Journal of Environmental Studies
Year:2018 | Volume:44 | Issue:3 |Papers Count:16

Introduction Rapid urbanization and continuous demand for land for infrastructural development in urban areas have placed great stress on the local environment. Consequently, varieties of environmental problems have emerged, which among them toxic metal pollution is a major issue, especially in urban soil and street's dust. Street's dust receives varying inputs of heavy metals, mineral constituents, organic matter (humus), living organism, air, and water; the anthropogenic materials are vehicle exhaust particles, lubricating oil residues, tire wear particles etc… ; and the natural biogenic materials are tree leaves and other plant matters. To certain degree, street's dust is a more pertinent indicator to urban environ-mental quality than single compartmental monitoring of air, water and soil, because it reflects pollutants from the different sources. Heavy metals may come from many different sources in urbanized areas, including vehicle emissions, industrial discharges and other activities. It is important to identify the origin and distribution of heavy metals in street dust, and estimate population from heavy metal exposure via street's dust in smelting district. There were many recent investigations on Heavy metals from many different sources in urbanized areas, including vehicle emissions, industrial discharges and other activities. Ahvaz, a metropolis city located in Southwest Iran, with a population of over 1. 2 million, has experienced a rapid urbanization and industrialization in the last few decades. Industrial growth along with expansion of population and increase in number of vehicles in Ahvaz caused increase of heavy metals accumulation in airborne particles and urban soils. Ahvaz city is considered as one of the heavily polluted cities in the world. However, the spatial distribution patterns and contamination levels of heavy metals in road dust in the area is still not clear. The aim of this paper is to: 1) identifying the patterns of spatial distribution of Cd, Cr, Cu, Pb, As and Zn; 2) assess contamination levels of these metals by integrated pollution index (IPI) and Nemerow integrated pollution index (NIPI). Multivariate statistical methods and spatial analyses were used to achieve these goals. Geographic Information System (GIS) mapping was applied to evaluate the results by visualizing the spatial patterns. Material and Methods Soil sampling and analytical methods A total of 115 street's dust samples were collected form urban area in July 2014 when it was dry season. The sampling compagain was chosen in driest month of the year to avoid rain-washing out the heavy metals. The weather condition was stable during the sampling period and no rain had occurred during one month prior to sample collection. The street dust samples were mainly collected by sweeping an area of about 1×1 m2 from road pavement using a clean plastic dustpan and brushes for each sampling site. The sampling points and background of samples locations are marked in Fig. 1. Geographical coordinates of samples collection locations were recorded at each sampling point with a GPS device. The streets' dust samples were analyzed for toxic metals by Atomic Absorption Spectrophotometer (AAS). Fig. 1. location of samples sites in Ahvaz city Metal pollution index PI was calculated for all the six elements under study and the minimum, maximum, and mean values of PI are given in Table 4. The pollution index IPI is defined as the mean value of the pollution index PI of an element. It is classified as. Non-pollution (PI≤ 1), low level pollution (13). The NIPI of the six metals for each sampling site was defined as follows: The NIPI was classified as: non-pollution (NIPI<0. 7); non pollution (NIPI≤ 0. 7); warning line of pollution (0. 73). Results and Discussion The mean concentration of Pb, Cu, Zn, Cr, As and Cd concentrations in the street dust samples were 179. 75, 179. 60, 150. 15, 101, 67. 27 and 5. 60 mg/kg respectively, and they were 5. 4, 12. 7, 5. 3, 1. 1, 21. 6 and 62. 2 times as high as the background values in street dust samples. Table )1( compares the concentration of heavy metals measured in road dusts of metropolis city of Ahvaz with other metropolitan cities in the world. Concentrations of heavy metals in street dust particles vary considerably among cities depending on the density of industrial activities in the area and technologies employed. As summarized in Table 1, the mean concentration of Cu in Ahvaz street dust (present work) is lower than mean concentration of Guangzhou, Baoji, Ottawa, Calcutta, Luanda, Oslo and Nanjing cities and higher than Tehran city. Pb concentration in Ahvaz street dust is higher than Oslo and Nanjing and lower than Guangzhou, Baoji, Ottawa, Calcutta, and Tehran. The mean concentration of Zn in Ahvaz is higher than Tehran, Ottawa, Calcutta, Baoji and lower than Oslo, Guangzhou and Nanjing. The mean concentration of Cr is lower than Baoji and Nanjing higher than Tehran, Guangzhou, Ottawa, Calcutta, and Oslo. The mean concentration of As in street dust of Ahvaz is higher than other metropolitan cities. The mean concentration of Cd in street dust of Ahvaz is higher than other metropolitan cities except Tehran. The range of PI values for all the elements under consideration were determined, and their behavior was found to be as follows: 0. 31 to 17. 32 for Pb, 1. 3 to 15. 29 for Zn, 0. 28 to 7. 69 for Cu, 0. 2 to 22. 5 for As, 0. 7 to 2. 06 for Cr and 0. 05 to 9. 45 for Cd. Spatial distribution map of PI indicated that there are several clear trends in the distribution of the PI values in the studied area. In the old urban area, most of the street dust samples collected were from the areas with high levels of pollution, which can significantly attributed to traffic emission and long-term accumulation of heavy metals. On the contrary, most of the streets dust samples collected with low levels of pollution were from the new urban areas and city suburban areas. Moreover, the areas closed to manufacturing companies were with high levels of pollution. These trends can be attributed to urbanization, distribution of industrial and commercial areas. The NIPI of all of the Samples collected varied between 0. 71 to 59. 01 with an average of 9. 66. Assessment of geochemical data indicates that there were 24 soil samples collected (20. 8% of samples) with an NIPI<0. 7, while 26 soil samples collected (22. 6%) of all soil samples had a NIPI between 0. 7 and 1. About 16. 5% of samples had a NIPI between 1 and 2 and about 16. 5 of all samples had a NIPI between 2 and 3. Finally 28 soil samples (24. 3%) had NIPI>3 with high level of pollution. Fig. 2 shows the spatial distribution of NIPIs in Ahvaz city. Overall, these findings suggest that the street dust of Ahvaz city has been polluted by anthropogenic emission. Soil samples with high and moderate pollutions were located in area with high dense pollution, high traffic volume, manufacturing industries such as smelting, chemical industry, industrial towns, and oil drilling activities. Fig. 2. Spatial distribution of NIPI in the study area Conclusion The present study examines the content of metals in the urban soils in Ahvaz city. The mean concentration of heavy metals were significantly higher than the other cities. The results of spatial distribution reflect the influence of urbanization and industrialization on the areas considered. The PI values indicate that a significant degree of metal pollution exist in some street dusts within the urban area with high population density, high traffic volume and also areas with high industrial activities such as oil drilling. Then NIPI values also indicate that Ahvaz street dusts have high degree of pollution. These finding indicate that more attention should be paid to metal pollution of the urban street dust and urban topsoil’ s in Ahvaz.

Hyrcania is a highly productive forest along the southern coast of the Caspian Sea (northern Iran). The forests are mostly uneven-aged, oriental beech (Fagus orientalis)-dominated hardwood mixtures. These forests often include the presence of Carpinus betulus, Alnus subcurdate, Acer velutinum, and several other tree species and shrubs. These forests are mostly broadleaved, but Taxus bacata and Cupressus spp. do appear on some specialized sites. These forests are home to about 80 different tree species and 50 shrub species. Hyrcanian forests have multiple ecological functions, such as provide for (i) the production of wood fiber and lumber, (ii) the protection of watersheds, including their water and soils, and (iii) the conservation of biodiversity. The topic of biodiversity has become a primary focal point in deliberations of sustainability worldwide, as a result of the rampant decline and degradation of natural environments initiated by urbanization, unrestrained resource extraction, and wanton disregard for nature. Furthermore, global climate change broadens our need to incorporate significant amounts of knowledge on biodiversity and functionality in developing contemporary forest management plans, which is not always easy to achieve. In this chapter, we develop a computational framework that relates measures of tree diversity (based on actual field surveys) to modelled physical (abiotic) variables. Here, we calculate tree diversity using the Shannon-Weiner index; an index commonly used to characterize species diversity in plant communities by accounting for both species abundance and evenness. The plot network in the Gorazbon section is designed on a rectangular grid (150 m × 200 m) and consists of 258 fixed-area circular plots of 0. 1 ha each. Tree species richness was determined at each plot from basic tree species identification and tallying. Prominent tree species in plots include Fagus orientalis, Carpinus betulus, Acer velutinum, Acer campestre, Alnus subcordata, Quercus castaneifolia, Parrotia persica, Tillia begonifolia, and Ulmus glabra. Total number of plots available for the current analysis was 202; many of the unused plots had missing site information, including GPS (global positioning system) coordinates, preventing their geo-referencing. Development of numerical surfaces Fundamental to the spatial calculation of abiotic surfaces or their surrogates at mid-resolution is the DTM of the Gorazbon section. DTM-height data is derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30-m resolution Global Digital Elevation Model v. 2 (GDEM; http: //asterweb. jpl. nasa. gov/gdem. asp, last accessed on June 2014). Descriptions of the various abiotic and associated surfaces, including their proxies and their derivation, can be found in Table 1. Values of abiotic and proxy variables at forest-plot locations were summarised separately as averages of values falling within each individual 0. 1-ha plot (Fig. 1b). Relating plot-estimates of environmental variables to Symbolic regression, or symbolic function identification, is used to determine from the list of independent variables in Table 1, which site variables are particularly crucial in explaining spatial variability in. Symbolic regression is a procedure founded on evolutionary computation in searching for algebraic equations, while reducing the difference between target values and values calculated with the equations generated with the procedure. Different from conventional regression techniques that determine parameters of known equations, no specific mathematical expression is needed as a starting point to the approach. Rather, primary expressions are formed by randomly combining primitive base functions of input variables (linear or otherwise) with algebraic operators. Equations retained by the procedure are those that replicate the target output data better than others; undesirable solutions are rejected. The procedure stops whenever the desired accuracy in data replication has been reached. In order to balance the relative contribution of each plot-estimate of in the development of a generalised expression of,-values were weighted as a function of the inverse of their occurrence (i. e., number of times it occurs) in the dataset. This was done to ensure that values that are not commonly observed (e. g., = 7 species per 0. 1 ha plot) contribute as much to the explanation of as values that are more frequently observed (e. g., = 2-4 species per 0. 1 ha plot). This research examines the possible ecological controls on tree diversity in an unmanaged region of the Hyrcanian forests (i. e., the Kheyrud experimental forest). Key to the study are computer-generated abiotic surfaces and associated plot estimates of (i) growing-season-cumulated cloud-free solar radiation, (ii) seasonal air temperature, (iii) topographic wetness index (TWI) in representing soil water distribution, and (iv) wind velocity generated from simulation of fluid-flow dynamics in complex terrain (Fig. 1). Fig. 1. Model-generated abiotic surfaces of (a) growing-season cumulated cloud-free solar radiation (MJ m-2), (b) mean seasonal air temperature (oC), (c) topographic wetness index (TWI; unitless), and (d) wind velocity within the study area (m s-1). Plot-level estimates (Fig. 2) are used in the generation of a three-variable equation (eq. 1) of tree diversity by means of symbolic regression (Schmidt and Lipson, 2009): (1) where W is the wind velocity (m s-1), S annually-cumulated cloud-free solar radiation (MJ m-2), and T mean annual air temperature near the ground surface (oC). In the regression process, diversity values are weighted according to TWI. Fig. 2. Spatial variation in plot-level estimates of tree species diversity; size of the circles coincides with level of species diversity based on a calculation of the Shannon-Weiner index; the large circles coincide with high index values (high species diversity with an upper value of 1. 6), whereas small circles coincide to low values (low species diversity with a lower value of 0. 1). Localised topographic wetness index (TWI) is shown to be unimportant with explaining spatial patterns in tree diversity. The approach shows that plot-level estimates of W, S, and T in combination can explain roughly 70% of the spatial variation in tree diversity in the validation data (Fig. 3). Fig. 3. Plot-level estimates of the Shannon-Weiner index (blue circles) compared to modelled values (red line). Concluding Remarks The chapter develops the methodology, the results, and discussion regarding the research. Reference Schmidt, M., and Lipson, H. 2009. Distilling free-form natural laws from experimental data. Science, 324(5923): 81-85.

In mangroves assessing the threat of multiple environmental hazards is important to inform effective management decisions to protect these habitats and to reduce or prevent damage as a result of environmental impacts. In this study, the threat of multiple environmental hazards including drought, reducing surface runoff of upstream catchments, strong winds, extreme temperatures, fishing activities and loss at seaward edges of mangroves in the northern coast of the Persian Gulf and Oman Sea in Iran are assessed and mapped. In this study a Mann-Kendall (MK) test was employed using MAKESENSE 1. 0 to detect trends in standardized precipitation index (SPI) values at a confidence level of 95% and 99%. Based on changes in Z values and implementation of the natural break command in ArcGIS, a map of changes as a result of drought was categorized into four classes of low (code 1), moderate (code 2), high (code 3) and very high (code 4), based on the values of Z | ≥ 1. 96 (increasing trend of drought severity). This was used to assess the threat to mangroves. In order to map changes in surface runoff from upstream catchments during the 30-year period (1986-2016), the time series of changes in runoff coefficient values were evaluated. A 30-year time series of land use / land cover changes (LULC) in upstream catchments of mangroves was also prepared using data from 210 Landsat images. Using the LULC map, the Runoff coefficient of the catchment is calculated from the runoff coefficient for permeable areas (Cper). Cper was calculated from a weighted sum of land use, soil type and slope factors, respectively, the first, second, and third term in the right-hand side of Eq. 1: (1) C_per=w1(0. 02/n)+w2(θ _w/(1-θ _w ))+w3(s/(10+s)) In equation (1), n is the Manning’ s roughness coefficient dependent on the LULC, θ _w is the volumetric soil water content at wilting point, and S is the land surface slope in percentage land surface slope in percentage. The value of (θ _w/(1-θ _w )) was calculated using the soil texture map of upstream catchments of mangroves obtained from Iranian Forests, Range and Watershed Management Organization (FRWMO). The values of the coefficients W1, W2 and W3 were considered as 0. 4, 0. 3 and 0. 3 respectively. Using a 30-year time series of runoff coefficients and annual precipitation values, a 30-year time series of surface runoff changes in catchments was prepared. The time series of the changes in surface runoff values was used to create a map of the reduction of catchments using four classes of low (code 1), moderate (code 2), high (code 3) and very high (Code 4) to assess the threat to mangroves. Classification was undertaken using the natural break command in ArcGIS. Changes in the intensity of fishing activities in mangrove habitats were mapped using the location of the fishing ports and the number of active launches and boats, determined by reviewing the satellite imagery of Google Earth Pro (© DigitalGlobe Inc., © GeoEye Inc. ) and visiting the coasts. The coastal waters area was divided into 4×4 km GIS grid cells (598 cells) and in each of the grid cells, the intensity of the fishing activity was calculated and finally a map of the intensity of the fishing activity in coastal waters was prepared. The map of the Fishing Index (FI) was derived. Using the Fishing Index (FI) map, fishing activity intensity was classified using the natural break command in ArcGIS as low (code 1), moderate (code 2), high (code 3) and very high (Code 4), this was used to assess the threat to the mangroves. In this study, the threat from wind speeds greater than 8 m/s was mapped, this cut off was used as this velocity is considered as potentially damaging to the structure and function of these mangroves. A 30-year time series (1986-2016) of daily wind speed data from synoptic stations adjacent to the mangroves was used. In this study, the Weibull function was used to calculate the probability of wind speeds greater than 8 m/s. Wind speeds greater than 8 m/s were extracted and their average was calculated and multiplied by the probability of occurrence for each of stations during the thirty year period. A risk map of winds speeds greater than 8 m /s was prepared and classified using the natural break command in ArcGIS, the four classes were low (code 1), moderate (code 2), high (code 3) and very high (Code 4), and these were used to assess the threat to mangroves. Based on previous studies and for the analysis of spatial variations in the occurrence of extreme temperatures, a temperature of 38° C was selected as the threshold temperature for mangrove damage. All daily temperatures equal to and greater than 38° C were extracted from the long-term dataset of daily temperatures for the 30-year period (1986-2016). By dividing the number of days with a temperature equal to and greater than 38° C by the total number of daily temperature records in the 30-year period, the probability of occurrence of temperatures above this threshold was calculated for each of the synoptic stations. At each synoptic station, the mean value of all temperatures equal to and greater than 38° C was calculated and multiplied by the probability of occurrence calculated for that station. Finally, using ArcGIS, a risk map of temperatures equal to and greater than 38 ° C was prepared within the coastal areas and classified using the natural breaks command as low (code 1), moderate (code 2), high (Code 3) and very high (code 4) to assess the threat to the mangroves. In this study, Landsat images of 1986, 2000, and 2016 were used to analyse the rates of changes in the seaward edges of mangroves over the 30-year period (1986-2016). To separate mangroves from surrounding water and coastal land areas and to identify the final borders of the study sites, an NDVI vegetation index was used. In this study, 2701 transects 30 m apart were mapped using the DSAS software to calculate the rate of changes in the seaward edges of mangroves. As with previous studies, the linear regression rate (LRR) method was used to measure the rates of changes in the seaward edges of mangroves. These were used to create a map of rates of regression of mangroves classified as: no regression (code 1), low rate of regression (code 2), moderate rate of regression (code 3) and high rate of regression (code 4) using the natural break command in ArcGIS, to assess the threat to the mangroves. At this stage, classified hazard maps were combined using ArcGIS and equation (4): (4) TI=√ ((a×b×c×d×e×f)/6) Where TI = Threat Index, a = drought, b = surface runoff, C = wind, d = air temperature, e = fishing activity and f = loss of seaward edges of mangroves. The TI was used to create a threat map for the mangroves at all four sites classified as: low, medium and high using the natural break command in ArcGIS. The results of this study showed that, considering the severity and probability of occurrence of the hazards, Khamir and Jask mangrove habitats are highly threatened by environmental hazards (Figure 1). Investigating the severity of occurrence of environmental hazards in mangrove habitats shows that Khamir and Jask habitats are considered to have a high to very high threat level from drought, reduced runoff from the catchments and extent of loss, significantly higher than the Tiab and Sirik sites. It is likely that the increase in the severity and risk from these hazards has had adverse effects on the structure and functions of these mangrove habitats as found in other studies conducted in other regions of the world that show that reducing rainfall and increasing the risk of drought reduces the extent of mangrove area and increases their vulnerability to other environmental hazards.

Although considering a varied mix of goals in environmental planning improves its results, it simultaneously increases the complexity of the process as well. This complexity forces researchers to use methods such as mathematical programming and optimization algorithms. In this context, linear programming as a sub-set of mathematical programming is one of the alternative methods for decision makers. As linear programming is not basically a spatial technique, its use in the context of spatial decision making problems such as land use planning is always considered in conjunction with GIS. This method has some limitations in terms of computational intensity and the time to achieve solution that is increased exponentially with increasing number of decision variables. This is a normal situation in land use planning. This study attempts to solve a land use planning problem with respect to optimization of four land uses: agriculture, forest, rangeland and development areas in Gorgan Township. In this regard, three objectives including minimizing allocating cost, land use conversion cost and maximizing compactness in the form of linear programming problem have been considered. Introduction: Inevitably, land use planning is the main clue in the process leading to sustainable development. Every unit of land cannot be allocated to more than one land use simultaneously. In this regard, land use planning defines the proportions and locations of special use for each spatial unit of land. With this definition place becomes an important issue when deciding about the most appropriate land use. It is the reason why we call land use planning a place-based decision making. Usually, in this process, the study area is modeled througha raster layer in which the cells act like land units waiting to be allocated their special uses. The important and crucial criterion in this process is suitability maps produced from overlaying several thematic maps. However, suitability maps are not sufficient for planning and optimization of land use for a region and without integration of other constraints, the result may become a fragmented land uses layer. In this study we attempt to solve a land use planning problem with respect to optimizing four categories including agriculture, forest, rangeland and development areas in GorganTownship. Here we incorporate three objectives including minimizing allocating cost, use conversion cost and maximizing compactness in the form of a linear programming problem. Methods: Linear programming is one of the well-known methods in decision making that in cases has been used integrated with GIS. Decision variables, constraints and objectives are three main and critical elements of linear programming. Decision variables are the questions of problems. In this domain, problem can be defined as minimizing or maximizing a problem based on suitability or unsuitability of the considered objectives. Land use planning or land use optimal allocation in the minimizing form and with respect to three objectives of this study including minimizing allocating cost, conversion cost and maximizing compactness can be considered as linear programming equations (1-8): X_ijkis binary variable that equals 1 when land use k is allocated to cell (i, j) and equals 0 otherwise. C_ijk refers to the cost of cell (i, j) for kth land use, S_ijkis the cost of converting the current land use into a new one (land use k), Y_ijkis an integer variable introduced to the model to define compactness without violating the special linearity condition in linear programming problems. It is clear that the problem is integer linear programming because decision variables (X_ijk)are binary. As linear programming is an exact method that enumerate all solutions to find optimal one, adding integer variables imposes a huge burden on computational processes and intensively increases the required processing time. This burden nearly makes it impossible to find optimal solutions. Therefore, we relaxed the problem from integrity and changed it to usual and classic linear programming. So, the values obtained for X_ijkwas ranged at [0 1]. The other difficulty was that one cell could not be allocated to more than one land use simultaneously. Therefore, we ranked the final responses that had been obtained from implementing linear model for every cell of the study area and then selected the maximum value in every cell and equaled other values with zero. After this selection, we had a solution map of the study area that showed land uses with the maximum value in every cell. We found that nearly none of the values were less than 0. 5. So, we completely allocated every cell to land use with the maximum value. Although this result could not guarantee optimality but was very close to solution of the exact method of linear programming. Results and Discussion: In order to have an equal base for comparison, we implemented the problem with the same target area in MOLA algorithm in IDRISI. We selected MOLA because it works on the base of ranking the value of cells related to distance to ideal point which is the highest value possible after standardization of the suitability values. It is necessary to say that compactness and conversion cost objectives have not been considered in MOLA. Table (1) shows the number of cells (area) of every land use before and after implementing the above mentioned model (target area) and Table (2) shows the number of cells for every land uses before and after application of MOLA. Conclusions As figure (1) shows, fragmentation of land use in the suggested model is less than that for MOLA. Also, it is possible to change objectives or some other criterion in the linear programming model but MOLA in IDRISI is a crisp module that cannot accept other objectives like compactness for improving the results. If we accept the fact that land use planning is a main clue through achieving sustainable development, its importance becomes ever more clear. What improves land use planning and makes it more practical and powerful is paying attention to many aspects, stakeholders and sources that are involved and affected by the process which makes even more complex. Use of optimization algorithms is the way to address this complexity. The powerful feature of linear programming is achieving the exact solution which guarantees optimality. This feature besides its simplicity is sufficient to make this algorithm attractive and worthy of more in-depth studies. In this regard, this study attempts to introduce a way for application of linear programming in land use planning and optimization.

Introduction: Atmospheric aerosols have different sources that we can refer to volcanic activities, dust, salt particles in the seas and oceans, or they due to human activities that we can refer to activities that such as industrial activities, transportation, fuel costs and … . aerosols have very important role in transitive radiation and chemical process that they are the earth’ s climate controller. Among the internationally-conducted works in this area can refer to the Olcese et al., 2015 which have been done based on the use of the artificial neural network model (MLP). They used previous values of the AOD at two stations as input of artificial neural network model to estimate the AOD under cloudy conditions and in situations where little data is available. This method was used to predict the values of AOD on nine stations with 440nm wavelengths on the east coast of the United States during the 1999 to 2012. The calculated R2=0. 85 between the observed and predicted AOD indicate a good performance of this model. To date, there is no research to estimate AOD by using different models such as Multiple linear Regression, Principal Component Regression, Artificial Neural Networks and Autoregressive integrated moving average model in Iran. Therefor in this research estimation AOD examined in two cases including estimate for areas with no Pyranometer stations and long-lasting estimation in stations with solar radiation detector for the future under. Material and methods: In this study related data to Pyranometer were collected for understudied are though the Meteorology office in center of Kurdistan province ranged 2005/01/01 until 2016/12/31. Thus, the total number of available data for the mentioned time period was 4382 data in the study area, and since there was no solar radiation for some days of the year, the total number of data used for Sanandaj city was reduced to 3956. Study area: Sanandaj is the capital of Kurdistan province. About geographic location this city is located in within limits 35 degree and 20 minutes north latitude and 47 degree east longitude from Greenwich Hour circle and in the 1373/4 meters height above sea level. Multiple linear Regression Model: In the Multiple linear Regression turn to check the relation between a dependent variable and several independent by earned relationship for them in the SPSS software, in the Multiple linear Regression the measure of AOD serve as dependent variable and meteorology numeral quantity such as temperature, relative humidity, wind speed and also altitude atmosphere were considered as independent variable. The general formula for the MLR model is as follows: Y=β _0+β _1 x_1+⋯ +β _n x_n+ε In this case, y is dependent variable. X1, . . ., Xn denote the independent variables, and also nβ 0, . . ., β report the fixed constants. Ԑ also indicates the remaining values. Principal Component Regression Model: Principal Component Regression Model is a combination of Principal Component Analysis (PCA) and Multiple Linear Regression (MLR). These calculations are as follows: Y=φ β _PCR+e Where φ is the matrix of base components, which is obtained as n * k, and β PCR represents the first of the components of the K score. The vector of e is a random error which defined as n٭ 1. Mark and scores for the components are based on the original version of the OLS method as follows: β _PCR=(φ ^' 〖 φ )〗 ^(-1) φ ^' y=(L^2 )^(-1) φ ^' y In this case, L2 is the amount of slice of the matrix, which is based on the Kth parameter, which also indicates the slip of the parameter k⅄ . Finally, the following equation was reached. β _PCR=∑ _(K=1)^K▒ (υ _k u_k^')/d_k y, K

Cracking of landfill’ s clay liners after implementation reduces life expectancy and quality of the liners by increasing hydraulic conductivity due to preferential flow. The objective of this study was to produce a liner with less cracking potential, during and after implementation and more absorption capacity of contaminants. Furthermore, it was aimed to come up with simpler and more inexpensive liner compared to geosynthetic liners. For this purpose, three different levels of polypropylene fibers including 0. 5, 0. 75 and 1. 0 of the weight of the soil were applied to a simple clay liner. To increase the contaminants absorption ability of liner, 0. 2 percent Di-calcium phosphate was added. The clay behavior, its cracking and permeability of simple clay liner, liner with fiber and liner with fiber and DCP were examined in laboratory scale. The obtained results indicated that 0. 75% of fiber can significantly reduce the surface and subsurface cracks. Although the use of 0. 75% fiber has led to increase permeability compares to the simple liner at laboratory scale, but it was remained in the acceptable range of clay liners. The examined physical parameters also showed that use of the additives do not have any adverse effect on liner. This implys that the quantity of permeability, shear strength and the elimination of cracking in the presence of 0. 75% fiber and 0. 2% DCP are optimal for this purpose. Materials & Methods The collected soil sample were transferred to the laboratory, dried and prepared for testing. Based on the unified system of soil classification, the texture of samples was clay loam (CL) and had a plastic and liquid limit of 25 and 10, respectively. The specific gravity of this soil was measured at 2. 65 g / cm3. The fiber used in this research was polypropylene (C3H6) with an approximate length of 2. 5 cm. Di-calcium phosphate (DCP) with the chemical formula CaHPO4, 2H2O, was used as a phosphate additive. Three different types of samples were prepared and tested in this study. To prepare a simple liner sample, 500 mm of water was added to 3000 g of soil was sample, so that the percentage of moisture in the samples was 16. 6%. Then the specimens were allowed to reach a uniform paste condition. This sample was transferred into the test cell and loaded for a period of time to prepare the permeability coefficient measurement. Fibrous samples were prepared similar to the simple one, except that before adding water to the soil, the required amount of fiber was randomly added to the soil and mixed well. Polypropylene fibers were used in three levels with 0. 5, 0. 75 and 1% by weight of soil. Third, in addition to the fibers, DCP was also applied. To prepare these samples, before adding water to the soil, di-calcium phosphate was added to the water and mixed with the mixer to form a gray-colored liquid. The resulting liquid was then added as sample water. The DCP value in all experiments was considered as 0. 2% by weight of the soil sample. The prepared samples were then placed in a humidification room for 10 days in order to obtain the chemical equilibrium, and the effect of the additive on the soil behavior was stable. In order to evaluate the performance of improved dense clay liner with polypropylene fibers and di-calcium phosphate, permeability tests, direct cutting and determination of cracking value were performed on seven different samples each with two replicates. Some permeability tests were carried out based on ASTM-D2434 (2006) standard in a cylindrical cell made of Plexiglass with a diameter of 150 mm and a height of 130 mm. Direct cutting test was performed according to ASTM D3080 / D3080M-11 (2011) instructions. Discussion of Results & Conclusions The permeability of simple clay liner measured in 24 hours, was a value of about (0. 8-1)×10-8 centimeters per second. The infiltration coefficient was increased by about four times in samples with 0. 5% fiber compared to clay without fibers, and was obtained a value between (3-3. 5)×10-8 cm/s. This process was also observed for the application of 0. 75 and 0. 1% fiber, and the permeability changes acquired in these two states between (3-4)×10-8 and (4-5)×10-8 cm / s. respectively. In other words, the hydraulic conductivity after application of these two quantities of fibers, on average, was about 3. 5 and 4. 5 times than permeability of simple clay, respectively. By adding 0. 5% fiber in the presence of DCP, the permeability value was obtained to be about (2-3)×10-8 cm/s. Similar to the non-DCP samples, adding DCP simultaneously with fibers in the clay linear increase the conductivity by about three times than the simple clay sample. The permeability of the clay sample with 0. 75% fiber plus DCP was 3 to 5 times larger than the simple clay sample. In samples with 0. 1% fiber plus DCP, the hydraulic conductivity of the clay liner increased about 4 to 6 times than the simple one. Therefore, the fiber increases permeability, but the addition of DCP does not have much effect on penetration changes. The results of the direct shear test showed that the maximum shear strength in the case where DCP was present was not significantly different from that in which the fiber was used alone. It seems that, by adding 0. 5% of the fiber to the soil, the φ of sample increased, but in the following, with increasing fiber content, ie, 0. 75 and 0. 1% of the fibers, φ decreased. The reason for this soil behavior is the high volume of fibers compared to its light weight. At each step, increasing fiber, adhesion has also increased. In fact, the increase in fiber has contributed to the increased adhesion of clay linings. The results indicated that the addition of fibers and DCP can increase the shear strength of the maximum soil. In addition, observing the effect of the fiber on the reduction of surface and inside cracking of clay liners is also a very important factor. Because these cracks have a significant adverse effect on the mechanical condition and the permeability of the lining. It also reduces the longevity and performance of the clay liners. The simple liner specimen, first had hairy cracks, but after passing the time and drying the surface, cracked deep and wide. The samples made in this study show a very small scale of clay liners, and in reality the dimensions of the liners are much larger. As a result, fractures and cracks will have a lot more size and depth. In samples with 0. 75% fibers, the desiccation cracks completely faded and a smooth and uniform sample was obtained. From the integrity of this sample, it can be concluded that this fiber percentage has also been able to greatly eliminate deep cracks. 1% Polypropylene fiber has a lot of volume based on its light weight, which may cause improperly distribution. That's why it cannot completely absorb soil particles and completely prevent desiccation cracks. The results observed in the fiber samples were also the same in the presence of DCP. That is, in clay sample with 0. 5% fiber and in the presence of DCP, cracks behavioral had a similar effect as 0. 5% of non-additive fibers. The analysis of the results obtained from the experiments shows that the hydraulic conductivity of the fibrous clay liners were lower than simple one, but the advantage of using the fibers is the reduction of the desiccation cracks, which can be a major factor in the significant increase in the hydraulic conductivity of the liners as a result of the preferential flow. In general, the elimination of contractions with 0. 75% fibers was more pronounced than 0. 5%, and the appropriate range for using fibers seems to be between 0. 5% and 0. 75%.

Introduction Currently, the international community has recognized the common problems associated with the quantity and quality of water. Many diseases that can be transmitted through unhealthy drinking water occur in developing countries of Asia, Central and South Africa. Therefore, provision of safe water for the community is one of the most effective and permanent technologies for improving the health of the community. According to the World Health Organization's 2008 report, the mortality rate associated with water borne diseases was more than 5 million per year. Therefore, information on water quality and pollution of resources is significant for sustainable water management strategies. The analysis of people's economic and social demands helps to anticipate health needs and deficiencies, among which these people can value the people who drink for drinking water and express it by expressing the amount of willingness to pay. Measuring the willingness to pay in social projects in developing countries is done by conditional valuation. Given the fact that there is no explicit market for improving some products such as safe drinking water, the use of non-market methods and the trading of such goods in this market is recommended. Contingent Valuation is one common method used by economists, policymakers and water organizations to improve water supply. It has also been implemented in many water supply and sanitation projects, especially in the provision of rural water in developed and developing countries. Matherials & Methods The present research was carried out in the north and east of Kermanshah city (elahiya, Jahad, Nobahar, Cornachi) in December and December, 2016. Data were collected using questionnaires completed and interviews by random sampling. The Cochran formula was used to calculate the number of samples needed in the sampling method. According to the formula, the number of samples required 384 questionnaires was obtained, but to achieve better results and with the probability that some filled in questionnaires were invalid 400 questionnaires were distributed among residents of Kermanshah city 39 of the questionnaires were deleted after completing the study due to mistakes and defects. Finally, 361 questionnaires were used for the final analysis. Face-to-face interviews were conducted at the respondents' place of residence. Socio-economic characteristics, population, water resources, drinking water quality, presence of suspended particles in water, willingness to pay and reasons for unwillingness to pay for respondents were questioned. Respondents were also asked about how to use tap water (boiling, filtering) before drinking water. The conditional valuation method was used to calculate the average willingness to pay the improvement of drinking water quality in the residents of Kermanshah. Also, the binary logistic regression method was used to obtain the tendency to pay. In this method, regression analysis is based on dependent variables and binary classification variables. McFadden R2 indicators and statistics likelihood ratio was used for a good fit. the right-of-proportion ratio statistic compares the right-exponential function statistic in the bound state (all coefficients of zero) and unconstrained, and shows the simultaneous meaning of all the coefficients. If this statistic is meaningful in relation to the probability of stating the right ratio, this shows that the explanatory variables in the model have been able to describe well the dependent variables. The software packages used in this research are SPSS, Shazam, and Wolfram Alpha. Discussion of Results In the preliminary study, experts' opinions were used to assess the validity of the questionnaire. After reviewing and correcting, the validity of the questionnaire was assured. To assess the reliability of the questionnaire, a pre-test was performed with 55 questionnaires and the Cronbach's alpha coefficient for the questionnaire was 0. 55, which indicates that the questions are highly valid. Table 1 shows the reasons for people's dissatisfaction with drinking water in Kermanshah. As shown in the table, the most reason for people's dissatisfaction with drinking water is related to the remaining salts in the container. The results of the willingness to pay individuals show that among 361 samples, 16% of the respondents offered 5000 USD, 51% offered 4000 USD and 16. 4% proposed 2000 USD for improving water quality. Also, 16. 6% were not willing to pay a sum to improve the quality of drinking water. Table 2 shows the results of a lack of willingness to pay or a willingness to pay low in order to improve the quality of drinking water in the city of Kermanshah. According to the results, unemployment among the five problems (unemployment, high living costs, low water quality, water crisis and environmental pollution), the highest and low water quality were the least significant among the respondents. Also, 76. 5% of respondents believe that the quality of drinking water is fairly good and 23. 5% of respondents believe that the quality of water is poor. In general, 80. 2% of respondents did not rely on drinking water (piped water) and Concerned about diseases transmitted through drinking water. Considering that 95. 8% of the statistical population sources their drinking water as piped water, therefore, 64. 3 percent of the respondents were willing to pay a fee to improve the quality of tap water so that the water and sewage office assured them that no disease would spread through drinking water. The results of estimating the Logit model are shown in Table 3. The dependent variable accepts a bid amount of 0 and one and variables such as recommendation rate, education, age, water quality concern, assurance of safe water quality and the quality of drinking water quality are independent variables. Also, variables that were not statistically significant were eliminated in the Logit model to help achieve better results. Conclusions Environmental valuation methods have been widely used to value recreational sites, but we must consider that all environmental benefits need to be valued to achieve sustainable development. Therefore, in this study, using the conditional valuation, the willingness to pay the residents of Kermanshah city was estimated to improve the quality of drinking water. Public awareness and raising public awareness about the standards of safe water and transmissible diseases through contaminated water will greatly help people's attitudes towards water quality. It is natural for people in the community to be aware of the quality of water, demand for improved drinking water quality and if such a blue is not available, they will be willing to pay for it. Regarding the results of the present study, in the logit model, the relationship between income variable and pay-as-you-go income was not significant. It can be noted that all strata of society, not just high-income people, are demanding safe and safe drinking water, and in order to achieve such a goal, they will have financial support from the government, and in particular the water and sewage administrations. considering the importance of drinking water for the residents of Kermanshah and the desire to pay them high in order to have a healthy and high quality water, it is clear that people of the community can play an important role in supplying water. Therefore, in the area of management and planning process, conscious efforts must be made to fulfill the community's needs and address their concerns regarding the quality of drinking water. Because these are people who must meet them when they are in trouble.

Introduction Probabilistic prediction model is obtained by development of spot prediction model. Unlike definite prediction, probabilistic prediction provides an amplitude of probable amounts for water demand. It is clear that there is more trust to amplitude values in decision making process. Probabilistic prediction of water demand is calculated through the distribution of uncertainty in independent variables by water demand model and production of a probabilistic distribution function for water demand in the point of interest in future. Looking for the probabilistic distribution allocation, Monte Carlo simulation process is used to develop probabilistic prediction of water demand according to the spot model by using Stone-Geary utility function. For each repeat in Mont Carlo simulation, a value of allocated distributions to each descriptive variable is used randomly. In this research both methods of determination of probable density functions consist of analyzing the past information and other experiences are used. Normal and uniform distribution functions were used because of their easiness in determination of parameters whenever their real distribution is not clear or it cannot be determined easily. Materials and methods To apply uncertainty on independent variables used in prediction of domestic water demand consist of per capita real income, the real price of water, stock prices of goods and services, the average maximum temperature and the number of literates, the best distribution of their monthly values, resulted from probabilistic distribution function, were used as the entrance of water demand function and the outputs obtained as the monthly water demand. For validation of water demand prediction model, the predicted values of water demand was compared with its real values in a few periods. In this research the LARS-WG micro scale statistical exponential model was used to predict the annually maximum temperature. Neyshabur city, located in Razavi Khorsan province, was selected as a case study and the statistical society of domestic water branches was investigated. The required information was obtained from its subsidiaries annually function report and statistics of meteorological organization. Independent variables which were used for water demand estimation are per capita real income, the real price of water, stock prices of goods and services, the average maximum temperature and the number of literates. The best distribution was selected by applying distribution functions on the value of variables, considering results of the Anderson test, selecting the minimum Anderson multiplier and attending to the others studies and experiences. The final model for long term water demand according to the Ston-Geary utility function is as equation 1: (1) t=1, … , 120 MP: is the average price of water (Rial) I: is the per capita income of consumer (Rial) PO: is the stock price of goods & services in Razavi khorasan province Perc: is the amount of per capita consumption (m3) E: is the number of literates MT: is the average maximum temperature U: is the disturbing element Discussion of results and conclusions Calculated elasticity of price, income, intersecting, maximum temperature and number of literates by considering their average values in investigated time periods for Neyshabur city are presented in Table 1. Table 1. Calculated elasticity of water demand for Neyshabur city Price Income Intersecting Maximum temperature Number of literates-0. 117 0. 195-0. 078 0. 054 0. 402 Table 1 shows if the price of water increases one unit, it will result to decrease of just 0. 117 unit in water demand that indicates the possibility of using pricing policies for decreasing water consumption. Relatively low income elasticity (0. 195) shows the small share of water in the family income. Negative intersecting elasticity indicates that water is a complementary good. Temperature elasticity is positive (0. 054) that notes the tendency of using water by temperature rise. In this research the elasticity for number of literates obtained equal to 0. 402. So one percent increase in the number of literates will result to 0. 402 percent increase in water demand. To assess the accuracy of the model the per capita water demand was predicted by using independently observed variables for the period of 1997 to 2008 and the results were compared with observed information that showed a good match. Also results of RMSE and MSE tests for assessing the accuracy of the models was 0. 22 and 0. 12 respectively that emphasizes on acceptable accuracy of the model. After finalizing the model the per capita water demand of Neyshabur was predicted by the spot method and summary of results are showed in Table 2. Table 2. Summary of the per capita water demand prediction by spot method. Unit Year Percentage changes Average of yearly changes 2008 2011 2016 2021 2026 2031 M3 51. 18 53. 35 60. 06 66. 03 71. 93 79. 31 48. 6% 2% Finally the per capita water demand was predicted for Neyshabur through the probabilistic model and summary of the result is presented in Figure 1. Fig. 1. Comparison of the observed values and 90% confidence interval of the annual per capita water demand forecast. As it can be seen in Figure 1 the annual amount of predicted mathematical expectation for the per capita water demand in year 2031 is calculated equal to 80. 36 m3 which shows 50% increase in comparison with year 2011. The average annual increase in water demand is about 2% which is consistent with the results of the spot prediction model. For year 2011 the defined confidence interval for water demand has obtained between 48. 34 and 58. 36 m3 averagely. While for year 2031 this range has grown and broadened to 67. 66 and 98. 64 m3. Furthermore, probability function shows 90% confidence interval for all probable predictions with considering uncertainty of independent and explanatory variables.

Introduction There are two approaches for water quality standardization and monitoring the pollution loads discharged into the water bodies, like rivers and estuaries. In the conventional system of command and control, the monitoring organization focuses on limiting the concentrations of physicochemical parameters of water, such as dissolved oxygen (DO), biochemical oxidation demand (BOD), chemical oxidation demand (COD), total nitrogen (TN), total phosphorous (TP), total kjeldahl nitrogen (TKN), and etc in the effluents of point-sources. This framework is easy for monitoring and penalizing, particularly for industrial and domestic polluters with continuous annual discharge flow. However, it has several shortcomings. The main weakness is the inflexibility of water quality standards regarding the environmental conditions of rivers, their self purification and vulnerability potential, and the seasonal variations of water quality and quantity of rivers. Besides, the conventional approach neglects controlling the discharges of non-point sources (NPS), including agricultural activities, as they may not be continuous or precise in location for sampling. These faults are introduced as a reason of pollution accumulation and Eutrophication in surface waters. In the second approach termed as controlling ambient discharges, the water quality standards are determined in local scales regarding the environmental potential and conditions of rivers. Here, water quality monitoring is focused on the critical points in the river itself and limiting the pollution loads rather than concentrations in these stations. This approach in monitoring considers other issues like the self-purification potential of river, and the total pollution loads (TPL) discharged by both point and non-point sources upstream. However, there are some challenges that make this framework more complicated. 1) Finding a proper standardization and TPL in a multi-parameter framework, 2) waste load allocation (WLA) and fair sharing of penalties among polluters, and 3) uncertainties regarding the seasonal variations of emissions and the fluctuations in river water quality and quantity. In this research, a methodology is introduced regarding the ambient discharge framework to calculate an optimal multi-parameter WLA among emission sources. This intends to determine an allowable TPL in a river with high seasonal variations and challenges in the aquatic life. For this purpose, we chose Tajan River in northern Iran as the study area. This river has 51 km length with annual average water volume of 15 million m3. It ends to the Caspian Sea where the estuary currently encounters DO deficiency in some seasons and endangers the aquatic life. This may be due to the pollutions discharged from point and non-point sources, including paddy fields, pulp and paper industry and municipal effluents of Sari city with the rural areas upstream. Methodology In order to find a proper WLA and TPL, a simulation is carried out on Tajan River with 18 reaches by Qual2kw software with 100 times iteration for calibration. This simulation includes two steps. In the farming season (FS) of the study area, more than 5 m3/s of water is allocated for paddy fields that reduces one third of river overall flow at headwater. This lessens the remediation potential of river for diluting pollutions discharged particularly the nutrients concentrations exist in the drainage of NPS. Conversely, in non-farming seasons (NFS), DO profile and base-flow of river increases and environmental pollution limits to the point sources. Therefore, simulation is calibrated with respect to the sampling results in the first scenario of FS and later validated by other data in NFS. Regarding the fitness function and auto-calibration based on the genetic algorithm, the simulated model with 100 iterations presented 71% accuracy. For that, the water quality data sampled from three stations between 2014 and 2015 in the upper, middle and lower lands of river are used. Results Figure 1 illustrates DO deficiency of river in two periods. It is obvious that in FS, DO deficiency exceeds 2. 5 mg/L (for a DO saturation of 8. 5 mg/L) that endangers aquatic life in the last 15 km of river to the terminus point but this is rather normal in NFS. Besides, in FS the concentrations of nutrients like TN and TP respectively increases more than 5 and 1. 5 folds in comparison with NFS. It should be noted that about 40% of TN is made of TKN in FS that shows two points. First, chemical fertilizers are the main pollution origins of NPS discharges, and second, it may devour considerable amount of DO in the nitrification process. Therefore, NPS like agricultural activities are introduced as the main reason of seasonal pollutions. In addition, both nutrients and carbonaceous compounds are highlighted as influential parameters on DO reduction. Therefore, DO is assumed as the key factor in multi-parameter WLA and decision-making. Here, it is assumed that 5 mg/L should be met as the minimum limit of DO throughout a year even in the most polluted periods FS, while 6 mg/L must be met annually in average. The sensitivity analysis on the origins of pollutions showed that the self purification potential of river for nutrients reduction will not exceed 10%, but it easily reaches 50% for carbonaceous organic loads. This result adds up the significance of NPS pollution control in decision-making for WLA in river. Therefore, regarding the simulated pollution loads of the terminus point in FS and NFS, the annual TPL in WLA is determined in a way that DO profile responds to the assumed limits. As shown in Table 1, the maximum allowable loads of TN and COD are respectively considered 2500 tons/yr and 4500 tons/yr. TPL for other parameters like TKN, nitrate, and TP are respectively 500, 2000, and 250 tons/yr. By these limits, the local concentrations of pollutants can be set as the standard level for better monitoring. For TN, TP and COD the recommended monitoring concentrations are 5, 0. 5 and 9 mg/L, respectively. By these conditions, it is expected that DO remains on the assumed standard level as shown in Figure 2. Here, WLA is set on 45% removal of pollutions discharged by NPS. This value may reduce 34% of TN, 46% of TP and 14% of COD at the terminus point. Conclusion In this research a method in introduced with respect to the ambient-based framework for water quality monitoring to find TPL and consequently the annual average concentrations of main water quality parameters. In the case of Tajan River, it is realized that the estuary is highly sensitive to the seasonal variations of water quality and quantity. The main source of these variations is marked as the agricultural activities of paddy fields that recommended to be mainly focused for multi-parameter WLA and decision-making. For this purpose, it is also recommended that DO is selected as the key controlling index because it reflects the effects of both carbonaceous and nitrogenous compounds and is crucial for the aquatic life. Finally, with respect to the self purification potential of river, TPL and WLA are determined. This approach can be similarly used in other cases to find local standards for water quality monitoring.

1. Introduction In recent decades, the development of higher education in Iran has always been associated with the development of physical training centers that this process, especially in the last ten years with the establishment of new fields and increasing the capacity of educational institutions and universities in different sections intensified. However, the quality of the centers of higher education institutions has not involved uniform by population growth and other factors. Open and green spaces are factors have the important role in the evaluation quality of academic centers that usually ignored in the process of physical development centers. Educational spaces, regardless of their buildings and facilities, due to the quality of the environment and your perspective can impact cognitive, emotional and behavioral users to leave. The quality of a school is judged by its sense of place and by the activities going on across the campus. Prospective students, their parents, and faculty count the overall feel of a campus as part of their decision when selecting a school. It also contributes significantly to a university’ s ongoing efforts to attract and sustain the best students, faculty and staff, and to reflect its social purpose in a positive way. Few people are happy attending a campus that recognizes the importance of campus landscape spaces; it is no longer the leftover of buildings. Ferdowsi University of Mashhad is the third university in Iran witch due to the diversity of the audience, scope and focus of the units has led the campus landscape and one of the most important and immediate collection of the university. In this research, the main problem of is to evaluate the measures affecting the quality of the campus landscape environment from aspect of Ferdowsi university students. To analyze the components of effective strategies for improving quality, planning and development of university provide current perspectives. One of the goals of this study was to identify the components of the quality of the campus landscape environment of Ferdowsi University and providing visual and environmental quality is to improve quality of life and education. Other objectives of this study are:-Strengthening the genius loci on campus landscape environment-Assessment and improvement of University open and green spaces.-Attention to beauty and aesthetics from the perspective of users, and its use in the planning and development of the current landscape.-Assessment of ecological effects of landscape and green space from aspect of users. 2. Methodology The study is analytical research and was used questionnaire to obtain information. For university campus, it is easy to identify the majority end-users of campus landscape spaces are students. Faculty, staffs, parents, visitors and surrounding community members are also important users. Only the major end-users (college students) were chosen to simplify the process. Among college students, the study population included all male and female students living in hostels are the number about 9, 000. According to Morgan table, we selected 367 students as a sample size, with half of them from different genders by random sampling. A reliability analysis of the questionnaires is necessary to test how real they reflect the facts. We use Cronbach’ s Alpha coefficient to test on the questionnaire items by means of internal reliability consistency. The analysis by software (spss) was performed. Factor analysis to obtain the effect of each variable was used sub-parts on evaluating the quality of campus landscape environment. Also, to obtain the relationship between variables correlation test was used. Pearson correlations and among the parameters has been conducted to build an effective linear regression equation and illustrate whether there is a significant correlation between the items of natural landscape, belonging to the place, visual assessment, health and ecological aspects. 3. Results The results showed that visual-perception variable, are explained 21. 8% of the variance by random sampling and are the highest representation that evaluate by an agent. The highest Pearson’ s correlation coefficient with the variable quality, related to the visual-perception with (0. 93), respectively. Genius loci and vitality and mental health and nuanced next. Physical aspect of environment has the lowest correlation with the quality of the environment. So, results show the environment quality from aspect of the women with (19. 98), less than men (47/21), respectively. Due to the significant mean difference in level of more than 99% is significant. The conclusion we can draw is that a significant positive correlation consists in the relation between the visual assessment, belonging to the place, health, ecological aspects and architectural environment. 4. Discussion Results of this study provide a framework that takes user’ s needs and requirements into account, and the traceability of these needs to design attributes. It also provides other advantages; yet it is not without some tradeoff and limitation. The testing results of scale reliability demonstrate that all the sub-scales are reliable as a whole. On this condition, through the Pearson correlations analysis among the independent variables and between the independent values and dependent variables, we can see that the visual assessment, belonging to the place, health, ecological aspects and architectural environment correlate positively with environment quality, which accords with the hypothesis test. By the following approach, the multiple regression analysis has been conducted in the article, which verifies that the visual assessment, belonging to the place, health, ecological aspects and architectural environment are the independent variables in the regression equation and that all three factors can well predict environment quality. In addition, the coefficients the visual assessment, belonging to the place and health are put in a descending order. The relatively highest coefficient of these three factors reveals the fact that students depend more on subjective variables in campus environment. 5. Conclusion The results show that the quality of the campus landscape environment directly related with the perception elements. The judicious use of characteristics of landscape elements such as form, color, texture and the like can be used to increase the quality of environmental impact. In addition, visual quality, to create a sense of place and identity formation has a direct effect. These factors led to the formation of students' minds, and it reinforced the university environment. The important note is physical and environmental variables that indicate a lack of attention to the combination of green open spaces and architectural elements. Also, due to the lack of differentiation and function of sex and use of existing spaces, the quality of the environment for the female students to male students is a significant difference in the health. In landscape planning and design, little attention was given to listening to user’ s needs or concerns in the past. It is not unusual to see design professionals formulate their design ideas solely based on their experience, “ insight” , creativity and artistic training. Although with this approach, design professional may create great designs, however, faced with a set of complex and occasionally conflicting individual and community issues, resolution of the community and project needs may be limited and incomplete. As an important landscape design, the design of campus landscape spaces affects millions of people. Nowadays university campuses are no longer ignored on the global outcry of taking serious actions to safeguard the environment. Universities should be modeled as centers that can enhance teaching and learning and accommodate the needs of all learners and to serve as center of the community for promoting sustainability that could support the concept that high institutions are important symbols of ‘ ‘ place’ ’ . Universities should also be welcoming to all members of their community and promote partnership and collaboration with all stakeholders in policymaking and planning a sustainable environment for learning and research. This can result in problem solving and innovations that support the goals of a sustainable campus.

Introduction Climate factors such as temperature, humidity, and precipitation play an important role on environments. Aerosol is one of the factors influencing on the climate system. Approximately 40% of aerosols in troposphere are dust. Aerosols and dust particles can affect the equilibrium energy of earth radiative, dynamic clouds, and their microphysics, both directly and indirectly impacts (Nabat et al., 2015). Rosenfeld (2000), used some satellite data, examined the processes of rainfall formation in Australian urban and industrial areas. The results indicated that the effective radius of cloud droplets have reduced as result of industrial and anthropogenic aerosols and then, coalescences droplets declined. These leaded to diminishing formation of raindrops and suppressed rainfall. Koehler et al. (2010) applied laboratory studies to give information about effect of temperature changes on several types of dust. Dust activated at less relative humidity for heterogeneous ice nucleus when temperature was higher. Additionally, those particles covered with secondary organic aerosols in lower temperatures require higher relative humidity for ice nucleus. Nabat et al. (2015) and Gu et al. (2016) studied direct and semi-direct aerosols effect on regional climate. They showed that the effect of dust on rainfall varies and can enhance or even suppress it. Absorbtion and scattering of shortwave by dust, cause the air columns to be heat. As a result, the strong vertical updraft movements are being created. On the other hand, forced radiative can dwindle surface temperature and weakens updrafts which lead to constant air. These processes can change rainfalls. Using observations with lidar in a region of China from 2010 to 2013, Wu and Yi (2017) investigated interaction of aerosols and moisture layers in cloud. Their results suggested that ice nucleus is growing with increased relative humidity thus evaluate rainfall. Ilam province is located in the western part of Iran and affected by dust storms. Hence, dust impacts were studied upon some climate factors such as temperature, humidity, cloudiness, and precipitation of Ilam province by using meteorological data from autumn and winter 2000-2013. Materials & Methods Ilam province is located in the western part of the Zagros chain mountain at Iran, from 31 ˚ 58' to 34 ˚ 15' northern latitude and 45 ˚ 24' to 48 ˚ 10' eastern longitude (Fig. 1). Fig. 1. Location of Ilam province in Iran showing the synoptic and hydrology stations. In this work, temperature, humid relatively, cloudiness, and rainfall information gathered from Iran Meteorological Organization in daily scale from 2000 to 2013 for Ilam province. Precipitation data collected from hydrology station of the Ministry of Energy as well. Statistical variance analysis in SPSS software was done to study relation among dust, temperature, relative humidity, and cloudiness. Because of the high volume of observation, MATLAB software was used to separate variables on scheduled days included dusty days as well as the days before and after. The stations were selected as control stations were those stations which had the highest correlation coefficient of daily precipitation with the target stations (Fig. 1). A regression model is used to forecast the target rainfalls by controlling rainfalls as a function of dust impact. The statistical ratio is used to evaluate climate change projections and operational cloud seeding programs on streamflow or rainfall at target stations (Gabriel., 2002; Silverman., 2010). The statistical ratio for historical regression was calculated target stations with observed and predicted rainfalls. The Monte Carlo permutation test was conducted for evaluation of dust impacts on rainfalls (Silverman., 2010). Results The results of statistical test presented in fig. 1 include temperature, relative humidity, and cloudiness in dusty days with the days before and after it for Ilam, Eyvan, and Dehloran. Cloudiness Relative Humidity (%) Temperature (º C) Fig. 1. Comparison of modified temperature (ɪ ), humidity (ɪ ɪ ) and cloudiness (ɪ ɪ ɪ ) on dusty days with days before and after it a: two days ago b: one day ago c: dusty d: two days later c: on day later Temperature variations had the lowest records in the dusty days of Ilam, Eyvan, and Dehloran with values of 10. 5º C, 10. 4º C and 19. 8º C, respectively (Fig. 1. I). Statistical comparison of relative humidity values during these days showed that the highest relative humidity of each station was related to the days that dust occurs (Fig. 1. II). Average of relative humidity for dusty days were 54. 4%, 46. 9%, and 43. 8%, respectively for Ilam, Eyvan, and Dehloran. Studied cloud cover for stations showed that cloudiness had the highest value for days with dust. Values of cloudiness were 3. 4, 3. 1, and 3 for Ilam, Eyvan, and Dehloran on dusty days, respectively. Table 1. Monte-Carlo permutation test for statistical ratio index for historical regression target stations 90% confidence intervals significant level Statistical Ratio Station confidence lower confidence upper 0. 68 1. 28 0. 05 0. 98 Ilam 0. 5 1. 1 0. 04 0. 8 Eyvan 0. 4 1 0. 03 0. 7 Dehloran Table 1 shows a statistical evaluation of dust impacts on rainfalls by using Monte Carlo Permutation test. The statistical ratios for historical regression were 0. 98, 0. 8, and 0. 7, respectively for Ilam, Eyvan, and Dehloran. Obtained statistical ratios are less than one which shows negative effect of dust on rainfalls. However, the negative effects of dust were difference on rainfalls for studied stations. Conclusions Results of temperature variable showed that they decreased on dusty days. Atmospheric aerosols, such as dust, play a significant role on the radiative budget of the earth-atmosphere system. Temperature declines due to downdraft reduction shortwave flux during dust storm (Wu & Yi., 2017). Stations temperature decreased for Ilam, Eyvan, and Dehloran when dust occurred and is similar to results of Nabat et al., 2015 and Wu & Yi., 2017. Result of variance analysis of studied days showed relative humidity enhancement on dusty days in all station in this work. The latent heat is altered on earth surface by changing radiative energy under dust effect, which changes specific humidity (Dessens & Bü cher., 1995). Relative humidity increased in Ilam, Eyvan, and Dehloran that agrees with Gu et al., 2016 and some other studies. Gu et al., 2016 reported that if temperature becomes less and specific humidity is constant, saturation humidity is reducing and then relative humidity is increasing. Cloudiness of studied stations showed that it increased on dusty days. One of the most important mechanisms for the cloud’ s formation is sufficient relative humidity (Weare et al., 1995) which can influence on cloud cover (Wu & Yi., 2017). Changed temperature by dust impacts can vary the relative humidity. Dust effect on solar radiation at the upper atmosphere and earth surface, therefore temperature may change. These processes alter relative humidity and cloud cover. Here, cloudiness enhanced on the dusty days. These results match with McFiggans et al., 2006 that showed rising humidity may increase cloud cover due to temperature reduction. The results of Monte Carlo of permutation analysis showed that dust had negative effect on station rainfalls. In this study, by comparing statistical ratio of each station with its relative humidity it can come to an end that effect of dust on rainfall variations are under influence of relative humidity. Interaction of water vapor and mineral dust can lead to cloud condensation nuclei and ice nucleus (Wu & Yi., 2017). The relative humidity can strongly control the surface precipitation rate (Ackerman et al., 2004). If relative humidity isn’ t enough during dust storm, precipitation reduces or suppresses (Rosenfeld., 2000). Ilam station had 0. 98 statistical ratio which had 54. 4% relative humidity that dust had the lowest negative effect on the rainfalls. On the other hand, Dehloran station had 0. 7 statistical ratio with 43. 8% relative humidity. Dust effects had the highest negative effect on the rainfall of Dehloran’ s station in the studied stations. These results accord with some studies that suggest suppression of precipitation on storm dust because of cloud condensation and ice nuclei in insufficient relative humidity (Wu & Yi., 2017).

Introduction Over the past three decades, in response to the rising coastal problems, most countries have moved towards adopting integrated coastal zone management. However, most of the approaches were not successful. This was due to the lack of attention to the inadequate institutional and structural capacities of the social system. Management approaches cannot succeed without looking at the local social, cultural, economic, political and environmental contexts. The governance of natural resources is defined as "interaction among structures, processes and traditions that determine the distribution of power and responsibilities, how decisions are made and how actors or other stakeholders are expressed". One of the main components of governance is the participation between stakeholders. The purpose of this paper is to propose a framework for assessing the governance structure of coastal zones in Iran, with an emphasis on improving the outcomes of integrated coastal zone management. Since the stakeholder participation is an important component in improving governance and resource management, so this study also has a special focus on this component. Therefore, the proposed framework is derived based on the review of the former research and global experiences related to governance and public participation approaches in coastal resources management. Also, this framework was evaluated and used for designing the public participation strategies and its appropriate capacity building program in the coastal zones of Hormozgan province, Iran. Materials and methods The economic features of the coasts of Hormozgan province, along with its problems, such as disproportionate population, oil pollution, sediment and erosion, degradation of mangrove forests and water scarcity, require the integrated coastal zone management approach to address the existing problems. For this purpose, two basic steps are taken in this article. Initially, a framework for assessing the structure of coastal governance and the implementation of the public participation approach under integrated coastal zone management for coastal zones of Iran has been developed. In the second step, the public participation strategies of the coastal zones of Hormozgan province along with their capacity building program were derived based on the framework proposed in the first step. In the first step, the objective was to propose a Coastal Governance Assessment Framework with an emphasis on implementation of the public participation. Tthe basic stages of the ICZM were considered as five main stages including: identification of issues, analysis of stakeholders, planning and decision making, policy implementation, and monitoring and evaluation. The main focus of this framework is on the three pillars of governance assessment, the use of public participation and the providing capacity building (training) program. The assessment of the governance structure is presented by defining and adopting 26 indicators under 7 categories of components and dimensions. These seven dimensions include capability and resources, adaptation, transparency and accountability, social capacity, participation, legitimacy and integration. In the framework of the public participation, based on the analysis and classification of the stakeholders using the indexes of interest, power and knowledge and awareness, the level and strategy of the stakeholder participation under ICZM plan was developed. The level of participation in this framework is considered as five levels of informing, consultation, Shared Decisions, Shared Working and empowerment. The third pillar, which is related to the designing of the capacity building program, was carried out in five main stages of setting the goals, defining courses, identification of audiences, determining the most effective tools, and setting out the authorities in charge of the capacity building process. The focus of the capacity building program is to train stakeholders. Also, for evaluation public participation at the final stage of the ICZM approach, which is monitoring and evaluation, indicators of information and informing, open space, agreement, accountability, participatory methods, trust and interaction, learning and capacity building, and level of participation were developed. In the second step, with the aim of proposing a public participation strategy for integrated coastal zone management of Hormozgan province, the framework presented in the first step was used. First, the related stakeholders were identified by snowball sampling method. Then they were evaluated and classified by indicators introduced in the framework (interest / power / information, knowledge and awareness), and their level of participation was determined. Based on the level of knowledge and awareness of the stakeholders, the capacity building program was also determined. The process of collecting the data required for this assessment was carried out by holding several workshops with experts related to coastline management project of Hormozgan province. Therefore qualitative methods for collection and analysis were used. Discussion of Results & Conclusions The Integrated Coastal Zone Management (ICZM) approach aims to exploit and sustainably develop natural resources in coastal zones along with solving socio-ecological problems in those areas. From this perspective, regardless of the social context of coastal zones and the problems encountered in it, the adoption of practical and emergent solutions to solve issues face with difficulty and failure. In addition to pay attention to the governance structure, the necessity of public participation between stakeholders with the aim of adopting solutions, implementation of plans and projects, and monitoring and evaluating their outcomes have been recognized. However, there was no specific, integrated and clear framework for these goals and gaps, and on this basis, the purpose of this paper was to provide a framework for assessing governance and public participation with respect to the coastal zones of Iran. It was also attempted to provide a clear methodology for designing the capacity-building program for stakeholders. Considering the importance of public participation and capacity building of stakeholders to prepare for ICZM projects, the second goal of this paper was to design the public participation strategies and capacity building program of the stakeholders of Hormozgan province by using the proposed framework. The public participation strategy is, in fact, a way for stakeholders to engage in ICZM's planning and implementation. Therefore, the list of stakeholders involved in the issue and the actors involved in the ICZM process (problem identification, decision making and planning, implementation, monitoring and evaluation) were first provided. In the next step, according to the analysis and assessment of the identified stakeholders, the level of participation was determined. Participation levels showed a wide range of participation from awareness to implementation, but none of stakeholders in the current situation has the capacity to initiate local governance and fully participate in ICZM programs. This weakness has been identified especially at the level of knowledge and awareness of the stakeholders. Therefore, the capacity building and training program for those stakeholders was designed. In this respect, various capacity-building programs were developed for the purpose of achieving their ability to participate. The main objectives and goals of the Capacity Building Program were explained in the seven groups as of creating a common knowledge, creating common concerns, improving adaptability, improving the level of stakeholders’ expectations, improving insights on coastal issues, improving the level of participation between all stakeholders, and creating and improving professional skills. Finally, it can be concluded that, based on the categorization and the determined capacity building packages, the process of implementation and participation in the ICZM project should be started by the stakeholders, and according to the level of Participation, participants in different stages and processes will be involved. Based on the above framework, the roadmap of implementation of the public participation with regard to the different levels of stakeholders will be designed in two basic steps. In the first step, the main priorities of capacity building should be those who are at the current level of participation in decision making and implementation. In this step, in the capacity building matrix, priority is given to the titles and courses that improve knowledge and awareness of these stakeholders. In the second step, after achieving the results of the capacity building process, the implementation of the ICZM at various levels of engagement by each of the stakeholders will be carried out.