1
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Liu Q, Xiang R, Zhao Y, Cui L. Exploration of the adsorption and desorption performance of volatile organic compounds by activated carbon with different shapes based on fixed-bed experiments. CHEMOSPHERE 2024; 364:143161. [PMID: 39178967 DOI: 10.1016/j.chemosphere.2024.143161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 07/12/2024] [Accepted: 08/20/2024] [Indexed: 08/26/2024]
Abstract
Activated carbon (AC) has been widely used in volatile organic compounds (VOCs) treatment of industrial exhaust gases. Rather than modifying specific pore size distributions and surface properties, altering the shape of AC offers a more feasible approach to enhance its adsorption performance. This study investigates the adsorption-desorption performance of two different shaped ACs with highly similar properties for the removal of VOCs. The clover-shaped AC (CSAC) has a 27.46% lower internal void fraction and a 39.10% higher external void fraction compared to cylindrical AC (CAC), resulting in denser packing and longer contact time with VOCs. Adsorption experiments showed the CSAC has 40% longer adsorption breakthrough (BT) times for ethanol, ethyl acetate, and n-hexane on average, and 20% higher saturation adsorption capacity per unit volume. CSAC also has higher partition coefficients, with the highest values for ethanol, ethyl acetate, and n-hexane being 0.0187, 0.0382, and 0.0527 mol kg-1·Pa-1, respectively. The desorption process for selected VOCs is non-spontaneous and endothermic. Optimal desorption conditions were identified as an inlet space velocity of 3535 h-1, a desorption temperature of 150 °C, and a pulsed inlet method. To investigate the possibility of the application of CSAC in real-world scenarios, xylene was chosen as a representative industrial VOC. Results showed CSAC has 20% higher BT time and saturation adsorption capacity for xylene compared to CAC under different bed heights. The desorption efficiency for xylene on both ACs is below 40%. With increasing xylene inlet concentration, the mass transfer zone (MTZ) height initially increases but stabilizes beyond 1704 mg m-3. At identical bed heights, the MTZ height of CSAC is 29% shorter than CAC, indicating a higher bed utilization efficiency.
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Affiliation(s)
- Qin Liu
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environment, South-Central Minzu University, Wuhan, 430074, China
| | - Ruyi Xiang
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environment, South-Central Minzu University, Wuhan, 430074, China
| | - Yufeng Zhao
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environment, South-Central Minzu University, Wuhan, 430074, China.
| | - Longzhe Cui
- Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environment, South-Central Minzu University, Wuhan, 430074, China.
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2
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He C, Liu J, Zhou Y, Zhou J, Zhang L, Wang Y, Liu L, Peng S. Synergistic PM 2.5 and O 3 control to address the emerging global PM 2.5-O 3 compound pollution challenges. ECO-ENVIRONMENT & HEALTH 2024; 3:325-337. [PMID: 39281068 PMCID: PMC11400616 DOI: 10.1016/j.eehl.2024.04.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/05/2024] [Accepted: 04/02/2024] [Indexed: 09/18/2024]
Abstract
In recent years, the issue of PM2.5-O3 compound pollution has become a significant global environmental concern. This study examines the spatial and temporal patterns of global PM2.5-O3 compound pollution and exposure risks, firstly at the global and urban scale, using spatial statistical regression, exposure risk assessment, and trend analyses based on the datasets of daily PM2.5 and surface O3 concentrations monitored in 120 cities around the world from 2019 to 2022. Additionally, on the basis of the common emission sources, spatial heterogeneity, interacting chemical mechanisms, and synergistic exposure risk levels between PM2.5 and O3 pollution, we proposed a synergistic PM2.5-O3 control framework for the joint control of PM2.5 and O3. The results indicated that: (1) Nearly 50% of cities worldwide were affected by PM2.5-O3 compound pollution, with China, South Korea, Japan, and India being the global hotspots for PM2.5-O3 compound pollution; (2) Cities with PM2.5-O3 compound pollution have exposure risk levels dominated by ST + ST (Stabilization) and ST + HR (High Risk). Exposure risk levels of compound pollution in developing countries are significantly higher than those in developed countries, with unequal exposure characteristics; (3) The selected cities showed significant positive spatial correlations between PM2.5 and O3 concentrations, which were consistent with the spatial distribution of the precursors NOx and VOCs; (4) During the study period, 52.5% of cities worldwide achieved synergistic reductions in annual average PM2.5 and O3 concentrations. The average PM2.5 concentration in these cities decreased by 13.97%, while the average O3 concentration decreased by 19.18%. This new solution offers the opportunity to construct intelligent and healthy cities in the upcoming low-carbon transition.
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Affiliation(s)
- Chao He
- College of Resources and Environment, Yangtze University, Wuhan 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
| | - Jianhua Liu
- College of Resources and Environment, Yangtze University, Wuhan 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan 430100, China
| | - Yiqi Zhou
- School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
| | - Jingwei Zhou
- Hydrology and Environmental Hydraulics Group, Wageningen University and Research, Wageningen 6700 HB, the Netherlands
| | - Lu Zhang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Yifei Wang
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, School of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Lu Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Sha Peng
- Collaborative Innovation Center for Emissions Trading System Co-constructed by the Province and Ministry, Hubei University of Economics, Wuhan 430205, China
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3
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Geng XZ, Hu JT, Zhang ZM, Li ZL, Chen CJ, Wang YL, Zhang ZQ, Zhong YJ. Exploring efficient strategies for air quality improvement in China based on its regional characteristics and interannual evolution of PM 2.5 pollution. ENVIRONMENTAL RESEARCH 2024; 252:119009. [PMID: 38679277 DOI: 10.1016/j.envres.2024.119009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 04/16/2024] [Accepted: 04/22/2024] [Indexed: 05/01/2024]
Abstract
Fine particulate matter (PM2.5) harms human health and hinders normal human life. Considering the serious complexity and obvious regional characteristics of PM2.5 pollution, it is urgent to fill in the comprehensive overview of regional characteristics and interannual evolution of PM2.5. This review studied the PM2.5 pollution in six typical areas between 2014 and 2022 based on the data published by the Chinese government and nearly 120 relevant literature. We analyzed and compared the characteristics of interannual and quarterly changes of PM2.5 concentration. The Beijing-Tianjin-Hebei region (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) made remarkable progress in improving PM2.5 pollution, while Fenwei Plain (FWP), Sichuan Basin (SCB) and Northeast Plain (NEP) were slightly inferior mainly due to the relatively lower level of economic development. It was found that the annual average PM2.5 concentration change versus year curves in the three areas with better pollution control conditions can be merged into a smooth curve. Importantly, this can be fitted for the accurate evaluation of each area and provide reliable prediction of its future evolution. In addition, we analyzed the factors affecting the PM2.5 in each area and summarize the causes of air pollution in China. They included primary emission, secondary generation, regional transmission, as well as unfavorable air dispersion conditions. We also suggested that the PM2.5 pollution control should target specific industries and periods, and further research need to be carried out on the process of secondary production. The results provided useful assistance such as effect prediction and strategy guidance for PM2.5 pollution control in Chinese backward areas.
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Affiliation(s)
- Xin-Ze Geng
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China.
| | - Jia-Tian Hu
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Zi-Meng Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Zhi-Ling Li
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Chong-Jun Chen
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Yu-Long Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Zhi-Qing Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Ying-Jie Zhong
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
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4
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Hou X, Wang X, Cheng S, Qi H, Wang C, Huang Z. Elucidating transport dynamics and regional division of PM 2.5 and O 3 in China using an advanced network model. ENVIRONMENT INTERNATIONAL 2024; 188:108731. [PMID: 38772207 DOI: 10.1016/j.envint.2024.108731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/05/2024] [Accepted: 05/07/2024] [Indexed: 05/23/2024]
Abstract
Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.
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Affiliation(s)
- Xiaosong Hou
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment Science and Engineering, Beijing University of Technology, Beijing 100124, China
| | - Xiaoqi Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment Science and Engineering, Beijing University of Technology, Beijing 100124, China.
| | - Shuiyuan Cheng
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment Science and Engineering, Beijing University of Technology, Beijing 100124, China
| | - Haoyun Qi
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment Science and Engineering, Beijing University of Technology, Beijing 100124, China
| | - Chuanda Wang
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment Science and Engineering, Beijing University of Technology, Beijing 100124, China
| | - Zijian Huang
- Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment Science and Engineering, Beijing University of Technology, Beijing 100124, China
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5
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Zhao H, Chen Z, Li C. Changes of PM 2.5 and O 3 and their impact on human health in the Guangdong-Hong Kong-Macao Greater Bay Area. Sci Rep 2024; 14:11190. [PMID: 38755236 PMCID: PMC11099087 DOI: 10.1038/s41598-024-62019-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024] Open
Abstract
In recent years, the combined pollution of PM2.5 and O3 in China, particularly in economically developed regions such as the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), has garnered significant attention due to its potential implications. This study systematically investigated the changes of PM2.5 and O3 and their associated human health effects in the GBA, utilizing observational data spanning from 2015 to 2019. The findings revealed a spatial trend indicating a gradual decrease in PM2.5 levels from the northwest to the southeast, while the spatial distribution of MDA8 O3 demonstrated an opposing pattern to that of PM2.5. The monthly fluctuations of PM2.5 and MDA8 O3 exhibited V-shaped and M-shaped patterns, respectively. Higher MDA8 O3 concentrations were observed in autumn, followed by summer and spring. Over the five-year period, PM2.5 concentrations exhibited a general decline, with an annual reduction rate of 1.7 μg m-3/year, while MDA8 O3 concentrations displayed an annual increase of 3.2 μg m-3. Among the GBA regions, Macao, Foshan, Guangzhou, and Jiangmen demonstrated notable decreases in PM2.5, whereas Jiangmen, Zhongshan, and Guangzhou experienced substantial increases in MDA8 O3 levels. Long-term exposure to PM2.5 in 2019 was associated with 21,113 (95% CI 4968-31,048) all-cause deaths (AD), 1333 (95% CI 762-1714) cardiovascular deaths (CD), and 1424 (95% CI 0-2848) respiratory deaths (RD), respectively, reflecting declines of 27.6%, 28.0%, and 28.4%, respectively, compared to 2015. Conversely, in 2019, estimated AD, CD, and RD attributable to O3 were 16,286 (95% CI 8143-32,572), 7321 (95% CI 2440-14,155), and 6314 (95% CI 0-13,576), respectively, representing increases of 45.9%, 46.2%, and 44.2% over 2015, respectively. Taken together, these findings underscored a shifting focus in air pollution control in the GBA, emphasizing the imperative for coordinated control strategies targeting both PM2.5 and O3.
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Affiliation(s)
- Hui Zhao
- School of Resources and Environmental Engineering, Jiangsu University of Technology, Changzhou, 213001, China.
- Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China.
| | - Zeyuan Chen
- No.2 High School of East China Normal University, Shanghai, 201203, China
| | - Chen Li
- School of Electronic and Information Engineering, Wuxi University, Wuxi, 214105, China
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Liu J, He C, Si Y, Li B, Wu Q, Ni J, Zhao Y, Hu Q, Du S, Lu Z, Jin J, Xu C. Toward Better and Healthier Air Quality: Global PM 2.5 and O 3 Pollution Status and Risk Assessment Based on the New WHO Air Quality Guidelines for 2021. GLOBAL CHALLENGES (HOBOKEN, NJ) 2024; 8:2300258. [PMID: 38617028 PMCID: PMC11009431 DOI: 10.1002/gch2.202300258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/14/2023] [Indexed: 04/16/2024]
Abstract
To reduce the high burden of disease caused by air pollution, the World Health Organization (WHO) released new Air Quality Guidelines (AQG) on September 22, 2021. In this study, the daily fine particulate matter (PM2.5) and surface ozone (O3) data of 618 cities around the world is collected from 2019 to 2022. Based on the new AQG, the number of attainment days for daily average concentrations of PM2.5 (≤ 15 µg m-3) and O3 (≤ 100 µg m-3) is approximately 10% and 90%, respectively. China and India exhibit a decreasing trend in the number of highly polluted days (> 75 µg m-3) for PM. Every year over 68% and 27% of cities in the world are exposed to harmful PM2.5 (> 35 µg m-3) and O3 (> 100 µg m-3) pollution, respectively. Combined with the United Nations Sustainable Development Goals (SDGs), it is found that more than 35% of the world's cities face PM2.5-O3 compound pollution. Furthermore, the exposure risks in these cities (China, India, etc.) are mainly categorized as "High Risk", "Risk", and "Stabilization". In contrast, economically developed cities are mainly categorized as "High Safety", "Safety", and "Deep Stabilization." These findings indicate that global implementation of the WHO's new AQG will minimize the inequitable exposure risk from air pollution.
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Affiliation(s)
- Jianhua Liu
- College of Resources and EnvironmentYangtze UniversityWuhan430100China
- Hubei Key Laboratory of Petroleum Geochemistry and EnvironmentYangtze UniversityWuhan430100China
| | - Chao He
- College of Resources and EnvironmentYangtze UniversityWuhan430100China
- Hubei Key Laboratory of Petroleum Geochemistry and EnvironmentYangtze UniversityWuhan430100China
| | - Yajun Si
- College of Water Resources and Architectural EngineeringNorthwest A&F UniversityYanglingShaanxi712100China
| | - Bin Li
- College of Resources and EnvironmentYangtze UniversityWuhan430100China
- Hubei Key Laboratory of Petroleum Geochemistry and EnvironmentYangtze UniversityWuhan430100China
| | - Qian Wu
- School of Resource and Environmental ScienceWuhan UniversityWuhanHubei430079China
| | - Jinmian Ni
- College of Resources and EnvironmentYangtze UniversityWuhan430100China
- Hubei Key Laboratory of Petroleum Geochemistry and EnvironmentYangtze UniversityWuhan430100China
| | - Yue Zhao
- College of Resources and EnvironmentYangtze UniversityWuhan430100China
- Hubei Key Laboratory of Petroleum Geochemistry and EnvironmentYangtze UniversityWuhan430100China
| | - Qixin Hu
- College of Resources and EnvironmentYangtze UniversityWuhan430100China
- Hubei Key Laboratory of Petroleum Geochemistry and EnvironmentYangtze UniversityWuhan430100China
| | - Shenwen Du
- College of Resources and EnvironmentYangtze UniversityWuhan430100China
- Hubei Key Laboratory of Petroleum Geochemistry and EnvironmentYangtze UniversityWuhan430100China
| | - Zhendong Lu
- Interdisciplinary Graduate Program in InformaticsThe University of IowaIowa CityIA52242USA
| | - Jiming Jin
- College of Resources and EnvironmentYangtze UniversityWuhan430100China
- Hubei Key Laboratory of Petroleum Geochemistry and EnvironmentYangtze UniversityWuhan430100China
| | - Chao Xu
- College of Resource and EnvironmentXinjiang Agricultural UniversityUrumqi830052China
- Xinjiang Key Laboratory of Soil and Plant Ecological ProcessesUrumqi830052China
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7
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Liu P, Dong J, Song H, Zheng Y, Shen X, Wang C, Wang Y, Yang D. Response of fine particulate matter and ozone concentrations to meteorology and anthropogenic precursors over the "2+26" cities of northern China. CHEMOSPHERE 2024; 352:141439. [PMID: 38342145 DOI: 10.1016/j.chemosphere.2024.141439] [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: 07/18/2023] [Revised: 02/02/2024] [Accepted: 02/09/2024] [Indexed: 02/13/2024]
Abstract
Analyzing the influencing factors of fine particulate matter and ozone formation and identifying the coupling relationship between the two are the basis for implementing the synergistic pollutants control. However, the current research on the synergistic relationship between the two still needs to be further explored. Using the Geodetector model, we analyzed the effects of meteorology and emissions on fine particulate matter and ozone concentrations over the "2 + 26" cities at multiple timescales, and also explored the coupling relationship between the two pollutants. Fine particulate matter concentrations showed overall decreasing trends on inter-season and inter-annual scale from 2015 to 2021, whereas ozone concentrations showed overall increasing trends. While ozone concentrations displayed an inverted U-shaped distribution from month to month, fine particulate matter concentrations displayed a U-shaped fluctuation. On inter-annual scale, climatic factors, with planet boundary layer height as the main determinant, have higher effects for both pollutants than human precursors. In summer and autumn, sunshine duration had the most influence on fine particulate matter, while planet boundary layer height was the greatest factor in winter. Fine particulate matter is the leading impacting factor on ozone concentrations in summer, and there were positive associations between them on both annual and seasonal scale. The impact of nitrogen oxides and volatile organic compounds for both pollutants concentrations varied significantly between seasons. The two pollutants concentration were enhanced by the interactions between the various components. On inter-annual scale, interactions between the planet boundary layer height and other factors dominated the concentrations of the two pollutants, whereas in summer, interactions between fine particulate matter and other factors dominated the concentrations of ozone. The study has implications for the treatment of atmospheric pollution in China and other nations and can serve as an important reference for the creation of integrated atmospheric pollution regulation policies over the "2 + 26" cities.
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Affiliation(s)
- Pengfei Liu
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng, 475004, China; College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China; Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China.
| | - Junwu Dong
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng, 475004, China; College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China; College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China.
| | - Hongquan Song
- College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China; Institute of Urban Big Data, College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, 475004, China.
| | - Yiwen Zheng
- College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, 475004, China.
| | - Xiaoyu Shen
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng, 475004, China.
| | - Chaokun Wang
- College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, 475004, China.
| | - Yansong Wang
- College of Geography and Environmental Science, Henan University, Kaifeng, 475004, China; Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, 475004, China.
| | - Dongyang Yang
- Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng, 475004, China.
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Wang L, Yang X, Dong J, Yang Y, Ma P, Zhao W. Evolution of surface ozone pollution pattern in eastern China and its relationship with different intensity heatwaves. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 338:122725. [PMID: 37827354 DOI: 10.1016/j.envpol.2023.122725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/23/2023] [Accepted: 10/09/2023] [Indexed: 10/14/2023]
Abstract
With climate warming, eastern China has experienced a significant increase in temperature accompanied by intensified ozone pollution. We aimed to investigate the spatiotemporal patterns and relationships between ozone levels and temperature in eastern China using observation-based ozone data from 418 air quality monitoring stations and temperature data from ERA5. The summer maximum temperature and annual ozone concentration in eastern China increased significantly between 2015 and 2022, with increases rate of 10% and 2.84 μg/m3 yr-1, respectively. The baseline ozone concentration was increasing over time. The average difference in MDA8 O3 concentration in spring, summer, and autumn decreased, with more ozone pollution spreading into spring and autumn, indicating a trend of prolonging the ozone season. During the June-July-August (JJA) period of 2015-2022, heatwaves increased significantly in eastern China. The frequency of heatwave events >10 days played a vital role in exacerbating ozone pollution. During the JJA period, the increase rate in MDA8 O3 concentration was 9.31 μg/m3 yr-1 during heatwave periods, significantly higher than that during non-heatwave periods (4.01 μg/m3 yr-1). The correlation between MDA8 O3 concentration and temperature was as high as 0.99, indicating that temperature was vital in ozone formation during the JJA period in eastern China. This study suggests that more stringent actions are needed to control ozone-precursor compounds during frequent summertime heatwaves in eastern China.
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Affiliation(s)
- Lili Wang
- College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China
| | - Xingchuan Yang
- College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China.
| | - Junwu Dong
- College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China
| | - Yang Yang
- College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China
| | - Pengfei Ma
- Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment/ State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing, 100094, China
| | - Wenji Zhao
- College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China
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9
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Boari A, Pedruzzi R, Vieira-Filho M. Air pollution trends and exceedances: ozone and particulate matter outlook in Brazilian highly urbanized zones. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1058. [PMID: 37592139 DOI: 10.1007/s10661-023-11654-3] [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: 04/25/2023] [Accepted: 07/29/2023] [Indexed: 08/19/2023]
Abstract
In Brazil, scarce air quality data hinders air pollutant chemical understanding and policy decisions regarding public health and environmental impacts. From this perspective, our study assessed the O3, PM2.5, and PM10 yearly and seasonal trends and also the WHO Air Quality Guidelines 2021 exceedance trends at 40 air quality stations located in four highly urbanized zones in Brazil (Belo Horizonte, São Paulo, Rio de Janeiro, and Espírito Santo) from early 1990s up to 2019. We applied the Mann-Kendall test aligned with Sen's Slope estimator to assess the trends and the Cox-Stuart test to verify the WHO AQG 2021 exceedances trends. Our findings pointed out that the current national legislation is outdated when compared to WHO AQG 2021 values, leading to multiple exceedances episodes. We also found out that 62% of São Paulo's stations presented O3 increasing trends, while in Rio de Janeiro 85.7% presented decreasing trends. The Cox-Stuart test pointed out that PM2.5 exceedance trends showcase positive values, and most of the significative values are located in São Paulo stations. Therefore, we endorse that the national legislation needs to be updated meanwhile the air monitoring network needs to expand its coverage.
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Affiliation(s)
- Arthur Boari
- Departamento de Engenharia Ambiental, Universidade Federal de Lavras, Campus Sede, Lavras, Minas Gerais, 37200-900, Brazil
| | - Rizzieri Pedruzzi
- Departamento de Engenharia Sanitária e Meio Ambiente, Universidade do Estado do Rio de Janeiro, Campus Maracanã, Rio de Janeiro, 20550-900, Brazil
| | - Marcelo Vieira-Filho
- Departamento de Engenharia Ambiental, Universidade Federal de Lavras, Campus Sede, Lavras, Minas Gerais, 37200-900, Brazil.
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10
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Wang S, Ren Y, Xia B, Liu K, Li H. Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis. CHEMOSPHERE 2023; 331:138830. [PMID: 37137395 DOI: 10.1016/j.chemosphere.2023.138830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/11/2023] [Accepted: 04/30/2023] [Indexed: 05/05/2023]
Abstract
Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) unit to predict the O3 and PM2.5 levels in the atmosphere, as well as an air quality index (AQI). The prediction results given by the proposed model are compared with those from CNN-LSTM and LSTM models as well as random forest and support vector regression models. The proposed model achieves a correlation coefficient between the predicted and observed values of more than 0.90, outperforming the other four models. The model errors are also consistently lower when using the proposed approach. Sobol-based sensitivity analysis is applied to identify the variables that make the greatest contribution to the model prediction results. Taking the COVID-19 outbreak as the time boundary, we find some homology in the interactions among the pollutants and meteorological factors in the atmosphere during different periods. Solar irradiance is the most important factor for O3, CO is the most important factor for PM2.5, and particulate matter has the most significant effect on AQI. The key influencing factors are the same over the whole phase and before the COVID-19 outbreak, indicating that the impact of COVID-19 restrictions on AQI gradually stabilized. Removing variables that contribute the least to the prediction results without affecting the model prediction performance improves the modeling efficiency and reduces the computational costs.
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Affiliation(s)
- Siyuan Wang
- School of Environment, Nanjing Normal University, Nanjing, 210023, PR China; School of Mathematics and Computer Science, Yan'an University, Yan'an, 716000, PR China
| | - Ying Ren
- School of Mathematics and Computer Science, Yan'an University, Yan'an, 716000, PR China
| | - Bisheng Xia
- School of Mathematics and Computer Science, Yan'an University, Yan'an, 716000, PR China
| | - Kai Liu
- School of Environment, Nanjing Normal University, Nanjing, 210023, PR China
| | - Huiming Li
- School of Environment, Nanjing Normal University, Nanjing, 210023, PR China.
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11
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Guo Q, He Z, Wang Z. Change in Air Quality during 2014-2021 in Jinan City in China and Its Influencing Factors. TOXICS 2023; 11:210. [PMID: 36976975 PMCID: PMC10056825 DOI: 10.3390/toxics11030210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014-2021. The results indicate that the annual average concentrations of PM10, PM2.5, NO2, SO2, CO, and O3 and AQI values all declined year after year during 2014-2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM2.5 concentration was the highest in winter and PM2.5 concentration was the lowest in summer, while it was the opposite for O3 concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM2.5, and PM10. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.
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Affiliation(s)
- Qingchun Guo
- School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
- Institute of Huanghe Studies, Liaocheng University, Liaocheng 252000, China
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
| | - Zhenfang He
- School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
- Institute of Huanghe Studies, Liaocheng University, Liaocheng 252000, China
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Zhaosheng Wang
- National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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12
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Wang Z, Tian X, Li J, Wang F, Liang W, Zhao H, Huang B, Wang Z, Feng Y, Shi G. Quantitative evidence from VOCs source apportionment reveals O 3 control strategies in northern and southern China. ENVIRONMENT INTERNATIONAL 2023; 172:107786. [PMID: 36738582 DOI: 10.1016/j.envint.2023.107786] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
Ground-level ozone (O3) pollution has received widespread attention because its rising trend and adverse ecological impacts. However, the extremely strong photochemical reactions of its precursor volatile organic compounds (VOCs) increase the difficulty of reducing VOCs emissions to alleviate O3. Here, we carried out a one-year comprehensive observation in two representative cities, Tianjin (TJ, Northern China) and Guangzhou (GZ, Southern China). By revealing the concentration characteristics of three different types of VOCs, i.e., initial VOCs without photochemical reaction (In-VOCs), consumed VOCs (C-VOCs), and measured VOCs after the reaction (M-VOCs), we elucidated the important role of C-VOCs in the formation of O3. Although the overall trends were similar in both cities, the average concentration level of VOCs in GZ was 8.2 ppbv higher than that in TJ, and the photochemical loss of VOCs was greater by 2.2 ppbv. In addition, various drivers affecting O3 generation from C-VOCs were specifically explored, and it was found that most alkenes of TJ were key substances for rapid O3 formation compared to aromatics of GZ. Meanwhile, favorable meteorological conditions such as high temperature (T > 31 °C in TJ, and T > 33 °C in GZ), low relative humidity (56% in TJ and 45% in GZ), and stable atmospheric environment (proper pressure and gentle wind speed) also contribute to the generation of O3. More importantly, we combined chemical kinetics and receptor model to quantify the three-type VOCs source contributions and assess the potential impact of C-VOCs sources on O3 production, thus proposing environmental abatement technologies corresponding to the three types of VOCs. The differences in the comparison results of the three-type VOCs highlight the need to reduce O3 pollution from C-VOCs sources, which provides insights for future clean air policies development.
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Affiliation(s)
- Zhenyu Wang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Xiao Tian
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Jie Li
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Feng Wang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Weiqing Liang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Huan Zhao
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Bo Huang
- Guangzhou Hexin Instrument Co., Ltd, Guangzhou 510530, China
| | - Zaihua Wang
- Institute of Resources Utilization and Rare Earth Development, Guangdong Academy of Sciences, Guangzhou 510650, China.
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Guoliang Shi
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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