Multi-Season Mobile Monitoring Campaign of on-Road Air Pollution in Bengaluru, India: High-Resolution Mapping and Estimation of Quasi-Emission Factors

Abstract

Mobile monitoring can supplement regulatory measurements, particularly in low-income countries where stationary monitors’ density is low. Here, we report results from a ~year-long mobile monitoring campaign of on-road black carbon (BC) mass concentration, ultrafine particle (UFP) number concentration, and carbon dioxide (CO2) in Bengaluru, India. The study route included ~150 unique kms covering urban and peri-urban residential neighborhoods and the central business district (CBD); ~22 repeat measurements were made per monitored road-segment, covering most seasons. After cleaning the data for known instrument artifacts and sensitivities, we generated 30 m high-resolution stable ‘data only’ spatial maps of BC, UFP, and CO2 for the study route. For the urban residential areas, the mean BC levels for residential roads, arterials, and highways were ~10, 22, and 56 µgm-³, respectively. A similar pattern (highways being characterized by highest pollution levels) was also observed for the UFP and CO2. Using the data from repeat measurements, we carried out a Monte Carlo subsampling analysis to understand the minimum number of repeat measures to generate stable maps of pollution in the city. Leveraging the simultaneous nature of the measurements, we also mapped the quasi-emission factors (QEF) of the pollutants under investigation. Finally, the results are discussed in the context of technical aspects of the campaign, limitations, and their policy relevance.

Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India

“…The study area was divided into four daily routes: the central business district (CBD), Kannuru (KAN), a peri-urban transecting route, and two routes in Malleshwaram (MAL), a residential neighborhood in Bengaluru. The entire study area was typically covered once a week. , There were high variations in pollutant concentrations along the routes, consistent with previous findings. , The highest pollution levels were found on the major roads, followed by arterials and residential areas. More information about the air quality monitoring campaign, device setup, and data correction have been described by Upadhya et al .…”

“…The entire study area was typically covered once a week. 24,25 There were high variations in pollutant concentrations along the routes, consistent with previous findings. 7,26 The highest pollution levels were found on the major roads, followed by arterials and residential areas.…”

“…7,26 The highest pollution levels were found on the major roads, followed by arterials and residential areas. More information about the air quality monitoring campaign, device setup, and data correction have been described by Upadhya et al 25 The air pollution instrumentation measurements were used here as the ground truth, whereas the video footage was used for feature set crafting that was then fed into the CNN model.…”