Geogenic Imprint on Groundwater and Its Quality in Parts of the Mamfe Basin, Manyu Division, Cameroon

Groundwater studies in parts of the Mamfe basin are sparse and the Mamfe area has the highest population density in the Mamfe basin. An in-depth study of groundwater rock interaction and groundwater quality is of vital importance. This same part of the basin is the economic centre and as such development of businesses in this area requires knowledge of the groundwater quality. Therefore, this study was undertaken to determine the input of the rock formations on the groundwater solute chemistry and groundwater domestic-agro-industrial quality using hydrogeochemical tools and physicochemical parameters: Ionic ratios, Gibbs diagrams, Piper diagrams, Durov diagrams and water quality indices. From physicochemical parameters, in the rainy season, pH ranged from, 4.3 - 8.6;EC, 3 - 1348 μS/cm;Temperature, 24.4℃ - 30.1℃? andTDS, 2.01 - 903.16 mg/L and in the dry season, pH ranged from 5.5 - 9.3;EC, 6 - 994 μS/cm;Temperature, 25℃?- 38.6℃?andTDS, 4.02 - 632.48 mg/L. Forty groundwater samples: 20 per season, wet and dry were analysed. The major ions fell below WHO acceptable limits for both seasons. The sequences of abundance of major ions were: Ca2+ > K+ > Mg2+ > ?> Na+, Cl- > ?> ?> ?> NO3 in wet season and Ca2+ > Mg2+ > K+ > Na+, ?> Cl- > ?> ?> ?in dry season. Ion-exchange, simple dissolution and uncommon dissolution processes determined groundwater character. Groundwater ionic content was as a result of ion exchange from rock-weathering. Water types are: CaSO4 and MgHCO3 in both seasons. Hydrogeochemical facies are Ca-Mg-Cl-SO4 and Ca-Mg-HCO3. SAR for wet season 0.05 - 0.06 and dry season 0.00 - 0.05, %Na wet season 3.64 - 16.59 and dry season 1.22 - 10.97, KR wet season 0.01 - 0.02 and 0.00 <span style="font-size:10pt;

Sanitary Surveys and Hydrochemistry of Groundwater in Two Urban Towns (Ado-Ekiti and Ijero-Ekiti), Southwestern Nigeria

Groundwater contamination in urban cities is imminent in the phase of increased anthropogenic activities apart from the contribution of geogenic contaminants. This study examined the sanitary surveys and hydrochemistry of groundwater in Ado-Ekiti and Ijero-Ekiti to establish the contaminants’ sources, decipher the effects of urbanization on population and explain any relationship between the surveys and the groundwater chemistry. Sanitary surveys of 30 randomly selected wells each from Ado-Ekiti and Ijero-Ekiti were executed by administering and processing appropriately designed questionnaires that addressed salient problems of hygiene and sanitation. The results of the surveys were grouped into very high risk, high risk, intermediate risk, and low risk classes. Subsequently, at each location, in situ parameters (temperature (°C), pH and EC (μS/cm)) were measured using a portable Multi-parameter TestrTM 35 Series S/N: 1382654. At each well, water samples were collected into clean polyethylene bottles in triplicates for cation, anions and e-coli evaluations, respectively. Water samples for cations were acidified by adding two drops of concentrated nitric acid. All samples were kept in a refrigerator at a low temperature of about 4°C before being taken to the Federal University of Technology, Akure, for analyses. Ion chromatography was employed for the anions analysis while the cations were determined using an Atomic Absorption Spectrophotometer Buck 210 model. Membrane filter technique was employed for the e-coli estimation. From the results of the hydrochemistry, the Nitrate Pollution Index (NPI) and Modified Nitrate Pollution Index (MNPI) were estimated and classified into;clean unpolluted, light pollution, moderate pollution, significant pollution, very significant pollution waters. Sanitary surveys in the two cities showed that in the very low risk, intermediate and high-risk categories, Ado-Ekiti had 33.33%, 56.67% and 10% representations, while Ijero-Ekiti had 50%, 23.33% and 26.67% representations, respectively. This observation showed that Ado-Ekiti with higher population and humans’ activities compared to Ijero-Ekiti was less susceptible to pollution. Urbanization has no direct effects on sanitary surveys. The pH of wells’ water in Ado-Ekiti ranged from 4.8 - 8.2, EC (μS/cm) from 101 - 1008, while at Ijero-Ekiti, the pH and EC (μS/cm) varied from 2.1 - 13.8 and 80 - 1008 respectively. Ado-Ekiti wells’ water was more acidic than that of Ijero-Ekiti. Chemical concentrations (mg/L) of Ca2+, Mg2+, Na+, K+, , and Clˉ of the wells’ water in both cities were within WHO-approved standards for drinking water. However, with average concentrations of 142.17 (mg/L) and 252.71 (mg/L) at Ado-Ekiti and Ijero-Ekiti, respectively, exceeded the standard in many locations. Susceptibility to pollution classification employing TDS, NPI and MNPI showed that Ijero-Ekiti was more susceptible to pollution compared to Ado-Ekiti. This assertion was supported by statistical analysis employing correlation, cluster analysis, and principal component analysis. This study showed that urbanization had no direct effects on sanitary surveys and groundwater quality. Pollution of wells’ water in the two cities was, mainly from anthropogenic activities. However, Ijero-Ekiti, with significant anthropogenic activities, had its wells’ water more susceptible to pollution. Sanitary surveys are a complementary method to water quality monitoring.

瑞士饮用水安全保障技术措施:检测、处理与保护

饮用水安全一直是全球关注的一项重要议题。检测手段的低时效性、地球成因污染物的存在以及微污染物的出现对饮用水安全造成了很大的威胁。为此,瑞士Eawag研究人员采用基于流式细胞术的微生物检测手段,对水中的微生物进行了快速、准确的定量检测,并对地球成因污染物及微污染物的去除方法及工艺进行了有益的探索。此外,还对与地下水保护密切相关的河水补给地下水行进时间及所占地下水水量比例的确定方法进行了阐述。

基于树的机器学习方法预测地质成因劣质地下水空间分布

截止到2020年,全球78亿人中仍有20亿人无法获得或只能获得有限的安全饮用水。地质成因劣质地下水(GCG)的广泛存在是造成这种严酷现实的重要原因之一,因此识别GCG已成为全球关注的热点。近年来,基于树的机器学习方法不仅成为揭示GCG空间分布和防范公共健康风险的有力工具,而且能帮助我们更好地理解地下水中劣质组分的水文生物地球化学行为。为促进基于树的机器学习方法在水文地质尤其是地下水水质与健康领域更为广泛的运用,综述了近20年来分类和回归树、随机森林和增强回归树等基于树的机器学习方法在GCG研究中的应用,讨论了如何应对正确优化模型超参数、细心选择强有力的预测变量和合理评估模型性能等诸多挑战。

基于极端梯度提升模型预测江汉平原高碘地下水的空间分布

长期摄入高碘地下水(碘浓度>100μg/L)会造成人体甲状腺机能损伤,掌握区域高碘地下水的空间分布规律对于有效规避劣质地下水,保障地下水资源的可持续安全供给至关重要。但大规模地下水水质调查耗费大量的人力、财力、物力。基于江汉平原177组常规的浅层地下水水质调查数据,选取DOC、HCO^(-)_(3)、Mg^(2+)、Fe^(2+)、NH^(+)_(4)-N、SO_(4)^(2-)等水质参数作为预测变量,建立江汉平原高碘地下水风险极端梯度提升机器学习预测模型,用于预测研究区高碘地下水的空间分布。结果表明:该模型通过测试数据集检验,预测的准确率达到86.4%;模型预测结果显示,江汉平原高碘地下水主要分布在长江河曲沿岸,零星分布在平原腹地河湖区,并识别出江汉平原西北部丘陵前缘的汉江沿岸也是高碘地下水分布的潜在区域。该研究结果将有助于圈划高碘地下水的空间分布范围,可为确定未来地下水水质监测的优先区域提供科学指导。