Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning.
Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning.
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DOI：
10.3390/ijerph20116022
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发表时间：
2023-05-31
影响因子：
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通讯作者：
Tchounwou, Paul B
中科院分区：
文献类型：
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作者：
Tuluri, Francis;Remata, Reddy;Walters, Wilbur L;Tchounwou, Paul B
为控制COVID-19的迅速传播，我们采取了策略性措施，包括社交距离措施及就地庇护令，以限制流动及交通。在主要大都市地区，交通使用量估计减少了50%至90%。COVID-19封城的次级效应预期将改善空气质量，导致呼吸道疾病减少。本研究考察了美国密西西比州（MS）COVID-19封锁期间流动性对空气质量的影响。选择研究区域是因为其非大都市和非工业环境。从2011年到2020年，从美国环境保护局收集了空气污染物-颗粒物2.5（PM2.5），颗粒物10（PM10），臭氧（O3），氮氧化物（NO2），二氧化硫（SO2）和一氧化碳（CO）的浓度。由于数据可用性的限制，MS杰克逊的空气质量数据被假定为代表该州的整个地区。气象数据（温度、湿度、压力、降水、风速和风向）收集自美国国家海洋和大气管理局。2020年与交通相关的数据（交通）来自谷歌。我们使用R Studio的统计和机器学习工具处理数据，以研究封锁期间空气质量的变化（如有）。模拟业务情景（BAU）的天气标准化机器学习模型预测NO2，O3和CO的观测值和预测值的平均值存在显著差异（p <0.05）。由于封锁，NO2和CO的平均浓度分别下降了− 4.1 ppb和− 0.088 ppm，而O3的平均浓度则增加了0.002 ppm。观察到的和预测的空气质量结果与观察到的过境减少-50.5%（基线变化百分比）以及在封锁期间观察到的MS哮喘患病率下降一致。本研究证明了简单、方便和通用的分析工具的有效性和使用，以帮助政策制定者在大流行或自然灾害的情况下估计空气质量的变化，并在检测到空气质量恶化时采取缓解措施。
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.