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Jiang H, Zhao H, Lai Y, Kang Y, Cao Y, Fu M, Ding Y. Characterizing ammonia emission from light-duty gasoline vehicles under the influence of multiple factors and its correlation with conventional pollutants. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:173967. [PMID: 38897474 DOI: 10.1016/j.scitotenv.2024.173967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/29/2024] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
Ammonia (NH3), which is a precursor of secondary particulate matter (PM), can be produced through three-way catalyst (TWC) side reactions in light-duty gasoline vehicles (LDGVs), posing a threat to human health and air quality. To explore ammonia emission characteristics, 8 LDGVs and 1 hybrid electric light-duty vehicle (HEV) with various mileages traveled were analyzed with a chassis dynamometer system during regulation driving cycles. The emission factors of the adopted China VI in-use LDGVs were 7.04 ± 2.61 mg/km under cold-start conditions and 4.94 ± 1.69 mg/km under hot-start conditions. With increasing mileage traveled, the total ammonia emissions increased, and the difference between the cold/hot-start results decreased. The emissions of in-use LDGVs with bi-fuel engines were analyzed, and more ammonia was generated in the compressed natural gas (CNG) mode through the hydrocarbon (HC) reforming reaction. The relationship between the emissions of ammonia and conventional pollutants was established. During the initial cold-start phase, a delay in ammonia formation was observed, and the ammonia emissions conformed with the CO and HC emissions after exhaust heating. Vehicle specific power (VSP) analysis revealed that the interval of highest ammonia emissions corresponded to acceleration events at high speeds. For the HEV, the transition from motor to engine drive conditions contributed to ammonia emission occurrence because of the more pronounced cold-start events. The use of HEV technology could introduce additional uncertainties in controlling urban ammonia emissions. Detailed analysis of emission characteristics could provide data support for future research on ammonia emission standards and control strategies for LDGVs.
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Affiliation(s)
- Han Jiang
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Haiguang Zhao
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yitu Lai
- Xiamen Environment Protection Vehicle Emission Control Technology Center, Xiamen 361023, China
| | - Yu Kang
- Department of Automation, University of Science and Technology of China, Hefei 230026, China
| | - Yang Cao
- Department of Automation, University of Science and Technology of China, Hefei 230026, China
| | - Mingliang Fu
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
| | - Yan Ding
- State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Vehicle Emission Control Center of Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
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2
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Mei H, Peng J, Wang T, Zhou T, Zhao H, Zhang T, Yang Z. Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array. NANO-MICRO LETTERS 2024; 16:269. [PMID: 39141168 PMCID: PMC11324646 DOI: 10.1007/s40820-024-01489-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Accepted: 07/21/2024] [Indexed: 08/15/2024]
Abstract
As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.
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Affiliation(s)
- Haixia Mei
- Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, People's Republic of China
| | - Jingyi Peng
- Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, People's Republic of China
| | - Tao Wang
- Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, People's Republic of China.
| | - Tingting Zhou
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, People's Republic of China
| | - Hongran Zhao
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, People's Republic of China
| | - Tong Zhang
- State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, People's Republic of China.
| | - Zhi Yang
- National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
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3
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Zou X, Wang S, Liu J, Zhu J, Zhang S, Xue R, Gu C, Zhou B. Role of gas-particle conversion of ammonia in haze pollution under ammonia-rich environment in Northern China and prospects of effective emission reduction. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 934:173277. [PMID: 38754510 DOI: 10.1016/j.scitotenv.2024.173277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/09/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024]
Abstract
As an important precursor of secondary inorganic aerosols (SIAs), ammonia (NH3) plays a key role in fine particulate matter (PM2.5) formation. In order to investigate its impacts on haze formation in the North China Plain (NCP) during winter, NH3 concentrations were observed at a high-temporal resolution of 1 min by using the SP-DOAS in Tai'an from December 2021 to February 2022. During the observation period, the average NH3 concentration was 11.84 ± 5.9 ppbv, and it was determined as an ammonia-rich environment during different air quality conditions. Furthermore, the average concentrations of sulfate (SO42-), nitrate (NO3-) and ammonium (NH4+) were 9.54 ± 5.97 μg/m3, 19.09 ± 14.18 μg/m3 and 10.72 ± 6.53 μg/m3, respectively. Under the nitrate-dominated atmospheric environment, aerosol liquid water content (ALWC) was crucial for NH3 particle transformation during haze aggravation, and the gas-particle partitioning of ammonia played an important role in the SIAs formation. The reconstruction of the molecular composition further indicated that ammonium nitrate (NH4NO3) plays a dominant role in the increase of PM2.5 during haze events. Consequently, future efforts to mitigate fine particulate pollution in this region should focus on controlling NH4NO3 levels. In ammonia-rich environments, NO3- formation is more dependent on the concentration of nitric acid (HNO3). The sensitive analysis of TNO3 (HNO3 + NO3-) and NHX (NH3 + NH4+) reduction using the thermodynamic model suggested that the NO3- concentration decreases linearly with the reduction of TNO3. And the concentration of NO3- decreases rapidly only when NHX is reduced by 50-60 %. Reducing NOX emissions is the most effective way to alleviate nitrate pollution in this region.
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Affiliation(s)
- Xueting Zou
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Shanshan Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), No. 20 Cuiniao Road, Shanghai 202162, China.
| | - Jiaqi Liu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jian Zhu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Sanbao Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Ruibin Xue
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Chuanqi Gu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Bin Zhou
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), No. 20 Cuiniao Road, Shanghai 202162, China; Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China.
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4
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Wen Z, Ma X, Xu W, Si R, Liu L, Ma M, Zhao Y, Tang A, Zhang Y, Wang K, Zhang Y, Shen J, Zhang L, Zhao Y, Zhang F, Goulding K, Liu X. Combined short-term and long-term emission controls improve air quality sustainably in China. Nat Commun 2024; 15:5169. [PMID: 38886390 PMCID: PMC11183230 DOI: 10.1038/s41467-024-49539-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 06/10/2024] [Indexed: 06/20/2024] Open
Abstract
The effectiveness of national policies for air pollution control has been demonstrated, but the relative effectiveness of short-term emission reduction measures in comparison with national policies has not. Here we show that short-term abatement measures during important international events substantially reduced PM2.5 concentrations, but air quality rebounded to pre-event levels after the measures ceased. Long-term adherence to strict emission reduction policies led to successful decreases of 54% in PM2.5 concentrations in Beijing, and 23% in atmospheric nitrogen deposition in China from 2012 to 2020. Incentivized by "blue skies" type campaigns, economic development and reactive nitrogen pollution are quickly decoupled, showing that a combination of inspiring but aggressive short-term measures and effective but durable long-term policies delivers sustainable air quality improvement. However, increased ammonia concentrations, transboundary pollutant flows, and the complexity to achieving reduction targets under climate change scenarios, underscore the need for the synergistic control of multiple pollutants and inter-regional action.
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Affiliation(s)
- Zhang Wen
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
- State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Xin Ma
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Wen Xu
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Ruotong Si
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Lei Liu
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Mingrui Ma
- State Key Laboratory of Pollution Control & Resource Reuse and School of Environment, Nanjing University, Nanjing, 210008, China
| | - Yuanhong Zhao
- College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China
| | - Aohan Tang
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Yangyang Zhang
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Kai Wang
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Ying Zhang
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Jianlin Shen
- Instute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China
| | - Yu Zhao
- State Key Laboratory of Pollution Control & Resource Reuse and School of Environment, Nanjing University, Nanjing, 210008, China
| | - Fusuo Zhang
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
| | - Keith Goulding
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, AL5 2JQ, UK
| | - Xuejun Liu
- State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China.
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5
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Lee YJ, Lin BL, Inoue K. Inorganic PM 2.5 reduction in Kanto, Japan: The role of ammonia and its emission sources control strategies. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 349:123926. [PMID: 38580059 DOI: 10.1016/j.envpol.2024.123926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/07/2024]
Abstract
Ammonia (NH3) is attracting attention as a carbon-free energy source and a significant precursor to inorganic PM2.5 (hereafter PM2.5), aside from NOx and SOx. Since the emission of NH3 has often been overlooked compared to NOx and SOx, this study aims to reveal the role of NH3 and its emission control on PM2.5 in Kanto, Japan. With the aid of gas ratio (GR) quantitatively defining the stoichiometry between the three precursors to PM2.5, and the aid of atmospheric modeling software ADMER-PRO, coupled with thermodynamics calculations, the spatiotemporal distribution along with PM2.5 reduction under different NH3 emission cutoff strategies in Kanto had been revealed for the first time. The cutoff of NH3 emission could effectively reduce the PM2.5 concentration, with sources originated from agriculture, human/pet activities, and vehicle sources, overall giving a 93.32% PM2.5 reduction. Different cutoff strategies lead to distinct reduction efficiencies of the overall and local PM2.5 concentrations, with GR as a crucial factor. The regions with GR ∼1, are sensitive to the NH3 concentration for forming PM2.5, at which the NH3 reduction strategies should be applied with high priority. On the other hand, installing a new NH3 emission source should be avoided in the region with GR < 1, suppressing the so-yielded PM2.5 pollution. The future PM2.5 pollution control related to the NH3 emission control strategies based on GR, which is stoichiometry-based and applicable to regions other than Kanto, has been discussed.
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Affiliation(s)
- Yu-Jen Lee
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), 16-1 Onogawa, Tsukuba, Ibaraki, 305-8569, Japan
| | - Bin-Le Lin
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), 16-1 Onogawa, Tsukuba, Ibaraki, 305-8569, Japan.
| | - Kazuya Inoue
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), 16-1 Onogawa, Tsukuba, Ibaraki, 305-8569, Japan
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6
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Xu L, Bao Y, Man H, Zhang Z, Chen J, Shao X, Zhu B, Liu H. Influencing factors on ammonia emissions from gasoline vehicles: A systematic review and meta-analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 923:171467. [PMID: 38447721 DOI: 10.1016/j.scitotenv.2024.171467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/18/2024] [Accepted: 03/02/2024] [Indexed: 03/08/2024]
Abstract
Ammonia, a significant precursor for secondary inorganic aerosols, plays a pivotal role in new particle formation. Inventories and source apportionment studies have identified vehicular exhaust as a primary source of atmospheric ammonia in urban regions. Existing research on the factors influencing ammonia emissions from gasoline vehicles exhibits substantial inconsistencies in both test results and analyses. The lack of a uniform pattern in ammonia emissions across different standard vehicles and the significant overlap in test results across diverse operational conditions highlight the complexities in this field of study. While individual results can be interpreted through a mechanistic lens, disparate studies often lack a common explanatory framework. To address this gap, our study leverages the robust and comprehensive approach of meta-analysis to reconcile these inconsistencies and provide a more precise understanding of the factors influencing ammonia emissions from gasoline vehicles. A large number (N = 537) of ammonia emission factors were extracted after screening >1628 publications. The combined ammonia emission factor was 23.57 ± 24.94 mg/km. Emission standards, engine type, ambient temperatures, mileage, vehicle speed, and engine displacement have a significant impact on ammonia emission factors, explaining the ammonia emission factor by up to 50.63 %, with speed being the most significant factor. All these factors are attributed to the interplay of catalyst properties, lambda, and residence time (space velocity). In the current fleet, ammonia emission control is relatively insufficient under low-speed and ultra-high speed, low temperature, and ultra-high mileage conditions. Since ammonia emission factors do not monotonically decrease with the upgrading of motor vehicle emission standards, it is called for the addition of ammonia emission factors indicators in motor vehicle emission standards, and stipulation of targeted testing procedures and testing instruments.
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Affiliation(s)
- Lizhong Xu
- College of Environmental and Resource Sciences, Fujian Key Laboratory of Pollution Control & Resource Reuse, Fujian Normal University, Fuzhou 350007, China; Digital Fujian Internet-of-things Laboratory of Environmental Monitoring, Fuzhou 350007, China
| | - Yumeng Bao
- College of Environmental and Resource Sciences, Fujian Key Laboratory of Pollution Control & Resource Reuse, Fujian Normal University, Fuzhou 350007, China
| | - Hanyang Man
- College of Environmental and Resource Sciences, Fujian Key Laboratory of Pollution Control & Resource Reuse, Fujian Normal University, Fuzhou 350007, China; Digital Fujian Internet-of-things Laboratory of Environmental Monitoring, Fuzhou 350007, China.
| | - Zhining Zhang
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jiawei Chen
- College of Environmental and Resource Sciences, Fujian Key Laboratory of Pollution Control & Resource Reuse, Fujian Normal University, Fuzhou 350007, China
| | - Xiaohan Shao
- College of Environmental and Resource Sciences, Fujian Key Laboratory of Pollution Control & Resource Reuse, Fujian Normal University, Fuzhou 350007, China
| | - Bo Zhu
- College of Environmental and Resource Sciences, Fujian Key Laboratory of Pollution Control & Resource Reuse, Fujian Normal University, Fuzhou 350007, China; Digital Fujian Internet-of-things Laboratory of Environmental Monitoring, Fuzhou 350007, China
| | - Huan Liu
- State Key Joint Laboratory of ESPC, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing 100084, China
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7
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Li Z, Xiao H, Walters WW, Hastings MG, Min J, Song L, Lu W, Wu L, Yan W, Liu S, Fang Y. Nitrogen isotopic characteristics of aerosol ammonium in a Chinese megacity indicate the reduction from vehicle emissions during the lockdown period. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 922:171265. [PMID: 38417516 DOI: 10.1016/j.scitotenv.2024.171265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/01/2024] [Accepted: 02/23/2024] [Indexed: 03/01/2024]
Abstract
The role of agricultural versus vehicle emissions in urban atmospheric ammonia (NH3) remains unclear. The lockdown due to the outbreak of COVID-19 provided an opportunity to assess the role of source emissions on urban NH3. Concentrations and δ15N of aerosol ammonium (NH4+) were measured before (autumn in 2017) and during the lockdown (summer, autumn, and winter in 2020), and source contributions were quantified using SIAR. Despite the insignificant decrease in NH4+ concentrations, significantly lower δ15N-NH4+ was found in 2020 (0.6 ± 1.0‰ in PM2.5 and 1.4 ± 2.1‰ in PM10) than in 2017 (15.2 ± 6.7‰ in PM2.5), which indicates the NH3 from vehicle emissions has decreased by∼50% during the lockdown while other source emissions are less affected. Moreover, a reversed seasonal pattern of δ15N-NH4+ during the lockdown in Changsha has been revealed compared to previous urban studies, which can be explained by the dominant effect of non-fossil fuel emissions due to the reductions of vehicle emissions during the lockdown period. Our results highlight the effects of lockdown on aerosol δ15N-NH4+ and the importance of vehicle emissions to urban atmospheric NH3, providing conclusive evidence that reducing vehicle NH3 emissions could be an effective strategy to reduce PM2.5 in Chinese megacities.
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Affiliation(s)
- Zhengjie Li
- National Engineering Laboratory for Applied Technology of Forestry & Ecology in Southern China, College of Biological Science and Technology, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
| | - Hongwei Xiao
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Wendell W Walters
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA
| | - Meredith G Hastings
- Institute at Brown for Environment and Society, Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI 02912, USA
| | - Juan Min
- National Engineering Laboratory for Applied Technology of Forestry & Ecology in Southern China, College of Biological Science and Technology, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
| | - Linlin Song
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China; Key Laboratory of Stable Isotope Techniques and Applications, Liaoning Province 110016, China
| | - Weizhi Lu
- National Engineering Laboratory for Applied Technology of Forestry & Ecology in Southern China, College of Biological Science and Technology, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
| | - Libin Wu
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Wende Yan
- National Engineering Laboratory for Applied Technology of Forestry & Ecology in Southern China, College of Biological Science and Technology, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
| | - Shuguang Liu
- National Engineering Laboratory for Applied Technology of Forestry & Ecology in Southern China, College of Biological Science and Technology, Central South University of Forestry and Technology, Changsha, Hunan 410004, China.
| | - Yunting Fang
- CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China; Key Laboratory of Stable Isotope Techniques and Applications, Liaoning Province 110016, China; Qingyuan Forest CERN, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China.
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8
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Wu L, Wang P, Zhang Q, Ren H, Shi Z, Hu W, Chen J, Xie Q, Li L, Yue S, Wei L, Song L, Zhang Y, Wang Z, Chen S, Wei W, Wang X, Zhang Y, Kong S, Ge B, Yang T, Fang Y, Ren L, Deng J, Sun Y, Wang Z, Zhang H, Hu J, Liu CQ, Harrison RM, Ying Q, Fu P. Dominant contribution of combustion-related ammonium during haze pollution in Beijing. Sci Bull (Beijing) 2024; 69:978-987. [PMID: 38242834 DOI: 10.1016/j.scib.2024.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/08/2023] [Accepted: 12/12/2023] [Indexed: 01/21/2024]
Abstract
Aerosol ammonium (NH4+), mainly produced from the reactions of ammonia (NH3) with acids in the atmosphere, has significant impacts on air pollution, radiative forcing, and human health. Understanding the source and formation mechanism of NH4+ can provide scientific insights into air quality improvements. However, the sources of NH3 in urban areas are not well understood, and few studies focus on NH3/NH4+ at different heights within the atmospheric boundary layer, which hinders a comprehensive understanding of aerosol NH4+. In this study, we perform both field observation and modeling studies (the Community Multiscale Air Quality, CMAQ) to investigate regional NH3 emission sources and vertically resolved NH4+ formation mechanisms during the winter in Beijing. Both stable nitrogen isotope analyses and CMAQ model suggest that combustion-related NH3 emissions, including fossil fuel sources, NH3 slip, and biomass burning, are important sources of aerosol NH4+ with more than 60% contribution occurring on heavily polluted days. In contrast, volatilization-related NH3 sources (livestock breeding, N-fertilizer application, and human waste) are dominant on clean days. Combustion-related NH3 is mostly local from Beijing, and biomass burning is likely an important NH3 source (∼15%-20%) that was previously overlooked. More effective control strategies such as the two-product (e.g., reducing both SO2 and NH3) control policy should be considered to improve air quality.
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Affiliation(s)
- Libin Wu
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Peng Wang
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China; IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Hong Ren
- Air Environmental Modeling and Pollution Controlling Key Laboratory of Sichuan Higher Education Institute, Chengdu University of Information Technology, Chengdu 610225, China
| | - Zongbo Shi
- Division of Environmental Health & Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Wei Hu
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Jing Chen
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Qiaorong Xie
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Linjie Li
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Siyao Yue
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Lianfang Wei
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Linlin Song
- Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China
| | - Yonggen Zhang
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Zihan Wang
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Shuang Chen
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Wan Wei
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Xiaoman Wang
- Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China; IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China
| | - Yanlin Zhang
- Yale-NUIST Center on Atmospheric Environment, International Joint Laboratory on Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Shaofei Kong
- Department of Atmospheric Sciences, School of Environmental Studies and Department of Environmental Science and Technology, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
| | - Baozhu Ge
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Ting Yang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yunting Fang
- Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China
| | - Lujie Ren
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Junjun Deng
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Yele Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Zifa Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Hongliang Zhang
- IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China; Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
| | - Jianlin Hu
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Cong-Qiang Liu
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Roy M Harrison
- Division of Environmental Health & Risk Management, School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK; Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Qi Ying
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station TX 77843-3136, USA
| | - Pingqing Fu
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
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9
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Zhu C, Li R, Qiu M, Zhu C, Gai Y, Li L, Yang N, Sun L, Wang C, Wang B, Yan G, Xu C. High spatiotemporal resolution ammonia emission inventory from typical industrial and agricultural province of China from 2000 to 2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 918:170732. [PMID: 38340857 DOI: 10.1016/j.scitotenv.2024.170732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/01/2024] [Accepted: 02/03/2024] [Indexed: 02/12/2024]
Abstract
As a typical industrial and agricultural province, Shandong is one of China's most seriously air-polluted regions. One comprehensive ammonia emission inventory with a high spatial resolution (1 km × 1 km) for 136 county-level administrative divisions in Shandong from 2000 to 2020 is developed based on county-level activity data with the corrected and updated emission factors of seventy-seven subcategories. Annual ammonia emissions decrease from 1003.3 Gg in 2000 to 795.9 Gg in 2020, with an annual decrease rate of 1.2 %. Therein, the ammonia emissions associated with livestock and farmland ecosystems in 2020 account for 50.8 % and 32.9 % of the provincial total ammonia emission, respectively. Laying hen and wheat are the livestock and crop with the highest ammonia emissions, accounting for 23.3 % and 36.3 % of ammonia emissions from livestock and the application of synthetic fertilizers, respectively. Furthermore, waste treatment, humans and vehicles are the top three ammonia emission sources in urban areas, accounting for 5.0 %, 4.7 % and 1.3 % of total ammonia emissions, respectively. The spatial distribution of grids with high ammonia emissions is consistent with the distribution of intensive farms. Significant emission intensity areas mainly concentrate in western Shandong (e.g., Caoxian of Heze, Qihe of Dezhou, Yanggu of Liaocheng, Liangshan of Jining) due to the large area of arable land and the high levels of agricultural activity. Overall, prominent seasonal variability characteristics of ammonia emission are observed. Ammonia emissions tend to be high in summer and low in winter, and the August to January-emission ratio is 5.6. The high temperature and fertilization for maize are primarily responsible for Shandong's increase in ammonia emissions in summer. Finally, the validity of the estimates is further evaluated using uncertainty analysis and comparison with previous studies. This study can provide information to determine preferentially effective PM2.5 control strategies.
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Affiliation(s)
- Chuanyong Zhu
- College of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
| | - Renqiang Li
- College of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Mengyi Qiu
- State Grid of China Technology Collage, State Grid, Jinan 250002, China
| | - Changtong Zhu
- College of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Yichao Gai
- College of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Ling Li
- Ecology Institute of Shandong Academy of Science, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Na Yang
- College of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Lei Sun
- College of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Chen Wang
- College of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Baolin Wang
- College of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Guihuan Yan
- Ecology Institute of Shandong Academy of Science, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Chongqing Xu
- Ecology Institute of Shandong Academy of Science, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
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10
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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. JOURNAL OF HAZARDOUS MATERIALS 2024; 465:133073. [PMID: 38039816 DOI: 10.1016/j.jhazmat.2023.133073] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [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.
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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
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11
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Zhuang Z, Li D, Zhang S, Hu Z, Deng W, Lin H. Short-Term Exposure to PM 2.5 Chemical Components and Depression Outpatient Visits: A Case-Crossover Analysis in Three Chinese Cities. TOXICS 2024; 12:136. [PMID: 38393231 PMCID: PMC10892610 DOI: 10.3390/toxics12020136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 02/25/2024]
Abstract
BACKGROUND The association between specific chemical components of PM2.5 and depression remains largely unknown. METHODS We conducted a time-stratified case-crossover analysis with a distributed lag nonlinear model (DLNM) to evaluate the relationship of PM2.5 and its chemical components, including black carbon (BC), organic matter (OM), sulfate (SO42-), nitrate (NO3-), and ammonium (NH4+), with the depression incidence. Daily depression outpatients were enrolled from Huizhou, Shenzhen, and Zhaoqing. RESULTS Among 247,281 outpatients, we found the strongest cumulative effects of PM2.5 and its chemical components with the odd ratios (ORs) of 1.607 (95% CI: 1.321, 1.956) and 1.417 (95% CI: 1.245, 1.612) at the 50th percentile of PM2.5 and OM at lag 21, respectively. Furthermore, the ORs with SO42- and NH4+ at the 75th percentile on the same lag day were 1.418 (95% CI: 1.247, 1.613) and 1.025 (95% CI: 1.009, 1.140). Relatively stronger associations were observed among females and the elderly. CONCLUSIONS Our study suggests that PM2.5 and its chemical components might be important risk factors for depression. Reducing PM2.5 emissions, with a particular focus on the major sources of SO42- and OM, might potentially alleviate the burden of depression in South China.
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Affiliation(s)
- Zitong Zhuang
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Dan Li
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Shiyu Zhang
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Zhaoyang Hu
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Road 2, Guangzhou 510080, China
| | - Wenfeng Deng
- Huizhou Center for Disease Control and Prevention, No. 10 Jiangbei Fumin Road, Huizhou 516003, China;
| | - Hualiang Lin
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Road 2, Guangzhou 510080, China
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12
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Yan F, Chen W, Wang X, Jia S, Mao J, Cao J, Chang M. Significant Increase in Ammonia Emissions in China: Considering Nonagricultural Sectors Based on Isotopic Source Apportionment. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:2423-2433. [PMID: 38270134 DOI: 10.1021/acs.est.3c07222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Isotopic source apportionment results revealed that nonagricultural sectors are significant sources of ammonia (NH3) emissions, particularly in urban areas. Unfortunately, nonagricultural sources have been substantially underrepresented in the current anthropogenic NH3 emission inventories (EIs). Here, we propose a novel approach to develop a gridded EI of nonagricultural NH3 in China for 2016 using a combination of isotopic source apportionment results and the emission ratios of carbon monoxide (CO) and NH3. We estimated that isotope-corrected nonagricultural NH3 emissions were 4370 Gg in China in 2016, accounting for an increase in the total NH3 emissions from 7 to 31%. As a result, compared to the original NH3 EI, the annual emissions of total NH3 increased by 35%. Thus, in comparison to the simulation driven by the original NH3 EI, the WRF-Chem model driven by the isotope-corrected NH3 EI has reduced the model biases in the surface concentrations and dry deposition flux of reduced nitrogen (NHx = gaseous NH3 + particulate NH4+) by 23 and 31%, respectively. This study may have wide-ranging implications for formulating targeted strategies for nonagricultural NH3 emissions controls, making it facilitate the achievement of simultaneously alleviating nitrogen deposition and atmospheric pollution in the future.
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Affiliation(s)
- Fenghua Yan
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, China
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Weihua Chen
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, China
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Xuemei Wang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, China
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Shiguo Jia
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou 510275, China
| | - Jingying Mao
- Scientific Research Academy of Guangxi Environmental Protection, Nanning 530022, China
| | - Jiachen Cao
- School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Ming Chang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, China
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
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13
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Shi W, Li Y, Zhao JV. Long-term exposure to ambient air pollution with sarcopenia among middle-aged and older adults in China. J Nutr Health Aging 2024; 28:100029. [PMID: 38388113 DOI: 10.1016/j.jnha.2023.100029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/19/2023] [Indexed: 02/24/2024]
Abstract
BACKGROUND Few studies have examined the role of outdoor air pollution exposure in sarcopenia in Asia. We aimed to investigate the association of outdoor air pollutants exposure with sarcopenia among Chinese adults. METHODS This nationally population-representative study used data from the China Health and Retirement Longitudinal Study (CHARLS) in 2015, 11,700 participants at least 45 years old from 125 Chinese cities were included. Sarcopenia status was identified according to the Asian Working Group for Sarcopenia 2019 (AWGS 2019) criteria. Ambient annual average air pollutants including fine particulate matter (PM2.5), inhalable particles (PM10), coarse particulate matter (PMcoarse), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) were estimated by satellite models and ground-based measurements. Multinomial logistic regression models were performed to examine the associations of air pollutants exposure with different status of sarcopenia (including possible sarcopenia and sarcopenia). Stratified analyses were utilized to assess the effect modifiers. RESULTS Among the 11,700 participants (52.6% women), the average age was 61.0 years. Each 10 μg/m3 increment of annual PMcoarse was associated with a higher risk of possible sarcopenia (odds ratio (OR) = 1.08, 95% confidence interval (CI) 1.04-1.11). Stratified analyses showed a positive risk of possible sarcopenia in women after exposure to PM10, PMcoarse, and NO2. Ambient NO2 exposure was positively associated with sarcopenia (OR = 1.13, 95% CI 1.04-1.22) in those aged 65 years and older. However, we have not observed differences by sex, age, residence, smoking, and drinking. Robustness results were found for PMcoarse in the sensitivity analyses. CONCLUSION This nationwide study suggested that long-term exposure to outdoor air pollution, especially for PMcoarse, was associated with the risk of sarcopenia among Chinese adults. Our findings provide epidemiological implications for protecting healthy ageing by improving air quality.
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Affiliation(s)
- Wenming Shi
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 999077, Hong Kong SAR, China
| | - Yongzhen Li
- Clinical Nutrition Department, Starkids Children's Hospital, Shanghai, New Hong Qiao Campus for Children's Hospital of Fudan University, Shanghai, 201106, China
| | - Jie V Zhao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 999077, Hong Kong SAR, China.
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14
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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. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 38261755 DOI: 10.1021/acs.est.3c07545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [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.
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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
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15
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Edwards TM, Puglis HJ, Kent DB, Durán JL, Bradshaw LM, Farag AM. Ammonia and aquatic ecosystems - A review of global sources, biogeochemical cycling, and effects on fish. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:167911. [PMID: 37871823 DOI: 10.1016/j.scitotenv.2023.167911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/27/2023] [Accepted: 10/16/2023] [Indexed: 10/25/2023]
Abstract
The purpose of this review is to better understand the full life cycle and influence of ammonia from an aquatic biology perspective. While ammonia has toxic properties in water and air, it also plays a central role in the biogeochemical nitrogen (N) cycle and regulates mechanisms of normal and abnormal fish physiology. Additionally, as the second most synthesized chemical on Earth, ammonia contributes economic value to many sectors, particularly fertilizers, energy storage, explosives, refrigerants, and plastics. But, with so many uses, industrial N2-fixation effectively doubles natural reactive N concentrations in the environment. The consequence is global, with excess fixed nitrogen driving degradation of soils, water, and air; intensifying eutrophication, biodiversity loss, and climate change; and creating health risks for humans, wildlife, and fisheries. Thus, the need for ammonia research in aquatic systems is growing. In response, we prepared this review to better understand the complexities and connectedness of environmental ammonia. Even the term "ammonia" has multiple meanings. So, we have clarified the nomenclature, identified units of measurement, and summarized methods to measure ammonia in water. We then discuss ammonia in the context of the N-cycle, review its role in fish physiology and mechanisms of toxicity, and integrate the effects of human N-fixation, which continuously expands ammonia's sources and uses. Ammonia is being developed as a carbon-free energy carrier with potential to increase reactive nitrogen in the environment. With this in mind, we review the global impacts of excess reactive nitrogen and consider the current monitoring and regulatory frameworks for ammonia. The presented synthesis illustrates the complex and interactive dynamics of ammonia as a plant nutrient, energy molecule, feedstock, waste product, contaminant, N-cycle participant, regulator of animal physiology, toxicant, and agent of environmental change. Few molecules are as influential as ammonia in the management and resilience of Earth's resources.
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Affiliation(s)
- Thea M Edwards
- U.S. Geological Survey, Columbia Environmental Research Center, Columbia, MO 65201, USA.
| | - Holly J Puglis
- U.S. Geological Survey, Columbia Environmental Research Center, Columbia, MO 65201, USA
| | - Douglas B Kent
- U.S. Geological Survey, Earth Systems Processes Division, Menlo Park, CA, USA
| | - Jonathan López Durán
- U.S. Geological Survey, Columbia Environmental Research Center, Columbia, MO 65201, USA
| | - Lillian M Bradshaw
- U.S. Geological Survey, Columbia Environmental Research Center, Columbia, MO 65201, USA
| | - Aïda M Farag
- U.S. Geological Survey, Columbia Environmental Research Center, Jackson Field Research Station, Jackson, WY, USA
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16
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Terry B, Shakya KM. Monitoring gaseous pollutants using passive sampling in the Philadelphia region. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2024; 74:52-69. [PMID: 37934867 DOI: 10.1080/10962247.2023.2279733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023]
Abstract
Air pollution can have deleterious impacts on human health and the environment. Historically, air pollution studies have focused more on cities. However, it is also important to consider the impact on large suburban populations living closer to the major cities. In this study, nitrogen oxides (nitrogen dioxide and nitric oxide), sulfur dioxide, ozone, and ammonia concentrations were measured from fifteen sites in the Greater Philadelphia area, Pennsylvania, USA using Ogawa passive samplers from September 2021 to May 2022. The fall season had the highest mean NOx concentrations (11.03 ± 4.51 ppb), and spring had the highest mean O3 concentration (18.65 ± 6.71 ppb) compared to other seasons. NOx concentrations were higher at suburban (30.43 ± 33.79 ppb) and urban sites (22.49 ± 12.54 ppb) compared to semi-rural sites (11.08 ± 9.20 ppb). SO2 was not detected in most of the measurements. The positive statistically significant correlation between NO and NH3 in urban (R2 = 0.33, p-value <0.05) and suburban sites (R2 = 0.37, p-value <0.05) during winter and spring, respectively, suggests a high attribution of traffic emissions to NH3 at urban and suburban sites. Influence of traffic emissions on air pollutant values for the study region is also supported by similar NOx concentrations between suburban and urban sites as well as decreasing NO2/NOx ratios with increased distance from expressways. This study shows that passive sampling can be effectively used for assessing spatial and seasonal variations in air pollutants within an area of diverse land use.Implications: This study presents the findings of temporal and seasonal patterns for nitrogen dioxide, nitric oxide, tropospheric ozone, and ammonia at urban, suburban, and semi-rural areas of the greater Philadelphia region. The main objective of the study is to monitor air pollution in suburban and semi-rural areas which are not monitored for air pollution. We monitored from a total of fifteen sites in three seasons to assess air pollution in suburban and semi-rural areas near the major city in the United States - Philadelphia. The findings are important to learn how air quality is affected in suburban and semi-rural areas near the major city. The study also shows the useful application of inexpensive passive sampling technique for measuring air pollution.
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Affiliation(s)
- Bryan Terry
- Department of Geography and the Environment, Villanova University, Villanova, Pennsylvania, USA
| | - Kabindra M Shakya
- Department of Geography and the Environment, Villanova University, Villanova, Pennsylvania, USA
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17
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Zhang S, Xiong Y, Liang X, Wang F, Liang S, Wu Y. Spatial and Cross-Sectoral Transfer of Air Pollutant Emissions from the Fleet Electrification in China by 2030. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21249-21259. [PMID: 38054598 DOI: 10.1021/acs.est.3c04496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Fleet electrification shifts emission sources from the tailpipe to electricity generation and automotive supply chains subsequently, with emission transfer among regions. Such a spatial and cross-sectoral transfer of air pollutant emissions might embody uncertain environmental benefits spatially, which has not been comprehensively quantified, mainly due to the complexity of manufacturing processes of electric vehicle (EV) components (e.g., battery). We developed a hybrid life cycle assessment by combining inventory data of major processes and cross-sectoral input-output information and identified how China's EV deployment would influence the spatial redistribution of air pollutant emissions currently (2017) and in the future (2030). The results indicate that fleet electrification could readily reduce life cycle nitrogen oxides (NOx) and nonmethane volatile organic compound (NMVOC) emissions by 12-93%, and the reductions are estimated to be concentrated in major cities and urban agglomerations. However, increased demand for electricity and power battery production could increase PM2.5 and SO2 emissions in 17-55% of grids under all the scenarios, which emerge in coal-rich (e.g., Inner Mongolia, Shanxi) and industrial (e.g., Shandong, Henan, Jiangsu) provinces. By tracing the upstream, 31-55% of vehicle-cycle emissions are from deep supply chains but exhibit diverse sources. It suggests the necessity to relieve emissions leakage of fleet electrification by synchronizing effective environmental management across multiple sectors through EV supply chains.
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Affiliation(s)
- 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
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
| | - Yiling Xiong
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Xinyu Liang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Fang Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Sai Liang
- School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, Guangdong 510006, 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
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
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18
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Xu J, Lu M, Guo Y, Zhang L, Chen Y, Liu Z, Zhou M, Lin W, Pu W, Ma Z, Song Y, Pan Y, Liu L, Ji D. Summertime Urban Ammonia Emissions May Be Substantially Underestimated in Beijing, China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:13124-13135. [PMID: 37616592 DOI: 10.1021/acs.est.3c05266] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
Ammonia (NH3) is critical to the nitrogen cycle and PM2.5 formation, yet a great deal of uncertainty exists in its urban emission quantifications. Model-underestimated NH3 concentrations have been reported for cities, yet few studies have provided an explanation. Here, we explore reasons for severe WRF-Chem model underestimations of NH3 concentrations in Beijing in August 2018, including simulated gas-particle partitioning, meteorology, regional transport, and emissions, using spatially refined (3 km resolution) NH3 emission estimates in the agricultural sector for Beijing-Tianjin-Hebei and in the traffic sector for Beijing. We find that simulated NH3 concentrations are significantly lower than ground-based and satellite observations during August in Beijing, while wintertime underestimations are much more moderate. Further analyses and sensitivity experiments show that such discrepancies cannot be attributed to factors other than biases in NH3 emissions. Using site measurements as constraints, we estimate that both agricultural and non-agricultural NH3 emission totals in Beijing shall increase by ∼5 times to match the observations. Future research should be performed to allocate underestimations to urban fertilizer, power, traffic, or residential sources. Dense and regular urban NH3 observations are necessary to constrain and validate bottom-up inventories and NHx simulation.
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Affiliation(s)
- Jiayu Xu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Mengran Lu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
- Department of Ecology and Environment of Shanxi Province, Taiyuan 030024, China
| | - Yixin Guo
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Youfan Chen
- Sichuan Academy of Environmental Policy and Planning, Chengdu 610041, China
| | - Zehui Liu
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Mi Zhou
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
- Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey 08540, United States
| | - Weili Lin
- Key Laboratory of Ecology and Environment in Minority Areas (Minzu University of China), National Ethnic Affairs Commission, Beijing 100081, China
| | - WeiWei Pu
- Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
| | - Zhiqiang Ma
- Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
| | - Yu Song
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yuepeng Pan
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Liu
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
| | - Dongsheng Ji
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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19
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Jiang Y, Liang X, Zhang S, Hu Z, Hove A, Wu Y. The future air quality impact of electric vehicle promotion and coordinated charging in the Beijing-Tianjin-Hebei region. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023:121928. [PMID: 37271362 DOI: 10.1016/j.envpol.2023.121928] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/12/2023] [Accepted: 05/28/2023] [Indexed: 06/06/2023]
Abstract
The synergetic benefit of air quality improvements together with greenhouse gas (GHG) mitigations from fleet electrification can be maximized if the power used for electric vehicles (EVs) is from renewables. The worth-noting mismatch between renewable power generation and EV fleet charging demand requires appropriate coordination strategy. Here, we analyze the environmental benefits from increased EVs penetration in Beijing-Tianjin-Hebei (BTH) regions by integrating various scenarios of fleet electrification with coordinated charging strategies to examine the air quality improvement and GHG abatement. The study found that fleet electrification could bring substantial reduction on urban PM2.5 in BTH, especially in December by 0.8 ± 0.5 μg/m3. The coordinated charging strategy could further improve the air quality in BTH, albeit smaller than that of fleet electrification itself. PM2.5 reduction benefit from EV adoption could be significantly more pronouncing when ammonia emission reduction was considered, by more than 0.3 μg/m3 in both December and July, validating the great significance of vehicle NH3 emission control and the necessity of prioritizing the electrification of high ammonia emitting fleet. The outcome of this study helps to formulate the effective on-road transportation pollutant abatement strategies and offers technological support for policy makers to conduct more sensible sequence of future fleet electrification process.
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Affiliation(s)
- Yiliang Jiang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, China
| | - Xinyu Liang
- 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, School of Environment, Tsinghua University, Beijing, 100084, China.
| | - Zechun Hu
- Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China
| | - Anders Hove
- Deutsche Gesellschaft fuer Internationale Zusammenarbeit (GIZ) GmbH, Germany
| | - 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
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