Model of ecological resilience in Hyrcanian forests that combines the decision-making trial and evaluation method (DEMATEL) and system dynamics.

The present study assessed the resilience model of Hyrcanian forests with Navroud-Asalem watershed as a case study. The Navroud-Assalem watershed was selected for study because of its special environmental characteristics and access to relatively acceptable information. In order to model resilience, the appropriate indices influencing resilience in Hyrcanian forests were identified and selected. The criteria of biological diversity and forest health and vitality were selected along with the indices of species diversity, forest-type diversity, mixed stands, and the infected area percentage forests with disturbance factors. Thirteen sub-indices and 33 variables were determined and the relationship between the indices and criteria was identified by developing a questionnaire using the decision-making trial and evaluation laboratory (DEMATEL) method. The weights of each index were estimated in Vensim software using the fuzzy analytic hierarchy process. Collecting and analyzing the regional information, the conceptual model was developed and formulated quantitatively and mathematically and was entered into Vensim for resilience modeling of the selected parcels. The DEMATEL method indicated that the species diversity indices and the percentage of affected forests had the greatest influence and interaction with other factors in the system. The parcels studied had different slopes and were affected by the input variables. They were considered as resilient if they were found to maintain current conditions. Avoidance of exploitation, infestation by pests, severe fires in the region, and increased livestock grazing compared to the existing situation were among the prerequisites for resilience in the region. Vensim modeling represents that in control parcel no. 232 the nondimensional resilience parameter is 3025 (the most resilient parcel), while in the disturbed parcel no. 278 this amount is 1775 (the least resilient parcel).

An integrated of fuzzy-WASPAS and E-FMEA methods for environmental risk assessment: a case study of petrochemical industry, Iran.

Due to the scope and volume of activities, the petrochemical industry has a high potential for risk to humans and the environment. This study aimed to evaluate the environmental risks caused by the ammonia and urea production process. To screen the risks identified in the follow-up phase, the process hazard analysis (PHA) was used. The environmental aspects were also assessed using environmental failure mode and effects analysis (EFMEA). The most significant environmental aspect with a Risk Priority Number (RPN) of 100 was related to CO2 emissions from the disposal tower. To rank the final aspects, the criteria "severity," "probability of occurrence," "probability of detection," and the "extent of contamination" were first weighed by the fuzzy Shannon entropy method. Then, each aspect was prioritized based on the mentioned criteria and using fuzzy Weighted Aggregated Sum Product Assessment (WASPAS). According to this technique, among the 24 environmental aspects, the highest score (with a value of 0.702) was given to CO2 emissions from the disposal tower. Finally, suggestions were made to mitigate the risks.

Arsenic Removal of Contaminated Soils by Phytoremediation of Vetiver Grass, Chara Algae and Water Hyacinth.

This research has been carried out for assessing phytoremediation of contaminated soils with 4 concentrations of arsenic by three plants, namely Vetiver grass (Vetiveria zizanioides), Chara algae (Chara vulgaris) and Water hyacinth (Hyacintus orientalis). The experimental results showed that at least two sampling times were significantly different. In addition, at least two plants were also significantly different in terms of percentages of total arsenic that were removed from the soil of the pots, as well as significant interactions between plant and arsenic concentrations. The results obtained from the thermodynamic studies show that, obtained by zero Gibbs free-energy, the process reached an equilibrium on the 60th day of the experiment, and, in fact, the adsorption of arsenic after the 60th day would be negligible.

A comprehensive multi-criteria decision making-based land capability assessment for municipal solid waste landfill sitting.

The municipal waste management has always been one of the most challenging environmental concerns. Today, although different strategies have been developed, sanitary disposal of municipal waste is still considered as one of the most widely used alternatives, especially in developing countries such as Iran. To investigate the land capability of Pishva, for landfill sitting, the important criteria were categorized in two groups of ecological and socioeconomic and then a multi-criteria decision-making model was used with decision-making trial and evaluation decision-making trial and evaluation (DEMATEL)-analytical network process (ANP) approach. First and foremost, the interaction of criteria was determined implementing DEMATEL. It was found that the soil depth criterion not only is the most effective but also is the most influenced one. Moreover, ANP structure was developed to weigh the criteria. In comparison to socioeconomic criteria, ecological ones play a more significant role. Afterwards, factor maps and constraints were standardized using fuzzy and Boolean logic, respectively, and all layers were combined to generate the final capability map of Pishva applying WLC method. The capability map showed that 71% of the area is not capable of landfilling, and only 5% of Pishva has a high capability. The results proved the great effectiveness of the methods proposed in this study.

Application of decision tree for prediction of cutaneous leishmaniasis incidence based on environmental and topographic factors in Isfahan Province, Iran.

Cutaneous Leishmaniasis (CL) is a neglected tropical disease that continues to be a health problem in Iran. Nearly 350 million people are thought to be at risk. We investigated the impact of the environmental factors on CL incidence during the period 2007- 2015 in a known endemic area for this disease in Isfahan Province, Iran. After collecting data with regard to the climatic, topographic, vegetation coverage and CL cases in the study area, a decision tree model was built using the classification and regression tree algorithm. CL data for the years 2007 until 2012 were used for model construction and the data for the years 2013 until 2015 were used for testing the model. The Root Mean Square error and the correlation factor were used to evaluate the predictive performance of the decision tree model. We found that wind speeds less than 14 m/s, altitudes between 1234 and 1810 m above the mean sea level, vegetation coverage according to the normalized difference vegetation index (NDVI) less than 0.12, rainfall less than 1.6 mm and air temperatures higher than 30°C would correspond to a seasonal incidence of 163.28 per 100,000 persons, while if wind speed is less than 14 m/s, altitude less than 1,810 m and NDVI higher than 0.12, then the mean seasonal incidence of the disease would be 2.27 per 100,000 persons. Environmental factors were found to be important predictive variables for CL incidence and should be considered in surveillance and prevention programmes for CL control.

Climate and environmental factors affecting the incidence of cutaneous leishmaniasis in Isfahan, Iran.

Cutaneous leishmaniasis (CL) is a vector-borne human disease caused by Leishmania, a parasite transmitted by sand flies. CL is endemic in the Isfahan Province, Iran. This study was designed to identify the climate and environmental factors associated with CL incidence in Isfahan Province. Data included incident cases of CL, climate, and environmental factors, which were collected across 23 districts of province from 2007 to 2015. Analyses were performed with generalized linear models (GLMs) to fit a function to the relationships between the response and predictors. We used negative binomial regression due to over-dispersed distribution of CL cases. The effects of all seven climate and environmental factors were found to be significant (all p < 0.01), and the model explained 40% of the deviance of CL incidence. There was a positive relation between mean temperature, relative humidity, and slope of area with disease incidence; however, negative association was demonstrated between maximum wind speed, rainfall, altitude, and vegetation cover with CL incidence. Cutaneous leishmaniasis continues to be a widespread challenge, especially in northwestern parts of Iran. Climate and environmental factors should be considered when selecting the most appropriate strategies for preventing and controlling CL.

Spatial analysis of cutaneous leishmaniasis in an endemic area of Iran based on environmental factors.

Leishmaniasis is a parasitic disease caused by different species of protozoan parasites. Cutaneous leishmaniasis (CL) is still a great public health problem in Iran, especially in Isfahan Province. Distribution and abundance of vectors and reservoirs of this disease is affected by different factors such as climatic, socioeconomic and cultural. This study aimed to identify the hotspot areas for CL in Isfahan and assess the relations between the climatic and topographic factors with CL incidence using spatial analysis. We collected data on the total number of CL cases, population at risk, vegetation coverage, altitude and climatic data for each district of the province from 2011 to 2015. Global Moran's Index was used to map clustering of CL cases across districts and the Getis-Ord (Gi*) statistics was used to determine hotspots areas of the disease in Isfahan. We applied overlay analysis to assess the correlation between the climatic and topographic factors with CL incidence. We found the CL distribution significantly clustered (Moran's Index=0.17, P<0.001) with the Ardestan and Aran va Bidgol (P<0.01) districts along with the Naein and Natanz districts (P<0.05) to be strong hotspot areas. Overlay analysis revealed a high incidence of CL in areas with relative humidity of 27-30%, mean temperature of 15-19°C, mean precipitation of 5-20 mm, maximum wind speed about 12-16 m/s and an altitude of 600-1,800 m. Our study showed that spatial analysis is a feasible approach for identifying spatial disease pattern and detecting hotspots of this infectious disease.

Development and validation of sustainability criteria of administrative green schools in Iran.

Environmental responsibility in school has led to the emergence of a variety of criteria to administer green schools' contributions to sustainability. Sustainability criteria of administrative green schools need validity, reliability and norms. The aim of the current study was to develop and validate assessment criteria for green schools in Iran based on the role of academia. A national survey was conducted to obtain data on sustainability criteria initiatives for green schools and the Iranian profile was defined. An initial pool of 71 items was generated and after its first edition, 63 items were selected to comprise the sustainability criteria. Engineering-architectural and behavioral aspects of this sustainability criteria were evaluated through a sample of 1218 graduate students with environmental degrees from Iran's universities. Exploratory factor analysis using principal components and promax rotation method showed that these 9 criteria have simple structures and are consistent with the theoretical framework. The reliability coefficients of subscales ranged between 0.62 (participation) and 0.84 (building location and position). The study's survey of correlation coefficients between items and subscales illustrated that those coefficients varied between 0.24 and 0.68.

Analysis of land suitability for urban development in Ahwaz County in southwestern Iran using fuzzy logic and analytic network process (ANP).

The ever-increasing development of cities due to population growth and migration has led to unplanned constructions and great changes in urban spatial structure, especially the physical development of cities in unsuitable places, which requires conscious guidance and fundamental organization. It is therefore necessary to identify suitable sites for future development of cities and prevent urban sprawl as one of the main concerns of urban managers and planners. In this study, to determine the suitable sites for urban development in the county of Ahwaz, the effective biophysical and socioeconomic criteria (including 27 sub-criteria) were initially determined based on literature review and interviews with certified experts. In the next step, a database of criteria and sub-criteria was prepared. Standardization of values and unification of scales in map layers were done using fuzzy logic. The criteria and sub-criteria were weighted by analytic network process (ANP) in the Super Decision software. Next, the map layers were overlaid using weighted linear combination (WLC) in the GIS software. According to the research findings, the final land suitability map was prepared with five suitability classes of very high (5.86 %), high (31.93 %), medium (38.61 %), low (17.65 %), and very low (5.95 %). Also, in terms of spatial distribution, suitable lands for urban development are mainly located in the central and southern parts of the Ahwaz County. It is expected that integration of fuzzy logic and ANP model will provide a better decision support tool compared with other models. The developed model can also be used in the land suitability analysis of other cities.

Urban development change detection based on Multi-Temporal Satellite Images as a fast tracking approach--a case study of Ahwaz County, southwestern Iran.

Rapid land-use/land-cover changes in suburbs of metropolitan cities of Iran have recently caused serious environmental damages. Detection of these changes can be a very important step in urban planning and optimal use of natural resources. Accordingly, the present study was carried out to track land-use/land-cover (LULC) changes of Ahwaz County in southwestern Iran using remote sensing techniques over a period of 26 years, from 1987 to 2013. For this, ISODATA algorithm and Maximum Likelihood were initially used for unsupervised and supervised classifications of the satellite images. The accuracy of the LULC maps was checked by the Kappa Coefficient and the Overall Accuracy methods. As the final step, the LULC changes were detected using the cross-tabulation technique. The obtained results indicated that urban and agricultural areas have been increased about 57.5 and 84.5 %, respectively, from 1987 to 2013. Further, the area of poorly vegetated regions, in the same period, has been decreased to approximately 36 %. The largest land conversion area belongs to the poorly vegetated regions, which have been declined to about 10,371 and 1,334 ha during 1987-2007 and 2007-2013, respectively. Approximately 1,670 and 382 ha of the agricultural lands have also been changed to built-up areas by about 1,670 and 382 ha during the same periods. As a result, it was found that the northwest, southwest, and south of the county were highly subjected to urban development. This would be of great importance for urban planning decision-making faced by the planners of the city in the present and future.