The eddy covariance (EC) technique is a precise and reliable method for quantifying net ecosystem exchange (NEE) of carbon dioxide (CO2) at the field scale. Despite the sustained increase in atmospheric CO2 levels, seasonality in NEE remains substantial, driven by the absorption and emission dynamics of terrestrial ecosystems. This study monitors seasonal variability in NEE using EC flux-tower data and Sentinel-2A multispectral satellite imagery. Flux tower data from six Prairie View A&M University sites and Sentinel-2A data were integrated to estimate gross primary production (GPP) using an empirically derived Light Use Efficiency (LUE) model. The R2 values ranged from 0.53 to 0.68 across the flux tower locations, with RMSE of 40.93 gC m 2 and MAE of 33.19 gC m⁻2 10 d⁻1, indicating the LUE model captured the seasonal dynamics of GPP with moderate skill between EC and Sentinel-2A satellite observations. Seasonally, the carbon sink increased markedly during spring and summer, corresponding to the hottest and wettest periods, and decreased post-harvest, such as fall and winter, reflecting the influence of agricultural practices. In addition, pastureland exhibits more stable and sustained carbon uptake, whereas croplands show greater variability driven by phenological cycles and management practices. This study underscores the potential of integrating satellite observations with EC data to scale up local flux tower measurements to broader ecosystem assessments. Such approaches provide valuable insights into the spatiotemporal dynamics of carbon fluxes, advancing our understanding of ecosystem responses to environmental and anthropogenic drivers.
Desalination’s high energy demand has increasingly limited the sustainable supply of fresh water in arid coastal areas, posing a major challenge within the water-energy nexus. This paper develops and validates a comprehensive techno-economic framework that decouples water security from fossil fuel price volatility. Specifically, this study evaluates the integration of intermittent wind energy and Public-Private Partnership (PPP) financial models, using the Dakhla region of Morocco as a baseline case. The assessment rigorously examines the mutual dependence of surplus grid electricity sales, public capital subsidies, and desalination load factors across four distinct techno-economic scenarios. The results demonstrate a profound financial synergy: the combination of wind energy export revenues and targeted public grants drastically reduces the Levelized Cost of Water (LCOW) from a fossil-dependent baseline of 8,38 MAD/m3 down to an optimal 2,71 MAD/m3. The findings reveal that utilizing the national electrical grid to monetize surplus wind energy is the most financially viable strategy to overcome the high costs of renewable desalination. Furthermore, closing the water affordability gap for agriculture cannot be achieved by technology alone; it requires a synergistic approach combining these dynamic energy-wheeling regulations with targeted public subsidies. By doing so, actively decoupling desalination from fossil fuels transforms a historically cost-prohibitive process into a sustainable, scalable driver for both agricultural expansion and regional decarbonisation. Ultimately, this research demonstrates that large-scale desalination achieves long-term economic sustainability only when engineered as a fully integrated renewable energy asset rather than an isolated water facility.
Water and energy are critical yet interconnected resources in agriculture, linking to land use, crop production, and greenhouse gas (GHG) emissions within the Water-Energy-Food (WEF) and Carbon nexus. This study develops a single-objective constrained nonlinear programming model to maximize total cropping profit by optimizing irrigation water and land allocation, while internalizing relevant environmental costs. The profit function integrates crop revenues minus variable costs, including water, energy, and a carbon price applied to GHG emissions. Crop yield is simulated using Stewart’s water production functions; energy use is linked to irrigation water via diesel pumping; and emissions are quantified for both irrigation and all other farm operations. Constraints include total water availability, maximum irrigation rates, and irrigated area limits. Applied to a case study of cotton-wheat rotation in Toowoomba region, Australia, using government data and expert interviews, the model demonstrates optimal allocations for different resources. Under current costs and a 38.92 GL water limit, the optimum allocation is 72% (7,768 ha) of irrigated area to wheat (2.02 ML/ha) and 28% (3,003 ha) to cotton (7.74 ML/ha). Gross margins are AUD 4,132/ha for cotton and AUD 1,584/ha for wheat. Total GHG emissions are 20.91 ktCO2e (wheat) and 9.77 ktCO2e (cotton), with intensities of 2.69 tCO2e/ha (wheat) and 3.25 tCO2e/ha (cotton). The model provides a transferable tool for quantifying trade-offs between profitability, resource efficiency, and environmental performance in irrigated cropping systems.
The installation of sand filters in cooling water systems is essential to maintain optimum performance and minimise maintenance downtime. Sand filters are commonly used in cooling towers to remove suspended solids, algae and other particles that can clog the system and reduce efficiency. The benefits of sand filters in cooling water systems include reducing fouling in the cooling water stream, where sand filters significantly reduce fouling of heat exchangers and other components in the cooling system, helping to reduce operating costs and downtime. The importance of cooling water treatment with sand filters is to improve water quality. Sand filters can remove total suspended solids (TSS), reduce conductivity, chemical oxygen demand (COD), biochemical oxygen demand (BOD) and turbidity. The purpose of this study was to investigate the performance of installing a sand filter in the cooling water return line to reduce the amount of effluent due to high levels of contaminants in the cooling water and also to reduce the use of fresh water. TSS dropped from 1200 mg/L to 350 mg/L, conductivity from 1700 µS/cm to 700 µS/cm, COD from 230 mg/L to 150 mg/L, BOD from 35 mg/L to 15.6 mg/L and turbidity from 5.6 NTU to 3.2 NTU after the sand filter was installed. After the sand filter was installed, the recirculation rate increased by approximately 16.5%, the blowdown rate decreased by approximately 24%, the evaporation rate decreased by approximately 20.5% and the water make-up decreased by approximately 14.2%.
Water pollution is one of the most serious environmental challenges the world faces nowadays. Adsorption has emerged as a promising method for addressing this issue. In this study, novel adsorbents were synthesized by combining zeolitic imidazolate framework-8 (ZIF-8) and zeolitic imidazolate framework-67 (ZIF-67) with layered triple hydroxide (LTH)/calcined layered triple hydroxide (cLTH) through various synthesis approaches. These adsorbents were thoroughly characterized using a range of techniques to determine their textural properties, crystallinity, and functional groups before being applied to treat wastewater samples contaminated with a variety of pollutants, including acid red 1 (AR1), methyl orange (MO), crystal violet (CV), methylene blue (MB), 2-nitrophenol (2NP), bisphenol A (BPA), lead (Pb(II)), and chromium (Cr(VI)). Rather than assuming that hybridization would generally enhance adsorption performance, this work adopts a systematic comparative screening approach to evaluate pristine ZIFs, LTH/cLTH, and their nanocomposites across a broad pollutant matrix, revealing selective, pollutant-dependent enhancement and establishing a performance map that identifies best-in-class adsorbent–pollutant pairings. For instance, the adsorption capacities of CV onto cLTH, ZIF-8, and cLTH@ZIF-8 nanocomposite were found to be 286.3, 406.8, and 1335.3 mg/g, respectively, demonstrating the superiority of the nanocomposite. Additionally, the LTH@ZIF-67 nanocomposite showed superior CV adsorption performance (1027.9 mg/g) relative to pristine LTH (318.0 mg/g) and ZIF-67 (892.3 mg/g). Zeta potential measurements and the textural properties of the synthesized materials suggest that the adsorption of the aforementioned pollutants onto the synthesized adsorbents (including the nanocomposites) is likely governed by multiple mechanisms. Overall, the findings reported herein reveal the potential of the novel adsorbents synthesized in this study in boosting water decontamination.
Energy efficiency in reverse osmosis desalination plants has significantly improved over the last few decades thanks to the integration of high-efficiency pumps, sophisticated membranes, and energy recovery devices. However, further reductions in energy consumption are still required to enable wider adoption of this technology. To deal with this challenge, this work employs a hybrid Genetic Algorithm-Particle Swarm Optimization approach in MATLAB/Simulink to enhance the energy efficiency of a high-pressure piston pump driven by a Permanent Magnet Synchronous Motor, which applies pressure to an RO membrane. Under an osmotic pressure of 46 bar and a semi-permeable membrane area of 37.2 m2, the results show a transmembrane pressure difference of 61 bar, a permeate flux of 32.1 LMH, and a water recovery rate of 45%. The feed, permeate, and concentrate flow rates satisfy mass conservation, with respective values of 0.75 Kg/s, 0.34 kg/s, and 0.41 kg/s. The PMSM achieves a rotational mechanical speed of 300 rad/s, without overshoot, while torque and electrical stress remain low, resulting in fewer spikes in active/reactive power, and lower Joule losses of 500 W. Without the integration of an energy recovery device, the system shows a specific energy consumption of around 1.6 kWh/m3 using the hybrid GA-PSO optimization.
In the Huang–Huai–Hai (HHH) Plain, a major agricultural region in North China, the pursuit of food security has increasingly intensified pressure on water resources, energy consumption, and ecological systems, posing substantial sustainability challenges. However, integrated assessment and optimization of crop production sustainability under the water–energy–food–ecology (WEFE) nexus remain limited at fine spatial scales, constraining the development of targeted management strategies. To address this gap, we developed a composite index framework integrating the information substitutability method and entropy weight method (ISM–EWM–CIF) to assess sustainability status, trade-offs, driving factors, and optimization pathways at the county level. Using this framework, we evaluated crop production sustainability across 479 counties from 2000 to 2020. Results showed that the composite sustainability index declined from 0.34 to 0.31, with water, energy, and ecological sustainability generally lower in the northern counties and higher in the southern counties. Synergistic relationships were identified among water, energy, and ecology subsystems, whereas food production exhibited trade-offs with water (−0.46), energy (−0.41), and ecology (−0.46), highlighting the environmental costs of agricultural intensification. The coupling coordination degree declined in 67.18% of counties, with water scarcity identified as the dominant limiting factor. In addition, a higher proportion of grain crops in total sown area was consistently associated with lower composite sustainability, suggesting unintended ecological costs of grain-oriented production policies. Scenario analysis showed that energy-focused (En) and water–energy combined (WEn) measures were the most effective strategies under single- and dual-factor optimization scenarios, accounting for 67.40% and 46.17% of counties, respectively, mainly in the central HHH Plain. By explicitly quantifying cross-sectoral trade-offs and spatial heterogeneity, this study provides a transferable framework for diagnosing WEFE nexus challenges and supporting region-specific strategies for sustainable agricultural intensification.
This study evaluates the feasibility of a municipal solid waste (MSW) incineration-based power generation system in Dhaka, Bangladesh, addressing critical issues of waste management and energy deficits driven by rapid urbanization, high moisture content of waste, inadequate segregation practices, and infrastructural limitations. A 10 MW waste-to-energy (WTE) facility operating at 22.5% efficiency was analyzed using key economic metrics, including Net Present Value (NPV), Payback Period, Internal Rate of Return (IRR), Modified Internal Rate of Return (MIRR), Levelized Cost of Energy (LCOE), and Levelized Cost of Waste (LCOW). The analysis reveals an NPV of $4.99 million, an IRR exceeding 10%, and a payback period of 11.06 years, indicating conditional economic viability under supportive policy and tariff frameworks. However, the calculated LCOE of $0.1298 USD/kWh exceeds the current electricity tariff in Bangladesh, highlighting financial constraints that necessitate regulatory incentives, preferential financing mechanisms, or feed-in tariff adjustments. Additionally, the plant is projected to reduce annual CO2 emissions by 0.045 million tons and decrease landfill volume by approximately 80%, contributing to environmental sustainability. Despite the technological maturity of incineration systems globally, implementation challenges remain, including high capital investment, emission control compliance requirements, and regulatory uncertainty. From a forward-looking perspective, integrating WTE within Bangladesh’s broader renewable energy strategy—supported by public–private partnerships and carbon credit mechanisms—could enhance long-term economic resilience and environmental performance. The findings demonstrate that waste-to-energy technology offers a viable solution for mitigating urban waste challenges while enhancing energy security, providing a sustainable pathway for Dhaka’s development and a scalable model for rapidly urbanizing cities in developing economies.
As a representative large shallow lake on the Yunnan-Guizhou Plateau, Dianchi Lake has experienced severe ecological degradation primarily driven by eutrophication. This study systematically investigates the spatiotemporal evolution and driving mechanisms of the macrophyte-algae ecosystem in Dianchi Lake, utilizing remote sensing data (1987-2022), historical monitoring records, and a comprehensive field survey conducted in 2022. The results demonstrate that since the 1970s, Dianchi Lake has experienced an abrupt regime shift from a macrophyte-dominated clear-water state to an algae-dominated turbid-water state: submerged vegetation coverage declined sharply from 90% in the 1960s to virtually 0% by the 2010s, while the algal bloom area peaked at 41.07 km2 in 1998 and 54.11 km2 in 2007. Although the extent of algal blooms has diminished in recent years, periodic outbreaks continue to occur. Mechanistic analysis indicates that the decline of submerged plants was initially triggered by hydrodynamically induced light limitation. Subsequent nutrient enrichment and stoichiometric imbalance further accelerated the establishment of an algae-dominated state through a self-reinforcing “nutrient-algae-turbidity” feedback loop. Additionally, bioavailable nitrogen stored in the sediments contributed to the persistence of turbidity. The interplay of multiple stressors, combined with ecological hysteresis, complicates the ecological restoration of Dianchi Lake. The conceptual framework of regime shifts in shallow plateau lakes proposed in this study highlights the critical need for integrated management strategies that combine external nutrient reduction with internal load control and hydrodynamic regulation. These insights provide a theoretical foundation and practical guidance for the ecological management of similar lakes worldwide.
Given the increasing attention in achieving carbon neutrality, carbon emissions and material flow are critical for the sustainability of wastewater treatment plants (WWTPs). This research firstly integrated carbon emission accounting, material flow analysis (MFA), and machine learning-based optimization to characterize a full-scale WWTP with the anaerobic-anoxic–oxic process. It was found that the annual carbon emission intensity was 0.734 ± 0.069 kgCO2·m−3, with direct and indirect emissions contributing 36.8% and 63.2%, respectively. Notably, the anaerobic tank was identified as the primary source of direct emissions from nitrous oxide and methane production, while the aerobic tank was the largest contributor of indirect emissions, accounting for about 40% of the total electricity consumption. Additionally, nitrogen and organic load had a significant impact on direct emissions, while the poly aluminium chloride dosage had the largest effect on chemical consumption-related carbon emissions (r = 0.853, p < 0.001). Innovatively, machine learning was employed to predict the full-process carbon emissions of the plant and then the differential evolution algorithm was used to optimize the operation of the plant, resulting in 30.36% reduction of carbon emissions, 33.19% reduction of operating costs and 25.21% reductions of pollutants for per ton of water. MFA revealed distinct removal patterns for the three pollutants: (1) chemical oxygen demand—12.49% removed in pretreatment, 81.34% in biological treatment, with relatively low carbon recovery potential; (2) total nitrogen—15.35% removed in pretreatment, 62.00% in biological treatment, and 18.26% discharged with effluent; (3) total phosphorus—80.94% removed in the aerobic tank through polyphosphate-accumulating organisms with an additional 18.76% removed by coagulants in sedimentation, indicating high phosphorus recovery potential through sludge. Based on these material flow characteristics showing high nitrogen and phosphorus recovery potential, a multi-algae system composed of Spirulina and freshwater Chlorella with intelligent control was proposed in an embedded algal pond to maximize nitrogen and phosphorus recovery while serving as a carbon sink, thereby reducing energy consumption and chemical usage. In summary, this study provides valuable insights for carbon reduction, pollution mitigation, and resource recovery in urban WWTPs.
Drought is an environmental phenomenon and an integral part of climate change that can occur anywhere. Drought can take different forms, including meteorological, hydrological, agricultural, and socioeconomic droughts. The main goal of this study was to identify how meteorological droughts can cause hydrological droughts. The study confirmed that the area experienced both hydrological and meteorological droughts in 1987, 2003, and 2015. Index-based drought analysis results showed a significant (P<0.05) correlation between hydrological and meteorological droughts. Particularly, it was determined that there was a better association between the Streamflow Drought Index and Reconnaissance Drought Index than between the Standardized Precipitation Index. This demonstrates how strongly evapotranspiration influences streamflow, a factor that the Reconnaissance Drought Index considers when calculating its value. Based on projected scenarios for streamflow and climate factors, all variables in the watershed are expected to decline over the next ten years, other than maximum temperature. From the prediction process results, SARIMA catched better of the seasonality of temperature than rainfall or streamflow. Investigating index-based droughts is essential to warn the public and decision-makers about impending droughts and help them put mitigation and adaptation plans for water management into action.
As global water demand rises, optimising energy consumption in water treatment processes is essential to improve both environmental sustainability and economic efficiency.This study fills a critical research gap by applying a comprehensive methodology to analyse correlations between energy consumption and raw water quality parameters, aiming to identify drivers of high energy use and optimise drinking water treatment processes.This case study focuses on the Apartadura Drinking Water Treatment Plant (DWTP), which draws water by gravity from the Apartadura reservoir in Portugal. Total energy consumption over the six-year period (2017–2022) was calculated by considering both direct energy, derived from electricity bills, and indirect energy, estimated from reagent dosages, transportation distances, and sludge disposal.The total energy consumption of treated water was 3.6 MJ/m3, of which 1.9 MJ/m3 (54%) corresponded to indirect energy and 1.7 MJ/m3 (46%) to direct energy. The main contributors to direct energy included ozone generators (180 kWh each), CO2 recirculation pumps (150 kWh each), and filter backwash pumps (7.7 kWh each). Indirect energy was largely due to the use of calcium hydroxide and carbon dioxide..Statistical analysis showed that raw water quality parameters, including alkalinity, pH, Langelier Index (LI), manganese (Mn), and total coliforms (TC), significantly influenced energy consumption, primarily by means of adjustments in reagent dosing and process performance. This methodology has proven effective in identifying suboptimal operational patterns, including via remote evaluation. Its broader application provides key advantages like: (1) a reproducible metric for total energy consumption across facilities; (2) integration of water quality and energy data to highlight optimisation opportunities; and (3) support for decision-making regarding energy-efficient and sustainable operation of DWTPs amid climate change challenges. This integrated approach provides a valuable tool for water treatment managers and policymakers to optimise energy consumption and improve sustainability, particularly in regions experiencing climate variability and change
The global demand for freshwater and the environmental concerns related to reverse osmosis reject (ROR) water have highlighted the need for sustainable wastewater treatment solutions. Microbial fuel cells (MFCs) have emerged as an innovative solution for simultaneous wastewater treatment and bioelectricity production. This study developed a multi-chamber MFC system to evaluate the potential of bioelectricity generation from institutional wastewater supplemented with ROR water. The MFC consisted of three anodic chambers, referred to as MFC1, MFC2, and MFC3, filled with different compositions of ROR and wastewater: 75% ROR and 25% WW, 50% ROR and 50% WW, and 25% ROR and 75% WW, respectively, and a central cathodic chamber filled with aerated distilled water. The study analyzed key performance parameters including open circuit voltage (OCV), voltage, power density, current density, chemical oxygen demand (COD), and coulombic efficiency (CE). MFC1 demonstrated the highest performance, recording a maximum OCV of 374 mV, a power density of 233 mW/m2, and 76% COD removal with a CE of 14.6%. The findings show that the supplementation of ROR water enhances microbial metabolism and electron transfer due to its higher content of inorganic ions, resulting in improved energy recovery and wastewater treatment. This study demonstrates the feasibility of integrating ROR water in MFC systems as a substrate amplifier for sustainable energy and water recovery.
Identifying the carbon emission characteristics of wastewater treatment plants (WWTPs) is fundamental for taking effective energy-saving and carbon reduction measures. This study conducted a comprehensive carbon emission analysis of a representative sludge-water co-treatment facility in Shanghai through establishing an integrated full-process accounting framework that encompasses both wastewater and sludge treatment lines, utilizing five-year operational data from 2020 to 2024. Carbon emissions were quantified by using the emission factor methodology, and their characteristics including total emissions, intensity, and driving factors were analyzed using statistical methods, notably power-law regression. Results show that while the wastewater treatment section contributed 60.8% of total emissions, the sludge treatment system demonstrated substantial emission potential by exceeding 30% contribution even when processing internally generated sludge, with its proportional impact amplifying progressively as treatment capacity increased, thereby highlighting the necessity of holistic system boundaries. The plant’s overall carbon emission intensity peaked at 0.904 kg CO2-eq/m3, which exceeded Shanghai’s average level of 0.75 kg CO2/m3. Carbon emission intensity exhibited significant negative power-law correlations and scale effects with wastewater treatment volume, CODcr removal, and sludge throughput. Crucially, critical operational thresholds emerged in these nonlinear relationships, exemplified by the approximate 70% utilization rate of sludge treatment design capacity, where sub-threshold operations triggered exponential growth in emission intensity whereas supra-threshold expansions yielded progressively diminishing marginal returns. Energy consumption dominated emission sources at 42.3% of the total, with electricity demand constituting 73.3% of energy-related emissions. Notably, comparative analysis suggests that applying default IPCC N2O emission factors could overestimate direct emissions by 18-22%, underscoring the imperative for developing region-specific emission parameters.These findings demonstrate that coordinated control of treatment scale around critical thresholds, coupled with process optimization, is crucial for achieving low-carbon operations in sludge-water co-treatment systems and provide scientific support for targeted carbon reduction strategies.
Groundwater is a vital resource sustaining communities in the Niger Delta, where access to clean drinking water is limited. However, the increasing hydrocarbon extraction in the Niger Delta has led to the release of hydrocarbon contaminants (HCCs), compromising groundwater quality, environmental integrity, and posing a threat to public health systems. Groundwater systems in the Niger Delta are heavily polluted with Total Petroleum Hydrocarbon (TPH) levels reaching 42,200 µg/L, far beyond international safety limits. Elevated levels of Heavy metals (HMs) such as cadmium, nickel, lead, and volatile organic pollutants exceed regulatory thresholds, rendering local water unsafe for consumption and harming aquatic ecosystems. The consequences include agricultural decline, biodiversity loss, and increased health risks such as reproductive disorders and cancer. Despite regulatory frameworks, weak enforcement, vast rever networks, and corporate negligence, contamination is exacerbated. Sustainable solutions, including stricter environmental policies, advanced remediation techniques, and community engagement in decision-making, are critical. Bioremediation, renewable energy adoption, and improved waste management can mitigate long-term damage while fostering ecological restoration. Strengthening compliance mechanisms and ensuring transparency in hydrocarbon operations are essential to safeguarding groundwater resources. This study highlights the pressing need for integrated scientific and policy-driven approaches to safeguard water sources and public health, advocating for sustainable environmental management in the Niger Delta. Without immediate intervention, the continued degradation of groundwater threatens both ecosystems and human livelihoods, highlighting the pressing necessity of enforceable solutions for lasting impact.
The drainage of stormwater, a critical element of the urban water cycle, has become a major issue in cities worldwide. Rapid and unplanned urbanization, coupled with increasingly frequent high-intensity rainfall events due to climate change, has reduced the effectiveness of existing drainage infrastructure. Stormwater Control Measures (SCMs) have emerged as sustainable solutions to address these challenges. As highlighted in this review, SCMs effectively reduce stormwater runoff volumes and peak discharges, while also improving water quality through the removal of pollutants such as nitrogen, phosphorus, E. coli, and heavy metals. However, their long-term success depends on sound design, regular maintenance, and consideration of climate variability. This study systematically reviews both quantitative and qualitative aspects of SCMs, emphasizing their design parameters, performance efficiency, and resilience under changing climatic conditions. A total of approximately 240 peer-reviewed research articles published over the last two decades were carefully selected for inclusion, forming a structured database covering six major SCM categories. The selection was based on recent and high-impact studies retrieved primarily from Scopus, Web of Science, and Google Scholar. Extracted performance metrics were synthesized and compared across SCM types using a literature-based quantitative approach supported by comparative tables. The review also evaluates how design considerations and site-specific factors influence SCM performance and how climate change affects their operational sustainability. The discussion concludes with key design recommendations and directions for future research, highlighting the importance of adaptive approaches and data-driven monitoring for improved SCM planning and management.
Sediment microbial fuel cells or SMFCs are innovative bio-electrochemical systems that address the interconnected global issues of energy scarcity, wastewater pollution, and sediment contamination. This comprehensive review examines the progression from early 20th-century microbial fuel cells to contemporary SMFCs, emphasizing significant developments including mediator-less electron transfer in the 1990s and in-situ sediment applications in the 2000s. Advancements in electrode materials, such as biochar modifications and nanostructured carbons, have increased power densities to 3.31 W/m2 in laboratory settings and improved remediation efficiencies, resulting in over 97% COD removal, 93% Cr(VI) reduction, and notable mitigation of nutrients and heavy metals. Lab-to-field transitions highlight enduring challenges such as environmental variability and scaling difficulties, which can be addressed through AI-optimized designs, synthetic biology for improved biofilms, and hybrid systems such as constructed wetland-SMFCs. Projections suggest that field outputs will consistently reach or exceed 1 W/m2 by 2050, facilitating significant energy recovery and enabling an annual avoidance of 0.4 to 0.9 Gt of CO2 emissions through global implementation. Policy implications correspond with UN SDGs 6, 7, 9, and 13, promoting incentives for commercialization to facilitate net-zero transitions. Despite achieving TRL 5–6 maturity, addressing internal resistance and longevity challenges is essential for mainstream integration, thereby establishing SMFCs as an important asset for sustainable development.
Biosorption has become attractive in the recent past as a cost-effective and environmentally friendly methodology. As biosorbents are usually heterogeneous consisting of many organic functionalities, change in solution pH would cause differences of their reactivities thereby affecting the extent and the modes of mass transfer of adsorbates from the solution phase to the solid biosorbent phase. Consequently, it is important to have model studies to extrapolate toward large scale treatment suitable for commercialization. Although model studies have been established for small-scale batch experiments, biosorption models for dynamic studies have not been much elaborated. In order to fill this void, various static and dynamic models were applied for the biosorption system consisting of Cd2+ adsorbate and peel of Artocarpus nobilis fruit as the biosorbent. Systematic investigation conducted on the biosorbent leads to an excellent Cd2+ removal of 88% in batch experiments under optimized conditions of 150 min shaking time, 15 min settling time, 5.5 – 7.0 pH range at 150 rpm rotation speed. The extent of removal of Cd2+ is independent of both the heating time and heating temperature. Fitting of equilibrium biosorption data on linearized Langmuir adsorption isotherm models leads to the regression coefficients of 0.976 with biosorption capacity of 13.7 mg g−1. Moreover, dynamic adsorption models, namely, the Thomas, Adam’s-Bohart and Yoon Nelson, are also successfully fitted to the Cd2+ removal data taken under dynamic conditions. Moreover, the effect of boundary layer thickness explained by the intra-particle diffusion model could be considered in extending the removal of Cd2+ at large-scale.
In wastewater treatment, AGS technology is a revolutionary breakthrough that overcomes the drawbacks of the conventional Activated Sludge (AS) method. Large geographic footprints, energy-intensive processes, and complicated biomass-water separation are all overcome by AGS systems. AGS use compact, dense microbial granules to effectively remove carbon, nitrogen, and phosphorus in a single reactor. This review explores the benefits, challenges, and operating principles of AGS, with a focus on accelerating granulation through the use of inoculation and polymer additives. Key topics include the granule formation mechanisms, the role of diverse microbial communities and comparison between AS and AGS. AGS technology has several benefits, but it also has drawbacks, including long granulation periods, granule instability, and environmental limitations. By overcoming these obstacles, AGS can become a viable and affordable option to meet the world’s wastewater treatment requirements, opening the door to more significant financial and environmental advantages. Different methods like bioaugmentation, inoculum and polymer additives can be used to get rapid startup of granulation and enhanced stability. Some future directions for future research, innovation and rapid formation in AGS technology is also discussed.
Evaluating the water quality is one of the most important steps in creating surface water resources for drinking water consumption in the Mahanadi River Basin, Odisha. Surface water possesses a number of water quality problems, such as turbidity, dissolved solids, and coliform contamination, all of which are harmful to human health. The use of a Water Quality Index (WQI) is regarded as a successful technique for assessing the quality of water. In the present study, a total of 13 surface water samples for a duration of 2019-2024, were analysed for eleven monitoring locations. This study’s goal was to assess the performance of several indexing techniques such as Geographical Information System (GIS), Pollution index of Surface water (PIS), and Nitrate Pollution Index (NPI). In addition to the fixed-weight WQI, to predict the water quality, the Entropy (E) weighted-WQI, and Multivariate Statistical Models like PCA (Principal Component Analysis) factor based WQI assessments were used. The findings showed that HCO3- was the main anion and that magnesium (Mg2+) dominated the cations. In almost 100% of the surface water samples, the concentration of turbidity, TDS, and coliform, respectively, displays above the WHO-established acceptable limit. Depending on the PIS model, about 9.09%, 9.09%, 36.36%, 18.18%, and 27.27% of examined water samples were categorized as insignificant, low, moderate, high, and very high polluted water quality. The EWQI values indicated that 72.73% of samples exhibited poor or extremely polluted zone. It showed notable 18.18% of samples, rated as medium for drinking. The entropy score revealed that three samples (27.27%) were classified as potable for human consumption. Almost 3 samples (Z-1, 3, and 4) are unanimously defined as clean or light zone, across the NPI rating. Across 5 water quality locations corresponds to significant/very significant pollution, with a nominal agreement of 45.45%. A few tests have extremely concerned results, necessitating quick action to ensure safe drinking water. The PCA analysis extracted four factors for the studied datasets within eigen values > 1, representing 87.602% of total variance. The PCA loadings were categorized as strong, moderate, and weak, respectively, accordingly to the loading values of > 0.75, 0.75 – 0.5, and 0.50 – 0.30. Observation depicts that variable in PC1 namely Cl- (0.75), SO42- (0.87), NO3- (0.78), turbidity (0.89), and coliform (0.85) were the most critical parameters affecting surface water chemistry through anthropogenic and natural occurrences. Based on the findings, the Bland-Altman plot utilized to confirm and reaffirm the validity of the current approach for surface water characterisation by comparing it with PWQI. The results showed that the error chance is very small. The north-eastern and south-eastern regions were found to be severely contaminated by agricultural and human-induced activities, according to spatial analysis. The presence of substantial levels of turbidity, magnesium, and coliform in surface water in the majority of the samples, together with their dependency, may lead to future pollution through the dissolution of parameters. At relevant places of the studied region, its water resource is under threat from urbanization, agriculture, industrialization, and population growth. Moreover, the resultant WQI model produces better forecast accuracy with fewer input parameters and makes a substantial contribution to the sustainable management of surface water resources in arid regions.
