Metal-contaminated potato crops and potential human health risk in Bolivian mining highlands

This study assessed metals in irrigation water, soil and potato crops impacted by mining discharges, as well as potential human health risk in the high desert near the historic mining center of Potosí, Bolivia. Metal concentrations were compared with international concentration limit guidelines. In addition, an ingested average daily dose and minimum risk level were used to determine the hazard quotient from potato consumption for adults and children. Irrigation water maximum concentrations of Cd, Pb and Zn in mining-impacted sites were elevated 20- to 1100-fold above international concentration limit guidelines. Agricultural soils contained total metal concentrations of As, Cd, Pb and Zn that exceeded concentration limits in agricultural soil guidelines by 22-, 9-, 3- and 12-fold, respectively. Potato tubers in mining-impacted sites had maximum concentrations of As, Cd, Pb and Zn that exceeded concentration limits in commercially sold vegetables by 9-, 10-, 16- and fourfold, respectively. Using conservative assumptions, hazard quotients (HQ) for potatoes alone were elevated for As, Cd and Pb among children (range 1.1–71.8), in nearly all of the mining-impacted areas; and for As and Cd among adults (range 1.2–34.2) in nearly all of the mining-impacted areas. Only one mining-impacted area had a Pb adult HQ for potatoes above 1 for adults. Toxic trace elements in a major regional dietary staple may be a greater concern than previously appreciated. Considering the multitude of other metal exposure routes in this region, it is likely that total HQ values for these metals may be substantially higher than our estimates.     

Trace elements in surface sediments of the Hooghly (Ganges) estuary: distribution and contamination risk assessment

Our objective was to evaluate distribution and accumulation of trace elements (TEs) in surface sediments along the Hooghly (Ganges) River Estuary, India, and to assess the potential risk with view to human health. The TE concentrations (mg kg^?1 dry weight) exhibited a wide range in the following order: Al (31.801 ± 15.943) > Fe (23.337 ± 7584) > Mn (461 ± 147) > S (381 ± 235) > Zn (54 ± 18) > V (43 ± 14) > Cr (39 ± 15) > As (34 ± 15) > Cu (27 ± 11) > Ni (24 ± 9) > Se (17 ± 8) > Co (11 ± 3) > Mo (10 ± 2) > Hg (0.02 ± 0.01). Clay, silt, iron, manganese and sulphur were important for the accumulation of TE in the sediments as confirmed by factor analysis and Pearson correlation. The accumulation and dispersal of TEs were most likely to be governed by both tide-induced processes and anthropogenic inputs from point and non-point sources. Enrichment factor analysis and geoaccumulation index revealed serious contamination of the sediments with Se and As, while comparing the consensus-based sediment quality guidelines (SQGs), adverse biological effects to benthic fauna might be caused by As, Cu, Ni and Cr. This investigation may serve as a model study and recommends continuous monitoring of As, Se, Cu, Ni and Cr to ascertain that SQGs with respect to acceptable levels of TEs to safeguard geochemical health and ecology in the vicinity of this estuary.     

Quantity and biochemical composition of sedimentary organic matter around offshore gas extraction platforms of the Adriatic Sea

Gas platforms can exert relevant effects on various ecosystem properties of the hosting area, modifying patterns of productivity and particle sedimentation. We hypothesised that the presence of gas platforms is associated with higher organic matter (OM) contents and we tested the null hypothesis by which benthic trophic conditions do not vary significantly among gas structures with different architectural characteristics. To test these hypotheses, we investigated sedimentary OM contents and biochemical composition at increasing distance from two distinct structures: one subsea well-site and one four-leg platform (FLP). We found that sediments surrounding the structures were characterised by similar OM contents, but different biochemical compositions, and that sedimentary OM compositional change with increasing distance from the structure is confirmed only at the FLP. These results suggest that: (i) benthic eutrophication is not consistent among different structures; (ii) OM biochemical composition changes are more evident than those in OM quantity; and (iii) the effect, wherever present, disappears at a very narrow distance from the platform. As biochemical composition of OM reflects food availability for benthic organisms, our results suggest that the enrichment of macrozoobenthic communities around these structures could be the consequence of increased nutritional quality of resources.     

Geochemical disturbance of soil cover in the nonferrous mining centers of the Selenga River basin

The anthropogenic geochemical transformation of soil cover in large nonferrous mining centers of the Selenga River basin was assessed. The results of the geochemical survey of 2010–2012 revealed the spatial distribution patterns and abundances of 18 hazardous heavy metals and metalloids in the soils of Erdenet (Mongolia) and Zakamensk (Buryat republic, Russian Federation). In both cities, mining activities disturbed soil cover which accumulates Mo, Cu, As, Sb, W in Erdenet and Bi, W, Cd, Be, Pb, Mo, Sb in Zakamensk. Maximum accumulation of elements in Erdenet is restricted to the industrial zone. In Zakamensk, it has spread on ½ of the territory with the degree of multielemental pollution exceeding the extremely dangerous level by 16 times. The effect of mining centers on the state of the river system is local and does not spread to the Selenga River. Downstream from Erdenet, an artificial pool intercepts heavy metal and metalloid flows of the Erdenetii-Gol River. By contrast, downstream from the tailing dumps of the Dzhida tungsten–molybdenum plant the concentrations of ore elements W and Mo and their accessories Bi and Cd in the Modonkul River exceed background values by 146, 20, 57, and 21 times, respectively, decreasing by an order of magnitude 30 km downstream.     

Assessment of the ecological risk of technogenic soil pollution on the basis of the statistical distribution of the occurrence of micromycete species

A methodology of substantiating the ecological risk of technogenic soil pollution has been described according to the results of bioindication using microfungal communities. At the first state, the values of the factors under which the frequency of each species is maximal were estimated using ordination methods. The statistical distribution of the species sensitivity was simulated according to these data. The methodology was illustrated using the results of analysis of the toxicity of soil samples from uranium mine dumps (the village of Kadzhi-Sai, Kyrgyzstan). The preliminary estimates of the critical values of six soil pollution indices that provide the defined allowable probability of the ecological risk have been given.     

Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.     

Constraining SWAT Calibration with Remotely Sensed Evapotranspiration Data

Historically, many watershed studies have been based on using the streamflow flux, typically from a single gauge at the basin's outlet, to support calibration. In this setting, there is great potential for equifinality of parameters during the optimization process, especially for parameters that are not directly related to streamflow. Therefore, some of the optimal parameter values achieved during the autocalibration process may be physically unrealistic. In recent decades a vast array of data from land surface models and remote sensing platforms can help to constrain hydrologic fluxes such as evapotranspiration (ET). While the spatial resolution of these ancillary datasets varies, the continuous spatial coverage of these gridded datasets provides flux measurements across the entire basin, in stark contrast to point‐based streamflow data. This study uses Global Land Evaporation: the Amsterdam Model data to constrain Soil and Water Assessment Tool parameter values associated with ET to a more physically realistic range. The study area is the Little Washita River Experimental Watershed, in southern Oklahoma. Traditional objective metrics such as the Nash‐Sutcliffe coefficients record no performance improvement after application of this method. However, there is a dramatic increase in the number of days with receding flow where simulations match observed streamflow.     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.