A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China

Lan Jin, Jesse Berman, Joshua L. Warren, Jonathan I. Levy, George Thurston, Y. Zhang, X. Xu, Shuxiao Wang, Y. Zhang, Michelle L. Bell

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13 Scopus citations

Abstract

Background: Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings. Objective: A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings. Methods: In each of four seasons in 2016–2017, NO2 was measured using Ogawa badges for 2 weeks at 75 ground-level sites. PM2.5 was measured using DataRAM for shorter time intervals at a subset (N = 38) of the 75 sites. Vertical profile measurements were conducted on 9 stories at 2 high-rise buildings (N = 18), with one building facing traffic and another facing away from traffic. The average seasonal concentrations of NO2 and PM2.5 at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models. Results: We developed robust LUR models at the ground level for estimated annual averages of NO2 (R2: 0.71, adjusted R2: 0.67, and Leave-One-Out Cross Validation (LOOCV) R2: 0.64) and PM2.5 (R2: 0.77, adjusted R2: of 0.73, and LOOCV R2: 0.67) in the urban core of Lanzhou, China. The LUR models for the estimated seasonal averages of NO2 showed similar patterns. Vertical variation of NO2 and PM2.5 differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations. Conclusions: Ground-level NO2 and PM2.5 showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.

Original languageEnglish (US)
Article number108597
JournalEnvironmental Research
Volume177
DOIs
StatePublished - Oct 2019

Bibliographical note

Funding Information:
This work was funded by Air & Waste Management Association, Yale MacMillan Center, Yale Institute for Biospheric Studies, Yale Hixon Center for Urban Ecology, Yale Tropical Resources Institute, Yale Global Health Initiative, Yale Graduate School, and Yale Council on East Asian Studies. This manuscript 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. This manuscript was also developed under the Ministry of Science and Technology of the People's Republic of China grant 2016YFC1302500 and National Natural Science Foundation of China grant 21625701.We acknowledge Mr. Qiusheng Jin and Ms. Bei Zhang, who assisted Ms. Lan Jin with the field work. We acknowledge Dr. Jane Cloughterty for lending Ogawa Badges, and Dr. Mikhail Wolfson and Dr. Marco Martins at Dr. Petros Koutrakis's lab for providing trainings and analyzing Ogawa pads. Funding for the fieldwork was provided by: Air & Waste Management Association, Yale MacMillan Center, Yale Institute for Biospheric Studies, Yale Hixon Center for Urban Ecology, Yale Tropical Resources Institute, Yale Global Health Initiative, Yale Graduate School, and Yale Council on East Asian Studies. 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, and National Natural Science Foundation of China grant 21625701.

Funding Information:
We acknowledge Mr. Qiusheng Jin and Ms. Bei Zhang, who assisted Ms. Lan Jin with the field work. We acknowledge Dr. Jane Cloughterty for lending Ogawa Badges, and Dr. Mikhail Wolfson and Dr. Marco Martins at Dr. Petros Koutrakis’s lab for providing trainings and analyzing Ogawa pads. Funding for the fieldwork was provided by: Air & Waste Management Association , Yale MacMillan Center , Yale Institute for Biospheric Studies , Yale Hixon Center for Urban Ecology , Yale Tropical Resources Institute , Yale Global Health Initiative , Yale Graduate School , and Yale Council on East Asian Studies . 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 , and National Natural Science Foundation of China grant 21625701 .

Funding Information:
This work was funded by Air & Waste Management Association , Yale MacMillan Center , Yale Institute for Biospheric Studies , Yale Hixon Center for Urban Ecology , Yale Tropical Resources Institute , Yale Global Health Initiative , Yale Graduate School , and Yale Council on East Asian Studies . This manuscript 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. This manuscript was also developed under the Ministry of Science and Technology of the People’s Republic of China grant 2016YFC1302500 and National Natural Science Foundation of China grant 21625701 .

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

  • Land use regression
  • NO
  • PM
  • Urban
  • Vertical variation

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