EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development of land use regression (LUR) models for criteria air pollutants in Delhi: Use of regulatory monitoring data

Adithi Upadhya1, Padmavati Kulkarni2, Mahesh Kalshetty2, Srishti Srishti2, Meenakshi Kushwaha1, Pratyush Agrawal2, and Sreekanth Vakacherla2
Adithi Upadhya et al.
  • 1ILK Labs, Bengaluru, India
  • 2Center for Study of Science, Technology, and Policy, Bengaluru, India

High-resolution spatial maps of air pollution can be useful for air quality management. In low- and middle-income countries, regulatory measurements of criteria pollutants are typically insufficient to generate spatial maps, due to the sparsely located monitoring stations. Alternatively, high-resolution spatial maps of air pollution can be achieved by dispersion (physics-based) and statistical regression (training-based) modelling. Resolutions of up to ~25 meters can be achieved by Land Use Regression (LUR) modelling based spatial predictions. In this study, we leveraged the high density of regulatory monitors located in New Delhi, India, and developed LUR models for all the major criteria pollutants (PM10, PM2.5, SO2, NO2, and Ozone). New Delhi is one of the most heavily polluted cities in the world. We used data from 40 continuous ambient air quality monitoring stations’ (CAAQMS) for the year 2019 to develop seasonal and annual LUR models, following the ESCAPE (European Study of Cohorts for Air Pollution Effects) stepwise supervised regression method. Model predictors included land use parameters, road lengths, rail track lengths, population, satellite pollution, and NDVI data, along with air pollution point source location data and reanalysis meteorology. The models were validated using leave one station out (LOSO) and 10-fold cross validations (CV). The model adjusted R2 values varied between 0.08 and 0.64. Particle pollutant models (PM2.5 and PM10) performed better than those of gaseous pollutants. Further, ozone models performed the least. Across seasons, summer models performed the best (least) for PM (gaseous pollutants). Models with adjusted R2 were used for spatial predictions at 50-m resolution for the Delhi National Capital Territory region. Spatio-seasonal characteristics of air pollution were studied using the generated high-resolution maps.

How to cite: Upadhya, A., Kulkarni, P., Kalshetty, M., Srishti, S., Kushwaha, M., Agrawal, P., and Vakacherla, S.: Development of land use regression (LUR) models for criteria air pollutants in Delhi: Use of regulatory monitoring data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5586,, 2023.