Physics

Geology

Meteorology

Organic chemistry

Consistency (knowledge bases)

Mathematics

Term (time)

Collocation (remote sensing)

Statistics

Mass concentration (chemistry)

Machine learning

Calibration

Artificial intelligence

Chemistry

Particulates

Aerosol

Computer science

Quantum mechanics

Environmental science

Remote sensing

Thermodynamics

Aerosol Science and Technology

Low-cost sensors for the measurement of fine particulate matter mass (PM2.5) enable dense networks to increase the spatial resolution of air quality monitoring. However, these sensors are affected by environmental factors such as temperature and humidity and their effects on ambient aerosol, which must be accounted for to improve the in-field accuracy of these sensors. We conducted long-term tests of two low-cost PM2.5 sensors: Met-One NPM and PurpleAir PA-II units. We found a high level of self-consistency within each sensor type after testing 25 NPM and 9 PurpleAir units. We developed two types of corrections for the low-cost sensor measurements to better match regulatory-grade data. The first correction accounts for aerosol hygroscopic growth using particle composition and corrects for particle mass below the optical sensor size cut-point by collocation with reference Beta Attenuation Monitors (BAM). A second, fully-empirical correction uses linear or quadratic functions of environmental variables based on the same collocation dataset. The two models yielded comparable improvements over raw measurements. Sensor performance was assessed for two use cases: improving community awareness of air quality with short-term semi-quantitative comparisons of sites and providing long-term reasonably quantitative information for health impact studies. For the short-term case, both sensors provided reasonably accurate concentration information (mean absolute error of ∼4 µg/m3) in near-real time. For the long-term case, tested using year-long collocations at one urban background and one near-source site, error in the annual average was reduced below 1 µg/m3. Hence, these sensors can supplement sparse networks of regulatory-grade instruments, perform high-density neighborhood-scale monitoring, and be used to better understand spatial patterns and temporal air quality trends across urban areas.Copyright © 2019 American Association for Aerosol Research

Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation