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, 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, China.
Ecotoxicol Environ Saf. 2024 Nov 15;287:117285. doi: 10.1016/j.ecoenv.2024.117285. Epub 2024 Nov 5.
Many studies have confirmed that PM exposure can cause a variety of diseases. Because people spend most of their time indoors, exposure to PM in indoor environments is critical to population health. Large-population, long-term, continuous, and accurate indoor PM 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 concentration data in 22 typical air pollution cities in China from 2013 to 2017. The test R value of this model reached as high as 0.89, and the RMSE of the model was 9.13. The predicted annual indoor PM concentrations of the cities ranged from 54.6 μg/m to 82.7 μg/m, and showed a decreasing trend year by year. The pollution level exceeds the recommended AQG level of PM and has potential impact on human health. The results could take a breakthrough in obtaining accurate big data of indoor PM 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 PM big data, which could support the research of indoor PM and health.
许多研究证实，PM 暴露会导致多种疾病。由于人们大部分时间都在室内，因此接触室内环境中的 PM 对人口健康至关重要。由于难以大规模监测室内空气质量，因此大量、长期、连续且准确的室内 PM 数据非常重要，但却很稀缺。模型模拟提供了新的研究方向。在这项研究中，使用环境健康大数据构建了一个先进的机器学习模型，以预测 2013 年至 2017 年中国 22 个典型污染城市的每日室内 PM 浓度数据。该模型的测试 R 值高达 0.89，模型的 RMSE 为 9.13。这些城市的预测年室内 PM 浓度范围为 54.6μg/m 至 82.7μg/m，且呈逐年下降趋势。污染水平超过了 PM 的 AQG 推荐水平，对人体健康具有潜在影响。该结果有望在获取室内 PM 大数据方面取得突破，并为中国的室内空气质量和人类健康研究做出贡献。
