Determination of seawater COD spectra using double-loop contraction and sorted frog optimization.

This study develops a novel double-loop contraction and C value sorting selection-based shrinkage frog-leaping algorithm (double-contractive cognitive random field [DC-CRF]) to mitigate the interference of complex salts and ions in seawater on the ultraviolet-visible (UV-Vis) absorbance spectra for chemical oxygen demand (COD) quantification. The key innovations of DC-CRF are introducing variable importance evaluation via C value to guide wavelength selection and accelerate convergence; a double-loop structure integrating random frog (RF) leaping and contraction attenuation to dynamically balance convergence speed and efficiency. Utilizing seawater samples from Jiaozhou Bay, DC-CRF-partial least squares regression (PLSR) reduced the input variables by 97.5% after 1,600 iterations relative to full-spectrum PLSR, RF-PLSR, and CRF-PLSR. It achieved a test R2 of 0.943 and root mean square error of 1.603, markedly improving prediction accuracy and efficiency. This work demonstrates the efficacy of DC-CRF-PLSR in enhancing UV-Vis spectroscopy for rapid COD analysis in intricate seawater matrices, providing an efficient solution for optimizing seawater spectra.

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm.

Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Demand (COD) modeling. To address the first problem and obtain optimal modeling range, the spectra are preprocessed using six methods including Standard Normal Variate, Savitzky-Golay Smoothing Filtering (SG) etc. Subsequently, the 190-350 nm spectral range is divided into 10 subintervals, and Interval Partial Least Squares (IPLS) is used to perform PLS modeling on each interval. The results indicate that it is best modeled in the 7th range (238~253 nm). The values of Mean Square Error (MSE), Mean Absolute Error (MAE) and R2score of the model without pretreatment are 1.6489, 1.0661, and 0.9942. After pretreatment, the SG is better than others, with MSE and MAE decreasing to 1.4727, 1.0318 and R2score improving to 0.9944. Using the optimal model, the predicted COD for three samples are 10.87 mg/L, 14.88 mg/L, and 19.29 mg/L. To address the problem of the small dataset, using Generative Adversarial Networks for data augmentation, three datasets are obtained for Support Vector Machine (SVM) modeling. The results indicate that, compared to the original dataset, the SVM's MSE and MAE have decreased, while its accuracy has improved by 2.88%, 11.53%, and 11.53%, and the R2score has improved by 18.07%, 17.40%, and 18.74%.

Profiling wastewater characteristics in intra-urban catchments using online monitoring stations.

This study aims to investigate the differences in intra-urban catchments with different characteristics through real-time wastewater monitoring. Monitoring stations were installed in three neighbourhoods of Barcelona to measure flow, total chemical oxygen demand (COD), pH, conductivity, temperature, and bisulfide (HS-) for 1 year. Typical wastewater profiles were obtained for weekdays, weekends, and holidays in the summer and winter seasons. The results reveal differences in waking up times and evening routines, commuting behaviour during weekends and holidays, and water consumption. The pollutant profiles contribute to a better understanding of pollution generation in households and catchment activities. Flows and COD correlate well at all stations, but there are differences in conductivity and HS- at the station level. The article concludes by discussing the operational experience of the monitoring stations.

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants.

Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants' operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

Monitoring of university wastewater within the sewage system and its performance evaluation through integrated constructed wetlands.

Rapid increase in population and industrialization has not only improved the lifestyle but adversely affected the quality and availability of water leading to ample amount of wastewater generation. The major contribution towards wastewater production is from sewage. Regular monitoring and treatment of sewage water is necessary to conserve and enhance the quality of water. The present study focuses on monitoring of sewage water within the sewage system of a residential university. A total of 16 samples from different manholes were collected for physicochemical and heavy metals analysis and compared with final effluent collected from integrated constructed wetlands (ICWs) to assess its removal efficiency. The mean concentrations of influent and effluent were compared with national environmental quality standards (NEQS) for municipal discharge (pH 6-9, COD 150 mg/L, TSS 200 mg/L and TDS 3500 mg/L) and international agricultural reuse standards (IARS) (pH 6-8, COD <150 mg/L, TSS < 100 mg/L) respectively. Among all physicochemical parameters, influent values for chemical oxygen demand (COD) (169.56-258.36) mg/L exceeded the limit of NEQS for discharge into inland waters, whereas for total suspended solids (TSS) the concentration exceeded for discharge into STP (406 mg/L) and inland waters (202.33 mg/L). However, effluent concentrations for all the parameters were found within the permissible limit set by IARS. The removal efficiency for different parameters such as phosphate- phosphorus (PO43-P), COD, TSS, total dissolved solids (TDS) and total kjeldahl nitrogen (TKN) were 52, 53, 54, 35, and 36%, respectively. Heavy metal concentrations were compared with WHO guidelines among which lead (Pb) in effluent and chromium (Cr) in influent exceeded the limit (Pb 0.01 and Cr 0.05 mg/L). Interpolation results showed that zone 2 was highly contaminated in comparison to zone 1 & 3. Statistical analysis showed that correlation of physicochemical parameters and heavy metals was found significant (p < 0.05).

Comparison of treatment performance and microbial community evolution of typical dye wastewater by different combined processes.

The degradation of typical dye wastewater is a focus of research in the printing and dyeing industry. In this study, a combined micro-electrolysis and microbial treatment method was established to treat refractory dye wastewater, and the pivotal factors in the microbial treatment were optimized. In the series and coupled modes, the removal rates of chroma reached 98.75% and 92.50%, and the removal rates of chemical oxygen demand (COD) reached 96.17% and 82.29%, respectively. The high-throughput sequencing results showed that the microbial communities in the microbial system varied at different treatment stages. From the culture stage to the domestication stage, the dominant phylum was Proteobacteria; however, the community abundance of microorganisms decreased. A combination of micro-electrolysis and biological methods can alter the characteristics of the microbial community, increase the number of dominant phyla, and increase the abundance of microorganisms. The degradation effect of the series mode and the overall strengthening effect of micro-electrolysis on the microorganisms were better than those of the coupled mode. In actual wastewater, the maximum removal rates of chroma, COD, total nitrogen (TN), ammonia nitrogen (NH3-N), and total phosphorus (TP) are 97.50%, 98.90%, 94.35%, 93.95%, and 91.17%, respectively. Three-dimensional fluorescence spectrum analysis showed that microbial processes could significantly degrade fluorescent components in wastewater, and methanogenic active enzymes in anaerobic processes could continue to react. The combined process can realize the efficient treatment of toxic dye wastewater by reducing the toxicity of wastewater and efficiently degrading organic matter, which has important guiding significance for the treatment of refractory dye wastewater.

Effects of chemical oxygen demand and chloramphenicol on attached microalgae growth: Physicochemical properties and microscopic mass transfer in biofilm.

During the wastewater treatment and resource recovery process by attached microalgae, the chemical oxygen demand (COD) can cause biotic contamination in algal culture systems, which can be mitigated by adding an appropriate dosage of antibiotics. The transport of COD and additive antibiotic (chloramphenicol, CAP) in algal biofilms and their influence on algal physiology were studied. The results showed that COD (60 mg/L) affected key metabolic pathways, such as photosystem II and oxidative phosphorylation, improved biofilm autotrophic and heterotrophic metabolic intensities, increased nutrient demand, and promoted biomass accumulation by 55.9 %, which was the most suitable COD concentration for attached microalgae. CAP (5-10 mg/L) effectively stimulated photosynthetic pigment accumulation and nutrient utilization in pelagic microalgal cells. In conclusion, controlling the COD concentration (approximately 60 mg/L) in the medium and adding the appropriate CAP concentration (5-10 mg/L) are conducive to improving attached microalgal biomass production and resource recovery potential from wastewater.

Performance evaluation and chemical oxygen demand removal of tannery wastewater through the aerobic-anaerobic route.

With characterized for complex and maximum substance (suspended solids, broke up oil, a mixture of inorganic and chromium sulfides), tannery wastewater was subjected to a treatment process on removal of chemical oxygen demand (COD) via upstream anaerobic sludge blanket reactor where we found reduced departure efficiencies and that process limits were affected by the assortments in regular stacking rates, closeness of chromium, and sulfides. Hence, a combination of the aerobic-anaerobic hybrid reactor was set up for sequential treatment to determine possible COD reduction. This study investigated the biological degradation of tannery wastewater in a laboratory-scale sequential up-flow aerobic-anaerobic reactor. The aerobic zone at the top was packed with spherical ball-shaped polyhedral polypropylene, and the anaerobic zone at the bottom was packed medium with granular media. The aeration flow rate varied by 2 L/min, 4 L/min, and 6 L/min in the aerobic zone, and the reactor maintained an organic loading rate (OLR) of 5 kg COD/m3/d. Parameters like COD and gas yield assess the performance of the reactor. The maximum COD of 86% is removed in the anaerobic zone with an aeration rate of 6 L/min, and the 1800-mL methane gas yield is measured by the 29th day.

The performance of pharmaceutical wastewater treatment system of electrocoagulation assisted adsorption using perforated electrodes to reduce passivation.

The integrated electrocoagulation-assisted adsorption (ECA) system with a solar photovoltaic power supply has gained more attention as an effective approach for reduction chemical oxygen demand (COD) from pharmaceutical wastewater (PhWW). In this research, the ECA system was used for the treatment of PhWW. Several operating parameters were investigated, including electrode number, configuration, distance, operating time, current density, adsorption time, and temperature. A current density of 6.656 mA/cm2, six electrodes, a 20-min time, a 4 cm distance, an MP-P configuration, and a 45 °C temperature produced the maximum COD reductions, where the operating cost of conventional energy was 0.273 $/m3. The EC, adsorption, and combination of EC and adsorption processes achieved efficient COD reductions of 85.4, 69.1, and 95.5%, respectively. The pseudo-second-order kinetic model and the Freundlich isotherm fit the data of the endothermic adsorption process. Therefore, it was found that the combination processes were superior to the use of these processes in isolation to remove COD.

Investigation of biochemical, enzymatic, and metagenomic profiles of garbage enzymes and its application in dumping site leachate treatment.

The current article focuses on the preparation and characterization of garbage enzyme (GE) and explores its applications in treating leachate. GE is prepared from fruit and vegetable wastes and characterized via analysis of metabolites, carbohydrates, proteins, antioxidants, and enzymatic activities. This study extends our understanding of GE by reporting the presence of various metabolites. Moreover, a metagenomic analysis of GE is presented, shedding light on the microbial diversity. Firmicutes emerged as the dominant phylum, surpassing other phyla, including Proteobacteria and Actinobacteria. When exploring the potential for leachate treatment, the results indicate that vegetable GE shows 68% reduction in COD (chemical oxygen demand) and 39% reduction in ammoniacal nitrogen. Similarly, non-citrus GE also showed 64% reduction in COD and a 37% reduction in ammoniacal nitrogen, followed by citrus GE with a 33% reduction in COD and a 34% reduction in ammoniacal nitrogen compared to the control.

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning.

The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment.

Dissolved organic material changes during combined treatment of a mixture of landfill leachate and anaerobic digestate using deammonification and chemical coagulation.

The current study investigates the combined treatment of wastewater of anaerobic digestate and landfill leachate, using deammonification and coagulation/flocculation processes. The deammonification section studies the performance of a full-scale deammonification plant in nitrogen and chemical oxygen demand (COD) removal, monitored over 2 years. For further COD reduction from the deammonification effluent (DE) to meet the environmental regulatory standards, coagulation/flocculation using three different Al-based coagulants was used to treat the DE. Results revealed that the deammonification plant showed 80% average ammonium removal from the mixed feed over the study period. Additionally, 30% of the feed COD was removed in the deammonification plant. COD analysis after treatment using coagulants revealed that the polyaluminum chloride modified with Fe had the best performance in reducing COD to meet the environmental standards. Excitation-emission matrix-parallel factor analysis (EEM-PARAFAC) of the dissolved organic material (DOM) samples indicated that fluorescents were the compounds mostly affected by the coagulant types. DOM analysis using 2D correlation Fourier-transform infrared spectroscopy revealed that the applied coagulants showed minor differences in removing different functional groups, despite having different COD reduction performance. Wastewater elemental analysis indicated elevated metal concentrations in low pH conditions (<6) due to re-stabilization of the flocs using coagulants.

Effect of tourist activity on wastewater quality in selected wastewater treatment plants in the Balearic Islands (Spain).

Unregulated sewage discharge into the sea poses a considerable danger to marine ecosystems, with coastal regions being particularly vulnerable to this because of the impact of tourism. This issue is amplified during the summer season, as the Balearic Islands are a heavily frequented destination. This study aims to determine the water quality in five different wastewater treatment plants (WWTPs) representative on the islands. For this purpose, we analysed several parameters, including biochemical oxygen demand (BOD), chemical oxygen demand (COD), treated water flow, suspended solids (SS), nitrates (N) and phosphorus (P), at the inlet and outlet of the WWTPs for 5 years. We set particular thresholds for each parameter and documented any breach by comparing the findings with the existing regulations. The least favourable results indicate non-compliance regarding N and P levels throughout the entire study period, as well as a lack of reduction percentage. Furthermore, flow analysis reflects the significant influence of tourism on water quality, with notable increases in both population and treated water volume during the peak tourist season. Overall, the investigation offers a robust foundation for comprehending water quality in relation to coastal landscape in the Balearic Islands. It pinpoints significant worry spots and underscores tourism's immediate impact on this ecological feature.

Resilience of aerobic sludge biomass under chlorpyrifos stress and its recovery potential.

Non-agricultural sources of pesticides in urban areas are responsible for their presence in domestic wastewater. Therefore, pesticides are typically found in sewage treatment plants in developed and developing countries as micro-pollutant. The presence of pesticides in the wastewater can impart stress on the aerobic sludge biomass and disrupt the functioning of the plant. However, there exists a knowledge gap regarding the resilience of aerobic sludge biomass towards stress due to the presence of pesticides in the wastewater. This study investigated the impact of chlorpyrifos (CPS) - a widely used pesticide, on sludge biomass and explored its recovery capability when CPS is discontinued in the influent. Four duplicate reactors were operated with different CPS concentrations ranging from 50 to 200 mg/L. Chemical oxygen demand (COD) removal for reactors has ranged within 18-73 % at the steady state of the stressed phase, whereas COD removal for the control reactor was 91 %. CPS stress slightly inhibited filamentous biomass growth. Biomass activity and cell viability have decreased significantly, whereas biochemical contents have varied slightly under CPS stress. The activities of the enzymes dehydrogenase and urease were significantly inhibited when compared to catalase and protease. Amplified ribosomal DNA restriction analysis reflected changes in the microbial community. The discontinuation of CPS has allowed aerobic sludge biomass to recover in its organic degradation capability (COD removal of more than 88 % at steady-state conditions of recovery phase operation), biomass growth, and cell viability. In addition, enzyme activities have retrieved to their original levels, and 78-93 % similarity of microbial community structure has been displayed between CPS-exposed and control reactor biomasses. Overall, the present study has indicated the orderly changes in the quality of aerobic sludge biomass under CPS stress through physico-chemical and biological characteristics. The study also has highlighted the self-recovery of sludge biomass characteristics stressed with different concentrations of CPS.

Optimization of Simultaneous Nutrients and Chemical Oxygen Demand Removal from Anaerobically Digested Liquid Dairy Manure in a Two-Step Fed Sequencing Batch Reactor System Using Taguchi Method and Grey Relational Analysis.

The technological development for efficient nutrient removal from liquid dairy manure is critical to a sustainable dairy industry. A nutrient removal process using a two-step fed sequencing batch reactor (SBR) system was developed in this study to achieve the applicability of simultaneous removal of phosphorus, nitrogen, and chemical oxygen demand from anaerobically digested liquid dairy manure (ADLDM). Three operating parameters, namely anaerobic time:aerobic time (min), anaerobic DO:aerobic DO (mg L-1), and hydraulic retention time (days), were systematically investigated and optimized using the Taguchi method and grey relational analysis for maximum removal efficiencies of total phosphorus (TP), ortho-phosphate (OP), ammonia-nitrogen (NH3-N), total nitrogen (TN), and chemical oxygen demand (COD) simultaneously. The results demonstrated that the optimal mean removal efficiencies of 91.21%, 92.63%, 91.82%, 88.61%, and 90.21% were achieved for TP, OP, NH3-N, TN, and COD at operating conditions, i.e., anaerobic:aerobic time of 90:90 min, anaerobic DO:aerobic DO of 0.4:2.4 mg L-1, and HRT of 3 days. Based on analysis of variance, the percentage contributions of these operating parameters towards the mean removal efficiencies of TP and COD were ranked in the order of anaerobic DO:aerobic DO > HRT > anaerobic time:aerobic time, while HRT was the most influential parameter for the mean removal efficiencies of OP, NH3-N, and TN followed by anaerobic time:aerobic time and anaerobic DO:aerobic DO. The optimal conditions obtained in this study are beneficial to the development of pilot and full-scale systems for simultaneous biological removal of phosphorus, nitrogen, and COD from ADLDM.

A pilot-scale electrocoagulation-treatment wetland system for the treatment of landfill leachate.

In the present study, the technical feasibility of an electrocoagulation-treatment wetland continuous flow system, for the removal of organic matter from landfill leachate (LL), was evaluated. The response surface methodology (MSR) was used to assess the individual and combined effects of the applied potential and distance between electrodes, on the removal efficiency and optimization of the electrocoagulation process. The hybrid treatment wetland system consisted of a vertical flow system coupled to a horizontal subsurface flow system, both planted with Canna indica. For a chemical oxygen demand (COD) concentration - without pretreatment of 5142.8 ± 2.5 mg L-1, the removal percentage for the electrocoagulation system was 79.4 ± 0.16%, under the optimal working conditions (Potential: 20 V; Distance: 2.0 cm). The COD removal efficiency in the treatment wetland with Canna indica showed a dependence with the hydraulic retention time, reaching 59.2 ± 0.2 % over 15 days. The overall efficiency of the system was about 91.5 ± 0.02 % removal of COD. In addition, a decrease in the biochemical oxygen demand (94.8 ± 0.14%) and total suspended solids (88.2 ± 0.22%), also related to the contamination levels of the LL, were obtained. This study, for the first time, shows that the coupling of electrocoagulation together with a treatment wetland system is a good alternative for the removal of organic contaminants present in LL.

Combination of advanced biological systems and photocatalysis for the treatment of real hospital wastewater spiked with carbamazepine: A pilot-scale study.

Over the past few decades, the increase in dependency on healthcare facilities has led to the generation of large quantities of hospital wastewater (HWW) rich in chemical oxygen demand (COD), total suspended solids (TSS), ammonia, recalcitrant pharmaceutically active compounds (PhACs), and other disease-causing microorganisms. Conventional treatment methods often cannot effectively remove the PhACs present in wastewater. Hence, hybrid processes comprising of biological treatment and advanced oxidation processes have been used recently to treat complex wastewater. The current study explores the performance of pilot-scale treatment of real HWW (3000 L/d) spiked with carbamazepine (CBZ) using combinations of moving and stationary bed bio-reactor-sedimentation tank (MBSST), aerated horizontal flow constructed wetland (AHFCW), and photocatalysis. The combination of MBSST and AHFCW could remove 85% COD, 93% TSS, 99% ammonia, and 30% CBZ. However, when the effluent of the AHFCW was subjected to photocatalysis, an enhanced CBZ removal of around 85% was observed. Furthermore, the intermediate products (IPs) formed after the photocatalysis was also less toxic than the IPs formed during the biological processes. The results of this study indicated that the developed pilot-scale treatment unit supplemented with photocatalysis could be used effectively to treat HWW.

Modeling spatiotemporal domestic wastewater variability: Implications for measuring treatment efficiency.

Continuously measuring the efficiency of wastewater treatment plants is crucial to progress in sanitation management. Regulations for decentralized wastewater treatment plants (WWTP) can include rudimentary specifications for sporadic sampling, unencouraging continuous monitoring, and missing crucial domestic wastewater (DW) variability, especially in low- and middle-income countries. However, few studies have focused on modeling and understanding spatiotemporal DW variability. We developed and calibrated an agent-based model (ABM) to understand spatial and temporal DW variability, its role in estimated WWTP efficiency, and provide recommendations to improve sampling regulations. We simulated DW variability at various spatial and temporal resolutions in Santa Ana Atzcapotzaltongo, Mexico, focusing on chemical oxygen demand (COD) and total suspended solids (TSS). The model results show that DW variability increases at higher spatiotemporal resolutions. Without a proper understanding of DW variability, treatment efficiency can be overestimated or underestimated by as much as 25% from sporadic sampling. Sensor measurements at 6-min intervals over 3 hours are recommended to overcome uncertainty resulting from temporal variability during heavy drinking water demand in the morning. Reporting of sewage catchment areas, population sizes, and sampling times and intervals is recommended to compare WWTP efficiencies to overcome uncertainty resulting from spatiotemporal variability. The proposed model is a useful tool for understanding DW variability. It can be used to estimate the impact of spatiotemporal variability when measuring WWTP efficiencies, support improvements to sampling regulations for decentralized sanitation, and alternatively for designing and operating WWTPs.

Insight into the dynamics of dissolved organic matter components under latitude change perturbation.

Dissolved organic matter (DOM) which can help the transportation of nutrients and pollutants plays essential role in the aquatic ecosystems. However, the dynamics of individual DOM component under the change of latitude have not been elucidated to date. The composition and dynamics of DOM were assessed in this study. Two individual parallel factor analysis (PARAFAC) components were found in each sampling site in Heilongjiang. To further characterize the inner change of the identified PARAFAC components, two-latitude correlation spectroscopy (2DCOS) technique was applied to the excitation loadings data. Interestingly, not all the fluorophore in a PARAFAC component change in the same direction as the overall change of a component. From upstream to downstream, the peak A1 in PARAFAC component C1 showed a downward trend, but peak A2 presented an upward trend. In PARAFAC component C2, the peak T2 and peak T3 showed an inverse changing trend under latitude perturbation. Furthermore, basic nutrients parameters in Heilongjiang were also characterized in each sampling sites. The relationships between DOM and nutrients showed that component C1 made a significant contribution to chemical oxygen demand (COD) and biochemical oxygen demand (BOD5). The evolutions of DOM peak A1 and peak A2 were accompanied by the changing of Total phosphorus (TP). The findings in this study could make a contribution to explore the fate of DOM in high humic-like substance containing river.

Sustainable power production from petrochemical industrial effluent using dual chambered microbial fuel cell.

Dual chambered microbial fuel cell (DMFC) is an advanced and effective treatment technology in wastewater treatment. The current work has made an effort to treat petrochemical industrial wastewater (PWW) as a DMFC substrate for power generation and organic substance removal. Investigating the impact of organic load (OL) on organic reduction and electricity generation is the main objective of this study. At the OL of 1.5 g COD/L, the highest total chemical oxygen demand (TCOD) removal efficiency of 88%, soluble oxygen demand (SCOD) removal efficiency of 80% and total suspended solids (TSS) removal efficiency of 71% were seen, respectively. In the same optimum condition of 1.5 g COD/L, the highest current and power density of about 270 mW/m2 and 376 mA/m2 were also observed. According to the results of this study, using high-strength organic wastewater in DMFC can assist in addressing the issue of the petrochemical industries and minimize the energy demand.