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Dong Z, Li S, Jiang Y, Wang S, Xing J, Ding D, Zheng H, Wang H, Huang C, Yin D, Zhao B, Hao J. Health-Oriented Emission Control Strategy of Energy Utilization and Its Co-CO 2 Benefits: A Case Study of the Yangtze River Delta, China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:12320-12329. [PMID: 38973717 DOI: 10.1021/acs.est.3c10693] [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: 07/09/2024]
Abstract
Reducing air pollutants and CO2 emissions from energy utilization is crucial for achieving the dual objectives of clean air and carbon neutrality in China. Thus, an optimized health-oriented strategy is urgently needed. Herein, by coupling a CO2 and air pollutants emission inventory with response surface models for PM2.5-associated mortality, we shed light on the effectiveness of protecting human health and co-CO2 benefit from reducing fuel-related emissions and generate a health-oriented strategy for the Yangtze River Delta (YRD). Results reveal that oil consumption is the primary contributor to fuel-related PM2.5 pollution and premature deaths in the YRD. Significantly, curtailing fuel consumption in transportation is the most effective measure to alleviate the fuel-related PM2.5 health impact, which also has the greatest cobenefits for CO2 emission reduction on a regional scale. Reducing fuel consumption will achieve substantial health improvements especially in eastern YRD, with nonroad vehicle emission reductions being particularly impactful for health protection, while on-road vehicles present the greatest potential for CO2 reductions. Scenario analysis confirms the importance of mitigating oil consumption in the transportation sector in addressing PM2.5 pollution and climate change.
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Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dian Ding
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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Chen Q, Miao R, Geng G, Shrivastava M, Dao X, Xu B, Sun J, Zhang X, Liu M, Tang G, Tang Q, Hu H, Huang RJ, Wang H, Zheng Y, Qin Y, Guo S, Hu M, Zhu T. Widespread 2013-2020 decreases and reduction challenges of organic aerosol in China. Nat Commun 2024; 15:4465. [PMID: 38796477 PMCID: PMC11127919 DOI: 10.1038/s41467-024-48902-0] [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: 10/24/2023] [Accepted: 05/17/2024] [Indexed: 05/28/2024] Open
Abstract
High concentrations of organic aerosol (OA) occur in Asian countries, leading to great health burdens. Clean air actions have resulted in significant emission reductions of air pollutants in China. However, long-term nation-wide trends in OA and their causes remain unknown. Here, we present both observational and model evidence demonstrating widespread decreases with a greater reduction in primary OA than in secondary OA (SOA) in China during the period of 2013 to 2020. Most of the decline is attributed to reduced residential fuel burning while the interannual variability in SOA may have been driven by meteorological variations. We find contrasting effects of reducing NOx and SO2 on SOA production which may have led to slight overall increases in SOA. Our findings highlight the importance of clean energy replacements in multiple sectors on achieving air-quality targets because of high OA precursor emissions and fluctuating chemical and meteorological conditions.
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Affiliation(s)
- Qi Chen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China.
- Institute of Carbon Neutrality, Peking University, Beijing, China.
| | - Ruqian Miao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Guannan Geng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | | | - Xu Dao
- China National Environmental Monitoring Centre, Beijing, China
| | - Bingye Xu
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Zhejiang Province Environment Monitoring Centre, Hangzhou, China
| | - Jiaqi Sun
- China National Environmental Monitoring Centre, Beijing, China
| | - Xian Zhang
- China National Environmental Monitoring Centre, Beijing, China
| | - Mingyuan Liu
- China National Environmental Monitoring Centre, Beijing, China
| | - Guigang Tang
- China National Environmental Monitoring Centre, Beijing, China
| | - Qian Tang
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Zhejiang Province Environment Monitoring Centre, Hangzhou, China
| | - Hanwen Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | - Ru-Jin Huang
- State Key Laboratory of Loess and Quaternary Geology, Center for Excellence in Quaternary Science and Global Change, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China
| | - Hao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yan Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Yue Qin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
| | - Song Guo
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
| | - Min Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
| | - Tong Zhu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Innovation Center for Engineering Science and Advanced Technology, International Joint Laboratory for Regional Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China
- Institute of Carbon Neutrality, Peking University, Beijing, China
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Li Z, Zhao B, Yin D, Wang S, Qiao X, Jiang J, Li Y, Shen J, He Y, Chang X, Li X, Liu Y, Li Y, Liu C, Qi X, Chen L, Chi X, Jiang Y, Li Y, Wu J, Nie W, Ding A. Modeling the Formation of Organic Compounds across Full Volatility Ranges and Their Contribution to Nanoparticle Growth in a Polluted Atmosphere. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:1223-1235. [PMID: 38117938 DOI: 10.1021/acs.est.3c06708] [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: 12/22/2023]
Abstract
Nanoparticle growth influences atmospheric particles' climatic effects, and it is largely driven by low-volatility organic vapors. However, the magnitude and mechanism of organics' contribution to nanoparticle growth in polluted environments remain unclear because current observations and models cannot capture organics across full volatility ranges or track their formation chemistry. Here, we develop a mechanistic model that characterizes the full volatility spectrum of organic vapors and their contributions to nanoparticle growth by coupling advanced organic oxidation modeling and kinetic gas-particle partitioning. The model is applied to Nanjing, a typical polluted city, and it effectively captures the volatility distribution of low-volatility organics (with saturation vapor concentrations <0.3 μg/m3), thus accurately reproducing growth rates (GRs), with a 4.91% normalized mean bias. Simulations indicate that as particles grow from 4 to 40 nm, the relative fractions of GRs attributable to organics increase from 59 to 86%, with the remaining contribution from H2SO4 and its clusters. Aromatics contribute much to condensable organic vapors (∼37%), especially low-volatility vapors (∼61%), thus contributing the most to GRs (32-46%) as 4-40 nm particles grow. Alkanes also contribute 19-35% of GRs, while biogenic volatile organic compounds contribute minimally (<13%). Our model helps assess the climatic impacts of particles and predict future changes.
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Affiliation(s)
- Zeqi Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xiaohui Qiao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yiran Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jiewen Shen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yicong He
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xing Chang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
| | - Xiaoxiao Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yuliang Liu
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
| | - Yuanyuan Li
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
| | - Chong Liu
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
| | - Ximeng Qi
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
| | - Liangduo Chen
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
| | - Xuguang Chi
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yuyang Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jin Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Wei Nie
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
| | - Aijun Ding
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing 210023, Jiangsu Province, China
- Jiangsu Provincial Collaborative Innovation Center for Climate Change, Nanjing University, Nanjing 210093, China
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Shen X, Che H, Yao Z, Wu B, Lv T, Yu W, Cao X, Hao X, Li X, Zhang H, Yao X. Real-World Emission Characteristics of Full-Volatility Organics Originating from Nonroad Agricultural Machinery during Agricultural Activities. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37419883 DOI: 10.1021/acs.est.3c02619] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Nonroad agricultural machinery (NRAM) emissions constitute a significant source of air pollution in China. Full-volatility organics originating from 19 machines under 6 agricultural activities were measured synchronously. The diesel-based emission factors (EFs) for full-volatility organics were 4.71 ± 2.78 g/kg fuel (average ± standard deviation), including 91.58 ± 8.42% volatile organic compounds (VOCs), 7.94 ± 8.16% intermediate-volatility organic compounds (IVOCs), 0.28 ± 0.20% semivolatile organic compounds (SVOCs), and 0.20 ± 0.16% low-volatility organic compounds (LVOCs). Full-volatility organic EFs were significantly reduced by stricter emission standards and were the highest under pesticide spraying activity. Our results also demonstrated that combustion efficiency was a potential factor influencing full-volatility organic emissions. Gas-particle partitioning in full-volatility organics could be affected by multiple factors. Furthermore, the estimated secondary organic aerosol formation potential based on measured full-volatility organics was 143.79 ± 216.80 mg/kg fuel and could be primarily attributed to higher-volatility-interval IVOCs (bin12-bin16 contributed 52.81 ± 11.58%). Finally, the estimated emissions of full-volatility organics from NRAM in China (2021) were 94.23 Gg. This study provides first-hand data on full-volatility organic EFs originating from NRAM to facilitate the improvement of emission inventories and atmospheric chemistry models.
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Affiliation(s)
- Xianbao Shen
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Hongqian Che
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
| | - Zhiliang Yao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Bobo Wu
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Tiantian Lv
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
| | - Wenhan Yu
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
| | - Xinyue Cao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Xuewei Hao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Xin Li
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Hanyu Zhang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Xiaolong Yao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China
- State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
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