Analyzing the Biochemical Alteration of Green Algae During Chronic Exposure to Triclosan Based on Synchrotron-Based Fourier Transform Infrared Spectromicroscopy.

The study explored the chronic toxicity of triclosan to green microalga Chlorococcum sp. under multiple interactions among multiple environmental conditions. This is the first study on chronic algal toxicity to combine synchrotron-based Fourier transform infrared spectromicroscopy, factorial analysis, principal component analysis, and stepwise-cluster analysis. Such a combination helps to reveal the toxic mechanism at the molecular level and explore the inner correlationship among multiple environmental conditions. In the 120-h test, nitrogen content became the most significant factor of the physiochemical properties. Some insignificant factors in the 48-h test became significant in the 120-h test. Temperature * nitrogen content, temperature * phosphorus content, and pH * phosphorus content were the most significant two-order interactions. Temperature * pH * NaCl concentration and temperature * NaCl concentration * phosphorus content were the most significant three-order interactions. More high-order interactions became significant in the 120-h test, indicating the complexity and impacts of all the factors may increase when time was extended. The chronic toxicity of triclosan presented more distinguishable variations among treatments based on biochemical alterations. These results demonstrate that the sensitivity and fragility of algae to triclosan can be amplified with time extension. Long-term exposure can be applied to better evaluate and predict the environmental toxicity behavior of triclosan. It can also help with environmental evaluation and risk management of real-world triclosan toxicity.

Wastewater treatment in amine-based carbon capture.

Amine-based CO2 capture (ACC) has become one cost-effective method for reducing carbon emissions in order to mitigate climate changes. The amine-rich wastewater (ARWW) generated from ACC may contain a series of degradation products of amine-based solvents (ABSs). These products are harmful for ecological environment and human health. Effective and reliable ARWW treatment methods are highly required for mitigating the harmfulness. However, there is a lack of a comprehensive review of the existing limited methods that can guide ARWW-related technological advancements and treatment practices. To fill this gap, the review is achieved in this study. All available technologies for treating the ARWW from washwater, condenser, and reclaimer units in ACC are examined based on clarification of degradation mechanisms and ARWW compounds. A series of significant findings and recommendations are revealed through this review. For instance, ARWW treatment methods should be selected according to degradation conditions and pollution concentrations. UV light can be only used for treating wastewater from washwater and condenser units in ACC. Biological activated carbon is feasible for removing nitrosamines from washwater and condenser units. Sequence batch reactors, microbial fuel cells, and the other techniques for removing amines and similar degradation products are applicable for treating ARWW. This review provides scientific support for the selection and improvement of ARWW treatment techniques, the mitigation of ACC's consequences in environment, health and other aspects, and the extensive development and applications of ACC systems.

Nanomaterials in the Environment: Research Hotspots and Trends.

Research on the field of nanomaterials in environment has continued to be a major area of interest in recent years. To present the up-to-date progress in this field, a bibliometric study is conducted to analyze 7087 related publications in the Science Citation Index (SCI) core collection of Web of Science based on the expanded SCI. These publications are identified through using representative keywords in the research directions environment of the Web of Science. This study finds that China and the United States dominate the field; one difference between them is that China issued more independent publications and the United States issued more cooperative publications. In addition, the number of the related publications in Asian countries has exceeded that of European and American ones. A Chinese institution, the Chinese Academy of Sciences, has an absolute dominance in this field. Traditional high-impact environmental journals have ruled this field. The number of publications in the Energy and Environmental Science field has gradually decreased. In addition, a co-citation analysis shows that previous studies in this field can be divided into four major branches, and that graphene oxide and nano-inorganic particles are increasingly becoming research hotspots.

Municipal solid waste management planning for Xiamen City, China: a stochastic fractional inventory-theory-based approach.

In this study, a stochastic fractional inventory-theory-based waste management planning (SFIWP) model was developed and applied for supporting long-term planning of the municipal solid waste (MSW) management in Xiamen City, the special economic zone of Fujian Province, China. In the SFIWP model, the techniques of inventory model, stochastic linear fractional programming, and mixed-integer linear programming were integrated in a framework. Issues of waste inventory in MSW management system were solved, and the system efficiency was maximized through considering maximum net-diverted wastes under various constraint-violation risks. Decision alternatives for waste allocation and capacity expansion were also provided for MSW management planning in Xiamen. The obtained results showed that about 4.24 × 106 t of waste would be diverted from landfills when p i is 0.01, which accounted for 93% of waste in Xiamen City, and the waste diversion per unit of cost would be 26.327 × 103 t per $106. The capacities of MSW management facilities including incinerators, composting facility, and landfills would be expanded due to increasing waste generation rate.

Identification of water quality management policy of watershed system with multiple uncertain interactions using a multi-level-factorial risk-inference-based possibilistic-probabilistic programming approach.

In this study, a multi-level-factorial risk-inference-based possibilistic-probabilistic programming (MRPP) method is proposed for supporting water quality management under multiple uncertainties. The MRPP method can handle uncertainties expressed as fuzzy-random-boundary intervals, probability distributions, and interval numbers, and analyze the effects of uncertainties as well as their interactions on modeling outputs. It is applied to plan water quality management in the Xiangxihe watershed. Results reveal that a lower probability of satisfying the objective function (θ) as well as a higher probability of violating environmental constraints (q i ) would correspond to a higher system benefit with an increased risk of violating system feasibility. Chemical plants are the major contributors to biological oxygen demand (BOD) and total phosphorus (TP) discharges; total nitrogen (TN) would be mainly discharged by crop farming. It is also discovered that optimistic decision makers should pay more attention to the interactions between chemical plant and water supply, while decision makers who possess a risk-averse attitude would focus on the interactive effect of q i and benefit of water supply. The findings can help enhance the model's applicability and identify a suitable water quality management policy for environmental sustainability according to the practical situations.

Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part II: scheme analysis and mechanism revelation.

As presented in the first companion paper, distributed mixed-integer fuzzy hierarchical programming (DMIFHP) was developed for municipal solid waste management (MSWM) under complexities of heterogeneities, hierarchy, discreteness, and interactions. Beijing was selected as a representative case. This paper focuses on presenting the obtained schemes and the revealed mechanisms of the Beijing MSWM system. The optimal MSWM schemes for Beijing under various solid waste treatment policies and their differences are deliberated. The impacts of facility expansion, hierarchy, and spatial heterogeneities and potential extensions of DMIFHP are also discussed. A few of findings are revealed from the results and a series of comparisons and analyses. For instance, DMIFHP is capable of robustly reflecting these complexities in MSWM systems, especially for Beijing. The optimal MSWM schemes are of fragmented patterns due to the dominant role of the proximity principle in allocating solid waste treatment resources, and they are closely related to regulated ratios of landfilling, incineration, and composting. Communities without significant differences among distances to different types of treatment facilities are more sensitive to these ratios than others. The complexities of hierarchy and heterogeneities pose significant impacts on MSWM practices. Spatial dislocation of MSW generation rates and facility capacities caused by unreasonable planning in the past may result in insufficient utilization of treatment capacities under substantial influences of transportation costs. The problems of unreasonable MSWM planning, e.g., severe imbalance among different technologies and complete vacancy of ten facilities, should be gained deliberation of the public and the municipal or local governments in Beijing. These findings are helpful for gaining insights into MSWM systems under these complexities, mitigating key challenges in the planning of these systems, improving the related management practices, and eliminating potential socio-economic and eco-environmental issues resulting from unreasonable management.

Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part I: System identification and methodology development.

Due to the existence of complexities of heterogeneities, hierarchy, discreteness, and interactions in municipal solid waste management (MSWM) systems such as Beijing, China, a series of socio-economic and eco-environmental problems may emerge or worsen and result in irredeemable damages in the following decades. Meanwhile, existing studies, especially ones focusing on MSWM in Beijing, could hardly reflect these complexities in system simulations and provide reliable decision support for management practices. Thus, a framework of distributed mixed-integer fuzzy hierarchical programming (DMIFHP) is developed in this study for MSWM under these complexities. Beijing is selected as a representative case. The Beijing MSWM system is comprehensively analyzed in many aspects such as socio-economic conditions, natural conditions, spatial heterogeneities, treatment facilities, and system complexities, building a solid foundation for system simulation and optimization. Correspondingly, the MSWM system in Beijing is discretized as 235 grids to reflect spatial heterogeneity. A DMIFHP model which is a nonlinear programming problem is constructed to parameterize the Beijing MSWM system. To enable scientific solving of it, a solution algorithm is proposed based on coupling of fuzzy programming and mixed-integer linear programming. Innovations and advantages of the DMIFHP framework are discussed. The optimal MSWM schemes and mechanism revelations will be discussed in another companion paper due to length limitation.

Groundwater level prediction using a SOM-aided stepwise cluster inference model.

Accurate groundwater level (GWL) prediction can contribute to sustaining reliable water supply to domestic, agricultural and industrial uses as well as ecological services, especially in arid and semi-arid areas. In this paper, a regional GWL modeling framework was first presented through coupling both spatial and temporal clustering techniques. Specifically, the self-organizing map (SOM) was applied to identify spatially homogeneous clusters of GWL piezometers, while GWL time series forecasting was performed through developing a stepwise cluster multisite inference model with various predictors including climate conditions, well extractions, surface runoffs, reservoir operations and GWL measurements at previous steps. The proposed modeling approach was then demonstrated by a case of an arid irrigation district in the western Hexi Corridor, northwest China. Spatial clustering analysis identified 6 regionally representative central piezometers out of 30, for which sensitivity and uncertainty analysis were carried out regarding GWL predictions. As the stepwise cluster tree provided uncertain predictions, we added an AR(1) error model to the mean prediction to forecast GWL 1 month ahead. Model performance indicators suggest that the modeling system is a useful tool to aid decision-making for informed groundwater resource management in arid areas, and would have a great potential to extend its applications to more areas or regions in the future.

Synchronic interval Gaussian mixed-integer programming for air quality management.

To reveal the synchronism of interval uncertainties, the tradeoff between system optimality and security, the discreteness of facility-expansion options, the uncertainty of pollutant dispersion processes, and the seasonality of wind features in air quality management (AQM) systems, a synchronic interval Gaussian mixed-integer programming (SIGMIP) approach is proposed in this study. A robust interval Gaussian dispersion model is developed for approaching the pollutant dispersion process under interval uncertainties and seasonal variations. The reflection of synchronic effects of interval uncertainties in the programming objective is enabled through introducing interval functions. The proposition of constraint violation degrees helps quantify the tradeoff between system optimality and constraint violation under interval uncertainties. The overall optimality of system profits of an SIGMIP model is achieved based on the definition of an integrally optimal solution. Integer variables in the SIGMIP model are resolved by the existing cutting-plane method. Combining these efforts leads to an effective algorithm for the SIGMIP model. An application to an AQM problem in a region in Shandong Province, China, reveals that the proposed SIGMIP model can facilitate identifying the desired scheme for AQM. The enhancement of the robustness of optimization exercises may be helpful for increasing the reliability of suggested schemes for AQM under these complexities. The interrelated tradeoffs among control measures, emission sources, flow processes, receptors, influencing factors, and economic and environmental goals are effectively balanced. Interests of many stakeholders are reasonably coordinated. The harmony between economic development and air quality control is enabled. Results also indicate that the constraint violation degree is effective at reflecting the compromise relationship between constraint-violation risks and system optimality under interval uncertainties. This can help decision makers mitigate potential risks, e.g. insufficiency of pollutant treatment capabilities, exceedance of air quality standards, deficiency of pollution control fund, or imbalance of economic or environmental stress, in the process of guiding AQM.