Dilhara R. Ranasinghe1, Wonsik Choi1,3, Arthur M. Winer2, Suzanne E. Paulson 1

  • 1 Department of Atmospheric & Oceanic Sciences, University of California, Los Angeles, CA 90095-1565, USA
  • 2 Department of Environmental Health Sciences, Fielding School of Public Health, University of California, Los Angeles, CA 90095-1772, USA
  • 3 BK21 plus Project of the Graduate School of Earth Environmental Hazard System, Pukyong National University, Busan, Korea

Received: July 31, 2015
Revised: January 30, 2016
Accepted: May 24, 2016
Download Citation: ||https://doi.org/10.4209/aaqr.2015.07.0484 


Cite this article:
Ranasinghe, D.R., Choi, W., Winer, A.M. and Paulson, S.E. (2016). Developing High Spatial Resolution Concentration Maps Using Mobile Air Quality Measurements. Aerosol Air Qual. Res. 16: 1841-1853. https://doi.org/10.4209/aaqr.2015.07.0484


HIGHLIGHTS

  • High spatial resolution concentration maps are developed using mobile measurements.
  • The non-uniform spatial resolution of data and GPS uncertainties present challenges.
  • A reference grid and interpolation are used to give equal weight to sampling runs.
  • Urban 1 s data can be used to produce 5 m spatial resolution concentration maps.
  • Generally 15–21 runs are needed to make representative UFP concentration maps.

 

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.


Keywords: Air pollution mapping; Mobile monitoring; Urban pollution dispersion; Ultrafine particles; Exposure


Impact Factor: 2.735

5-Year Impact Factor: 2.827


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