Water scarcity, its necessity in food production, and environmental protection in the world have forced human beings to seek new water sources. Nowadays, application of unconventional water resources (wastewater) has been proposed in countries facing the crisis of water resources shortage; however, a few studies have dealt with this issue. The present study has evaluated the changes in the elements of the soil, irrigated with wastewater. For so doing, an experiment has been conducted on a randomized complete block design with three replications. Soil samples have been collected from the studied regions at two depths of 0-30 cm and 30-60 cm and the studied parameters have included sodium, total calcium, magnesium, some acidity, and electrical conductivity of the soil. Three regions of study (namely no irrigation, irrigation with treated wastewater, and irrigation with river waters) have been taken into consideration. Results have shown increased calcium, magnesium, and pH of the effluent from Zabol Wastewater Treatment Plant compared to the control; however, electrical conductivity and chloride have decreased in wastewater-irrigated soil. The electrical conductivity in the surface layer of wastewater samples, treated with an amount of 2.25 (ds/m), has had the most significant difference to the control and other treatments. It can be concluded that wastewater increases some soil properties, contributing to its restoration.

The high amount of Electrical Conductivity (EC) in the groundwater is one of the major negative Geo-environmental problems which has a considerable effect on the quality of drinking water. To address this challenging problem we proposed an intelligent Machine Learning (ML) based approach to predict EC in urban areas. We applied the deep learning technique as one of the most applicable ML techniques with high capabilities for intelligent predictions. Five different deep neural networks (Net 1 to Net 5) were developed in this study and their reliability to predict EC with an emphasis on different settings of inputs, features, functions, and the number of hidden layers was evaluated. The achieved results showed that deep neural networks can predict EC parameters using minimum and economic input parameters. Results showed parameters Cl and SO4 with a high range of correlation and pH with a low range of Pearson correlation properties are influential parameters to be used as the input of neural networks. Activation function Relu, optimization function Adam with a learning rate of 0.0005 and loss function Mean Squared Error with the minimum of two hidden dense layers from Keras laboratory of Tensor Flow developed an efficient and fast network to predict the EC parameter in urban areas. Maximum epochs for developed networks were defined up to 2000 iterations while epochs are reducible up to 200 to drive minimum loss function outcome. The maximum training and testing R2 for developed networks was 0.99 in both the training and testing parts.

The aim of this study is to determine the amount of quantitative and qualitative changes in groundwater in the Sarvestan plain in south of Fars province, which is one of the critical plains in Iran in terms of water resources. In this research, zoning maps of electrical conductivity of water in GIS were prepared and various hydrochemical diagrams were illustrated. Different quality parameters of water resources were compared according to the statistical data collected and the experiments performed at the beginning of the 8-year period of the research. Chemical analysis of water samples shows that the groundwater type of most of the studied wells at the beginning of the period (2013) has changed from Ca-Cl and Mg-Cl types to Na-Cl type at end of the time period (2020). Determining the trend of chemical changes shows that the diversity of water samples in terms of anions and cations in water with increasing salinity at the end of the period is less than the variety of samples at the beginning of the period. According to the results of chemical experiments, evaporation, crystallization, and weathering of rocks are the factors that control the composition of groundwater in the study area. This study shows increasing the salinity of groundwater in addition to decreasing precipitation and high water use for agricultural application, due to the type of geological formations, especially the presence of salt domes at groundwater inlets to the plain on the east side of the study area.

peer reviewed | The impact of climate change on water resource availability and soil quality is more and more emphasized under the Mediterranean basin, mostly characterized by drought and extreme weather conditions. The present study aims to investigate how electromagnetic induction technique and soil mapping combined with crop yield data can be used to optimize phosphorus (P) use efficiency by chickpea crop under drip fertigation system. The study was carried out on a 2.5-ha agricultural plot and the agronomic experiments in two growing cycles of chickpea crop. Soil spatial variability was first assessed by the measurement of soil apparent electrical conductivity (ECa) using the CMD Mini-Explorer sensor, and then, soil physicochemical properties were evaluated based on an oriented soil sampling scheme to explore other soil spatial variabilities influencing chickpea yield and quality. Data from the first agronomic experiment were used in geostatistical, multiple linear regression (MLR), and fuzzy c-means unsupervised classification algorithms to properly identify P drip fertigation management zones (MZs). Results from the Person's correlation analysis revealed that chickpea grain yield was more influenced by soil ECa (r = - 0.56), pH (r = - 0.84), ECe (r = - 0.6), P content (r = 0.72), and calcium (Ca) content (r = - 0.83). The proposed MLR-based model to predict chickpea grain yield showed good performances with a normalized root mean square error (NRMSE) of 0.11% and a coefficient of determination (R2) equal to 0.69. The identified MZs were verified by the one-way variance analysis for the studied soil and plant attributes, revealing that the first MZ1 presents a high grain yield, high soil P content, and low ECa. The low fertility MZ2 located in the south part of the studied site presented a low chickpea grain yield due to the low P content and the high ECa. Moreover, the application of P-variable rate fertigation regimes in the second field experiment significantly improved P use efficiency, chickpea grain yield, seed quality, and farmer income by 18%, 12%, 9%, and 136 $/ha, respectively, as compared to the conventional drip fertigation practices. The approach proposed in this study can greatly contribute to optimizing agro-input use efficiency under drip fertigation system, thereby improving farmers' incomes, preserving the ecosystem, and ensuring sustainable cropping systems in the Mediterranean climate.

The presence of microplastics (MPs) in freshwater systems that receive discharge of urban effluent implies a great environmental impact. In order to be able to generate proposals that solve this problem, it is necessary to know in detail the contributions of different MPs sources. The aim of this work was to study the contribution of urban sewage discharge to MPs pollution in a stream that runs through a medium-sized city. The spatial distribution of MPs with sizes between 100 μm and 1.5 mm present in surface water was measured and their characteristics, dimensions, shapes and identification were determined. Physical-chemical parameters of the stream water were measured, and a decrease in water quality was found due to wastewater treatment plants. The main source of MPs was effluent from the plants (97% of the total MPs), while the rest came from storm drains and discharge of tributaries. The maximum concentration of MPs found was around 72,000 MP/L (equivalent to 53 million MPs/s), at a point after discharge from both plants. Around 70% of MPs correspond to microfibers with a mean length of around 300 μm and a mean width of around 15 μm, and they are mainly polyethylene fibers. The remaining 30% of MPs are particles with lengths of around 140 μm. The transport of MPs between a point located after discharge of the plants and another point located about 3 km further on was studied, and no significant variation was found in the concentration of MPs. Electrical conductivity was used as a conservative tracer of MPs concentration. This work presents for the first time a detailed analysis of different contributions of MPs to a freshwater system in South America, which receives discharge of wastewater treatment plants, evidencing its important role in pollution.

In this work, the novel technology was used to remove heavy metal from sludge. The coupled with biodegradable ethylenediamine disuccinic acid (EDDS) and approaching anode electrokinetic (AA-EK) technique was used to enhance heavy metal removing from sludge. Electric current, sludge and electrolyte characteristics, heavy metal removal efficiency and residual content distribution, and heavy metal fractions percentage of variation were evaluated during the electrokinetic remediation process. Results demonstrated that the coupled with EDDS and AA-EK technique obtain a predominant heavy metal removal efficiency, and promote electric current increasing during the enhanced electrokinetic remediation process. The catholyte electrical conductivity was higher than the anolyte, and electrical conductivity of near the cathode sludge achieved a higher value than anode sludge during the coupled with EDDS and AA-EK remediation process. AA-EK technique can produce a great number of H⁺, which caused the sludge acidification and pH decrease. Cu, Zn, Cr, Pb, Ni and Mn obtain the highest extraction efficiency after the coupled with EDDS and AA-EK remediation, which were 52.2 ± 2.57%, 56.8 ± 3.62%, 60.4 ± 3.62%, 47.2 ± 2.35%, 53.0 ± 3.48%, 54.2 ± 3.43%, respectively. Also, heavy metal fractions analysis demonstrated that the oxidizable fraction percentage decreased slowly after the coupled with EDDS and AA-EK remediation.

Calcium peroxide (CaO₂) has been proven to oxidize various organic pollutants when they exist as a single class of compounds. However, there is a lack of research on the potential of unactivated CaO₂ to treat mixed chlorinated organic pollutants in soils. This study examined the potential of CaO₂ in treating soils co-contaminated with p-dichlorobenzene (p-DCB) and p-chloromethane cresol (PCMC). The effects of CaO₂ dosage and treatment duration on the rate of degradation were investigated. Furthermore, the collateral effects of the treatment on treated soil characteristics were studied. The result showed that unactivated CaO₂ could oxidize mixed chlorinated organic compounds in wet soils. More than 69% of the pollutants in the wet soil were mineralized following 21 days of treatment with 3% (w/w) CaO₂. The hydroxyl radicals played a significant role in the degradation process among the other decomposition products of hydrogen peroxide. Following the oxidation process, the treated soil pH was increased due to the formation of calcium hydroxide. Soil organic matter, cation exchange capacity, soil organic carbon, total nitrogen, and certain soil enzyme activities of the treated soil were decreased. However, the collateral effects of the system on electrical conductivity, available phosphorus, and particle size distribution of the treated soil were not significant. Likewise, since no significant release of heavy metals was seen in the treated soil matrix, the likelihood of metal ions as co-pollutants after treatment was low. Therefore, CaO₂ can be a better alternative for treating industrial sites co-contaminated with chlorinated organic compounds.

Soil microbes influence the uptake of toxic metals (TMs) by changing soil characteristics, bioavailability and translocation of TMs, and soil health indicators in polluted environment. The potential effect of Streptomyces pactum (Act12) and Bacillus consortium (B. subtilis and B. licheniformis; 1:1) on soil enzymes and bacterial abundance, bioavailability and translocation of Zn and Cd by Symphytum officinale, and physiological indicators in soil acquired from Fengxian (FX) mining site. Act12 and Bacillus consortium were applied at 0 (CK), 0.50 (T1), 1.50 (T2), and 2.50 (T3) g kg⁻¹ in a split plot design and three times harvested (H). Results showed that soil pH significantly dropped, whereas, electrical conductivity increased at higher Act12 and Bacillus doses. The extractable Zn lowered and Cd increased at each harvest compared to their controls. Soil β-glucosidase, alkaline phosphatase, urease and sucrase improved, whereas, dehydrogenase reduced in harvest 2 and 3 (H2 and H3) as compared to harvest 1 (H1) after Act12 and Bacillus treatments. The main soil phyla individually contributed ∼5–55.6%. Soil bacterial communities’ distribution was also altered by Act12 and Bacillus amendments. Proteobacteria, Acidobacteria, and Bacteroidetes increased, whereas, the Actinobacteria, Chloroflexi, and Gemmatimonadetes decreased during the one-year trial. The Zn and Cd concentration significantly decreased in shoots at each harvest, whereas, the roots concentration was far higher than the shoots, implicating the rhizoremediation by S. officinale. Accumulation factor (AF) and bioconcentration ratio (BCR) of Zn and Cd in shoots were lower and remained higher in case of roots than the standard level (≥1). BCR values of roots indicated that S. officinale can be used for rhizoremediation of TMs in smelter/mines-polluted soils. Thus, field trials in smelter/mines contaminated soils and the potential role of saponin and tannin exudation in metal translocation by plant will broaden our understanding about the mechanism of rhizoremediation of TMs by S. officinale.

Textile wastewater contains a huge amount of azo dyes and heavy metals and catastrophically deteriorates the agricultural field by affecting its phyisco-chemical/biological and nutritional properties when directly drained to agricultural lands without any treatment. Recently, biogenic copper nanoparticles (CuNPs) have gained considerable attention for photocatalytic degradation of wastewater pollutants owing to their unique physico-chemical and biological properties, low cost and environmental sustainability. The current study reports the synthesis of CuNPs by a native copper-resistant bacterial strain Escherichia sp. SINT7 and evaluation of the photocatalytic activity of the biogenic CuNPs for azo dye degradation and treatment of textile effluents. Scanning electron microscopy and transmission electron microscopy revealed the spherical shape of biogenic CuNPs with particle size ranging from 22.33 to 39 nm. Moreover, X-ray diffraction data revealed that the CuNPs have spherical crystalline shapes with an average particle size of 28.55 nm. FTIR spectra showed the presence of coating proteins involved in the stabilization of nanomaterial. Azo dye degradation assays indicated that CuNPs decolorized congo red (97.07%), malachite green (90.55%), direct blue-1 (88.42%) and reactive black-5 (83.61%) at a dye concentration of 25 mg L⁻¹ after 5 h of sunlight exposure. However, at 100 mg L⁻¹ dye concentration, the degradation percentage was found to be 83.90%, 31.08%, 62.32% and 76.84% for congo red, malachite green, direct blue-1 and reactive black-5, respectively. Treatment of textile effluents with CuNPs resulted in a significant reduction in pH, electrical conductivity, turbidity, total suspended solids, total dissolved solids, hardness, chlorides and sulfates as compared to the non-treated samples. Thus, the promising dye detoxification and textile effluent recycling efficiency of biogenic CuNPs may lead to the development of eco-friendly and cost-efficient process for large-scale wastewater treatment.

Taihu Lake is one of the largest freshwater lakes in China and serves as an important source for drinking water. This lake is suffering from eutrophication, cyanobacterial blooms and fecal pollution, and the inflow Tiaoxi River is one of the main contributors. The goal here was to characterize the bacterial community structure of Tiaoxi River water by next-generation sequencing (NGS), paying attention to bacteria that are either fecal-associated or pathogenic, and to examine the relationship between environmental parameters and bacterial community structure. Water samples collected from 15 locations in three seasons, and fecal samples collected from different hosts and wastewater samples were used for bacterial community analysis. The phyla Proteobacteria, Actinobacteria, Bacteroidetes, and Cyanobacteria were predominant in most of the water samples tested. In fecal samples, Bacteroidetes, Firmicutes, and Proteobacteria were abundant, while wastewater samples were dominated by Proteobacteria, Bacteroidetes, Acidobacteria, and Chloroflexi. The cluster analysis and principal coordinate analysis indicated that bacterial community structure was significantly different between water, fecal and sewage samples. Shared OTUs between water samples and chicken, pig, and human fecal samples ranged from 4.5 to 9.8% indicating the presence of avian, pig and human fecal contamination in Tiaoxi River. At genus level, five bacterial genera of fecal origin and sequences of seven potential pathogens were detected in many locations and their presence was correlated well with the land use pattern. The sequencing data revealed that Faecalibacterium could be a potential target for human-associated microbial source-tracking qPCR assays. Our results suggest that pH, conductivity, and temperature were the main environmental factors in shaping the bacterial community based on redundancy analysis. Overall, NGS is a valuable tool for preliminary investigation of environmental samples to identify the potential human health risk, providing specific information about fecal and potentially pathogenic bacteria that can be followed up by specific methods.