Principal factor and hierarchical cluster analyses for the performance assessment of an urban wastewater treatment plant in the Southeast of Spain

Machine learning analysis of PM1 impact on visibility with comprehensive sensitivity evaluation of concentration, composition, and meteorological factors

This study introduces a novel approach to visibility modelling, focusing on PM1 concentration, its chemical composition, and meteorological conditions in two distinct Polish cities, Zabrze and Warsaw. The analysis incorporates PM1 concentration measurements as well as its chemical composition and meteorological parameters, including visibility data collected during summer and winter measurement campaigns (120 samples in each city). The developed calculation procedure encompasses several key steps: formulating a visibility prediction model through machine learning, identifying data in clusters using unsupervised learning methods, and conducting global sensitivity analysis for each cluster. The multi-layer perceptron methods developed demonstrate high accuracy in predicting visibility, with R values of 0.90 for Warsaw and an RMSE of 1.52 km for Zabrze. Key findings reveal that air temperature and relative humidity significantly impact visibility, alongside PM1 concentration and specific heavy metals such as Rb, Vi, and Cd in Warsaw and Cr, Vi, and Mo in Zabrze. Cluster analysis underscores the localized and complex nature of visibility determinants, highlighting the substantial but previously underappreciated role of heavy metals. Integrating the k-means clustering and GSA methods emerges as a powerful tool for unravelling complex mechanisms of chemical compound changes in particulate matter and air, significantly influencing visibility development.

Organic stabilization and methane production under different organic loading rates in UASB treating swine wastewater

This study proposes the was to evaluate the stability and methane production with organic load differents in an upflow anaerobic sludge blanket reactor (UASB) treating swine wastewater by methods of multivariate analysis. Four organic loads were used with average hydraulic holding times of one day. The methods of data analysis of linear regression, Pearson correlation, principal component analysis and hierarchical clustering analysis were used for understanding stability and methane production in the reactor. The highest concentrations of bicarbonate alkalinity of 683 mg L-1 CaCO3 and total volatile acids of 1418 mg L-1 HAc with maximum organic loading applied were obtained. The optimal stability conditions occurred at an intermediate and partial alkalinity ratio between 0.24 and 0.25 observed in initial phases with a chemical oxygen demand (COD) removal of 47-57%. Maximum methane production was 9.0 L CH4 d-1 observed with linear regression positive and occurred at the highest applied organic load, corresponding to the highest COD removal efficiency and increased microbial biomass. Positive and negative correlation between functional stability in anaerobic digestion showed regular activity between acids, alkalinity and organic matter removal. This fact was also proven by the analysis of principal components that showed three components responsible for explaining 83.2% of the data variability, and the alkalinity, organic matter influent and organic acids had the greatest effects on the stability of the UASB reactor. Hierarchical clusters detected the formation of five groupings with a similarity of 50.1%, indicating that temperature and pH were variables with unitary influences on data dimensionality.

Microplastics and Endocrine Disruptors in Typical Wastewater Treatment Plants in Megacity Shanghai

The fast development of China's urbanization has led to a notable release of emerging pollutants, including microplastics (MPs) and endocrine disruptors (EDCs). Generally, these pollutants enter the coastal environment through the discharge of wastewater treatment plants (WWTPs) and finally threaten the organisms in the receiving waterbody. The study investigated the environmental behavior of MPs and EDCs in two typical WWTPs in one of the megacities in China, Shanghai. The abundance of MPs in the influent ranged from 321 to 976 items/L. Four shapes (films, fragments, fibers, and microbead) were found, while fibers and films dominated. Transparent (31-63%) and white (20-47%) MPs were more frequently observed, while polyethylene terephthalate, cellulose, and cellophane were the main polymetric materials. The size of the MPs fell between 15.8 μm and 2220 μm, and the smaller one (<500 μm) dominated. The removal efficiencies of the two WWTPs for MPs ranged from 64% to 92%, and both WWTPs performed better for large pieces of MPs (>500 μm). For EDCs, total concentrations in the influent were detected, ranging from 113 to 2780 ng/L. Two groups, including phenolic estrogens (PEs) and steroid estrogens (SEs), were detected, and PEs, especially bisphenol A (BPA), were the predominant individuals among the studied EDCs. Specifically, PEs ranged from 82.8 to 2637 ng/L, while SEs ranged from 27.3 to 143 ng/L. The removal efficiencies of the WWTPs for EDCs varied (82.8-100%) as well, possibly due to the different treatment compartments and contamination load in the influent. Seasonal variations for both MPs and EDCs were observed. Specifically, concentrations of MPs and EDCs in WWTPs influent were higher in the wet season, as well as the removal efficiency. Furthermore, there was a correlation observed between the concentrations of MPs and EDCs, suggesting that MPs and EDCs may originate from the same source and that EDCs released by MPs cannot be ignored during treatment. Finally, the study evaluated the environmental risk of the effluents. MPs led to a minor risk (Level I), while EDCs might lead to an adverse impact on algae (RQs = 0.0014-0.024) and fish (RQs = 3.4-30.2). In summary, WWTPs received considerable amounts of MPs and EDCs. Although the WWTPs removed the contaminants efficiently, the environmental risk of the effluent needs to be noted.

Characterization and removal efficiencies of microplastics discharged from sewage treatment plants in Southeast Spain

Microplastics (MPs) are ubiquitous pollutants that can effectively harm different ecosystems. The information on the relative contribution of wastewater treatment plants (WWTPs) to the surrounding environment is important, in order to understand ecological health risks and implement measures to reduce their presence. This focus article presents a quantitative assessment on the relative concentration and types of MPs delivered from four WWTPs located at the Southeast of Spain. Samples from WWTPs were collected throughout a four-year period, comprising more than 1,200 L of analyzed wastewater and 3,215 microparticles isolated. Density extraction with 1.08 g/mL NaCl salt solution was systematically used as the main separation method, in a simple and reliable manner, and repeat extraction cycles did not play any significant impact on the study outcomes. The four WWTPs had removal efficiencies between 64.3% and 89.2% after primary, secondary, and tertiary treatment phases, without diurnal or daily variations. Advanced treatment methods displayed a lower removal rate for fibers than for particulate MPs. The abundance of MPs was always higher and with a lower mean size in wastewater samples collected in Autumn than for the rest of seasons. MPs dumped from WWTPs in large quantities into the environment are meant to be regarded as an important point source for aquatic and terrestrial environments.

Identification of the Contamination Sources by PCBs Using Multivariate Analyses: The Case Study of the Annaba Bay (Algeria) Basin

Persistent Organic Pollutants (POPs), particularly the indicator polychlorinated biphenyls (PCBs), were first quantified in water and sediments of two wadis, Boujemaâ and Seybouse, as well as in the effluents from a fertilizer and phytosanitary production industrial plant (Fertial). Since these contaminated discharges end in Annaba Bay (Algeria) in the Mediterranean Sea, with a significant level of contamination, all the potential sources should be identified. In this work, this task is conducted by a multivariate analysis. Liquid-liquid extraction and gas chromatography/mass spectrometry (GC-MS) methods were applied to quantify seven PCB congeners, usually taken as indicators of contamination. The sum of the PCB concentrations in the sediments ranged from 1 to 6.4 μg/kg dw (dry weight) and up to 0.027 μg/L in waters. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for the multivariate analysis, indicating that the main sources of PCB emissions in the bay are urban/domestic and agricultural/industrial. The outfalls that mostly contribute to the pollution of the gulf are the Boujemaâ wadi, followed by the Seybouse wadi, and finally by the Fertial cluster and more precisely the annex basin of the plant. Although referring to a specific site of local importance, the work aims to present a procedure and a methodological analysis that can be potentially applicable to further case studies all over the world.

Occurrence, seasonal distribution, and ecological risk assessment of microplastics and phthalate esters in leachates of a landfill site located near the marine environment: Bushehr port, Iran as a case

Plastic wastes are produced in a large amount everywhere, and are commonly disposed in landfills. So landfill leachate seems an obvious source of microplastics (MPs) and phthalate esters (PAEs) due to a huge usage as plastic additives and plasticizers. But this issue still lacks attention and the present study provides the first information on the levels of MPs and PAEs in the fresh landfill leachate of Bushehr port during different seasons. The mean levels of MPs and PAEs in the fresh leachate in all seasons were 79.16 items/L and 3.27 mg/L, respectively. Also, the mean levels of PAEs in MPs were 48.33 μg/g. A statistically significant difference was detected in the levels of MPs and PAEs among different seasons with the highest values in summer and fall. MPs with a size of >1000 μm had the highest abundance in all seasons. The most prominent shape, color, and type of MPs in the leachate were fibers black, and nylon, respectively. Dibutyl phthalate (DBP) and Di(2-ethylhexyl) phthalate (DEHP) were the most dominant PAEs present in the leachate samples. The results of this study revealed high hazard index (HI) and pollution load index (PLI) of MPs in all seasons. Dioctyl phthalate (DOP), DEHP, DBP, diisobutyl phthalate (DiBP), butyl benzyl phthalate (BBP), and diethyl phthalate (DEP) represented a high risk to the sensitive organisms. The results of this study showed that significant levels of MPs and PAEs may release into the surrounding environment from the landfill sites without sufficient protection. This issue is more critical when the landfill sites in particular are located near the marine environments like the Bushehr landfill that is located near the Persian Gulf, which can lead to serious environmental problems. Thus permanent control and monitor of landfills, especially in the coastal areas are highly needed to prevent further pollution.

Performance Assessment of Natural Wastewater Treatment Plants by Multivariate Statistical Models: A Case Study

Waste stabilization ponds (WSPs) as natural wastewater treatment plants are commonly utilized for wastewater treatment due to their simple design, low cost, and low-skilled operator requirements. Large-scale studies assessing the performance of WSPs using multivariate statistical models are scarce. Therefore, this study was conducted to assess the performance of 16 full-scale WSPs regarding physicochemical parameters, algae, bacterial indicators, and pathogens (e.g., Cryptosporidium, Entamoeba histolytica) by using multivariate statistical models. The principal component analysis revealed that the chemical pollutants were removed significantly (p < 0.001) through the treatment stages of 16 WSPs, indicating that the treatment stages made a substantial change in the environmental parameters. The non-multidimensional scale analysis revealed that the treatment stages restructured the bacterial indicators significantly (p < 0.001) in the WSPs, implying that the bacterial indicators were removed with the progress of the treatment processes. The algal community exhibited a distinct pattern between the geographical location (i.e., upper WSPs versus lower WSPs) and different treatment stages (p < 0.001). Four out of the sixteen WSPs did not comply with the Egyptian ministerial decree 48/1982 for discharge in agriculture drainage; three of these stations are in lower Egypt (M.K., Al-Adlia, and Ezbet El-Borg), and one is in upper Egypt (Armant). The continuous monitoring of WSPs for compliance with regulatory guidelines with the aid of multivariate statistical models should be routinely performed.

Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods

One of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at the inflow to wastewater treatment plants, the values of which depend only on the amount of inflowing wastewater. The methodology of an expert system to predict selected indicators of wastewater quality at the inflow to the treatment plant (biochemical oxygen demand, chemical oxygen demand, total suspended solids, and ammonium nitrogen) on the example of a selected WWTP—Sitkówka Nowiny, was presented. In the considered system concept, a division of the values of measured wastewater quality indices into lower (reduced values of indicators in relation to average), average (typical and most common values), and upper (increased values) were adopted. On the basis of the calculations performed, it was found that the values of the selected wastewater quality indicators can be identified with sufficient accuracy by means of the determined statistical models based on the support vector machines and boosted trees methods.

Addition of Fibroblast-Stromal Cell Markers to Immune Synovium Pathotypes Better Predicts Radiographic Progression at 1 Year in Active Rheumatoid Arthritis

Objectives

This study aims to investigate if addition of fibroblast-stromal cell markers to a classification of synovial pathotypes improves their predictive value on clinical outcomes in rheumatoid arthritis (RA).

Methods

Active RA patients with a knee needle synovial biopsy at baseline and finished 1-year follow-up were recruited from a real-world prospective cohort. Positive staining for CD20, CD38, CD3, CD68, CD31, and CD90 were scored semiquantitatively (0-4). The primary outcome was radiographic progression defined as a minimum increase of 0.5 units of the modified total Sharp score from baseline to 1 year.

Results

Among 150 recruited RA patients, 123 (82%) had qualified synovial tissue. Higher scores of CD20+ B cells, sublining CD68+ macrophages, CD31+ endothelial cells, and CD90+ fibroblasts were associated with less decrease in disease activity and greater increase in radiographic progression. A new fibroblast-based classification of synovial pathotypes giving more priority to myeloid and stromal cells classified samples as myeloid-stromal (57.7%, 71/123), lymphoid (31.7%, 39/123), and paucicellular pathotypes (10.6%, 13/123). RA patients with myeloid-stromal pathotype showed the highest rate of radiographic progression (43.7% vs. 23.1% vs. 7.7%, p = 0.011), together with the lowest rate of Boolean remission at 3, 6, and 12 months. Baseline synovial myeloid-stromal pathotype independently predicted radiographic progression at 1 year (adjusted OR: 3.199, 95% confidence interval (95% CI): 1.278, 8.010). Similar results were obtained in a subgroup analysis of treatment-naive RA.

Conclusions

This novel fibroblast-based myeloid-stromal pathotype could predict radiographic progression at 1 year in active RA patients which may contribute to the shift of therapeutic decision in RA.

Spatial distribution of heavy metal contamination and uncertainty-based human health risk in the aquatic environment using multivariate statistical method

Heavy metal contamination in the aquatic environment is one of the most serious health issues worldwide. In this study, an evaluation framework is developed to identify the sources and health risk of heavy metals (i.e., As, Hg, Cr, Cu, Zn, Pb, and Cd) contamination in the North Canal of Fengtai District, China, which is based on multiple approaches, including multivariate statistical method, health risk assessment, and uncertainty analysis. Spatial distribution of these heavy metals could exhibit their impact on the aquatic environment. Pearson's correlation analysis shows that a majority of the correlations between different heavy metals are not significant due to the differences in sources of heavy metals. Principal component analysis indicates that there are four principal components to explain 91.381% of the total variance. Moreover, health risk reveals that hazard quotient values are in low levels, ranging from 0.48 to 0.74, relative higher quotient levels could be observed in the northern section. The carcinogenic risk of Cd has exceeded the acceptable level in S1, S3, and S7. Sensitivity analysis ensures the reliability of health risk assessments. Furthermore, some specific recommendations are given to help decision-makers develop more comprehensive strategies for improving water environment quality.

Investigating Industrial Effluent Impact on Municipal Wastewater Treatment Plant in Vaal, South Africa

Industrial effluents with high concentrations of toxic heavy metals are of great concern because of their persistence and non-degradability. However, poor operation and maintenance of wastewater treatment infrastructure is a great concern in South Africa. In this study, physico-chemical parameters and heavy metals (HMs) concentration of wastewater from five different industries, Leeuwkuil wastewater treatment plant (WWTP) inflow and effluent, and Vaal River water samples were monitored between January and September 2017, to investigate the correlation between heavy metal pollution and the location of industries and ascertain the effectiveness of the municipal WWTP. Physico-chemical variables such as pH, biological oxygen demand (BOD), dissolved oxygen (DO), chemical oxygen demand (COD), total dissolved solids (TDS) and electrical conductivity (EC) exhibited both temporal and spatial variations with the values significantly higher in the industrial samples. Inductively coupled plasma optical emission spectrometry (ICP-OES) results also showed that aluminium (Al), copper (Cu), lead (Pb) and zinc (Zn) were significantly higher in industrial effluents (p < 0.05), with only Zn and Al exhibiting significant seasonal variability. Statistical correlation analysis revealed a poor correlation between physicochemical parameters and the HMs compositional quality of wastewater. However, toxic HMs (Zn, Cu and Pb) concentrations in treated wastewater from WWTP were above the permissible limits. Although the WWTP was effective in maintaining most of the wastewater parameters within South African Green drop Standards, the higher Cu, Zn, Pb and COD in its final effluent is a concern in terms of Vaal river health and biological diversity. Therefore, we recommend continuous monitoring and maintenance of the WWTPs infrastructure in the study area.

Microplastics in an urban wastewater treatment plant: The influence of physicochemical parameters and environmental factors

This paper presents the abundance, concentration and variability of microplastics (MP) in an urban wastewater treatment plant (WWTP), according to different water parameters and environmental factors, their possible sources and removal efficiency. A total of 352.6 L of wastewater from four stages of the treatment process were processed following a standardized extraction protocol by density separation, trinocular microscopic identification and polymeric analysis by Fourier transform infrared spectroscopy. MP comprised a 46.6% of total microlitter, with a statistically significant removal of 90.3% in the final effluent of the WWTP. Five different shapes were isolated; i.e. fragment, film, bead, fiber, and foam. The most prominent MP forms in the final effluent were fragments and fibers, with the most common size class being 400-600 μm. Seventeen different polymer families were identified, with low-density polyethylene being the most prevalent one (52.4%) in a film form (27.7%), mostly from agriculture greenhouses near the sewage plant and single plastic bags (it is noted that only a year ago consumers are charged for them in Spain). Influent wastewater with high concentrations of suspended solids proved to have a low MP burden with a larger MP size, possibly due to a hetero-aggregation with particulate matter. Agglomeration of polystyrene and polyethylene terephthalate with organic material is also suggested, both with surface energies higher than 25 mN m^-1 enough for a high biofouling rate. The sewage plant cushions sharp-point microplastic concentrations during the warm season, allowing a stable performance of the WWTP.

Adequacy analysis of drinking water treatment technologies in regard to the parameter turbidity, considering the quality of natural waters treated by large-scale WTPs in Brazil

This paper seeks to present a performance evaluation of large-scale water treatment plants and verify the adjustment of the treatment to the parameter turbidity of natural waters. Nonparametric and multivariate statistical tools were used to analyze raw water and treated water turbidity of a large on-line monitoring databank for the period from 2013 to 2015, from six large-scale treatment plants utilizing different technologies. Cluster analysis was able to differentiate adequately groups of treatment plants with similar raw and treated water quality. Considering the effluent turbidity as a marker parameter, the results indicated that selection of the technology to be applied must be well studied to always seek the best solution, and that other factors than only the raw water characteristics should be evaluated. It was also demonstrated that utilization of the same treatment technology does not always result in the same effluent quality, since there are many factors related to operation, maintenance, raw water variability, climatic interferences, and others.

Application of the Mathematical Simulation Methods for the Assessment of the Wastewater Treatment Plant Operation Work Reliability

The aim of the present work was the modeling of the wastewater treatment plant operation work using Monte Carlo method and different random variables probability distributions modeling. The analysis includes the following pollutants indicators; BOD5 (Biochemical Oxygen Demand), CODCr (Chemical Oxygen Demand), Total Suspended Solids (SSt), Total Nitrogen (TN), and Total Phosphorus (TP). The Anderson–Darling (A–D) test was used for the assessment of theoretical and empirical distributions compatibility. The selection of the best-fitting statistical distributions was performed using peak-weighted root mean square (PWRMSE) parameter. Based on the performed calculations, it was stated that pollutants indicators in treated sewage were characterized by a significant variability. Obtained results indicate that the best-fitting pollutants indicators statistical distribution is Gauss Mixed Model (GMM) function. The results of the Monte Carlo simulation method confirmed that some problems related to the organic and biogenic pollutants reduction may be observed in the Wastewater Treatment Plant, in Jaworzno.

Spatio-temporal distribution and chemical characterization of groundwater quality of a wastewater irrigated system: A case study

Wastewater irrigation is a common livelihood practice in many parts of the developing world. With the continuous irrigation supply, groundwater systems in these regions perceive adverse impacts due to inadequate infrastructure to treat the wastewater. The current study area, Musi River irrigation system, is one such case study located in the peri-urban Hyderabad of South India. The Musi River water, which is used for irrigation, is composed of untreated and secondary treated wastewater from Hyderabad city. Kachiwani Singaram micro-watershed in the peri-urban Hyderabad is practicing wastewater irrigation for the last 40 years. The current quality of (untreated) wastewater used for irrigation is expected to have adverse impacts on the local aquifers, but detailed investigations are lacking. To elucidate the groundwater quality dynamics and seasonality of the wastewater irrigation impacts on the peri-urban agricultural system, we analyzed the groundwater quality on a monthly basis for one hydrological year in the wastewater and groundwater irrigated areas, which exist next to each other. The spatio-temporal variability of groundwater quality in the watershed was analyzed with respect to wastewater irrigation and seasonality using multivariate statistical analysis, multi-way modeling and self-organizing maps. This study indicates the significance of combining various statistical techniques for detailed evaluation of the groundwater processes in a wastewater irrigated agricultural system. The results suggest that concentrations of the major ionic substances increase after the monsoon season, especially in wastewater irrigated areas. Multi-way modeling identified the major polluted groundwaters to come from the wastewater irrigated parts of the watershed. Clusters of chemical variables identified by using self-organizing maps indicate that groundwater pollution is highly impacted by mineral interactions and long-term wastewater irrigation. The study recommends regular monitoring of water resources and development of sustainable management strategies to mitigate the aquifer pollution in wastewater irrigation systems.

Patrilineal background of the She minority population from Chaoshan Fenghuang Mountain, an isolated mountain region, in China

The She ethnic minority population is distributed in southern China. The origin of the She population has been controversial. The purpose of this work was to investigate the genomic diversity of She. The Chaoshan She population living in the Chaoshan Fenghuang mountain is a relatively isolated population. We detected 14 Y chromosome biallelic markers (Y-SNPs) and 6 Y chromosome short tandem repeat (Y-STR) loci in Chaoshan She people. Y-SNP analysis showed the Chaoshan She was closely related to the Chaoshan Hakka, Chaoshanese, Tujia and Gaoshan national minority. Compared with the Fujian She, the Chaoshan She maintained a more southern native genetic structure. Y-STR analysis revealed the Chaoshan She population was more closely related to the Hakka population than the other Hans. We concluded the Chaoshan She population had a closer genetic relationship with the southern national minority and Hakka Han and it may be representative of She ancestors' patrilineal genetic structure.

Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis

For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs.

Development of a protocol to optimize electric power consumption and life cycle environmental impacts for operation of wastewater treatment plant

In wastewater treatment plants (WWTPs), the portion of operating costs related to electric power consumption is increasing. If the electric power consumption decreased, however, it would be difficult to comply with the effluent water quality requirements. A protocol was proposed to minimize the environmental impacts as well as to optimize the electric power consumption under the conditions needed to meet the effluent water quality standards in this study. This protocol was comprised of six phases of procedure and was tested using operating data from S-WWTP to prove its applicability. The 11 major operating variables were categorized into three groups using principal component analysis and K-mean cluster analysis. Life cycle assessment (LCA) was conducted for each group to deduce the optimal operating conditions for each operating state. Then, employing mathematical modeling, six improvement plans to reduce electric power consumption were deduced. The electric power consumptions for suggested plans were estimated using an artificial neural network. This was followed by a second round of LCA conducted on the plans. As a result, a set of optimized improvement plans were derived for each group that were able to optimize the electric power consumption and life cycle environmental impact, at the same time. Based on these test results, the WWTP operating management protocol presented in this study is deemed able to suggest optimal operating conditions under which power consumption can be optimized with minimal life cycle environmental impact, while allowing the plant to meet water quality requirements.