Abstract
Mobile mapping of air pollution has the potential to provide pollutant concentration data at unprecedented spatial scales. Characterizing instrument performance in the mobile context is challenging, but necessary to analyze and interpret the resulting data. We used robust statistical methods to assess mobile platform performance using data collected with the Aclima Inc. mobile air pollution measurement and data acquisition platform installed on three Google Street View cars. They were driven throughout the greater Denver metropolitan area between July 25, 2014 and August 14, 2014, measuring ozone (O3), nitrogen dioxide (NO2), nitric oxide (NO), black carbon (BC), and size-resolve particle number counts (PN) between 0.3 μm and 5.0 μm diameter. August 6, 2014 was dedicated to parked and moving collocations among the three cars, allowing an assessment of measurement precision and bias. We used the median absolute deviation (MAD) to estimate instrument precision from outdoor, parked collocations. Bias was assessed by measurements obtained from parked cars using the standard deviation of median values over a collocated measurement period, as well as by Passing-Bablok regression statistics while the cars were moving and collocated. For the moving collocation periods, we compared the distribution of 1-σ standard deviations among the 3 cars to the estimated distribution assuming only measurement uncertainty (precision and bias). The distribution of mobile measurements agreed well with the theoretical uncertainty distribution at the lower end of the distribution for O3, NO2, and PN. We assert that the difference between the actual and theoretical distributions is due to real spatial variability between pollutants. The agreement between the parked car estimates of uncertainty and that measured during the mobile collocations (at the lower quantiles) provides evidence that on-road collocation while parked could be sufficient for estimating measurement uncertainties of a mobile platform, even when extended to the moving environment.
Highlights
Measurements form the basis of our understanding of air pollution; scientists, regulators, and the public use these measurements to understand atmospheric chemistry, determine air quality levels, link concentrations to health effects, and evaluate advanced air quality models
The analyses we present are based on either entire statistical distributions, in which case the higher end of the distribution can influence the rest of the distribution, or outlier-resistant robust statistics, in which the values of the high outliers do not impact the resulting statistics
black carbon (BC) and nitric oxide (NO) vary more than NO2 and O3 on a relative scale and are skewed towards higher values
Summary
Measurements form the basis of our understanding of air pollution; scientists, regulators, and the public use these measurements to understand atmospheric chemistry, determine air quality levels, link concentrations to health effects, and evaluate advanced air quality models. In the United States, local, state, and tribal air quality agencies measure air quality using well characterized regulatory grade instruments, typically with 1-hr to 24-hr time resolution These measurements are spatially limited, and to some extent temporally limited, and do not capture the full variability of pollutant concentrations that exist in urban environments over fine spatial and temporal scales. Cost, and power requirements associated with stationary monitoring sites prohibit widespread deployment of laboratory or regulatory grade air pollution measurement instrumentation to study spatial variability on sub-kilometer scales. The cars were equipped with high time resolution (0.5 hz or 1 hz data reporting rate) laboratory-grade air pollution monitors that measured ozone (O3), nitrogen dioxide (NO2), nitric oxide (NO), black carbon (BC), and size-fractionated particle numbers (PN).
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