Abstract
Low-cost sensors for particulate matter mass (PM) enable spatially dense, high temporal resolution measurements of air quality that traditional reference monitoring cannot. Low-cost PM sensors are especially beneficial in low and middle-income countries where few, if any, reference grade measurements exist and in areas where the concentration fields of air pollutants have significant spatial gradients. Unfortunately, low-cost PM sensors also come with a number of challenges that must be addressed if their data products are to be used for anything more than a qualitative characterization of air quality. The various PM sensors used in low-cost monitors are all subject to biases and calibration dependencies, corrections for which range from relatively straightforward (e.g. meteorology, age of sensor) to complex (e.g. aerosol source, composition, refractive index). The methods for correcting and calibrating these biases and dependencies that have been used in the literature likewise range from simple linear and quadratic models to complex machine learning algorithms. Here we review the needs and challenges when trying to get high-quality data from low-cost sensors. We also present a set of best practices to follow to obtain high-quality data from these low-cost sensors.
Highlights
Low-cost sensors have the potential to greatly alter how, where, and when air pollution monitoring is done
All commercially available low-cost particulate matter mass (PM) sensors utilize light-scattering as their principle of operation
The fact that comparatively simple transfer functions are relatively accurate implies that the main drawbacks of measuring aerosol mass from nephelometric response are less important for the expected applications of low-cost PM sensors
Summary
Low-cost sensors have the potential to greatly alter how, where, and when air pollution monitoring is done. Purpleair.com/]) (Ford et al, 2019; Karagulian et al, 2019; Kelly et al, 2017) to large city-scale distributed network deployments (Eilenberg et al, 2020; Jiao et al, 2016) These projects may include more sparsely dispersed reference monitors to increase the network’s accuracy. We provide a set of suggestions supported by the literature for how to generate high-quality data from low-cost sensors. This manuscript is based on the experiences and lessons learned by the authors’ deployments of over 200 low-cost PM sensors over much of the world for the past five years, including the USA, South Asia, and multiple countries in Europe and Africa. B These sensors are essentially the same but are packaged differently by different companies. c The PMS 1003/5003 flowrate is estimated to be approximately 0.1 lpm by Sayahi et al (2019). d Sensor is an integrated sensor; all other sensors are stand alone
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