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Particulate matter (PM) pollution is a crucial environmental issue. Considering its adverse health impacts, especially on children's immune systems, Korean regulations require annual PM2.5 measurements in daycare centers. Therefore, we developed a low-cost PM2.5 sensor calibration model for measuring the indoor PM concentrations in daycare centers using long short-term memory (LSTM) algorithms. Moreover, we trained the model to predict the PM2.5 based on temperature and humidity, and optimized its hyperparameters. The model achieved a high accuracy and outperformed traditional calibration methods. The optimal lookback period was 76, which led to a high calibration performance with root mean and mean squared errors, a coefficient of determination, and mean absolute errors of 3.57 and 12.745, 0.962, and 2.7, respectively. The LSTM model demonstrated a better calibration performance than those of the linear (r2 = 0.57) and multiple (r2 = 0.75) linear regression models. The developed calibration model provided precise short-term measurement values for the optimal management of indoor PM concentrations. This methodology can be applied to similar environments to obtain new learning and hyper-parameters. Our results will aid in improving the accuracy of low-cost sensors for measuring indoor PM concentrations, thereby providing cost-effective solutions for enhancing children's health and well-being in daycare centers and other multiuse facilities. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) pollution is a crucial environmental issue. Considering its adverse health impacts, especially on children’s immune systems, Korean regulations require annual PM2.5 measurements in daycare centers. Therefore, we developed a low-cost PM2.5 sensor calibration model for measuring the indoor PM concentrations in daycare centers using long short-term memory (LSTM) algorithms. Moreover, we trained the model to predict the PM2.5 based on temperature and humidity, and optimized its hyperparameters. The model achieved a high accuracy and outperformed traditional calibration methods. The optimal lookback period was 76, which led to a high calibration performance with root mean and mean squared errors, a coefficient of determination, and mean absolute errors of 3.57 and 12.745, 0.962, and 2.7, respectively. The LSTM model demonstrated a better calibration performance than those of the linear (r2 = 0.57) and multiple (r2 = 0.75) linear regression models. The developed calibration model provided precise short-term measurement values for the optimal management of indoor PM concentrations. This methodology can be applied to similar environments to obtain new learning and hyper-parameters. Our results will aid in improving the accuracy of low-cost sensors for measuring indoor PM concentrations, thereby providing cost-effective solutions for enhancing children’s health and well-being in daycare centers and other multiuse facilities.