Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater.

Pentachlorophenol (PCP) is a commonly found recalcitrant and toxic groundwater contaminant that resists degradation, bioaccumulates, and has a potential for long-range environmental transport. Taking proper actions to deal with the pollutant accounting for the life cycle consequences requires a better understanding of its behavior in the subsurface. We recognize the huge potential for enhancing decision-making at contaminated groundwater sites with the arrival of machine learning (ML) techniques in environmental applications. We used ML to enhance the understanding of the dynamics of PCP transport properties in the subsurface, and to determine key hydrochemical and hydrogeological drivers affecting its transport and fate. We demonstrate how this complementary knowledge, provided by data-driven methods, may enable a more targeted planning of monitoring and remediation at two highly contaminated Swedish groundwater sites, where the method was validated. We evaluated 6 interpretable ML methods, 3 linear regressors and 3 non-linear (i.e., tree-based) regressors, to predict PCP concentration in the groundwater. The modeling results indicate that simple linear ML models were found to be useful in the prediction of observations for datasets without any missing values, while tree-based regressors were more suitable for datasets containing missing values. Considering that missing values are common in datasets collected during contaminated site investigations, this could be of significant importance for contaminated site planners and managers, ultimately reducing site investigation and monitoring costs. Furthermore, we interpreted the proposed models using the SHAP (SHapley Additive exPlanations) approach to decipher the importance of different drivers in the prediction and simulation of critical hydrogeochemical variables. Among these, sum of chlorophenols is of highest significance in the analyses. Setting that aside from the model, tetra chlorophenols, dissolved organic carbon, and conductivity found to be of highest importance. Accordingly, ML methods could potentially be used to improve the understanding of groundwater contamination transport dynamics, filling gaps in knowledge that remain when using more sophisticated deterministic modeling approaches.

Production of Graphene Membranes from Rice Husk Biomass Waste for Improved Desalination.

Inexpensive and efficient desalination is becoming increasingly important due to dwindling freshwater resources in view of climate change and population increase. Improving desalination techniques of brackish water using graphene-based materials has the possibility to revolutionize freshwater production and treatment. At the same time, graphene matter can be cheaply mass-produced from biowaste materials. In view of this, graphene material was obtained from a four-step production approach starting from rice husk (RH), including pre-carbonation, desilication, chemical activation, and exfoliation. The results showed that the produced samples contained a mixture of graphene layers and amorphous carbon. The activation ratio of 1:5 for carbonized RH and potassium hydroxide (KOH), respectively, provided higher graphene content than the 1:4 ratio of the same components, while the number of active layers remained unaffected. Further treatment with H2O2 did not affect the graphene content and exfoliation of the amorphous carbon. Preparation of the graphene material by the NIPS technique and vacuum filtration displayed different physicochemical characteristics of the obtained membranes. However, the membranes' main desalination function might be related more to adsorption rather than size exclusion. In any case, the desalination properties of the different graphene material types were tested on 35 g/L saltwater samples containing NaCl, KCl, MgCl2, CaSO4, and MgSO4. The produced graphene materials efficiently reduced the salt content by up to 95%. Especially for the major constituent NaCl, the removal efficiency was high.

Automated Low-Cost Soil Moisture Sensors: Trade-Off between Cost and Accuracy.

Automated soil moisture systems are commonly used in precision agriculture. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 tested under lab and field conditions. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor response in dry soil. During the second stage of testing, the sensors were coupled to a low-cost monitoring station and installed in the field. The sensors were capable of measuring daily and seasonal oscillations in soil moisture resulting from solar radiation and precipitation. The low-cost sensor performance was compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors provide single-point information with high reliability but at a high acquisition cost, while low-cost sensors can be acquired in larger numbers at a lower cost, allowing for more detailed spatial and temporal observations, but with medium accuracy. The use of SKU sensors is then indicated for short-term and limited-budget projects in which high accuracy of the collected data is not required.

Evapotranspiration in Semi-Arid Climate: Remote Sensing vs. Soil Water Simulation.

Estimating crop evapotranspiration (ETa) is an important requirement for a rational assessment and management of water resources. The various remote sensing products allow the determination of crops' biophysical variables integrated in the evaluation of ETa by using surface energy balance (SEB) models. This study compares ETa estimated by the simplified surface energy balance index (S-SEBI) using Landsat 8 optical and thermal infra-red spectral bands and transit model HYDRUS-1D. In semi-arid Tunisia, real time measurements of soil water content (θ) and pore electrical conductivity (ECp) were made in the crop root zone using capacitive sensors (5TE) for rainfed and drip irrigated crops (barley and potato). Results show that HYDRUS model is a fast and cost-effective assessment tool for water flow and salt movement in the crop root layer. ETa estimated by S-SEBI varies according to the available energy resulting from the difference between the net radiation and soil flux G0, and more specifically according to the assessed G0 from remote sensing. Compared to HYDRUS, the ETa from S-SEBI was estimated to have an R2 of 0.86 and 0.70 for barley and potato, respectively. The S-SEBI performed better for rainfed barley (RMSE between 0.35 and 0.46 mm·d-1) than for drip irrigated potato (RMSE between 1.5 and 1.9 mm·d-1).

Health Risk Assessment of Nitrate in Drinking Water with Potential Source Identification: A Case Study in Almaty, Kazakhstan.

Infant mortality in Kazakhstan is six times higher compared with the EU. There are several reasons for this, but a partial reason might be that less than 30% of Kazakhstan's population has access to safe water and sanitation and more than 57% uses polluted groundwater from wells that do not comply with international standards. For example, nitrate pollution in surface and groundwater continues to increase due to intensified agriculture and the discharge of untreated wastewater, causing concerns regarding environmental and human health. For this reason, drinking water samples were collected from the water supply distribution network in eight districts of Almaty, Kazakhstan, and water quality constituents, including nitrate, were analyzed. In several districts, the nitrate concentration was above the WHO and Kazakhstan's maximum permissible limits for drinking water. The spatial distribution of high nitrate concentration in drinking water was shown to be strongly correlated with areas that are supplied with groundwater, whereas areas with lower nitrate levels are supplied with surface water sources. Based on source identification, it was shown that groundwater is likely polluted by mainly domestic wastewater. The health risk for infants, children, teenagers, and adults was assessed based on chronic daily intake, and the hazard quotient (HQ) of nitrate intake from drinking water was determined. The non-carcinogenic risks increased in the following manner: adult < teenager < child < infant. For infants and children, the HQ was greater than the acceptable level and higher than that of other age groups, thus pointing to infants and children as the most vulnerable age group due to drinking water intake in the study area. Different water management options are suggested to improve the health situation of the population now drinking nitrate-polluted groundwater.

DynSus: Dynamic sustainability assessment in groundwater remediation practice.

Decision-making processes for clean-up of contaminated sites are often highly complex and inherently uncertain. It depends not only on hydrological and biogeochemical site variability, but also on the associated health, environmental, economic, and social impacts of taking, or not taking, action. These variabilities suggest that a dynamic framework is required for promoting sustainable remediation. For this, the decision support system DynSus is presented here for integrating a predeveloped contaminant fate and transport model with a sustainability assessment tool. Implemented within a system dynamics framework, the new tool uses model simulations to provide remediation scenario analysis and handling of uncertainty in various data. DynSus was applied to a site in south Sweden, contaminated with pentachlorophenol (PCP). Simulation scenarios were developed to enable a comparison between alternative remediation strategies and combinations of these. Such comparisons are provided for selected sustainability indicators and remediation performance (in terms of concentration at the recipient). This leads to identifying the most critical variables to ensure that sustainable solutions are chosen. Simulation results indicated that although passive practices, e.g., monitored natural attenuation, were more sustainable at first (5-7 years after beginning remediation measures), they failed to compete with more active practices, e.g., bioremediation, over the entire life cycle of the project (from the beginning of remedial action to achieving the target concentration at the recipient). In addition, statistical tools (clustering and genetic algorithms) were used to further assess the available hydrogeochemical data. Taken together, the results reaffirmed the suitability of the simple analytical framework that was implemented in the contaminant transport model. DynSus outcomes could therefore enable site managers to evaluate different scenarios more quickly and effectively for life cycle sustainability in such a complex and multidimensional problem.

A hybrid approach based on simulation, optimization, and estimation of conjunctive use of surface water and groundwater resources.

Due to limited groundwater resources in arid and semi-arid areas, conjunctive use of surface water and groundwater is becoming increasingly important. In view of this, there are needs to improve the methods for conjunctive use of surface and groundwater. Using numerical models, optimization algorithms, and machine learning, we created a new comprehensive methodological structure for optimal allocation of surface and groundwater resources and optimal extraction of groundwater. The surface and groundwater system was simulated by MODFLOW to reflect groundwater transport and aquifer conditions. The important Marvdasht aquifer in the south of Iran was used as an experimental study area to test the methodology. In this context, we developed an optimal conjunctive exploitation model for dry and wet years using two new evolutionary algorithms, i.e., whale optimization algorithm (WOA) and firefly algorithm (FA). These were used in combination with the group method of data handling (GMDH) and least squares support vector machine (LS-SVM) to estimate sustainable groundwater withdrawal. The results show that the FA is more efficient in calculating optimal conjunctive water supply so that about 61% of water needs were met in the worst scenario for surface water resources, while it was 52% using the WOA. By applying the optimal conjunctive model during the simulation period, the groundwater level increased by about 0.4 and 0.55 m using the WOA and FA, respectively. The results of Taylor's diagram, box plot diagram, and rock diagram with error evaluation criteria, i.e., root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE), showed that the GMDH (RMSE = 6.04 MCM, MAE = 3.89 MCM, and NSE = 0.99) was slightly better than LS-SVM (RMSE = 6.36 MCM, MAE = 4.50 MCM, and NSE = 0.98) to estimate optimal groundwater use. The results show that machine learning models are cost- and time-effective solutions to estimate optimal exploitation of groundwater resources in complex combined surface and groundwater supply problems. The methodology can be used to better estimate sustainable exploitation of groundwater resources by water resources managers.

Parasitological Assessment of Sewage Sludge Samples for Potential Agricultural Reuse in Tunisia.

Wastewater sludge represents an important resource for reuse in agriculture. However, potentially harmful pathogens are a main threat in this context. Thus, the aim of this study was to examine the presence of helminth ova and protozoan cysts in dried sewage sludge samples collected from ten wastewater treatment plants (WWTPs) located in eight governorates in Tunisia. Based on morphological criteria, protozoan cysts of Giardia spp., Entamoeba histolytica/dispar/moshkovskii, and Entamoeba coli, were detected in all dried sludge composite samples (N = 116) from the investigated WWTPs. The mean concentration ranged from 1.4 to 10.7 cysts per 100 g dry matter (DM). The identified helminth eggs were Ascaris spp., Strongyles, Taeniid eggs, Hymenolepis nana, Enterobius vermicularis, and hookworm species. Ascaris spp. and Taeniid eggs were detected in 56.9 and 74.1% of analyzed samples, respectively. The presence of Trichuris spp., Hymenolepis diminuta, and Toxocara spp. eggs in dried sewage sludge samples was low (0.9, 1.7, and 2.6%, respectively). The mean concentration of helminth eggs during the three-year study was less than 1 egg/100 g DM. All examined dried sewage sludge sample contents were below the WHO (2006) and US EPA (2003) recommendations, and thus, the sludge can potentially be reused in agriculture.

Vicia-Micronucleus Test Application for Saline Irrigation Water Risk Assessment.

In view of climate change, increasing soil salinity is expected worldwide. It is therefore important to improve prediction ability of plant salinity effects. For this purpose, brackish/saline irrigation water from two areas in central and coastal Tunisia was sampled. The water samples were classified as C3 (EC: 2.01-2.24 dS m-1) and C4 (EC: 3.46-7.00 dS m-1), indicating that the water was questionable and not suitable for irrigation, respectively. The water samples were tested for their genotoxic potential and growth effects on Vicia faba seedlings. Results showed a decrease in mitotic index (MI) and, consequently, growth parameters concomitant to the appearance of micronucleus (MCN) and chromosome aberrations when the water salinity increased. Salt ion concentration had striking influence on genome stability and growth parameters. Pearson correlation underlined the negative connection between most ions in the water inappropriate for irrigation (C4) and MI as well as growth parameters. MI was strongly influenced by Mg2+, Na+, Cl-, and to a less degree Ca2+, K+, and SO42-. Growth parameters were moderately to weakly affected by K+ and Ca2+, respectively. Re-garding MCN, a very strong positive correlation was found for MCN and K+. Despite its short-term application, the Vicia-MCN Test showed a real ability to predict toxicity induced by salt ions confirming that is has a relevant role in hazard identification and risk assessment of salinity effects.

Distribution of heavy metals and related health risks through soil ingestion in rural areas of western Japan.

The distribution of heavy metal concentrations and related human health risks were investigated for Shimabara City, Nagasaki Prefecture. The purpose was to clarify the potential for heavy metal contamination in an area already known to be affected by nitrate pollution. A total of 188 soil samples was collected at 47 sites. The heavy metal content of the soil was measured in laboratory using an X-ray analyzer. The highest contents of heavy metals exceeded common background concentration in Japanese soils. The highest concentrations of Cu and Zn appear to be related to application of livestock waste in agriculture. Principal component and cluster analyses were performed to classify the sampling sites based on soil content of heavy metals. Three principal components (PCs) were extracted with the first PC explaining crustal constituents, the second explaining application of livestock waste, and the third explaining other types of anthropogenic pollutants. The cluster analysis resulted in 5 groups regarding the sampling locations. In total, 44% of sampling locations belonged to Group 1 and 46% to Group 2, distributed over the agricultural land in the northern part of the city and the urban area in the southern part of the city, respectively. There is a potential temporal health risk for the Pb content at specific locations in the area.

Novel approach for predicting groundwater storage loss using machine learning.

Comprehensive national estimates of groundwater storage loss (GSL) are needed for better management of natural resources. This is especially important for data scarce regions with high pressure on groundwater resources. In Iran, almost all major groundwater aquifers are in a critical state. For this purpose, we introduce a novel approach using Artificial Intelligence (AI) and machine learning (ML). The methodology involves water budget variables that are easily accessible such as aquifer area, storage coefficient, groundwater use, return flow, discharge, and recharge. The GSL was calculated for 178 major aquifers of Iran using different combinations of input data. Out of 11 investigated variables, agricultural water consumption, aquifer area, river infiltration, and artificial drainage were highly associated to GSL with a correlation of 0.84, 0.79, 0.70, and 0.69, respectively. For the final model, 9 out of the totally 11 investigated variables were chosen for prediction of GSL. Results showed that ML methods are efficient in discriminating between different input variables for reliable GSL estimation. The Harris Hawks Optimization Adaptive Neuro-Fuzzy Inference System (HHO-ANFIS) and the Least-Squares Support Vector Machine (LS-SVM) gave best results. Overall, however, the HHO-ANFIS was most efficient to predict GSL. AI and ML methods can thus, save time and costs for these complex calculations and point at the most efficient data inputs. The suggested methodology is especially suited for data-scarce regions with a great deal of uncertainty and a lack of reliable observations of groundwater levels and pumping.

Efficient organic mulch thickness for soil and water conservation in urban areas.

The use of organic mulch is important for urban green applications. For urban areas in arid and semiarid regions receiving short high-intensive rainfall, rainfall characteristics, and soil slope play an important role for mulch functioning. These properties of mulch were studied. For this purpose, rainfall simulation experiments using organic mulching were conducted in Jiufeng National Forestry Park to analyze the influence of organic mulch under different slope and heavy rainfall events. The results showed that soil water content displayed a decreasing tendency with increasing mulch application. Compared to bare soil, a mulch application of 0.25 kg/m2 and 0.50 kg/m2 led to maximum soil water content and maximum runoff decrease occurred for 0.50 kg/m2 mulch. Higher application rate of mulch displayed less soil water content and greater runoff. The runoff amount and runoff generation rate decreased by 28-83% and 21-83%, respectively, as compared to bare soil. With a mulch application of 0.25-1.00 kg/m2, soil drainage accounted for 56-60% of total rainfall. Overall, an efficient mulch application was found to be 0.25-0.50 kg/m2. The results of this study are relevant for arid and semiarid urban regions that experience heavy rainfall.

Spatiotemporal variation of nitrate concentrations in soil and groundwater of an intensely polluted agricultural area.

Nitrate pollution in groundwater is a serious problem in many parts of the world. However, due to the diffuse and common spatially over-lapping character of potential several non-point pollution sources, it is often difficult to distinguish main nitrate sources responsible for the pollution. For this purpose, we present a novel methodology applied to groundwater for an intensely polluted area. Groundwater samples were collected monthly from April 2017 to March 2018 in Shimabara City, Nagasaki, Japan. Soil samples were collected seasonally at soil surface and 50 cm depth at 10 locations during the same period. Sequential extraction by water and extract agents was performed using calcium phosphate for anions and strontium chloride for cations. Mean nitrate concentration in groundwater close to a livestock waste disposal site (hereinafter called "LWDS") was 14.2 mg L-1, which is exceeding Japanese drinking water standards (10 mg L-1). We used coprostanol concentration, which is a fecal pollution indicator, to identify pollution sources related to livestock waste. For this purpose, we measured coprostanol (5ß) and cholestanol (5α) and then calculated the sterol ratio (5ß/(5ß + 5α)). The ratios for three groundwater sampling sites were 0.28, 0.26, and 0.10, respectively. The sterol ratios indicated no pollution (< 0.3). However, the detection of coprostanol originating from animal and human waste showed that groundwater was clearly affected by this pollution source. Nitrate levels in the soil were relatively high in samples collected close to the LWDS and coprostanol contents were affected by livestock waste. Soil and groundwater nitrate concentrations displayed a complex but strong relationship. Nitrate contents were shown to be transported downstream from source areas in both soil and groundwater.

Assessment of hydro-geochemical properties of groundwater under the effect of desalination wastewater discharge in an arid area.

Desalination to increase irrigation water supply for agricultural production is becoming important in water-scarce regions. While desalination has positive effects on the potential irrigation water quantity and quality, the technique may also be a considered potential source of groundwater pollution. The present study investigated the effects of desalination wastewater discharge on groundwater quality in an arid area in southern Iran for the 2012-2017 period. The chemical composition of the groundwater samples was evaluated considering pH, EC, Na+, Ca2+, Mg2+, SO42+, Cl-, and HCO3-. The suitability of groundwater for drinking and irrigation purposes as well as spatial pattern of groundwater pollution was analyzed. The results showed that mean concentration of Na+, Ca2+, Mg2+, SO42-, and Cl- in all investigated wells increased from 148, 94, 46, 247, and 257 mg/L in 2012 to 282, 146, 71, 319, and 582 mg/L in 2017, respectively. Using Gibb's diagram, it was shown that the groundwater quality is slightly alkaline and primarily controlled by evaporation. Based on our findings, about 78% of the study aquifer displayed groundwater with good to excellent water quality that can be used for drinking and irrigation purposes. However, the eastern part of the aquifer was classified as unsuitable for use due to the disposal of desalination plant wastewater. The spatial distribution of WQI and other indices such as SAR, TDS, and TH showed that groundwater in the eastern part of the aquifer has deteriorated since the establishment of the desalination plants. To reverse this trend, it is important to implement regulations against wastewater discharge from desalination plants.

INSIDE: An efficient guide for sustainable remediation practice in addressing contaminated soil and groundwater.

Modeling criteria interaction in decision-making problems is complex and often neglected. In complicated problems, like contaminated site remediation projects, independency of involved criteria is not a realistic assumption. INfluence based deciSIon guiDE (INSIDE) is a methodology that enables sustainable decision making and management in contaminated site remediation practice. Unlike most previous decision-support methods, INSIDE considers realistic interactions among all involved criteria. The method not only gives a one-time best option for choosing a remediation method for the project at hand, but also a management plan for further improvements of the system. INSIDE recognizes economic, environmental, social, and technological considerations for the most sustainable practice. Eight criteria are defined based on these aspects and they can be interrelated. This means that a criterion, e.g., remediation time, does not need to belong to any pre-defined category such as economic, environmental, social, or technical, but can interact with other criteria. This allows for a system with many degrees of freedom that is more realistic for practical problem-solving. In INSIDE, the DEcision MAking Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) techniques are combined for assigning weights to criteria and scoring of remediation alternatives, respectively. Thus, the proposed methodology gives a managerial cone of influence versus importance for all involved criteria in the system. The method is applied to a data-scarce case study in Iran to prioritize between remediation methods for a contaminated groundwater aquifer. The results show that human health risk and environmental impacts are more influential than other evaluated criteria. The suggested methodology should be further tested on a variety of actual remediation problems for additional evaluation.

Impact of the Sediment Organic vs. Mineral Content on Distribution of the Per- and Polyfluoroalkyl Substances (PFAS) in Lake Sediment.

Contamination of the water and sediment with per- and polyfluoroalkyl substances (PFAS) was studied for the lake impacted by the release of PFAS-containing aqueous film forming foam (AFFF). PFAS concentrations were analyzed in lake water and sediment core samples. ΣPFAS concentrations were in the range of 95-100 ng L-1 in the lake water and 3.0-61 µg kg-1 dry weight (dw) in sediment core samples, both dominated by perfluorohexane sulfonate, perfluorooctane sulfonate; 6:2 fluortelomer sulfonate was inconsistently present in water and sediment core samples. The sediment-water partitioning coefficients (log Kd) were estimated and ranged 0.6-2.3 L kg-1 for individual perfluoroalkyl carboxylates (PFCAs) and 0.9-5.6 L kg-1 for individual perfluoroalkane sulfonates (PFSAs). The influence of the sediment inorganic content and organic matter on PFAS distribution was investigated. In studied sediments, the mineral content (corresponding to <5% of the bulk media mass) was mainly represented by sulfur, iron and calcium. The PFAS distribution was found strongly connected to the sediment mineral content (i.e., Fe, Pb, Rb and As), whereas the sediment organic carbon content did not to have a direct influence on the PFAS distribution. The aim of this study was to improve our understanding of the PFAS distribution in the natural heterogeneous media.

Evaluation of a new X-band weather radar for operational use in south Sweden.

The performance of a new type of X-band weather radar (WR) for Sweden during a pilot run is studied. Compared to the conventional C-band WRs, the X-band WR covers a smaller area but with a higher spatiotemporal resolution, making it suitable for urban hydrological applications. Rainfall estimations from different elevation angles of the radar (levels) are compared at one-minute and single-event timescales with the observations of several rain gauges at different ranges using hyetographs. In general, the estimations aligned well with observations and the best match appeared for ranges as long as 5-10 km. Seemingly, radar estimations suffered from overshooting of lower lying showers by higher level scans in longer ranges (19-30 km) and from the reflectivity contamination due to moving objects in short ranges (<1 km). Also, the effective range of the radar dropped sharply for the moments when a cloudburst was located over the radar. Although various sources of error could affect the X-band WR rainfall estimates, higher resolution spatiotemporal rainfall monitoring for wider areas will benefit from an integration of data from a network of X-band WRs.

Laboratory Calibration and Field Validation of Soil Water Content and Salinity Measurements Using the 5TE Sensor.

Capacitance sensors are widely used in agriculture for irrigation and soil management purposes. However, their use under saline conditions is a major challenge, especially for sensors operating with low frequency. Their dielectric readings are often biased by high soil electrical conductivity. New calculation approaches for soil water content (θ) and pore water electrical conductivity (ECp), in which apparent soil electrical conductivity (ECa) is included, have been suggested in recent research. However, these methods have neither been tested with low-cost capacitance probes such as the 5TE (70 MHz, Decagon Devices, Pullman, WA, USA) nor for field conditions. Thus, it is important to determine the performance of these approaches and to test the application range using the 5TE sensor for irrigated soils. For this purpose, sandy soil was collected from the Jemna oasis in southern Tunisia and four 5TE sensors were installed in the field at four soil depths. Measurements of apparent dielectric permittivity (Ka), ECa, and soil temperature were taken under different electrical conductivity of soil moisture solutions. Results show that, under field conditions, 5TE accuracy for θ estimation increased when considering the ECa effect. Field calibrated models gave better θ estimation (root mean square error (RMSE) = 0.03 m3 m-3) as compared to laboratory experiments (RMSE = 0.06 m3 m-3). For ECp prediction, two corrections of the Hilhorst model were investigated. The first approach, which considers the ECa effect on K' reading, failed to improve the Hilhorst model for ECp > 3 dS m-1 for both laboratory and field conditions. However, the second approach, which considers the effect of ECa on the soil parameter K0, increased the performance of the Hilhorst model and gave accurate measurements of ECp using the 5TE sensor for irrigated soil.

Temporal trends and sediment-water partitioning of per- and polyfluoroalkyl substances (PFAS) in lake sediment.

The use of per- and polyfluoroalkyl substances (PFAS) containing aqueous film forming foams (AFFF) at fire training facilities can have an adverse impact on the surrounding environment. The aim of the present study was to study the distribution and temporal trend of 26 PFAS in water and sediment cores for a lake and a pond affected by AFFF release from a fire training facility in Luleå, northern Sweden. In the aqueous phase, maximum ΣPFAS concentration was 1.700 ±â€¯90 ng L-1. Dominant PFAS groups were perfluoroalkane sulfonates (PFSAs) with 70% of the ΣPFAS, followed by perfluoroalkyl carboxylates (PFCAs, 29%), whereas the contribution of 6:2 fluorotelomer carboxylate (FTSAs) was low (<1%). In the sediment core samples, ΣPFAS concentrations ranged between <1 µg kg-1 dry weight (dw) and 76 µg kg-1 dw, where perfluorooctane sulfonate (PFOS) and perfluorohexane sulfonate (PFHxS) had an average contribution of ∼71% and ∼23% of the ΣPFAS. The sediment core analysis indicated that the PFAS contamination began about 1994 and the highest accumulation rate was observed for the period 2003-2009. The PFAS flux increased from 2.3 µg m-2 yr-1 dw in 1994 to 12 µg m-2 yr-1 dw by 2009. Over the accumulation period 1994-2009, the lake sediment surface received 213 µg m-2 dw for Æ©PFAS, where PFOS contributed with 125 µg m-2 yr-1 dw and PFHxS with 65 µg m-2 dw. Results point to that sediment cores collected near PFAS hotspot areas can be used as a contamination record to reconstruct release history.

Recent and future trends in sea surface temperature across the Persian Gulf and Gulf of Oman.

Climate change's effect on sea surface temperature (SST) at the regional scale vary due to driving forces that include potential changes in ocean circulation and internal climate variability, ice cover, thermal stability, and ocean mixing layer depth. For a better understanding of future effects, it is important to analyze historical changes in SST at regional scales and test prediction techniques. In this study, the variation in SST across the Persian Gulf and Gulf of Oman (PG&GO) during the past four decades was analyzed and predicted to the end of 21st century using a proper orthogonal decomposition (POD) model. As input, daily optimum interpolation SST anomaly (DOISSTA) data, available from the National Oceanic and Atmospheric Administration of the United States, were used. Descriptive analyses and POD results demonstrated a gradually increasing trend in DOISSTA in the PG&GO over the past four decades. The spatial distribution of DOISSTA indicated: (1) that shallow parts of the Persian Gulf have experienced minimum and maximum values of DOISSTA and (2) high variability in DOISSTA in shallow parts of the Persian Gulf, including some parts of southern and northwestern coasts. Prediction of future SST using the POD model revealed the highest warming during summer in the entire PG&GO by 2100 and the lowest warming during fall and winter in the Persian Gulf and Gulf of Oman, respectively. The model indicated that monthly SST in the Persian Gulf may increase by up to 4.3 °C in August by the turn of the century. Similarly, mean annual changes in SST across the PG&GO may increase by about 2.2 °C by 2100.