Journal of Environmental Resources Research
Volume:9 Issue: 2, Summer-Autumn 2021

The continuous growth in civilization and population have led to upsurge in generation of construction and demolition (C&D) wastes. A considerable part of C&D wastes is gypsum wastes that together with its derivatives are classified as a group of binding agents in soil stabilization and immobilization and upgrading soil durability. In this study, the possibility of using gypseous wastes as a binding agent was investigated. Moreover, soil stabilized with recycled gypsum was tested under different dry-wet cycles as well as multiple dry-rewetting to assess the stability of improved soil. Different amounts of gaseous waste (0, 5, 10, and 20%) and 5% cement and 5% lime were added to clay soil at various curing conditions (0, 7, 14, and 21 days). Then, durability of samples was tested by wetting/drying cycles (0, 1, 2, and 3 cycles). Soil characteristics including compaction, unconfined compression strength, Atterberg limits and soil durability were assessed for all samples. Results demonstrated significant increase of the unconfined compressive strength in clay by addition of gypsum waste, cement, and lime. However, we detected a significant reduction in the unconfined compressive strength of the samples in the third cycle of wetting-drying test.

The impact of regional plans on water resources and has always been a concern for researchers. In the meantime, one of the issues which is being raised by drought is how to assess the quality of regional plans considering the drought. In this research, Fu and Tang plan assessment method called "Awareness-Analysis-Action" is used to assess the quality of six regional plans of Chaharmahal and Bakhtiari Province in Iran. Findings show that the awareness component in all plans is at an average level. Regarding the analysis component, comprehensive regional plans are in a good level, and other plans were at an average level. The action component was rated moderate in all plans and the scores related to the component of action in the plans were not different significantly. In terms of the overall plan score, only one plan received a score above average. Results of ANOVA test showed that the mean of the “Analysis component” in the plans is more than the two other components and there is no significant difference between the score of the three components of the plans. In other words, regional development plans have failed to succeed in three components of awareness, analysis, and action regarding drought adaptation.

Erosive soil processes in arid ecosystems render local heterogeneities and associated ecological and hydrological diversities within the landscape. Spatial heterogeneity exhibits simultaneous opposing degrading and developing conditions with varying degrees of resilience. While it would have been expected that heterogeneity-induced response diversity should increase the ecosystem resilience, highly heterogeneous ecosystems promote irreversible shifts. The major question is whether heterogeneity accelerates dryland degradation or provides an opportunity for increasing sustainability. To understand this paradox, recent studies were reviewed to answer (1) the causes of spatial heterogeneity in patterns of soil biotic-abiotic properties; (2) how heterogeneity simultaneously exhibits seemingly opposite effects in dryland dynamics through the emergence of resilience thresholds. Until heterogeneity can retain multiple resilience thresholds, it will have facilitative effects on resilience of the landscape. When the distance between fragments exceeds a dispersal threshold, the disappearance of resilience thresholds promotes destructive effects of the heterogeneity, stimulating irreversible transitions. It is hoped that this review, in emphasizing the importance of the relationship between erosive soil disturbances and soil biotic-abiotic variables in the dynamics of spatial heterogeneity can provide an effective basis to quantify critical heterogeneity thresholds as an early warning sign for anticipating the future evolution trend of landscape.

Environmental pollution is nowadays one of the main challenges of the world. The global economic growth and the associated environmental degradation have put environmental protection at the forefront of attention. Regarding the aim of economic growth and its impact on environmental quality, the effect of various economic and human factors on environmental pollution and degradation is increasingly gaining importance in Iran. This study investigated the effects of economic growth, energy consumption, trade liberalization, urbanization, financial development, and human development on ecological footprint and environmental degradation in Iran over the period 1971-2015 using the Markov switching-error correction method (MS-ECM). Based on the results, the environmental Kuznets hypothesis was confirmed for the ecological carbon footprint with a reversed-U shape. Economic growth, urbanization, and energy use were found to have a positive and significant effect on environmental degradation, while financial development negatively influenced environmental degradation. Also, human development had a positive effect on the ecological carbon footprint. Finally, it is recommended to save energy, especially fossil fuels, by using clean energy, increase environmental awareness, and enforce strict environmental monitoring.

In this study, spatio-temporal forest canopy cover density changes in the Golestan National Park (Iran) were assessed from 1967 to 2016 using aerial photos and satellite images in a Geographical Information Systems environment. The digital aerial orthophoto mosaics were created after georeferencing photos of the years 1967 and 1994. A special canopy interpretation key was used for interpreting and determining the forest canopy cover classes on the digital orthophoto mosaics and Google earth images and for providing the canopy cover density maps. The results indicated that throughout the period from 1967 to 1994, the non-forest areas in some places were converted into forest due to physical and technical management measures. Results also showed that between 1967 and 2016, the canopy cover class 10-25% had the highest increase (16.6%) followed by the 25-50% class that increased by 21.17%. Our research exhibited that applying the physical preservation and protection management plans are the major factors in conversion of non-forest to forest areas and increase of the canopy cover density. These results emphasize that active physical protection and proper implementation of management methods are required for forest restoration in the study area.

Many of the serious today’s environmental problems are, directly or indirectly, caused by humans' irresponsible behavior. The current study, then, aimed at modeling the environmental behavior of tourists in destinations found in Birjand, Iran. The effects of independent constructs such as environmental perception (P), satisfaction (S), attitude (A), and active involvement (AI) on the dependent construct of the environmental behavior (EB) of tourists were investigated. The Partial Least Squares Structural Equation Modeling (PLS-SEM) was used via SmartPLS 3 software. According to the results, the reliability of the model, and Cronbach's alpha values of latent and observant variables were acceptable (EB=0.851, P= 0.898, S=0.920, A=0.852, AI= 0.903); the combined reliability (rho or CR) for constructs ranged between 0.874 and 0.931. The average variance extracted (AVE) for EB, P, S, A, and AI were measured at 0.385, 0.555, 0.530, 0.530, and 0.595, respectively. The correlation between latent variables, according to Fornell and Larker method showed that the A and AI constructs were more positively correlated with each other. As a whole, the results showed that perception has a significant and positive effect on the tourists’ environmental behavior in natural and historical destinations. This is while no significant correlation was found between satisfaction, attitude, and active involvement. This research provided a scientific basis for tourism management and offered some suggestions that can be useful for formulating policies, and wise tourism development in the context of environmental protection in relevant administrative departments.

The plant growth-promoting rhizobacteria (PGPR) are one of the famous sources when applying drought stress management in rangeland plants. This research was conducted to investigate the effect of PGPRs on the germination and growth of Astragalus cyclophyllon Beck under drought stress. A factorial experiment in a completely randomized design with three replications, was conducted in the seed laboratory. The effects of the main factors of PGPR inoculation as well as control and drought stress at four levels and their interactions on weight, length, and germination indices were investigated. Bacillus cereus had the highest effect on increasing shoot, seedling dry weight, and vigour indices. Pseudomonas aeruginosa had the highest effect on promoting radicle length. Additionally, results revealed that -0.8 MPa drought stress had a significant effect on radicle and shoot fresh weight, shoot and seedling length, germination percentage, germination rate and mean daily germination as compared to 0.0, -0.2 and -0.4 MPa indicating A. cyclophyllon is a drought stress resistance species. B. cereus at the control level of drought stress significantly increased shoot and seedling dry weight and vigour indices, whilst, P. aeruginosa at the drought stress level of -0.8 MPa significantly increased shoot lengths compared to control. In general, B. cereus significantly increased biomass by increasing shoot and seedling dry weight but P. aeruginosa affected elongation growth of the plant by increasing radicle and shoot lengths of A. cyclophyllon. Suitable PGPR as biotic elicitors can enhance the growth of the rangeland plants.

River flow forecasting is of immense importance for reliable planning, designing, and management of water resources projects. This study investigated the performance of wavelet neural network, support vector machine, artificial neural network, and Multiple Models Driven by Artificial Neural Networks (MMANN) in predicting flow time series of the Kashkan River in Lorestan, Iran. Daily flow time series was created from the records of Kashkan hydrometric and rain gauge stations for a 10-year period from 2006 to 2016. To determine the best input-output mapping, estimations were repeated with different combinations of inputs derived from previous daily river flow data. Performance of the models was evaluated in terms of correlation coefficient, root mean square error, and mean absolute error. Performance comparisons showed that the MMANN model with a correlation coefficient of 0.960, root mean square error of 0.021, and mean absolute error of 0.001 generates the best daily flow estimates for the studied river.

Aquatic ecosystems contaminated by heavy metals are a worldwide concern. These metals are toxic and persistent. This research aimed to measure mercury in the hair of fishermen, working at Chahnimeh Reservoirs, Iran. Samples were collected from April 2012 through October 2012 from 40 local fishermen in Chahnimeh Reservoirs, Iran. Fishermen with teeth filled with amalgam (a source of mercury) were excluded. The average amount of mercury in the samples was 1.56 μg/g (ranging from 0.1 to 3.65 μg/g). In 29 fishermen (72%) hair mercury levels exceeded the reference dose of the USEPA. The results in this study showed that height and age was not significantly related to mercury levels, but body weight and BMI was significantly correlated.Fish consumption (p=0.001), smoking (p=0.018), weight (r=0.35, p=0.02) and BMI (r = 0.35, p=0.02) were significantly related to hair mercury levels. Fruit (p=0.017) and dairy consumption (p<0.001) were inversely related to the concentration of mercury in hair.

The Lonsdorf model simply considers the potential of land covers in providing nesting habitat and floral resources for mapping pollination services in different landscapes. However, this model does not take into account topographic and climatic factors in its modeling and does not include edge effects of factors such as roads, rivers, lakes, and wetlands, which affect the presence of pollinators. To overcome these problems, we used the ESTIMAP model to improve the results of the Lonsdorf model. For this, we included the effects of roads, railways, rivers, wetlands, lakes, altitude, climate, and ecosystem boundaries in the ESTIMAP modeling and compared the results with the Lonsdorf model. The results of the Lonsdorf model showed that the majority of Iran had a very low potential for providing pollination service and only three percent of the northern and western parts of Iran had high potential. However, the results of the ESTIMAP model showed that 16% of Iran had a high potential to provide pollination that covers most of the northern and southern parts of the country. The results of the ESTIMAP model for pollination mapping in Iran showed the Lonsdorf model of estimating pollination service can be improved through considering other relevant factors.

Water is one of the critical natural resources in the world and one of the scarce inputs of the agriculture sector in arid and semi-arid areas, including Iran. Consecutive droughts, lower groundwater levels, and lower water quality are among the concerns of farmers and policymakers in Iran. Therefore, the impacts of various factors, including the knowledge-based economy index, on the water crisis in Iran were investigated. In the present study, the Bayesian Network has been applied for modeling water crisis management in Iran. Therefore, key factors related to water crisis management were identified. The U.N. indicator was used to assess the water crisis. The composite index of the knowledge-based economy was calculated using the Knowledge Assessment Methodology. The results of the U.N. indicator indicated that Iran is in a state of a severe water crisis. The modeling results showed that advances in the knowledge-based economy index could alleviate the water crisis. Also, increased economic growth can exacerbate the water crisis. Sensitivity analysis showed that drought has the most significant impact on the water crisis in Iran. Therefore, planning and policymaking to advance the various components of the knowledge-based economy and moving towards sustainable development can help manage the water crisis in Iran.

Rangelands are one of the most important terrestrial ecosystems that are very important in the carbon sequestration process. In this study, the amount of carbon pool was measured and compared in the habitats of Haloxylon ammodendron and Calligonum comosum in Mirjaveh plain, Sistan and Baluchestan province (in 2021). The research was based on a completely randomized design. Above and belowground biomass was harvested along 100 m transects. In order to determine the soil carbon pool of plant species, sampling was performed from three depths )0-15, 15-30 and 30-45 cm (. The amount of soil and plant organic carbon, bulk density and carbon storage of plant and soil were measured. Data were analyzed using SPPSS.20. The results showed that in both habitats, the highest and lowest amounts of organic carbon and soil carbon pool were related to depths of 0-15 and 15-30 cm, respectively. Results showed that in both habitats, soil carbon pool, bulk density, organic carbon had the maximum levels in the 0-15 cm soil layer. In C. comosum habitat, Cp in the 0-15 cm depth was more than the same soil depth in H. ammodendron habitat. In both plants, Cp in the belowground biomass was significantly more than the aboveground biomass (p<0.01). In particular, our study showed that C. comosum has more potential to store carbon compared with H. ammodendron. The use of shrubs in biological practices can increase the carbon pool in arid lands but, the efficiency of more plant species needs to be assessed.

Astragalus podolobus is a thorn less half-shrub plant that is considered as one of the most valuable species in the Iran rangelands. Land-use changes and rangelands degradation represent a real threat to it. To determine how to manage it better, the potential spatial distribution of this species was mapped using a bivariate statistical model (FR: frequency ratio) for the Maraveh Tapeh rangelands of Golestan province, north-east Iran. A total of 115 occurrences of A. podolobus were recorded using GPS during field surveys from April to September in 2018 and 2019, then 80 data points (70%) were modeled, and 35 data points (30%) were used to evaluate the model. In the form of digital layers, 8 variables potentially affecting the habitat suitability of the plant were selected as independent variables, including; distance from road and river, elevation, plan curvature, precipitation, slope percentage slope aspect and temperature. The results of the relationship between the effective variables and the presence of the plant species showed that the variables of temperature, precipitation, and distance from the road have a greater effect on the presence of the A. podolobus. . The newly derived habitat suitability map produced using the FR model is a powerful tool harnessed for the development of conservation projects of the degraded habitat of A. podolobus. The analytical framework used in this study could be applied to other arid and semi-arid environments to determine suitability habitat of A. podolobus and stabilize this species, also to introduce new adaptive rules for rangeland management.

Soil is an essential natural resource for life that provides the required substrate on which plants grow and flourish. One of the challenges for environmental specialists is to accurately estimate and control soil erosion. MPSIAC (Modified model of Pacific Southwest Inter-Agency Committee) is a common model for estimating erosion and sedimentation rate. In this study, we used MPSIAC, regression and artificial neural networks (ANN) to estimate sediment yield in Kordan Basin, a region in Alborz Province of Iran. The erosion and sedimentation data of the region were collated using the opinions of sedimentation experts. A linear regression was performed in Weka software to determine the factors influencing the sedimentation rate. Based on the results and the opinion of the experts, the factors with less impact on the sedimentation were removed. ANN was implemented using NeuroSolutions and Matlab software. The neural network was a Multi-Layer Perceptron (MLP) with one hidden layer and five neurons. The hidden layer consisted of tan-sigmoid activation function, and the output layer had a linear-sigmoid activation function. The algorithm used for training the neural network was Levenberg-Marquardt. The ANN results were superior to that of regression and the Matlab's output was more accurate than that of NeuroSolutions, with a mean square error of 0.009 for sediment yield. Finally, Matlab's neural network was extracted in the form of a function for later applications without the need to further training.