Air Quality Assessment: Analyzing PM Distribution and Calibrating Low-Cost Sensors for Precise Measurements in Indoor and Outdoor Environments

Air pollution is one of the leading causes of premature death across the world. To gain valuable insights into ambient fine particulate matter (PM) concentrations, a combination of regulatory monitoring networks, satellite retrievals of air-quality-related substances, and air quality models are typically employed. Studies reveal persistent exceedance of World Health Organization and national standards, particularly in developing nations. It is crucial to recognize that numerous regions in Asia and Africa still need proper monitoring systems to understand the emission sources and concentrations. A major obstacle to better spatiotemporal monitoring is the high cost involved in setting up the monitors.

This research investigates the distribution and proportion of PM1, PM2.5, and PM10 in an educational campus using low-cost sensors (PMS5003, PMS 7003, Winsen ZH 06, SPS 30, Honeywell). A comparative analysis was conducted to evaluate the performance of these sensors against the TSI DRX DustTrak 8533 and calibrated with a beta attenuation monitor (BAM). Additionally, to enhance the accuracy and reliability of LCS measurements, the calibration through various regression and machine-learning (ML) techniques was explored under diverse environmental conditions. In the absence of calibration, the PM2.5 correlation (R²) between LCS and DustTrak is 0.62 to 0.73, indicating a moderate to strong relationship. However, compared to BAM, LCS correlation decreases (0.20 to 0.26), suggesting a weaker association. Utilizing ML with meteorological variables improves R² values to 0.82 to 0.96 for DustTrak and 0.40 to 0.56 for BAM, with lower mean absolute and root mean square errors. The time-series results demonstrated typical seasonal patterns of winter highs and summer/monsoon lows. We also explored the PM concentrations in the kitchen and common dining facility using a combination of validated low-cost PM sensors (LCS) and DustTrak 8433. It is found that the prolonged cooking durations involved in high-heat cooking methods like stir-frying and deep-frying resulted in a rise in PM2.5, causing a higher exposure to PM. Overall, the findings of the study have provided valuable insights into the dynamics of PM2.5 emissions, the impact of cooking activities, effect of chimney and the importance of ventilation to reduce exposure to PM and implementing mitigation strategies to improve indoor air quality and protect human health.