Elevated inorganic arsenic concentrations in groundwater has become a major public and environmental health concern in different parts of the world. Currently, As-contaminated groundwater issue in many countries and regions is a major topic for publications at global level. However, there are many regions worldwide where the problem has still not been resolved or fully understood due to inadequate hydrogeochemical investigations. Hence, this study evaluates for the first time the hydrogeochemical behavior of the arid and previously unexplored inland basin of Sirjan Plain, south east (SE) Iran, in order to assess the controlling factors which influence arsenic (As) mobility and its distribution through groundwater resources. Total inorganic arsenic concentration was measured using inductive-coupled plasma optical emission spectrometry (ICP-OES). Arsenic content in groundwater of this region ranged between 2.4 and 545.8 μg/L (mean value: 86.6 μg/L) and 50% of the samples exceeded the World Health Organization (WHO) guideline value of 10 μg/L in drinking water. Groundwater was mainly of Na-Cl type and alkaline due to silicate weathering, ion exchange and evaporation in arid conditions. Elevated As concentrations were generally observed under weakly alkaline to alkaline conditions (pH > 7.4). Multivariate statistical analysis including cluster analysis and bi-plot grouped As with pH and HCO3 and demonstrated that the secondary minerals including oxyhydroxides of Fe are the main source of As in groundwater in this region. The desorption of As from these mineral phases occurs under alkaline conditions in oxidizing arid environments thereby leading to high levels of As in groundwater. Moreover, evaporation, ion exchange and saltwater intrusion were the secondary processes accelerating As release and its mobility in groundwater. Based on the results of this study, desorption of As from metal oxy-hydroxides surfaces under alkaline conditions, evaporation and intrusion of As-rich saline water are considered to be the major factors causing As enrichment in arid inland basins such as those in southeast Iran. This study proposes the regular monitoring and proper groundwater management practices to mitigate high levels of arsenic in groundwater and related drinking water wells of Sirjan Plain.

Polychlorinated biphenyls (PCBs) are persistent organic pollutants that, due to their high toxicity, lipophilic property and widespread dispersal in the global environment, present a danger for human health and ecological systems. Although the inventory and use of PCBs are extensively reported worldwide, the status of PCBs in Iran is still unknown. In this study, the concentrations of PCBs were determined in the environmental matrices and in five commercially important fish species from Larak coral Island, Persian Gulf, Iran, in winter and summer 2015. A positive correlation was found among PCBs levels and congeners profiles in seawater (0.97–3.10 ng L⁻¹), surface sediments (2.95–7.95 ng g⁻¹dw) and fish samples (7.20–90.19 ng g⁻¹dw), indicating fish as suitable bioindicator of environmental PCBs contamination. In all matrices, a high contribution of light and medium chlorinated congeners was detected in both seasons. In fish, the higher PCBs levels were found for both sexes in both seasons in liver and kidney than other tissues (skin, gonad, muscle) due to their high lipid content and PCBs lipophilicity. More importantly, the risks for human health associated with fish consumption were also evaluated, and it was found that all the toxicity indices measured for PCBs were within the World Health Organization (WHO) permissible limit of food consumption. However, it is highly recommended to inform the local population about potential risks attributable to dietary incorporation of locally caught fish, and establish a surveillance monitoring programme on PCBs in this region.

From a health prospective, it is critical to provide a comprehensive model which integrates all the parameters involved in virus transmission and its consequences on human body. In order to estimate the health risks, for workers and residents, associated with an exposure airborne viruses emitted from a wastewater treatment (WWTP), the concentration levels of viruses in emitted bioaerosols over a twelve-month period were measured by real-time polymerase chain reaction (RT-PCR). A combined Gaussian plum dispersion model and quantitative microbial risk assessment (QMRA) with Monte-Carlo simulation served as suitable explanatory tools to estimate the risk of acquiring gastrointestinal illness (GI) due to exposure to air containing Rotavirus (RoV) and Norovirus (NoV) bioaerosols. Additionally, DALY metric was applied to quantify the disability and mortality for workers and residents. RoV and NoV were detected above aeration tank with annual mean concentration 27 and 3099 (Viruses/m³.h), respectively. The medium calculated DALY indicator based on viral loads in contaminant source (RoV:5.76 × 10⁻² and NoV:1.23 × 10⁻¹) and estimated in different distances away (300–1000 m) (RoV:2.87 × 10⁻²- 2.75 × 10⁻² and NoV:1.14 × 10⁻¹-1.13 × 10⁻¹) were markedly higher than the threshold values recommended by US EPA (10⁻⁴ DALY pppy) and WHO (10⁻⁶ DALY pppy). The sensitivity analysis highlighted dose exposure and disease burden per case (DBPC) as two most influential factors for both workers and residents following exposure to two pathogens of concern. Due to high resistance and high concentration in the environment, the presence of RoV and NoV can intensify the consequences of diarrhea especially for children under five years of age; A comprehensible and transparent presentation of DALYs and QMRA can help decision makers and responsibilities to justify the priorities of exposure to wastewater in comparison with other risks of daily life.

The present study aims to investigate the spatial distribution and associated various geochemical mechanisms responsible for fluoride (F⁻) contamination in groundwater of unconfined aquifer system along major rivers in Sindh and Punjab, Pakistan. The concentration of F⁻ in groundwater samples ranged from 0.1 to 3.9 mg/L (mean = 1.0 mg/L) in Sindh and 0.1–10.3 mg/L (mean = 1.0 mg/L) in Punjab, respectively with 28.9% and 26.6% of samples exhibited F⁻ contamination beyond WHO permissible limit value (1.5 mg/L). The geochemical processes regulated F⁻ concentration in unconfined aquifer mainly in Sindh and Punjab were categorized as follows: 1) minerals weathering that observed as the key process to control groundwater chemistry in the study areas, 2) the strong correlation between F⁻ and alkaline pH, which provided favorable environmental conditions to promote F⁻ leaching through desperation or by ion exchange process, 3) the 72.6% of samples from Sindh and Punjab were dominated by Na⁺- Cl⁻ type of water, confirmed that the halite dissolution process was the major contributor for F⁻ enrichment in groundwater, 4) dolomite dissolution was main process frequently observed in Sindh, compared with Punjab, 5) the arid climatic conditions promote evaporation process or dissolution of evaporites or both were contributing to the formation of saline groundwater in the study area, 6) the positive correlation observed between elevated F⁻ and fluorite also suggested that the fluorite dissolution also played significant role for leaching of F⁻ in groundwater from sediments, and 7) calcite controlled Ca2⁺ level and enhanced the dissolution of F-bearing minerals and drive F⁻ concentration in groundwater. In a nut shell, this study revealed the worst scenarios of F⁻ contamination via various possible geochemical mechanisms in groundwater along major rivers in Sindh and Punjab, Pakistan, which need immediate attention of regulatory authorities to avoid future hazardous implications.

Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum of means over 35 ppb) in their health impact assessments. Is this paper, the artificial neural network (ANN) approach was used to forecast SOMO35 at the national level for a set of 24 European countries, mostly EU members. Available ozone precursors’ emissions, population and climate data for the period 2003–2013 were used as inputs. Trend analysis had been performed using the linear regression of SOMO35 over time, and it has demonstrated that majority of the studied countries have a decreasing trend of SOMO35 values.The created models have made majority of predictions (≈60%) with satisfactory accuracy (relative error <20%) on testing, while the best performing model had R² = 0.87 and overall relative error of 33.6%. The domain of applicability of the created models was analyzed using slope/mean ratio derivate from the trend analysis, which was successful in distinguishing countries with high from countries with low prediction errors. The overall relative error was reduced to <14%, after the pool of countries was reduced based on the abovementioned criterion.

Stable isotopes ratios (‰) of Hydrogen (δ2H) and Oxygen (δ1⁸O) were used to trace the groundwater recharge mechanism and geochemistry of arsenic (As) contamination in groundwater from four selected sites (Larkana, Naudero, Ghari Khuda Buksh and Dokri) of Larkana district. The stable isotope values of δ2H and δ1⁸O range from 70.78‰ to −56.01‰ and from −10.92‰ to −7.35‰, relative to Vienna Standard for Mean Ocean Water (VSMOW) respectively, in all groundwater samples, thus indicating the recharge source of groundwater from high-salinity older water. The concentrations of As in all groundwater samples were ranged from 2 μg/L to 318 μg/L, with 67% of samples exhibited As levels exceeding than that of World Health Organization (WHO) permissible limit 10 μg/L and 42% of samples expressed the As level exceeding than that of the National Environmental Quality Standard (NEQS) 50 μg/L. The leaching and vertical mixing with return irrigation water are probably the main processes controlling the enrichment of As in groundwater of Larkana, Naudero, Ghari Khuda Buksh and Dokri. The weathering of minerals mostly controlled the overall groundwater chemistry; rock-water interactions and silicate weathering generated yielded solutions that were saturated in calcite and dolomite in two areas while halite dissolution is prominent with high As area.

Thousands of chemicals exist in hydraulic-fracturing (HF) fluids and wastewater from unconventional oil gas development. The carcinogenicity of these chemicals in HF fluids and wastewater has never been systematically evaluated.In this study, we assessed the carcinogenicity of 1,173 HF-related chemicals in the HF chemical data from the US Environmental Protection Agency (EPA).We linked the HF chemical data with the agent classification data from the International Agency for Research on Cancer (IARC) at the World Health Organization (WHO) (N = 998 chemicals) to evaluate human carcinogenic risk of the chemicals and with the Carcinogenic Potency Database (CPDB) from Toxnet (N = 1,534 chemicals) to evaluate potential carcinogenicity of the chemicals.The Chemical Abstract Service Registry Numbers (CASRNs) for chemicals were used for data linkage. Among 1,173 chemicals, 1,039 were identified only in HF fluids, 97 only in wastewater, and 37 in both. Compared with IARC, we found information of 104 chemicals, and 48 of them may have potentially carcinogenic risk to human, among which 14 are definitely carcinogenic, 7 probably carcinogenic, and 27 possibly carcinogenic. Using the CPDB data, it suggests that 66 chemicals are potentially carcinogenic based on rats and mouse models.Conclusions Our evaluation suggests that exposure to some chemicals in HF fluids and wastewater may increase cancer risk, and the identified chemicals could be selected as the priority list for drinking water exposure assessment or cancer-related health studies.

The removal of arsenic from groundwater is an important issue for environmental safety and human health. Research focused on the comparative assessment of arsenic removal from arsenic-bearing groundwater and arsenic-containing-synthetic water (2 mg/L) using natural magnetic material (NMM) (rock) and synthesized magnetic material (SMM) by Bacillus pasteurii and humic acid. The arsenic-bearing groundwater (97.56 ± 0.05 μg/L) exceed the WHO limit (10 μg/L) of arsenic concentration for drinking water. The NMM contains dominantly magnetite, hematite, ferrihydrate, coesite, quartz, and stishovite. The NMM of natural rock exhibited the existence of iron (6.25–8.86% Fe₃O₄), which is widespread and important component in sedimentary rocks. The investigation on vibrating sample magnetometers (VSM) of NMM and SMM demonstrated the typical magnetization properties, which can be separated after arsenic removal process. The thermogravimetric analysis (TGA) of SMM displayed the existence of organic matter decomposition during particle synthesis. The TEM and SEM exhibited the nanoparticle particle formation within the range of 10–39 nm (10–20 nm particle Fe₃O₄ through B. pasteurii). FTIR spectrum (before and after removal of arsenic) indicated the existence and binding nature in between arsenic and iron. >90% of arsenic was removed from arsenic-bearing groundwater using Fe₃O₄, Fe₃O₄ (N₂-Environment), Fe₃O₄ with humic acid, and Fe₃O₄ with B. pasteurii after 25 min, 8 min, 13 min and 120 min, respectively. In case of NMM in Site-A, the arsenic removal was observed very fast as 85–87% within 30 s, whereas 95–99%, 93–95% and 88–91% removal detected using the sample of Site-A, Site-B, and Site-C respectively, after 120 min at natural pH (8.31 ± 0.05) of arsenic-bearing groundwater. Thus, NMM, (ecofriendly green material), can be applicable for arsenic removal from arsenic-bearing groundwater.

In this study, data collected from an urban air quality monitoring network are being used for the purpose of evaluating various methodologies used for spatial interpolation in the context of proposing an effective yet simple to apply scheme for PM spatial point estimations. The examined methods are the Inverse Distance Weighting, two linear regression models, the Multiple Linear Regression and the Linear Mixed Model, along with a Feed Forward Neural Network (FFNN) model. These schemes utilize daily PM₁₀ and PM₂.₅ concentrations collected from five and three air quality monitoring sites respectively. In order to obtain the resulted estimations, the leave-one-out cross-validation methodology is used for all methods. The evaluation of their predictive ability is performed by using a combination of difference and correlation statistical measures, scatter plots and statistical tests. The results indicate the usefulness of FFNNs as they are found to be statistically significantly superior for modelling the particulate matter spatial variability. The model performance statistics show that in most cases the error values are considerably lower for the FFNN model. Additionally, the rank and Wilcoxon rank tests reveal that the null hypothesis for equal predictive accuracy is rejected for the majority of monitoring sites and schemes (values lower than the critical t-value). According to the comparison results, the FFNN model is selected for forecasting air quality limit exceedances set by the European Union and World Health Organization air quality standards. For two monitoring sites in which the largest number of exceedances occurred, the probability of detection is high while the probability of false detection is very low, further establishing the neural networks’ predictive ability.

The objective of this research is to investigate the effluent water quality of a treatment plant in Turkey fed from surface and groundwater, according to water quality index (WOI) and health risk assessment (HRA). In order to achieve this goal, the quality of the influent and effluent water of the treatment plant was monitored monthly from January 2017 to January 2019. Water quality parameter results were compared with the Turkish drinking water standards and the World Health Organization (WHO), revealing that all parameters were within approved limits. Principal component analysis (PCA) was applied to determine the water quality parameter impacts in the overall quality of water and the most attractive parameters were trace elements, heavy metals, NH₃-N, NO₃, and TKN. To evaluate water quality and the impacts on human health, WQI and HRA, including hazard quotient (HQ) and hazard index (HI), were used. The WQI values were calculated by taking into account PCA results. WQI results demonstrated that the influent and effluent of water treatment plant values have a small number of WQI ranking that expressed the water category was “excellent” for drinking purpose. Finally, metal contamination in influent and effluent waters was assessed and the associated health risks to rural populations were estimated for different age groups, children and adults in the service area of the treatment plant. The health risk assessment with similar to WQI results, the acute, sub-chronic, and chronic risks of trace elements was “negligible” level, i.e., to a level affecting 1 person in 1,000,000 inhabitants.