Integrating Address Geocoding, Land Use Regression, and Spatiotemporal Geostatistical Estimation for Groundwater Tetrachloroethylene

Geographic Information Systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for Tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend

Spatiotemporal profiles of ultrafine particles differ from other traffic-related air pollutants : lessons from long-term measurements at fixed sites and mobile monitoring

Peer reviewe

Arsenic in North Carolina: Public Health Implications

Arsenic is a known human carcinogen and relevant environmental contaminant in drinking water systems. We set out to comprehensively examine statewide arsenic trends and identify areas of public health concern. Specifically, arsenic trends in North Carolina private wells were evaluated over an eleven-year period using the North Carolina Department of Health and Human Services (NCDHHS) database for private domestic well waters. We geocoded over 63,000 domestic well measurements by applying a novel geocoding algorithm and error validation scheme. Arsenic measurements and geographical coordinates for database entries were mapped using Geographic Information System (GIS) techniques. Furthermore, we employed a Bayesian Maximum Entropy (BME) geostatistical framework, which accounts for geocoding error to better estimate arsenic values across the state and identify trends for unmonitored locations. Of the approximately 63,000 monitored wells, 7,712 showed detectable arsenic concentrations that ranged between 1 and 806 μg/L. Additionally, 1,436 well samples exceeded the EPA drinking water standard. We reveal counties of concern and demonstrate a historical pattern of elevated arsenic in some counties, particularly those located along the Carolina terrane (Carolina slate belt). We analyzed these data in the context of populations using private well water and identify counties for targeted monitoring, such as Stanly and Union Counties. By spatiotemporally mapping these data, our BME estimate revealed arsenic trends at unmonitored locations within counties and better predicted well concentrations when compared to the classical kriging method. This study reveals relevant information on the location of arsenic-contaminated private domestic wells in North Carolina and indicates potential areas at increased risk for adverse health outcomes

Extreme precipitation and emergency room visits for influenza in Massachusetts: a case-crossover analysis

Abstract Background Influenza peaks during the wintertime in temperate regions and during the annual rainy season in tropical regions – however reasons for the observed differences in disease ecology are poorly understood. We hypothesize that episodes of extreme precipitation also result in increased influenza in the Northeastern United States, but this association is not readily apparent, as no defined ‘rainy season’ occurs. Our objective was to evaluate the association between extreme precipitation (≥ 99th percentile) events and risk of emergency room (ER) visit for influenza in Massachusetts during 2002–2008. Methods A case-crossover analysis of extreme precipitation events and influenza ER visits was conducted using hospital administrative data including patient town of residence, date of visit, age, sex, and associated diagnostic codes. Daily precipitation estimates were generated for each town based upon data from the National Oceanic and Atmospheric Administration. Odds ratio (OR) and 95% confidence intervals (CI) for associations between extreme precipitation and ER visits for influenza were estimated using conditional logistic regression. Results Extreme precipitation events were associated with an OR = 1.23 (95%CI: 1.16, 1.30) for ER visits for influenza at lag days 0–6. There was significant effect modification by race, with the strongest association observed among Blacks (OR = 1.48 (1.30, 1.68)). Conclusions We observed a positive association between extreme precipitation events and ER visits for influenza, particularly among Blacks. Our results suggest that influenza is associated with extreme precipitation in a temperate area; this association could be a result of disease ecology, behavioral changes such as indoor crowding, or both. Extreme precipitation events are expected to increase in the Northeastern United States as climate change progresses. Additional research exploring the basis of this association can inform potential interventions for extreme weather events and influenza transmission

Spatial variation of fine particulate matter levels in Nairobi before and during the COVID-19 curfew: implications for environmental justice

Abstract: The temporary decrease of fine particulate matter (PM2.5) concentrations in many parts of the world due to the COVID-19 lockdown spurred discussions on urban air pollution and health. However there has been little focus on sub-Saharan Africa, as few African cities have air quality monitors and if they do, these data are often not publicly available. Spatial differentials of changes in PM2.5 concentrations as a result of COVID also remain largely unstudied. To address this gap, we use a serendipitous mobile air quality monitoring deployment of eight Sensirion SPS 30 sensors on motorbikes in the city of Nairobi starting on 16 March 2020, before a COVID-19 curfew was imposed on 25 March and continuing until 5 May 2020. We developed a random-forest model to estimate PM2.5 surfaces for the entire city of Nairobi before and during the COVID-19 curfew. The highest PM2.5 concentrations during both periods were observed in the poor neighborhoods of Kariobangi, Mathare, Umoja, and Dandora, located to the east of the city center. Changes in PM2.5 were heterogeneous over space. PM2.5 concentrations increased during the curfew in rapidly urbanizing, the lower-middle-class neighborhoods of Kahawa, Kasarani, and Ruaraka, likely because residents switched from LPG to biomass fuels due to loss of income. Our results indicate that COVID-19 and policies to address it may have exacerbated existing air pollution inequalities in the city of Nairobi. The quantitative results are preliminary, due to sampling limitations and measurement uncertainties, as the available data came exclusively from low-cost sensors. This research serves to highlight that spatial data that is essential for understanding structural inequalities reflected in uneven air pollution burdens and differential impacts of events like the COVID pandemic. With the help of carefully deployed low-cost sensors with improved spatial sampling and at least one reference-quality monitor for calibration, we can collect data that is critical for developing targeted interventions that address environmental injustice in the African context

Long-term exposure to ultrafine particles and incidence of cardiovascular and cerebrovascular disease in the EPIC-NL cohort

Background: There is a small but growing evidence base that exposure to ultrafine particles (UFP – particles smaller than 100nm) may play an important role in the etiology of several illnesses, including cardiovascular disease (CVD). However, this has been under-explored in population-level studies. Methods: Using Cox proportional hazard models we studied the association between long-term exposure to UFP (predicted via recently developed land use regression models) and incident cardiovascular disease in the Dutch arm of the European Prospective Investigation into Cancer cohort (EPIC-NL), which contains 33,831 Dutch residents. Hazard ratios (HR) for UFP were compared to HRs for more routinely monitored air pollutants, including PM$_{10}$, PM$_{coarse}$, PM$_{2.5}$, PM$_{2.5}$ absorbance, NO$_{x}$, and NO$_{2}$. Joint-pollutant effects were also evaluated in two-pollutant models. Results: Long-term exposure to UFP was associated with increased HRs for all incident cardiovascular disease (HR = 1.18 per 10,000 particles/cm$_{3}$, 95% CI: 1.03, 1.34), myocardial infarction (HR = 1.34, 95% CI: 1.00, 1.79), and heart failure (HR = 1.76, 95% CI: 1.17, 2.66). Positive associations were also observed for NO2 (HR for heart failure = 1.22, 95% CI: 1.01, 1.48 per 20 μg/m$^{3}$) and coarse PM (HR for all CVD = 1.21, 95% CI: 1.01, 1.45 per 10 μg/m$^{3}$). CVD was not positively associated with PM$_{2.5}$ (HR for all CVD = 0.95, 95% CI: 0.75, 1.28 per 5 μg/m$^{3}$). HRs for UFP and cerebrovascular diseases were positive, but not significant. In two-pollutant models (UFP + NO$_{2}$ and UFP + PM$_{coarse}$), positive associations tended to remain for UFP, while HRs for PM$_{coarse}$ and NO$_{2}$ generally attenuated towards the null. Conclusions: These findings strengthen the overall evidence that UFP exposure plays an important role in cardiovascular health and that risks of ambient air pollution, based on conventional air pollution metrics, may underestimate the true population risk.ment data and biological responses as viability (AlamarBlue assay), cytotoxicity (LDH release), and release of cytokines during long-term exposure are reported