Assessment of human and meteorological influences on PM10 concentrations: Insights from machine learning algorithms

Abstract

Air pollution prediction assists residents in planning activities and avoiding regions with elevated pollution levels. This study employed Random Forest (RF), Extreme Gradient Boosting (XGB), and Histogram-Based Gradient Boosting (HistGB) based models to evaluate PM10 under 10-year average meteorological conditions (2012–2021) for Rocklea monitoring station in Queensland, Australia. The meteorological normalisation method adjusted air pollution data was assessed by removing weather-related effects to focus on the impact of human activities. By comparing observed and meteorologically normalised PM10 values, the impact of weather and human activities was assessed. The performance of these models was evaluated through k-Fold cross-validation, with a particular emphasis on their ability to predict PM10 concentrations accurately. Contrary to initial expectations, the RF model exhibited superior performance compared to XGB and HistGB, demonstrating the highest accuracy and consistency in predicting PM10 levels. This result underscores the effectiveness of the RF model in environmental air quality prediction. A key insight from our analysis was the role of weather-related factors in influencing PM10 levels. While the observed PM10 values showed a general decreasing trend from 2012 to 2021, except for an anomaly in 2019, the meteorologically normalised values remained relatively stable. This finding suggests that weather variations, rather than human activities, have been the more significant influence on the observed trend in PM10 levels over the past decade. The consistent meteorologically normalised values further reinforce the importance of considering natural meteorological variability in air quality studies.