All

Biosorption potential of neem leave powder for the sequestration of arsenic and chromium metal ions

Anandh Babu M, Hemavathi S, Kousalyadevi G, and Shanmuga Priya S P, Pages:1-14

DOI:

https://doi.org/10.30955/gnj.005287

Paper Topic:

Water and Wastewater Treatment

Issue:

Issue 10

Get Full Paper

Abstract

In this study, neem leaf biosorbent, prepared through chemical synthesis and activated with concentrated sulfuric acid, was utilized as an organic adsorbent for the removal of heavy metal ions, specifically arsenic (As) and chromium (Cr), from wastewater. The characteristics of the biosorbent were thoroughly analyzed using BET, FTIR, SEM, and EDX analysis to evaluate the surface morphology and functional groups. The experimental results indicated that under optimal conditions, with a pH of 10.0, a biosorbent dose of 3 g/L, an initial concentration of 20 mg/L, and a contact time of 60 minutes, 89.74% of As(II) and 82.32% of Cr(VI) metal ions were efficiently removed from synthetic solutions. To understand the nature of the adsorption process, various isothermal studies were conducted to determine whether it was homogeneous or heterogeneous. In addition to the above-mentioned experiments, kinetic studies were conducted to assess the physical or chemical nature of the metal ion adsorption on the adsorbent. Thermodynamic investigations were also performed to determine whether the adsorption of metal ions was exothermic or endothermic and whether the process was spontaneous. The results from the desorption studies revealed that the addition of 0.3N HCl resulted in the highest rate of desorption. This effectively facilitated the recovery of the metal ions from the spent adsorbent, indicating the effectiveness of 0.3N HCl in desorbing the adsorbed metal ions.

Influence of Utilizing Prosopis Juliflora Ash as Cement on Mechanical Properties of Cement Mortar and Concrete

Karthikeyan G Leema Margret A Muruganantam R Harshani R, Pages:1-7

DOI:

https://doi.org/10.30955/gnj.005402

Paper Topic:

Ecology, environmental change and management

Issue:

Issue 1

Get Full Paper

Abstract

The infrastructure of the country heavily depends on the construction material concrete. The primary ingredient in conventional concrete is cement, fly ash, or another cementitious substance. The need to explore alternative sources is driven by the challenges posed by global environmental warming, which stems from widespread factors, and the escalating costs associated with the extraction of cement from quarries. To replace cement, sand, and aggregate, numerous research investigations have been conducted. The Prosopis Juliflora Ash (PJA) as an additional cementitious component in cement mortar and concrete are examined in this experimental study. The amount of cement produced in the nation will decrease if Prosopis Juliflora (PJ) ash is used as an additional substitute. It will also encourage the use of sustainable materials for concrete and mortar. The objective of this research is to investigate the impact of incorporating PJA as a binding agent on the compressive strength, split tensile strength, and flexural strength of both concrete and cement mortar. In this research, Prosopis Juliflora Ash (commonly known as Seemaikaruvelam) was employed to substitute cement in both cement mortar and concrete, with varying proportions of 10%, 20%, 30%, 40%, and 50%.According to the results of this experiment, 30% of PJA has the requisite strength qualities. This experimental investigation provides valuable insights into the mechanical characteristics of concrete and mortar incorporating PJ ash and emphasizes its potential as a sustainable alternative in the construction industry.

A Critical probation for potentiality of bio-fuel with exquisite concoction of natural anti-oxidant additive in engines for pollutants reduction

Sithivinayagam N. Anandavelu K., Pages:1-7

DOI:

https://doi.org/10.30955/gnj.005491

Paper Topic:

Air Quality

Issue:

Issue 1

Get Full Paper

Abstract

Considering the present global fuel scenario, it becomes highly inevitable to opt for biofuel. Yet the use of biodiesel had increased pollutants which are emitted to the atmosphere such as Carbon monoxide, Unburnt Hydrocarbon, Nitrogen oxides and Smoke density in the combustion products. For this analysis, a novel and naturally obtainable antioxidant (Coffea Arabica antioxidant) has been experimented. With POME20 (20% palm oil Methyl ester and 80% diesel fuel), a proportion yielding favorable results based on several studies, Coffea Arabica antioxidant additive was infused in different proportions (ppm’s) to obtain the test blends. The thus derived blends PSBC500, PSBC1000, PSBC1500 and PSBC2000 in addition to plain DF and POME20 were used in diesel engine that is of the type, single cylinder DI water cooled TV-1 Kirloskar, under different load conditions. The test results showed improved BSFC and BTE for Coffea Arabica antioxidant infused blends. The atmospheric pollutants emitted from the engine such as CO, UHBC, NOx and SD were reduced considerably for the antioxidant additive infused POME20 blends simultaneously not creating any adverse impact in the performance of the engine. Among the test blends, PSBC 2000 was found performing with the closer tolerating characters as the plain diesel fuel.

Investigation of Key Technologies for Energy Saving and Consumption Reduction in Chongqing Municipal Wastewater Treatment Plants Based on Carbon Emission Reduction Contribution

Guanyu X, Qiyu Y, Hanyi Y, Yongheng W, Pages:1-12

DOI:

https://doi.org/10.30955/gnj.005642

Paper Topic:

Water and Wastewater Treatment

Issue:

Issue 2

Get Full Paper

Abstract

In this paper, based on the factors of treatment process, treatment scale, as well as water quality, the level and distribution of electricity consumption, drug consumption, and indirect carbon emission of five typical wastewater treatment plants (WWTPs) were examined. The distribution of electricity consumption within the WWTPs were analyzed in terms of wastewater treatment units. The results uncovered that the biological treatment unit was the treatment unit with a high percentage of electricity consumption in the WWTPs. Carbon emissions of main units in the WWTPs presented that the aeration blower, sewage lifting pump, submersible pusher, phosphorus remover and return sludge pump of the biological treatment unit were the top 5 emission units of carbon emissions in the WWTPs, and the key influencing factors of the carbon emissions of the main carbon emission units had been analyzed. Combined with the current situation of sewage treatment energy consumption in Chongqing, the analysis put forward a library of energy saving and consumption reduction measures for Chongqing WWTPs, which applied them to the sewage treatment plant A energy saving and consumption reduction renovation project.

Exploring the Effect of Salinity as a Primary Cause of Teratology in Freshwater Diatoms

Noune F. Chaib N. Metallaoui S. Kaddeche H. Dzizi S. Blanco S. , Pages:1-12

DOI:

https://doi.org/10.30955/gnj.005586

Paper Topic:

Water Quality

Issue:

Issue 3

Get Full Paper

Abstract

Increased water conductivity levels have been proposed as a key factor to explain the occurrence of teratological forms in freshwater diatom assemblages. The current study aimed to carry out an experiment on a laboratory scale to assess the response of periphytic diatoms to artificially increased salinity levels. The obtained results show that: a) the proportion of aberrant cells increased in high conductivity treatments, b) developed teratologies were preserved in diatom populations even after being translocated to normal conditions, and c) the degree of valve outline deformation in the dominant species was proportional to the induced water conductivity. All these data support previous field observations that linked high electrolyte content and the abundance of aberrant cells among microalgal communities in continental aquatic ecosystems.

Optimizing Drought Resilience in Okra (Abelmoschus esculentus) Through Synergistic Actions of Drought-Tolerant Rhizobacterial Strains and Brassinosteroids Hormone Application: A Jar Trial Study

Mahmood K. Hussain A. Ahmad M. Dar A. Akhtar M.F.Z. Iqbal R. Alsakkaf W. Alkahtani J. AbdelGawwad M.R., Pages:1-15

DOI:

https://doi.org/10.30955/gnj.005730

Paper Topic:

Ecology, environmental change and management

Issue:

Issue 4 (In progress)

Get Full Paper

Abstract

The study addresses the critical issue of water scarcity in agriculture, which causes significant losses by disrupting plant-water connections and increasing oxidative damage to biological components. In this study, we evaluated the synergistic potential of pre-isolated and identified exopolysaccharide-producing strains (ZE15 and ZE11) in combination with the drought-alleviating hormone Brassinosteroids (BR) to boost okra development under PEG-6000-induced drought stress. The experiment was carried out in a controlled environment at the Soil Microbiology and Biotechnology Laboratory. It used a completely randomized design with factorial arrangements and three replications. Individual rhizobacterial strains were evaluated and also used in a consortium (ZE15+ZE11) at two different Brassinosteroids (BR) concentrations (10-4 M and 10-8 M). The results showed that the consortium of (ZE15+ZE11) with foliar spray of BR (10-8 M) greatly increased okra production. Under both control and drought stress conditions, shoot length (37 and 34%), root length (41 and 40%), root surface area (32, 34%), and root volume (35 and 33%) improved significantly compared to the control group. Furthermore, this treatment demonstrated the most promising results in alleviating drought-induced oxidative damage by improving antioxidant defense mechanisms. Key enzyme activities, such as MDA (30 and 31%), GR (32 and 30%), SOD (36 and 37%), and CAT (31 and 34%), rose significantly under both control and drought stress conditions when compared to the control. To summarize, the use of drought-tolerant bacteria in concert with plant growth hormone provides a synergistic approach to mitigating the negative impacts of drought, resulting in considerable gains in okra growth and antioxidant defenses. This technique shows promise as a way to increase agricultural productivity in arid and semi-arid areas.

Removal of gaseous ammonia released from leachate by adsorption on carbon-based adsorbents prepared from agro-industrial wastes

Feliciano M. Lima T. Dias de Tuesta J. Furst L. Roman F. Silva A. Wilken A. Silva A. Gomes H., Pages:1-8

DOI:

https://doi.org/10.30955/gnj.004862

Paper Topic:

Water and Wastewater Treatment

Issue:

Issue 4 (In progress)

Get Full Paper

Abstract

Landfill facilities and organic waste treatment plants typically are known sources of odour pollution, such as gaseous NH3, among others. In this work, the removal of gaseous NH3 released from a composting line of a mechanical and biological treatment plant of undifferentiated municipal solid waste was assessed in a fixed-bed column loaded with carbon-based adsorbents (CBAs) prepared from olive stone and malt bagasse as carbon precursors. CBAs were prepared by hydrothermal carbonization (HTC) assisted by H2SO4 and pyrolysis, resulting in materials with different physical and chemical properties. The hydrochar derived from olive stone by H2SO4-assisted HTC was found as the best adsorbent for NH3 removal (10.4 mg g-1). This result was ascribed to the high acid character of the adsorbent (2.34 mmol g-1), since it was found that acidity contributed significantly more than the specific surface of the adsorbents for the removal of NH3 (BET surface of 4 m2 g-1 was obtained for the CBA with the highest uptake capacity, whereas other adsorbents reach values of 172 m2 g-1 and NH3 uptake capacities of 0.07 mg g-1). The NH3-saturated hydrochar was regenerated by washing with water and subsequently reused in the adsorption of NH3, with a performance more than 70% compared to its first use.

Trace Metals Pollution in Ambient Air of Urban and Rural Coastal Environments

Huboyo HS Isworo S Oetari PS Ramadan BS Manullang OR , Pages:1-7

DOI:

https://doi.org/10.30955/gnj.005174

Paper Topic:

Environmental data analysis and modelling

Issue:

Issue 1

Get Full Paper

Abstract

Air pollution in coastal areas will have a different pattern from mountainous areas due to different wind patterns during the day and night. The city of Semarang, as a representation of urban areas, and Jepara Regency, as a representation of the rural regions, were chosen for this sampling. 24-h fine particulate matter (PM2.5) was collected within 3 months of sampling. In this study, seven metal elements derived from this PM2.5 were quantified using ICP MS. The analytical method used is a descriptive analysis of the obtained metal element data, enrichment factor (EF), Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) modeling and sources pollutant estimation. The results of the study showed that the stannum (Sn) metal was found in almost all study locations. Sn, K, and Fe are the dominant metals found in urban areas, whereas, in rural areas, only the Sn metal is quite abundant. Nevertheless, the results of EF calculations show that the most significant enrichment in both villages and cities is Sn. Allegedly, this enrichment occurred because of dense activity in the transportation sector and metal industry in urban areas, which brought metals to three villages in Jepara Regency. This research can be used as the main reference in determining health risks associated with metal pollution in atmospheric particulate matter.

Spatial variation of air pollutants by using GIS modelling

Sidharthan K. Gandhimathi A. Akshayaa M., Pages:1-6

DOI:

https://doi.org/10.30955/gnj.005230

Paper Topic:

Air Quality

Issue:

Issue 2

Get Full Paper

Abstract

Air pollution in Tamil Nadu has become a pressing issue, as various pollutants have surpassed the limits established by the pollution control board. The major air pollutants in the region, namely sulphur dioxide, nitrogen dioxide, and suspended particulate matter, have been identified. This study aims to monitor and analyse the levels of air pollution in Tamil Nadu, with a specific focus on key parameters such as SO2, NO2, PM2.5, PM10, and ozone. By examining the atmospheric levels of these pollutants and their correlation with temperature fluctuations, the impact on human health and respiratory problems can be evaluated. Moreover, the study will assess the contribution of traffic emissions and vehicular pollution to the concentration of PM10 in different areas. Additionally, GIS technology will be utilized to spatially and temporally investigate pollution concentrations and evaluate their effect on the air quality index. By comparing the pollution concentration from thermal power stations and vehicular pollution, the study will identify the sources of pollutants, with a particular emphasis on SO2 and NOx emissions from vehicles and higher levels of suspended particulate matter from thermal stations. The analysis of these pollutants and their sources will provide valuable insights for the implementation of effective pollution control measures and the management of environmental pollution in Tamil Nadu. Given the concerns regarding pollutants exceeding the prescribed limits set by the pollution control board, monitoring air pollution in Tamil Nadu is of utmost importance.

A Real-time Environmental Air Pollution Predictor Model Using Dense Deep Learning Approach in IoT Infrastructure

Dr.Preethi P Dr.Saravanan T Dr.Mohanraj R Mrs.Gayathri PG, Pages:1-16

DOI:

https://doi.org/10.30955/gnj.005666

Paper Topic:

Air Quality

Issue:

Issue 3

Get Full Paper

Abstract

With the technical advancements in Deep Learning (DL), it is probable to construct the predictor model for monitoring and controlling pollution from real-time data. Here, IoT techniques are used for sensing the emission rate from various factors and the predictor model is constructed using the available data, for instance, carbon monoxide prediction. Modern sensors are embedded to evaluate the level of pollutants and using these modern techniques, the source of emission rate is identified and notified to the specific environment. Deep learning concepts are used for predicting the pollution level based on the current and previous data attained from the sensors. Here, we have implemented a learning solution to predict carbon monoxide concentration hourly using the novel Dense Residual Convolutional Network Model with Bi-LSTM (Bidirection-Long Short Term Memory) with the spatial and temporal features by integrating the features of the present and previous pollutant data. The side output from the residual network model is used to evaluate prediction quality. The performance is compared with existing approaches like standard LSTM, CNN, pre-trained network model, etc. The experimentation is done in a Python environment, and the proposed model facilitates more prediction accuracy for the pollutants CO,SO_2,O_3 and NO_2 than other conventional network models and establishes a better trade-off.

You are here

All

Biosorption potential of neem leave powder for the sequestration of arsenic and chromium metal ions

Influence of Utilizing Prosopis Juliflora Ash as Cement on Mechanical Properties of Cement Mortar and Concrete

A Critical probation for potentiality of bio-fuel with exquisite concoction of natural anti-oxidant additive in engines for pollutants reduction

Investigation of Key Technologies for Energy Saving and Consumption Reduction in Chongqing Municipal Wastewater Treatment Plants Based on Carbon Emission Reduction Contribution

Exploring the Effect of Salinity as a Primary Cause of Teratology in Freshwater Diatoms

Optimizing Drought Resilience in Okra (Abelmoschus esculentus) Through Synergistic Actions of Drought-Tolerant Rhizobacterial Strains and Brassinosteroids Hormone Application: A Jar Trial Study

Removal of gaseous ammonia released from leachate by adsorption on carbon-based adsorbents prepared from agro-industrial wastes

Trace Metals Pollution in Ambient Air of Urban and Rural Coastal Environments

Spatial variation of air pollutants by using GIS modelling

A Real-time Environmental Air Pollution Predictor Model Using Dense Deep Learning Approach in IoT Infrastructure

Pages

Follow us

You are here

All

Pages

Share Global NEST

Follow us