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Assessing the Effects of Urban Morphology Parameters on PM 2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method

Author

Listed:
  • Peng Cui

    (School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

  • Chunyu Dai

    (School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

  • Jun Zhang

    (School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

  • Tingting Li

    (School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

Abstract

The dispersion of urban pollutants is affected by the urban morphology parameters. The objective of this study was to investigate the correlation between PM 2.5 distribution and urban morphology parameters in a cold-climate city in China. Field measurements were performed to record the PM 2.5 concentration and microclimate parameters at 25 points in a 10 km 2 urban area in Harbin, China. It was found that the maximum difference of PM 2.5 concentration among the measuring points at the same time could be up to 69.03 μg/m 3 . In this study, a geographic information system (GIS) was used to extract and screen the urban morphology parameter data under reasonable buffer radius, the gradient boosted regression trees model (GBRT) was used to carry out the prediction experiment of PM 2.5 concentration and explore the nonlinear influence of urban morphology factors on PM 2.5 concentration. In addition, random forest (RF), decision trees (DT), and multiple linear regression (MLR) models were selected to compare the prediction accuracy of the GBRT model. The results show that the GBRT model has the highest accuracy, with R 2 reaching 0.981; building density (57%) and average building height (49%) were the two most significant factors affecting PM 2.5 concentration.

Suggested Citation

  • Peng Cui & Chunyu Dai & Jun Zhang & Tingting Li, 2022. "Assessing the Effects of Urban Morphology Parameters on PM 2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2618-:d:757231
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