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Leveraging Citizen Science and Low-Cost Sensors to Characterize Air Pollution Exposure of Disadvantaged Communities in Southern California

ABSTRACT: Assessing exposure to fine particulate matter (PM2.5) across disadvantaged communities is understudied, and the air monitoring network is inadequate. We leveraged emerging low-cost sensors (PurpleAir) and engaged community residents to develop a community-based monitoring program across disadvantaged communities (high proportions of low-income and minority populations) in Southern California. We recruited 22 households from 8 communities to measure residential outdoor PM2.5 concentrations from June 2021 to December 2021. We identified the spatial and temporal patterns of PM2.5 measurements as well as the relationship between the total PM2.5 measurements and diesel PM emissions. We found that communities with a higher percentage of Hispanic and African American population and higher rates of unemployment, poverty, and housing burden were exposed to higher PM2.5 concentrations. The average PM2.5 concentrations in winter (25.8 µg/m3) were much higher compared with the summer concentrations (12.4 µg/m3). We also identified valuable hour-of-day and day-of-week patterns among disadvantaged communities. Our results suggest that the built environment can be targeted to reduce the exposure disparity. Integrating low-cost sensors into a citizen-science-based air monitoring program has promising applications to resolve monitoring disparity and capture “hotspots” to inform emission control and urban planning policies, thus improving exposure assessment and promoting environmental justice.

SUBMITTER: Lu T

PROVIDER: S-EPMC9322770 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:An Environmental Justice (EJ) analysis was carried out using full Chemical Transport Models (CTMs) over Los Angeles, California, to determine how the combination of domain size and spatial resolution affects predicted air pollution disparities in present day and future simulations when data support from measurements is not available. One set of simulations used the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF/Chem) with spatial resolution ranging from 250 m to 36 km, comparable to census tract sizes, over domains ranging in size from 320 km2 to 10,000 km2. A second set of simulations used the UCD/CIT CTM with spatial resolution ranging from 4 km to 24 km over domains ranging in size from 98,000 km2 to 1,000,000 km2. Overall WRF/Chem model accuracy improved approximately 9% as spatial resolution increased from 4 km to 250 m in present-day simulations, with similar results expected for future simulations. Exposure disparity results are consistent with previous findings: the average Non-Hispanic White person in the study domain experiences PM2.5 mass concentrations 6–14% lower than the average resident, while the average Black and African American person experiences PM2.5 mass concentrations that are 3–22% higher than the average resident. Predicted exposure disparities were a function of the model configuration. Increasing the spatial resolution finer than approximately 1 km produced diminishing returns because the increased spatial resolution came at the expense of reduced domain size in order to maintain reasonable computational burden. Increasing domain size to capture regional trends, such as wealthier populations living in coastal areas, identified larger exposure disparities but the benefits were limited. CTM configurations that use spatial resolution/domain size of 1 km/103 km2 and 4 km/104 km2 over Los Angeles can detect a 0.5 μg m−3 exposure difference with statistical power greater than 90%. These configurations represent a balanced approach between statistical power, sensitivity across socio-economic groups, and computational burden when predicting current and future air pollution exposure disparities in Los Angeles. Air quality; Environmental disparity; PM2.5; Chemical transport model.

Project description:BackgroundAir pollution exposure has been associated with increased risk of COVID-19 incidence and mortality by ecological analyses. Few studies have investigated the specific effect of traffic-related air pollution on COVID-19 severity.ObjectiveTo investigate the associations of near-roadway air pollution (NRAP) exposure with COVID-19 severity and mortality using individual-level exposure and outcome data.MethodsThe retrospective cohort includes 75,010 individuals (mean age 42.5 years, 54% female, 66% Hispanic) diagnosed with COVID-19 at Kaiser Permanente Southern California between 3/1/2020-8/31/2020. NRAP exposures from both freeways and non-freeways during 1-year prior to the COVID-19 diagnosis date were estimated based on residential address history using the CALINE4 line source dispersion model. Primary outcomes include COVID-19 severity defined as COVID-19-related hospitalizations, intensive respiratory support (IRS), intensive care unit (ICU) admissions within 30 days, and mortality within 60 days after COVID-19 diagnosis. Covariates including socio-characteristics and comorbidities were adjusted for in the analysis.ResultOne standard deviation (SD) increase in 1-year-averaged non-freeway NRAP (0.5 ppb NOx) was associated with increased odds of COVID-19-related IRS and ICU admission [OR (95% CI): 1.07 (1.01, 1.13) and 1.11 (1.04, 1.19) respectively] and increased risk of mortality (HR = 1.10, 95% CI = 1.03, 1.18). The associations of non-freeway NRAP with COVID-19 outcomes were largely independent of the effect of regional fine particulate matter and nitrogen dioxide exposures. These associations were generally consistent across age, sex, and race/ethnicity subgroups. The associations of freeway and total NRAP with COVID-19 severity and mortality were not statistically significant.ConclusionsData from this multiethnic cohort suggested that NRAP, particularly non-freeway exposure in Southern California, may be associated with increased risk of COVID-19 severity and mortality among COVID-19 infected patients. Future studies are needed to assess the impact of emerging COVID-19 variants and chemical components from freeway and non-freeway NRAP.

Project description:BackgroundStudies of effects of air pollution on gestational diabetes mellitus (GDM) have not been consistent, and there has been little investigation of effects of exposure preceding pregnancy. In previous studies, the temporal relationship between exposure and GDM onset has been difficult to establish.MethodsData were obtained for 239,574 pregnancies between 1999 and 2009 in a population-based health care system with comprehensive electronic medical records. Concentrations of ambient nitrogen dioxide (NO2), particulate matter (PM) ≤2.5 μm in aerodynamic diameter (PM2.5) and ≤10 μm (PM10), and ozone (O3) during preconception and the first trimester of pregnancy at the residential birth address were estimated from regulatory air monitoring stations. Odds ratios (ORs) of GDM diagnosed in the second and third trimesters in association with pollutant exposure were estimated using generalized estimating equation models adjusted for birth year, medical center service areas, maternal age, race/ethnicity, education, census-tract household income, and parity.ResultsIn single-pollutant models, preconception NO2 was associated with increased risk of GDM (OR = 1.10 per 10.4 ppb, 95% confidence interval [CI]: 1.07, 1.13). First trimester NO2 was weakly associated with GDM, and this was not statistically significant (OR = 1.02 per 10.4 ppb, 95% CI: 0.99, 1.05). Preconception NO2 associations were robust in multi-pollutant models adjusted for first trimester NO2 with another co-pollutant from both exposure windows. In single-pollutant models, preconception PM2.5 and PM10 associations were associated with increased risk of GDM (OR = 1.04 per 6.5 μg/m3, 95% CI: 1.01, 1.06; OR = 1.03 per 16.1 μg/m3, 95% CI: 1.00, 1.06, respectively), but these effect estimates were not robust to adjustment for other pollutants. In single-pollutant models, preconception and first trimester O3 were associated with reduced risk of GDM (OR = 0.94 per 15.7 ppb, 95% CI: 0.92, 0.95; OR = 0.95 per 15.7 ppb, 95% CI: 0.94, 0.97), associations that were robust to adjustment for co-pollutants.ConclusionsMaternal exposure to NO2 during the preconception trimester may increase risk of GDM.

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Leveraging Citizen Science and Low-Cost Sensors to Characterize Air Pollution Exposure of Disadvantaged Communities in Southern California

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