Synergistic algal/bacterial interaction in membrane bioreactor for detoxification of 1,2-dichloroethane-rich petroleum wastewater

The study addressed the challenge of treating petroleum industry wastewater with high concentrations of 1,2-dichloroethane (1,2-DCA) ranging from 384 to 1654 mg/L, which poses a challenge for bacterial biodegradation and algal photodegradation. To overcome this, a collaborative approach using membrane bioreactors (MBRs) that combine algae and bacteria was employed. This synergistic method effectively mitigated the toxicity of 1,2-DCA and curbed MBR fouling. Two types of MBRs were tested: one (B-MBR) used bacterial cultures and the other (AB-MBR) incorporated a mix of algal and bacterial cultures. The AB-MBR significantly contributed to 1,2-DCA removal, with algae accounting for over 20% and bacteria for approximately 49.5% of the dechlorination process. 1,2-DCA metabolites, including 2-chloroethanol, 2-chloro-acetaldehyde, 2-chloroacetic acid, and acetic acid, were partially consumed as carbon sources by algae. Operational efficiency peaked at a 12-hour hydraulic retention time (HRT) in AB-MBR, enhancing enzyme activities crucial for 1,2-DCA degradation such as dehydrogenase (DH), alcohol dehydrogenase (ADH), and acetaldehyde dehydrogenase (ALDH). The microbial diversity in AB-MBR surpassed that in B-MBR, with a notable increase in Proteobacteria, Bacteroidota, Planctomycetota, and Verrucomicrobiota. Furthermore, AB-MBR showed a significant rise in the dominance of 1,2-DCA-degrading genus such as Pseudomonas and Acinetobacter. Additionally, algal-degrading phyla (e.g., Nematoda, Rotifera, and Streptophyta) were more prevalent in AB-MBR, substantially reducing the issue of membrane fouling.

Quantifying the impact of urban road traffic on air quality: activity pre-pandemic and during partial and full lockdowns

The impact of partial and full COVID lockdowns in 2020 on vehicle miles traveled (VMT) in Kuwait was estimated using data extracted from the Directions API of Google Maps and a Python script running as a cronjob. This approach was validated by comparing the predictions based on the app to measuring traffic flows for 1 week across four road segments considered in this study. VMT during lockdown periods were compared to VMT for the same calendar weeks before the pandemic. NOx emissions were estimated based on VMT and were used to simulate the spatial patterns of NOx concentrations using an air quality model (AERMOD). Compared to pre-pandemic periods, VMT was reduced by up to 25.5% and 42.6% during the 2-week partial and full lockdown episodes, respectively. The largest reduction in the traffic flow rate occurred during the middle of these 2-week periods, when the traffic flow rate decreased by 35% and 49% during the partial and full lockdown periods, respectively. The AERMOD simulation results predicted a reduction in the average maximum concentration of emissions directly related to VMT across the region by up to 38%, with the maximum concentration shifting to less populous residential areas as a result of the lockdown.

Artificial intelligence-enabled optimization of Fe/Zn@biochar photocatalyst for 2,6-dichlorophenol removal from petrochemical wastewater: A techno-economic perspective

While numerous studies have addressed the photocatalytic degradation of 2,6-dichlorophenol (2,6-DCP) in wastewater, an existing research gap pertains to operational factors' optimization by non-linear prediction models to ensure a cost-effective and sustainable process. Herein, we focus on optimizing the photocatalytic degradation of 2,6-DCP using artificial intelligence modeling, aiming at minimizing initial capital outlay and ongoing operational expenses. Hence, Fe/Zn@biochar, a novel material, was synthesized, characterized, and applied to harness the dual capabilities of 2,6-DCP adsorption and degradation. Fe/Zn@biochar exhibited an adsorption energy of -21.858 kJ/mol, effectively capturing the 2,6-DCP molecules. This catalyst accumulated photo-excited electrons, which, upon interaction with adsorbed oxygen and/or dissolved oxygen generated •O2-. The •OH radicals could also be produced from h+ in the Fe/Zn@biochar valence band, cleaving the C-Cl bonds to Cl- ions, dechlorinated byproducts, and phenols. An artificial neural network (ANN) model, with a 4-10-1 topology, "trainlm" training function, and feed-forward back-propagation algorithm, was developed to predict the 2,6-DCP removal efficiency. The ANN prediction accuracy was expressed as R2 = 0.967 and mean squared error = 5.56e-22. The ANN-based optimized condition depicted that over 90% of 2,6-DCP could be eliminated under C0 = 130 mg/L, pH = 2.74, and catalyst dosage = 168 mg/L within ∼4 h. This optimum condition corresponded to a total cost of $7.70/m3, which was cheaper than the price estimated from the unoptimized photocatalytic system by 16%. Hence, the proposed ANN could be employed to enhance the 2,6-DCP photocatalytic degradation process with reduced operational expenses, providing practical and cost-effective solutions for petrochemical wastewater treatment.

Electron donor addition for stimulating the microbial degradation of 1,4 dioxane by sequential batch membrane bioreactor: A techno-economic approach

The presence of 1,4 dioxane in wastewater is associated with severe health and environmental issues. The removal of this toxic contaminant from the industrial effluents prior to final disposal is necessary. The study comprehensively evaluates the performance of sequential batch membrane bioreactor (MBR) for treating wastewater laden with 1,4 dioxane. Acetate was supplemented to the wastewater feed as an electron donor for enhancing and stimulating the microbial growing activities towards the degradation of 1,4 dioxane. The removal efficiency of 1,4 dioxane was maximized to 87.5 ± 6.8% using an acetate to dioxane (A/D) ratio of 4.0, which was substantially dropped to 31.06 ± 3.7% without acetate addition. Ethylene glycol, glyoxylic acid, glycolic acid, and oxalic acid were the main metabolites of 1,4 dioxane biodegradation using mixed culture bacteria. The 1,4 dioxane degrading bacteria, particularly the genus of Acinetobacter, were promoted to 92% at the A/D ratio of 4.0. This condition encouraged as well the increase of the main 1,4 dioxane degraders, i.e., Xanthomonadales (12.5%) and Pseudomonadales (9.1%). However, 50% of the Sphingobacteriales and 82.5% of Planctomycetes were reduced due to the inhibition effect of the 1,4 dioxane contaminate. Similarly, the relative abundance of Firmicutes, Verrucomicrobia, Chlamydiae, Actinobacteria, Chloroflexi, and Nitrospirae was reduced in the MBR at the A/D ratio of 4.0. The results derived from the microbial analysis and metabolites detection at different A/D ratios indicated that acetate supplementation (as an electron donor) maintained an essential role in encouraging the microorganisms to produce the monooxygenase enzymes responsible for the biodegradation process. Economic feasibility of such a MBR system showed that for a designed flow rate of 30 m³∙d^-1, the payback period from reusing the treated wastewater would reach 6.6 yr. The results strongly recommend the utilization of mixed culture bacteria growing on acetate for removing 1,4 dioxane from the wastewater industry, achieving dual environmental and economic benefits.

Sulphonated graphene catalyst incorporation with sludge enhanced the microbial activities for biomethanization of crude rice straw

Biomethanization of crude rice straw (RS) was enhanced by a coupled effectiveness of sulphonated graphene (SGR) with sludge rich anaerobes (SRA). A reduction of 19.2 ± 1.32% for cellulose, 40.8 ± 3.7% for hemicellulose and 30.8 ± 2.4% for lignin was achieved with addition of SRA after fermentation of 60 days. The abundance of hydrolytic microbes in SRA i.e. Acidobacteria, Bacteroidetes, Chloroflexi and Proteobacteria caused RS structure liquefaction and dissolution. The reduction of cellulose, hemicellulose and lignin boosted to 92.3 ± 1.5, 84.9 ± 3.5 and 97.0 ± 1.8% respectively with SGR catalyst addition of 100 mg/gVS. Reducing sugars, phenols and volatile fatty acids (VFAs) were subsequently utilized by bacteria and archaea species of Methanosphaera, Methanocella, Candidatus Methanoregula, Methanolinea and Methanosaeta. The biogas yield was 92 ± 3.1 mL/gRS and methane content amounted to 68 ± 4.6% % at SGR catalyst of 80 mg/gVS. These findings show the potential of using SRA/SGR to improve the RS fermentation with a novel application for biogas productivity.

Membrane based reactors for sustainable treatment of Coronopus didymus L. by developing Iodine doped potassium oxide Catalyst under Dynamic conditions

Green nano-technology together with the availability of eco-friendly and alternative sources are the promising candidates to combat environment deteriorations and energy clutches globally. The current work focuses on the synthesis and application of newly synthesized nano catalyst of Iodine doped Potassium oxide I (K₂O) for producing sustainable biodiesel from novel non-edible seed oils of Coronopus didymus L. using membrane based contactor to avoid emulsification and phase separation issues. Highest biodiesel yield (97.03%) was obtained under optimum conditions of 12:1 methanol to oil ratio, reaction temperature of 65 °C for 150 min with the 1.0 wt% catalyst concentration. The lately synthesized, environment friendly and recyclable Iodine doped Potassium oxide K (IO)₂ catalyst was synthesized via chemical method followed by characterization via advanced techniques including EDX, XRD, FTIR and SEM analysis. The catalyst was proved to be stable and efficient with the reusability of five times in transesterification reaction. These analysis have reported the sustainability, stability and good quality of biodiesel from seed oil of Coronopus didymus L. using efficient Iodine doped potassium oxide catalyst. Thus, non-edible, environment friendly and novel Coronopus didymus L. seeds and their extracted oil along with Iodine doped potassium oxide catalyst seems to be highly affective, sustainable and better alternative source to the future biodiesel industry. Also, by altering the reaction equilibrium and lowering the purification phases of the process, these studies show the potential of coupling transesterification and a membrane contactor.

An integrated framework for exploring the tradeoffs between cost-optimized fuel allocation and regional air quality impacts in a water-energy nexus infrastructure

This paper presents an integrated framework in which an air quality dispersion model is combined with an economic dispatch model to address the environmental tradeoffs of a cost-optimized fuel allocation strategy. A unit commitment dispatch model was developed to re-allocate fuel between power generation and desalination plants. Then, an air quality dispersion model was run for a 1-year period to simulate the spatiotemporal transport of pollutants and the possible formation of air pollution hotspots. The results showed that optimizing fuel allocation can reduce the associated fuel cost by as much as 16.5% of the total cost (1.08 billion USD). The optimized fuel allocation approach resulted in reducing the base case emissions of NOx, SO₂, CO, and PM₁₀ by 25%, 4.6%, 3.1%, and 7.6%, respectively. However, the air quality impact of the optimized fuel allocation scheme was not as favorable. The 1-h-averaged maximum concentration of SO₂ increased, and NOx concentrations were slightly above the allowable limits. Although fewer pollutants were emitted over the study period in the optimized fuel allocation case, the variability in how fuel was allocated between power and desalination plants concentrated emissions near residential areas. As a result of this trend, the maximum 1-h concentrations of all pollutants increased, with increases ranging from 1% for CO to 29% for PM₁₀. In addition, the total number of hourly SO₂ concentration violations increased dramatically, leading to additional hotspot areas. Therefore, the effectiveness of any environmental-economic fuel dispatch strategy should be tested based on additional indicators such as the allowable limits of pollutant concentrations and not exclusively the overall emissions of the system. This approach could promote the selection of the most economic fuel dispatch method while simultaneously considering regional air quality impacts.

Numerical simulation of gas dispersion from rooftop stacks on buildings in urban environments under changes in atmospheric thermal stability

The prediction of dispersion of gases emitted from rooftop stacks in a built environment is important for preventing or minimizing their harmful effects on human health. In this study, the wind flow and dispersion of exhaust gas emitted from rooftop stacks on buildings in an urban environment under different atmospheric thermal stabilities were investigated using numerical simulations. The wind flow field and dispersion contaminants were simulated using a computational fluid dynamics model with the k-ε turbulent schemes being resolved by the Reynolds-averaged Navier-Stokes approach. An isolated building was modeled under conditions of varying thermal stratification of the boundary layers (neutral, unstable, and stable conditions). The diffusion flow field within the building wake zone was investigated for various stack sites (center, right side, and left side). Experiments were conducted in a wind tunnel to validate the numerical simulation results, by using the data qualitatively and quantitatively. The numerical simulation results were consistent with the experimental observations. The results indicated that the pollutant concentration of the plume spread was high near the stack and decreased with increasing distance from the stack. Under stable conditions, the flow motion and separation increased in the wake zone, and the pollutant concentration of the lateral spread at the average human height decreased. Under unstable conditions, the flow of the vortex circulation was fast and strong, and the pollutant concentration of the vertical spread was high.

The impact of power plant emission variability and fuel switching on the air quality of Kuwait

Power plant emissions have a significant impact on air quality, and a frequent assumption made in estimating impacts is to assume annual or monthly average emission rates. This study investigates the impact, on predicted ambient concentrations, of assuming annual average emissions, compared to resolving emissions on an hourly basis (base case). A case study of emissions from power plants in Kuwait, for the year 2014, is presented. In Kuwait, power plants operate on a mix of natural gas, gas oil, crude oil, and heavy fuel oil, and the type of fuel used varies on an hourly basis. Because of this fuel variability, a fuel switching strategy was also simulated in this work, replacing high sulfur fuels with natural gas during hours with high predicted SO₂ concentrations. Emissions estimates were combined with an air quality dispersion model to simulate the temporal variability and spatial dispersion of sulfur dioxide (SO₂) and nitrogen dioxide (NO₂) in Kuwait, for a one-year episode. The results indicate that emission averaging and fuel switching operations result in lower area-wide annual maximum SO₂ concentrations compared to the base case (1747 μg/m³, 1063 μg/m³, 616 μg/m³ for base case, annual average emissions and fuel switching scenarios, respectively). The number of receptor sites recording daily exceedances of the SO₂ standard for annual average emissions were one seventh of those predicted for hourly averaged emissions and 92% lower for the fuel switching scenario. For NO₂, while the overall number of exceedances of air quality criteria was much lower than for SO₂, the numbers of exceedances were also predicted to be lower using annual averaged emissions compared to the base case. These results document the importance of using emission estimates that capture hourly variability over annually averaged emissions, particularly in locations such as Kuwait where multiple fuels are used in power production.

Regional air quality impacts of increased natural gas production and use in Texas

Natural gas use in electricity generation in Texas was estimated, for gas prices ranging from $1.89 to $7.74 per MMBTU, using an optimal power flow model. Hourly estimates of electricity generation, for individual electricity generation units, from the model were used to estimate spatially resolved hourly emissions from electricity generation. Emissions from natural gas production activities in the Barnett Shale region were also estimated, with emissions scaled up or down to match demand in electricity generation as natural gas prices changed. As natural gas use increased, emissions decreased from electricity generation and increased from natural gas production. Overall, NOx and SO2 emissions decreased, while VOC emissions increased as natural gas use increased. To assess the effects of these changes in emissions on ozone and particulate matter concentrations, spatially and temporally resolved emissions were used in a month-long photochemical modeling episode. Over the month-long photochemical modeling episode, decreases in natural gas prices typical of those experienced from 2006 to 2012 led to net regional decreases in ozone (0.2-0.7 ppb) and fine particulate matter (PM) (0.1-0.7 μg/m(3)). Changes in PM were predominantly due to changes in regional PM sulfate formation. Changes in regional PM and ozone formation are primarily due to decreases in emissions from electricity generation. Increases in emissions from increased natural gas production were offset by decreasing emissions from electricity generation for all the scenarios considered.