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Lorenz C, Libonati R, Belém LBC, Oliveira A, Chiaravalloti RM, Nunes AV, Batista EKL, Fernandes GW, Chiaravalloti-Neto F, Damasceno-Junior GA, Berlinck CN, Roque FO. Wildfire and smoke association with COVID-19 cases in the Pantanal wetland, Brazil. Public Health 2023; 225:311-319. [PMID: 37972494 DOI: 10.1016/j.puhe.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 10/04/2023] [Accepted: 10/13/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVES In 2020, Brazil experienced two concurrent public health challenges related to respiratory disease: wildfires and increased mortality due to the coronavirus (COVID-19) pandemic. Smoke from these wildfires contributed to a variety of air pollutants, including fine particulate matter (PM2.5). The present study aims to investigate the effects of environmental and socio-economic factors on COVID-19 hospitalisation in the Pantanal. STUDY DESIGN Ecological retrospective study. METHODS We applied a multilevel negative binomial model to relate monthly hospitalisation data with environmental variables. RESULTS We showed that monthly PM2.5 concentration levels had the greatest influence on the increase in hospitalisations by COVID-19 in the elderly (23 % increase). The Gini index, a coefficient that reflects income inequalities, also had a positive association with COVID-19 hospitalisations (18 % increase). Higher temperatures and humidity were protective factors, showing a 15 % and 14 % decrease in hospitalisations, respectively. The results of the present study suggest that high PM2.5 exposure contributed to the increase in COVID-19 hospitalisations, as did the social inequalities of each municipality. CONCLUSIONS The present study highlights the importance of gathering evidence supported by multiple information sources to guide decision-making and identify populations needing better public health systems.
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Affiliation(s)
- C Lorenz
- Instituto de Estudos Avançados, Universidade de São Paulo, R. do Anfiteatro, 513 - Butantã, São Paulo/SP, 05508-060, São Paulo, Brazil.
| | - R Libonati
- Departamento de Meteorologia, Universidade Federal Do Rio de Janeiro, Cidade Universitária, Av. Athos da Silveira Ramos, 274, Ilha do Fundão, 21941-916, Rio de Janeiro, Brazil
| | - L B C Belém
- Departamento de Meteorologia, Universidade Federal Do Rio de Janeiro, Cidade Universitária, Av. Athos da Silveira Ramos, 274, Ilha do Fundão, 21941-916, Rio de Janeiro, Brazil
| | - A Oliveira
- Departamento de Meteorologia, Universidade Federal Do Rio de Janeiro, Cidade Universitária, Av. Athos da Silveira Ramos, 274, Ilha do Fundão, 21941-916, Rio de Janeiro, Brazil
| | - R M Chiaravalloti
- University College London, Anthropology Department, 14 Taviton Street, WC1H 0BW, London, United Kingdom
| | - A V Nunes
- Instituto de Biociências, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Av. Costa e Silva - Pioneiros, MS, 79070-900, Campo Grande, Brazil
| | - E K L Batista
- National Research Center for Carnivores Conservation, Chico Mendes Institute for the Conservation of Biodiversity, Estrada Municipal Hisaichi Takebayashi 8600, Atibaia, 12952-011, São Paulo, Brazil
| | - G W Fernandes
- Evolutionary Ecology & Biodiversity (DGEE ICB) Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos, 6627 - Pampulha, Belo Horizonte, 31270-901, Minas Gerais, Brazil
| | - F Chiaravalloti-Neto
- Departamento de Epidemiologia, Faculdade de Saúde Pública da Universidade de São Paulo, Av. Dr. Arnaldo 715, 01246-904, São Paulo/SP, Brazil
| | - G A Damasceno-Junior
- Laboratório de Botânica/Laboratório de Ecologia Vegetal, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Av. Costa e Silva - Pioneiros, MS, 79070-900, Campo Grande, Brazil
| | - C N Berlinck
- National Research Center for Carnivores Conservation, Chico Mendes Institute for the Conservation of Biodiversity, Estrada Municipal Hisaichi Takebayashi 8600, Atibaia, 12952-011, São Paulo, Brazil
| | - F O Roque
- Instituto de Biociências, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Av. Costa e Silva - Pioneiros, MS, 79070-900, Campo Grande, Brazil; Centre for Tropical Environmental and Sustainability Science and College of Science and Engineering, James Cook University, 1 James Cook Dr, Douglas, Cairns, 4811, Queensland, Australia
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2
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Chen B, Wang Y, Huang J, Zhao L, Chen R, Song Z, Hu J. Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 864:160928. [PMID: 36539084 DOI: 10.1016/j.scitotenv.2022.160928] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/21/2022] [Accepted: 12/10/2022] [Indexed: 06/17/2023]
Abstract
Ozone (O3) is an important greenhouse gas in the atmosphere. Stratospheric ozone protects human beings, but high near-surface ozone concentrations threaten environment and human health. Owing to the uneven distribution of ground-monitoring stations and the low time resolution of polar orbiting satellites, it is difficult to accurately evaluate the refinement and synergistic pollution of near-surface ozone in China. Besides, atmospheric circulation patterns also affect ozone concentrations greatly. In this study, a new generation of geostationary satellite is used to estimate the hourly near-surface ozone concentration with a spatial resolution of 0.05°. First, the Pearson correlation coefficient and maximum information coefficient were used to study the correlation between the top of atmospheric radiation (TOAR) of Himawari-8 satellite and O3 concentration; seven TOAR channels were selected. Second, based on an interpretable deep learning model, the hourly ozone concentration in China from September 2015 to August 2021 was obtained using the TOAR-O3 model. Finally, the self-organizing map method was used to determine six major summer weather circulation patterns in China. The results showed that (1) the near-surface O3 concentration can be accurately estimated; the R2 (RMSE: μg/m3) values of the daily, monthly, and annual tenfold cross validation results were 0.91 (12.74), 0.97 (5.64), and 0.98 (1.75), respectively. The feature importance of the model showed that the temperature, TOAR, and boundary layer height contributed 38 %, 22 %, and 13 %, respectively. (2) The O3 concentration showed obvious spatiotemporal difference and gradually increased from 10:00 to 15:00 (Beijing time) every day. In most areas of China, O3 concentration had increased significantly. (3) The O3 concentration in northern China was the highest under the circulation pattern of the Meiyu front over the Yangtze River Delta, while in southern China, it was the highest under the circulation pattern of the northeast cold vortex controlling most of China.
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Affiliation(s)
- Bin Chen
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou 730000, China.
| | - Yixuan Wang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou 730000, China
| | - Jianping Huang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou 730000, China
| | - Lin Zhao
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou 730000, China
| | - Ruming Chen
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou 730000, China
| | - Zhihao Song
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou 730000, China
| | - Jiashun Hu
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou 730000, China
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3
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Zhan C, Jiang W, Min H, Gao Y, Tse CK. Human migration-based graph convolutional network for PM2.5 forecasting in post-COVID-19 pandemic age. Neural Comput Appl 2023; 35:6457-6470. [PMID: 36467631 PMCID: PMC9684777 DOI: 10.1007/s00521-022-07876-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 09/21/2022] [Indexed: 11/24/2022]
Abstract
Due to the coronavirus disease 2019 pandemic, local authorities always implanted non-pharmaceutical interventions, such as maintaining social distance to reduce human migration. Besides, previous studies have proved that human migration highly influenced air pollution concentration in an area. Therefore, this study aims to explore whether human migration can work as a significant factor in the post-pandemic age to help PM2.5 concentration forecasting. In this work, we first analyze the variations of PM2.5 in 11 cities of Hubei from 2015 to 2020 and further compare PM2.5 trends with the migration trends of Hubei province in 2020. Experimental results indicate that the human migration indirectly affected the urban PM2.5 concentration. Then, we established a graph data structure based on the migration network describing the migration flow size between any two areas in the Hubei province and proposed a migration attentive graph convolutional network (MAGCN) for forecasting PM2.5. Combined with the migration data. The proposed model can attentively aggregate the information of neighbor nodes through migration weights. Experimental results indicate that the proposed MAGCN can forecast PM2.5 concentration accurately.
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Affiliation(s)
- Choujun Zhan
- School of Computer, South China Normal University, Guangzhou, Guangdong China ,School of Electrical and Computer Engineering, Nanfang College Guangzhou, Guangzhou, Guangdong China
| | - Wei Jiang
- School of Electrical and Computer Engineering, Nanfang College Guangzhou, Guangzhou, Guangdong China
| | - Hu Min
- School of Electrical and Computer Engineering, Nanfang College Guangzhou, Guangzhou, Guangdong China
| | - Ying Gao
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong China
| | - C. K. Tse
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
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Puiu S, Udriștioiu MT, Velea L. Air Pollution Management: A Multivariate Analysis of Citizens' Perspectives and Their Willingness to Use Greener Forms of Transportation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14613. [PMID: 36361493 PMCID: PMC9656880 DOI: 10.3390/ijerph192114613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/03/2022] [Accepted: 11/05/2022] [Indexed: 06/16/2023]
Abstract
The present research aims to understand how air pollution can be managed by public authorities, both central and local, starting from citizens' perspectives on the issue. Air quality is a real problem, affecting people at multiple levels. Thus, we introduced the following variables to better understand the problem and to be able to formulate theoretical and practical implications for public management: the involvement of authorities in reducing air pollution; the involvement of citizens in reducing air pollution; financial incentives for citizens and companies for adopting behaviors that reduce air pollution; green investments in the city; the impact of air pollution on the community; and the need for independent bodies to monitor air pollution. The research methodology used is partial least squares structural equation modelling (PLS-SEM) and the required data were gathered from issuing a survey to citizens from the most important cities in Romania where pollution poses important challenges for the community and for the authorities. The results are useful to public managers in local and central institutions for creating better strategies meant to reduce air pollution, increase air quality, and improve the quality of the citizens' lives.
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Affiliation(s)
- Silvia Puiu
- Department of Management, Marketing and Business Administration, Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania
| | | | - Liliana Velea
- Department of Humanities, University Ca’Foscari, 30123 Venice, Italy
- National Meteorological Administration, Sos. Bucuresti-Ploiesti 97, Sect 1, 013686 București, Romania
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5
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Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning. ATMOSPHERE 2022. [DOI: 10.3390/atmos13081205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM2.5, and PM10) and meteorological factors in indoor and outdoor environments to discover patterns that allow predicting future confirmed cases of COVID-19 is a challenge within a long pandemic. In this study, a hybrid approach based on machine learning and deep learning is proposed to predict confirmed cases of COVID-19. On the one hand, a clustering algorithm based on K-means allows the discovery of behavior patterns by forming groups with high cohesion. On the other hand, multivariate linear regression is implemented through a long short-term memory (LSTM) neural network, building a reliable predictive model in the training stage. The LSTM prediction model is evaluated through error metrics, achieving the highest performance and accuracy in predicting confirmed cases of COVID-19, using data of PM2.5 and PM10 concentrations and meteorological factors of the outdoor environment. The predictive model obtains a root-mean-square error (RMSE) of 0.0897, mean absolute error (MAE) of 0.0837, and mean absolute percentage error (MAPE) of 0.4229 in the testing stage. When using a dataset of PM2.5, PM10, and meteorological parameters collected inside 20 households from 27 May to 13 October 2021, the highest performance is obtained with an RMSE of 0.0892, MAE of 0.0592, and MAPE of 0.2061 in the testing stage. Moreover, in the validation stage, the predictive model obtains a very acceptable performance with values between 0.4152 and 3.9084 for RMSE, and a MAPE of less than 4.1%, using three different datasets with indoor environment values.
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6
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Assessing the Relationship among Land Transfer, Fertilizer Usage, and PM2.5 Pollution: Evidence from Rural China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148387. [PMID: 35886238 PMCID: PMC9323440 DOI: 10.3390/ijerph19148387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022]
Abstract
Concern for environmental issues is a crucial component in achieving the goal of sustainable development of humankind. Different countries face various challenges and difficulties in this process, which require unique solutions. This study investigated the relationship between land transfer, fertilizer usage, and PM2.5 pollution in rural China from 2000 to 2019, considering their essential roles in agricultural development and overall national welfare. A cross section dependence test, unit root test, and cointegration test, among other methods, were used to test the panel data. A Granger causality test was used to determine the causal relationship between variables, and an empirical analysis of the impulse response and variance decomposition was carried out. The results show that the use of chemical fertilizers had a significant positive impact on PM2.5 pollution, but the impact of land transfer on PM2.5 pollution was negative. In addition, land transfer can reduce the use of chemical fertilizers through economies of scale, thus reducing air pollution. More specifically, for every 1% increase in fertilizer usage, PM2.5 increased by 0.17%, and for every 1% increase in land transfer rate, PM2.5 decreased by about 0.07%. The study on the causal relationship between land transfer, fertilizer usage, and PM2.5 pollution in this paper is helpful for exploring environmental change—they are supplements and innovations which are based on previous studies and provide policy-makers with a basis and inspiration for decision-making.
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7
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He C, Zhou W, Li T, Zhou T, Wang Y. East Asian summer monsoon enhanced by COVID-19. CLIMATE DYNAMICS 2022; 59:2965-2978. [PMID: 35382257 PMCID: PMC8969407 DOI: 10.1007/s00382-022-06247-8] [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: 11/01/2021] [Accepted: 03/07/2022] [Indexed: 06/14/2023]
Abstract
UNLABELLED Anthropogenic emissions decreased dramatically during the COVID-19 pandemic, but its possible effect on monsoon is unclear. Based on coupled models participating in the COVID Model Intercomparison Project (COVID-MIP), we show modeling evidence that the East Asian summer monsoon (EASM) is enhanced by 2.2% in terms of precipitation and by 5.4% in terms of the southerly wind at lower troposphere, and the amplitude of the forced response reaches about 1/3 of the standard deviation for interannual variability. The enhanced EASM during COVID-19 pandemic is a fast response to reduced aerosols, which is confirmed by the simulated response to the removal of all anthropogenic aerosols. The observational evidence, i.e., the anomalously strong EASM observed in 2020 and 2021, also supports the simulated enhancement of EASM. The essential mechanism for the enhanced EASM in response to COVID-19 is the enhanced zonal thermal contrast between Asian continent and the western North Pacific in the troposphere, due to the reduced aerosol concentration over Asian continent and the associated latent heating feedback. As the enhancement of EASM is a fast response to the reduction in aerosols, the effect of COVID-19 on EASM dampens soon after the rebound of emissions based on the models participating in COVID-MIP. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s00382-022-06247-8.
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Affiliation(s)
- Chao He
- Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
- Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China
| | - Wen Zhou
- Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China
| | - Tim Li
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center On Forecast of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
- International Pacific Research Center, Department of Atmospheric Sciences, University of Hawaii, Honolulu, USA
| | - Tianjun Zhou
- State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Yuhao Wang
- Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center On Forecast of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
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8
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Bhatti UA, Zeeshan Z, Nizamani MM, Bazai S, Yu Z, Yuan L. Assessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19. CHEMOSPHERE 2022; 288:132569. [PMID: 34655644 PMCID: PMC8514250 DOI: 10.1016/j.chemosphere.2021.132569] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/01/2021] [Accepted: 10/12/2021] [Indexed: 05/21/2023]
Abstract
Following the outbreak of the novel coronavirus in early 2020, to effectively prevent the spread of the disease, major cities across China suspended work and production. While the rest of the world struggles to control COVID-19, China has managed to control the pandemic rapidly and effectively with strong lockdown policies. This study investigates the change in air pollution (focusing on the air quality index (AQI), six ambient air pollutants nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), carbon monoxide (CO), particulate matter with aerodynamic diameters ≤10 μm (PM10) and ≤2.5 μm (PM2.5)) patterns for three periods: pre-COVID (from 1 January to May 30, 2019), active COVID (from 1 January to May 30, 2020) and post-COVID (from 1 January to May 30, 2021) in the Jiangsu province of China. Our findings reveal that the change in air pollution from pre-COVID to active COVID was greater than in previous years due to the government's lockdown policies. Post-COVID, air pollutant concentration is increasing. Mean change PM2.5 from pre-COVID to active COVID decreased by 18%; post-COVID it has only decreased by 2%. PM10 decreased by 19% from pre-COVID to active COVID, but post-COVID pollutant concentration has seen a 23% increase. Air pollutants show a positive correlation with COVID-19 cases among which PM2.5, PM10 and NO2 show a strong correlation during active COVID-19 cases. Metrological factors such as minimum temperature, average temperature and humidity show a positive correlation with COVID-19 cases while maximum temperature, wind speed and air pressure show no strong positive correlation. Although the COVID-19 pandemic had numerous negative effects on human health and the global economy, the reduction in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits; the government must implement policies to control post-COVID environmental issues.
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Affiliation(s)
- Uzair Aslam Bhatti
- School of Geography, Nanjing Normal University, Nanjing, 210023, China; Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, No. 1 Wenyuan Road, Nanjing, China
| | | | - Mir Muhammad Nizamani
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Sibghatullah Bazai
- School of Natural and Computational Sciences, Massey University, Auckland, 0632, New Zealand; Department of Computer Engineering, BUITEMS, Quetta 87300, Pakistan
| | - Zhaoyuan Yu
- School of Geography, Nanjing Normal University, Nanjing, 210023, China; Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, No. 1 Wenyuan Road, Nanjing, China
| | - Linwang Yuan
- School of Geography, Nanjing Normal University, Nanjing, 210023, China; Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, No. 1 Wenyuan Road, Nanjing, China.
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9
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Chen B, Song Z, Pan F, Huang Y. Obtaining vertical distribution of PM 2.5 from CALIOP data and machine learning algorithms. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 805:150338. [PMID: 34537706 DOI: 10.1016/j.scitotenv.2021.150338] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/10/2021] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
Aerosol optical depth (AOD) has been widely used to estimate the near-surface PM2.5 (fine particulate matter with particle size less than 2.5 μm). However, the total-column AOD obtained by passive remote sensing instruments can neither distinguish the contribution of AOD in various altitude layers nor obtain the vertical PM2.5 concentration. In this study, we compared several AOD-PM2.5 models including Extra Trees (ET), Random Forest (RF), Deep Neural Network (DNN), and Gradient Boosting Regression Tree (GBRT), and analyzed the corresponding results using AOD of different altitudes and auxiliary data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The results indicate that the ET model performs best in terms of the model effectiveness and feature interpretation on the training dataset. We conclude that the feature importance of the bottom layer AOD is higher than that of the upper and total column AOD. The results showed that regional differences existed in the optimal height of the AOD-PM2.5 correlation in study area. The results of cross-validation indicate that ET manages the most appealing overall performance with an R2 (RMSE) of 0.85 (17.77 μg/m3). Regarding the 729 sites involved in this study, 73% had R2 > 0.7, and the region or season with higher AOD feature importance achieves better model performance. The results of the AOD-PM2.5 model in each layer were corrected using the AOD weight, to obtained the PM2.5 vertical concentrations from 2015 to 2019. The results highlight that the high PM2.5 concentration area is primarily near the ground and decreases with height. Additionally, the PM2.5 vertical concentration in Beijing-Tianjin-Hebei (-1.80 μg/m3, P < 0.001), Central China (-1.62 μg/m3, P < 0.001), and Pearl River Delta (-0.66 μg/m3, P < 0.001) show an apparent downward trend. We believe that the vertical distribution analysis of PM2.5 can provide meaningful information for studying air pollution.
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Affiliation(s)
- Bin Chen
- College of Atmospheric Science, Lanzhou University, Lanzhou 730000, China.
| | - Zhihao Song
- College of Atmospheric Science, Lanzhou University, Lanzhou 730000, China
| | - Feng Pan
- College of Atmospheric Science, Lanzhou University, Lanzhou 730000, China
| | - Yue Huang
- College of Atmospheric Science, Lanzhou University, Lanzhou 730000, China
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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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11
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The Impact of Traffic and Meteorology on Urban Particle Mass and Particle Number Concentrations: Student-Led Studies Using Mobile Measurements before, during, and after the COVID-19 Pandemic Lockdowns. ATMOSPHERE 2021. [DOI: 10.3390/atmos13010062] [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
A series of student-led research activities were performed using a cargo bicycle equipped with air chemistry instrumentation to study the dynamics of aerosol particles in urban air before, during, and after the COVID-19 lockdown periods. The studies examined a high-traffic route and a low-traffic route around the city center of Münster, Germany. A complex picture emerged for how the particle number concentrations (PN) and particle mass concentrations (PM, specifically PM10) were affected by the day of the week, the route selected, the meteorological conditions, and the traffic intensity. Traffic had the most impact on PN through the multitude of exhaust plumes from motorized vehicles. The impact of traffic on PM10 was rather low, which is also mirrored in the general pattern of the low response of PM10 to the pandemic lockdown in Germany. Instead, PM10 responded more to the day of the week. Presumably, PM10 responds either to a specific traffic component, such as commercial low-duty vehicles, or to other business, such as construction activity. Meteorological conditions exert their impact mostly through the relative humidity, which affects particle growth. As such, future research could examine PN and PM10 responses over all seasons of the year. In any case, this student-led study in which high-resolution data were acquired deepened our understanding and broadened our view on particle dynamics in urban air. Well-trained university graduates will contribute to meeting further challenges in studying and combatting air pollution.
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12
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The Impact of Large-Scale Social Restriction Phases on the Air Quality Index in Jakarta. ATMOSPHERE 2021. [DOI: 10.3390/atmos12070922] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
We reported the result of our study on the impact of Large-Scale Social Restriction (LSSR) phases due to the COVID-19 outbreak on the air quality in Jakarta. Specifically, this study covered the change of Air Quality Index (AQI) based on five pollutants, PM10, SO2, CO, O3, and NO2, contained in Jakarta’s air before and during LSSR. The AQI data were provided by the Ministry of Environment and Forestry, Indonesia, from January 2019 to December 2020 at five different locations in Jakarta, with missing data for March and September 2020 due to unknown reasons. These data were grouped into the period before the LSSR from January 2019 to February 2020 and the period during LSSR from April 2020 to December 2020. In order to measure the change in the air quality of Jakarta before and during LSSR, we ran a chi-squared test to the AQI for each location and LSSR phase as well as paired one-sided t-test for the seasonal trend. The result of this study showed that, in general, LSSR improved the air quality of Jakarta. The improvement was mainly contributed by reduced transportation activities that were induced by LSSR. Further analysis on the seasonal pollutants trend showed a variation of AQI improvement in each phase due to their unique characteristics.
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13
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Air Pollution Observations in Selected Locations in Poland during the Lockdown Related to COVID-19. ATMOSPHERE 2021. [DOI: 10.3390/atmos12070806] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The coronavirus disease (COVID-19) has caused huge changes in people’s daily habits and had a significant impact on the economy. The lockdowns significantly reduced road traffic and meant that many people worked remotely. Therefore, the question arose as to how the reduced road traffic and stays of residents at home affected the degree of pollution and the structure of major air pollutants. To answer this question, the article presents an analysis of changes in typical air pollutants (PM10, PM2.5, NO2) in the five largest Polish cities and one of the voivodships. The data from the Polish State Environmental Monitoring were used for the analysis. The analysis showed that the period of the first lockdown in Poland (April 2020), despite the reduced road traffic, resulted in a significant increase in PM10 emissions (9–91% during working days and an average of 30% on Saturdays and Sundays), a slight increase in PM2.5 emissions (on average from 2% to 11% for all analyzed locations), and a reduction in NO2 emissions (on average from 6% to 11% for all analyzed locations) compared to the period before the lockdown. However, the changes were not homogeneous—in Łódź and Warsaw, in most cases, an increase in all analyzed pollutants was observed, and the greatest decrease in pollution took place in Małopolska voivodship (including Kraków). Comparing the data from April 2020 to the data from April 2019, the overall difference in the PMs concentrations was small, although there are places where there has been a significant decrease (Wrocław, Poznań), and there were also places where the concentration increased (Warsaw, Łódź, Małopolska). In the case of nitrogen dioxide, pollution concentration decreased in most locations. The only exception was the background stations in Warsaw, where the increase was 27%.
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Rahman MM, Paul KC, Hossain MA, Ali GGMN, Rahman MS, Thill JC. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:72420-72450. [PMID: 34786314 PMCID: PMC8545207 DOI: 10.1109/access.2021.3079121] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 05/07/2021] [Indexed: 05/19/2023]
Abstract
The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.
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Affiliation(s)
- Md. Mokhlesur Rahman
- The William States Lee College of EngineeringUniversity of North Carolina at CharlotteCharlotteNC28223USA
- Department of Urban and Regional PlanningKhulna University of Engineering and Technology (KUET)Khulna9203Bangladesh
| | - Kamal Chandra Paul
- Department of Electrical and Computer EngineeringThe William States Lee College of EngineeringUniversity of North Carolina at CharlotteCharlotteNC28223USA
| | - Md. Amjad Hossain
- Department of Computer Science, Mathematics and EngineeringShepherd UniversityShepherdstownWV25443USA
| | - G. G. Md. Nawaz Ali
- Department of Applied Computer ScienceUniversity of CharlestonCharlestonWV25304USA
| | - Md. Shahinoor Rahman
- Department of Earth and Environmental SciencesNew Jersey City UniversityJersey CityNJ07305USA
| | - Jean-Claude Thill
- Department of Geography and Earth SciencesSchool of Data ScienceUniversity of North Carolina at CharlotteCharlotteNC28223USA
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