Unveiling the impact of the COVID-19 pandemic on water quality: a case study on Ain-Silan and Ain-Karma sources Open Access

The preference for groundwater and springs over surface water sources is long-standing, largely due to the perception that they are less pollution-resistant. The suitability of groundwater for drinking purposes is contingent upon attaining specific physicochemical and biological quality standards (Moldovan et al. 2020; Ahmed et al. 2022a, b). The quality of groundwater is influenced by several factors, including the infiltrating water's chemical composition, the reservoir rocks' geological properties, and human activities (Chen et al. 2019). The impact of climate change on groundwater recharge is evidenced by the findings of Islam et al. (2016). During the period of the global pandemic caused by the novel coronavirus, concerns were raised about the potential for contamination of water sources, both before and after the outbreak. This emphasized the need for rigorous monitoring of water quality and remediation efforts to be implemented (Ohwoghere-Asuma et al. 2019; Ali et al. 2024). The ongoing pandemic has significant implications for society, including the quality of drinking water sources (Ahmed et al. 2018; Facciolà et al. 2021). It has underscored the critical importance of access to clean drinking water in maintaining public health and hygiene, underlining the necessity for efficacious strategies to manage water quality (Spearing et al. 2020). Several studies have identified potential issues with the impact of the pandemic on drinking water quality, particularly concerning the presence of contaminants and pollutants (Quinete & Hauser-Davis 2021; Gani et al. 2025). These concerns have been particularly prevalent in communities at risk during the ongoing pandemic (Lebel et al. 2022). The restrictions and behavioral changes brought about by the pandemic have resulted in alterations to water usage patterns, which may affect contaminant levels in drinking water sources (Mare et al. 2023). Research has been conducted to assess changes in water quality during the pandemic, particularly focusing on determining whether drinking water quality has improved or deteriorated because of lockdown measures (Shahrir & Hayder 2022). Moreover, maintaining hygienic practices concerning drinking water sources has been emphasized during the ongoing pandemic. There has been a notable increase in the focus on ensuring the cleanliness and safety of water reservoirs (Herniwanti 2022).

The challenges posed by the pandemic also extended to issues of access to drinking water. Indeed, reports indicate difficulties in accessing clean water during the COVID-19 crisis (Zahra et al. 2023). The maintenance of water quality, particularly in community settings, has been a topic of concern, prompting discussions on the efficacy of chlorination and other water treatment methods in ensuring the safety of drinking water during the pandemic (García-Ávila et al. 2020). Furthermore, the pandemic has prompted inquiries into the potential impact of the pandemic on antibiotic sensitivity patterns in drinking water sources, underscoring the necessity to monitor and address potential alterations in water quality (Petromelidou et al. 2024). Although some studies have indicated an improvement in water quality on a global scale following the pandemic, there is still a need for ongoing monitoring and evaluation of drinking water sources to ensure public health and safety (Farrokhi et al. 2023). The maintenance of sound sanitation practices and the preservation of water quality have been identified as pivotal elements in the prevention of the transmission of diseases such as COVID-19, underscoring the interconnectivity between water quality and public health outcomes (Da Silva et al. 2020). Modeling approaches have been proposed to predict and manage residual chlorine levels in drinking water systems during the pandemic and to maintain water quality standards (García-Ávila et al. 2021). The impact of the pandemic on drinking water quality has been inextricably linked with broader environmental and public health concerns, necessitating the implementation of comprehensive strategies to safeguard water resources and public health during the ongoing pandemic and beyond (Baklanov et al. 2021).

The ongoing pandemic has underscored the paramount importance of maintaining and monitoring drinking water quality to safeguard public health (Donde et al. 2021). Robust practices for managing water quality, sanitation measures, and access to clean drinking water have become necessary due to the challenges presented by the pandemic (World Health Organization 2020). A health risk assessment is an essential element of environmental research, particularly in the context of evaluating the impact of water quality on human health (Dong et al. 2015; Shivarajappa et al. 2023). The results of studies conducted in different locations have highlighted the importance of different parameters in predicting water quality and associated health risks (Wu et al. 2019). The statistical significance of regression models in source partitioning underscores their reliability in guiding sustainable water management practices and health risk assessment (Liu et al. 2018).

This study aims to elucidate the efficacy of regression models in predicting water quality, with a particular focus on the impact of the ongoing pandemic on groundwater quality and the importance of effective water quality management. We hypothesize that the pandemic has influenced the chemical quality of Ain-Silan and Ain-Karma water.

Regression equations:

Regression coefficients:

• Slope (b₁)

This formula calculates the slope b₁ of the regression line.

• o⁠: This is the sum of the product of deviations of xx and yy from their respective means.

• on: The number of data points.

• o⁠: This is the sum of the squared deviations of xx from its mean.

• o The formula can be simplified to the covariance of xx and yy (⁠⁠) divided by the variance of xx (⁠⁠).

• Intercept (b₀)

This formula calculates the intercept b₀ of the regression line. It represents the expected value of yy when xx is zero, adjusted by the slope and means of xx and yy.

To assess the significance of the regression coefficients, we employed a significance level of α = 0.05. This threshold allows us to determine whether the relationships observed in the data are statistically significant or if they could have arisen by chance (Chen & Mkumbo 2020; Konca 2024).

The samples were analyzed and the results were determined at the laboratory of environmental analysis and chemical testing of materials LACIP GROUPE in Ain M'lila. A part of the results was obtained from the laboratory of ADE (Algerian Water Company) in Khenchela. Standards are according to the WHO and Algerian norms. The data collected for the parameters were processed statistically using Excel software (Table 1).

To discuss the statistical analysis of water quality parameters (SO₄, Cl, NO₃, Ca, Mg, EC, pH, Na, NH₄, and NO₂) during COVID-19 in Ain-Silan and Ain-Karma, we have to consider the following points (Table 1):

• 1. The absence of effects on certain parameters during the pandemic is noted.

Ain-Silan: The parameters pH, Ca, Cl, and NO₂ showed minor changes during COVID-19. This suggests that these parameters were not influenced by the pandemic-related changes in human activity or environmental conditions (Gagan et al. 2022).

Both sources: The Na parameter showed insignificant change in both Ain-Silan and Ain-Karma, indicating that sodium levels remained stable regardless of the pandemic (Imbulana et al. 2021).

• 2. An increase in specific parameters post-COVID-19 is observed.

Ain-Silan: There was an increase in NH₄ (0.11 ± 0.06) after the COVID-19 period. This could be attributed to changes in agricultural runoff, wastewater discharge, or other anthropogenic activities that were altered during the pandemic.

Ain-Karma: The parameters EC (722 ± 81.6), NO₃ (15.5 ± 2.99), and Cl (19.8 ± 4.7) showed an increase. This is due to the changes in land use (rehabilitation activities) and/or variations in precipitation patterns during the pandemic (Yapabandara et al. 2023).

• 3. A decrease in parameters with a high effect is highlighted.

Ain-Silan: There was a significant decrease in NO₃ and EC, indicating a reduction in nitrate pollution and electrical conductivity. This could be due to decreased human activities.

Mg and SO₄: These parameters showed a high effect during the COVID-19 period, suggesting that the pandemic had a substantial impact on magnesium and sulfate levels due to changes in natural processes (Karunanidhi et al. 2021).

• 4. A positive to high positive effect on certain parameters during the pandemic is noted.

Ain-Karma: The parameters pH, NO₂, Mg, SO₄, Ca, and NH₄ showed a positive to high positive effect during the pandemic. This indicates that these parameters were significantly influenced by the pandemic, potentially due to changes in human activities, environmental policies, or natural processes (Cheval et al. 2020).

These findings align with broader research on the impact of the COVID-19 pandemic on water quality parameters globally. Studies have shown variations in water quality characteristics during the pandemic, influenced by factors such as changes in human activities, environmental policies, and natural processes (Rupani et al. 2020; Ahmed et al. 2022; Heal et al. 2022; Pešić et al. 2023).

The findings encompass a series of linear regression models analyzing multiple water quality indicators over 7 years from 2015 to 2022 at two distinct sites such as Ain-Silan and Ain-Karma. Parameters examined include EC, pH, Na, Mg, Ca, NH₄, NO₃, Cl, SO₄, and NO₂.

At Ain-Silan, the regression models reveal R² values of 0.8708, 0.850, and 0.7495 for Mg, SO₄, and NO₃, respectively, indicating that these parameters change significantly over time and serve as important predictors of water quality at this location. Meanwhile, at Ain-Karma, the R² values for NO₂, NH₄, and Mg are 0.8418, 0.7947, and 0.8347, respectively. These high R² values underscore the critical roles of Mg, NH₄, and NO₃ as predictors of water quality at Ain-Karma. Strong correlations suggest fluctuations in these parameters heavily influence measured water quality indicators, potentially reflecting both human activities and natural environmental processes (Xu et al. 2023).

These models demonstrate robust relationships with water quality parameters, reflecting various factors influencing pollutant sources and environmental conditions (Guo et al. 2019; Cheng et al. 2022). Consistency in pollutant sources over time significantly contributes to these relationships, ensuring stable water system inputs (Loi et al. 2022). Effective monitoring practices enhance data reliability, capturing fluctuations in pollutant levels for accurate model fits (McDowell et al. 2024). Controlled variables, such as stable environmental conditions, clarify relationships between independent variables (like time) and water quality parameters, reinforcing model robustness (Guo et al. 2020). Environmental factors such as climate and geological features further influence pollutant consistency, affecting model strength (Roy et al. 2023). Proactive management practices such as wastewater treatment and pollution controls support more predictable water quality trends observed in the models (Piao et al. 2023). High-quality data with minimal errors ensures precise model outcomes, highlighting the importance data integrity in environmental research. Ultimately, statistical significance underscores the reliability of these regression models in source apportionment, guiding sustainable water management practices (Zhao et al. 2024).

During the COVID-19 pandemic, the analysis of water quality parameters in Ain-Silan and Ain-Karma has revealed a complex interplay of factors influencing these critical metrics. This examination has provided insights into how global health crises can impact environmental dynamics and human activities, underscoring the intricate relationship between public health and ecological resilience. The linear regression (OLS) was found to be suitable for the analysis of water quality parameters at Ain-Silan and Ain-Karma during the COVID-19 pandemic and revealed significant insights into the dynamic relationship between human activities, environmental conditions, and water quality. At Ain-Silan, regression models demonstrated high R² values of 0.8708 for Mg, 0.850 for SO₄, and 0.7495 for NO₃, indicating substantial changes over time and highlighting their importance as predictors of water quality. Similarly, at Ain-Karma, NO₂, NH₄, and Mg showed high R² values of 0.8418, 0.7947, and 0.8347, respectively, underscoring their critical roles in water quality prediction. The pandemic, characterized by lockdowns and shifts in human behavior, had varied impacts on these parameters. While pH levels remained relatively stable in both locations, suggesting robust buffering capacities or minimal industrial influence, other parameters experienced more pronounced fluctuations. This variability necessitates ongoing monitoring and nuanced analysis to distinguish short-term anomalies from long-term trends. Sustainable water resource management strategies must account for the intricate interplay between natural processes, human activities, and global events like pandemics. Robust monitoring networks and adaptive management practices are essential to mitigate risks and ensure the resilience of aquatic ecosystems.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Nevertheless, we would like to thank the Laboratory of Biotechnology, Water, Environment and Health, Abbes Laghrour University Khenchela, Algeria, and the laboratory of ADE in Khenchela.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.