Retrospective Analysis Of Midsummer Hypoxic Area And Volume In The Northern Gulf Of Mexico, 1985-2011

Robust estimates of hypoxic extent (both area and volume) are important for assessing the impacts of low dissolved oxygen on aquatic ecosystems at large spatial scales. Such estimates are also important for calibrating models linking hypoxia to causal factors, such as nutrient loading and stratification, and for informing management decisions. In this study, we develop a rigorous geostatistical modeling framework to estimate the hypoxic extent in the northern Gulf of Mexico from data collected during midsummer, quasi-synoptic monitoring cruises (1985-2011). Instead of a traditional interpolation-based approach, we use a simulation-based approach that yields more robust extent estimates and quantified uncertainty. The modeling framework also makes use of covariate information (i.e., trend variables such as depth and spatial position), to reduce estimation uncertainty. Furthermore, adjustments are made to account for observational bias resulting from the use of different sampling instruments in different years. Our results suggest an increasing trend in hypoxic layer thickness (p = 0.05) from 1985 to 2011, but less than significant increases in volume (p = 0.12) and area (p = 0.42). The uncertainties in the extent estimates vary with sampling network coverage and instrument type, and generally decrease over the study period

Ensemble Modeling Informs Hypoxia Management In The Northern Gulf Of Mexico

A large region of low-dissolved-oxygen bottom waters (hypoxia) forms nearly every summer in the northern Gulf of Mexico because of nutrient inputs from theMississippi River Basin andwater column stratification. Policymakers developed goals to reduce the area of hypoxic extent because of its ecological, economic, and commercial fisheries impacts. However, the goals remain elusive after 30 y of research and monitoring and 15 y of goal-setting and assessment because there has been little change in river nitrogen concentrations. An intergovernmental Task Force recently extended to 2035 the deadline for achieving the goal of a 5,000-km(2) 5-y average hypoxic zone and set an interim load target of a 20% reduction of the spring nitrogen loading from the Mississippi River by 2025 as part of their adaptive management process. The Task Force has asked modelers to reassess the loading reduction required to achieve the 2035 goal and to determine the effect of the 20% interim load reduction. Here, we address both questions using a probabilistic ensemble of four substantially different hypoxia models. Our results indicate that, under typical weather conditions, a 59% reduction in Mississippi River nitrogen load is required to reduce hypoxic area to 5,000 km(2). The interim goal of a 20% load reduction is expected to produce an 18% reduction in hypoxic area over the long term. However, due to substantial interannual variability, a 25% load reduction is required before there is 95% certainty of observing any hypoxic area reduction between consecutive 5-y assessment periods

Ensemble modeling informs hypoxia management in the northern Gulf of Mexico

A large region of low-dissolved-oxygen bottom waters (hypoxia) forms nearly every summer in the northern Gulf of Mexico because of nutrient inputs from theMississippi River Basin andwater column stratification. Policymakers developed goals to reduce the area of hypoxic extent because of its ecological, economic, and commercial fisheries impacts. However, the goals remain elusive after 30 y of research and monitoring and 15 y of goal-setting and assessment because there has been little change in river nitrogen concentrations. An intergovernmental Task Force recently extended to 2035 the deadline for achieving the goal of a 5,000-km(2) 5-y average hypoxic zone and set an interim load target of a 20% reduction of the spring nitrogen loading from the Mississippi River by 2025 as part of their adaptive management process. The Task Force has asked modelers to reassess the loading reduction required to achieve the 2035 goal and to determine the effect of the 20% interim load reduction. Here, we address both questions using a probabilistic ensemble of four substantially different hypoxia models. Our results indicate that, under typical weather conditions, a 59% reduction in Mississippi River nitrogen load is required to reduce hypoxic area to 5,000 km(2). The interim goal of a 20% load reduction is expected to produce an 18% reduction in hypoxic area over the long term. However, due to substantial interannual variability, a 25% load reduction is required before there is 95% certainty of observing any hypoxic area reduction between consecutive 5-y assessment periods

Ozone and PM(2.5) Exposure and Acute Pulmonary Health Effects: A Study of Hikers in the Great Smoky Mountains National Park

To address the lack of research on the pulmonary health effects of ozone and fine particulate matter (≤ 2.5 μm in aerodynamic diameter; PM(2.5)) on individuals who recreate in the Great Smoky Mountains National Park (USA) and to replicate a study performed at Mt. Washington, New Hampshire (USA), we conducted an observational study of adult (18–82 years of age) day hikers of the Charlies Bunion trail during 71 days of fall 2002 and summer 2003. Volunteer hikers performed pre- and posthike pulmonary function tests (spirometry), and we continuously monitored ambient O(3), PM(2.5), temperature, and relative humidity at the trailhead. Of the 817 hikers who participated, 354 (43%) met inclusion criteria (nonsmokers and no use of bronchodilators within 48 hr) and gave acceptable and reproducible spirometry. For these 354 hikers, we calculated the posthike percentage change in forced vital capacity (FVC), forced expiratory volume in 1 sec (FEV(1)), FVC/FEV(1), peak expiratory flow, and mean flow rate between 25 and 75% of the FVC and regressed each separately against pollutant (O(3) or PM(2.5)) concentration, adjusting for age, sex, hours hiked, smoking status (former vs. never), history of asthma or wheeze symptoms, hike load, reaching the summit, and mean daily temperature. O(3) and PM(2.5) concentrations measured during the study were below the current federal standards, and we found no significant associations of acute changes in pulmonary function with either pollutant. These findings are contrasted with those in the Mt. Washington study to examine the hypothesis that pulmonary health effects are associated with exposure to O(3) and PM(2.5) in healthy adults engaged in moderate exercise

Using a B ayesian hierarchical model to improve L ake E rie cyanobacteria bloom forecasts

The last decade has seen a dramatic increase in the size of western Lake Erie cyanobacteria blooms, renewing concerns over phosphorus loading, a common driver of freshwater productivity. However, there is considerable uncertainty in the phosphorus load‐bloom relationship, because of other biophysical factors that influence bloom size, and because the observed bloom size is not necessarily the true bloom size, owing to measurement error. In this study, we address these uncertainties by relating late‐summer bloom observations to spring phosphorus load within a Bayesian modeling framework. This flexible framework allows us to evaluate three different forms of the load‐bloom relationship, each with a particular combination of statistical error distribution and response transformation. We find that a novel implementation of a gamma error distribution, along with an untransformed response, results in a model with relatively high predictive skill and realistic uncertainty characterization, when compared to models based on more common statistical formulations. Our results also underscore the benefits of a hierarchical approach that enables assimilation of multiple sets of bloom observations within the calibration processes, allowing for more thorough uncertainty quantification and explicit differentiation between measurement and model error. Finally, in addition to phosphorus loading, the model includes a temporal trend component indicating that Lake Erie has become increasingly susceptible to large cyanobacteria blooms over the study period (2002–2013). Results suggest that current phosphorus loading targets will be insufficient for reducing the intensity of cyanobacteria blooms to desired levels, so long as the lake remains in a heightened state of bloom susceptibility. Key Points Bayesian model relates cyanobacteria bloom size to phosphorus load Algal bloom forecast uncertainty well characterized by gamma distribution Lake Erie increasingly susceptible to large cyanobacteria bloomsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109610/1/wrcr21154.pd

Relating soil geochemical properties to arsenic bioaccessibility through hierarchical modeling

<p>Interest in improved understanding of relationships among soil properties and arsenic (As) bioaccessibility has motivated the use of regression models for As bioaccessibility prediction. However, limits in the numbers and types of soils included in previous studies restrict the usefulness of these models beyond the range of soil conditions evaluated, as evidenced by reduced predictive performance when applied to new data. In response, hierarchical models that consider variability in relationships among soil properties and As bioaccessibility across geographic locations and contaminant sources were developed to predict As bioaccessibility in 139 soils on both a mass fraction (mg/kg) and % basis. The hierarchical approach improved the estimation of As bioaccessibility in studied soils. In addition, the number of soil elements identified as statistically significant explanatory variables increased when compared to previous investigations. Specifically, total soil Fe, P, Ca, Co, and V were significant explanatory variables in both models, while total As, Cd, Cu, Ni, and Zn were also significant in the mass fraction model and Mg was significant in the % model. This developed hierarchical approach provides a novel tool to (1) explore relationships between soil properties and As bioaccessibility across a broad range of soil types and As contaminant sources encountered in the environment and (2) identify areas of future mechanistic research to better understand the complexity of interactions between soil properties and As bioaccessibility.</p