نشریه محیط زیست طبیعی
سال هفتاد و سوم شماره 1 (بهار 1399)

Polycyclic aromatic hydrocarbons (PAHs) are counted as important pollutants due to their ubiquitous occurrence and toxicity, mutagenic and carcinogenicity characteristics. They are counted as the main pollutants of soil. One of the most effective, environmentally friendly and cost-effective methods is bioremediation of contaminated soils. Fungi have been identified as the most successful microorganisms in the removal of these compounds. The purpose of this study was to identify endemic fungal strains compatible with PAH compounds. Contaminated dumping site of acidic sludge, produced from the recycling process of used oils in Eshtehard industrial complex was selected as the study area. Some endemic fungal species including Aspergillus fumigatus, Alternaria chlamydosporigena and Penicillium chrysogenum were isolated from contaminated soil and inoculated into contaminated soil. To examine the efficiency of fungal remediation of PAHs, after 21 and 50 days of inoculation, residual PAHs concentrations were measured by Gas chromatography mass spectrometry (GC/MS). The results interpreted by the mean and standard deviation of data using Excel software and comparison of means by Tukey HSD test. The results revealed that all three fungal species, identified in this study, were significantly effective in eliminating PAHs. The highest removal rate related to Naphthalene and Acenaphthene was due to the volatility of these compounds. Removal of Benzo [a] pyrene, which is the most cancerous PAH and has the highest molecular weight in this study, was 50%, 40% and 32% by Aspergillus fumigatus, Alternaria chlamydosporigena and Penicillium chrysogenum isolates, respectively. In addition, there was a significant difference between different species in reducing the concentration of different PAHs, at α = .05.

Drilling operation is one of the difficult and costly operations in petroleum industry. One of the challenges encountered with oil and gas industries is contaminations caused by drilling waste. Chemical compounds that used for adjusting the physical and rheological properties of drilling fluid and drill cuttings can have destructive effects on the environment. In this study, application of diatomaceous earth as a natural material for the treatment of drill cuttings was investigated on waste samples from Cheshme-Khosh field. To evaluate the quality of the stabilized waste using diatomaceous earth, Sheen, Can and Retort tests were carried out. Moreover, the possibility of soil regeneration was investigated by wheat seed cultivation. The results of the experimental tests done on the stabilized waste showed that moisture adsorption and cuttings fixation was well done. Unlike other chemicals, no changes were observed in stabilized waste pH using diatomaceous earth. The optimum amount of diatomaceous earth for fixation of waste samples contain 65% and 78% solid particles were obtained 0.038 and 0.025 gr/cm3, respectively. Wheat seed growth in stabilized samples indicated the compatibility of the method with the environment. In this method, not only the drilling wastes contamination has been removed from the environment but also the soil regeneration becomes possible. The results indicated that the diatomaceous earth has a great potential for moisture removal and drilling waste stabilization

Leonardite is containing humic and fulvic acids, and possesses number of functional groups that play an important role on forming complex with heavy. In order to investigate the effect of Leonardite on the desorption kinetics of Cd, Zn and Pb in soils with different maternal sources, an experiment was conducted as a factorial based on a completely randomized design with two factors: 1. soil type (Syenite, Schist and Gabbro) and 2. Leonardite levels (0, 2 and 5%) in three replications. To carry out the experiment, soils were spiked with studied heavy metals and after adding Leonardite, samples were taken at 2, 24, 48, 7, 15, 30, 45, 60 and 90 days. Some physical and chemical properties, mineralogy (XRD) and desorption kinetics of heavy metals were performed at different levels of Leonardite. The amount of metals desorption in the sample treated with Leonardite decreased with time. Kinetics equations of exponential function, exponential function and parabolic diffusion were selected with a high coefficient of explanation (R2) and the lowest standard error (SE). The results showed that the composition and type of clay minerals were different. According to kinetics studies, the amount of metal desorption was significant between the different levels of Leonardite as well as the soils studied and the highest amount of desorption (a and b) of the heavy metals was observed in the Syenite soil.So, due to lower pH and CEC of Syenite soil, diffusion coefficient (Kd) showed a significant increase for the heavy metals (Cd,Pb and Zn) in comparison with other soils. Thus, Leonardite application must on the basis economic and physical and chemical properties of soil will be done.

Atmospheric emissions from cement industry are one of the major sources of environmental pollution. Heavy metals are among the most relevant substances emitted during the process of cement manufacture. The purpose of this study was to investigate the contamination of heavy metals of lead and manganese in the lands around the Yasouj Cement Factory. Sixty one soil samples of land around the factory were sampled at depths of 0-30 cm. Some soil properties such as total concentration of lead, manganese, acidity (pH), electrical conductivity (EC), organic matter and calcium carbonate equivalent were measured. To verify the normality of the data the Kolmogorov-Smirnov test was used. In the normal data series, Kriging method was used to prepare zoning maps. In an abnormal data series, the IDW method was used to prepare zoning maps. PH values ranged from 3.72 to 8.02, the electrical conductivity ranged from 0.07 to 0.96, the organic matter ranged from 0.82 to 2.19 and calcium carbonate equivalent ranged from 18.44 to 54 percent. Lead concentrations in soil samples varied from 2.25 to 168.75 mg kg-1 and manganese values ranged from 45 to 708 mg kg-1. The results of zoning in the study area showed that the highest amount of lead and manganese are inside the factory. The levels of lead and manganese in a number of samples harvested within the factory were higher than the global standard of USEPA, that indicating the accumulation of these metals in the soil and contamination of this factory. From these findings, it is highly recommended that environmental auditing of the cement production line be carried out to reduce the release of pollutants. It is also important that remediation activities be carried out on the soil to reduce the levels of lead and manganese to avert potential ecological disasters.

Planning for management of the forests in the future is not possible without having sufficient information about the forest degradation conditions in the past. The present study was conducted to model the forest degradation levels using logistic regression in the Masal watershed NO: 12. In this study, the factors of slope, altitude, geographic direction, distance from rivers, distance from the road networks, distance from residential areas, population centers and distance from barns as independent variables and different degrees of forest degradation entered into the regression model as dependent variables. Extraction of independent variables is done by using the digital maps of the area and recording the forest degradation degrees is done through terrestrial field work. To determinate the impact of each factor a hierarchical analysis method and to model the factors the logistic regression with five connects functions including: Cauchit, Negative log-log, Complementary log-log, Logit and Probit were used. The statistical tests of similarity and Wald were used to examine the significance of the model and its coefficients. The results showed that the model constructed with the Probit connection function has the appropriate capability in modeling the forest degradation. The results also showed that 20760.32 hectares of studied forests (equivalent to 83 percent of the total forest area) were degraded from very low to moderate levels. As a result, the conservation plans for 83% of the watershed forest areas and the implementation of recovery operations for 17% of the forest area (according to the forest degradation map presented in this study) have a favorable effect on improvement of the forest condition in the studied watershed.

Designation of protected areas is the most important action in conservation of biodiversity. Previous Ad hoc methods and focusing on charismatic species resulted in unrepresentative networks of protected areas. To compensate this problem systematic conservation planning is introduced and applied. Nearly, 10 percent of Iran is designated as protected areas that should be added up to 17% until 2020, as suggested by UNEP. Therefore, we applied Zonation conservation prioritization algorithm to introduce new high priority areas to be added to the current network. We chose 36 endemic and globally threatened enlisted in IUCN red list mammals, birds, reptiles and amphibians as surrogate species. We developed maximum entropy modeling on surrogates to develop habitat suitability models. Zonation approach was applied with all of the three cell removal rules of additive benefit function (ABF), core area zonation (CAZ) and Target based planning (TBP). The current area network was included in all the rules. Selection of the 20% of country area with highest conservation rank based on each rule lead to increase of species habitat conservation by an average of 16%. The ABF is the most efficient rule with conservation of 34.98% of surrogate habitats on average in comparison with CAZ (28.74%) and TBP (31.1%).

This study examines the effects of economic growth, energy consumption, biological capacity and trade openness on the ecological footprint (EF) of consumption as index of environmental degradation using a panel data during the period 1990–2013. A number of panel unit root tests confirm that the data are first-difference stationary. Results from Pedroni and Kao panel co-integration tests show that the variables are co-integrated. Therefore, the panel Fully Modified Ordinary Least Square (FMOLS) method is then employed to estimate the long run relationship between the variables. The results indicate a positive and significant relationship between ecological footprint (EF) and biological capacity, and a negative and insignificant impact of trade openness on EF. Energy consumption is also led to increase EF. The results showed the N-shape relationship between EF and economic growth. Therefore, economic growth will lead to further environmental degradation in this region.

Eichhornia crassipes is one of the world’s most invasive aquatic weed which in recent years has been considered as a serious threat to most northern Iranian wetlands. The aim of this research was to investigate the effects of compost and vermicompost of this plant as a growing media for lily flower. First, compost and vermicompost were made of water hyacinth. The experiment was a completely randomized design with nine treatments including peat 50% + perlite 50% (control treatment) and 25%, 50%, 75% and 100% compost and vermicompost. Most of cultivars had significant effect on most traits. The highest plant height and number of open buds were produced in peat + perlite. The highest number of bulblet was in 25% compost. The maximum amount of chlorophyll b was in the 25 or 100% compost. The highest air root was found in 100% vermicompost. The concentration of phosphorus in Peat + perlite was maximal. The lowest concentration of potassium was observed in 75% and 100% vermicompost. The highest concentration of calcium was in Peat + perlite, which had no significant difference with 25 and 75% compost. According to the results, Eichhornia crassipes compost and lower vermicompost percentage could be a substitute for peat in lilium plant.

One of the methods used by planners and managers to manage the land use change is its modeling. The aim of this research is to forecast the land use changes in Minab Esteqlal Dam Watershed using Multilayer Perceptron Network and Markov chain in 1409 horizon. The images of Landsat 5 TM (1995), Landsat 7 ETM+ (2003), and Landsat 8 OLI (2016) were used to prepare the land use maps. For this purpose, Maximum Likelihood Algorithm was used in three mentioned time periods. Transition potential modeling was done using Perceptron Neural Network and some of the static and dynamic variables and Markov chain was used to forecast the changes of land use in future. The parameters of GEOMOD method and Kappa statistics were used to evaluate the accuracy of the forecasting. Results of evaluating the calibration periods using GEOMOD methods and parameters of N (n), N (m), H (m), M (m), K (m), P (m), and P (p) and Kappa statistics indicated that the calibration period of 1995 to 2016 had the highest accuracy to forecast the land use in 2030. Results of the land use change in calibration period indicated that among six land use categories of forest, rangeland, agriculture, residential areas, bare lands, and water resources, the highest increase was related to the agriculture land use with an area of 627.05 Km2 and the highest decrease was related to the rangeland land use with an area of 580.35 Km2. Rangeland degradation has been done for developing the agricultural lands and residential areas. Also, results of land use change modeling for 2030 indicated that, agricultural areas has increased by 101128 Km2 and it has increased from 7.36% to 16.9% in the study time period. The area of rangelands decreased by 1000Km2 and reached from 56.8% to 47.4%. If the current trend continues until 1409, decreasing the area of rangelands and their conversion to agricultural lands will occur to increase land use productivity.

Tehran is the most polluted city in the country, which can have long-term and short-term effects on human health. Therefore, predicting the concentration of pollutants can be useful in prevention and control planning. There are different methods for predicting, and over the years, neural network methods have made significant progress in predicting pollution. In this study, an artificial neural network of three-layer perceptron was used to predict the concentrations of PM10, CO and air quality index (AQI) in air in Tehran. The concentrations of pollutants were collected from the Tehran Air Quality Control Department and the weather data collected from the Office of the Iranian Meteorological Organization during 2013 and 2015. The highest correlation coefficient (R2) for PM10 pollutant was 0.83 in warm seasons and the highest CO emission factor correlated with cold seasons (R2 = 0.74). Finally, the highest correlation coefficient of AQI was in cold season (R2 = 0.57). In linear regression model, the highest correlation coefficient with 0.58 for PM10 pollutant was in hot seasons. The highest correlation coefficient in this model was for the CO pollutant (0.33) in the cold season. Finally, the highest correlation coefficient was AOI (R2 = 0.31) in the warm season.

Each year fire damages the natural environment remarkably. Cellular automata is a way of modeling and simulating processes in the real world. We can take precautionary steps toward preventing fire and damages after, by knowing how it spreads and providing risk plans. In this research, the fire risk model has been used for modeling fire occurrence which is composed of factors such as slope, slope direction, vegetation cover, and prevailing wind direction. During this process, areas with a high risk of fire have been determined and by using the cellular automata model. In this model, the Alexandridis method in MATLAB was used. it became possible to simulate the fire spread model of Arasbaran forests in the northwest of Iran. This area holds the record of most fire statistics in the whole province and considering huge resources of flora and fauna available there, it is essential to protect this area most of all from fire. Results have shown that 56.36% of this area is highly exposed to fire. Slope gradient and slope direction are two important factors in the start and spreading of fire. Human factors play a great role in the development of fire. also, results indicate that using cellular automata along with GIS, not only accelerates the simulation of fire but also it has great accuracy.

Here, the spatio-temporal variability of nutrients, chlorophyll a, and physicochemical parameters, with regard to sensitive zones such as swimming and recreational areas, and entrances of urban sewages, at 23 stations of coastal waters of Bushehr city were investigated in hot (September, 2017) and cold (January, 2018) seasons. The results show that the dissolved oxygen and pH values were high in some coastal areas. Macroalgae were extensively observed at these areas. Statistical results show that the temperature, electrical conductivity, salinity, dissolved oxygen values and concentrations of nitrate, nitrite, ammonium, and silicate have significant difference between the two seasons (p<0.05). Nitrate and silicate have higher percentage than other nutrients in both hot and cold seasons. The nitrate and ammonium concentrations were higher in clod season (were 324.79 µg/l and 12.66 µg/l, respectively) than in hot season (were 123.43 µg/l and 1.91 µg/l, respectively). Furthermore, in most of the coastal stations, the nitrate concentrations were high, so that the nitrate concentrations of the coastal stations and 5 km away from the coastline stations were 229.91 µg/l and 81.56 µg/l, respectively. In general, using a water quality index, it can be said that, at the time of doing this research, the nutrients and chlorophyll a status of Bushehr coastal water is not in a dangerous situation. The results of this study can be used by local decision makers in the health, environmental, and tourism sections.

Heavy metals are among the pollutants that come into the aquifer through direct or indirect sources of man-made or natural materials. Beheshtakad River is one of the most important water sources in Chaharmahal and Bakhtiari Province. The present study was designed to evaluate the amount of heavy metals (arsenic, cadmium, chromium, copper, lead and zinc) in the sediment of Chaharmahal and Bakhtiari Beheshtabat River. The sediment samples were collected in two periods each year from a height of 6 cm above the bed. The sediment samples were transferred to the laboratory in an ice-free box at 4 ° C. The concentration of total arsenic, cadmium, chromium, copper, lead and zinc elements was determined by ICP-MASS. Descriptive statistics were performed using SPSS software. The average concentrations of arsenic, cadmium, chromium, copper, lead and zinc were 1.34, 0.18, 25.96, 11.51, 9.7 and 29.57 mg / kg, which were less toxic. According to the results of the potential ecological risk index, contamination factor, geochemical index and enrichment factor, there was no significant and severe pollution of heavy metals in river sediments based on the background concentration. The concentration of copper in some study stations has an adverse biological effect on aquatic organisms. Generally, the origin of heavy metals in the sediments of the Beheshtabad River is natural due to the results, and to prevent contamination, the prevention of the input of pollutants due to agricultural production and fish farming and household sewage to the river was prevented.

Mallard city has two aquifers comprising Tehran-Karaj and Ghate-Chahar that providing most of drinking water for villages of this City. Accordingly, there is a dire need to investigate quality and quantity of groundwater in this area. The aim of this study is to survey the impact of soil physical and chemical properties on quality and quantity of Mallard groundwater. in view of this data from thirty-one drinking water wells during a 6-year period were used. the soil physical e.g. soil texture, uniformity coefficient, curvature factor, permeability coefficient, saturation percentage, effective porosity percentage and chemical including electrical conductivity level, sodium, potassium, calcium carbonate, total dissolved solids, pH and dissolved salt percentage properties were measured in seventeen soil samples. the results showed that in eastern areas due to loamy-clay texture of the soil and suitability of permeability coefficient and saturation percent rate the discharge of the wells is better in comparison to other areas. Qualitative parameters of the soil such as Calcium Carbonate, Electrical Conductivity, Total dissolved solids, Sodium, Potassium have strong Positive correlation with Qualitative parameters of groundwater in alluvial Terraces. In agricultural and residential areas, the of nitrate concentration in drinking water have relatively strong positive correlation with soil EC, TDS and dissolved Salt.

Being an essentially open system, cages are usually characterized by a high degree of interaction with environment and cage systems are highly likely to produce large bulk of wastes that are released directly into the aquatic ecosystem if optimal use of inputs, especially fish food, is not available. Nevertheless, measurement of side effects of fish cage culture, along with providing solutions for the optimal use of fish food, can be used as an effective tool to reduce the side effects of this system. So the purpose of this study is to quantify nutrient loadings in rainbow trout (Oncorhynchus mykiss) marine cage farms as the most important nutrients released to the marine and near-shore ecosystem and to make suggestions for reducing the emissions of these nutrients in Mazandaran province. In the production cycle of 2017-18, there were 9 active fish cage farms which the nutrient loadings from each farm were calculated based on an indirect method. According to the results, the environmental loss amounted to 73.979 kg N t-1, 13.893 kg P t-1, and 488.353 kg C t-1 of fish produced. Feed conversion ratio (FCR = feed supplied/ body weight gained) varied between 0.897- 4 with an average of 1.249 representing severe inefficiencies of feeding. Based on study findings, weak technical knowledge of farmers, especially in choosing the right time for start and the end of production cycles, and feeding mismanagement will increase the amount of nutrients released into the environment.