A probabilistic methodology to assess the risk of groundwater quality degradation

An approach to assess the risk of groundwater quality degradation with regard to fixed standards, based on DisjunctiveKriging (DK) is presented. The DK allows one to evaluate the Conditional Probability (CP) of overriding a given threshold of concentration of a pollutant at a given time, and at a generic point in a consideredgroundwater system. The result of such investigation over the considered area can be plotted in form of maps of spatial risk. By repeating this analysis at different times, severalspatial riskmaps will be produced, one for each consideredtime. By means of non-parametric statistics, the temporal trendof the CPs can be evaluated at every point of the considered area. The trend index, assessed by means of a sort of classification of the trend values obtained as described above,can be superimposed on the most recent values of the spatialrisk (i.e.: the most recent values of probability). Consequentlya classification of the risk of groundwater quality degradationresults with which to weigh both the spatial distribution and thetemporal behaviour of the probability to exceed a given standardthreshold. The methodology has been applied to values of nitrateconcentration sampled in the monitoring well network of theModena plain, northern Italy. This area is characterised by intensive agricultural exploitation and hog breeding along withindustrial and civil developments. The influence of agriculture on groundwater results in a high nitrate pollution that limitsits use for potable purposes.     

Application and evaluation of kriging and cokriging methods on groundwater depth mapping

Groundwater and water resources management play a key role in conserving the sustainable conditions in arid and semi-arid regions. Applying some techniques that can reveal the critical and hot conditions of water resources seem necessary. In this study, kriging and cokriging methods were evaluated for mapping the groundwater depth across a plain in which has experienced different climatic conditions (dry, wet, and normal) and consequently high variations in groundwater depth in a 12 year led in maximum, minimum, and mean depths. During this period groundwater depth has considerable fluctuations. Results obtained from geostatistical analysis showed that groundwater depth varies spatially in different climatic conditions. Furthermore, the calculated RMSE showed that cokriging approach was more accurate than kriging in mapping the groundwater depth though there was not a distinct difference. As a whole, kriging underestimated the real groundwater depth for dry, wet, and normal conditions by 5.5, 2.2, and 5.3%, while cokriging underestimations were 3.3, 2, and 2.2%, respectively; which showed the unbiasedness in estimations. Results implied that in the study area farming and cultivation in dry conditions needs more attention due to higher variability in groundwater depth in short distances compared to the other climate conditions. It is believed that geostatistical approaches are reliable tools for water resources managers and water authorities to allocate groundwater resources in different environmental conditions.     

某金矿周边土壤和地下水中砷的健康风险评价

以湖北某含砷金矿污染场地为例,依据《污染场地风险评估技术导则》(HJ 25.3—2014),对其周边土壤和地下水中砷元素进行健康风险评价,结果表明:该金矿周边土壤和地下水中砷分别通过经口摄入土壤、皮肤接触土壤和吸入土壤颗粒物、饮用地下水4种暴露途径的致癌风险均超过可接受风险水平(10-6),对周边居民造成潜在危害。计算得到经口摄入、皮肤接触和吸入途径基于致癌效应的土壤风险控制值分别为1.6 mg/kg、9.3 mg/kg和12.1 mg/kg,应予以重视,并建议对矿山进行综合治理和目标值修复。     

Assessment of the impact of landfill on groundwater quality: A case study of the Pirana site in western India

In present study focus has been given on estimating quality and toxicity of waste with respect to heavy metals and its impact on groundwater quality, using statistical and empirical relationships between different hydrochemical data, so that easy monitoring may be possible which in turn help the sustainable management of landfill site and municipal solid waste. Samples of solid waste, leachate and groundwater were analyzed to evaluate the impact of leachates on groundwater through the comparison of their hydrochemical nature. Results suggest the existence of an empirical relationship between some specific indicator parameters like heavy metals of all three above mentioned sample type. Further, K/Mg ratio also indicates three groundwater samples heavily impacted from leachate contamination. A good number of samples are also showing higher values for and Pb than that of World Health Organization (WHO) drinking water regulation. Predominance of Fe and Zn in both groundwater and solid waste samples may be due to metal plating industries in the area. Factor analysis is used as a tool to explain observed relation between numerous variables in term of simpler relation, which may help to deduce the strength of relation. Positive loading of most of the factors for heavy metal clearly shows landfill impact on ground water quality especially along the hydraulic gradient. Cluster analysis, further substantiates the impact of landfill. Two major groups of samples obtained from cluster analysis suggest that one group comprises samples that are severely under the influence of landfill and contaminated leachates along the groundwater flow direction while other assorted with samples without having such influence.     

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.