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Babaan J, Wong PY, Chen PC, Chen HL, Lung SCC, Chen YC, Wu CD. Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121198. [PMID: 38772239 DOI: 10.1016/j.jenvman.2024.121198] [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: 02/20/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 05/23/2024]
Abstract
Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.
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Affiliation(s)
- Jennieveive Babaan
- Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City, Philippines
| | - Pei-Yi Wong
- Department of Environmental and Occupational Health, National Cheng Kung University, Tainan City, Taiwan
| | - Pau-Chung Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan; Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, Taipei City, Taiwan; Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei City, Taiwan; Department of Public Health, National Taiwan University College of Public Health, Taipei City, Taiwan
| | - Hsiu-Ling Chen
- Department of Food Safety/Hygiene and Risk Management, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Shih-Chun Candice Lung
- Research Center for Environmental Changes, Academia Sinica, Taipei City, Taiwan; Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan; Institute of Environmental Health, School of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Cheng Chen
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Chih-Da Wu
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan; Department of Geomatics, National Cheng Kung University, Tainan City, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung City, Taiwan; Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
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Lee HW, Lee HJ, Oh S, Lee JK, Heo EY, Kim DK. Combined effect of changes in NO 2, O 3, PM 2.5, SO 2 and CO concentrations on small airway dysfunction. Respirology 2024; 29:379-386. [PMID: 38378265 DOI: 10.1111/resp.14687] [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: 09/19/2023] [Accepted: 01/30/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND AND OBJECTIVE When multiple complex air pollutants are combined in real-world settings, the reliability of estimating the effect of a single pollutant is questionable. This study aimed to investigate the combined effects of changes in air pollutants on small airway dysfunction (SAD). METHODS We analysed Korea National Health and Nutrition Examination Survey (KNHANES) V-VIII database from 2010 to 2018 to elucidate the associations between annual changes in air pollutants over a previous 5-year period and small airway function. We estimated the annual concentrations of five air pollutants: NO2, O3, PM2.5, SO2 and CO. Forced expiratory flow between 25% and 75% of vital capacity (FEF25%-75%) <65% was defined as SAD. Using the quantile generalized-Computation (g-Computation) model, the combined effect of the annual changes in different air pollutants was estimated. RESULTS A total of 29,115 individuals were included. We found significant associations between SAD and the quartiles of annual changes in NO2 (OR = 1.10, 95% CI = 1.08-1.12), O3 (OR = 1.03, 95% CI = 1.00-1.05), PM2.5 (OR = 1.03, 95% CI = 1.00-1.05), SO2 (OR = 1.04, 95% CI = 1.02-1.08) and CO (OR = 1.16, 95% CI = 1.12-1.19). The combined effect of the air pollutant changes was significantly associated with SAD independent of smoking (OR = 1.31, 95% CI = 1.26-1.35, p-value <0.001), and this trend was consistently observed across the entire study population and various subgroup populations. As the estimated risk of SAD, determined by individual-specific combined effect models, increased and the log odds for SAD increased linearly. CONCLUSION The combined effect of annual changes in multiple air pollutant concentrations were associated with an increased risk of SAD.
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Affiliation(s)
- Hyun Woo Lee
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Hyo Jin Lee
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Sohee Oh
- Medical Research Collaborating Center, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Jung-Kyu Lee
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Eun Young Heo
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Deog Kyeom Kim
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
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Alari A, Ranzani O, Olmos S, Milà C, Rico A, Ballester J, Basagaña X, Dadvand P, Duarte-Salles T, Nieuwenhuijsen M, Vivanco-Hidalgo RM, Tonne C. Short-term exposure to air pollution and hospital admission after COVID-19 in Catalonia: the COVAIR-CAT study. Int J Epidemiol 2024; 53:dyae041. [PMID: 38514998 DOI: 10.1093/ije/dyae041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 03/01/2024] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19. METHODS The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240 902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7 days. RESULTS Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant. CONCLUSIONS Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization.
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Affiliation(s)
- Anna Alari
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Otavio Ranzani
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Sergio Olmos
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Carles Milà
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Alex Rico
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Joan Ballester
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
| | - Xavier Basagaña
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Payam Dadvand
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Mark Nieuwenhuijsen
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | | | - Cathryn Tonne
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
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Li Y, Li R. A hybrid model for daily air quality index prediction and its performance in the face of impact effect of COVID-19 lockdown. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2023; 176:673-684. [PMID: 37350802 PMCID: PMC10264166 DOI: 10.1016/j.psep.2023.06.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 05/22/2023] [Accepted: 06/06/2023] [Indexed: 06/24/2023]
Abstract
Accurate and dependable air quality forecasting is critical to environmental and human health. However, most methods usually aim to improve overall prediction accuracy but neglect the accuracy for unexpected incidents. In this study, a hybrid model was developed for air quality index (AQI) forecasting, and its performance during COVID-19 lockdown was analyzed. Specifically, the variational mode decomposition (VMD) was employed to decompose the original AQI sequence into some subsequences with the parameters optimized by the Whale optimization algorithm (WOA), and the residual sequence was further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On this basis, a deep learning method bidirectional long short-term memory coupled with added time filter layer and attention mechanism (TFA-BiLSTM) was employed to explore the latent dynamic characteristics of each subsequence. This WOA-VMD-CEEMDAN-TFA-BiLSTM hybrid model was used to forecast AQI values for four cities in China, and results verified that the accuracy of the hybrid model outperformed other proposed models, achieving R2 values of 0.96-0.97. In addition, the improvement in MAE (34.71-49.65%) and RMSE (32.82-48.07%) were observed over single decomposition-based model. Notably, during the epidemic lockdown period, the hybrid model had significant superiority over other proposed models for AQI prediction.
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Affiliation(s)
- Yuting Li
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, PR China
| | - Ruying Li
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, PR China
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Tuluri F, Remata R, Walters WL, Tchounwou PB. Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6022. [PMID: 37297626 PMCID: PMC10252722 DOI: 10.3390/ijerph20116022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants-particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)-were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by -4.1 ppb and -0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by -50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.
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Affiliation(s)
- Francis Tuluri
- Department of Industrial Systems & Technology, Jackson State University, Jackson, MS 39217, USA
| | - Reddy Remata
- Department of Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA;
| | - Wilbur L. Walters
- College of Sciences, Engineering & Technology, Jackson State University, Jackson, MS 39217, USA;
| | - Paul B. Tchounwou
- RCMI Center for Health Disparities Research, Jackson State University, Jackson, MS 39217, USA
- RCMI Center for Urban Health Disparities Research and Innovation, Morgan State University, Baltimore, MD 21251, USA
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6
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Ajagbe SA, Adigun MO. Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-35. [PMID: 37362693 PMCID: PMC10226029 DOI: 10.1007/s11042-023-15805-z] [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: 12/17/2022] [Revised: 03/06/2023] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps in the timely detection of any infectious disease (IDs) and is essential to the management of diseases and the prediction of future occurrences. Many scientists and scholars have implemented DL techniques for the detection and prediction of pandemics, IDs and other healthcare-related purposes, these outcomes are with various limitations and research gaps. For the purpose of achieving an accurate, efficient and less complicated DL-based system for the detection and prediction of pandemics, therefore, this study carried out a systematic literature review (SLR) on the detection and prediction of pandemics using DL techniques. The survey is anchored by four objectives and a state-of-the-art review of forty-five papers out of seven hundred and ninety papers retrieved from different scholarly databases was carried out in this study to analyze and evaluate the trend of DL techniques application areas in the detection and prediction of pandemics. This study used various tables and graphs to analyze the extracted related articles from various online scholarly repositories and the analysis showed that DL techniques have a good tool in pandemic detection and prediction. Scopus and Web of Science repositories are given attention in this current because they contain suitable scientific findings in the subject area. Finally, the state-of-the-art review presents forty-four (44) studies of various DL technique performances. The challenges identified from the literature include the low performance of the model due to computational complexities, improper labeling and the absence of a high-quality dataset among others. This survey suggests possible solutions such as the development of improved DL-based techniques or the reduction of the output layer of DL-based architecture for the detection and prediction of pandemic-prone diseases as future considerations.
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Affiliation(s)
- Sunday Adeola Ajagbe
- Department of Computer & Industrial Production Engineering, First Technical University Ibadan, Ibadan, 200255 Nigeria
- Department of Computer Science, University of Zululand, Kwadlangezwa, 3886 South Africa
| | - Matthew O. Adigun
- Department of Computer Science, University of Zululand, Kwadlangezwa, 3886 South Africa
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Yang J, Ji Q, Pu H, Dong X, Yang Q. How does COVID-19 lockdown affect air quality: Evidence from Lanzhou, a large city in Northwest China. URBAN CLIMATE 2023; 49:101533. [PMID: 37122825 PMCID: PMC10121109 DOI: 10.1016/j.uclim.2023.101533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/04/2023] [Accepted: 04/14/2023] [Indexed: 05/03/2023]
Abstract
Coronavirus disease (COVID-19) has disrupted health, economy, and society globally. Thus, many countries, including China, have adopted lockdowns to prevent the epidemic, which has limited human activities while affecting air quality. These affects have received attention from academics, but very few studies have focused on western China, with a lack of comparative studies across lockdown periods. Accordingly, this study examines the effects of lockdowns on air quality and pollution, using the hourly and daily air monitoring data collected from Lanzhou, a large city in Northwest China. The results indicate an overall improvement in air quality during the three lockdowns compared to the average air quality in the recent years, as well as reduced PM2.5, PM10, SO2, NO2, and CO concentrations with different rates and increased O3 concentration. During lockdowns, Lanzhou's "morning peak" of air pollution was alleviated, while the spatial characteristics remained unchanged. Further, ordered multi-classification logistic regression models to explore the mechanisms by which socioeconomic backgrounds and epidemic circumstances influence air quality revealed that the increment in population density significantly aggravated air pollution, while the presence of new cases in Lanzhou, and medium- and high-risk areas in the given district or county both increase the likelihood of air quality improvement in different degrees. These findings contribute to the understanding of the impact of lockdown on air quality, and propose policy suggestions to control air pollution and achieve green development in the post-epidemic era.
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Affiliation(s)
- Jianping Yang
- State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
| | - Qin Ji
- State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hongzheng Pu
- School of Management, Chongqing University of Technology, Chongqing 400054, China
| | - Xinyang Dong
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China
| | - Qin Yang
- State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
- University of Chinese Academy of Sciences, Beijing, China
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Wang Y, Ge Q. The positive impact of the Omicron pandemic lockdown on air quality and human health in cities around Shanghai. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2023:1-26. [PMID: 37362999 PMCID: PMC9975847 DOI: 10.1007/s10668-023-03071-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 02/21/2023] [Indexed: 06/28/2023]
Abstract
The Omicron pandemic broke out in Shanghai in March 2022, and some infected people spread to some cities in the Yangtze River Delta (YRD) region. To achieve the dynamic zero-COVID target as soon as possible, Shanghai and nine cities that were heavily affected by Shanghai implemented the lockdown measures. This paper aims to quantify the impact of the lockdown on air quality and human health. A difference-in-difference (DID) model was first used to measure the impact of the lockdown on air quality in these ten cities. Based on the results of the DID model, we estimated the PM2.5-related health and economic benefits using the concentration-response function and the value of statistical life method. Results showed that the lockdown has reduced the concentrations of PM2.5, PM10, SO2, NO2, and CO by 9.87 μg/m3, 17.31 μg/m3, 0.75 μg/m3, 9.03 μg/m3, and 0.07 mg/m3, respectively. The number of avoided premature deaths due to PM2.5 reduction was estimated to be 35,342. The resulting economic benefits totaled 18.86 billion US dollars. We investigated the reasons for the air quality improvement in these ten cities and found the "3 + 11" policy has had a great impact on air quality. Compared with the first COVID-19 lockdown in early 2020, the effect of the lockdown in 2022 was smaller. These findings demonstrated that reductions in anthropogenic emissions would achieve substantial air quality improvement and health benefits. This paper re-emphasized continuous efforts to improve air quality are essential to protect public health.
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Affiliation(s)
- Yu Wang
- Business School, University of Shanghai for Science and Technology, 334 Jungong Rd, Shanghai, 200093 People’s Republic of China
| | - Qingqing Ge
- College of Business, Yancheng Teachers University, 2 South Hope Avenue, Yancheng, 224051 People’s Republic of China
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Wu WL, Shan CY, Liu J, Zhao JL, Long JY. Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20054199. [PMID: 36901210 PMCID: PMC10002059 DOI: 10.3390/ijerph20054199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 06/03/2023]
Abstract
This study aimed to analyze the main factors influencing air quality in Tangshan during COVID-19, covering three different periods: the COVID-19 period, the Level I response period, and the Spring Festival period. Comparative analysis and the difference-in-differences (DID) method were used to explore differences in air quality between different stages of the epidemic and different years. During the COVID-19 period, the air quality index (AQI) and the concentrations of six conventional air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3-8h) decreased significantly compared to 2017-2019. For the Level I response period, the reduction in AQI caused by COVID-19 control measures were 29.07%, 31.43%, and 20.04% in February, March, and April of 2020, respectively. During the Spring Festival, the concentrations of the six pollutants were significantly higher than those in 2019 and 2021, which may be related to heavy pollution events caused by unfavorable meteorological conditions and regional transport. As for the further improvement in air quality, it is necessary to take strict measures to prevent and control air pollution while paying attention to meteorological factors.
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Schatke M, Meier F, Schröder B, Weber S. Impact of the 2020 COVID-19 lockdown on NO 2 and PM 10 concentrations in Berlin, Germany. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2022; 290:119372. [PMID: 36092472 PMCID: PMC9450488 DOI: 10.1016/j.atmosenv.2022.119372] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 05/27/2023]
Abstract
In March 2020, the World Health Organization declared a pandemic due to the rapid and worldwide spread of the SARS-CoV-2 virus. To prevent spread of the infection social contact restrictions were enacted worldwide, which suggest a significant effect on the anthropogenic emission of gaseous and particulate pollutants in urban areas. To account for the influence of meteorological conditions on airborne pollutant concentrations, we used a Random Forest machine learning technique for predicting business as usual (BAU) pollutant concentrations of NO2 and PM10 at five observation sites in the city of Berlin, Germany, during the 2020 COVID-19 lockdown periods. The predictor variables were based on meteorological and traffic data from the period of 2017-2019. The differences between BAU and observed concentrations were used to quantify lockdown-related effects on average pollutant concentrations as well as spatial variation between individual observation sites. The comparison between predicted and observed concentrations documented good overall model performance for different evaluation periods, but better performance for NO2 (R2 = 0.72) than PM10 concentrations (R2 = 0.35). The average decrease of NO2 was 21.9% in the spring lockdown and 22.3% in the winter lockdown in 2020. PM10 concentrations showed a smaller decrease, with an average of 12.8% in the spring as well as the winter lockdown. The model results were found sensitive to depict local variation of pollutant reductions at the different sites that were mainly related to locally varying modifications in traffic intensity.
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Affiliation(s)
- Mona Schatke
- Climatology and Environmental Meteorology, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, 38106, Braunschweig, Germany
| | - Fred Meier
- Chair of Climatology, Institute of Ecology, Technische Universität Berlin, Rothenburgstraße 12, 12165, Berlin, Germany
| | - Boris Schröder
- Landscape Ecology and Environmental Systems Analysis, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, 38106, Braunschweig, Germany
- Berlin-Brandenburg Institute of Advanced Biodiversity Research BBIB, Altensteinstraße 6, D, 14195, Berlin, Germany
| | - Stephan Weber
- Climatology and Environmental Meteorology, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, 38106, Braunschweig, Germany
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Zhao L, Wang Y, Zhang H, Qian Y, Yang P, Zhou L. Diverse spillover effects of COVID-19 control measures on air quality improvement: evidence from typical Chinese cities. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2022; 25:7075-7099. [PMID: 35493768 PMCID: PMC9035376 DOI: 10.1007/s10668-022-02353-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 04/05/2022] [Indexed: 06/03/2023]
Abstract
The COVID-19 prevention and control measures are taken by China's government, especially traffic restrictions and production suspension, had spillover effects on air quality improvement. These effects differed among cities, but these differences have not been adequately studied. To provide more knowledge, we studied the air quality index (AQI) and five air pollutants (PM2.5, PM10, SO2, NO2, and O3) before and after the COVID-19 outbreak in Shanghai, Wuhan, and Tangshan. The pollution data from two types of monitoring stations (traffic and non-traffic stations) were separately compared and evaluated. We used monitoring data from the traffic stations to study the emission reduction caused by traffic restrictions. Based on monitoring data from the non-traffic stations, we established a difference-in-difference model to study the emission reduction caused by production suspension. The COVID-19 control measures reduced AQI and the concentrations of all pollutants except O3 (which increased greatly), but the magnitude of the changes differed among the three cities. The control measures improved air quality most in Wuhan, followed by Shanghai and then Tangshan. We investigated the reasons for these differences and found that differences in the characteristics of these three types of cities could explain these differences in spillover effects. Understanding these differences could provide some guidance and support for formulating differentiated air pollution control measures in different cities. For example, whole-process emission reduction technology should be adopted in cities with the concentrated distribution of continuous process enterprises, whereas vehicles that use cleaner energy and public transport should be vigorously promoted in cities with high traffic development level.
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Affiliation(s)
- Laijun Zhao
- Business School, University of Shanghai for Science and Technology, 334 Jungong Rd, Shanghai, 200093 People’s Republic of China
| | - Yu Wang
- Business School, University of Shanghai for Science and Technology, 334 Jungong Rd, Shanghai, 200093 People’s Republic of China
| | - Honghao Zhang
- Business School, University of Shanghai for Science and Technology, 334 Jungong Rd, Shanghai, 200093 People’s Republic of China
| | - Ying Qian
- Business School, University of Shanghai for Science and Technology, 334 Jungong Rd, Shanghai, 200093 People’s Republic of China
| | - Pingle Yang
- Business School, University of Shanghai for Science and Technology, 334 Jungong Rd, Shanghai, 200093 People’s Republic of China
| | - Lixin Zhou
- Business School, University of Shanghai for Science and Technology, 334 Jungong Rd, Shanghai, 200093 People’s Republic of China
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