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利用机器学习研究美国密西西比州 COVID-19 封锁期间区域流动性对空气质量的影响

Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning.

机构信息

Department of Industrial Systems & Technology, Jackson State University, Jackson, MS 39217, USA.

Department of Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA.

出版信息

Int J Environ Res Public Health. 2023 May 31;20(11):6022. doi: 10.3390/ijerph20116022.

DOI:10.3390/ijerph20116022
PMID:37297626
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10252722/
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 (O), nitrogen oxide (NO), sulfur dioxide (SO), 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 NO, O, and CO ( < 0.05). Due to the lockdown, the mean concentrations decreased for NO and CO by -4.1 ppb and -0.088 ppm, respectively, while it increased for O 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.

摘要

社交距离措施和就地避难令限制了流动性和交通，这些措施是控制 COVID-19 快速传播的战略措施之一。在主要大都市区，公共交通的使用量估计减少了 50%至 90%。COVID-19 封锁的二次效应预计将改善空气质量，导致呼吸道疾病减少。本研究考察了 COVID-19 封锁期间美国密西西比州（MS）的流动性对空气质量的影响。选择该研究区域是因为其位于非大都市区和非工业区。从 2011 年到 2020 年，从美国环境保护署收集了空气污染物浓度-细颗粒物 2.5（PM2.5）、细颗粒物 10（PM10）、臭氧（O）、氮氧化物（NO）、二氧化硫（SO）和一氧化碳（CO）。由于数据可用性的限制，假设密西西比州杰克逊市的空气质量数据代表该州的整个地区。从美国国家海洋和大气管理局收集了天气数据（温度、湿度、压力、降水、风速和风向）。2020 年从谷歌获取了与交通相关的数据。使用 R 工作室的数据统计和机器学习工具来研究封锁期间空气质量是否发生了变化。天气归一化机器学习模型模拟了（BAU）预测，观察值和预测值的均值在 NO、O 和 CO 方面存在显著差异（<0.05）。由于封锁，NO 和 CO 的平均浓度分别下降了 4.1 ppb 和 0.088 ppm，而 O 的浓度则增加了 0.002 ppm。观察到的空气质量结果与观察到的交通减少 50.5%相吻合，这是基线的百分比变化，以及在封锁期间密西西比州哮喘发病率的观察下降。本研究证明了简单、易用和通用的分析工具的有效性和用途，这些工具可以帮助决策者估计大流行或自然灾害情况下的空气质量变化，并在检测到空气质量恶化时采取措施加以缓解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/163b7a76093c/ijerph-20-06022-g017.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/4cdff93d306c/ijerph-20-06022-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/d0e6a5876571/ijerph-20-06022-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/e86ab3724b13/ijerph-20-06022-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/9fe194ed733a/ijerph-20-06022-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/163b7a76093c/ijerph-20-06022-g017.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/363b69a66679/ijerph-20-06022-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/902fd29d9071/ijerph-20-06022-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/8c264cf9591e/ijerph-20-06022-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/f6dbfb50877f/ijerph-20-06022-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/702339623727/ijerph-20-06022-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/72214a1e24ce/ijerph-20-06022-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/4cdff93d306c/ijerph-20-06022-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/6ee29013be47/ijerph-20-06022-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/f5cf0c49e2cd/ijerph-20-06022-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/b36faec1448f/ijerph-20-06022-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/955e28023d42/ijerph-20-06022-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/d0e6a5876571/ijerph-20-06022-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/e86ab3724b13/ijerph-20-06022-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/9fe194ed733a/ijerph-20-06022-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2987/10252722/163b7a76093c/ijerph-20-06022-g017.jpg

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