Weighting Nitrogen and Phosphorus Pixel Pollutant Loads to Represent Runoff and Buffering Likelihoods

Watershed models often estimate annual nitrogen (N) or phosphorus (P) pollutant loads in rural areas with export coefficient (EC) (kg/ha/yr) values based on land cover, and in urban areas as the product of spatially uniform event mean concentration (EMC) (mg/L) values and runoff volume. Actual N and P nonpoint source (NPS) pollutant loading has more spatial complexity due to watershed variation in runoff likelihood and buffering likelihood along surface and subsurface pathways, which can be represented in a contributing area dispersal area (CADA) NPS model. This research develops a CADA NPS model to simulate how watershed properties of elevation, land cover, and soils upslope and downslope of each watershed pixel influence nutrient loading. The model uses both surface and subsurface runoff indices (RI), and surface and subsurface buffer indices (BI), to quantify the runoff and buffering likelihood for each watershed pixel, and generate maps of weighted EC and EMC values that identify NPS pollutant loading hotspots. The research illustrates how CADA NPS model maps and pixel loading values are sensitive to the spatial resolution and accuracy of elevation and land cover data, and model predictions can represent the lower and upper bounds of NPS loading. The model provides managers with a tool to rapidly visualize, rank, and investigate likely areas of high nutrient export.     

Spring-harvested game birds in the Western James Bay region of Northern Ontario, Canada: the amount of organochlorines in matched samples of breast muscle, skin, and abdominal fat

We examined matched-tissue samples (the right pectoral muscle plus the associated skin and fat was considered a breast portion) of 81 spring-harvested waterfowl and 19 summer-harvested godwits (Limosa spp.) to assess the potential of these water birds contributing to the body burden of PCBs and DDT noted in First Nation people of the western James Bay region, northern Ontario, Canada. In general, the dabbling ducks (mallard duck, Anas platyrhynchos; and northern pintail, A. acuta) had significantly lower percent lipid (gravimetrically determined) values in skin tissue, fat tissue, and breast muscle compared to the goose species (Canada goose, Branta canadensis; lesser snow goose, Chen caerulescens); godwits had percent lipid values not significantly different than ducks and geese. Also, the percent lipid values in skin for all species of birds examined approached those found in fat tissue. Organochlorine data were expressed as the amount (microg) of each contaminant per breast portion to show contaminant consumption in terms of typical and easily recognizable dietary portions; direct comparisons were made to acceptable daily intake (ADI) or tolerable daily intake (TDI) values as recommended by Health Canada. Significant differences in the amount of organochlorines between bird species for skin, fat tissue, and breast muscle samples were found. In general, breast portions from snow geese contained the least amount of organochlorines, followed by godwits (except for mirex) and then Canada geese; the dabbling ducks had the greatest amount of organochlorines on a breast portion basis. However, on average, no 60 kg person would exceed the calculated organochlorine ADI/TDI values consuming one breast portion (i.e., breast + associated skin and fat), but the maximum value of SigmaPCBs for skin tissue alone in male mallards (47 microg) was more than twice the ADI/TDI (18 microg/day); while, that in fat tissue alone (17 microg) approached the ADI/TDI. Thus, the consumption of dabbling ducks by children is an issue that should be explored further, if tissue other than breast muscle is eaten. Lastly, the consumption of waterfowl was a source of PCBs for people of Fort Albany and Kashechewan, but not DDT, as this organochlorine was infrequently detected.     

Relationship between metal speciation in soil solution and metal adsorption at the root surface of ryegrass

The total metal content of the soil or total metal concentration in the soil solution is not always a good indicator for metal availability to plants. Therefore, several speciation techniques have been developed that measure a defined fraction of the total metal concentration in the soil solution. In this study the Donnan Membrane Technique (DMT) was used to measure free metal ion concentrations in CaCl(2) extractions (to mimic the soil solution, and to work under standardized conditions) of 10 different soils, whereas diffusive gradients in thin-films (DGT) and scanning chronopotentiometry (SCP) were used to measure the sum of free and labile metal concentrations in the CaCl(2) extracts. The DGT device was also exposed directly to the (wetted) soil (soil-DGT). The metal concentrations measured with the speciation techniques are related to the metal adsorption at the root surface of ryegrass (Lolium perenne L.), to be able to subsequently predict metal uptake. In most cases the metal adsorption related pH-dependently to the metal concentrations measured by DMT, SCP, and DGT in the CaCl(2) extract. However, the relationship between metal adsorption at the root surface and the metal concentrations measured by the soil-DGT was not-or only slightly-pH dependent. The correlations between metal adsorption at the root surface and metal speciation detected by different speciation techniques allow discussion about rate limiting steps in biouptake and the contribution of metal complexes to metal bioavailability.     

In vitro cytotoxicity assessment of a West Virginia chemical spill mixture involving 4-methylcyclohexanemethanol and propylene glycol phenyl ether

Thousands of gallons of industrial chemicals, crude 4-methylcyclohexanemethanol (MCHM) and propylene glycol phenyl ether (PPh), leaked from industrial tanks into the Elk River in Charleston, West Virginia, USA, on January 9, 2014. A considerable number of people were reported to exhibit symptoms of chemical exposure and an estimated 300,000 residents were advised not to use or drink tap water. At the time of the spill, the existing toxicological data of the chemicals were limited for a full evaluation of the health risks, resulting in concern among those in the impacted regions. In this preliminary study, we assessed cell viability and plasma membrane degradation following a 24-h exposure to varying concentrations (0–1000 μM) of the two compounds, alone and in combination. Evaluation of different cell lines, HEK-293 (kidney), HepG2 (liver), H9c2 (heart), and GT1-7 (brain), provided insight regarding altered cellular responses in varying organ systems. Single exposure to MCHM or PPh did not affect cell viability, except at doses much higher than the estimated exposure levels. Certain co-exposures significantly reduced metabolic activity and increased plasma membrane degradation in GT1-7, HepG2, and H9c2 cells. These findings highlight the importance of examining co-exposures to fully understand the potential toxic effects.     

Marine Habitat Classification for Ecosystem-Based Management: A Proposed Hierarchical Framework

Creating a habitat classification and mapping system for marine and coastal ecosystems is a daunting challenge due to the complex array of habitats that shift on various spatial and temporal scales. To meet this challenge, several countries have, or are developing, national classification systems and mapping protocols for marine habitats. To be effectively applied by scientists and managers it is essential that classification systems be comprehensive and incorporate pertinent physical, geological, biological, and anthropogenic habitat characteristics. Current systems tend to provide over-simplified conceptual structures that do not capture biological habitat complexity, marginalize anthropogenic features, and remain largely untested at finer scales. We propose a multi-scale hierarchical framework with a particular focus on finer scale habitat classification levels and conceptual schematics to guide habitat studies and management decisions. A case study using published data is included to compare the proposed framework with existing schemes. The example demonstrates how the proposed framework’s inclusion of user-defined variables, a combined top-down and bottom-up approach, and multi-scale hierarchical organization can facilitate examination of marine habitats and inform management decisions.     

Quantification of emission factor uncertainty

Emissions factors are important for estimating and characterizing emissions from sources of air pollution. There is no quantitative indication of uncertainty for these emission factors, most factors do not have an adequate data set to compute uncertainty, and it is very difficult to locate the data for those that do. The objectives are to compare the current emission factors of Electric Generating Unit NOx sources with currently available continuous emission monitoring data, develop quantitative uncertainty indicators for the Environmental Protection Agency (EPA) data quality rated emission factors, and determine the possible ranges of uncertainty associated with EPA's data quality rating of emission factors. EPA's data letter rating represents a general indication of the robustness of the emission factor and is assigned based on the estimated reliability of the tests used to develop the factor and on the quantity and representativeness of the data. Different sources and pollutants that have the same robustness in the measured emission factor and in the representativeness of the measured values are assumed to have a similar quantifiable uncertainty. For the purposes of comparison, we assume that the emission factor estimates from source categories with the same letter rating have enough robustness and consistency that we can quantify the uncertainty of these common emission factors based on the qualitative indication of data quality which is known for almost all factors. The results showed that EPA's current emission factor values for NOx emissions from combustion sources were found to be reasonably representative for some sources; however AP-42 values should be updated for over half of the sources to reflect current data. The quantified uncertainty ranges were found to be 25-62% for A rated emission factors, 45-75% for B rated emission factors, 60-82% for C rated emission factors, and 69-86% for D rated emission factors, and 82-92% for E rated emission factors.     

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.     

The secret life of pets: The intersection of animals and organizational life

Although often overlooked, pets and other animals intersect with organizations in interesting, important ways. We seek to define how various animals intersect with organizations, highlight opportunities for theory development, and illustrate important areas for future research. We also explore how pandemics such as COVID-19 might affect the animals we highlight.