Strategies of method selection for fine-scale PM 2.5 mapping in an intra-urban area using crowdsourced monitoring

Xu, Shan; Zou, Bin; Lin, Yan; Zhao, Xiuge; Li, Shenxin; Hu, Chenxia

Fine particulate matter (PMinline-formula2.5) is of great concern to the public due to its significant risk to human health. Numerous methods have been developed to estimate spatial PMinline-formula2.5 concentrations in unobserved locations due to the sparse number of fixed monitoring stations. Due to an increase in low-cost sensing for air pollution monitoring, crowdsourced monitoring of exposure control has been gradually introduced into cities. However, the optimal mapping method for conventional sparse fixed measurements may not be suitable for this new high-density monitoring approach. This study presents a crowdsourced sampling campaign and strategies of method selection for 100 m scale PMinline-formula2.5 mapping in an intra-urban area of China. During this process, PMinline-formula2.5 concentrations were measured by laser air quality monitors through a group of volunteers during two 5 h periods. Three extensively employed modelling methods (ordinary kriging, OK; land use regression, LUR; and regression kriging, RK) were adopted to evaluate the performance. An interesting finding is that PMinline-formula2.5 concentrations in micro-environments varied in the intra-urban area. These local PMinline-formula2.5 variations can be easily identified by crowdsourced sampling rather than national air quality monitoring stations. The selection of models for fine-scale PMinline-formula2.5 concentration mapping should be adjusted according to the changing sampling and pollution circumstances. During this project, OK interpolation performs best in conditions with non-peak traffic situations during a lightly polluted period (holdout validation inline-formulaR2: 0.47–0.82), while the RK modelling can perform better during the heavily polluted period (0.32–0.68) and in conditions with peak traffic and relatively few sampling sites (fewer than inline-formula∼100) during the lightly polluted period (0.40–0.69). Additionally, the LUR model demonstrates limited ability in estimating PMinline-formula2.5 concentrations on very fine spatial and temporal scales in this study (0.04–0.55), which challenges the traditional point about the good performance of the LUR model for air pollution mapping. This method selection strategy provides empirical evidence for the best method selection for PMinline-formula2.5 mapping using crowdsourced monitoring, and this provides a promising way to reduce the exposure risks for individuals in their daily life.



Xu, Shan / Zou, Bin / Lin, Yan / et al: Strategies of method selection for fine-scale PM2.5 mapping in an intra-urban area using crowdsourced monitoring. 2019. Copernicus Publications.


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