Urban stormwater runoff quantity and quality are strongly dependent upon catchment properties. Models are used to simulate the runoff characteristics, but the output from a stormwater management model is dependent on how the catchment area is subdivided and represented as spatial elements. For green infrastructure (GI) modeling, we suggest a discretization method that distinguishes directly connected impervious area from the total impervious area. We recommend identifying pervious buffers, which receive runoff from upgradient impervious areas, as a separate subset of the entire pervious area. This separation improves model representation of the runoff process. The rational and demonstration of the performance of this approach is presented and discussed in detail in Lee et. al. 2017.
Using these criteria for categorizing important land cover components governing runoff hydrology, an approach to spatial discretization for projects using the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM) is demonstrated for the Shayler Crossing (SHC) headwatershed, a well-monitored, residential suburban area occupying 100 ha, east of Cincinnati, Ohio. The model relies on a highly resolved spatial database of urban land cover, stormwater drainage features, and topography. The approach accommodates the distribution of runoff contributions from different spatial components and flow pathways that would impact GI performance. In headwatersheds with relatively homogeneous landscape properties throughout the system like SHC, all subcatchments are discretized with the same land cover types, and instead of using a j × k array of calibration parameters, based on j subcatchments and k parameters per subcatchment, the values used for the parameter set for one subcatchment can be applied in all cases (i.e., just k parameters), reducing the number of modeled parameters to consider during calibration. Depending on the size of the watershed being modeled and the heterogeneity of the landscape, grouping subcatchments into categories, such as steep slope vs gentle slope, for example, may be necessary. This would result in an additional parameter set for consideration during calibration, but still limits the domain of parameter values compared to when each subcatchment is parameterized independently.
This report was written to outline the spatial database and SWMM model set-up steps required to simulate GI scenarios at a small watershed scale. We use the SHC headwatershed as the case study for describing the processes for model set-up and conducting simulations. While some modeling results are given, they are provided for context and guidance only, and were not meant for detailed discussion. The main purpose of the report is to provide SWMM model users interested in GI considerations at a watershed scale a framework for using common computer analytical software tools to configure a SWMM model for GI scenario analysis. The report is staged in
Federal Reserve Board announces termination of enforcement action with Heartland Bank and denies motion to void fine and permanent ban on former foreign exchange (FX) trader
Solar radiation exposure can increase the toxicity of bioaccumulated oil compounds in a diversity of aquatic species. We investigated the photoenhanced toxicity of weathered South Louisiana crude oil in sediment and water accommodated fractions (WAF) to larval zebrafish. Larvae were first exposed for 24 h to one of six treatments: no oil (sediment or water), 7.5 g oil/kg sediment, oil-only WAF, oil WAF plus the dispersant Corexit 9500A, or dispersant alone. Larvae were then exposed to high or low levels of sunlight in control water for 3 or 3.5 h. Hydrocarbon concentrations were measured in exposure media, including alkanes, polycyclic aromatic compounds and total petroleum hydrocarbons. Significant phototoxicity was observed in larvae exposed to oiled sediment, oil-only WAF, and oil plus dispersant WAF. The results indicated that petroleum from the northern Gulf of Mexico can be phototoxic to larval fish exposed to oil in either the water column or sediment.
For the first time, scientists have obtained direct, quantifiable observations of cloud seeding for increased snowfall — from the growth of ice crystals, through the processes that occur in clouds, to the eventual snowfall.
The National Science Foundation (NSF)-supported project, dubbed SNOWIE (Seeded and Natural Orographic Wintertime Clouds — the Idaho Experiment), took place from Jan. 7 to March 17, 2017, in and near Idaho’s Payette Basin, located approximately 50 miles north of …
More at https://www.nsf.gov/news/news_summ.jsp?cntn_id=244215&WT.mc_id=USNSF_51&WT.mc_ev=click
This is an NSF News item.
Air pollution is less common in southern China, but Guangdong province can still get pretty hazy.
Near-coastal (0-200 depth) ecosystems and species are under threat from increasing temperatures, ocean acidification, and sea level rise. However, species vary in their vulnerability to specific climatic changes and climate impacts will vary geographically. For management to respond in a scientifically-sound fashion, it is critical to know the extent and geographic patterns of risk. To address these needs, we developed a rule-based framework to predict the relative risk of near-coastal species due to climate change at regional scales. The framework synthesizes risks from biotic traits and population trends with risks associated with increasing ocean temperatures, ocean acidification, and sea level rise. Key objectives in crafting the framework were to: a) predict climate risks for rare species as well as for better studied species; b) identify major climate stressor(s) affecting each species within each region; c) predict geographic patterns for the importance of different climate stressors; and d) assess how risks change under different climate scenarios.
A unique feature of the current effort is that we developed an ecoinformatics website called the Coastal Biodiversity Risk Analysis Tool (CBRAT; http://www.cbrat.org/) to conduct climate risk analyses and to serve as a tool for managers and researchers to address specific climate and species inquiries. CBRAT uses an algorithm approach where the risk is automatically generated from a centralized knowledgebase and a set of explicit rules, thereby avoiding the limitations and potential biases of risks based on expert opinion.
The first method to identify species at risk was a set of rules using “baseline/status”; biotic traits, such as endemicity, population trends and relative abundance, that are associated with increased climate vulnerability or resilience. These baseline/status rules help identify species that might not be identified as at risk through the analysis of specific climate stressors. The core method to predict temperature risks was the Ecoregional Thermal Windows approach that compares the projected sea surface temperature (SST) in each ecoregion to the historic range of SST values in the “warmest occupied ecoregion” or WOE. Temperatures in the WOE are assumed to represent the upper ecological thermal limit for a species to maintain a viable population. Though ocean acidification is the least well understood of the climate stressors, it is possible to conduct a first-order risk assessment by treating it as a contaminant. Specifically, we derive “maximum allowable toxicant concentrations” (MATCs) for pH and aragonite saturation state from a synthesis of exposure experiments. Because species within a taxon vary greatly in their sensitivity, we developed an approach to generate high, moderate, and low sensitivity effects thresholds to pH, though the analysis is limited by the paucity of experiments based on endpoints directly related to population
Several models were used to describe the partitioning of ammonia, water, and organic compounds between the gas and particle phases for conditions in the southeastern US during summer 2013. Existing equilibrium models and frameworks were found to be sufficient, although additional improvements in terms of estimating pure-species vapor pressures are needed. Thermodynamic model predictions were consistent, to first order, with a molar ratio of ammonium to sulfate of approximately 1.6 to 1.8 (ratio of ammonium to 2 × sulfate, RN∕2S ≈ 0.8 to 0.9) with approximately 70 % of total ammonia and ammonium (NHx) in the particle. Southeastern Aerosol Research and Characterization Network (SEARCH) gas and aerosol and Southern Oxidant and Aerosol Study (SOAS) Monitor for AeRosols and Gases in Ambient air (MARGA) aerosol measurements were consistent with these conditions. CMAQv5.2 regional chemical transport model predictions did not reflect these conditions due to a factor of 3 overestimate of the nonvolatile cations. In addition, gas-phase ammonia was overestimated in the CMAQ model leading to an even lower fraction of total ammonia in the particle. Chemical Speciation Network (CSN) and aerosol mass spectrometer (AMS) measurements indicated less ammonium per sulfate than SEARCH and MARGA measurements and were inconsistent with thermodynamic model predictions. Organic compounds were predicted to be present to some extent in the same phase as inorganic constituents, modifying their activity and resulting in a decrease in [H+]air (H+ in µg m−3 air), increase in ammonia partitioning to the gas phase, and increase in pH compared to complete organic vs. inorganic liquid–liquid phase separation. In addition, accounting for nonideal mixing modified the pH such that a fully interactive inorganic–organic system had a pH roughly 0.7 units higher than predicted using traditional methods (pH = 1.5 vs. 0.7). Particle-phase interactions of organic and inorganic compounds were found to increase partitioning towards the particle phase (vs. gas phase) for highly oxygenated (O : C ≥ 0.6) compounds including several isoprene-derived tracers as well as levoglucosan but decrease particle-phase partitioning for low O : C, monoterpene-derived species.
Globally, hydrologic modifications such as ditching and subsurface drainage have significantly reduced wetland water storage capacity (i.e., the volume of surface water a wetland can retain) and consequent wetland functions. While wetland area has been well documented across many landscapes and used to guide restoration efforts, few studies have directly quantified the associated wetland storage capacity. Here, we present a novel raster-based approach to quantify both the contemporary and potential (i.e., restorable) storage capacities of individual wetlands across landscapes. We demonstrate the utility of this method by applying it to the Delmarva Peninsula, a region punctuated by both depressional wetlands and drainage ditches. Across the entire peninsula, we estimated a potential increase in storage capacity of 76%. Focusing on an individual watershed, we found that over 60% of restorable storage capacity occurs within 20 m of the drainage network, and that 89% of restorable capacity is 1 m or less above the drainage network elevation. Our demonstration highlights the ubiquitous nature of ditching in this landscape; spatial patterns of both contemporary and potential storage capacities; and clear opportunities for hydrologic restoration. In Delmarva and more broadly, our novel approach can inform targeted landscape-scale conservation and restoration efforts to optimize hydrologically mediated wetland functions.
The Maximum Cumulative Ratio (MCR) quantifies the degree to which a single chemical drives the cumulative risk of an individual exposed to multiple chemicals. Phthalates are a class of chemicals with ubiquitous exposures in the general population that have the potential to cause adverse health effects in humans. This work used the MCR to evaluate coexposures to six phthalates as measured in biomonitoring data from the most recent cycle (2013–2014) of the National Health and Nutrition Examination Survey (NHANES). The values of MCR, Hazard Index (HI), and phthalate-specific Hazard Quotients (HQs) were determined for 2663 NHANES participants aged six years and older by using reverse dosimetry techniques to calculate steady-state doses consistent with concentrations of metabolites of six phthalates in urine and using Tolerable Daily Intake values. There were 21 participants (0.8% of the NHANES sample) with HI > 1. Of those, 43% (9/21) would have been missed by chemical-by-chemical assessments (i.e. all HQs were less than one). The mean MCR value in the 21 participants was 2.1. HI and MCR values were negatively correlated (p 1 was not driven by age, gender, or ethnicity. The cumulative exposures of concern largely originated from a subset of three of the fifteen possible pairs of the six phthalates. These findings suggest that cumulative exposures were a potential concern for a small portion of the surveyed participants involving a subset of the phthalates explored. The largest risks tended to occur in individuals whose exposures were dominated by a single phthalate.
Water resources support more than 60 million people in the Lower Mekong Basin (LMB) and are important for food security—especially rice production—and economic security. This study aims to quantify water yield under near- and long-term climate scenarios and assess the potential impacts on rice cultivation. The InVEST model (Integrated Valuation of Ecosystem Services and Tradeoffs) forecasted water yield, and land evaluation was used to delineate suitability classes. Pattern-downscaled climate data were specially generated for the LMB. Predicted annual water yields for 2030 and 2060, derived from a drier overall scenario in combination with medium and high greenhouse gas emissions, indicated a reduction of 9–24% from baseline (average 1986–2005) runoff. In contrast, increased seasonality and wetter rainfall scenarios increased annual runoff by 6–26%. Extreme drought decreased suitability of transplanted rice cultivation by 3%, and rice production would be reduced by 4.2 and 4%, with and without irrigation projects, relative to baseline. Greatest rice reduction was predicted for Thailand, followed by Lao PDR and Cambodia, and was stable for Vietnam. Rice production in the LMB appears sufficient to feed the LMB population in 2030, while rice production in Lao PDR and Cambodia are not expected to be sufficient for domestic consumption, largely due to steep topography and sandy soils as well as drought. Four adaptation measures to minimize climate impacts (i.e., irrigation, changing the planting calendar, new rice varieties, and alternative crops) are discussed.
A next generation air quality modeling system is being developed at the U.S. EPA to enable modeling of air quality from global to regional to (eventually) local scales. We envision that the system will have three configurations: 1. Global meteorology with seamless mesh refinement and online atmospheric chemistry; 2. Regional (limited area) online meteorology and chemistry; 3. Offline (sequential) regional meteorology and chemistry. A one-dimensional air quality (AQ) component, built from state of the science chemistry and aerosol modules from the Community Multiscale Air Quality (CMAQ) model will be used in all three configurations. For the Global online configuration, the AQ component will be coupled to the Model for Prediction Across Scales – Atmosphere (MPAS-A), which is a global meteorological model with seamless mesh refinement developed at the National Center for Atmospheric Research (NCAR). The regional configurations will be coupled with WRF although we may also use a regional version of MPAS that has recently been developed at NCAR.
In the presentation we will describe our modifications to MPAS to improve its suitability for retrospective air quality applications and show evaluations of global and regional meterological simulations. Our modifications include addition of physics schemes that we developed for WRF that are particularly designed for air quality applications: the Pleim surface layer (PSL), the Pleim-Xiu (PX) land surface model with fractional land use for a 40-class National Land Cover Database (NLCD40), and the Asymmetric Convective Model 2 (ACM2) planetary boundary layer scheme. We also added analysis nudging four-dimensional data assimilation (FDDA) to control error growth for long term retrospective simulations. In addition, we updated the KF scheme in MPAS to the latest version which adds subgrid-scale cumulus cloud feedback to the radiation schemes, multiple convective triggers, and a scale-aware convective time scale (Jerry Herwehe will present this work). We will also show preliminary MPAS-AQ results where we have incorporated CMAQ modules for atmospheric chemistry and deposition in MPAS.
The US EPA is charged with screening chemicals for their ability to be endocrine disruptors through interaction with the estrogen, androgen and thyroid axes. The agency is starting to explore the use of high-throughput in vitro assays to use in the Endocrine Disruptor Screening Program (EDSP), potentially as replacements for lower-throughput in vitro and in vivo tests. The first replacement is an integrated computational and experimental model for estrogen receptor (ER) pathway activity, to be used as an alternative to the Tier 1 in vitro ER binding and transactivation assays and the in vivo uterotrophic bioassay. The experimental component of the ER agonist model uses a set of 16 in vitro assays that incorporate a variety of technologies and cell lines and probe multiple points in the ER pathway. Here, we demonstrate that it is possible to achieve equivalent levels of performance against both in vitro and in vivo reference chemical sets as the full ER agonist model using various subsets of assays. The simplest “subset” model that achieves maximum accuracy against multiple metrics uses only 4 assays. There are multiple accurate subsets, allowing flexibility in the construction of a multiplexed assay battery. We also discuss the issue of challenging chemicals, i.e. those that tend to give false positive results in certain assays, and could hence be more problematic when only a few assays are being used.
This is the final Provisional Peer-Review Toxicity Value report generated for EPA’s Superfund Program.
Cumulative risk assessment (CRA) methods promote the use of a conceptual site model (CSM) to apportion exposures and integrate risk from multiple stressors. While CSMs may encompass multiple species, evaluating end points across taxa can be challenging due to data availability and physiological differences among organisms. Adverse outcome pathways (AOPs) describe biological mechanisms leading to adverse outcomes (AOs) by assembling causal pathways with measurable intermediate steps termed key events (KEs), thereby providing a framework for integrating data across species. In this work, we used a case study focused on the perchlorate anion (ClO4-) to highlight the value of the AOP framework for cross-species data integration. Computational models and dose-response data were used to evaluate the effects of ClO4- in 12 species and revealed a dose-response concordance across KEs and taxa. The aggregate exposure pathway (AEP) tracks stressors from sources to the exposures and serves as a complement to the AOP. We discuss how the combined AEP-AOP construct helps to maximize the use of existing data and advances CRA by (1) organizing toxicity and exposure data, (2) providing a mechanistic framework of KEs for integrating data across human health and ecological end points, (3) facilitating cross-species dose-response evaluation, and (4) highlighting data gaps and technical limitations.
A flexible framework has been created for modeling multi-dimensional hydrological and water quality processes within stormwater green infrastructures (GIs). The framework models a GI system using a set of blocks (spatial features) and connectors (interfaces) representing different functional components of a GI. The blocks are used to represent spatial features with the ability to store water (e.g., pond, soil, benthic sediments, manhole, and a storage zone) and water quality constituents including reactive chemical constituents and multiple classes of particles. The connectors represent the flow and mass transfer between each pair of blocks. Each block and connector, depending on their identity, can be assigned different constitutive relationships controlling the head-storage (H-S) and flow-head (Q-H) relationship, respectively. The computational engine of this flexible model solves equations describing critical mechanisms related to GI model performance that can be grouped into three categories: 1) hydraulics, 2) particle fate and transport, and 3) coupled dissolved and particle-associated reactive transport of water quality constituents (e.g., pollutants). Regarding the hydraulics, the model can solve a combination of Richards equation, kinematic/diffusive wave, Darcy, and other user-provided flow models simultaneously. The particle transport model is based on performing mass-balance on particles in different phases, e.g., mobile and deposited in soil with constitutive theories controlling their transport, settling, deposition, and release. The reactive transport modules allow constituents to be in dissolved, sorbed or bound to particles, and also to undergo user-defined transformations. The numerical solution is based on an adaptive time-step implicit Newton-Raphson method. A graphical user interface (GUI) has also been developed that allows users to visualize the conceptual layout of the GI system being modeled as well as define and parameterize the transport and fate mechanisms. Four applications of the modeling framework are presented that demonstrate its flexibility for simulating the performance of common urban GI types, but each with unique implementation or modeling objectives: 1) the hydraulic processes within a serial rain garden system, 2) a porous pavement system, 3) the hydraulic, carbon/nitrogen transport and transformation, and coupled dissolved/colloid-associated transport of zinc underneath a hypothetical infiltration basin and 4) carbon/nitrogen cycling in a wet pond.
A number of industrial chemicals and therapeutic agents cause liver tumors in rats and mice by activating the nuclear receptor peroxisome proliferator-activated receptor α (PPARα). The molecular and cellular events by which PPARα activators induce rodent hepatocarcinogenesis have been extensively studied elucidating a number of consistent mechanistic changes linked to the increased incidence of liver neoplasms. The weight of evidence relevant to the hypothesized mode of action (MOA) for PPARα activator-induced rodent hepatocarcinogenesis is summarized here. Chemical-specific and mechanistic data support concordance of temporal and dose–response relationships for the key events associated with many PPARα activators. The key events (KE) identified in the MOA are PPARα activation (KE1), alteration in cell growth pathways (KE2), perturbation of hepatocyte growth and survival (KE3), and selective clonal expansion of preneoplastic foci cells (KE4), which leads to the apical event—increases in hepatocellular adenomas and carcinomas (KE5). In addition, a number of concurrent molecular and cellular events have been classified as modulating factors, because they potentially alter the ability of PPARα activators to increase rodent liver cancer while not being key events themselves. These modulating factors include increases in oxidative stress and activation of NF-kB. PPARα activators are unlikely to induce liver tumors in humans due to biological differences in the response of KEs downstream of PPARα activation. This conclusion is based on minimal or no effects observed on cell growth pathways and hepatocellular proliferation in human primary hepatocytes and absence of alteration in growth pathways, hepatocyte proliferation, and tumors in the livers of species (hamsters, guinea pigs and cynomolgus monkeys) that are more appropriate human surrogates than mice and rats at overlapping dose levels. Despite this overwhelming body of evidence and almost universal acceptance of the PPARα MOA and lack of human relevance, several reviews have selectively focused on specific studies that, as discussed, contradict the consensus opinion and suggest uncertainty. In the present review, we systematically address these most germane suggested weaknesses of the PPARα MOA.
Infiltrative rain gardens add retention capacity to sewersheds, yet, their capacity for detention and redistribution of stormwater runoff is dynamic and often unverified by monitoring. Over a 4-year period, we tracked whole system water fluxes in a two-tier rain garden network, and assessed near-surface hydrology and soil development across construction and operational phases. The monitoring data provided a quantitative basis for determining network effectiveness with implications for return-on-investment. Based on 233 monitored warm-season rainfall events, at least 50 percent of total inflow volume was detained, with 90 percent of all events producing no flow to the combined sewer. For the larger events that did result in flow to the combined sewer into the local combined sewer system, the rain gardens delayed flows for an average of 5.5 hours. Multivariate analysis of hydrologic fluxes showed that total event rainfall depth was the predominant hydrologic driver for network out flow during both phases, with average event intensity and daily evapotranspiration as additional, independent factors in regulating retention in the operational phase. Despite lower-than-design infiltration rates, tradeoffs among soil profile development and hydrology, and resilience to sediment loading contributed to maintaining hydrologic effectiveness. We suggest that a monitoring framework to evaluate effectiveness is useful in guiding adaptation of stormwater control measures toward functional green infrastructures.