Health risk assessments due to nitrate levels in drinking water in villages of Azadshahr,northeastern Iran

Groundwater is the main water source used for drinking and cooking purposes globally. Nitrate level in most groundwater resources in arid and semi-arid areas has increased in the past several decades as a result of human activities and natural processes. This may exert a great impact on human health. To learn the contamination circumstances of groundwater nitrate in villages of Azadshahr, Iran and assess its probable risk to the health of adults, children and infants, fifty-eight groundwater samples were collected from wells and springs in 2018. Nitrate concentrations had a wide spatial variability in wells and springs of the studied villages, with values going from 1 up to 51 mg/L. Exceedances of the EPA standard value were limited to two village springs (villages Nili and Narab, with nitrate level of 51 and 46 mg/L, respectively). The hazard quotients (HQ) values for 41% of children and infants were above the safety level (i.e., HQ?>?1), suggesting that groundwater nitrate would have significant health effects on these age groups. Therefore, appropriate control measures and sanitation improvement programs should be put in place to protect the health of the residents in the contaminated villages.     

Mineral chemistry and Ti in zircon thermometry: Insights into magmatic evolution of the Sangan igneous rocks,NE Iran

The Sangan Magmatic complex (SMC) is, a large I-type magmatic complex, located in the northeastern Iran. Zircons extracted from the intrusive and volcanic rocks within the SMC record a similar Hf compositions and REE patterns, indicating that these chemical signatures have likely been inherited from the same source and simple history of magmatic crystallization during the evolution of the orogeny. The zircon from volcanic rocks yield Ti-in-zircon crystallization temperatures of 667–1145?°C with average temperatures of 934?°C while those from granitoids indicate crystallization temperatures of 614–898?°C with an average of 812?°C. Ti-in-zircon, Ti in biotite thermometries also indicates that the crystallization temperatures of volcanic rocks are relatively higher than those of granitoids. The biotite chemistry studies reveal that this mineral crystallized at approximately 725°–800?°C and 758° to 816?°C for granitoid and volcanic rocks, respectively, which is similar to obtained temperatures by Zir-saturation of Eq. (1). T_zicsat and T_magma trend lines on the T-SiO₂ diagram cross at high silica contents of ～68?wt.%, at which temperature the magma becomes zircon-saturated and new zircons are crystallized. The zircon REE data including Ce/Ce*, Eu/Eu*, and Th/U ratios suggest that SMC igneous rocks are formed from oxidized magma. However, the zircon Th/U and Hf data suggest that the SMC became progressively more oxidized and also indicate lower temperatures from volcanic and plutonic rock with decreasing time.     

A multi-dimensional assessment of urban vulnerability to climate change in Sub-Saharan Africa

In this paper, we develop and apply a multi-dimensional vulnerability assessment framework for understanding the impacts of climate change-induced hazards in Sub-Saharan African cities. The research was carried out within the European/African FP7 project CLimate change and Urban Vulnerability in Africa, which investigated climate change-induced risks, assessed vulnerability and proposed policy initiatives in five African cities. Dar es Salaam (Tanzania) was used as a main case with a particular focus on urban flooding. The multi-dimensional assessment covered the physical, institutional, attitudinal and asset factors influencing urban vulnerability. Multiple methods were applied to cover the full range of vulnerabilities and to identify potential response strategies, including: model-based forecasts, spatial analyses, document studies, interviews and stakeholder workshops. We demonstrate the potential of the approach to assessing several dimensions of vulnerability and illustrate the complexity of urban vulnerability at different scales: households (e.g., lacking assets); communities (e.g., situated in low-lying areas, lacking urban services and green areas); and entire cities (e.g., facing encroachment on green and flood-prone land). Scenario modeling suggests that vulnerability will continue to increase strongly due to the expected loss of agricultural land at the urban fringes and loss of green space within the city. However, weak institutional commitment and capacity limit the potential for strategic coordination and action. To better adapt to urban flooding and thereby reduce vulnerability and build resilience, we suggest working across dimensions and scales, integrating climate change issues in city-level plans and strategies and enabling local actions to initiate a ‘learning-by-doing’ process of adaptation.

     

Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods

Groundwater is the most valuable natural resource in arid areas. Therefore, any attempt to investigate potential zones of groundwater for further management of water supply is necessary. Hence, many researchers have worked on this subject all around the world. On the other hand, the Generalized Additive Model (GAM) has been applied to environmental and ecological modelling, but its applicability to other kinds of predictive modelling such as groundwater potential mapping has not yet been investigated. Therefore, the main purpose of this study is to evaluate the performance of GAM model and then its comparison with three popular GIS-based bivariate statistical methods, namely Frequency Ratio (FR), Statistical Index (SI) and Weight-of-Evidence (WOE) for producing groundwater spring potential map (GSPM) in Lorestan Province Iran. To achieve this, out of 6439 existed springs, 4291 spring locations were selected for training phase and the remaining 2147 springs for model evaluation. Next, the thematic layers of 12 effective spring parameters including altitude, plan curvature, slope angle, slope aspect, drainage density, distance from rivers, topographic wetness index, fault density, distance from fault, lithology, soil and land use/land cover were mapped and integrated using the ArcGIS 10.2 software to generate a groundwater prospect map using mentioned approaches. The produced GSPMs were then classified into four distinct groundwater potential zones, namely low, moderate, high and very high classes. The results of the analysis were finally validated using the receiver operating characteristic (ROC) curve technique. The results indicated that out of four models, SI is superior (prediction accuracy of 85.4%) following by FR, GAM and WOE, respectively (prediction accuracy of 83.7, 77 and 76.3%). The result of groundwater spring potential map is helpful as a guide for engineers in water resources management and land use planning in order to select suitable areas to implement development schemes and also government entities.     

Developing an expert group method of data handling system for predicting the geometry of a stable channel with a gravel bed

Predicting the geometry of channels and alluvial rivers is of primary importance in river engineering science. Appropriately designing channels and predicting stable river cross‐sections can decrease costs and prevent the destruction of installations and agricultural land by rivers. Consequently, researchers have applied different empirical and regression methods to achieve relations for predicting stable channel and river geometry. In this study, Group Method of Data Handling ]GMDH) models are used to predict three geometric variables of stable channels, namely width (w), depth (h) and slope (s). The effect of different input parameters, such discharge (Q), median grain size (d₅₀) and the Shields parameter (τ^*) on the GMDH models is assessed with regard to predicting stable channel geometry. The results indicate that the GMDH model with mean absolute percentage error (MAPE) of 5.53%, 4.05% and 4.89% for channel width, depth and slope prediction respectively, exhibits good accuracy. Moreover, a comparison of the GMDH models with previous theoretical equations (based on regression analysis) indicates the superiority of GMDH model performance, with error reductions of one‐fifth, one‐eighth and one‐sixth compared with the regression equations for channel width, depth and slope prediction, respectively. Copyright © 2016 John Wiley & Sons, Ltd.     

GIS-Based Groundwater Potential Mapping in Khorramabad in Lorestan,Iran, using Frequency Ratio (FR) and Weights of Evidence (WoE) Models

Water Resources - In this study groundwater potential map of Khorramabad in Lorestan Province, Iran was produced using two different methods; Frequency Ratio (FR) and Weights of Evidence (WoE)...     

Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm

Natural Hazards - The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer...     

Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models

Natural Hazards - The Urmia Lake basin is one of the most important basins in Iran, facing many problems due to poor water management and rainfall reduction. Under current circumstances, it becomes...     

Assessment of Empirical Pressure–Volume–Temperature Correlations in Gas Condensate Reservoir Fluids: Case Studies

Natural Resources Research - Measurement and modeling of fluid properties and phase behavior of gas condensate reservoir fluids are challenging tasks. Many researchers proposed various empirical...     

Time trend analysis of some agroclimatic variables during the last half century over Iran

Theoretical and Applied Climatology - We assessed the trends of precipitation, maximum and minimum temperature (Tmax and Tmin), diurnal temperature range (DTR), water requirement of autumn-planted...