In Low- and Middle-Income Countries, rapid urbanization has led to poorer air quality, yet pollution monitoring networks are often sparse or non-existent. Few previous studies have sought to understand the unique predictors of air pollution exposure in Indian urban environments. Our study monitored and modeled nitrogen dioxide (NO2) in Mysore, a rapidly urbanizing city in India. NO2 sampling was conducted in four seasonal campaigns (each lasting 2 weeks) in 2016–2017, at 150 sites throughout Mysore. Seasonal spatial interpolation of NO2 levels was conducted using 2 distinct models, the first utilizing a land use regression (LUR) approach and the second using universal kriging methods. Model performance was determined using adjusted R2, and validated using leave-one-out cross validation. Measured NO2 concentrations ranged from 0.3 to 51.9 ppb across the four seasons of the study period, with higher concentrations in the center of the city. In the LUR model (R2 = 0.535), proximity to major roads, point sources of pollution such as industrial sites and religious points of interest (PoI), land uses with high human activity, and high population density were associated with higher levels of NO2. Proximity to minor roads and coverage of land uses characterized by low human activity were inversely associated with air pollution. Cross-validation of results confirmed the reliability of each model. Few studies have applied spatially heterogeneous sampling to assess ambient air pollution levels in India. The combination of passive NO2 sampling and LUR/kriging modeling methods allowed for characterization of NO2 patterns in Mysore. While previous work indicates traffic pollution as a major contributor to ambient air pollution levels in urbanizing centers in Asia, our results indicate the influence of other pollution factors (e.g., point sources), as well as highly localized characteristics of the urban environment (e.g., proximity to religious points of interest) in urban India. Areas of Mysore consistently experienced pollution in excess of World Health Organization (WHO) health-protective guidelines for NO2.
Bibliographical noteFunding Information:
The authors would like to acknowledge Dr. P. Barry Ryan (Emory University Rollins School of Public Health), Dr. Ana Rule (Johns Hopkins Bloomberg School of Public Health), and Timothy Green (Johns Hopkins Bloomberg School of Public Health) for their assistance with laboratory analysis.This work was funded by the U.S. Environment Protection Agency (Grant number: FP-91782101-0) Science to Achieve Results Fellowship, the Air & Waste Management Association's Air Quality Research and Study fellowship, the DBT-Ramalingaswami Re-entry Fellowship, DBT-SERB (EEQ/2016/000341) the Yale Tropical Resources Institute, Yale Macmillan Center, and Yale Hixon Center for Urban Ecology.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.
This work was funded by the U.S. Environment Protection Agency (Grant number: FP-91782101-0) Science to Achieve Results Fellowship, the Air & Waste Management Association's Air Quality Research and Study fellowship, the DBT-Ramalingaswami Re-entry Fellowship, DBT-SERB ( EEQ/2016/000341 ) the Yale Tropical Resources Institute , Yale Macmillan Center, and Yale Hixon Center for Urban Ecology.
© 2020 Elsevier Ltd
- Air pollution
- Nitrogen dioxide
- Spatial interpolation