Assessing the Value of Complex Refractive Index and Particle Density for Calibration of Low-Cost Particle Matter Sensor for Size-Resolved Particle Count and PM2.5 Measurements

Commercially available low-cost particulate matter (PM) sensors provide output as total or size-specific particle counts and mass concentrations. These quantities are not measured directly but are estimated by the original equipment manufacturers' (OEM) proprietary algorithms and have inherent limitations since particle scattering depends on their composition, size, shape, and complex index of refraction (CRI). Hence, there is a need to characterize and calibrate their performance under a controlled environment. We present calibration algorithms for Plantower PMS A003 sensor as a function of particle size and concentration. A standardized experimental protocol was used to control the PM level, environmental conditions and to evaluate sensor-to-sensor reproducibility. The calibration was based on tests when PMS A003 were exposed to different polydisperse standardized testing aerosols. The results suggested particle size distribution from PMS A003 was shifted compared to reference instrument measures. For calibration of number concentration, linear model without adjusting aerosol properties corrects the raw PMS A003 measurement for specific size bins with normalized mean absolute error within 4.0% of the reference instrument. Although the Bayesian Information Criterion suggests that models adjusting for particle optical properties and relative humidity are technically superior, they should be used with caution as the particle properties used in fitting were within a narrow range for challenge aerosols. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM's mass concentrations estimates and demonstrated lower error. These results have significant implications for using PMS A003 in high concentration environments, including indoor air quality and occupational/industrial exposure assessments, wildfire smoke, or near-source monitoring scenarios

Evaluating Columbus, Georgia, Tree Canopy Interactions with Air Pollutants Using High Spectral Imagery and Portable PM Sensors

Trees provide environmental, economic, and social advantages in urban areas. Knowing the extent and location of tree canopy in a municipality is an important step in quantifying these benefits. Spatial and temporal tree canopy analysis was performed for the city of Columbus, Georgia, by categorizing the National Agriculture Imagery Program (NAIP) aerial imagery for 2005, 2010 and 2015 into tree versus non-tree land cover type using unsupervised classification procedures. Air pollution removal rates from the i-Tree program were applied to this evaluation providing an estimate of the city’s tree air quality benefits. The city’s canopy overall has remained steady at 52% of the 38,143 hectares that compose the municipality for the years 2005 (89% accuracy), 2010 (93% accuracy) and 2015 (93% accuracy). Percent tree canopy within the city’s 53 census tracts ranged from 13 to 75%. Tree loss due to development in south central, north, and north-eastern areas was offset by forest regrowth, having been cleared prior to 2005. These trees remove 1,700 tonnes of five critical air pollutants (CO, NO2, O3, PM2.5, PM10, and SO2.) and sequester 256,000 tonnes of CO2 annually, based on i-Tree’s first-order valuations. Since trees influence fine particulate matter (PM2.5) and the health impacts of PM2.5 are great, a second study was conducted to better understand how tree stand formation controls PM2.5. Three portable, fine PM sensors (AirBeams) were used among three tree canopy configurations (dense tree buffer, n=5; small tree line, n=6; and U-shaped, n=4) to determine if stand design effects PM2.5 concentrations in open areas near trees. AirBeams were evaluated and found to have reliability, ease of use, repeatability among units, and stability across the study period. Overall results between open and tree concentrations were not significantly different. Site by site observations indicated that dense tree buffers (3 of the 5 sites) trap PM2.5 resulting in higher tree particulate concentrations in the buffer zone and small tree lines (5 sites) had no effect on PM2.5. U-shaped tree stands interactions are dependent on location of the open area within the tree stand in relation to notable PM sources. While wind direction played a role in particulates reaching sampling locations, proximity to and type of PM source had the largest impact on local PM2.5 concentrations. Urban canopy cover recommendations are made so cities can benefit from ecosystem services that trees provide, but simply adding trees does not mean these benefits are fully utilized. Tree type, tree design, and tree placement, i.e. in available space and proximity to pollution source, need to be considered. Utilizing high spectral imagery and low-cost, portable sensors can help cities determine the best tree placement and design to aide in air pollution reduction