Optimizing a twin-chamber system for direct ozone production rate measurement

High Ozone Production Rate (OPR) leads to O3 pollution episodes and adverse human health outcomes. OPR observation (Obs-OPR) and OPR modelling (Mod-OPR) have been obtained from observed and modelled peroxy radicals and nitrogen oxides. However, discrepancies between them remind of an imperfect understanding of O3 photochemistry. Direct measurement of OPR (Mea-OPR) by a twin-chamber system emerges. Herein, we optimized Mea-OPR design, i.e., minimizing the chamber surface area to volume ratio (S/V) to 9.8 m-1 from 18 m-1 and the dark uptake coefficient of O3 to 9.9 × 10-9 from 7.1 × 10-8 in the literature. In addition, control experiments further revealed and quantified a photo-enhanced O3 uptake, and therefore recommended an essential correction of Mea-OPR. We finally characterized a measurement uncertainty of ±38% and a detection limit of 3.2 ppbv h-1 (3SD), which suggested that Mea-OPR would be sensitive enough to measure OPR in urban or suburban environments. Further application of this system in urban Beijing during the Beijing 2022 Olympic Winter Games recorded a noontime OPR of 7.3 (±3.3, 1SD) ppbv h-1. These observational results added up to our confidence in future field application of Mea-OPR, to facilitate pollution control policy evaluation and to shed light on O3 photochemistry puzzle.

Microbiome data analysis via machine learning models: Exploring vital players to optimize kitchen waste composting system

Composting, reliant on microorganisms, effectively treats kitchen waste. However, it is difficult to precisely understand the specific role of key microorganisms in the composting process by relying solely on experimental research. This study aims to employ machine learning models to explore key microbial genera and to optimize composting systems. After introducing a novel microbiome preprocessing approach, Stacking models were constructed (R2 is about 0.8). The SHAP method (SHapley Additive exPlanations) identified Bacillus, Acinetobacter, Thermobacillus, Pseudomonas, Psychrobacter, and Thermobifida as prominent microbial genera (Shapley values ranging from 3.84 to 1.24). Additionally, microbial agents were prepared to target the identified key genera, and experiments demonstrated that the composting quality score was 76.06 for the treatment and 70.96 for the control. The exogenous agents enhanced decomposition and improved compost quality in later stages. In summary, this study opens up a new avenue to identifying key microorganisms and optimizing the biological treatment process.

Optimization of integrated anaerobic digestion and pyrolysis for biogas, biochar and bio-oil production from the perspective of energy flow

Valorization of lignocellulosic biomass via anaerobic digestion (AD) is limited by its reluctant structure, leading to a substantial energy remaining in the solid digestate. To mitigate this effect, the integration of AD and pyrolysis has attracted attention in recent years. However, the energy recovery efficiency of this cascading system is still unclear, especially the time node. Herein, a comprehensive evaluation of this integration, using varied AD periods, was conducted, to produce biogas, bio-oil and biochar, and to enhance the energy recovery, from the perspective of energy flow. The result indicated that the accumulative CH₄ yields increased from 33.23 to 249.20 mL/g VS as the AD time increased from 3 to 15 days. Pyrolysis of the obtained solid digestate obtained biochar from 28.81 to 35.96 %, while the bio-oil and pyrolysis gas slowly decreased. The highest energy efficiency of 71.9 % with a net energy gain of 2.0 MJ/kg wet biomass was achieved by the coupled system optimization at an AD time of 12 days as suggested by the energy flow analysis. This study provides new insight for the maximal conversion of biomass waste into energy products and provides a new way of recycling it.

Production of total dissolved gas supersaturation at hydropower facilities and its transport: A review

Total dissolved gas supersaturation (TDG) is a common issue in hydropower facilities as a result of water conveyance structures that increase the amount of air entrainment from the atmosphere and dissolved into the water. Water with TDG supersaturation can negatively impact fish, aquatic invertebrates and their habitats. This study comprehensively reviewed the physical mechanisms of TDG generation and predictive TDG generation models at various facility types. To establish TDG mitigation strategies, it is essential to develop predictive tools for TDG generation that consider both facility geometry as well as the hydrology of the downstream environment. Applications of TDG prediction at different discharge modes included plunging flows, trajectory jets, plunging jets, free-falling jets, and submerged jets were discussed. TDG transport models in downstream rivers involving mixing and dissipation were introduced, which can be integrated with TDG generation models into a platform to describe TDG distribution in river systems. Subsequently, risk ranking procedures for assessing the degree of TDG risk on fish were provided. Potential measures for mitigating TDG supersaturation were reviewed and included engineering, operational, and technical solutions. Outcomes from this review considered a diverse suite of studies on TDG issues in regulated rivers and allowed for recommendations to reduce uncertainties and improve environmental performance at facilities where TDG risks occur.

An interval two-stage fuzzy fractional programming model for planning water resources management in the coastal region - A case study of Shenzhen, China

In this study, an interval two-stage fuzzy fractional programming (TFFP) method is developed to facilitate collaborative governance of economy and water resources. Methods of interval programming, fuzzy programming, two-stage programming, and fractional programming are integrated within a general system optimization framework. The main contribution of TFFP is simultaneously addressing various uncertainties and tackling trade-offs between environmental and economic objectives in the optimized schemes for water resources allocation. A case study of a highly urbanized coastal city (i.e., Shenzhen) in China is provided as an example for demonstrating the proposed approach. According to the results, industrial sectors should receive 34.8% of total water supply, while agricultural sectors should receive 1.5%. For the spatial allocation of water resources, Bao An, Long Gang, and Fu Tian districts should be allocated 21.6%, 20.5%, and 14.8% water to promote the economic development. The discharge analysis indicates that chemical oxygen demand (COD_cr) and total phosphorus (TP) would be key pollutants. Moreover, the optimized seawater desalination volume would be negligibly influenced by price, while the upper bounds of desalination would be increased with the raising acceptable credibility levels in the period of 2031-2035. Analysis of desalination prices also reveals that the decision-makers should increase the scale of desalination in the period of 2021-2025. In addition, the effectiveness and applicability of TFFP would be evaluated under economic maximization scenarios. The result showed that the economic maximization scenario could obtain higher economic benefits, but it would be accompanied by a larger number of pollutant discharges. It is expected that this study will provide solid bases for planning water resources management systems in coastal regions.

An integrated model for medical expense system optimization during diagnosis process based on artificial intelligence algorithm

In the era of artificial intelligence, the healthcare industry is undergoing tremendous innovation and development based on sophisticated AI algorithms. Focusing on diagnosis process and target disease, this study theoretically proposed an integrated model to optimize traditional medical expense system, and ultimately helps medical staff and patients make more reliable decisions. From the new perspective of total expense estimation and detailed expense analysis, the proposed model innovatively consists of two intelligent modules, with theoretical contribution. The two modules are SVM-based module and SOM-based module. According to the rigorous comparative analysis with two classic AI techniques, back propagation neural networks and random forests, it is demonstrated that the SVM-based module achieved better capability of total expense estimation. Meanwhile, by designing a two-stage clustering process, SOM-based module effectively generated decision clusters and corresponding cluster centers were obtained, that clarified the complex relationship between detailed expense and patient information. To achieve practical contribution, the proposed model was applied to the diagnosis process of coronary heart disease. The real data from a hospital in Shanghai was collected, and the validity and accuracy of the proposed model were verified with rigorous experiments. The proposed model innovatively optimized traditional medical expense system, and intelligently generated reliable decision-making information for both total expense and detailed expense. The successful application on the target disease further indicates that this model is a user-friendly tool for medical expense control and therapeutic regimen strategy.

Process optimization for simultaneous antibiotic removal and precious metal recovery in an energy neutral process

Conventional chemical and physical methods to remove antibiotics from wastewater consume large amount of energy and chemicals, and the efficiency of biological process in converting antibiotics is relatively low. Microbial electrolysis cell (MEC) has been employed to degrade recalcitrant organic compounds recently. Given it is an energy consuming device, it would be more sustainable if driven by renewable energy, e.g. power from microbial fuel cell (MFC). Here, chloramphenicol (CAP) was chosen as a representative antibiotic that is abundant in the environment, and Ag ion contained wastewater as electron acceptor in MFC, to demonstrate the feasibility of a self-driven system for recalcitrant removal and resource recovery. It was found that CAP removal in MEC can be successfully driven by Ag(I) reduced MFC without external energy consumption. Method of one-factor-at-a-time (OFAT) and response surface methodology (RSM) with central composite design were used to evaluate the system performance. Under the optimum condition, 99.8% of Ag(I) in MFC and 98.8% of CAP in MEC can be converted. EDX and XPS revealed that pure silver was obtained on the surface of electrode in MFC, reflecting Ag(I) was reduced to valuable product. The concept and methods developed in this study can be also applied to design other types of self-driven BES systems for simultaneous pollutants removal and resources recovery.

Development of a water cycle management approach to Sponge City construction in Xi'an, China

In recent years, climate change, population growth, and inefficient use of water have exacerbated the water resources scarcity problems around the world. Hence, this paper establishes a new approach of Sponge City construction (SCC) based on water cycle management (WCM) for the sustainable exploitation of groundwater, recycled wastewater and rainwater in the Xi'an Siyuan University. The University is located in an isolated area that is far away from the city center so that no centralized water supply system could be utilized. To mitigate water scarcity problems in the University, 39% of the annual rainfall is harvested and stored from impervious surfaces and grasslands by using the Curve Number (CN) method. This stored water is reused for non-potable purposes: 40% for toilet flushing and 60% as miscellaneous water. According to findings, the available rainwater of500-700 m³/d accounts for 16-23% of the non-potable water from April to December. Moreover, the utilization rate of water resources increases from 204% to 227%. With the minimum volume of large-scale rainwater harvesting cistern of 52,760 m³, the environment could be adequately watered while improving the expansion and development conditions on the campus. Furthermore, water scarcity problems could be mitigated through optimization of the water resources utilization system. This study demonstrates that this new approach of SCC based on WCM could alleviate water resources scarcity problems in Xi'an Siyuan University effectively. It is hoped that this study will provide a model and example of the new approach for future applications.

Theoretical framework for designing a desalination plant based on membrane capacitive deionization

Despite significant progress made in multiple aspects of capacitive deionization (CDI), a rational framework is in need for optimizing the design and operation of a large desalination system based on CDI. In this work, we develop a theoretical framework for guiding the design of a desalination plant based on CDI with ion exchange membranes (i.e. membrane CDI, or MCDI). This framework is established by identifying (1) the practical design constraints, (2) the inter-relationships between different design and operating parameters, (3) a set of independent variables, and (4) the key performance metrics. The proposed design framework reduces the degrees of freedom of the system and facilitates more focused and systematic analysis of the overall performance of an MCDI-based desalination plant. Careful analysis using the proposed design framework suggests the presence of an optimal tradeoff curve that comprises all the possible optima of design and operating conditions with which an MCDI-based desalination plant is the most cost-effective. We also show that the typical practice of using equal flowrates for charging and discharge yields very good performance compared to the optima, as long as water recovery is not too high. Finally, we also briefly explain the implication of this framework on cost-based optimization of the design and operation of an MCDI-based desalination plant.

Decision support toolkit for integrated analysis and design of reclaimed water infrastructure

Planning of water reuse systems is a complex endeavor. We have developed a software toolkit, IRIPT (Integrated Urban Reclaimed Water Infrastructure Planning Toolkit) that facilitates planning and design of reclaimed water infrastructure for both centralized and hybrid configurations that incorporate satellite treatment plants (STPs). The toolkit includes a Pipeline Designer (PRODOT) that optimizes routing and sizing of pipelines for wastewater capture and reclaimed water distribution, a Selector (SelWTP) that assembles and optimizes wastewater treatment trains, and a Calculator (CalcBenefit) that estimates fees, revenues, and subsidies of alternative designs. For hybrid configurations, a Locator (LocSTP) optimizes siting of STPs and associated wastewater diversions by identifying manhole locations where the flowrates are sufficient to ensure that wastewater extracted and treated at an adjacent STP can generate the revenue needed to pay for treatment and delivery to customers. Practical local constraints are also applied to screen and identify STP locations. Once suitable sites are selected, System Integrator (ToolIntegrator) identifies a set of centralized and hybrid configurations that: (1) maximize reclaimed water supply, (2) maximize reclaimed water supply while also ensuring a financial benefit for the system, and (3) maximize the net financial benefit for the system. The resulting configurations are then evaluated by an Analyst (SANNA) that uses monetary and non-monetary criteria, with weights assigned to appropriate metrics by a decision-maker, to identify a preferred configuration. To illustrate the structure, assumptions, and use of IRIPT, we apply it to a case study for the city of Golden, CO. The criteria weightings provided by a local decision-maker lead to a preference for a centralized configuration in this case. The Golden case study demonstrates that IRIPT can efficiently analyze centralized and hybrid water reuse configurations and rank them according to decision-makers' preferences.

Phase sensitivity evaluation and its application to phase shifting interferometry

In quantitative phase imaging, sensitivity is a key measure of system reproducibility. Despite continuous experimental breakthroughs in achieving highly sensitive detection, in-depth studies of theoretical constraints on sensitivity are inadequate and comparisons between different techniques are difficult. In this paper, we introduce the method to evaluate the sensitivity of phase shifting interferometry which is a major category of quantitative phase imaging techniques. The method discusses in detail several key concepts of sensitivity evaluation, including a general three-level evaluation framework, a complete interference signal model, Cramér-Rao bound and algorithm sensitivity, algorithm and system efficiencies, as well as energy efficiency of an algorithm. In discussions of specific phase shifting algorithms, we focus on the shot noise-limited model. This simplified model not only reflects the rapid developments in modern detectors that are often dominated by shot noise, but also permits the calculation of theoretical sensitivities based on measured data, which is important in evaluating experimental performance. As examples, we study several common phase shifting interferometric techniques. The results of different techniques are compared to provide insights into algorithm optimization and energy efficiency of sensitivity. A normalized algorithm sensitivity table is also provided for readers to conveniently estimate their system's algorithm sensitivity and compare against experiments.

Intrinsic tradeoff between kinetic and energetic efficiencies in membrane capacitive deionization

Significant progress has been made over recent years in capacitive deionization (CDI) to develop novel system configurations, predictive theoretical models, and high-performance electrode materials. To bring CDI to large scale practical applications, it is important to quantitatively understand the intrinsic tradeoff between kinetic and energetic efficiencies, or the relationship between energy consumption and the mass transfer rate. In this study, we employed both experimental and modeling approaches to systematically investigate the tradeoff between kinetic and energetic efficiencies in membrane CDI (MCDI). Specifically, we assessed the relationship between the average salt adsorption rate and specific energy consumptions from MCDI experiments with different applied current densities but a constant effluent salinity. We investigated the impacts of feed salinity, diluted water salinity, diluted water volume per charging cycle, and electrode materials on the kinetics-energetics tradeoff. We also demonstrate how this tradeoff can be employed to optimize the design and operation of CDI systems and compare the performance of different electrode materials and CDI systems.

Attempts to improve nitrogen utilization efficiency of aquaponics through nitrifies addition and filler gradation

Aquaponics has attracted worldwide attention in recent years and is considered as an alternative technology for conventional aquaculture. In this study, common carp (Cyprinus carpio) and pakchoi (Brassica chinensis) were cultured in lab-scale aquaponics, and attempts were conducted to enhance its nitrogen utilization efficiency (NUE) through two optimization methods, i.e., nitrifies addition (NA) and filler gradation (FG). Results showed that NA and FG could improve the NUE of aquaponics by 8.8 and 16.0%, respectively, compared with control. The total ammonia (TAN) and nitrite (NO2(-)) concentrations in NA and FG systems were maintained at relatively low level (TAN < 0.5 mg/L, NO2(-) < 0.1 mg/L), which demonstrated that both the NA and FG could provide non-toxic water environment for fish culture. Nitrous oxide conversion ratio of the control, NA, and FG were 0.8, 1.2, and 1.7%, respectively, indicating that media-based aquaponics also contributed to global warming. Although the two proposed attempts in this study caused more N2O emission, they made new breakthrough in improving the NUE of aquaponics.