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.
Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic
