ghorban vahabzadekebriya

Although the exploitation of mineral resources is very useful for the country, it has a negative impact on human and plant life with its destructive effects. These destructive effects appear as pollution in the soil, which can lead to the imbalance of ecosystems and ultimately endanger human health. In addition, economic development and the expansion of industrial areas, especially coal mining mines, cause heavy metal contamination of the soil to become more severe and cause the destruction of soil resources and the deterioration of ecosystems in different regions of the world. The present study was conducted to investigate the contamination of the two elements Pb and Zn in the surface soil of the Komarzd mining area in Mazandaran province (Iran) due to coal mining.

Komarzd mines in Mazandaran province are one of the largest and oldest coal-producing areas in the central Alborz coal field. Komarzd mines are located 48 km from the southern district of Qaemshahr and 25 km from Alasht. Four indices, including CF, mCF, RI, and Igeo were calculated for 110 samples from surface soil prepared at a depth of 5-15 cm. After separating the rubble, the collected soils were kept in closed plastic bags with a unique code, and a GPS device recorded their location. The samples prepared in a dry environment were transferred to the laboratory. The ICP-MS method at 75 microns was used to analyze and measure the concentration of heavy elements in the collected samples. According to the presence of different elements measured in the region's soil and the compounds in it, two metals Zn and Pb were selected for evaluation and analysis. Sampling was randomly selected based on saved points and drilling tunnels. PCA statistical test is a type of multivariate analysis widely used in sediment, water, and soil pollution studies. Varimax Rotation is one of the most common types of PCA tests to interpret the results and contamination components of the method. This statistical method can be calculated using R and SPSS softwares.

The results showed that the ordinary kriging model is the most suitable model to show the region's concentration distribution of the two metals, PB and Zn. Also, the statistical status of the elements showed that the lowest amount of Zn for the Igeo index is equal to -0.53 and the highest value for the CF index is equal to 0.72. The amount of Pb concentration measured based on Igeo, CF, mCF, and RI indices equals 0.08, 1.03, and 213 mg/kg, respectively (RI<CF<Igeo). The results of the CF and mCF showed that the region has a low or moderate pollution status in terms of the concentration of the two determining elements; in other words, the value of this factor for Zn is less than 1 (CF<1) and the amount for Pb is (3 > CF> 1). The Igeo index showed that Zn with a concentration lower than zero is not polluted, but Pb with a value (0< Igo <1) has a non-polluted to slightly polluted state. The RI also showed that the concentration of the two elements is less than 150 mg/kg, so this index is also in the low-risk category of contamination (RI ≤ 150). By evaluating the graphs obtained from the PCA analysis, it was found that the two elements Zn and Pb in the correlation analysis graph have a good correlation and distribution, and the changes in the two axes show 47.9% and 24%, respectively, for the Igeo. This indicates the presence of environmental factors in the distribution of this element in the environment. The CF also showed that their distribution in region three negatively correlated with environmental factors. Its value for the two axes is 32.5 and 21.9 %, respectively. The correlation of the Zn element is less than -0.5, but the Pb element is more than -0.5. In general, the value of this variable is 8 and 20, respectively, based on the diagram of two minimum and maximum values. The mCF and RI indices showed that environmental factors influence the distribution of the two elements PB and Zn, and among the two elements, the distribution of the Pb element in the mCF and RI, results of 32.8 and 34.4 has the most influence from the distribution of the drilling tunnel and had environmental factors and has a lower correlation than Zn. The effect percentage of these two elements is 32.8 and 23.2%, respectively, and the two elements Zn and Pb have a good correlation and distribution with environmental changes and human factors.

According to the measurements and the results of four indices, the area is uncontaminated or has low contamination in terms of the concentration of two elements, PB and Zn. Therefore, mineral tailings and coal exploitation do not significantly affect the distribution of the mentioned elements. To improve the research results, other indices can be used to determine the role of environmental factors. Also, considering the agricultural lands and water resources of Talar River, it is better to use other elements that directly affect human health and analyze the region's conditions in terms of environmental pollution to achieve better results and accuracy. One of the main disadvantages of measuring the sources of pollution is the many problems in laboratory work and the preparation of laboratory materials. Still, with this research, it is possible to determine the existing doubts in the field of soil pollution for residents and show the impact of mining on the spread of pollutants. A suggestion to improve this research is to consider elements other than Pb and Zn in different environments such as water sources; In general, for a more detailed investigation, other elements that play a role in human health should be investigated.

In the present study, the effect of sand extraction from the bed and bank of Khatirkuh river in Mazandaran province on social, ecological and environmental issues was investigated using a questionnaire and field visits, experts and local people. Then, by determining the sample size using the Cochran relation and evaluating the reliability and reliability of the questionnaires by Cronbach's alpha method and finally its validity was confirmed by experts and professors. Then 70 questionnaires were distributed between local communities and experts and using the SWOT matrix of external and internal factors, strategies for the effects of sand extraction in the region were identified. The results showed that the total secondary coefficient of strengths and weaknesses were 1.938 and 1.076, respectively, and the total secondary coefficient of opportunities and threats were 2.307 and 1.907, Respectively. According to the SO and WT factors for development planning in the region, offensive strategy is a priority. The above strategy maximize  opportunities and strengths and thereby reducing mining problems.The most important ecological factor of this region is the entry of invasive plants, forest and pasture fires, which have reduced growth, vegetation density and pasture destruction. The most important strength of  mining in this region is direct  and indirect employment related to sand and gravel mines.

Landslides are natural hazards that destroy nature and often cause severe damage to natural resources and loss of life and human property (Intaravichin and Dasananda, 2010; Feyzzadeh et al., 2013). Despite significant advances in risk analysis and landslide risk management, this natural hazard remains a real threat to the lives and property of people in mountainous areas (Getz et al., 2011). Natural factors affecting landslides can be rainfall, lithology, slope, and the like (Talebi and Niazi, 1390). Among the natural factors affecting the occurrence of landslides, we can mention factors such as rainfall, lithology, slope, and the like (Talebi and Niazi, 1390). In general, Iran is considered a high-risk country due to favorable geographical conditions, lack of comprehensive environmental management, and non-compliance with environmental thresholds. Approximately 38 hazards have been identified and recorded in Iran, and due to the multiplicity, diversity, recurrence, and severity of natural hazards and environmental unrest are among the ten most prosperous countries in the world (Mohammadi et al., 2004). Considering landslide hazard zoning map is essential for a proper understanding of its mechanisms in recent research (Sun, 2014).

In order to prepare a landslide susceptibility map using Dempster-Schiffer theory, first, using the landslide database, the General Department of Natural Resources of Mazandaran province, and field studies, landslide points in the region were identified. During the field observations, the accuracy of the collected information was evaluated, and the position of the landslide points was checked using the Global Positioning System (GPS), and 26 new landslides were recorded. A total of 134 slide points were examined. In this research, 12 information layers including altitude map, slope direction, slope degree, slope shape, distance from fault, distance from road, land use, geology, distance from waterway, Slope length or sediment transport index (STI), topographic moisture index (TWI) and vegetation were prepared and classified using ArcMap and ENVI software using 1: 50,000 topographic maps, 1: 100,000 geology, geology and satellite imagery, IRS for 2012. In order to evaluate the Dempster-Schaefer theory in landslide risk analysis, the relative performance curve of the relative efficiency of variables (ROC) is used. ROC curve analysis is used to determine the accuracy and efficiency of the model (Egan, 1975; Soots, 1988; Williams et al., 1999). This curve is one of the most efficient methods in providing diagnostic properties, probability identification, and prediction systems that quantitatively estimate the modelchr('39')s accuracy (Soots, 1988). The area under the ROC curve represents the prediction value of the system by describing its ability to accurately estimate its occurrence events (landslide occurrence) and its non-occurrence events (non-landslide occurrence). Therefore, the area of ​​the curve is used as a measure of the accuracy of the model. In this study, out of 134 slip points, several points were used for modeling, and part of it was used to evaluate the model (with a ratio of about 70 to 30%) (Matthew et al., 2007; Pourghasemi et al., 2009; Constantine et al., 2010).

The study of the slope factor shows that although the highest percentage of landslides is related to the class of 15-30 degrees with 52.127 percent, which is consistent with the views (Moradi et al., 2010). However, the highest weight is related to the class of 5-15 degrees, which can be attributed to road construction and unprincipled constructions that have caused the slope to be cut in the region. No landslides have been observed or recorded in areas with a slope above 50 degrees, which can be due to the rocky outcrop and the lack of soil layer in the high slopes. According to the results of this study, the western and southern slopes with 19.15% and 18.1%, respectively, had the highest percentage of landslides in the region, which with the results of studies (Pour Ghasemi et al., 2009) and (Lee, 2007) corresponds. The study of altitude factors indicates that the altitude class of 1000-500 with 56.38% of landslides, the highest number of landslides, and the altitude class of 0-500 have the highest weight, also, with increasing altitude in the study area, the sensitivity to landslides decreases, which is consistent with the studies of Moradi et al. (2010) and Pourghasemi et al. (2011). At altitudes above 2000 m, no landslides have been observed or recorded, which indicates a decrease in sensitivity to landslides with increasing altitude (Moradi et al., 2010). The highest weight for the geological factor belongs to group 1, which includes deposits and unhardened alluvial deposits of the Quaternary period, while the highest percentage of landslides occurred in the units. The lithology of the Shemshak Formation is located at 37.23%, which is consistent with the views (Naji, 2006), (Shadfar et al., 2008), (Pourghasemi et al., 2011). The study of the linear element of the distance from the waterway has shown that because the highest percentage of landslides occurred at a distance of more than 400 meters at the rate of 31.915%. However, the highest weight in calculations by class is allocated 0-100 meters, which is in accordance with the opinions (Pourghasemi et al., 2009), (Dokota et al., 2013). Investigation of the distance from the road showed that the highest percentage of landslides occurred at a distance of more than 500 meters (30.85%); however, the highest weight in the calculations to the class 0-100 for the distance from the road is allocated which is consistent with the results of studies of Mazouki et al. (2011), Pourghasemi et al. (2009, 2012).