Assessment of children's personal and land use regression model-estimated exposure to NO2 in Springfield, Massachusetts

Ambient nitrogen dioxide (NO₂) is derived from tailpipe vehicle emission and is linked with various of health outcomes. Personal exposure monitoring is crucial for accurate assessment of the associated disease risks. This study aimed to evaluate the utility of a wearable air pollutant sampler in determining the personal NO₂ exposure of school children for comparison with a model-based personal exposure assessment. We employed cost-effective, wearable passive samplers to directly measure personal exposure of 25 children (aged 12-13 years) in Springfield, MA to NO₂ over a five-day period in winter 2018. NO₂ levels were additionally measured at 40 outdoor sites in the same region using stationary passive samplers. A land use regression (LUR) model was developed based on the ambient NO₂ measures, with a good prediction performance (R² = 0.72) using road lengths, distance to highway, and institutional land area as predictor variables. Time-weighted averages (TWA), which incorporated the time-activity patterns of participants and LUR-derived estimates in children's primary microenvironments (homes, the school and commute paths), were calculated as an indirect measure of personal NO₂ exposure. Results indicated that the conventional residence-based exposure estimate approach, often used in epidemiological studies, differed from the direct personal exposure and could overestimate the personal exposure by up to 109 %. TWA improved personal NO₂ exposure estimates by accounting for the time activity patterns of individuals, a difference of 5.4 % ± 34.2 % was found for exposures compared to wristband measurements. Nevertheless, the personal wristband measurements exhibited a large variability due to the potential contributions from indoor and in-vehicle NO₂ sources. The findings suggest that exposure to NO₂ can be highly personalized based on individual activities and contact with pollutants in specific microenvironments, reaffirming the importance of measuring personal exposure.

Improving Water Quality in the Short Beach Neighborhood of Branford, Connecticut, 2019-A Citizen Science Project

We initiated a collaboration between local government, academia, and citizen scientists to investigate high frequencies of elevated Escherichia coli bacteria levels in the coastal Short Beach neighborhood of Branford, Connecticut. Citizen scientist involvement enabled collection of short-duration postprecipitation outfall flow water samples (mean E. coli level = 4930 most probable number per 100 mL) and yielded insights into scientific collaboration with local residents. A records review and sanitary questionnaire identified aging properties with septic systems (3.3%) and holding tanks (0.6%) as potential sources of the E. coli contamination. (Am J Public Health. 2022;112(9):1261-1264. https://doi.org/10.2105/AJPH.2022.306943).