Analysing and predicting the fine-scale distribution of traffic particulate matter in urban nonmotorized lanes by using wavelet transform and random forest methods

• Published in: Stochastic environmental research and risk assessment 2023-07, Vol.37 (7), p.2657-2676
• Main Authors: Luo, Binru, Cao, Ruhui, Yang, Wenbin, Wang, Zhanyong, Hu, Xisheng, Xu, Jinqiang, Fan, Zhongmou, Zhang, Lanyi
• Format: Article
• Language: English
• Subjects: Air pollution; Air pollution control; Air temperature; Aquatic Pollution; Atmospheric pressure; Black carbon; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Earth and Environmental Science; Earth Sciences; Environment; Environmental risk; Land use; Math. Appl. in Environmental Science; Microenvironments; Original Paper; Outdoor air quality; Particulate emissions; Particulate matter; Physics; Predictions; Probability Theory and Stochastic Processes; Relative humidity; Roadsides; Spatial distribution; Statistics for Engineering; Traffic; Variation; Waste Water Technology; Water Management; Water Pollution Control; Wavelet transforms
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-023-02411-6

Dynamic traffic and complex roadside environments always cause fine variations in traffic pollutants with many uncertainties in nonmotorized lanes located close to motorways; thus, reliable methods for identifying pollution risks are urgently needed so that measures can be taken to reduce these slow-moving risks. Focusing on the nonmotorized lanes along an expressway in Fuzhou, China, in this study, we established a cycling platform instrumented by portable detectors to collect fine particle (PM 2.5 ), coarse particle (PM 10 ), and black carbon (BC) concentrations at a high spatiotemporal resolution; then, wavelet transform (WT) and random forest (RF) methods were combined to reveal the fine-scale distribution of different particulate matter types. The results indicated that WT was able to accurately decompose the total measurement value ( C t ) of each particulate matter into immediate vehicle-emitted ( C v ) and background-contributed ( C b ) values, thereby successfully identifying the spatiotemporal variations in traffic-induced pollution hotspots rather than background-disguised hotspots. Furthermore, the RF results were substantially better than the land-use regression results with regards to the fine-scale prediction of each particle in nonmotorized lanes. Although the RF predictions of C t and C v particles differed, traffic pollution hotspots could still be captured by the results. Compared to the measurements, the spatial distributions of the PM 2.5 and PM 10 predictions presented R 2 values larger than 0.96, higher than those of BC (R 2  = 0.77); this was the result of the different impacts of the same predictors, especially their differentiated determinants such as barometric pressure, relative humidity and air temperature. This study highlights the potential of using WT and RF methods to reveal fine-scale variations in roadside traffic pollution, which is beneficial for preventing and controlling air pollution in road microenvironments.