The agricultural sector is one of the most important sources of CO2 emissions. Thus, the current study predicted CO2 emissions based on data from the agricultural sectors of 25 provinces in Iran. The gross domestic product (GDP), the square of the GDP (GDP2), energy use, and income inequality (Gini index) were used as the inputs. The study used support vector machine (SVM) models to predict CO2 emissions. Multiobjective algorithms (MOAs), such as the seagull optimization algorithm (MOSOA), salp swarm algorithm (MOSSA), bat algorithm (MOBA), and particle swarm optimization (MOPSO) algorithm, were used to perform three important tasks for improving the SVM models. Additionally, an inclusive multiple model (IMM) used the outputs of the MOSOA, MOSSA, MOBA, and MOPSO algorithms as the inputs for predicting CO2 emissions. It was observed that the best kernel function based on the SVM-MOSOA was the radial function. Additionally, the best input combination used all the gross domestic product (GDP), squared GDP (GDP2), energy use, and income inequality (Gini index) inputs. The results indicated that the quality of the obtained Pareto front based on the MOSOA was better than those of the other algorithms. Regarding the obtained results, the IMM model decreased the mean absolute errors of the SVM-MOSOA, SVM-MOSSA, SVM-MOBA, and SVM-PSO models by 24, 31, 69, and 76%, respectively, during the training stage. The current study showed that the IMM model was the best model for predicting CO2 emissions.
A high-voltage generator (HVG) is an essential part of a radio frequency identification electrically erasable programmable read-only memory (RFID-EEPROM). An HVG circuit is used to generate a regulated output voltage that is higher than the power supply voltage. However, the performance of the HVG is affected owing to the high-power dissipation, high-ripple voltage and low-pumping efficiency. Therefore, a regulator circuit consists of a voltage divider, comparator and a voltage reference, which are respectively required to reduce the ripple voltage, increase pumping efficiency and decrease the power dissipation of the HVG. Conversely, a clock driving circuit consists of the current-starved ring oscillator (CSRO), and the non- overlapping clock generator is required to drive the clock signals of the HVG circuit. In this study, the Mentor Graphics EldoSpice software package is used to design and simulate the HVG circuitry. The results showed that the designed CSRO dissipated only 4.9 μW at 10.2 MHz and that the phase noise was only -119.38 dBc/Hz at 1 MHz. Moreover, the proposed charge pump circuit was able to generate a maximum VPP of 13.53 V and it dissipated a power of only 31.01 μW for an input voltage VDD of 1.8 V. After integrating all the HVG modules, the results showed that the regulated HVG circuit was also able to generate a higher VPP of 14.59 V, while the total power dissipated was only 0.12 mW with a chip area of 0.044 mm2. Moreover, the HVG circuit produced a pumping efficiency of 90% and reduced the ripple voltage to <4 mV. Therefore, the integration of all the proposed modules in HVG ensured low-ripple programming voltages, higher pumping efficiency, and EEPROMs with lower power dissipation, and can be extensively used in low-power applications, such as in non-volatile memory, radiofrequency identification transponders, on-chip direct current DC-DC converters.
Organic pollution continues to be an important worldwide obstacle for tackling health and environmental concerns that require ongoing and prompt response. To identify the LAB content levels as molecular indicators for sewage pollution, surface sediments had obtained from the South region of Malaysia. The origins of the LABs were identified using gas chromatography-mass spectrometry (GC-MS). ANOVA and a Pearson correlation coefficient at p
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.
The seasonal variation of petroleum pollution including n-alkanes in surface sediments of the Selangor River in Malaysia during all four climatic seasons was investigated using GC-MS. The concentrations of n-alkanes in the sediment samples did not significantly correlate with TOC (r = 0.34, p > 0.05). The concentrations of the 29 n-alkanes in the Selangor River ranged from 967 to 3711 µg g-1 dw, with higher concentrations detected during the dry season. The overall mean per cent of grain-sized particles in the Selangor River was 85.9 ± 2.85% sand, 13.5 ± 2.8% clay, and 0.59 ± 0.34% gravel, respectively. n-alkanes are derived from a variety of sources, including fresh oil, terrestrial plants, and heavy/degraded oil in estuaries. The results of this study highlight concerns and serve as a warning that hydrocarbon contamination is affecting human health. As a result, constant monitoring and assessment of aliphatic hydrocarbons in coastal and riverine environments are needed.
To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machine learning techniques was employed in order to make an estimation of the performance of reduction catalysis in the context of ecologically detrimental nitrophenols and azo dyes contaminants. The catalyst utilized in the experiment was Ag@CMC, which proved to be highly effective in eliminating various contaminants found in water, like 4-nitrophenol (4-NP). The experiments were carefully conducted at various time intervals, and the machine learning procedures used in this study were all employed to forecast catalytic performance. The evaluation of the performance of such algorithms were done by means of Mean Absolute Error. The noteworthy findings of this research indicated that the ADAM and LSTM algorithm exhibited the most favourable performance in the case of toxic compounds i.e. 4-NP. Moreover, the Ag@CMC catalyst demonstrated an impressive reduction efficiency of 98 % against nitrophenol in just 8 min. Thus, based on these compelling results, it can be concluded that Ag@CMC works as a highly effective catalyst for practical applications in real-world scenarios.
This work reports silver nanoparticles (AgNPs) supported on biopolymer carboxymethyl cellulose beads (Ag-CMC) serves as an efficient catalyst in the reduction process of p-nitrophenol (p-NP) and methyl orange (MO). For Ag-CMC synthesis, first CMC beads were prepared by crosslinking the CMC solution in aluminium nitrate solution and then the CMC beads were introduced into AgNO3 solution to adsorb Ag ions. Field emission scanning electron microscopy (FE-SEM) analysis suggests the uniform distribution of Ag nanoparticles on the CMC beads. The X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) analysis revealed the metallic and fcc planes of AgNPs, respectively, in the Ag-CMC catalyst. The Ag-CMC catalyst exhibits remarkable reduction activity for the p-NP and MO dyes with the highest rate constant (kapp) of a chemical reaction is 0.519 and 0.697 min-1, respectively. Comparative reduction studies of Ag-CMC with CMC, Fe-CMC and Co-CMC disclosed that Ag-CMC containing AgNPs is an important factore in reducing the organic pollutants like p-NP and MO dyes. During the recyclability tests, the Ag-CMC also maintained high reduction activity, which suggests that CMC protects the AgNPs from leaching during dye reduction reactions.
A catalytic system has been developed, utilizing metal nanoparticles confined within a chitosan‑carbon black composite hydrogel (M-CH/CB), aimed at improving ease of use and recovery in catalytic processes. The M-CH/CBs were characterized by XPS, XRD, SEM, and EDX, the M-CH/CB system demonstrated exceptional catalytic activity in producing hydrogen gas (H2) from water and methanol, and in reducing several hazardous materials including 2-nitrophenol (2-NP), 4-nitrophenol (4-NP), 2,6-dinitrophenol (2,6-DNP), acridine orange (ArO), methyl orange (MO), congo red (CR), methylene blue (MB), and potassium ferricyanide (PFC). Among the tested nanocatalysts, CH/CB showed the highest efficiency for H₂ production, while Fe0-CH/CB excelled in contaminant reduction (7.0 min). In addition to the synthesis and characterization of the catalytic system, various factors, such as NaBH₄ amount, catalyst quantity, pollutant concentration, and reaction temperature were optimized to maximize its overall efficacy and efficiency. Fe0-CH/CB achieved the best reaction rate of 0.850 min-1 for 4-NP reduction, while CH/CB had a hydrogen generation rate (HGR) of 3500 ml.g-1.min-1. The Fe0-CH/CB was able to achieve the 4-NP reduction percentage of >95 % over 5 times during the recyclability tests. However, a slight decrease in reduction time was observed as the reaction rate dropped to 0.716 min-1 after 5 cycles, but the catalyst remained effective, underscoring its practical potential for environmental remediation, water treatment, and sustainable energy production.
This review article provides a thorough examination of an interaction between linear alkylbenzenes (LABs) and ecosystems. The review covers various aspects of LABs' impact on ecosystems, focusing on detection and treatment strategies to mitigate ecological consequences. It delves into LABs' role as molecular markers for sewage pollution, their physicochemical properties contributing to persistence, and their effects on aquatic and terrestrial organisms, including disruptions to endocrine systems. The diverse sources of LABs, including domestic wastewater and industrial effluents, are explored, along with their ratios in different matrices for assessing contamination origins. Biodegradation pathways of LABs, both aerobic and anaerobic, are scrutinized, considering their interaction with microbes. Distribution patterns in aquatic environments are discussed, encompassing sediment, water, sewage, and soils. An investigation is conducted on the relationship between LABs and total organic carbon (TOC) as a means of evaluating sewage pollution. It is assessed how sewage treatment facilities (STPs) contribute to biodegradation.
Climate change poses an escalating threat to the safety of high-hazard embankment dams, increases flood discharge impacting dam overtopping risk by altering the hydrological load of the original dam designed capacity. This paper's primary aims are to evaluate climate change's influence on extreme rainfall events and their impact on dam safety and to assess the overtopping risk of Batu Dam under various climate scenarios. This study focusses on assessing the overtopping risk of Batu Dam in Malaysia, utilizing regional climate model projections from the Coupled Model Intercomparison Project 5 (CMIP5) spanning 2020 to 2100. Three Representative Concentration Pathways (RCPs)-RCP4.5, RCP6.0, and RCP8.5 as the scenario and divide into 3 period of study: early century (2020-2046), mid (2047-2073) and late-century (2074-2100) evaluated with hydrological analysis to access the dam safety. Using the Linear Scaling Method (LSM), we corrected the bias projection rainfall data from three Regional Climate Models (RCMs) for the RCPs. Future Probable Maximum Precipitation (PMP) was estimated using statistical analysis techniques developed by the National Hydraulic Research Institute of Malaysia (NAHRIM). Additionally, Rainfall Intensity-Duration-Frequency (IDF) curves were updated based on climate scenarios outlined in the Hydrological Procedure 2021 and the associated Climate Change Factors. The HEC-HMS hydrological model was employed to simulate PMF and IDF for ARIs ranging from 1 to 100,000 years, providing a comprehensive analysis of risks under future climatic conditions. Across all future climate scenarios, inflow events were projected to exceed the dam design inflow, with RCP8.5 indicating the highest inflow values, particularly later in the century, highlighting probability of overtopping risks. Late-century projections show inflow for ARI 50 under RCP8.5 exceeding PMF by 20%, while mid-century RCP6.0 results indicate a 15% higher inflow for ARI 50000. Early-century RCP4.5 shows a 10% increase for ARI 100000 compared to PMF. The study advocates adaptive dam safety management and flood protection measures. This research provides crucial insights for embankment dam owners, stressing the urgent need to address Batu Dam's vulnerability to extreme flooding amidst climate change and emphasizing proactive measures to fortify critical infrastructure and protect downstream communities.