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Introduction: Flood is caused by a sudden increase in the water level of a river due to heavy rainfall, in which excess water flows out of the basin and the riverbed and enters adjacent areas, causing extensive damage to the region's ecosystems and infrastructure. In order to determine and implement a strategic plan in the direction of crisis management, understanding the mechanism and development of the flood phenomenon and the amount of subsequent runoff is a priority. In recent years, flood events have occurred in the catchment area of ​​the dry river of Shiraz; The most terrible flood event occurred on the 25th and 26th of March 2019 in the basin and caused severe economic, social and human damage to the people of the city. By recognizing the mechanisms and developing atmospheric patterns of flood generation, the first step can be taken in flood control; Also, by predicting the amount of runoff produced, how to deal with it can be identified. For this reason, in order to manage the environment of the dry river basin of Shiraz, the present study tries to approach flood management based on recognizing flood generating patterns and simulating the amount of runoff produced.Material and Methods: In order to study rainfall in the dry river of Shiraz, the daily rainfall was obtained from 12 rain gauge stations in the area in the period 2001 to 2019. The rainfalls were then sorted on an annual and monthly scale, and the rainfall was zoned using the Kriging algorithm in ArcGIS. Also, rainfalls of more than 40 mm were extracted from 12 rain gauge stations and were considered as flood events. For each rainfall station, the number of flood events was determined and flood events were zoned by the kriging algorithm in ArcGIS. Next, three events of flood on 16 February 2017, 26 March 2019 and 23 January 2020, when the average rainfall in the Shiraz watershed was 100 mm every three days, were selected and interacted with a synoptic approach. Atmospheric conditions and flood patterns were identified. Next, using the HEC-HMS hydrological model, the amount of rainfall to runoff conversion in each of the sub-basins in the Shiraz watershed was simulated.Results and Discussion: The results showed that the maximum precipitation occurs in February and the minimum in July. The maximum annual rainfall is 627 mm in the mountainous areas of Qalat and Golestan and the minimum is 245 mm in Maharloo Lake. Synoptic results showed that the low altitude and cold-water trough at the level of 500 hPa in the eastern Mediterranean along with the dynamic low-pressure system in Iran caused surface air instability in Fars Province and by injecting moisture from the Persian Gulf to the massif. The weather has caused floods in Shiraz. The results of rainfall-runoff modeling showed that the highest runoff in Sadra and Qalat sub-basins were 5773 and 5076 thousand m3, respectively, and the maximum peak discharge in Sadra and Qalat was 666 and 389 m3/sec, respectively. It happened at 17:00. The highest volume of rainfall penetration in Qalat and Chenar Rahdar was 5423 and 2546 thousand m3, respectively, and the lowest level of penetration in the Quran Gate was 247 thousand m3, the main reason being the high density of residential-commercial use in this sub-basin.Conclusion: Therefore, it is suggested that in order to manage the flood crisis in Shiraz, controlling runoff produced in northern sub-basins such as Qalat, Golestan and Sadra and increasing the level of permeability can be effective in controlling floods.

Introduction: Emissions of particulate matter from car exhaust, tire wear, engine lubricants and street fence wear contain heavy metals, which are dangerous for the urban ecosystem. However, most of the particles are deposited on the canopy and soil of plants, which are absorbed by them. Pollution-resistant species with a high capacity to absorb suspended particles can be used to remove suspended particles from the urban environment. For this reason, in this study, concentrations of heavy metals in the leaves and soil of tree species in Bandar Abbas were measured and evaluated. Also, species with the highest potential for adsorption of heavy metals were identified. Material and methods: First, three tree species including Azadirachta indica, Conocarpus and Prosopis juliflora were selected as the dominant plants in Bandar Abbas and in 30 points of the fields with 5 points of the non-polluted environment outside the city (control), and sampled from the surface soil and leaves of these species. After preparing and digesting the samples, the concentration of heavy metals (cadmium, manganese, zinc and lead) emitted from urban traffic was measured using atomic absorption spectrometry in soil and tree leaf samples. Next, the difference in heavy metal concentrations between tree species was analyzed by ANOVA and between soil and leaves and between urban and control environments was analyzed by t-student test. Then, to determine the tree species with high accumulation of heavy metals, two indices of heavy metal accumulation (BCF) and bioaccumulation index (MAI) were used.Results and discussion: The results showed that the pattern of heavy metals in soil and leaves of tree species was Mn> Zn> Pb> Cd. The maximum concentrations of heavy metals in the soil and leaves were detected in Conocarpus, Azadirachta indica and Prosopis juliflora, respectively. Spatially, the highest concentration of heavy metals in the sampling points was related to Imam Hossein Street. However, the minimum concentration of heavy metals was observed in the Persian Gulf Street. The concentrations of Mn and Pb in the soil of plant species were different and their differences were significant at 95% level, but the concentration of Cd and Zn was not different in the soil of tree species. Pb, Mn and Zn concentrations were not different in the shoots of the studied tree species and their differences were not significant at the 95% level. However, the concentration of Cd in the shoots of the species was different and the difference between the species was significant at the 95% level. There was a significant difference in the concentration of heavy metals between soil and leaves at 95 and 99%. Also, the concentration of heavy metals in soil and leaves between urban and control fields was significantly different at 95%. The pattern of the BCF index in all species was Zn> Pb> Mn> Cd and heavy metals had moderate accumulation for tree species. Bioaccumulation index (MAI) showed that Conocarpus had the highest adsorption potential of heavy metals and the lowest adsorption potential was observed in Prosopis juliflora.Conclusion: Conocarpus, by absorbing pollutants from urban traffic, purifies the polluted air of Bandar Abbas; However, Prosopis juliflora, which is one of the migratory trees in the city of Bandar Abbas, with strong and deep roots, dries up the surface and groundwater of the region. On the other hand, its potential to absorb urban air pollutants is weak. Therefore, despite their resistance to drought and heat, Conocarpus trees have a high potential to purify urban pollution and can replace Prosopis juliflora.

Extended abstractIntroduction:The idea of ​​resilience of different social, economic, physical, and managerial orientations has entered urban and regional studies on a large scale. This resilient system can absorb temporary or permanent crises and adapt to rapidly changing conditions without losing its function. Among these, resilience against natural disasters can be explained by how social, economic, institutional, political, and executive capacities of societies affect the increase of resilience and understanding of its dimensions in the society. Environmental crises, such as earthquakes, floods, fires, and climate pollution, have caused environmental vulnerability in cities and consequently created threats to their securities, especially in District 20 of Tehran City. By recognizing the dimensions of vulnerability in District 20 of this city against environmental crises, management strategies can be developed to reduce vulnerability and risks and enhance resilience. For this reason, the main purpose of this study was to evaluate resilience of the neighborhoods in District 20 of Tehran City against environmental crises. To achieve this goal, the Fuzzy Multi-Criteria Decision Model (FMCDM) and K-mean method of classification were used. Methodology:To identify and assess the resilience of District 20 of Tehran against environmental crises, a database was created based on the crises and its spatial information was prepared in 4 criteria and 26 sub-criteria. After creating the spatial database of the mentioned district and compiling the criteria and sub-criteria, a layer of information was prepared in ArcGIS software and a distance map was drawn for each sub-criterion through Euclidean distance mapping in order to measure and manage the resilience. Then, fuzzy operators were applied to draw each fuzzy map (subscale) with a value between 0 and 1. Analytic Network Process (ANP) method was utilized to weight and evaluate the research criteria and sub-criteria. Next, the map of each criterion and sub-criterion was drawn by combining the Euclidean distance and fuzzy operators multiplied by their fuzzy weights obtained from the ANP model in ArcGIS software. Thus, the final map was prepared for each criterion and sub-criterion, which showed their values of resilience to the environmental crises. Then, fuzzy superimposing operators were applied to superimpose the fuzzy weighting maps and a superimposed map of 26 sub-criteria (4 criteria) was obtained for each fuzzy operator. To identify the best fuzzy operator by superimposing the research sub-criteria, analysis of spatial relationships between the independent variables and the dependent variable was done through the Ordinary Least Squares (OLS) regression. Finally, the classical K-mean clustering method was employed to classify the neighborhoods from the perspective of resilience to environmental crises. Discussion:The results showed that the weights and values of the socio-economic criteria, road infrastructure, land use and accessibility in resilience measures were 0.49, 0.23, 0.16, and 0.11, respectively. In the socio-economic, road infrastructure, land use, and accessibility criteria, the sub-criteria of house strength, pedestrian bridge, access to social places, and access to medical centers with the weights of 0.33, 0.43, 0.32, and 0.29 had the highest values in resilience. Among the fuzzy superposition operators, the algebraic addition operator (SUM) had the highest correlation with the research criteria in identifying the resilience of the neighborhoods. The northeast and southeast neighborhoods, as well as the central neighborhoods of District 20 of Tehran, were the most resilient neighborhoods to environmental crises. In the final step of the current research, the classical K-mean method was used to cluster the existing neighborhoods in District 20 of Tehran City based on their resilience to environmental crises. The results revealed that the neighborhoods were divided into 3 clusters. In the first cluster showing a lot of patience, the neighborhoods of Javanmard Qassab, Mansouria and Mangal, Hamzehabad, Sartakht, Ibn Babavieh and Zahirabad, Taghiabad, and Abbasabad were located. In the second cluster indicating moderate tolerance, Dolatabad and Shahadat, Sadeghieh, Shahid Ghayuri, Deilman, Aqdasiyeh, Estakhr, and Alain neighborhoods were situated. Finally, the neighborhoods of Sizdeh Aban, Shahid Beheshti, Firoozabadi, Valiabad, and Hashemabad were located in the third cluster with poor productivity. Conclusion:Environmental crises, such as earthquake, flood, drought, air and water pollution, and fire, have the potential to become harmful in areas where there are no crisis management and risk mitigation. In the 21st century, the world has been hit by such environmental crises as Asian tsunamis, Hurricanes Katrina and Rita, successive earthquakes, flash floods, desert dust storms, and widespread fires. Although predictive tools are able to predict some disasters, future crises cannot be forecast based on empirical evidence. Therefore, increasing the ability of a system called resilience is very important for responding to such crises; yet, its resilience must first be measured. In the present study, the resilience of District 20 of Tehran City to environmental crises was evaluated based on socio-economic, road infrastructure, land use, and accessibility criteria. The results of this modeling led to the extraction of 3 clusters for the resilience of the neighborhoods of District 20 of Tehran against environmental crises. The neighborhoods in the west region had the highest resilience compared to the urban areas. Keywords: resilience, home strength, Analytic Network Process (ANP), fuzzy operator, regression References- Asadzadeh, A., Kötter, T., & Zebardast, E. (2015). An augmented approach for measurement of disaster resilience using connective factor analysis and analytic network process (F’ANP) model. International Journal of Disaster Risk Reduction, 14, 504-518.- Bacud, S. T. (2018). Integration of Indigenous and Scientific Knowledge in Disaster Risk Reduction: Resilience Building of a Marginalized Sampaguita Growing Community in the Philippines. Procedia engineering, 212, 511-518.‏- Borsekova, K., Nijkamp, P., & Guevara, P. (2018). Urban resilience patterns after an external shock: An exploratory study. 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Strengthening institutional and financial mechanisms for building urban resilience in India. International Journal of Disaster Risk Reduction, 101549.- Harpin, S. B. (2019). Adverse childhood experiences and resilience: implications for marginalized and vulnerable young people. Journal of Adolescent Health, 64(1), 3-4.‏- Kabir, M. H., Sato, M., Habbiba, U., & Yousuf, T. B. (2018). Assessment of Urban Disaster Resilience in Dhaka North City Corporation (DNCC), Bangladesh. Procedia engineering, 212, 1107-1114.- Landry, F., Dupras, J., & Messier, C. (2020). Convergence of urban forest and socio-economic indicators of resilience: A study of environmental inequality in four major cities in eastern Canada. Landscape and Urban Planning, 202, 103856.‏- Moghadas, M., Asadzadeh, A., Vafeidis, A., Fekete, A., & Kötter, T. (2019). A multi-criteria approach for assessing urban flood resilience in Tehran, Iran. International journal of disaster risk reduction, 35, 101069.- Mullick, M. R. A., Tanim, A. H., & Islam, S. S. (2019). Coastal vulnerability analysis of Bangladesh coast using fuzzy logic based geospatial techniques. Ocean & Coastal Management, 174, 154-169.‏- Ran, J., MacGillivray, B. H., Gong, Y., & Hales, T. C. (2019). The application of frameworks for measuring social vulnerability and resilience to geophysical hazards within developing countries: A systematic review and narrative synthesis. Science of the total environment, 134486.- Suárez, M., Gómez-Baggethun, E., Benayas, J., & Tilbury, D. (2016). Towards an urban resilience Index: a case study in 50 Spanish cities. Sustainability, 8(8), 774.- Wills, G., & Hofmeyr, H. (2019). Academic resilience in challenging contexts: Evidence from township and rural primary schools in South Africa. International Journal of Educational Research, 98, 192-205.‏- Zhang, W., Su, S., Wang, B., Hong, Q., & Sun, L. (2020). Local k-NNs pattern in Omni-Direction graph convolution neural network for 3D point clouds. Neurocomputing, 413, 487-498.‏- Zhang, X., Song, J., Peng, J., & Wu, J. (2019). Landslides-oriented urban disaster resilience assessment—a case study in ShenZhen, China. Science of the Total Environment, 661, 95-106.- Fig 1. Geographical location of District 20 Tehran- Table 1- Fuzzy membership of sub-criteria in resilience of District 20 of Tehran against environmental hazards- Fig 2. Diagram of the steps of the work method in the present study- Table 1- The weight of research criteria in resilience of District 20 of Tehran against environmental hazards- Tab 3- Weight of criteria socio-economic in the resilience of Tehran's 20th districtFigure 3- Zoning of population and young population sub-criteria in the resilience of District 20 of Tehran- Figure 4- Zoning of Economic participation and employment rates sub-criteria in the resilience of District 20 of Tehran- Figure 5- Zoning of Home strength and literacy rates sub-criteria in the resilience of District 20 of Tehran- Tab 4- Weight of criteria and sub-criteria of land cover in the resilience of Tehran's 20th district- Figure 6- Zoning of Access to parks and social sites sub-criteria in the resilience of District 20 of Tehran- Figure 7- Zoning of Distance from the flood and access to water sources sub-criteria in the resilience of District 20 of Tehran- Figure 8- Zoning of Distance from agricultural lands and urban green space sub-criteria in the resilience of District 20 of Tehran- Figure 9- Zoning of Distance from the green belt and outdoor rates sub-criteria in the resilience of District 20 of Tehran- Tab 5- Standard weight and sub-criteria of accesses in Tehran 20 district resilience- Figure 10- Zoning of Access to fuel station and security police sub-criteria in the resilience of District 20 of Tehran- Figure 11- Zoning of Access to educational and administrative centers sub-criteria in the resilience of District 20 of Tehran- Figure 12- Zoning of Access to Commercial and service centers sub-criteria in the resilience of District 20 of Tehran- Figure 13- Zoning of Access to Medical centers and distance from the factory sub-criteria in the resilience of District 20 of Tehran- Tab 6- Standard weight and sub-criteria of road infrastructure in Tehran 20 district- Figure 14- Zoning of Access to bus and freeway stations sub-criteria in the resilience of District 20 of Tehran- Figure 15- Zoning of Access to the pedestrian bridge and railway station sub-criteria in the resilience of District 20 of Tehran- Figure 16- Zoning of Criteria for access to urban services and socio-economic criteria in the resilience of District 20 of Tehran- Figure 17- Zoning of Land use criteria and access to road infrastructure criteria in the resilience of District 20 of Tehran- Tab 7. Correlation coefficient between fuzzy overlay operators with research criteria- Fig 18. Overlapping of research criteria with SUM operator and resilience modeling of Tehran Region 20

Water resources have always been a limiting factor in environmental planning. Understanding the status of this critical resource can have a significant impact on proper land use planning. This study aimed to estimate the water supply and demand as an ecosystem service and to identify areas with water stress in the Sirvan transboundary basin. This goal could be achieved using the concept of ecosystem services, which is new thinking in the earth sciences, and using geographical, climatic data, and satellite images. After relative corrections, Landsat satellite images for 2019 were prepared, and the LULC map was produced. Then precipitation, evapotranspiration, and root depth layers were created. The latest inputs, including the Evapotranspiration coefficients of the land cover and related tables, were prepared and modeled in the InVEST 3.8.9 software environment. The results showed that the amount of water yield in this watershed is 5,381 million m3/yr, where the sub-basin 5, 11, and 1 have the highest water yield with 1426, 906, and 621 million m3/yr. and sub-basin 2 with 100 million m3/yr have the lowest water yield. The sub-basin 5 with 110 million m3/yr has the highest consumption, and the sub-basin 2 with 7 Mm3/yr has the lowest consumption. Sub-basin 4, where the city of Sanandaj is located, has the highest water stress.

Introduction: Place attachment is an emotional connection to a specific location or perspective where usually includes physical and social elements (Devine-Wright, 2013). From Hummon's point of view, the community attachment is defined as an emotional investment in one place (Buta et al., 2014). Today, environmental concepts deeply have social implications, and a large part of the people’s environmental beliefs and behaviors changes under the influence of cultural and social systems (Kalantari et al., 2016). The purpose of this study was to investigate the relationship between community attachment and environmental behaviors considering the role of mediating attachment of the place among residents of Tajrish neighbourhood located in the district One of Tehran. Material and methods: The present research was a descriptive, analytical-correlational survey. A sample of 284 was selected from inhabitants of Tajrish neighbourhood based on random sampling method. The participants were asked to fill out the research questionnaire whose reliability and validity were confirmed. The obtained data were statistically analyzed using the correlation analysis and structural equation modeling in SPSS-21 and AMOS-21. Results and discussion: The results of standard coefficients obtained from structural equation modeling showed that community attachment can directly and positively predict place attachment (p < 0.01, β=0.65) and place attachment also can directly and positively predict pro-environmental civic participation (p < 0.01, β=0.48). Finally, the study findings revealed that community attachment affects pro-environmental civic participation both directly (p < 0.01, β=0.41) and indirectly, through the mediating role of place attachment, (p < 0.01, β=0.31). In order to assess the fitness of the model, in this research, fit indices from eight main indicators were utilized. In this study, the relative Chi-square, (CMIN/Df) was estimated at 1.375, indicating the acceptable condition of the model. The Root Mean Square Error of Approximation (RMSEA) of 0.036 also demonstrated the validity of the model developed in this study. The GFI and AGFI indices, absolute fit indices, should be in the range of zero to one, and values greater than 0.9 for the GFI index and 0.85 for the AGFI index indicate the optimal fit of the model. In the model measured in the present study, the value of 0.990 for the GFI index and 0.965 for the AGFI index showed a highly favorable fit of the model. For the Increasing Fitting Index (IFI), the Tucker-Lewis Index (TLI), and the Comparative Fit Index (CFI), values greater than 0.9 represent the ideal fit of the model. IFI, TLI, and CFI were, respectively, 0.996, 0.990, and 0.996 in this study. The (Bentler-Bonett) Normed Fit Index (NFI) is based on the correlation between the present variables in the model in a way that the coefficients of correlation between them would lead to high values of the adaptive fit index. The acceptable value of this index should be greater than 0.9 which was obtained in the present study and is an indication of the confirmation of the research model. Conclusions: The present study demonstrated that community attachment has a direct and significant effect on the place attachment and the environmental behavior. On the other hand, community attachment indirectly affects the creation of environmental behaviors in citizens through involving the sense of attachment to the .place; hence, place attachment is considered as an intermediary factor in creating environmentalist behaviors

In this paper, among multi-criteria models for complex decision-making and multiple-attribute models for assigning the most preferable choice, the technique for order preference by similarity ideal solution (TOPSIS) is implied. The main objective of this research is to identify potential natural hazards in Bandar Abbas city, Iran, using TOPSIS model, which is based on an analytical hierarchy process structure. A set of 12 relevant geomorphologic parameters, including earthquake frequency, distance from the earthquake epicentre, number of faults, flood, talus creep, landslide, land subsidence, tide, hurricane and tidal wave, dust storms with external source, wind erosion and sea level fluctuations are considered to quantify inputs of the model. The outputs of this study indicate that one region, among three assessed regions, has the maximum potential occurrence of natural hazards, while it has been urbanized at a greater rate compared to other regions. Furthermore, based on Delphi method, the earthquake frequency and the landslide are the most and the least dangerous phenomena, respectively.