Big data from population surveys and environmental monitoring-based machine learning predictions of indoor PM in 22 cities in China.

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.