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1.
The study region comprises the Sidi Bouzid shallow aquifer, which is located in the western part of Central Tunisia. It is mainly occupied by agricultural land with intensive use of chemical fertilizers especially nitrates. For this reason, nitrate measurement was performed in 38 water samples to evaluate and calibrate the obtained models. Several environmental parameters were analyzed using groundwater nitrate concentrations, and different statistical approaches were applied to assess and validate the groundwater vulnerability to nitrate pollution in the Sidi Bouzid shallow aquifer. Multiple linear regression (MLR), analyses of covariance (ANCOVA), and logistic regression (LR) were carried out for studying the nitrate effects on groundwater pollution. Statistical analyses were used to identify major environmental factors that control the groundwater nitrate concentration in this region. Correlation and statistical analyses were conducted to examine the relationship between the nitrate (dependent variable) and various environmental variables (independent variables). All methods show that “groundwater depth” and “land use” parameters are statistically significant at 95% level of confidence. Groundwater vulnerability map was obtained by overlaying these two thematic layers which were obtained in the GIS environment. It shows that the high vulnerability area coincides with the likelihood that nitrate concentration exceeds 24.5 mg/l in groundwater. The relationship between the groundwater vulnerability classes and the nitrate concentrations provides satisfactory results; it showed an Eta-squared correlation coefficient of 64%. So, the groundwater vulnerability map can be used as a synthetic document for realistic management of groundwater quality.  相似文献   

2.
Spatial relations between land use and groundwater quality in the watershed adjacent to Assateague Island National Seashore, Maryland and Virginia, USA were analyzed by the use of two spatial models. One model used a logit analysis and the other was based on geostatistics. The models were developed and compared on the basis of existing concentrations of nitrate as nitrogen in samples from 529 domestic wells. The models were applied to produce spatial probability maps that show areas in the watershed where concentrations of nitrate in groundwater are likely to exceed a predetermined management threshold value. Maps of the watershed generated by logistic regression and probability kriging analysis showing where the probability of nitrate concentrations would exceed 3 mg/L (>0.50) compared favorably. Logistic regression was less dependent on the spatial distribution of sampled wells, and identified an additional high probability area within the watershed that was missed by probability kriging. The spatial probability maps could be used to determine the natural or anthropogenic factors that best explain the occurrence and distribution of elevated concentrations of nitrate (or other constituents) in shallow groundwater. This information can be used by local land-use planners, ecologists, and managers to protect water supplies and identify land-use planning solutions and monitoring programs in vulnerable areas.  相似文献   

3.
A study was carried in Mettur taluk, Salem district of Tamilnadu, India to develop a DRASTIC vulnerability index in GIS environment owing to groundwater pollution with increasing population, industries, and agricultural activities. Seven DRASTIC layers were created from available data (depth to water table, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity) and incorporated into DRASTIC model to create a groundwater vulnerability map by overlaying the hydrogeological parameters. The output map indicates southwestern part of the study area with high pollution potential, northern and northwestern parts as moderate pollution potential and northeastern parts as low and no risk of pollution potential. For validating the vulnerability assessment, a total of 46 groundwater samples were collected from different vulnerability zones of the study area for two different seasons (pre- and post-monsoon) and analyzed for major anions and cations. Higher ionic concentrations were noted in wells located near highly industrialized, urbanized, and agricultural active zones. The water types represent Na–Mg–HCO3 and Na–Cl–HCO3 type indicating dominance of anthropogenic-related activities. Nitrate and chloride were demarcated as pollution indicators and correlated with DRASTIC vulnerability map. The results show that southwestern, northwestern, and northern parts of the study area recorded with high and moderate vulnerable zones, record higher nitrate values. In contrast to DRASTIC method predicted, low vulnerable zones show higher chloride concentration may be due to agricultural and urban development.  相似文献   

4.
为研究滹沱河冲洪积扇地区地下水硝酸盐污染机制,对滹沱河冲洪积扇地区地下水和地表水进行了采样监测,运用环境健康风险评价模型对研究区硝酸盐进行评价,采用水化学和多元统计方法研究了滹沱河冲洪积扇地区地下水硝酸盐污染问题。结果表明:研究区地表水NO-3污染较轻,NO-3均值为19.54 mg/L,所有水样均未超出我国地表水环境质量标准（45 mg/L）;但是,地下水已经受到了NO-3的严重污染,NO-3均值为75.84 mg/L,且有30.43%水样超出我国地下水质量标准（88. 6 mg/L）。研究区3个水文地质单元地下水硝酸盐的平均个人年健康风险分别为4.94×10-8、1.99×10-8和2.61×10-9,低于国际辐射防护委员会（ICRP）推荐的最大可接受风险水平（5.0×10-5/a）,因此,认为不会对人群构成严重危害。水文地质单元和地下水埋深对硝酸盐污染有显著影响,但是,土地利用类型对硝酸盐浓度的影响不显著。滹沱河冲洪积扇地区地下水硝酸盐的主要污染来源是生活污水和化肥。此外,强烈开采地下水也是该地区NO-3污染的诱因。  相似文献   

5.
The intrinsic vulnerability of groundwater in the Comunidad de Madrid (central Spain) was evaluated using the DRASTIC and GOD indexes. Groundwater vulnerability to nitrate pollution was also assessed using the composite DRASTIC (CD) and nitrate vulnerability (NV) indexes. The utility of these methods was tested by analyzing the spatial distribution of nitrate concentrations in the different aquifers located in the study area: the Tertiary Detrital Aquifer, the Moor Limestone Aquifer, the Cretaceous Limestone Aquifer and the Quaternary Aquifer. Vulnerability maps based on these four indexes showed very similar results, identifying the Quaternary Aquifer and the lower sub-unit of the Moor Limestone Aquifer as deposits subjected to a high risk of nitrate pollution due to intensive agriculture. As far as the spatial distribution of groundwater nitrate concentrations is concerned, the NV index showed the greatest statistical significance (p?<?0.01). This new type of multiplicative model offers greater accuracy in estimations of specific vulnerability with respect to the real impact of each type of land use. The results of this study provide a basis on which to guide the designation of nitrate vulnerable zones in the Comunidad de Madrid, in line with European Union Directive 91/676/EEC.  相似文献   

6.
A DRASTIC-model method based on a geographic information system (GIS) was used to study groundwater vulnerability in Egirdir Lake basin (Isparta, Turkey), an alluvial area that has suffered agricultural pollution. ‘Lineament’ and ‘land use’ were added to the DRASTIC parameters, and an analytic hierarchy process (AHP) method determined the rating coefficients of each parameter. The effect of lineament and land-use parameters on the resulting vulnerability maps was determined with a single-parameter sensitivity analysis. Of the DRASTIC parameters, land use affects the aquifer vulnerability map most and lineament affects it least, after topography. A simple linear regression analysis assessed the statistical relation between groundwater nitrate concentration and the aquifer vulnerability areas; the highest R 2 value was obtained with the modified-DRASTIC-AHP method. The DRASTIC vulnerability map shows that only the shoreline of Egirdir Lake and the alluvium units have high contamination potential. In this respect, the modified DRASTIC vulnerability map is quite similar. According to the modified-DRASTIC-AHP method, the lakeshore areas of Senirkent-Uluborlu and Hoyran plains, and all of the Yalvaç-Gelendost plain, have high contamination potential. Analyses confirm that groundwater nitrate content is high in these areas. By comparison, the modified-DRASTIC-AHP method has provided more valid results.  相似文献   

7.
Increasing pressure on water resources worldwide has resulted in groundwater contamination, and thus the deterioration of the groundwater resources and a threat to the public health. Risk mapping of groundwater contamination is an important tool for groundwater protection, land use management, and public health. This study presents a new approach for groundwater contamination risk mapping, based on hydrogeological setting, land use, contamination load, and groundwater modelling. The risk map is a product of probability of contamination and impact. This approach was applied on the Gaza Strip area in Palestine as a case study. A spatial analyst tool within Geographical Information System (GIS) was used to interpolate and manipulate data to develop GIS maps of vulnerability, land use, and contamination impact. A groundwater flow model for the area of study was also used to track the flow and to delineate the capture zones of public wells. The results show that areas of highest contamination risk occur in the southern cities of Khan Yunis and Rafah. The majority of public wells are located in an intermediate risk zone and four wells are in a high risk zone.  相似文献   

8.
Groundwater plays a key role in arid regions as the majority of water is supplied by it. Groundwater pollution is a major issue, because it is susceptible to contamination from land use and other anthropogenic impacts. A study was carried out to build a vulnerability map for the Ordos Plateau using the DRASTIC model in a GIS environment. The map was designed to show the areas of the highest potential for groundwater pollution based on hydrogeological conditions. Seven environmental parameters, such as depth to water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone media, and hydraulic conductivity of the aquifer, were incorporated into the DRASTIC model and GIS was used to create a groundwater vulnerability map by overlaying the available data. The results of this study show that 24.8 % of the study area has high pollution potential, 24.2 % has moderate pollution potential, 19.7 % has low pollution potential, and the remaining 31.3 % of the area has no risk of groundwater pollution. The regional distribution of nitrate is well correlated with the DRASTIC vulnerability index. In contrast to this, although the DRASTIC model indicated that the western part had no risk, nitrate concentrations were higher in some of these areas. In particular, higher nitrate concentrations were recorded along river valleys and around lakes, such as the Mulin River valley. This is mainly caused by the intensive agricultural development and favorable conditions for recharge along river valleys.  相似文献   

9.
The existing different human activities and planned land uses put the groundwater resources in Jordan at considerable risk. There are evidences suggesting that the quality of groundwater supplies in north Jordan is under threat from a wide variety of point and non-point sources including agricultural, domestic, and industrial. Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. DRASTIC method incorporates the major geological and hydrogeological factors that affect and control groundwater movement: depth to groundwater (D), net recharge (R), lithology of the aquifer (A), soil texture (S), topography (T), lithology of vadose zone (I), and hydraulic conductivity (C). The main goal of this study is to produce vulnerability maps of groundwater resources in the Yarmouk River basin by applying the DRASTIC method to determine areas where groundwater protection or monitoring is critical. ArcGIS 9.2 was used to create the groundwater vulnerability maps by overlaying the available hydrogeological data. The resulting vulnerability maps were then integrated with lineament and land use maps as additional parameters in the DRASTIC model to assess more accurately the potential risk of groundwater to pollution. The general DRASTIC index indicates that the potential for polluting groundwater is low in the whole basin, whereas the resulting pesticide DRASTIC vulnerability map indicates that about 31% of the basin is classified as having moderate vulnerability, which may be attributed to agricultural activities in the area. Although high nitrate concentrations were found in areas of moderate vulnerability, DRASTIC method did not depict accurately the nitrate distribution in the area.  相似文献   

10.
DRASTIC indexing and integrated electrical conductivity (IEC) modeling are approaches for assessing aquifer vulnerability to surface pollution. DRASTIC indexing is more common, but IEC modeling is faster and more cost-effective because it requires less data and fewer processing steps. This study aimed to compare DRASTIC indexing with IEC modeling to determine whether the latter is sufficient on its own. Both approaches are utilized to determine zones vulnerable to groundwater pollution in the Nile Delta. Hence, assessing the nature and degree of risk are important for realizing effective measures toward damage minimization. For DRASTIC indexing, hydrogeological factors such as depth to aquifer, recharge rate, aquifer media, soil permeability, topography, impact of the vadose zone, and hydraulic conductivity were combined in a geographical information system environment for assessing the aquifer vulnerability. For IEC modeling, DC resistivity data were collected from 36 surface sounding points to cover the entire area and used to estimate the IEC index. Additionally, the vulnerable zones identified by both approaches were tested using a local-scale resistivity survey in the form of 1D and 2D resistivity imaging to determine the permeable pathways in the vadose zone. A correlation of 0.82 was obtained between the DRASTIC indexing and IEC modeling results. For additional benefit, the obtained DRASTIC and IEC models were used together to develop a vulnerability map. This map showed a very high vulnerability zone, a high-vulnerability zone, and moderate- and low-vulnerability zones constituting 19.89, 41, 27, and 12%, respectively, of the study area. Identifying where groundwater is more vulnerable to pollution enables more effective protection and management of groundwater resources in vulnerable areas.  相似文献   

11.
A wellhead protection study for the city of Sturgeon Bay, Wisconsin, USA, demonstrates the necessity of combining detailed hydrostratigraphic analysis with groundwater modeling to delineate zones of contribution for municipal wells in a fractured dolomite aquifer. A numerical model (MODFLOW) was combined with a particle tracking code (MODPATH) to simulate the regional groundwater system and to delineate capture zones for municipal wells. The hydrostratigraphic model included vertical and horizontal fractures and high-permeability zones. Correlating stratigraphic interpretations with field data such as geophysical logs, packer tests, and fracture mapping resulted in the construction of a numerical model with five high-permeability zones related to bedding planes or facies changes. These zones serve as major conduits for horizontal groundwater flow. Dipping fracture zones were simulated as thin high-permeability layers. The locations of exposed bedrock and surficial karst features were used to identify areas of enhanced recharge. Model results show the vulnerability of the municipal wells to pollution. Capture zones for the wells extend several kilometers north and south from the city. Travel times from recharge areas to all wells were generally less than one year. The high seasonal variability of recharge in the study area made the use of a transient model necessary. Electronic Publication  相似文献   

12.
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13.
Groundwater, the most vital water resource being used for irrigation, domestic and industrial purposes is nowadays under severe threat of contamination. Groundwater contamination risk assessment is an effective tool for groundwater management. In the study, a DRASTIC model which is based on the seven hydrogeological parameters viz: depth of water, net-recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity was used to evaluate the groundwater pollution potentiality of upper Betwa watershed. ArcGIS was used to create the ground water vulnerability map by overlaying the seven layers. Based on groundwater vulnerability map, the watershed has been divided in three vulnerable zones viz; low vulnerability zone with 42.83 km2 of area, moderate with 369.21 km2 area and high having 270.96 km2 of area. Furthermore, the DRASTIC model has been validated by nitrate concentration over the area. Results of validation have shown that in low vulnerable zone, no nitrate contamination has been recorded. While in the moderate zone nitrate has been found in the range of 1.6-10ppm. However, in high vulnerable zone 11-40ppm of nitrate concentration in groundwater has been recorded, which proves that the DRASTIC model is applicable for the prediction of groundwater vulnerability in the watershed and in similar areas too.  相似文献   

14.
Submerged aquatic vegetation (SAV) provides many important ecosystem functions, but SAV has been significantly reduced in many estuaries. We used spatial–statistical models to identify estuarine shoreline characteristics that explain variations in SAV abundance among subestuaries of the Chesapeake Bay and mid-Atlantic Coastal Bays. We summarized digital spatial data on shoreline construction, shoreline land use, physical characteristics, watershed land cover, and salinity for each subestuary. We related SAV abundance to shoreline characteristics and other stressors using univariate regression and multivariate models. The strongest univariate predictors of SAV abundance were percent shoreline forest, percent shoreline marsh, the percentage of shoreline that is 5–10 m tall, percent riprap, the percentage of subestuary area <2 m deep, percent herbaceous wetland, and percent shrubland. Shoreline marsh, bulkhead, and shoreline forest had different effects on SAV in different salinity zones. Percent riprap shoreline was the most important variable in a regression tree analysis of all the subestuaries, and percent deciduous forest in the watershed was the most important variable in a separate regression tree analysis on the mesohaline subestuaries. Subestuaries with <5.4 % riprap followed a significantly different temporal trajectory than those with >5.4 % riprap. SAV abundance has increased steadily since 1984 in subestuaries with <5.4 % riprap, but has not increased since 1996–1997 in subestuaries with >5.4 % riprap. Some shoreline characteristics interact with larger-scale factors like land cover and salinity zone to affect the distribution of SAV, while the effects of other shoreline characteristics are consistent among subestuaries with different salinities or local watershed land covers. Many shoreline characteristics can be controlled by management decisions, and our results help identify factors that managers should consider in efforts to increase SAV abundance.  相似文献   

15.
The purpose of this study is to develop statistical models for groundwater quality assessment in urban areas using Geographic Information Systems (GIS). To develop the models, the concentrations of nitrate (expressed as nitrogen, NO3-N), which are different according to the type of land use, well depth and distribution of rainfall, were analyzed in the Seoul (the capital of South Korea) area. Data such as land use, location of wells and groundwater quality data for nitrate contamination were collected and a database constructed within GIS. The distribution of NO3-N concentrations is not normal, and the results of the Mann-Whitney U-test analysis show the difference of NO3-N concentration by well depth and by distribution of rainfall. In both the shallow and deep wells, the radius of influence is 200 m in the dry season and 250 m in the rainy season, showing the tendency to increase in the rainy season. The results of correlation and regression analysis indicate that mixed residential and business areas and cropped field areas are likely to be the major contributor of increasing NO3-N concentration. Land uses are better correlated with NO3-N in deep wells than in shallow wells.  相似文献   

16.
This study developed methodology for statistically assessing groundwater contamination mechanisms. It focused on microbial water pollution in low-income regions. Risk factors for faecal contamination of groundwater-fed drinking-water sources were evaluated in a case study in Juba, South Sudan. The study was based on counts of thermotolerant coliforms in water samples from 129 sources, collected by the humanitarian aid organisation Médecins Sans Frontières in 2010. The factors included hydrogeological settings, land use and socio-economic characteristics. The results showed that the residuals of a conventional probit regression model had a significant positive spatial autocorrelation (Moran’s I?=?3.05, I-stat?=?9.28); therefore, a spatial model was developed that had better goodness-of-fit to the observations. The most significant factor in this model (p-value 0.005) was the distance from a water source to the nearest Tukul area, an area with informal settlements that lack sanitation services. It is thus recommended that future remediation and monitoring efforts in the city be concentrated in such low-income regions. The spatial model differed from the conventional approach: in contrast with the latter case, lowland topography was not significant at the 5% level, as the p-value was 0.074 in the spatial model and 0.040 in the traditional model. This study showed that statistical risk-factor assessments of groundwater contamination need to consider spatial interactions when the water sources are located close to each other. Future studies might further investigate the cut-off distance that reflects spatial autocorrelation. Particularly, these results advise research on urban groundwater quality.  相似文献   

17.
Depth to water, net recharge, aquifer media, soil media, topography, impact of the vadose zone media, and hydraulic conductivity of the aquifer (DRASTIC) model based on a geographic information system (GIS) is the most widely adopted model for the evaluation of groundwater vulnerability. However, the model had its own disadvantages in various aspects. In this work, several methods and the technologies have been introduced to improve on the traditional model. The type of the aquifer was replaced by the thickness of the aquifer, and the index of topography was removed. The indexes of the exploitation of the groundwater and the type of land use that reflected the special vulnerability were added to the system. Furthermore, considering the wideness of the study area, the fixed weights in the DRASTIC model were not suitable. An analytic hierarchy process (AHP) method and an entropy weight (Ew) method were introduced to calculate the weights of parameters. Then, the Spearman Rho correlation coefficients between IVI and the Nemerow synthetical pollution index (NI) of the groundwater quality were significantly improved, after the four steps of modification. The level differences with little gaps between Nemerow comprehensive pollution indexes and groundwater vulnerability occupied the proportion of the area from 75.68 to 84.04%, and finally, a single-parameter sensitivity analysis for the two models was used to compute the effective weights of these parameters. By comparison, the DRMSICEL model seems to perform better than the DRASTIC model in the study area. And the results show discrepancies between the vulnerability indices and groundwater quality as indicated by existence of vulnerable areas with bad water quality and vice versa.  相似文献   

18.
Groundwater contamination from intensive fertilizer application affects conservation areas in a plain. The DRASTIC model can be applied in the evaluation of groundwater vulnerability to such pollution. The main purpose of using the DRASTIC model is to map groundwater susceptibility to pollution in different areas. However, this method has been used in various areas without modification, thereby disregarding the effects of pollution types and their characteristics. Thus, this technique must be standardized and be approved for applications in aquifers and particular types of pollution. In this study, the potential for the more accurate assessment of vulnerability to pollution is achieved by correcting the rates of the DRASTIC parameters. The new rates were calculated by identifying the relationships among the parameters with respect to the nitrate concentration in groundwater. The methodology was implemented in the Kerman plain in the southeastern region of Iran. The nitrate concentration in water from underground wells was tested and analyzed in 27 different locations. The measured nitrate concentrations were used to associate and correlate the pollution in the aquifer to the DRASTIC index. The Wilcoxon rank-sum nonparametric statistical test was applied to determine the relationship between the index and the measured pollution in Kerman plain. Also, the weights of the DRASTIC parameters were modified through the sensitivity analysis. Subsequently, the rates and weights were computed. The results of the study revealed that the modified DRASTIC model performs more efficiently than the traditional method for nonpoint source pollution, particularly in agricultural areas. The regression coefficients showed that the relationship between the vulnerability index and the nitrate concentration was 82 % after modification and 44 % before modification. This comparison indicated that the results of the modified DRASTIC of this region are better than those of the original method.  相似文献   

19.
This study constructs a hazard map for ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using a probability (frequency ratio) model, a statistical (logistic regression) model, and a Geographic Information System (GIS). To evaluate the factors related to ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, Global Positioning System (GPS) data, land use map, lineaments, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed from field investigations and reports on the existing ground subsidence areas at the study site. Nine major factors causing ground subsidence were extracted from the probability analysis of the existing ground subsidence area: (1) depth of drift; (2) DEM and slope gradient; (3) groundwater level, permeability, and rock mass rating (RMR); (4) lineaments and geology; and (5) land use. The frequency ratio and logistic regression models were applied to determine each factor’s rating, and the ratings were overlain for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with existing subsidence areas. The verification results showed that the logistic regression model (accuracy of 95.01%) is better in prediction than the frequency ratio model (accuracy of 93.29%). The verification results showed sufficient agreement between the hazard map and the existing data on ground subsidence area. Analysis of ground subsidence with the frequency ratio and logistic regression models suggests that quantitative analysis of ground subsidence near AUCMs is possible.  相似文献   

20.
In recent years, nitrate contamination of groundwater has become a growing concern for people in rural areas in North China Plain (NCP) where groundwater is used as drinking water. The objective of this study was to simulate agriculture derived groundwater nitrate pollution patterns with artificial neural network (ANN), which has been proved to be an effective tool for prediction in many branches of hydrology when data are not sufficient to understand the physical process of the systems but relative accurate predictions is needed. In our study, a back propagation neural network (BPNN) was developed to simulate spatial distribution of NO3-N concentrations in groundwater with land use information and site-specific hydrogeological properties in Huantai County, a typical agriculture dominated region of NCP. Geographic information system (GIS) tools were used in preparing and processing input–output vectors data for the BPNN. The circular buffer zones centered on the sampling wells were designated so as to consider the nitrate contamination of groundwater due to neighboring field. The result showed that the GIS-based BPNN simulated groundwater NO3-N concentration efficiently and captured the general trend of groundwater nitrate pollution patterns. The optimal result was obtained with a learning rate of 0.02, a 4-7-1 architecture and a buffer zone radius of 400 m. Nitrogen budget combined with GIS-based BPNN can serve as a cost-effective tool for prediction and management of groundwater nitrate pollution in an agriculture dominated regions in North China Plain.  相似文献   

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