Development and Validation of a Sub-National, Satellite-Based Land-Use Regression Model for Annual Nitrogen Dioxide Concentrations in North-Western China

Igor Popovic, Ricardo J. Soares Magalhães, Shukun Yang, Yurong Yang, Erjia Ge, Boyi Yang, Guanghui Dong, Xiaolin Wei, Guy B. Marks and Luke D. Knibbs
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Igor Popovic: Faculty of Medicine, School of Public Health, University of Queensland, Herston 4006, Australia
Ricardo J. Soares Magalhães: UQ Spatial Epidemiology Laboratory, School of Veterinary Science, University of Queensland, Gatton 4343, Australia
Shukun Yang: Department of Radiology, The Second Affiliated Hospital of Ningxia Medical University, The First People’s Hospital in Yinchuan, Yinchuan 750004, China
Yurong Yang: Department of Pathogenic Biology & Medical Immunology, School of Basic Medical Science, Ningxia Medical University, Yinchuan 750004, China
Erjia Ge: Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A1, Canada
Boyi Yang: Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510085, China
Guanghui Dong: Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510085, China
Xiaolin Wei: Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A1, Canada
Guy B. Marks: South Western Sydney Clinical School, University of New South Wales, Liverpool 2170, Australia
Luke D. Knibbs: Centre for Air Pollution, Energy and Health Research, Glebe 2037, Australia

IJERPH, 2021, vol. 18, issue 24, 1-12

Abstract: Existing national- or continental-scale models of nitrogen dioxide (NO 2 ) exposure have a limited capacity to capture subnational spatial variability in sparsely-populated parts of the world where NO 2 sources may vary. To test and validate our approach, we developed a land-use regression (LUR) model for NO 2 for Ningxia Hui Autonomous Region (NHAR) and surrounding areas, a small rural province in north-western China. Using hourly NO 2 measurements from 105 continuous monitoring sites in 2019, a supervised, forward addition, linear regression approach was adopted to develop the model, assessing 270 potential predictor variables, including tropospheric NO 2 , optically measured by the Aura satellite. The final model was cross-validated (5-fold cross validation), and its historical performance (back to 2014) assessed using 41 independent monitoring sites not used for model development. The final model captured 63% of annual NO 2 in NHAR (RMSE: 6 ppb (21% of the mean of all monitoring sites)) and contiguous parts of Inner Mongolia, Gansu, and Shaanxi Provinces. Cross-validation and independent evaluation against historical data yielded adjusted R 2 values that were 1% and 10% lower than the model development values, respectively, with comparable RMSE. The findings suggest that a parsimonious, satellite-based LUR model is robust and can be used to capture spatial contrasts in annual NO 2 in the relatively sparsely-populated areas in NHAR and neighbouring provinces.

Keywords: air pollution modelling; nitrogen dioxide; satellite-based model; land-use regression; exposure assessment; China (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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