Accurate, Low Cost PM2.5 Measurements Demonstrate the Large Spatial Variation in Wood Smoke Pollution in Regional Australia and Improve Modeling and Estimates of Health Costs

Abstract

The accuracy and utility of low-cost PM2.5 sensors was evaluated for measuring spatial variation and modeling population exposure to PM2.5 pollution from domestic wood-heating (DWH) in Armidale, a regional town in New South Wales (NSW), Australia, to obtain estimates of health costs and mortality. Eleven ‘PurpleAir’ (PA) monitors were deployed, including five located part of the time at the NSW government station (NSWGov) to derive calibration equations. Calibrated PA PM2.5 were almost identical to the NSWGov tapered element oscillating microbalance (TEOM) and Armidale Regional Council’s 2017 DustTrak measurements. Spatial variation was substantial. National air quality standards were exceeded 32 times from May–August 2018 at NSWGov and 63 times in one residential area. Wood heater use by about 50% of households increased estimated annual PM2.5 exposure by over eight micrograms per cubic meter, suggesting increased mortality of about 10% and health costs of thousands of dollars per wood heater per year. Accurate real-time community-based monitoring can improve estimates of exposure and avoid bias in estimating dose-response relationships. Efforts over the past decade to reduce wood smoke pollution proved ineffective, perhaps partly because some residents do not understand the health impacts or costs of wood-heating. Real-time Internet displays can increase awareness of DWH and bushfire pollution and encourage governments to develop effective policies to protect public health, as recommended by several recent studies in which wood smoke was identified as a major source of health-hazardous air pollution.

The Summer 2019–2020 Wildfires in East Coast Australia and Their Impacts on Air Quality and Health in New South Wales, Australia

“…Low-cost sensors could be used to increase the availability of air quality measurement data in areas where standard monitoring network stations are lacking, and modelling output data can then be used to blend with these data using techniques such as the Bayesian method to increase the accuracy of the estimated pollutant concentration for health impact calculations. In 2020, Robinson [ 30 ] used low-cost sensor data in addition to the data from the DPIE monitoring station in Armidale (northern NSW) to show the spatial variability of PM 2.5 and thus help to improve the accuracy of exposure and health impact estimates due to wood heater use during winter in this region.…”

Development and Application of a United States wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

“…The low cost of outdoor PurpleAir sensors ($230-$260 U.S. dollars) has enabled them to be widely used with thousands of sensors publicly reporting across the U.S. Previous work has explored the performance and accuracy of the PurpleAir sensors (Magi et al, 2019;Feenstra et al, 2019;Mehadi et al, 2019;Malings et al, 2019;Kim et al, 2019;Sayahi et al, 2019;Tryner et al, 2020a;Singer and Delp, 2018;Kelly et al, 2017;Li et al, 2020;Wang et al, 2020b;Gupta et al, 2018;Delp and Singer, 2020;Zou et al, 2020b;Stavroulas et al, 2020;Holder et al, 2020;Ardon-Dryer et al, 2020;Schulte et al, 2020;Zou et al, 2020a;Robinson, 2020;Bi et al, 2020) and their dual Plantower PMS5003 laser scattering particle sensors (He et al, 2020;Tryner et al, 2019;Kuula et al, 2019;Ford et al, 2019;Si et al, 2020;Zou et al, 2020b;Tryner et al, 2020b). Although not true of all types of PM2.5 sensors, previous work with PurpleAir sensors and other models of Plantower sensors have shown that the sensors are precise, with sensors of the same model measuring similar PM2.5 concentrations (Barkjohn et al, 2020a;Pawar and Sinha, 2020;Malings et al, 2019).…”

The Summer 2019-2020 Wildfires in East Coast Australia and Their Impacts on Air Quality and Health in New South Wales, Australia

“…Low cost sensors could be used to increase the availability of air quality measurement data in areas where standard monitoring network stations are lacking, and modelling output data can then be used to blend with these data using techniques such as Bayesian method to increase the accuracy of the estimated pollutant concentration for health impact calculation. Robinson 2020 [25] has used low cost sensor data in additional to DPIE monitoring station in Armidale (northern NSW) to show the spatial variability of PM2.5 and hence help to improve the accuracy of exposure and health impact estimate due to wood heater during winter in this region.…”