Assessing the Impact of Straw Burning on PM 2.5 Using Explainable Machine Learning: A Case Study in Heilongjiang Province, China

Zehua Xu, Baiyin Liu (), Wei Wang, Zhimiao Zhang and Wenting Qiu
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Zehua Xu: Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Baiyin Liu: Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Wei Wang: Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Zhimiao Zhang: Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Wenting Qiu: Chinese Research Academy of Environmental Sciences, Beijing 100012, China

Sustainability, 2024, vol. 16, issue 17, 1-15

Abstract: Straw burning is recognized as a significant contributor to deteriorating air quality, but its specific impacts, particularly on PM 2.5 concentrations, are still not fully understood or quantified. In this study, we conducted a detailed examination of the spatial and temporal patterns of straw burning in Heilongjiang Province, China—a key agricultural area—utilizing high-resolution fire-point data from the Fengyun-3 satellite. We subsequently employed random forest (RF) models alongside Shapley Additive Explanations (SHAPs) to systematically evaluate the impact of various determinants, including straw burning (as indicated by crop fire-point data), meteorological conditions, and aerosol optical depth (AOD), on PM 2.5 levels across spatial and temporal dimensions. Our findings indicated a statistically nonsignificant downward trend in the number of crop fires in Heilongjiang Province from 2015 to 2023, with hotspots mainly concentrated in the western and southern parts of the province. On a monthly scale, straw burning was primarily observed from February to April and October to November—which are critical periods in the agricultural calendar—accounting for 97% of the annual fire counts. The RF models achieved excellent performance in predicting PM 2.5 levels, with R 2 values of 0.997 for temporal and 0.746 for spatial predictions. The SHAP analysis revealed the number of fire points to be the key determinant of temporal PM 2.5 variations during straw-burning periods, explaining 72% of the variance. However, the significance was markedly reduced in the spatial analysis. This study leveraged machine learning and interpretable modeling techniques to provide a comprehensive understanding of the influence of straw burning on PM 2.5 levels, both temporally and spatially. The detailed analysis offers valuable insights for policymakers to formulate more targeted and effective strategies to combat air pollution.

Keywords: straw burning; Fengyun-3; machine learning; interpretable model; PM 2.5 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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