Partial Least Squares Regression Models for Outdoor Air Pollutant Forecasting

This is the authors' abstract. We don't add key points for this paper.

Atmospheric pollution presents a significant global health burden, yet the establishment and maintenance of extensive ‘gold standard’ monitoring networks remain prohibitively expensive. We explore the potential of machine learning to create virtual monitoring sites for predicting and reconstructing urban air pollutant concentrations, offering a cost‐effective alternative. Partial least squares (PLS) and support vector regression (SVR) models were developed and validated for daily concentrations of nitrogen dioxide (NO 2 ) and particulate matter (PM 2.5 and PM 10 ) within a well‐monitored central London area. Our methodology involved comprehensive data pre‐processing, including normalization, and rigorous model validation using RMSE, MSE, MAE, bias and R 2 metrics. A key finding was the superior predictive performance of PLS models incorporating meteorological data alongside pollutant concentrations, compared to models including land‐use data. Specifically, these PLS models exhibited lower error metrics across all pollutants, with NO 2 and PM 2.5 predictions showing R 2 = 0.891 and R 2 = 0.688, respectively, in the transformed space. Upon back‐transformation to original units (μg/m 3 ), the PLS models maintained high performance, particularly for PM 2.5 (RMSEP = 2.261, R 2 = 0.796). Whereas SVR models occasionally showed slightly higher R 2 values in the transformed space, their error metrics were consistently larger than those of PLS models, especially for PM 2.5 . This research underscores the effectiveness of PLS for accurate urban air pollutant prediction and highlights the critical influence of meteorological data over land‐use data in densely built‐up areas. The Air‐01 London dataset , a compilation of air pollutant, meteorological and land‐use data created for this study, has been made openly available to facilitate further research.