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 scarc...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Ecotoxicology and Environmental Safety |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651324013617 |
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| author | Yanjun Du Yingying Zhang Yaoling Li Qiang Huang Yanwen Wang Qing Wang Runmei Ma Qinghua Sun Qin Wang Tiantian Li |
| author_facet | Yanjun Du Yingying Zhang Yaoling Li Qiang Huang Yanwen Wang Qing Wang Runmei Ma Qinghua Sun Qin Wang Tiantian Li |
| author_sort | Yanjun Du |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5ab6ad227a824d4ea32b10447dad73f6 |
| institution | Kabale University |
| issn | 0147-6513 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-5ab6ad227a824d4ea32b10447dad73f62024-11-21T06:02:03ZengElsevierEcotoxicology and Environmental Safety0147-65132024-11-01287117285Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in ChinaYanjun Du0Yingying Zhang1Yaoling Li2Qiang Huang3Yanwen Wang4Qing Wang5Runmei Ma6Qinghua Sun7Qin Wang8Tiantian Li9China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, China; Corresponding authors at: China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, ChinaChina CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China; National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, China; Corresponding authors at: China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, 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.http://www.sciencedirect.com/science/article/pii/S0147651324013617Big dataMachine learningIndoor airPM2.5 |
| spellingShingle | Yanjun Du Yingying Zhang Yaoling Li Qiang Huang Yanwen Wang Qing Wang Runmei Ma Qinghua Sun Qin Wang Tiantian Li Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China Ecotoxicology and Environmental Safety Big data Machine learning Indoor air PM2.5 |
| title | Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China |
| title_full | Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China |
| title_fullStr | Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China |
| title_full_unstemmed | Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China |
| title_short | Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM2.5 in 22 cities in China |
| title_sort | big data from population surveys and environmental monitoring based machine learning predictions of indoor pm2 5 in 22 cities in china |
| topic | Big data Machine learning Indoor air PM2.5 |
| url | http://www.sciencedirect.com/science/article/pii/S0147651324013617 |
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