Abstract
Background: Land use regression (LUR) has been widely used to estimate air pollution exposure in recent epidemiology studies. However, few LUR studies were conducted in China, and even fewer used purposefully designed monitoring networks. The objectives of this study are to obtain preliminary understanding of fine-scale air pollution distributions, and to provide a foundation for a future extended study in Lanzhou, China, a major industrial city. Methods: A pilot monitoring network was designed using stratified-random sampling, and purposeful selection in gaps of spatial predictor distributions. Based on this network, NO2 were measured using Palmes tubes for 2 weeks in summer 2015, which were used to develop a pilot LUR model considering spatial information of traffic and population densities, elevation, land cover, and land use. We developed linear regression, kriging models, including ordinary kriging, universal kriging, and compared them using AIC. Results: The sampling sites of the pilot monitoring network represented wide ranges of spatial predictors (N = 47). The pilot LUR model explained 71% of the variance in the measured NO2 at the sampling sites. The spatial predictors in the model included road densities, elevation, and district indicator. Predicted NO2 concentrations were higher in the east of the city, which is more developed and has dense road networks. Linear regression model performed better than the kriging models due to the lowest AIC. Conclusions: This study developed a pilot monitoring network that can effectively capture variability in spatial characteristics and developed a robust LUR model capturing small-scale spatial variations of air pollution in an understudied area. The predicted and measured NO2 showed substantial spatial heterogeneity that was not captured by the limited government monitors. A future study with extended monitoring network and measurements from more seasons is needed to fully understand the distribution of air pollution in Lanzhou, China.
Original language | English (US) |
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Pages (from-to) | 253-262 |
Number of pages | 10 |
Journal | Atmospheric Environment |
Volume | 210 |
DOIs | |
State | Published - Aug 1 2019 |
Bibliographical note
Funding Information:We would acknowledge Mr. Qiusheng Jin and Ms. Bei Zhang, assisting Ms. Lan Jin in the field work. The fieldwork was funded by the following funding agencies: Yale Hixon Center for Urban Ecology Research Fellowship, Yale Tropical Resources Institute Endowment Fellowship, Yale Global Health Initiative Field Experience Award, and Yale Graduate School's John F. Enders Fellowship. This publication was developed under Assistance Agreement No. RD835871awarded by the U.S. Environmental Protection Agency to Yale University. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. We also acknowledge the Ministry of Science and Technology of the People's Republic of China grant 2016YFC1302500. We acknowledge Dr. Xibao Xu at Nanjing Institute of Geography and Limnology and Dr. Shuxiao Wang at Tsinghua University for sharing land use data and data on major point sources in Lanzhou, China.
Funding Information:
We would acknowledge Mr. Qiusheng Jin and Ms. Bei Zhang, assisting Ms. Lan Jin in the field work. The fieldwork was funded by the following funding agencies: Yale Hixon Center for Urban Ecology Research Fellowship , Yale Tropical Resources Institute Endowment Fellowship , Yale Global Health Initiative Field Experience Award , and Yale Graduate School's John F. Enders Fellowship . This publication was developed under Assistance Agreement No. RD835871 awarded by the U.S. Environmental Protection Agency to Yale University . It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. We also acknowledge the Ministry of Science and Technology of the People's Republic of China grant 2016YFC1302500 . We acknowledge Dr. Xibao Xu at Nanjing Institute of Geography and Limnology and Dr. Shuxiao Wang at Tsinghua University for sharing land use data and data on major point sources in Lanzhou, China.
Publisher Copyright:
© 2019 Elsevier Ltd
Keywords
- China
- Land use regression
- Monitoring network
- Traffic pollution