Deciphering heavy metal contamination zones in soils of a granitic terrain of southern India using factor analysis and GIS

Soil contamination by heavy metals has been a major concern for last few decades due to increase in urbanization and industrialization. The main objective of this research was to identify the heavy metal contaminated zones in the study area. Twenty five soil samples collected throughout the agriculture, residential and industrial areas were analysed by X-ray Fluorescence Spectrometer (XRF) for trace metals and major oxides. These metals can affect the quality of soil and infiltrate through the soil, thereby causing groundwater pollution. Based on the chemical analysis of major oxides (SiO_2, Al₂O_3, ?Fe₂O_3, MnO, MgO, CaO, Na₂O, K₂O, TiO₂, and P₂O₅) and their distribution; it is observed that these soils are predominantly siliceous type with slight enrichment of alumina component in the study area. Correlation matrix (CM) and factor analysis (FA) is employed to the heavy metal variables, viz., Ba, Cr, Cu, Ni, Pb, Rb, Sr, V, Y, Zn and Zr of the soil to determine the dominant factors contributing to the soil contamination in the area. In the analysis, five factors emerged as significant contributors to the soil quality. The total contribution of these five factors is about 90%. The contribution of the first factor is about 45% and has significant positive loadings of Co, Cr, Cu, Ni and Zn. The contribution of second factor is 22% and has significant positive loadings of Rb, Sr and Y. The contribution of third, fourth and fifth factors is 10, 8 and 5% and show positive loadings for lead, molybdenum and barium respectively to the soil contamination. The spatial variation maps deciphering different zones of heavy metal concentration in the soil were generated in a GIS (geographic information system) based environment using ArcGIS 9.3.1. The results reveal that heavy metal contamination in the area is mainly due to anthropogenic activities.     

A Periglacial Palaeoenvionment in the Upper Carboniferous–Lower Permian Tobra Formation of the Salt Range, Pakistan

The Upper Carboniferous—Lower Permian(Upper Pennsylvanian-Asselian) Tobra Formation is exposed in the Salt and Trans Indus ranges of Pakistan.The formation exhibits an alluvial plain(alluvial fan-piedmont alluvial plain) facies association in the Salt Range and Khisor Range.In addition,a stream flow facies association is restricted to the eastern Salt Range.The alluvial plain facies association is comprised of clast-supported massive conglomerate(Gmc),diamictite(Dm)facies,and massive sandstone(Sm) Hthofacies whereas the stream flow-dominated alluvial plain facies association includes fine-grained sandstone and siltstone(Fss),fining upwards pebbly sandstone(Sf),and massive mudstone(Fm) Hthofacies.The lack of glacial signatures(particularly glacial grooves and striatums) in the deposits in the Tobra Formation,which are,in contrast,present in their timeequivalent and palaeogeographically nearby strata of the Arabian peninsula,e.g.the AI Khlata Formation of Oman and Unayzah B member of the Saudi Arabia,suggests a pro-to periglacial,i.e.glaciofluvial depositional setting for the Tobra Formation.The sedimentology of the Tobra Formation attests that the Salt Range,Pakistan,occupied a palaeogeographic position just beyond the maximum glacial extent during Upper Pennsylvanian-Asselian time.     

Prevailing trends of climatic extremes across Indus-Delta of Sindh-Pakistan

This study examines the variability and change in the patterns of climatic extremes experienced in Indus-Delta of Sindh province of Pakistan, comprising regions of Karachi, Badin, Mohenjodaro, and Rohri. The homogenized daily minimum and maximum temperature and precipitation data for a 36-year period were used to calculate 13 and 11 indices of temperature and precipitation extremes with the help of RClimDex, a program written in the statistical software package R. A non-parametric Mann–Kendall test and Sen’s slope estimates were used to determine the statistical significance and magnitude of the calculated trend. Temperatures of summer days and tropical nights increased in the region with overall significant warming trends for monthly maximum temperature as well as for warm days and nights reflecting dry conditions in the study area. The warm extremes and nighttime temperature indices showed greater trends than cold extremes and daytime indices depicting an overall warming trends in the Delta. Historic decrease in the acreage of major crops and over 33% decrease in agriculture credit for Sindh are the indicators of adverse impacts of warmer and drier weather on Sindh agriculture. Trends reported for Karachi and Badin are expected to decrease rice cultivation, hatching of fisheries, and mangroves forest surrounding these cities. Increase in the prevailing temperature trends will lead to increasingly hotter and drier summers resulting to constraints on cotton, wheat, and rice yield in Rohri and Mohenjodaro areas due to increased crop water requirements that may be met with additional groundwater pumping; nonetheless, the depleted groundwater resources would have a direct impact on the region’s economy.     

Prediction of pore-water pressure response to rainfall using support vector regression

Nonlinear complex behavior of pore-water pressure responses to rainfall was modelled using support vector regression (SVR). Pore-water pressure can rise to disturbing levels that may result in slope failure during or after rainfall. Traditionally, monitoring slope pore-water pressure responses to rainfall is tedious and expensive, in that the slope must be instrumented with necessary monitors. Data on rainfall and corresponding responses of pore-water pressure were collected from such a monitoring program at a slope site in Malaysia and used to develop SVR models to predict pore-water pressure fluctuations. Three models, based on their different input configurations, were developed. SVR optimum meta-parameters were obtained using k-fold cross validation and a grid search. Model type 3 was adjudged the best among the models and was used to predict three other points on the slope. For each point, lag intervals of 30 min, 1 h and 2 h were used to make the predictions. The SVR model predictions were compared with predictions made by an artificial neural network model; overall, the SVR model showed slightly better results. Uncertainty quantification analysis was also performed for further model assessment. The uncertainty components were found to be low and tolerable, with d-factor of 0.14 and 74 % of observed data falling within the 95 % confidence bound. The study demonstrated that the SVR model is effective in providing an accurate and quick means of obtaining pore-water pressure response, which may be vital in systems where response information is urgently needed.     

Timing of metamorphism,melting and exhumation of the Leo Pargil dome,northwest India

The Leo Pargil dome, northwest India, is a 30 km‐wide, northeast‐trending structure that is cored by gneiss and mantled by amphibolite facies metamorphic rocks that are intruded by a leucogranite injection complex. Oppositely dipping, normal‐sense shear zones that accommodated orogen‐parallel extension within a convergent orogen bound the dome. The broadly distributed Leo Pargil shear zone defines the southwest flank of the dome and separates the dome from the metasedimentary and sedimentary rocks in the hanging wall to the west and south. Thermobarometry and in‐situ U–Th–Pb monazite geochronology were conducted on metamorphic rocks from within the dome and in the hanging wall. These data were combined with U–Th–Pb monazite geochronology of leucogranites from the injection complex to evaluate the relationship between metamorphism, crustal melting, and the onset of exhumation. Rocks within the dome and in the hanging wall contain garnet, kyanite, and staurolite porphyroblasts that record prograde Barrovian metamorphism during crustal thickening that reached ～530–630 °C and ～7–8 kbar, ending by c. 30 Ma. Cordierite and sillimanite overgrowths on Barrovian assemblages within the dome record dominantly top‐down‐to‐the‐west shearing during near‐isothermal decompression of the footwall rocks to ～4 kbar by 23 Ma during an exhumation rate of 1.3 mm year^?1. Monazite growth accompanied Barrovian metamorphism and decompression. The leucogranite injection complex within the dome initiated at 23 Ma and continued to 18 Ma. These data show that orogen‐parallel extension in this part of the Himalaya occurred earlier than previously documented (>16 Ma). Contemporaneous onset of near‐isothermal decompression, top‐down‐to‐the‐west shearing, and injection of the decompression‐driven leucogranite complex suggests that early crustal melting may have created a weakened crust that was proceeded by localization of strain and shear zone development. Exhumation along the shear zone accommodated decompression by 23 Ma in a kinematic setting that favoured orogen‐parallel extension.     

APHRODITE降水数据驱动的融雪径流模拟

针对流域内气象观测站点稀少和融雪径流过程的特点,利用APHRODITE降水数据进行插值,应用日有效活动温度改进度日数;依据季节性冻土受有效活动积温影响的特点,建立有效活动积温与径流系数的关系,提高模型中融雪速率和径流系数的计算精度。结合气象、水文资料和MODIS遥感积雪产品,应用改进的融雪径流模型（SRM）对开都河流域2000年与2006年融雪期的径流进行了率定和验证模拟。改进模型在率定期和验证期的模拟结果远远优于用日平均温度作为度日数的结果。结果表明,用APHRODITE降水数据及改进的度日数和径流系数作为SRM模型参数输入,能够较好模拟开都河流域融雪径流过程,大大提高模型模拟精度。     

Assessment of Metals Concentration and Ecotoxicology of the Sediment Core of Rehri Creek, Karachi Coast, Pakistan

Trace metal concentrations were investigated in a recent sediment core collected from the Rehri Creek area of the Karachi coast,Sindh-Pakistan.The core was sliced horizontally at 2.5-cm intervals to determine grain size,sediment composition,pH,organic matter,and acid-leachable trace metals:cadmium,chromium,copper,lead,and zinc.The trace metals were analyzed by ICP.To separate anthropogenic from geogenic input,several approaches were made,including comparison with sediment quality guidelines—ecotoxicological sense of heavy metal contamination and classification by quantitative indexes.Grain-size analysis and sediment composition of core sample show a sandy nature with neutral pH.Elemental sequence(ES)of the trace metals is in the order of Zn(19.2-109.56 ppm)>Si(66.46-101.71 ppm)>Ba(12.05-26.86 ppm)>As(8.18-17.36 ppm)>Ni(4.2-14.69 ppm)>Cr(3.02-9.62 ppm)>Pb(2.79-6.83 ppm)>Cu(2.2-5.29 ppm)>Co(0.9-2.05 ppm).Thus it is likely that the area may face a serious threat of metal pollution with the present deposition rates unless stringent pollution control norms are adopted.The Sediment Geo-accumulation Index shows that there is no Cr,Cu,Ni,Pb,Zn,or Fe pollution;however,the former index and the Pollution Load Index indicate arsenic pollution in the sediments.     

Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy model.The Standardized Precipitation Index（SPI） was used as a proxy for drought conditions.Among the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation（AMO）,Atlantic Meridional Mode（AMM）,the Bivariate ENSO Time series（BEST）,the East Central Tropical Pacific Surface Temperature（NINO 3.4）,the Central Tropical Pacific Surface Temperature（NINO 4）,the North Tropical Atlantic Index（NTA）,the Southern Oscillation Index（SOI）,and the Tropical Northern Atlantic Index（TNA）.These indices accounted for 81% of the variance in the Principal Components Analysis（PCA） method.The Atlantic surface temperature（SST：Atlantic） had an inverse relationship with SPI,and the AMM index had the highest correlation.Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.     

Pesticide Health Risk Mapping and Sensitivity Analysis of Parameters in Groundwater Vulnerability Assessment

The extensive use of pesticides for increasing the agricultural production is affecting the quality of groundwater. The objectives of this article are to (i) develop pesticide relative leaching ranks for well sites, (ii) develop maps for human health risks due to pesticide applications, and (iii) identify the most significant parameters in pesticide simulations for groundwater vulnerability assessment. The methods include (i) development of acifluorfen relative leaching ranks for 25 well sites using ArcPRZM‐3, (ii) development of health risk maps using model simulated maximum dissolved bentazon concentrations on the basis of USA drinking water quality guidelines, (iii) sensitivity analysis for 14 ArcPRZM‐3 input parameters using the Plackett–Burman method. ArcPRZM‐3 is a user‐friendly system for spatial modeling of pesticide leaching from surface to groundwater. Thirteen acifluorfen relative leaching potential ranks were developed in which the pesticide leaching decrease from 1 to 13. The model predicted ranks for well 34 and well 9 were 2nd and 3rd, respectively, and acifluorfen was detected in both wells during the physical monitoring. The percentages of high health risks in the agricultural areas were 48.38 and 72.72% for Randolph and Independence Counties, respectively. The most significant parameters were thickness of horizon compartment, runoff curve number of antecedent moisture condition II for cropping, soil bulk density, and total application of pesticide. The irrigation, soil permeability, and numerical dispersion could impact the pesticide leaching in soils toward groundwater. The ArcPRZM‐3 system could be efficiently applied for spatial modeling and mapping of pesticide concentrations for groundwater vulnerability assessment on a large scale.