Spatiotemporal distribution prediction for PM based on STXGBoost model and high-density monitoring sensors in Zhengzhou High Tech Zone, China.

The increasing demand for air pollution control has driven the application of low-cost sensors (LCS) in air quality monitoring, enabling higher observation density and improved air quality predictions. However, the inherent limitations in data quality from LCS necessitate the development of effective methodologies to optimize their application. This study established a hybrid framework to enhance the accuracy of spatiotemporal predictions of PM, standard instrument measurements were employed as reference data for the remote calibration of LCS. To account for local emission characteristics, the calibration model was trained using statistical values from LCS during periods of reduced anthropogenic emissions. This calibration approach significantly improved data quality, increasing R values of LCS data from 0.60 to 0.85. Subsequently, an advanced predictive model, STXGBoost, was developed, combining Kriging interpolation technology with high-density LCS data to integrate temporal trends and geographic spatial correlations. The STXGBoost model effectively captured the spatiotemporal variability of PM data, producing accurate and high spatiotemporal resolution PM prediction maps, with R values of 0.96, 0.92, and 0.89 for 1-h, 4-h, and 48-h predictions, respectively. These findings demonstrate the feasibility of generating high-resolution urban air pollution maps by integrating high-density ground monitoring data with advanced computational approaches. This framework provides valuable support for precise management and informed decision-making in urban atmospheric environments.