Modeling the intraurban variation in nitrogen dioxide in urban areas in Kathmandu Valley, Nepal.

BACKGROUND: With growing urbanization, traffic has become one of the main sources of air pollution in Nepal. Understanding the impact of air pollution on health requires estimation of exposure. Land use regression (LUR) modeling is widely used to investigate intraurban variation in air pollution for Western cities, but LUR models are relatively scarce in developing countries. In this study, we developed LUR models to characterize intraurban variation of nitrogen dioxide (NO2) in urban areas of Kathmandu Valley, Nepal, one of the fastest urbanizing areas in South Asia.

METHODS: Over the study area, 135 monitoring sites were selected using stratified random sampling based on building density and road density along with purposeful sampling. In 2014, four sampling campaigns were performed, one per season, for two weeks each. NO2 was measured using duplicate Palmes tubes at 135 sites, with additional information on nitric oxide (NO), NO2, and nitrogen oxide (NOx) concentrations derived from Ogawa badges at 28 sites. Geographical variables (e.g., road network, land use, built area) were used as predictor variables in LUR modeling, considering buffers 25-400m around each monitoring site.

RESULTS: Annual average NO2 by site ranged from 5.7 to 120ppb for the study area, with higher concentrations in the Village Development Committees (VDCs) of Kathmandu and Lalitpur than in Kirtipur, Thimi, and Bhaktapur, and with variability present within each VDC. In the final LUR model, length of major road, built area, and industrial area were positively associated with NO2 concentration while normalized difference vegetation index (NDVI) was negatively associated with NO2 concentration (R(2)=0.51). Cross-validation of the results confirmed the reliability of the model.

CONCLUSIONS: The combination of passive NO2 sampling and LUR modeling techniques allowed for characterization of nitrogen dioxide patterns in a developing country setting, demonstrating spatial variability and high pollution levels.