Our work encompasses the analysis of ambient air pollutants using reference- or research-grade instruments and low-cost sensors. We go beyond PM mass concentrations to investigate aerosol composition with state-of-the-art filter-based laboratory analysis and in-situ instrumentation. Our expertise in the proper, science-guided use of these techniques is obtained by conducting field campaigns, source apportionment studies, and systematic evaluations of low-cost sensors, as well as literature reviews. We share this knowledge with the wider community through capacity-building workshops, data portals, and scientific publications. Our data are used to inform policy and support model development.
Multiple PM Low-Cost Sensors, Multiple Seasons’ Data, and Multiple Calibration Models
In this study, we combined state-of-the-art data modelling techniques (machine learning [ML] methods) and data from state-of-the-art low-cost particulate matter (PM) sensors (LCSs) to improve the accuracy of LCS-measured PM2.5 (PM with aerodynamic diameter less than 2.5 microns) mass concentrations. We collocated nine LCSs and a reference PM2.5 instrument for 9 months, covering all local seasons, in Bengaluru, India. Using the collocation data, we evaluated the performance of the LCSs and trained around 170 ML models to reduce the observed bias in the LCS-measured PM2.5.
Mapping air pollution in Bengaluru using low-cost sensors and mobile monitoring data
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.
Inter-versus Intracity Variations in the Performance and Calibration of Low-Cost PM2.5 Sensors: A Multicity Assessment in India
Low-cost sensors (LCSs) have revolutionized the air pollution monitoring landscape. However, the sensitivities of particulate matter (PM) LCS measurements to various particle microphysical properties and meteorological aspects warrant an accuracy investigation. We investigated the inter- and intracity variations in the accuracy of LCS-measured PM2.5 across geographically and demographically distinct Indian cities.
Bias in PM2.5 measurements using collocated reference-grade and optical instruments
Optical PM2.5 measurements are sensitive to aerosol properties that can vary with space and time. Here, we compared PM2.5 measurements from collocated reference-grade (beta attenuation monitors, BAMs) and optical instruments (two DustTrak II and two DustTrak DRX) over 6 months. We performed inter-model (two different models), intra-model (two units of the same model), and inter-type (two different device types: optical vs. reference-grade) comparisons under ambient conditions.
Best Practices for Deploying and Maintaining a Low-Cost PM2.5 Sensor Network
Strategically placed sensors can monitor air pollution and provide a detailed picture of air quality and its variability within a region. Low-cost sensors (LCSs) that measure PM2.5 are becoming increasingly popular because of their low cost, ease of use, and portability. However, the portability and low cost come with trade-offs on data quality, reliability, and shelf life. The typical shelf life of LCSs is around a year to two. Also, the raw data from these LCSs need to be calibrated. This report documents the best practices for establishing and maintaining an LCS network.