Soil Chemistry and Pollution
Alireza Zarasvandi; Majid Heidari; Ahmadreza Lahijanzadeh; Sedigheh Jalali; Mohsen Rezaei; Madineh Saed; Zahra Fereydouni
Abstract
Introduction Dust storms or sand storms are some of the meteorological phenomena that demonstrate differences with one another terminologically. These kinds of storms usually occur under arid and semiarid areas in circumstances which the blowing speed of a gale is higher than the erosion threshold. In ...
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Introduction Dust storms or sand storms are some of the meteorological phenomena that demonstrate differences with one another terminologically. These kinds of storms usually occur under arid and semiarid areas in circumstances which the blowing speed of a gale is higher than the erosion threshold. In other words, Dust and sand storms are persistent problems in the Middle East Region. The regional dust storms have bad effects on the health of human life which can cause asthma, bronchitis and lung diseases, due to their carrying micro-organisms (such as bacteria, fungi, spores, viruses and pollen) and their sharp edged particles. Several studies researches have shown that microorganisms mobilized into the atmosphere along with desert soils are capable of surviving long-range transport on a global scale. Dust-borne microorganisms in particular can. directly impact human health via pathogenesis,exposure of sensitive individuals to cellular components. The chemical components of dust are affecting the microbial life besides the precipitation, wind direction, time of day, season and atmosphere inversion conditions, all affecting the survival of total bacteria communities associated with dust particles, and the microbes are capable of surviving long distance transport. Dust storms have become a major environmental concern during the last decades in the oil- and gas-rich Khuzestan province in the southwestern Iran. Dust storms frequently occur in Khuzestan mainly during summer, and intense dust storms are particularly associated with easterly-blowing winds. High frequency (10-15%) and health outcomes of local dust storms in Khuzestan province, requires an extensive study on various factors of local storms such as heavy metal geochemistry and its environmental consequences are very important. In this paper, we present an overview of the geochemical and geo-environmental characteristics of dust storms in Khuzestan. Materials and Methods Information about dust storms of source and coverage was obtained from meteorological stations in Khuzestan province. In this study, airborne dust samples were collected to obtain TSP and PM10 by using the high-volume air (HVA) sampler model TCR. The geochemistry of airborne dust samples was analyzed at the Actlabs (Canada). The concentration of V, Co, Ni, As, Cd, Pb and Zn was determined in Actlab, Canada, using ICP-MS method. Results and Discussion The obtained results showed that Pb concentration in TSP samples ranges between 8.11 and 197 ppm with an average and median value of 23.6 and 11.15 ppm, respectively. The zinc content in PM10 samples, ranges between 4670 and 5000 ppm. Also, Ni has high concentration that ranges between 5.8 - 43.2 in PM10 samples. Lowest concentration of Cobalt is present in PM10 samples that ranges between 0.6 and 4.7 ppm. Vanadium has the highest concentration in Ahvaz samples. Also, PM10 samples include higher Arsenic concentration than TSP samples. Finally, Cd has the lowest concentration in all of the studied heavy metals with the mean value of 0.12 ppm. Positive correlation (0.9) between Cu with V, Co and Ni shows probably a similar source for these elements. Investigation of heavy metals concentration in various dust storms confirms that arsenic has a higher concentration in local storms. Seasonal studies show that V, Co and As have high concentrations in warm periods and Pb has the highest frequency in the cold season, in Khuzestan province. Based on the study of Contamination Factor (CF), the mean CF of heavy metals was in the order Pb > Zn > Cd >As > Ni > Co> V. Also, Degree of Contamination (DC) factor of the studied heavy metals in PM10 samples with mean value of 40 ppm is higher than TSP samples with average value of 10 ppm. In relation to the Enrichment Factor (EF) for V, Co, Ni, As, Cd, Pb and Zn, the EF mean of these metals was in the order Cd > Zn > Ni > Pb > As > V > Co. The highest Arsenic enrichment factor can be seen in PM10 samples. Conclusion The obtained results from calculation of Integrated Pollution Index (IPI) in PM10 samples showed that, V, Co, Ni and Pb are non-polluted. Also, Cadmium, Arsenic and Zinc showed a low, medium and high levels of pollution, respectively. Furthermore, based on IPI data in TSP samples, Co, As and V were non-pollution and Pb, Ni, Zn showed low level of contamination. Finally, Cadmium in TSP samples in dicated a high level of Integrated Pollution Index.
Negin Hassanvandi; Ahmad Landi; Hamid Reza Matinfar; Mohammad Hassan Taherzadeh
Volume 37, Issue 1 , September 2014, , Pages 23-34
Abstract
Soil salinity and salinization are destructive phenomena and limiting factors for agricultural development. Therefore, it is necessary to monitor and mange salinity. Identification of saline soils and production of saline soils maps are the first steps for their management. The objective of this study ...
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Soil salinity and salinization are destructive phenomena and limiting factors for agricultural development. Therefore, it is necessary to monitor and mange salinity. Identification of saline soils and production of saline soils maps are the first steps for their management. The objective of this study was to achieve a map of saline soils of the south of Ahvaz using ETM+ images. Soil sampling from 0-15 cm was accomplished using visual interpretations and differences in spectral reflection of various phenomena. Electrical conductivity (EC) was measured in soil paste. Spectral values of each sample were obtained in different bands and the relationship between spectral values and samples electrical conductivity was analyzed by determining correlated bands and selecting desirable bands for contributing to the model. The results showed that the correlation between electrical conductivity values with B4, B2 bands, brightness and SAVI was significant at 1% level. Using the correlation between satellite data (Landsat ETM+ 2010) and numerical value of soil salinity, salinity maps were produced in GIS. Finally, a salinity map was prepared using a multiple variable regression model. The accuracy of the produced map by electrical conductivity values showed that the correlation method in mapping soil salinity can be recommended.
