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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2007.07.014 Byline: Ratnakar Dhakate, V.S. Singh, B.C. Negi, Subhash Chandra, V. Ananda Rao Keywords: Groundwater; Geomorphology; Lineaments and vertical electrical sounding Abstract: The increasing demand for fresh water has necessitated the exploration for new sources of groundwater, particularly in hard rock terrain, where groundwater is a vital source of fresh water. A fast, cost effective and economical way of exploration is to study and analyze remote sensing data. Interpreted remote sensing data was used to select sites for carrying out surface geophysical investigations. Various geomorphologic units were demarcated and the lineaments were identified by interpretation of remote sensing satellite images. The potential for occurrence of groundwater in the watershed areas was classified as very good, good, moderate and poor by interpreting the images. Sub-surface geophysical investigations, namely vertical electrical soundings, were carried out to delineate potential water-bearing zones. Integrated studies of interpretation of geomorphologic and geophysical data were used to prepare a groundwater potential map. The studies reveal that the groundwater potential of shallow aquifers is due to geomorphologic features and the potential of deeper aquifers is determined by lineaments such as faults and joints. Author Affiliation: National Geophysical Research Institute, Uppal Road, Hyderabad 500007, Andhra Pradesh, India Article History: Received 12 September 2006; Revised 14 June 2007; Accepted 7 July 2007

• The research focused on dust removal from solar panels through optimal cleaning. • Totally 42 literature on optimal cleaning schedules in PV panels were collected. • The SVM-DCNN combined with HBBHO predicts the dust particles. The research aims to develop a system that determines the optimal intervals for cleaning solar PV panels to maximize their efficiency and minimize power loss due to dust accumulation. The research utilizes data related to daily solar photovoltaic tariff rates, power generation, and conditions of dust accumulation on PV panels. To monitor the performance of PV panels, the research involves forecasting power generation. If the power generation falls below a predefined threshold, it indicates a need for cleaning. Dust particle loss conditions are analyzed using an optimized Support Vector Machine (SVM) coupled with a Deep Convolutional Neural Network (CNN) model. This model processes PV panel images to identify dusty conditions. The Deep CNN model extracts features from PV panel images, such as color-based statistical features and local gradient patterns. These features are crucial for identifying dusty conditions. The optimal cleaning schedule is determined based on the detection of dust particle loss conditions. Cleaning is initiated when the conditions warrant it. The novelty of this research lies in the development of a deep learning model for both detecting dust particle loss and forecasting power generation. This model is trained using data that includes daily tariff rates and dust particle loss information. The research proposes a new optimization method called "biography-based Helianthus optimization (HBBHO)" specifically designed to suit the characteristics of sunlight. This method is employed to schedule cleaning activities. The proposed approach achieves impressive accuracy, precision, sensitivity, and specificity, with values reaching 96.53% for most metrics, except for specificity, which is at 96.21%. [ABSTRACT FROM AUTHOR]

Hydrogen fluoride (HF) is a highly dangerous and corrosive gas that can cause severe burns and respiratory damage. The density functional theory method (DFT) used to study the interaction between the HF gas and the surface of a carbon nanocone (CNC) doped with gallium atom as a chemical sensor. The results showed that CNC wasn't a good candidate to sense the HF gas and consequently its electrical properties are changed insignificant. To improve the properties of the CNC, several strategies were tried: functionalizing by pyridinol (Pyr) and pyridinol oxide (PyrO), decorated with metals (M = B, Al, and Ga), and doped with element of third group (M = B, Al, and Ga). The obtained data demonstrated that the promising results were obtained by doping the CNC with Ga atom. After full optimization, we achieved one stable configuration between the HF gas and CNC-Ga structure (S15 configuration) with E ads = −19.86 kcal/mol. The electronic properties of the CNC-Ga structure is sensible changed after the HF molecule is adsorbed. According to calculated the energy gap between HOMO and LUMO orbitals of S15 configuration are increased which could be applied a chemical signal. Eventually, one could propose that the CNC-Ga has the ability to act as a Φ-type sensor based on its physical adsorption energy and quick recovery time and doped with gallium atom is a promising strategy. [Display omitted] • Pristine CNC isn't a good candidate to sense the HF gas. • Several strategies containing 1) functionalized 2) decorated and 3) doped strategies for possible sensing of HF. • One stable configuration between the HF gas and the CNC-Ga structure with Eads = −19.86 kcal/mol was formed. • The CNC-Ga can be played as an Φ-type sensor due to physical adsorption energy and low recovery time. [ABSTRACT FROM AUTHOR]

Abstract The study reports an investigation into the effects of temperature and duration of the pyrolysis on the yield and properties of the rice straw biochar and their optimization for application in soil based on the results of seed germination test. As a continuation, the effects of rice straw biochar on the growth of mung beans germinated using extracts of biochar, which were maintained at different pHs was studied. Biochar solvent extraction was done by mixing a predetermined dose of rice straw biochar to solutions initially maintained at five different pH conditions (4, 6, 7, 8, and 10). The biochar extracts were also characterized for their pH, electrical conductivity, and nutrient values and correlated with the germinated seed lengths. Further, this study based on the results of seed germination optimized the rice straw biochar production conditions with such properties to promote maximum seedling growth. Results showed that the yield of biochar decreased from 45.04% to 33.45% as temperature raised from 400 °C to 700 °C. The properties of biochar are primarily affected by the temperature as compared to the heating duration of the pyrolysis. The crystallinity index in the rice straw biochar decreased by 15.25% with the increase in the temperature from 400 °C to 700 °C. Correlation test among seed lengths and biochar extracts properties showed that the seed growth was directly correlated with the increase in the nutrient values at all pH conditions. Based on the germination test, the optimized conditions for the maximum seedling growth observed as for high nutrients, carbon (35–40%) and moderate nitrogen (3–4%), ash (32–35%), yield (35–40%) and cation exchange capacity (40–45 cmol/kg), carbon-nitrogen ratio (22–25:1), low oxygen-carbon ratio (0.7–0.8), ideal pH (8–9), and low volatiles (44–48%). The optimized pyrolytic temperature and duration to produce rice straw biochar with above-listed properties modelled as 500–600 °C and 80–100 min respectively. Graphical abstract Image 1 Highlights • The crystallinity in the biochar decreased with the increase in the temperature. • The aromaticity in biochar increased with the pyrolysis temperature. • The surface response curves used as a regression model with pyrolysis parameters. • Seed germination test used to predict effectiveness of biochar various soils. • The pyrolysis conditions were optimized for biochar production. [ABSTRACT FROM AUTHOR]