In arid inland irrigated areas, the role of human activities on fluoride enrichment in groundwater is not fully understood. There is an extremely arid climate, high-intensity irrigation, and severe soil salinization in the Hotan Oasis within the Tarim Basin, Northwestern China. In this study, hydrogeochemistry and environmental isotope methods were combined to explore the distribution characteristics and controlling processes of fluoride enrichment in groundwater. The F⁻ concentration in groundwater had a range of 1.12–9.4 mg/L. F⁻ concentrations of all the groundwater samples were higher than 1.0 mg/L (Chinese Standards for Drinking Water Quality), and about 89% were higher than 1.5 mg/L (WHO Guidelines for Drinking Water Quality). High fluoride groundwater was mainly distributed downstream of the river and in the middle of the interfluvial zone. Vertically, the fluoride concentration was higher when the sampling depth was less than 15 m. There was a significant positive correlation between F⁻ concentration and salinity in groundwater. F⁻ in groundwater was mainly derived from river water fluoride, which could be imported to groundwater with infiltration of rivers and irrigation canals as well as irrigation return flow. Anthropogenic inputs may be partly responsible for fluoride enrichment in groundwater. Fluoride accumulated in the vadose zone by strong evapotranspiration and then leached into groundwater with irrigation return flow was the main mechanism of F⁻ enrichment in groundwater in the study area. This work is a clear example of how human activities together with natural processes can affect the chemical quality of groundwater, which is essential to safeguard the sustainable management of water and soil resources inland arid oasis areas.

In the article the results of major and trace elements investigation in freshwater lakes of Vecherny Oasis (Enderby Land, East Antarctica) are considered. Water sampling was carried out during seasonal Belarusian Antarctic expeditions in 2011–2017. Totally 22 water samples from four lakes, three temporal ponds and one water course were collected for major and trace elements determination. The total concentrations of Ag, Al, As, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Na, Mg, Mn, Mo, Ni, Pb, Sb, Se, Th, Tl, V, W, Zn in all samples as well as more than 40 additional trace elements in 3 samples from lakes were determined using ICP-MS method.It is shown that increase of heavy metals concentration (Cd, Pb, Zn, Sb, Co, Ni, Se, Mn) and As in the lakes compared to temporary ponds can be explained by anthropogenic impact including previous human activity in the oasis in late 1970th – early 1990th. The maximum concentrations of a number of technophilic elements (Pb, Mo, Mn, V, Sb, Zn) in Lake Nizhneye are possibly connected with the its lowest hypsometric location in catchment and the drainage of the territory impacted by past and present human activity.

This study reports the development of a multi-residue method for determining 48 compounds of emerging concern (CEC) including three diclofenac transformation products (TP) in Slovenian wastewater (WW) and surface water (SW). For solid-phase extraction (SPE), Oasis™ Prime cartridges were favoured over Oasis HLB™. The validated method was then applied to 43 SW and 52 WW samples collected at nine locations. Ten bisphenols in WW and 14 bisphenols in SW were traced in Europe for the first time. Among all of the 48 targeted CEC, 21 were >LOQ in the influents and 20 in the effluents. One diclofenac TP was also quantified in WWs (3.04–78.1 ng L⁻¹) for the first time. As expected, based on mass loads in the wastewater treatment plant influents, caffeine is consumed in high amounts (105,000 mg day⁻¹ 1000 inhab.⁻¹) in Slovenia, while active pharmaceutical ingredients (APIs) are consumed in lower amounts compared to other European countries. Removal was lower in winter in the case of four bisphenols (17–78%), one preservative (36%) and four APIs (-14–91%), but remained constant for caffeine, one API, two UV-filters and three preservatives (all >85.5%). Overall, a constructed wetland showed the lowest (0–80%) and most inconsistent removal efficiencies (SD > 40% for some CECs) of CECs including caffeine, two UV-filters, two preservatives and two APIs compared to other treatment technologies. The method was also able to quantify Bisphenol S in SW (<36.2 ng L⁻¹). Environmental risk was assessed via risk quotients (RQs) based on WW and SW data. Two UV-filters (oxybenzone and dioxybenzone), estrone and triclosan, despite their low abundance posed a medium to high environmental risk with RQs between 0.282 (for HM-BP) and 15.5 (for E1).

In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water, must satisfy certain preconditions (e.g., flat water surface and ideal radiation geometry). In this study, we hypothesized that drone-borne hyperspectral imagery of emergent plants could be better applied to retrieval total nitrogen (TN) concentration in water regardless of preconditions possibly due to the spectral responses of emergent plants on nitrogen removal and water purification. To test this hypothesis, a total of 200 groups of bootstrap samples were used to examine the relationship between the extracted TN concentrations from the drone-borne hyperspectral imagery of emergent plants and the experimentally measured TN concentrations in Ebinur Lake Oasis using four machine learning (ML) models (Partial Least Squares (PLS), Random Forest (RF), Extreme Learning Machine (ELM), and Gaussian Process (GP)). Through the introduction of the fractional order derivative (FOD), we build a decision-level fusion (DLF) model to minimize the regression results’ biases of individual ML models. For individual ML model, GP performed the best. Still, the amount of uncertainty in individual ML models renders their performance to be subpar. The introduction of the DLF model greatly minimizes the regression results’ biases. The DLF model allows to reduce potential uncertainties without sacrificing accuracy. In conclusion, the spectral response caused by nitrogen removal and water purification on emergent plants could be used to retrieve TN concentration in water with a DLF model framework. Our study offers a new perspective and a basic scientific support for water quality monitoring in arid regions.

The rapid expansion of urban areas and industrial units has put much strain on natural environments and biodiversity. Quantifying the impact of human pressures on avian biodiversity is vital for the identification, preservation, and restoration of important areas. Here, data collected in 11 coastal Mediterranean oases were used to assess the impact of urban and industrial landscapes and habitat structure on the richness of breeding birds. Results of generalized linear mixed models analyses showed a quadratic effect of distance to the industrial complex on breeding bird richness, being optimal (6.41 ± 0.89) at 24 km. The results also showed a negative effect of the cover of urban areas. Our analysis also emphasized the importance of southern oases for breeding bird richness mostly because of their remoteness from the industrial complex and their significant coverage of fruit trees and natural ground cover. Variation partitioning analysis revealed that the shared fraction of industrial landscape, oasis habitat structure, and space was relevant in explaining the richness of breeding birds. It is highly recommended to (i) uninstall the Gabès industrial complex from this Mediterranean area, (ii) enhance the habitat quality in southern oases by planting other fruit trees, such as pomegranate and olive, and (iii) pursue scientific research in these Mediterranean coastal oases, as they offer a good opportunity for assessment and improvement of knowledge on both the impact of industrialization on quality of habitats and the richness of bird species.

Identifying the magnitude and seasonal variability of groundwater nitrogen (N) under various land use types and quantifying the contribution of their environmental factors are of great importance when attempting to implement prioritizing effective strategies for mitigating groundwater N pollution. In this study, hydrochemical investigation was used to assess the magnitude and temporal variability of groundwater N in arid regions. Spatial distributions of N species (total N (TN), nitrate-N (NO₃––N), ammonium-N (NH₄⁺–N), and nitrous-N (NO₂––N)) were mapped using geostatistical techniques. Redundancy analysis (RDA) was conducted to determine environmental factors controlling hydrochemistry. The results showed that residential areas (town and village) and cropland had higher groundwater N concentrations than natural (forest and grassland) and unused land. And the concentrations of N species in rain season (August) were greater than those in the dry season (March) and normal season (November). The N species spatial patterns showed that there is a risk of TN and NO₃––N pollution in groundwater of town and surrounding developed cropland, and that NH₄⁺–N and NO₂––N pollution were negligible. Selected environmental factors explained a total of 77.4% of data variance in N concentrations. These factors indicated that water environmental factors (dissolved oxygen (DO), oxidation–reduction potential (ORP), water temperature (WT), and pH) affect groundwater concentrations and forms of N by influencing the process of nitrification and denitrification, which explained about 60% of the variance of the data. Approximately 10.8 and 8.3% of the variability was explained by shallow groundwater depth and soil texture, indicating that N concentrations in groundwater had heterogeneous influence. The high N excessive pollution ratio was observed in towns and cropland indicating that artificial N input is the main reason for groundwater N pollution in the study area. Hence, ameliorating anthropogenic agricultural practices and reducing N input in urban areas are critical to alleviating groundwater N pollution in the research area.

A thorough understanding of the processes and driving factors of ion migration, dilution, and enrichment in arid inland river basins is the basis for implementing water resources management. In this study, we analyzed the water chemistry of streamflow, groundwater, and precipitation and the behavior of main elements in the Shiyang River Basin by means of the hydrochemical diagram and multivariate statistical analysis. The spatial variation of water chemistry was obvious, and the conversion between different water bodies was frequent. The ions migrated from the mountain area to the oasis and desert and accumulated near the terminal lake finally. There were obvious differences in hydrochemistry between surface water and groundwater. From the mountain to the basin, the hydrochemical type of surfer water has varied, and the hydrochemical type of groundwater has changed from Ca–Cl type to Na–Cl type. The hydrochemistry of the basin was controlled by silicate weathering. However, the influence of water–rock interaction on surface water and groundwater was different, and the surface water was more complex. Significantly, agricultural activities and sewage discharge had a negative impact on the water environment. Interbasin water transfer (IBWT) was a form of external ions input from outside the basin, which affected the chemical characteristics of surface water in the lower reaches to a certain extent. In arid areas, human impact on water chemistry needs to be paid attention. These results are helpful to strengthen the understanding of the relationship between different regions and different water bodies in the arid basin.

Global environment changes rapidly alter regional hydrothermal conditions, which undoubtedly affects the spatiotemporal dynamics of vegetation, especially in arid and semi-arid areas. However, identifying and quantifying the dynamic evolution and driving factors of vegetation greenness under the changing environment are still a challenge. In this study, gradual trend analysis was applied to calculate the overall spatiotemporal trend of the normalized difference vegetation index (NDVI) time series of Xinjiang province in China, the abrupt change analysis was used to detect the timing of breakpoint and trend shift, and two machine learning methods (boosted regression tree and random forest) were used to quantify the key factors of vegetation change and their relative contribution rate. The results have shown that vegetation has experienced overall recovery over the past 20 years in Xinjiang, and greenness increased at a rate of 17.83 10⁻⁴ year⁻¹. Cropland, grassland, and sparse vegetation were the main biome types where vegetation restoration is happening. Nearly 10% of the pixels (about 166000 km²) were detected to have breakpoints from 2004 to 2016 of the monthly NDVI, and most of the breakpoints were concentrated in the ecotone of various biomes. CO₂ concentration was the most prevalent environmental factor to increase vegetation greenness, because continuous emission of CO₂ greatly enhanced the fertilization effect, further promoted vegetation growth. Besides, cropland expansion and desertification control were the vital anthropogenic factors to vegetation turning “green” in Xinjiang, and most areas under anthropogenic were mainly in oasis areas. These findings provide new insights and measures for the regional response strategies and terrestrial ecosystem protection.

Analytical procedures for the determination of pharmaceuticals from different therapeutic groups were proposed. These groups included the corticosteroids prednisolone and dexamethasone; the β-blockers sotalol, metoprolol, propranolol, and carvedilol; and the analgesic nonsteroidal anti-inflammatory drugs paracetamol, aspirin, metamizole, and ketoprofen. Reversed-phase ultrahigh performance liquid chromatography with an ultraviolet detector, different columns, different mobile phases, and gradient elution programmes were used to obtain the best separations within the shortest possible time. Solid-phase extraction was examined as a preconcentration step. The Oasis HLB column, with the highest recoveries (over 90% for most of the drugs), was chosen for the analysis of surface waters. Limits of detection ranged from 0.06 to 0.39 μg L⁻¹ for all drugs after optimisation of all analytical steps.

Transport of nitrogen compounds to groundwater, especially from agricultural fertilization, is a main problem in aquifer contamination. Ammonium (NH₄⁺-N) is one of the most common nitrogen fertilizer forms. In order to evaluate the risk of agricultural fertilizer pollution to aquifers caused by infiltration, a soil column experiment and breakthrough modeling simulation were employed to study NH₄⁺-N retention by a sandy soil profile (10–60 cm depth). The soil used was collected from an irrigated semi-arid cropland in Bulanghe Town, Yuyang District, Yulin City, Shaanxi Province of China, and recombined in a laboratory soil column. Column experiments were accomplished using aqueous solutions containing various concentrations of NH₄⁺-N and the breakthrough curves were determined. The linear soil-water partition coefficients (Kd) were determined from batch equilibrium experiments, and the retardation factor (Rd) for the saturated sandy soil was computed. The lowest NH₄⁺-N concentrations at the depth range of 10-20 cm suggested the strongest NH₄⁺-N sorption. The chemical nonequilibrium model in Hydrus-1D can better simulate the breakthrough of NH₄⁺-N through the soil column. The soil sorption capacity decreases as the irrigation flow rate increases. The results provide a scientific basis for optimization of fertilizer application in agricultural management under irrigation in desert oasis, and those semi-arid areas with similar soil texture and vadose zones of small thickness.