Assessment of a mass balance equation for estimating community-level prevalence of COVID-19 using wastewater-based epidemiology in a mid-sized city.

Wastewater-based epidemiology (WBE) has emerged as a valuable epidemiologic tool to detect the presence of pathogens and track disease trends within a community. WBE overcomes some limitations of traditional clinical disease surveillance as it uses pooled samples from the entire community, irrespective of health-seeking behaviors and symptomatic status of infected individuals. WBE has the potential to estimate the number of infections within a community by using a mass balance equation, however, it has yet to be assessed for accuracy. We hypothesized that the mass balance equation-based approach using measured SARS-CoV-2 wastewater concentrations can generate accurate prevalence estimates of COVID-19 within a community. This study encompassed wastewater sampling over a 53-week period during the COVID-19 pandemic in Gainesville, Florida, to assess the ability of the mass balance equation to generate accurate COVID-19 prevalence estimates. The SARS-CoV-2 wastewater concentration showed a significant linear association (Parameter estimate = 39.43, P value < 0.0001) with clinically reported COVID-19 cases. Overall, the mass balance equation produced accurate COVID-19 prevalence estimates with a median absolute error of 1.28%, as compared to the clinical reference group. Therefore, the mass balance equation applied to WBE is an effective tool for generating accurate community-level prevalence estimates of COVID-19 to improve community surveillance.

Association between PM2.5 and respiratory hospitalization in Rio Branco, Brazil: Demonstrating the potential of low-cost air quality sensor for epidemiologic research.

BACKGROUND: There is currently a scarcity of air pollution epidemiologic data from low- and middle-income countries (LMICs) due to the lack of air quality monitoring in these countries. Additionally, there is limited capacity to assess the health effects of wildfire smoke events in wildfire-prone regions like Brazil's Amazon Basin. Emerging low-cost air quality sensors may have the potential to address these gaps. OBJECTIVES: We investigated the potential of PurpleAir PM2.5 sensors for conducting air pollution epidemiologic research leveraging the United States Environmental Protection Agency's United States-wide correction formula for ambient PM2.5. METHODS: We obtained raw (uncorrected) PM2.5 concentration and humidity data from a PurpleAir sensor in Rio Branco, Brazil, between 2018 and 2019. Humidity measurements from the PurpleAir sensor were used to correct the PM2.5 concentrations. We established the relationship between ambient PM2.5 (corrected and uncorrected) and daily all-cause respiratory hospitalization in Rio Branco, Brazil, using generalized additive models (GAM) and distributed lag non-linear models (DLNM). We used linear regression to assess the relationship between daily PM2.5 concentrations and wildfire reports in Rio Branco during the wildfire seasons of 2018 and 2019. RESULTS: We observed increases in daily respiratory hospitalizations of 5.4% (95%CI: 0.8%, 10.1%) for a 2-day lag and 5.8% (1.5%, 10.2%) for 3-day lag, per 10 µg/m3 PM2.5 (corrected values). The effect estimates were attenuated when the uncorrected PM2.5 data was used. The number of reported wildfires explained 10% of daily PM2.5 concentrations during the wildfire season. DISCUSSION: Exposure-response relationships estimated using corrected low-cost air quality sensor data were comparable with relationships estimated using a validated air quality modeling approach. This suggests that correcting low-cost PM2.5 sensor data may mitigate bias attenuation in air pollution epidemiologic studies. Low-cost sensor PM2.5 data could also predict the air quality impacts of wildfires in Brazil's Amazon Basin.

Assessment of per- and polyfluoroalkyl substances (PFAS) in the Indian River Lagoon and Atlantic coast of Brevard County, FL, reveals distinct spatial clusters.

Per- and polyfluoroalkyl substances (PFAS) constitute a class of highly stable and extensively manufactured anthropogenic chemicals that have been linked to a variety of adverse health effects in humans and wildlife. These compounds are ubiquitously distributed in the environment and have been measured in aquatic systems globally. However, there are limited data on longitudinal comprehensive assessments of PFAS profiles within sensitive aquatic ecosystems. Surface water samples were collected from the Indian River Lagoon (IRL) and the Atlantic coast within Brevard County (BC), FL in December of 2019 (n = 57) and again from corresponding locations in February of 2021 (n = 40). Samples were analyzed by ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) to determine the occurrence, concentration, and distribution of 92 PFAS. No significant difference in total PFAS concentrations were identified between samples collected in 2019 (87 ng/L) and those collected in 2021 (77 ng/L). However, comparisons of PFAS among four natural sub-regions within Brevard County revealed site- and regional-specific differences. The Banana River exhibited the greatest concentration of total PFAS, followed by the southern Indian River, the northern Indian River, and then the Atlantic coast. Six distinct PFAS profiles were identified with the novel application of multivariate statistical cluster analysis, which may be useful for identifying potential sources of PFAS. Elevated total PFAS and unique compound mixtures identified in the Banana River are most likely a result of industrial discharge and extensive historical use of aqueous film-forming foams (AFFF). The environmental persistence of PFAS threatens key ecosystem services and the ecological homeostasis of the Indian River Lagoon - the most biologically diverse estuary in North America. Brevard County offers a unique model site that may be used to investigate potential exposure and health implications for wildlife and adjacent coastal communities, which could be extrapolated to better understand and manage other critical coastal systems.

A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda.

The application of land use regression (LUR) modeling for estimating air pollution exposure has been used only rarely in sub-Saharan Africa (SSA). This is generally due to a lack of air quality monitoring networks in the region. Low cost air quality sensors developed locally in sub-Saharan Africa presents a sustainable operating mechanism that may help generate the air monitoring data needed for exposure estimation of air pollution with LUR models. The primary objective of our study is to investigate whether a network of locally developed low-cost air quality sensors can be used in LUR modeling for accurately predicting monthly ambient fine particulate matter (PM2.5) air pollution in urban areas of central and eastern Uganda. Secondarily, we aimed to explore whether the application of machine learning (ML) can improve LUR predictions compared to ordinary least squares (OLS) regression. We used data for the entire year of 2020 from a network of 23 PM2.5 low-cost sensors located in urban municipalities of eastern and central Uganda. Between January 1, 2020 and December 31, 2020, these sensors collected highly time-resolved measurement data of PM2.5 air concentrations. We used monthly-averaged PM2.5 concentration data for LUR prediction modeling of monthly PM2.5 concentrations. We used eight different ML base-learner algorithms as well as ensemble modeling. We applied 5-fold cross validation (80% training/20% test random splits) to evaluate the models with resampling and Root mean squared error (RMSE). The relative explanatory power and accuracy of the ML algorithms were evaluated by comparing coefficient of determination (R2) and RMSE, using OLS as the reference approach. The overall average PM2.5 concentration during the study period was 52.22 µg/m3 (IQR: 38.11, 62.84 µg/m3)-well above World Health Organization PM2.5 ambient air guidelines. From the base-learner and ensemble models, RMSE and R2 values ranged between 7.65 µg/m3 - 16.85 µg/m3 and 0.24-0.84, respectively. Extreme gradient boosting (xgbTree) performed best out of the base learner algorithms (R2 = 0.84; RMSE = 7.65 µg/m3). Model performance from ensemble modeling with Lasso and Elastic-Net Regularized Generalized Linear Models (glmnet) did not outperform xgbTree, but prediction performance was comparable to that of xgbTree. The most important temporal and spatial predictors of monthly PM2.5 levels were monthly precipitation, percent of the population using solid fuels for cooking, distance to Lake Victoria, and greenspace (NDVI) within a 500-m buffer of air monitors. In conclusion, data from locally developed low-cost PM sensors provide evidence that they can be used for spatio-temporal prediction modeling of air pollution exposures in Uganda. Moreover, the non-parametric ML and ensemble approaches to LUR modeling clearly outperformed OLS regression algorithm for the prediction of monthly PM2.5 concentrations. Deploying low-cost air quality sensors in concert with implementation of data quality control measures, can help address the critical need for expanding and improving air quality monitoring in resource-constrained settings of sub-Saharan Africa. These low-cost sensors, in conjunction with non-parametric ML algorithms, may provide a rapid path forward for PM2.5 exposure assessment and to spur air pollution epidemiology research in the region.

"Freedom to Breathe": Youth Participatory Action Research (YPAR) to Investigate Air Pollution Inequities in Richmond, CA.

Air pollution is a major contributor to human morbidity and mortality, potentially exacerbated by COVID-19, and a threat to planetary health. Participatory research, with a structural violence framework, illuminates exposure inequities and refines mitigation strategies. Home to profitable oil and shipping industries, several census tracts in Richmond, CA are among the most heavily impacted by aggregate burdens statewide. Formally trained researchers from the Center for Environmental Research and Children's Health (CERCH) partnered with the RYSE youth justice center to conduct youth participatory action research on air quality justice. Staff engaged five youth researchers in: (1) collaborative research using a network of passive air monitors to quantify neighborhood disparities in nitrogen dioxide (NO2) and sulfur dioxide (SO2), noise pollution and community risk factors; (2) training in environmental health literacy and professional development; and (3) interpretation of findings, community outreach and advocacy. Inequities in ambient NO2, but not SO2, were observed. Census tracts with higher Black populations had the highest NO2. Proximity to railroads and major roadways were associated with higher NO2. Greenspace was associated with lower NO2, suggesting investment may be conducive to improved air quality, among many additional benefits. Youth improved in measures of empowerment, and advanced community education via workshops, Photovoice, video, and "zines".

A time series analysis of the ecologic relationship between acute and intermediate PM2.5 exposure duration on neonatal intensive care unit admissions in Florida.

Admissions of newborn infants into Neonatal Intensive Care Units (NICU) has increased in the US over the last decade yet the role of environmental exposures as a risk factor for NICU admissions is under studied. Our study aims to determine the ecologic association between acute and intermediate ambient PM2.5 exposure durations and rates of NICU admissions, and to explore whether this association differs by area-level social stressors and meteorological factors. We conducted an ecologic time-series analysis of singleton neonates (N = 1,027,797) born in Florida hospitals between December 26, 2011 to April 30, 2019. We used electronic medical records (EMRs) in the OneFlorida Data Trust and included infants with a ZIP code in a Metropolitan Statistical Areas (MSA) and excluded extreme preterm births (<24wks gestation). The study outcome is the number of daily NICU admission at 28 days old or younger for each ZIP code in the study area. The exposures of interest are average same day, 1- and 2-day lags, and 1-3 weeks ambient PM2.5 concentration at the ZIP code-level estimated using inverse distance weighting (IDW) for each day of the study period. We used a zero-inflated Poisson regression mixed effects models to estimate adjusted associations between acute and intermediate PM2.5 exposure durations and NICU admissions rates. NICU admissions rates increased over time during the study period. Ambient 7-day average PM2.5 concentrations was significantly associated with incidence of NICU admissions, with an interquartile range (IQR = 2.37 µg/m3) increase associated with a 1.4% (95% CI: 0.4%, 2.4%) higher adjusted incidence of daily NICU admissions. No other exposure duration metrics showed a significant association with daily NICU admission rates. The magnitude of the association between PM2.5 7-day average concentrations with NICU admissions was significantly (p < 0.05) higher among ZIP codes with higher proportions of non-Hispanic Blacks, ZIP codes with household incomes in the lowest quartile, and on days with higher relative humidity. Our data shows a positive relationship between acute (7-day average) PM2.5 concentrations and daily NICU admissions in Metropolitan Statistical Areas of Florida. The observed associations were stronger in socioeconomically disadvantaged areas, areas with higher proportions with non-Hispanic Blacks, and on days with higher relative humidity. Further research is warranted to study other air pollutants and multipollutant effects and identify health conditions that are driving these associations with NICU admissions.

DNA methylation and socioeconomic status in a Mexican-American birth cohort.

Background: Maternal social environmental stressors during pregnancy are associated with adverse birth and child developmental outcomes, and epigenetics has been proposed as a possible mechanism for such relationships. Methods: In a Mexican-American birth cohort of 241 maternal-infant pairs, cord blood samples were measured for repeat element DNA methylation (LINE-1 and Alu). Linear mixed effects regression was used to model associations between indicators of the social environment (low household income and education, neighborhood-level characteristics) and repeat element methylation. Results from a dietary questionnaire were also used to assess the interaction between maternal diet quality and the social environment on markers of repeat element DNA methylation. Results: After adjusting for confounders, living in the most impoverished neighborhoods was associated with higher cord blood LINE-1 methylation (ß = 0.78, 95%CI 0.06, 1.50, p = 0.03). No other neighborhood-, household-, or individual-level socioeconomic indicators were significantly associated with repeat element methylation. We observed a statistical trend showing that positive association between neighborhood poverty and LINE-1 methylation was strongest in cord blood of infants whose mothers reported better diet quality during pregnancy (pinteraction = 0.12). Conclusion: Our findings indicate a small yet unexpected positive association between neighborhood-level poverty during pregnancy and methylation of repetitive element DNA in infant cord blood and that this association is possibly modified by diet quality during pregnancy. However, our null findings for other adverse SES indicators do not provide strong evidence for an adverse association between early-life socioeconomic environment and repeat element DNA methylation in infants.