文献类型：期刊文献

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

作者：Xu, Wei[1];Riley, Erin A.[2];Austin, Elena[2];Sasakura, Miyoko[2];Schaal, Lanae[3];Gould, Timothy R.[4];Hartin, Kris[2];Simpson, Christopher D.[2];Sampson, Paul D.[3];Yost, Michael G.[2];Larson, Timothy V.[2,4];Xiu, Guangli[1];Vedal, Sverre[2]

机构：[1]East China Univ Sci & Technol, Dept Environm Engn, Shanghai, Peoples R China;[2]Univ Washington, Dept Environm & Occupat Hlth Sci, Seattle, WA 98195 USA;[3]Univ Washington, Dept Stat, Seattle, WA 98195 USA;[4]Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA

年份：2017

卷号：27

期号：2

起止页码：184

外文期刊名：JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000394456500005)】；

基金：Wei Xu was supported by a scholarship awarded by the Chinese Scholarship Council. This publication was made possible by USEPA grant (RD-83479601-0). Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication.

语种：英文

外文关键词：air pollution monitoring; exposure models; land use regression; partial least squares; universal kriging

摘要：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 (NOX) and ozone (O-3) 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 NOX and O3, with LOOCV R(2)s 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 NOX had LOOCV R2s 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 O3. Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NOX and O3 and are a better source of data for these models than 2-week passive badge data.