Impact of waste dump on surface water quality and aquatic insect diversity of Deepor Beel (Ramsar site), Assam, North-east India

Feature selection approaches for predictive modelling of cadmium sources and pollution levels in water springs

The World Health Organization lists cadmium (Cd) as one of the top ten chemicals of public health concern. Cd is toxic at relatively low exposure levels and has acute and chronic effects on both health and the environment. In this study, we investigate a suite of data-driven methods that could assist decision-makers in estimating Cd levels in water springs, and in identifying polluting sources. Machine learning (ML) regression models were used to identify sources of contamination and predict Cd levels based on support vector machines and a variety of tree-based models, including Random Forests, M5Tree, CatBoost, and gradient boosting. Feature selection analysis revealed that heavy traffic and distance to a major power plant in the sampled area play a leading role in springs Cd contamination, together with precipitation levels and average of slopes of the closest waste dumps upstream to sampled springs. Our best performing ML model was the Adaboost regression tree using all the features (RMSE = 19.36, R^2 = 0.64). Our findings highlight the effectiveness of predictive data-driven modeling in addressing environmental challenges, particularly in high-risk areas with low resources.

Diversity of aquatic insects and biomonitoring of water quality in the upper Ganga River, a Ramsar site: a preliminary assessment

Monitoring of freshwater habitats through aquatic insects is widely used. A study was carried out in March, 2019 at 14 sites in the Upper Ganga River between Brijghat and Narora, a riverine Ramsar site in India, to document the diversity of three major aquatic predatory insect groups—Odonata, Coleoptera, and Hemiptera—and determine their biomonitoring potential. The study recorded three species of Coleoptera, four Hemiptera, 14 dragonflies, and eight damselflies. The Shannon diversity index (H′) ranged from 2.465 to 2.782, Pielou’s Evenness index (J′) from 0.841 to 0.894, and Berger–Parker index of dominance (d) from 0.122 to 0.243. Families Libellulidae (Odonata), Coenagrionidae (Odonata) and Gerridae (Hemiptera) had high relative abundance and dominant status. The stream invertebrate grade number-average level (SIGNAL2) score (for family) ranged from 2.316 to 3.174, lying within quadrant 2 of the SIGNAL2 (family) quadrant diagram. This suggested that the water in the area is likely to have high levels of turbidity, salinity, or nutrients, caused  naturally or by anthropogenic activities, and the water has low levels of most toxic chemicals.

Impact of landfill garbage on insect ecology and human health

The landfill garbage includes organic and inorganic matter. The organic matter covers more than 50% of the total waste material. Due to improper management of landfill garbage, it causes serious risks to human health directly by the emission of toxic gasses. On the other hand, landfill sites are the natural habitat of several microbes and arthropods. The present discussion illustrates the impact of landfill garbage on insect ecology and human health. Here, we highlighted the arthropod density as well as diversity. Moreover, the population of insect vectors of various diseases, insect scavengers as well as pollinators has been pinpointed. It shows that landfill sites and adjacent areas are hotspots for a wide variety of arthropods. The proper management of landfill sites could reduce the population dynamics of various insect pests, and health risks could be decreased in low-and middle-income countries.

An investigation on water quality variability and identification of ideal monitoring locations by using entropy based disorder indices

Optimizing between information needs and information gathered from water quality monitoring networks involve complicated decision making processes and management strategies. The present study investigates upon entropy based variability of water quality using disorder indices. Employing the Shannon's diversity index and the principle of maximum entropy (POME), the study identifies locations which have encountered the highest influence of cumulative factors such as discharge of sewage, lowering of water table, dilution and surface run-off, which lead to water quality variability in a waterbody over a monitoring period. A case study on 19 sampling locations over the monitoring period of 2017-2018 has been done on the entire expanse of Deepor Beel (DB), a wetland of international importance designated as a Ramsar site in 2002. Geospatial analysis and isoinformation lines have been employed to generate geospatial maps that clearly depict ideal monitoring locations which encountered highest variability in their water quality over the monitoring period with respect to physico-chemical parameters, BOD, COD and heavy metals. Results indicated 5 sampling locations DB1, DB10, DB11, DB12 and DB18 for physico-chemical parameters and 4 sampling locations DB1, DB4, DB15 and DB17 for heavy metals encountered highest variability and are recommended ideal monitoring locations. The present study introduces an innovative approach to derive maximum useful information by pinpointing sampling stations for regular monitoring purposes. Risk assessment due to heavy metals was also done by using average daily dose and hazard index in the monitoring period. Cr and Pb were critical to human health on consumption.

Water Quality and Brain Function

In the United States, regulations are in place to ensure the quality of drinking water. Such precautions are intended to safeguard the health of the population. However, regulatory guidelines may at times fail to achieve their purpose. This may be due to lack of sufficient data regarding the health hazards of chronic low dose exposure to contaminants or the introduction of new substances that pose a health hazard risk that has yet to be identified. In this review, examples of different sources of contaminants in drinking water will be discussed, followed by an evaluation of some select individual toxicants with known adverse neurological impact. The ability of mixtures to potentially cause additive, synergistic, or antagonistic neurotoxic responses will be briefly addressed. The last section of the review will provide examples of select mechanisms by which different classes of contaminants may lead to neurological impairments. The main objective of this review is to bring to light the importance of considering trace amounts of chemicals in the drinking water and potential brain abnormalities. There is continued need for toxicology studies to better understand negative consequences of trace amounts of toxins and although it is beyond the scope of this brief overview it is hoped that the review will underscore the paucity of studies focused on determining how long-term exposure to minute levels of contaminants in drinking water may pose a significant health hazard.