Understanding of aquaporins (AQPs) facilitating the transport of water and many other small solutes including metalloids like silicon (Si) and arsenic (As) is important to develop stress tolerant cultivars. In the present study, 40 AQPs were identified in the genome of pigeonpea (Cajanus cajan), a pulse crop widely grown in semi-arid region and areas known to affected with heavy metals like As. Conserved domains, variation at NPA motifs, aromatic/arginine (ar/R) selectivity filters, and pore morphology defined here will be crucial in predicting solute specificity of pigeonpea AQPs. The study identified CcNIP2-1 as an AQP predicted to transporter Si (beneficial element) as well as As (hazardous element). Further Si quantification in different tissues showed about 1.66% Si in leaves which confirmed the predictions. Furthermore, scanning electron microscopy showed a higher level of Si accumulation in trichomes on the leaf surface. A significant alleviation in level of As, Sb and Ge stress was also observed when these heavy metals were supplemented with Si. Estimation of relative water content, H₂O₂, lipid peroxidation, proline, total chlorophyll content and other physiological parameters suggested Si derived stress tolerance. Extensive transcriptome profiling under different developmental stages from germination to senescence was performed to understand the tissue-specific regulation of different AQPs. For instance, high expression of TIP3s was observed only in reproductive tissues. Co-expression network developed using transcriptome data from 30 different conditions and tissues, showed interdependency of AQPs. Expression profiling of pigeonpea performed using real time PCR showed differential expression of AQPs after Si supplementation. The information generated about the phylogeny, distribution, molecular evolution, solute specificity, and gene expression dynamics in article will be helpful to better understand the AQP transport system in pigeonpea and other legumes.

River ecosystems are under increasing stress in the background of global change and ever-growing anthropogenic impacts in Central Asia. However, available water quality data in this region are insufficient for a reliable assessment of the current status, which come as no surprise that the limited knowledge of regulating processes for further prediction of solute variations hinders the development of sustainable management strategies. Here, we analyzed a dataset of various water quality variables from two sampling campaigns in 2019 in the catchments of two major rivers in Central Asia—the Amu Darya and Syr Darya Rivers. Our results suggested high spatial heterogeneity of salinity and major ion components along the longitudinal directions in both river catchments, pointing to an increasing influence of human activities toward downstream areas. We linked the modeling outputs from the global nutrient model (IMAGE-GNM) to riverine nutrients to elucidate the effect of different natural and anthropogenic sources in dictating the longitudinal variations of the riverine nutrient concentrations (N and P). Diffuse nutrient loadings dominated the export flux into the rivers, whereas leaching and surface runoff constituted the major fractions for N and P, respectively. Discharge of agricultural irrigation water into the rivers was the major cause of the increases in nutrients and salinity. Given that the conditions in Central Asia are highly susceptible to climate change, our findings call for more efforts to establish holistic management of water quality.

Transient storage model (TSM) is the most popular model for simulating solutes transport in natural streams. Accurate estimate of TSM parameters is essential in many hydraulic and environmental problems. In this study, an improved version of high-level Moth-Swarm Algorithm (IMSA) was used to predict the TSM parameters. First, the performance of the improved model was successfully assessed through several benchmark functions. Next, a series of 58 measured hydraulic and geometric datasets was used to validate the model. The data were divided into two series randomly, 38 datasets were selected for derivation and the remaining 20 datasets were used to verification. Then the results of IMSA were compared with other algorithms proposed by previous researchers. Two statistical indices of root mean square error (RMSE) and coefficient of correlation (CC) were employed to evaluate the performance of the model. The results showed that despite the high complexity and uncertainty associated with the dispersion processes, the IMSA algorithm could accurately predict the TSM parameters.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal).This article has been retracted at the request of the authors due to the results of a detailed investigation of the data quality conducted by the Central Analytical Laboratory (CAL) after relocation to the University of Wisconsin (UW) – Wisconsin State Laboratory of Hygiene. Using a subset of the 30 samples with the highest bromide ion (Br-) concentrations, the CAL at UW found 6 samples that could not be verified or were incorrect. Because the extent of the incorrect data is unknown, the NADP Executive Committee voted unanimously in May 2019 to discontinue public access to these data, and they decided to sequester all Br- data prior to June 2018. These issues were not obvious to the authors when the paper was written.The authors apologize for the inconvenience caused.

Overburden and tailings materials from oil sands production were used as construction materials as part of a novel attempt to create a self-sustaining, peat accumulating fen-upland ecosystem. To evaluate the potential for elemental release from the construction materials, total elemental concentrations in the tailings sand, petroleum coke and peat used to construct a fen ecosystem were determined using microwave-assisted acid digestions and compared to a leaching experiment conducted under environmentally-relevant conditions. A comparison of solid phase to aqueous Na, Ca, S and Mg concentrations showed they were highly leachable in the materials. Given that the concentrations of these elements can affect plant community structure, it is important to understand their leachability and mobility as they migrate between materials used to construct the system. To that end, a mass balance of aqueous Na, Ca, S and Mg was conducted based on leaching experiments and materials analysis coupled with existing data from the constructed system. The data indicate that there is a large pool of leachable Na, Ca, S and Mg in the system, estimated at 27 t of Na, 14 t of Ca, 37.3 t of S and 8.8 t of Mg. Since recharge mainly drives the fen-upland system water regime, and discharge in the fen, evapo-accumulation of these solutes on the surface may occur.

Insufficient understanding of the hydrogeochemistry of aquifers makes it necessary to conduct a preliminary water quality assessment in the southern region of Ordos Basin, an arid area in the world. In this paper, the major ions of groundwater have been studied aiming at evaluating the hydrogeochemical processes that probably affect the groundwater quality using 150 samples collected in 2015. The two prevalent hydrochemical facies, HCO3Mg·Na·Ca and HCO3Mg·Ca·Na type water, have been identified based on the hydrochemical analysis from Piper trilinear diagram. Compositional relations have been used to assess the origin of solutes and confirm the predominant hydrogeochemical processes responsible for the various ions in the groundwater. The results show that the ions are derived from leaching effect, evaporation and condensation, cation exchange, mixing effect and human activities. Finally groundwater quality was assessed with single factor and set pair methods, the results indicate that groundwater quality in the study region is generally poor in terms of standard of national groundwater quality. The results obtained in this study will be useful to understand the groundwater quality status for effective management and utilization of the groundwater resource.

In reverse osmosis, a frequently used technology in water desalination processes, wastewater (RO concentrate) is generated containing the retained solutes as well as so-called antiscalants (AS), i.e. chemical substances that are commonly applied to prevent membrane-blocking. In this study, a risk assessment of a possible discharge of concentrate into a small stream was conducted. The acute toxicity of two concentrates containing two different ASs and of concentrate without AS to the amphipods Gammarus pulex and Gammarus roeseli was studied. Mortality of gammarids exposed to the concentrate without AS was not different to the control, whereas concentrates including ASs caused mortality rates up to 100% at the highest test concentrations after 168 h. Resulting EC50-values were 36.2–39.4% (v/v) after 96 h and 26.6–58.0% (v/v) after 168 h. These results suggest that the ecotoxicological relevance of antiscalants is greater than currently assumed.

Isosteric heats (ΔH) of sorption/desorption of phenanthrene were determined for carbonaceous materials (Pahokee peat, lignite, and high-volatile bituminous coal) and two soils based on reported equilibrium sorption/desorption isotherms at four different temperatures (4, 20, 46 and 77 °C). In addition, ΔH for desorption of native phenanthrene was determined to elucidate the “aging” effect by equilibrating samples with water at six temperatures (20, 40, 53, 61, 73, and 86 °C). Isosteric heats decreased with increasing solute concentration and were in a range of 19–35 kJ mol−1. Values higher than the heat of octanol–water phase transfer for phenanthrene (19 kJ mol−1) imply that both partitioning and adsorption processes are involved for these materials, where the sorptive contributions from both processes were estimated based on the phenanthrene thermodynamic data. Moreover, on the basis of ΔH values of desorption, release of native and spiked phenanthrene from our samples was similar.

Simulation modelling with CHUM-AM was carried out to investigate the accumulation and release of atmospherically-deposited heavy metals (Ni, Cu, Zn, Cd and Pb) in six moorland catchments, five with organic-rich soils, one with calcareous brown earths, in the Pennine chain of northern England. The model considers two soil layers and a third layer of weathering mineral matter, and operates on a yearly timestep, driven by deposition scenarios covering the period 1400-2010. The principal processes controlling heavy metals are competitive solid-solution partitioning of solutes, chemical interactions in solution, and chemical weathering. Agreement between observed and simulated soil metal pools and surface water concentrations for recent years was generally satisfactory, the results confirming that most contemporary soil metal is from atmospheric pollution. Metals in catchments with organic-rich soils show some mobility, especially under more acid conditions, but the calcareous mineral soils have retained nearly all anthropogenic metal inputs. Complexation by dissolved organic matter and co-transport accounts for up to 80% of the Cu in surface waters.

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., KXAD₋A) are indispensable to obtain POPs concentration, and the KXAD₋A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of KXAD₋A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting KXAD₋A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R²ₐdⱼ and Q²ₑₓₜ were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R²ₐdⱼ = 0.991, Q²ₑₓₜ = 0.935, RMSEₜᵣₐ = 0.271 and RMSEₑₓₜ = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting KXAD₋A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.