Strategies for a Sustainable Economy: Optimizing Processes for BOD, COD and TSS Removal from Wastewater

Abstract

In the current global context of the natural resource crisis and the need for environmental protection, sustainable economy strategies are becoming imperative. These strategies aim to optimize wastewater treatment processes, with a particular focus on the removal of biological and chemical quality indicators such as BOD, COD and TSS. By developing and implementing advanced technologies and effective resource management methods, this article explores ways the industry can reduce its negative environmental impact and contribute to a sustainable future. The proposed research investigates the impact of 40% ferric chloride on the purification processes of domestic wastewater using biological contactors. The study evaluates the efficiency of pollutant removal through measurements such as biochemical oxygen demand over 5 days (BOD), chemical oxygen demand (COD) and total suspended solids (TSS). Through the statistical analysis of the obtained results, the research identifies opportunities for innovative strategies in the sustainable economy, thus contributing to the optimization of purification process efficiency and significantly reducing pollution’s impact on the environment. In conclusion, this research highlights the use of 40% ferric chloride as an effective and sustainable method to improve the efficiency of wastewater treatment processes, focusing on BOD, COD and TSS removal. The findings demonstrate significant pollutant reduction and environmental impact mitigation, underlining its potential for Sustainable Development Goals. The study supports innovative strategies for optimizing water quality and recommends further evaluation of long-term impacts on human and environmental health.

1. Introduction

The wastewater issue is one of the greatest challenges for the environment and public health. Water quality indicators such as biochemical oxygen demand over 5 days (BOD), chemical oxygen demand (COD) and total suspended solids (TSS) are critical for assessing pollution levels and determining the efficiency of treatment processes [1,2,3,4,5,6,7,8]. With population growth and the pace of industrialization, the volume of wastewater has significantly increased, necessitating innovative and sustainable treatment solutions [9,10,11,12,13,14].

By 2030, Romania aims to achieve ambitious goals for wastewater treatment in line with European Union requirements and the 2030 Agenda for Sustainable Development [15,16,17,18,19,20,21]. These goals focus on improving infrastructure and complying with European directives on wastewater management [21,22,23,24].

A key element in achieving these ambitious investment goals is the implementation of an institutional model that allows larger, stronger, and more experienced operators to provide water supply and sewage services across multiple administrative–territorial units, under a single delegated management contract for these services [25,26,27,28].

The integration of ferric chloride (FeCl₃) into wastewater treatment processes has been extensively studied, demonstrating its efficacy in enhancing pollutant removal. FeCl₃ serves as a coagulant, effectively aggregating suspended particles and facilitating their removal. Studies have shown that FeCl₃ achieves higher removal efficiencies for color, turbidity, and organic materials compared to other coagulants like alum [29].

In membrane bioreactor (MBR) systems, FeCl₃ dosing has been observed to improve treatment performance by enhancing phosphorus removal without adversely affecting biological activity. This is achieved through the formation of a more porous foulant layer, which prevents the development of a strongly attached cake layer and pore blocking [30].

The application of FeCl₃ in anaerobic membrane bioreactors (AnMBRs) treating municipal wastewater has also been investigated. Long-term studies indicate that the addition of FeCl₃ enhances the removal efficiencies of COD and BOD, while also improving membrane performance by reducing fouling. This improvement is attributed to the reduction of colloidal and soluble substances in the sludge, leading to a more efficient treatment process [31,32].

Furthermore, FeCl₃ has been utilized for odor control in wastewater treatment plants by precipitating sulfides, thereby reducing hydrogen sulfide emissions. This application not only improves air quality around treatment facilities but also contributes to the overall efficiency of the treatment process [33].

This study aims to advance wastewater treatment strategies by investigating the integration of a 40% ferric chloride solution in biological treatment processes, focusing on its influence on pollutant removal efficiencies, specifically for COD, BOD and TSS. By analyzing data collected over three years (2021–2023) under varying treatment conditions, the research seeks to identify quantifiable improvements attributed to the chemical’s introduction.

Raw and treated wastewater was monitored at the Dumbraveni wastewater treatment plant (Figure 1) located in Sibiu County, Romania in the urban area of the locality with the same name, on a 25,869 square meter plot of land on the east bank of the Târnava Mare River [34].

The treatment process for the Dumbraveni domestic wastewater treatment plant is biological treatment with rotating biological contactors [35].

The Dumbraveni wastewater treatment plant is designed for a relatively small number of inhabitants (7100 PE) and only the removal of organic and suspended solids was taken into account [36] according to NTPA-011/2002 for localities with a population of less than 10,000 PE [37].

The novelty of the study lies in its specific context of applying high-concentration ferric chloride within a compact wastewater treatment plant serving small urban areas, a relatively underexplored scale in current research. Furthermore, the analysis includes comparative assessments before and after the implementation, allowing precise insights into the chemical’s operational efficacy. These findings aim to offer innovative approaches for enhancing wastewater management processes, aligning with European sustainability directives while addressing the unique challenges of smaller communities.

2. Experimental Setup

Sampling was carried out in accordance with the provisions of the standard on wastewater sampling [38] in polyethylene recipients and the volume sampled was 2 L wastewater.

The determination of the physicochemical parameters relevant for the operating efficiency of the treatment plant was carried out according to the standardized methods presented in

Table 1. Parameters and their analytical methods used in the Dumbraveni WWTP.

Parameter	Analytical Standard or Method	Equipment	Maximal Admitted Limits mg/L
pH	SR ISO 10523/2012, PO-01	WTW pH/conductivity multimeter model 330i with SENTIX® 41 electrode—Wissenschaftlich-Technische Werkstätte, Weilheim, Germania	6.5–8.5
COD	SR ISO 6060/1996	Velp eco 16 thermoreactor VELP Scientifica, Usmate Velate, Italia Merck Spectroquant® multispectrophotometer—Merck KGaA, Darmstadt, Germania	125.0
BOD	SR EN ISO 5815-1/2020 Method WTW 997230 OxiTop, PO-07	WTW incubator model TS 606/2-I—Wissenschaftlich-Technische Werkstätte, Weilheim, Germania WTW OxiTop® bottles—Wissenschaftlich-Technische Werkstätte, Weilheim, Germania	25.0
TSS	SR EN 872/2009	Classical filtration equipment	35.0

.

The choice of 40% ferric chloride concentration was motivated by several technical and operational factors, which are based on specialized literature and current industrial practices.

The 40% ferric chloride concentration represents an optimal balance between the chemical efficiency of the coagulant and safety considerations in handling and storage. It is frequently used in drinking and industrial water treatments, being a well-documented standard in the specialized literature [29,30,31,32,33] which ensures a compromise between performance and costs. In addition, this concentration is compatible with most dosing systems used in treatment plants, facilitating the implementation and control of the process.

The determination of the dosage amount of FeCl₃ coagulant for wastewater treatment at the Dumbraveni wastewater treatment plant was performed by laboratory tests using the jar test method.

The “jar test” method is a laboratory procedure often used to determine the optimum operating conditions for water or wastewater treatment. This method allows pH adjustments, variations in coagulant or polymer dosage, alternation of mixing rates, or testing coagulants or polymers of different types on a smaller scale to predict the performance of a large-scale treatment operation.

The jar test apparatus consists of six 1 L recipients fitted with six paddles that mix the contents of each recipient. One of the recipients acts as a control point, while in the other five recipients the operating conditions may vary. A measuring instrument for determining the speed—revolutions per minute (rpm)—located in the upper part of the center of the device allows uniform control of the mixing speed in all recipients.

Procedure: 1000 mL of sample water was introduced into the recipients of the device (sample I. mechanically purified waste water—after primary decantation and sample II. waste water—biological purification). One of the recipients is used for the control and the other five recipients can be adjusted according to the required conditions. For example, the pH in the jars can be changed or variations in coagulant dosages can be added to determine the primary operating conditions.

The wastewater samples selected for this research were taken from the Dumbraveni wastewater treatment plant and labeled as follows: mechanically treated wastewater after the primary settling process, before treatment with rotary biological contactors: Dumbraveni mechanical treatment (MT). In Figure 2 presents the flowchart of the Dumbraveni wastewater treatment plant.

The coagulant ferric chloride 40% was added to each container, recipient 1 = 0.5 mg/L (MTI 1), recipient 2 = 1 mg/L (MT2), recipient 3 = 5 mg/L (MT3), recipient 4 = 7.5 mg/L (MT4), recipient 5 = 10 mg/L (MT5), by mixing for about one minute at 100 rpm. Then, the influence of the amounts of coagulant on turbidity and on the quality indicators followed was observed. The rapid mixing step helps to disperse the coagulant in each recipient. One recipient was used for control, denoted mechanical treatment (MT), and the other five recipients can be adjusted according to the required conditions.

The residual turbidity, monitored parameters vs. coagulant dose, can be plotted graphically to determine the optimum conditions. The values obtained are correlated and adjusted to account for the actual treatment system.

During the monitoring period from the analysis of the experimental data it appears that the turbidity decreases with increasing coagulant dose (Figure 3).

3. Results and Discussion

The dosage of the coagulant ferric chloride min. 40% for COD, BOD and TSS removal was calculated based on the average values of physicochemical indicators and average daily flow during the study years.

During the study period, the ferric chloride 40% was dosed at the biofilter inlet, after primary settling, taking into account the values of the inlet parameters of the treatment plant at all times.

The improvement of the operation of the technological process with rotary biological contactors was realized by dosing the coagulant ferric chloride 40% as the advantages following chemical precipitation are numerous [29,30,31,32,33]:

−

Total phosphorus can be reduced below the permitted values.

−

The effectiveness of the treatment plant also increases in the removal of organic matter.

−

Prevents excessive growth of filamentous micro-organisms.

−

Helps to form sludge with good settling qualities.

−

Increases the dry matter content of the sludge, corrects density and the degree of dewatering.

The values for COD, BOD and TSS in the effluent obtained during the biological treatment process are shown in Figure 4. These values are compared both for the year in which ferric chloride was not dosed and for the years in which dosing was carried out, with reference to NTPA 001.

The monthly averages of the removal efficiencies of these three pollutants over the three years are presented, which are used as a data series in the SPSS computerized processing, with the column Year 2021 showing the efficiencies in the period before the use of ferric chloride and the columns Year 2022 and Year 2023 showing the efficiencies in the period when it was applied.

The data are represented in Figure 5, Figure 6 and Figure 7, showing the monthly averages of the removal efficiencies of the three pollutants over the three years.

The graph of monthly averages of COD removal efficiency shows higher values of this indicator after application of ferric chloride solution, and stabilized around 90%, compared to the values with very high fluctuations recorded previously.

Also, for BOD, there is a significant increase in removal efficiency in 2022 and 2023, followed by stabilization of the values at a level above 90% after fluctuations in 2021.

Regarding the evolution of TSS removal, overall, there is an increase in and some stabilization of efficiency in 2022 and 2023. However, it can be seen on the graph that some peaks reached by this indicator before the use of ferric chloride were very close to or even exceeded the levels recorded when the solution was used.

The annual evolution of the removal efficiency of COD, BOD and TSS pollutants is shown in Figure 8.

Figure 8 shows that the lack of ferric chloride dosing in the non-dosing year indicates a reduction in the removal efficiency of the three pollutants compared to the years when ferric chloride is used in the treatment process.

There is a significant, strong increase in the average annual efficiency for all three pollutants in Year 2022 compared to Year 2021 (with the use of ferric chloride). In Year 2023, the average COD and BOD increase very little, less than 2 percentage points, while the average TSS removal shows a decrease of almost 2 percentage points.

In order to determine the magnitude of the effect of ferric chloride on the removal efficiencies of the three pollutants, we next analyze the tables generated by the SPSS application.

The non-significant results of the Kolmogorov–Smirnov test confirm normal distributions for all data series analyzed, through Z-values correlated with Sig. levels much higher than the 0.05 threshold (

Table 2. Normal distribution test. One-sample Kolmogorov–Smirnov test.

No.	2021 (COD)	2022 (COD)	2023 (COD)	2021 (BOD)	2022 (BOD)	2023 (BOD)	2021 (TSS)	2022 (TSS)	2023 (TSS)
Deviation	12.9107	2.3519	3.54358	6.44280	1.18280	1.61946	12.01264	1.98607	3.52880
Dif. Absolute	0.167	0.141	0.220	0.152	0.127	0.213	0.223	0.175	0.214
Positive	0.083	0.141	0.193	0.122	0.081	0.176	0.153	0.091	0.148
Negative	0.167	0.137	0.220	0.152	0.127	0.213	0.223	0.175	0.214
Kolmogorov–Smirnov	0.578	0.489	0.763	0.527	0.440	0.736	0.773	0.607	0.740
Asymo. Sig.	0.892	0.971	0.606	0.944	0.990	0.650	0.589	0.854	0.644

).

The tests of sphericity, related to the simple ANOVA method with repeated measures, verify the fulfillment by the variable “removal efficiency” of the homogeneity condition of the correlation coefficients between each two steps. Test results are presented in

Table 3. Sphericity test elimination. Mauchly’s test of sphericity.

Within Subjects Effect	Mauchly’s W	Approx. Chi- Square	df	Epsilon
				Greenhouse–Geisser	Huynh–Feldt	Lower Bound
Efficiency_COD	0.119	21.268	2	0.532	0.541	0.500
Efficiency_BOD	0.192	16.503	2	0.553	0.570	0.500
Efficiency_TSS	0.247	13.998	2	0.570	0.593	0.500

.

As can be seen, in all three cases the sphericity condition is not satisfied, the W test results and p-values (Sig.) < 0.05 being statistically significant. For this reason, the analysis will continue considering the degrees of freedom (df) adjusted by the Greenhouse–Geisser correction coefficient value.

Intrasubject effects are shown in

Table 4. Within-subjects effects test.

Source		Type III Sum of Squares	df	Mean Square	F	Sig.
Efficiency_COD	Sphericity Assumed	2668.976	2	1334.488	20.102	0.000
	Greenhouse–Geisser	2668.976	1.063	2509.885	20.102	0.001
	Huynh–Feldt	2668.976	1.083	2464.594	20.102	0.001
	Lower Bound	2668.976	1.000	2668.976	20.102	0.001
Error(Efficiency_COD)	Sphericity Assumed	1460.498	22	66.386
	Greenhouse–Geisser	1460.498	11.697	124.858
	Huynh–Feldt	1460.498	11.912	122.605
	Lower Bound	1460.498	11.000	132.773
Efficiency_BOD	Sphericity Assumed	2535.391	2	1267.695	100.586	0.000
	Greenhouse–Geisser	2535.391	1.106	2292.008	100.586	0.000
	Huynh–Feldt	2535.391	1.140	2224.954	100.586	0.000
	Lower Bound	2535.391	1.000	2535.391	100.586	0.000
Error(Efficiency_BOD)	Sphericity Assumed	277.269	22	12.603
	Greenhouse–Geisser	277.269	12.168	22.787
	Huynh–Feldt	277.269	12.535	22.120
	Lower Bound	277.269	11.000	25.206
Efficiency TSS	Sphericity Assumed	639.011	2	319.505	5.705	0.010
	Greenhouse–Geisser	639.011	1.141	560.206	5.705	0.030
	Huynh–Feldt	639.011	1.185	539.030	5.705	0.028
	Lower Bound	639.011	1.000	639.011	5.705	0.036
Error(Efficiency TSS)	Sphericity Assumed	1232.056	22	56.003
	Greenhouse–Geisser	1232.056	12.547	98.192
	Huynh–Feldt	1232.056	13.040	94.481
	Lower Bound	1232.056	11.000	112.005

by comparing the effects of the ferric chloride solution on the removal efficiency of each pollutant within the same calendar month over the three years.

The test results (F) represent a comparison of the homogeneity of variances, which should be relatively equal for each of the three stages. Since sphericity has been assumed by adjusting the degrees of freedom, from the tables above we extract the values corresponding to the Greenhouse–Geisser rows.

Thus, we analyze the values:

−

for COD: F(1.063; 11.697) = 20.102, significant value at a threshold p = 0.001 < 0.05: there is a significant effect of the use of ferric chloride solution on the efficiency of COD removal from wastewater (99.9% confidence statement);

−

for BOD: F(1.106; 12.168) = 100.586, significant value at a threshold p = 0.000 < 0.05: there is a significant effect of the use of ferric chloride solution on the efficiency of BOD removal from wastewater (100% confidence);

−

for TSS: F(1.141; 12.547) = 5.705, significant value at a threshold p = 0.030 < 0.05: there is a significant effect of the use of ferric chloride solution on the efficiency of removal of TSS from wastewater (97% confidence level).

The contrast test generates

Table 5. Tests of within-subjects contrasts.

Source	Efficiency_COD	Type III Sum of Squares	df	Mean Square	F	Sig.
Efficiency_COD	Level 1 vs. Level 2	3873.613	1	3873.613	22.760	0.001
	Level 2 vs. Level 3	4.083	1	4.083	0.367	0.557
Error(Efficiency_COD)	Level 1 vs. Level 2	1872.107	11	170.192
	Level 2 vs. Level 3	122.437	11	11.131
Efficiency_BOD	Level 1 vs. Level 2	3594.941	1	3594.941	87.474	0.000
	Level 2 vs. Level 3	10.830	1	10.830	3.308	0.096
Error(Efficiency_BOD)	Level 1 vs. Level 2	452.069	11	41.097
	Level 2 vs. Level 3	36.010	11	3.274
Efficiency_TSS	Level 1 vs. Level 2	1154.441	1	1154.441	7.769	0.018
	Level 2 vs. Level 3	54.187	1	54.187	3.449	0.090
Error(Efficiency_TSS)	Level 1 vs. Level 2	1634.569	11	148.597
	Level 2 vs. Level 3	172.802	11	15.709

, which show the differences between the experimental steps:

Test values for COD removal efficiency:

−

F(1; 22.760), p = 0.001 < 0.05—there are statistically significant differences between Year 2021 and Year 2022 stages (“Level 1 vs. Level 2”);

−

F(1; 0.367), p = 0.557 > 0.05—no statistically significant differences between Year 2022 and Year 2023 (“Level 2 vs. Level 3”).

Test values for BOD removal efficiency:

−

F(1; 87.474), p = 0.000 < 0.05—there are statistically significant differences between Year 2021 and Year 2022 (“Level 1 vs. Level 2”) stages;

−

F(1; 3.308), p = 0.096 > 0.05—no statistically significant differences between Year 2022 and Year 2023 (“Level 2 vs. Level 3”).

Test values in the case of TSS removal efficiency:

−

F(1; 7.759), p = 0.018 < 0.05—there are statistically significant differences between the stages Year 2021 and Year 2022 (“Level 1 vs. Level 2”);

−

F(1; 3.449), p = 0.090 > 0.05—there are no statistically significant differences between the stages Year 2022 and Year 2023 (“Level 2 vs. Level 3”).

The magnitude of the effect size of the ferric chloride solution (coefficient r) for the removal efficiency of the three pollutants is calculated below for the stages between which there are significant differences. In relation to the reference levels, the values obtained are interpreted accordingly:

COD:

$$r_{year2022-year2021}=\sqrt{{\displaystyle \frac{F_{contrast}}{F(d{f}_{intergroup})+d{f}_{intragroup}}}}=\sqrt{{\displaystyle \frac{22.760}{20.102\cdot 1.063+11.697}}}=0.83$$

The ferric chloride solution, in the first stage of use, had a very strong effect on the increase in COD removal efficiency.

Eighty-three percent of the increase in COD removal efficiency was due to the application of the ferric chloride solution.

BOD:

$${\mathrm{r}}_{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}2022-\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}2021}=\sqrt{{\displaystyle \frac{{\mathrm{F}}_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{s}\mathrm{t}}}{\mathrm{F}(\mathrm{d}{\mathrm{f}}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{p}})+\mathrm{d}{\mathrm{f}}_{\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{r}\mathrm{o}\mathrm{u}\mathrm{p}}}}}=\sqrt{{\displaystyle \frac{87.474}{100.586\cdot 1.106+12.168}}}=0.84$$

The ferric chloride solution, in the first stage of use, had a very strong effect on the BOD removal efficiency increase.

Eighty-four percent of the increase in BOD removal efficiency was due to the application of the ferric chloride solution.

TSS:

$$r_{year2022-year2021}=\sqrt{{\displaystyle \frac{F_{contrast}}{F(d{f}_{intergroup})+d{f}_{intragroup}}}}=\sqrt{{\displaystyle \frac{7.769}{5.705\cdot 1.141+12.547}}}=0.64$$

The ferric chloride solution, in the first stage of use, had a strong effect on increasing the removal efficiency of TSS.

Sixty-four percent of the increase in TSS removal efficiency was due to the application of ferric chloride solution.

The calculation of Pearson correlation coefficients helps to detect the links between the removal efficiencies of the three pollutants within each stage,

Table 6. Correlations between removal efficiencies of COD, BOD and TSS for Year 2021.

		2021 (COD)	2021 (BOD)	2021 (TSS)
2021 (COD)	Pearson Correlation	1	0.225	0.590
	Sig. (2-tailed)		0.483	0.043
	N	12	12	12
2021 (BOD)	Pearson Correlation	0.225	1	0.114
	Sig. (2-tailed)	0.483		0.665
	N	12	12	12
2021 (TSS)	Pearson Correlation	0.590	0.144	1
	Sig. (2-tailed)	0.043	0.655
	N	12	12	12

,

Table 7. Correlations between removal efficiencies of COD, BOD and TSS for Year 2022.

		2022 (COD)	2022 (BOD)	2022 (TSS)
2022 (COD)	Pearson Correlation	1	0.856	0.150
	Sig. (2-tailed)			0.090
	N	12	12	12
2022 (BOD)	Pearson Correlation	0.856	1	0.428
	Sig. (2-tailed)			0.165
	N	12	12	12
2022 (TSS)	Pearson Correlation	0.510	0.428	1
	Sig. (2-tailed)	0.090	0.165
	N	12	12	12

and

Table 8. Correlations between removal efficiencies of COD, BOD and TSS for Year 2023.

		2023 (COD)	2023 (BOD)	2023 (TSS)
2023 (COD)	Pearson Correlation	1	0.971	0.366
	Sig. (2-tailed)			0.242
	N	12	12	12
2023 (BOD)	Pearson Correlation	0.971	1	0.258
	Sig. (2-tailed)			0.419
	N	12	12	12
2023 (TSS)	Pearson Correlation	0.366	0.258	1
	Sig. (2-tailed)	0.242	0.419
	N	12	12	12

.

In 2021, a strong, negative correlation is observed between COD removal efficiency and TSS removal efficiency. The value −0.590 is statistically significant as Sig. = 0.043 < 0.05. The negative correlation indicates an opposite evolution of the two indicators, as can be easily observed in Figure 9. Thus, when COD removal efficiency increases, TSS removal efficiency decreases and increases.

Regarding the correlations between the efficiency of COD removal and BOD removal, as well as between BOD and TSS, we observe negative values (−0.255 and −0.14, respectively). However, these are not statistically significant, as the p-values (Sig.) are much higher than the accepted threshold of 0.05. This leads to the rejection of the hypothesis that there is a significant influence or relationship between these parameters.

In 2022, we find a very strong, positive correlation between COD removal efficiency and BOD removal efficiency. The value 0.856 is statistically significant as Sig. = 0.000 < 0.05. The positive correlation indicates a similar evolution of the two indicators, almost identical in our case, which is illustrated in Figure 10.

Importantly, with the use of ferric chloride, the negative correlation between COD and TSS, manifested in 2021, disappears. Moreover, we observe that in the year 2022 these indicators are very close to a strong positive correlation (0.510), the significance threshold Sig. = 0.09 being close to the minimum admissible level of certainty (0.05).

In this year, the correlation between the efficiency of BOD removal and TSS removal becomes positive (0.428); however, according to the acceptability threshold it remains insignificant (Sig. = 0.165 > 0.05).

In 2023, the very strong, positive correlation between COD removal efficiency and BOD removal efficiency is maintained. The value of 0.971 is statistically significant as Sig. = 0.000 < 0.05. Figure 11 is relevant in this respect.

The correlations between the efficiency of COD removal and TSS removal, as well as between BOD removal and TSS removal, are positive (0.366 and 0.258, respectively) but still statistically insignificant due to the p-values (Sig.) of 0.242 and 0.419.

By analyzing the data obtained, it can be concluded that the use of ferric chloride had a significant impact on the removal efficiency of the three pollutants COD, BOD and TSS. In particular, a significant improvement in removal efficiency was observed in the years when ferric chloride dosing was applied. This finding suggests that the use of ferric chloride can be an effective method in wastewater treatment processes, contributing to the reduction of pollution levels. By dosing ferric chloride 40% solution in the technological process with rotary biological contactors in the Dumbraveni WWTP, the results obtained are promising, not only the decrease in the maximum admissible concentrations of pollutants in the effluent but also the compliance with the obligation (according to the Water Management Authorization) that the treated water meets the quality conditions.

4. Conclusions

Adopting treatment technology for wastewater is a very complex process.

The use of ferric chloride had a significant impact on the removal efficiency of COD, BOD and TSS pollutants in wastewater treatment processes. The observed efficiency of the ferric chloride treatment method suggests that this technique could be a viable and effective solution for improving water quality in wastewater treatment systems.

Statistical data show that the use of ferric chloride in wastewater treatment processes can be associated with a significant improvement in pollutant removal efficiency, which indicates an important potential for environmental protection and conservation of water resources.

Pollutant reduction methods, such as BOD, COD and TSS, can contribute to improving water quality and optimizing wastewater treatment processes.

These findings emphasize the importance of implementing and using efficient and sustainable technologies in wastewater management and treatment to protect the environment and public health.

The data obtained from this research can serve as a basis for the development of effective wastewater management strategies and policies, contributing to environmental and public health objectives.

The 40% ferric chloride solution treatment method was found to be a sustainable treatment method with low environmental impact compared to other more invasive or potentially polluting technologies. It shows a significant reduction of the environmental impact, highlighting the effectiveness of the treatment process at the Dumbraveni wastewater treatment plant in reducing pollutants and improving the quality of wastewater, thus contributing to the protection of the local ecosystem.

These findings support the idea that the use of 40% ferric chloride in wastewater treatment can contribute to achieving Sustainable Development Goals by promoting more responsible and efficient water resource management practices.

The recommendation to extend studies to investigate the full environmental and human health impacts of ferric chloride use could provide important additional information for decision making in the field of wastewater management.

These conclusions emphasize the importance of continuing efforts towards the use of sustainable technologies and wastewater management practices that protect the environment and contribute to building a more sustainable and healthy future for future generations.