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Atmos Environ (1994)

Title:		Correlations between short-term mobile monitoring and long-term passive sampler measurements of traffic-related air pollution
Author(s):		Riley EA; Schaal L; Sasakura M; Crampton R; Gould TR; Hartin K; Sheppard L; Larson T; Simpson CD; Yost MG;
Address:		"University of Washington Department of Environmental and Occupational Health Sciences, Box 357234, Seattle, WA, 98198. +1 (206) 543-3222. University of Washington Department of Biostatistics, Box 357232 Seattle, WA, 98198. University of Washington Department of Civil & Environmental Engineering, Box 352700 Seattle, WA, 98198. University of Washington Department of Environmental and Occupational Health Sciences, Box 357234, Seattle, WA, 98198. +1 (206) 543-3222; University of Washington Department of Biostatistics, Box 357232 Seattle, WA, 98198. University of Washington Department of Environmental and Occupational Health Sciences, Box 357234, Seattle, WA, 98198. +1 (206) 543-3222; University of Washington Department of Civil & Environmental Engineering, Box 352700 Seattle, WA, 98198"
Journal Title:		Atmos Environ (1994)
Year:		2016
Volume:		132
Issue:
Page Number:		229 - 239
DOI:		10.1016/j.atmosenv.2016.03.001
ISSN/ISBN:		1352-2310 (Print) 1352-2310 (Linking)
Abstract:		"Mobile monitoring has provided a means for broad spatial measurements of air pollutants that are otherwise impractical to measure with multiple fixed site sampling strategies. However, the larger the mobile monitoring route the less temporally dense measurements become, which may limit the usefulness of short-term mobile monitoring for applications that require long-term averages. To investigate the stationarity of short-term mobile monitoring measurements, we calculated long term medians derived from a mobile monitoring campaign that also employed 2-week integrated passive sampler detectors (PSD) for NO(x), Ozone, and nine volatile organic compounds at 43 intersections distributed across the entire city of Baltimore, MD. This is one of the largest mobile monitoring campaigns in terms of spatial extent undertaken at this time. The mobile platform made repeat measurements every third day at each intersection for 6-10 minutes at a resolution of 10 s. In two-week periods in both summer and winter seasons, each site was visited 3-4 times, and a temporal adjustment was applied to each dataset. We present the correlations between eight species measured using mobile monitoring and the 2-week PSD data and observe correlations between mobile NO(x) measurements and PSD NO(x) measurements in both summer and winter (Pearson's r = 0.84 and 0.48, respectively). The summer season exhibited the strongest correlations between multiple pollutants, whereas the winter had comparatively few statistically significant correlations. In the summer CO was correlated with PSD pentanes (r = 0.81), and PSD NO(x) was correlated with mobile measurements of black carbon (r = 0.83), two ultrafine particle count measures (r =0.8), and intermodal (1-3 mum) particle counts (r = 0.73). Principal Component Analysis of the combined PSD and mobile monitoring data revealed multipollutant features consistent with light duty vehicle traffic, diesel exhaust and crankcase blow by. These features were more consistent with published source profiles traffic-related air pollutants than features based on the PSD data alone. Short-term mobile monitoring shows promise for capturing long-term spatial patterns of traffic-related air pollution, and is complementary to PSD sampling strategies"
Keywords:		Traffic-related air pollution black carbon mobile monitoring ozone ultrafine particles;
Notes:		"PubMed-not-MEDLINERiley, Erin A Schaal, LaNae Sasakura, Miyoko Crampton, Robert Gould, Timothy R Hartin, Kris Sheppard, Lianne Larson, Timothy Simpson, Christopher D Yost, Michael G eng P30 ES007033/ES/NIEHS NIH HHS/ T32 ES015459/ES/NIEHS NIH HHS/ England 2016/04/19 Atmos Environ (1994). 2016 May; 132:229-239. doi: 10.1016/j.atmosenv.2016.03.001"