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.

Abstract. We report on the long-term performance of a popular low-cost PM2.5 sensor, the PurpleAir PA-II, at multiple sites in India, with the aim of identifying robust calibration protocols. We established 3 distinct sites in India (North India: Delhi, Hamirpur; South India: Bangalore), where we collocated PA-II with reference beta-attenuation monitors to characterize sensor performance and to model calibration relationships between PA-IIs and reference monitors for hourly data. Our sites remained in operation across all major seasons of India. Without calibration, the PA-IIs had high precision (Normalized Root Mean Square Error [NRMSE] among replicate sensors ≤ 15 %) and tracked the overall seasonal and diurnal signals from the reference instruments well (Pearson’s r ≥ 0.9) but were inaccurate (NRMSE ≥ 40 %). We used a comprehensive feature selection process to create optimized site-specific calibrations. Relative to the uncalibrated data, parsimonious least-squares long-term calibration models improved PA-II performance at all sites (cross-validated NRMSE: 20–30 %, R2: 0.82–0.95), particularly by reducing seasonal and diurnal biases. Because aerosol properties and meteorology vary regionally, the form of these long-term models differed by site. Likewise, using a moving-window calibration, we find a calibration scheme using seasonally specific information somewhat improves performance relative to a static long-term calibration model. In contrast, we demonstrate that a successful short-term calibration exercise for one season may not transfer reliably to other seasons. Overall, we demonstrate how the PA-II, when paired with a careful calibration scheme, can provide actionable information on PM2.5 in India with only modest irreducible uncertainty.

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Table S1. Most relevant parameters selected through sequential feature selection (SFS) for each PM2.5 channel (CF1, ATM, ALT) from Delhi collocation. Parameters: relative humidity (RH), temperature (T), and dew point (D). As expected, the PM2.5 was selected as the most relevant, followed by RH features.

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