Novel strategy for treating high salinity oilfield produced water: Pyrite-activated peroxymonosulfate coupled with heterotrophic ammonia assimilation

Degradation of conjugated estrogen in visible light-driven intimately coupled photocatalysis and biodegradation system

Visible light-driven intimately coupled photocatalysis and biodegradation (VDICPB) is an efficient technology for removing recalcitrant contaminants, but the degradation pathway on 17β-estradiol 3-Sulfate (E2-3S) is still not clear. In this study, VDICPB based on N-doped TiO2 as a photocatalyst was established to investigate the removal and transformation of E2-3S in synthetic wastewater. VDICPB showed a satisfactory removal efficiency of 97.8 ± 0.4 %, which was much higher than that of independent photocatalysis (84.0 ± 2.2 %) or biodegradation system (71.4 ± 1.8 %). Steroid C/D-rings of E2-3S was broken in VDICPB since the transformation process reached terminal central pathway. Primary metabolites did not accumulate in VDICPB, resulting in a low expression of functional genes. E2-3S was mainly removed by cooperative interaction of photocatalysis and co-metabolism of biofilm. Photocatalysis led to deconjugation and microbes acted to mineralization. This study provides technical reference and theoretical support for the removal of new pollutants.

Recovery mechanism of heterotrophic ammonia assimilation system under chromium hexavalent stress

Heterotrophic ammonia assimilation (HAA), an innovative technology for high-salinity wastewater treatment, demonstrates self-recovery capability following Cr (VI) stress. This study investigated the inhibitory effects and self-restoration mechanisms of Cr (VI) at various stress levels. The removal efficiencies of NH4+-N and Cr (VI) in the HAA gradually decreased with increasing influent Cr (VI) concentration. Exposure to Cr (VI) increased the amounts of high-molecular-weight proteins in soluble microbial products and stimulated the generation of extracellular polymeric substances. Heterotrophic functional microorganisms with Cr (VI) tolerance, such as Marinobacter and Planktosalinus, were enriched. An assimilation pathway gene (glnA) and a Cr (VI)-related gene (atoB) were also upregulated. After ceasing Cr (VI) addition, the HAA system demonstrated a 17.1 % increase in the removal efficiency of NH4+-N, which was attributable to its self-recovery ability. This study provides a scientific and theoretical foundation for the HAA process in resisting the impact of heavy-metal-containing wastewater and self-recovery.

Nutrients recovery by coupled bioreactor of heterotrophic ammonia assimilation and microbial fuel cell in saline wastewater

Heterotrophic ammonia assimilation (HAA) process had been widely used in the treatment of high salt wastewater, but the electro enhanced coupling process and electron transfer process were rarely studied. In this study, a HAA process coupled microbial fuel cell (MFC) system was established to treat ammonia-containing wastewater under increasing salinity to achieve nitrogen recovery and electricity generation. Up to 95.4 % NH4+-N and 96.4 % COD removal efficiencies were achieved at 2 % salinity in HAA-MFC. The maximum power density and current density at 2 % salinity were 29.93 mW/m2 and 182.37 mA/m2, respectively. The residual organic matter in the cathode effluent was effectively removed by the anode. The increase of salinity not only enhanced the sludge settling performance and activity, but also promoted the enzyme activity and amino acid production of the ammonia assimilation pathway. Marinobacter and Halomonas were gradually enriched at the anode and cathode with increased salinity to promote ammonia assimilation and electron production. This research offered a promising solution to overcome salinity-related challenges in wastewater treatment and resource recovery.

Utilizing machine learning for reactive material selection and width design in permeable reactive barrier (PRB)

Permeable reactive barrier (PRB) is an important groundwater treatment technology. However, selecting the optimal reactive material and estimating the width remain critical and challenging problems in PRB design. Machine learning (ML) has advantages in predicting evolution and tracing contaminants in temporal and spatial distribution. In this study, ML was developed to design PRB, and its feasibility was validated through experiments and a case study. ML algorithm showed a good prediction about the Freundlich equilibrium parameter (R2 0.94 for KF, R2 0.96 for n). After SHapley Additive exPlanation (SHAP) analysis, redefining the range of the significant impact factors (initial concentration and pH) can further improve the prediction accuracy (R2 0.99 for KF, R2 0.99 for n). To mitigate model bias and ensure comprehensiveness, evaluation index with expert opinions was used to determine the optimal material from candidate materials. Meanwhile, the ML algorithm was also applied to predict the width of the mass transport zone in the adsorption column. This procedure showed excellent accuracy with R2 and root-mean-square-error (RMSE) of 0.98 and 1.2, respectively. Compared with the traditional width design methodology, ML can enhance design efficiency and save experiment time. The novel approach is based on traditional design principles, and the limitations and challenges are highlighted. After further expanding the data set and optimizing the algorithm, the accuracy of ML can make up for the existing limitations and obtain wider applications.