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Evaluation of Soil Pollution Levels in Al-Qadisiyah Governorate, Iraq Using Contamination Index and GIS

Al-Khuzaie Marwah M., Abdul Maulud Khairul Nizam

Journal of Ecological Engineering

2022

Vol. 23, nr 3

206--213

The precise determination of trace element concentrations in the soil of the Al-Qadisiyah Governorate is part of the Iraqi sedimentary plain is required to eliminate high levels of harmful elements in polluted soils. The soil samples were collected from 28 representative profiles in Al-Shamiyah city. The soil profiles were defined using virtual characterization. In this study, I-geo was used to analyze soil pollution. The goals and destinations of the I-geo readings Contamination of Cd, Ni, Pb, and Zn in various soil strata. I-geo (Cd) generally range from 0.58 to 4.71, I-geo (Ni) range from 0.09 to 4.07, I-geo (Pb) ranged range from 0.07 to 2.79, and I-geo (Zn) ranges from zero to 2.79, depicting the local differences in I-geo for pollutants in the research area. Suggesting that the research area had been heavily polluted from Cd in the varied layers of the soils. On the maps pertaining to Zn and Pb, the majority of the research area was primarily covered in the orange and blue hues, suggesting that a significant portion of the research area was likely to be severely polluted from Cd and Ni. Moreover, the land cover layouts of Ni in layers of the soils revealed concentrations rising towards to the western sections, which could be attributed to proximity to a major drain. The results display that its I-geo value of four trace metals generally range from non-pollute to significantly heavily polluted. The I-geo data show significant differences in levels of the Ni, Cd, Zn, and Pb in different soils strata. Including these findings, the soil in Al-Shamiya, Al-Qadisiyah Governorate contains high levels of Cd, Ni, Pb, and Zn. Industries of fossil fuel combustion, as well as other man-made wastes include agricultural nutrients, soil conditioners, and sludge, particularly, ammonium phosphate pollution in soils. The pollutant load index (PLI) reveals a baseline level of contamination in 28 locations, as well as a decline in soil quality in four others. Finally, assessing the danger of contamination for trace metals utilizing the I-geo and PLI by using the GIS method and multimodal models is a helpful and relevant strategy.

Comprehensive prediction of coal seam thickness by using in‑seam seismic surveys and Bayesian kriging

Zhu Mengbo, Cheng Jianyuan, Cui Weixiong, Yue Hui

Acta Geophysica

2019

Vol. 67, no. 3

825--836

Quantitative determination of the coal seam thickness distribution within the longwall panel is one of the primary works before integrated mining. In-seam seismic (ISS) surveys and interpolations are essential methods for predicting thickness. In this study, a new quantitative method that combines ISS and Bayesian kriging (BK), called ISS–BK, is proposed to determine the thickness distribution. ISS–BK consists of the following six steps. (1) The group velocity of Love waves is plotted by using the simultaneous iterative reconstruction technique under a constant frequency value. (2) An approximate quantitative relationship between the thickness and the group velocity is fitted based on sampling points of the coal seam thickness, which are measured during the process of entry development. (3) The group velocity map is translated into a primary thickness map according to the above-mentioned fitted equation. (4) By subtracting the ISS prediction result from the actual thickness at a sampling point, the residual variable is created. (5) The residual distribution is interpolated within the whole longwall panel by applying BK. The residual map establishes the interconnection between the ISS survey and BK. (6) A refined thickness distribution map can be obtained by overlapping the primary thickness map and the residual map. The application of this method to the No. 2408 longwall panel of Yuhua Coal Mine using ISS–BK showed a considerable improvement in thickness prediction accuracy over ISS. The residuals of ISS and ISS–BK mainly lie in the intervals (− 3.0, 3.0 m) and (− 1.0, 3.0 m), respectively. The accurate prediction rates [where the residual lies in the interval (0, 0.1 m)] of ISS and ISS–BK are 9.39% and 50.28%, respectively, and the effective prediction rates (where the residual is less than 1.0 m) of ISS and ISS–BK are 61.88% and 77.90%, respectively. All the above statistics reflect a considerable improvement in the ISS–BK method over the ISS method.