{"title":"基于人口调查大数据和环境监测的机器学习预测中国 22 个城市的室内 PM2.5","authors":"","doi":"10.1016/j.ecoenv.2024.117285","DOIUrl":null,"url":null,"abstract":"<div><div>Many studies have confirmed that PM<sub>2.5</sub> exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM<sub>2.5</sub> in indoor environments is critical to population health. Large-population, long-term, continuous, and accurate indoor PM<sub>2.5</sub> data are important but scarce because of the difficulties in monitoring the indoor air quality on a large scale. Model simulation provides a new research direction. In this study, an advanced machine learning model was constructed using environmental health big data to predict the daily indoor PM<sub>2.5</sub> concentration data in 22 typical air pollution cities in China from 2013 to 2017. The test R<sup>2</sup> value of this model reached as high as 0.89, and the RMSE of the model was 9.13. The predicted annual indoor PM<sub>2.5</sub> concentrations of the cities ranged from 54.6 μg/m<sup>3</sup> to 82.7 μg/m<sup>3</sup>, and showed a decreasing trend year by year. The pollution level exceeds the recommended AQG level of PM<sub>2.5</sub> and has potential impact on human health. The results could take a breakthrough in obtaining accurate big data of indoor PM<sub>2.5</sub> and contribute to research on the indoor air quality and human health in China.</div></div><div><h3>Synopsis</h3><div>This study established a machine learning model and predicted indoor PM<sub>2.5</sub> big data, which could support the research of indoor PM<sub>2.5</sub> and health.</div></div>","PeriodicalId":303,"journal":{"name":"Ecotoxicology and Environmental Safety","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China\",\"authors\":\"\",\"doi\":\"10.1016/j.ecoenv.2024.117285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Many studies have confirmed that PM<sub>2.5</sub> exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM<sub>2.5</sub> in indoor environments is critical to population health. Large-population, long-term, continuous, and accurate indoor PM<sub>2.5</sub> data are important but scarce because of the difficulties in monitoring the indoor air quality on a large scale. Model simulation provides a new research direction. In this study, an advanced machine learning model was constructed using environmental health big data to predict the daily indoor PM<sub>2.5</sub> concentration data in 22 typical air pollution cities in China from 2013 to 2017. The test R<sup>2</sup> value of this model reached as high as 0.89, and the RMSE of the model was 9.13. The predicted annual indoor PM<sub>2.5</sub> concentrations of the cities ranged from 54.6 μg/m<sup>3</sup> to 82.7 μg/m<sup>3</sup>, and showed a decreasing trend year by year. The pollution level exceeds the recommended AQG level of PM<sub>2.5</sub> and has potential impact on human health. The results could take a breakthrough in obtaining accurate big data of indoor PM<sub>2.5</sub> and contribute to research on the indoor air quality and human health in China.</div></div><div><h3>Synopsis</h3><div>This study established a machine learning model and predicted indoor PM<sub>2.5</sub> big data, which could support the research of indoor PM<sub>2.5</sub> and health.</div></div>\",\"PeriodicalId\":303,\"journal\":{\"name\":\"Ecotoxicology and Environmental Safety\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecotoxicology and Environmental Safety\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0147651324013617\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecotoxicology and Environmental Safety","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0147651324013617","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China
Many studies have confirmed that PM2.5 exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM2.5 in indoor environments is critical to population health. Large-population, long-term, continuous, and accurate indoor PM2.5 data are important but scarce because of the difficulties in monitoring the indoor air quality on a large scale. Model simulation provides a new research direction. In this study, an advanced machine learning model was constructed using environmental health big data to predict the daily indoor PM2.5 concentration data in 22 typical air pollution cities in China from 2013 to 2017. The test R2 value of this model reached as high as 0.89, and the RMSE of the model was 9.13. The predicted annual indoor PM2.5 concentrations of the cities ranged from 54.6 μg/m3 to 82.7 μg/m3, and showed a decreasing trend year by year. The pollution level exceeds the recommended AQG level of PM2.5 and has potential impact on human health. The results could take a breakthrough in obtaining accurate big data of indoor PM2.5 and contribute to research on the indoor air quality and human health in China.
Synopsis
This study established a machine learning model and predicted indoor PM2.5 big data, which could support the research of indoor PM2.5 and health.
期刊介绍:
Ecotoxicology and Environmental Safety is a multi-disciplinary journal that focuses on understanding the exposure and effects of environmental contamination on organisms including human health. The scope of the journal covers three main themes. The topics within these themes, indicated below, include (but are not limited to) the following: Ecotoxicology、Environmental Chemistry、Environmental Safety etc.