Fine particulate matter (PM 2.5 ) is of great concern to the public due to its significant risk to human health. Numerous methods have been developed to estimate spatial PM 2.5 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 PM 2.5 mapping in an intra-urban area of China. During this process, PM 2.5 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 PM 2.5 concentrations in microenvironments varied in the intra-urban area. These local PM 2.5 variations can be easily identified by crowdsourced sampling rather than national air quality monitoring stations. The selection of models for fine-scale PM 2.5 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 R 2 : 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 ∼ 100) during the lightly polluted pe-riod (0.40-0.69). Additionally, the LUR model demonstrates limited ability in estimating PM 2.5 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 PM 2.5 mapping using crowdsourced monitoring, and this provides a promising way to reduce the exposure risks for individuals in their daily life.

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Abstract. Fine particulate matters (PM2.5) are of great concern to public due to their significant risk to human health. Numerous methods have been developed to estimate spatial PM2.5 concentrations at unobserved locations due to the sparse fixed monitoring stations. On the other hand, as the rising of low-cost sensing for air pollution monitoring, crowdsourcing activities has been gradually introduced into fine exposure control in cities. However, the optimal mapping method for conventional sparse fixed measurements may not suit this new high-density monitoring way. This study therefore for the first time presents a crowdsourcing sampling campaign and strategies of method selection for hundred meter-scale level PM2.5 mapping in intra-urban area of China. In this process, the crowdsourcing sampling campaign was developed through a group of volunteers and their smart phone applications; the best performed mapping approach was chosen by comparing three widely used modelling method (ordinary kriging (OK), land use regression (LUR), and universal kriging combined OK and LUR (UK)) with increasing training sites. Results show that crowdsourcing based PM2.5 measurements varied significantly by sites (i.e. urban microenvironments) (Period 1: 28–136 µg m−3; Period 2: 115–266 µg m−3) and clearly differed from those at national monitoring sites (Period 1: 20–58 µg m−3; Period 2: 146–219 µg m−3). Despite the performance of the three models in estimating PM2.5 concentrations all improved as the number of training sites increase, OK interpolation performed best under conditions with non-peak traffic (9:00–11:00) in Period 1 (i.e. light-polluted period) with the hold-out validation R2 ranging from 0.47 to 0.82. Meanwhile, the accuracy of UK was the highest for 8:00 and 12:00 with less than 70 % training sites (0.40–0.69) and all five hours of Period 2 (i.e. heavy-polluted period) (0.32–0.68). Comparatively, LUR demonstrated limited ability in PM2.5 concentration simulations (0.04–0.55). Moreover, spatial distributions of PM2.5 concentrations based on the selected model with crowdsourcing data clearly illustrated their hourly intra urban variations which are generally concealed by the results from national air quality monitoring sites. This method selection strategy provides solid experimental evidence for method selection of PM2.5 mapping under crowdsourcing monitoring and a promising access to the prevention of exposure risks for individuals in their daily life.

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