Modeling marine oily wastewater treatment by a probabilistic agent-based approach

Preparation of Biochar Composite Microspheres and Their Ability for Removal with Oil Agents in Dyed Wastewater

Oil agents produced from the degreasing treatment of synthetic fibers are typical pollutants in wastewater from printing and dyeing, which may cause large-scale environmental pollution without proper treatment. Purifying oily dye wastewater (DTY) at a low cost is a key problem at present. In this study, biochar microspheres with oil removal ability were prepared and derived from waste bamboo chips using the hydrothermal method. The structure of the biochar microsphere was regulated by activation and modification processes. Biochar microspheres were characterized, and their adsorption behaviors for oily dye wastewater were explored. The results show that the adsorption efficiency of biochar microspheres for oily dye wastewater (DTY) was improved significantly after secondary pyrolysis and the lauric acid grafting reaction. The maximum COD removal quantity of biochar microspheres for DTY was 889 mg/g with a removal rate of 86.06% in 30 min. In addition, the kinetics showed that chemisorption was the main adsorption manner. Considering the low cost of raw materials, the application of biochar microspheres could decrease the cost of oily wastewater treatment and avoid environmental pollution.

Comparison of O3, UV/O3, and UV/O3/PS processes for marine oily wastewater treatment: Degradation performance, toxicity evaluation, and flocs analysis

Efficient on-site treatment technology is crucial for mitigating marine oily wastewater pollution. This work investigates the ozone (O₃), ultraviolet (UV)/O₃, UV/O₃/persulfate (PS) processes for the treatment of marine oily wastewater, including degradation performance, acute toxicity evaluation, and oil flocs analysis in a benchtop circulating flow photoozonation reactor. Degradation performances have been studied by measuring the degradation rate of total oil concentrations, specific oil components (n-alkanes and polycyclic aromatic hydrocarbons (PAHs)), and total organic carbon (TOC). The results show that UV/O₃/PS could significantly enhance the removal efficiency than the other two processes, with above 90% of removal efficiency in 30 min. Acute toxicity analysis further shows that the wastewater quality is significantly improved by four-fold of the EC₅₀ of Vibrio fischeri, and the mortality of Artemia franciscana decreases from 100% to 0% after 48 h exposure. Further, the morphology and functional groups of flocs have been further characterized, showing that the floating flocs could be further degraded especially in UV/O₃/PS process. Our study further raised discussions regarding the future on-site application of O₃-based systems, based on the results generated from the treatment efficiency, toxicity, and flocs characterization. The regulation of the oxidation strength and optimization of the reaction systems could be a practical strategy for on-site marine oily wastewater treatment.

Machine Learning-Aided Causal Inference Framework for Environmental Data Analysis: A COVID-19 Case Study

Links between environmental conditions (e.g., meteorological factors and air quality) and COVID-19 severity have been reported worldwide. However, the existing frameworks of data analysis are insufficient or inefficient to investigate the potential causality behind the associations involving multidimensional factors and complicated interrelationships. Thus, a causal inference framework equipped with the structural causal model aided by machine learning methods was proposed and applied to examine the potential causal relationships between COVID-19 severity and 10 environmental factors (NO₂, O₃, PM2.5, PM10, SO₂, CO, average air temperature, atmospheric pressure, relative humidity, and wind speed) in 166 Chinese cities. The cities were grouped into three clusters based on the socio-economic features. Time-series data from these cities in each cluster were analyzed in different pandemic phases. The robustness check refuted most potential causal relationships' estimations (89 out of 90). Only one potential relationship about air temperature passed the final test with a causal effect of 0.041 under a specific cluster-phase condition. The results indicate that the environmental factors are unlikely to cause noticeable aggravation of the COVID-19 pandemic. This study also demonstrated the high value and potential of the proposed method in investigating causal problems with observational data in environmental or other fields.

A model for the estimation of the mixing zone behind large sea vessels

One of the important tasks associated with reducing the concentration of contaminants in the sea surface layer is the determination of their mixing volume, as of the most active systems for mixing the sea surface layer are sea vessels. The wake of a ship is a highly mixed medium. The study of the wake development over time is important when evaluating the mixing of various pollutants in the wake with neutralizing chemicals. As shown in some previous works, in the wake of a vessel that crosses a contaminated surface, the concentration of harmful impurities decreases to background values; however, the problem of determining the volumetric characteristics of this wake remains. In our work, we propose a relatively simple model for assessing the characteristics of a turbulent wake in the near zone behind a vessel. Based on the actual parameters of the vessels, the parameter F= (penetration depth) / (draft) was calculated, which characterizes the potential mixing effects caused by turbulence in the wake. The proposed simple model can be used, for example, to assess the mixing of oil when it is being bottled, with chemicals, to assess possible scenarios of increasing its dilution.

Minimizing membrane bioreactor environmental footprint by multiple objective optimization

This paper presents a modelling study aimed at minimizing the environmental foot print of a membrane bioreactor (MBR) for wastewater treatment. Specifically, an integrated model for MBR was employed in view of the management optimization of an MBR biological nutrient removal (BNR) pilot plant in terms of operational costs and direct greenhouse gases emissions. The influence of the operational parameters (OPs) on performance indicators (PIs) was investigated by adopting the Extended-FAST sensitivity analysis method. Further, a multi-objective analysis was performed by applying the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results show-up that the sludge retention time is the OP mostly affecting all the investigated PIs. By applying the set of optimal OPs, there was a reduction of 48% and 10% of the operational costs and direct emissions, respectively.

Decision support systems (DSS) for wastewater treatment plants - A review of the state of the art

The use of decision support systems (DSS) allows integrating all the issues related with sustainable development in view of providing a useful support to solve multi-scenario problems. In this work an extensive review on the DSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide an updated compendium on DSSs in view of supporting researchers and engineers on the selection of the most suitable method to address their management/operation/design problems. Results showed that DSSs were mostly used as a comprehensive tool that is capable of integrating several data and a multi-criteria perspective in order to provide more reliable results. Only one energy-focused DSS was found in literature, while DSSs based on quality and operational issues are very often applied to site-specific conditions. Finally, it would be important to encourage the development of more user-friendly DSSs to increase general interest and usability.