Optimization Of Wastewater Treatment Processes Research Articles

Machine learning algorithm and neural network architecture for optimization of pharmaceutical and drug manufacturing industrial effluent treatment using activated carbon derived from breadfruit (Treculia africana)

In a recent development, attention has shifted to the application of artificial intelligence for the optimization of wastewater treatment processes. This research compared the performances of the machine learning (ML) model: random forest, decision tree, support vector machine, artificial neural network, convolutional neural network, long-short term memory, and multiple linear regressors for optimization in effluent treatment. The training, testing, and validation datasets were obtained via the design of an experiment conducted on the removal of total dissolved solids (TDS) from pharmaceutical effluent. The breadfruit-activated carbon (BFAC) adsorbent was characterized using scanning electron microscopy and X-ray diffraction techniques. The predictive capacity of an ML algorithm, and neural network architecture implemented to optimize the treatment process using statistical metrics. The results showed that MSE ≤ 1.68, MAE ≤ 0.95, and predicted-R2 ≥ 0.9035 were recorded across all ML. The ML output with minimum error functions that satisfied the criterion for clean discharge was adopted. The predicted optimum conditions correspond to BFAC dosage, contact time, particle size, and pH of 2.5 mg/L, 10 min, 0.60 mm, and 6, respectively. The optimum transcends to a reduction in TDS concentration from 450 mg/L to a residual ≤ 40 mg/L and corresponds to 90% removal efficiency, indicating ± 1.01 standard deviation from the actual observation practicable. The findings established the ML model outperformed the neural network architecture and affirmed validation for the optimization of the adsorption treatment in the pharmaceutical effluent domain. Results demonstrated the reliability of the selected ML algorithm and the feasibility of BFAC for use in broad-scale effluent treatment.

Multiobjective Operation Optimization of Wastewater Treatment Process Based on Reinforcement Self-Learning and Knowledge Guidance.

This article proposes a multiobjective operation optimization method based on reinforcement self-learning and knowledge guidance for quality assurance and consumption reduction of wastewater treatment process (WWTP) with nonstationary time-varying dynamics. First, operation optimization models are developed by online sequential random vector functional-link (OS-RVFL) neural network, which can realize online sequential learning of model parameters. Then, a knowledge base is established to store typical optimization cases for knowledge guiding the subsequent optimizations. Based on it, a reinforcement self-learning-based multiobjective particle swarm optimization (RSL-MOPSO) algorithm is proposed to perform optimization calculation. In this algorithm, reinforcement self-learning is used for interaction learning between environment and action in optimization, and the particle motion trend of algorithm is adjusted according to the feedback information of the optimization process. The effects of wastewater state parameters on particles are recorded and reused to improve the solution quality and calculation efficiency of optimization. Moreover, to make good use of the information of the previous optimizations and balance the coordination between global search in the early stage and local search in the later stage, a selective information feedback mechanism is further proposed to ensure the diversity and convergence of the algorithm. Finally, prediction-based intelligent decision making is performed to select the final optimization solution as the final setpoints for the lower-level controllers from the Pareto frontier with considering specific technical requirements. Data experiments show that the proposed method can effectively reduce energy consumption and ensure effluent quality.

SPEA2 based on grid density search and elite guidance for multi-objective operation optimization of wastewater treatment process

It is challenging to introduce an optimization method to improve the operation performance of wastewater treatment process (WWTP) on account of its high energy consumption and poor water quality characteristics. In this paper, an improved Strength Pareto Evolutionary Algorithm 2 (SPEA2) based on grid density search and elite guidance (GDSEG-SPEA2) is proposed for multi-objective operation optimization of WWTP. First, the external archive is divided by an improved adaptive grid method to determine the distribution density of the solutions, and a neighborhood circle strategy as well as a mixed perturbation strategy are designed to search the neighborhood of sparse and crowded solutions, resulting in a more uniformly distributed Pareto front. Then, in order to avoid SPEA2 falling into local optimum after adding the neighborhood search strategies, the crossover and mutation operations based on individual information are proposed to generate new individuals. Finally, an elite guidance strategy, in which the poor-performing individuals of the population learn from the best-performing individuals to update their positions, is introduced into the algorithm to improve convergence. The test function verification shows that the proposed algorithm can obtain the Pareto front with better distribution and convergence than other existing algorithms. The operation optimization control experiment of WWTP shows that the proposed algorithm can better purify water quality and reduce more energy consumption on the premise of ensuring that effluent quality meets the discharge standards, which is better than other optimization algorithms in comparison.

Unraveling the evolution of dissolved organic matter in full-scale A/A/O wastewater treatment process using size exclusion chromatography with PARAFAC and nonnegative matrix factorization analysis

Changes in spectral features and molecular weight (MW) of dissolved organic matter (DOM) along the A/A/O processes in eight full-scale wastewater treatment plants (WWTPs) were characterized using size exclusion chromatography with a diode array detector, a fluorescence detector and an organic carbon detector in tandem (SEC-DAD-FLD-OCD) as well as bulk water quality parameters. The parallel factor (PARAFAC) and the nonnegative matrix factorization (NMF) analyses have been effectively applied to the postprocessing of SEC-FLD fingerprints and SEC-OCD chromatograms, respectively. Individual SEC-FLD-PARAFAC or SEC-OCD-NMF components may span a broad range of MW, indicating that these SEC fractions within the same component were cognate and varied coherently across the dataset samples. The SEC-FLD-PARAFAC modeling and SEC-OCD-NMF analysis have clearly and concisely presented that the dramatic decreases of dissolved organic carbon, UV absorbance at 254 nm and protein-like fluorescence at Ex280/Em350 nm in the anaerobic process were primarily associated with the degradation of the large MW proteinaceous and polysaccharide-like biopolymers. It has also revealed that fluorescence of humic acid-like fractions increased significantly during the anaerobic process, but fluorescence of fulvic acid-like and humic substances’ building blocks decreased slightly. Laboratory experiments further confirmed the presence of the humification process in anaerobic processes, and the formation of humic acid-like fluorophores may be associated with carbohydrate metabolism. The combination of SEC-FLD-PARAFAC and SEC-OCD-NMF helped to establish the links between changes in bulk water quality parameters and the evolution of SEC MW fractions, which provides a more in-depth insight into wastewater DOM treatability and enables the optimization of wastewater treatment processes.

Delineation of the complex microbial nitrogen-transformation network in an anammox-driven full-scale wastewater treatment plant

Microbial-driven nitrogen removal is a crucial step in modern full-scale wastewater treatment plants (WWTPs), and the complexity of nitrogen transformation is integral to the various wastewater treatment processes. A full understanding of the overall nitrogen cycling networks in WWTPs is therefore a prerequisite for the further enhancement and optimization of wastewater treatment processes. In this study, metagenomics and metatranscriptomics were used to elucidate the microbial nitrogen removal processes in an ammonium-enriched full-scale WWTP, which was configured as an anaerobic-anoxic-anaerobic-oxic system for efficient nitrogen removal (99.63%) on a duck breeding farm. A typical simultaneous nitrification-anammox-denitrification (SNAD) process was established in each tank of this WWTP. Ammonia was oxidized by ammonia-oxidizing bacteria (AOB), archaea (AOA), and nitrite-oxidizing bacteria (NOB), and the produced nitrite and nitrate were further reduced to dinitrogen gas (N2) by anammox and denitrifying bacteria. Visible red anammox biofilms were formed successfully on the sponge carriers submerged in the anoxic tank, and the nitrogen removal rate by anammox reaction was 4.85 times higher than that by denitrification based on 15N isotope labeling and analysis. This supports the significant accumulation of anammox bacteria on the carriers responsible for efficient nitrogen removal. Two distinct anammox bacteria, named “Ca. Brocadia sp. PF01” and “Ca. Jettenia sp. PF02”, were identified from the biofilm in this investigation. By recovering their genomic features and their metabolic capabilities, our results indicate that the highly active core anammox process found in PF01, suggests extending its niche within the plant. With the possible contribution of the dissimilatory nitrate reduction to ammonium (DNRA) reaction, enriching PF02 within the biofilm may also be warranted. Collectively, this study highlights the effective design strategies of a full-scale WWTP with enrichment of anammox bacteria on the carrier materials for nitrogen removal and therefore the biochemical reaction mechanisms of the contributing members.

Dynamic optimization of wastewater treatment process based on novel multi-objective ant lion optimization and deep learning algorithm

In this paper, a novel dynamic optimization control based on multi-objective ant lion optimization (DMOALO) and deep learning algorithm is proposed, which could optimize energy consumption (EC) and effluent quality (EQ) simultaneously in the wastewater treatment processes. In order to overcome the difficulty that there is no clear function relationship between the dynamic parameters and the performance indicators, a novel deep belief network (DBN) model for predicting EC and EQ as objective function is proposed. Then, this objective function with constraints is solved by DMOALO method, and the optimal solution would be selected by the intelligent decision system. Finally, Proportional Integral (PI) controllers would be used to track and control these optimal dynamic parameters. DBN-DMOALO-PI optimization control strategy is evaluated in benchmark simulation model 1(BSM1), the simulation results demonstrated this novel optimization control strategy could reduce the EC significantly while meeting the standards of effluent quality parameters. EC is decreased by 3.31% compared with PI optimization control strategy. Therefore, this novel method may reduce the cost of wastewater treatment process effectively, and realize the carbon neutrality in wastewater treatment process.

Chemical and biological tracking in decentralized sanitation systems: The case of artificial constructed wetlands

Given that the social and economic sustainability of rural areas is highly based on the protection of natural resources, biodiversity and human health, simple-operated and cost-effective wastewater treatment systems, like artificial constructed wetlands (CWs), are widely proposed for minimizing the environmental and human impact of both water and soil pollution. Considering that the optimization of wastewater treatment processes is vital for the reduction of effluents toxic potential, there is imperative need to establish appropriate management strategies for ensuring CW performance and operational efficiency. To this end, the present study aimed to assess the operational efficiency of a horizontal free water surface CW (HFWS-CW) located in a world heritage area of Western Greece, via a twelve-month duration Toxicity Identification Evaluation (TIE)-like approach, including both chemical and biological tracking tools. Conventional chemical tracking, by means of pH, conductivity, total COD, and nitrogen-derived components, like nitrates and ammonia–nitrogen, were monthly recorded in both influents and effluents to monitor whether water quality standards are maintained, and to assess potent CW operational deficiencies occurring over time. In parallel, Whole Effluent Toxicity (WET) bioassays were thoroughly applied, using freshwater algae and higher plant species (producers), crustaceans and rotifers (consumers), as well as human lymphocytes (in terms of Cytokinesis Block Micronucleus assay) to evaluate the acute and short-term toxic and hazardous potential of both influents and effluents. The integrated analysis of abiotic (physicochemical parameters) and biotic (toxic endpoints) parameters, as well as the existence of “cause-effect” interrelations among them, revealed that CW operational deficiencies, mainly based on poorly removal rates, could undermine the risk posed by treated sewage. Those findings reinforce the usage of WET testing, thus giving rise to the importance of applying appropriate water management strategies and optimization actions, like oxygen enrichment of surface and bottom of HFWS-CW basins, expansion of the available land, the enhancement of bed depth and seasonal harvesting of plants, for ensuring sewage quality, in favor of water resources protection and sustainable growth in rural areas.

Ionization selectivity of electrospray and atmospheric pressure photoionization FT-ICR MS for petroleum refinery wastewater dissolved organic matter.

Dissolved organic matter (DOM) in petroleum refinery wastewater is an extremely complex mixture. A better understanding of chemical compositions of DOM at the molecular level is necessary for the design and optimization of wastewater treatment processes. In this study, two largely different DOM samples, one from a petroleum refinery wastewater and the other from the Suwannee river water, were characterized by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) coupled with positive-/negative-ion electrospray ionization (ESI), and positive-ion atmospheric pressure photoionization (APPI). For wastewater DOM, a total of 6226 molecular formulae were assigned in the three ionization modes. However, only 1182 molecular formulae were common in all three mass spectra, indicating that the techniques were highly complementary in the types of molecules they ionize. Acid Ox (x = 1-9) and basic N1Ox (x = 0-2) classes were dominant in the wastewater DOM detected in negative-ion and positive-ion ESI mode, respectively. And the wastewater DOM contains considerable amounts of polycyclic aromatic hydrocarbons that did not respond to ESI but can be ionized selectively by APPI. Compared with riverine DOM, the refinery wastewater DOM has a higher molecular complexity and is more enriched in hydrocarbon, and nitrogen- and sulfur-containing compounds. The results show that the major components of refinery wastewater DOM were distinctive from those of the natural organic matter. Though not quantitative, the results obtained by various ionization techniques were found to be complementary, and are helpful to our understanding of the selectivity of different ionization techniques as well as the molecular compositions of DOM.

Characteristics of bacterial populations in an industrial scale petrochemical wastewater treatment plant: Composition, function and their association with environmental factors

The efficiency of petrochemical wastewater biological treatment is dependent upon complex bacterial communities. A well understanding of the structure and function of bacterial community and their association with environmental variables is essential for the elucidation of contaminant removal mechanisms and optimization of wastewater treatment processes. In this study, the bacterial communities and metabolic functions in the primary hydrolysis acidification unit (PHAU), cyclic activated sludge system (CASS), secondary hydrolysis acidification unit (SHAU), and biological aerated filter (BAF) of a petrochemical wastewater treatment plant (WWTP) were studied via Illumina high-throughput sequencing. The correlations between bacterial community and environmental variables were also investigated. The phylum Proteobacteria, Planctomycetes, Chloroflexi, Acidobacteria and Bacteroidetes were dominant in the petroleum WWTP. The bacterial communities varied with wastewater characteristics and operational parameters, as a result of the differences in biosystems functions. Phylogenetic analysis showed that the genes involved in the degradation of benzoate, nitrotoluene and aminobenzoate degradation were abundant in PHAU, and the genes related to the degradation of benzoate, aminobenzoate, chloroalkane, chloroalkene, caprolactam, naphthalene and toluene were abundant in CASS, SHAU and BAF. The Redundancy analysis (RDA) suggested that biochemical oxygen demand (BOD5), NH4+-N and total nitrogen concentrations exhibited significant impacts in shaping the structure of bacterial community. Variance partitioning analysis (VPA) showed that 18.6% of the community variance was related to wastewater characteristics, higher than operational parameters of 4.5%. These results provide insight into microbial community structure and metabolic function during petrochemical wastewater treatment, and discern the relationships between bacterial community and environmental variables, which can provide basic data and a theoretical analysis of the design and operation optimization in petrochemical WWTP.

Integration of wastewater treatment into process design of lignocellulosic biorefineries for improved economic viability

BackgroundProduction and use of bio-based products offer advantages over conventional petrochemicals, yet the relatively high cost of production has restricted their mainstream adoption. Optimization of wastewater treatment processes could reduce capital expenditures, lowering the barrier to market entry for lignocellulosic biorefineries. This paper characterizes wastewater associated with lignocellulosic ethanol production and evaluates potential wastewater treatment operations.ResultsIt is found that organic material is intrinsic to bioconversion wastewater, representing up to 260 kg of biological oxygen demand per tonne of feedstock processed. Inorganics in the wastewater largely originate from additions during pretreatment and pH adjustments, which increase the inorganic loading by 44 kg per tonne of feedstock processed. Adjusting the ethanol production process to decrease addition of inorganic material could reduce the demands and therefore cost of waste treatment. Various waste treatment technologies—including those that take advantage of ecosystem services provided by feedstock production—were compared in terms of capital and operating costs, as well as technical feasibility.ConclusionsIt is concluded that wastewater treatment technologies should be better integrated with conversion process design and feedstock production. Efforts to recycle resources throughout the biofuel supply chain through application of ecosystem services provided by adjacent feedstock plantations and recovery of resources from the waste stream to reduce overall capital and operating costs of bioconversion facilities.

Taguchi Method and Response Surface Methodology in the Treatment of Highly Contaminated Tannery Wastewater Using Commercial Potassium Ferrate.

The potential implementation of Envifer®, a commercial product containing potassium ferrate (40.1% K2FeO4), for the purification of highly contaminated tannery wastewater from leather dyeing processes was proposed. The employment of the Taguchi method for optimization of experiments allowed the discoloration (98.4%), chemical oxygen demand (77.2%), total organic carbon (75.7%), and suspended solids (96.9%) values to be lowered using 1.200 g/L K2FeO4 at pH 3 within 9 min. The application of the central composite design (CCD) and the response surface methodology (RSM) with the use of 1.400 g/L K2FeO4 at pH 4.5 diminished the discoloration, the chemical oxygen demand, the total organic carbon, and suspended solids within 9 min. The Taguchi method is suitable for the initial implementation, while the RSM is superior for the extended optimization of wastewater treatment processes.

Optimization of wastewater treatment processes using molecular bacteriology

The study aims at evaluating the microbiome within different treatment steps, and the efficiency of ultra filtration (UF) and chlorination processes on bacteria removal for safe water reuse. Next-generation sequencing (NGS) techniques to characterize bacteria persistence in relation to the treatment facilities were applied. Results introduced comprehensive taxonomy of bacteria at family, class and phylum levels. Wastewater treatment processes are effective in reducing the bacterial community of family and species. The alpha diversity and taxonomical summaries of the samples suggested that the UF is essential in reducing bacteria species diversity. However, chlorination disinfection, the final step of treatment, needs further optimization because the UF process before chlorination showed better possible removal of bacterial communities when compared to the quality after chlorination. Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes persisted through the stages of treatment. Proteobacteria remained contently present; whereas bacteria of the other three phyla showed decrease between 2-fold to 100 fold following either ultrafiltration or chlorination. More studies are required to distinguish between viable and non-viable microorganisms.

Optimization of Wastewater Treatment Process on Energy Costs at Truck and Tractor Washing Posts

The paper considers a process pertaining to purification of oily effluents while using electrocoagulation, electro-flotation and an electromagnetic hydro-cyclone, implemented with the help of a laboratory unit (Patent of the Republic of Belarus for the invention No 21229). The investigations have been carried out with the purpose to optimize specific energy consumption for the process of cleaning fuel and lubricants effluents. The following investigation methods have been applied – a literature review, a comparative analysis, an experiment execution, a mathematical modeling. Content of oil products in wastewater before and after treatment has been evaluated in the analytical laboratory of the Belarusian State Agrarian Technical University in accordance with standard methods. Purification of the oily effluents has been made with the help of a laboratory unit while using electroflotocoagulation method with removal of floated sludge in an electromagnetic hydrocyclone. This cleaning method makes it possible to increase a degree of water disinfection, provides a closed water supply, rational use of water resources, reduction of harmful effects of pollutants on the environment. An experimental design technique has been worked out and a three-level Box – Behnken design has been implemented in the paper. The problem concerning optimization of the purification process on energy costs has been solved without worsening the required concentration indices of petroleum products in water after treatment . Mathematical models have been obtained and optimal purification modes have been determined at the lowest specific energy costs and with sufficiently high degree of purification. Significance of regression coefficients has been estimated by Student criterion. It has been established that while using the proposed unit it is possible to obtain a degree of wastewater purification at automotive equipment washing stations with its performance G = 0.7 l/s, current density j = 150 A/m2. The degree of purification, determined by ratio of pollutant concentrations before and after purification, is up to 99.9 %. The results can be used in implementation of microprocessor control of cleaning mode while taking an initial concentration of pollution, unit capability, current density of an electroflotocoagulator as control action factors.

Fate of estrogens in a pilot-scale step-feed anoxic/oxic wastewater treatment system controlling by nitrogen and phosphorus removal.

The control measures for estrogens in the aquatic environment are topics of growing concern. It is a meaningful issue to finding optimal process parameters for efficient removal of estrogens with the purpose of efficient total nitrogen (TN) or total phosphorus (TP) removal in sewage treatment plants. The present paper is concerned with the relationships between the estrogen removal and TN or TP removal in a pilot-scale three-stage anoxic/oxic (A/O) system treating real municipal wastewater. The total removal efficiency for estrone (E1) and 17β-estradiol (E2) and their sulfate and glucuronide conjugates were on average 87% in the pilot-scale system. The concentrations of the sulfate and glucuronide conjugates of estrogens (E1 and E2) in the system were much lower than the estrogens, which might be caused by the rapid degradation of conjugates in the pilot-scale system. The average removal efficiencies of E1 and E2 and their sulfate and glucuronide conjugates were significantly lower under high TP removal conditions than those under high TN removal conditions that suggested that the ammonia oxidation promotes estrogen degradation. When the system achieved efficient TN removal, the concentrations of both E1 and E2 were generally lower in the aerobic zones than those in the anoxic zones. Instead, when the system achieved efficient TP removal conditions, the estrogen concentrations were higher in the aerobic zones than in the anoxic zones. However, it was thought that the variation of the concentrations of the estrogen conjugates had weak influence on concentrations of the free estrogens. The increase of the free estrogens in the aerobic zones could be attributed to the release of the estrogens adsorbed on the sludge. The variation of estrogens in a three-stage A/O system can be properly estimated and measured by a binary linear regression model with the variables of TP and TON (NO2--N and NO3--N), which is probably the important information for the improvement and optimization of wastewater treatment processes to obtain higher removal efficiency for estrogens.

Combination of Coagulation-flocculation and Ultrafiltration Processes using Cellulose Acetate Membrane for Wastewater Treatment of Tofu Industry

The tofu industry is generally a household-scale industry so that the efficiency of water used and wastewater treatment are mistreated. Wastewater from tofu industry is very hazardous when discharged directly into the aquatic environment as it contains high organic pollutants which is indicated by high concentrations of BOD (biological oxygen demand), COD (chemical oxygen demand), TSS (total suspended solid), TDS (total dissolved solid) and turbidity. The optimization of wastewater treatment process is needed to meet the quality standards set by the government and also needed to obtain more effective and efficient effluent treatment. The aim of this study is to evaluate the performance of combination coagulation-flocculation and ultrafiltration processes for tofu industry wastewater treatment. The process of pretreatment coagulation and flocculation using poly aluminum chloride (PAC) was conducted to extend the life of the ultrafiltration membrane and improve the performance of ultrafiltration membrane separation. The experimental results showed that TSS and turbidity decreased with precipitation time whereas COD did not significantly change. TDS after coagulation-flocculation has increased due to the addition of PAC. The TSS, TDS, COD and turbidity of wastewater were drastically reduced after experiencing ultrafiltration process. After the ultrafiltration process, the TSS, Turbidity, COD and TDS rejection were 98.8%, 98.1%, 71.0% and 50.6%, respectively.

Water Environmental Management in Industrial Park - A Case Study of Xinjin in Chengdu City

The mode of industrial park is becoming a vital national strategy of circular economy in China and a means to enhance energy efficiency, environmental performance, and economic competitiveness. It is becoming increasingly important to control the discharge of industrial wastewater from industrial parks in order to reduce water pollution. In this paper, as taking Chengdu Xinjin chemical cluster area for example, industrial layout of the park, environmental pollution treatment measures and management situation was analyzed surrounding with water environmental issues, factors of industrial park, like layout planning, industrial structure, management mode etc. were considered as significant influence on water environment. Enterprises in the park have different discharge standards and without any supervision. The influent of the wastewater treatment plant in the park is unstable, and the total phosphorus discharge exceeds the standard for a long time, and there is no large emergency pool in the park to prepare for sudden pollution incidents. In order to solve these problems, this paper puts forward some measures, such as setting up on-line monitoring, improving the standard of enterprise water discharge, and establishing emergency command system etc. In addition, some suggestions were put forward on how to improve the industrial park water environment status and management level, to provide references for the development of industrial park. The results could have supports for the design and optimization of wastewater treatment processes, as well as for evaluation of how centralized wastewater treatment plants can help to control water pollution.

Pollution control and cost analysis of wastewater treatment at industrial parks in Taihu and Haihe water basins, China

It is becoming increasingly important to control the discharge of industrial wastewater from industrial parks in order to reduce water pollution. In this study, a comprehensive investigation of water treatment at industrial parks in two large basins in China was carried out, involving comparative and correlation analysis of wastewater quality, pollutant removal, sludge production and cost. In both basins, the average influent chemical oxygen demand, ammonia, and total nitrogen at the centralized wastewater treatment plants in chemical parks were higher than those in comprehensive parks. Anaerobic-anoxic-oxic and anoxic-oxic were the most widely used treatment processes at both comprehensive and chemical parks in the two basins—similar to municipal wastewater treatment plants in China. The operating costs of centralized wastewater treatment plants in the comprehensive and chemical parks comprised the following components: electricity > labor > chemical costs. Reductions in chemical oxygen demand, ammonia and total nitrogen and total phosphorus in the centralized wastewater treatment plants in the parks led to an average reduction of 28% in total pollutants in municipal and industrial wastewater in the two basins. These results could have supports for the design and optimization of wastewater treatment processes, as well as for evaluation of how centralized wastewater treatment plants can help to control water pollution.