Statistical analysis of the physico-chemical characteristics of industrial effluents: Case of evaporating basins used as final storage for effluent

2.1 Overview of the research region

To collect a sampling of effluent from two evaporating basins during 2021/ 2022, a renewable solar power plant in the south of Morocco that uses an indoor water treatment facility was selected to perform the experiences. Fig. 1 depicts the precise location of the study area as presented in the map.

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Fig. 1

Location map of the study area.

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The evaporation ponds constructed using geomembrane materials have dimensions of 165 m in length, 85 m in width, and 2 m in depth.

2.1.1

2.1.1 Meteorological pattern

The Moroccan southern region is known for its arid climate and continental characteristics. Both the south-western Ocean and desert air currents have an impact, although the former predominates (Web, 2020).

• Annual rainfall:

Limited precipitation is a distinctive feature of the region. About 187 mm of rainfalls on average each year.

The climate variability dispersion of precipitation pattern has a highly irregular pattern. The area has both hot seasons, with just a seasonal rainfall of 65 mm, together with warmer seasons, with average annual precipitation as high as 293 mm.

There are just a few days with hail in the area each year on average, and there are only a few days with thunderstorms. The project location and its immediate environs seldom get snow.

• The region’s temperatures:

The region maintains a global average temperature of 26.8 °C. The warmest months of the year, July and August, experience average high temperatures reaching 40.0 °C. In contrast, January exhibits the coolest median high temperature, recorded at 15.0 °C. The wintertime ranges in temperature from 10 °C up to 26.0 °C. The extent of summertime temperatures is 26.0 °C up to 41.2 °C. Just a handful of days during the seasons, the temperatures are lower than zero degrees.

This region had temperatures as high as 42.5 °C and as low as 09.0 °C, all in 2021 (Fig. 2.).

• Humidity:

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Fig. 2

Umbro rainfall and heat graph in the south region of Morocco (Web, 2020).

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The levels of the average humidity decreased when the average temperature increases. Relative humidity varies by month, from 41% in July to 63% in December, according to data gathered in the area between 2000 and 2018.

The maximum level of evaporation exceeds 1.8 m/year, is considered the greatest in Morocco, in this region, a peak in July and the highest concentration of evaporation from May to September (which accounts for 50% of the total evaporation).

2.2 Sampling’s effluent analysis

The sampling was gathered between April 2022 and November 2021 using a certified automated tester, the SIGMA type 940P.

We used conventional techniques at the Laboratory of chemical and environmental to do the Physicochemical study of the wastewater.

All the sampling was delivered in several unique bottles since the effluent from the evaporating basin included oil residues (type glass bottle). This sampling was analyzed on the same day they were collected from the power plant.

The Common Techniques for the Examination of Effluent provides a complete description of standard processes for wastewater analysis. (Silva et al., 2022).

The measurements for the physicochemical analysis are highlighted below (Table 1):

3.1 Statistical regression and correlation

When evaluating effluent conformity in the evaporating basin, regression and correlation examination is helpful. This statistical evaluation was conducted using Minitab software.The correlation coefficients (r) between physicochemical parameters in industrial effluents were computed for this study using the Pearson correlation method. The most common correlation types employed in the main statistical analysis are Pearson and Spearman. The most common method for determining how closely two variables are connected linearly is Pearson correlation. The following equation is used to compute the Pearson coefficient (r) correlation: (Javadzadeh et al., 2020): $$r=\frac{N\mathrm{\Sigma }xy-\mathrm{\Sigma }x\mathrm{\Sigma }y}{\sqrt{\left[N\sum x^2-{(\sum x)}^2\right]\left[N\sum y^2-{(\sum y)}^2\right]}}$$

In this equation, r is the Pearson correlation coefficient, N represents the number of data points, x represents the values of a variable and y represents the values of the other variable.

∑x and ∑y are the sum of all × and y scores respectively, ∑x² and ∑y² are the sum of × and y squared scores, where ∑xy is the sum of the products of × and y paired scores.

This least-squares regression method in statistical data is used to predict how reliant factors will change over time. Amongst datasets, there must be one that perfectly matches the trend being taken into account, according to the least-squares approach. Regression analysis may be used to see the association between the dependent variable and several independent ones (Amaral and Ferreira, 2005). The following regression equation was created using the parameter estimates and the proposed model for this relationship (Stoichev et al., 2020): $$y=ax+b$$

In this equation, y is the dependent, x is the independent, a is the coefficient of linear correlation and b is the constant at the origin.

The aim of this statistical study is to assess relationships between the physico-chemical properties of effluent. This study uses a bivariate correlation research to evaluate the strength of the link between two factors as well as the direction of the connection (Javadzadeh et al., 2020).

The coefficient (r) of −1 or + 1 indicates the largest negative and positive link for two variables; furthermore, when the obtained value is close to 1, the linear connection level for the variables is extremely strong. And when the correlation coefficient value decreases toward zero, the linear association among those variables will diminish in significance (Kumar et al., 2006).

Through using p-value, the linear correlation's strength was therefore evaluated. Consequently, the correlation in this instance is strong when the p-value is lower than 0.05 or lower than 0.01. Nevertheless, the linear connection is not strong if the p-value is above 0.05. At 0.01 and 0.05 values, the relevance is assessed (second tailed assessment) (Shroff et al., 2015).

4.1 Evolution of the physico-chemical characteristics of the effluents over time

Table 2 presents the outcomes regarding Physicochemical examination of the effluent for a year.

Four effluent parameters (EC, BOD₅, Na, and Cl₂) surpass the limit maximum values established by Moroccan regulations for effluents discharged into surface resources, according to Table 2 (Moroccan limit-values-of-discharges, 2017):

The average EC value of 16.2 ms/cm is greater than the maximum limit value which is 2.7 ms/cm.

The average BOD₅ value of 108 mg O2/l is more than the maximum fixed limit which is 100 mg O2/l.

The average of Na value of 603 mg/l is pretty high.

The median chlorine value of 0.09 mg/l is more than the limit value of 0.05 mg/l.

The bacteriological investigation demonstrates that there are no microorganisms in this effluent.

The following Figs. 3–6 illustrate the yearly variations in the Physicochemical properties of non-compliant effluent as compared to limit criteria.

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Fig. 3

DBO5 Analysis results vs the limit value.

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Fig. 4

Sodium Analysis results vs the limit value.

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Fig. 5

Chlorine Analysis results vs the limit value.

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Fig. 6

EC Analysis results vs the limit value.

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4.2 Correlation results

The correlation data for the entire year (winter and summer) are summarized in Table 3.

4.3 Regression analysis outcomes

Employing a statistical tool, the regression model for the effluent characteristics was undertaken.

Table 5 presents the regression model’s last square focused on effluent variables with statistically strong correlations.

• Regression graphs:

The Figs. 7–19 illustrate linear relationships between effluent Physico-chemical variables:

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Fig. 7

Linear plot between TSS and T.

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Fig. 8

Linear plot between Na and TSS.

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Fig. 9

Linear plot between DBO₅ and EC.

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Fig. 10

Linear plot between TSS and EC.

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Fig. 11

Linear plot between DBO₅ and Fe.

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Fig. 12

Linear plot between DBO₅ and P.

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Fig. 13

Linear plot between TN and Zn.

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Fig. 14

Linear plot between TN and Fe.

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Fig. 15

Linear plot between pH and Zn.

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Fig. 16

Linear plot between pH and Fe.

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Fig. 17

Linear plot between P and Fe.

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Fig. 18

Linear plot between P and EC.

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Fig. 19

Linear plot between AOX and DCO.

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The plots demonstrated that TSS has a direct linear and positive connection with EC and Na, but a negative association with T°. Using linear regression, the regression coefficients (R) and (R2) for these relationships were determined.

The graph reveals that Na and EC are dependent on TSS, with an increase in TSS leading to an increase in EC and Na.

Also, BOD₅ with EC,P and Fe have a direct linear and positive connection; hence, EC,P and Fe are dependent on BOD₅, with an increase in BOD₅ leading to an increase in EC,P and Fe.

TN has a positive direct linear relationship with Zn and Fe, with an increase in TN leading to a rise in Zn and Fe.

pH with Zn and Fe and have the same positive direct linear relationship, a simple increase of pH resulting in another increase of Zn and Fe.

P has a positive direct linear relationship with EC and Fe, with a rise in P leading to an increase in EC and Fe.

DCO and AOX and have a positive direct linear relationship, with an increase of DCO resulting in another increase of AOX.

Ordinarily, a regression analysis is conducted primarily for one of two reasons: to anticipate the significance of the effluent physicochemical dependent parameters for those for whom some information about the response variable is available, or to estimate the impact of some explanatory physicochemical parameters on the dependent ones, hence have complete understanding of the wastewater pollutants.

4.4 Analysis of results

The outcomes of evaluating Physico-Chemical Characteristics of the effluent released in evaporating basins indicate that evaporation concept shows a negative impact on wastewater, as demonstrated by a huge rise in different effluent properties (EC, Na, TSS..etc). Such impact may have adverse environmental and ecological consequences.

The large impact of the temperature on the generation of Na, Fe, and TSS in effluent can be understood by evaporation factors that increase the salinity and saturation of the effluent.

The oxidation of Fe ions by atmospheric oxygen can also result in elevated TSS and Na levels in the evaporating basins.

AOX may be used to measure the quantity of organic halogens, which are compounds comprising chlorine, phosphorus, or salt, in an effluent test. Typically, increases are associated with water contamination.

Microbial degradation in the effluent of evaporating basins might underlie the high EC level, which would be associated to a rise in several physicochemical variables; EC is increased in case of a plenty of organic contaminants.

Hydrogen may be produced through the concentration of Zn in the effluent, which reacts significantly with oxygen. In wastewater, zinc ions in high concentrations cause turbulence and the liberation of iron. In consequence, zinc may provide an unpleasant odor to the effluent in the evaporation basins.

The occurrence of peroxidation reliability of organic substances, especially in the effluent pit, the appearance of Na might contribute to the maximum DBO₅ and AOX interpretations. Since some particles are limited or not biodegradable, pathogens could be required to acclimate for a prolonged period before it can alter the organic matter.

The presence of Cl₂ residual in the effluent may indicate that sufficient chlorine was initially added to render the bacteria dormant.

The quantity of unintentional residual Cl₂ in the effluent is related to the absence of microorganisms and the occurrence of a significant EC.

In surface evaporating basins, oil and grease can generate unsightly deposits and interfere with living life, which could be described by a reduction in oxygen concentration and phosphorus.

Total Nitrogen is essential for all living creatures. Nevertheless, an excess of nitrogen in the wastewater can lead to low levels of dissolved oxygen and have devastating effects on a variety of species of plants and animals.

The linear correlation between regional climate and effluent features imply that warm and hot weather has an effect on effluent characteristics.

Based on these findings, we advocate reusing the effluent recovered in the evaporating basins by applying a sustainable approach like the solar still techniques, especially in arid and hot regions.