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Mobile air pollution monitoring offers an opportunity to map pollutants with much higher spatial resolution than sparse stationary monitors. We develop a framework to address the challenges and constraints to developing higher spatial resolution maps from mobile data. The challenges include the non-uniform spatial resolution and distribution of the measurements; that measurements are made at slightly different locations in each pass of the mobile monitoring platform along a specific route (each run); in some cases, the poor precision of global positioning system coordinate data; potential for over/underweighting data; and varying urban background concentrations. We find that use of a reference grid and piecewise cubic Hermite spline interpolation between measurements to give equal weight to each sampling run at each grid reference point addresses many of the challenges effectively. A background correction was implemented to facilitate averaging over several sessions. For 1 s time resolution data collected at normal city driving speeds, we show that concentration maps of 5 m spatial resolution can be obtained, by including up to 21% interpolated values. Finally, we use ultrafine particle concentrations to consider the minimum number of sampling runs needed to make a representative concentration map with a specific spatial resolution, finding that generally between 15 to 21 repeats of a particular route under similar traffic and meteorological conditions is sufficient. The concentration maps can afford insights into factors influencing pollutant concentrations at the city block and sub-block scale; information that is useful in urban planning strategies to reduce pollution exposure. Methodical analysis of mobile monitoring data will facilitate meaningful comparison of concentration maps of different routes/studies.

Developing High Spatial Resolution Concentration Maps Using Mobile Air Quality Measurements