Scenario 1 Research Articles

Evaluating machine learning performance in predicting sodium adsorption ratio for sustainable soil-water management in the eastern Mediterranean

Soil salinization is a critical global issue for sustainable agriculture, impacting crop yields and posing a threat to achieving the Sustainable Development Goal (SDG) of ensuring food security. It is necessary to monitor it in detail and uncover its underlying factors at a regional scale. In this context, the present study aimed to evaluate soil health in the eastern Mediterranean region by using the Sodium Adsorption Ratio (SAR) as an indicator of soil salinity in three distinct soil horizons. The main objective of the research was to evaluate the performance of four machine learning (ML) models, including Random Forest (RF), Nu Support Vector Regression (NuSVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Gradient Boosting Regression (GBR), for accurate prediction of SAR following the Recursive Feature Elimination (RFE) as a feature selection method. Moreover, SHapely Additive exPlanations (SHAP) was applied as sensitivity analysis to identify the most influential covariates. Main findings of the research revealed that the average clay content in the surface horizon (H10-25cm) was 50.5% ± 10.4, which significantly increased to 57.5% ± 8.7 (p < 0.05). No significant mean differences were detected between the studied horizons for SAR and Na+. ML output revealed that NuSVR outperformed other algorithms in accurately predicting outcomes during both the training and testing stages. Moreover, Scenario 2 (SC2) with seven selected features from the RFE method facilitated highly accurate SAR predictions. Overall, the performance of ML models is ranked as NuSVR > GBR > ANN-MLP > RF. Lastly, SHAP sensitivity analysis identified CEC, Ca+2, Mg+2, and Na+ as the most influential variables for SAR prediction in both the training and testing stages. Hence, the research yielded valuable insights for efficient agricultural soil management at a regional level using state-of-the-art technology.

Recycled ionic liquid vs. deep eutectic solvent in cellulose nanocrystals production: Characterization, techno-economic analysis, and life cycle assessment

Three scenarios involving aqueous mixtures of neoteric solvents have been evaluated as solvents and catalysts for the cellulose hydrolysis reaction to obtain CNC in high yields (>70%). The scenarios considered were: scenario 1 (S1) involves a recyclable ionic liquid dilution of [Hmim][(HSO4)(H2SO4)]/H2O (64 wt% IL); scenario 2 (S2) another recyclable dilution of [Hmim][(HSO4)(H2SO4)]//H2O (80 wt% IL) and scenario 3 (S3) a non-recyclable ternary deep eutectic solvent (60 wt% DES: choline chloride: oxalic acid/30 wt% PA/10 wt% water). Experimental analysis, techno-economic, and life cycle analysis (LCA) indicate that S1 emerges as a suitable scenario among the three analyzed. S1 demonstrated the highest competitiveness, with a lower raw material cost per gram of CNC produced (US$0.81/g) and lower environmental contributions concerning climate change and fossil fuel depletion, accounting mainly for its recyclability. Therefore, the recyclable dilution of 64 wt% IL used in S1 appears to be the most viable option for sustainable and environmentally conscious CNC production. Besides, the physicochemical properties of the CNC were revealed: aspect ratio range 7–8, high dispersion stability related to zeta potential > −40 mV, good crystallinity range 50–80%, and thermal stability with Tonset> 300 °C. These results guided towards a scenario with crucial advantages: a diluted IL that retains its acidity after reuse cycles by a facile recovery process, maintaining high CNC production performance and providing low environmental impacts.

Life cycle assessment of magnesium phosphate cement production

Magnesium phosphate cement (MPC) is gaining attention in the field of rapid repair and coatings due to its superior qualities, yet little research has been done on the environmental impacts of MPC preparation. It's necessary to investigate its environmental implications for the sustainable development and application of MPC. This study develops a life cycle inventory of the production of MgO with three different production processes and the production of two phosphates and applies the inventory to conduct a life cycle assessment from cradle to gate for four scenarios of MPC production: Scenario 1 (S1) - dry route MgO and potassium dihydrogen phosphate (PDP) as raw materials; Scenario 2 (S2) - wet route MgO and PDP as raw materials; Scenario 3 (S3) - salt lake MgO and PDP as raw materials; Scenario 4 (S4) - dry route MgO and ammonium dihydrogen phosphate (ADP) as raw materials. Moreover, the sensitivity analysis and cost analysis of MPC are carried out. In order to comprehensively evaluate the low carbon properties of MPC, the carbon emissions of MPC and the commonly used rapid repair materials and coatings are compared. Results indicate S2 shows the highest carbon emission, and the global warming of S2 is 3.97%, 74.55%, and 1.41% higher than that of S1, S3, and S4, respectively. The environmental impact of MPC mainly comes from the production stage of MgO and phosphate (47%–67%). In three MgO production routes, S3 shows the best environmental performance. In two phosphates, S4 has a smaller impact on most impact categories. Sensitivity analysis also indicates that MgO and phosphate have a greater impact on the production of MPC. In addition, the cost of S2 is 2.91%, 0.37%, and 15.40% higher than that of S1, S3, S4, respectively, and S4 shows the best economic benefit. Furthermore, MPC has great advantages in rapid repair and coating, and can reduce carbon emissions by 12%–98% compared to other rapid repair materials and coatings.

Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms

Frequent coastal harmful algal blooms (HABs) threaten the ecological environment and human health. Biscayne Bay in southeastern Florida also faces algal bloom issues; however, the mechanisms driving these blooms are not fully understood, emphasizing the importance of HAB prediction for effective environmental management. The overarching goal of this study is to offer a robust HAB predictive framework and try to enhance the understanding of HAB dynamics. This study established three scenarios to predict chlorophyll-a concentrations, a recognized representative of HABs: Scenario 1 (S1) using single nonlinear machine learning (ML) algorithms, hybrid Scenario 2 (S2) combining linear models and nonlinear ML algorithms, and hybrid Scenario 3 (S3) combining temporal decomposition and ML (TD-ML) algorithms. The novel-developed S3 TD-ML hybrid models demonstrated superior predictive capabilities, achieving all R2 values above 0.9 and MAPE under 30% in tests, significantly outperforming the S1 with an average R2 of 0.16 and the S2 with an R2 of −0.06. S3 models effectively captured the algal dynamics, successfully predicting complex time series with extremes and noise. In addition, we unveiled the relationship between environmental variables and chlorophyll-a through correlation analysis and found that climate change might intensify the HABs in Biscayne Bay. This research developed a precise predictive framework for early warning and proactive management of HABs, offering potential global applicability and improved prediction accuracy to address HAB challenges.

Thermodynamic analysis of an evacuated tube solar collector system for industrial building application: A case study

In this study, a modified solar-Organic Rankine Cycle (ORC) assisted hydrogen production system for two different scenarios was experimentally investigated. In the study, evacuated tube solar collector (ETSC), ORC system, proton exchanger membrane electrolyzer (PEMe) unit is discussed with a holistic perspective. The presented work includes energy and exergy analysis of solar-ORC assisted hydrogen production. According to scenario 1 (S-1), only the case of hydrogen production from the installed system is considered. According to Scenario 2 (S-2), the both electrical energy and hydrogen production from the installed system is examined. As a result of the study, the energy efficiency in S-1 and S-2 operating modes was calculated as 9.553% and 16.02%, respectively. Additionally, the highest exergy efficiency for both scenarios was calculated as 6.129% in the S-2 scenario. Furthermore, hydrogen production amount of the S-1 and S-2 scenarios was calculated as 743.1 kg/annual and 371.5 kg/annual, respectively.

Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins

This study addresses the challenge of utilizing incomplete long-term discharge data when using gridded precipitation datasets and data-driven modeling in Iran’s Karkheh basin. The Multilayer Perceptron Neural Network (MLPNN), a rainfall-runoff (R-R) model, was applied, leveraging precipitation data from the Asian Precipitation—Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climatic Research Unit (CRU). The MLPNN was trained using the Levenberg–Marquardt algorithm and optimized with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Input data were pre-processed through principal component analysis (PCA) and singular value decomposition (SVD). This study explored two scenarios: Scenario 1 (S1) used in situ data for calibration and gridded dataset data for testing, while Scenario 2 (S2) involved separate calibrations and tests for each dataset. The findings reveal that APHRODITE outperformed in S1, with all datasets showing improved results in S2. The best results were achieved with hybrid applications of the S2-PCA-NSGA-II for APHRODITE and S2-SVD-NSGA-II for GPCC and CRU. This study concludes that gridded precipitation datasets, when properly calibrated, significantly enhance runoff simulation accuracy, highlighting the importance of bias correction in rainfall-runoff modeling. It is important to emphasize that this modeling approach may not be suitable in situations where a catchment is undergoing significant changes, whether due to development interventions or the impacts of anthropogenic climate change. This limitation highlights the need for dynamic modeling approaches that can adapt to changing catchment conditions.

Energy and exergy analysis of waste heat and solar energy assisted for hydrogen production system: A case study

In the presented study, the focus is on the exergy efficiency of a textile factory that has the potential to optimize itself using solar energy and waste heat. Two distinct scenarios are considered in this study. In Scenario 1 (S-1), the whole system components operate solely for hydrogen production, while in Scenario 2 (S-2), the whole system components are utilized for both electricity, heat water and hydrogen production. For the harnessing waste heat, a 25 kW capacity Organic Rankine Cycle (ORC) system, 20 evacuated tube solar collectors (ETSC) for solar energy utilization, and a water electrolyzer for hydrogen production are employed. As a result of the thermodynamic analysis of the whole system, the energy and exergy efficiency is calculated as 5.17 %, 2.63% and 39.32%, 21.74%, respectively, according to the S-1 and S-2 scenarios. Furthermore, considering S-1 and S-2 conditions, hydrogen production amounts to 5197 kg/annual and 2209 kg/annual, respectively.

Assessing multifunctional retrofit potential of urban roof areas and evaluating the power and carbon benefits under efficient retrofit scenarios

Green roof installations and photovoltaic (PV) systems are widely employed roof retrofits that aid cities in mitigating climate change impacts, while avoiding the need for increased land utilization. By integrating PV systems with vegetation on urban roofs, a photovoltaic-green (PV-Green) system can be achieved for multifunctional use of the roof space, thereby simultaneously achieving PV and greening benefits. However, current research solely based on one retrofit type cannot meet the requirement for assessing the multifunctional retrofit potential of urban roofs. In this study, an assessment method is proposed to identify and quantify the multiple retrofit potential of urban roofs by integrating roof attributes (slope, orientation, and area), roof type (gable or flat), solar attributes (radiation and irradiation duration), and biogeochemical simulation. Moreover, three roof retrofit scenarios, Scenario 1 (S1): maximization of PV-Green roofs, Scenario 2 (S2): maximization of PV economic benefits, and Scenario 3 (S3): maximization of public subjective well-being through roof greening, were designed to allocate the use of urban roof spaces and evaluate their respective potential power and carbon benefits at the city scale. Using Shanghai's downtown as an example, the results showed that 85,722 roofs (or 7310.86 ha) were available for multifunctional use. S1 revealed that applying PV-Green roofs can increase the additional green biomass by 0.74 × 107 kg C/yr compared to only installing PV roofs. Moreover, S1 produced the highest power output of 2.31 × 1010 kWh/yr to meet 15.4% of Shanghai's electricity demand. S2 identified 609 flat roofs and 70,527 gable roofs that were uneconomical for PV system installation. This indicated that the solar radiation received by most gable roofs was insufficient to cover the installation cost. S3 offered a biomass production of 1.48 × 107 kg C/yr and increased carbon stocks in Shanghai by 0.87%. This assessment method provides urban planners and policymakers with an analytical tool to optimize the use of urban roof spaces, thereby enhancing urban livability and sustainability.

Development of an economically viable plastic recovery waste management system in Cibodas and Padamukti Village, West Java

Out of the 2,142 existing community-managed sorting and recycling facilities in Indonesia, known as TPS-3R, only 14% are functioning optimally due to economic challenges, highlighting the need for financially viable TPS-3R. To address this issue, the research aims to recommend an economically viable plastic recovery waste management system with optimum residue and income. Cibodas and Padamukti Villages are chosen as the case study location, as it has only 20% waste collection coverage and is located in the Citarum Watershed, whose river is a source of water for 60 million people and is part of the Citarik Ecotourism Living Lab. Primary data collection was carried out through interviews with recyclers in the villages and through waste volume and composition characterization. Three scenarios were developed for the Plastic Recovery Facility (PRF) objectives: Scenario 1 (S1) serves as the baseline of PRF, Scenario 2 (S2) buys plastic wastes from existing traders to be processed with more advanced plastic recycling technologies, and Scenario 3 (S3) is a self-sustaining PRF that could be replicated for the whole villages. Data analysis involved mass balance, financial models, BCR, NPV, and multicriteria decision-making. The residue disposed of in landfills is 32%, 17%, and 28% of the total waste managed for S1, S2, and S3. Financial modeling yielded BCR values of 1.06, 1.15, and 1.24, as well as NPV +IDR78,036,332; +IDR465,224,754; and +IDR354,414,072. The results highlight the importance of detailed analysis of mass balance and financial modeling according to the waste composition in any TPS-3R location, especially the differences in the value of rigid and flexible plastics, which will affect the types of technology and products. While the recommended scenario, S3, does not eliminate the need for capital investment from the government, it provides a framework to plan PRF with an operating surplus, which will increase the community’s willingness to run it and ensure the PRF’s long-term functionality to yield social and environmental benefits.

Biodiversity burdens in Spanish conventional and low-impact single-family homes

Biodiversity loss caused by housing is not a well-defined sector of environmental impact. This research quantifies effects on biodiversity of an average Spanish Single-Family House (SFH) with 180 m2 of built surface. The current Spanish SFH stock GWP amounts to 1.16 Gt CO2eq in a 50-year life cycle, 40 % of which is embodied in the building materials and the 60 % are emissions due to the use of the building. This stock also impacts with 10.2 Gt 1,4-DCB the land, water and human health. SFHs also drive 6052 species extinct in a 50 year life cycle, and account for 3.03 M years of life lost due to premature death or lived with a disability. Divided by the 16 M people living in Spanish SFHs, each one lost 0.19 years of their lives (68.1 days) due to their home's impacts on human health.The article compares a reference conventional building against three low-impact cases, to understand how different building techniques and materials influence environmental outcomes that keep biodiversity loss the lowest possible. Scenarios include a standard brick and concrete house as Scenario 0 (SC0, Base), a timber Passivhaus as Scenario 1 (SC1), a straw-bale house with renewable energies as Scenario 2 (SC2), and an earth bioclimatic house as Scenario 3 (SC3). An initial Global Warming Potential (GWP) analysis was performed to relate previous building Life Cycle Assessment (LCA) studies with biodiversity metrics. Three main biodiversity metrics; ecotoxicity (as midpoint indicator), biodiversity loss and damage to human health (both as endpoint indicators) have been considered.Compared to SC0 with 1292 kgCO2-eq·m−2 (516 embodied) of GWP, we found that SC1 emitted −47.0 % of that, SC2–41.4 % and SC3–80.9 %. Concerning ecotoxicity, where SC0 has 11,399 kg 1,4 DCB, the results are −27.9 % in SC1, −19.2 % in SC2, and −45.6 % in SC3. Regarding biodiversity loss, where SC0 has 7.54 E−06 species.yr·m−2, the impacts are −30.9 % in SC1, −32.6 % in SC2, and −58.6 % in SC3.Human health damage in SC0 being 3.37 E−03 DALY, has been reduced in the timber home (SC1) is −44.2 %, of the Straw SFH (SC2) −39.2 %, and of the earth house (SC3) −67.1 %.This article shows that with current existing technological solutions GWP could be reduced in −80.9 %, ecotoxicity in −45.6 %, biodiversity loss in −58.6 % and human health in −67.1 %. Spanish Single-Family Houses built in timber, earth or straw-bale are real alternatives to current cement traditional building.

Ecosystem Service Optimisation in the Central Plains Urban Agglomeration Based on Land Use Structure Adjustment

By using the methods of scenario analysis, model simulation, and the multi-objective spatial optimisation algorithm Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Pareto optimal solutions for water supply, water purification (N retention), as well as carbon storage and sequestration service (carbon service) of the Central Plains Urban Agglomeration (CPUA) were sought by adjusting the land use structure. It showed that, to reach the Pareto optimal solution goal, (1) in Scenario 1 (S1), the water supply service needs to increase by 10.682 billion cubic metres, the water purification (N retention) service needs to decrease by 11,400 tons, and the carbon service need to decrease by 2.487 billion tons. In Scenario 2 (S2), the water supply service needs to increase by 8.243 billion cubic metres, the water purification (N retention) service needs to decrease by 11,000 tons, and the carbon service needs to decrease by 2.466 billion tons. In Scenario 3 (S3), the water supply service needs to increase by 4.089 billion cubic metres, the water purification (N retention) service needs to decrease by 10,800 tons, and the carbon service needs to decrease by 2.380 billion tons. (2) After land use optimisation and adjustment, the S3 ecological land structure is complete and consistent with the vision of ecological protection and urban development in the study area, which is the optimal scenario. (3) Optimising the ecosystem service supply pattern through land use structure adjustment could balance the overall ecosystem service supply pattern of the study area In regions wherein ecosystem supply is insufficient and there is a spatial mismatch between supply and demand for ecosystem services, this study can guide regional land planning and assist in the formulation of ecosystem service management policies.

Using a Gaussian model to estimate the level of particle matter concentration on paved and unpaved roads in urban environment: A case study

Introduction: In both developed and developing countries, re-suspension of dust particles along the road owing to tire and brake wear is the most common source of Particulate Matters (PM) pollution in metropolitan areas. This study in Douala analyses the effects of paved and unpaved roads on particle matters concentration thresholds in urban environments.&#x0D; Materials and methods: The United States Environmental Protection Agency (US EPA)'s model AP-42 equations were used to calculate the amount of particle matter emissions on the roads. Between 6 am and 8 pm, a traffic analysis using information from the city of Douala was conducted. The busiest times for traffic were from 8 to 9 a.m. and 6 to 7 p.m. We applied a two-dimensional Gaussian model to determine the particle concentration. Two different scenarios were taken into account: Compared to Scenario 2 (S2), Scenario 1 (S1) represents an unpaved road. The PM10 and PM2.5 types of particles were the main topics of interest.&#x0D; Results: We obtained for S , around 917.70 µg/m3 and 559.00 µg/m3 respectively for PM and PM . We got roughly 170.00 µg/m3 and 103.90 10 2.5 µg/m3 for S , respectively for the two particles. The amount of silt deposited on the road, the kind of road (paved or unpaved), the number, and the types of vehicles moving all influence the emission of road dust re-suspension. Regardless of particle size, these pollution levels are beyond World Health Organization (WHO) recommended norms.&#x0D; Conclusion: This study offers important information on Douala's pollution levels, which can be a significant cause of disease in the area and should be considered.

Optimal emission reductions pathway for polybrominated diphenyl ethers in typical household e-waste dismantling products

Waste printed circuit boards (WPCBs) and waste epoxy resin powder (WERP) generated after crushing are the most crucial hazardous materials in the recycling process of household e-waste. In this study, a sustainable treatment approach was established in response to the drawbacks of traditional treatment methods. The baseline and hypothetical scenarios were as follows: (1) scenario 1 (S1): WPCBs mechanical treatment, WERP safe landfill; (2) scenario 2 (S2): WPCBs mechanical treatment, WERP imitation stone bricks production. Based on the material flow analysis and comprehensive evaluation, the most profitable and environmentally friendly scenario was selected and assumed to be promoted in Jiangsu area and China from 2013 to 2029. The analysis result showed that S2 had the best economic performance and polybrominated diphenyl ethers (PBDEs) emission reductions potential. S2 is the best option that can gradually replace the traditional recycling model. With the promotion of S2, China would reduce the emissions of PBDEs by 700.8 kg. Meanwhile, it could save $542.2 million in WERP landfill costs, produce 1260.2 kt of imitation stone bricks, and generate $2308.5 million in economic benefits. In conclusion, this study can offer a new idea for dismantling products treatment of household e-waste and provide scientific knowledge to improve the sustainable management.

Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China

Machine learning models have been effectively applied to predict certain variable in several engineering applications where the variable is highly stochastic in nature and complex to identify utilizing the classical mathematical models. Therefore, this study investigates the capability of various machine learning algorithms in predicting the power production of a reservoir located in China using data from 1979 to 2016. In this study, different supervised and unsupervised machine learning algorithms are proposed: artificial neural network (ANN), AutoRegressive Integrated Moving Aveage (ARIMA) and support vector machine (SVM). Three different scenarios are examined, such as scenario1 (SC1): used to predict daily power generation, scenario 2 (SC2): used to predict power generation for monthly prediction and scenario 3 (SC3): used to predict hydropower generation (HPG) seasonally. The statistical analysis and pre-processing techniques were applied to the raw data before developing the models. Five statistical indexes are employed to evaluate the performances of various models developed. The results indicate that the proposed models can be used to predict HPG efficiently and could be an effective method for energy decision-makers. The sensitivity analyses found the most effective models for predicting HPG for three scenarios using graphical distribution data (Taylor diagram). Regarding the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models for ANN and SVM. The results presented that the value of 95PPU for all models falls into the range between 80% and 100%. As for the d-factor, all values in all scenarios are less than one.

Exploration of algal biorefinery frameworks: Optimization, quantification of environmental impacts and economics

The integrated algal biorefinery (IABR) was considered as a feasible alternative to fossil fuels. After the processes of algae cultivation and pretreatment, the algae biomass with different carbohydrate/lipid contents is split into the carbohydrate process for producing fuel/chemicals and the oil process for producing fuel/energy. Four frameworks of IABR are presented: (i) Scenario 1 (SC1) is designed to produce lactic acid/biodiesel, (ii) Scenario 2 (SC2) is an extension of SC1 by increasing the production of biooil, (iii) Scenario 3 (SC3) is an extension of SC2 by adding the combined heat and power (CHP) process, and (iv) Scenario 4 (SC4) is an extension of SC3 by increasing the production of acetone, butanol, and ethanol (ABE). Through sensitivity analysis of environmental impacts and economics to assess the influences of parameters of four scenarios, it shows that (i) SC1 ensures the lower total eCO2 emission than other scenarios, (ii) SC3 with the low carbohydrate content and the low percentage of carbohydrate process could suppress eCO2 emission, (iii) SC3 with the high lipid content and the low percentage of carbohydrate process has the higher total profit than SC2 due to the extra green electricity from CHP for sale. To address an IABR with high economic potential and low environmental impact, the process optimization of SC4 under prescribed algae carbohydrate/lipid contents shows that (i) the total profit of SC4 is higher than SC3 by 81.8 % and (ii) the eCO2 emission of SC4 is lower than SC3 by 51.9 %.

Assessment of water quality variations due to operation of wastewater treatment plant in Ca Mau city

The wastewater treatment plant (WWTP) in Ca Mau city, which is being planned for construction, will be responsible for treating domestic wastewater with a capacity of 8,000 m3/day. After being treated by the physic-biological method, the plant’s wastewater will be discharged into the Thong Nhat canal and the Tac Thu river. As a result, this article applies the MIKE21 model to calculate the range, the distance, and the concentration of typical pollutants for domestic wastewater, including TSS, BOD5, NH4+, and total Coliforms in the dry season, to provide evidence for water resource management following the plant’s construction. Two scenarios were established; in scenario 1 (S1), the WWTP is being operated inefficiently, and the wastewater has not been treated up to standards. Maximum calculation in case the processing performance = 0; in scenario 2 (S2), the WWTP is being operated efficiently. The concentration of 4 components in wastewater is lower than the critical value of QCVN 14:2008/BTNMT (column B, K = 1). The results show that the waste source will highly affect the water in the Thong Nhat canal both downstream and upstream due to the irregular semi-diurnal tide regime in case of the plant experiences problems (S1). In the case of the stable operation plant (S2), the pollutant concentrations at all positions will be lower than the critical value of QCVN 08-MT:2015/BTNMT (column B1) except for the TSS. Concentration variation is evident over time and distance: after about 1 hour and about 1 km from the source, the concentration of pollutants reaches the maximum value, then gradually decreases and ends at about 4 km after 7 hours.

Study on sustainable defluoridization approaches for rural drinking water treatment in northern China

Groundwater for potable uses containing high fluoride is a worldwide threat to human health. Electrocoagulation is a sustainable approach for decentralized treatment. This study aims to analyse the economic and environmental impacts of electrocoagulation applied at different scale in rural area of northern China by life cycle cost (LCC) and life cycle assessment (LCA) methods. Decentralized treatment for defluoridization in rural Fangshan District, Beijing, where the groundwater quality is typical in northern China, is surveyed as a case study. Three scenarios were evaluated. In scenario 1 (S1) all domestic water is electrocoagulated at community scale without dual water supply system. In scenario 2 (S2) only water for potable uses is electrocoagulated at community scale and raw water and treated water are distributed via dual water pipeline systems. In scenario 3 (S3) water for potable uses is electrocoagulated at household scale. Life cycle assessment is performed with openLCA software and Ecoinvent database. S1 has the highest impacts for eutrophication and global warming. S2 has the lowest impact for toxicity but highest impact for ozone layer depletion. S3 has the lowest impact for global warming and acidification but highest impact for human toxicity. S2 is the costliest while S3 is the most economical. In this case, life cycle cost and global warming impact are the most restricted factors so electrocoagulation at household scale is recommended.

Cost-Effectiveness of Waiving Coinsurance for Follow-Up Colonoscopy after a Positive Stool-Based Colorectal Screening Test in a Medicare Population.

Follow-up colonoscopy after a positive stool-based test is necessary to complete the colorectal cancer-screening process. This analysis demonstrated that in a simulated Medicare population, waiving coinsurance for a follow-up colonoscopy improved estimated outcomes and was cost-effective when it was assumed that waiving the coinsurance modestly increased screening adherence. See related Spotlight, p. 641.

Environmental Benefits of Ammonia Reduction in an Agriculture-Dominated Area in South Korea

Agricultural activity greatly contributes to the secondary PM2.5 concentrations by releasing relatively large amounts of ammonia emissions. Nonetheless, studies and air quality policies have traditionally focused on industrial emissions such as NOx and SOx. To compare them, this study used a three-dimensional modeling system (e.g., WRF/CMAQ) to estimate the effects of emission control policies of agricultural and industrial emissions on PM2.5 pollution in Chungcheong, an agriculturally active region in Korea. Scenario 1 (S1) was designed to estimate the effect of a 30% reduction in NH3 emissions from the agro-livestock sector on air pollution. Scenario 2 (S2) was designed to show the air quality under a mitigation policy on NOx, SOx, VOCs, and primary PM2.5 from industrial sources, such as power plants and factories. The results revealed that monthly mean PM2.5 in Chungcheong could decrease by 3.6% (1.1 µg/m3) under S1 with agricultural emission control, whereas S2 with industrial emission control may result in only a 0.7~1.1% improvement. These results indicate the importance of identifying trends of multiple precursor emissions and the chemical environment in the target area to enable more efficient air quality management.

Life cycle assessment of digestate post-treatment and utilization

Three digestate utilization scenarios for bio-fertilizer production are evaluated with life cycle assessment. The aim is to determine the environmental performance of the digestate post-treatment with the goal to decrease the loss of nitrogen and phosphorus, support circular nutrient management, and increase the substitution of mineral fertilizers. The functional unit (FU) of the study is the utilization of 1 kg dry matter raw digestate, in three scenario designs. Scenario 1 (S1) describes a system where the raw digestate is directly spread on soil. In scenario 2 (S2) the raw digestate is processed by centrifugation with two recovered phases (liquid and solid digestate), which are spread on agricultural soil. In scenario 3 (S3) a more advanced post-treatment system is modelled, where the raw digestate is phase separated with centrifugation followed by drying of the solid digestate and further processing of the liquid digestate with a membrane filtration and a reverse osmosis unit. The studied scenarios show a global warming potential ranging from −0.14 (S3) to −0.36 (S1) kg CO2 eq per FU. The fossil resource depletion per FU was decreased in scenario 1 (−0.053 kg oil eq) and scenario 2 (−0.049 kg oil eq) but increased in scenario 3 (0.002 kg oil eq). The terrestrial acidification potential ranges from 0.09 (S3) to 0.18 (S1) kg SO2 per FU. The digestate post-treatment is a sustainable solution able to tackle the problem of excess nutrients and their management in agricultural areas. It could replace conventional nitrogen removal processes (aerobic biological treatment) by a valorization chain keeping the nutrients in closed loop.