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.
{"title":"A Real-time Environmental Air Pollution Predictor Model Using Dense Deep Learning Approach in IoT Infrastructure","authors":"","doi":"10.30955/gnj.005666","DOIUrl":"https://doi.org/10.30955/gnj.005666","url":null,"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. \u0000","PeriodicalId":502310,"journal":{"name":"Global NEST: the international Journal","volume":"16 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139531198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, air pollution has increased with industrialization and urbanization globally. It is an important hazardous factor that causes severe health issues to community’s health. Among the number of pollutants in air, PM2.5 is very dangerous due to its very small, 2.5µm, diameter. The PM2.5 concentration in air causes severe life-threatening to humans. In this paper, RFBIGRU model is proposed to predict PM2.5 in the atmospheric air. RFBIGRU improves PM2.5 prediction accuracy using Random Forest (RF) feature selector and Bidirectional Gated Recurrent Unit (BIGRU) deep neural network. The PM2.5 concentration in air depends on other pollutants' concentration in the air. However, the consideration of several other pollutants increases the curse of dimensionality and overfitting issues. So, in RFBIGRU, first, the relevant pollutants to PM2.5 are identified using random forest feature importance. Then the nonlinear and temporal patterns of the time series air pollutant data are extracted both in forward and backward direction using Bidirectional GRU. The RFBIGRU reduces the curse of dimensionality, overfitting and improves the PM2.5 prediction accuracy compared to other deep learning methods. The experimental result proves RFBIGRU outperforms others by producing least Root Mean Square Error of 42.217 and 6.813 for Delhi and Amaravathi regions.
{"title":"Prediction of Particulate Matter PM2.5 Using Bidirectional Gated Recurrent Unit with Feature Selection","authors":"","doi":"10.30955/gnj.005631","DOIUrl":"https://doi.org/10.30955/gnj.005631","url":null,"abstract":"In recent years, air pollution has increased with industrialization and urbanization globally. It is an important hazardous factor that causes severe health issues to community’s health. Among the number of pollutants in air, PM2.5 is very dangerous due to its very small, 2.5µm, diameter. The PM2.5 concentration in air causes severe life-threatening to humans. In this paper, RFBIGRU model is proposed to predict PM2.5 in the atmospheric air. RFBIGRU improves PM2.5 prediction accuracy using Random Forest (RF) feature selector and Bidirectional Gated Recurrent Unit (BIGRU) deep neural network. The PM2.5 concentration in air depends on other pollutants' concentration in the air. However, the consideration of several other pollutants increases the curse of dimensionality and overfitting issues. So, in RFBIGRU, first, the relevant pollutants to PM2.5 are identified using random forest feature importance. Then the nonlinear and temporal patterns of the time series air pollutant data are extracted both in forward and backward direction using Bidirectional GRU. The RFBIGRU reduces the curse of dimensionality, overfitting and improves the PM2.5 prediction accuracy compared to other deep learning methods. The experimental result proves RFBIGRU outperforms others by producing least Root Mean Square Error of 42.217 and 6.813 for Delhi and Amaravathi regions. \u0000","PeriodicalId":502310,"journal":{"name":"Global NEST: the international Journal","volume":"26 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Temperature dynamics is a widely recognized indicator of the global warming phenomenon. Changes in temperature patterns in Bhubaneswar, India, were assessed by examining the monthly minimum and maximum temperature data for 60 years (1956–2015) sourced from the Indian Meteorological Department. SimCLIM climate change risk assessment software was used for projecting the temperature regime for four different representative concentration pathways. Further, a survey of 112 farmers was conducted to understand their perceptions of temperature variations in and around Bhubaneswar city using a multi-stage sampling technique. Mann–Kendall statistics and linear regression were used to analyze the monthly temperature data and trend detection. The study reveals a change of +4%,-4.44%, and +1.09% in the mean monthly maximum, minimum, and annual temperature. The results of the future projection show a temperature change of 0.81°C for RCP 2.6, 1.12°C for RCP 4.5, 1.03°C for RCP 6.0, and 1.54°C for RCP 8.5 for the year 2050. Confirming the analysis findings, most of the interviewed farmers also perceived increasing temperatures and decreasing precipitation in and around the city. The study outcome of temperature trend analysis and future projections will be helpful for farmers and policymakers in formulating adaptation strategies to climate change.
{"title":"Assessment of observed temperature trend patterns of Bhubaneswar city, India with special prominence on future projections using SimCLIM climate model and farmer’s perception","authors":"","doi":"10.30955/gnj.004859","DOIUrl":"https://doi.org/10.30955/gnj.004859","url":null,"abstract":"Temperature dynamics is a widely recognized indicator of the global warming phenomenon. Changes in temperature patterns in Bhubaneswar, India, were assessed by examining the monthly minimum and maximum temperature data for 60 years (1956–2015) sourced from the Indian Meteorological Department. SimCLIM climate change risk assessment software was used for projecting the temperature regime for four different representative concentration pathways. Further, a survey of 112 farmers was conducted to understand their perceptions of temperature variations in and around Bhubaneswar city using a multi-stage sampling technique. Mann–Kendall statistics and linear regression were used to analyze the monthly temperature data and trend detection. The study reveals a change of +4%,-4.44%, and +1.09% in the mean monthly maximum, minimum, and annual temperature. The results of the future projection show a temperature change of 0.81°C for RCP 2.6, 1.12°C for RCP 4.5, 1.03°C for RCP 6.0, and 1.54°C for RCP 8.5 for the year 2050. Confirming the analysis findings, most of the interviewed farmers also perceived increasing temperatures and decreasing precipitation in and around the city. The study outcome of temperature trend analysis and future projections will be helpful for farmers and policymakers in formulating adaptation strategies to climate change. \u0000","PeriodicalId":502310,"journal":{"name":"Global NEST: the international Journal","volume":"26 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139531582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays cement industry power plant bed wastes can be used to create aerated concrete blocks for widespread usage in the construction sector instead of sand. The optimum materials for building enclosures for a variety of uses include aerated concretes. To enhance the physical and mechanical qualities of Non-Autoclaved Aerated Concrete (NAAC) blocks, bed material is introduced in this study as a superior alternative material. The non-autoclaved concrete blocks in this study are made with cement, bed materials, fly ash, gypsum, and a consistent amount of 0.65 grams of aluminium powder. The mix preparation and method employed for manufacturing NAAC blocks, the composition of mix specimens and the dosing and mixing processes have been expounded upon, shedding light on the critical steps in the production. According to the suggested method in IS 2185 (Part III) of 1984, the proportion of bed materials was taken by volume of compacted dry material for NAAC of Size 22cm x 10.5cm x 7cm. Experiments into the NAAC block's compressive strength plus water absorption of the bed materials were followed by comparisons of these characteristics with clay and fly ash bricks sold in the market. As a result, NAAC blocks met the 6 MPa strength criteria specified by Indian Standard code IS2185 (Part III): 1984. However, the strength of the aforementioned NAAC brick at 28 days was 7.28 MPa for Sample T5. A more in-depth presentation of the testing methods, focusing on the compressive strength tests was conducted at various intervals (7, 14, 21, and 28 days). The density values and water absorption rates for each test sample (T1 to T5) are now presented with additional insights into the observed trends. According to the research, blocks manufactured with NAAC bed materials tend to be stronger and lighter than those made with conventional clay bricks. They also produce non-autoclaved concrete blocks. Therefore, the creation of such inexpensive blocks can be employed for extensive production.
{"title":"Utilization of Cement Power Plant Beds for Aerated Concrete Thermal Blocks","authors":"","doi":"10.30955/gnj.005432","DOIUrl":"https://doi.org/10.30955/gnj.005432","url":null,"abstract":"Nowadays cement industry power plant bed wastes can be used to create aerated concrete blocks for widespread usage in the construction sector instead of sand. The optimum materials for building enclosures for a variety of uses include aerated concretes. To enhance the physical and mechanical qualities of Non-Autoclaved Aerated Concrete (NAAC) blocks, bed material is introduced in this study as a superior alternative material. The non-autoclaved concrete blocks in this study are made with cement, bed materials, fly ash, gypsum, and a consistent amount of 0.65 grams of aluminium powder. The mix preparation and method employed for manufacturing NAAC blocks, the composition of mix specimens and the dosing and mixing processes have been expounded upon, shedding light on the critical steps in the production. According to the suggested method in IS 2185 (Part III) of 1984, the proportion of bed materials was taken by volume of compacted dry material for NAAC of Size 22cm x 10.5cm x 7cm. Experiments into the NAAC block's compressive strength plus water absorption of the bed materials were followed by comparisons of these characteristics with clay and fly ash bricks sold in the market. As a result, NAAC blocks met the 6 MPa strength criteria specified by Indian Standard code IS2185 (Part III): 1984. However, the strength of the aforementioned NAAC brick at 28 days was 7.28 MPa for Sample T5. A more in-depth presentation of the testing methods, focusing on the compressive strength tests was conducted at various intervals (7, 14, 21, and 28 days). The density values and water absorption rates for each test sample (T1 to T5) are now presented with additional insights into the observed trends. According to the research, blocks manufactured with NAAC bed materials tend to be stronger and lighter than those made with conventional clay bricks. They also produce non-autoclaved concrete blocks. Therefore, the creation of such inexpensive blocks can be employed for extensive production. \u0000","PeriodicalId":502310,"journal":{"name":"Global NEST: the international Journal","volume":"11 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the limitations of fossil fuels and the environmental problems associated with their usage, renewable energy sources have been exploited for desalination through the employment of various technologies and mediums. One of the most useful renewable energy sources for solar desalination, both directly and indirectly, is solar energy. The effectiveness of solar desalination is influenced by a variety of parameters, making it challenging to forecast their performance in particular circumstances. Artificial neural networks (ANNs), PSO, ANFIS, RO, and genetic algorithms would all be suitable techniques for their modeling and output predictions in this context. In the current research, multiple data-driven approaches are used in-depth to perform modeling of solar-based desalination facilities. By utilizing these methods with the proper inputs and structures, it can be deduced that the results of the solar desalination units can be consistently and correctly projected. Additionally, several suggestions are offered for future research in the relevant areas of the study.
{"title":"Predictive Modeling for Solar Desalination Using Artificial Neural Network Techniques: A Review","authors":"","doi":"10.30955/gnj.005481","DOIUrl":"https://doi.org/10.30955/gnj.005481","url":null,"abstract":"Due to the limitations of fossil fuels and the environmental problems associated with their usage, renewable energy sources have been exploited for desalination through the employment of various technologies and mediums. One of the most useful renewable energy sources for solar desalination, both directly and indirectly, is solar energy. The effectiveness of solar desalination is influenced by a variety of parameters, making it challenging to forecast their performance in particular circumstances. Artificial neural networks (ANNs), PSO, ANFIS, RO, and genetic algorithms would all be suitable techniques for their modeling and output predictions in this context. In the current research, multiple data-driven approaches are used in-depth to perform modeling of solar-based desalination facilities. By utilizing these methods with the proper inputs and structures, it can be deduced that the results of the solar desalination units can be consistently and correctly projected. Additionally, several suggestions are offered for future research in the relevant areas of the study. \u0000","PeriodicalId":502310,"journal":{"name":"Global NEST: the international Journal","volume":"18 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Compost stability is an essential parameter of composting. Recent studies have shown that the color of compost is influenced by the initial characteristics of the main organic substrates. In this study, the progression of CIELAB color variables and typical compost stability and maturity indices were monitored during composting of green waste (GW) with different characteristics from previous studies. Results showed that the color variables a*, b* and C* exhibited a constant downward trend and a strong correlation with composting time (R2 > 0.90). In addition, the color variables Δb* and ΔC* were found to be correlated with the humification of the compost, and in particular HA/FA with R2 values above 0.83. Δb* and ΔC* are not affected by the initial characteristics of the green waste. Therefore, they can be used to monitor the stability of GW compost, regardless of different composting parameters, such as windrow size, additional materials, conditions, initial properties, and waste treatment delays. Δb* and ΔC* values above 2.76 and 2.96, respectively, can be used as an indicator of an acceptable degree of GW humification. Color analysis is a quick and easy compost stability monitoring method, and it can complement standard stability physicochemical indices.
{"title":"Development of a novel green waste compost stability monitoring method using the CIELAB color model","authors":"","doi":"10.30955/gnj.005448","DOIUrl":"https://doi.org/10.30955/gnj.005448","url":null,"abstract":"Compost stability is an essential parameter of composting. Recent studies have shown that the color of compost is influenced by the initial characteristics of the main organic substrates. In this study, the progression of CIELAB color variables and typical compost stability and maturity indices were monitored during composting of green waste (GW) with different characteristics from previous studies. Results showed that the color variables a*, b* and C* exhibited a constant downward trend and a strong correlation with composting time (R2 > 0.90). In addition, the color variables Δb* and ΔC* were found to be correlated with the humification of the compost, and in particular HA/FA with R2 values above 0.83. Δb* and ΔC* are not affected by the initial characteristics of the green waste. Therefore, they can be used to monitor the stability of GW compost, regardless of different composting parameters, such as windrow size, additional materials, conditions, initial properties, and waste treatment delays. Δb* and ΔC* values above 2.76 and 2.96, respectively, can be used as an indicator of an acceptable degree of GW humification. Color analysis is a quick and easy compost stability monitoring method, and it can complement standard stability physicochemical indices. \u0000","PeriodicalId":502310,"journal":{"name":"Global NEST: the international Journal","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139624989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mateen Hosseinzadeh, Roya Mafigholami, E. R. Ghatromi
This study assessed the efficiency of Fe, Al, Ni and Sr removal from the steel sludge using the coupled bioleaching with Thiobacillus thiooxidans and ultrasonic waves. Growth conditions were optimized using the surface response method. The bacterium was adapted successively to three heavy metal-containing solutions with different concentrations of 100, 200, and 300 mg/ml. Samples were exposed to ultrasonic waves at frequencies of 30, 60 and 90 kHz and durations of 20, 30 and 40 min for two weeks. The highest Fe removal efficiency of 98.45% was obtained using the T. thiooxidation, wave frequency of 30 kHz for 40 min, and pulp density of 100 mg/ml. The maximum removal efficiency was found to be 99.74% for Al under a wave frequency of 90 kHz for 20 min and a pulp density of 300 mg/ml, approximately 100% for Ni under a wave frequency of 30 kHz for 20 min and a pulp density of 300 mg/ml, and 98.45% for Sr under a wave frequency of 90 kHz for 20 min and a pulp density of 300 mg/mL. Results showed that the removal efficiency of Ni and Al bioleaching improved significantly (P <0.05) under the ultrasonic irradiation while the removal efficiency of Fe and Sr remained statistically unchanged (P> 0.05) with and without the application of ultrasonic waves.
{"title":"Improving heavy metal removal efficiency from steel sludge: Application of the coupled ultrasonic-bioleaching treatment","authors":"Mateen Hosseinzadeh, Roya Mafigholami, E. R. Ghatromi","doi":"10.30955/gnj.005417","DOIUrl":"https://doi.org/10.30955/gnj.005417","url":null,"abstract":"This study assessed the efficiency of Fe, Al, Ni and Sr removal from the steel sludge using the coupled bioleaching with Thiobacillus thiooxidans and ultrasonic waves. Growth conditions were optimized using the surface response method. The bacterium was adapted successively to three heavy metal-containing solutions with different concentrations of 100, 200, and 300 mg/ml. Samples were exposed to ultrasonic waves at frequencies of 30, 60 and 90 kHz and durations of 20, 30 and 40 min for two weeks. The highest Fe removal efficiency of 98.45% was obtained using the T. thiooxidation, wave frequency of 30 kHz for 40 min, and pulp density of 100 mg/ml. The maximum removal efficiency was found to be 99.74% for Al under a wave frequency of 90 kHz for 20 min and a pulp density of 300 mg/ml, approximately 100% for Ni under a wave frequency of 30 kHz for 20 min and a pulp density of 300 mg/ml, and 98.45% for Sr under a wave frequency of 90 kHz for 20 min and a pulp density of 300 mg/mL. Results showed that the removal efficiency of Ni and Al bioleaching improved significantly (P <0.05) under the ultrasonic irradiation while the removal efficiency of Fe and Sr remained statistically unchanged (P> 0.05) with and without the application of ultrasonic waves.","PeriodicalId":502310,"journal":{"name":"Global NEST: the international Journal","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139227706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The greener approach offers a viable, sustainable and eco-friendly way to synthesize nanoparticles. This study used the seed extract of Vigna stipulacea (VS) as a bioreducing agent to synthesize iron nanoparticles (VS-Fe). The VS seed extract contains polyphenols and lignin content that acted as a bioreducing agent during VS-Fe formation. The Vigna stipulacea-mediated Fe nanoparticles were characterized using UV, XRD, FTIR, EDAX and BET surface analysis. The as-synthesized VS-Fe, comprised of Fe0 phase and Fe hydroxides, had an average crystallite size of 30.65 nm. It possessed a surface area of 199.189 m2/g and magnetic saturation of 11.21 m emu. The VS-Fe exhibited excellent adsorptive behavior during the sequestration of Pb2+ ions from an aqueous environment. The Pb2+ uptake was maximum (96.7%) under the optimal conditions of 60 min contact time, 0.01 g/ 100 mL VS-Fe dosage and pH 6. The equilibrium data of Pb2+ adsorption was more appropriate with pseudo-second-order kinetics (R2 = 0.9903) and Langmuir isotherm (R2 = 0.9941) with qmax of 1020.50 mg/g. Thus, the dominance of chemisorption in Pb2+ removal was revealed. It was further confirmed with the SEM micrograph of Pb-loaded VS-Fe nanoparticles. Overall, this study demonstrated the inexpensive and non-toxic way of synthesizing Fe nanoparticles and their utilization in effectively removing Pb2+ ions from water.
{"title":"Vigna stipulacea mediated Fe nanoparticles synthesis: A greener approach for sequestration of Pb2+ from aqueous environment","authors":"","doi":"10.30955/gnj.005132","DOIUrl":"https://doi.org/10.30955/gnj.005132","url":null,"abstract":"The greener approach offers a viable, sustainable and eco-friendly way to synthesize nanoparticles. This study used the seed extract of Vigna stipulacea (VS) as a bioreducing agent to synthesize iron nanoparticles (VS-Fe). The VS seed extract contains polyphenols and lignin content that acted as a bioreducing agent during VS-Fe formation. The Vigna stipulacea-mediated Fe nanoparticles were characterized using UV, XRD, FTIR, EDAX and BET surface analysis. The as-synthesized VS-Fe, comprised of Fe0 phase and Fe hydroxides, had an average crystallite size of 30.65 nm. It possessed a surface area of 199.189 m2/g and magnetic saturation of 11.21 m emu. The VS-Fe exhibited excellent adsorptive behavior during the sequestration of Pb2+ ions from an aqueous environment. The Pb2+ uptake was maximum (96.7%) under the optimal conditions of 60 min contact time, 0.01 g/ 100 mL VS-Fe dosage and pH 6. The equilibrium data of Pb2+ adsorption was more appropriate with pseudo-second-order kinetics (R2 = 0.9903) and Langmuir isotherm (R2 = 0.9941) with qmax of 1020.50 mg/g. Thus, the dominance of chemisorption in Pb2+ removal was revealed. It was further confirmed with the SEM micrograph of Pb-loaded VS-Fe nanoparticles. Overall, this study demonstrated the inexpensive and non-toxic way of synthesizing Fe nanoparticles and their utilization in effectively removing Pb2+ ions from water.","PeriodicalId":502310,"journal":{"name":"Global NEST: the international Journal","volume":"117 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139257085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to study the effective methods of green and low-carbon transformation in Chinese cities, this paper takes Rizhao City, Shandong Province, China, as the object of study. Based on the analysis of the research background and the literature review, and taking into account the characteristics of Rizhao City, the following five categories with a total of 25 indicators were selected to construct the assessment indicator system: the environmental quality of green and low-carbon transformation, the quality of the planning and construction of green and low-carbon transformation, the quality of the life and work in the green and low-carbon transformation, the quality of the process of the green transformation empowering the development of high-quality, and the quality of the results of the green transformation empowering high-quality development. Based on this, the two-level fuzzy comprehensive assessment model is reconstructed, and the relevant statistical data provided by the government is used to comprehensively assess the high-quality development performance of the green transformation and empowerment cities in Rizhao City from 2012 to 2022. It is found that the performance of high-quality development empowered by green transformation in Rizhao City, China, has shown a continuous upward trend, having risen from Level Ⅳ in 2012, to Level II by 2022, and remained at Level II during 2018-2022, with its assessment results and showing an upward trend. Finally, based on the specific research results, the policy suggestions to improve the high-quality development performance of cities empowered by the green and low-carbon transformation of Rizhao City, China are discussed.
{"title":"Research on Performance Assessment of High Quality Development in Urban Green and Low Carbon Transformation","authors":"","doi":"10.30955/gnj.005356","DOIUrl":"https://doi.org/10.30955/gnj.005356","url":null,"abstract":"In order to study the effective methods of green and low-carbon transformation in Chinese cities, this paper takes Rizhao City, Shandong Province, China, as the object of study. Based on the analysis of the research background and the literature review, and taking into account the characteristics of Rizhao City, the following five categories with a total of 25 indicators were selected to construct the assessment indicator system: the environmental quality of green and low-carbon transformation, the quality of the planning and construction of green and low-carbon transformation, the quality of the life and work in the green and low-carbon transformation, the quality of the process of the green transformation empowering the development of high-quality, and the quality of the results of the green transformation empowering high-quality development. Based on this, the two-level fuzzy comprehensive assessment model is reconstructed, and the relevant statistical data provided by the government is used to comprehensively assess the high-quality development performance of the green transformation and empowerment cities in Rizhao City from 2012 to 2022. It is found that the performance of high-quality development empowered by green transformation in Rizhao City, China, has shown a continuous upward trend, having risen from Level Ⅳ in 2012, to Level II by 2022, and remained at Level II during 2018-2022, with its assessment results and showing an upward trend. Finally, based on the specific research results, the policy suggestions to improve the high-quality development performance of cities empowered by the green and low-carbon transformation of Rizhao City, China are discussed.","PeriodicalId":502310,"journal":{"name":"Global NEST: the international Journal","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139257804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ioannis Ioannidis, I. Anastopoulos, Ioannis Pashalidis
The effect of temperature on the adsorption of U-232 and Am-241 by PN6 has been investigated in laboratory and environmental water samples (e.g. seawater and waste water) in the picomolar concentration range. Generally, increasing temperature favors radionuclide adsorption, indicating that radionuclide binding by PN6 is an endothermic and entropy-driven process. In environmental waters, Kd values are significantly lower than the corresponding values in de-ionized water solutions, because of the presence of various cations (e.g., Ca2+, Fe2+) that compete the radionuclide adsorption by PN6 and the presence of complexing anions (e.g. CO32-), which complex and stabilize the actinide cations in solution.
{"title":"The effect of temperature on the U-232 and Am-241 adsorption by PN6 microplastics in aqueous solutions.","authors":"Ioannis Ioannidis, I. Anastopoulos, Ioannis Pashalidis","doi":"10.30955/gnj.005392","DOIUrl":"https://doi.org/10.30955/gnj.005392","url":null,"abstract":"The effect of temperature on the adsorption of U-232 and Am-241 by PN6 has been investigated in laboratory and environmental water samples (e.g. seawater and waste water) in the picomolar concentration range. Generally, increasing temperature favors radionuclide adsorption, indicating that radionuclide binding by PN6 is an endothermic and entropy-driven process. In environmental waters, Kd values are significantly lower than the corresponding values in de-ionized water solutions, because of the presence of various cations (e.g., Ca2+, Fe2+) that compete the radionuclide adsorption by PN6 and the presence of complexing anions (e.g. CO32-), which complex and stabilize the actinide cations in solution.","PeriodicalId":502310,"journal":{"name":"Global NEST: the international Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139254997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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