Abstract
Abstract. Fine particulate matter (PM2.5) is of great concern to the public due to its significant risk to human health. Numerous methods have been developed to estimate spatial PM2.5 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 PM2.5 mapping in an intra-urban area of China. During this process, PM2.5 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 PM2.5 concentrations in micro-environments varied in the intra-urban area. These local PM2.5 variations can be easily identified by crowdsourced sampling rather than national air quality monitoring stations. The selection of models for fine-scale PM2.5 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 R2: 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 period (0.40–0.69). Additionally, the LUR model demonstrates limited ability in estimating PM2.5 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 PM2.5 mapping using crowdsourced monitoring, and this provides a promising way to reduce the exposure risks for individuals in their daily life.
Highlights
Fine particulate matter (PM2.5) has been associated with an increased risk of morbidity and mortality in both the long term and the short term (Beverland et al, 2012; Cohen et al, 2017; Di et al, 2017; Lelieveld et al, 2015)
regression kriging (RK) had the highest accuracy in Period 2 and at 08:00 and 12:00 of Period 1 with fewer than ∼ 100 training sites. These results suggest that ordinary kriging (OK) interpolation based on crowdsourced sampling is the best strategy for the PM2.5 mapping in the intra-urban area when the official air pollution levels are good and moderate for nonpeak traffic conditions in this study, while RK is the best strategy when the pollution levels are heavily polluted
This study presented strategies of method selection for efficient PM2.5 concentration mapping with an increasing number of training sites using crowdsourced monitoring
Summary
Fine particulate matter (PM2.5) has been associated with an increased risk of morbidity and mortality in both the long term and the short term (Beverland et al, 2012; Cohen et al, 2017; Di et al, 2017; Lelieveld et al, 2015). The persistent cumulative effects from exposure in daily activities, especially daily travelling, are critical (Kingham et al, 2013; Hankey et al, 2017). If individuals could consciously choose the location and time of their outdoor activities based on detailed knowledge about the spatio-temporal variation in PM2.5 concentration, their health protection could be improved. In situ measurement is the most reliable way to capture the PM2.5 concentrations across every corner of a city in real time. Fixed monitoring stations in conventional air quality monitoring networks are sparse. As a result, sitebased observations encounter challenges in capturing spatiotemporal variations in air pollutants, especially in intra-urban areas with unevenly distributed emission sources and disper-
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
