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Mobile Air Monitoring Data Processing Strategies and Effects on Spatial Air Pollution Trends
Citation:
Brantley, H., G. Hagler, Sue Kimbrough, R. Williams, S. Mukerjee, AND L. Neas. Mobile Air Monitoring Data Processing Strategies and Effects on Spatial Air Pollution Trends. Atmospheric Measurement Techniques. Copernicus Publications, Katlenburg-Lindau, Germany, 7(7):2169-2183, (2014).
Impact/Purpose:
This study utilizes a robust multipollutant mobile monitoring data set collected on a roadway network in North Carolina, USA, to evaluate common data-processing methods including time alignment optimization, emissions event detection, background standardization, and spatial and temporal smoothing.
Description:
The collection of real-time air quality measurements while in motion (i.e., mobile monitoring) is currently conducted worldwide to evaluate in situ emissions, local air quality trends, and air pollutant exposure. This measurement strategy pushes the limits of traditional data analysis with complex second-by-second multipollutant data varying as a function of time and location. Data reduction and filtering techniques are often applied to deduce trends, such as pollutant spatial gradients downwind of a highway. However, rarely do mobile monitoring studies report the sensitivity of their results to the chosen data processing approaches. The study being reported here utilized a large mobile monitoring dataset collected on a roadway network in central North Carolina to explore common data processing strategies including time-alignment, short-term emissions event detection, background estimation, and averaging techniques. One-second time resolution measurements of ultrafine particles <100 nm in diameter (UFPs), black carbon (BC), particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen dioxide (NO2) were collected on twelve unique driving routes that were repeatedly sampled. Analyses demonstrate that the multiple emissions event detection strategies reported produce generally similar results and that utilizing a median (as opposed to a mean) as a summary statistic may be sufficient to avoid bias in near-source spatial trends. Background levels of the pollutants are shown to vary with time, and the estimated contributions of the background to the mean pollutant concentrations were: BC (6%), PM2.5-10 (12%), UFPs (19%), CO (38%), PM10 (45%), NO2 (51%), PM2.5 (56%), and CO2 (86%). Lastly, while temporal smoothing (e.g., 5 second averages) results in weak pair-wise correlation and the blurring of spatial trends, spatial averaging (e.g., 10 m) is demonstrated to increase correlation and refine spatial trends.
