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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 Jan 2019

Research article | 07 Jan 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Strategies of Method Selection for Fine Scale PM2.5 Mapping in Intra-Urban Area Under Crowdsourcing Monitoring

Shan Xu1, Bin Zou1, Yan Lin2, Xiuge Zhao3, Shenxin Li1, and Chengxia Hu1 Shan Xu et al.
  • 1School of Geosciences and Info-Physics, Central South University, Changsha, Hunan, 410083, China
  • 2Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, New Mexico, 87131, United States
  • 3Chinese Research Academy of Environmental Sciences, Beijing, 100012, China

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: 28–136µgm−3; Period 2: 115–266µgm−3) and clearly differed from those at national monitoring sites (Period 1: 20–58µgm−3; Period 2: 146–219µgm−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.

Shan Xu et al.
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Shan Xu et al.
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Short summary
This study presents a crowdsourcing sampling campaign and strategies of method selection for hundred meter-scale PM2.5 mapping. What interesting is, PM2.5 concentrations in micro-environments varied significantly in intra urban area. And these local PM2.5 variations can be effectively revealed by crowdsourcing sampling rather than national air quality monitoring sites. The selection of models for fine scale PM2.5 mapping should be adjusted with the changing sampling and pollution circumstances.
This study presents a crowdsourcing sampling campaign and strategies of method selection for...