Low-Cost Air Quality Sensor Evaluation and Calibration in Contrasting Aerosol Environments

The use of low-cost sensors (LCS) in air quality monitoring has been gaining interest across all walks of society, including community and citizen scientists, academic research groups, environmental agencies, and the private sector. Traditional air monitoring, performed by regulatory agencies, involves expensive regulatory-grade equipment and requires ongoing maintenance and quality control checks. The low-price tag, minimal operating cost, ease of use, and open data access are the primary driving factors behind the popularity of LCS. This study discusses the role and associated challenges of PM _2.5 sensors in monitoring air quality. We present the results of evaluations of the PurpleAir (PA.) PA-II LCS against regulatory-grade PM _2.5 federal equivalent methods (FEM) and the development of sensor calibration algorithms. The LCS calibration was performed for 2 to 4 weeks during December 2019-January 2020 in Raleigh, NC, and Delhi, India, to evaluate the data quality under different aerosols loadings and environmental conditions. This exercise aims to develop a robust calibration model that uses PA measured parameters (i.e., PM _2.5 , temperature, relative humidity) as input and provides bias-corrected PM _2.5 output at an hourly scale. Thus, the calibration model relies on simultaneous measurements of PM _2.5 by FEM as target output during the calibration model development process. We applied various statistical and machine learning methods to achieve a regional calibration model. The results from our study indicate that, with proper calibration, we can achieve bias-corrected PM _2.5 data using PA sensors within 12% percentage mean absolute bias at hourly and within 6% for a daily average. Our study also suggests that pre-deployment calibrations developed at local or regional scales should be performed for the PA sensors to correct data from the field for scientific data analysis.