Real-Time Estimation Models of Particulate Matter at Construction Sites for Environmental Monitoring

Excessive dust pollution, referred to as particulate matter (PM), remains a major health concern in the construction industry, affecting both workers and nearby communities. Traditional PM monitoring methods, including sensor-based monitoring, face significant limitations due to sparse spatial coverage arising from the limited number of sensors. Consequently, dust control measures, such as water spraying, are often applied inefficiently without precise targeting. To overcome the shortcomings, this study proposes a real-time PM estimation model, for both PM2.5 and PM10, specifically designed for construction environments. The model was developed through an analysis of the dispersion characteristics of construction-generated PM and the formulation of an estimation framework based on a modified inverse distance weighting (IDW) method that accounts for both wind direction and speed, enabling prediction of PM levels in areas lacking sensor coverage. The predicted PM concentrations were visualized using three-dimensional site maps that clearly delineate high-risk zones requiring dust mitigation. The applicability and robustness of the models were validated across three types of construction sites—road, bridge, and building—with a mean absolute error (MAE) of 3.18, mean absolute percentage error (MAPE) of 0.10, and root mean square error (RMSE) of 4.30 for PM2.5, and an MAE of 3.93, MAPE of 0.10, and RMSE of 5.58 for PM10. The proposed model provides site managers with actionable, site-wide insights, facilitating proactive and spatially optimized dust control strategies.