A comprehensive approach for calibrating anthropogenic effects on atmosphere degradation

Geonhwa You

Renewable and Sustainable Energy Reviews, 2024, vol. 191, issue C

Abstract: Climate change is the most pervasive threat to the ecosystem, species survival, global economy, and urban lives. Global citizens and governments have perceived the alleviation of harmful anthropogenic effects as an urgent priority. However, conflicting interests between interest groups preclude achieving a common goal. The convincing solutions fundamentally proceed from the precise calibration. Considerable efforts have established calibration models where population density and road transportation have been investigated as major proxies. The model components generally involve spatiotemporal characteristics, but the consideration of time-varying, spatially correlated attributes is insufficient. This article presents a pilot approach in a comprehensive manner; it includes a spatial feature transformation procedure called Kriging for spatial consistency and applying model-fitting and explanation techniques. In the case study, the regression models fit by OLS, Ridge, and Lasso showed analogous coefficients for SO2, NO2, CO, O3, PM2.5, and PM10 emissions, whereas the magnitudes and directions extracted by classification techniques such as ANN and XGBoost vary with emission intensity. This quantitative interpretation based on coefficients or weights could be incompatible with qualitative aspects. As an alternative, this article applied SHAP technique to XGBoost so that the discovery of multidirectional relationships complemented this incongruity. In conclusion, the model design needs to encompass the whole process from recognizing data properties to eliciting high accuracy and scientific proof for efficacious policies and schemes. Environmental action building on valid models would promise to alleviate climate risks and sustain lives.

Keywords: Air pollution prediction; Multidirectional relationship; Modern vehicle technologies; Population structure; Real road traffic; Spatial autocorrelation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2023.114226

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