Developing High Spatial Resolution Concentration Maps Using Mobile Air Quality Measurements

Abstract

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.