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Tong M, Zhang Y, Li M, Wang Q, Tian X, Zhang D, Ge A, Song W, Xiong X, You Y, Xu Y, Huang Y, Yang X, Wang X. Onboard measurements of organic vapor emissions from river vessels under various operational conditions. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2025; 364:125332. [PMID: 39557354 DOI: 10.1016/j.envpol.2024.125332] [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: 09/30/2024] [Revised: 11/11/2024] [Accepted: 11/15/2024] [Indexed: 11/20/2024]
Abstract
The emission factors and characteristics of pollutants from river vessels are critical for understanding the environmental impact of ship emissions, particularly in inland waterways. However, research gaps remain regarding emissions of volatile organic compounds (VOCs) and intermediate-volatility organic compounds (IVOCs) from river vessels. In this study, we collected and analyzed organic vapor emissions, including non-methane hydrocarbon (NMHCs), oxygenated volatile organic compounds (OVOCs) and IVOCs, from three river vessels under different operating conditions. The results show that the average emission factors of NMHCs, and IVOCs from river vessels are significantly higher than those from ocean-going vessels. Inland waterways' proximity to residential areas increases the risk of pollutant transport to urban environments, heightening the importance of managing river vessel emissions. Notably, older auxiliary engines displayed higher organic vapor emissions compared to main engines, underscoring the need for better control measures for aging engines. By analyzing the emission characteristics of organic vapors from river vessels, it was found that, unlike other pollution sources where C12 n-alkanes are the major contributors of IVOCs, the contributions of C12-C15 n-alkanes in river vessel exhaust are similar, with C14 n-alkane having the highest contribution. OVOCs constituted more than 50% to ozone formation potentials of organic vapors, while IVOCs were responsible for over 90% of the secondary organic aerosol (SOA) formation. Given these findings, targeted efforts to reduce OVOCs and IVOCs emissions from river vessels should prioritized to mitigate their environmental impact.
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Affiliation(s)
- Mengxue Tong
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China; School of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Yanli Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China; School of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 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, China
| | - Qi Wang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China; School of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Xiao Tian
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China; School of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Dan Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China; School of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Aoqi Ge
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China; School of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Wei Song
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China; School of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Xin Xiong
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, China
| | - Yinong You
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, China
| | - Yongjang Xu
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, China
| | - Yihua Huang
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, China
| | - Xin Yang
- School of Environmental, Southern University of Science and Technology, Shenzhen, China
| | - Xinming Wang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China; School of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
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2
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Jiang Y, Zhang A, Zou Q, Zhang L, Zuo H, Ding J, Wang Z, Li Z, Jin L, Xu D, Sun X, Zhao W, Xu B, Li X. Long-Term Halocarbon Observations in an Urban Area of the YRD Region, China: Characteristic, Sources Apportionment and Health Risk Assessment. TOXICS 2024; 12:738. [PMID: 39453158 PMCID: PMC11511214 DOI: 10.3390/toxics12100738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2024] [Revised: 10/07/2024] [Accepted: 10/10/2024] [Indexed: 10/26/2024]
Abstract
To observe the long-term variations in halocarbons in the Yangtze River Delta (YRD) region, this study analyzes halocarbon concentrations and composition characteristics in Shanxi from 2018 to 2020, exploring their origins and the health effects. The total concentration of halocarbons has shown an overall increasing trend, which is driven by both regulated substances (CFC-11 and CFC-113) and unregulated substances, such as dichloromethane, chloromethane and chloroform. The results of the study also reveal that dichloromethane (1.194 ± 1.003 to 1.424 ± 1.004 ppbv) and chloromethane (0.205 ± 0.185 to 0.666 ± 0.323 ppbv) are the predominant halocarbons in Shanxi, influenced by local and northwestern emissions. Next, this study identifies that neighboring cities in Zhejiang Province and other YRD areas are potentially affected by backward trajectory models. Notably, chloroform and 1,2-dichloroethane have consistently surpassed acceptable thresholds, indicating a significant carcinogenic risk associated with solvent usage. This research sheds light on the evolution of halocarbons in the YRD region, offering valuable data for the control and reduction in halocarbon emissions.
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Affiliation(s)
- Yuchun Jiang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Anqi Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Qiaoli Zou
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Lu Zhang
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Hanfei Zuo
- College of Environmental and Resource Sciences, Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Zhejiang University, Hangzhou 310058, China
| | - Jinmei Ding
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Zhanshan Wang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Zhigang Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Lingling Jin
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Da Xu
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Xin Sun
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Wenlong Zhao
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Bingye Xu
- Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
- Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control, Hangzhou 310012, China
| | - Xiaoqian Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
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3
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Huo Y, Yao D, Guo H. Differences in aerosol chemistry at a regional background site in Hong Kong before and during the COVID-19 pandemic. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171990. [PMID: 38537818 DOI: 10.1016/j.scitotenv.2024.171990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/23/2024] [Accepted: 03/24/2024] [Indexed: 04/04/2024]
Abstract
Restrictions on human-related activities implemented in Hong Kong to curb the spread of the coronavirus disease 2019 (COVID-19) pandemic provided an opportunity to investigate the anthropogenic impact on organic aerosols (OA) composition. In this study, we conducted a comparative analysis of online measurements of non-refractory submicron particulate matters (NR-PM1) at a regional background site in Hong Kong, covering the periods before the COVID-19 control (November 2018) and during the COVID-19 control (October to November 2020), to investigate changes in OA sources and formation mechanisms. Among the measured NR-PM1 components, organics were the most dominant species with an average percentage of 51.0 ± 0.5 %, exceeding pre-control levels of 44.0 ± 0.7 %. Moreover, 88 % of the organics were attributed to oxygenated OA (OOA). Diurnal variations of all bulk components in NR-PM1 consistently showed afternoon peaks, indicating photochemical processes during COVID-19 control. Similar to the pre-restriction period, the positive matrix factorization (PMF) model showed that OOA was composed of three factors, including two less-oxidized oxygenated factors (LO-OOA1 and LO-OOA2) and one more-oxidized oxygenated factor (MO-OOA). The contribution of the LO-OOA2 factor remained small and stable during both sampling campaigns, which might imply background levels of OOA at this site. The formation of the two predominant components of organics (e.g., LO-OOA1 and MO-OOA) was further discussed. Compared with before control, observational evidence showed that the levels of MO-OOA exceeded LO-OOA1 during the control period, and the average concentration of odd oxygen (Ox = ozone + nitrogen dioxide) increased by 53 % during the COVID-19 control. Besides, the results showed that both LO-OOA1 and MO-OOA exhibited similar diurnal variations to Ox, and their concentrations generally enhanced with increasing Ox levels. This suggested that the formation of OOA was closely related to the photochemical oxidation processes when anthropogenic emissions were reduced. By correlating LO-OOA1 and MO-OOA with speciated OA markers, we found that the formation of LO-OOA1 remained associated with anthropogenic sources, while biogenic emissions contributed to the formation of MO-OOA during the COVID-19 control. Our findings highlight the interplay between emissions, atmospheric conditions, and aerosol composition, providing valuable insights to guide strategic decisions for future air quality improvement.
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Affiliation(s)
- Yunxi Huo
- Air Quality Studies, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Dawen Yao
- School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Hai Guo
- Air Quality Studies, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
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4
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Wang S, Zhu Y, Jang JC, Jiang M, Yue D, Zhong L, Yuan Y, Zhang M, You Z. Modeling assessment of air pollution control measures and COVID-19 pandemic on air quality improvements over Greater Bay Area of China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171951. [PMID: 38537836 DOI: 10.1016/j.scitotenv.2024.171951] [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: 12/19/2023] [Revised: 03/04/2024] [Accepted: 03/23/2024] [Indexed: 04/17/2024]
Abstract
A remarkable progress has been made toward the air quality improvements over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China from 2017 to 2020. In this study, for the first time, the emission reductions of regional control measures together with the COVID-19 pandemic were considered simultaneously into the development of the GBA's emission inventories for the years of 2017 and 2020. Based on these collective emission inventories, the impacts of control measures, meteorological variations together with temporary COVID-19 lockdowns on the five major air quality index pollutants (SO2, NO2, PM2.5, PM10, and O3, excluding CO) were evaluated using the WRF-CMAQ and SMAT-CE model attainment assessment tool over the GBA region. Our results revealed that control measures in the Pearl River Delta (PRD) region affected significantly the GBA, resulting in pollutant reductions ranging from 48 % to 64 %. In contrast, control measures in Hong Kong and Macao contributed to pollutant reductions up to 10 %. In PRD emission sectors, stationary combustion, on-road, industrial processes and dust sectors stand out as the primary contributors to overall air quality improvements. Moreover, the COVID-19 pandemic during period I (Jan 23-Feb 23) led to a reduction of NO2 concentration by 7.4 %, resulting in a negative contribution (disbenefit) for O3 with an increase by 2.4 %. Our findings highlight the significance of PRD control measures for the air quality improvements over the GBA, emphasizing the necessity of implementing more refined and feasible manageable joint prevention and control policies.
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Affiliation(s)
- Shaoyi Wang
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Yun Zhu
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
| | - Ji-Cheng Jang
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Ming Jiang
- Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510308, China
| | - Dingli Yue
- Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510308, China
| | - Liuju Zhong
- Guangdong Polytechnic of Environmental Protection Engineering, Foshan 528216, China
| | - Yingzhi Yuan
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Mengmeng Zhang
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Zhiqiang You
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
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5
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Wang L, Zhuang X, Bao H, Ma C, Ma C, Yang G. Chemical characterization and source apportionment of PM 2.5 in a Northeastern China city during the epidemic period. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:32901-32913. [PMID: 38668944 DOI: 10.1007/s11356-024-33473-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 04/22/2024] [Indexed: 05/29/2024]
Abstract
To investigate the influence of COVID-19 lockdown measures on PM2.5 and its chemical components in Shenyang, PM2.5 samples were continuously collected from January 1 to May 31, 2020. The samples were then analyzed for water-soluble inorganic ions, metal elements, organic carbon, and elemental carbon. The findings indicated a significant decrease in PM2.5 and its various chemical components during the lockdown period, compared to pre-lockdown levels (p < 0.05), suggesting a substantial improvement in air quality. Water-soluble inorganic ions (WSIIs) were identified as the primary contributors to PM2.5, accounting for 47% before the lockdown, 46% during the lockdown, and 37% after the lockdown. Ionic balance analysis revealed that PM2.5 exhibited neutral, weakly alkaline, and alkaline characteristics before, during, and after the lockdown, respectively. NH4+ was identified as the main balancing cation and was predominantly present in the form of NH4NO3 in the absence of complete neutralization of SO42- and NO3-. Moreover, the higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), along with the significant increase in PM2.5/EC, suggested intense secondary transformation during the lockdown period. The elevated OC/EC ratio during the lockdown period implied higher secondary organic carbon (SOC), and the notable increase in SOC/EC ratio indicated a significant secondary transformation of total carbon. The enrichment factor (EF) results revealed that during the lockdown, 9 metal elements (As, Sn, Pb, Zn, Cu, Sb, Ag, Cd, and Se) were substantially impacted by anthropogenic emissions. Source analysis of PMF was employed to identify the sources of PM2.5 in Shenyang during the study period, and the analysis identified six factors: secondary sulfate and vehicle emissions, catering fume sources, secondary nitrate and coal combustion emissions, dust sources, biomass combustion, and industrial emissions, with secondary sulfate and vehicle emissions and catering fume sources contributing the most to PM2.5.
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Affiliation(s)
- Lukai Wang
- College of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Xiaohong Zhuang
- College of Environmental Science, Liaoning University, Shenyang, 110036, China.
| | - Hongxu Bao
- College of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Chunlei Ma
- College of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Chen Ma
- College of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Guangchao Yang
- College of Environmental Science, Liaoning University, Shenyang, 110036, China
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6
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Zuo H, Jiang Y, Yuan J, Wang Z, Zhang P, Guo C, Wang Z, Chen Y, Wen Q, Wei Y, Li X. Pollution characteristics and source differences of VOCs before and after COVID-19 in Beijing. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:167694. [PMID: 37832670 DOI: 10.1016/j.scitotenv.2023.167694] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/14/2023] [Accepted: 10/07/2023] [Indexed: 10/15/2023]
Abstract
During the outbreak of the COVID-19, the change in the way of people's living and production provided the opportunity to study the influence of human activity on Volatile organic compounds (VOCs) in the atmosphere. Therefore, this study analyzed VOCs concentration and composition characteristics in urban area of Beijing from 2019 to 2020. The results showed that the concentration of VOCs in Chaoyang district in 2020 was 73.1ppbv, lower than that in 2019 (92.8ppbv), and alkanes (45 % and 47 %) were the most dominant components. The concentrations of isopentane, n-pentane, n-hexane, and OVOCs significantly increased in 2020. According to the results of the PMF model, the contribution of VOCs from vehicle and pharmaceutical-related emissions increased to 45.8 % and 27.1 % in 2020, while coal combustion decreased by 23.7 %. This is likely linked to the strict implementation of the coal conversion policy, as well as the increment in individual travel and pharmaceutical production during the pandemic. The calculation results of OFP and SOAFP indicated that toluene had an increased impact on the formation of O3 and SOA in the Chaoyang district in 2020. Notably, VOCs emitted by vehicles have the highest potential for secondary generation. In addition, VOCs from vehicles and industries pose the greatest health risks, together accounting for 77.4 % and 79.31 % of the total carcinogenic risk in 2019 and 2020. Although industrial emission with the high proportions of halocarbons was controlled to some extent during the pandemic, the carcinogenic risk in 2020 was 3.74 × 10-6, which still exceeded the acceptable level, and more attention and governance efforts should be given to.
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Affiliation(s)
- Hanfei Zuo
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; School of Materials Science and Chemical Engineering, Harbin Engineering University, Harbin 150006, China
| | - Yuchun Jiang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Jing Yuan
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; School of Materials Science and Chemical Engineering, Harbin Engineering University, Harbin 150006, China
| | - Ziqi Wang
- College of Arts and Sciences, University of Cincinnati, Cincinnati, State of Ohio 45221, USA
| | - Puzhen Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Chen Guo
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Zhanshan Wang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Ye Chen
- School of Materials Science and Chemical Engineering, Harbin Engineering University, Harbin 150006, China
| | - Qing Wen
- School of Materials Science and Chemical Engineering, Harbin Engineering University, Harbin 150006, China
| | - Yongjie Wei
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Xiaoqian Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; School of Materials Science and Chemical Engineering, Harbin Engineering University, Harbin 150006, China.
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7
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He J, Harkins C, O’Dell K, Li M, Francoeur C, Aikin KC, Anenberg S, Baker B, Brown SS, Coggon MM, Frost GJ, Gilman JB, Kondragunta S, Lamplugh A, Lyu C, Moon Z, Pierce BR, Schwantes RH, Stockwell CE, Warneke C, Yang K, Nowlan CR, González Abad G, McDonald BC. COVID-19 perturbation on US air quality and human health impact assessment. PNAS NEXUS 2024; 3:pgad483. [PMID: 38222466 PMCID: PMC10785034 DOI: 10.1093/pnasnexus/pgad483] [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: 04/28/2023] [Accepted: 12/21/2023] [Indexed: 01/16/2024]
Abstract
The COVID-19 stay-at-home orders issued in the United States caused significant reductions in traffic and economic activities. To understand the pandemic's perturbations on US emissions and impacts on urban air quality, we developed near-real-time bottom-up emission inventories based on publicly available energy and economic datasets, simulated the emission changes in a chemical transport model, and evaluated air quality impacts against various observations. The COVID-19 pandemic affected US emissions across broad-based energy and economic sectors and the impacts persisted to 2021. Compared with 2019 business-as-usual emission scenario, COVID-19 perturbations resulted in annual decreases of 10-15% in emissions of ozone (O3) and fine particle (PM2.5) gas-phase precursors, which are about two to four times larger than long-term annual trends during 2010-2019. While significant COVID-induced reductions in transportation and industrial activities, particularly in April-June 2020, resulted in overall national decreases in air pollutants, meteorological variability across the nation led to local increases or decreases of air pollutants, and mixed air quality changes across the United States between 2019 and 2020. Over a full year (April 2020 to March 2021), COVID-induced emission reductions led to 3-4% decreases in national population-weighted annual fourth maximum of daily maximum 8-h average O3 and annual PM2.5. Assuming these emission reductions could be maintained in the future, the result would be a 4-5% decrease in premature mortality attributable to ambient air pollution, suggesting that continued efforts to mitigate gaseous pollutants from anthropogenic sources can further protect human health from air pollution in the future.
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Affiliation(s)
- Jian He
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
- NOAA Chemical Sciences Laboratory, Boulder, CO 80305, USA
| | - Colin Harkins
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
- NOAA Chemical Sciences Laboratory, Boulder, CO 80305, USA
| | - Katelyn O’Dell
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
| | - Meng Li
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
- NOAA Chemical Sciences Laboratory, Boulder, CO 80305, USA
| | - Colby Francoeur
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
- NOAA Chemical Sciences Laboratory, Boulder, CO 80305, USA
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Kenneth C Aikin
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
- NOAA Chemical Sciences Laboratory, Boulder, CO 80305, USA
| | - Susan Anenberg
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
| | - Barry Baker
- NOAA Air Resources Laboratory, College Park, MD 20740, USA
| | - Steven S Brown
- NOAA Chemical Sciences Laboratory, Boulder, CO 80305, USA
| | | | | | | | - Shobha Kondragunta
- NOAA National Environmental Satellite, Data, and Information Service, Center for Satellite Applications and Research, College Park, MD 20740, USA
| | - Aaron Lamplugh
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
- NOAA Chemical Sciences Laboratory, Boulder, CO 80305, USA
| | - Congmeng Lyu
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
- NOAA Chemical Sciences Laboratory, Boulder, CO 80305, USA
| | - Zachary Moon
- NOAA Air Resources Laboratory, College Park, MD 20740, USA
- Earth Resources Technology (ERT) Inc., Laurel, MD 20707, USA
| | - Bradley R Pierce
- Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA
| | | | - Chelsea E Stockwell
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
- NOAA Chemical Sciences Laboratory, Boulder, CO 80305, USA
| | | | - Kai Yang
- Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA
| | - Caroline R Nowlan
- Center for Astrophysics, Harvard and Smithsonian, Cambridge, MA 02138, USA
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8
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Feng X, Ma Y, Lin H, Fu TM, Zhang Y, Wang X, Zhang A, Yuan Y, Han Z, Mao J, Wang D, Zhu L, Wu Y, Li Y, Yang X. Impacts of Ship Emissions on Air Quality in Southern China: Opportunistic Insights from the Abrupt Emission Changes in Early 2020. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:16999-17010. [PMID: 37856868 DOI: 10.1021/acs.est.3c04155] [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: 10/21/2023]
Abstract
In early 2020, two unique events perturbed ship emissions of pollutants around Southern China, proffering insights into the impacts of ship emissions on regional air quality: the decline of ship activities due to COVID-19 and the global enforcement of low-sulfur (<0.5%) fuel oil for ships. In January and February 2020, estimated ship emissions of NOx, SO2, and primary PM2.5 over Southern China dropped by 19, 71, and 58%, respectively, relative to the same period in 2019. The decline of ship NOx emissions was mostly over the coastal waters and inland waterways of Southern China due to reduced ship activities. The decline of ship SO2 and primary PM2.5 emissions was most pronounced outside the Chinese Domestic Emission Control Area due to the switch to low-sulfur fuel oil there. Ship emission reductions in early 2020 drove 16 to 18% decreases in surface NO2 levels but 3.8 to 4.9% increases in surface ozone over Southern China. We estimated that ship emissions contributed 40% of surface NO2 concentrations over Guangdong in winter. Our results indicated that future abatements of ship emissions should be implemented synergistically with reductions of land-borne anthropogenic emissions of nonmethane volatile organic compounds to effectively alleviate regional ozone pollution.
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Affiliation(s)
- Xu Feng
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Yaping Ma
- National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China
| | - Haipeng Lin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Shenzhen National Center for Applied Mathematics, Shenzhen 518055, Guangdong, China
- Center for Oceanic and Atmospheric Science at SUSTech (COAST), Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Yan Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Xiaolin Wang
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Aoxing Zhang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Yupeng Yuan
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Zimin Han
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jingbo Mao
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Dakang Wang
- School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, Guangdong, China
| | - Lei Zhu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Yujie Wu
- School of Public and International Affairs, Princeton University, Princeton, New Jersey 08544, United States
| | - Ying Li
- Center for Oceanic and Atmospheric Science at SUSTech (COAST), Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Department of Ocean Sciences and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Xin Yang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
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9
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Ni J, Liu SS, Lang XP, He Z, Yang GP. Sulfur hexafluoride in the marine atmosphere and surface seawater of the Western Pacific and Eastern Indian Ocean. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 335:122266. [PMID: 37499965 DOI: 10.1016/j.envpol.2023.122266] [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: 04/21/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023]
Abstract
Sulfur hexafluoride (SF6) is a powerful greenhouse gas with a high global warming potential. While SF6 emissions from urban areas have been extensively studied, our knowledge about SF6 concentrations in the oceanic atmosphere and its air-sea exchange remains limited. Herein, the concentrations of SF6 in the atmosphere and surface seawater of the WPO (Western Pacific Ocean) and EIO (Eastern Indian Ocean) were comprehensively characterized from 2019 to 2022 in the first long-term study. The mean mixing ratios of SF6 over the WPO and EIO during 2019-2020 (2021-2022) were 10.9 (11.2) and 10.9 (11.1) ppt, respectively. The atmospheric SF6 concentration over the WPO and EIO increased at rates of 0.40 ± 0.06 and 0.58 ± 0.28 ppt yr-1, respectively, surpassing previously reported annual growth rates. The faster growth was primarily attributed to the influence of polluted air masses originating from eastern Asian countries, particularly Japan, Northeast China, and India. This might explain why the radiative forcing caused by SF6 in the study region was higher than the global average. The concentrations of SF6 in the surface seawater of the WPO and EIO ranged from 0.33 to 2.54 fmol kg-1, and the distribution was affected by atmospheric concentrations and ocean currents. Estimated air-sea fluxes revealed that the ocean acted as a significant sink of atmospheric SF6, and the preliminary estimation suggested oceanic uptake accounts for about 7% of annual global SF6 emissions. Based on these findings, we tentatively suggest that the strength of the ocean as a sink of SF6 may warrant reassessment. The global oceanic uptake of SF6 has the potential to reduce its global abundance and environmental impacts.
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Affiliation(s)
- Jie Ni
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, And Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, And College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao, 266100, China
| | - Shan-Shan Liu
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, And Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, And College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao, 266100, China
| | - Xiao-Ping Lang
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, And Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, And College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao, 266100, China
| | - Zhen He
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, And Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, And College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao, 266100, China; Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China.
| | - Gui-Peng Yang
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, And Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, And College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao, 266100, China; Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China; Institute of Marine Chemistry, Ocean University of China, Qingdao, 266100, China
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10
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Yen YC, Ku CH, Hsiao TC, Chi KH, Peng CY, Chen YC. Impacts of COVID-19's restriction measures on personal exposure to VOCs and aldehydes in Taipei City. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 880:163275. [PMID: 37028680 PMCID: PMC10074730 DOI: 10.1016/j.scitotenv.2023.163275] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/21/2023] [Accepted: 03/31/2023] [Indexed: 05/23/2023]
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic provided an unprecedented natural experiment, that allowed us to investigate the impacts of different restrictive measures on personal exposure to specific volatile organic compounds (VOCs) and aldehydes and resulting health risks in the city. Ambient concentrations of the criteria air pollutants were also evaluated. Passive sampling for VOCs and aldehydes was conducted for graduate students and ambient air in Taipei, Taiwan, during the Level 3 warning (strict control measures) and Level 2 alert (loosened control measures) of the COVID-19 pandemic in 2021-2022. Information on the daily activities of participants and on-road vehicle counts nearby the stationary sampling site during the sampling campaigns were recorded. Generalized estimating equations (GEE) with adjusted meteorological and seasonal variables were used to estimate the effects of control measures on average personal exposures to the selected air pollutants. Our results showed that ambient CO and NO2 concentrations in relation to on-road transportation emissions were significantly reduced, which led to an increase in ambient O3 concentrations. Exposure to specific VOCs (benzene, methyl tert-butyl ether (MTBE), xylene, ethylbenzene, and 1,3-butadiene) associated with automobile emissions were remarkably decreased by ~40-80 % during the Level 3 warning, resulting in 42 % and 50 % reductions of total incremental lifetime cancer risk (ILCR) and hazard index (HI), respectively, compared with the Level 2 alert. In contrast, the exposure concentration and calculated health risks in the selected population for formaldehyde increased by ~25 % on average during the Level 3 warning. Our study improves knowledge of the influence of a series of anti-COVID-19 measures on personal exposure to specific VOCs and aldehydes and its mitigations.
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Affiliation(s)
- Yu-Chuan Yen
- National Institute of Environmental Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli, Taiwan
| | - Chun-Hung Ku
- National Institute of Environmental Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli, Taiwan
| | - Ta-Chih Hsiao
- Graduate Institute of Environmental Engineering, National Taiwan University, Taiwan
| | - Kai Hsien Chi
- Institute of Environmental and Occupational Health Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chiung-Yu Peng
- Department of Public Health, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Yu-Cheng Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, 35 Keyan Road, Zhunan Town, Miaoli, Taiwan; Department of Occupational Safety and Health, China Medical University, 91 Hsueh-Shih Road, Taichung, Taiwan; Department of Safety, Health and Environmental Engineering, National United University, No. 2, Lienda, Miaoli 360302, Taiwan; Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
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11
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Xiao S, Zhang Y, Zhang Z, Song W, Pei C, Chen D, Wang X. The contributions of non-methane hydrocarbon emissions by different fuel type on-road vehicles based on tests in a heavily trafficked urban tunnel. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 873:162432. [PMID: 36841415 DOI: 10.1016/j.scitotenv.2023.162432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Automobile exhaust is a major source of volatile organic compounds (VOCs) in metropolitan areas, yet it is difficult to accurately determine the contributions of different types of on-road vehicles. Tunnel tests are an effective way to measure real-world vehicle emissions, and the data collected are also suitable for receptor modeling to analyze the contributions of non-methane hydrocarbons (NMHCs) from different types of vehicles, as the closed environment ensures good mixing and minimal aging. In this study, tunnel tests were conducted inside a heavily trafficked city tunnel in Guangzhou in south China, and the positive matrix factorization (PMF) model was applied to the inlet-outlet incremental NMHC data. The results revealed that gasoline vehicles (GVs), Liquefied Petroleum Gas vehicles (LPGVs), and diesel vehicles (DVs) were responsible for 39 %, 45 % and 16 % of NMHCs, and 52 %, 23 %, and 24 % of the ozone formation potentials, respectively. LPGVs were the largest contributor of (56 %) alkanes, and GVs were the largest contributor of aromatics (61 %) and C2-C4 alkenes (55 %). With the video-recorded traffic counts the emissions of different fuel types are further compared on a per-vehicle-per-kilometer basis, and the results reveal that LPGVs and GVs were comparable in the OFPs of NMHCs emitted per kilometer, while on average a DV emitted 2.0 times more NMHCs than a GV with 2.4 times more OFPs. This study highlights substantial contribution of reactive alkenes and aromatics by DVs and the benefits of strengthening diesel exhaust control in terms of preventing ozone pollution.
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Affiliation(s)
- Shaoxuan Xiao
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou, 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanli Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou, 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhou Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
| | - Wei Song
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou, 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chenglei Pei
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China; Guangdong Province Guangzhou Ecological Environment Monitoring Center Station, Guangzhou 510030, China
| | - Duohong Chen
- Guangdong Provincial Ecological Environment Monitoring Center, Guangzhou 510308, China
| | - Xinming Wang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou, 510640, China; University of Chinese Academy of Sciences, Beijing 100049, China
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12
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Lian M, Wang J, Wang B, Xin M, Lin C, Gu X, He M, Liu X, Ouyang W. Spatiotemporal variations and the ecological risks of organophosphate esters in Laizhou Bay waters between 2019 and 2021: Implying the impacts of the COVID-19 pandemic. WATER RESEARCH 2023; 233:119783. [PMID: 36842327 PMCID: PMC9943543 DOI: 10.1016/j.watres.2023.119783] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/23/2023] [Accepted: 02/20/2023] [Indexed: 05/21/2023]
Abstract
Organophosphate esters (OPEs) are a group of synthetic chemicals used in numerous consumer products such as plastics and furniture. The Coronavirus Disease 2019 (COVID-19) pandemic significantly slowed anthropogenic activities and reduced the emissions of pollutants. Meanwhile, the mismanagement of large quantities of disposable plastic facemasks intensified the problems of plastic pollution and leachable pollutants in coastal waters. In this study, the joint effects of the COVID-19 outbreak on the occurrence of 12 targeted OPEs in the waters of Laizhou Bay (LZB) were investigated. The results showed that the median total OPE concentrations were 725, 363, and 109 ng L-1 in the sewage treatment plant effluent, river water, and bay water in 2021, decreased significantly (p < 0.05) by 67%, 68%, and 70%, respectively, compared with those before the COVID-19 outbreak. The release potential of targeted OPEs from disposable surgical masks in the LZB area was ∼0.24 kg yr-1, which was insufficient to increase the OPE concentration in the LZB waters. The concentrations of most individual OPEs significantly decreased in LZB waters from 2019 to 2021, except for TBOEP and TNBP. Spatially, a lower concentration of OPEs was found in the Yellow River estuary area in 2021 compared with that before the COVID-19 pandemic due to the high content of suspended particulate matter in the YR. A higher total OPE concentration was observed along the northeastern coast of LZB, mainly owing to the construction of an artificial island since 2020. The ecological risks of the OPE mixture in LZB waters were lower than those before the COVID-19 outbreak. However, TCEP, TNBP, and BDP should receive continuous attention because of their potential ecological risks to aquatic organisms.
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Affiliation(s)
- Maoshan Lian
- Beijing Normal University, Beijing 100875, China
| | - Jing Wang
- Beijing Normal University, Beijing 100875, China
| | - Baodong Wang
- First Institute of Oceanography, Ministry of Natural Resources, 6 Xianxialing Road, Qingdao 266061, China
| | - Ming Xin
- First Institute of Oceanography, Ministry of Natural Resources, 6 Xianxialing Road, Qingdao 266061, China
| | - Chunye Lin
- Beijing Normal University, Beijing 100875, China.
| | - Xiang Gu
- Beijing Normal University, Beijing 100875, China
| | - Mengchang He
- Beijing Normal University, Beijing 100875, China
| | - Xitao Liu
- Beijing Normal University, Beijing 100875, China
| | - Wei Ouyang
- Beijing Normal University, Beijing 100875, China
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13
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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: 1.5] [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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14
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Guan Y, Shen Y, Liu X, Liu X, Chen J, Li D, Xu M, Wang L, Duan E, Hou L, Han J. Important revelations of different degrees of COVID-19 lockdown on improving regional air quality: a case study of Shijiazhuang, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:21313-21325. [PMID: 36269475 PMCID: PMC9589624 DOI: 10.1007/s11356-022-23715-0] [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: 09/05/2022] [Accepted: 10/14/2022] [Indexed: 05/06/2023]
Abstract
To control the spread of COVID-19, Shijiazhuang implemented two lockdowns of different magnitudes in 2020 (lockdown I) and 2021 (lockdown II). We analyzed the changes in air quality index (AQI), PM2.5, O3, and VOCs during the two lockdowns and the same period in 2019 and quantified the effects of anthropogenic sources during the lockdowns. The results show that AQI decreased by 13.2% and 32.4%, and PM2.5 concentrations decreased by 12.9% and 42.4% during lockdown I and lockdown II, respectively, due to the decrease in urban traffic mobility and industrial activity levels. However, the sudden and unreasonable emission reductions led to an increase in O3 concentrations by 160.6% and 108.4%, respectively, during the lockdown period. To explore the causes of the O3 surge, the major precursors NOx and VOCs were studied separately, and the main VOCs species affecting ozone formation during the lockdown period and the source variation of VOCs were identified, and it is important to note that the relationship between diurnal variation characteristics of VOCs and cooking became apparent during the lockdown period. These findings suggest that regional air quality can be improved by limiting production, but attention should be paid to the surge of O3 caused by unreasonable emission reductions, clarifying the control priorities for urban O3 management.
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Affiliation(s)
- Yanan Guan
- School of Environmental Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China
- National Joint Local Engineering Research Center for Volatile Organic Compounds and Odorous Pollution Control, Shijiazhuang, 050018, China
| | - Ying Shen
- School of Environmental Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China
| | - Xinyue Liu
- School of Environmental Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China
| | - Xuejiao Liu
- School of Environmental Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China
| | - Jing Chen
- Shijiazhuang City Environmental Meteorological Center, Shijiazhuang, 050018, China
| | - Dong Li
- Shijiazhuang City Environmental Prediction and Forecast Center, Shijiazhuang, 050018, China
| | - Man Xu
- Shijiazhuang City Environmental Prediction and Forecast Center, Shijiazhuang, 050018, China
| | - Litao Wang
- School of Environmental Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China
- National Joint Local Engineering Research Center for Volatile Organic Compounds and Odorous Pollution Control, Shijiazhuang, 050018, China
| | - Erhong Duan
- School of Environmental Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China
- National Joint Local Engineering Research Center for Volatile Organic Compounds and Odorous Pollution Control, Shijiazhuang, 050018, China
| | - Li'an Hou
- Xi'an High-Tech Institute, Xi'an, 710025, Shaanxi, China
| | - Jing Han
- School of Environmental Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China.
- National Joint Local Engineering Research Center for Volatile Organic Compounds and Odorous Pollution Control, Shijiazhuang, 050018, China.
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