Abstract
In 2017, Assembly Bill 617 was approved in the state of California, which mandated the allocation of resources for addressing air pollutant exposure disparities in underserved communities across the state. The bill stipulated the implementation of community scale monitoring and the development of local emissions reductions plans. We aimed to develop a streamlined, robust, and accessible PM2.5 exposure assessment approach to support environmental justice analyses. We sought to characterize individual PM2.5 exposure over multiple 24-hr periods in the inland Southern California region, which includes the underserved community of San Bernardino, CA. Personal sampling took place over five weeks in the spring of 2019, and personal PM2.5 exposure was monitored for 18 adult participants for multiple, consecutive 24-hr periods. Exposure and location data were analyzed at 5-second resolution, and participant data recovery was 50.8% on average. A spatial clustering algorithm was used to classify data points as one of seven microenvironments. Mean and median personal-ambient PM2.5 ratios were aggregated along SES lines for eligible datasets. GIS-based spatial clustering facilitated efficient microenvironment classification for more than 900,000 data points. Mean (median) personal-ambient ratios ranged from 0.26 (0.14) to 2.78 (0.65) for each microenvironment when aggregated along SES-lines. Aggregated ratios indicated that participants from the lowest SES community experienced higher home exposures compared to participants of all other communities over consecutive 24-hr monitoring periods, despite high participant mobility and relatively low variability in ambient PM2.5 during the study. The methods described here highlight the robust and accessible nature of the personal sampling campaign, which was specifically designed to reduce participant fatigue and engage members of the inland Southern California community who may experience barriers when engaging with the scientific community. This approach is promising for larger-scale, community-focused, personal exposure campaigns for direct and precise environmental justice analyses.
Highlights
Ambient particulate matter (PM) has been widely studied, and researchers have carefully examined the impact of PM exposure on human health
We explored one other approach to adjust the raw data, for which we utilized using machine learning with random forest regression (RFR) to construct a pattern-based relationship between the reference and raw data
The mean bias for the four monitors ranged from -0.11 to 0.61, slopes ranged from 0.99 to 1.10, intercepts ranged from 0.012 to 0.75, and R2 ranged from 0.41-0.45 (Note S2)
Summary
Ambient particulate matter (PM) has been widely studied, and researchers have carefully examined the impact of PM exposure on human health. Further the sparseness of the monitoring network leads to low spatial resolution data and necessitates gap-filling, which affects the accuracy of PM exposure assessments that are based on ambient measurements Home and workplace are the two most dominant indoor microenvironments. Indoor PM originates from cooking, smoking, cleaning products, vacuuming, and dusting; while in offices, PM is emitted from printing, mechanical grinding, consumer products, and dusting. The Environmental Protection Agency (EPA) carried out the particulate total exposure assessment methodology (PTEAM) study on 178 non-smoking randomly selected homes in Riverside, CA. The study showed that indoor PM2.5 (PM with an aerodynamic diameter less than or equal to 2.5 μm) levels were slightly lower than outdoor levels during the day
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