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The study explores a synergistic two-phase system to treat olive mill wastewater (OMW), comprising a multilayer adsorbent filter (pretreatment) and a vertical flow constructed wetland (VFCW). The pretreatment phase includes layers of commercial granular activated carbon (CGAC) and volcanic tuff (VT), while the VFCW phase consists of planted tank with Phragmites australis reeds and unplanted tanks. Initially, municipal wastewater is introduced into the VFCW to establish the required microbial community. Then, pre-treated OMW is passed through the VFCW. The removal rates of various pollutants were assessed. The planted VFCW showed superior removal efficiencies, averaging 97.82% for total chemical oxygen demand (CODT), 92.78% for dissolved oxygen demand (CODd), 99.61% for total phenolic compounds (TPC), 98.94% for total nitrogen (TN), 96.96% for ammonium, and 95.83% for nitrate. In contrast, the unplanted VFCW displayed lower removal efficiencies, averaging 91.47% for CODT, 77.82% for CODd, 98.53% for TPC, 97.51% for TN, 92.04% for ammonium, and 90.82% for nitrate. These findings highlight the significant potential of VFCWs, which offer an integrated approach to OMW treatment by incorporating physical, chemical, and biological mechanisms within a single treatment system.

Bali Province, Indonesia, experiences serious water shortages and groundwater over-abstraction due to rapidly increasing water demand. Therefore, this study aimed to assess the potential for water reclamation and reuse in Bali Province, focusing on the operational performance of two wastewater treatment plants (WWTPs). Although the Suwung WWTP could increase its treatment capacity to produce reclaimed water for irrigation and landscape, there are multiple management issues to be addressed, including fluctuating water demand, limited customer base beyond hotels, concerns about water quality and safety, and cultural perceptions of reclaimed water. In addition, despite the organic loading rates being lower than the design value, the treatment performance of the Suwung WWTP was found to be significantly lower than that of the ITDC WWTP, which achieved high BOD, COD, and TSS removal rates by performing good maintenance of aerators and post-treatment based on dissolved air flotation (DAF). Causal loop analysis indicates that aerator malfunctioning causes multiple problems, such as low dissolved oxygen, poor BOD removal, sludge carryover, and low sludge concentrations. Therefore, regular maintenance of aerators, as well as the development of aerators robust against malfunctioning, are fundamental to producing effluents from stabilization ponds that meet the requirements for irrigation and landscape reuse.

This investigation presents a new approach for evaluating soil organic carbon (SOC) content in farming soils using a photocatalytic chemical oxygen demand (PeCOD) analyzer combined with geographic information system (GIS) technology for spatial analysis. Soil samples were collected at various sites throughout Canada and were analyzed using sieve analysis, followed by further SOC evaluation using three distinct techniques: loss on ignition (LOI), Walkley-Black, and PeCOD. The PeCOD system, which relies on the photochemical oxidation of organic carbon, showed an exciting correlation between its evaluations and SOC content, making it a prompt and reliable method to evaluate SOC. In this investigation, finer materials such as clayey soils (soil fractions of (<50 µm)) demonstrated high SOC content compared to coarser ones (soil fractions of (>75 µm)) and decreased SOC content with increased soil depth, generally below the 30 cm mark. It should be noted that this investigation revealed that other variables, such as land management practices, precipitation, and atmospheric temperature, have drastic effects on the formation and residence time of SOC. GIS georeferencing еnablеd mapping of the SOC distribution and identification of hotspot areas with high SOC content. The results of this study have implications for sustainable farming, climate change mitigation, and soil health operations by providing farmers with schemes that amplify carbon sequestration while simultaneously improving soil health.

Modeling insecurity under future climate change and socio-economic development is indispensable for adaptive planning and sustainable management of water resources. This case study strives to assess the water quality and quantity status for both the present and the near future in the Ciliwung River basin inside the Jakarta Province under different scenarios using population growth with planned additional wastewater management infrastructure by 2030 as mentioned in the local master plan, and comparing the above conditions with the addition of the effects of climate change. Biochemical oxygen demand (BOD), chemical oxygen demand (COD) and nitrate (NO3), the three important indicators of aquatic ecosystem health, were simulated to assess river pollution. Simulation results suggest that water quality in year 2030 will further deteriorate compared to the base year 2000 due to population growth and climate change, even considering the planned wastewater management infrastructure. The magnitude of impact from population growth is far greater than that from climate change. Simulated values of NO3, BOD and COD ranged from 6.07 to 13.34 mg/L, 7.65 to 11.41 mg/L, and 20.16 to 51.01 mg/L, respectively. Almost all of the water quality parameters exceeded the safe limit suitable for a healthy aquatic system, especially for the year 2030. The situation of water quality is worse for the downstream sampling location because of the cumulative effect of transport of untreated pollutants coming from upstream, as well as local dumping. This result will be useful for local policy makers and stakeholders involved in the water sector to formulate strategic and adaptive policies and plan for the future. One of the potential policy interventions is to implement a national integrated sewerage and septage management program on a priority basis, considering various factors like population density and growth, and global changes for both short- and long-term measures.

The effect of settling on mass balance and biodégradation characteristics of domestic wastewater and on denitrification potential was studied primarily using model calibration and evaluation of oxygen uptake rate profiles. Raw domestic wastewater was settled for a period of 30 minutes and a period of 2 hours to assess the effect of primary settling on wastewater characterization and composition. Mass balances in the system were made to evaluate the effect of primary settling on major parameters.Primary settling of the selected raw wastewater for 2 hours resulted in the removal of 32% chemical oxygen demand (COD), 9% total Kjeldahl nitrogen, 9% total phosphorus, and 47% total suspended solids. Respirometric analysis identified COD removed by settling as a new COD fraction, namely settleable slowly biodegradable COD (XssX characterized by a hydrolysis rate of 1.0 day\"⁻¹ and a hydrolysis half-saturation coefficient of 0.08. A model simulation to test the fate and availability of suspended (Xs ) and settleable (Xss) COD fractions as carbon sources for denitrification showed that both particulate COD components were effectively removed aerobically at sludge ages higher than 1.5 to 2.0 days. Under anoxic conditions, the biodegradation of both COD fractions was reduced, especially below an anoxic sludge retention time of 3.0 days. Consequently, modeling results revealed that the settleable COD removed by primary settling could represent up to approximately 40% of the total denitrification potential of the system, depending on the specific configuration selected for the nitrogen removal process. This way, the results showed the significant effect of primary settling on denitrification, indicating that the settleable COD fraction could contribute an additional carbon source in systems where the denitrification potential associated with the influent becomes rate-limiting for the denitrification efficiency.

The treatment of palm oil mill effluent (POME) poses a significant challenge for Malaysia’s palm oil industry, necessitating compliance with the Department of Environment (DOE) regulations prior to discharge. This study introduces an eco-friendly synthesis method utilizing mangosteen (Garcinia mangostana L.)-leaf aqueous extract to fabricate copper oxide (CuO), zinc oxide (ZnO) nanoparticles (NPs), and their nanocomposite (CuO-ZnO NCs). The physicochemical properties of these nanomaterials were characterized using various analytical tools and their effectiveness in reducing the chemical oxygen demand (COD) of palm oil mill effluent (POME) was assessed under the illumination of two types of light sources: monochromatic blue- and polychromatic white-light emitting diodes (LEDs). CuO-ZnO NCs demonstrated superior performance, with the lowest energy bandgap (1.61 eV), and achieved a COD removal efficiency of 63.27% ± 0.010 under blue LED illumination, surpassing the DOE’s discharge limit of 100 mg/L. This study offers a cost-effective and environmentally friendly method for synthesizing heterojunction materials, which show great potential as photocatalysts in reducing POME COD to permissible levels for discharge.

Black sludge is commonly found in the acid tanks of cold rolling pickling lines and in the pipelines of acid regeneration units, impacting system efficiency and causing negative effects on equipment and the environment. To address this issue, a method combining black sludge composition analysis and Chemical Oxygen Demand (COD) measurement of rinse water was proposed to study its origin and formation mechanisms. The results showed that the black sludge mainly contains carbon, chlorine, oxygen, and small amounts of iron, with significant organic matter loss between 300-400°C, indicating a high content of alkanes and cycloalkanes. The COD value of rinse water is higher when fresh acid is used, and alkanes and cycloalkanes are the main contributors to the formation of black sludge and the increase in COD values. Strengthening the management of fresh acid can help reduce sludge formation.

The standard method to determine chemical oxygen demand (COD) with K 2 Cr 2 O 6 uses harmful chemicals, has a long analysis time, and cannot be used for on-site online monitoring. It is therefore necessary to find a fast, cheap, and harmless alternative. The amperometric determination of COD on boron-doped diamond (BDD) electrodes is a promising approach. However, to be a suitable alternative, the electrochemical method must at least be able to determine the COD of water samples independently of the contained substances. Therefore, the current signal as a function of various organic materials was investigated for the first time. It was shown that the height of the signal current depended on the type of organic matter in single-substance solutions and that this substance dependency increases with the amount of COD. Those findings could be explained by the mechanism proposed for this reaction, showing that the selectivity of the reaction depends on the ratio of the concentration of hydroxyl radicals and organic species. We give an outlook on how to improve the method in order to increase the linear working range and avoid signal variance and how to further explain the signal variance.

Considering the exponential growth of today’s industry and the wastewater results of its processes, it needs to have an optimal treatment system for such effluent waters to mitigate the environmental impact generated by its discharges and comply with the environmental regulatory standards that are progressively increasing their demand. This leads to the need to innovate in the control and management information systems of the systems responsible to treat these residual waters in search of improvement. This paper proposes the development of an intelligent system that uses the data from the process and makes a prediction of its behavior to provide support in decision making related to the operation of the wastewater treatment plant (WWTP). To carry out the development of this system, a multilayer perceptron neural network with 2 hidden layers and 22 neurons each is implemented, together with process variable analysis, time-series decomposition, correlation and autocorrelation techniques; it is possible to predict the chemical oxygen demand (COD) at the input of the bioreactor with a one-day window and a mean absolute percentage error (MAPE) of 10.8%, which places this work between the adequate ranges proposed in the literature.

Water quality parameters like temperature, pH, total dissolved solids (TDS), total suspended solids (TSS), dissolved oxygen (DO), oil and grease, etc., are calculated from the field while parameters like biological oxygen demand (BOD) and chemical oxygen demand (COD) are interpreted through the laboratory tests. On one hand parameters like temperature, pH, DO, etc., can be accurately measured with the exceeding simplicity, whereas on the other hand calculation of BOD and COD is not only cumbersome but also inaccurate many times. A number of previous researchers have tried to use different empirical methods to predict BOD and COD but these empirical methods have their limitations due to their less versatile application. In this paper, an attempt has been made to calculate BOD and COD from simple field parameters like temperature, pH, DO, TSS, etc., using Artificial Neural Network (ANN) method. Datasets have been obtained from analysis of mine water discharge of one of the mines in Jharia coalfield, Jharkhand, India. 73 data sets were used to establish ANN architecture out of which 58 datasets were used to train the network while 15 datasets for testing the network. The results show encouraging similarity between experimental and predicted values. The RMSE values obtained for the BOD and COD are 0.114 and 0.983 %, respectively.