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Regional distribution pattern of groundwater heavy metals resulting from agricultural activities 总被引：18，自引：0，他引：18
Contaminations of groundwater by heavy metals due to agricultural activities are growing recently. The objective of this study
was to evaluate and map regional patterns of heavy metals (Cd, Zn and Cu) in groundwater on a plain with high agricultural
activities. The study was conducted to investigate the concentration of heavy metals and distribution in groundwater in regions
of Shush Danial and Andimeshk aquifers in the southern part of Iran. Presently, groundwater is the only appropriate and widely
used source of drinking water for rural and urban communities in this region. The region covers an area of 1,100 km2 between the Dez and Karkhe rivers, which lead to the Persian Gulf. For this study, the region was divided into four sub-regions
A, B, C and D. Additionally, 168 groundwater samples were collected from 42 water wells during the earlier months of 2004.
The flame atomic absorption spectrometry (AAS-Flame) was used to measure the concentration of heavy metals in water samples
and the Surfer software was used for determination of the contour map of metal distribution. The results demonstrated that
in all of the samples, Cd and Zn concentrations were below the EPA MCLG and EPA secondary standard, respectively. However,
the Cu contents of 4.8 % of all samples were higher than EPA MCL. It is also indicated that the concentrations of metals were
more pronounced at the southern part of the studied region than at the others. The analysis of fertilizers applied for agricultural
activities at this region also indicated that a great majority of the above-mentioned heavy metals were discharged into the
environment. Absence of confining layers, proximity to land surface, excess agricultural activities in the southern part and
groundwater flow direction that is generally from the north to the southern parts in this area make the southern region of
the Shush plain especially vulnerable to pollution by heavy metals than by other contaminants. 相似文献
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Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction 总被引：1，自引：1，他引：0
R Shirani Faradonbeh D Jahed Armaghani M. Z. Abd Majid M. MD Tahir B. Ramesh Murlidhar M. Monjezi H. M. Wong 《International Journal of Environmental Science and Technology》2016,13(6):1453-1464
Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model. 相似文献
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Edy?Tonnizam Mohamad Danial?Jahed ArmaghaniEmail author Mahdi?Hasanipanah Bhatawdekar?Ramesh?Murlidhar Mohd?Nur?Asmawisham?Alel 《Environmental Earth Sciences》2016,75(2):174
Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R 2), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site. 相似文献
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Zhou Jian Zhu Shuangli Qiu Yingui Armaghani Danial Jahed Zhou Annan Yong Weixun 《Acta Geotechnica》2022,17(4):1343-1366
Acta Geotechnica - The squeezing behavior of surrounding rock can be described as the time-dependent large deformation during tunnel excavation, which appears in special geological conditions, such... 相似文献
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Haris Ahmed KHAN Ali Asghar SHAHID Muhammad Jahangir KHAN Taher ZOUAGHI Maria Dolores ALVAREZ Syed Danial Mehdi NAQVI 《《地质学报》英文版》2023,97(1):256-268
This research is focused on the analysis of the sequence stratigraphic units of F3 Block, within a wave-dominated delta of Plio–Pleistocene age. Three wells of F3 block and a 3D seismic data, are utilized in this research. The conventional techniques of 3D seismic interpretation were utilized to mark the 11 surfaces on the seismic section. Integration of seismic sequence stratigraphic interpretation, using well logs, and subsequent 3D geostatistical modeling, using seismic data, aided to evaluat... 相似文献
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Mahdi Hasanipanah Danial Jahed Armaghani Hassan Bakhshandeh Amnieh Mohammadreza Koopialipoor Hossein Arab 《Geotechnical and Geological Engineering》2018,36(4):2247-2260
Flyrock is an adverse effect produced by blasting in open-pit mines and tunneling projects. So, it seems that the precise estimations and risk level assessment of flyrock are essential in minimizing environmental effects induced by blasting. The first aim of this research is to model the risk level associated with flyrock through rock engineering systems (RES) methodology. In this regard, 62 blasting were investigated in Ulu Tiram quarry, Malaysia, and the most effective parameters of flyrock were measured. Using the most influential parameters on flyrock, the overall risk of flyrock was obtained as 32.95 which is considered as low to medium degree of vulnerability. Moreover, the second aim of this research is to estimate flyrock based on RES and multiple linear regression (MLR). To evaluate performance prediction of the models, some statistical criteria such as coefficient of determination (R2) were computed. Comparing the values predicted by the models demonstrated that the RES has more suitable performance than MLR for predicting the flyrock and it could be introduced as a powerful technique in this field. 相似文献
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Manoj Khandelwal Amir Mahdiyar Danial Jahed Armaghani T. N. Singh Ahmad Fahimifar Roohollah Shirani Faradonbeh 《Environmental Earth Sciences》2017,76(11):399
Coal, as an initial source of energy, requires a detailed investigation in terms of ultimate analysis, proximate analysis, and its biological constituents (macerals). The rank and calorific value of each type of coal are managed by the mentioned properties. In contrast to ultimate and proximate analyses, determining the macerals in coal requires sophisticated microscopic instrumentation and expertise. This study emphasizes the estimation of the concentration of macerals of Indian coals based on a hybrid imperialism competitive algorithm (ICA)–artificial neural network (ANN). Here, ICA is utilized to adjust the weight and bias of ANNs for enhancing their performance capacity. For comparison purposes, a pre-developed ANN model is also proposed. Checking the performance prediction of the developed models is performed through several performance indices, i.e., coefficient of determination (R 2), root mean square error and variance account for. The obtained results revealed higher accuracy of the proposed hybrid ICA-ANN model in estimating macerals contents of Indian coals compared to the pre-developed ANN technique. Results of the developed ANN model based on R 2 values of training datasets were obtained as 0.961, 0.955, and 0.961 for predicting vitrinite, liptinite, and inertinite, respectively, whereas these values were achieved as 0.948, 0.947, and 0.957, respectively, for testing datasets. Similarly, R 2 values of 0.988, 0.983, and 0.991 for training datasets and 0.989, 0.982, and 0.985 for testing datasets were obtained from developed ICA-ANN model. 相似文献