Use of mobile and passive badge air monitoring data for NOX and ozone air pollution spatial exposure prediction models

Abstract

Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NO) and ozone (O) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NO and O, with LOOCV Rs of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NO had LOOCV Rs of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O. Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NO and O and are a better source of data for these models than 2-week passive badge data.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance

“…Mobile monitoring is considered as a cost-effective strategy. It requires less instrumentation and enables extensive spatial coverage in a short time (Apte et al, 2017;Robinson et al, 2019;Xu et al, 2017;Shi et al, 2016;Kerckhoffs et al, 2017a;Basu et al, 2019;Hankey et al, 2019). These help to leverage the ability of mobile monitoring to capture subtle changes in pollutants in dense urban areas and reduce the effects of inadequate monitoring coverage.…”

High-resolution mapping of traffic related air pollution with Google street view cars and incidence of cardiovascular events within neighborhoods in Oakland, CA

“…Although land use regression techniques have improved the ability to characterize some of the spatial variability of pollutants over large areas, these methods are still limited in their ability to characterize the full distribution of highly spatially variable TRAP exposures within highly variable or idiosyncratic urban neighborhoods [ 7 ] and predictions are sensitive to variable selection [ 6 , 8 ]. New research is emerging on the use of mobile monitoring to better characterize spatial variability of air pollutants without needing to carry out extensive modelling and prediction [ 9 – 11 ], but few studies have examined TRAP exposures estimated by mobile monitoring in relation to clinical health outcomes.…”

Mobile Monitoring of Personal NOx Exposures during Scripted Daily Activities in Chicago, IL

“…Some of these studies have shown moderate correlations between personal and ambient and/or indoor NO 2 /NO x concentrations for some populations (Sørensen et al, 2005;Meng et al, 2012b), while others have shown almost no correlation (Quackenboss et al, 1986;Kousa et al, 2001;Lai et al, 2004;Meng et al, 2012a). Many previous personal or microenvironmental NO 2 /NO x studies have been limited to long sampling intervals using passive integrated samplers and most have focused on either NO 2 or total NO x (Esplugues et al, 2010;Borge et al, 2016;Xu et al, 2016), which limits understanding of important spatiotemporal variations in personal exposures to NO/NO 2 /NO x that could affect short-term health effects and elucidate contributions from various sources. Moreover, most field campaigns that have made microenvironmental NO/NO 2 /NO x measurements with higher temporal resolution used chemiluminescence monitors, some of which have been shown to be subject to interference by species common to urban environments including HONO, HNO 3 , and peroxyacyl nitrates (McClenny et al, 2002;Gerboles et al, 2003;Dunlea et al, 2007;Steinbacher et al, 2007;Kebabian et al, 2008).…”