Spatial dimensions of water quality value in New England river networks.

Households' willingness to pay (WTP) for water quality improvements-representing their economic value-depends on where improvements occur. Households often hold higher values for improvements close to their homes or iconic areas. Are there other areas where improvements might hold high value to individual households, do effects on WTP vary by type of improvement, and can these areas be identified even if they are not anticipated by researchers? To answer these questions, we integrated a water quality model and map-based, interactive choice experiment to estimate households' WTP for water quality improvements throughout a river network covering six New England states. The choice experiment was implemented using a push-to-web survey over a sample of New England households. Voting scenarios used to elicit WTP included interactive geographic information system (GIS) maps that illustrated three water quality measures at various zoom levels across the study domain. We captured data on how respondents maneuvered through these maps prior to answering the value-eliciting questions. Results show that WTP was influenced by regionwide quality improvements and improvements surrounding each respondent's home, as anticipated, but also by improvements in individualized locations identifiable via each respondent's map interactions. These spatial WTP variations only appear for low-quality rivers and are focused around particular areas of New England. The study shows that dynamic map interactions can convey salient information for WTP estimation and that predicting spatial WTP heterogeneity based primarily on home or iconic locations, as typically done, may overlook areas where water quality has high value.

Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation.

An accurate forecast of the red tide respiratory irritation level would improve the lives of many people living in areas affected by algal blooms. Using a decades-long database of daily beach conditions, two conceptually different models to forecast the respiratory irritation risk level one day ahead of time are trained. One model is wind-based, using the current days' respiratory level and the predicted wind direction of the following day. The other model is a probabilistic self-exciting Hawkes process model. Both models are trained on beaches in Florida during 2011--2017 and applied to the red tide bloom during 2018-2019. For beaches where there is enough historical data to develop a model, the model which performs best depends on the beach. The wind-based model is the most accurate at half the beaches, correctly predicting the respiratory risk level on average about 84% of the time. The Hawkes model is the most accurate (81% accuracy) at nearly all of the remaining beaches.

Respiratory illness, hospital visits, and health costs: Is it air pollution or pollen?

BACKGROUND: Both air pollution and airborne pollen can cause respiratory health problems. Since both are often jointly present in ambient air, it is important to control for one while estimating the effect of the other when considering pollution-abating policies. To date only a limited number of studies have considered the health effects of both irritants jointly for a general population, and for a sufficiently long time period to allow for variation in seasonal concentrations of both components. The primary goal of this study is to determine the causal impact of fine particulate matter (PM2.5) on hospital visits and related treatment costs, while controlling for potentially confounding pollen effects. Our study area is the metropolitan hub of Reno/Sparks in Northern Nevada. METHODS: Taking advantage of a rare sample of daily pollen counts over a prolonged period of time (2009-2015), we model the effects of PM2.5 and pollen on respiratory-related hospital admissions for the population at large, plus specific age groups. Pollen data are provided by a local allergy clinic. Data on PM2.5 and other air pollutants are obtained from the U.S. Environmental Protection Agency's air quality data web site. We collect daily meteorological data from the National Centers for Environmental Information's data repository. Data on hospital admissions are given by the Nevada Center for Surveys, Evaluations, and Statistics. Our econometric approach centers on a fully robust count data (Poisson) model, estimated via Quasi-Maximum Likelihood. RESULTS: We find that for our sample PM2.5 effects are largely robust to the inclusion of both pollen counts and temporal indicators. In contrast, pollen effects vanish when time fixed effects are added, pointing at their correlation with unobserved temporal confounders. At the same time, model fit improves with the inclusion of temporal indicators. Based on our preferred specification, we find a significant PM2.5 effect of approximately 0.5% additional hospital visits per day due to a one µg/m3 increase in PM2.5. This translates into expected augmented treatment costs of $2700 per day for the same unit-change in PM2.5. These figures can mount quickly when more pronounced and/or longer episodes of particulate matter pollution are considered, perhaps due to wildfire smoke. For instance, the expected increase in patients and costs due to a month-long 10-unit-jump of PM2.5 over the long-run annual average would amount to an extra 70 patients and approximately $680,000 in additional treatment costs.