1
|
Driscoll C, Milford JB, Henze DK, Bell MD. Atmospheric reduced nitrogen: Sources, transformations, effects, and management. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2024; 74:362-415. [PMID: 38819428 DOI: 10.1080/10962247.2024.2342765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 04/02/2024] [Indexed: 06/01/2024]
Abstract
Human activities have increased atmospheric emissions and deposition of oxidized and reduced forms of nitrogen, but emission control programs have largely focused on oxidized nitrogen. As a result, in many regions of the world emissions of oxidized nitrogen are decreasing while emissions of reduced nitrogen are increasing. Emissions of reduced nitrogen largely originate from livestock waste and fertilizer application, with contributions from transportation sources in urban areas. Observations suggest a discrepancy between trends in emissions and deposition of reduced nitrogen in the U.S., likely due to an underestimate in emissions. In the atmosphere, ammonia reacts with oxides of sulfur and nitrogen to form fine particulate matter that impairs health and visibility and affects climate forcings. Recent reductions in emissions of sulfur and nitrogen oxides have limited partitioning with ammonia, decreasing long-range transport. Continuing research is needed to improve understanding of how shifting emissions alter formation of secondary particulates and patterns of transport and deposition of reactive nitrogen. Satellite remote sensing has potential for monitoring atmospheric concentrations and emissions of ammonia, but there remains a need to maintain and strengthen ground-based measurements and continue development of chemical transport models. Elevated nitrogen deposition has decreased plant and soil microbial biodiversity and altered the biogeochemical function of terrestrial, freshwater, and coastal ecosystems. Further study is needed on differential effects of oxidized versus reduced nitrogen and pathways and timescales of ecosystem recovery from elevated nitrogen deposition. Decreases in deposition of reduced nitrogen could alleviate exceedances of critical loads for terrestrial and freshwater indicators in many U.S. areas. The U.S. Environmental Protection Agency should consider using critical loads as a basis for setting standards to protect public welfare and ecosystems. The U.S. and other countries might look to European experience for approaches to control emissions of reduced nitrogen from agricultural and transportation sectors.Implications: In this Critical Review we synthesize research on effects, air emissions, environmental transformations, and management of reduced forms of nitrogen. Emissions of reduced nitrogen affect human health, the structure and function of ecosystems, and climatic forcings. While emissions of oxidized forms of nitrogen are regulated in the U.S., controls on reduced forms are largely absent. Decreases in emissions of sulfur and nitrogen oxides coupled with increases in ammonia are shifting the gas-particle partitioning of ammonia and decreasing long-range atmospheric transport of reduced nitrogen. Effort is needed to understand, monitor, and manage emissions of reduced nitrogen in a changing environment.
Collapse
Affiliation(s)
- Charles Driscoll
- Department of Civil and Environmental Engineering, Syracuse University, Syracuse, NY, USA
| | - Jana B Milford
- Department of Mechanical Engineering, University of Colorado, Boulder, CO, USA
| | - Daven K Henze
- Department of Mechanical Engineering, University of Colorado, Boulder, CO, USA
| | - Michael D Bell
- Ecologist, National Park Service - Air Resources Division, Boulder, CO, USA
| |
Collapse
|
2
|
Hu W, Zhao Y, Lu N, Wang X, Zheng B, Henze DK, Zhang L, Fu TM, Zhai S. Changing Responses of PM 2.5 and Ozone to Source Emissions in the Yangtze River Delta Using the Adjoint Model. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:628-638. [PMID: 38153406 DOI: 10.1021/acs.est.3c05049] [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: 12/29/2023]
Abstract
China's industrial restructuring and pollution controls have altered the contributions of individual sources to varying air quality over the past decade. We used the GEOS-Chem adjoint model and investigated the changing sensitivities of PM2.5 and ozone (O3) to multiple species and sources from 2010 to 2020 in the central Yangtze River Delta (YRDC), the largest economic region in China. Controlling primary particles and SO2 from industrial and residential sectors dominated PM2.5 decline, and reducing CO from multiple sources and ≥C3 alkenes from vehicles restrained O3. The chemical regime of O3 formation became less VOC-limited, attributable to continuous NOX abatement for specific sources, including power plants, industrial combustion, cement production, and off-road traffic. Regional transport was found to be increasingly influential on PM2.5. To further improve air quality, management of agricultural activities to reduce NH3 is essential for alleviating PM2.5 pollution, while controlling aromatics, alkenes, and alkanes from industry and gasoline vehicles is effective for O3. Reducing the level of NOX from nearby industrial combustion and transportation is helpful for both species. Our findings reveal the complexity of coordinating control of PM2.5 and O3 pollution in a fast-developing region and support science-based policymaking for other regions with similar air pollution problems.
Collapse
Affiliation(s)
- Weiyang Hu
- State Key Laboratory of Pollution Control and Resource Reuse and School of the Environment, Nanjing University, 163 Xianlin Road, Nanjing, Jiangsu 210023, China
| | - Yu Zhao
- State Key Laboratory of Pollution Control and Resource Reuse and School of the Environment, Nanjing University, 163 Xianlin Road, Nanjing, Jiangsu 210023, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Jiangsu 210044, China
| | - Ni Lu
- Laboratory for Climate and Ocean-Atmosphere Sciences, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Xiaolin Wang
- Laboratory for Climate and Ocean-Atmosphere Sciences, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Daven K Henze
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Sciences, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Tzung-May Fu
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Shixian Zhai
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
- Division of Environment and Sustainability, HKUST Jockey Club Institute for Advanced Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, China
| |
Collapse
|
3
|
Ma G, Liu X, Wang J, Li M, Dong Z, Li X, Wang L, Han Y, Cao J. Characteristics and health risk assessment of indoor and outdoor PM 2.5 in a rural village, in Northeast of China: impact of coal and biomass burning. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2023; 45:9639-9652. [PMID: 37787830 DOI: 10.1007/s10653-023-01755-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/05/2023] [Indexed: 10/04/2023]
Abstract
Fine particulate matter (PM2.5) has health effects that may depend on its sources and chemical composition. In this study, characteristics of PM2.5 chemical composition and health risk assessment from Songyuan, China, were investigated during day and night in indoor and outdoor from February 4 to 19, 2021. Relative high concentrations of PM2.5 were obtained in indoor environment than outdoor, with 503.95 ± 209.62 μg/m3 during the day and 357.52 ± 232.81 μg/m3 at night for the indoor environment. Relatively high total carbon, organic carbons, elemental carbons, polycyclic aromatic hydrocarbons (PAHs), and oxygenated polycyclic aromatic hydrocarbons (OPAHs) were obtained in indoor environment. However, the average concentrations of PAHs were higher during night (73.57 ± 43.09 ng/m3) in indoor and OPAHs during day (6.027 ± 2.960 ng/m3) in outdoor. They had different I/O distributions of these compounds during day and night. Indeno(1,2,3-cd) pyrene was the dominant PAHs, and benzanthrone was the dominant OPAHs; this is different with the previous studies. The high indoor/outdoor ratios showed the indoor coal and biomass burning greatly affect the indoor pollutants. Average ILCR health risk assessment for PAHs was all higher than 10-6 for different age gender, suggesting there has potential cancer risk existed for populations living in the rural coal and biomass burning area Songyuan, China.
Collapse
Affiliation(s)
- Ge Ma
- National Demonstration Center for Experimental Geography Education, School of Geography and Tourism, Shaanxi Normal University, No. 620 West Chang'an Road, Chang'an Zone, Xi'an, 710119, China
| | - Xiuqun Liu
- National Demonstration Center for Experimental Geography Education, School of Geography and Tourism, Shaanxi Normal University, No. 620 West Chang'an Road, Chang'an Zone, Xi'an, 710119, China
| | - Jingzhi Wang
- National Demonstration Center for Experimental Geography Education, School of Geography and Tourism, Shaanxi Normal University, No. 620 West Chang'an Road, Chang'an Zone, Xi'an, 710119, China.
- Key Lab of Aerosol Chemistry & Physics, State Key Lab of Loess and Quaternary Geology (SKLLQG), Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China.
| | - Minrui Li
- National Demonstration Center for Experimental Geography Education, School of Geography and Tourism, Shaanxi Normal University, No. 620 West Chang'an Road, Chang'an Zone, Xi'an, 710119, China
| | - Zhibao Dong
- National Demonstration Center for Experimental Geography Education, School of Geography and Tourism, Shaanxi Normal University, No. 620 West Chang'an Road, Chang'an Zone, Xi'an, 710119, China
| | - Xiaoping Li
- National Demonstration Center for Experimental Geography Education, School of Geography and Tourism, Shaanxi Normal University, No. 620 West Chang'an Road, Chang'an Zone, Xi'an, 710119, China
| | - Lijun Wang
- National Demonstration Center for Experimental Geography Education, School of Geography and Tourism, Shaanxi Normal University, No. 620 West Chang'an Road, Chang'an Zone, Xi'an, 710119, China
| | - Yongming Han
- Key Lab of Aerosol Chemistry & Physics, State Key Lab of Loess and Quaternary Geology (SKLLQG), Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China
| | - Junji Cao
- Key Lab of Aerosol Chemistry & Physics, State Key Lab of Loess and Quaternary Geology (SKLLQG), Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, China
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
4
|
Li Y, Xue L, Tao Y, Li Y, Wu Y, Liao Q, Wan J, Bai Y. Exploring the contributions of major emission sources to PM 2.5 and attributable health burdens in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 322:121177. [PMID: 36731741 DOI: 10.1016/j.envpol.2023.121177] [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: 05/15/2022] [Revised: 01/27/2023] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
Ambient fine particulate matter (PM2.5) pollution is the principal environmental risk factor for health burdens in China. Identifying the sectoral contributions of pollutant emissions sources on multiple spatiotemporal scales can help in the formulation of specific strategies. In this study, we used sensitivity analysis to explore the specific contributions of seven major emission sources to ambient PM2.5 and attributable premature mortality across mainland China. In 2016, about 60% of China's population lived in areas with PM2.5 concentrations above the Chinese Ambient Air Quality Standard of 35 μg/m3. This percentage was expected to decrease to 35% and 39% if industrial and residential emissions were fully eliminated. In densely populated and highly polluted regions, residential sources contributed about 50% of the PM2.5 exposure in winter, while industrial sources contributed the most (29-51%) in the remaining seasons. The three major sectoral contributors to PM2.5-related deaths were industry (247,000 cases, 35%), residential sources (219,000 cases, 31%), and natural sources (87,000, 12%). The relative contributions of the different sectors varied in the different provinces, with industrial sources making the largest contribution in Shanghai (65%), while residential sources predominated in Heilongjiang (63%), and natural sources dominated in Xinjiang (82%). The contributions of the agricultural (11%), transportation (6%), and power (3%) sources were relatively low in China, but emissions mitigation was still effective in densely populated areas. In conclusion, to effectively alleviate health burdens across China, priority should be given to controlling residential emissions in winter and industrial emissions all year round, taking additional measures to curb emissions from other sources in urban hotspots, and formulating air pollution control strategies tailored to local conditions.
Collapse
Affiliation(s)
- Yong Li
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China; Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China
| | - Liyang Xue
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China; Gansu Ecological Environment Emergency and Accident Investigation Center, Lanzhou, 730030, China
| | - Yan Tao
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
| | - Yidu Li
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Yancong Wu
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Qin Liao
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China; Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Junyi Wan
- School of Natural Science, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Yun Bai
- School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China
| |
Collapse
|
5
|
Quantification of SO2 Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations. ATMOSPHERE 2022. [DOI: 10.3390/atmos13030470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In this research, a new time-resolved emission inversion system was developed to investigate variations in SO2 emission in China during the COVID-19 (Corona Virus Disease 2019) lockdown period based on a four-dimensional variational (4DVar) inversion method to dynamically optimize the SO2 inventory by assimilating the ground-based hourly observation data. The inversion results obtained were validated in the North China Plain (NCP). Two sets of experiments were carried out based on the original and optimized inventories during the pre-lockdown and lockdown period to quantify the SO2 emission variations and the corresponding prediction improvement. The SO2 emission changes due to the lockdown in the NCP were quantified by the differences in the averaged optimized inventories between the pre-lockdown and lockdown period. As a response to the lockdown control, the SO2 emissions were reduced by 20.1% on average in the NCP, with ratios of 20.7% in Beijing, 20.2% in Tianjin, 26.1% in Hebei, 18.3% in Shanxi, 19.1% in Shandong, and 25.9% in Henan, respectively. These were mainly attributed to the changes caused by the heavy industry lockdown in these areas. Compared to the model performance based on the original inventory, the optimized daily SO2 emission inventory significantly improved the model SO2 predictions during the lockdown period, with the correlation coefficient (R) value increasing from 0.28 to 0.79 and the root-mean-square error (RMSE) being reduced by more than 30%. Correspondingly, the performance of PM2.5 was slightly improved, with R-value increasing from 0.67 to 0.74 and the RMSE being reduced by 8% in the meantime. These statistics indicate the good optimization ability of the time-resolved emission inversion system.
Collapse
|
6
|
The Different Impacts of Emissions and Meteorology on PM2.5 Changes in Various Regions in China: A Case Study. ATMOSPHERE 2022. [DOI: 10.3390/atmos13020222] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Emissions and meteorology are significant factors affecting aerosol pollution, but it is not sufficient to understand their relative contributions to aerosol pollution changes. In this study, the observational data and the chemical model (GRAPES_CUACE) are combined to estimate the drivers of PM2.5 changes in various regions (the Beijing–Tianjin–Hebei (BTH), the Central China (CC), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)) between the first month after COVID-19 (FMC_2020) (i.e., from 23 January to 23 February 2020) and the corresponding period in 2019 (FMC_2019). The results show that PM2.5 mass concentration increased by 26% (from 61 to 77 µg m−3) in the BTH, while it decreased by 26% (from 94 to 70 µg m−3) in the CC, 29% (from 52 to 37 µg m−3) in the YRD, and 32% (from 34 to 23 µg m−3) in the PRD in FMC_2020 comparing with FMC_2019, respectively. In the BTH, although emissions reductions partly improved PM2.5 pollution (−5%, i.e., PM2.5 mass concentration decreased by 5% due to emissions) in FMC_2020 compared with that of FMC_2019, the total increase in PM2.5 mass concentration was dominated by more unfavorable meteorological conditions (+31%, i.e., PM2.5 mass concentration increased by 31% due to meteorology). In the CC and the YRD, emissions reductions (−33 and −36%) played a dominating role in the total decrease in PM2.5 in FMC_2020, while the changed meteorological conditions partly worsened PM2.5 pollution (+7 and +7%). In the PRD, emissions reductions (−23%) and more favorable meteorological conditions (−9%) led to a total decrease in PM2.5 mass concentration. This study reminds us that the uncertainties of relative contributions of meteorological conditions and emissions on PM2.5 changes in various regions are large, which is conducive to policymaking scientifically in China.
Collapse
|
7
|
Wang S, Huang G, Hu K, Wang L, Dai T, Zhou C. The deep blue day is decreasing in China. THEORETICAL AND APPLIED CLIMATOLOGY 2022; 147:1675-1684. [PMID: 35095143 PMCID: PMC8782681 DOI: 10.1007/s00704-021-03898-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 12/11/2021] [Indexed: 06/14/2023]
Abstract
UNLABELLED The deep blue sky is an indicator of a lower concentration of aerosols and a cloudless sky. With increasing human emissions, a trend towards days with fewer deep blue skies might indicate a decline in a good living environment for humans. This study investigates the long-term changes of the deep blue sky in China from 1980 to 2018. Due to a lack of direct measurements, we use atmospheric visibility and low cloud cover to classify blue sky days into three grades: light blue day, medium blue day, and deep blue day. Climatologically, annual deep blue days increase from southeast China to northwest China, with the maximum number in Xinjiang and eastern Inner Mongolia and the minimum number in western Qinghai and southern Hebei. From 1980 to 2018, annual deep blue days show a prominent decreasing trend in most of China, with area-mean annual deep blue days decreasing by -0.48 days per year (d/y) in China, and the variation becomes more obvious after 2013. The maximum decreasing trend is observed in eastern China. The most prominent decreases of deep blue days are seen in winter. Both air pollution and the change in meteorological conditions contribute to the decrease of wintertime deep blue days in China. Specifically, the decrease in surface wind speed hinders the cleaning of air by winds, the increase in surface air temperature, and decrease in relative humidity is favorable for low cloud increase, and the increasing emission of pollution reduces atmospheric visibility. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s00704-021-03898-1.
Collapse
Affiliation(s)
- Su Wang
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Gang Huang
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237 China
| | - Kaiming Hu
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Lin Wang
- Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
| | - Tie Dai
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
| | - Chunjiang Zhou
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| |
Collapse
|
8
|
Adam MG, Tran PTM, Balasubramanian R. Air quality changes in cities during the COVID-19 lockdown: A critical review. ATMOSPHERIC RESEARCH 2021; 264:105823. [PMID: 34456403 PMCID: PMC8384485 DOI: 10.1016/j.atmosres.2021.105823] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 08/11/2021] [Accepted: 08/21/2021] [Indexed: 05/04/2023]
Abstract
In response to the rapid spread of coronavirus disease-2019 (COVID-19) within and across countries and the need to protect public health, governments worldwide introduced unprecedented measures such as restricted road and air travel and reduced human mobility in 2020. The curtailment of personal travel and economic activity provided a unique opportunity for researchers to assess the interplay between anthropogenic emissions of primary air pollutants, their physical transport, chemical transformation, ultimate fate and potential health impacts. In general, reductions in the atmospheric levels of outdoor air pollutants such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and volatile organic compounds (VOCs) were observed in many countries during the lockdowns. However, the levels of ozone (O3), a secondary air pollutant linked to asthma and respiratory ailments, and secondary PM were frequently reported to remain unchanged or even increase. An increase in O3 can enhance the formation of secondary PM2.5, especially secondary organic aerosols, through the atmospheric oxidation of VOCs. Given that the gaseous precursors of O3 (VOCs and NOx) are also involved in the formation of secondary PM2.5, an integrated control strategy should focus on reducing the emission of the common precursors for the co-mitigation of PM2.5 and O3 with an emphasis on their complex photochemical interactions. Compared to outdoor air quality, comprehensive investigations of indoor air quality (IAQ) are relatively sparse. People spend more than 80% of their time indoors with exposure to air pollutants of both outdoor and indoor origins. Consequently, an integrated assessment of exposure to air pollutants in both outdoor and indoor microenvironments is needed for effective urban air quality management and for mitigation of health risk. To provide further insights into air quality, we do a critical review of scientific articles, published from January 2020 to December 2020 across the globe. Finally, we discuss policy implications of our review in the context of global air quality improvement.
Collapse
Affiliation(s)
- Max G Adam
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Phuong T M Tran
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
- Faculty of Environment, University of Science and Technology, The University of Danang, 54 Nguyen Luong Bang Street, Lien Chieu District, Danang City, Viet Nam
| | - Rajasekhar Balasubramanian
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
| |
Collapse
|
9
|
Zhao X, Wang G, Wang S, Zhao N, Zhang M, Yue W. Impacts of COVID-19 on air quality in mid-eastern China: An insight into meteorology and emissions. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2021; 266:118750. [PMID: 34584487 PMCID: PMC8461319 DOI: 10.1016/j.atmosenv.2021.118750] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 09/11/2021] [Accepted: 09/22/2021] [Indexed: 05/09/2023]
Abstract
The coronavirus disease (COVID-19) spread rapidly worldwide in the first half of 2020. Stringent national lockdown policies imposed by China to prevent the spread of the virus reduced anthropogenic emissions and improved air quality. A weather research and forecasting model coupled with chemistry was applied to evaluate the impact of meteorology and emissions on air quality during the COVID-19 outbreak (from January 23 to February 29, 2020) in mid-eastern China. The results show that air pollution episodes still occurred on polluted days and accounted for 31.6%-60.5% of the total number of outbreak days in mid-eastern China from January 23 to February 29, 2020. However, anthropogenic emissions decreased significantly, indicating that anthropogenic emission reduction cannot completely offset the impact of unfavorable meteorological conditions on air quality. Favorable meteorological conditions in 2019 improved the overall air quality for a COVID-19 outbreak in 2019 instead of 2020. PM2.5 concentrations decreased by 4.2%-29.2% in Beijing, Tianjin, Shijiazhuang, and Taiyuan, and increased by 6.1%-11.5% in Jinan and Zhengzhou. PM2.5 concentrations increased by 10.9%-20.5% without the COVID-19 outbreak of 2020 in mid-eastern China, and the frequency of polluted days increased by 5.3%-18.4%. Source apportionment of PM2.5 during the COVID-19 outbreak showed that industry and residential emissions were the dominant PM2.5 contributors (32.7%-49.6% and 26.0%-44.5%, respectively) followed by agriculture (18.7%-24.0%), transportation (7.7%-15.5%), and power (4.1%-5.9%). In Beijing, industrial and residential contributions to PM2.5 concentrations were lower (32.7%) and higher (44.5%), respectively, than in other cities (38.7%-49.6% for industry and 26.0%-36.2% for residential). Therefore, enhancing regional cooperation and implementing a united air pollution control are effective emission mitigation measures for future air quality improvement, especially the development of new technologies for industrial and cooking fumes.
Collapse
Affiliation(s)
- Xiuyong Zhao
- National Environmental Protection Research Institute for Electric Power Co., Ltd. State Environmental Protection Key Laboratory of Atmospheric Physical Modeling and Pollution Control, Nanjing 210031, China
| | - Gang Wang
- Department of Environmental and Safety Engineering, College of Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, China
| | - Sheng Wang
- National Environmental Protection Research Institute for Electric Power Co., Ltd. State Environmental Protection Key Laboratory of Atmospheric Physical Modeling and Pollution Control, Nanjing 210031, China
| | - Na Zhao
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Ming Zhang
- National Environmental Protection Research Institute for Electric Power Co., Ltd. State Environmental Protection Key Laboratory of Atmospheric Physical Modeling and Pollution Control, Nanjing 210031, China
| | - Wenqi Yue
- Department of Environmental Art Engineering, Nanjing Technical Vocational College, Nanjing 210019, China
| |
Collapse
|
10
|
Liu C, Zhang S, Gao Y, Wang Y, Sheng L, Gao H, Fung JCH. Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 773:145580. [PMID: 33582338 DOI: 10.1016/j.scitotenv.2021.145580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 01/13/2021] [Accepted: 01/28/2021] [Indexed: 06/12/2023]
Abstract
Attributing sources of air pollution events by deploying an efficient observational network is an important and interesting problem in air quality control and forecast studies, but it is very challenging. In order to estimate the sensitivities of pollution events to emission sources, a comprehensive framework is built based on a horizontal 2-dimensional transport model and its adjoint in solving this problem. In an analysis of an idealized air pollution event of PM2.5 over the region of North China, an objective function is defined to optimally estimate the initial concentrations and emission sources through a series of minimization procedures. Results by means of the 4-dimensional variational approach show that, with the optimal initial conditions and emission sources, the model can successfully forecast the pollution event in a few days. The optimal observing network based on sensitivity analysis takes only one third of the cost but greatly retains predictability skill compared to the full-grid observing system, while nearly no predictability skill is detectable if the same number of observational sites is randomly deployed. We evaluate air pollution predictability in the point of focusing on to what degree the root mean square errors between the modeled concentration and the targeted air pollution are limited by the optimal observational network. Results show that air pollution predictability in association with the optimal observational network is limited in the time scales about 6 days. With the high efficiency and in an economic fashion, such a sensitivity-based optimal observing system holds promise for accurately predicting an air pollution event in the targeted area once the adjoint and variational procedure of a realistic atmosphere model including transport and chemical processes is performed.
Collapse
Affiliation(s)
- Caili Liu
- The College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
| | - Shaoqing Zhang
- The College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China; Key Laboratory of Physical Oceanography, MOE, Institute for Advanced Ocean Study, Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ocean University of China, China; Ocean Dynamics and Climate Function Lab, Pilot National Laboratory for Marine Science and Technology (QNLM), Qingdao, China; International Laboratory for High-Resolution Earth System Model and Prediction (iHESP), Qingdao, China.
| | - Yang Gao
- Key Laboratory of Marine Environment and Ecology, and Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ministry of Education, Ocean University of China, Qingdao 266100, China; Ocean Dynamics and Climate Function Lab, Pilot National Laboratory for Marine Science and Technology (QNLM), Qingdao, China.
| | - Yuhang Wang
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
| | - Lifang Sheng
- The College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
| | - Huiwang Gao
- Key Laboratory of Marine Environment and Ecology, and Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ministry of Education, Ocean University of China, Qingdao 266100, China
| | - J C H Fung
- Division of Environment and Sustainability, Hong Kong University of Science and Technology, China
| |
Collapse
|
11
|
Zhang Q, Pan Y, He Y, Walters WW, Ni Q, Liu X, Xu G, Shao J, Jiang C. Substantial nitrogen oxides emission reduction from China due to COVID-19 and its impact on surface ozone and aerosol pollution. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 753:142238. [PMID: 33207485 PMCID: PMC7474802 DOI: 10.1016/j.scitotenv.2020.142238] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 09/03/2020] [Accepted: 09/04/2020] [Indexed: 05/05/2023]
Abstract
A top-down approach was employed to estimate the influence of lockdown measures implemented during the COVID-19 pandemic on NOx emissions and subsequent influence on surface PM2.5 and ozone in China. The nation-wide NOx emission reduction of 53.4% due to the lockdown in 2020 quarter one in China may represent the current upper limit of China's NOx emission control. During the Chinese New Year Holiday (P2), NOx emission intensity in China declined by 44.7% compared to the preceding 3 weeks (P1). NOx emission intensity increased by 20.3% during the 4 weeks after P2 (P3), despite the unchanged NO2 column. It recovered to 2019 level at the end of March (P4). The East China (22°N - 42°N, 102°E - 122°E) received greater influence from COVID-19. Overall NOx emission from East China for 2020 first quarter is 40.5% lower than 2019, and in P4 it is still 22.9% below the same period in 2019. The 40.5% decrease of NOx emission in 2020 first quarter in East China lead to 36.5% increase of surface O3 and 12.5% decrease of surface PM2.5. The elevated O3 promotes the secondary aerosol formation through heterogeneous pathways. We recommend that the complicated interaction between PM2.5 and O3 should be considered in the emission control strategy making process in the future.
Collapse
Affiliation(s)
- Qianqian Zhang
- National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
| | - Yuepeng Pan
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Yuexin He
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Wendell W Walters
- Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, RI 02912, USA; Institute at Brown for Environment and Society, Brown University, Providence, RI 02912, USA
| | - Qianyin Ni
- Sinopec Yanshan Petrochemical Company, Beijing 102500, China
| | - Xuyan Liu
- National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
| | - Guangyi Xu
- Hebei Provincial Academy of Environmental Sciences, Shijiazhuang 050037, China
| | - Jiali Shao
- National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
| | - Chunlai Jiang
- Research Center for Total Pollution Load Control and Emission Trading, CAEP, Beijing 100012, China
| |
Collapse
|
12
|
Wang Y, Liao H. Effect of emission control measures on ozone concentrations in Hangzhou during G20 meeting in 2016. CHEMOSPHERE 2020; 261:127729. [PMID: 32763646 DOI: 10.1016/j.chemosphere.2020.127729] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 07/13/2020] [Accepted: 07/14/2020] [Indexed: 06/11/2023]
Abstract
The effect of emission control measures on ozone (O3) concentrations in Hangzhou during G20 (The Group of Twenty Finance Ministers and Central Bank Governors) meeting during 24 August to 6 September of 2016 was evaluated using the nested version of a global chemical transport model. During G20, observed concentrations of PM10, PM2.5, SO2, NO2, and CO were all below national air quality standards, whereas those of MDA8 O3 were above national standard (with an averaged value of 160.2 μg m-3) but had a decreasing trend. Model sensitivity studies show that, MDA8 O3 concentrations in Hangzhou during G20 were reduced by 11.3 μg m-3 (6.8%), 14.8 μg m-3 (8.9%), and 19.5 μg m-3 (11.7%) with emission control measures in the core area, Zhejiang province, and the Yangtze River Delta (YRD) region, respectively, indicating that control measures were the most effective when carried out jointly in YRD. Considering the ratios of NOx to VOCs during G20, Hangzhou and most areas of Zhejiang province were in transitional regime; reductions in either NOx or VOCs could reduce O3 concentrations. We also quantified how sensitive O3 concentrations respond to emission reductions in sectors of industry, power, residential and transportation in the whole of YRD during G20. The removal of emissions in industry and transportation sectors would lead to the largest reductions of 17.6 μg m-3 (10.5%) and 12.3 μg m-3 (7.4%) in MDA8 O3 concentrations in Hangzhou during G20, respectively. This study has important implications for the control of high O3 levels in eastern China.
Collapse
Affiliation(s)
- Ye Wang
- 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 & Technology, Nanjing 210044, China
| | - Hong Liao
- 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 & Technology, Nanjing 210044, China.
| |
Collapse
|
13
|
Sun Y, Lei L, Zhou W, Chen C, He Y, Sun J, Li Z, Xu W, Wang Q, Ji D, Fu P, Wang Z, Worsnop DR. A chemical cocktail during the COVID-19 outbreak in Beijing, China: Insights from six-year aerosol particle composition measurements during the Chinese New Year holiday. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 742:140739. [PMID: 32721760 PMCID: PMC7334657 DOI: 10.1016/j.scitotenv.2020.140739] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/01/2020] [Accepted: 07/02/2020] [Indexed: 05/18/2023]
Abstract
The rapidly spread coronavirus disease (COVID-19) has limited people's outdoor activities and hence caused substantial reductions in anthropogenic emissions around the world. However, the air quality in some megacities has not been improved as expected due to the complex responses of aerosol chemistry to the changes in precursors and meteorology. Here we demonstrate the responses of primary and secondary aerosol species to the changes in anthropogenic emissions during the COVID-19 outbreak in Beijing, China along with the Chinese New Year (CNY) holiday effects on air pollution by using six-year aerosol particle composition measurements. Our results showed large reductions in primary aerosol species associated with traffic, cooking and coal combustion emissions by 30-50% on average during the CNY, while the decreases in secondary aerosol species were much small (5-12%). These results point towards a future challenge in mitigating secondary air pollution because the reduced gaseous precursors may not suppress secondary aerosol formation efficiently under stagnant meteorological conditions. By analyzing the long-term measurements from 2012 to 2020, we found considerable increases in the ratios of nitrate to sulfate, secondary to primary OA, and sulfur and nitrogen oxidation capacity despite the overall decreasing trends in mass concentrations of most aerosol species, suggesting that the decreases in anthropogenic emissions have facilitated secondary formation processes during the last decade. Therefore, a better understanding of the mechanisms driving the chemical responses of secondary aerosol to the changes in anthropogenic emissions under complex meteorological environment is essential for future mitigation of air pollution in China.
Collapse
Affiliation(s)
- Yele Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Lu Lei
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Zhou
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chun Chen
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yao He
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaxing Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhijie Li
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiqi Xu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Qingqing Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Dongsheng Ji
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Pingqing Fu
- Institute of Surface-Earth System Science, Tianjin University, Tianjin 300072, China
| | - Zifa Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | | |
Collapse
|
14
|
Mao YH, Zhao X, Liao H, Zhao D, Tian P, Henze DK, Cao H, Zhang L, Li J, Li J, Ran L, Zhang Q. Sources of black carbon during severe haze events in the Beijing-Tianjin-Hebei region using the adjoint method. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 740:140149. [PMID: 32563001 DOI: 10.1016/j.scitotenv.2020.140149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/08/2020] [Accepted: 06/10/2020] [Indexed: 06/11/2023]
Abstract
The Beijing-Tianjin-Hebei (BTH) region in China has been frequently suffering from severe haze events (observed daily mean surface fine particulate matter PM2.5 concentrations larger than 150 μg m-3) partially caused by certain types of large-scale synoptic patterns. Black carbon (BC), as an important PM2.5 component and a primarily emitted species, is a good tracer for investigating sources and formation mechanisms leading to severe haze pollutions. We apply GEOS-Chem model and its adjoint to quantify the source contributions to BC concentrations at the surface and at the top of the planetary boundary layer (PBL) during typical types of severe haze events for April 2013-2017 in BTH. Four types of severe haze events, mainly occurred in December-January-February (DJF, 62.3%) and in September-October-November (SON, 26.3%), are classified based on the associated synoptic weather patterns using principal component analysis. Model results reasonably capture the daily variations of BC measurements at three ground sites in BTH. The adjoint method attributes BC concentrations to emissions from different source sectors and from local versus regional transport at the model spatial and temporal resolutions. By source sectors, the adjoint method attributes the daily BC concentrations during typical severe haze events (in winter heating season) in Beijing largely to residential emissions (48.1-62.0%), followed by transportation (16.8-25.9%) and industry (19.1-29.5%) sectors. In terms of regionally aggregated source influences, local emissions in Beijing (59.6-79.5%) predominate the daily surface BC concentrations, while contributions of emissions from Beijing, Hebei, and outside BTH regions are comparable to the daily BC concentrations at the top of PBL (~200-400 m). Our adjoint analyses would provide a scientific support for joint regional and targeted control policies on effectively mitigating the particulate pollutions when the dominant synoptic weather patterns are predicted.
Collapse
Affiliation(s)
- Yu-Hao Mao
- 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 (NUIST), Nanjing 210044, China; Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/International Joint Research Laboratory on Climate and Environment Change (ILCEC), NUIST, Nanjing 210044, China.
| | - Xincheng Zhao
- 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 (NUIST), Nanjing 210044, China
| | - Hong Liao
- 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 (NUIST), Nanjing 210044, China; Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/International Joint Research Laboratory on Climate and Environment Change (ILCEC), NUIST, Nanjing 210044, China
| | - Delong Zhao
- Beijing Weather Modification Office, Beijing 100089, China
| | - Ping Tian
- Beijing Weather Modification Office, Beijing 100089, China
| | - Daven K Henze
- Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
| | - Hansen Cao
- Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
| | - Lin Zhang
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100081, China
| | - Jiandong 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 (NUIST), Nanjing 210044, China
| | - Jing Li
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100081, China
| | - Liang Ran
- Key Laboratory of Middle Atmosphere and global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Xianghe Observatory of Whole Atmosphere, Institute of Atmospheric Physics, Chinese Academy of Sciences, Xianghe 065400, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
| |
Collapse
|
15
|
Feng S, Jiang F, Wang H, Wang H, Ju W, Shen Y, Zheng Y, Wu Z, Ding A. NO x Emission Changes Over China During the COVID-19 Epidemic Inferred From Surface NO 2 Observations. GEOPHYSICAL RESEARCH LETTERS 2020; 47:e2020GL090080. [PMID: 33041389 PMCID: PMC7537042 DOI: 10.1029/2020gl090080] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/14/2020] [Accepted: 09/20/2020] [Indexed: 05/21/2023]
Abstract
The COVID-19 epidemic has substantially limited human activities and affected anthropogenic emissions. In this work, daily NO x emissions are inferred using a regional data assimilation system and hourly surface NO2 measurement over China. The results show that because of the coronavirus outbreak, NO x emissions across the whole mainland China dropped sharply after 31 January, began to rise slightly in certain areas after 10 February, and gradually recover across the country after 20 February. Compared with the emissions before the outbreak, NO x emissions fell by more than 60% and ~30% in many large cities and most small to medium cities, respectively. Overall, NO x emissions were reduced by 36% over China, which were mainly contributed by transportation. Evaluations show that the inverted changes over eastern China are credible, whereas those in western China might be underestimated. These findings are of great significance for exploring the reduction potential of NO x emissions in China.
Collapse
Affiliation(s)
- Shuzhuang Feng
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System ScienceNanjing UniversityNanjingChina
| | - Fei Jiang
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System ScienceNanjing UniversityNanjingChina
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina
| | - Hengmao Wang
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System ScienceNanjing UniversityNanjingChina
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina
| | - Haikun Wang
- School of Atmospheric SciencesNanjing UniversityNanjingChina
| | - Weimin Ju
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System ScienceNanjing UniversityNanjingChina
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina
| | - Yang Shen
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System ScienceNanjing UniversityNanjingChina
| | - Yanhua Zheng
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System ScienceNanjing UniversityNanjingChina
| | - Zheng Wu
- Chongqing Institute of Meteorological SciencesChongqingChina
| | - Aijun Ding
- School of Atmospheric SciencesNanjing UniversityNanjingChina
| |
Collapse
|
16
|
Jiang Z, Jolleys MD, Fu TM, Palmer PI, Ma Y, Tian H, Li J, Yang X. Spatiotemporal and probability variations of surface PM 2.5 over China between 2013 and 2019 and the associated changes in health risks: An integrative observation and model analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 723:137896. [PMID: 32208211 DOI: 10.1016/j.scitotenv.2020.137896] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 03/10/2020] [Accepted: 03/11/2020] [Indexed: 06/10/2023]
Abstract
We used statistical methods and the GEOS-Chem model to interpret the observed spatiotemporal and probability variations of surface PM2.5 concentrations in China from December 2013 to November 2019, as well as to assess the drivers for the variations and the implications for health risks associated with long-term and short-term exposure to PM2.5. Annual and seasonal PM2.5 concentrations have decreased over most areas in China during the 6-year period. We decomposed the observed day-to-day variation of PM2.5 concentrations in eastern Chinese cities and found that it showed two distinct major spatial modes, which fluctuated in strength seasonally. The first mode, characterized by most of Eastern China being in the same phase, was mainly associated with the regional ventilation of pollutants. The second mode showed a dipolar pattern between the Beijing-Tianjin-Hebei area and the Yangtze River Delta area and was more prominent in summer. Using model simulations, we showed that this dipole mode was chemically driven by the secondary formation of sulfate in summer. We further used a gamma distribution to succinctly interpret the changes in the probability distributions of PM2.5. We found that the nationwide decline in seasonal mean PM2.5 concentrations mainly reflected decreased occurrences of extremely high PM2.5 concentrations, which was strongly driven by the interannual variation of meteorology. These changes in the annual means and probability distributions of PM2.5 since December 2013 has led to significant decline of the estimated mortality risks associated with long-term and short-term PM2.5-exposures. Regions that were less polluted saw the largest relative benefit per unit decrease in PM2.5 concentration, due to the steepness of the exposure-response curve at the low-concentration end. Our integrated methodology effectively diagnosed the drivers of PM2.5 variability and the associated health risks and can be used as part of the decision tool for PM2.5 pollution management over China.
Collapse
Affiliation(s)
- Zhongjing Jiang
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
| | | | - Tzung-May Fu
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province, China; Shenzhen Institute of Sustainable Development, Southern University of Science and Technology, Shenzhen, Guangdong Province, China.
| | - Paul I Palmer
- School of GeoSciences, University of Edinburgh, Edinburgh, UK.
| | - Yaping Ma
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
| | - Heng Tian
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
| | - Jing Li
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
| | - Xin Yang
- School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong Province, China; Shenzhen Institute of Sustainable Development, Southern University of Science and Technology, Shenzhen, Guangdong Province, China
| |
Collapse
|
17
|
Peng L, Li L, Lin Q, Li M, Zhang G, Bi X, Wang X, Sheng G. Does atmospheric processing produce toxic Pb-containing compounds? A case study in suburban Beijing by single particle mass spectrometry. JOURNAL OF HAZARDOUS MATERIALS 2020; 382:121014. [PMID: 31445413 DOI: 10.1016/j.jhazmat.2019.121014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 08/01/2019] [Accepted: 08/13/2019] [Indexed: 06/10/2023]
Abstract
A single particle aerosol mass spectrometry (SPAMS) was deployed to investigate the mixing state and chemical processing of Pb-rich particles in suburban Beijing. Based on a large dataset of mass spectra, Pb-rich particles were classified into Pb-O-Cl-N-S (55%), Pb-N (17%), Pb-N-S (15%), and Pb-EC (7%). Residual coal combustion, industrial activities, and meteorological conditions were identified as main factors regulating the variations of Pb-rich particles in the atmosphere. The highest abundance of the Pb-rich particles was observed during heating period (HP) primarily due to the increase in coal usage. Pb in Pb-O-Cl-N-S type was identified in forms of PbO, PbCl2, and Pb(NO3)2. Dominantly presented in the form of Pb(NO3)2, Pb-N type represented the completely transformed Pb-rich particles from PbO/PbCl2 by atmospheric processes. It is found that PbCl2 and PbO could be transformed to Pb(NO3)2, highly dependent on the amount of NO2 and RH. Significant enhancement of nitrate in Pb-O-Cl-N-S particles was observed when the RH was greater than 60%, emphasizing the importance of heterogeneous hydrolysis of N2O5 on the formation of Pb(NO3)2. Compared with non-carcinogenic PbCl2/PbO and insoluble PbO, soluble and carcinogenic Pb(NO3)2 produced by atmospheric processes may significantly enhance negative effects of Pb-rich particles on human health and the ecosystem.
Collapse
Affiliation(s)
- Long Peng
- State Key Laboratory of Organic Geochemistry and Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China; University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Lei Li
- 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, PR China
| | - Qinhao Lin
- State Key Laboratory of Organic Geochemistry and Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China
| | - Mei Li
- 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, PR China
| | - Guohua Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China.
| | - Xinhui Bi
- State Key Laboratory of Organic Geochemistry and Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China
| | - Xinming Wang
- State Key Laboratory of Organic Geochemistry and Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China
| | - Guoying Sheng
- State Key Laboratory of Organic Geochemistry and Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China
| |
Collapse
|
18
|
Qu Z, Henze DK, Theys N, Wang J, Wang W. Hybrid Mass Balance/4D-Var Joint Inversion of NO x and SO 2 Emissions in East Asia. JOURNAL OF GEOPHYSICAL RESEARCH. ATMOSPHERES : JGR 2019; 124:8203-8224. [PMID: 31763108 PMCID: PMC6853212 DOI: 10.1029/2018jd030240] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 05/22/2019] [Accepted: 05/27/2019] [Indexed: 05/27/2023]
Abstract
Accurate estimates of NO x and SO2 emissions are important for air quality modeling and management. To incorporate chemical interactions of the two species in emission estimates, we develop a joint hybrid inversion framework to estimate their emissions in China and India (2005-2012). Pseudo observation tests and posterior evaluation with surface measurements demonstrate that joint assimilation of SO2 and NO2 can provide more accurate constraints on emissions than single-species inversions. This occurs through synergistic change of O3 and OH concentrations, particularly in conditions where satellite retrievals of the species being optimized have large uncertainties. The percentage changes of joint posterior emissions from the single-species posterior emissions go up to 242% at grid scales, although the national average of monthly emissions, seasonality, and interannual variations are similar. In China and India, the annual budget of joint posterior SO2 emissions is lower, but joint NO x posterior emissions are higher, because NO x emissions increase to increase SO2 concentration and better match Ozone Monitoring Instrument SO2 observations in high-NO x regions. Joint SO2 posterior emissions decrease by 16.5% from 2008 to 2012, while NO x posterior emissions increase by 24.9% from 2005 to 2011 in China-trends which are consistent with the MEIC inventory. Joint NO x and SO2 posterior emissions in India increase by 15.9% and 19.2% from 2005 to 2012, smaller than the 59.9% and 76.2% growth rate using anthropogenic emissions from EDGARv4.3.2. This work shows the benefit and limitation of joint assimilation in emission estimates and provides an efficient framework to perform the inversion.
Collapse
Affiliation(s)
- Zhen Qu
- Department of Mechanical EngineeringUniversity of Colorado BoulderBoulderCOUSA
| | - Daven K. Henze
- Department of Mechanical EngineeringUniversity of Colorado BoulderBoulderCOUSA
| | - Nicolas Theys
- Belgian Institute for Space Aeronomy (BIRA‐IASB)BrusselsBelgium
| | - Jun Wang
- Center for Global and Regional Environmental Research, Department of Chemical and Biochemical EngineeringUniversity of IowaIowa CityIAUSA
| | - Wei Wang
- China National Environmental Monitoring CenterBeijingChina
| |
Collapse
|
19
|
New positive feedback mechanism between boundary layer meteorology and secondary aerosol formation during severe haze events. Sci Rep 2018; 8:6095. [PMID: 29666505 PMCID: PMC5904139 DOI: 10.1038/s41598-018-24366-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 04/03/2018] [Indexed: 11/15/2022] Open
Abstract
Severe haze events during which particulate matter (PM) increases quickly from tens to hundreds of microgram per cubic meter in 1–2 days frequently occur in China. Although it has been known that PM is influenced by complex interplays among emissions, meteorology, and physical and chemical processes, specific mechanisms remain elusive. Here, a new positive feedback mechanism between planetary boundary layer (PBL), relative humidity (RH), and secondary PM (SPM) formation is proposed based on a comprehensive field experiment and model simulation. The decreased PBL associated with increased PM increases RH by weakening the vertical transport of water vapor; the increased RH in turn enhances the SPM formation through heterogeneous aqueous reactions, which further enhances PM, weakens solar radiation, and decreases PBL height. This positive feedback, together with the PM-Radiation-PBL feedback, constitutes a key mechanism that links PM, radiation, PBL properties (e.g. PBL height and RH), and SPM formation, This mechanism is self-amplifying, leading to faster PM production, accumulation, and more severe haze pollution.
Collapse
|
20
|
Pan Y, Tian S, Zhao Y, Zhang L, Zhu X, Gao J, Huang W, Zhou Y, Song Y, Zhang Q, Wang Y. Identifying Ammonia Hotspots in China Using a National Observation Network. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:3926-3934. [PMID: 29499112 DOI: 10.1021/acs.est.7b05235] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The limited availability of ammonia (NH3) measurements is currently a barrier to understanding the vital role of NH3 in secondary aerosol formation during haze pollution events and prevents a full assessment of the atmospheric deposition of reactive nitrogen. The observational gaps motivated us to design this study to investigate the spatial distributions and seasonal variations in atmospheric NH3 on a national scale in China. On the basis of a 1-year observational campaign at 53 sites with uniform protocols, we confirm that abundant concentrations of NH3 [1 to 23.9 μg m-3] were identified in typical agricultural regions, especially over the North China Plain (NCP). The spatial pattern of the NH3 surface concentration was generally similar to those of the satellite column concentrations as well as a bottom-up agriculture NH3 emission inventory. However, the observed NH3 concentrations at urban and desert sites were comparable with those from agricultural sites and 2-3 times those of mountainous/forest/grassland/waterbody sites. We also found that NH3 deposition fluxes at urban sites account for only half of the emissions in the NCP, suggesting the transport of urban NH3 emissions to downwind areas. This finding provides policy makers with insights into the potential mitigation of nonagricultural NH3 sources in developed regions.
Collapse
Affiliation(s)
- Yuepeng Pan
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics , Chinese Academy of Sciences , Beijing 100029 , China
| | - Shili Tian
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics , Chinese Academy of Sciences , Beijing 100029 , China
| | - Yuanhong Zhao
- Department of Atmospheric and Oceanic Sciences, School of Physics , Peking University , Beijing , 100871 , China
| | - Lin Zhang
- Department of Atmospheric and Oceanic Sciences, School of Physics , Peking University , Beijing , 100871 , China
| | - Xiaying Zhu
- National Climate Center , China Meteorological Administration , Beijing 100081 , China
| | - Jian Gao
- State Key Laboratory of Environmental Criteria and Risk Assessment , Chinese Research Academy of Environmental Sciences , Beijing 100012 , China
| | - Wei Huang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics , Chinese Academy of Sciences , Beijing 100029 , China
| | - Yanbo Zhou
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics , Chinese Academy of Sciences , Beijing 100029 , China
| | - Yu Song
- Department of Environmental Science , Peking University , Beijing 100871 , China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science , Tsinghua University , Beijing 100084 , 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
| |
Collapse
|
21
|
Liu Y, Yan C, Zheng M. Source apportionment of black carbon during winter in Beijing. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 618:531-541. [PMID: 29149737 DOI: 10.1016/j.scitotenv.2017.11.053] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 11/02/2017] [Accepted: 11/04/2017] [Indexed: 05/12/2023]
Abstract
Black carbon (BC) in PM2.5 was measured at an urban site in Beijing during winter 2015 using an aethalometer. The characteristics and sources of BC during pollution episodes and clean days were analyzed. The average hourly mass concentration of BC during the study period was 5.31±6.27μg/m3. BC was highly correlated with PM2.5 (R2=0.80), with its concentration ranging from 0.17μg/m3 in clean days to 35.33μg/m3 in haze days. Source apportionment results showed that the average contribution of liquid fuel source (e.g., vehicle emission) to BC was around 50% in clean days. While during the pollution episodes, solid fuel sources including coal combustion and biomass burning were the predominant sources, accounting for 61-83% of BC. Specific source tracers suggested that coal combustion and biomass burning dominated in different pollution episodes. Ratios of BC/PM2.5 and BC/CO as well as source tracers provided further supportive evidences for the source apportionment results. Our findings suggest that it is more important to control solid fuel sources such as coal combustion for BC abatement in Beijing during haze days, while liquid fuel source (e.g., vehicle emission) plays a relatively more important role in clean days compared to pollution episodes.
Collapse
Affiliation(s)
- Yue Liu
- SKL-ESPC and BIC-ESAT, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Caiqing Yan
- SKL-ESPC and BIC-ESAT, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Mei Zheng
- SKL-ESPC and BIC-ESAT, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China.
| |
Collapse
|