Abstract
The dispersion of urban pollutants is affected by the urban morphology parameters. The objective of this study was to investigate the correlation between PM2.5 distribution and urban morphology parameters in a cold-climate city in China. Field measurements were performed to record the PM2.5 concentration and microclimate parameters at 25 points in a 10 km2 urban area in Harbin, China. It was found that the maximum difference of PM2.5 concentration among the measuring points at the same time could be up to 69.03 μg/m3. 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 PM2.5 concentration and explore the nonlinear influence of urban morphology factors on PM2.5 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 R2 reaching 0.981; building density (57%) and average building height (49%) were the two most significant factors affecting PM2.5 concentration.
Highlights
RadaPM2.5 refers to the particulate matter in the atmosphere with a diameter of 2.5 μm or less, often called lungable particulate matter or fine particulate matter
It was found that there was a big difference in PM2.5 concentration among different measurement points, and the maximum difference can reach 69.03μg/m3
After 10:00, the concentration begins to decline rapidly, and it drops to a minimum between 15:00 and 16:00 when a valley appears
Summary
RadaPM2.5 refers to the particulate matter in the atmosphere with a diameter of 2.5 μm or less, often called lungable particulate matter or fine particulate matter. There are differences in the PM2.5 situation in different regions because factors such as climate and urban morphology will have an impact on the formation and dispersion of PM2.5 [2]. It is very important to understand the spatial distribution and related dynamic changes of PM2.5 , which is conducive to formulating effective measures to reduce and control the harm caused by PM2.5 combined with the actual situation. PM2.5 pollution is very serious in cold-climate cities of northeast China. It is worth noting that the demand for wind and cold protection of cold-climate cities forces the urban morphology design to be relatively simple and closed, which is not conducive to the dispersion of air pollutants [5]. It is of great practical significance to study the distribution of PM2.5 in cold-climate cities of northeast China
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