Fluctuations In Water Quality Research Articles

Data-Driven Techniques and Data Analytics in Water Treatment Facilities: Innovative Safety Protocols and Optimization

Aim: To examine harnessing of data-driven techniques and data analytics in water treatment facilities considering innovative safety protocols and process optimization. Problem Statement: The most significant part of human existence and industrial operations has been linked to water which often becomes vulnerable to hazardous contaminants which are brought on through natural processes and human activity. Significance of Study: The use of water treatment facilities in order to make water a sustainable natural resource for the whole world is vital. To achieve this, it is imperative to monitor and classify water quality. Methodology: Recent literature materials in form of books, journals and relevant published articles in the area of data-driven techniques and data analytics in water treatment facilities were consulted. Discussion: This review article has critically examined harnessing of data-driven and data analytics in water treatment facilities with consideration also given to innovative safety protocols and process optimization. The basic fundamental principle of water treatment facilities were discussed alongside the application of data-driven techniques. Safety protocols in water treatment facilities were discussed. Data driven techniques in water treatment facilities and applications of machine learning and artificial intelligence techniques to water treatment were explained. It was noticed that AI-Driven solutions transforms real-time water quality analysis and offers a proactive approach to water treatment. The integration of machine learning and AI algorithms capabilities in water treatment facilities can now respond quickly to fluctuations and continuously monitor key parameters fluctuations in water quality. Consideration was given to artificial intelligence as data analytics optimization tool for water treatment facilities. Future prospects, limitations and challenges of the study were stated. Conclusion: AI algorithms and historical data can be harnessed into water treatment facilities in order to assess performance patterns, predict equipment malfunctions and proactively plan the maintenance activities.

Development of a Macroinvertebrate Multimetric Index for the Evaluation of the Ecological Quality of Lake Nokoué

Nowadays, new tools for assessing the biotic integrity of aquatic environments are used. In Benin, the Macroinvertebrate MultiMetric Lake Index (M3LI) was designed to reveal the current state of Lake Nokoué. The inventory was carried out in eight seasonal data collection campaigns from 2019 to 2021 using an Eckman type grab and a trouble net. The composition, diversity and different metrics were calculated. Thirty-nine metrics grouped into five categories were defined and subjected to selection using a PCA in order to identify the relevant metrics for calculating the index. In the lake, 83 taxa divided into 25 orders and 54 families representing 32,770 specimens were determined. Insects, Molluscs, Crustaceans and Annelids constituted the essential macrofauna which was numerically dominated by Gastropods, Crustaceans, Bivalves and Insects. Of the 5 food functional groups obtained, crushers and collector-gatherers were dominant. Ten metrics relevant and well explained by environmental variables were used to calculate M3LI. The M3LI calculated varied from 6.44 at the station near Dantokpa to 3.38 at the Cotonou channel station with fluctuating water quality between poor quality and good quality. These results clearly explain the responses of the living communities of the lake faced with the various stresses due to daily marine intrusion, the role of receptacle of different continental waters and the various anthropogenic activities plunging the environment into a hypereutrophic state. It is therefore urgent to take measures to restoration of this environment to allow living species to regain their balance of life. This index is also a departure for the establishment of a biomonitoring program for the restoration of the lake.

An Algorithm for Nutrient Mixing Optimization in Aquaponics

Controlled environment agriculture is a promising alternative to conventional production methods, as it is less affected by climate change and is often more sustainable, especially in circular and recycling frameworks such as aquaponics. A major cost factor in such facilities, however, is the need for skilled labor. Depending on available resources, there are endless possibilities on how to choose ingredients to realize a desired nutrient solution. At the same time, the composition of the desired solution is subject to fluctuations in fish water quality, fertilizer availability, weather, and plant development. In high-evaporation scenarios, e.g., summer, nutrient solutions might be mixed multiple times per day. This results in a complex, multi-variable task that is time-consuming to solve manually, yet requires frequent resolution. This work aims to help solve this challenge by providing methods to automate the nutrient mixing procedure. A simple mass-balance-based model of a nutrient mixing tank with connections to different water sources, drains, and fertilizers is provided. Using methods of static optimization, a program was developed which, in consideration of various process constraints and optimization variables, is able to calculate the necessary steps to mix the desired solution. The program code is provided in an open-source repository. The flexibility of the method is demonstrated in simulation scenarios. The program is easy to use and to adapt, and all necessary steps are explained in this paper. This work contributes to a higher automation level in CEA.

Evaluation of water quality fluctuation in the tidal reach under the impact of on shore wastewater discharges based on MIKE 21 model in dongguan, China

The text discusses the study of water quality simulation in the lower reaches of the Dongjiang River basin—tidal river section. Utilizing MIKE 21 software, an integrated 2D hydrodynamic-water quality coupled mathematical model was constructed for the lower reaches and estuary area of the Dongjiang River. The model simulated the hydrodynamic characteristics near the wastewater outlet and the impact of pollutant discharge on the water quality in the study area. The simulation results indicate: (1) During wet and dry seasons, the overall flow pattern remains consistent, with the ebb current velocity slightly greater than the flood tide current velocity beside the outlet. The flow velocity is generally greater during wet season compared to dry season. Interestingly, the flow direction during flood and ebb tides is completely opposite and tides have a great impact on the maximum flow velocity in Xialu Connecting channel. (2) Under normal discharge conditions, the increment of COD, NH3-N, and TP in most areas of Zhongtang Waterway, Daoyun Waterway, and Hengchong Waterway is less than 5 mg/L, 0.1 mg/L, and 0.02 mg/L, respectively, indicating a minor impact on water quality; While in the event of an accidental discharge, the concentration increment is in the range of 5–30 mg/L, 0.1–0.25 mg/L, and 0.02–0.05 mg/L, respectively. COD causes the most significant water quality changes and will severely impact the water environment of the Dongjiang River basin. Furthermore, pollutant dispersion is more extensive during dry seasons due to reduced river flow and weaker dilution capacity. (3) Overall, the results underscore the critical importance of accounting for both tidal and seasonal dynamics in simulation of estuarine water quality.

Evaluation of Indian white shrimp (Penaeus indicus) rearing technique at Balai Besar Perikanan Budidaya Air Payau (BBPBAP) Jepara

Abstract Indian white shrimp is one of the important fishery commodities with high economic value after banana prawns (Penaeus merguiensis) in the northern coastal waters of Central Java Province. This shrimp needs to be more developed since its aquaculture technique was not quite as well-known as in other shrimps such as P. vannamei or P. monodon. This study aimed to unveil the techniques for rearing white shrimp and its influencing factors. The method in this research used active participation and descriptive methods. The data collection method used was from primary and secondary data. Both primary data were FCR (Feed Conversion Rate), ABW (Average Body Weight), ADG (Average Daily Growth), and water quality parameters while the secondary data were literature review and staff information. The results showed that the FCR values in the three pools were high (1: 5.5, 2: 24.57, 3: 10.36). For growth parameters, the ABW values for the three ponds were 29.57, 20.00, and 23.91 while the final ADG values were 0.53, 0.14, and 0.52 respectively. It can be concluded that the enlargement activity of Indian white shrimp needs to be improved due to high FCR value and fluctuating water quality. Higher FCR value would reduce the feed efficiency so that a lot of feed was given to the shrimps but their growth is less than optimal. The Enlargement activities at Jepara should use feed with a minimum protein content of 33% by the protein requirements of Indian white shrimp so that it can support gonad maturity. Additionally, it is necessary to rearrange the pond size to conform to the standard size (2000-5000 m2), as well as manage water quality through daily siphoning so that the water quality remains optimal.

Analysis of the Water Quality Status and Its Historical Evolution Trend in the Mainstream and Major Tributaries of the Yellow River Basin

The Yellow River basin, an area of extreme water scarcity, has faced significant challenges in water quality management due to rapid economic and social development since the 1980s. This study analyzes the water quality evolution over nearly 40 years, focusing on primary pollutants like chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and permanganate index (CODMn). In the 1990s, sections of the river were severely polluted, with some areas failing to meet the lowest national standards. In 2000, 32% of the river water was classified as inferior Class V. However, enhanced water resource management and stricter pollutant regulations introduced after 2000 have significantly improved water quality. By 2010, water quality reached its nadir, with 16% of water classified as inferior Class V and 25% as Class IV–V. By 2020, water quality showed marked improvement, with a significant reduction in segments classified as inferior Class V and Class IV–V. Recent years have seen water quality stabilize, with COD meeting Class I standards and NH3-N and CODMn meeting Class II standards based on national criteria. The study also highlights discrepancies in water quality between the mainstream and tributaries of the Yellow River. While the mainstream generally maintains good water quality, many tributaries remain severely polluted. In 2022, 85% of the water in tributaries was classified as Class I to III, 12.3% as Class IV to V, and only 2.7% as Class V. However, all water in the mainstream reached Class I–III, with 86% achieving Class II and 14% achieving Class I. A detailed analysis of the Huayuankou section over the past three decades shows a general decline in pollution indicators. Seasonal water quality fluctuations, correlated with flow rates and temperatures, were observed, often exhibiting normal distribution patterns. These findings underscore the effectiveness of sustained pollution control and the need for continuous, adaptive management strategies to improve and maintain water quality in the Yellow River basin.

The effect of sampling frequency and spatial and temporal variation in the density of fecal indicator bacteria on the assessment of coastal bathing water quality

The time and location of sampling as well as the number of samples per season can influence a reliable assessment of bathing water quality. In this study, we investigated the spatio-temporal variation of fecal indicator bacteria (FIB) density and the effects of FIB variability and sampling frequency on the assessment of a single sample and the annual and final assessment of coastal bathing water quality. Increasing the number of samples from 10 to 20 per bathing season had a significant impact on bathing water quality assessment at sites where water quality fluctuations had previously been observed, resulting in a change in water quality to a lower category in 36 % of annual and 54 % of final assessments, suggesting that the minimum number of samples per season should be increased at such sites. Increasing the number of samples at sites assessed as excellent over a longer period had no impact on water quality assessment.Spatial and temporal variations in FIB density were significant at a considerable number of sites both in the single sample assessment and in the annual and final assessment. Bathing water quality was spatially unrepresentative at a quarter of the sites analyzed and temporally unrepresentative at a fifth, as there was at least one additional point with a lower bathing water quality than the official sampling point or the bathing water quality was lower in the afternoon than in the morning.When revising the current recreational water quality regulations, the impact of sampling frequency of and of spatio-temporal variation of FIB density on the relevance of bathing water quality assessment at sites subjected to pollution needs to be seriously considered.

Analysis of Spatiotemporal Variation and Driving Factors of Water Quality in the Xiangjiang River Basin from 1990 to 2016

In order to explore the spatiotemporal variation characteristics and driving mechanism of water quality in the Xiangjiang River Basin, the data of 16 water quality parameters at 113 stations over 26 years from 1990 to 2016 in the Xiangjiang River Basin were collected for synthetically assessing the water quality and identifying its main pollutants through the water quality index and other methods. The causal mechanism of water quality, especially the driving effect of water level and land use pattern, was analyzed. The results showed that： ① The overall water quality grade of the Xiangjiang River Basin during the study period was "good." However, the water quality deteriorated first （from 1990 to 2003） and then improved （from 2004 to 2016）. The season variation in water quality was not obvious, but the water quality fluctuation of the wet season was larger. The water pollution load of the main stream decreased successively from the middle reaches, downstream reaches, and upstream reaches. The upstream tributaries had the best water quality, whereas areas with poor water quality were mainly distributed at the mouth of the middle and downstream tributaries, especially in areas where multiple tributaries converged. ② Toxic heavy metals had the characteristics of toxicity, persistence, and bioaccumulation. If they exceeded a certain concentration in water, they were difficult to purify, posing great harm to the natural environment and human health. The toxic metal index （CI1） was the leading factor affecting water quality, in which Hg and Cd were the main parameters affecting the overall water quality of the Xiangjiang River Basin. ③ The water level had a positive impact on the water quality of the Xiangjiang River by diluting environmental parameters. Land type had little effect on heavy metal concentration, whereas forest land could improve water quality. Grassland had a negative correlation with permanganate index over a large scale range （≥ 5 km）. The increase in water bodies, arable land, and impermeable surface areas within the watershed increased the probability of high fecal coliform concentration in the water body. ④ With the increase in buffer distance, the water quality explained by the land use pattern increased. On the scale of 10 km buffer zone in the riparian zone, the explanation degree by land use pattern on water quality was the highest, which was an effective scale for water quality control of the Xiangjiang River. This research showed that the driving factors of heavy metal pollution and other pollution were different, but their regional differences were all obvious in the Xiangjiang River Basin. Therefore, pollution control should be classified and taken according to local conditions.

Study of Fluctuation in Surface Water Quality in Can Tho Province (Mekong Delta, Vietnam)

Study of Fluctuation in Surface Water Quality in Can Tho Province (Mekong Delta, Vietnam)

Study on the Removal Effect of 3DEPs on Microplastics in Groundwater and Desalination Mechanism

Owing to water scarcity, groundwater quality has become an important constraint on sustainable regional development, especially in coastal areas. Coastal groundwater is subject to natural conditions and intense human activities, and the evolution of water quality is extremely complex, with the possibility of seawater intrusion into the groundwater, leading to the salinisation of freshwater aquifers. At present, the commonly used methods for removing groundwater pollution include coagulation, adsorption, biological method, membrane technology, etc., among which the coagulation method is more commonly used. At present, three-dimensional electrode electrochemical technology is widely used in high salinity organic wastewater and many organic wastewaters, but there are few studies related to microplastic removal by three-dimensional electrode electrochemical technology using metallic aluminium particles as particle electrodes. The treatment of microplastics in coastal groundwater is a relatively novel issue, and with the increasing application of three-dimensional electrochemical technology in the study of desalination, the three-dimensional electrode electrochemical technology will be promising due to the high salinity of coastal groundwater, the increasingly serious pollution of microplastics, and the fluctuation of water quality and quantity.

Advanced treatment and reuse of dye wastewater using thermo-irreversible on/off switch starch with disruption of dissolution/precipitation dynamic equilibrium

The development of irreversible on/off switching materials is a potential strategy for unidirectional capture and encapsulation of pollutants, preventing the pollutant leakage problem resulting from the reversible dissolution of flocculants. Herein, a thermo-irreversible on/off switch starch (TISS) is prepared through modifying starch by etherification grafting glycidyl phenyl ether and 2,4-bis(dimethylamino)-6-chloro-[1,3,5]-triazine. It breaks the dissolution/precipitation dynamic equilibrium across heating–cooling cycles by thermal-induced irreversible coil-to-globule self-assembly of polymer chains, resulting in a 50-fold decrease in polymer solubility. Particularly, TISS shows a superior double-locking effect on pollutants and flocculants through its unique irreversible conformation memory capability, leading to a high-quality reuse water. 99.9 % of reactive brilliant red dye and 97.9 % of TISS remain fixed within sludge flocs even after prolonged immersion in cold water at 24 °C for 60 days. Furthermore, direct recycling and reuse of dye-bath energy can be realized through the isothermal flocculation and dyeing method, showing a 75 % decrease in energy consumption after three cycles compared to traditional dyeing techniques. This work presents a novel approach to constructing an irreversible pollutant delivery system using thermo-irreversible on/off switch starch, addressing the problems of high energy dissipation and water quality fluctuations during wastewater treatment.

A four-season exploration of surface water quality and trophic status in the highly dynamic waters of Karun-4 Dam Lake, SW Iran

This study explores seasonal fluctuations in water quality and phytoplankton communities within Karun-4 Dam Lake, the largest double-arch dam in the Middle East situated in Iran. Employing a systematic approach, 26 sampling stations were strategically selected to collect surface water samples at a depth of 50 cm during the midpoints of each season throughout 2019. Significant seasonal variations in water quality parameters and phytoplankton composition were observed. Predominant species included Chrysophyceae (38%) and Bacillariophyceae (32%), indicative of nutrient-rich conditions, particularly during spring and summer, as evidenced by the eutrophic state (Carlson Trophic State Index: 59.43 and 53.96, respectively). Summer exhibited the highest diversity (Shannon–Wiener Index = 2.27) and lowest evenness (Pielou’s Evenness Index = 0.21). PCA and CCA analyses revealed season-specific preferences for nutrients and ions among phytoplankton species. Water temperature emerged as a crucial factor in spring and summer, while environments with elevated bicarbonate and alkalinity levels were less favorable during winter and fall. This study provides essential insights into Karun-4 Lake's dynamic ecological conditions, underscoring the necessity for ongoing monitoring to discern long-term trends and anthropogenic impacts for effective ecosystem management.

Assembly mechanism and stability of zooplankton communities affected by China's south-to-north water diversion project

Water diversion can effectively alleviate water resource shortages and improve water environmental conditions, while also causing unknown ecological consequences, in particular, the assembly mechanism of zooplankton communities in the affected areas will become more complex after long-term water transfer. Taking Nansi Lake, the second largest impounded lake along the eastern route of China's South to North Water Diversion Project (SNWDP), as an example, the composition and diversity of zooplankton communities in the lake area and estuaries during the water diversion period (WDP) and non-water diversion period (NWDP) were studied. The potential assembly process of zooplankton communities was further explored, and the stability of communities in different regions during different periods was compared. The related results indicated that the changes in water quality conditions induced by water diversion had a relatively weak impact on the zooplankton communities. In the assembly mechanism of zooplankton communities, stochastic process played a more important role during both WDP or NWDP, and the proportion of deterministic process was relatively higher during NWDP, which may be related to the greater role of total nitrogen (TN) in the assembly of the zooplankton communities. The network analysis and cohesion calculation results showed that the stability of the zooplankton communities in the lake area sites was higher than that in the estuary sites, and the stability during NWDP was higher than that during WDP. In sum, the stability of zooplankton communities displayed a degree of change affected by water diversion activities, but the community assembly was not significantly influenced by the water quality fluctuations after about relatively long-term water diversion. This study provides an in-depth understanding of the ecological effects of water diversion on the biological communities in the affected lake, which is beneficial to the management and regulation of long-term water diversion projects.

Water Quality Prediction Model for the Pearl River Estuary Based on BiLSTM Improved with Attention Mechanism

To improve the accuracy and stability of water quality prediction in the Pearl River Estuary, a water quality prediction model was proposed based on BiLSTM improved with an attention mechanism. The feature attention mechanism was introduced to enhance the ability of the model to capture important features, and the temporal attention mechanism was added to improve the mining ability of time series correlation information and water quality fluctuation details. The new model was applied to the water quality prediction of eight estuaries of the Pearl River, and the prediction performance test, generalization ability test, and characteristic parameter expansion test were carried out. The results showed that:① The new model achieved high prediction accuracy in the water quality prediction of the Zhuhaidaqiao section. The root-mean-square error (RMSE) between the predicted value and the measured value was 0.004 1 mg·L-1, and the coefficient of determination (R2) was 98.3 %. Compared with that of Multi-BiLSTM, Multi-LSTM, BiLSTM, and LSTM, the results showed that the new model had the highest prediction accuracy, which verified the accuracy of the model. ② Both the number of training samples and the number of forecasting steps affected the prediction accuracy of the model, and the prediction accuracy of the model increased with the increase of the training samples. When predicting the total phosphorus of the Zhuhaidaqiao section, more than 240 training samples could obtain higher prediction accuracy. Increasing the number of prediction steps caused the prediction accuracy of the model to decline rapidly, and the reliability of the model prediction could not be guaranteed when the number of prediction steps was greater than 5. ③ When the new model was applied to the prediction of different water quality indexes in eight estuaries of the Pearl River, the prediction results had high precision and the model had strong generalization ability. The input data of upstream water quality, rainfall, and other characteristic parameters associated with the section prediction index of the object could improve the prediction accuracy of the model. Through many tests, the results showed that the new model could meet the requirements of precision, applicability, and expansibility of water quality prediction in the Pearl River Estuary and thus is a new exploration method for high-precision prediction of water quality in complex hydrodynamic environments.

Influence of water quality and seasonal variations on freshwater macroinvertebrate diversity and community structure in wastewater treatment ponds, Phetchaburi Province, Thailand

Wastewater originating from the Phetchaburi municipality undergoes treatment in a series of five distinct stages at the King’s Royally Initiated Leam Phak Bia Environmental Research and Development Project (LERD) in Phetchaburi province, Thailand. These stages involve a sedimentation (pond 1), three oxidation ponds (ponds 2 to 4), and a final stabilization pond (pond 5). These ponds serve as habitats for macroinvertebrates; consequently, their diversity and composition might be influenced by fluctuations in water quality and seasonal variations. The primary aim of this research was to analyze the diversity and species composition of macroinvertebrate communities concerning varying levels of organic contamination across the five wastewater treatment ponds at LERD. This investigation spanned three seasons: cold season (December 2019), rainy season (July 2020), and hot season (April 2021). The findings revealed that the diversity and species composition of macroinvertebrate communities displayed distinct alterations across multiple environmental gradients, especially identifying the significant influence of organic loading levels observed in ponds 1 to 5. The macroinvertebrate communities exhibited two distinct groupings, with the Chironomidae and Candonidae or ostracods prevailing prominently in ponds 1 and 2 (heterogenous environments). This prevalence was attributed to the high levels of detrital food and the robust resilience of chironomid larvae and ostracods to organic pollution, thriving even in environments characterized by low dissolved oxygen levels. Conversely, the prevalence of snails from the Thiaridae family in ponds 3 to 5 (homogenous environments) indicated improved water quality conditions, notably lower organic matter levels, and a higher dissolved oxygen content. In addition, the study identified seasonal variations in macroinvertebrates, likely influenced by the differing organic loading and environmental conditions. Thus, this research provided insights into the factors shaping macroinvertebrate communities in a wastewater treatment system.

Water quality fluctuations prediction and Debi estimation based on stochastic optimized weighted ensemble learning machine

Today, humanity faces the complex phenomenon of global development. Problems such as limited resources, financial constraints, time constraints, and the involved nature of issues surrounding water resources management pose challenges to effectively monitor water resources issue parameter management (WRIPM). Nevertheless, the importance of this issue cannot be ignored. The focus of this study is on WRIPM issues, and new machine learning models are utilized to investigate and estimate fluctuations in WRIPM parameters. The initial dataset utilized in this study was sourced from the US Geological Survey, specifically sampling stations information from the South Platte River in the United States. Two new models were developed: the Ensemble Bagged Machine (EBM) and the Stochastic Weighted Ensemble Bagged Machine (SWEBM), which further optimized the characteristics of the EBM model using Bayesian Optimization (BN). These models were employed to simulate Dissolved Oxygen (DO), Electrical Conductivity (EC), Power of hydrogen (pH), and river flow rate (Debi) parameters. Additionally, the research employed various scenarios for evaluation and validation. Uncertainties were calculated using the Wilson analysis. The results demonstrated the superiority of the SWEBM in all modeling aspects, yielding the best indices, namely, R2, MAE, and RMSE, at 0.984, 0.0259, and 0.0394, respectively. Performing an receiver operating characteristic analysis revealed that the area over the RROC curve value for the superior model was 36.16, demonstrating excellent performance in modeling the pH parameter. The WM analysis method confirmed that the SWEBM model was the most trustworthy, delivering the greatest precision and best modeling for the pH parameter (WOUB=0.00683, MOPE=0.00138, and STD=0.1665) even though it was slightly overestimated. The probability density function analysis revealed that the SWEBM was the best model. While other models had results that were relatively close. This research represents valuable findings that can be shared with stakeholders, including experts, scholars, and organizations responsible for overseeing water quality administration.

Comparison of water quality fluctuations and subterranean stem biomass in different methods of <i>Nymphaea</i> spp. Eradication

Comparison of water quality fluctuations and subterranean stem biomass in different methods of <i>Nymphaea</i> spp. Eradication

Log-Linear Model and Delivery Load Analysis for Improvements in Water Quality through TMDL in the Gyeongan Stream Watershed, Republic of Korea

The effectiveness of implementing the total maximum daily load (TMDL) in the Gyeongan stream watershed was evaluated to assess its impact on water quality. The relationships between water quality and flow rate and load and water quality were reinterpreted using a log-linear model and the delivery load, respectively. To estimate annual water quality trends and analyze the effects of water quality improvement in each parameter, an improved multivariable log-linear model addressing the limitations of the traditional L-Q equation was applied to analyze the relationship between water quality and flow rate, excluding the effects of flow rate and seasonality. The effect of total phosphorus (T-P) was particularly prominent. A new delivery load was developed and applied to address the limitations of the original unit-based delivery load. Evaluations showed that water quality continuously improved across all parameters, and all methods (excluding the influence of flow rate on water quality fluctuations) were highly effective in estimating water quality changes attributable to anthropogenic pollution sources. The analysis of pollutant contributions revealed that managing point sources is necessary for controlling the biochemical oxygen demand and total nitrogen, while point and non-point sources require T-P management. Future policy development should consider this when formulating management strategies.

A pilot-scale anoxic-anaerobic-anoxic-oxic combined with moving bed biofilm reactor system for advanced treatment of rural wastewater

Rural domestic poses a significant challenge to treatment technologies due to significant fluctuations in both water quality, particularly in terms of carbon concentration, and quantity. Conventional biological technology, such as anaerobic-anoxic-oxic (A2O) systems, is inefficient. In this work, a continuous pilot-scale anoxic-anaerobic-anoxic-oxic (A3O) reactor with a moving bed biofilm reactor (MBBR) system was constructed and optimized to improve the treatment efficiency of rural domestic wastewater. The sludge return ratio, volume ratio of the oxic-to-anoxic zone (Voxi/Vano), step-feeding and hydraulic retention time (HRT) at low temperature were considered the main parameters for optimization. Microbial analysis was performed on both the mixed liquor and carrier of the A3O-MBBR system under initial and post-optimized conditions. The results indicated that the A3O-MBBR improved the treatment efficiency of rural domestic wastewater, especially for total phosphorus (TP), which increased by 20 % compared with that of the A2O-MBR. In addition, the removal efficiencies of nitrogen and phosphorus were further optimized, and the average concentrations of total nitrogen (TN) and TP in the effluent reached 2.46 and 0.364 mg/L, respectively, at a sludge reflux ratio of 100 or 150 %, Voxi/Vano =200 %, step-feeding of 0.5Q/0.5Q (anaerobic/anoxic) and HRT of 15 h at low temperature in the A3O-MBBR, which met standard A of GB18918–2002, China (TN < 15 mg/L, TP < 0.5 mg/L). The average rate of attaining the standard increased by 58.63 % (post optimization). The microbial analysis showed an increase in species diversity and richness after the parameters were optimized. Moreover, compared to the microbial community structure before optimization, the post-optimization exhibited a more stable microbial structure with a significant enrichment of functional bacteria. Defluviimonas, Novosphingobium and Bifidobacterium, considered as the dominant nitrification or denitrifying bacteria, were enriched in the suspended sludge of the MBBR reactor, which the relative abundance increased by 3.11 %, 3.84 %, and 3.24 %, respectively. Further analysis of the microbial community in the carrier revealed that the abundance of Nitrospira and the denitrifying bacteria carried by the carrier were much greater than those in the suspended sludge. Consequently, the microorganism cooperation between suspended sludge and biofilm might be responsible for the improved performance of the optimized A3O-MBBR.

An attempt to augment performance of machine learning models in a pilot-scale urban wastewater treatment system

The challenge we face is striking a balance between ensuring the standards for wastewater treatment processes and minimizing energy consumption. As such, accurate predictive models become critical. With machine learning algorithms having the ability to discern relationships between inputs and outputs, broad interest has been shown in their applications in wastewater treatment. A key research focus is on effectively adjusting these generic machine learning algorithms to adapt to frequent fluctuations of water quality, inflow volume, and environmental variations. This study developed a Nonlinear Autoregressive neural network (NARX) model having an adaptive real-time updating module to predict the ammonium concentration in the treated effluent. The model has exhibited high predictive accuracy on the testing data, achieving an R2 value of 0.997. By incorporating an adaptive real-time updating module, the model's capability to predict effluent ammonium concentration on novel data sets has been significantly improved, with the R2 value for these predictions increasing from an initial 0.918 to a more refined range of 0.966 to 0.984. Our findings confirm that integrating real-time updating capabilities with machine learning algorithms substantially improves the stability and predictive performance of the models. These outcomes will prompt a broader integration of artificial intelligence in the domain of environmental engineering, which shall foster the implementation of more accurate and eco-friendly wastewater treatment strategies.