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The development and spread of antibiotic resistance (AR) have become major concerns because they pose pressing public health problems worldwide, and aquatic ecosystems are recognized reservoirs for antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARB). We reviewed the sources, distribution, and factors influencing ARGs and ARB in surface waters, and the methods used to measure and assess the risks posed to human and ecological health. The prevalence of ARGs and ARB is largely attributed to environmental contamination from fecal matter. Therefore, the distribution of AR on both regional and seasonal scales is significantly impacted by agriculture, which is related to economic development. In risk assessments, the risk of ARGs is mainly evaluated based on their mobility, pathogen carriage, and regional distribution, while the risk assessment for ARB is primarily focused on the quantities and diversities of pathogen-associated resistant bacteria. Based on this information, we suggest seven priority research questions regarding antibiotic resistance management in water environments: control of AR dissemination, advanced monitoring technologies, integrative impacts evaluation of antibiotics on resistance mechanisms and microbial communities, quantitative microbial risk assessment for ARB and ARGs, implications of horizontal gene transfer in non-pathogenic bacteria, synergistic risks of multiple resistance elements, and identification of high-risk ARGs and ARB in aquatic ecosystems. We also advocate for the implementation of national actions that focus on source management and environmental monitoring.

Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.

Integrating renewable energy sources (RES) into island microgrids is usually done to provide a cost-effective electricity supply. The integration process is carried out by scheduling generating unit operations with a unit commitment (UC) scheme to ensure low system operating costs. This article discusses developing a UC optimization method for integrating solar photovoltaic plants in Indonesia's Eastern Sumba microgrid power system. The scope of this study is the optimization algorithm of the UC, which consists of a priority list (PL) for the UC stage and an economic dispatch (ED) that relies on a genetic algorithm (GA) to minimize total operating costs (TOC). The results show that the PL-GA algorithm performs better than the extended priority list (EPL), and combinations of genetic algorithm and Lagrange, by applying continuous problem dispatch and improved binary GA hourly dispatch to meet ramping constraints. The application of RES incentive programs, such as carbon taxes and incentives for RES generation in calculating the TOC, shows an improvement in the financial feasibility analysis of the internal rate of return (IRR) and net present value (NPV) of actual projects in Indonesia. [ABSTRACT FROM AUTHOR]

Electric utilities in many Small Island Developing States (SIDS) of the Caribbean are characterized by a nearly exclusive use of thermal generation and operate using rudimentary optimization approaches based on assumed characteristics. This is due to financial constraints which limit the resources that can be spent in collecting detailed data required for proper generation optimization. This paper presents a case study investigating the generation system parameters that such a financially constrained electric utility located in the Caribbean should invest in knowing more precisely. This task is accomplished by performing a sensitivity analysis on the cost related parameters in the Unit Commitment (UC) problem. The results of this analysis can guide the Utility on how to prioritize their investments in. The results provide insights for similar utilities in the Caribbean and other SIDS in their investment considerations.

Minimizing the trade-offs between agricultural production, development and forest conservation is key to ensure that conservation policies can achieve long-term positive impacts. Taking the case of the Brazilian Amazon in the context of the Action Plan for the Prevention and Control of Deforestation in the Legal Amazon, I estimate the impact of the prioritizing of municipalities with high deforestation risk from soybean production (a major driver of deforestation), exports and land-use changes relying on a difference-in-differences and generalized synthetic control approach. I find that, although effective in reducing deforestation, the policy is unlikely to have undermined the soybean production and exports. On the contrary, the results suggest that the soybean sector benefited from the changes in land use following the implementation of the priority list. However, I do not find evidence that land restriction triggered the intensification of soybean production, which suggests that the soybean sector benefited from intra-crops reallocation and pasture conversion. [ABSTRACT FROM AUTHOR]

Through a systematic review of literature references from 2007 to 2022, we compiled a comprehensive national dataset comprising over 67,000 records and covering information on 129 antibiotics detected in the surface water and sediments of China's major rivers. Our analysis revealed notably high antibiotic concentrations in the Liaohe and Yellow Rivers. Among the antibiotics examined, sulfonamides, quinolones, and tetracyclines exhibited relatively high median concentrations in river water. Regional distribution analysis highlighted increased antibiotic levels in Shandong and Tianjin compared to other areas. Partial least squares path modeling revealed that animal production and pollution discharge positively influenced antibiotic levels in river water, whereas natural and socioeconomic factors had negative impacts. Based on the ecological risk assessment, we formulated a prioritized national list of antibiotics, with sulfonamides having the largest number of entries, followed by quinolones. Importantly, our analysis revealed a declining trend in antibiotic concentrations and the associated risk levels across China during the study period. This study not only enhances our understanding of antibiotic distribution in China's water systems, but also contributes to the development of a scientifically sound approach for prioritizing antibiotics. Ultimately, these findings will inform targeted antibiotic management and control strategies. Antibiotics, posing threats to ecosystems and human health, exhibit pseudo-persistence in the environment. we compiled a national dataset of over 67,000 records on antibiotics, our study scrutinized antibiotic distribution in China's major river water and sediment. Through this analysis, we identified key factors influencing distribution patterns and crafted a national priority ranking for antibiotics. These findings deepen our understanding of antibiotic presence and contribute to the development of targeted management strategies aimed at minimizing environmental impact. [Display omitted] • A national dataset comprising 129 antibiotics found in China's rivers was compiled. • Shandong and Tianjin showed higher antibiotic concentrations in their river water. • Animal production had the greatest effect on antibiotic levels in river water. • A national-level priority rank list of antibiotics was proposed. [ABSTRACT FROM AUTHOR]

This study proposes a novel binary optimization method for solving unit commitment (UC) problems. Developed from moth-flame optimization (MFO), the proposed method is called binary alternative MFO (BAMFO). Originally, each MFO moth is restricted to fly toward a predetermined flame (solution), causing it to be trapped in the local optimum of that flame and reducing its performance. To address these drawbacks, BAMFO represents four alternative characteristics for moths to discover better flames all over the search space. The exploration and exploitation performances of BAMFO via these characteristics are balanced by a random parameter competing with the criteria of each characteristic. A repair strategy working with the priority list is created to adjust any unit status from the BAMFO simulation to satisfy all UC constraints. Then, Lambda-iteration method is adopted to solve economic dispatch problems. The proficiency of BAMFO is verified in various scales of unit systems for short-term operational scheduling. The results show that BAMFO yields better solutions than do other methods. The Wilcoxon signed rank test was conducted to prove the predominance of BAMFO.

Constructive Heuristics for the Resource Constraint Project Scheduling Problems (RCPSP) are preferred scheduling methods when the project network broadens. Then, to generate a good schedule from these heuristics, the priority list used in the algorithm becomes crucial. This paper proposes a Cluster-Based Priority List (CB-PL) method for generating lists to improve makespans of schedules obtained from constructive heuristics. The method creates more intellectual priority lists that generate lower makespans. The approach is built and fine-tuned upon the existing relative literature. The performance of the method is measured by comparing the makespan results. The experiment for the comparison uses serial and parallel scheduling schemes with seven priority rules. Then the experiment is tested through a set of benchmark data. Finally, schedules obtained through the CB-PL showed significant makespan reductions and increases in an overall number of better solutions. [ABSTRACT FROM AUTHOR]

Integration of renewable energy generators with existing electrical power systems is an effective strategy to strengthen energy security in islands and remote areas. However, in the case of high penetration of renewable energy sources (RES), operations and economic aspects must be considered. One of the power plant integration schemes is the Unit Commitment (UC), a daily scheduling method to achieve optimization goals while still meeting various operational, environmental, and technical constraints. However, the existing UC method is a generic one, therefore it is necessary to develop an optimization method with a new approach based on improved PL-GA to manage the generation scheduling in microgrid power systems. The proposed solution method is the PL-GA Algorithm, an improvement from the previous studies, with optimization on economic dispatch using GA. This algorithm uses a simple and fast PL technique for the generation scheduling and dispatch stages to obtain the initial population. Then, an accurate and more flexible GA technique is used for the economic dispatch stage to avoid local optima to get the lowest total operating costs. This method has been simulated on the electricity system in Eastern Indonesia, a developing electricity system with intermittent RES power plants with good results. [ABSTRACT FROM AUTHOR]

The Unit Commitment Problem (UCP) is an operational research problem commonly encountered in energy management. It refers to the optimum scheduling of the generating units in a power system to efficiently meet the electricity demand. UCP comprises two interrelated sub-problems: the Unit Commitment for deciding the operating state of the units at each scheduling period and the Economic Dispatch (ED) for allocating the demand among them. Various Evolutionary Algorithms (EA) have been adopted for solving UCP, commonly assisted by the Lambda iteration method for solving the ED. In this study, an EA-based method is proposed for dealing with both sub-problems, avoiding binary variables through a simple transformation function. The method takes advantage of a repair mechanism utilizing the Priority List (PL) to steer the search towards adequate generating schedules. The impact of the cost metric chosen for creating the PL on the computational results is investigated and the use of a Plurality of PL is suggested to alleviate the biases introduced by employing constant cost metrics. Furthermore, an Elitist Mutation strategy is developed to enhance the performance of the proposed EA-based method. Simulation results on various power systems validate the beneficial effect of the proposed modifications. Compared to state of the art, the algorithm proposed has been at least equivalent, exhibiting consistently solutions of lower or competitive costs in all systems examined. [ABSTRACT FROM AUTHOR]

The discovery of the significant lethal impacts of the tire additive transformation product N -(1,3-dimethylbutyl)- N '-phenyl- p -phenylenediamine quinone (6PPD-Q) on coho salmon has garnered global attention. However, the bioaccumulation and trophic transfer of tire additives and their transformation products (TATPs) within food webs remain obscure. This study first characterized the levels and compositions of 15 TATPs in the Pearl River Estuary, estimated their bioaccumulation and trophic transfer potential in 21 estuarine species, and identified priority contaminants. Our observations indicated that TATPs were prevalent in the estuarine environment. Eight, six, seven, and 10 TATPs were first quantified in the shrimp, sea cucumber, snail, and fish samples, with total mean levels of 45, 56, 64, and 67 ng/g (wet weight), respectively. N , N '-Diphenyl- p -phenylenediamine (DPPD) and N , N '-bis(2-methylphenyl)-1,4-benzenediamine (DTPD) exhibited high bioaccumulation. Significant biodilution was only identified for benzothiazole, while DPPD and DTPD displayed biomagnification trends based on Monte Carlo simulations. The mechanisms of bioaccumulation and trophodynamics of TATPs could be explained by their chemical hydrophobicity, molecular mass, and metabolic rates. Based on a multicriteria scoring technique, DPPD, DTPD, and 6PPD-Q were characterized as priority contaminants. This work emphasizes the importance of biomonitoring, particularly for specific hydrophobic tire additives.

Horticulture is one of the main pathways of deliberate introduction of non-native plants, some of which might become invasive. Of the 914 commercial ornamental outdoor plant species sold in Spain, 700 (77%) are non-native (archaeophytes excluded) marketed species. We classified these into six different lists based on their invasion status in Spain and elsewhere, their climatic suitability in Spain and their potential environmental and socioeconomic impacts. We found sufficient information for 270 species. We provide a Priority List of eight regulated invasive species that were still available on the market. We also established an Attention List with 68 non-regulated invasive and potentially invasive species that might cause various impacts. To prioritise the species within the Attention List, we further assessed the risk of invasion of these species by using an adaptation of the Australian WRA protocol and the level of societal interest estimated from values of the Google Trends tool. We also propose a Green List of seven species with probably no potential to become invasive, a Watch List with 27 potentially invasive species with few potential impacts and an Uncertainty List with 161 species of known status but with insufficient information to include them in any of the previous lists. We did not find sufficient information for 430 (61%) of the marketed non-native plant species, which were compiled into a Data Deficient List. Our findings of prohibited species for sale highlight the need for stronger enforcement of the regulations on invasive plant species in Spain. In addition, our results highlight the need for additional information on potential impacts and climate suitability of horticultural plants being sold in Spain, as insufficient information could be found to assess the invasion risk for most species. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic multi-layer energy management for a multi-microgrid (MMG) based smart distribution network (SDN). Also, all network constraints have been regarded to evaluate the effect of the active power loss on the energy management of the entities. Besides, demand response (DR) programs have been considered in the optimization process, which is one of the features of the SDN and could be used to manage the consumed power of each individual entity. Furthermore, in order to contemplate environmental issues, emission constraints for dispatchable generators have been taken into account. In this procedure, first, each microgrid (MG) performs an energy management to determine the schedule of its units and the shortage/surplus power. Then using the received data, the SDN operator (SDNO) prepares a priority list (PL) of units capable of injecting power to the SDN based on their average cost and capacity. The preparation of the PL entices the investors for using renewable based generators. Afterwards, it executes a global energy management. In the proposed approach, scenario based stochastic optimization has been utilized to model the uncertainties of the renewable energy resources. Finally, simulations using the modified IEEE 33-bus test feeder are done to demonstrate that how the proposed method encourages each MG for deploying clean productions as their main generation units. Moreover, the executed results illustrate the changes in the schedules of the MGs in the case of considering active power loss. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm-binary glowworm swarm optimization (BGSO) to solve the unit commitment (UC) problem. After a certain quantity of initial feasible solutions is obtained by using the priority list and the decommitment of redundant unit, BGSO is applied to optimize the on/off state of the unit, and the Lambda-iteration method is adopted to solve the economic dispatch problem. In the iterative process, the solutions that do not satisfy all the constraints are adjusted by the correction method. Furthermore, different adjustment techniques such as conversion from cold start to hot start, decommitment of redundant unit, are adopted to avoid falling into local optimal solution and to keep the diversity of the feasible solutions. The proposed BGSO is tested on the power system in the range of 10–140 generating units for a 24-h scheduling period and compared to quantum-inspired evolutionary algorithm (QEA), improved binary particle swarm optimization (IBPSO) and mixed integer programming (MIP). Simulated results distinctly show that BGSO is very competent in solving the UC problem in comparison to the previously reported algorithms.

With the rapid growth of thermostatically controlled loads, active power fluctuation and peak demand growth within an autonomous micro-grid become serious problems. This paper tries to suppress power fluctuation and shave peak demand for a micro-grid through optimizing domestic electric water heaters (controllable load). In this paper, domestic electric water heater models are first built to optimize power flow within a single water heater. Subsequently, the Monte Carlo method is proposed to simulate power consumption of a cluster of domestic electric water heaters. After that, the temperature state priority list method is presented to suppress power flow and shave peak demand for a given micro-grid. Optimization results show that the proposed temperature state priority list method can reduce peak demand by 12.5%. However, it has a wider active power fluctuation range and needs a longer reaction time compared with the simplified temperature state priority list method. In addition, the optimization results show that by increasing the number of controllable loads participating in load scheduling, active power fluctuation can be reduced and the maximum active power of the given micro-grid can be cut. However, to achieve this, about 1.2% of extra electrical energy needs to be generated by the external grid. [ABSTRACT FROM AUTHOR]

Volatile organic compounds emissions from building materials can be a major pollution source in low-occupant-density spaces. Composite-style indoor air quality references, which reflect the combined effects of multiple volatile organic compounds, can be used to determine ventilation rate requirements based on building material emissions. The lowest concentration of interest concept was adopted to implement the idea. Twenty-eight building materials selected from the National Research Council of Canada database were subjected to emission modelling, resulting in 101 volatile organic compounds as a starting volatile organic compound pool. A method was proposed to generate a volatile organic compound priority list that determines ventilation rate requirements while considering ozone-initiated reactions. Three priority lists were obtained based on three lowest concentration of interest schemes, i.e., AFSSET, AgBB and EU-LCI, with each consisting of 17–21 volatile organic compounds that were most likely to attribute to large ventilation rate requirements. Also, analyses of selected volatile organic compounds showed that the changes in the composition of the priority lists due to ozone-initiated reactions could be ignored at a typical indoor ozone concentration level. The application of priority lists was discussed for source control and air cleaning device testing. This paper provides a method to prioritize the chemicals based on ventilation rate requirements with a goal of developing volatile organic compound control strategies at building design stage. [ABSTRACT FROM AUTHOR]