Comparative Analysis of the Impact of Built Environment and Land Use on Monthly and Annual Mean PM<sub>2.5</sub> Levels
Urban planners are progressively recognizing the significant effects of the built environment and land use on PM<sub>2.5</sub> levels. However, in analyzing the drivers of PM<sub>2.5</sub> levels, researchers’ reliance on annual mean and seasonal means may overlook the monthl...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/6/682 |
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| Summary: | Urban planners are progressively recognizing the significant effects of the built environment and land use on PM<sub>2.5</sub> levels. However, in analyzing the drivers of PM<sub>2.5</sub> levels, researchers’ reliance on annual mean and seasonal means may overlook the monthly variations in PM<sub>2.5</sub> levels, potentially impeding accurate predictions during periods of high pollution. This study focuses on the area within the Sixth Ring Road of Beijing, China. It utilizes gridded monthly and annual mean PM<sub>2.5</sub> data from 2019 as the dependent variable. The research selects 33 independent variables from the perspectives of the built environment and land use. The Extreme Gradient Boosting (XGBoost) method is employed to reveal the driving impacts of the built environment and land use on PM<sub>2.5</sub> levels. To enhance the model accuracy and address the randomness in the division of training and testing sets, we conducted twenty comparisons for each month. We employed Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP) to interpret the models’ results and analyze the interactions between the explanatory variables. The results indicate that models incorporating both the built environment and land use outperformed those that considered only a single aspect. Notably, in the test set for April, the R<sup>2</sup> value reached up to 0.78. Specifically, the fitting accuracy for high pollution months in February, April, and November is higher than the annual mean, while July shows the opposite trend. The coefficient of variation for the importance rankings of the seven key explanatory variables exceeds 30% for both monthly and annual means. Among these variables, building density exhibited the highest coefficient of variation, at 123%. Building density and parking lots density demonstrate strong explanatory power for most months and exhibit significant interactions with other variables. Land use factors such as wetlands fraction, croplands fraction, park and greenspace fraction, and forests fraction have significant driving effects during the summer and autumn seasons months. The research on time scales aims to more effectively reduce PM<sub>2.5</sub> levels, which is essential for developing refined urban planning strategies that foster healthier urban environments. |
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| ISSN: | 2073-4433 |