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Huang J, Wang D, Zhu Y, Yang Z, Yao M, Shi X, An T, Zhang Q, Huang C, Bi X, Li J, Wang Z, Liu Y, Zhu G, Chen S, Hang J, Qiu X, Deng W, Tian H, Zhang T, Chen T, Liu S, Lian X, Chen B, Zhang B, Zhao Y, Wang R, Li H. An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere. FUNDAMENTAL RESEARCH 2024; 4:430-441. [PMID: 38933199 PMCID: PMC11197502 DOI: 10.1016/j.fmre.2023.05.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 04/29/2023] [Accepted: 05/16/2023] [Indexed: 06/28/2024] Open
Abstract
Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.
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Affiliation(s)
- Jianping Huang
- Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
- College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Danfeng Wang
- Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Yongguan Zhu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Zifeng Yang
- National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease (Guangzhou Medical University), Guangzhou 510230, China
| | - Maosheng Yao
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Taicheng An
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| | - Xinhui Bi
- 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
| | - Jiang Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Zifa Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Yongqin Liu
- Center for Pan-third Pole Environment, Lanzhou University, Lanzhou 730000, China
| | - Guibing Zhu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Siyu Chen
- Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
- College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Jian Hang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 510640, China
| | - Xinghua Qiu
- State Key Joint Laboratory for Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, and Center for Environment and Health, Peking University, Beijing 100871, China
| | - Weiwei Deng
- Shenzhen Key Laboratory of Soft Mechanics & Smart Manufacturing and Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100101, China
| | - Tengfei Zhang
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Sijin Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Xinbo Lian
- Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
- College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Bin Chen
- Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
- College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Beidou Zhang
- Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
- College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Yingjie Zhao
- Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
- College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Rui Wang
- Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
- College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Han Li
- Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
- College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
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Jia W, Zhang X, Wang D, Wang J, Yang Y, Wang H, Liu H, Wang Y. Impacts of emissions and meteorological conditions in three different phases of aerosol pollution during 2013-2022 in Anhui, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 925:171582. [PMID: 38494029 DOI: 10.1016/j.scitotenv.2024.171582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/20/2024] [Accepted: 03/06/2024] [Indexed: 03/19/2024]
Abstract
The PM2.5 concentrations in Anhui, which links the Yangtze River Delta region, China's fastest growing economy area, with the Beijing-Tianjin-Hebei (BTH) region, China's most polluted region, are influenced not only by emissions, but also by variation of meteorological conditions. A comprehensive understanding of the relative impacts of meteorology and emissions on heavy pollution in Anhui at three phases (i.e., phase1: from 2013 to 2017; phase2: from 2018 to 2020; phase 3: from 2021 to 2022) from 2013 to 2022, which can provide suggestions for pollution prevention and control in the future. The decrease in pollutant concentrations from 2013 to 2022 is mainly attributed to the continued reduction in emissions, while the year-to-year fluctuations in pollutant concentrations are largely influenced by meteorological conditions. Although emissions are decreasing, the proportions of residential biofuel combustion and cement are increasing. In addition to the effects of prevailing northeasterly and northwesterly winds (i.e., Type1 and Type2), there is also concern about the influences of static weather and neighboring regional transport (i.e., Type5 and Type6), especially in 2016. The contribution of emissions is greater in phase 2 and phase 3, with a 17 % increase compared to phase 1. Overall, approximately 57 % of explosive growth in PM2.5 concentration during the cumulative stage (CS) can be regarded as the feedback effect of the deteriorating meteorological conditions. Therefore, statistical analyses show that limiting PM2.5 concentrations below about 73 μg m-3 would weaken the feedback effects, which in turn would avoid most of the explosive growth processes in the CS of the 60 heavy pollution processes, which can provide a reference for the government to set a target for sustained emission reduction.
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Affiliation(s)
- Wenxing Jia
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China; Center for Excellence in Regional Atmospheric Environment, IUE, Chinese Academy of Sciences, Xiamen 361021, China.
| | - Deying Wang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Jizhi Wang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yuanqin Yang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Hong Wang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Hongli Liu
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Yaqiang Wang
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
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Kovács KD, Haidu I. Modeling NO 2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 342:122973. [PMID: 37989406 DOI: 10.1016/j.envpol.2023.122973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/29/2023] [Accepted: 11/14/2023] [Indexed: 11/23/2023]
Abstract
By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.
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Affiliation(s)
- Kamill Dániel Kovács
- Université de Lorraine, Laboratoire LOTERR-EA7304, Île Du Saulcy, 57045, Metz, France.
| | - Ionel Haidu
- Université de Lorraine, Laboratoire LOTERR-EA7304, Île Du Saulcy, 57045, Metz, France
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Rudke AP, Martins JA, Hallak R, Martins LD, de Almeida DS, Beal A, Freitas ED, Andrade MF, Koutrakis P, Albuquerque TTA. Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak. REMOTE SENSING OF ENVIRONMENT 2023; 289:113514. [PMID: 36846486 PMCID: PMC9941323 DOI: 10.1016/j.rse.2023.113514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/11/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO2 column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO2 and particulate material (PM; coarse: PM10 and fine: PM2.5)]. For this purpose, tropospheric NO2 obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM10, most stations showed correlations lower than 0.2, which were not significant. The results for PM2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO2 proved to be a good predictor for NO2 concentrations at ground level. Considering all stations with NO2 measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO2 throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO2) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO2). Our results demonstrate that Tropospheric NO2 column densities can serve as good predictors of NO2 concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.
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Affiliation(s)
- A P Rudke
- Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Av. Pres. Antônio Carlos, 6627, 31270-901 Belo Horizonte, Brazil
- Federal University of Technology - Paraná, Av. Dos Pioneiros, 3131, 86036-370 Londrina, Brazil
| | - J A Martins
- Federal University of Technology - Paraná, Av. Dos Pioneiros, 3131, 86036-370 Londrina, Brazil
| | - R Hallak
- Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, Rua do Matão, 1226, Cidade Universitária, 05508-090, São Paulo, Brazil
| | - L D Martins
- Federal University of Technology - Paraná, Av. Dos Pioneiros, 3131, 86036-370 Londrina, Brazil
| | - D S de Almeida
- Federal University of Technology - Paraná, Av. Dos Pioneiros, 3131, 86036-370 Londrina, Brazil
- Federal University of São Carlos, Rod. Washington Luiz, Km 235, SP310, 13565-905, São Carlos, Brazil
| | - A Beal
- Federal University of Technology - Paraná, Av. Dos Pioneiros, 3131, 86036-370 Londrina, Brazil
| | - E D Freitas
- Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, Rua do Matão, 1226, Cidade Universitária, 05508-090, São Paulo, Brazil
| | - M F Andrade
- Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, Rua do Matão, 1226, Cidade Universitária, 05508-090, São Paulo, Brazil
| | - P Koutrakis
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA 02114, USA
| | - T T A Albuquerque
- Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Av. Pres. Antônio Carlos, 6627, 31270-901 Belo Horizonte, Brazil
- Post Graduation Program on Environmental Engineering - Federal University of Espírito Santo, Av. Fernando Ferrari, 514, 29075-910 Vitória, Brazil
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Wang Y, Huang L, Huang C, Hu J, Wang M. High-resolution modeling for criteria air pollutants and the associated air quality index in a metropolitan city. ENVIRONMENT INTERNATIONAL 2023; 172:107752. [PMID: 36709673 DOI: 10.1016/j.envint.2023.107752] [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: 09/15/2022] [Revised: 01/05/2023] [Accepted: 01/12/2023] [Indexed: 06/18/2023]
Abstract
The Air Quality Index (AQI), which jointly accounts for levels of criteria air pollutants relative to their guidelines, is largely reported at the city level. Little is known about the spatial patterns of the AQI in terms of the magnitude, temporal variability, and predominant air pollutant contributions at the hyperlocal scale within a city. To fill this research gap, we developed spatiotemporal models for each criteria air pollutant based on an advanced geostatistical framework and estimated daily AQI levels at 100-meter resolution in a metropolitan city in 2019. The model prediction ability (cross-validation, CV, Coefficient of determination, R2, and root mean square error, RMSE) ranged from 0.43 and 1.86 µg/m3 for sulfur dioxide (SO2) to 0.92 and 6.25 µg/m3 for fine particulate matter (PM2.5) across the six air pollutants, leading to good performance in the subsequent AQI estimations (CV R2 = 0.86, RMSE = 10.05). The AQI varies substantially over space at a fine scale and differs from the distributions of individual air pollutants. The unhealthy air quality (AQI > 100 over 75 days) spatial pattern was dominated by excessive ground-level ozone exposure in a large area. Our research provides a useful tool for accurately estimating AQI spatiotemporal variations for population health studies.
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Affiliation(s)
- Yiyi Wang
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China; State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Lei Huang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Conghong Huang
- College of Land Management, Nanjing Agricultural University, Nanjing 210095, China; National & Local Joint Engineering, Research Center for Rural Land Resources Use and Consolidation, Nanjing 210095, China; Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY 14214, USA
| | - Jianlin Hu
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China.
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY 14214, USA; RENEW Institute, University at Buffalo, Buffalo, NY, USA; Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA.
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Han B, Yao T, Li G, Song Y, Zhang Y, Dai Q, Yu J. Marginal reduction in surface NO 2 attributable to airport shutdown: A machine learning regression-based approach. ENVIRONMENTAL RESEARCH 2022; 214:114117. [PMID: 35985489 DOI: 10.1016/j.envres.2022.114117] [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: 06/13/2022] [Revised: 08/03/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
Emissions from aviation and airport-related activities degrade surface air quality but received limited attention relative to regular transportation sectors like road traffic and waterborne vessels. Statistically, assessing the impact of airport-related emissions remains a challenge due to the fact that its signal in the air quality time series data is largely dwarfed by meteorology and other emissions. Flight-ban policy has been implemented in a number of cities in response to the COVID-19 spread since early 2020, which provides an unprecedented opportunity to examine the changes in air quality attributable to airport closure. It would also be interesting to know whether such an intervention produces extra marginal air quality benefits, in addition to road traffic. Here we investigated the impact of airport-related emissions from a civil airport on nearby NO2 air quality by applying machine learning predictive model to observational data collected from this unique quasi-natural experiment. The whole lockdown-attributable change in NO2 was 16.7 μg/m3, equals to a drop of 73% in NO2 with respect to the business-as-usual level. Meanwhile, the airport flight-ban aviation-attributable NO2 was 3.1 μg/m3, accounting for a marginal reduction of 18.6% of the overall NO2 change that driven by the whole lockdown effect. The airport-related emissions contributed up to 24% of the local ambient NO2 under normal conditions. Additionally, the average impact of airport-related emissions on the nearby air quality was ∼0.01 ± 0.001 μg/m3 NO2 per air-flight. Our results highlight that attention needs to be paid to such a considerable emission source in many places where regular air quality regulatory measures were insufficient to bring NO2 concentration into compliance with the health-based limit.
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Affiliation(s)
- Bo Han
- School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin, China; Research Centre for Environment and Sustainable Development of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin, China.
| | - Tingwei Yao
- Research Centre for Environment and Sustainable Development of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin, China
| | - Guojian Li
- Airline Operating Center, Xiamen Airlines, Xiamen, China
| | - Yuqin Song
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, China
| | - Yiye Zhang
- Research Centre for Environment and Sustainable Development of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin, China
| | - Qili Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, China.
| | - Jian Yu
- Research Centre for Environment and Sustainable Development of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin, China
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Xu C, Gao L, Lyu Y, Qiao L, Huang D, Liu Y, Li D, Zheng M. Molecular characteristics, sources and environmental risk of aromatic compounds in particulate matter during COVID-2019: Nontarget screening by ultra-high resolution mass spectrometry and comprehensive two-dimensional gas chromatography. ENVIRONMENT INTERNATIONAL 2022; 167:107421. [PMID: 35868078 DOI: 10.1016/j.envint.2022.107421] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/02/2022] [Accepted: 07/14/2022] [Indexed: 06/15/2023]
Abstract
Aromatic compounds, including many polycyclic aromatic hydrocarbons (PAHs), are suspected carcinogens and may originate from different sources. To investigate the impact of anthropogenic emission reductions on unknown aromatic compounds in particulate matter, we collected samples during the pre-COVID period in 2020, the COVID-19 lockdown period in 2020, and the same period as the lockdown in 2019. Besides the 16 PAHs, other aromatic compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. Four main compound classes were identified: CH, CHO, CHNO, and CHOS. Hierarchical cluster analysis showed the aromatic compounds varied during the different periods. Compared with before the pandemic, the relative abundances of aromatic compounds with low degrees of unsaturation and long alkyl chains (e.g., alkylbenzenes) increased. These compounds probably mainly arose from fossil fuel combustion and petrochemical industry emissions. The CHO compounds, which were dominated by those with high degrees of oxidation, might originate from secondary organic aerosols. Aromatic aldehydes (e.g., cyclamen aldehyde) and benzoates (e.g., 2-ethylhexyl benzoate) probably with high toxicity deserve more attention. During lockdown, nitro derivatives of condensed PAHs were the main CHNO compounds, and the numbers of homologs decreased perhaps because of significant reductions in NOx and PAHs. CHOS compounds with long carbon chains and low degrees of unsaturation were predominant and the numbers of homologs increased. Five compounds (e.g. 1,3-dimethyl pyrene) were predicted to possibly exhibit persistent and bio-accumulated by EPI Suite model, which need further research. The results provide insight on aromatic compounds and their source appointment in atmospheric particulate matter.
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Affiliation(s)
- Chi Xu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Bejing 100012, China
| | - Lirong Gao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China; School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310000, China.
| | - Yibing Lyu
- State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Bejing 100012, China
| | - Lin Qiao
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Di Huang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Da Li
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
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Li K, Ni R, Jiang T, Tian Y, Zhang X, Li C, Xie C. The regional impact of the COVID-19 lockdown on the air quality in Ji'nan, China. Sci Rep 2022; 12:12099. [PMID: 35840644 PMCID: PMC9284497 DOI: 10.1038/s41598-022-16105-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 07/05/2022] [Indexed: 12/23/2022] Open
Abstract
A number of strict lockdown measures were implemented in the areas most affected by COVID-19 in China, including Ji’nan city, from 24 January to 7 February 2020. Due to these forced restrictions, the pollution levels in cities across the country drastically decreased within just a few days. Since traffic pollution and industrial emissions are important factors affecting regional air quality, congestion has a significant impact on the environment. Therefore, using the aid of air quality data for six pollutants (PM10, PM2.5, SO2, NO2, CO and O3) from 11 monitoring stations (located in urban, suburban and urban-industrial regions) across Ji’nan, we employed the air quality index (AQI) to investigate the spatial pattern of air quality in the pre-COVID-19 (pre-COVID) and COVID-19-related lockdown (COVID lockdown) periods. The results showed that air quality significantly improved during the COVID lockdown period. Among the selected pollutants, compared to the corresponding pre-COVID levels, the greatest reduction was observed for the concentration of NO2 (54.02%), while the smallest reduction was observed for the concentration of SO2 (27.92%). The PM2.5 (38.73%), PM10 (44.92%) and CO (30.60%) levels also decreased during the COVID lockdown period; only the O3 concentration increased (37.42%) during this period. Overall, air quality improved by approximate improvements of 37.33% during the COVID lockdown period. Approximately 35.48%, 37.01% and 43.43% in the AQI were observed in urban, suburban and urban-industrial regions, respectively. Therefore, the AQI exhibited remarkable regional differences in Ji'nan. This study demonstrates the contributions of the transportation sector and local emissions to improving air quality in typical urban areas, and these research results can provide guidance for the further monitoring of air pollution in northern Chinese cities.
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Affiliation(s)
- Kun Li
- Forestry College of Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China.,Mountain Tai Forest Ecosystem Research Station of State Forestry Administration/Key Laboratory of State Forestry Administration for Silviculture of the Lower Yellow River, Tai'an, 271018, Shandong, China
| | - Ruiqiang Ni
- Forestry College of Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China.,Mountain Tai Forest Ecosystem Research Station of State Forestry Administration/Key Laboratory of State Forestry Administration for Silviculture of the Lower Yellow River, Tai'an, 271018, Shandong, China
| | - Tenglong Jiang
- Jinan Eco-environmental Monitoring Center of Shandong Province, Ji'nan, 250014, Shandong, China
| | - Yaozhen Tian
- Forestry College of Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China.,Mountain Tai Forest Ecosystem Research Station of State Forestry Administration/Key Laboratory of State Forestry Administration for Silviculture of the Lower Yellow River, Tai'an, 271018, Shandong, China
| | - Xinwen Zhang
- Forestry College of Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
| | - Chuanrong Li
- Forestry College of Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China. .,Mountain Tai Forest Ecosystem Research Station of State Forestry Administration/Key Laboratory of State Forestry Administration for Silviculture of the Lower Yellow River, Tai'an, 271018, Shandong, China.
| | - Chunying Xie
- Forestry College of Shandong Agricultural University, Tai'an, 271018, Shandong, People's Republic of China
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9
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Sun J, Zhou T, Wang D. Relationships between urban form and air quality: A reconsideration based on evidence from China's five urban agglomerations during the COVID-19 pandemic. LAND USE POLICY 2022; 118:106155. [PMID: 35450142 PMCID: PMC9010237 DOI: 10.1016/j.landusepol.2022.106155] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 02/28/2022] [Accepted: 04/11/2022] [Indexed: 05/19/2023]
Abstract
The outbreak of Coronavirus disease 2019 (COVID-19) led to the widespread stagnation of urban activities, resulting in a significant reduction in industrial pollution and traffic pollution. This affected how urban form influences air quality. This study reconsiders the influence of urban form on air quality in five urban agglomerations in China during the pandemic period. The random forest algorithm was used to quantitate the urban form-air quality relationship. The urban form was described by urban size, shape, fragmentation, compactness, and sprawl. Air quality was evaluated by the Air Quality Index (AQI) and the concentration of six pollutants (CO, O3, NO2, PM2.5, PM10, SO2). The results showed that urban fragmentation is the most important factor affecting air quality and the concentration of the six pollutants. Additionally, the relationship between urban form and air quality varies in different urban agglomerations. By analyzing the extremely important indicators affecting air pollution, the urban form-air quality relationship in Beijing-Tianjin-Hebei is rather complex. In the Chengdu-Chongqing and the Pearl River Delta, urban sprawl and urban compactness are extremely important indicators for some air pollutants, respectively. Furthermore, urban shape ranks first for some air pollutants both in the Triangle of Central China and the Yangtze River Delta. Based on the robustness test, the performance of the random forest model is better than that of the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model.
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Affiliation(s)
- Jianing Sun
- School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China
| | - Tao Zhou
- School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China
- Research Center for Construction Economy and Management, Chongqing University, Chongqing 400044, China
| | - Di Wang
- School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China
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10
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Wu Q, Li T, Zhang S, Fu J, Seyler BC, Zhou Z, Deng X, Wang B, Zhan Y. Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2022; 278:119083. [PMID: 35350168 PMCID: PMC8949849 DOI: 10.1016/j.atmosenv.2022.119083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/04/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
Meteorological normalization refers to the removal of meteorological effects on air pollutant concentrations for evaluating emission changes. There currently exist various meteorological normalization methods, yielding inconsistent results. This study aims to identify the state-of-the-art method of meteorological normalization for characterizing the spatiotemporal variation of NOx emissions caused by the COVID-19 pandemic in China. We obtained the hourly data of NO2 concentrations and meteorological conditions for 337 cities in China from January 1, 2019, to December 31, 2020. Three random-forest based meteorological normalization methods were compared, including (1) the method that only resamples meteorological variables, (2) the method that resamples meteorological and temporal variables, and (3) the method that does not need resampling, denoted as Resample-M, Resample-M&T, and Resample-None, respectively. The comparison results show that Resample-M&T considerably underestimated the emission reduction of NOx during the lockdowns, Resample-None generates widely fluctuating estimates that blur the emission recovery trend during work resumption, and Resample-M clearly delineates the emission changes over the entire period. Based on the Resample-M results, the maximum emission reduction occurred during January to February 2020, for most cities, with an average decrease of 19.1 ± 9.4% compared to 2019. During April of 2020 when work resumption initiated to the end of 2020, the emissions rapidly bounced back for most cities, with an average increase of 12.6 ± 15.8% relative to those during the strict lockdowns. Consequently, we recommend using Resample-M for meteorological normalization, and the normalized NO2 concentration dynamics for each city provide important implications for future emission reduction.
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Affiliation(s)
- Qinhuizi Wu
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Tao Li
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Shifu Zhang
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Jianbo Fu
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Barnabas C Seyler
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Zihang Zhou
- Chengdu Academy of Environmental Sciences, Chengdu, Sichuan, 610072, China
| | - Xunfei Deng
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, Zhejiang, 310021, China
| | - Bin Wang
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Yu Zhan
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
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11
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Fioravanti G, Cameletti M, Martino S, Cattani G, Pisoni E. A spatiotemporal analysis of NO 2 concentrations during the Italian 2020 COVID-19 lockdown. ENVIRONMETRICS 2022; 33:e2723. [PMID: 35574514 PMCID: PMC9087439 DOI: 10.1002/env.2723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/24/2021] [Accepted: 02/24/2022] [Indexed: 06/15/2023]
Abstract
When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify-in space and time-the effectiveness of the adopted strategy. The lockdown measures taken worldwide in 2020 to reduce the spread of the SARS-CoV-2 virus can be envisioned as a policy intervention with an indirect effect on air quality. In this paper we propose a statistical spatiotemporal model as a tool for intervention analysis, able to take into account the effect of weather and other confounding factor, as well as the spatial and temporal correlation existing in the data. In particular, we focus here on the 2019/2020 relative change in nitrogen dioxide (NO 2 ) concentrations in the north of Italy, for the period of March and April during which the lockdown measure was in force. We found that during March and April 2020 most of the studied area is characterized by negative relative changes (median values around - 25%), with the exception of the first week of March and the fourth week of April (median values around 5%). As these changes cannot be attributed to a weather effect, it is likely that they are a byproduct of the lockdown measures. There are two aspects of our research that are equally interesting. First, we provide a unique statistical perspective for calculating the relative change in the NO 2 by jointly modeling pollutant concentrations time series. Second, as an output we provide a collection of weekly continuous maps, describing the spatial pattern of the NO 2 2019/2020 relative changes.
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Affiliation(s)
- Guido Fioravanti
- Istituto Superiore per la Protezione e la Ricerca AmbientaleRomeItaly
| | | | - Sara Martino
- Norwegian University of Science and TechnologyTrondheimNorway
| | - Giorgio Cattani
- Istituto Superiore per la Protezione e la Ricerca AmbientaleRomeItaly
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12
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Zhang Y, Pan X, Tian Y, Liu H, Chen X, Ge B, Wang Z, Tang X, Lei S, Yao W, Ren Y, Tian Y, Li J, Fu P, Xin J, Sun Y, Cao J, Wang Z. Transport Patterns and Potential Sources of Atmospheric Pollution during the XXIV Olympic Winter Games Period. ADVANCES IN ATMOSPHERIC SCIENCES 2022; 39:1608-1622. [PMID: 35400782 PMCID: PMC8983323 DOI: 10.1007/s00376-022-1463-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/02/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
The attainment of suitable ambient air quality standards is a matter of great concern for successfully hosting the XXIV Olympic Winter Games (OWG). Transport patterns and potential sources of pollutants in Zhangjiakou (ZJK) were investigated using pollutant monitoring datasets and a dispersion model. The PM2.5 concentration during February in ZJK has increased slightly (28%) from 2018 to 2021, mostly owing to the shift of main potential source regions of west-central Inner Mongolia and Mongolian areas (2015-18) to the North China Plain and northern Shanxi Province (NCPS) after 2018. Using CO as an indicator, the relative contributions of the different regions to the receptor site (ZJK) were evaluated based on the source-receptor-relationship method (SRR) and an emission inventory. We found that the relative contribution of pollutants from NCPS increased from 33% to 68% during 2019-21. Central Inner Mongolia (CIM) also has an important impact on ZJK under unfavorable weather conditions. This study demonstrated that the effect of pollution control measures in the NCPS and CIM should be strengthened to ensure that the air quality meets the standard during the XXIV OWG.
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Affiliation(s)
- Yuting Zhang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Xiaole Pan
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
| | - Yu Tian
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
| | - Hang Liu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
| | - Xueshun Chen
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
| | - Baozhu Ge
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
| | - Zhe Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
| | - Xiao Tang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
| | - Shandong Lei
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Weijie Yao
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Yuanzhe Ren
- Inner Mongolia Autonomous Region environmental monitoring central station, Hohhot, 010090 China
| | - Yongli Tian
- Inner Mongolia Autonomous Region environmental monitoring central station, Hohhot, 010090 China
| | - Jie Li
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
| | - Pingqing Fu
- Institute of Surface-Earth System Science, Tianjin University, Tianjin, 300072 China
| | - Jinyuan Xin
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049 China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, 210044 China
| | - Yele Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049 China
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
| | - Junji Cao
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
| | - Zifa Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029 China
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049 China
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China
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13
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Itahashi S, Yamamura Y, Wang Z, Uno I. Returning long-range PM 2.5 transport into the leeward of East Asia in 2021 after Chinese economic recovery from the COVID-19 pandemic. Sci Rep 2022; 12:5539. [PMID: 35365707 PMCID: PMC8972671 DOI: 10.1038/s41598-022-09388-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 03/22/2022] [Indexed: 12/26/2022] Open
Abstract
Changes in the aerosol composition of sulfate (SO42−) and nitrate (NO3−) from 2012 to 2019 have been captured as a paradigm shift in the region downwind of China. Specifically, SO42− dramatically decreased and NO3− dramatically increased over downwind locations such as western Japan due to the faster reduction of SO2 emissions than NOx emissions and the almost constant trend of NH3 emissions from China. Emissions from China sharply decreased during COVID-19 lockdowns in February–March 2020, after which China’s economic situation seemed to recover going into 2021. Given this substantial change in Chinese emissions, it is necessary to clarify the impact of long-range PM2.5 transport into the leeward of East Asia. In this study, ground-based aerosol compositions observed at three sites in western Japan were analysed. The concentrations of PM2.5, SO42− and NO3− decreased in 2020 (during COVID-19) compared with 2018–2019 (before COVID-19). In 2021 (after COVID-19), PM2.5 and NO3− increased and SO42− was unchanged. This suggests the returning long-range PM2.5 transport in 2021. From numerical simulations, the status of Chinese emissions during COVID-19 did not explain this returning impact in 2021. This study shows that the status of Chinese emissions in 2021 recovered to that before COVID-19.
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Affiliation(s)
- Syuichi Itahashi
- Sustainable System Research Laboratory (SSRL), Central Research Institute of Electric Power Industry (CRIEPI), Abiko, Chiba, 270-1194, Japan.
| | - Yuki Yamamura
- Fukuoka Institute of Health and Environmental Science, Dazaifu, Fukuoka, 818-0135, Japan
| | - Zhe Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), Beijing, 100029, China
| | - Itsushi Uno
- Research Institute for Applied Mechanics (RIAM), Kyushu University, Kasuga, Fukuoka, 816-8580, Japan
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14
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Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements. REMOTE SENSING 2022. [DOI: 10.3390/rs14020419] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO2 by TROPOMI decreased by 39.2–71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53–69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO2 vertical profiles. For instance, in Beijing, MAX-DOAS NO2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO2 can largely explain the differences between satellite and in situ NO2 variations. In the post-epidemic era of 2021, satellite NO2 TVCD and in situ NO2 concentrations decreased by 10.42–64.96% and 1.05–34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China’s urban NO2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production.
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15
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Shen F, Hegglin MI, Luo Y, Yuan Y, Wang B, Flemming J, Wang J, Zhang Y, Chen M, Yang Q, Ge X. Disentangling drivers of air pollutant and health risk changes during the COVID-19 lockdown in China. NPJ CLIMATE AND ATMOSPHERIC SCIENCE 2022; 5:54. [PMID: 35789740 PMCID: PMC9244310 DOI: 10.1038/s41612-022-00276-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 06/06/2022] [Indexed: 05/07/2023]
Abstract
The COVID-19 restrictions in 2020 have led to distinct variations in NO2 and O3 concentrations in China. Here, the different drivers of anthropogenic emission changes, including the effects of the Chinese New Year (CNY), China's 2018-2020 Clean Air Plan (CAP), and the COVID-19 lockdown and their impact on NO2 and O3 are isolated by using a combined model-measurement approach. In addition, the contribution of prevailing meteorological conditions to the concentration changes was evaluated by applying a machine-learning method. The resulting impact on the multi-pollutant Health-based Air Quality Index (HAQI) is quantified. The results show that the CNY reduces NO2 concentrations on average by 26.7% each year, while the COVID-lockdown measures have led to an additional 11.6% reduction in 2020, and the CAP over 2018-2020 to a reduction in NO2 by 15.7%. On the other hand, meteorological conditions from 23 January to March 7, 2020 led to increase in NO2 of 7.8%. Neglecting the CAP and meteorological drivers thus leads to an overestimate and underestimate of the effect of the COVID-lockdown on NO2 reductions, respectively. For O3 the opposite behavior is found, with changes of +23.3%, +21.0%, +4.9%, and -0.9% for CNY, COVID-lockdown, CAP, and meteorology effects, respectively. The total effects of these drivers show a drastic reduction in multi-air pollutant-related health risk across China, with meteorology affecting particularly the Northeast of China adversely. Importantly, the CAP's contribution highlights the effectiveness of the Chinese government's air-quality regulations on NO2 reduction.
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Affiliation(s)
- Fuzhen Shen
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, 210044 Nanjing, China
- Department of Meteorology, University of Reading, Reading, RG6 6BX UK
- Institute of Energy and Climate Research, IEK-7: Stratosphere, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Michaela I. Hegglin
- Department of Meteorology, University of Reading, Reading, RG6 6BX UK
- Institute of Energy and Climate Research, IEK-7: Stratosphere, Forschungszentrum Jülich, 52425 Jülich, Germany
| | | | - Yue Yuan
- Jining Meteorological Bureau, 272000 Shandong, China
| | - Bing Wang
- Henley Business School, University of Reading, Reading, RG6 6UD UK
| | | | - Junfeng Wang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, 210044 Nanjing, China
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138 USA
| | - Yunjiang Zhang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, 210044 Nanjing, China
| | - Mindong Chen
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, 210044 Nanjing, China
| | - Qiang Yang
- Hongkong University of Science and Technology, 999007 Hong Kong, China
| | - Xinlei Ge
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, 210044 Nanjing, China
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16
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de Souza A, Aristone F, Abreu MC, de Oliveira-Júnior JF, Fernandes WA, Pobocikova I. Spatio-temporal variations of tropospheric nitrogen dioxide in South Mato Grosso based on remote sensing by satellite. METEOROLOGY AND ATMOSPHERIC PHYSICS 2022; 134:19. [PMCID: PMC8771657 DOI: 10.1007/s00703-021-00855-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 12/13/2021] [Indexed: 11/19/2023]
Abstract
The study evaluates the characteristics of tropospheric nitrogen dioxide (NO2) concentrations from 2005 to 2020 in eight cities in the State of Mato Grosso do Sul (MS), Midwestern Brazil, using data available from the Ozone Monitoring Instrument (OMI). The average concentration varies from 2981 × 1015 molecules/cm2 in Campo Grande, where commercial, industrial and vehicular activities take place, to 2906 × 1015 molecules/cm2 in Corumbá and 3035 × 1015 molecules/cm2 in Porto Murtinho, regions of livestock and biomass burning. The results, based on the Mann–Kendall (MK) test, show a significant increase (p < 0.05) in the NO2 column levels in the region. For each of the eight cities studied, a significant seasonal cycle of NO2 columns was determined. The maximum value of NO2 concentration was observed in the dry period, from July to September, while the minimum value was registered in the rainy period, from October to March.
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Affiliation(s)
- Amaury de Souza
- Universidade Federal de Mato Grosso do Sul, Campo Grande, MS Brazil
| | - Flavio Aristone
- Universidade Federal de Mato Grosso do Sul, Campo Grande, MS Brazil
| | | | | | | | - Ivana Pobocikova
- Universidade Federal de Mato Grosso do Sul, Campo Grande, MS Brazil
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17
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García-Dalmau M, Udina M, Bech J, Sola Y, Montolio J, Jaén C. Pollutant Concentration Changes During the COVID-19 Lockdown in Barcelona and Surrounding Regions: Modification of Diurnal Cycles and Limited Role of Meteorological Conditions. BOUNDARY-LAYER METEOROLOGY 2021; 183:273-294. [PMID: 34975160 PMCID: PMC8711231 DOI: 10.1007/s10546-021-00679-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 10/11/2021] [Indexed: 06/01/2023]
Abstract
One of the consequences of the COVID-19 lockdowns has been the modification of the air quality in many cities around the world. This study focuses on the variations in pollutant concentrations and how important meteorological conditions were for those variations in Barcelona and the surrounding area during the 2020 lockdown. Boundary-layer height, wind speed, and precipitation were compared between mid-March and April 2016-2019 (pre-lockdown) and the same period in 2020 (during lockdown). The results show the limited influence of meteorological factors on horizontal and vertical dispersion conditions. Compared with the pre-lockdown period, during lockdown the boundary-layer height slightly increased by between 5% and 9%, mean wind speed was very similar, and the fraction of days with rainfall increased only marginally, from 0.33 to 0.34, even though April 2020 was extremely wet in the study area. Variations in nitrogen dioxide ( NO 2 ), particulate matter with a diameter less than 10 μ m (PM10), and ozone ( O 3 ) concentrations over a 10-year period showed a 66% reduction in NO 2 , 37% reduction in PM10, and 27% increase in O 3 at a traffic station in Barcelona. The differences in the daily concentration cycle between weekends and weekdays were heavily smoothed for all pollutants considered. The afternoon NO 2 peak at the traffic station was suppressed compared with the average daily cycle. The analysis of ozone was extended to the regional scale, revealing lower concentrations at rural sites and higher ones in urban zones, especially in Barcelona and the surrounding area. The results presented not only complement previous air quality COVID-19 lockdown studies but also provide insights into the effects of road-traffic reduction.
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Affiliation(s)
- Miguel García-Dalmau
- Departament de Física Aplicada–Meteorologia, Universitat de Barcelona, Barcelona, Spain
| | - Mireia Udina
- Departament de Física Aplicada–Meteorologia, Universitat de Barcelona, Barcelona, Spain
| | - Joan Bech
- Departament de Física Aplicada–Meteorologia, Universitat de Barcelona, Barcelona, Spain
| | - Yolanda Sola
- Departament de Física Aplicada–Meteorologia, Universitat de Barcelona, Barcelona, Spain
| | - Joan Montolio
- Departament de Física Aplicada–Meteorologia, Universitat de Barcelona, Barcelona, Spain
- DT Catalonia, AEMET, Barcelona, Spain
| | - Clara Jaén
- Departament de Física Aplicada–Meteorologia, Universitat de Barcelona, Barcelona, Spain
- Institute of Environmental Assessment and Water Research (IDAEACSIC), Barcelona, Spain
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18
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Analyzing the Contribution of Human Mobility to Changes in Air Pollutants: Insights from the COVID-19 Lockdown in Wuhan. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2021. [DOI: 10.3390/ijgi10120836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions in air pollutants, we implemented sensitivity experiments by Random Forest (RF) models, with the comparison of the contributions of meteorological conditions, human mobility, and emissions from industry and households between different periods. In addition, we conducted scenario analyses to suggest an appropriate limit for control of human mobility. Different mechanisms for air pollutants were shown in the pre-pandemic, pre-lockdown, lockdown, and post-pandemic periods. Wind speed and the Within-city Migration index, representing intra-city mobility intensity, were excluded from stepwise multiple linear models in the pre-lockdown and lockdown periods. The results of sensitivity experiments show that, in the COVID-19 lockdown period, 73.3% of the reduction can be attributed to decreased human mobility. In the post-pandemic period, meteorological conditions control about 42.2% of the decrease, and emissions from industry and households control 40.0%, while human mobility only contributes 17.8%. The results of the scenario analysis suggest that the priority of restriction should be given to human mobility within the city than other kinds of human mobility. The reduction in the NO2 concentration tends to be smaller when human mobility within the city decreases by more than 70%. A limit of less than 40% on the control of the human mobility can achieve a better effect, especially in cities with severe traffic pollution.
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Munir S, Luo Z, Dixon T. Comparing different approaches for assessing the impact of COVID-19 lockdown on urban air quality in Reading, UK. ATMOSPHERIC RESEARCH 2021; 261:105730. [PMID: 36540719 PMCID: PMC9756911 DOI: 10.1016/j.atmosres.2021.105730] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/10/2021] [Accepted: 06/10/2021] [Indexed: 05/28/2023]
Abstract
Many studies investigated the impact of COVID-19 lockdown on urban air quality, but their adopted approaches have varied and there is no consensus as to which approach should be used. In this paper we compare three of the main approaches and assess their performance using both estimated and measured data from several air quality monitoring stations (AQMS) in Reading, Berkshire UK. The approaches are: (1) Sequential approach - comparing pre-lockdown and lockdown periods 2020; (2) Parallel approach - comparing 2019 and 2020 for the equivalent time of the lockdown period; and (3) Machine learning modelling approach - predicting pollution levels for the lockdown period using business as usual (BAU) scenario and comparing with the observations. The parallel and machine learning approaches resulted in relative higher reductions and both showed strong correlation (0.97) and less error with each other. The sequential approach showed less reduction in NO and NOx, showed positive gain in PM10 and NO2 at most of the sites and demonstrated weak correlation with the other two approaches, and is not recommended for such analysis. Overall, the sequential approach showed -14, +4, -32, and + 56% change, the parallel approach showed -46, -43, -43 and + 7% change, and the machine learning approach showed -47, -44, -38 and + 5% change in NOx, NO2, NO and PM10 concentrations, respectively. The pollution roses demonstrated that the UK received easterly polluted winds from the central and eastern Europe, promoting secondary particulates and O3 formation during the lockdown. Changes in pollutant concentrations vary both in space and time according to the approach used, environment type of the monitoring site and the data type (e.g., deweathered vs. raw data). Therefore, the reported results (here or elsewhere) should be viewed in light of these factors before making any conclusion.
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Affiliation(s)
- Said Munir
- Department of Construction Management & Engineering, School of the Built Environment, University of Reading, Reading RG6 6AW, UK
| | - Zhiwen Luo
- Department of Construction Management & Engineering, School of the Built Environment, University of Reading, Reading RG6 6AW, UK
| | - Tim Dixon
- Department of Construction Management & Engineering, School of the Built Environment, University of Reading, Reading RG6 6AW, UK
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Tsai IC, Lee CY, Lung SCC, Su CW. Characterization of the vehicle emissions in the Greater Taipei Area through vision-based traffic analysis system and its impacts on urban air quality. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 782:146571. [PMID: 33838380 DOI: 10.1016/j.scitotenv.2021.146571] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/01/2021] [Accepted: 03/15/2021] [Indexed: 06/12/2023]
Abstract
In recent years, many surveillance cameras have been installed in the Greater Taipei Area, Taiwan; traffic data obtained from these surveillance cameras could be useful for the development of roadway-based emissions inventories. In this study, web-based traffic information covering the Greater Taipei Area was obtained using a vision-based traffic analysis system. Web-based traffic data were normalized and applied to the Community Multiscale Air Quality (CMAQ) model to study the impact of vehicle emissions on air quality in the Greater Taipei Area. According to an analysis of the obtained traffic data, sedans were the most common vehicles in the Greater Taipei Area, followed by motorcycles. Moderate traffic conditions with an average speed of 30-50 km/h were most prominent during weekdays, whereas traffic flow with an average speed of 50-70 km/h was most common during weekends. The proportion of traffic flows in free-flow conditions (>70 km/h) was higher on weekends than on weekdays. Two peaks of traffic flow were observed during the morning and afternoon peak hours on weekdays. On the weekends, this morning peak was not observed, and the variation in vehicle numbers was lower than on weekdays. The simulation results suggested that the addition of real-time traffic data improved the CMAQ model's performance, especially for the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations. According to sensitivity tests for total and vehicle emissions in the Greater Taipei Area, vehicle emissions contributed to >90% of CO, 80% of nitrogen oxides (NOx), and approximately 50% of PM2.5 in the downtown areas of Taipei. The vehicle emissions contribution was affected by both vehicle emissions and meteorological conditions. The connection between the surveillance camera data, vehicle emissions, and regional air quality models in this study can also be used to explore the impact of special events (e.g., long weekends and COVID-19 lockdowns) on air quality.
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Affiliation(s)
- I-Chun Tsai
- Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan, ROC.
| | - Chen-Ying Lee
- Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan, ROC
| | | | - Chih-Wen Su
- Department of Information and Computer Engineering, Chung Yuan Christian University, Taoyuan City, Taipei, Taiwan, ROC
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21
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Dai Q, Hou L, Liu B, Zhang Y, Song C, Shi Z, Hopke PK, Feng Y. Spring Festival and COVID-19 Lockdown: Disentangling PM Sources in Major Chinese Cities. GEOPHYSICAL RESEARCH LETTERS 2021; 48:e2021GL093403. [PMID: 34149113 PMCID: PMC8206764 DOI: 10.1029/2021gl093403] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/07/2021] [Accepted: 05/15/2021] [Indexed: 05/23/2023]
Abstract
Responding to the 2020 COVID-19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO2, O3, and PM2.5 concentrations changed by -29.5%, +31.2%, and -7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ∼14.1% decrease in NO2, ∼6.6% increase in O3 and a mixed effect on PM2.5 in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: -15.4%, +24.6%, and -9.7% for NO2, O3, and PM2.5, respectively.
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Affiliation(s)
- Qili Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and ControlCollege of Environmental Science and EngineeringNankai UniversityTianjinChina
- CMA‐NKU Cooperative Laboratory for Atmospheric Environment‐Health ResearchTianjinChina
| | - Linlu Hou
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and ControlCollege of Environmental Science and EngineeringNankai UniversityTianjinChina
- CMA‐NKU Cooperative Laboratory for Atmospheric Environment‐Health ResearchTianjinChina
| | - Bowen Liu
- Department of EconomicsUniversity of BirminghamBirminghamUK
| | - Yufen Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and ControlCollege of Environmental Science and EngineeringNankai UniversityTianjinChina
- CMA‐NKU Cooperative Laboratory for Atmospheric Environment‐Health ResearchTianjinChina
| | - Congbo Song
- School of Geography Earth and Environment SciencesUniversity of BirminghamBirminghamUK
| | - Zongbo Shi
- School of Geography Earth and Environment SciencesUniversity of BirminghamBirminghamUK
| | - Philip K. Hopke
- Department of Public Health SciencesUniversity of Rochester School of Medicine and DentistryRochesterNYUSA
- Institute for a Sustainable EnvironmentClarkson UniversityPotsdamNYUSA
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and ControlCollege of Environmental Science and EngineeringNankai UniversityTianjinChina
- CMA‐NKU Cooperative Laboratory for Atmospheric Environment‐Health ResearchTianjinChina
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22
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Assessment of Air Pollution before, during and after the COVID-19 Pandemic Lockdown in Nanjing, China. ATMOSPHERE 2021. [DOI: 10.3390/atmos12060743] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
A unique illness, the coronavirus disease 2019 (COVID-19), emerged in Wuhan, People’s Republic of China, in December 2019. To reduce the spread of the virus, strict lockdown policies and control measures were put in place all over the world. Due to these enforced limitations, a drastic drop in air pollution and an improvement in air quality were observed. The present study used six air pollutants (PM10, PM2.5, SO2, NO2, CO and O3) to observe trends before, during and after the COVID-19 lockdown period in Nanjing, China. The data were divided into six phases: P1–P3, pre-lockdown (1 October–31 December 2019), lockdown (1 January–31 March 2020), after lockdown (1 April–30 June 2020), P4–P6: the same dates as the lockdown but during 2017, 2018 and 2019. The results indicate that compared with the pre-lockdown phase, the PM10 and PM2.5 average concentrations decreased by –27.71% and –5.09%. Compared with the previous three years, 2017–2019, the reductions in PM10 and PM2.5 were –37.99% and –33.56%, respectively. Among other pollutants, concentrations of SO2 (–32.90%), NO2 (–34.66%) and CO (–16.85%) also decreased during the lockdown, while the concentration of O3 increased by approximately 25.45%. Moreover, compared with the pre- and during lockdown phases, PM10, PM2.5 and NO2 showed decreasing trends while SO2, CO and O3 concentrations increased. These findings present a road map for upcoming studies and provide a new path for policymakers to create policies to improve air quality.
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23
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COVID-19 and air pollution in Vienna-a time series approach. Wien Klin Wochenschr 2021; 133:951-957. [PMID: 33959810 PMCID: PMC8101341 DOI: 10.1007/s00508-021-01881-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/20/2021] [Indexed: 11/12/2022]
Abstract
We performed a time series analysis in Vienna, Austria, investigating the temporal association between daily air pollution (nitrogen dioxide, NO2 and particulate matter smaller than 10 µm, PM10) concentration and risk of coronavirus disease 2019 (COVID-19) infection and death. Data covering about 2 months (March–April 2020) were retrieved from public databases. Infection risk was defined as the ratio between infected and infectious. In a separate sensitivity analysis different models were applied to estimate the number of infectious people per day. The impact of air pollution was assessed through a linear regression on the natural logarithm of infection risk. Risk of COVID-19 mortality was estimated by Poisson regression. Both pollutants were positively correlated with the risk of infection with the coefficient for NO2 being 0.032 and for PM10 0.014. That association was significant for the irritant gas (p = 0.012) but not for particles (p = 0.22). Pollutants did not affect COVID-19-related mortality. The study findings might have wider implications on an interaction between air pollution and infectious agents.
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24
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Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation. REMOTE SENSING 2021. [DOI: 10.3390/rs13091798] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Air quality is strongly influenced by both local emissions and regional transport. Atmospheric chemical transport models can distinguish between emissions and regional transport sources in air pollutant concentrations. However, quantifying model inventories is challenging due to emission changes caused by the recent strict control measures taken by the Chinese government. In this study, we use NO2 column observations from the Tropospheric Monitoring Instrument to retrieve top-down nitrogen oxide (NOX) emissions and quantify the contributions of local emissions and regional transport to NOx in Beijing (BJ), from 1 November 2018 to 28 February 2019 (W_2018) and 1 November 2019 to 29 February 2020 (W_2019). In W_2018 and W_2019, the BJ bottom-up NOX emissions from the multi-resolution emission inventory for China in 2017 were overestimated by 11.8% and 40.5%, respectively, and the input of NOX from other cities to BJ was overestimated by 10.9% and 51.6%, respectively. The simulation using our adjusted inventory exhibited a much higher spatial agreement (slope = 1.0, R2 = 0.79) and reduced a mean relative error by 45% compared to those of bottom-up NOX emissions. The top-down inventory indicated that (1) city boundary transport contributes approximately 40% of the NOX concentration in BJ; (2) in W_2019, NOX emissions and transport in BJ decreased by 20.4% and 17.2%, respectively, compared to those of W_2018; (3) in W_2019, NOX influx substantially decreased (−699 g/s) in BJ compared to that of W_2018 despite negative meteorological conditions that should have increased NOx influx by +503 g/s. Overall, the contribution of intercity input to NOx in BJ has declined with decreasing emissions in the surrounding cities due to regional cooperative control measures, and the role of local emissions in BJ NOx levels was more prominent. Our findings indicate that local emissions may play vital roles in regional center city air quality.
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25
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Gorris ME, Anenberg SC, Goldberg DL, Kerr GH, Stowell JD, Tong D, Zaitchik BF. Shaping the Future of Science: COVID-19 Highlighting the Importance of GeoHealth. GEOHEALTH 2021; 5:e2021GH000412. [PMID: 34084984 PMCID: PMC8144838 DOI: 10.1029/2021gh000412] [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: 02/23/2021] [Revised: 04/26/2021] [Accepted: 05/02/2021] [Indexed: 06/12/2023]
Abstract
From the heated debates over the airborne transmission of the novel coronavirus to the abrupt Earth system changes caused by the sudden lockdowns, the dire circumstances resulting from the coronavirus disease 2019 (COVID-19) pandemic have brought the field of GeoHealth to the forefront of visibility in science and policy. The pandemic has inadvertently provided an opportunity to study how human response has impacted the Earth system, how the Earth system may impact the pandemic, and the capacity of GeoHealth to inform real-time policy. The lessons learned throughout our responses to the COVID-19 pandemic are shaping the future of GeoHealth.
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Affiliation(s)
- Morgan E. Gorris
- Information Systems and ModelingLos Alamos National LaboratoryLos AlamosNMUSA
| | - Susan C. Anenberg
- Department of Environmental and Occupational HealthMilken Institute School of Public HealthGeorge Washington UniversityWashingtonDCUSA
| | - Daniel L. Goldberg
- Department of Environmental and Occupational HealthMilken Institute School of Public HealthGeorge Washington UniversityWashingtonDCUSA
| | - Gaige Hunter Kerr
- Department of Environmental and Occupational HealthMilken Institute School of Public HealthGeorge Washington UniversityWashingtonDCUSA
| | - Jennifer D. Stowell
- Department of Environmental HealthBoston University School of Public HealthBostonMAUSA
| | - Daniel Tong
- Department of Atmospheric, Oceanic, & Earth SciencesGeorge Mason UniversityFairfaxVAUSA
| | - Benjamin F. Zaitchik
- Department of Earth and Planetary SciencesJohns Hopkins UniversityBaltimoreMDUSA
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26
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Meteorological Drivers of Permian Basin Methane Anomalies Derived from TROPOMI. REMOTE SENSING 2021. [DOI: 10.3390/rs13050896] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The launch of the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S-5P) satellite has revolutionized pollution observations from space. The purpose of this study was to link spatiotemporal variations in TROPOMI methane (CH4) columns to meteorological flow patterns over the Permian Basin, the largest oil and second-largest natural gas producing region in the United States. Over a two-year period (1 December 2018–1 December 2020), the largest average CH4 enhancements were observed near and to the north and west of the primary emission regions. Four case study periods—two with moderate westerly winds associated with passing weather disturbances (8–15 March 2019 and 1 April–10 May 2019) and two other periods dominated by high pressure and low wind speeds (16–23 March 2019 and 24 September–9 October 2020)—were analyzed to better understand meteorological drivers of the variability in CH4. Meteorological observations and analyses combined with TROPOMI observations suggest that weakened transport out of the Basin during low wind speed periods contributes to CH4 enhancements throughout the Basin, while valley and slope flows may explain the observed western expansion of the Permian Basin CH4 anomaly.
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27
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Quantifying the Impact of the Covid-19 Lockdown Measures on Nitrogen Dioxide Levels throughout Europe. ATMOSPHERE 2021. [DOI: 10.3390/atmos12020131] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
In this paper, the effect of the lockdown measures on nitrogen dioxide (NO2) in Europe is analysed by a statistical model approach based on a generalised additive model (GAM). The GAM is designed to find relationships between various meteorological parameters and temporal metrics (day of week, season, etc.) on the one hand and the level of pollutants on the other. The model is first trained on measurement data from almost 2000 monitoring stations during 2015–2019 and then applied to the same stations in 2020, providing predictions of expected concentrations in the absence of a lockdown. The difference between the modelled levels and the actual measurements from 2020 is used to calculate the impact of the lockdown measures adjusted for confounding effects, such as meteorology and temporal trends. The study is focused on April 2020, the month with the strongest reductions in NO2, as well as on the gradual recovery until the end of July. Significant differences between the countries are identified, with the largest NO2 reductions in Spain, France, Italy, Great Britain and Portugal and the smallest in eastern countries (Poland and Hungary). The model is found to perform best for urban and suburban sites. A comparison between the found relative changes in urban surface NO2 data during the lockdown and the corresponding changes in tropospheric vertical NO2 column density as observed by the TROPOMI instrument on Sentinel-5P revealed good agreement despite substantial differences in the observing method.
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