Abstract

Groundwater is a reliable source of fresh water for domestic and agricultural water users. It supports subsurface ecosystem by balancing the geo-biological and bio-geochemical processes at micro- and macro-scales of the ecosystem. Overexploitation, anthropogenic activities and improper agricultural practices have contributed to the pollution of groundwater sources all around the globe. The water quality index (WQI) is the most extensively used indicator which transforms the water quality information derived from several parameters into a single value/rating to categorize and provide a general perception of water quality standard. Groundwater quality analysis and mapping via geographical information system (GIS) proves to be beneficial in identifying the locations where the groundwater quality is deteriorating. In the current study, the WQI of groundwater was determined for the samples collected from open and tube wells located within the Udupi district of Karnataka state, India. The groundwater quality parameters such as pH, hardness, calcium, chlorides, nitrates, iron, fluoride, sulfates, manganese, sodium, magnesium, potassium, turbidity, and phosphate were analyzed for water samples collected from 112 randomly chosen open/tube wells in order to determine the WQI. Interpolation approaches such as inverse distance weighting (IDW) and kriging were adopted in the GIS environment to quantify the spatial variability of groundwater quality over the entire geographical area. The groundwater quality maps were generated using the best fit models. The results portray that, the accuracy of interpolation using IDW and kriging methods was dependent on the measures of central tendency and variability of water quality data of different parameters. The kriging interpolation was much accurate for most of the groundwater quality parameters compared to IDW maps. The WQI maps, perhaps signposted the poor quality of groundwater quality in about 1.88% of the geographical area of Udupi district. Further, about 21.69% of the area was affected by poor quality of groundwater where suitable strategies for replenishment of groundwater resources should be taken up by the concerned authorities. The spatial distribution maps of groundwater quality aid to locate vulnerable places where immediate action is required.

Highlights

• Water quality index mapping clearly depicts the critical areas that need policy measures for the groundwater sustainability.

• Current study acts as a decision support system for taking up water quality management activities for groundwater remediation in the study area.

• Geostatistical methods prove to be ideal for the evaluation of spatial groundwater quality assessment and distribution.

中文翻译：

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地下水水质的空间变异性：以印度卡纳塔克邦乌杜皮区为例

摘要

对于家庭和农业用水者而言，地下水是可靠的淡水来源。它通过在生态系统的微观和宏观尺度上平衡地球生物学和生物地球化学过程来支持地下生态系统。过度开发，人为活动和不当的农业作法加剧了全球地下水源的污染。水质指数（WQI）是使用最广泛的指标，它将从多个参数得出的水质信息转换为单个值/等级，以进行分类并提供对水质标准的一般理解。事实证明，通过地理信息系统（GIS）进行的地下水质量分析和制图有助于识别地下水质量恶化的位置。在目前的研究中 确定了从印度卡纳塔克邦乌杜皮区的裸眼和管井中采集的样品的地下水水质指数。分析了从112个随机选择的裸露/管井中采集的水样的地下水质量参数，例如pH，硬度，钙，氯化物，硝酸盐，铁，氟化物，硫酸盐，锰，钠，镁，钾，浊度和磷酸盐。确定WQI。GIS环境中采用了反距离权重（IDW）和克里金法等插值方法来量化整个地理区域内地下水质量的空间变异性。使用最佳拟合模型生成了地下水水质图。结果表明，使用IDW和克里金法进行插值的准确性取决于不同参数的水质数据的集中趋势和变异性。与IDW图相比，克里格插值法对于大多数地下水质量参数而言非常准确。WQI地图可能标志着乌杜皮地区约1.88％的地下水水质较差。此外，约21.69％的地区受到地下水质量差的影响，有关当局应采取适当的补充地下水资源的策略。地下水质量的空间分布图有助于确定需要立即采取行动的脆弱地区。与IDW图相比，克里格插值法对于大多数地下水质量参数而言非常准确。WQI地图可能标志着乌杜皮地区约1.88％的地下水水质较差。此外，约21.69％的地区受到地下水质量差的影响，有关当局应采取适当的补充地下水资源的策略。地下水质量的空间分布图有助于确定需要立即采取行动的脆弱地区。与IDW图相比，克里格插值法对于大多数地下水质量参数而言非常准确。WQI地图可能标志着乌杜皮地区约1.88％的地下水水质较差。此外，约21.69％的地区受到地下水质量差的影响，有关当局应采取适当的补充地下水资源的策略。地下水质量的空间分布图有助于确定需要立即采取行动的脆弱地区。该地区69％的地区受到地下水质量差的影响，有关当局应采取适当的地下水资源补充策略。地下水质量的空间分布图有助于确定需要立即采取行动的脆弱地区。该地区69％的地区受到地下水质量差的影响，有关当局应采取适当的地下水资源补充策略。地下水质量的空间分布图有助于确定需要立即采取行动的脆弱地区。

强调

• 水质指数图清楚地描绘了需要采取政策措施以实现地下水可持续性的关键领域。

• 当前的研究作为决策支持系统，用于承担研究区域地下水修复的水质管理活动。

• 事实证明，地统计学方法是评估空间地下水质量评估和分布的理想方法。