Total Environment Research Themes最新文献

The WHO tentatively called the new virus 2019 novel corona virus (COVID-19), which is become a effectively contagious disease in global health concern. India is the world's seventh-largest country, with twenty-eight (28) states and eight (8) union territories. In India, the highest COVID-19 case was recorded on 6th may 2021 which is 4,14,433 cases in the single day. On September 2020th, Maharashtra (11,45,840), Tamil Nadu (5,25,420) and Delhi (2,34,701) have the foremost COVID-19, and on May 2021st, Maharashtra (51,01,737), Tamil Nadu (13,80,259) and Delhi (13,23,567) have the foremost COVID-19 in India. Also, these states show the high death rate than other states. The positive COVID-19 cases are very drastically increased and death cases in these states due to the high population density. Hand hygiene, social distancing, and quarantine are some of the precautions that must be taken to prevent the virus from spreading in society. By increasing the detecting and testing capacity of the COVID-19 positive patients are also the way to enable the reduction of secondary cases with stricter quarantine rules.

Alkaline-treated natural zeolite was prepared by sodium hydroxide treatment of natural zeolite and applied in the adsorption of Pb(II). The response surface methodology (RSM) analysis was conducted under manipulated variables of initial concentration (X₁ = 50–400 mg/L), pH (X₂ = 2–10), and adsorbent dosage (X₃ = 0.5–5.0 g/L). The optimal condition was attained at X₁ = 240 mg/L, X₂ = 6, and X₃ = 1.07 g/L, with Pb(II) removal of 60.75 %. The characterization of alkaline-treated natural zeolite of fresh and spent confirmed the adsorption of Pb(II) onto alkaline-treated natural zeolite. The reusability and regeneration experiments revealed the ability of the alkaline-treated natural zeolite in multiple cycles of the adsorption process. This study proved that alkaline-treated natural zeolite could be an alternative low-cost adsorbent for wastewater treatment containing Pb(II).

The present work was carried out to understand the wind energy potential in the selected location of Tamil Nadu State, India to estimate the wind energy statistical data for power generation. The meteorology parameters and frequency distribution data were used to interpret the wind energy characteristics at monitoring locations. The wind energy potential data were collected at 100 m height from MSL (Mean Sea Level) in 2016. The wind power density was analyzed using the Weibull distribution expression. Based on observed data, site 1, 2, and 3 was suitable for recommending wind energy exploitation. The Indian wind power density classification suggests that nearly 35 % of the annual mean wind velocity falls under class 7. Further, the above results also suggest that station S1 and S3 followed by S2 is suitable for installing wind turbines.

Energy conversion efficiency of solar photovoltaic (PV) panels decreases with an increase in their surface temperature. Hence, cooling down the surface temperature is the most attractive method to enhance its performance. This paper presents an experimental study of the water-cooling front surface of a PV panel to increase the efficiency of solar energy conversion to electricity. Two panels of mono-crystalline type with 50 Wp of each panel are used for a non-cooled and cooled cases in this study. The experiment is conducted during the days of April 2022, in Sisattanark district, Vientiane Capital, Lao PDR. Results revealed that the cooled panel has got better performance than the non-cooled panel. This is confirmed by exergy analysis that the average exergy efficiency is 2.91 % and 12.76 % for the non-cooled and cooled panels, respectively. In conclusion, the water-cooling solar PV panel enables the improvement of electrical characteristics generated thus, enhancing the efficiency of the panel.

Growth pattern (Length-Weight Relationship-LWR), condition factor(K) and phylogenetic linkage of sourced Clarias gariepinus from Oueme valley(main catfish brooder source in Benin) was investigated to understand possible sources of diversity in growth pattern in spawned C. gariepinus.

Mean length-ML cohorts(1:1 sex ratio) of 30 randomly selected C. gariepinus ‘labelled’ brooders from sellers in each of Adjohoun(wild-habitat); Dangbo(culture-habitat) and Porto-Novo(culture-habitat) were characterized for differences(p < 0.05) in Mean Weight-MW(g), condition factor(K) and growth behavior(LWR intercept-a; slope-b) in sexes and unsexed populations. 16S-barcode gene sequences of 10 random tissue subsamples from the populations were analyzed for genetic linkages using Maximum Likelihood Method. ML ranged 34.26 ± 2.17 cm-35.54 ± 2.07 cm. MW was similar across Dangbo(326.18 ± 51.64) and Adjohoun(364.57 ± 64.48), significantly highest in Porto-novo(410.42 ± 54.67). K (0.72 ± 0.03-Dangbo − 1.02 ± 0.09-Porto-novo) differ across populations; ML and MW similar across Adjohoun sexes; different (male > female) across Dangbo and Porto-Novo. Sexes similar in K in Portonovo and Adjohoun but differ in Dangbo. At p = 0.00, r2 ranged 0.81–0.94, ‘a’ 0.45(Porto-Novo) to 1.09(Adjohoun), ‘b’ 0.18(Adjohoun) to 0.42(Porto-Novo) in unsexed population. Male and females ‘a’ were 1.01:1.16(Adjohoun), 0.77:0.85(Dangbo), −0.41:0.86(Porto-Novo); ‘b’ 0.21:0.15(Adjohoun), 0.31:0.28(Dangbo) and 0.75:0.26(Porto-Novo) at p = 0.00, r2 range 0.72–0.90. 80 %Porto-novo were closest to Clarias batrachus; 20 % closest to C. gariepinus × C. batrachus-crossbred; 70 %Dangbo closest to C. gariepinus × C. batrachus-crossbred, 10 % closest to Clarias batrachus. 20 % closest to C. gariepinus. 90 %Adjohoun was closest to C. gariepinus × C. batrachus-crossbred, 10 % closest to C. gariepinus.

The populations diverged in weight, condition factor and sexual dimorphism, and demonstrated negative allometry irrespective of sexes to implicate sub-optimum robustness. Each contained mixed strains of Catfish species mistaken as C. gariepinus whose growth could be influenced by growth attributes of dominant strain. Dominance of C. batrachus × C. gariepinus-crossbreds’ in culture-Dangbo and Wild-Adjohoun signals possible introgression of invasive C. batrachus gene, indicating dire need for stricter management and conservation of local catfish gene pool for posterity.

Paddy is the staple food for more than 50% of 138 billion Indian population. Inorder to meet with the growing demand, farmers often resort to application of synthetic fertilizers and plant protection chemicals indiscriminately. Rice is susceptible to diseases, pests, and nutrient deficiencies likewise other crops. Ignorant about the reasons for damage farmers apply synthetic chemicals that too in exorbitant rates. Excessive use of these chemical molecules alters the soil characteristics and causes environmental pollution as well. As a result, entire eco system gets affected. To overcome this, it is necessary to identify the reason for damage early and necessary treatments should be done in the beginning stages itself. Early detection can be done by assessing the leaves and culm of paddy. Assessment by naked eye may misinterpret symptoms and if artificial intelligence is used such misinterpretations can be minimised. This study proposes an automatic classification system using artificial intelligence and image processing for identification of diseased, pest infested and nutrient deficient crop using symptoms exhibited in the leaves and culm of paddy. Kaggle data set was being used to test the performance of the proposed classification system for metrics specificity, precision, sensitivity, F1-score and accuracy. The proposed work provides a specificity, precision, sensitivity, F1-score and accuracy of 97.1%, 97.6%, 96.2%, 96.8%, and 98.1% respectively. The evaluation results indicate that the proposed algorithm outperforms other recent rice leaf disease, pest and nutrient deficiency classification algorithms. Thus, precise identification of reasons for infection allows farmers to use specific control methods with less toxic chemicals or through eco-friendly methods. Thus, environmental pollution and soil characteristics can be saved and in turn can save the environment and its creatures.

This works encompasses a short review of the adversity of various types of organic pollutants and the viability of ZnO in abating their occurrence in water bodies. The classes of organic pollutants that have been selected for the analysis were: dyes, pesticides, antibiotics and analgesics, and finally stimulants. As a result of the established data analysis, it can be construed that the toxicity of the organic pollutant can pose serious threats to the environment, public health, and well-being. To combat this matter, earlier studies proposed the reliability of ZnO catalyst in elevating the efficiency of the organic pollutant degradation process due to its outstanding photophysical properties. However, as the optical response range of ZnO is often limited to UV light, various modification strategies in ZnO architecture have been executed to extend its functionality in solar irradiation. Overall, it can be concluded that ZnO exerts remarkable potential to remediate the contaminated wastewater. Nevertheless, there are still a lot of aspects that need to be enhanced before its actual utilization as a part of the future wastewater treatment technology.

The present research work mainly highlights the simultaneous detection of five antibiotics i.e., ceftriaxone (CTX), metronidazole (MTZ), amoxicillin (AMX), amikacin (AMK) and ciprofloxacin (CPFX) in hospital wastewater located in Sagar district (Madhya Pradesh, India). All these antibiotics make their way through drainage systems into the environment in the form of metabolized and unmetabolized compounds. Growing concern about the antibiotic resistance and contamination of wastewater by antibiotics requires fast, sensitive and eco-friendly techniques. Therefore a simple, rapid and eco-friendly chromatographic method has been developed for simultaneous determination of CTX, MTZ, AMX, AMK and CPFX in hospital wastewater samples. Optimization of the method was accomplished using response surface methodology (RSM) with Box-Behnken design (BBD). The optimized mobile phase was 0.15 M SDS-0.01 M NaH₂PO₄-7% (v/v) 1-butanol, pH 3 which provided a chromatographic run time of 11 min. for the simultaneous determination of selected antibiotics. The correlation coefficient (r²) values were satisfactory between 0.996 and 0.999 over the linear concentration range of 0.04–12 μg/mL. Limits of detection (LODs) and the limits of quantification (LOQs) for the five antibiotics were in the range of 0.02–0.18 μg/mL and 0.04–0.25 μg/mL, respectively. The developed method is simple, rapid, cost-effective and green which could be used for complex matrix (wastewater) without any sample pretreatment other than filtration. The results indicated that the MLC-PDA method proved to be more suitable than reverse phase-high performance liquid chromatography for the simultaneous separation of selected antibiotics.

Pharmaceuticals in wastewater are rapidly becoming new emerging pollutants, affecting humans and the aquatic ecosystem, and can go undetected due to their microscopic nature. Adsorption proves to be a promising technology for the removal of pharmaceuticals from effluent wastewater owning to its low cost, flexibility, and renewability. Adsorbents are porous materials such as silica, clay, resins, and carbon-based materials (e.g., biochars, carbon nanotubes, and activated carbon) often used to remove pharmaceutical micropollutants during adsorption. Among them biochar is an emerging, cost-effective, and eco-friendly sorbent. Modeling methods such as linear correlativity and multilinear regressions, are often employed to explain the adsorption mechanism, however they show limited accuracy and applicability. On the contrary, data driven machine learning (ML) methods is a powerful tool that could be used to study the complex relationship between adsorption performances and biochar properties. This review provides an overview of recent advances in the use of machine learning (ML) methods to explore the field of pharmaceutical adsorption onto biochar. An introduction to different ML algorithms and their advantages and limitations is provided. Furthermore, the challenges and future prospects of ML applications to study the adsorption mechanism is outlined.

The expansion of the e-waste management business at the national level is hampered by regulatory laws and a lack of knowledge among residents in most countries. Countries all around the globe are still struggling to raise public awareness and take effective efforts to safeguard the natural environment from fast degradation. An electronic device, like televisions, cell phones, and refrigerators, has a finite lifespan, which necessitates their replacement on a frequent basis, resulting in e-waste. Because of the aforementioned factors, proper e-waste management is a must around the clock. E-waste is the fastest increasing municipal solid trash, with a global production of around 20–50 million tonnes per year. Environmental health is a major problem when it comes to e-waste handling. Workers and those who live near a recycling centre in underdeveloped nations, where the majority of the informal and recyclable e-waste is recycled, are exposed to dangerous substances that have long-term negative health impacts. In Africa, India, Ghana, and Nigeria are among the countries where such recycling is common. This review paper, discussed the e-waste situation and provide information on the hazardous materials found among them and then it will have an impact on health and the environment. Electrical and electronic electrical equipment management in developed and developing countries will be explored in a way that relates to reusable components that will lead to the development of a circular economy using increased productivity. The current state of the e-waste industry will be assessed along with serious damage to the illegal e-waste trade and environment of developed countries to address the challenges associated with the re-use of e-waste.