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Lu X, Zhang D, Wang L, Wang S, Zhang X, Liu Y, Chen K, Song X, Yin S, Zhang R, Wang S, Yuan M. Establishment and verification of anthropogenic speciated VOCs emission inventory of Central China. J Environ Sci (China) 2025; 149:406-418. [PMID: 39181653 DOI: 10.1016/j.jes.2024.01.033] [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: 07/30/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 08/27/2024]
Abstract
Improving the accuracy of anthropogenic volatile organic compounds (VOCs) emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution. In this study, an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km × 3 km spatial resolution based on the emission factor method. The 2019 VOCs emission in Henan Province was 1003.5 Gg, while industrial process source (33.7%) was the highest emission source, Zhengzhou (17.9%) was the city with highest emission and April and August were the months with the more emissions. High VOCs emission regions were concentrated in downtown areas and industrial parks. Alkanes and aromatic hydrocarbons were the main VOCs contribution groups. The species composition, source contribution and spatial distribution were verified and evaluated through tracer ratio method (TR), Positive Matrix Factorization Model (PMF) and remote sensing inversion (RSI). Results show that both the emission results by emission inventory (EI) (15.7 Gg) and by TR method (13.6 Gg) and source contribution by EI and PMF are familiar. The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R-value of 0.73. The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable.
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Affiliation(s)
- Xuan Lu
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Dong Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Lanxin Wang
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Shefang Wang
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Xinran Zhang
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Yali Liu
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Keying Chen
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, China; Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Xinshuai Song
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Shasha Yin
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China.
| | - Ruiqin Zhang
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Shanshan Wang
- Research Institute of Environmental Sciences, Zhengzhou University, Zhengzhou 450001, China; School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China
| | - Minghao Yuan
- Zhengzhou Ecological Environment Monitoring Center of Henan Province, Zhengzhou 450000, China
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Wang R, Wang L, Li Y, Sun J, Li K, Huang H, You Y, Wang Y. The reward and penalty for ozone pollution control caused by natural sources and regional transport: A case study in Guangdong province. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 949:174984. [PMID: 39053537 DOI: 10.1016/j.scitotenv.2024.174984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/03/2024] [Accepted: 07/21/2024] [Indexed: 07/27/2024]
Abstract
Ground-level O3 pollution in the Pearl River Delta region (PRD) is closely related to anthropogenic, natural emissions and regional transport. However, the interactions among different sources and natural intervention in modulating anthropogenic management have not been comprehensively assessed. Here, the WRF-CMAQ-MEGAN modeling system was utilized to simulate an O3 episode over PRD. The integrated source apportionment method (ISAM) and brute-force top-down combined with factor separation approach (BF-TD-FSA) were applied to quantify source contributions, impacts of individual or multiple sources on O3, and decouple interactions among various emissions; additionally, based on ISAM, O3 isopleths visualized MDA8 O3 response of different source types to anthropogenic perturbations. ISAM concluded considerable MDA8 O3 contributions of regional transport in PRD/NPRD (non-PRD areas in Guangdong province) (38.8 %/35.7 %), followed by anthropogenic (32.7 %/24.8 %), BVOC (biogenic volatile organic compounds, 23.8 %/20.3 %) and SNO (soil NO, ∼4 %) emissions. Compared to concentrated anthropogenic contributions, widespread natural contributions were observed across their source areas and along the transport pathways. The BF-TD also revealed that regional transport had the largest impact (>90 μg m-3) on MDA8 O3 while anthropogenic and BVOC emissions greatly affected downwind PRD (64.5 and 7.7 μg m-3). Negative synergy between anthropogenic and natural emissions (especially BVOC emission) suggested potential natural-induced intensification of anthropogenic impact during O3 management. The MDA8 O3 isopleths further demonstrated significant BVOC-induced reward and regional transport-induced penalty for anthropogenic NOx (ANOx) emission control benefits, leading to additional maximum MDA8 O3 decrease and increase by -27.5 and 13.8 μg m-3 in polluted high-emission grids. The natural-induced reward effect could impose loose requirements on anthropogenic reduction (decreased by 13.3 %-17.7 %) if there were no regional transport-induced control penalty. It is advisable to prioritize ANOx control and seek collaboration on air quality management with neighboring provinces to maximize the natural-induced control reward and achieve desired targets with minimal human efforts.
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Affiliation(s)
- Runyu Wang
- State Key Laboratory of Atmospheric Environment and Extreme Meteorology, 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
| | - Lili Wang
- State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Yuanyuan Li
- Xinjiang Weather Modification Office, Urumqi 830002, China
| | - Jiaren Sun
- Guangdong Province Engineering Laboratory for Air Pollution Control, Guangdong Provincial Key Laboratory of Water and Air Pollution Control, South China Institute of Environmental Sciences, MEE, Guangzhou 510655, China.
| | - Ke Li
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Hanjie Huang
- College of Environment and Climate, Institute of Mass Spectrometry and Atmospheric Environment, Jinan University, Guangzhou 510632, China
| | - Yanbin You
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuesi Wang
- State Key Laboratory of Atmospheric Environment and Extreme Meteorology, 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
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3
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Zhou J, Wang W, Wang Y, Zhou Z, Lv X, Zhong M, Zhong B, Deng M, Jiang B, Luo J, Cai J, Li XB, Yuan B, Shao M. Intercomparison of measured and modelled photochemical ozone production rates: Suggestion of chemistry hypothesis regarding unmeasured VOCs. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175290. [PMID: 39117234 DOI: 10.1016/j.scitotenv.2024.175290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/21/2024] [Accepted: 08/03/2024] [Indexed: 08/10/2024]
Abstract
Ozone (O3) pollution is a severe environmental problem in China. The incomplete understanding of atmospheric photochemical reaction mechanisms prevents us from accurately understanding the chemistry of O3 production. Here, we used an improved dual-channel reaction chamber technique to measure net photochemical O3 production rate (P(O3)net) directly in Dongguan, a typical industrial city in China. The maximum P(O3)net was 46.3 ppbv h-1 during the observation period, which is at a relatively high level compared to previous observations under different environment worldwide. We employed an observation-based box model coupled with the state-of-the-art atmospheric chemical mechanism (MCM v3.3.1) to investigate the chemistry of O3 production. Under the base scenario, the modelling underestimates P(O3)net by ~30 %. Additionally considering HO2 uptake by ambient aerosols, inorganic deposition, and Cl chemistry only caused a small change (< 13 %) in the simulation of P(O3)net. Further analysis indicates that unmeasured reactive volatile organic compounds (VOCs), such as oxygenated VOCs and branched alkenes are potential contributors to the underestimation of P(O3)net. This study underscores the underestimation of P(O3)net in conventional atmospheric modelling setups, providing a crucial scientific foundation for further investigation aimed at promoting our understanding of photochemical O3 formation.
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Affiliation(s)
- Jun Zhou
- College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China.
| | - Wenjie Wang
- Minerva Research Group, Max Planck Institute for Chemistry, Mainz 55128, Germany
| | - Yan Wang
- College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Zhen Zhou
- Guangdong dongguan ecological and environmental monitoring station, Dongguan 523009, China
| | - Xiongzhou Lv
- Guangdong dongguan ecological and environmental monitoring station, Dongguan 523009, China
| | - Meijin Zhong
- Guangdong dongguan ecological and environmental monitoring station, Dongguan 523009, China
| | - Bowen Zhong
- College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Minhui Deng
- College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Bin Jiang
- College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Junqing Luo
- College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Jiahua Cai
- College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Xiao-Bing Li
- College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Bin Yuan
- College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Min Shao
- College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
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4
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Li J, Yuan B, Yang S, Peng Y, Chen W, Xie Q, Wu Y, Huang Z, Zheng J, Wang X, Shao M. Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 932:173011. [PMID: 38719052 DOI: 10.1016/j.scitotenv.2024.173011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 05/14/2024]
Abstract
Ozone pollution presents a growing air quality threat in urban agglomerations in China. It remains challenge to distinguish the roles of emissions of precursors, chemical production and transportations in shaping the ground-level ozone trends, largely due to complicated interactions among these 3 major processes. This study elucidates the formation factors of ozone pollution and categorizes them into local emissions (anthropogenic and biogenic emissions), transport (precursor transport and direct transport from various regions), and meteorology. Particularly, we attribute meteorology, which affects biogenic emissions and chemical formation as well as transportation, to a perturbation term with fluctuating ranges. The Community Multiscale Air Quality (CMAQ) model was utilized to implement this framework, using the Pearl River Delta region as a case study, to simulate a severe ozone pollution episode in autumn 2019 that affected the entire country. Our findings demonstrate that the average impact of meteorological conditions changed consistently with the variation of ozone pollution levels, indicating that meteorological conditions can exert significant control over the degree of ozone pollution. As the maximum daily 8-hour average (MDA8) ozone concentrations increased from 20 % below to 30 % above the National Ambient Air Quality Standard II, contributions from emissions and precursor transport were enhanced. Concurrently, direct transport within Guangdong province rose from 13.8 % to 22.7 %, underscoring the importance of regional joint prevention and control measures under adverse weather conditions. Regarding biogenic emissions and precursor transport that cannot be directly controlled, we found that their contributions were generally greater in urban areas with high nitrogen oxides (NOx) levels, primarily due to the stronger atmospheric oxidation capacity facilitating ozone formation. Our results indicate that not only local anthropogenic emissions can be controlled in urban areas, but also the impacts of local biogenic emissions and precursor transport can be potentially regulated through reducing atmospheric oxidation capacity.
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Affiliation(s)
- Jin Li
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Bin Yuan
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China.
| | - Suxia Yang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Yuwen Peng
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Weihua Chen
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Qianqian Xie
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Yongkang Wu
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Zhijiong Huang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Junyu Zheng
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Xuemei Wang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Min Shao
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
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5
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Wu L, Yu F, Luo H, Zhu M, Liao S, Liu J, Wu C, Horchler EJ, Ristovski Z, Zheng J. Characterization of ammonia emissions from light-duty gasoline vehicles based on real-world driving and dynamometer measurements. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172644. [PMID: 38649054 DOI: 10.1016/j.scitotenv.2024.172644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 04/25/2024]
Abstract
Ammonia (NH3) contributes significantly to the formation of particulate matter, and vehicles represent a major source of NH3 in urban areas. However, there remains a lack of comprehensive understanding regarding the emission characteristics of NH3 from vehicles. This study conducted real-world driving emission (RDE) measurements and dynamometer measurements on 33 light-duty gasoline vehicles (LDGVs) to investigate their emission characteristics and impact factors. The tested vehicles include China 3 to China 6 emission standards. The results show that the average NH3 emission factors of LDGVs decreased by >80 % from China 3 to China 6 emission standards. The results obtained from dynamometer measurements reveal that independent from other conventional pollutants (such as HCHO and NOx), NH3 emissions do not exhibit significant emission peaks during the hot- or cold-start phase. The RDE measurement covers a more comprehensive range of the vehicle's real-world driving conditions, resulting in higher NH3 emission factors compared with dynamometer measurements. The analysis of RDE measurements revealed that NH3 emissions are influenced by vehicle speeds and accelerations. Acceleration processes contribute approximately 50 % of total NH3 emissions over a driving period. Finally, using real driving speed, acceleration, and road gradient as input parameters, an NH3 emission rate model based on vehicle specific power was developed. This emission rate model enables a more precise reflection of LDGVs' NH3 emissions of LDGVs across diverse driving conditions and provides valuable data support for high-resolution inventories of vehicle NH3 emissions.
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Affiliation(s)
- Lili Wu
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Fei Yu
- School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China.
| | - Haoming Luo
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Manni Zhu
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Songdi Liao
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Junwen Liu
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Changda Wu
- Department of Chemistry, University of British Columbia, Vancouver V6T 1Z1, Canada
| | - Eva Johanna Horchler
- Department of Chemistry, Faculty of Natural Science, Aarhus University, Aarhus C 8000, Denmark
| | - Zoran Ristovski
- International Laboratory for Air Quality and Health, School of Earth and Atmospheric Sciences, Queensland University of Technology, Brisbane 4000, Australia
| | - Junyu Zheng
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511458, China.
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Liu G, Ma X, Li W, Chen J, Ji Y, An T. Pollution characteristics, source appointment and environmental effect of oxygenated volatile organic compounds in Guangdong-Hong Kong-Macao Greater Bay Area: Implication for air quality management. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 919:170836. [PMID: 38346658 DOI: 10.1016/j.scitotenv.2024.170836] [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: 11/22/2023] [Revised: 01/24/2024] [Accepted: 02/07/2024] [Indexed: 02/17/2024]
Abstract
Same as other bay areas, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is also suffering atmospheric composite pollution. Even a series of atmospheric environment management policies have been conducted to win the "blue sky defense battle", the atmospheric secondary pollutants (e.g., O3) originated from oxygenated volatile organic compounds (OVOCs) still threaten the air quality in GBA. However, there lacks a systematic summary on the emission, formation, pollution and environmental effects of OVOCs in this region for further air quality management. This review focused on the researches related to OVOCs in GBA, including their pollution characteristics, detection methods, source distributions, secondary formations, and impacts on the atmosphere. Pollution profile of OVOCs in GBA revealed that the concentration percentage among total VOCs from Guangzhou and Dongguan cities exceeded 50 %, while methanol, formaldehyde, acetone, and acetaldehyde were the top four highest concentrated OVOCs. The detection technique on regional atmospheric OVOCs (e.g., oxygenated organic molecules (OOMs)) underwent an evolution of off-line derivatization method, on-line spectroscopic method and on-line mass spectrometry method. The OVOCs in GBA were mainly from primary emissions (up to 80 %), including vehicle emissions and biomass combustion. The anthropogenic alkenes and aromatics in urban area, and natural isoprene in rural area also made a significant contribution to the secondary emission (e.g., photochemical formation) of OVOCs. About 20 % in average of ROx radicals was produced from photolysis of formaldehyde in comparison with O3, nitrous acid and rest OVOCs, while the reaction between OVOCs and free radical accelerated the NOx-O3 cycle, contributing to 15 %-60 % cumulative formation of O3 in GBA. Besides, the heterogeneous reactions of dicarbonyls generated 21 %-53 % of SOA. This review also provided suggestions for future research on OVOCs in terms of regional observation, analytical method and mechanistic study to support the development of a control and management strategy on OVOCs in GBA and China.
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Affiliation(s)
- Guanyong Liu
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong Technology Research Center for Photocatalytic Technology Integration and Equipment Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Xiaoyao Ma
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong Technology Research Center for Photocatalytic Technology Integration and Equipment Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Wanying Li
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong Technology Research Center for Photocatalytic Technology Integration and Equipment Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Jiangyao Chen
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong Technology Research Center for Photocatalytic Technology Integration and Equipment Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China; Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China.
| | - Yuemeng Ji
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong Technology Research Center for Photocatalytic Technology Integration and Equipment Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China; Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
| | - Taicheng An
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, Guangdong Technology Research Center for Photocatalytic Technology Integration and Equipment Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China; Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
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7
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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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Wang R, Wang L, Sun J, Zhang L, Li Y, Li K, Liu B, Zhang J, Wang Y. Maximizing ozone control by spatial sensitivity-oriented mitigation strategy in the Pearl River Delta Region, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:166987. [PMID: 37717781 DOI: 10.1016/j.scitotenv.2023.166987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/06/2023] [Accepted: 09/09/2023] [Indexed: 09/19/2023]
Abstract
The Pearl River Delta (PRD) has long been plagued by severe O3 pollution, particularly during the autumn. A regional O3 pollution episode influenced by the Western Pacific Subtropical High in September 2021 was characterized by near-surface O3 escalation due to strong photochemical reactions within the planetary boundary layer. This event was targeted to develop effective control strategies through investigation of precursor control type and scope based on the high-order decoupled direct method (HDDM) and integrated source apportionment method (ISAM) of CMAQ. Generally, the majority of areas (67.0 %) were under NOx-limited regime, which should strengthen afternoon NOx control inferred by positive convex O3 responses. However, high emission and heavily polluted areas located in central PRD were under VOC-limited regime (11.6 %) or mixed regime (15.0 %). The remaining areas (6.4 %) were under NOx-titration or insensitive conditions. Regarding source apportionment, Guangdong province contributed 32.3 %-58.4 % to MDA8 O3 of PRD, especially higher proportion (>50 %) to central areas. Overall, local-focused NOx/VOC emission reductions had limited effects on O3 mitigation for receptor cities compared to regional-cooperative regulation. When region-wide VOC emission reduction was implemented, MDA8 O3 in VOC-limited grids exhibited the largest declines (2.3 %-4.1 %, 3.9- 7.0 μg·m-3). However, unified NOx control contributed to increasing MDA8 O3 in VOC-limited grids (most stations located for air quality evaluation) whereas decreased MDA8 O3 by 2.1 %- 5.7 %, 3.0- 8.2 μg·m-3 in large-scale NOx-limited grids. The sensitivity-oriented regional control avoided O3 rebound and achieved the greatest decline of 3.4 %- 5.0 %, 5.7- 8.4 μg·m-3 in VOC-limited grids; additionally, time-refined dynamic aggressive NOx control decreased peak O3 by an extra 1.2- 6 μg·m-3, both of which facilitate the regulation for the forecasting O3 episodes. These findings suggest that in heavily polluted environments, the enhancement of O3 regulation benefits requires meticulous, coordinated, and dynamic NOx and VOC controls spanning the entire region based on high-resolution analysis of heterogeneous O3-NOx-VOC sensitivity. Furthermore, emission reduction gains should be more reasonably reflected through increasing in-situ observations covering multi-sensitivity regions.
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Affiliation(s)
- Runyu Wang
- 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
| | - Lili Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Jiaren Sun
- Guangdong Province Engineering Laboratory for Air Pollution Control, Guangdong Provincial Key Laboratory of Water and Air Pollution Control, South China Institute of Environmental Sciences, MEE, Guangzhou 510655, China.
| | - Lei Zhang
- 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
| | - Yuanyuan Li
- Xinjiang Weather Modification Office, Urumqi 830002, China; Xinjiang Weather Modification Engineering Technology Research Center, Urumqi 830002, China
| | - Ke Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Boya Liu
- 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
| | - Jiaxin Zhang
- 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
| | - Yuesi Wang
- 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
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9
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Wang C, Duan W, Cheng S, Zhang J. Multi-component emission characteristics and high-resolution emission inventory of non-road construction equipment (NRCE) in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 877:162914. [PMID: 36933727 DOI: 10.1016/j.scitotenv.2023.162914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 02/11/2023] [Accepted: 03/13/2023] [Indexed: 05/06/2023]
Abstract
With the continuous abatement of industries and vehicles in the past years in China, the comprehensive understanding and scientific control of non-road construction equipment (NRCE) may play an important role in alleviating PM2.5 and O3 pollution in the next stage. In this study, the emission rates of CO, HC, NOx, PM2.5, CO2 and the component profiles of HC and PM2.5 from 3 loaders, 8 excavators and 4 forklifts under different operating conditions were tested for a systematic representation of NRCE emission characteristics. With the fusion of field tests, construction land types and population distributions, the NRCE emission inventory with a 0.1° × 0.1° resolution in nationwide and with a 0.01° × 0.01° resolution in Beijing-Tianjin-Hebei region (BTH) were established. The sample testing results suggested prominent differences in instantaneous emission rates and the composition characteristics among different equipment and under different operating modes. Generally, for NRCE, the dominant components are OC and EC for PM2.5, and HC and olefin for OVOC. Especially, the proportion of olefins in idling mode is much higher than that in working mode. The measurement-based emission factors of various equipment exceeded the Stage III standard to varying degrees. The high-resolution emission inventory suggested that highly developed central and eastern areas, represented by BTH, showed the most prominent emissions in China. This study is a systematic representations of China's NRCE emissions, and the NRCE emission inventory establishment method with multiple data fusion has important methodological reference value for other emission sources.
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Affiliation(s)
- Chuanda Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Wenjiao Duan
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
| | - Shuiyuan Cheng
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Junfeng Zhang
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
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10
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Chen Y, Fung JCH, Yuan D, Chen W, Fung T, Lu X. Development of an integrated machine-learning and data assimilation framework for NO x emission inversion. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 871:161951. [PMID: 36737010 DOI: 10.1016/j.scitotenv.2023.161951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/10/2023] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
As major air pollutants, nitrogen oxides (NOx, mainly comprising NO and NO2) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NOx simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NOx emission, which integrates the simulation results of the 3D-CTM, surface NO2 measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NOx emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NOx emissions, the root-mean-square error, normalized mean error, and normalized mean bias decreased by approximately 40 %, 40 %, and 60 % in NO2 simulation using posterior NOx emissions corrected by the framework proposed in this work. Compared with the emissions for 2015, the NOx emission generally decreased by an average of 5 % in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24 % and 19 %, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. The framework can provide reliable and up-to-date emission information and can thus contribute to both scientific research and policy development relating to NOx pollution.
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Affiliation(s)
- Yiang Chen
- Division of Environment and Sustainability, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
| | - Jimmy C H Fung
- Division of Environment and Sustainability, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China; Department of Mathematics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
| | - Dehao Yuan
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Wanying Chen
- Division of Environment and Sustainability, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
| | - Tung Fung
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Xingcheng Lu
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
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11
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Hu Y, Shi B, Yuan X, Zheng C, Sha Q, Yu Y, Huang Z, Zheng J. VOC emission caps constrained by air quality targets based on response surface model: A case study in the Pearl River Delta Region, China. J Environ Sci (China) 2023; 123:430-445. [PMID: 36522004 DOI: 10.1016/j.jes.2022.09.004] [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: 01/15/2022] [Revised: 09/02/2022] [Accepted: 09/04/2022] [Indexed: 06/17/2023]
Abstract
Because of the recent growth in ground-level ozone and increased emission of volatile organic compounds (VOCs), VOC emission control has become a major concern in China. In response, emission caps to control VOC have been stipulated in recent policies, but few of them were constrained by the co-control target of PM2.5 and ozone, and discussed the factor that influence the emission cap formulation. Herein, we proposed a framework for quantification of VOC emission caps constrained by targets for PM2.5 and ozone via a new response surface modeling (RSM) technique, achieving 50% computational cost savings of the quantification. In the Pearl River Delta (PRD) region, the VOC emission caps constrained by air quality targets varied greatly with the NOx emission reduction level. If control measures in the surrounding areas of the PRD region were not considered, there could be two feasible strategies for VOC emission caps to meet air quality targets (160 µg/m3 for the maximum 8-hr-average 90th-percentile (MDA8-90%) ozone and 25 µg/m3 for the annual average of PM2.5): a moderate VOC emission cap with <20% NOx emission reductions or a notable VOC emission cap with >60% NOx emission reductions. If the ozone concentration target were reduced to 155 µg/m3, deep NOx emission reductions is the only feasible ozone control measure in PRD. Optimization of seasonal VOC emission caps based on the Monte Carlo simulation could allow us to gain higher ozone benefits or greater VOC emission reductions. If VOC emissions were further reduced in autumn, MDA8-90% ozone could be lowered by 0.3-1.5 µg/m3, equaling the ozone benefits of 10% VOC emission reduction measures. The method for VOC emission cap quantification and optimization proposed in this study could provide scientific guidance for coordinated control of regional PM2.5 and O3 pollution in China.
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Affiliation(s)
- Ya'nan Hu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511436, China
| | - Bowen Shi
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511436, China
| | - Xin Yuan
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511436, China
| | - Chuanzeng Zheng
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511436, China
| | - Qing'e Sha
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511436, China
| | - Yufan Yu
- Guangdong Polytechnic of Environmental Protection Engineering, Guangzhou 528216, China
| | - Zhijiong Huang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511436, China.
| | - Junyu Zheng
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511436, China.
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12
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Mo H, You Y, Wu L, Yan F, Chang M, Wang W, Wang P, Wang X. Potential impact of industrial transfer on PM 2.5 and economic development under scenarios oriented by different objectives in Guangdong, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 316:120562. [PMID: 36332706 DOI: 10.1016/j.envpol.2022.120562] [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: 08/31/2022] [Revised: 10/27/2022] [Accepted: 10/29/2022] [Indexed: 06/16/2023]
Abstract
For sustainable regional development, industrial transfer is an important trend that will potentially change the spatial and temporal pattern of air pollution and economic development. Aiming to better regulate industrial transfer and guide policy-making, this study proposes an assessment framework for industrial transfer that combines precise enterprise data, GIS technology and a 3-D air quality model. Taking Guangdong Province as an example, this study simulates the redistribution of 4015 high-pollution and high-energy-consumption (double-high) enterprises in the Pearl River Delta (PRD) to surrounding areas, and the potential impact on air quality is further evaluated. Three mutually independent transfer scenarios with different objectives are designed-ENV (ENVironment), ENT (ENTerprise), and GOV (GOVernment)-which aim to maximize benefits from the standpoint of the residents of Guangdong, the enterprises themselves, and local governments, respectively. Results show that Western Guangdong (WG), Northern Guangdong (NG), and Eastern Guangdong (EG) would be the primary transfer-in regions under the ENV, ENT, and GOV scenarios due to different resource endowment. Controlled by the different scenarios, the redistribution of enterprises presented different characteristics regarding the transport of pollutant emissions and economic added value between the PRD and surrounding areas. The average concentration of PM2.5 and the related population-weighted concentrations (PWC) showed a slight decrease over the PRD (-0.75 to -0.62 μg/m3 and -0.35 to -0.49 μg/m3 per person) but increased dramatically in surrounding areas under the three scenarios (0.46-7.68 μg/m3 and 0.07-4.44 μg/m3 per person). The transfer of double-high enterprises could potentially decrease the industrial fossil fuel consumption intensity (fossil energy consumption per unit of industrial GDP) of most of the cities while exacerbating pollution intensity (concentration of PM2.5 per unit of industrial GDP), reflecting the huge gap in the regional industrial development pattern in Guangdong Province at this stage, and illustrating the importance of emission control of these enterprises for improvement of regional air quality in the future. The research perspective on industrial transfer proposed in this study could provide a reference for future studies.
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Affiliation(s)
- Haihua Mo
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, China
| | - Yingchang You
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, China
| | - Liping Wu
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, China
| | - Fenghua Yan
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, China
| | - Ming Chang
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, China
| | - Weiwen Wang
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, China
| | - Peng Wang
- Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, Guangdong, China
| | - Xuemei Wang
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, China.
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13
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Zhang C, Li J, Zhao W, Yao Q, Wang H, Wang B. Open biomass burning emissions and their contribution to ambient formaldehyde in Guangdong province, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:155904. [PMID: 35569659 DOI: 10.1016/j.scitotenv.2022.155904] [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: 01/07/2022] [Revised: 04/09/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Formaldehyde (HCHO) plays a vital role in atmospheric chemistry and O3 formation. Open biomass burning (OBB) is considered to be an important source of HCHO; however, its quantitative contribution to ambient HCHO remains poorly understood due to the lack of reliable high-resolution emission inventories. In this study, a satellite-based method coupled with local emission factors was developed to estimate the hourly primary emissions of HCHO and volatile organic compound (VOC) precursors from OBB in Guangdong (GD) Province of southern China. Furthermore, the contribution of OBB to ambient HCHO was quantified using the Community Multi-scale Air Quality model. The results suggested that in average OBB emissions contributed 5293 tons of primary HCHO per year, accounting for ~14% of the total anthropogenic HCHO emissions in GD. The ambient HCHO concentration ranged from 0.3 ppbv to 8.7 ppbv during normal days, and from 8 ppbv to 45 ppbv in downwind area during OBB impacted days. The monthly contribution of OBB to local HCHO levels reached up to 50% at locations with frequent fires and over 70% during a forest fire event. Ambient HCHO was heavily affected by primary OBB emissions near the source region and by the oxidation of OBB-emitted VOCs in the downwind area. Secondary HCHO formation from OBB emissions was enhanced during photochemical pollution episodes, especially under conditions of high O3 and low NOx. OBB-emitted ethene was identified as the most important VOC precursor of HCHO and contributed to the formation of ~50% of the secondary HCHO. The HCHO formation potential of cropland fires was 26% higher than that of forest fires. Our results suggest that OBB can elevate ambient HCHO levels significantly. Thus, strict control policies on OBB should be implemented, especially for open burning agricultural residues in upwind areas on serious photochemical pollution days.
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Affiliation(s)
- Chunlin Zhang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; Australia-China Centre for Air Quality Science and Management (Guangdong), Guangzhou 511443, China; Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Jiangyong Li
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Wenlong Zhao
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China
| | - Qian Yao
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China
| | - Hao Wang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; Australia-China Centre for Air Quality Science and Management (Guangdong), Guangzhou 511443, China; Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China.
| | - Boguang Wang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; Australia-China Centre for Air Quality Science and Management (Guangdong), Guangzhou 511443, China; Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China.
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14
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Wu N, Geng G, Qin X, Tong D, Zheng Y, Lei Y, Zhang Q. Daily Emission Patterns of Coal-Fired Power Plants in China Based on Multisource Data Fusion. ACS ENVIRONMENTAL AU 2022; 2:363-372. [PMID: 37101967 PMCID: PMC10125283 DOI: 10.1021/acsenvironau.2c00014] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Daily emission estimates are essential for tracking the dynamic changes in emission sources. In this work, we estimate daily emissions of coal-fired power plants in China during 2017-2020 by combining information from the unit-based China coal-fired Power plant Emissions Database (CPED) and real-time measurements from continuous emission monitoring systems (CEMS). We develop a step-by-step method to screen outliers and impute missing values for data from CEMS. Then, plant-level daily profiles of flue gas volume and emissions obtained from CEMS are coupled with annual emissions from CPED to derive daily emissions. Reasonable agreement is found between emission variations and available statistics (i.e., monthly power generation and daily coal consumption). Daily power emissions are in the range of 6267-12,994, 0.4-1.3, 6.5-12.0, and 2.5-6.8 Gg for CO2, PM2.5, NO x , and SO2, respectively, with high emissions in winter and summer caused by heating and cooling demand. Our estimates can capture sudden decreases (e.g., those associated with COVID-19 lockdowns and short-term emission controls) or increases (e.g., those related to a drought) in daily power emissions during typical socioeconomic events. We also find that weekly patterns from CEMS exhibit no obvious weekend effect compared to those in previous studies. The daily power emissions will help to improve chemical transport modeling and facilitate policy formulation.
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Affiliation(s)
- Nana Wu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Guannan Geng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Xinying Qin
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Dan Tong
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Yixuan Zheng
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Yu Lei
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
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15
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Huang J, Zhang Z, Tao J, Zhang L, Nie F, Fei L. Source apportionment of carbonaceous aerosols using hourly data and implications for reducing PM 2.5 in the Pearl River Delta region of South China. ENVIRONMENTAL RESEARCH 2022; 210:112960. [PMID: 35189099 DOI: 10.1016/j.envres.2022.112960] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 02/10/2022] [Accepted: 02/12/2022] [Indexed: 05/28/2023]
Abstract
Ambient fine particulate matter (PM2.5) levels in South China have been decreasing in the past decade, but the decreasing rates differed between its major chemical components, e.g., with much small rates for carbonaceous aerosols than for secondary inorganic aerosols. To investigate the sources of carbonaceous aerosols in this region, a comprehensive campaign was carried out in urban Guangzhou in the winter of 2019-2020 using a combination of various instruments. Data generated from this campaign include hourly total carbon (TC), black carbon (BC), criteria air pollutants and meteorological parameters, 4-hourly particle-bound elements, and chemically-resolved daily PM2.5. Similar diurnal patterns were observed for TC, CO and NO2, suggesting TC was very likely related to vehicle exhaust emission. Secondary organic carbon (SOC) estimated using the Minimum R squared (MRS) method accounted for 35 ± 17% of OC, indicating strong atmospheric oxidation capacity. Four major source factors for carbonaceous aerosols were identified by positive matrix factorization (PMF) model, including coal combustion, traffic emissions, soil dust and ship emissions, which accounted for 37 ± 23%, 39 ± 23%, 14 ± 10% and 10 ± 13%, respectively, of TC mass concentration, 38 ± 24%, 38 ± 23%, 14 ± 10% and 10 ± 12%, respectively, of OC mass concentration, and 29 ± 21%, 43 ± 22%, 14 ± 11% and 14 ± 15%, respectively, of EC mass concentration. Among these sources, traffic emission was the most important one, suggesting the necessity for promoting clean energy vehicles and relieving urban traffic congestion.
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Affiliation(s)
- Junjun Huang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Zhisheng Zhang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Guangzhou, China.
| | - Jun Tao
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China; South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Guangzhou, China.
| | - Leiming Zhang
- Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, Toronto, Canada
| | - Fuli Nie
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Guangzhou, China
| | - Leilei Fei
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, China; Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Guangzhou, China
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16
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Du Y, Zhao K, Yuan Z, Luo H, Ma W, Liu X, Wang L, Liao C, Zhang Y. Identification of close relationship between large-scale circulation patterns and ozone-precursor sensitivity in the Pearl River Delta, China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 312:114915. [PMID: 35313148 DOI: 10.1016/j.jenvman.2022.114915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 03/07/2022] [Accepted: 03/14/2022] [Indexed: 06/14/2023]
Abstract
To curb the continuous deterioration of ozone (O3) pollution in China, identifying the O3-precursor sensitivity (OPS) and its driving factors is a prerequisite for formulating effective O3 pollution control measures. Traditional OPS identification methods have limitations in terms of spatiotemporal representation and timeliness; therefore, they are not appropriate for making OPS forecasts for O3 contingency control. OPS is not only influenced by local precursor emissions but is also closely related to meteorological conditions governed by large-scale circulation (LSC). In this study, a localized three-dimensional numerical modeling system was used to investigate the relationship between LSC and OPS in the Pearl River Delta (PRD) of China during September 2017, a month with continuous O3 pollution. Our results highlighted that there was a close relationship between LSC and OPS over the PRD, and the four dominant LSC patterns corresponded well to the NOx-limited, NOx-limited, VOC-limited, and transitional regimes, respectively. The clear linkage between LSC and OPS was mainly driven by the spatial heterogeneity of NOx and VOC emissions within and beyond the PRD along the prevailing winds under different LSC patterns. A conceptual model was developed to highlight the intrinsic causality between the LSC and OPS. Because current technology can accurately forecast LSC 48-72 h in advance, the LSC-based OPS forecast method provided us with a novel approach to guide contingency control and management measures to reduce peak O3 at a regional scale.
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Affiliation(s)
- Yi Du
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China
| | - Kaihui Zhao
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China.
| | - Zibing Yuan
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China.
| | - Huihong Luo
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China
| | - Wei Ma
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China
| | - Xuehui Liu
- School of Environment and Energy, South China University of Technology, Guangzhou, 510006, China
| | - Long Wang
- Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
| | - Chenghao Liao
- Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
| | - Yongbo Zhang
- Guangdong Provincial Academy of Environmental Science, Guangzhou, 510045, China
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17
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Jia G, Huang Z, Tang X, Ou J, Lu M, Xu Y, Zhong Z, Sha Q, Wu H, Zheng C, Deng T, Chen D, He M, Zheng J. A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China. J Environ Sci (China) 2022; 114:233-248. [PMID: 35459489 DOI: 10.1016/j.jes.2021.08.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/22/2021] [Accepted: 08/23/2021] [Indexed: 11/29/2022]
Abstract
The conventional Ensemble Kalman filter (EnKF), which is now widely used to calibrate emission inventories and to improve air quality simulations, is susceptible to simulation errors of meteorological inputs, making accurate updates of high temporal-resolution emission inventories challenging. In this study, we developed a novel meteorologically adjusted inversion method (MAEInv) based on the EnKF to improve daily emission estimations. The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs. For demonstration, we used the MAEInv to inverse daily carbon monoxide (CO) emissions in the Pearl River Delta (PRD) region, China. In the case study, 60% of the total CO simulation biases were associated with sensitive meteorological inputs, which would lead to the overestimation of daily variations of posterior emissions. Using the new inversion method, daily variations of emissions shrank dramatically, with the percentage change decreased by 30%. Also, the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%, indicating that posterior CO emissions might be overestimated using the conventional EnKF. Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration, with a higher correlation coefficient (R, +37.0%) and lower normalized mean bias (NMB, -17.9%). Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants, the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.
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Affiliation(s)
- Guanglin Jia
- School of Environment and Energy, South China University of Technology, University Town Campus, Guangzhou 510006, China
| | - Zhijiong Huang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China.
| | - Xiao Tang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Jiamin Ou
- Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, CH 3584, the Netherlands
| | - Menghua Lu
- School of Environment and Energy, South China University of Technology, University Town Campus, Guangzhou 510006, China
| | - Yuanqian Xu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China
| | - Zhuangmin Zhong
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China
| | - Qing'e Sha
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China
| | - Huangjian Wu
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Chuanzeng Zheng
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China
| | - Tao Deng
- Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510640, China
| | - Duohong Chen
- State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Monitoring Center, Guangzhou 510308, China
| | - Min He
- Department of Environmental Science and Engineering, College of Architecture and Environment, Sichuan University, Chengdu 610065, China.
| | - Junyu Zheng
- School of Environment and Energy, South China University of Technology, University Town Campus, Guangzhou 510006, China; Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China
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18
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Chen Y, Yang Y, Yao Y, Wang X, Xu Z. Spatial and dynamic effects of air pollution on under-five children's lower respiratory infections: an evidence from China 2006 to 2017. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:25391-25407. [PMID: 34841486 DOI: 10.1007/s11356-021-17791-x] [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: 09/07/2021] [Accepted: 11/23/2021] [Indexed: 06/13/2023]
Abstract
Air pollution has been a deeply concerned issue posing an immediate and profound threat to human's lower respiratory health in China. The health of children under 5 years old, regarded as a key index of public health progress in a country, is closely related to the long-term human capital development. Hence, it is vital to investigate the potential association between air pollution and children's lower respiratory health outcomes and to explore related policy implications regarding the public health and the pollution regulation. As air pollutants diffuse across adjacent regions rather easily, considering the spatial spillover effect is meaningful in course of acquiring the aforementioned association. Based on the proposed province-level panel dataset of China during 2006-2017, this study constructs a dynamic spatial panel Durbin model to investigate the impact of air pollution on under-five children's lower respiratory infections. As a result, (1) both air pollution and children's respiratory health have obvious spatial spillover effects, and the latter has an outstanding characteristic of path dependence in time. (2) In the short term, air pollution presents significant negative impact on children's respiratory health, while in the long run, the impact decreases dramatically. (3) Regional comparison indicates that children in the western China are the most susceptible to air pollution followed by children in the central and eastern regions. (4) Other control variables have significant and varying impacts both in the short and long term. Particularly, this paper proves the existence of "siphon effect" in children healthcare system in China. From a broader and more comprehensive perspective, this study provides effective and constructive basis for policy making, in favor of improving children's health under air pollution and promoting sustainable development in China.
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Affiliation(s)
- Yi Chen
- Business School, Sichuan University, Chengdu, 610065, Sichuan, China
| | - Yining Yang
- Desautels Faculty of Management, McGill University, Montreal, QC, H3A 0C8, Canada
| | - Yongna Yao
- National Office for Maternal and Child Health Surveillance of China, West China Second University Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Xuehao Wang
- China Europe International Business School, Shanghai, 201206, China
| | - Zhongwen Xu
- Business School, Sichuan University, Chengdu, 610065, Sichuan, China.
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19
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Xu D, Yuan Z, Wang M, Zhao K, Liu X, Duan Y, Fu Q, Wang Q, Jing S, Wang H, Zhao X. Multi-factor reconciliation of discrepancies in ozone-precursor sensitivity retrieved from observation- and emission-based models. ENVIRONMENT INTERNATIONAL 2022; 158:106952. [PMID: 34717174 DOI: 10.1016/j.envint.2021.106952] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 05/22/2023]
Abstract
Ground-level O3 pollution has been continuously worsening in China despite gradual improvement in other major pollutant levels. Understanding the sensitivity of O3 production to its precursors (OPS) is a prerequisite for formulating effective O3 control measures, but this has been hampered by significant discrepancies in OPS produced by traditional identification approaches using observation-based models (OBM) and emission-based models (EBM). In this study, by applying OBM and EBM in parallel within a month having significant O3 pollution in Shanghai, China, we demonstrated that a lack of carbonyl input, overestimation in NO2 monitoring data, and differences in simulation period and emission reduction area were the core factors leading to OPS discrepancies, and that a reliable OPS cannot be obtained unless these factors are reconciled. By collectively addressing these factors, the number of days with a consistent OPS from both models increased from 6-7 to 20-21 in a month, and the R value defined to quantify the discrepancy decreased by ∼55%. The contributions of these factors to OPS discrepancy differed greatly in urban and suburban settings, mainly caused by differences in pollutant emission and transport characteristics. Overall, OPS identified solely by OBM or EBM is associated with great uncertainty, while reliable OPS estimation can be achieved by a collective application of OBM and EBM with consensus on the above factors. The method demonstrated here could be applied to other photo-chemically active regions worldwide as part of efforts to address ozone pollution.
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Affiliation(s)
- Danni Xu
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
| | - Zibing Yuan
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.
| | - Ming Wang
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
| | - Kaihui Zhao
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
| | - Xuehui Liu
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
| | - Yusen Duan
- Shanghai Environmental Monitoring Center, Shanghai 200235, China
| | - Qingyan Fu
- Shanghai Environmental Monitoring Center, Shanghai 200235, China
| | - Qian Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Shengao Jing
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Xin Zhao
- Nanjing Intelligent Environmental Science and Technology Co., Ltd., Nanjing 211800, China
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Huang Z, Sha Q, Zhu M, Xu Y, Yu F, Liu H, Zhou W, Zhang X, Zhang X, Rao S, Jiang F, Liu J, Zheng J. Status and quality evaluation of precursor emission inventories for PM<sub>2.5</sub> and ozone in China. CHINESE SCIENCE BULLETIN-CHINESE 2021. [DOI: 10.1360/tb-2021-0783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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21
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Lu M, Zheng J, Huang Z, Wu C, Zheng C, Jia G, Zhang L, Jiang F, Li Z, Liu J, Chen D. Insight into the characteristics of carbonaceous aerosols at urban and regional sites in the downwind area of Pearl River Delta region, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 778:146251. [PMID: 34030391 DOI: 10.1016/j.scitotenv.2021.146251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 02/26/2021] [Accepted: 02/27/2021] [Indexed: 06/12/2023]
Abstract
Carbonaceous aerosols (CAs) take up a substantial fraction of fine particle (PM2.5) in the atmosphere, yet high temporal resolution and seasonal variations of their emission sources and formation mechanisms are still poorly characterized in the regions with strong anthropogenic activities. In this study, the spatiotemporal characteristics of CAs and their subfractions, i.e., organic carbon (OC) and elemental carbon (EC), were studied in one of China's key city clusters, the Pearl River Delta (PRD) region. Results show that the annual mean OC and EC concentrations are 5.89 ± 3.32 μg/m3 and 1.60 ± 1.00 μg/m3 at the urban site, respectively. Such levels are consistently higher than those at the regional site (4.94 ± 3.34 μg/m3 of OC and 1.45 ± 0.82 μg/m3 of EC), suggesting the strong impact of human activities on OC and EC concentration. Moreover, the OC concentration peak sharply appears at 19:00 across all seasons at the urban site due to the direct influence of traffic exhaust and cooking activities. At regional site, OC peaks in fall afternoon due to intensive photochemical reactions derived combustion-related secondary organic carbon (SOCcom) contributions to the downwind PRD region. Correlations between SOCcom and influence factors were found at both regional and urban sites, suggesting that SOCcom formation is more regionally homogenous and mainly originates from the Zhaoqing-Foshan-Jiangmen belt. In addition, there are significantly different formation mechanisms of non-combustion-related secondary organic carbon (SOCnon-com) in the downwind PRD region. This study provides a solid evidence for collaborative efforts in the mitigation of secondary aerosols in the PRD region.
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Affiliation(s)
- Menghua Lu
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
| | - Junyu Zheng
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China; Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China.
| | - Zhijiong Huang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Cheng Wu
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for on-Line Source Apportionment System of Air Pollution, Jinan University, Guangzhou 510632, China
| | - Chuanzeng Zheng
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Guanglin Jia
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Lihang Zhang
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
| | - Fan Jiang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Zhen Li
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China
| | - Junwen Liu
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511486, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 510632, China.
| | - Duohong Chen
- State Environmental Protection Key Laboratory of Regional Monitoring Air Quality, Guangdong Provincial Environmental Monitoring Center, Guangzhou 510635, China
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