ANN-based mapping of monthly reference crop evapotranspiration by using altitude, latitude and longitude data in Fars province, Iran

The main goal of this study was to evaluate the different feed-forward back-propagation artificial neural networks’ (ANNs) potential to estimate and interpolate the reference crop evapotranspiration (ET₀) in Fars province of Iran. ET₀ was calculated using the FAO-56 Penman–Monteith method over 24 synoptic stations. Then, altitude, latitude, longitude and the month’s number as inputs and the monthly ET₀ as output (target) were used to train the ANNs. In addition, the three-layered ANNs optimized with different training algorithms including gradient descent back-propagation (gd), gradient descent with adaptive learning rate back-propagation (gda), gradient descent with momentum and adaptive learning rate back-propagation (gdx) and scaled conjugate gradient back-propagation (scg). The results indicated that scg algorithm with architecture (4 2 1) had more satisfactory results with the RMSE and R correlation coefficient equal to 18.538 mm and 0.967 in validation phase, respectively. Based on the mentioned architecture of scg algorithm, and input data form different parts of Fars province and surrounding areas, monthly ET₀ maps were produced and annual one achieved by summation of monthly maps. The maps particularly annual one showed that highest values of ET₀ could be found in the southern and especially southeastern regions, while the lowest values of ET₀ could be seen in the northern parts. Contribution of geographic and topographic variables improved the accuracy and spatial details of the resulting maps. It is interesting to note that the fundamental capability of this model is the usage of just a few parameters for ET₀ mapping. Since ET₀ is a key parameter in water demand planning, therefore, the derived maps could be useful and applicable for many purposes mainly irrigation scheduling in Fars province, Iran.     

River water quality assessment using environmentric techniques: case study of Jakara River Basin

Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH₄). Partial correlation analysis (r _p) results showed that there is a strong relationship between salinity and turbidity (r _p?=?0.930, p?=?0.001) and BOD₅ and COD (r _p?=?0.839, p?=?0.001) controlling for the linear effects of conductivity and NH₄, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD₅, COD, SS, NH₄, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.     

Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Evaluation of the anthropogenic influx of metal and metalloid contaminants into the Moulay Bousselham lagoon, Morocco, using chemometric methods coupled to geographical information systems

Superficial and cored sediment samples from the Moulay Bousselham lagoon and sub-watershed were analyzed for Al, Fe, Cu, Zn, Pb, Mn, Ni, Cr, As, Hg, and Cd. The temporal and spatial distributions of the main contamination sources of heavy metals were identified and described using chemometric and geographic information system (GIS) methods. Sediments from coastal lagoons near urban and agricultural areas are commonly contaminated with heavy metals, and the concentrations found in surface sediments are significantly higher than those from 50–100 years ago. The concentrations of these elements decrease sharply with depth in the sediment column, and the elements are preferentially enriched in the <2-μm-sized fraction of the sediment. The zones of enhanced risk of heavy metals were detected by means of GIS-based geostatistical modeling. According to sediment pollution indices and statistical analysis, heavy metals (Pb, Cu, Ni, Zn, Cr, and Hg) that pose a risk have become largely enriched in the lagoon sediments during the recent period of agricultural intensification.     

Abundance of denitrification genes under different peizometer depths in four Irish agricultural groundwater sites

This study examined the relationship between the abundance of bacterial denitrifiers in groundwater at four sites, differing with respect to overlaying land management and peizometer depth. Groundwater was sourced from 36 multilevel piezometers, which were installed to target different groundwater zones: (1) subsoil, (2) subsoil to bedrock interface, and (3) bedrock. The gene copy concentrations (GCCs), as gene copies per liter, for bacterial 16S rRNA genes and the denitrifying functional genes, nirK, nirS, and nosZ, were determined using quantitative polymerase chain reaction assays. The results were related to gaseous nitrogen emissions and to the physicochemical properties of the four sites. Overall, nirK and nirS abundance appeared to show no significant correlation to N₂O production (P?=?0.9989; P?=?0.3188); and no significant correlation was observed between nosZ and excess N₂ concentrations (P?=?0.0793). In the majority of piezometers investigated, the variation of nirK and nirS gene copy concentrations was considered significant (P?<?0.0001). Dissolved organic carbon (DOC) decreased with aquifer depth and ranged from 1.0–4.0 mg l^?1, 0.9–2.4 mg l^?1, and 0.8–2.4 mg l^?1 within piezometers located in the subsoil, subsoil/bedrock interface, and bedrock depths, respectively. The availability of increasing DOC and the depth of the water table were positively correlated with increasing nir and nosZ GCCs (P?=?0.0012). A significant temporal correlation was noted between nirS and piezometer depth (P?<?0.001). Interestingly, the nirK, nirS, and nosZ GCCs varied between piezometer depths within specific sites, while GCCs remained relatively constant from site to site, thus indicating no direct impact of agricultural land management strategies investigated on denitrifier abundance.     

The Fate of Hydrogen Peroxide as an Oxygen Source for Bioremediation Activities within Saturated Aquifer Systems

ABSTRACT

In situ bioremediation is an innovative technique for the remediation of contaminated aquifers that involves the use of microorganisms to remediate soils and groundwaters polluted by hazardous substances. During its application, this process may require the addition of nutrients and/or electron acceptors to stimulate appropriate biological activity. Hydrogen peroxide has been commonly used as an oxygen source because of the limited concentrations of oxygen that can be transferred into the groundwater using above-ground aeration followed by reinjection of the oxygenated groundwater into the aquifer or subsurface air sparging of the aquifer. Because of several potential interactions of H₂O₂ with various aquifer material constituents, its decomposition may be too rapid, making effective introduction of the H₂O₂ into targeted treatment zones extremely difficult and costly. Therefore, a bench-scale study was conducted to determine the fate of H₂O₂ within subsurface aquifer environments. The purpose of this investigation was to identify those aquifer constituents, both biotic and abiotic, that are most active in controlling the fate of H₂O₂. The decomposition rates of H₂O₂ were determined using both equilibrated water samples and soil slurries. Results showed H₂O₂ decomposition to be effected by several commonly found inorganic soil components; however, biologically mediated catalytic reactions were determined to be the most substantial.     

Effects of N-acetyl-L-cysteine on fish hepatoma cells treated with mercury chloride and ionizing radiation

Organisms are exposed to natural radiations from cosmic or terrestrial origins. Furthermore the combined action of radiation with various chemicals is an inevitable feature of modern life. Radiation is known to cause cell death, mainly due to its ability to produce reactive oxygen species in cells. N-acetyl-l-cysteine (NAC) is a well-known sulfhydryl-containing antioxidant whose role in radioprotection has been reported. Synergistic effects of radiation and mercury chloride on human cells was previously reported by the authors. Based on the previous report, this study was designed to assess the synergistic effects of radiation and mercury chloride on fish hepatoma cells, as well as to investigate the protective effects of NAC on the cells. The cytotoxicity of radiation was enhanced in the presence of mercury chloride. NAC in lower concentrations prevented cells from death after irradiation with lower doses (<300 Gy) while it did not prevent cells from radiation-induced death after irradiation with higher doses (300, 500 Gy). The intracellular glutathione (GSH) levels significantly decreased after irradiation while the combined treatment of NAC and radiation alleviated the decrease in the GSH levels. The investigations give a clue for the action mechanism of synergistic or protective effects of NAC on the cells. Due to their high resistance to ionizing radiation, the PLHC-1 cells can be effectively used as a screening tool for assessing the combined effects of radiation with toxic chemicals.     

Eco-Friendly, Vascular Shape and Interpenetrating Poly (Acrylic Acid) Grafted Pectin Hydrogels; Biosorption and Desorption Investigations

The synthesis and characterization of poly (acrylic acid) grafted pectin hydrogel followed by biosorption and desorption characteristics of cadmium, as a model heavy metal, have been studied. The grafted eco-friendly pectin based interpenetrating hydrogel was prepared in the presence of gluteraldehyde crosslinker under N₂ atmosphere and characterized using ¹H-NMR, FTIR, TGA and SEM techniques. Gluteraldehyde was found to form one-arm and two-arm crosslinks in the copolymer. Upon grafting, two-dimensional sheet structures bounded to tubular and vascular cylindrical rods were observed. The biosorption and desorption data, determined experimentally, were fitted to pseudo-second order reaction kinetics. At higher ionic strength values, the maximum metal uptake value (q _max) was lowered and pseudo-second order rate constant (k ₂) was increased. Whereas, at higher pH values the maximum metal uptake value (q _max) was increased and Pseudo-second order rate constant (k ₂) was decreased. 0.1?M HCl solution was a suitable eluent to regenerate the hydrogel surface and recover the adsorbed cadmium metal ions. Pectin based copolymer could be used as an efficient candidature biosorbent for the recovery of cadmium metal ions from aqueous solutions.     

Uranium biosorption by <Emphasis Type="Italic">Padina</Emphasis> sp. algae biomass: kinetics and thermodynamics

Introduction  

Kinetic, thermodynamic, and equilibrium isotherms of the biosorption of uranium ions onto Padina sp., a brown algae biomass, in a batch system have been studied.     

How renewable energy and service growth influence environmental quality: Evidence from a sustainable development perspective

The present study inspects the relationships between the carbon dioxide (CO₂) emissions, real GDP, renewable and non-renewable energy consumption, as well as the service growth for top ten countries (TTC) in service activities. The empirical modeling used in the study involves the procedures of cointegration and tests of Granger causality to inspect the dynamic interaction between the variables during the period from 1980 to 2018. The results of the present study suggest that the variables are cross-sectionally dependent. In addition, the variables appear to be cointegrated based on several tests. The long-run outcomes revealed an inversed U-shaped form between emissions-GDP proving the validity of the environmental Kuznets curve assumption. The fully modified ordinary least squares and dynamic OLS estimates show that the non-renewable energy and economic growth contribute to the increase of CO₂ emissions, while service value-added leads to decrease emissions. Furthermore, the renewable energy coefficient comes through as negative but insignificant for the selected panel. The TTC in service should stimulate the usage of renewable energy in various service events for following the path of sustainable development. Devising the investment plans associated with the use of renewable energies is quite essential for the advancement of the service sector leading to mitigating emissions portion.