Mitigating Impact of Devils Lake Flooding on the Sheyenne River Sulfate Concentration

Devils Lake is a terminal lake located in northeast North Dakota. Because of its glacial origin and accumulated salts from evaporation, the lake has a high concentration of sulfate compared to the surrounding water bodies. From 1993 to 2011, Devils Lake water levels rose by ~10 m, which flooded surrounding communities and increased the chance of an overspill to the Sheyenne River. To control the flooding, the State of North Dakota constructed two outlets to pump the lake water to the river. However, the pumped water has raised concerns about of water quality degradation and potential flooding risk of the Sheyenne River. To investigate these perceived impacts, a Soil and Water Assessment Tool (SWAT) model was developed for the Sheyenne River and it was linked to a coupled SWAT and CE‐QUAL‐W2 model that was developed for Devils Lake in a previous study. While the current outlet schedule has attempted to maintain the total river discharge within the confines of a two‐year flood (36 m³/s), our simulation from 2012 to 2018 revealed that the diversion increased the Sheyenne River sulfate concentration from an average of 125 to >750 mg/L. Furthermore, a conceptual optimization model was developed with a goal of better preserving the water quality of the Sheyenne River while effectively mitigating the flooding of Devils Lake. The optimal solution provides a “win–win” outlet management that maintains the efficiency of the outlets while reducing the Sheyenne River sulfate concentration to ≤600 mg/L.     

Siting MSW landfill using weighted linear combination and analytical hierarchy process (AHP) methodology in GIS environment (case study: Karaj)

Selection of landfill site is a complex process and needs many diverse criteria. The purpose of this paper is to evaluate the suitability of the studied site as landfill for MSW in Karaj. Using weighted linear combination (WLC) method and spatial cluster analysis (SCA), suitable sites for allocation of landfill for a 20-year period were identified. For analyzing spatial auto-correlation of the land suitability map layer (LSML), Maron’s I was used. Finally, using the analytical hierarchy process (AHP), the most preferred alternative for the landfill siting was identified. Main advantages of AHP are: relative ease of handling multiple criteria, easy to understand and effective handling of both qualitative and quantitative data.As a result, 6% of the study area is suitable for landfill siting and third alternative was identified as the most preferred for siting MSW landfill by AHP.The ranking of alternatives were obtained only by applying the WLC approach showed different results from the AHP. The WLC should be used only for the identification of alternatives and the AHP is used for prioritization. We suggest the employed procedure for other similar regions.     

Impact of untreated wastewater irrigation on soils and crops in Shiraz suburban area, SW Iran

In this study an assessment is made of the negative impacts of wastewater irrigation on soils and crops sampled along the Khoshk River channel in suburban area of Shiraz City, SW Iran. For this purpose, samples of soil profiles (0–60 cm in depth) and crops were collected from two wastewater irrigated sites and a tube well-irrigated (control) site. Total concentrations of the five heavy metals (Ni, Pb, Cd, Zn and Cr) and their phytoavailable contents were determined. The Pollution Load Indexes (PLIs) and Contamination Factors (CFs) for soils and Hazard quotients (ΣHQ) for some vegetables were also calculated. The results showed the use of untreated wastewater has caused the following changes as compared to control site: (1) a 20–30% increase in organic matter content of soil; (2) increase in pH by 2–3 units; (3) significant concentration increase in Ex-Ca especially in top layers of soil resulting in high CEC; (4) build up of heavy metals (notably Pb and Ni) in topsoil above Maximum Permissible Limits (MPLs) indicating a moderate contamination (PLI > 1, CF > 2.5); (5) contamination of some vegetables (spinach and lettuce) with Cd due to its high phytoavailability in topsoil causing a HQ > 1; (6) excessive accumulation of Ni and Pb in wheat due to continual addition of heavy metals through long-term wastewater application. The study concludes that strict protection measures, stringent guidelines and an integrated system for the treatment and recycling of wastewater are needed to minimize the negative impacts of wastewater irrigation in the study area.     

Application of artificial intelligence in groundwater ecosystem protection: a case study of Semnan/Sorkheh plain,Iran

Groundwater ecosystems have unparalleled environmental value. Accurate modeling of groundwater level (GWL) fluctuations is a vital requirement for the protection of the groundwater ecosystems. The GWL modeling is a challenge due to complexities of the underground geological structure. Among the various modeling methods, artificial intelligence (AI)-based approaches serve as desirable alternatives due to their distinctive and potent properties. One of the most practical AI-based approaches is an artificial neural network (ANN) model. The purpose of the current study was to apply time delay neural networks (TDNN) with different network structures and input delays to model the GWL fluctuations. The variables used in the construction and validation of the models were average weekly GWL from January 2002 to January 2013 in two monitoring sites in Semnan/Sorkheh plain, Iran. The study area is an arid region, where overutilization of groundwater threatens the water security in this area. The computational results of the current research demonstrated that the TDNN model is a practical tool in modeling time-series GWL compared to the other state-of-the-art AI-based approaches. Future studies are recommended to explore application of proposed model for more sustainable and effective Groundwater Resources Management (GWRM).

     

Regression-type estimators for adaptive two-stage sequential sampling

Adaptive two-stage sequential sampling (ATSSS) design was developed to observe more rare units and gain higher efficiency, in the sense of having a smaller variance estimator, than conventional sampling designs with equal effort for rare and spatially cluster populations. For certain rare populations, incorporating auxiliary variables into a sampling design can further improve the observation of rare units and increase efficiency. In this article, we develop regression-type estimators for ATSSS so that auxiliary variables can be incorporated into the ATSSS design when warranted. Simulation studies on two populations show that the regression-type estimators can significantly increase the efficiency of ATSSS and the detection of more rare units as compared to conventional sampling counterparts. Simulation of sampling of desert shrubs in Inner Mongolia (one of the two populations studied) showed that by incorporating a GIS auxiliary variable into ATSSS with the regression estimators resulted in a gain in efficiency over ATSSS without the auxiliary variable. Further, we found that the use of the GIS auxiliary variable in a conventional two-stage design with a regression estimator did not show a gain in efficiency.     

Investigation of Great Egret (Casmerodius albus) breeding success in Hara Biosphere Reserve of Iran

Study of Great Egret breeding success was carried out for the first time in Hara Biosphere Reserve of Iran. Since Great Egret is considered as wading bird as well as wetland-dependent species which is located on top of the food chain in this ecosystem, its breeding study is an appropriate means for evaluating food supply fluctuations and environmental threatening factors by comparison of different years. On the other hand, Great Egret is considered a suitable indicator to examination of biological changes, impact of pollutions, and other effective human activities on Hara Biosphere Reserve. Therefore, read-ahead is required for area management planning in order to maintain the health of mangrove ecosystem and control threatening factors of the sensitive biodiversity of area. The results indicate that the average breeding success of Great Egret in different stages of hatching success, fledging success, and breeding success were equal to 0.54, 0.61, and 0.50 in 2008 and 0.61, 0.59, and 0.42 in 2009, respectively.     

Examining Relationships Between Socioeconomic Factors and Landscape Metrics in the Southern Basin of the Caspian Sea

Socioeconomic forces are not only among the main drivers of landscape dynamics; they are also influenced by landscape patterns. Landscape structure and functions are closely related to natural and social factors. The objective of this study was to investigate the relationships among some human-related factors and landscape ecological metrics as landscape pattern indicators and to identify suitable metrics for modeling these relationships. To this goal, landscape ecological metrics were calculated for each of the 32 counties of Mazandaran and Guilan provinces located in the southern basin of the Caspian Sea using land use/cover maps in class level. Stream network metrics were calculated using a digital elevation model, road density metrics were calculated using map of main roads separately, and significant metrics were selected according to results of correlation tests and factor analysis. The correlations between these metrics and socioeconomic factors were tested, and their relationships were modeled with multiple linear regressions. Significant relationships were found among socioeconomic factors and landscape ecological metrics, and land use/cover data are applicable for modeling socioeconomic factors, especially demographic and employment structure factors. Among the landscape metrics applied in this study, road density, mean patch size, mean nearest neighbor distance, and percentage of a land use/cover class in landscape were important metrics for predicting socioeconomic factors. Our findings indicated that road density metric and percentages of urban class are useful for predicting urban socioeconomic factors and percentage of agriculture and forest classes in the landscape are suitable metrics for predicting rural socioeconomic factors.     

Analysis of the traffic injury severity on two-lane,two-way rural roads based on classification tree models

Two-lane, two-way roads constitute a major portion of the rural roads in most countries of the world. This study identifies the factors influencing crash injury severity on these roads in Iran. Classification and regression trees (CART), which is one of the most common methods of data mining, was employed to analyze the traffic crash data of the main two-lane, two-way rural roads of Iran over a 3-year period (2006–2008). In the analysis procedure, the problem of three-class prediction was decomposed into a set of binary prediction models, which resulted in a higher overall accuracy of the predictions of the model. In addition, the prediction accuracy of the fatality class, which was nearly 0% in some of the previous studies, increased significantly. The results indicated that improper overtaking and not using a seatbelt are the most important factors affecting the severity of injuries.