Work

Evaluating the impact of anthropogenic drivers and meteorological factors on air pollutants by explainable machine learning in Shandong Province, China

Year: 2025

Type: article

Abstract: Unexpected haze in North China Plain during the COVID-19 lockdown has been regarded as a natural window to explore the meteorological impact on formatting PM 2.5 pollution but with limitations in quantifying weather elements’ contributions. In this study, daily data of six air pollutants (including PM 2.5 , PM 10 , SO 2 , NO 2 , O 3 , and CO) and six meteorological factors (including temperature, pressure, relative humidity (RH), wind speed (WS), wind direction (WD), and precipitation) from 2015 to 2020 across 16 capital cities in Shandong province, China, was used to drive the Machine Learning and the SHapley Additive exPlanation (SHAP) models. By applying these models, contributions from anthropogenic drivers to pollutant reductions and contributions from meteorological factors to the haze event were investigated. Results show that the COVID-19 lockdown measures reduced concentrations of NO 2 , PM 2.5 , PM 10 , CO and SO 2 by −52.1 %, −40.0 %, −45.5 %, −29.4 % and −38.7 % respectively. On average, an 18.9 % increase in O 3 was observed. PM 2.5 pollution was mainly driven by temperature with a SHAP value of 19.7 μg/m 3 , followed by RH (5.8 μg/m 3 ), precipitation (0.9 μg/m 3 ), WD (0.3 μg/m 3 ), pressure (0.1 μg/m 3 ) and WS (0.1 μg/m 3 ) during the haze period. Relative to the post-haze period, high-pressure systems coupled with lower temperatures and weakened surface winds hindered the dispersion of PM 2.5 whilst higher RH was in favour of PM 2.5 production during the haze period. This study underscores the intricate interplay between emissions, meteorological conditions, and regulatory measures in air pollution, offering critical insights into future air quality management strategies by air pollution prediction. • Air pollutants and meteorological factors from 2015 to 2020 were used to drive Machine Learning and SHAP models. • Contributions from different drivers to pollutants variations were quantified during the COVID-19 lockdown. • Contributions of different meteorological factors to haze event were quantified in Shandong, China. • SHAP results agree well with the results of analyses disturbance of meteorological conditions during haze event.

Source: Atmospheric Pollution Research

DOI: 10.1016/j.apr.2025.102694