mozhgan ahmadi nadoushan

Significant changes occur in land cover, infrastructure and population density, due to urban growth and urbanization, leading to modifications in local weather patterns and climate. This phenomenon, known as the urban heat island effect, primarily contributes to urban warming. Individual urban residents have a substantial impact on mitigating and addressing the urban heat island effect. This study aims to investigate the adaptive behaviors of the citizens of Isfahan City in facing to the hot season temperature in two parts: mitigating and adapting behaviors.

To collect data from citizens, a random-probability method was used and 100 individuals from the city of Isfahan were selected for this study. Participants were asked to provide information about their perception of climate change, their access to urban infrastructure and their individual coping strategies for high summer temperatures. The level of proximity to urban infrastructure, especially the urban transportation network and green parks, and the rank of adopting two categories of mitigating and adapting behaviors to temperature were collected on a Likert scale. After collecting questionnaire data and ensuring its validity and reliability, descriptive statistics were used to better interpret the results. A linear regression model was used to investigate the relationship between the temporal distance of individuals from urban infrastructure and the rank of adopting behaviors corresponding to it.

The results showed that the largest number of individuals (29 people) were between the ages of 38 and 48. The average travel time to the first bus and subway station for the individuals was 21.65 minutes and to the first city park was 20.35 minutes. Among the obtained ratings, disconnecting electrical appliances that are not in use with an average of 2.83 had the highest average rank among the temperature-reducing factors in the 10 questions in this field. Also, the use of temperature-adaptive clothing such as hats, wearing temperature-adaptive clothes, and staying at home during the very hot hours of the day were also among the factors that were assigned the highest ranks of temperature-adaptive factors. The results of the linear regression showed that proximity to infrastructure significantly affects the ratings given by citizens to adaptive behaviors.   

According to the results of this study, optimal human performance and improved adoption of heat coping strategies in the summer require a multifaceted approach that encompasses individual actions, home design, and urban infrastructure. Isfahan is one of the dry and semi-arid cities in the center of Iran, which in recent years has faced many environmental problems, especially the lack of water and the increase in temperature in hot seasons. This has led to numerous efforts to increase temperature adaptation by social and managerial groups, from urban planning to citizens. If proper activities are carried out regarding the adaptation to the increased temperature of the city, it is anticipated that the adaptation to the rising temperatures in the coming years will be achieved with the minimal environmental footprint. However, without paying attention to the citizens' perception and understanding of this phenomenon and the lack of effort to improve it, this is very difficult.

The purpose of this study was to investigate the effect of using tubular settlers on the energy required for backwashing filters and the frequency of backwashing.

In this experimental study, two accelerator clarifiers connected to the raw water transfer channel No. 1 of the Isfahan Water Treatment Plant (WPT), Iran, were equipped with approximately 350 tubular settlers at an installation angle of 60 degrees.

The results of sampling the outlets showed that the average concentration of suspended solids in the clarifiers with tubular settlers was 3.20 ± 16.00 mg/l, while in clarifiers without tubular settlers, it was 4.41 ± 20.00 mg/l. The average removal efficiency of suspended solids in clarifiers equipped with tubular settlers was 64.86 ± 34.00%, compared to 52.10 ± 46.00% in other clarifiers. Additionally, the number of backwashing cycles of filters decreased by 25%, and energy consumption was reduced by 50%-60%.

The use of tube settlers to improve the quality of water entering filters reduces the number of backwashing cycles per day and ultimately saves energy for backwashing filters, which is very effective.

Parks play a crucial role in modern city structures, serving various ecological functions. This study investigates the role of urban parks in the spatial distribution of PM 2.5.

Using the Inverse Distance Weighting (IDW) method, the daily values of the concentration of this pollutant, collected by 16 ground monitoring stations, were obtained for a full year from March 22, 2022, to March 20, 2023. Processing Landsat-8 satellite images, the entire green cover of Isfahan city was extracted. Subsequently, the annual concentration of this pollutant was mapped into a continuous map. 

Based on the results, Region 11 in the northwest of the city showed the highest average concentration of PM2.5 in the amount of 62.047 and a standard deviation of 4.091 among the 15 urban regions of Isfahan, indicating high pollution levels in this region. The results of the correlation analysis (R2 = 0.203) between the annual concentration of PM2.5 and the total area of parks indicate that the greater the cumulative area of parks in an urban area, the lower the average concentration of PM2.5. The results showed that only the size of each park and its normalized difference vegetation index with the concentration of annual PM2.5 have a negative and significant correlation of -0.572 and -0.748, respectively, at the one percent level (p-value = 0.00).

In conclusion, it can be inferred that utilizing the natural filtration capacity of park vegetation and larger green spaces significantly improves air quality. 

Urban parks serve numerous social and ecological functions, providing citizens with a convenient escape from urban life and an opportunity to reconnect with nature. Therefore, the management of urban parks should prioritize enhancing their utility through the incorporation of natural elements. The main objective of this research was to gain knowledge and to know the aesthetic preferences and willingness to pay of the Isfahan residents in using urban park.

In this study, 6 parks including Soffeh, Najwan, Ghadir, Hasht-Behesht, Laleh and Qalamestan were selected and the opinions of 150 visitors were analyzed and evaluated by convenience sampling method from different ranges of citizens. After designing and collecting the data of the researcher-made questionnaire and ensuring its validity and reliability, the descriptive statistics of the data were used for a better interpretation of the results. Information about access to the park was classified in the categories of travel time, time of visit per week, vehicle used to reach the park, and accompanying persons. A 5-point Likert scale was used to collect answers in each field. Analysis of variance (ANOVA) was also used to check the differences between the opinions of different groups. In this study, a desirability index was designed to measure people's willingness to attend and use the park.

The most frequent age group visiting the parks was 18 to 25 years old, comprising 24.28% of the visitors. Among the selected parks, Soffeh Park had the longest average travel time of 40.13 minutes, followed by Hashtbehesht Park with 38.73 minutes. The study also demonstrates that the choice of transportation means is influenced by the park's type and location. Soffeh Park and Hashtbehesht Park were predominantly accessed by public transportation, accounting for over 40% and 38% of visitors, respectively. Regarding the connection with nature, Soffeh and Najvan Parks exhibited a significant difference (p-value ≤ 0.05) compared to other parks. Overall, the results indicate that Najvan and Soffeh parks are commonly chosen for recreational purposes rather than their proximity to visitors' residences. On the other hand, parks with public designs are frequently utilized by local residents but less often by tourists or specific interest groups. Notably, visitors displayed a greater willingness to contribute financially to the preservation of Soffeh and Najvan parks, likely influenced by the natural ambiance these parks offer.

Based on the study's findings, it is recommended to enhance the naturalness of parks, expand their size, and consider designing parks tailored for specific purposes rather than general public use. Such measures are more likely to gain acceptance among citizens, leading to increased willingness to contribute financially and heightened desirability of the parks. It is suggested to classify the design elements of the parks and the quality of their presence and arrangement inside the park should also be considered as one of the important criteria of desirability. Considerations such as the per capita of each park, their size, and location are also one of the most important factors that affect the quality and desirability of the park.

Drought is a natural phenomenon that occurs almost in most regions of the world, and due to its relationship with agricultural products and water resources, it is considered as one of the most important issues in environmental sciences. The effects of this phenomenon are greater in arid and semi-arid regions due to their less annual rainfall. In contrast to traditional methods, the use of remote sensing techniques and satellite images has been considered as a useful tool for agricultural drought monitoring. The main objective of this study is to investigate changes in agricultural land use using normalized vegetation difference index and satellite images.

In this study, Landsat satellite images were used to investigate the trend of agricultural land use changes in the Zayandeh Rood catchment during 1984-2023. To do this study, the normalized plant difference index was used for each year. Since various patterns of cultivation with different time differences are present in the study area during a year, it is not possible to use a selected image as the basis of a year, on the contrary, it is necessary to examine different images for different times of the year. To identify and specify the set of all the pixels that have gone under the cultivation surface in one crop year. Since this process would be very time-consuming, an innovative approach was used. First, in the Google Earth Engine system, all the annual Landsat images were called year by year. Then, the images with cloud cover were removed and the maximum filter was applied to the bands of the remaining images. Then, the normalized vegetation difference index of new annual images was created and by applying a threshold of 0.2, agricultural lands were separated from other lands. The extent of agricultural land was calculated in each year and the linear regression model was used to identify the change process. In other words, the extent of agricultural land was used as a dependent variable and time was used as an independent parameter on an annual scale.

The extent of agricultural land in 1984 was about 25 thousand hectares, which with a decreasing trend over time reached 21700 and 15180 hectares in 1994 and 2014, and finally reached its lowest value in 1401. It has reached 11.250 hectares. This trend shows a 55% reduction in the abandonment of agricultural land at this point in time. Also, the value of the normalized plant difference index in agricultural lands has experienced a decreasing trend over time, which indicates the change in the cultivation pattern towards low-density crops with low biomass such as wheat.

The results of the changes in the extent of agricultural use in the study area showed a decreasing pattern, so, there has been a loss of agricultural land, which is consistent with the decreasing pattern of the water level of the Zayandeh Rood watershed. This phenomenon can be directly attributed to the reduction of water resources in the region. In the last decade, the amount of water allocated to carry out agricultural activities in this region has decreased a lot due to the water volume of the Zayandeh Rood watershed approaching critical limits.

Identification of citizens' climate perception is one of the key elements in formulating policies related to mitigating the heat island effect. This study also examines some influential aspects of climate perception and its relationship with individual characteristics in the city of Isfahan. For this purpose, the central core of Isfahan City was divided into four temperature zones ranging from 27 to 43 degrees Celsius. A random sample of 100 individuals was selected from each temperature zone, and their personal information, along with their perception of temperature increase, disruption in life, location-dependent temperature differences, and voluntary willingness to change behavior, were collected. The results showed that as the temperature increased in each temperature zone, citizens' views on the increased difficulty of life and efforts to adapt to higher temperatures were more pronounced. They indicated that the air temperature inside Isfahan City is relatively higher than in the surrounding rural areas. Regarding the perception of the urban heat island, a significant correlation was found between the intensity of the heat island and age, and educational level. The temperature increase elicited similar responses among all interviewees in all parts of the city, and the existence of an urban heat island has led to various disturbances (in terms of intensity) in the lives of different individuals residing in different temperature zones. Overall, attention can be focused on individuals in younger age groups and those with higher education as target groups for improving their climatic conditions and maximizing citizen participation as a benchmark.

Protected areas are vital tools in the management and conservation of both terrestrial and marine environments. However, the conflict between conservation priorities and resource extraction from protected areas is increasing worldwide. Due to its high biodiversity, Mouteh National Park and Wildlife Refuge hold significant conservation value; nevertheless, in recent years, it has been heavily exploited due to rich mineral attractions. Consequently, the current study investigates the Cd concentration in the soil and plants in the Mouteh Basin, its transfer factor, and the influencing factors.

45 soil samples were collected from a depth of 0-30 cm, and leaf samples of the Artemisia sieberi plant were obtained at 15 locations within the plain region of the Mouteh National Park and Wildlife Refuge, which spans an area of 423 km2. The concentrations of Cd in these samples were determined using an atomic absorption spectrometer. The uptake of Cd by the plants was calculated and interpreted using the transfer factor. The Inverse Distance Weighting method in ArcGIS software was used to identify areas contaminated with Cd, with concentrations exceeding 5 mg/kg in soil and 2.0 mg/kg in plant leaves, based on the standard limits set by the WHO and U.S. EPA. Finally, linear, logarithmic, power, exponential and polynomial regression models were employed to examine the impact of road transportation on Cd pollution.

The mean Cd concentrations in plants and soil (mg/kg) were found to be 0.39 ± 0.11 and 2.97 ± 0.15, respectively. The highest soil concentration of this element was recorded in the central areas of the region, reaching 15.00 ± 0.69 mg/kg. This concentration gradually decreased towards the southern parts of the region. The highest concentration of Cd in the leaves of Artemisia sieberi was observed in the northern sections of the region, at 0.62 ± 0.05 mg/kg, while the lowest value was recorded in the central part (0.32 ± 0.05 mg/kg). The transfer factor of Cd ranged from 0.005 to 0.544, with a mean of 0.934. According to the standard limit of 5 mg/kg for Cd in soil, an area equivalent to 20.80% of the region was identified as Cd-contaminated. However, considering the permissible limit of 0.2 mg/kg in plants, a significant portion of the vegetation cover in the area (more than 95%) was found to be contaminated with Cd. The best regression model was achieved in the exponential form with a coefficient of determination of 0.301, indicating that road transportation is one of the most significant human factors contributing to the increase in Cd pollution in the Mouteh soil and plant species Artemisia sieberi.

The exploration of gold and the establishment of multiple mines in the Mouteh region, as well as the construction of highways in the northern province of Isfahan, have led to significant threats to the ecosystem and species of this area. Based on the results, the soils of the Mouteh plain region have been contaminated with Cd. The high concentration of this element in soils near the roads and its decrease with increasing distance from the road are indicators of the impact of road transportation on soil pollution. Therefore, preventing the construction of transportation routes in areas with rich biodiversity, improving the quality of fuels and vehicles, and reducing traffic congestion are among the most important measures for protecting the environment and reducing pollution from potentially toxic elements in natural areas.

The development of technology in drilling oil and gas wells has created serious problems for the environment. Drilling fluids have some unwanted compounds such as heavy metals, hydrocarbons, and salt. The increase in the volume of these activities has caused the increasing spread of waste in the lands adjacent to the drilling rigs. The discharge of these compounds in the soil causes damage to the environment. Due to the importance of drilling waste management to reduce the environmental pollution caused by them, native plants of the region were selected and compared to the examination of salinity and heavy metal pollution levels, equivalent to the concentrations of drilling mud, which were prepared and implemented in the laboratory. To evaluate germination indicators under salinity stress and heavy metals (Cr.Cd.Pb), an experiment was conducted in the form of a completely randomized design in the year 2020 in the laboratory of the Faculty of Agriculture of IslamicAzad University of Isfahan (Khorasgan).The treatments included the concentration of metals. heavy and salinity at the level of 0,5,10,15%, four plants (Triticum wheat, Hordeum vulgare barley, Avena sativa oats, and Chenopodium quinoa) were selected with three replications. Comparison of the average fresh and dry weight of seedlings, seed germination index, and germination speed of wheat, barley, quinoa, and oat species showed that with the increase of salinity and metal stress treatments and their combination treatment, these traits decreased significantly. According to the research results, the quinoa plant species had the highest fresh and dry weight of seedlings among plant species.The allometric coefficient in the oat plant species was lower than the three wheat, barley, and quinoa plant species. 10% salinity treatment followed by 5% salinity showed the highest allometric coefficient in the quinoa plant with 1.93 and 1.86, respectively, which increased by 8% and 4%, respectively, compared to the control treatment. The oat plant had the highest seed germination index under 10 and 15% salinity conditions. The highest germination rate was related to the wheat plant, followed by the oat plant, and the barley plant had the lowest germination rate in all experimental treatments. In the conditions of salinity and metals and their combined treatments (salinity+metals), the quinoa plant had a higher germination percentage than the other three plant species. It can be concluded that wheat and quinoa plants are more suitable for the bio assay of heavy metals and salinity than the other two plants.

Water, as one of the most basic needs of life, currently includes our drinking, agriculture, industry, economic-social and security-political issues for survival and expansion. Due to the importance of this issue, the basic management and exploitation of water resources has become one of the important global issues. Nowadays, remote sensing technique has many uses in agriculture, such as determining the area under cultivation and predicting crop yield. In this research, the evaluation of the detection method of zones, water levels and rivers with Normalized Difference Water Index (NDWI) using Landsat 9 satellite images in the period of 1984-2022 was used to separate the water cover. The obtained results showed that there is a noticeable trend in reducing the water level in the desired points, which can be directly related to water harvesting for agricultural purposes in different parts of the route. In other words, the amount of water that the NDWI index shows in each station has a significant difference with other stations, which indicates significant water extraction in all parts of the Zayandeh-Rood. Therefore, in the present study, it was found that the normalized differential water index was easily able to extract water from satellite images and is a suitable index for predicting the condition of the Zayandeh-Rood Watershed

The Isfahan water treatment plant, with a final capacity of 12.5 cubic meters per second, is one of the largest water treatment plants in the Middle East. It has 10 sedimentation basins with a retention time ranging from 1.91 to 2.75 hours; each basin has a volume of 9172 cubic meters and a surface load of 14.2 to 2.85 cubic meters per square meter per hour. This research was conducted to investigate the efficiency of the combined use of accelerator sedimentation basins equipped with tube settlers on a full and field scale. The goal was to achieve greater efficiency in improving the quality of output water and managing the reduction of water wastage, backwashing time wastage, as well as reducing the need for repairs and maintenance of treatment plant equipment. In this study, the accelerator sedimentation ponds related to two stream 1 clarifiers from the first phase of the treatment plant were equipped with rapid tubular settlers made of food-grade polypropylene. These settlers are hexagonal in shape with a hydraulic diameter of 50 to 80 mm; placed at a 60-degree angle to the surface, and have a length of 90 cm. Stream 2 was operated without the simultaneous use of rapid tube settlers in the circuit. The amount of backwashing’s of filters, the number of times the aerators and backwashing pumps are engaged in the circuit, the amount of water wastage, and the time required for each wash in streams 1 and 2 were compared with each other. The results of this study showed that the number of times of reverse washing of filters was reduced by an average of 25% compared to stream 2. This action also reduced the amount of water used for backwashing the filters and consequently reduced the number of working hours of the electrical and mechanical equipment of the water treatment plant.

The purpose of this research was to investigate the impact of pH, EC, and organic carbon (OC) on the amount of total nitrogen in the soil of the Zarin Shahr Region using statistical analyses (borderline analysis and step-by-step regression). In the Zarinshahr region, a comprehensive study was conducted for a duration of one year across all four seasons. The study employed GPS technology to select a total of 60 points in agricultural, tree plantations, and bare soil areas while ensuring an average distance of 546 meters between each point. The plots areas were 50x50x20 cm in length, width, and height. The various soil samples collected from these plots were analyzed. Then, the maximum reaction of a biogeochemical process to a certain environmental factor was determined using borderline analysis. According to the findings, the soil's EC exhibited higher readings during the hotter seasons of the year, in comparison to other seasons. Specifically, during the summer, the maximum value of this parameter was approximately 1587 dS/m, while in the winter, the values were nearly 1140 dS/m. Also, pH levels of the soil was generally alkaline. The seasonal variations in soil OC and organic matter percentage were evident, with notably lower values (ranging from 0.04-2.52) in the autumn and winter, followed by a marked increase during the summer and spring (0.12-2.39). Boundary line analysis was used to investigate the relationships between variables and the values of indicators that caused the maximum increase in soil nitrogen. It was ascertained that OC percentage and soil nitrogen percentage exhibited a significant linear correlation in all land uses. Hence, increase in OC percentage resulted in an increase in the total nitrogen content in the soil. The step-by-step regression analysis in a systematic manner revealed that, within the variables under investigation, the percentage of total nitrogen in the soil can be determined based on the impact of two key parameters, namely, EC and the percentage of OC. The results suggest that use of boundary line analysis and step-by-step regression represent a viable approach for determining the effects of the significant factors on the levels of nitrogen content in the soil.

Particulate matter is an important air pollutant in urban areas with unknown effects on the fetus. The present study investigated the effect of PM2.5 concentration on the level of liver enzymes in the neonate born in Isfahan city, one of the most polluted cities in Iran was investigated.

This case study was conducted between March 2019 to September 2020 in Isfahan. Blood samples were collected from the umbilical cord of 200 newborns to measure the level of liver enzymes from their blood serum using quantitative diagnostic kits and the spectrophotometry method. Seasonal distribution of PM2.5 concentrations was estimated using the data of monitoring stations; thus, modeled and used to measure their correlations with liver enzyme levels at trimester in buffers of 500, 1000, and 2000 meters.

The mean levels of liver enzymes were 38.42 (AST), 10.09 (ALT), 407 (ALP), and 152 (GGT) IU/L. Spring and fall with PM2.5 concentrations of 24.35 and 35.35 and μg/m3 had the lowest and highest levels, respectively. Enzyme levels within the third trimester and the whole period of pregnancy in the 2000-m buffers had the highest correlation coefficient, which indicates the effect of high PM2.5 concentrations on neonatal liver function, especially in late pregnancy.

Proximity to particulate matter sources in cities will increase the likelihood of elevated neonatal liver enzymes during the fetal period which is higher and more effective in late pregnancy.

PM2.5 concentration has become a public concern in cities due to its harmful effects on human health. In this study, due to the importance of air pollution, the effect of urban traffic conditions on the emission of urban air pollutants (PM2.5) was studied using geographic weight regression model (GWR) and IDW interpolation method.

For this purpose, concentration of PM2.5 in 2019 was collected from 9 air pollution monitoring stations of Isfahan Municipality and population data and traffic in the city were collected and entered into the model. Interpolation IDW method was used for preparing seasonal air pollutants dispersion maps. After performing geographical weight regression on the model parameters and in order to evaluate the validity of the model, the RMSE parameter was used, which is obtained from the difference between the actual value of the concentration and the predicted value and indicates the predictive power of the model. Finally, R2 values ​​were calculated and Moran's index was used to examine the spatial autocorrelation test.

After performing geographical weight regression on the model parameters, in order to evaluate the validity of the model, the value of R2 was calculated and the Moran index was used to examine the spatial autocorrelation test.

According to the amount of R2=0.75 for PM2.5, a direct correlation has been shown between this pollutant and independent variables, especially in the summer. The Moran index results showed that the GWR model was a good model for investigating the spatial temporal pattern of suspended particles.

 AERMOD model is a Gaussian model, which is widely used to model the emission of air pollutants from different sources and for distances less than 50 km from emission sources. Nowadays, petrochemical industries are one of the essential and cost-effective industries for the country. So, along with the progress of these industries, air pollution control should be considered. The purpose of this study is to model the dispersion of two pollutants, carbon monoxide, and nitrogen oxides, emitted from Maroon petrochemical stacks.

 In this study, the emission of carbon monoxide and nitrogen oxides from the stacks of the Maroon Petrochemical Complex was modeled using the AERMOD model in 2019. For modeling the dispersion of pollutants using AERMOD, which is the model proposed by the EPA for comprehensive studies of air pollution, the three-hour data from Bandar Mahshahr synoptic station as well as a digital elevation model with 30 m pixel size were gathered and then modeling was done in an area of 2500 square kilometers. 

 The results indicated that the average annual concentration of nitrogen dioxide and carbon monoxide pollutants was 1.44 and 1.3 mg/m3, respectively, at a distance of 900 meters from the stacks. This land area includes Maroon Petrochemical Complex and does not affect residential areas.

 The results of this study also indicated that the AERMOD model is suitable for comprehensive studies, with a focus on pollution concentration. The results of this study showed that the concentration of carbon monoxide and nitrogen dioxide pollutants is lower than the standards assigned by the Iranian Department of Environment. Maintenance programs, employee health monitoring, output control, and appropriate controls under EPA recommendations have a significant impact on controlling and reducing pollutant emissions. The result of this study also showed that the AERMOD model is highly efficient in comprehensive air pollution studies that require a detailed investigation of pollutant concentration.

Soil is one of the most important natural resources that provides more than 97% of human food needs. Soil organic matter (SOM) is an important soil quality factor that greatly affects soil’s physical, chemical, and biological properties. Modeling and mapping of soil properties are critical in many environmental, climatic, ecological, and hydrological applications. The main objective of this study is to model the distribution of soil organic matter and organic carbon using satellite images and random forest and kriging models in Lenjan County.

In this study, digital maps of four main soil parameters including soil organic carbon, soil organic matter, electrical conductivity, and pH are prepared using random forest and Kriging methods in Lenjan County. Based on homogeneous land units, a total of 110 points in the study area are determined, and in these points, samples are taken from a depth of 0 to 30 cm of soil surface. Sampling is done in July 2021 and Sentinel-2 satellite images are acquired from the same month because better information is available this month due to fewer clouds and increased direct reflection from the soil surface. In addition, 16 environmental variables affecting the distribution of soil parameters are used. Various auxiliary variables such as NDVI, NDWI, DEM, and Slope are used for prediction, which are all directly or indirectly extracted from satellite images.

The maps obtained by the random forest method showed more accuracy than the kriging method. The zoning map prepared using the random forest method displays much more details than the map prepared by kriging method. The output of the random forest model with the combination of different auxiliary variables showed values ​​equal to 0.312, 0.54, 0.73 and 0.16 of the modeling error for soil organic carbon, organic matter, electrical conductivity and pH, respectively. In the study area, the maximum values of soil organic carbon and organic matter were observed in urban areas and the highest values of electrical conductivity and pH were observed in agricultural lands. The most important variables affecting the spatial distribution of organic carbon and soil organic matter are clay, slope and silt. While in modeling electrical conductivity, silt BI and Aspect and in modeling pH, MNDWI, NDWI and DEM variables are recorded as more important than other variables.

In general, this study demonstrates that land use regression models based on random forest method can help mapping soil parameters faster and more efficiently. There is a strong need for efficient and accurate methods, including land use regression, for continuous monitoring of changes in soil quality in different landscapes. Land use regression contributes developing advanced maps of soil quality parameters using cost-effective and accessible spatial information.

Surface water resources are limited in many regions in Iran, so groundwater resources are considered the most suitable resources for supplying water. The excessive use of chemical pests and fertilizers has resulted in groundwater pollution. Therefore, it is necessary to determine the vulnerable areas to preserve these vital resources. In this study, the DRASTIC model was used to analyze the vulnerability of groundwater in Khaled-Abad- Bardood plain. Seven factors including the nutrition of pure water, groundwater depth, unsaturated area, soil texture, Hydraulic conductivity, aquifer media, and topography were used in the model. After the preparation of 7 layers, appropriate weight was assigned to these factors. Finally, by mapping these layers and applying their weight to them in ArcGIS 10.5, the vulnerability maps of the groundwater were generated. To validate the model, sampling and measuring the amount of nitrate for 13 wells located in the Khaled-Abad-Bardood plain were conducted. The results indicated that 22% of the plain was vulnerable based on the DRASTIC model, which was located in the vicinity of Bardood city and its eastern areas. The results also showed that the correlation of the DRASTIC vulnerability map with the measured nitrate was found to be 74 %.

Regarding the status of air pollution in Isfahan, this study aims to evaluate the air quality of Isfahan due to Particulate Matter and find the relationship between landscape patterns and suspended particles.

to measure the concentration of the suspended particles using a Met One dust meter in 52 points, the city was randomly sampled. The land use map of Isfahan city was prepared after downloading satellite images from the site of the United States Geological Survey. The land use map was generated in six classes with the maximum likelihood classification method in Terrset software. To create the distribution map of suspended particles, the information of 52 stations and inverse distance weighting method in ArcGIS 10.5 was used. Landscape metrics (Normalized Entropy, Edge Density, Patch Area, Relative Richness, and Patch Compactness) were used to quantify the pattern of landscape. The landscape metrics were quantified using FRAGSTATS software.

The results showed that vegetation has a reducing effect on air pollution. A positive and significant correlation was observed between the amount of suspended particles and relative richness. Moreover, a significant negative correlation was observed between PM2.5 and (NDVI), which means that the amount of suspended particles decreased with increasing vegetation density. 

In general, by calculating the concentration of suspended particles in Isfahan, it was found that the southwest, south, and southeast are exposed to more suspended particles. The high density of green space landscaping leads to a reduction of particulate matter pollution. From the analysis of changes in suspended particles in the region, it was inferred that the more we move to areas with weaker vegetation, the higher the concentration of suspended particles.

Industrialization, urbanization, and population growth are considered as the main causes of urban air pollution that is responsible for millions of deaths per year worldwide. Besides, the impact of urban air pollution on health is considerable. Respiratory, lung diseases, and heart attacks are largely due to urban air pollution. However, there is a lack of air pollution monitoring stations (hereafter simply stations) in most cities worldwide because of their high expenses, and, thus, access to high spatial and temporal coverage of air pollutants and their distribution is limited. To address this issue, the main purpose of this study was to estimate CO concentration in Isfahan, Iran, based on air pollution monitoring stations and Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2018 to 2019.

In the present work, we used ANFIS and Random Forest (RF) algorithms to estimate the CO concentration level. To implement the ANFIS algorithm, based on collected air pollution data from the stations and Aerosol Optical Depth (AOD) data from MODIS imagery, the basic fuzzy rules were extracted. Further, with the integration of fuzzy rules and artificial neural network algorithm, ANFIS algorithm was implemented to model the dispersion of CO level in Isfahan city. To model the dispersion of CO using the RF algorithm, air pollution data and AOD data were used. Since the number of trees and the number of variables in each node are two basic parameters in the success of the RF algorithm, a 10-fold cross-validation method was used to identify value for these two variables.

Our findings indicated that the RF algorithm was more efficient and accurate in spatial modeling the dispersion of CO because it achieved better RMSE and MAE results than the ANFIS algorithm. The RMSE error value of the RF and ANFIS algorithms were 0.724 and 0.809 ppm, respectively. Furthermore, the MAE error value of the RF and ANFIS algorithms were 0.636 and 0.792 ppm, respectively. In the case of spatial dispersion of CO pollutants, the ANFIS algorithm showed that the amount of this pollutant varies in the city. For example, the central and northern regions of Isfahan had the most pollution and the eastern and western regions of Isfahan had the least pollution based on the ANFIS algorithm. Regarding the RF algorithm, it was observed that by moving from the southeast to the northwest of Isfahan, the amount of CO pollutant increases, and the northwestern regions of Isfahan had the highest CO pollution. The examination of numerical values obtained from the ANFIS algorithm showed that the lowest amount of CO pollution in Isfahan city was equal to 1.43 ppm and the highest amount was 2.13 ppm. In contrast, obtained results from the RF algorithm showed that the lowest amount of CO pollution in the city was equal to 0.57 ppm and the highest amount was 2.27 ppm.

Overall, it can be concluded that since ANFIS and RF algorithms are appropriate and accurate methods in modeling environmental problems due to their nonlinear modeling, the ability to reduce the negative effects of outgoing data, and less sensitivity to the local minimum problem. It should be noted that a significant part of the error observed in the results of ANFIS and RF methods was related to the intrinsic properties of MODIS imagery (i.e., cloud cover and mixed pixel problem due to the coarse resolution of MODIS imagery), point measurements of air pollution data collected from the stations, and recorded data error at the stations.

Wetlands, as one of the most important natural habitats in the world, play an important role in supplying fresh water and maintaining biological balance. The aim of this study was evaluating the spatial distribution of heavy metal concentrations of lead, zinc and cadmium in the sediments of Astara's Steel wetland. Data were obtained through sediment sampling with Van Veen grab sampler and analyzed through laboratory and statistical analysis. Sampling was done in autumn and winter of 2019-2020 monthly and randomly from wetland sediments in 4 stations in north, south, east and west of Steel Wetland. The results showed that the concentration of heavy metals in the sediments of the northern and western parts, especially the northwestern part of the wetland is higher than the southern and eastern parts. Experimental data indicated that the metals zinc, cadmium and lead with concentrations of 1.2, 1.2 and 0.99 ppm, respectively, had the highest concentration in the western station and the lowest concentration (zero ppm) at the Southern Station. The results of statistical analysis showed that the difference between the concentrations of heavy metals lead, cadmium and zinc in wetland sediments at different stations was significant (p<0.05). Pearson correlation method was used to investigate the correlation between heavy metals in the wetland. The results showed that the correlation between cadmium and zinc concentrations was not significant (p>0.01). Also, the correlation between lead and zinc concentrations was not significant (p>0.01). The correlation between cadmium and lead concentrations was significant (p<0.01).

Soil is one of the important natural resources of any country, which plays an important role in preserving the environment and producing food. Increasing and decreasing the amount of total soil nitrogen due to various agricultural methods, the entry of industrial wastewater into water and other factors, leads to microbial contamination of soil, reduced vegetation and deficiency in agricultural products needed by humans. Mapping soil nutrient distribution helps mangers in decisions. Since laboratory analysis of these parameters is time consuming and costly across large scales, attempts have been made in recent years to study soil nitrogen based on remote sensing techniques. In this regard, the present study investigated the applicability of remote sensing predicting soil total nitrogen in the east of Lenjan city.

Nitrogen reference points were identified by analyzing 50 randomly-selected surface soil samples from 0-20 cm depth. Nitrogen of soil samples was measured by Kjeldahl method after drying soil at 25 ° C, passing through a 2 mm mesh sieve and transferring to the laboratory, to compare the final results obtained from field sampling and remote sensing technology. Landsat 8 OLI Satellite Image of 2019 (Path 164/Row 37) was obtained and geometric and radiometric correction were applied. Cloud cover for image provided was less than 10%. To reduce the effect of atmospheric diffusion on the quality of image, radiation and atmospheric correction were performed using the FLASH model. the Landsat-8 satellite image (rows 164 and 37) taken on 15 Sep. 2019 and along with three topographic indices of elevation, slope and topographic wetness index (TWI) were introduced to the multiple linear regression and random forest models. 

The digital elevation map of the area showed elevation values ​​between 1100 and 2050 meters. The slope of the study area was less than eight percent. Numerical values ​​of TWI index near water bodies were found to be 0.77. DVI and EVI index values ​​increased with increasing vegetation cover. NDVI index showed values ​​higher than 0.3 and NDWI index as a water index showed a maximum value of 0.77. The SAVI index showed high differences from areas without cover to sparse cover and areas with strong vegetation. SBI index and SI salinity indices showed very high variability in terms of soil parameters in barren lands. Nitrogen regression model was built using three indices RVI, DVI and TWI with p-values of 0.049 and 0.00, respectively. In the nitrogen random forest model, however, plant and soil indices played a more important role in model construction with an of r2 value of 0.44.

Total soil nitrogen in soil parameters showed correlation with density and sand and clay from soil texture and in topographic parameters with elevation and spectral indices with EVI RVI, SAVI, NDWI, NDVI and DVI at the level of 0.01 and with SI3 of salinity indices at the 0.05 level. In soil parameters, silt is correlated with sand and clay at the level of 0.05 and sand with clay as well as density with clay are correlated at the level of 0.01. The results of this study showed that the topographic condition of the region along with red and near infrared-based indices had a significant role in predicting soil total nitrogen. Results also showed a slight difference was observed between the two models in predicting soil nitrogen.

The present study aims at investigating land use changes (as one of the effective human factors on water systems) as well as its relation with water quality at spatial scales of the entire basin, sub-basin and defined buffers (10 and 15 km) in Zayandeh-Rud Basin, Isfahan, Iran. By means of supervised classification method along with maximum likelihood algorithm, it classifies the land use map into five categories, including agriculture, bare lands, urban areas, vegetation, and water. The research collects data for 11 water quality parameters in seven sampling stations of Zayandeh-Rud Basin in 2002, 2009, and 2015 from Isfahan Water and Sewerage Organization. Correlation analysis is then conducted to investigate the effect of land use changes on water quality at different spatial scales. Land use analysis in the entire basin shows that despite an increase in urban and agricultural lands from 2002 to 2015, bare lands, vegetation, and water covers have had a decreasing trend. Moreover, various land uses at different scales show some correlation with water quality parameters. The strongest correlations in this study belong to sub-basin scale. Therefore, it is recommended to use this spatial scale to investigate the relation between land use and water quality parameters

Nowadays, soil salinization is one of the world’s major threats that reduce soil productivity by intensifying the process of desertification and land degradation. Since laboratory analysis is mostly time consuming and costly, especially in large scales, attempts have been made to study soil salinity using remote sensing techniques in recent years. The present study assessed the potential of remote sensing in predicting soil surface salinity in the east of Lenjan                 City. Salinity reference points were identified based on analyzing 50 randomly selected surface soil samples. Satellite indices including DVI, NDVI, EVI, MSAVI, SAVI, RVI, NDWI, SI1, SI2, SI3 and SBI were derived from a Landsat-8 satellite image (path and row of 164 and 37) acquired on September 13, 2019. These indices along with three topographic indices of elevation, slope and topographic wetness index (TWI) were introduced to the Multiple Linear regression and Random Forest models. The linear regression model was built using band 6, RVI, NDVI, elevation and TWI with a p-value of 0.049. In the Random Forest model, band 7, slope, band 5 and elevation were among the most important parameters. The r2 value of this model was 0.21. The results of this study showed that topographic indices had also great importance in salinity prediction. Moreover, comparison of the results indicated that Random Forest had a higher accuracy than the regression model for determining salinity in the study area.

Noise pollution is one of the most important environmental problems in metropolitan areas which, threats humans' health in different aspects of mental and physical. Isfahan like other large cities faces to traffic congestion due to vehicles traffic and lack of an effective mechanism to restrict the use of personal vehicles. The purpose of this study was to investigate and map the distribution of noise pollution in areas 3 and 4 of Isfahan to determine the critical points. 16 stations were selected, and sound parameters were measured three times a day during the winter and spring by Bruel & Kjaer sound meter. The results showed that the highest rate of pollution in January recorded in station near Bozorgmehr bridge was 100 dB and the lowest pollution recorded in station before Roknodoleh street and station after Apadana intersection with 69 dB. The highest level of pollution in February was in station near Bozorgmehr bridge at 103 Db. In March, stationafter Mobarezan street had the highest pollution. In April, the highest pollution rate belonged to station near Bozorgmehr bridge at 106 dB. In June, stations 1, 2, 9, 10 and 11 were the most polluted stations w. The results of noise pollution emission mapping using Inverse distance weighted method showed that the highest noise emission was in the area of Bozorgmehr and Ahmadabad Square, which decreased with the distance from the main street. Geostatistical tools for preparing the map of sound dispersion is an useful tool. Results of this study are useful as reference and guideline for future planning and regulations on noise limit to be implemented for urban areas.

In recent years, population growth, increasing vehicles, and industrial development have caused air pollution. One of the major disasters, creating significant problems for human and endangering their health is air pollution. The main objective of the present study was to select best sites for air pollution monitoring stations in zones 1 and 3 of Isfahan city using fuzzy technique and analytical hierarchy process (AHP). Accurate monitoring of the air pollution condition requires accurate and appropriate dispersion of monitoring stations throughout the study area, so that the optimal locations of these stations can be selected using fuzzy and AHP methods. In the present study, considering the necessity of determining the location of air pollution monitoring stations, the best sites for these stations were selected based on important criteria such as distance from market, recreation areas, parks, parking areas and existing stations using fuzzy and AHP methods. The results showed that Fuzzy-AHP method was flexible and accurate method to determine the appropriate areas for air pollutant monitoring stations.

The usage of water resources requires an understanding of water quality, since water resources are the ultimate recipients of pollution from human activities. The main objectives of this study were to investigate the water quality in Choghakhor wetland located in Chaharmahal and Bakhtiari Province using field surveys and to measure the chlorophyll A, using satellite images and to calculate the correlation between the amount of chlorophyll A derived from satellite images and field measurements. For this purpose, water quality of wetland was evaluated using laboratory methods and chlorophyll A was measures using NDCI index and laboratory method. Parameters measured using laboratory methods were DO, EC, PH, TDS, TSS, chlorophyll A, nitrates, water depth, water temperature and the ambient temperature. Sampling was done in 4 stations in the wetland and all tests were carried out within 24 hours after sampling in the laboratory. Statistical analysis of data was done by two-way ANOVA and mean comparison was performed based on Duncan's multiple range test at significance level of 5 percent. Chlorophyll A concentration was measured using NDCI index in ArcGIS 10.3 software. The amount of chlorophyll A derived from satellite images were correlated against the amount of chlorophyll A derived from water sampling and results showed a good correlation (R2 =0.92) between the data obtained from remote sensing and data driven from field survey. This showed that satellite imagery could be used as a useful tool to estimate the amount of chlorophyll A.