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| Titel |
Mobile air monitoring data-processing strategies and effects on spatial air pollution trends |
| VerfasserIn |
H. L. Brantley, G. S. W. Hagler, E. S. Kimbrough, R. W. Williams, S. Mukerjee, L. M. Neas |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 7, no. 7 ; Nr. 7, no. 7 (2014-07-22), S.2169-2183 |
| Datensatznummer |
250115851
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| Publikation (Nr.) |
 copernicus.org/amt-7-2169-2014.pdf |
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| Zusammenfassung |
| 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 40 h (> 140 000 observations) of mobile monitoring data
collected on a roadway network in central North Carolina to explore common
data-processing strategies including local emission plume detection,
background estimation, and averaging techniques for spatial trend analyses.
One-second time resolution measurements of ultrafine particles (UFPs), black
carbon (BC), particulate matter (PM), carbon monoxide (CO), and nitrogen
dioxide (NO2) were collected on 12 unique driving routes that were
each sampled repeatedly. The route with the highest number of repetitions was
used to compare local exhaust plume detection and averaging methods. Analyses
demonstrate that the multiple local exhaust plume detection strategies
reported produce generally similar results and that utilizing a median of
measurements taken within a specified route segment (as opposed to a mean)
may be sufficient to avoid bias in near-source spatial trends. A time-series-based method of estimating background concentrations was shown to produce
similar but slightly lower estimates than a location-based method. For the
complete data set the estimated contributions of the background to the mean
pollutant concentrations were as follows: BC (15%), UFPs (26%), CO (41%),
PM2.5-10 (45%), NO2 (57%), PM10 (60%), PM2.5
(68%). Lastly, while temporal smoothing (e.g., 5 s 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. |
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