Search for: All records

Abstract Early forecasts give people in a storm’s path time to prepare. Less is known about the cost to society when forecasts are incorrect. In this observational study, we examine over 700,000 births in the path of Hurricane Irene and find exposure was associated with impaired birth outcomes. Additional warning time was associated with decreased preterm birth rates for women who experienced intense storm exposures documenting a benefit of avoiding a type II forecasting error. A larger share of this at-risk population experienced a type I forecasting error where severe physical storm impacts were anticipated but not experienced. Disaster anticipation disrupted healthcare services by delaying and canceling prenatal care, which may contribute to storm-impacted birth outcomes. Recognizing storm damages depend on human responses to predicted storm paths is critical to supporting the next generation’s developmental potential with judicious forecasts that ensure public warning systems mitigate rather than exacerbate climate damages. 

Domestic wells provide drinking water to 44 million people nationwide. Many of these wells, which remain federally unregulated and rarely tested for pollutants, serve rural populations clustered near surface-contaminated sites (e.g., hazardous waste sites, animal agriculture operations, coal ash ponds, etc.). The potential for natural disasters to deteriorate drinking water quality is well documented. Less understood is whether opportunistic post-disaster sampling might underrepresent vulnerable populations. When disaster strikes, well water sampling campaigns offer a glimpse into the quality of water for exposed residents. We examined over 8,000 well water samples from 2016 and 2017 to measure Hurricane Matthew’s impact on the presence of indicator bacteria. Bacteria presence was predicted at the household level following Hurricane Matthew’s landfall. The residential addresses associated with birth records as well as clinically estimated dates of conception and birth dates were used to predict the likelihood of indicator bacteria in drinking water sources that were unsampled but likely to have served pregnant women. We estimate that opportunistic well water sampling captures the average predicted contamination rates among households with pregnant women. Our approach documents a distribution of contamination risk where 2.7% of the vulnerable sample (670 unsampled households) have a 75% likelihood of total coliform presence. The predicted likelihood of indicator bacteria is elevated for a small share of households nearby swine lagoons that experienced the most torrential rainfall. However, the gap between sampled and unsampled households cannot otherwise be explained by the storm event or proximity to surface-contaminated sites. Findings suggest that sophisticated and holistic water quality prediction models may support post-disaster sampling campaigns by targeting individual households within vulnerable groups that are likely to experience higher risks from groundwater contamination. 

Ergodicity (or at least the tantalizing promise of it) is a core animating principle of molecular-dynamics (MD) simulations: Put simply, sample for long enough (in time), and you will make representative visits to states of a system all throughout phase space, consistent with the desired statistical ensemble. However, one is not guaranteed a priori that the chosen window of sampling in a production run is sufficiently long to avoid problematically non-ergodic observations; one is also not guaranteed that successive measurements of an observable are statistically independent of each other. In this paper, we investigate several particularly striking and troublesome examples of statistical correlations in MD simulations of nanoconfined fluids, which have profound implications on the quantification of uncertainty for transport phenomena in these systems. In particular, we show that these correlations can lead to confidence intervals on the fluid self-diffusion coefficient that are dramatically overconfident and estimates of this transport quantity that are simply inaccurate. We propose a simple approach—based on the thermally accelerated decorrelation of fluid positions and momenta—that ameliorates these issues and improves our confidence in MD measurements of nanoconfined fluid transport properties. We demonstrate that the formation of faithful confidence intervals for measurements of self-diffusion under nanoscale confinement typically requires at least 20 statistically independent samples, and potentially more depending on the sampling technique used.