This research also focuses on evaluating the radiological risk associated with water ingestion. In this regard, consuming 95.5% of the drinking water samples analysed would not imply a health risk to the population as the annual effective doses calculated were below 0.1 mSv/year.  https://www.selleckchem.com/products/sitagliptin.html  There was only one sample that exceeded this level with a value of 0.33 mSv/year. 226Ra activity concentration was the radionuclide that mainly contributed to this dose.Unlike collectively treatable industrial wastewaters where only one or a few pollutants have concentrations much higher than the relevant standards, geothermal waters, in which multiple harmful constituents coexist, are usually discharged dispersedly, provoking a big challenge for their effective treatment. Here, a Mg/Fe layered double hydroxide with OH- intercalated (Mg-Fe-OH-LDH) was synthesized in a mechanochemical way and then applied in the treatment of various types of high-temperature geothermal waters in western Yunnan (China) containing a variety of harmful anions (As, Sb, W, and F) and inducing local environmental pollution. Due to the endothermic nature of removal of aqueous As, Sb, W, and F by Mg-Fe-OH-LDH, the original high temperatures of the geothermal waters could promote their sorption effectively. Batch sorption experiments demonstrated that over 94% and 80% of the As and W removal amounts could be reached within the first 10 and 20 min, respectively. On-site column experiments confirmed thaived pollution.Understanding the spatial distribution of soil salinity is required to conserve land against degradation and desertification. Against this background, this study is the first attempt to predict soil salinity in the Jaghin basin, in southern Iran, by applying and comparing the performance of four deep learning (DL) models (deep convolutional neural networks-DCNNs, dense connected deep neural networks-DenseDNNs, recurrent neural networks-long short-term memory-RNN-LSTM and recurrent neural networks-gated recurrent unit-RNN-GRU) and six shallow machine learning (ML) models (bagged classification and regression tree-BCART, cforest, cubist, quantile regression with LASSO penalty-QR-LASSO, ridge regression-RR and support vectore machine-SVM). To do this, 49 environmental landsat8-derived variables including digital elevation model (DEM)-extracted covariates, soil-salinity indices, and other variables (e.g., soil order, lithology, land use) were mapped spatially. For assessing the relationships between soil salinityoses in environmental sciences.The concentration of PM2.5 is one of the main factors in evaluating the air quality in environmental science. The severe level of PM2.5 directly affects the public health, economics and social development. Due to the strong nonlinearity and instability of the air quality, it is difficult to predict the volatile changes of PM2.5 over time. In this paper, a hybrid deep learning model VMD-BiLSTM is constructed, which combines variational mode decomposition (VMD) and bidirectional long short-term memory network (BiLSTM), to predict PM2.5 changes in cities in China. VMD decomposes the original PM2.5 complex time series data into multiple sub-signal components according to the frequency domain. Then, BiLSTM is employed to predict each sub-signal component separately, which significantly improved forecasting accuracy. Through a comprehensive study with existing models, such as the EMD-based models and other VMD-based models, we justify the outperformance of the proposed VMD-BiLSTM model over all compared models. The results show that the prediction results are significantly improved with the proposed forecasting framework. And the prediction models integrating VMD are better than those integrating EMD. Among all the models integrating VMD, the proposed VMD-BiLSTM model is the most stable forecasting method.One of most reduction reasons of simple conventional solar still productivity is the coupling between high solar intensity and the high ambient temperature in the same time. The high intensity increases the saline water temperature, while the outside temperature increases the glass temperature, and consequently reduction in saline water and glass temperature difference leads to reduction in condensation and productivity. The present theoretical study focuses on the completion of the absorbed solar energy in the basin to be constant during the day. The basin water will be in high temperature level all day especially at the time of low outside temperature far away the noon. The absorbed heat in the basin is held constant at αw Imax by extra heat from wind turbine power with battery storage system all day hours. The results show that the solar still productivity with constant heat supply is more than that with same amount of variable energy during sun rise time only (6 AM to 6 PM) by 69.133%. So, constant absorbed heat in the water basin (αw Imax) through the 24 h of the day enhances the performance with productivity up to 248% with the hybrid solar and electric power consumption of the wind turbine power. The water in the basin is held constant at 2 cm via makeup water to compensate the evaporation rate.With per capita water resources at only around a quarter of the world average, China's water resources are limited and unevenly distributed. Past research on water resource utilization has mainly focused on industrial water use (agriculture and industry), water plant ownership efficiencies (private or public operation), or water resources and economic production; however, there have been few studies focused on water supply livelihood. Therefore, this paper considered both industrial production water services, non-production water services (public sector and residential water use), and water leakage losses, which is a water supply problem seldom mentioned in other studies. An undesirable directional distance function (DDF) dynamic data envelopment analysis (DEA) model was employed for the dynamic analysis as it was able to deal with both desirable and undesirable outputs at the same time. The model examined collected water supply and water leak efficiency data from 30 Chinese provinces/municipalities from 2007 to 2018, from which it was found that (1) Beijing, Gansu, Guangdong, and Ningxia had efficient water supply and water leak losses from 2007 to 2018 and the most improved province was Jiangxi; (2) the eastern provinces, in general, had better water efficiencies and the central and western provinces needed greater improvements; and (3) the lowest water leakage loss efficiencies were in Inner Mongolia, Jiangxi, and Heilongjiang, all of which required significant improvements.