Journal:Construction of control charts to help in the stability and reliability of results in an accredited water quality control laboratory

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Full article title Construction of control charts to help in the stability and reliability of results in an accredited water quality control laboratory
Journal Sustainability
Author(s) da Silva, Flávia M.O.; Silvério, Karina S.; Castanheira, Maria I.; Raposo, Mariana; Imaginário, Maria J.; Simões, Isabel; Almeida, Maria A.
Author affiliation(s) Polytechnic Institute of Beja
Primary contact Email: flavia dot silva at ipbeja dot pt
Editors Kazak, J.K.; Sciavicco, G.; Kamińska, J.A.
Year published 2022
Volume and issue 14(22)
Article # 15392
DOI 10.3390/su142215392
ISSN 2071-1050
Distribution license Creative Commons Attribution 4.0 International
Download (PDF)


Overall, laboratory water quality analysis must have stability in their results, especially in laboratories accredited by ISO/IEC 17025. Accredited parameters should be strictly reliable. Using control charts to ascertain divergences between results is thus very useful. The present work applied a methodology of analysis of results through control charts to accurately monitor the results for a wastewater treatment plant. The parameters analyzed were pH, biological oxygen demand for five days (BOD5), chemical oxygen demand (COD), total suspended solids (TSS), and total phosphorus (TP). The stability of the results was analyzed from the control charts and 30 analyses performed in the last 12 months. From the results, it was possible to observe whether the results were stable, according to the rehabilitation factor, which cannot exceed WN = 1.00, and the efficiency of removal of pollutants, which remained above 70% for all parameters. The method of determining the technological reliability and stability of the treatment station by use of control charts proves to be an efficient tool for detecting any instability in the results. As such, this method helps to monitor the results of such analyses more clearly and thus enables a rapid response to possible disturbances and maintains the quality of the analysis control, as well as determining the accreditation entities.

Keywords: water quality, control charts, reliability, wastewater treatment


Quality management in accredited laboratories consists of a set of actions based on the international regulatory standard ISO/IEC 17025. Maintaining quality across lab operations is seen as fundamental to maintaining the suitability of the lab's services and products. Organizations are increasingly looking for improvement in their processes and procedures (P&P) and have tools contributing to the achievement of their goals. Implementing and improving statistical controls, continuous improvement, training, and participation reduce a process’s variability and consequently increases quality and productivity. Some tools even help to ensure quality control, including check sheets, histograms, and control charts.[1][2] As Samohyl[3] notes, mindfully implementing these and other tools further ensures quality by minimizing variations in the characteristics of products and procedures.

A control chart—and charting in general—is of the main statistical tools used to control and monitor processes. Control charts signal the presence of accidental causes and special causes in a process.[4] According to Corrêa[5], every process has variation, and a natural or common cause is within the control limits. In contrast, so-called special causes need more attention because they indicate values outside the control limits and therefore need rapid correction. Using control charts to manage routine analyses allows easy detection of negative trends in analytical work, enabling quick corrective action, reducing out-of-specification (OOS) results, and consequently avoiding non-compliance.[6] They can be applied in several areas and are used worldwide. A study carried out by Razif[7] proves that the use of control charts contributed to faster detection of anomalies in a daily cycle of analysis and showed similarities in the characteristics of the water quality data of the Surabaya river in Indonesia between 2014 and 2015 for several parameters such as biological oxygen demand for five days (BOD5), chemical oxygen demand (COD), total suspended solids (TSS).

In an analogous study in Brazil using control charts in conjunction with other statistical tools, it was possible to identify a problem in the domestic sewage treatment plant. The treatment did not produce an effluent with characteristics that meet the specifications or release standards of the environmental legislation, indicating the need for restructuring and correction of the efficiency of the process.[8]

The international standard ISO 7870-1:2019 Control charts — Part 1: General guidelines addresses several objectives regarding control charts, including[9]

  • indicating if the process is stable, comparing information from samples that represent the current state of the process against the control limits that reflect this variability;
  • estimating the magnitude of the variability inherent to the process; and
  • aiming to determine if the variability of the process has remained stable or if there are oscillations.

A 2022 study by Śliz and Bugajski[10] concluded that control charts could be an effective tool for assessing the operation of a sewage treatment plant, allowing the detection of any disturbances during the sewage treatment process in the tested facility and the rapid correction of those disturbances, ensuring the natural reservoir’s water quality. The Shewhart-type control chart is the most used and has broad applicability. In building this model, a preliminary period of subsequent sample analysis is needed to determine the control limits.[11] After this analysis period, statistical treatment is applied to obtain the control charts.

According to Zan et al.[12], control charts are used to monitor whether a process is controlled or not. They have long been used for quality monitoring of manufacturing processes. If only random causes affect the operation, the production process is considered to be natural or normal.[13] At this time, the control chart tends to fluctuate randomly in the symmetrical coordinate system. The traditional control chart easily detects the abnormity beyond the boundary. Still, it is challenging to identify the range of abnormity given that it usually requires human judgment and is easily affected by various factors.

The problem of water pollution in urban areas is highly relevant today due to progressive urbanization, aging of ecological infrastructure, and high population density.[10][14] The Member States of the European Union, in agreement with the Water Framework Directive (WFD), are obliged to use and protect their water resources rationally. These include the proper treatment of wastewater.

This study aims to apply a methodology of analysis of results through control charts, aiming at accurately monitoring the results for a wastewater treatment plant from a slaughterhouse in a specific region of Alentejo in Portugal. The parameters analyzed were pH, BOD5, COD, TSS, and total phosphorus (TP).

According to Reilly et al.[15], slaughterhouse wastewater presents a biological risk to humans and other animals due to the presence of pathogens, pharmaceuticals, and toxic chemicals used for plant cleaning.[16] This harmful potential means that the disposal of slaughterhouse and dairy waste is often subject to local legislation which has been put in place to protect public health.[17] For example, COD in slaughterhouse wastewater often requires a 95% reduction, with similar levels of treatment being required for TP before final discharge into the environment.[16] Currently, the UK dairy processing and slaughterhouse industries use technologies such as chemical dosing, reverse osmosis, anaerobic digestion, dissolved air floatation, and membrane bioreactors to treat their wastewater. They have been obtaining good results.[18][19][20]

This study will show the construction of control charts helps guide analysts when there are results outside the control lines, making it possible to reassess the results faster if necessary. Going forward, this method will assist laboratories in managing the quality of the results of their water and wastewater analysis.

Materials and methods

Description of the study

The School of Agriculture of the Polytechnic Institute of Beja has a water quality control laboratory accredited to ISO/IEC 17025, where water quality is analyzed. This laboratory is located in Beja, Portugal. The technological process of the wastewater treatment plant analyzed includes mechanical, biological, and chemical treatment of wastewater. The values of pollution indicators for sewage discharged into the municipality to the slaughterhouse may not exceed the following values: pH 19, BOD5 500.0 mg O2·L−1, COD 1500.0 mg O2·L−1, TSS 1000.0 mg·L−1, and TP 25 mg P·L−1. These values are accorded between the company and the municipality.

The objective was thus to create control charts for the pH, BOD5, COD, TSS, and TP parameters in analyzing a wastewater treatment plant from a slaughterhouse in Alentejo for 18 months (2021–2022).


The methodology was made according to Figure 1. The laboratory receives the wastewater samples, analyzes them, and creates a database with the data. The work was applied from this step forward, where the data was analyzed, and the control charts were created.

Fig1 daSilva Sustain22 14-22.png

Figure 1. Process method

The creation of the control charts is structured with an upper control line (UCL), a lower control line (LCL), the process means or target (CL), and the observed points. This statistical tool shows the evolution over time of a characteristic, allowing the identification of the purpose of variations and assisting in a continuous improvement of the process to produce itself according to the specifications, keeping the process under statistical control. Statistical control is ensured through the lines of the control limits that allow real-time analysis of the progress of the process. This represents the statistic related to the variable of interest in case one or more points exceed the control limits, indicating that the process has a problem.[21]

For pollution indicators in treated sewage, control charts were within the boundaries of the helplines, and the control lines and the center line were determined considering the tree-sigma rule for the normal distribution N (μ, σ), where μ = average of the analyzed values, and σ = standard deviation of the analyzed variable.[10][22][23] Formulas were:

  • Lower control line (LCL): LCL = μ
  • Lower warning line (LWL): LWL = μ
  • Lower helpline (LHL): LHL = μ
  • Center line (CL): CL = μ
  • Upper helpline (UHL): UHL = μ +
  • Upper warning line (UWL): UWL = μ +
  • Upper control line (UCL): UCL = μ +

To analyze the technological reliability and stability of the wastewater treatment station in Alentejo, Portugal, with the use of control charts, some specifics were necessary. The coefficient of technical reliability was used, as with Śliz and Bugajski[10]:

where WN = plant reliability factor [−], xsr = average value of the analyzed pollution index in treated sewage [mg·dm−3], and xdop = permissible value of the analyzed pollution index in treated sewage [mg·dm−3].

The effectiveness of wastewater treatment was calculated according to the following formula:

where: ƞ = reduction of a particular pollutant index in treated sewage [%], Ss = value of the pollution index in raw sewage [mg·dm−3], and S0 = value of the pollution index in treated sewage [mg·dm−3].

To verify interruption or instability of the effluent treatment process from control charts, we can use the following parameters, as with Andraka[24]: eight consecutive points on one side of the central line, one point outside the control limits, two of the three points outside the ±2σ warning lines, and four of five consecutive points beyond the ±1σ extension lines.[10]

Control charts have three fundamental objectives: reducing variability, monitoring, and estimating process quality parameters.[25] In constructing control charts, it is essential and valuable to distinguish the two phases of implementation and construction.

In phase 1, a set of process data is analyzed retrospectively to understand the variation of the process over time, evaluate the stability of the process, and model the performance of the process under control. This last step is usually carried out by estimating the parametric model in phase 2, in which the process has been previously estimated. Phase 1 thus corresponds to a retrospective check of the process where the experimental control limits are calculated, while phase 2 is concerned with monitoring the process itself.[26]

Phase 2 begins after collecting a set of process data under stable conditions and is representative of the performance of the process under control. In phase 2, a control chart is used to monitor the process, comparing the sample statistics for each successive sample as extracted from the process with the control limits.[27]


The results show the control charts for the inlet and outlet effluents of an existing wastewater treatment plant in a slaughterhouse in Alentejo, Portugal.

Figure 2 and Figure 3 show the inlet and outlet effluent behavior for the pH parameter. The control charts show the variation during the 30 days of treatment for wastewater.

Fig2 daSilva Sustain22 14-22.png

Figure 2. The control chart for pH inlet

Fig3 daSilva Sustain22 14-22.png

Figure 3. The control chart for pH outlet

It can be seen that after treatment, the results are stabilized; this is the possible interpretation of the analysis definition of the control lines. Figure 4 shows the COD inlet results; compared to Figure 5, the COD outlet shows an improved system. For Figure 5, there is a first point close to the LWL, but it is not necessary to take control because the next point is far away from the LWL. Only two points cross the control lines, and this behavior does not jeopardize the treatment outcome.

Fig4 daSilva Sustain22 14-22.png

Figure 4. The control chart for COD inlet

Fig5 daSilva Sustain22 14-22.png

Figure 5. The control chart for COD outlet

Analyzing the results for Figure 6 and Figure 7, it is possible to see the same stability of the results at the outlet of the treated effluent, with only one point crossing the UCL control line. Due to all the results obtained having this same point outside the standard, it is believed that the treatment was ineffective on this day.

Fig6 daSilva Sustain22 14-22.png

Figure 6. The control chart for BOD5 inlet

Fig7 daSilva Sustain22 14-22.png

Figure 7. The control chart for BOD5 outlet

For total phosphorus results, Figure 8 and Figure 9 show similar results. The resulting line remains stable and only crosses the UHL line once; the results stay close to the LC control line across all samples.

Fig8 daSilva Sustain22 14-22.png

Figure 8. The control chart for TP inlet

Fig9 daSilva Sustain22 14-22.png

Figure 9. The control chart for TP outlet

For the TSS inlet chart (Figure 10 and Figure 11) one sample exceeds the UWL line, and when observing the TSS outlet chart, it can be noted that the post-treatment results are stable, except on three occasions. In this situation, it may alert the company to a possible problem in the treatment in that specific period.

Fig10 daSilva Sustain22 14-22.png

Figure 10. The control chart for TSS inlet

Fig11 daSilva Sustain22 14-22.png

Figure 11. The control chart for TSS outlet

All control charts obtained were analyzed, and most points were under statistical control in Figures 2–11. In accordance with Montgomery[27], only rule 1, the presence of special causes of variation, was evaluated, and there was only one particular cause. With the process under statistical control in phase 1, it passes to phase 2, which consists of monitoring the process.

Note that the purpose of using the rules is to increase the sensitivity of control charts. However, care must be taken when using a set of rules, as an excessive number of false alarms may occur. The higher the number of rules to be used, the greater the number of false alarms.[27]

Table 1 shows the technological reliability coefficient against the average efficiency of the analyzed pollutant removal in the sewage treatment plant. It can be seen that the efficiency of reduction from treatment is good enough to reach more than 70% in all parameters analyzed, with better results for the BOD5 indicator, reaching 89%.

Table 1. Value of the technological reliability coefficient against the average efficiency of the analyzed pollutant removal in the sewage treatment plant in the Alentejo slaughterhouse.
Indicator BOD5 COD TSS TP
Reliability coefficient WN 0.37 0.57 0.45 0.84
Reduction efficiency η (%) 89 78 76 70

The BOD5 parameter showed the lowest rehabilitation coefficient (WN) of 0.37 and, consequently, the most excellent technological reliability, which indicates a highly satisfactory result for this parameter. For COD and TSS parameters, the rehabilitation coefficient showed results of 0.57 and 0.45, respectively, indicating values that can be considered reasonably good. In the case of TP, the indicated value is within the legislation in force. However, it is the highest value for the rehabilitation coefficient and consequently has the lowest treatment efficiency among the analyzed parameters since the indicators are inversely proportional. A study by Mlyński[28] used the indicator to prove the technological reliability of the treatment plant, where it obtained results below 1.00. However, this was slightly higher when compared to the results obtained in the present study.


Although there were some lower deviations in the LC, most of the results were stable after the treatment, which can be seen from the construction of the control charts.

The graphs demonstrate stability after water treatment, except for occasional events in a given period. These points that suffered oscillation may indicate a lower treatment efficiency because this anomaly is in the same period and with different parameters such as COD and BOD5. For the TSS input graph, one sample reaches the UWL line, while the TSS output graph shows that the post-treatment results are stable, except for three points that, in this situation,, may alert the company to a possible problem in the treatment in that specific period. Another potential cause of this difference in results may be related to the error in sample collection.

In sample 18, all parameters were altered beyond the control lines UCL, UWL, and UHL, except for the parameter pH. This behavior leads us to believe that the wastewater treatment plant had some technical problems that led to the inefficiency of treatment or stoppage of the process on this day. It is verified that this is the only sample we obtained this type of change. In the previous and subsequent samples, the results were satisfactory within the control lines, as expected. According to Nagendra and Rai[29], the chart series size, sample size, and sampling interval are the three main factors in detecting changes efficiently. Thus, for further investigation of the cause of this variation, a larger number of samples is required. In a similar study, Moore[30] suggests that in relation to the Shewhart chart of averages (x), the errors depend on the degree of non-normality and the sample size (or subgroup). These errors can be reduced by using a larger sample size.

Studies related to wastewater treatment from slaughterhouses indicate the need for efficiency in treatment due to environmental risks and human health. When comparing the results obtained by Reilly et al.[15] in their studies for COD (75%) and BOD5 (85%), the results obtained in this study show a good removal efficiency, with values of 78% and 89%, respectively. These data assist in analyzing results compared to the values established in the decrees of laws in force for Portugal, such as Decree-Law No. 236/98.[31] According to 236/98[31], phosphorus in wastewater may have a concentration of 10 mg P/L in the forms of orthophosphates, polyphosphates (P2O7), and organic phosphorus.[32] Wastewater treatment is carried out to avoid risks to public health, pollution of water resources, and the environment in general. It is essential to be able to control these results. The work performed by Luizi[33] created control charts similar to this work and obtained values of 95% for removal efficiency and 0.49 for the rehabilitation coefficient for TP. Luizi's values are better than those found in Table 1 for the TP, and a justification for these results is the origin of the residual water. Although there is a similarity in the construction of the control charts, we cannot, however, compare the results since they are wastewater from different sources.

These results demonstrate how control charts help with the analysis of data in the laboratory, thus establishing greater quality control. As such, when a result is outside the line control (LC) in the respective control chart, it can be more readily said that there was an error in the treatment process, facilitating the search for corrections.


This study’s main objective was based on the application of statistical control of the process of analyzing wastewater from a food industry company, more specifically in the meat sector (a slaughterhouse). From the experimental point of view, it started by diagnosing OOS results via control charts that allow laboratorians to better identify the main problems and situations to be corrected and monitored.

Most of the results of the studied parameters in question oscillated around the central line and did not show any crossing of the control lines or grouping of samples below or above the characteristic lines. As such, the control charts were effective for what was proposed, and variations could be observed in the analyzed period. These results confirmed that the treatment is largely stable according to the rehabilitation coefficient and treatment efficiency results.

With the help of control charts, it could also be observed that on the eighteenth day of collection, all parameters exceeded the UWL. This behavior was due to a possible isolated failure of treatment. This OOS result could be observed in all control charts but the pH charts, thus demonstrating how rapid detection of variations in small scales on control charts allows the lab to better act upon identifying potential causes of variability. Finally, the results of this work show it is possible to state that all points are under statistical control, and thus by extension these types of control charts can have routine value in the laboratory.

Abbreviations, acronyms, and initialisms

  • BOD5: biological oxygen demand
  • CL: center line
  • COD: chemical oxygen demand
  • LCL: lower control line
  • LHL: lower helpline
  • LWL: lower warning line
  • OOS: out of specification
  • TP: total phosphorus
  • TSS: total solid suspense
  • UCL: upper control line
  • UHL: upper helpline
  • UWL: upper warning line
  • WFD: Water Framework Directive


Thanks to the Water Quality Control Laboratory of the Agrarian School of Beja Polytechnic Institute, Beja.

Author contributions

Conceptualization, F.M.O.d.S. and K.S.S.; methodology, F.M.O.d.S. and M.A.A.; validation, F.M.O.d.S., K.S.S. and M.A.A.; formal analysis, M.I.C., M.R., I.S. and M.J.I.; investigation, F.M.O.d.S. and K.S.S.; data curation, M.I.C., M.R., I.S. and M.J.I.; writing—original draft preparation, F.M.O.d.S.; writing—review and editing, F.M.O.d.S. and K.S.S.; supervision M.A.A. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Conflicts of interest

The authors declare no conflict of interest.


  1. Costa, A.F.B.; Epprecht, E.K.; Carpinetti, L.C.R. (2010). Controle Estatístico da Qualidade (2nd ed.). Atlas. 
  2. Perez, Valéria Vasconcelos; Diacenco, Adriana Amaro; Paulista, Paulo Henrique (25 April 2017). "ANÁLISE DAS SETE FERRAMENTAS ESTATÍSTICAS DA QUALIDADE UTILIZADAS NOS SISTEMAS PRODUTIVOS". Revista Univap 22 (40): 807. doi:10.18066/revistaunivap.v22i40.1629. ISSN 2237-1753. 
  3. Samohyl, R. (2009). Controle Estatístico de Qualidade (1st ed.). Elsevier. pp. 352. ISBN 9788535232202. 
  4. Ramos, E.M.L.S. (2003). "Aperfeiçoamento e desenvolvimento de ferramentas do controle estatístico da qualidade: utilizando quartis para estimar o desvio padrão". Repositório Institucional. Universidade Federal de Santa Catarina. 
  5. Corrêa, H.L. (2009). Administração da Produção e Operações: Manufatura e Serviços: Uma Abordagem Estratética (2nd ed.). Atlas. pp. 690. ISBN 9788522442126. 
  6. Simonet, B.M. (1 June 2005). "Quality control in qualitative analysis" (in en). TrAC Trends in Analytical Chemistry 24 (6): 525–531. doi:10.1016/j.trac.2005.03.011. 
  7. Razif, M. (2022). "BOD, COD, and TSS Predictions from DO measurement results for the Surabaya River, Indonesia". Journal of Civil Engineering, Planning, and Design 1 (1): 1–7. doi:10.31284/j.jcepd.2022.v1i1.3047. 
  8. Orssatto, Fábio; Vilas Boas, Marcio A.; Nagamine, Ricardo; Uribe-Opazo, Miguel A. (1 August 2014). "Shewhart's control charts and process capability ratio applied to a sewage treatment station". Engenharia Agrícola 34 (4): 770–779. doi:10.1590/S0100-69162014000400016. ISSN 0100-6916. 
  9. International Organization for Standardization (November 2019). "ISO 7870-1:2019 Control charts — Part 1: General guidelines". International Organization for Standardization. 
  10. 10.0 10.1 10.2 10.3 10.4 Śliz, Paulina; Bugajski, Piotr (2022). "Assessment of the stability and reliability of the water treatment plant in Nowy Sącz using control cards". Journal of Water and Land Development 52 (I–III): 251–6. doi:10.24425/JWLD.2022.140396. 
  11. Turuta, Tatiana Barbosa (5 March 2015) (in pt). Aplicação de cartas de controle como ferramenta de melhoria frente às dificuldades operacionais de laboratórios acreditados na ABNT NBR ISO/IEC 17025. São Carlos. doi:10.11606/d.75.2015.tde-20052015-094414. 
  12. Zan, Tao; Wang, Min; Fei, Ren Yuan (1 March 2010). "Pattern Recognition for Control Charts Using AR Spectrum and Fuzzy ARTMAP Neural Network". Advanced Materials Research 97-101: 3696–3702. doi:10.4028/ ISSN 1662-8985. 
  13. Hadian, Hengameh; Rahimifard, Ali (1 April 2019). "Multivariate statistical control chart and process capability indices for simultaneous monitoring of project duration and cost" (in en). Computers & Industrial Engineering 130: 788–797. doi:10.1016/j.cie.2019.03.021. 
  14. Wagner, Iwona; Breil, Pascal (2013). "The role of ecohydrology in creating more resilient cities" (in en). Ecohydrology & Hydrobiology 13 (2): 113–134. doi:10.1016/j.ecohyd.2013.06.002. 
  15. 15.0 15.1 Reilly, Matthew; Cooley, Andrew P; Tito, Duarte; Tassou, Savvas A; Theodorou, Michael K (1 March 2019). "Electrocoagulation treatment of dairy processing and slaughterhouse wastewaters" (in en). Energy Procedia 161: 343–351. doi:10.1016/j.egypro.2019.02.106. 
  16. 16.0 16.1 Bustillo-Lecompte, Ciro; Mehrvar, Mehrab (3 May 2017), Farooq, Robina; Ahmad, Zaki, eds., "Slaughterhouse Wastewater: Treatment, Management and Resource Recovery" (in en), Physico-Chemical Wastewater Treatment and Resource Recovery (InTech), doi:10.5772/65499, ISBN 978-953-51-3129-8, Retrieved 2022-12-17 
  17. Tirado, Lydia; Gökkuş, Ömür; Brillas, Enric; Sirés, Ignasi (1 December 2018). "Treatment of cheese whey wastewater by combined electrochemical processes" (in en). Journal of Applied Electrochemistry 48 (12): 1307–1319. doi:10.1007/s10800-018-1218-y. ISSN 0021-891X. 
  18. Kolev Slavov, Aleksandar (2017). "Dairy Wastewaters – General Characteristics and Treatment Possibilities – A Review". Food Technology and Biotechnology 55 (1). doi:10.17113/ftb. PMC PMC5434364. PMID 28559730. 
  19. Bazrafshan, Edris; Kord Mostafapour, Ferdos; Farzadkia, Mehdi; Ownagh, Kamal Aldin; Mahvi, Amir Hossein (29 June 2012). Marr, Andrew C.. ed. "Slaughterhouse Wastewater Treatment by Combined Chemical Coagulation and Electrocoagulation Process" (in en). PLoS ONE 7 (6): e40108. doi:10.1371/journal.pone.0040108. ISSN 1932-6203. PMC PMC3387025. PMID 22768233. 
  20. Şengil, İ. Ayhan; özacar, Mahmut (21 September 2006). "Treatment of dairy wastewaters by electrocoagulation using mild steel electrodes" (in en). Journal of Hazardous Materials 137 (2): 1197–1205. doi:10.1016/j.jhazmat.2006.04.009. 
  21. Henning, E. (25 October 2012). "Aperfeiçoamento e desenvolvimento dos gráficos combinados Shewhart-Cusum binomiais". Repositório Institucional. Universidade Federal de Santa Catarina. 
  22. Krzanowski, S.; Walega, A. (2006). "Wykorzystanie teorii niezawodności i statystycznej kontroli jakości do oceny eksploatacyjnej wiejskich oczyszczalni ścieków". Infrastruktura i Ekologia Terenów Wiejskich 3 (2): 17-37. 
  23. Krzanowski, S.; Walega, A.; Pasmionka, I. (2008). "Oczyszczanie ścieków z wybranych zakładów przemysłu spożywczego". Infrastruktura i Ekologia Terenów Wiejskich 1: 1-89. 
  24. Śliz, P. (2018). "Wykorzystanie statystycznej kontroli jakości w ocenie działalności Oczyszczalni Ścieków „Kujawy” w Krakowie". Biuletyn Komitetu Przestrzennego Zagospodarowania Kraju PAN (272): 382--391. 
  25. Batista, Luciana Teixeira; Franco, José Ricardo Queiroz; Fakury, Ricardo Hall; Porto, Marcelo Franco; Braga, Carmela Maria Polito (9 May 2022). "Methodology for Determining Sustainable Water Consumption Indicators for Buildings" (in en). Sustainability 14 (9): 5695. doi:10.3390/su14095695. ISSN 2071-1050. 
  26. Woodall, William H.; Montgomery, Douglas C. (1 October 1999). "Research Issues and Ideas in Statistical Process Control" (in en). Journal of Quality Technology 31 (4): 376–386. doi:10.1080/00224065.1999.11979944. ISSN 0022-4065. 
  27. 27.0 27.1 27.2 Montgomery, Douglas C. (2005). Introduction to statistical quality control (5th ed ed.). Hoboken, N.J: John Wiley. ISBN 978-0-471-65631-9. 
  28. Młyński, Dariusz; Bugajski, Piotr; Młyńska, Anna (26 April 2019). "Application of the Mathematical Simulation Methods for the Assessment of the Wastewater Treatment Plant Operation Work Reliability" (in en). Water 11 (5): 873. doi:10.3390/w11050873. ISSN 2073-4441. 
  29. Nagendra, Y.; Rai, G. (1 September 1971). "Optimum Sample Size and Sampling Interval for Controlling the Mean of Non-Normal Variables" (in en). Journal of the American Statistical Association 66 (335): 637–640. doi:10.1080/01621459.1971.10482323. ISSN 0162-1459. 
  30. Moore, Peter G. (1 November 1957). "Normality in Quality Control Charts". Applied Statistics 6 (3): 171. doi:10.2307/2985602. 
  31. 31.0 31.1 Ministério do Ambiente (1 August 1998). "Decreto-Lei n.º 236/98, de 1 de agosto". Diário da República n.º 176/1998, Série I-A de 1998-08-01. República Portugesa. pp. 3676 - 3722. 
  32. Emídio, V.J.G. (2012). "A problemática do fósforo nas águas para consumo humano e águas residuais e soluções para o seu tratamento". Sapientia. Universidade do Algarve. 
  33. Luizi, R.P.S.L. (March 2012). "Operação de Sistemas de Tratamento de Águas Residuais por Lamas Activadas com Arejamento Prolongado" (PDF). Universidade Técnica de Lisboa. 


This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was tweaked significantly to improve readability. The PMCID and DOI were also added when they were missing from the original reference. No citation was made with ISO 7870-1:2019; a citation was added for this version.