To effectively manage air pollution, we need to measure it accurately and at high spatial resolution. However, maintaining a dense network of regulatory instruments is financially and technically burdensome for low- and middle-income countries. A hybrid approach that combines non-conventional, less expensive, short-term stationary, and mobile deployments may be a cost-effective solution. In the city of Bengaluru, India, we adopted such a hybrid measurement approach to generate high spatial resolution air pollution maps. We carried out a mobile monitoring campaign covering approximately 10% of roads in the city to measure on-road mass concentrations of fine particulate matter (PM2.5), black carbon (BC), and number concentrations of ultrafine particles (UFPs). We also conducted another campaign where we established and maintained a 55-node city-wide network of low-cost sensors to measure ambient PM2.5. Data from these two campaigns were corrected for their respective instrument biases and then used along with regulatory data from pollution control board monitors to predict pollutant levels at 50 m resolution using land-use regression models.
Adithi R Upadhya, Meenakshi Kushwaha, and Vinod Solomon (ILK Labs) co-authored this report.
