic aerosol in Beijing to emission reductions during the XXIV Olympic Winter Games. Sci Total Environ 2024; 914:170033. [PMID: 38220000 DOI: 10.1016/j.scitotenv.2024.170033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/05/2024] [Accepted: 01/07/2024] [Indexed: 01/16/2024]
Abstract
Organic aerosol (OA) serves as a crucial component of fine particulate matter. However, the response of OA to changes in anthropogenic emissions remains unclear due to its complexity. The XXIV Olympic Winter Games (OWG) provided real atmospheric experimental conditions on studying the response of OA to substantial emission reductions in winter. Here, we explored the sources and variations of OA based on the observation of aerosol mass spectrometer (AMS) combined with positive matrix factorization (PMF) analysis in urban Beijing during the 2022 Olympic Winter Games. The influences of meteorological conditions on OA concentrations were corrected by CO and verified by deweathered model. The CO-normalized primary OA (POA) concentrations from traffic, cooking, coal and biomass burning during the OWG decreased by 39.8 %, 23.2 % and 65.0 %, respectively. Measures controlling coal and biomass burning were most effective in reducing POA during the OWG. For the CO-normalized concentration of secondary OA (SOA), aqueous-phase related oxygenated OA decreased by 51.8 % due to the lower relative humidity and emission reduction in precursors, while the less oxidized‑oxygenated OA even slightly increased as the enhanced atmospheric oxidation processes may partially offset the efficacy of emission control. Therefore, more targeted reduction of organic precursors shall be enhanced to lower atmospheric oxidation capacity and mitigate SOA pollution.
Collapse
Affiliation(s)
- Qipeng Qu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Ruolan Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Chengrui Liang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Haowen Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Boyang Feng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xuan Hou
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jinhong Du
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yangxi Chu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yanning Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yifan Wen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xiaomeng Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jingnan Hu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
9
|
Zheng H, Li S, Jiang Y, Dong Z, Yin D, Zhao B, Wu Q, Liu K, Zhang S, Wu Y, Wen Y, Xing J, Henneman LRF, Kinney PL, Wang S, Hao J. Unpacking the factors contributing to changes in PM 2.5-associated mortality in China from 2013 to 2019. Environ Int 2024; 184:108470. [PMID: 38324930 DOI: 10.1016/j.envint.2024.108470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 02/09/2024]
Abstract
From 2013 to 2019, a series of air pollution control actions significantly reduced PM2.5 pollution in China. Control actions included changes in activity levels, structural adjustment (SA) policy, energy and material saving (EMS) policy, and end-of-pipe (EOP) control in several sources, which have not been systematically studied in previous studies. Here, we integrate an emission inventory, a chemical transport model, a health impact assessment model, and a scenario analysis to quantify the contribution of each control action across a range of major emission sources to the changes in PM2.5 concentrations and associated mortality in China from 2013 to 2019. Assuming equal toxicity of PM2.5 from all the sources, we estimate that PM2.5-related mortality decreased from 2.52 (95 % confidence interval, 2.13-2.88) to 1.94 (1.62-2.24) million deaths. Anthropogenic emission reductions and declining baseline incidence rates significantly contributed to health benefits, but population aging partially offset their impact. Among the major sources, controls on power plants and industrial boilers were responsible for the highest reduction in PM2.5-related mortality (∼80 %), followed by industrial processes (∼40 %), residential combustion (∼40 %), and transportation (∼30 %). However, considering the potentially higher relative risks of power plant PM2.5, the adverse effects avoided by their control could be ∼2.4 times the current estimation. Our power plant sensitivity analyses indicate that future estimates of source-specific PM2.5 health effects should incorporate variations in individual source PM2.5 effect coefficients when available. As for the control actions, while activity levels increased for most sources, SA policy significantly reduced the emissions in residential combustion and industrial boilers, and EOP control dominated the contribution in health benefits in most sources except residential combustion. Considering the emission reduction potential by source and control actions in 2019, our results suggest that promoting clean energy in residential combustion and enforcing more stringent EOP control in the iron and steel industry should be prioritized in the future.
Collapse
Affiliation(s)
- Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Kaiyun Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Ye Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yifan Wen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Lucas R F Henneman
- Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA
| | - Patrick L Kinney
- Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
10
|
Liang C, Feng B, Wang S, Zhao B, Xie J, Huang G, Zhu L, Hao J. Differentiated emissions and secondary organic aerosol formation potential of organic vapor from industrial coatings in China. J Hazard Mater 2024; 466:133668. [PMID: 38309167 DOI: 10.1016/j.jhazmat.2024.133668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/29/2023] [Accepted: 01/28/2024] [Indexed: 02/05/2024]
Abstract
Organic vapors emitted during solvent use are important precursors of secondary organic aerosols (SOAs). Industrial coatings are a major class of solvents that emit volatile and intermediate volatile organic compounds (VOCs and IVOCs, respectively). However, the emission factors and source profiles of VOCs and IVOCs from industrial coatings remain unclear. In this study, representative solvent- and water-based industrial paints were evaporated, sampled and tested using online and offline instruments. The VOC and IVOC emission factors for solvent-based paints are 129-254 and 25-80 g/kg, while for water-based paint are 13 and 32 g/kg, respectively. In solvent-based paints, the VOCs are mainly aromatics, while the IVOCs are composed of long-chain alkanes, alkenes, carbonyls and halocarbons. The VOCs and IVOCs in water-based paint are mostly oxygenates, such as ethanol, acetone, ethylene glycol, and Texanol. During the evaporation of solvent-based paints, the fraction of IVOCs increases along with those of alkenes and aldehydes, while the proportion of aromatics decreases. For water-based paint, the fraction of IVOCs slightly decreases with evaporation. The SOA formation potentials of solvent-based paints are 8.6-28.0 g/kg, much higher than that of water-based paint (0.65 g/kg); thus, substituting solvent-based paints with water-based paints may significantly decrease SOA formation.
Collapse
Affiliation(s)
- Chengrui Liang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Boyang Feng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jinzi Xie
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Guanghan Huang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Liang Zhu
- TOFWERK China, No. 320, Pubin Road, Pukou, Nanjing 211800, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
11
|
Wen Y, Zhang S, Wang Y, Yang J, He L, Wu Y, Hao J. Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment. Environ Sci Technol 2024. [PMID: 38261755 DOI: 10.1021/acs.est.3c07545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an "out-of-limit map" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.
Collapse
Affiliation(s)
- Yifan Wen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, P. R. China
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, P. R. China
| | - Yuan Wang
- Department of Earth System Science, Stanford University, Stanford, California 94305, United States
| | - Jiani Yang
- Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California 91125, United States
| | - Liyin He
- Department of Global Ecology, Carnegie Institution for Science, Stanford, California 94305, United States
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, P. R. China
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, P. R. China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, P. R. China
| |
Collapse
|
12
|
Tian X, Wang H, Xu S, Gao L, Cao J, Chen J, Zhang Q, Ning P, Hao J. Boosting the catalytic performance of Cu-SAPO-34 in NO x removal via hydrothermal treatment. J Environ Sci (China) 2024; 135:640-655. [PMID: 37778835 DOI: 10.1016/j.jes.2022.10.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 10/03/2023]
Abstract
Phosphate ions promoted Cu-SAPO-34 (P-Cu-SAPO-34) were prepared using bulk CuO particles as Cu2+ precursor by a solid-state ion exchange technique for the selective catalytic reduction of NOx with NH3 (NH3-SCR). The effects of high temperature (H-T) hydrothermal aging on the NOx removal (de-NOx) performance of Cu-SAPO-34 with and without phosphate ions were systematically investigated at atomic level. The results displayed that both Cu-SAPO-34 and P-Cu-SAPO-34 presented relatively poor NOx removal activity with a low conversion (< 30%) at 250-500°C. However, after H-T hydrothermal treatment (800°C for 10 hr at 10% H2O), these two samples showed significantly satisfied NOx elimination performance with a quite high conversion (70%-90%) at 250-500°C. Additionally, phosphate ions decoration can further enhance the catalytic performance of Cu-SAPO-34 after hydrothermal treatment (Cu-SAPO-34H). The textural properties, morphologies, structural feature, acidity, redox characteristic, and surface-active species of the fresh and hydrothermally aged samples were analyzed using various characterization methods. The systematical characterization results revealed that increases of 28% of the isolated Cu2+ active species (Cu2+-2Z, Cu (OH)+-Z) mainly from bulk CuO and 50% of the Brønsted acid sites, the high dispersion of isolated Cu2+ active component as well as the Brønsted acid sites were mainly responsible for the accepted catalytic activity of these two hydrothermally aged samples, especially for P-Cu-SAPO-34H.
Collapse
Affiliation(s)
- Xiaoyan Tian
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Huimin Wang
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Siyuan Xu
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Lianyun Gao
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Jinyan Cao
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China
| | - Jianjun Chen
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China; National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries, Kunming University of Science and Technology, Kunming 650500, China
| | - Qiulin Zhang
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China; National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries, Kunming University of Science and Technology, Kunming 650500, China.
| | - Ping Ning
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China; National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries, Kunming University of Science and Technology, Kunming 650500, China
| | - Jiming Hao
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China; School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| |
Collapse
|
13
|
Zhang J, Jiang Y, Wang Y, Zhang S, Wu Y, Wang S, Nielsen CP, McElroy MB, Hao J. Increased Impact of Aviation on Air Quality and Human Health in China. Environ Sci Technol 2023; 57:19575-19583. [PMID: 37991894 DOI: 10.1021/acs.est.3c05821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
China's civil aviation market has rapidly expanded, becoming the world's second-largest. However, the air quality and health impacts caused by its aircraft emissions have been inadequately assessed. Here, we leverage an updated emission inventory of air pollutants with improved temporal and spatial resolution based on hundreds of thousands of flight trajectories and simulate aviation-attributable contributions to ground-level air pollution in China. We find that in 2017, the annual-average aviation-attributed PM2.5 and O3 concentrations were 0.4-1.5 and 10.6-14.5 μg·m-3, respectively, suggesting that aviation emissions have become an increasingly important source of ambient air pollution. The contributions attributable to high-altitude emissions (climb/cruise/descent) were comparable to those at low altitudes (landing and takeoff). Aviation-attributed ambient PM2.5 and O3 exposures are estimated to have caused about 67,000 deaths in China in 2017, with populous coastal regions in Eastern China suffering the most due to the dense aviation activity. We recommend that industrial and policy stakeholders expedite an agenda of regulating air pollutants harmonized with decarbonization efforts for a more sustainable aviation future.
Collapse
Affiliation(s)
- Jingran Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- Harvard-China Project on Energy, Economy and Environment, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Yiliang Jiang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
| | - Yunjie Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| | - Shuxiao Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| | - Chris P Nielsen
- Harvard-China Project on Energy, Economy and Environment, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Michael B McElroy
- Harvard-China Project on Energy, Economy and Environment, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| |
Collapse
|
14
|
Yang Y, Liu Y, Xu R, Jiao Y, Hao J, Sun YE, Gu XP, Zhang W. [The predictive values of platelet mitochondrial mass and quantity during the perioperative period in elderly patients on the occurrence of postoperative delirium]. Zhonghua Yi Xue Za Zhi 2023; 103:3258-3262. [PMID: 37926568 DOI: 10.3760/cma.j.cn112137-20230627-01085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Objective: To investigate the changes of platelet mitochondrial mass and quantity during perioperative period in elderly patients, and assess their predictive values on the occurrence of postoperative delirium (POD). Methods: In this prospective study, 162 elderly patients scheduled for abdominal surgery under general anesthesia were enrolled from November 2021 to January 2022 in Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School. Among them, 20 patients [10 males, 10 females, aged (71.4±6.8) years] developed POD within 3 days after surgery (POD group), and another 20 patients[12 males, 8 females, aged (67.7±5.3) years] who did not develope POD were selected as controls (control group) using propensity score matching method. Blood samples were collected preoperatively, at the end of surgery and on the first postoperative day. Platelets were extracted and mitochondrial mass was detected with flow cytometry. Transmission electron microscopy was used to determine mitochondrial quantity. The receiver operating characteristic (ROC) curve was drawn to analyze the value of mitochondrial mass and quantity in predicting the occurrence of POD. Results: The mean fluorescence intensities of platelet mitochondrial mass were 193±46, 236±61, 264±53 preoperatively, at the end of surgery and on the first postoperative day in the POD group, respectively. The corresponding values were 209±61, 191±67 and 201±56 in the control group. The platelet mitochondrial mass of patients in the POD group was significantly increased on the first postoperative day compared to preoperative levels (P<0.001). In contrast, there was no significant difference in the control group (P=0.410). Patients in the POD group had higher platelet mitochondrial mass than patients in the control group on the first postoperative day(P=0.002). Meanwhile, platelets from patients in the POD group showed significantly higher number of mitochondria than platelets from patients in the control group [3 (2, 4) vs 2 (1, 2), P<0.001]. According to the ROC curve of platelet on the first postoperative day, at a mitochondrial mass cut-off value of>275.35, the sensitivity, specificity and area under the ROC curve to detect the occurrence of POD were 55%, 90% and 0.800 (95%CI: 0.666-0.934, P<0.001). At a mitochondrial quantity cut-off value of>2, the sensitivity, specificity and area under the ROC curve to detect the occurrence of POD were 53%, 78% and 0.680 (95%CI: 0.584-0.776, P<0.001). Conclusions: Patients who developed POD show higher platelet mitochondrial mass after surgery compared to preoperative levels. The mitochondrial mass of platelets on the first postoperative day has good predictive value on the occurrence of POD.
Collapse
Affiliation(s)
- Y Yang
- Department of Anesthesiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Y Liu
- Department of Anesthesiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - R Xu
- Department of Anesthesiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Y Jiao
- Department of Anesthesiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - J Hao
- Department of Anesthesiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Y E Sun
- Department of Anesthesiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - X P Gu
- Department of Anesthesiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - W Zhang
- Department of Anesthesiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| |
Collapse
|
15
|
Huang L, Zhao B, Wang S, Chang X, Klimont Z, Huang G, Zheng H, Hao J. Global Anthropogenic Emissions of Full-Volatility Organic Compounds. Environ Sci Technol 2023; 57:16435-16445. [PMID: 37853753 DOI: 10.1021/acs.est.3c04106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
Traditional global emission inventories classify primary organic emissions into nonvolatile organic carbon and volatile organic compounds (VOCs), excluding intermediate-volatility and semivolatile organic compounds (IVOCs and SVOCs, respectively), which are important precursors of secondary organic aerosols. This study establishes the first global anthropogenic full-volatility organic emission inventory with chemically speciated or volatility-binned emission factors. The emissions of extremely low/low-volatility organic compounds (xLVOCs), SVOCs, IVOCs, and VOCs in 2015 were 13.2, 10.1, 23.3, and 120.5 Mt, respectively. The full-volatility framework fills a gap of 18.5 Mt I/S/xLVOCs compared with the traditional framework. Volatile chemical products (VCPs), domestic combustion, and on-road transportation sources were dominant contributors to full-volatility emissions, accounting for 30, 30, and 12%, respectively. The VCP and on-road transportation sectors were the main contributors to IVOCs and VOCs. The key emitting regions included Africa, India, Southeast Asia, China, Europe, and the United States, among which China, Europe, and the United States emitted higher proportions of IVOCs and VOCs owing to the use of cleaner fuel in domestic combustion and more intense emissions from VCPs and on-road transportation activities. The findings contribute to a better understanding of the impact of organic emissions on global air pollution and climate change.
Collapse
Affiliation(s)
- Lyuyin Huang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xing Chang
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
| | - Zbigniew Klimont
- International Institute for Applied Systems Analysis (IIASA), Laxenburg 2361, Austria
| | - Guanghan Huang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
16
|
Wu D, Zheng H, Li Q, Wang S, Zhao B, Jin L, Lyu R, Li S, Liu Y, Chen X, Zhang F, Wu Q, Liu T, Jiang J, Wang L, Li X, Chen J, Hao J. Achieving health-oriented air pollution control requires integrating unequal toxicities of industrial particles. Nat Commun 2023; 14:6491. [PMID: 37838777 PMCID: PMC10576764 DOI: 10.1038/s41467-023-42089-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 09/29/2023] [Indexed: 10/16/2023] Open
Abstract
Protecting human health from fine particulate matter (PM) pollution is the ambitious goal of clean air actions, but current control strategies largely ignore the role of source-specific PM toxicity. Here, we proposed health-oriented control strategies by integrating the unequal toxic potencies of the most polluting industrial PMs. Iron and steel industry (ISI)-emitted PM2.5 exhibit about one order of magnitude higher toxic potency than those of cement and power industries. Compared with the current mass-based control strategy (prioritizing implementation of ultralow emission standards in the power sector), the proposed health-oriented control strategy (priority control of the ISI sector) could generate 5.4 times higher reduction in population-weighted toxic potency-adjusted PM2.5 exposure among polluting industries in China. Furthermore, the marginal abatement cost per unit of toxic potency-adjusted mass of ISI-emitted PM2.5 is only a quarter of that of the other two sectors under ultralow emission scenarios. We highlight that a health-oriented air pollution control strategy is urgently required to achieve cost-effective reductions in particulate exposure risks.
Collapse
Affiliation(s)
- Di Wu
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Qing Li
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China.
- Shanghai Institute of Eco-Chongming (SIEC), 20 Cuiniao Road, Chenjia Town, Chongming District, Shanghai, 202162, China.
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China.
| | - Bin Zhao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Ling Jin
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Rui Lyu
- China Huaneng Clean Energy Research Institute, Beijing, 102209, China
| | - Shengyue Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yuzhe Liu
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Xiu Chen
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Fenfen Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Tonghao Liu
- China National Environmental Monitoring Center, Beijing, 100012, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Lin Wang
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Xiangdong Li
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jianmin Chen
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
- Shanghai Institute of Eco-Chongming (SIEC), 20 Cuiniao Road, Chenjia Town, Chongming District, Shanghai, 202162, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| |
Collapse
|
17
|
Yu JS, Hao J, Huang H, Zhao J, Prayson R, Bao S. Sema3C Signaling is an Alternative Activator of the Canonical WNT Pathway in Glioblastoma. Int J Radiat Oncol Biol Phys 2023; 117:S138. [PMID: 37784353 DOI: 10.1016/j.ijrobp.2023.06.545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Wnt signaling maintains normal and cancer stem cells. The Wnt pathway is frequently dysregulated in many cancers, underscoring it as a therapeutic target. Although Wnt inhibitors appear promising in many preclinical studies, they have failed uniformly in clinical trials. Molecular mechanisms of resistance are poorly defined. Further dissection of the precise mechanisms of Wnt pathway activation in specific tumor types is needed to develop new Wnt pathway inhibitors with less toxicity. Here, we identify an alternative activator of the Wnt pathway that may mediate resistance to upstream Wnt inhibition in glioblastoma. MATERIALS/METHODS Glioma stem-like cells (GSCs) were enriched in defined media. GSCs were transduced with lentiviruses to knockdown or overexpress Sema3C or Wnt pathway components. Cell viability, proliferation, apoptosis, and self-renewal were assessed. Expression of Sema3C and Wnt pathway components were assessed in GSCs, mouse models of GBM, and human glioblastoma by qPCR, Western blot, and/or immunostaining. Beta-catenin subcellular localization was assessed by cell fractionation and immunofluorescence. GSC-derived orthotopic models of GBM were used to assess the impact of genetic or pharmacologic inhibition of Sema3C or Wnt pathway components alone or in combination on tumor growth and animal survival. RESULTS The axonal guidance protein Sema3C promotes the tumorigenicity of GSCs through binding its NRP/PlxnD1 receptor complex leading to Rac1 activation. Sema3C signaling directs beta-catenin nuclear accumulation in a Rac1-dependent process, leading to transactivation of Wnt target genes. Sema3C-driven Wnt signaling occurred despite suppression of Wnt ligand secretion, suggesting that Sema3C may drive canonical Wnt signaling independent of Wnt ligand binding. In human glioblastoma, Sema3C expression and Wnt pathway activation were highly concordant. In a mouse model of glioblastoma, combined depletion of Sema3C and beta-catenin partner TCF1 extended animal survival more than single target inhibition alone. CONCLUSION Sema3C signaling may represent an alternative mechanism of WNT pathway activation even when WNT ligand-receptor interaction is inhibited. Since Sema3C is overexpressed in >85% glioblastoma and is used to maintain GSCs but not normal neural progenitor cells, this pathway may represent a major mechanism of Wnt pathway activation and resistance to upstream Wnt pathway inhibitors in GSCs. Our data provide a therapeutic strategy to achieve clinically significant Wnt pathway inhibition in GSCs potentially without the toxicity of currently available WNT inhibitors.
Collapse
Affiliation(s)
- J S Yu
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH
| | - J Hao
- Cleveland Clinic, Cleveland, OH
| | - H Huang
- Cleveland Clinic, Cleveland, OH
| | - J Zhao
- Cleveland Clinic, Cleveland, OH
| | | | - S Bao
- Center for Cancer Stem Cell Research, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| |
Collapse
|
18
|
Wen Y, Liu M, Zhang S, Wu X, Wu Y, Hao J. Updating On-Road Vehicle Emissions for China: Spatial Patterns, Temporal Trends, and Mitigation Drivers. Environ Sci Technol 2023; 57:14299-14309. [PMID: 37706680 DOI: 10.1021/acs.est.3c04909] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Vehicle emissions in China have been decoupled from rapid motorization owing to comprehensive control strategies. China's increasingly ambitious goals for better air quality are calling for deep emission mitigation, posing a need to develop an up-to-date emission inventory that can reflect the fast-developing policies on vehicle emission control. Herein, large-sample vehicle emission measurements were collected to update the vehicle emission inventory. For instance, ambient temperature correction modules were developed to depict the remarkable regional and seasonal emission variations, showing that the monthly emission disparities for total hydrocarbon (THC) and nitrogen oxide (NOX) in January and July could be up to 1.7 times in northern China. Thus, the emission ratios of THC and NOX can vary dramatically among various seasons and provinces, which have not been considered well by previous simulations regarding the nonlinear atmospheric chemistry of ozone (O3) and fine particulate matter (PM2.5) formation. The new emission results indicate that vehicular carbon monoxide (CO), THC, and PM2.5 emissions decreased by 69, 51, and 61%, respectively, during 2010-2019. However, the controls of NOX and ammonia (NH3) emissions were not as efficient as other pollutants. Under the most likely future scenario (PC [1]), CO, THC, NOX, PM2.5, and NH3 emissions were anticipated to reduce by 35, 36, 35, 45, and 4%, respectively, from 2019 to 2025. These reductions will be expedited with expected decreases of 56, 58, 74, 53, and 51% from 2025 to 2035, which are substantially promoted by the massive deployment of new energy vehicles and more stringent emission standards. The updated vehicle emission inventory can serve as an important tool to develop season- and location-specific mitigation strategies of vehicular emission precursors to alleviate haze and O3 problems.
Collapse
Affiliation(s)
- Yifan Wen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Min Liu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, China
| | - Xiaomeng Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, China
| |
Collapse
|
19
|
Ma Q, Jiang L, Yang B, Xu B, Wang Q, Wu Q, Ning P, Zhang Y, Huang J, Hao J. Mn/Ce@HKUST-1 for Efficient Removal of Gaseous Thallium: Insights from Kinetic and Experimental Studies. Langmuir 2023; 39:13090-13102. [PMID: 37669076 DOI: 10.1021/acs.langmuir.3c01439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Gaseous thallium (Tl) pollution events, primarily caused by non-ferrous mineral refineries and fossil fuel combustion, have increased over the past few decades. To prevent gaseous Tl distribution from flue gas, MnO2/CeO2@HKUST-1 (MCH) was synthesized and found to achieve a gaseous Tl(I) removal level of up to 90% at 423 K, a weight hourly space velocity (WHSV) of 2000 h-1/mL with an Mn dose of 10%, maintained over 10 h. The best Mn/Ce ratio was found to be 9:1. To further investigate surface kinetic behavior, four commonly used kinetic models were applied, including the Eley-Rideal (ER) model, Langmuir-Hinshelwood (LH) model, Mars-van Krevelen (MVK) model, and pseudo-first-order (PFO) model. While the ER and LH models had the slightest deviation, the MVK model was the most reliable. The CatMAP software was also used to match the simulation deviation. This work demonstrated the Tl removal mechanism and provided insights into the accuracy of kinetic models on minor-radius heavy metal. Thus, this research may help promote the design of reactors, heavy metal removal rates, and flue gas purification technology selection.
Collapse
Affiliation(s)
- Qiang Ma
- Sichuan Provincial Engineering Research Center of City Solid Waste Energy and Building Materials Conversion & Utilization Technology, Chengdu University, Chengdu 610106, China
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Lijun Jiang
- Sichuan Provincial Engineering Research Center of City Solid Waste Energy and Building Materials Conversion & Utilization Technology, Chengdu University, Chengdu 610106, China
| | - Bowen Yang
- Sichuan Provincial Engineering Research Center of City Solid Waste Energy and Building Materials Conversion & Utilization Technology, Chengdu University, Chengdu 610106, China
| | - Bowen Xu
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Qingyuan Wang
- Sichuan Provincial Engineering Research Center of City Solid Waste Energy and Building Materials Conversion & Utilization Technology, Chengdu University, Chengdu 610106, China
| | - Qihong Wu
- Sichuan Provincial Engineering Research Center of City Solid Waste Energy and Building Materials Conversion & Utilization Technology, Chengdu University, Chengdu 610106, China
| | - Ping Ning
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
| | - Yingjie Zhang
- College of Agriculture and Biological Science, Dali University, Dali 671000, China
| | - Jin Huang
- Sichuan Provincial Engineering Research Center of City Solid Waste Energy and Building Materials Conversion & Utilization Technology, Chengdu University, Chengdu 610106, China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| |
Collapse
|
20
|
Liu K, Wang K, Wang S, Wu Q, Hao J. Tracking Carbon Flows from Coal Mines to Electricity Users in China Using an Ensemble Model. Environ Sci Technol 2023; 57:12242-12250. [PMID: 37551974 DOI: 10.1021/acs.est.3c01348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Accurately tracking carbon flows is crucial for preventing carbon leakage and allocating responsibility for reducing CO2eq emissions. In this study, we developed an ensemble model to effectively track carbon flows within China's power system. Our approach integrates coal quality tests, individual power plant datasets, a dynamic material-energy flow analysis model, and an extended version of an interconnected power grid model that incorporates transmission and distribution (T&D) losses. Our results not only provide accurate quantification of unit-based CO2eq emissions based on coal quality data but also enable the assessment of emissions attributed to T&D losses and emission shifts resulting from interprovincial coal and electricity trade. Remarkably, for CO2eq emissions from coal-fired units, the disparity between the guideline and our study can be as high as [-95%, 287%]. We identify Guangdong, Hebei, Jiangsu, and Zhejiang provinces as the major importers of both coal and electricity, responsible for transferring nearly half of their user-based emissions to coal and power bases. Significantly, T&D losses, often overlooked, contribute to 15-20% of provincial emissions at the user side. Our findings emphasize the necessity of up-to-date life cycle emissions and spatial carbon shifts in effectively allocating emission reduction responsibilities from the national level to provinces.
Collapse
Affiliation(s)
- Kaiyun Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Kun Wang
- Institute of Urban Safety and Environmental Science, Beijing Academy of Science and Technology, Beijing 100054, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
21
|
Yang YQ, Fan SJ, Lyu AG, Miao H, Guo L, Jia Q, Fan SY, Wang PW, Li ZD, Liu HR, Hao J, Hu JH, Han W, Wang NL. [Distribution and reference intervals of daytime intraocular pressure in the eye health screening population of Handan]. Zhonghua Yan Ke Za Zhi 2023; 59:620-626. [PMID: 37550969 DOI: 10.3760/cma.j.cn112142-20221013-00512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Objective: To describe the distribution and establish reference intervals (RI) of daytime intraocular pressure (IOP) in the eye health screening population of Handan. Methods: This cross-sectional study included subjects who participated in eye health screening at the Physical Examination Center of Handan First Hospital from May 2021 to June 2022. A complete general and ocular examination was performed, including measurements of visual acuity and IOP (using Goldmann tonometry), slit lamp microscopy, fundus photography, and anterior and posterior segment optical coherence tomography. Subjects with factors that could cause significant changes in IOP or affect the accuracy of IOP measurement, or with an inability to measure IOP were excluded. Simple random sampling was used to select participants, who were grouped by gender and age (18 to <30, 30 to <40, 40 to <50, 50 to <60, 60 to <70, and ≥70 years). Central corneal thickness and IOP at 8 to 11 o'clock in one eye of each participant were recorded. The independent sample t test and ANOVA were used for statistical analysis, and the RI of IOP values was calculated by x¯±1.96s. Results: A total of 9 310 subjects had their IOP measured, and 3 491 participants (3 491 eyes) were randomly selected from 7 886 healthy subjects. The age of the participants was (47.74±14.47) years old, ranging from 18 to 90 years old. There were 1 694 males and 1 797 females. The central corneal thickness of all participants was (525.56±49.39) μm. The daytime IOP of all participants was (15.40±2.54) mmHg (1 mmHg=0.133 kPa), and the RI was 10.42 to 20.39 mmHg. The IOP was (15.49±2.58) mmHg for males and (15.29±2.49) mmHg for females, and the gender difference was statistically significant (P<0.05). The RI of daytime IOP values was 10.43 to 20.54 mmHg for males and 10.41 to 20.18 mmHg for females. There were significant differences in daytime IOP [(15.13±2.58), (15.33±2.53), (15.49±2.50), (15.53±2.55), (15.39±2.62), and (15.28±2.52) mmHg] among 6 age groups (P<0.05). Conclusions: The distribution of daytime IOP in different gender and age groups in the eye health screening population of Handan and the RIs derived from the distribution were roughly the same as the international normal IOP RI (10 to 21 mmHg). It is recommended to refer to the RI of daytime IOP values of different genders and ages for clinical decision.
Collapse
Affiliation(s)
- Y Q Yang
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology & Visual Sciences, Beijing 100730, China
| | - S J Fan
- Department of Ophthalmology, Handan City Eye Hospital (The Third Hospital of Handan), Handan 056006, China
| | - A G Lyu
- Department of Ophthalmology, Handan City Eye Hospital (The Third Hospital of Handan), Handan 056006, China
| | - H Miao
- Department of Ophthalmology, Handan City Eye Hospital (The Third Hospital of Handan), Handan 056006, China
| | - L Guo
- Department of Ophthalmology, Handan City Eye Hospital (The Third Hospital of Handan), Handan 056006, China
| | - Q Jia
- Department of Ophthalmology, Handan City Eye Hospital (The Third Hospital of Handan), Handan 056006, China
| | - S Y Fan
- Department of Ophthalmology, Handan City Eye Hospital (The Third Hospital of Handan), Handan 056006, China
| | - P W Wang
- Department of Ophthalmology, Handan City Eye Hospital (The Third Hospital of Handan), Handan 056006, China
| | - Z D Li
- Department of Ophthalmology, Handan City Eye Hospital (The Third Hospital of Handan), Handan 056006, China
| | - H R Liu
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology & Visual Sciences, Beijing 100730, China
| | - J Hao
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology & Visual Sciences, Beijing 100730, China
| | - J H Hu
- Department of Ophthalmology, Handan City Eye Hospital (The Third Hospital of Handan), Handan 056006, China
| | - W Han
- Department of Ophthalmology, Handan City Eye Hospital (The Third Hospital of Handan), Handan 056006, China
| | - N L Wang
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology & Visual Sciences, Beijing 100730, China
| |
Collapse
|
22
|
Yang B, Ma Q, Hao J, Huang J, Wang Q, Wang D, Zhang J. Periodate-based advanced oxidation processes: A review focusing on the overlooked role of high-valent iron and manganese species. Chemosphere 2023:139442. [PMID: 37422211 DOI: 10.1016/j.chemosphere.2023.139442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/10/2023]
Abstract
Periodate-based advanced oxidation processes (AOPs) have received mounting attention in scientific research in the past two decades due to their fair oxidizing capability for satisfactory decontamination performance. Unlike iodyl (IO3•) and hydroxyl (•OH) radicals are widely recognized as the predominant species generated from periodate activation, the role of high-valent metal as a dominant reactive oxidant has been proposed recently. Although several excellent reviews concerning periodate-based AOPs have been reported, there are still prevalent knowledge roadblocks to high-valent metals' formation and reaction mechanisms. Therefore, this work aims to provide a comprehensive overview of high-valent metals, especially concerning the identification methods (e.g., direct and indirect strategies), formation mechanisms (e.g., formation pathways and interpretation based on density functional theory calculation), reaction mechanisms (e.g., nucleophilic attack, electron transfer, oxygen-atom transfer, electrophilic addition, and hydride and hydrogen-atom transfer), and reactivity performance (e.g., chemical properties, influencing factors, and practical applications). Furthermore, points for critical thinking and further prospects for high-valent metal-mediated oxidation processes are suggested, emphasizing the need for parallel efforts to enhance the stability and reproducibility of high-valent metal-mediated oxidation processes in real world applications.
Collapse
Affiliation(s)
- Bowen Yang
- School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, China; Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin, 541004, China
| | - Qiang Ma
- School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Jin Huang
- School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, China
| | - Qingyuan Wang
- School of Architecture and Civil Engineering, Chengdu University, Chengdu, 610106, China
| | - Dunqiu Wang
- Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin, 541004, China.
| | - Jun Zhang
- Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin, 541004, China.
| |
Collapse
|
23
|
Ge X, Yu Q, Duan L, Zhao Y, Posch M, Hao J. High-resolution maps of critical loads for sulfur and nitrogen in China. Sci Data 2023; 10:339. [PMID: 37258508 DOI: 10.1038/s41597-023-02178-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/24/2023] [Indexed: 06/02/2023] Open
Abstract
The critical load concept is an important scientific guideline for acid deposition control. It was not only a crucial scientific basis to determine the emission reduction targets in Europe, but also used in China's air pollution control, especially the designation of two control zones. Currently, critical loads of sulfur and nitrogen are still exceeded in Europe, America, and East Asia (mainly in China), and need to be continuously updated to meet the demands of further emission reductions. Critical loads of China were calculated and mapped in the 2000s, but are not sufficiently accurate due to methodological and data limitations. Here we present the latest high-quality critical loads for China, based on high-resolution basic data on soil, vegetation, and atmospheric base cations deposition, and up-to-date knowledge on important parameters. Our data, which is going to be included in GAINS-China, can be used to assess the ecological benefits of nitrogen and sulfur reductions in China at a regional or national scale, and to develop mitigation strategies in the future.
Collapse
Affiliation(s)
- Xiaodong Ge
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Qian Yu
- State Key Laboratory of Pollution Control & Resource Reuse and School of the Environment, Nanjing University, Nanjing, Jiangsu, 210023, China
| | - Lei Duan
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing, 100084, China.
| | - Yu Zhao
- State Key Laboratory of Pollution Control & Resource Reuse and School of the Environment, Nanjing University, Nanjing, Jiangsu, 210023, China
| | - Maximilian Posch
- International Institute for Applied System Analysis (IIASA), Schlossplatz 1, 2361, Laxenburg, Austria
| | - Jiming Hao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing, 100084, China
| |
Collapse
|
24
|
Wang J, Wang J, Cai R, Liu C, Jiang J, Nie W, Wang J, Moteki N, Zaveri RA, Huang X, Ma N, Chen G, Wang Z, Jin Y, Cai J, Zhang Y, Chi X, Holanda BA, Xing J, Liu T, Qi X, Wang Q, Pöhlker C, Su H, Cheng Y, Wang S, Hao J, Andreae MO, Ding A. Unified theoretical framework for black carbon mixing state allows greater accuracy of climate effect estimation. Nat Commun 2023; 14:2703. [PMID: 37164951 PMCID: PMC10172310 DOI: 10.1038/s41467-023-38330-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 04/26/2023] [Indexed: 05/12/2023] Open
Abstract
Black carbon (BC) plays an important role in the climate system because of its strong warming effect, yet the magnitude of this effect is highly uncertain owing to the complex mixing state of aerosols. Here we build a unified theoretical framework to describe BC's mixing states, linking dynamic processes to BC coating thickness distribution, and show its self-similarity for sites in diverse environments. The size distribution of BC-containing particles is found to follow a universal law and is independent of BC core size. A new mixing state module is established based on this finding and successfully applied in global and regional models, which increases the accuracy of aerosol climate effect estimations. Our theoretical framework links observations with model simulations in both mixing state description and light absorption quantification.
Collapse
Affiliation(s)
- Jiandong Wang
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044, Nanjing, China.
- China Meteorological Administration Aerosol-Cloud-Precipitation Key Laboratory, School of Atmospheric Physics, Nanjing University of Information Science and Technology, 210044, Nanjing, China.
| | - Jiaping Wang
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China.
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, 210023, Nanjing, China.
| | - Runlong Cai
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, 00014, Helsinki, Finland
| | - Chao Liu
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044, Nanjing, China
- China Meteorological Administration Aerosol-Cloud-Precipitation Key Laboratory, School of Atmospheric Physics, Nanjing University of Information Science and Technology, 210044, Nanjing, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084, Beijing, China
| | - Wei Nie
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, 210023, Nanjing, China
| | - Jinbo Wang
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Nobuhiro Moteki
- Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, Tokyo, 113-0033, Japan
| | - Rahul A Zaveri
- Atmospheric Sciences & Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Xin Huang
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Nan Ma
- Institute for Environmental and Climate Research, Jinan University, 511443, Guangzhou, China
| | - Ganzhen Chen
- China Meteorological Administration Aerosol-Cloud-Precipitation Key Laboratory, School of Atmospheric Physics, Nanjing University of Information Science and Technology, 210044, Nanjing, China
| | - Zilin Wang
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
| | - Yuzhi Jin
- China Meteorological Administration Aerosol-Cloud-Precipitation Key Laboratory, School of Atmospheric Physics, Nanjing University of Information Science and Technology, 210044, Nanjing, China
| | - Jing Cai
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, 00014, Helsinki, Finland
| | - Yuxuan Zhang
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, 210023, Nanjing, China
| | - Xuguang Chi
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, 210023, Nanjing, China
| | - Bruna A Holanda
- Max Planck Institute for Chemistry, 55128, Mainz, Germany
- Hessian Agency for Nature Conservation, Environment and Geology, 65203, Wiesbaden, Germany
| | - Jia Xing
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084, Beijing, China
| | - Tengyu Liu
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, 210023, Nanjing, China
| | - Ximeng Qi
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, 210023, Nanjing, China
| | - Qiaoqiao Wang
- Institute for Environmental and Climate Research, Jinan University, 511443, Guangzhou, China
| | | | - Hang Su
- Max Planck Institute for Chemistry, 55128, Mainz, Germany
| | - Yafang Cheng
- Max Planck Institute for Chemistry, 55128, Mainz, Germany
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084, Beijing, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084, Beijing, China
| | - Meinrat O Andreae
- Max Planck Institute for Chemistry, 55128, Mainz, Germany
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Geology and Geophysics, King Saud University, 11451, Riyadh, Saudi Arabia
| | - Aijun Ding
- Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, 210023, Nanjing, China.
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, 210023, Nanjing, China.
| |
Collapse
|
25
|
Liang C, Wang S, Hu R, Huang G, Xie J, Zhao B, Li Y, Zhu W, Guo S, Jiang J, Hao J. Molecular tracers, mass spectral tracers and oxidation of organic aerosols emitted from cooking and fossil fuel burning sources. Sci Total Environ 2023; 868:161635. [PMID: 36657674 DOI: 10.1016/j.scitotenv.2023.161635] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
Secondary organic aerosol (SOA) composes a substantial fraction of atmospheric particles, yet the formation and aging mechanism of SOA remains unclear. Here we investigate the initial oxidation of primary organic aerosol (POA) and further aging of SOA in winter Beijing by using aerosol mass spectrometer (AMS) measurements along with offline molecular tracer analysis. Multilinear engine (ME-2) source apportionment was conducted to capture the characteristic of source-related SOA, and connect them with specific POA. Our results show that urban cooking and fossil fuel burning sources contribute significantly (17 % and 20 %) to total organic aerosol (OA) in winter Beijing. Molecular tracer analysis by two-dimensional gas chromatography-time-of-flight mass spectrometer (GC × GC-ToF-MS) reveals that cooking SOA (CSOA) is produced through both photooxidation and aqueous-phase processing, while less oxidized SOA (LO-SOA) is the photooxidation product of fossil fuel burning OA (FFOA) and may experience aqueous-phase aging to form more-oxidized oxygenated OA (MO-OOA). Furthermore, CHOm/z 69 and CHOm/z 85 are mass spectral tracers indicating the initial photooxidation, while CHO2+ and C2H2O2+ imply further aqueous-phase aging of OA. Tracer analysis indicates that the formation of diketones is involved in the initial photooxidation of POA, while the formation of glyoxal and diacids is involved in the further aqueous-phase aging of SOA.
Collapse
Affiliation(s)
- Chengrui Liang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Ruolan Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Guanghan Huang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jinzi Xie
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yuyang Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Wenfei Zhu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Song Guo
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
26
|
Wu Q, Han L, Li S, Wang S, Cong Y, Liu K, Lei Y, Zheng H, Li G, Cai B, Hao J. Facility-Level Emissions and Synergistic Control of Energy-Related Air Pollutants and Carbon Dioxide in China. Environ Sci Technol 2023; 57:4504-4512. [PMID: 36877596 DOI: 10.1021/acs.est.2c07704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Boilers involve ∼60% of primary energy consumption in China and emit more air pollutants and CO2 than any other infrastructures. Here, we established a nationwide, facility-level emission data set considering over 185,000 active boilers in China by fusing multiple data sources and jointly using various technical means. The emission uncertainties and spatial allocations were significantly improved. We found that coal-fired power plant boilers were not the most emission-intensive boilers with regard to SO2, NOx, PM, and mercury but emitted the highest CO2. However, biomass- and municipal waste-fired combustion, regarded as zero-carbon technologies, emitted a large fraction of SO2, NOx, and PM. Future biomass or municipal waste mixing in coal-fired power plant boilers can make full use of the advantages of zero-carbon fuel and the pollution control devices of coal-fired power plants. We identified small-size boilers, medium-size boilers using circulating fluidized bed boilers, and large-size boilers located in China's coal mine bases as the main high emitters. Future focuses on high-emitter control can substantially mitigate the emissions of SO2 by 66%, NOx by 49%, PM by 90%, mercury by 51%, and CO2 by 46% at the most. Our study sheds light on other countries wishing to reduce their energy-related emissions and thus the related impacts on humans, ecosystems, and climates.
Collapse
Affiliation(s)
- Qingru Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Licong Han
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yan Cong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Kaiyun Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yu Lei
- Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Guoliang Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Bofeng Cai
- Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
27
|
Liang D, Lu X, Zhuang M, Shi G, Hu C, Wang S, Hao J. Author Correction: China's greenhouse gas emissions for cropping systems from 1978-2016. Sci Data 2023; 10:122. [PMID: 36878962 PMCID: PMC9988821 DOI: 10.1038/s41597-023-02027-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Affiliation(s)
- Dijuan Liang
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing, 100084, China.,Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xi Lu
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing, 100084, China. .,State Key Joint Laboratory of Environment Simulation and Pollution Control and State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing, 100084, P. R. China.
| | - Minghao Zhuang
- College of Resources and Environmental Sciences; National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions of MOE, China Agricultural University, Beijing, P.R. China
| | - Guang Shi
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Chengyu Hu
- School of Computer Science, China University of Geosciences, Wuhan, 430074, China
| | - Shuxiao Wang
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing, 100084, China.,College of Resources and Environmental Sciences; National Academy of Agriculture Green Development, Key Laboratory of Plant-Soil Interactions of MOE, China Agricultural University, Beijing, P.R. China
| | - Jiming Hao
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing, 100084, China.,State Key Joint Laboratory of Environment Simulation and Pollution Control and State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Tsinghua University, Beijing, 100084, P. R. China
| |
Collapse
|
28
|
Zhou B, He L, Zhang S, Wang R, Zhang L, Li M, Liu Y, Zhang S, Wu Y, Hao J. Variability of fuel consumption and CO 2 emissions of a gasoline passenger car under multiple in-laboratory and on-road testing conditions. J Environ Sci (China) 2023; 125:266-276. [PMID: 36375913 DOI: 10.1016/j.jes.2021.12.042] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/27/2021] [Accepted: 12/28/2021] [Indexed: 06/16/2023]
Abstract
An increasing divergence regarding fuel consumption (and/or CO2 emissions) between real-world and type-approval values for light-duty gasoline vehicles (LDGVs) has posed severe challenges to mitigating greenhouse gases (GHGs) and achieving carbon emissions peak and neutrality. To address this divergence issue, laboratory test cycles with more real-featured and transient traffic patterns have been developed recently, for example, the China Light-duty Vehicle Test Cycle for Passenger cars (CLTC-P). We collected fuel consumption and CO2 emissions data of a LDGV under various conditions based on laboratory chassis dynamometer and on-road tests. Laboratory results showed that both standard test cycles and setting methods of road load affected fuel consumption slightly, with variations of less than 4%. Compared to the type-approval value, laboratory and on-road fuel consumption of the tested LDGV over the CLTC-P increased by 9% and 34% under the reference condition (i.e., air conditioning off, automatic stop and start (STT) on and two passengers). On-road measurement results indicated that fuel consumption under the low-speed phase of the CLTC-P increased by 12% due to the STT off, although only a 4% increase on average over the entire cycle. More fuel consumption increases (52%) were attributed to air conditioning usage and full passenger capacity. Strong correlations (R2 > 0.9) between relative fuel consumption and average speed were also identified. Under traffic congestion (average speed below 25 km/hr), fuel consumption was highly sensitive to changes in vehicle speed. Thus, we suggest that real-world driving conditions cannot be ignored when evaluating the fuel economy and GHGs reduction of LDGVs.
Collapse
Affiliation(s)
- Boya Zhou
- China Automotive Technology and Research Center Co., Ltd. Tianjin 300300, China
| | - Liqiang He
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China.
| | - Shijian Zhang
- China Automotive Technology and Research Center Co., Ltd. Tianjin 300300, China
| | - Rui Wang
- China Automotive Technology and Research Center Co., Ltd. Tianjin 300300, China
| | - Luowei Zhang
- China Automotive Technology and Research Center Co., Ltd. Tianjin 300300, China
| | - Mengliang Li
- China Automotive Technology and Research Center Co., Ltd. Tianjin 300300, China
| | - Yu Liu
- China Automotive Technology and Research Center Co., Ltd. Tianjin 300300, China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China; Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China; Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China; Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| |
Collapse
|
29
|
Wang Y, Wen Y, Zhang S, Zheng G, Zheng H, Chang X, Huang C, Wang S, Wu Y, Hao J. Vehicular Ammonia Emissions Significantly Contribute to Urban PM 2.5 Pollution in Two Chinese Megacities. Environ Sci Technol 2023; 57:2698-2705. [PMID: 36700651 DOI: 10.1021/acs.est.2c06198] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Ammonia (NH3) plays a vital role in the formation of fine particulate matter (PM2.5). Prior studies have primarily focused on the control of agricultural NH3 emissions, the dominant source of anthropogenic NH3 emissions. The air quality impact from vehicular NH3 emissions, which could be particularly important in urban areas, has not been adequately evaluated. We developed high-resolution vehicular NH3 emission inventories for Beijing and Shanghai based on detailed link-level traffic profiles and conducted atmospheric simulations of ambient PM2.5 concentrations contributed by vehicular NH3 emissions. We found that vehicular NH3 emissions shared high proportions among total anthropogenic NH3 emissions in the urban areas of Beijing (86%) and Shanghai (45%), where vehicular NH3 was primarily emitted by gasoline vehicles. Local vehicular NH3 emissions could be responsible for approximately 3% of urban PM2.5 concentrations during wintertime, and the contributions could be much higher during polluted periods (∼3 μg m-3). We also showed that controlling vehicular NH3 emissions will be effective and feasible to alleviate urban PM2.5 pollution for megacities in the near future.
Collapse
Affiliation(s)
- Yunjie Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
| | - Yifan Wen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing100084, China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing100084, China
| | - Guangjie Zheng
- Minerva Research Group, Max Planck Institute for Chemistry, Mainz55128, Germany
| | - Haotian Zheng
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
| | - Xing Chang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai200233, China
| | - Shuxiao Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing100084, China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing100084, China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing100084, China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing100084, China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing100084, China
| |
Collapse
|
30
|
Dong Z, Wang S, Jiang Y, Xing J, Ding D, Zheng H, Hao J. An acid rain-friendly NH 3 control strategy to maximize benefits toward human health and nitrogen deposition. Sci Total Environ 2023; 859:160116. [PMID: 36379329 DOI: 10.1016/j.scitotenv.2022.160116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/05/2022] [Accepted: 11/06/2022] [Indexed: 06/16/2023]
Abstract
Ammonia (NH3) abatement remains controversial in China owing to its effectiveness in reducing PM2.5 pollution and nitrogen deposition but with the potential risk of promoting acid rain formation, necessitating scientific guidance. Here, we propose a novel method for designing an NH3 control strategy to mitigate both air pollution and nitrogen deposition without significantly exacerbating acid rain. This method involves extending the response surface model (RSM) to deposition using a delicately developed polynomial response function of deposition (i.e., dep-RSM). The Yangtze River Delta (YRD) dep-RSM application reveals that 16 out of 41 cities have NH3 control potentials from 15 % to 71 %. Excellent NH3 control potentials have been noted between April and June (78 %-92 %). From 2013 to 2017, the effective SO2 and NOx control significantly reduced wet sulfur and oxidized nitrogen deposition, providing considerable NH3 abatement potentials (15 %-24 %) to further reduce PM2.5 and nitrogen deposition by up to 2 % and 9 %, respectively, without acid rain exacerbation (the wet neutralization factor was maintained). Additionally, 57 % and 73 % NH3 emission reduction potentials were obtained under acid rain constraints with 75 % and 86 % reductions in the other precursors to reduce the average PM2.5 concentration below 25 and 15 μg/m3, and an additional 8408 and 14,459 premature deaths could only be avoided at an extra cost of 8.7 and 19.7 billion CNY, respectively. Meanwhile, the N deposition considerably reduced by 10 and 13 kgN/ha·yr. However, the YRD region could still simultaneously obtain substantial amounts of PM2.5 and N deposition mitigation using the strategy proposed herein. The expanded optimization system can be directly adopted by policymakers to implement coordinated control in regions or countries facing the same NH3 control conundrum.
Collapse
Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Dian Ding
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
31
|
Luo L, Bai X, Lv Y, Liu S, Guo Z, Liu W, Hao Y, Sun Y, Hao J, Zhang K, Zhao H, Lin S, Zhao S, Xiao Y, Yang J, Tian H. Exploring the driving factors of haze events in Beijing during Chinese New Year holidays in 2020 and 2021 under the influence of COVID-19 pandemic. Sci Total Environ 2023; 859:160172. [PMID: 36395856 PMCID: PMC9663379 DOI: 10.1016/j.scitotenv.2022.160172] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/09/2022] [Accepted: 11/09/2022] [Indexed: 05/23/2023]
Abstract
Unexpected outbreak of the 2019 novel coronavirus (COVID-19) has profoundly altered the way of human life and production activity, which posed visible impacts on PM2.5 and its chemical species. The abruptly emergency reduction in human activities provided an opportunity to explore the synergetic impacts of multi-factors on shaping PM2.5 pollution. Here, we conducted two comprehensive observation measurements of PM2.5 and its chemical species from 1 January to 16 February in Beijing 2020 and the same lunar date in 2021, to investigate temporal variations and reveal the driving factors of haze before and after Chinese New Year (CNY). Results show that mean PM2.5 concentrations during the whole observation were 63.83 and 66.86 μg/m3 in 2020 and 2021, respectively. Higher secondary inorganic species were observed after CNY, and K+, Cl- showed three prominent peaks which associated closely with fireworks burnings from suburb Beijing and surroundings, verifying that they could be used as two representative tracers of fireworks. Further, we explored the impacts of meteorological conditions, regional transportation as well as chemical reactions on PM2.5. We found that unfavorable meteorological conditions accounted for 11.0 % and 16.9 % of PM2.5 during CNY holidays in 2020 and 2021, respectively. Regional transport from southwest and southeast (south) played an important role on PM2.5 during the two observation periods. Higher ratio of NO3-/SO42- were observed under high OX and low RH conditions, suggesting the major pathway of NO3- and SO42- formation could be photochemical process and aqueous-phase reaction. Additionally, nocturnal chemistry facilitated the formation of secondary components of both inorganic and organic. This study promotes understandings of PM2.5 pollution in winter under the influence of COVID-19 pandemic and provides a well reference for haze and PM2.5 control in future.
Collapse
Affiliation(s)
- Lining Luo
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Xiaoxuan Bai
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yunqian Lv
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shuhan Liu
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Zhihui Guo
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Wei Liu
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yan Hao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
| | - Yujiao Sun
- College of Water Sciences, Beijing Normal University, Beijing 100875, China
| | - Jiming Hao
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Kai Zhang
- Department of Environmental Health Sciences School of Public Health University at Albany, State University of New York, One University Place, Rensselaer, NY 12144, United States of America
| | - Hongyan Zhao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shumin Lin
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shuang Zhao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yifei Xiao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Junqi Yang
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Hezhong Tian
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
| |
Collapse
|
32
|
Bai X, Tian H, Zhu C, Luo L, Hao Y, Liu S, Guo Z, Lv Y, Chen D, Chu B, Wang S, Hao J. Present Knowledge and Future Perspectives of Atmospheric Emission Inventories of Toxic Trace Elements: A Critical Review. Environ Sci Technol 2023; 57:1551-1567. [PMID: 36661479 DOI: 10.1021/acs.est.2c07147] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Toxic trace elements (TEs) can pose serious risks to ecosystems and human health. However, a comprehensive understanding of atmospheric emission inventories for several concerning TEs has not yet been developed. In this study, we systematically reviewed the status and progress of existing research in developing atmospheric emission inventories of TEs focusing on global, regional, and sectoral scales. Multiple studies have strengthened our understanding of the global emission of TEs, despite attention being mainly focused on Hg and source classification in different studies showing large discrepancies. In contrast to those of developed countries and regions, the officially published emission inventory is still lacking in developing countries, despite the fact that studies on evaluating the emissions of TEs on a national scale or one specific source category have been numerous in recent years. Additionally, emissions of TEs emitted from waste incineration and traffic-related sources have produced growing concern with worldwide rapid urbanization. Although several studies attempt to estimate the emissions of TEs based on PM emissions and its source-specific chemical profiles, the emission factor approach is still the universal method. We call for more extensive and in-depth studies to establish a precise localization national emission inventory of TEs based on adequate field measurements and comprehensive investigation to reduce uncertainty.
Collapse
Affiliation(s)
- Xiaoxuan Bai
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Hezhong Tian
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Chuanyong Zhu
- College of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Lining Luo
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yan Hao
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shuhan Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Zhihui Guo
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yunqian Lv
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Dongxue Chen
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Biwu Chu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100875, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100875, China
| |
Collapse
|
33
|
Dong Z, Xing J, Zhang F, Wang S, Ding D, Wang H, Huang C, Zheng H, Jiang Y, Hao J. Synergetic PM 2.5 and O 3 control strategy for the Yangtze River Delta, China. J Environ Sci (China) 2023; 123:281-291. [PMID: 36521990 DOI: 10.1016/j.jes.2022.04.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 06/17/2023]
Abstract
PM2.5 concentrations have dramatically reduced in key regions of China during the period 2013-2017, while O3 has increased. Hence there is an urgent demand to develop a synergetic regional PM2.5 and O3 control strategy. This study develops an emission-to-concentration response surface model and proposes a synergetic pathway for PM2.5 and O3 control in the Yangtze River Delta (YRD) based on the framework of the Air Benefit and Cost and Attainment Assessment System (ABaCAS). Results suggest that the regional emissions of NOx, SO2, NH3, VOCs (volatile organic compounds) and primary PM2.5 should be reduced by 18%, 23%, 14%, 17% and 33% compared with 2017 to achieve 25% and 5% decreases of PM2.5 and O3 in 2025, and that the emission reduction ratios will need to be 50%, 26%, 28%, 28% and 55% to attain the National Ambient Air Quality Standard. To effectively reduce the O3 pollution in the central and eastern YRD, VOCs controls need to be strengthened to reduce O3 by 5%, and then NOx reduction should be accelerated for air quality attainment. Meanwhile, control of primary PM2.5 emissions shall be prioritized to address the severe PM2.5 pollution in the northern YRD. For most cities in the YRD, the VOCs emission reduction ratio should be higher than that for NOx in Spring and Autumn. NOx control should be increased in summer rather than winter when a strong VOC-limited regime occurs. Besides, regarding the emission control of industrial processes, on-road vehicle and residential sources shall be prioritized and the joint control area should be enlarged to include Shandong, Jiangxi and Hubei Province for effective O3 control.
Collapse
Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Fenfen Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Dian Ding
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
34
|
Bai X, Liu W, Wu B, Liu S, Liu X, Hao Y, Liang W, Lin S, Luo L, Zhao S, Zhu C, Hao J, Tian H. Emission characteristics and inventory of volatile organic compounds from the Chinese cement industry based on field measurements. Environ Pollut 2023; 316:120600. [PMID: 36347407 DOI: 10.1016/j.envpol.2022.120600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 10/08/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Volatile organic compounds (VOCs) are major precursors of ozone (O3) and secondary organic aerosols (SOA), which degrade air quality and pose a serious risk to human health and ecological systems. Previous studies on the emission characteristics of VOCs have predominantly focused on petrochemical and solvent-using sources, while localized studies on the cement industry are scarce in China. Field measurements for four cement plants were carried out in this study to investigate the emission levels, source profiles, and secondary pollutant generation potential of 98 VOCs species emitted from rotary and shaft kilns in China. Furthermore, a species-differentiated VOCs emission inventory was compiled for the Chinese cement industry in 2019. The results demonstrated that the mass concentration of VOCs emitted from shaft kiln was more than 20-fold higher than that emitted from rotary kilns, and the alkanes was the dominant species (56%) in shaft kilns, while oxygenated VOCs (OVOCs) and halocarbons were the main species in rotary kilns. Moreover, alkenes & alkyne were the dominant contributors to ozone formation potential (OFP) in shaft kilns, whereas alkenes & alkyne and OVOCs were comparable and prominent contributors in rotary kilns. In contrast, secondary organic aerosol potential (SOAP) for the two types of kilns was dominated by aromatics. In 2019, approximately 18.18 kt VOCs were emitted from cement production and were found to be largely concentrated in the southeast and central provinces of China. Considering the influence on environmental conditions, high OFP-contributing species in cement kilns are suggested to be a priority in the pollution mitigation of O3. This study provides a new, comprehensive, and reasonable cognition of the current VOCs emissions from both rotary and shaft kilns in China, which will aid in a better understanding of VOCs emission characteristics and guide future policy-making.
Collapse
Affiliation(s)
- Xiaoxuan Bai
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Wei Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Bobo Wu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China; School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
| | - Shuhan Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Xiangyang Liu
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Yan Hao
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Weizhao Liang
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Shumin Lin
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Lining Luo
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Shuang Zhao
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China
| | - Chuanyong Zhu
- Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China; School of Environmental Science and Engineering, Qilu University of Technology, Jinan, 250353, China
| | - Jiming Hao
- School of Environment, Tsinghua University, Beijing, 100084, China
| | - Hezhong Tian
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China.
| |
Collapse
|
35
|
He L, You Y, Zheng X, Zhang S, Li Z, Zhang Z, Wu Y, Hao J. The impacts from cold start and road grade on real-world emissions and fuel consumption of gasoline, diesel and hybrid-electric light-duty passenger vehicles. Sci Total Environ 2022; 851:158045. [PMID: 35981594 DOI: 10.1016/j.scitotenv.2022.158045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/19/2022] [Accepted: 08/11/2022] [Indexed: 06/15/2023]
Abstract
Progressively stringent regulations regarding vehicle emissions and fuel economy have spurred technology diversification in light-duty passenger vehicles (LDPVs). To assess the real-world emissions and fuel economy performances of hybrid electric vehicles (HEVs) compared to conventional internal combustion engine (ICE) vehicles, on-road measurements of ten gasoline, four diesel and six full hybrid LDPVs were performed using portable emissions measurement systems (PEMS) in Macao, China. The hot-running emission results indicate that the high emission risks of gasoline vehicles are associated with high mileage and old model years. Diesel vehicles are found to be the highest pollutant emitters in this study due to the intentional removal of aftertreatment systems. Under hot-running conditions, HEVs, as expected, could achieve carbon-reduction benefits of approximately 30 % (i.e., lower CO2 emissions and fuel consumption) compared to their conventional gasoline counterparts, while no measurable reduction in pollutant emissions was observed except in NOX (~70 % reduction). In contrast, the cold-start extra emissions (CSEEs) of CO2 reached 120-364 g/start for these HEVs, even exceeding the maximum values of conventional gasoline vehicles. However, the higher CO2 CSEEs of HEVs can be far offset by their hot-running emission reduction benefits. For tailpipe pollutants, the CSEEs of the HEVs were reduced by 21 %-68 % on average in comparison to those of conventional gasoline vehicles. Furthermore, strong correlations (R2 values of 0.69-0.89) between the road grades and relative emissions were observed. These results can provide necessary information regarding the improvement of future LDPV emission models and inventories.
Collapse
Affiliation(s)
- Liqiang He
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Yan You
- National Observation and Research Station of Coastal Ecological Environments in Macao, Macao Environmental Research Institute, Macau University of Science and Technology, Macao SAR 999078, China
| | - Xuan Zheng
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China; Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| | - Zhenhua Li
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Zikai Zhang
- National Observation and Research Station of Coastal Ecological Environments in Macao, Macao Environmental Research Institute, Macau University of Science and Technology, Macao SAR 999078, China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China; Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China.
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China; Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| |
Collapse
|
36
|
Brökelmann K, Köller N, Linnartz C, Hao J, Wessling M. Lithium recovery and concentration by flow‐electrode capacitive deionization for a sustainable use of lithium‐ion batteries. CHEM-ING-TECH 2022. [DOI: 10.1002/cite.202271208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- K. Brökelmann
- RWTH Aachen University Chair of Chemical Process Engineering Forckenbeckstr. 51 52074 Aachen Germany
| | - N. Köller
- RWTH Aachen University Chair of Chemical Process Engineering Forckenbeckstr. 51 52074 Aachen Germany
| | - C. Linnartz
- RWTH Aachen University Chair of Chemical Process Engineering Forckenbeckstr. 51 52074 Aachen Germany
- DWI – Leibniz Institute for Interactive Materials Forckenbeckstr. 50 52074 Aachen Germany
| | - J. Hao
- RWTH Aachen University Chair of Chemical Process Engineering Forckenbeckstr. 51 52074 Aachen Germany
| | - M. Wessling
- RWTH Aachen University Chair of Chemical Process Engineering Forckenbeckstr. 51 52074 Aachen Germany
- DWI – Leibniz Institute for Interactive Materials Forckenbeckstr. 50 52074 Aachen Germany
| |
Collapse
|
37
|
Huang H, Yang Y, Liao L, Hao J, Zhou Y. High-Risk pT1-2N0 Breast Cancer may Benefit from Postmastectomy Radiotherapy: A Risk Stratification Analysis Based on Nomogram. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
38
|
Luo L, Bai X, Liu S, Wu B, Liu W, Lv Y, Guo Z, Lin S, Zhao S, Hao Y, Hao J, Zhang K, Zheng A, Tian H. Fine particulate matter (PM 2.5/PM 1.0) in Beijing, China: Variations and chemical compositions as well as sources. J Environ Sci (China) 2022; 121:187-198. [PMID: 35654509 DOI: 10.1016/j.jes.2021.12.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/02/2021] [Accepted: 12/11/2021] [Indexed: 05/16/2023]
Abstract
Particulate matter (i.e., PM1.0 and PM2.5), considered as the key atmospheric pollutants, exerts negative effects on visibility, global climate, and human health by associated chemical compositions. However, our understanding of PM and its chemical compositions in Beijing under the current atmospheric environment is still not complete after witnessing marked alleviation during 2013-2017. Continuous measurements can be crucial for further air quality improvement by better characterizing PM pollution and chemical compositions in Beijing. Here, we conducted simultaneous measurements on PM in Beijing during 2018-2019. Results indicate that annual mean PM1.0 and PM2.5 concentrations were 35.49 ± 18.61 µg/m3 and 66.58 ± 60.17 µg/m3, showing a positive response to emission controls. The contribution of sulfate, nitrate, and ammonium (SNA) played an enhanced role with elevated PM loading and acted as the main contributors to pollution episodes. Discrepancies observed among chemical species between PM1.0 and PM2.5 in spring suggest that sand particles trend to accumulate in the range of 1-2.5 µm. Pollution episodes occurred accompanied with southerly clusters and high formation of SNA by heterogeneous reactions in summer and winter, respectively. Results from positive matrix factorization (PMF) combined with potential source contribution function (PSCF) models showed that potential areas were seasonal dependent, secondary and vehicular sources became much more important compared with previous studies in Beijing. Our study presented a continuous investigation on PM and sources origins in Beijing, which provides a better understanding for further emission control as well as a reference for other cities in developing countries.
Collapse
Affiliation(s)
- Lining Luo
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Xiaoxuan Bai
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shuhan Liu
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Bobo Wu
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Wei Liu
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yunqian Lv
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Zhihui Guo
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shumin Lin
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Shuang Zhao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Yan Hao
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China
| | - Jiming Hao
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Kai Zhang
- Department of Environmental Health Sciences School of Public Health University at Albany, State University of New York One University Place Rensselaer, NY 12144, USA
| | - Aihua Zheng
- Analysis and Testing Center, Beijing Normal University, Beijing 100875, China
| | - Hezhong Tian
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China; Center for Atmospheric Environmental Studies, Beijing Normal University, Beijing 100875, China.
| |
Collapse
|
39
|
Yang B, Ma Q, Hao J, Sun X. Peroxymonosulfate Activation by Palladium(II) for Pollutants Degradation: A Study on Reaction Mechanism and Molecular Structural Characteristics. Int J Environ Res Public Health 2022; 19:13036. [PMID: 36293612 PMCID: PMC9603282 DOI: 10.3390/ijerph192013036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/07/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
Compared with certain transition metals (e.g., iron, cobalt, and manganese), noble metals are less frequently applied in peroxymonosulfate (PMS)-based advanced oxidation processes (AOPs). Palladium (Pd), as one of noble metals, has been reported to possess the possibility of both radical mechanisms and electron transfer mechanisms in a heterogeneous Pd/PMS system, however, data are still sparse on the homogeneous Pd/PMS system. Therefore, this work aims to explore the homogeneous reactivity of PMS by Pd(II) ions from the aspects of reaction parameters, radical or non-radical oxidation mechanisms, and the relationship between pollutants' degradation rate and their molecular descriptors based on both experimental data and density functional theory (DFT) calculation results. As a result, the reaction mechanism of Pd(II)/PMS followed a radical-driven oxidation process, where sulfate radicals (SO4•-), rather than hydroxyl radicals (HO•), were the primary reactive oxidant species. BOx and EHOMO played significant roles in pollutant degradation during the Pd(II)/PMS system. It turned out that the bond's stability and electron donation ability of the target compound was responsible for its degradation performance. This finding provides an insight into PMS activation by a noble metal, which has significant implications for scientific research and technical development.
Collapse
Affiliation(s)
- Bowen Yang
- Sichuan Provincial Engineering Research Center of City Solid Waste Energy and Buliding Materials Conversion & Utilization Technology, Chengdu University, Chengdu 610106, China
- School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China
| | - Qiang Ma
- Sichuan Provincial Engineering Research Center of City Solid Waste Energy and Buliding Materials Conversion & Utilization Technology, Chengdu University, Chengdu 610106, China
- School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Xiaojie Sun
- Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin 541004, China
| |
Collapse
|
40
|
Zhang S, Yang Y, Wen Z, Peng M, Zhou Y, Hao J. Sustainable development trial undertaking: Experience from China's innovation demonstration zones. J Environ Manage 2022; 318:115370. [PMID: 35752003 DOI: 10.1016/j.jenvman.2022.115370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/12/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
In December 2016, China proposed creating about ten sustainable development demonstration zones to create a batch of replicable and extendable demonstration models to fully realize the 2030 sustainable development goals (SDGs) and provide a reference for similar regions of emerging economies. It has now approved six cities that act as green and low carbon lifestyle laboratories. However, very few documents quantitatively evaluate this policy's natural, economic, and social impact. This article comprehensively uses dynamic stochastic general equilibrium (DSGE) methods and input-output methods to portray the urgency of sustainable development in China. This article sets the sustainable indicator system for the approved six cities and sets scenario simulations based on transformation needs for quantitative evaluation. The results show that demonstration zones policies would lead to a decline in the output of heavily polluting industries. However, in China's current coal-dominated energy structure, the degree of positive impact on the growth of clean industry output would be less than the intensity of the impact on heavily polluting industries.
Collapse
Affiliation(s)
- Sheng Zhang
- School of Environment, Tsinghua University, 30 Shuangqing Road, Beijing, 100084, China
| | - Yifu Yang
- School of Environment & Natural Resources, Renmin University of China, No.59 Zhongguancun Street, Haidian District, Beijing, 100872, China
| | - Zuhui Wen
- School of Environment & Natural Resources, Renmin University of China, No.59 Zhongguancun Street, Haidian District, Beijing, 100872, China
| | - Meng Peng
- School of Environment, Tsinghua University, 30 Shuangqing Road, Beijing, 100084, China
| | - Yunqiao Zhou
- State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jiming Hao
- School of Environment, Tsinghua University, 30 Shuangqing Road, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China.
| |
Collapse
|
41
|
Brökelmann K, Köller N, Linnartz C, Hao J, Wessling M. Lithium recovery and concentration by flow‐electrode capacitive deionization for a sustainable use of lithium‐ion batteries. CHEM-ING-TECH 2022. [DOI: 10.1002/cite.202255383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- K. Brökelmann
- RWTH Aachen University Chair of Chemical Process Engineering Forckenbeckstr. 51 52074 Aachen Germany
| | - N. Köller
- RWTH Aachen University Chair of Chemical Process Engineering Forckenbeckstr. 51 52074 Aachen Germany
| | - C. Linnartz
- RWTH Aachen University Chair of Chemical Process Engineering Forckenbeckstr. 51 52074 Aachen Germany
- DWI – Leibniz Institute for Interactive Materials Forckenbeckstr. 50 52074 Aachen Germany
| | - J. Hao
- RWTH Aachen University Chair of Chemical Process Engineering Forckenbeckstr. 51 52074 Aachen Germany
| | - M. Wessling
- RWTH Aachen University Chair of Chemical Process Engineering Forckenbeckstr. 51 52074 Aachen Germany
- DWI – Leibniz Institute for Interactive Materials Forckenbeckstr. 50 52074 Aachen Germany
| |
Collapse
|
42
|
Dong Z, Xing J, Wang S, Ding D, Ge X, Zheng H, Jiang Y, An J, Huang C, Duan L, Hao J. Responses of nitrogen and sulfur deposition to NH 3 emission control in the Yangtze River Delta, China. Environ Pollut 2022; 308:119646. [PMID: 35718044 DOI: 10.1016/j.envpol.2022.119646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/11/2022] [Accepted: 06/14/2022] [Indexed: 06/15/2023]
Abstract
NH3 emission control has proven to be of great importance in reducing PM2.5 concentrations in China, while how it affects nitrogen/sulfur (N/S) deposition is still unclear. This study expanded the response surface model method to quantify the responses of N/S deposition to the emission control of precursors (NOx, SO2, NH3, VOCs and primary PM2.5) in the Yangtze River Delta, China. NH3 control was found to have higher efficiency in reducing N/S deposition than NOx and SO2 alone. The reduced N deposition response to NH3 emission control was higher in the northern part of the YRD region, whereas oxidized N deposition decreased sharply in the region with a low N critical load. Synergetic effect was found in reducing N deposition when we controlled the NH3 and NOx emissions simultaneously. Compared with the sum effect of individual NH3 and NOx emission control, the extra benefits from the synergy controls accounted for 4.4% (1.23 kg N·ha-1·yr-1) of the total N deposition, of which 81% came from the oxidized N deposition. The YRD region could receive the largest synergetic effect with a 1:1 ratio of NOx:NH3 emission reduction. The NH3 emission control increases the dry deposition of acid substances and worsens acid rain though it reduces the wet S/oxidized N deposition. These findings highlight the effectiveness of NH3 emission control and suggest a multi-pollutant control strategy for reducing N/S deposition. The response surface model method for deposition also provides a reference for other regions in China and other countries.
Collapse
Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China.
| | - Dian Ding
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China; Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014, Helsinki, Finland
| | - Xiaodong Ge
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Jingyu An
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Lei Duan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| |
Collapse
|
43
|
Xie Y, Chen J, Wu X, Wen J, Zhao R, Li Z, Tian G, Zhang Q, Ning P, Hao J. Frustrated Lewis Pairs Boosting Low-Temperature CO 2 Methanation Performance over Ni/CeO 2 Nanocatalysts. ACS Catal 2022. [DOI: 10.1021/acscatal.2c02535] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Yu Xie
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
- National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries, Kunming University of Science and Technology, Kunming 650500, P. R. China
| | - Jianjun Chen
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
- National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries, Kunming University of Science and Technology, Kunming 650500, P. R. China
| | - Xi Wu
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
| | - Junjie Wen
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
- National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries, Kunming University of Science and Technology, Kunming 650500, P. R. China
| | - Ru Zhao
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
- National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries, Kunming University of Science and Technology, Kunming 650500, P. R. China
| | - Zonglin Li
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
- National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries, Kunming University of Science and Technology, Kunming 650500, P. R. China
| | - Guocai Tian
- Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China
| | - Qiulin Zhang
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
- National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries, Kunming University of Science and Technology, Kunming 650500, P. R. China
| | - Ping Ning
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
- National-Regional Engineering Center for Recovery of Waste Gases from Metallurgical and Chemical Industries, Kunming University of Science and Technology, Kunming 650500, P. R. China
| | - Jiming Hao
- Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, P. R. China
| |
Collapse
|
44
|
Cai R, Yin R, Yan C, Yang D, Deng C, Dada L, Kangasluoma J, Kontkanen J, Halonen R, Ma Y, Zhang X, Paasonen P, Petäjä T, Kerminen VM, Liu Y, Bianchi F, Zheng J, Wang L, Hao J, Smith JN, Donahue NM, Kulmala M, Worsnop DR, Jiang J. The missing base molecules in atmospheric acid-base nucleation. Natl Sci Rev 2022; 9:nwac137. [PMID: 36196118 PMCID: PMC9522409 DOI: 10.1093/nsr/nwac137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 07/06/2022] [Accepted: 07/08/2022] [Indexed: 11/30/2022] Open
Abstract
Transformation of low-volatility gaseous precursors to new particles affects aerosol number concentration, cloud formation and hence the climate. The clustering of acid and base molecules is a major mechanism driving fast nucleation and initial growth of new particles in the atmosphere. However, the acid–base cluster composition, measured using state-of-the-art mass spectrometers, cannot explain the measured high formation rate of new particles. Here we present strong evidence for the existence of base molecules such as amines in the smallest atmospheric sulfuric acid clusters prior to their detection by mass spectrometers. We demonstrate that forming (H2SO4)1(amine)1 is the rate-limiting step in atmospheric H2SO4-amine nucleation and the uptake of (H2SO4)1(amine)1 is a major pathway for the initial growth of H2SO4 clusters. The proposed mechanism is very consistent with measured new particle formation in urban Beijing, in which dimethylamine is the key base for H2SO4 nucleation while other bases such as ammonia may contribute to the growth of larger clusters. Our findings further underline the fact that strong amines, even at low concentrations and when undetected in the smallest clusters, can be crucial to particle formation in the planetary boundary layer.
Collapse
Affiliation(s)
- Runlong Cai
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University , Beijing , 100084 , China
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
| | - Rujing Yin
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University , Beijing , 100084 , China
| | - Chao Yan
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology , Beijing , 100029 , China
| | - Dongsen Yang
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology , Nanjing , 210044 , China
| | - Chenjuan Deng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University , Beijing , 100084 , China
| | - Lubna Dada
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
- Laboratory of Atmospheric Chemistry, Paul Scherrer Institute , Villigen , 5232 , Switzerland
| | - Juha Kangasluoma
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
| | - Jenni Kontkanen
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
| | - Roope Halonen
- Center for Joint Quantum Studies and Department of Physics, School of Science, Tianjin University , 135 Yaguan Road , Tianjin , 300350 , China
| | - Yan Ma
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology , Nanjing , 210044 , China
| | - Xiuhui Zhang
- Key Laboratory of Cluster Science, Ministry of Education of China, School of Chemistry and Chemical Engineering, Beijing Institute of Technology , Beijing , 100081 , China
| | - Pauli Paasonen
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
| | - Tuukka Petäjä
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
| | - Veli-Matti Kerminen
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
| | - Yongchun Liu
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology , Beijing , 100029 , China
| | - Federico Bianchi
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
| | - Jun Zheng
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology , Nanjing , 210044 , China
| | - Lin Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University , Shanghai , 200433 , China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University , Beijing , 100084 , China
| | - James N Smith
- Chemistry Department, University of California , Irvine , CA 92697 , USA
| | - Neil M Donahue
- Center for Atmospheric Particle Studies, Carnegie Mellon University , Pittsburgh , PA 15213 , USA
- Department of Chemistry, Carnegie Mellon University , Pittsburgh , PA 15213 , USA
| | - Markku Kulmala
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
| | - Douglas R Worsnop
- Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki , Helsinki , 00014 , Finland
- Aerodyne Research Inc., Billerica , MA , MA 01821 , USA
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University , Beijing , 100084 , China
| |
Collapse
|
45
|
Li X, Li Y, Cai R, Yan C, Qiao X, Guo Y, Deng C, Yin R, Chen Y, Li Y, Yao L, Sarnela N, Zhang Y, Petäjä T, Bianchi F, Liu Y, Kulmala M, Hao J, Smith JN, Jiang J. Insufficient Condensable Organic Vapors Lead to Slow Growth of New Particles in an Urban Environment. Environ Sci Technol 2022; 56:9936-9946. [PMID: 35749221 DOI: 10.1021/acs.est.2c01566] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Atmospheric new particle formation significantly affects global climate and air quality after newly formed particles grow above ∼50 nm. In polluted urban atmospheres with 1-3 orders of magnitude higher new particle formation rates than those in clean atmospheres, particle growth rates are comparable or even lower for reasons that were previously unclear. Here, we address the slow growth in urban Beijing with advanced measurements of the size-resolved molecular composition of nanoparticles using the thermal desorption chemical ionization mass spectrometer and the gas precursors using the nitrate CI-APi-ToF. A particle growth model combining condensational growth and particle-phase acid-base chemistry was developed to explore the growth mechanisms. The composition of 8-40 nm particles during new particle formation events in urban Beijing is dominated by organics (∼80%) and sulfate (∼13%), and the remainder is from base compounds, nitrate, and chloride. With the increase in particle sizes, the fraction of sulfate decreases, while that of the slow-desorbed organics, organic acids, and nitrate increases. The simulated size-resolved composition and growth rates are consistent with the measured results in most cases, and they both indicate that the condensational growth of organic vapors and H2SO4 is the major growth pathway and the particle-phase acid-base reactions play a minor role. In comparison to the high concentrations of gaseous sulfuric acid and amines that cause high formation rates, the concentration of condensable organic vapors is comparably lower under the high NOx levels, while those of the relatively high-volatility nitrogen-containing oxidation products are higher. The insufficient condensable organic vapors lead to slow growth, which further causes low survival of the newly formed particles in urban environments. Thus, the low growth rates, to some extent, counteract the impact of the high formation rates on air quality and global climate in urban environments.
Collapse
Affiliation(s)
- Xiaoxiao Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Yuyang Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Runlong Cai
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Chao Yan
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Xiaohui Qiao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Yishuo Guo
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Chenjuan Deng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Rujing Yin
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Yijing Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Yiran Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - Lei Yao
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Nina Sarnela
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Yusheng Zhang
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Tuukka Petäjä
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Federico Bianchi
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Yongchun Liu
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Markku Kulmala
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
- Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| | - James N Smith
- Chemistry Department, University of California, Irvine, California 92697, United Sates
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, 100084 Beijing, China
| |
Collapse
|
46
|
Xing J, Li S, Zheng S, Liu C, Wang X, Huang L, Song G, He Y, Wang S, Sahu SK, Zhang J, Bian J, Zhu Y, Liu TY, Hao J. Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder. Environ Sci Technol 2022; 56:9903-9914. [PMID: 35793558 DOI: 10.1021/acs.est.1c08337] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Accurate timely estimation of emissions of nitrogen oxides (NOx) is a prerequisite for designing an effective strategy for reducing O3 and PM2.5 pollution. The satellite-based top-down method can provide near-real-time constraints on emissions; however, its efficiency is largely limited by efforts in dealing with the complex emission-concentration response. Here, we propose a novel machine-learning-based method using a physically informed variational autoencoder (VAE) emission predictor to infer NOx emissions from satellite-retrieved surface NO2 concentrations. The computational burden can be significantly reduced with the help of a neural network trained with a chemical transport model, allowing the VAE emission predictor to provide a timely estimation of posterior emissions based on the satellite-retrieved surface NO2 concentration. The VAE emission predictor successfully corrected the underestimation of NOx emissions in rural areas and the overestimation in urban areas, resulting in smaller normalized mean biases (reduced from -0.8 to -0.4) and larger R2 values (increased from 0.4 to 0.7). The interpretability of the VAE emission predictor was investigated using sensitivity analysis by modulating each feature, indicating that NO2 concentration and planetary boundary layer (PBL) height are important for estimating NOx emissions, which is consistent with our common knowledge. The advantages of the VAE emission predictor in efficiency, flexibility, and accuracy demonstrate its great potential in estimating the latest emissions and evaluating the control effectiveness from observations.
Collapse
Affiliation(s)
- Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Siwei Li
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | | | - Chang Liu
- Microsoft Research Asia, Beijing 100080, China
| | - Xiaochun Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Lin Huang
- Microsoft Research Asia, Beijing 100080, China
| | - Ge Song
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Yihan He
- Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shovan Kumar Sahu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jia Zhang
- Microsoft Research Asia, Beijing 100080, China
| | - Jiang Bian
- Microsoft Research Asia, Beijing 100080, China
| | - Yun Zhu
- College of Environment and Energy, Guangzhou Higher Education Mega Center, South China University of Technology, Guangzhou 510006, China
| | - Tie-Yan Liu
- Microsoft Research Asia, Beijing 100080, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
47
|
Yang B, Ma Q, Ren X, Peng X, Wang H, Li L, Hao J. Supercritical Water Oxidation of Aniline, Nitrobenzene, and Indole: Effect of Catalysts on Nitrogen Conversion Mechanism. J Supercrit Fluids 2022. [DOI: 10.1016/j.supflu.2022.105680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
48
|
Hu R, Wang S, Zheng H, Zhao B, Liang C, Chang X, Jiang Y, Yin R, Jiang J, Hao J. Variations and Sources of Organic Aerosol in Winter Beijing under Markedly Reduced Anthropogenic Activities During COVID-2019. Environ Sci Technol 2022; 56:6956-6967. [PMID: 34786936 PMCID: PMC8610015 DOI: 10.1021/acs.est.1c05125] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/24/2021] [Accepted: 11/05/2021] [Indexed: 05/19/2023]
Abstract
The COVID-19 outbreak provides a "controlled experiment" to investigate the response of aerosol pollution to the reduction of anthropogenic activities. Here we explore the chemical characteristics, variations, and emission sources of organic aerosol (OA) based on the observation of air pollutants and combination of aerosol mass spectrometer (AMS) and positive matrix factorization (PMF) analysis in Beijing in early 2020. By eliminating the impacts of atmospheric boundary layer and the Spring Festival, we found that the lockdown effectively reduced cooking-related OA (COA) but influenced fossil fuel combustion OA (FFOA) very little. In contrast, both secondary OA (SOA) and O3 formation was enhanced significantly after lockdown: less-oxidized oxygenated OA (LO-OOA, 37% in OA) was probably an aged product from fossil fuel and biomass burning emission with aqueous chemistry being an important formation pathway, while more-oxidized oxygenated OA (MO-OOA, 41% in OA) was affected by regional transport of air pollutants and related with both aqueous and photochemical processes. Combining FFOA and LO-OOA, more than 50% of OA pollution was attributed to combustion activities during the whole observation period. Our findings highlight that fossil fuel/biomass combustion are still the largest sources of OA pollution, and only controlling traffic and cooking emissions cannot efficiently eliminate the heavy air pollution in winter Beijing.
Collapse
Affiliation(s)
- Ruolan Hu
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Chengrui Liang
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Xing Chang
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Rujing Yin
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation
and Pollution Control, School of Environment, Tsinghua
University, Beijing 100084, China
- State Environmental Protection Key Laboratory of
Sources and Control of Air Pollution Complex, School of Environment, Tsinghua
University, Beijing 100084, China
| |
Collapse
|
49
|
Zhang J, Zhang S, Zhang X, Wang J, Wu Y, Hao J. Developing a High-Resolution Emission Inventory of China's Aviation Sector Using Real-World Flight Trajectory Data. Environ Sci Technol 2022; 56:5743-5752. [PMID: 35418234 DOI: 10.1021/acs.est.1c08741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Economic growth and globalization have led to a surge in civil aviation transportation demand. Among the major economies in the world, China has experienced a 12-fold increase in terms of total passenger aviation traffic volume since 2000 and is expected to be the largest aviation market soon. To better understand the environmental impacts of China's aviation sector, this study developed a real-world flight trajectory-based emission inventory, which enabled the fine-grained characterization of four-dimensional (time, longitude, latitude, and altitude) emissions of various flight stages. Our results indicated that fuel consumption and CO2 emissions showed two peaks in altitude distribution: below 1,000 m and between 8,000 and 12,000 m. Various pollutants depicted different vertical distributions; for example, nitrogen oxides (NOX) had a higher fraction during the high-altitude cruise stage due to the thermal NOX mechanism, while hydrocarbons had a dominant fraction at the low-altitude stages due to the incomplete combustion under low-load conditions. This improved aviation emission inventory approach identified that total emissions of CO2 and air pollutants from short-distance domestic flights would be significantly underestimated by the conventional great-circle-based approach due to underrepresented calculation parameters (particularly, flight distance, duration, and cruise altitude). Therefore, we suggest that more real-world aviation flight information, especially actual trajectory records, should be utilized to improve assessments of the environmental impacts of aviation.
Collapse
Affiliation(s)
- Jingran Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| | - Xiaole Zhang
- Institute of Environmental Engineering (IfU), ETH Zürich, Zürich CH-8093, Switzerland
| | - Jing Wang
- Institute of Environmental Engineering (IfU), ETH Zürich, Zürich CH-8093, Switzerland
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
| |
Collapse
|
50
|
Wang J, Xing J, Wang S, Mathur R, Wang J, Zhang Y, Liu C, Pleim J, Ding D, Chang X, Jiang J, Zhao P, Sahu SK, Jin Y, Wong DC, Hao J. The pathway of impacts of aerosol direct effects on secondary inorganic aerosol formation. Atmos Chem Phys 2022; 22:5147-5156. [PMID: 36033648 PMCID: PMC9413026 DOI: 10.5194/acp-22-5147-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Airborne aerosols reduce surface solar radiation through light scattering and absorption (aerosol direct effects, ADEs), influence regional meteorology, and further affect atmospheric chemical reactions and aerosol concentrations. The inhibition of turbulence and the strengthened atmospheric stability induced by ADEs increases surface primary aerosol concentration, but the pathway of ADE impacts on secondary aerosol is still unclear. In this study, the online coupled meteorological and chemistry model (WRF-CMAQ; Weather Research and Forecasting-Community Multiscale Air Quality) with integrated process analysis was applied to explore how ADEs affect secondary aerosol formation through changes in atmospheric dynamics and photolysis processes. The meteorological condition and air quality in the Jing-Jin-Ji area (denoted JJJ, including Beijing, Tianjin, and Hebei Province in China) in January and July 2013 were simulated to represent winter and summer conditions, respectively. Our results show that ADEs through the photolysis pathway inhibit sulfate formation during winter in the JJJ region and promote sulfate formation in July. The differences are attributed to the alteration of effective actinic flux affected by single-scattering albedo (SSA). ADEs through the dynamics pathway act as an equally or even more important route compared with the photolysis pathway in affecting secondary aerosol concentration in both summer and winter. ADEs through dynamics traps formed sulfate within the planetary boundary layer (PBL) which increases sulfate concentration in winter. Meanwhile, the impact of ADEs through dynamics is mainly reflected in the increase of gaseous-precursor concentrations within the PBL which enhances secondary aerosol formation in summer. For nitrate, reduced upward transport of precursors restrains the formation at high altitude and eventually lowers the nitrate concentration within the PBL in winter, while such weakened vertical transport of precursors increases nitrate concentration within the PBL in summer, since nitrate is mainly formed near the surface ground.
Collapse
Affiliation(s)
- Jiandong Wang
- Key Laboratory of Aerosol and Cloud Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Rohit Mathur
- U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jiaping Wang
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
| | - Yuqiang Zhang
- Nicholas School of the Environment, Duke University, Durham, NC 27710, USA
| | - Chao Liu
- Key Laboratory of Aerosol and Cloud Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Jonathan Pleim
- U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Dian Ding
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Xing Chang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Peng Zhao
- Department of Health and Environmental Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China
| | - Shovan Kumar Sahu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yuzhi Jin
- Key Laboratory of Aerosol and Cloud Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - David C. Wong
- U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| |
Collapse
|