Groundwater quality assessment in the village of Lutfullapur Nawada, Loni, District Ghaziabad, Uttar Pradesh, India

The groundwater quality for drinking, domestic and irrigation in the village Lutfullapur Nawada, Loni, district Ghaziabad, U.P., India, has been assessed. Groundwater samples were collected, processed and analyzed for temperature, pH, conductivity, salinity, total alkalinity, carbonate alkalinity, bicarbonate alkalinity, total hardness, calcium hardness, magnesium hardness, total solids, total dissolved solids, total suspended solids, nitrate-nitrogen, chloride, fluoride, sulfate, phosphate, silica, sodium, potassium, calcium, magnesium, total chromium, cadmium, copper, iron, nickel, lead and zinc. A number of groundwater samples showed levels of electrical conductivity (EC), alkalinity, chloride, calcium, sodium, potassium and iron exceeding their permissible limits. Except iron, the other metals (Cr, Cd, Cu, Ni, Pb, and Zn) were analyzed below the permissible limits. The correlation matrices for 28 variables were performed. EC, salinity, TS and TDS had significant positive correlations among themselves and also with NO (3) (-) , Cl(-), alkalinity, Na(+), K(+), and Ca(2+). Fluoride was not significantly correlated with any of the parameters. NO (3) (-) was significantly positively correlated with Cl(-), alkalinity, Na(+), K(+) and Ca(2+). Chloride also correlated significantly with alkalinity, Na(+), K(+) and Ca(2+). Sodium showed a strong and positive correlation with K(+) and Ca(2+). pH was negatively correlated with most of the physicochemical parameters. This groundwater is classified as a normal sulfate and chloride type. Base-exchange indices classified 73% of the groundwater sources as the Na(+)-SO (4) (2-) type. The meteoric genesis indices demonstrated that 67% of groundwater sources belong to a deep meteoric water percolation type. Hydrochemical groundwater evaluations revealed that most of the groundwaters belong to the Na(+)-K(+)-Cl(-)-SO (4) (2-) type followed by Na(+)-K(+)-HCO (3) (-) type. Salinity, chlorinity and SAR indices indicated that majority of groundwater samples can be considered suitable for irrigation purposes.     

Exploring potential of C band synthetic aperture radar imagery to investigate rice crop growth mechanism and productivity

Environmental Science and Pollution Research - Development of satellite technology over decades established unique approach to analyze rice crop phenological parameters and supervise growth and...     

Current trends in diagnosis and treatment strategies of COVID-19 infection

Environmental Science and Pollution Research - Coronaviruses are terrifically precise and adapted towards specialized respiratory epithelial cells, observed in organ culture and human volunteers...     

A novel bagging ensemble approach for predicting summertime ground-level ozone concentration

Ozone pollution appears as a major air quality issue, e.g. for the protection of human health and vegetation. Formation of ground level ozone is a complex photochemical phenomenon and involves numerous intricate factors most of which are interrelated with each other. Machine learning techniques can be adopted to predict the ground level ozone. The main objective of the present study is to develop the state-of-the-art ensemble bagging approach to model the summer time ground level ozone in an industrial area comprising a hazardous waste management facility. In this study, the feasibility of using ensemble model with seven meteorological parameters as input variables to predict the surface level O3 concentration. Multilayer perceptron, RTree, REPTree, and Random forest were employed as the base learners. The error measures used for checking the performance of each model includes IoAd, R2, and PEP. The model results were validated against an independent test data set. Bagged random forest predicted the ground level ozone better with higher Nash-Sutcliffe coefficient 0.93. This study scaffolded the current research gap in big data analysis identified with air pollutant prediction.

Implications: The main focus of this paper is to model the summer time ground level O₃ concentration in an Industrial area comprising of hazardous waste management facility. Comparison study was made between the base classifiers and the ensemble classifiers. Most of the conventional models can well predict the average concentrations. In this case the peak concentrations are of importance as it has serious effect on human health and environment. The models developed should also be homoscedastic.     

Common property resources in drylands of India

Common property resources are areas of land or water being used by a community or a group of communities. These have special significance to peoples and communities who depend on them for their livelihood. The commons in all arid districts of India include village pastures, community forests, wasteland, common threshing grounds, waste dumps, watershed drainage, village baoris¹, talabs², nadis³ and ponds, and tanks, rivers, rivulets, wetlands, riverbeds, community conserved areas, protected areas, Dhaam⁴ or Dhooni⁵, culturable wastelands, barren & un-culturable land, etc. The area under commons often ranged from 9 to 28% of total village area. Appropriation of the commons by the state for building essential infrastructure such as schools, clinics, veterinary hospitals, housing for government functionaries, SEZ and industrial corridors, etc. is a cause of serious concern. Presently the ownership rights over CPRs are not clear and there are many who claim ownership, some at State level but also like local bodies. The 12^th plan of the Planning Commission of India recognized and highlighted the need for favourable land tenure arrangements, institutional design and programme architecture in order to achieve effective governance and management of the commons. The revitalization of CPRs is crucial for protecting livelihoods, as well as for biodiversity conservation and for the improvement in arid microclimatic conditions. Dialogue continues on the status of common property resources, the available legal framework and some policy related issues for its conservation through strengthening of local institutions and capacity building for stakeholders.     

Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)

Contamination of groundwater constrains its uses and poses a serious threat to the environment. Once groundwater is contaminated, the cleanup may be difficult and expensive. Identification of unknown pollution sources is the first step toward adopting any remediation strategy. The proposed methodology exploits the capability of a universal function approximation by a feed-forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes, and duration of activity. The back-propagation algorithm is utilized for training the ANN to identify the source characteristics based on simulated concentration data at specified observation locations in the aquifer. Uniform random generation and the Latin hypercube sampling method of random generation are used to generate temporal varying source fluxes. These source fluxes are used in groundwater flow and the transport simulation model to generate necessary data for the ANN model-building processes. Breakthrough curves obtained for the specified pollution scenario are characterized by different methods. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentation is also performed with different number of training and testing patterns. In addition, the effects of measurement errors in concentration measurements values are used to show the robustness of ANN based methodology for source identification in case of erroneous data.     

Solar oxidation and removal of arsenic from water: An experimental study

Arsenic poses a significant threat to both human health and the environment. Arsenic removal through solar oxidation has been investigated in a batch process. Arsenic was artificially added to both deionized and tap water to conduct the experiments. Clean, colorless, transparent, Polyethylene Terephthalate (PET) bottles were used for Solar Oxidation and Removal of Arsenic (SORAS) experiments. Various parameters including concentration of arsenic, iron, and photo-catalyst were varied during the experiments. The maximum arsenic removal efficiency obtained was 94% and 88% for deionized water and tap water respectively when ferrous ammonium sulphate and lemon juice were used. Maximum efficiency of 88% and 82% was obtained for deionized and tap water respectively when locally available ferrous alum and glacial acetic acid were used. The change in volume of the photo-catalyst (lemon juice and glacial acetic acid) also did not affect the SORAS process significantly. Therefore, the recommended volume for the photo-catalyst was 1–2 ml/L. SORAS can very well be used for areas contaminated with arsenic having concentrations less than 100 μg/L.     

Acid azo dye degradation by free and immobilized horseradish peroxidase (HRP) catalyzed process

Acid azo (Acid Black 10 BX) dye removal by plant based peroxidase catalyzed reaction was investigated. Horseradish peroxidase (HRP) was extracted from horseradish roots and its performance was evaluated in both free and immobilized form. HRP showed its ability to degrade the dye in aqueous phase. Studies are further carried out to understand the process parameters such as aqueous phase pH, H2O2 dose, dye and enzyme concentrations during enzyme-mediated dye degradation process. Experimental data revealed that dye (substrate) concentration, aqueous phase pH, enzyme and H2O2 dose play a significant role on the overall enzyme-mediated reaction. Acrylamide gel immobilized HRP showed effective performance compared to free HRP and alginate entrapped HRP. Alginate entrapped HRP showed inferior performance over the free enzyme due to the consequence of non-availability of the enzyme to the dye molecule due to polymeric immobilization. Standard plating studies performed with Pseudomonas putida showed enhanced degradation of HRP catalyzed dye compared to control.     

A risk-based model to establish threshold planning quantities of hazardous substances

The Threshold Planning Quantity (TPQ) is the minimum quantity of any hazardous substance stored or processed at an industry that can pose a defined level of risk up to a certain distance from the industrial premises should an accidental release occur. These are recommended by the concerned government departments of the respective countries. Often, concerned regulatory authorities focus on simple screening tools to prescribe TPQs of hazardous chemicals rather than on more detailed technical aspects. This is illustrated with the help of TPQs presently administered in India, which are followed based on Control of Industrial Major Accident Hazard regulations of the United Kingdom. In this paper, the risk ranking matrix (RRM) approach is described to evaluate the recommended TPQs and prescribe these for some extremely hazardous chemicals (EHS) commonly used in industries. The RRM presented here is unique in the sense that various acceptable risk criteria are reviewed to appropriately rank the risk potential and provide suitability criteria in terms of individual risk factors and geosocietal risk factors (GSRF). Based on this concept, an attempt has been made to develop a risk-based model to determine the TPQs of various EHS (acrolein, ammonia, chlorine, hydrogen fluoride, and phosgene).