Analysis of the impact of the COVID-19 pandemic lockdown on the spatiotemporal variations in water quality in three wetland areas in Oran, western Algeria

Analysis of the impact of the COVID-19 pandemic lockdown on the spatiotemporal variations in water quality in three wetland areas in Oran, western Algeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of the impact of the COVID-19 pandemic lockdown on the spatiotemporal variations in water quality in three wetland areas in Oran, western Algeria Abdelhalim BENDIB, Mohamed Lamine BOUTRID This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4476677/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In arid regions, water is a scarce and invaluable resource. Moreover, with urban expansions and socioeconomic changes, its quality has become a matter of significance and an indicator of environmental preservation. The objective of this study was to shed light on the impact of the COVID-19 pandemic on three wetlands in Oran, western Algeria (Lake of Dayet Oum Rhalez (DORh), Lake of Dhayat Morasli (DMo), and Lake of Sidi Chahmi (SCh)). Three parameters, namely, the chlorophyll-a concentration (Chl-a), trophic state index (TSI), and Secchi depth (SD), were selected and calculated for the period from 2019–2022. The results showed that, except for DORh, the Chl-a concentration decreased from 41.73 µg/l to 21.01 µg/l for DMo and from 42.82 µg/l to 23.08 µg/l for SCh between 2019 and 2021. The TSI decreased from 5.67 to 5.32 for DORh, from 5.95 to 5.36 for DMo, and from 5.32 to 4.12 for SCh. These results are also validated by the SD values, with an improvement in water transparency from 1.16 m to 2.61 m for DORh, from 1.31 m to 2.75 m for DMo, and from 1.4 m to 2.07 m for SCh. This reduction in biological activity justifies the impact of the applied lockdown on the improvement of water quality. Additionally, despite this improvement, the overall health of the three studied wetlands remains concerning (eutrophic ecological characteristics), and water quality is often mediocre. This study, in its entirety, can contribute to better decision-making and targeted actions for the preservation of these ecosystems. Wetlands Chlorophyll-a Water quality Oran Lockdown Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction According to the RAMSAR Convention, wetlands are regions where water is the primary determinant of the environment and is associated with plant and animal life (Ramsar 2007 ). These vital spaces provide various services, such as potable water, food, energy, industry, and recreation (Tundisi et al. 2008). They serve as crucial habitats for bird repopulation, where birds use them for nesting and as resting spaces during their seasonal migration (Kačergytė et al. 2021 ); (Yao et al. 2020 ). With demographic development and economic transformations, contemporary cities continue to expand into natural spaces, posing a threat to biodiversity (Bendib and Berghout 2023 ); (Dörnhöfer and Oppelt 2016 ). Statistics indicate that between 60 and 70% of all global wetlands have been lost due to agricultural drainage and urbanization (Davidson 2014 ); (Kačergytė et al. 2021 ). These vital yet fragile spaces continue to face pressures jeopardizing ecological balance and water quality (Calhoun et al. 2017 ). Wetland eutrophication is becoming an increasingly common environmental issue (Ogashawara et al. 2021 ). Along with certain agricultural practices, domestic and industrial wastewater discharge are identified as the main sources of this eutrophication (Converse 1995 ); (Australian Government 2016 ); (Quanz et al. 2021 ); (Convention on Wetlands 2022 ); (Ostad-Ali-Askari 2022 ). Currently, due to the vastness, remoteness, and difficulty of accessing certain wetland areas, ground-based monitoring methods have become expensive, costly, and often unfeasible. Due to the ease of acquisition and rapid processing of images (Guo et al. 2023 ), remote sensing techniques based on satellite imagery have become indispensable for monitoring water quality parameters, including chlorophyll-a and water transparency (Mishra and Mishra 2012 ); (Alikas and Kratzer 2017 ). Several multispectral satellites have been launched, including Landsat series data, MODIS data, and MERIS data. However, their low resolution affects the quality of studies, especially for small lakes. Furthermore, with the launch of the latest generation of spatial sensors (Sentinel-2 of the European Space Agency (ESA)), characterized by a short temporal series (Lisboa et al. 2020 ), a new opportunity to explore small aquatic systems has emerged (Ogashawara et al. 2021 ). Additionally, with the geospatial analysis capabilities of the Google Earth Engine (GEE) in addressing numerous issues such as deforestation, climate change, and water management (Sherjah et al. 2023 ), this study relies on this platform for monitoring water quality in three wetlands in the Oran region, western Algeria. Due to the significance of Oran's wetlands for biological diversity, numerous studies have been conducted. A study by (Ben Bayer et al. 2019 ) attempted to monitor the biological, physical, and chemical characteristics of Dhayat Morasli in 2017 using monthly samples taken around the lake. The results indicated that the concentrations of nitrates and phosphates exceeded the permissible standards, and the cadmium and lead concentrations increased compared to the recommended values. Consequently, this situation led to eutrophication of the lake. Another study on physicochemical and bacteriological quality was conducted by (Mahi et al. 2021 ). Three wetlands in western Algeria were selected for experimentation: Dhayat Morasli Lake, Dayet Oum Rhalez Lake, and Telamine Lake. Using eight parameters in addition to pH, the results revealed concentrations exceeding established standards for chloride, ammonium, and sulfate. Furthermore, the pH of Lake Dayet Morsli, nitrate concentration of Lake Telamine, chemical oxygen demand (COD), and suspended solids concentration of Lakes Dayet Morsli and Telamine exceeded international standards. These exceedances are mainly attributed to untreated domestic and industrial wastewater, which has been identified as the main cause of water quality deterioration. (Aibeche et al. 2020 ) conducted a comparative study between Lake Telamine and Lake Dayet Oum Rhalez. The study, conducted throughout the four seasons of 2016/2017, concluded that Lake Telamine is more polluted than Lake Dayet Oum Rhalez in terms of chemical oxygen demand (COD), chloride, and heavy metals such as chromium, copper, lead, and nickel. On the other hand, high values of the COD/DBO5 (biochemical oxygen demand over five days) ratio (> 3) indicate that the water in both lakes contains industrially derived organic matter that is difficult to biodegrade. From this literature, it is evident that most studies share a common idea: monitoring physical, chemical, and biological parameters by focusing on specific sites during a single year, while identifying areas where water quality deteriorates requires continuous monitoring over the entire water body (Sherjah et al. 2023 ). In our opinion, these approaches do not provide satisfactory answers and do not comprehensively reflect the situation of wetlands in terms of spatiotemporal variations in pollutants, especially if the goal is to develop robust long-term conservation strategies. Moreover, according to (Kislik et al. 2022 ), monitoring water surfaces through in situ sampling is both expensive and tedious, and satellite imagery provides a rapid and relatively cost-effective method. Building on this point, the novelty of our work lies in the use of remote sensing data as a complementary technique capable of providing a dynamic view of the water surface at different periods and scales, thus allowing a thorough understanding of the underlying causes. By adopting an approach that transcends simple annual monitoring, our study aims to capture the spatiotemporal evolution of pollutants, offering a more comprehensive perspective to guide the development of long-term conservation strategies. The primary objective of this study was to shed light on the effect of the COVID-19 lockdown on the spatiotemporal variations in pollutants in three wetlands in Oran using chlorophyll-a, an excellent indicator of water quality, especially in aquatic ecosystems. To achieve this goal, the following steps are essential: (1) use Sentinel-2/MSI data to characterize variations in chlorophyll-a (Chl-a), the trophic state index (TSI), and the Secchi depth (SD); (2) analyze the effect of meteorological factors (precipitation) on variations in the calculated parameter values; and (3) understand the effect of the applied lockdown on improving water quality and biological activity on each water surface. 2. Materials and methods 2.1 Description of the study area This study was conducted in the Oran region in western Algeria, focusing on three water bodies: DMo Lake, DORh Lake, and SCh Lake (Fig. 1). With an area of 114 hectares, DMo Lake is part of the wetland complex in western Algeria (Ramsar 2018 ). It is situated southeast of the Oran urban area within the municipality of Es Sénia, where urban development has gradually encroached upon this depression. The lake is bordered by industrial zones to the west and south. Oran's urban area and the road infrastructure border Dayet to the north, while agricultural lands surround the wetland to the east. However, peripheral urbanization around the city of Oran has isolated the wetland from its original watershed (MATET 2010 ). DMo is home to 53 bird species from 22 families and 36 genera, making it a suitable location for wintering and stopovers during bird migration. Covering an area of 17 hectares, SCh Lake is located in the municipality of Sidi Chahmi, in direct proximity to built-up areas. It serves as an excellent example for studying the impact of domestic discharge on water quality during the lockdown period. With an area of 328 hectares, DORh Lake is relatively distant from significant urban construction areas (except for a 10-hectare concentration at 500 meters). This surface is a preferred habitat for the northern shoveler duck ( Anas clypeata ), the ferruginous duck (Aythya nyroca), and the squacco heron (Ardeola ralloides). Table 1 summarizes the main characteristics of each water surface. Table 1 Main characteristics of the study area Name Site Type Localisation Area (ha) Lake of Dayet Oum Rhalez A Natural Lat: 35°36’2.36” N, long: 0°25’34.48” W 328 Lake of Sidi Chahmi B Natural Lat: 35°39’40.90” N, long: 0°31’45.52” W 17 Lake of Dhayat Morasli C Natural Lat: 35°40’3.24” N, long: 0°36’24.85” W 114 2.2 Data sources The primary objective of this study was to analyze the impact of the lockdown imposed in response to the COVID-19 pandemic on the improvement of water quality in three wetlands in the Oran region. To achieve this goal, we utilized Sentinel-2 multispectral instrument (MSI) level-2B data, which began operating in 2017 (Table 2). We employed images acquired between January and December of the years 2019, 2020, 2021, and 2022, with a maximum cloud coverage threshold of 20%, to ensure reliability. The Google Earth Engine (GEE) platform and a reference code developed by (Page et al. 2019 ), enhanced by Geography Lounge ( https://www.geography-lounge.com/courses/1726312/lectures/44199367 ), were modified and utilized to extract chlorophyll-a, trophic state index (TSI), and Secchi depth (SD). The choice of GEE has been validated by numerous studies demonstrating its applicability for water quality monitoring (Vaičiūtė et al. 2021 ); (Lobo et al. 2021 ); (Kislik et al. 2022 ); (Sherjah et al. 2023 ). The obtained images were then clipped, exported in raster format, and, using the ArcGIS environment, transformed into vectors (Shapefile). Additionally, before utilizing the data, it is crucial to transform them from the GCS WGS1984 reference system to the UTM WGS84 Z30 system. This enables the calculation of areas for comparative statistical analysis. Table 2 Details of the data used in the study Satellite data Band number Spatial resolution (m) Central wavelength (nm) Date of acquisition Sentinel 2B 4 10 664.9 2019, 2020, 2021, 2022 5 20 703.8 The selection of parameters is justified by the work of (Pavluk and Bij De Vaate 2008 ). According to this study, the trophic state concept is based on the notion that variations in nutrient levels (assessed by total phosphorus) lead to changes in algal biomass (assessed by chlorophyll-a), which, in turn, affects lake clarity (measured by the Secchi depth). 2.2.1 Chlorophyll-a Chlorophyll-a is the pigment shared by all phytoplankton species (Reinart and Kutser 2006 ). It plays a central role in photosynthesis, which is crucial for energy production in photosynthetic organisms (Björn et al. 2009 ). Indeed, the concentration of chlorophyll-an in water is often used as an indicator of water quality (Muduli et al. 2021 ); (Bramich et al. 2021 ), especially in aquatic systems such as lakes, rivers, and reservoirs. In this study, before calculating Chl-a, it is essential to compute the normalized difference chlorophyll index (NDCI), a commonly used vegetation index in remote sensing to assess chlorophyll concentrations in vegetation. According to (Muduli et al. 2021 ), the high accuracy (R2 = 0.95; p < 0.0001; range = 1–60 µg/l; RMSE = 2 µg/l) of the Chl-a model developed by (Mishra and Mishra 2012 ) has made it widely used in many remote sensing studies. According to (Bramich et al. 2021 ), the model is generally expressed in equations ( 1 ) and ( 2 ). $$Chl-a=14.039+86.115\left(NDCI\right)+194.325{\left(NDCI\right)}^{2}$$ 1 ……………………………… $$NDCI=\frac{{R}_{nir}-{R}_{red}}{{R}_{nir}+{R}_{red}}$$ 2 …………..….........................………………… where \({R}_{red}\) and \({R}_{nir}\) are MSI bands 4 at 665 nm and 5 at 704 nm, respectively. 2.2.2 Trophic State Index (TSI) The trophic state index (TSI) is a numerical scale widely used to assess and quantify water quality (Sherjah et al. 2023 ); (Zhu and Mao 2021 ). It indicates the trophic state, which refers to the level of biological productivity or nutrient enrichment in a body of water. The trophic state is often linked to factors such as algal growth, water clarity, and overall ecosystem health. The TSI is calculated using equations that combine these parameters. According to (Pavluk and Bij De Vaate 2008 ), the formulas for calculating the TSI are presented in Eq. 3 , Eq. 4 , and Eq. 5 . Each of these three formulas can theoretically be used to classify a body of water, and it is also feasible to consider the average between them. $$TSI=30.6+9.81\text{ln}\left(Chlorophyll-a(\mu g/l)\right)$$ 3 ……………………………… $$TSI=60-14.41\text{ln}\left(Secchi disk depth \left(m\right)\right)$$ 4 ……………………………… $$TSI=4.15+14.42\text{ln}\left(total phosphorus(\mu g/l)\right)$$ 5 …………………………… 2.2.3 Secchi depth (SD) The clarity of surface waters is frequently associated with the concentration of plant nutrients in the water (Gao et al. 2020 ). An increase in nutrient quantity promotes the proliferation of phytoplankton (Rose et al. 2021 ), leading to a decrease in water transparency (Pavluk and Bij De Vaate 2008 ). The Secchi disk is a straightforward yet effective tool utilized in water quality assessment (Liu et al. 2019 ). This disc is a flat, white disk with alternating black and white quadrants. It is lowered into the water until it becomes invisible, and the depth at which it disappears, referred to as the Secchi depth, is measured. The Secchi depth serves as an indicator of water transparency or clarity and is commonly employed in various environmental and ecological assessments (Lee et al. 2015 ). 3. Results and discussion Figure 2 shows the temporal variations in Chl-a, TSI, and SD for 2019, 2020, 2021, and 2022. The statistical analysis of chlorophyll-a (Fig. 2.a) in DMo Lake revealed that in 2019, the average concentration was 41.73 µg/l, with a marginal reduction of 0.18 µg/l recorded in 2020 (averaging 41.55 µg/l). Furthermore, a significant decrease of 21.01 µg/l was observed in 2021, followed by a slight increase of 2.82 µg/l in 2022 (a total of 23.83 µg/l). Similarly, the chlorophyll content of SCh Lake decreased to 42.82 µg/l in 2019, 32.17 µg/l in 2020, 23.08 µg/l in 2021, and 16.17 µg/l in 2022. For DORh Lake, chlorophyll decreased by 7.44 µg/l between 2019 and 2020 (53.77 µg/l in 2019 compared with 46.33 µg/l in 2020). Moreover, an increase to 61.96 µg/l was observed in 2021, followed by a decrease to 25.3 µg/l in 2022. For the TSI (Fig. 2.b), the values obtained indicate the positive effect of the lockdown on water quality. For DMo Lake, the average was 5.95 in 2019. In 2020, this average decreased to 5.86. A notable decline in TSI values is recorded in 2021, with an average of 5.36 (a decrease of 0.5). An increase of 0.51 (resulting in a TSI of 0.87) is expected to occur in 2022. For SCh Lake, the TSI increased by 0.41 between 2019 and 2020 (5.32 in 2019 against 5.73 in 2020), followed by a significant decrease of 1.61 in 2021. An increase to 4.81 is expected to occur in 2022. The same changes also affected DORh Lake, with an increase of 0.02 between 2019 and 2020, followed by a decrease to 5.32 in 2021 before the values increase again to reach 5.85 in 2022. Similarly, the Secchi depth (Fig. 2.c) confirmed the positive effect of the lockdown on the changes in the physicochemical characteristics of the water in the three studied wetlands. DMo Lake exhibited a significant improvement in water clarity, with an average depth of 1.31 m in 2019 and 1.51 m in 2020, reaching 2.75 m in 2021. This clarity is substantially reduced to a depth of 1.33 m in 2022. The same trend was also observed for SCh Lake, with improvements of 1.40, 1.43, and 2.07 meters in 2019, 2020, and 2021, respectively. This is followed by a decrease in depth to reach 1.31 m in 2022. For DORh Lake, the water quality significantly improved between 2019, 2020, and 2021, with depths of 1.16, 1.75, and 2.61 meters, respectively. However, a decrease in water quality is expected to occur in 2022, with a maximum depth of 1.4 meters. Chl-a can be used as a common parameter for the water quality index (Muduli et al. 2021 ). This study provides insights into the trophic state and overall health of aquatic ecosystems (Table 3 and Fig. 3). In 2019, 55.70% of DORh Lake was characterized by chlorophyll concentrations exceeding 61.52 µg/l, while 21.13% of the lakes had chlorophyll concentrations ranging between 50.87 µg/l and 61.51 µg/l. Additionally, low concentrations (< 22.10 µg/l) occupy barely 2.6% of the overall surface area. In 2020, there was a clear trend toward the dominance of medium concentrations (32.77–50.86 µg/l), covering 85.94% (262.06 ha) of the total area. Following the imposed lockdown, the Chl-a concentration gradually decreased in 2021, with 8.96% (23.66 ha) of the total area falling below 22.10 µg/l. The consequences of this lockdown are expected to reach optimal values in 2022, with 12.91% (39.01 ha) for the class < 22.10 µg/l, 86% for the class 22.11–32.76 µg/l, and 0% for concentrations exceeding 50.87 µg/l. Based on the Chl-a classification table and its ecological characteristics (Carlson and Simpson 1996 ), it is evident that in 2019, DORh Lake was dominated by a hypereutrophic ecological characteristic (76%), with water quality deemed uninhabitable. In 2020, water quality improved, with 85% of the surface exhibiting eutrophic ecological characteristics dominated by blue‒green algae, algal blooms, and macrophyte issues. However, despite the lockdown implemented in 2021, a considerable increase in the area dominated by hypereutrophic (59%) and eutrophic (15%) ecological characteristics was noted. In 2022, water quality significantly improved, with 86% exhibiting eutrophic characteristics (dominated by blue‒green algae, algal blooms, and macrophyte issues) and 12% displaying hypolimnetic anoxia, with possible macrophyte problems. For DMo Lake, chlorophyll-a concentrations were notably found in the medium (37.07–41.85 µg/l) and high (41.86–47.73 µg/l) classes, covering 33.14 ha (23.87%) and 96.18 ha (69.29%), respectively. Low concentrations ranging from 10.49 to 27.04 µg/l barely accounted for 1%. Water quality is expected to improve significantly in 2020, with values of 11%, 18%, and 45% for the 27.05–37.06 µg/l, 37.07–41.85 µg/l, and 41.86–47.73 µg/l classes, respectively. A notable improvement is observed in 2021 and 2022, with dominance at low concentrations. Specifically, 65% of the compounds were in the 10.49–27.04 µg/l class, 31% were in the 27.05–37.06 µg/l class in 2021, and 55% and 44% were in the same classes in 2022. The dominant ecological characteristic in 2019 was eutrophic (with the dominance of blue‒green algae, algal blooms, and macrophyte issues), indicating poor water quality. However, in 2020, an increase in hypereutrophic characteristics was easily identified. The COVID-19 pandemic contributed to improving water quality, with eutrophic characteristics and poor water quality. The same trend was observed for SCh Lake, with a dominance of medium (36.73–44.49 µg/l) and high (44.49–53.82 µg/l) concentrations in 2019, accounting for 21.28% and 60.86%, respectively. In 2020, there was an improvement in water quality, with an increase in concentrations below 25.32 µg/l reaching 6%, a considerable increase in the class ranging from 25.33 µg/l to 36.72 µg/l with 52%, and finally an increase in the class ranging from 36.73 µg/l to 44.49 µg/l, covering a total area of 33%. Moreover, with the lockdown, the chlorophyll concentration decreased significantly in 2021 and 2022. Concentrations below 36.72 µg/l accounted for 93% of the total in 2021, reaching 100% in 2022. In general, SCh Lake is no exception. The dominance of the hypereutrophic ecological characteristic was observed in 2019, followed by 2020, 2021, and 2022 by the eutrophic characteristic with the dominance of blue‒green algae, algal blooms, and macrophyte issues in 2020, and hypolimnetic anoxia with possible macrophyte problems in 2021 and 2022. Table 3. Variations in Chl-a for 2019, 2020, 2021 and 2022 class 2019 2020 2021 2022 Area (ha) Area (%) Area (ha) Area (%) Area (ha) Area (%) Area (ha) Area (%) Lake of Dayet Oum Rhalez 10.39 – 22.10 8.18 2.68 4.18 1.37 23.66 8.96 39.01 12.91 22.11 – 32.76 53.57 17.53 2.60 0.85 6.49 2.46 262.78 86.93 32.77 – 50.86 9.09 2.97 262.06 85.94 35.14 13.30 0.49 0.16 50.87 – 61.51 64.57 21.13 30.89 10.13 41.71 15.79 0.00 0.00 61.52 – 78.29 170.24 55.70 5.19 1.70 157.14 59.49 0.00 0.00 Lake of Dhayat Morasli 10.49 – 27.04 1.50 1.08 5.41 3.90 88.55 65.21 76.07 55.09 27.05 – 37.06 5.61 4.04 16.10 11.61 43.29 31.88 61.87 44.81 37.07 – 41.85 33.14 23.87 25.78 18.58 1.84 1.36 0.14 0.10 41.86 – 47.73 96.18 69.29 63.31 45.64 1.00 0.74 0.00 0.00 47.74 – 66.03 2.38 1.71 28.12 20.27 1.11 0.82 0.00 0.00 Lake of Sidi Chahmi 9.781 - 25.32 0.47 3.03 0.94 6.05 5.95 40.48 15.15 96.68 25.33 – 36.72 1.40 9.03 8.08 52.03 7.77 52.86 0.52 3.32 36.73 – 44.49 3.30 21.28 5.24 33.74 0.63 4.29 0.00 0.00 44.49 - 53.82 9.44 60.86 0.95 6.12 0.35 2.38 0.00 0.00 53.82 - 75.84 0.90 5.80 0.32 2.06 0.00 0.00 0.00 0.00 The trophic state index (Table 4 and Fig. 4) provides a quantitative measure of water quality by assessing the nutrient levels present. In 2019, 99% of DORh Lake was characterized by a high biological productivity level exceeding 50%, while less than 0.08% exhibited low nutrient levels (> 50%). In 2020, there was a slight improvement in water quality, with an increase in the area of biological productivity below 50% at 10% (28 ha). Unfortunately, this is accompanied by an increase in the class (> 60–100%) at the expense of the class (50–60%), with 85.45% and 4.40%, respectively. Improvement can be observed in 2021, with 11.55% for the class (< 30–40%) and 8.13% for the class (40–50%). The percentage of the class with biological activity exceeding 60% decreased to 64%. Conversely, water became uninhabitable again immediately after the lockdown, with 91% (260 ha) exhibiting biological activity levels exceeding 60% and 1.71% (4.89 ha) exhibiting levels below 40%. Overall, DORh Lake is characterized by high biological activity with a hypereutrophic water body. This indicates that the water quality is poor, even after the forced lockdown is applied. For DMo Lake, nutrient levels in 2019 exceeded 96% for the class above 60%, compared to 0.06% for the class below 40%. This reflects the poor state of water quality. A slight improvement was observed in 2020, with 1.99% for the 40–50% class and 5.05% for the 50–60% class. Following the lockdown, a significant decrease (38%) in biological activity was noted in the class above 60%, accompanied by an increase in biological activity in the class (50–60%) of 59.94% (75.45 ha). Indeed, water quality will degrade again in 2022. The biological productivity increased significantly, particularly for the class (> 60%), with a total area of 116.97 ha (92.93%). The results revealed an improvement in water quality in 2021, transitioning from a hypereutrophic water body in 2019 and 2020 to a eutrophic water body in 2021. In general, water quality fluctuates between poor and uninhabitable areas. The results obtained for SCh Lake are intriguing. The nutrient levels in 2019 were divided as follows: 9.44% (ha) for the 60% class represents 64% of the total class. This good water quality decreased in 2020 to yield the following results: 2.44% (0.31 ha) for the 60% class. In 2021, the lockdown imposed due to the COVID-19 pandemic significantly improved water quality, with 28.45% for classes below 40%, 35.93% for classes 40–50%, 30.73% for classes 50–60%, and 4.89% for classes above 60%. After the lockdown, an increase in biological productivity levels can be easily observed, particularly in the classes 50–60% and > 60% at 64.20% (8.14 ha) and 10.88% (1.38 ha), respectively. According to the trophic scale, the water bodies transitioned from eutrophic in 2019 to hypereutrophic in 2021. Moreover, in 2021, the biological activity was considered moderate, indicating a mesotrophic water body. In terms of water surface pollution, this suggests a passable quality. However, this situation will rapidly degrade in 2022, when the water quality will become poor and the eutrophic water body will dominate. Table 4. Variations in TSI for the years 2019, 2020, 2021 and 2022 class 2019 2020 2021 2022 Area (ha) Area (%) Area (ha) Area (%) Area (ha) Area (%) Area (ha) Area (%) Lake of Dayet Oum Rhalez 60 – 100 192.12 67.37 243.70 85.45 182.62 64.05 260.19 91.24 Lake of Dhayat Morasli 60 – 100 121.58 96.62 115.22 91.55 48.48 38.52 116.97 92.93 Lake of Sidi Chahmi 60 – 100 8.21 64.59 10.26 80.91 0.62 4.89 1.38 10.88 The Secchi depth (Table 5 and Fig. 5) is used to measure the depth at which the disk is no longer visible from the water surface. This measurement serves as an indicator of water transparency and quality (Harrison 2016 ). In 2019, 99.81% of the surface area of DORh Lake was characterized by a depth not exceeding 2 m, while depths exceeding 8 m barely accounted for 0.03%. This indicates a disastrous state of water quality (poor quality according to the trophic degree scale) with a dominance of the eutrophic mass. A marginal improvement in transparency was recorded in 2020, with 88.76% of the depths being visible below 2 m and 1% exceeding 8 m. Depths ranging from 2 m to 8 m constitute 10% of the lake. Moreover, following the enforced lockdown, a significant improvement in water clarity was observed in 2021. A considerable increase in depths exceeding 8 m at 1.4% was accompanied by an increase in depths of 2–4 m and 4–8 m at 32.80% and 12.75%, respectively. A total of 53.05% of the depths below 2 m decreased. The situation deteriorated in 2022, with 98% for depths below 2 m and 0.49% for depths exceeding 8 m. Generally, the dominant trophic class in DORh Lake is eutrophic, indicating high biological activity and poor water quality. However, with confinement, considerable areas transform into mesotrophic (92.75 ha) and oligotrophic (36.06 ha) areas, with water quality ranging between fair and good. The same trend was observed for DMo Lake, with a predominance of depths below 2 m of 99.48% in 2019 and 94.91% in 2020. This is followed by a slight improvement in depths of 2–4 m, 4–8 m, and 8–10 m at 4.76%, 5.58%, and 0.90%, respectively. In 2021, the positive effects of the lockdown were easily identifiable, with a significant improvement in transparency in the classes ranging from 2 m to 4 m (75.89%) and between 4 m and 8 m (6.85%). However, the water quality rapidly decreased in 2022, with 99.48% of the depths below 2 m and barely 0.05% of the depths exceeding 8 m. In summary, except for 2021 (with the water body classified as mesotrophic of moderate quality), the water quality of DMo Lake is poor, and its biological activity is classified as high with a eutrophic water body. Interesting results were obtained for SCh Lake between 2019 and 2020. In addition to classes 2–4 m, depths of 0–2 m, 4–8 m, and 8–10 m exhibited decreases in surface area, with 95.93% compared to 95.45%, 2.49% compared to 2.33%, and 0.45% compared to 0.36%, respectively. After the lockdown, water quality improved in 2021, and clarity exceeding 4 m occupied more than 10% of the water surface. However, 75% of the surface remains of inferior quality (visible at less than 2 m). In 2022, the situation is no exception, and water quality will deteriorate. A total of 97.15% of the surface is characterized by a depth not exceeding 2 m. According to these statistics, the water quality in this area is poor (eutrophic water body). Table 5. Variations in SD for the years 2019, 2020, 2021 and 2022 class 2019 2020 2021 2022 Area (ha) Area (%) Area (ha) Area (%) Area (ha) Area (%) Area (ha) Area (%) Lake of Dayet Oum Rhalez 0 – 2 284.64 99.81 251.92 88.76 150.02 53.05 278.22 98.00 2 - 4 0.14 0.05 13.52 4.76 92.75 32.80 1.36 0.48 4 – 8 0.33 0.12 15.83 5.58 36.06 12.75 2.93 1.03 8 - 10 0.078 0.03 2.56 0.90 3.97 1.40 1.39 0.49 Lake of Dhayat Morasli 0 – 2 125.13 99.48 119.04 94.91 21.54 17.13 125.19 99.48 2 - 4 0.30 0.24 3.53 2.81 95.42 75.89 0.38 0.30 4 – 8 0.35 0.28 2.39 1.91 8.61 6.85 0.21 0.17 8 - 10 0.008 0.01 0.46 0.37 0.17 0.14 0.06 0.05 Lake of Sidi Chahmi 0 – 2 11.94 95.93 11.86 95.45 9.43 75.93 11.95 97.15 2 - 4 0.14 1.12 0.23 1.85 1.66 13.37 0.09 0.73 4 – 8 0.31 2.49 0.29 2.33 1.21 9.74 0.18 1.46 8 - 10 0.056 0.45 0.045 0.36 0.12 0.97 0.08 0.65 The concentration of chlorophyll an in a lake is influenced by various environmental, biological, and anthropogenic factors (Huang et al. 2022 ); (Benkesmia et al. 2023 ); (Zhang et al. 2024 ). These factors may vary depending on specific lake characteristics, such as proximity to urban and industrial development and the presence or absence of direct discharge into the lake. Additionally, meteorological conditions, especially precipitation, can affect chlorophyll-a concentrations through variations in water levels in the lake (Das Sarkar et al. 2020 ); (Han et al. 2023 ). They can also influence nutrient runoff from the watershed into the lake. Heavy rainfall events can lead to sudden nutrient inputs. In this study, we aimed to explore the underlying factors behind spatiotemporal variations in environmental parameters such as the chlorophyll-a concentration (Chl-a), trophic state index (TSI), and Secchi depth (SD) in DORh, DMo, and SCh lakes. To do so, we use a correlation coefficient to help identify the main causes of these variations. Our hypothesis is as follows: if the obtained correlation coefficient is high, precipitation is the primary influencing factor. Conversely, if the correlation is low, this could indicate that the confinement imposed in response to the COVID-19 pandemic is the main cause of the observed variations in the three parameters. According to the results shown in Fig. 6, the effect of precipitation is weak in DMo Lake, with correlations of 16.7%, 26.22%, and 34.29% for Chl-a, TSI, and SD, respectively. Furthermore, for water bodies relatively distant from urban areas (especially DORh Lake), the effect of precipitation is significant at 81.55%, 60.19%, and 11.87%, respectively. Moreover, for SCh Lake, the effect of precipitation was relatively moderate, with 63.74%, 23.5%, and 45.02% for Chl-a, TSI, and SD, respectively. This suggests that the positive effect of confinement is recorded in DMo and to a lesser extent in SCh. This leads us to conclude that the observed pollution in DMo primarily results from untreated industrial discharge from the Es-Sénia industrial zone, specifically from factories lacking water treatment facilities, which significantly contributes to the degradation of water quality. Similarly, industrial discharge from units near SCh Lake constitutes the major source of pollution. The geographical proximity of these industrial units amplifies their impact on the ecological balance of the lake. Moreover, the implementation of confinement measures was a determining factor for the sharp decrease in chlorophyll-a concentrations recorded in 2021. In response to these measures, several industrial activities were either suspended or forced to drastically reduce their production. Consequently, industrial discharge into the DMo and SCh Lakes significantly decreased, explaining the abrupt decrease in chlorophyll-a concentrations during this period. Furthermore, DMo has been supplied with industrial discharge (estimated flow of 6000 m3/day) and is currently receiving water that has come into contact with landfills, inert waste, and drainage water from the surrounding road network (Fig. 7). In this lake, eutrophication and the presence of illegal dumps are visible signs of water pollution (Ben Bayer et al. 2019 ). In 2009, a study conducted by the Ministry of Territorial Planning, Environment, and Tourism at three lake sites (Table 6) revealed that the water is excessively polluted with organic matter due to its COD and BOD5 values. The pH and suspended matter content also indicate water pollution. The results obtained show that even though there are no longer untreated discharges from the Es-Sénia industrial zone today, the water is still excessively polluted due to the measured concentrations of cadmium, copper, and chromium. These findings are also supported by two recent studies (Mahi et al. 2021 ); (Ben Bayer et al. 2019 ). According to (Mahi et al. 2021 ), physicochemical and bacteriological analyses exceed international standards, indicating a trend of lake pollution, particularly with values of 9.4 for pH, 198 mg/l for suspended matter, and 123.93 mg/l for COD. Additionally, the significant increase in concentrations in DORh Lake in 2021 underscores a direct correlation with behavioral changes related to the confinement measures implemented. The small agglomeration of Khedaimia, located within 500 meters of the lake, played a crucial role in this dynamic. Due to the restrictions imposed by confinement, residents were compelled to stay at home for considerable periods. This situation led to a substantial change in lifestyle, characterized by an extended time spent at home. Consequently, domestic water consumption has notably increased, affecting various aspects of daily life. Particularly affected by these changes, the quantities of household and toilet water significantly increased. Furthermore, it is essential to note that the wetland of DORh is facing the discharge of untreated industrial water but at lower rates than DMo. This is supported by a study conducted by (Aibeche et al. 2020 ), in which the results of physicochemical water analyses exceeded the recommended levels in Algeria, with values of 376.27 mg/l for COD, 29.70 mg/l for BOD5, and 7.8 for pH. In conclusion, despite the interesting results obtained and the potential of remote sensing techniques, this study has limitations that must be considered. Every remote sensing study requires ground-based measurements for validation. Unfortunately, in our case, obtaining such in situ measurements is challenging and costly, potentially introducing uncertainties. Additionally, comparing our work with other studies conducted in the same area and within a closer temporal framework can validate our findings. Moreover, it is noteworthy that the fundamental question of this study revolves around the impact of confinement on water quality, which is addressed with satisfactory results. Table 6. Grid of the classification of surface water quality in Algeria and the results of the analyses (MATET 2010 ) Parameters Unit Good Moderate polluted Extremely polluted Analysis results Site 1 Site 2 Site 3 Physical quality pH / 6.5 – 8.5 6.5 – 8.5 8.5 – 9.0 9.0 9.43 8.93 9.13 Suspended matter mg/l 0-30 30-75 75-100 >100 190 358 258 Organic quality DBO5 mg/l 5 5-10 10-15 >15 178.9 202.8 229.8 DCO mg/l 20 20-40 40-50 >50 892 843 882 Oils and greases mg/l / / / / 25.18 14.08 8.11 Heavy metals quality Arsenic (As) mg/l / / > 0,01 / 0.361 0.108 0.164 Chrome (Cr+2) mg/l 0 0-0.05 0.05-0.5 >0.5 0.77 2.08 0.50 Copper (Cu+2) mg/l 0-0.02 0.02-0.05 0.05-1 >1 2.84 2.01 1.97 Cadmium (Cd) mg/l 0 0 0-0.01 >0.01 0.168 0.987 0.123 Lead (Pb) mg/l / / >0.01 / 0.97 0.87 1.16 4. Conclusion The wetlands of Oran constitute a vital ecosystem for bird repopulation, serving as nesting sites and resting spaces during their seasonal migration from north to south. However, with socioeconomic development and urban sprawl, they face serious environmental issues that threaten biodiversity. The water in wetlands becomes excessively polluted due to the measured concentrations of cadmium, copper, chrome, arsenic, and lead, as well as high levels of pH and organic matter (COD and BOD5). Two sources of pollution can be easily identified: (1) discharges of water from neighboring industrial hubs and current wastewater discharges from urban areas and (2) embankments and inert waste (demolition waste from informal settlements). In this study, Sentinel-2/MSI data were used to assess water quality and determine the impact of the COVID-19 epidemic on three wetlands: DORh, DMo, and SCh Lakes. According to three parameters (chlorophyll a (Chl-a), trophic state index (TSI), and Secchi depth (SD)), the results revealed a significant reduction in pollution levels in 2021. The average Chl-a concentration between 2019 and 2021 decreased by more than 20 µg/l for DMo and 19.74 µg/l for SCh. The TSI decreased from 5.67 to 5.32 for DORh, from 5.95 to 5.36 for DMo, and from 5.32 to 4.12 for SCh. These results are also supported by the SD values, with improvements in water transparency of 1.45 m for DORh, 1.44 m for DMo, and 0.67 m for SCh. This decrease in biological activity justifies the direct impact of the lockdown imposed in response to the COVID-19 pandemic on water quality improvement. However, despite the improvement observed in 2021, the overall health of the three studied wetlands remains a concern. The water quality in these areas is still poor, characterized by eutrophic ecological features dominated by blue‒green algae, algal scum, and macrophyte issues. Our results, through remote sensing techniques, provide valuable support for addressing an alarming situation and developing more effective action plans, especially for these fragile ecosystems. Declarations Funding (the present study was not funded by any company or person) Conflicts of interest/Competing interests (No conflicts of interest) Availability of data and material (not applicable) Code availability (Not applicable) There are no competing interests to declare. Author contribution: The authors' contributions are as follows: study conception and design: AB and MLB; data collection: AB; analysis and interpretation of results: AB and MLB; and draft manuscript preparation: AB and MLB. References Aibeche C, Sidhoum W, Djabeur A, Kaid-Harche A (2020) Effet des caractéristiques physico-chimiques sur la charge microbienne de l’eau des zones humides du nordouest algérien: cas du lac Télamine et de Dayet Oum Ghellaz, Oran). 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Ahmed","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Lamine","lastName":"BOUTRID","suffix":""}],"badges":[],"createdAt":"2024-05-25 11:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4476677/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4476677/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58056841,"identity":"c23d772e-39a8-4f68-98d1-1aac4e443c22","added_by":"auto","created_at":"2024-06-10 14:26:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":958941,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area (A) Dayet Oum Rhalez Lake, (B) Sidi Chahmi Lake, and (C) Dhayat Morasli Lake\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4476677/v1/9f9a9f15d76226308b6907ff.jpg"},{"id":58056432,"identity":"91b9acb1-21a8-455c-a4ea-79e9023698fb","added_by":"auto","created_at":"2024-06-10 14:18:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1848065,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variations in the average annual Chl-a concentration, SD, and TSI for 2019, 2020, 2021 and 2022\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4476677/v1/28a6f1bd6d4da4e29caa04a6.jpg"},{"id":58056840,"identity":"4f3c49a8-1f8a-419d-8521-18eb2f54eaeb","added_by":"auto","created_at":"2024-06-10 14:26:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1598075,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal variations inchlorophyll-a concentrations for the period 2019-2022\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4476677/v1/10fe206a4a12f264078f43d0.jpg"},{"id":58056435,"identity":"342ce183-fdcf-46f9-9051-6b9754aab779","added_by":"auto","created_at":"2024-06-10 14:18:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1441660,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal variations in TSI for the period of 2019-2022\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4476677/v1/0ebaa01ee27814a673371e8c.jpg"},{"id":58056838,"identity":"9abe6656-4e77-45e4-a53d-581aa2bd5099","added_by":"auto","created_at":"2024-06-10 14:26:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1473410,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal variations in SD for the period 2019-2022\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4476677/v1/e2a158cf4803f8bf628ecb32.jpg"},{"id":58056430,"identity":"f25e6d76-979f-4299-9bba-87d371e1c092","added_by":"auto","created_at":"2024-06-10 14:18:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1114706,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between precipitation and Chl-a, TSI, and SD\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4476677/v1/75f2a040ddd888ccbbbc4999.jpg"},{"id":58056837,"identity":"7c904d63-7570-4036-b744-c4a4c4c9bdf3","added_by":"auto","created_at":"2024-06-10 14:26:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1909316,"visible":true,"origin":"","legend":"\u003cp\u003eSome samples of (A) DMo lake pollution, (B) solid waste in SCh lake, (C) wastewater discharge in DORh lake, and (D) DORh lake pollution\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4476677/v1/4131e62b07e71416655564de.jpg"},{"id":64040588,"identity":"371eb37a-8ca4-4255-bc9e-2f02e40bc5c0","added_by":"auto","created_at":"2024-09-05 12:49:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11073745,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4476677/v1/26904b8e-80d5-4144-8330-18b8cb838de2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of the impact of the COVID-19 pandemic lockdown on the spatiotemporal variations in water quality in three wetland areas in Oran, western Algeria","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to the RAMSAR Convention, wetlands are regions where water is the primary determinant of the environment and is associated with plant and animal life (Ramsar \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These vital spaces provide various services, such as potable water, food, energy, industry, and recreation (Tundisi et al. 2008). They serve as crucial habitats for bird repopulation, where birds use them for nesting and as resting spaces during their seasonal migration (Kačergytė et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); (Yao et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With demographic development and economic transformations, contemporary cities continue to expand into natural spaces, posing a threat to biodiversity (Bendib and Berghout \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); (D\u0026ouml;rnh\u0026ouml;fer and Oppelt \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Statistics indicate that between 60 and 70% of all global wetlands have been lost due to agricultural drainage and urbanization (Davidson \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); (Kačergytė et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These vital yet fragile spaces continue to face pressures jeopardizing ecological balance and water quality (Calhoun et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Wetland eutrophication is becoming an increasingly common environmental issue (Ogashawara et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Along with certain agricultural practices, domestic and industrial wastewater discharge are identified as the main sources of this eutrophication (Converse \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1995\u003c/span\u003e); (Australian Government \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); (Quanz et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); (Convention on Wetlands \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); (Ostad-Ali-Askari \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, due to the vastness, remoteness, and difficulty of accessing certain wetland areas, ground-based monitoring methods have become expensive, costly, and often unfeasible. Due to the ease of acquisition and rapid processing of images (Guo et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), remote sensing techniques based on satellite imagery have become indispensable for monitoring water quality parameters, including chlorophyll-a and water transparency (Mishra and Mishra \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); (Alikas and Kratzer \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Several multispectral satellites have been launched, including Landsat series data, MODIS data, and MERIS data. However, their low resolution affects the quality of studies, especially for small lakes. Furthermore, with the launch of the latest generation of spatial sensors (Sentinel-2 of the European Space Agency (ESA)), characterized by a short temporal series (Lisboa et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), a new opportunity to explore small aquatic systems has emerged (Ogashawara et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, with the geospatial analysis capabilities of the Google Earth Engine (GEE) in addressing numerous issues such as deforestation, climate change, and water management (Sherjah et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), this study relies on this platform for monitoring water quality in three wetlands in the Oran region, western Algeria.\u003c/p\u003e \u003cp\u003eDue to the significance of Oran's wetlands for biological diversity, numerous studies have been conducted. A study by (Ben Bayer et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) attempted to monitor the biological, physical, and chemical characteristics of Dhayat Morasli in 2017 using monthly samples taken around the lake. The results indicated that the concentrations of nitrates and phosphates exceeded the permissible standards, and the cadmium and lead concentrations increased compared to the recommended values. Consequently, this situation led to eutrophication of the lake. Another study on physicochemical and bacteriological quality was conducted by (Mahi et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Three wetlands in western Algeria were selected for experimentation: Dhayat Morasli Lake, Dayet Oum Rhalez Lake, and Telamine Lake. Using eight parameters in addition to pH, the results revealed concentrations exceeding established standards for chloride, ammonium, and sulfate. Furthermore, the pH of Lake Dayet Morsli, nitrate concentration of Lake Telamine, chemical oxygen demand (COD), and suspended solids concentration of Lakes Dayet Morsli and Telamine exceeded international standards. These exceedances are mainly attributed to untreated domestic and industrial wastewater, which has been identified as the main cause of water quality deterioration. (Aibeche et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a comparative study between Lake Telamine and Lake Dayet Oum Rhalez. The study, conducted throughout the four seasons of 2016/2017, concluded that Lake Telamine is more polluted than Lake Dayet Oum Rhalez in terms of chemical oxygen demand (COD), chloride, and heavy metals such as chromium, copper, lead, and nickel. On the other hand, high values of the COD/DBO5 (biochemical oxygen demand over five days) ratio (\u0026gt;\u0026thinsp;3) indicate that the water in both lakes contains industrially derived organic matter that is difficult to biodegrade. From this literature, it is evident that most studies share a common idea: monitoring physical, chemical, and biological parameters by focusing on specific sites during a single year, while identifying areas where water quality deteriorates requires continuous monitoring over the entire water body (Sherjah et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our opinion, these approaches do not provide satisfactory answers and do not comprehensively reflect the situation of wetlands in terms of spatiotemporal variations in pollutants, especially if the goal is to develop robust long-term conservation strategies.\u003c/p\u003e \u003cp\u003eMoreover, according to (Kislik et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), monitoring water surfaces through in situ sampling is both expensive and tedious, and satellite imagery provides a rapid and relatively cost-effective method. Building on this point, the novelty of our work lies in the use of remote sensing data as a complementary technique capable of providing a dynamic view of the water surface at different periods and scales, thus allowing a thorough understanding of the underlying causes. By adopting an approach that transcends simple annual monitoring, our study aims to capture the spatiotemporal evolution of pollutants, offering a more comprehensive perspective to guide the development of long-term conservation strategies. The primary objective of this study was to shed light on the effect of the COVID-19 lockdown on the spatiotemporal variations in pollutants in three wetlands in Oran using chlorophyll-a, an excellent indicator of water quality, especially in aquatic ecosystems. To achieve this goal, the following steps are essential: (1) use Sentinel-2/MSI data to characterize variations in chlorophyll-a (Chl-a), the trophic state index (TSI), and the Secchi depth (SD); (2) analyze the effect of meteorological factors (precipitation) on variations in the calculated parameter values; and (3) understand the effect of the applied lockdown on improving water quality and biological activity on each water surface.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Description of the study area\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in the Oran region in western Algeria, focusing on three water bodies: DMo Lake, DORh Lake, and SCh Lake (Fig.\u0026nbsp;1). With an area of 114 hectares, DMo Lake is part of the wetland complex in western Algeria (Ramsar \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is situated southeast of the Oran urban area within the municipality of Es S\u0026eacute;nia, where urban development has gradually encroached upon this depression. The lake is bordered by industrial zones to the west and south. Oran's urban area and the road infrastructure border Dayet to the north, while agricultural lands surround the wetland to the east. However, peripheral urbanization around the city of Oran has isolated the wetland from its original watershed (MATET \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). DMo is home to 53 bird species from 22 families and 36 genera, making it a suitable location for wintering and stopovers during bird migration.\u003c/p\u003e\n\u003cp\u003eCovering an area of 17 hectares, SCh Lake is located in the municipality of Sidi Chahmi, in direct proximity to built-up areas. It serves as an excellent example for studying the impact of domestic discharge on water quality during the lockdown period. With an area of 328 hectares, DORh Lake is relatively distant from significant urban construction areas (except for a 10-hectare concentration at 500 meters). This surface is a preferred habitat for the northern shoveler duck (\u003cem\u003eAnas clypeata\u003c/em\u003e), the ferruginous duck (Aythya nyroca), and the squacco heron (Ardeola ralloides). Table\u0026nbsp;1 summarizes the main characteristics of each water surface.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1 Main characteristics of the study area\u003c/p\u003e\n\u003ctable border=\"1\" width=\"94%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eName\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eSite\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eType\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"41%\"\u003e\n\u003cp\u003eLocalisation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eLake of Dayet Oum Rhalez\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eNatural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"41%\"\u003e\n\u003cp\u003eLat: 35\u0026deg;36\u0026rsquo;2.36\u0026rdquo; N, long: 0\u0026deg;25\u0026rsquo;34.48\u0026rdquo; W\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e328\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eLake of Sidi Chahmi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eNatural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"41%\"\u003e\n\u003cp\u003eLat: 35\u0026deg;39\u0026rsquo;40.90\u0026rdquo; N, long: 0\u0026deg;31\u0026rsquo;45.52\u0026rdquo; W\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"27%\"\u003e\n\u003cp\u003eLake of Dhayat Morasli\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"9%\"\u003e\n\u003cp\u003eNatural\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"41%\"\u003e\n\u003cp\u003eLat: 35\u0026deg;40\u0026rsquo;3.24\u0026rdquo; N, long: 0\u0026deg;36\u0026rsquo;24.85\u0026rdquo; W\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"12%\"\u003e\n\u003cp\u003e114\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Data sources\u003c/h2\u003e\n\u003cp\u003eThe primary objective of this study was to analyze the impact of the lockdown imposed in response to the COVID-19 pandemic on the improvement of water quality in three wetlands in the Oran region. To achieve this goal, we utilized Sentinel-2 multispectral instrument (MSI) level-2B data, which began operating in 2017 (Table\u0026nbsp;2). We employed images acquired between January and December of the years 2019, 2020, 2021, and 2022, with a maximum cloud coverage threshold of 20%, to ensure reliability. The Google Earth Engine (GEE) platform and a reference code developed by (Page et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), enhanced by Geography Lounge (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geography-lounge.com/courses/1726312/lectures/44199367\u003c/span\u003e\u003c/span\u003e), were modified and utilized to extract chlorophyll-a, trophic state index (TSI), and Secchi depth (SD). The choice of GEE has been validated by numerous studies demonstrating its applicability for water quality monitoring (Vaičiūtė et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); (Lobo et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); (Kislik et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e); (Sherjah et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The obtained images were then clipped, exported in raster format, and, using the ArcGIS environment, transformed into vectors (Shapefile). Additionally, before utilizing the data, it is crucial to transform them from the GCS WGS1984 reference system to the UTM WGS84 Z30 system. This enables the calculation of areas for comparative statistical analysis.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2 Details of the data used in the study\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003eSatellite data\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003eBand number\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eSpatial resolution (m)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003eCentral wavelength (nm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eDate of acquisition\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"15%\"\u003e\n\u003cp\u003eSentinel 2B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e664.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"21%\"\u003e\n\u003cp\u003e2019, 2020, 2021, 2022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"15%\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"25%\"\u003e\n\u003cp\u003e703.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe selection of parameters is justified by the work of (Pavluk and Bij De Vaate \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). According to this study, the trophic state concept is based on the notion that variations in nutrient levels (assessed by total phosphorus) lead to changes in algal biomass (assessed by chlorophyll-a), which, in turn, affects lake clarity (measured by the Secchi depth).\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.1 Chlorophyll-a\u003c/h2\u003e\n\u003cp\u003eChlorophyll-a is the pigment shared by all phytoplankton species (Reinart and Kutser \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). It plays a central role in photosynthesis, which is crucial for energy production in photosynthetic organisms (Bj\u0026ouml;rn et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Indeed, the concentration of chlorophyll-an in water is often used as an indicator of water quality (Muduli et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); (Bramich et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), especially in aquatic systems such as lakes, rivers, and reservoirs. In this study, before calculating Chl-a, it is essential to compute the normalized difference chlorophyll index (NDCI), a commonly used vegetation index in remote sensing to assess chlorophyll concentrations in vegetation. According to (Muduli et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), the high accuracy (R2\u0026thinsp;=\u0026thinsp;0.95; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; range\u0026thinsp;=\u0026thinsp;1\u0026ndash;60 \u0026micro;g/l; RMSE\u0026thinsp;=\u0026thinsp;2 \u0026micro;g/l) of the Chl-a model developed by (Mishra and Mishra \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) has made it widely used in many remote sensing studies.\u003c/p\u003e\n\u003cp\u003eAccording to (Bramich et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), the model is generally expressed in equations (\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$Chl-a=14.039+86.115\\left(NDCI\\right)+194.325{\\left(NDCI\\right)}^{2}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$NDCI=\\frac{{R}_{nir}-{R}_{red}}{{R}_{nir}+{R}_{red}}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;.........................\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{red}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{nir}\\)\u003c/span\u003e\u003c/span\u003e are MSI bands 4 at 665 nm and 5 at 704 nm, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.2 Trophic State Index (TSI)\u003c/h2\u003e\n\u003cp\u003eThe trophic state index (TSI) is a numerical scale widely used to assess and quantify water quality (Sherjah et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); (Zhu and Mao \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). It indicates the trophic state, which refers to the level of biological productivity or nutrient enrichment in a body of water. The trophic state is often linked to factors such as algal growth, water clarity, and overall ecosystem health. The TSI is calculated using equations that combine these parameters. According to (Pavluk and Bij De Vaate \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e), the formulas for calculating the TSI are presented in Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, and Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Each of these three formulas can theoretically be used to classify a body of water, and it is also feasible to consider the average between them.\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$TSI=30.6+9.81\\text{ln}\\left(Chlorophyll-a(\\mu g/l)\\right)$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e\n\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ4\" class=\"mathdisplay\"\u003e$$TSI=60-14.41\\text{ln}\\left(Secchi disk depth \\left(m\\right)\\right)$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e\n\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ5\" class=\"mathdisplay\"\u003e$$TSI=4.15+14.42\\text{ln}\\left(total phosphorus(\\mu g/l)\\right)$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n\u003ch2\u003e2.2.3 Secchi depth (SD)\u003c/h2\u003e\n\u003cp\u003eThe clarity of surface waters is frequently associated with the concentration of plant nutrients in the water (Gao et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). An increase in nutrient quantity promotes the proliferation of phytoplankton (Rose et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), leading to a decrease in water transparency (Pavluk and Bij De Vaate \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). The Secchi disk is a straightforward yet effective tool utilized in water quality assessment (Liu et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). This disc is a flat, white disk with alternating black and white quadrants. It is lowered into the water until it becomes invisible, and the depth at which it disappears, referred to as the Secchi depth, is measured. The Secchi depth serves as an indicator of water transparency or clarity and is commonly employed in various environmental and ecological assessments (Lee et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eFigure 2 shows the temporal variations in Chl-a, TSI, and SD for 2019, 2020, 2021, and 2022. The statistical analysis of chlorophyll-a (Fig.\u0026nbsp;2.a) in DMo Lake revealed that in 2019, the average concentration was 41.73 \u0026micro;g/l, with a marginal reduction of 0.18 \u0026micro;g/l recorded in 2020 (averaging 41.55 \u0026micro;g/l). Furthermore, a significant decrease of 21.01 \u0026micro;g/l was observed in 2021, followed by a slight increase of 2.82 \u0026micro;g/l in 2022 (a total of 23.83 \u0026micro;g/l). Similarly, the chlorophyll content of SCh Lake decreased to 42.82 \u0026micro;g/l in 2019, 32.17 \u0026micro;g/l in 2020, 23.08 \u0026micro;g/l in 2021, and 16.17 \u0026micro;g/l in 2022. For DORh Lake, chlorophyll decreased by 7.44 \u0026micro;g/l between 2019 and 2020 (53.77 \u0026micro;g/l in 2019 compared with 46.33 \u0026micro;g/l in 2020). Moreover, an increase to 61.96 \u0026micro;g/l was observed in 2021, followed by a decrease to 25.3 \u0026micro;g/l in 2022.\u003c/p\u003e\n\u003cp\u003eFor the TSI (Fig.\u0026nbsp;2.b), the values obtained indicate the positive effect of the lockdown on water quality. For DMo Lake, the average was 5.95 in 2019. In 2020, this average decreased to 5.86. A notable decline in TSI values is recorded in 2021, with an average of 5.36 (a decrease of 0.5). An increase of 0.51 (resulting in a TSI of 0.87) is expected to occur in 2022. For SCh Lake, the TSI increased by 0.41 between 2019 and 2020 (5.32 in 2019 against 5.73 in 2020), followed by a significant decrease of 1.61 in 2021. An increase to 4.81 is expected to occur in 2022. The same changes also affected DORh Lake, with an increase of 0.02 between 2019 and 2020, followed by a decrease to 5.32 in 2021 before the values increase again to reach 5.85 in 2022.\u003c/p\u003e\n\u003cp\u003eSimilarly, the Secchi depth (Fig.\u0026nbsp;2.c) confirmed the positive effect of the lockdown on the changes in the physicochemical characteristics of the water in the three studied wetlands. DMo Lake exhibited a significant improvement in water clarity, with an average depth of 1.31 m in 2019 and 1.51 m in 2020, reaching 2.75 m in 2021. This clarity is substantially reduced to a depth of 1.33 m in 2022. The same trend was also observed for SCh Lake, with improvements of 1.40, 1.43, and 2.07 meters in 2019, 2020, and 2021, respectively. This is followed by a decrease in depth to reach 1.31 m in 2022. For DORh Lake, the water quality significantly improved between 2019, 2020, and 2021, with depths of 1.16, 1.75, and 2.61 meters, respectively. However, a decrease in water quality is expected to occur in 2022, with a maximum depth of 1.4 meters.\u003c/p\u003e\n\u003cp\u003eChl-a can be used as a common parameter for the water quality index (Muduli et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study provides insights into the trophic state and overall health of aquatic ecosystems (Table\u0026nbsp;3 and Fig.\u0026nbsp;3). In 2019, 55.70% of DORh Lake was characterized by chlorophyll concentrations exceeding 61.52 \u0026micro;g/l, while 21.13% of the lakes had chlorophyll concentrations ranging between 50.87 \u0026micro;g/l and 61.51 \u0026micro;g/l. Additionally, low concentrations (\u0026lt;\u0026thinsp;22.10 \u0026micro;g/l) occupy barely 2.6% of the overall surface area. In 2020, there was a clear trend toward the dominance of medium concentrations (32.77\u0026ndash;50.86 \u0026micro;g/l), covering 85.94% (262.06 ha) of the total area. Following the imposed lockdown, the Chl-a concentration gradually decreased in 2021, with 8.96% (23.66 ha) of the total area falling below 22.10 \u0026micro;g/l. The consequences of this lockdown are expected to reach optimal values in 2022, with 12.91% (39.01 ha) for the class\u0026thinsp;\u0026lt;\u0026thinsp;22.10 \u0026micro;g/l, 86% for the class 22.11\u0026ndash;32.76 \u0026micro;g/l, and 0% for concentrations exceeding 50.87 \u0026micro;g/l. Based on the Chl-a classification table and its ecological characteristics (Carlson and Simpson \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e), it is evident that in 2019, DORh Lake was dominated by a hypereutrophic ecological characteristic (76%), with water quality deemed uninhabitable. In 2020, water quality improved, with 85% of the surface exhibiting eutrophic ecological characteristics dominated by blue‒green algae, algal blooms, and macrophyte issues. However, despite the lockdown implemented in 2021, a considerable increase in the area dominated by hypereutrophic (59%) and eutrophic (15%) ecological characteristics was noted. In 2022, water quality significantly improved, with 86% exhibiting eutrophic characteristics (dominated by blue‒green algae, algal blooms, and macrophyte issues) and 12% displaying hypolimnetic anoxia, with possible macrophyte problems.\u003c/p\u003e\n\u003cp\u003eFor DMo Lake, chlorophyll-a concentrations were notably found in the medium (37.07\u0026ndash;41.85 \u0026micro;g/l) and high (41.86\u0026ndash;47.73 \u0026micro;g/l) classes, covering 33.14 ha (23.87%) and 96.18 ha (69.29%), respectively. Low concentrations ranging from 10.49 to 27.04 \u0026micro;g/l barely accounted for 1%. Water quality is expected to improve significantly in 2020, with values of 11%, 18%, and 45% for the 27.05\u0026ndash;37.06 \u0026micro;g/l, 37.07\u0026ndash;41.85 \u0026micro;g/l, and 41.86\u0026ndash;47.73 \u0026micro;g/l classes, respectively. A notable improvement is observed in 2021 and 2022, with dominance at low concentrations. Specifically, 65% of the compounds were in the 10.49\u0026ndash;27.04 \u0026micro;g/l class, 31% were in the 27.05\u0026ndash;37.06 \u0026micro;g/l class in 2021, and 55% and 44% were in the same classes in 2022. The dominant ecological characteristic in 2019 was eutrophic (with the dominance of blue‒green algae, algal blooms, and macrophyte issues), indicating poor water quality. However, in 2020, an increase in hypereutrophic characteristics was easily identified. The COVID-19 pandemic contributed to improving water quality, with eutrophic characteristics and poor water quality.\u003c/p\u003e\n\u003cp\u003eThe same trend was observed for SCh Lake, with a dominance of medium (36.73\u0026ndash;44.49 \u0026micro;g/l) and high (44.49\u0026ndash;53.82 \u0026micro;g/l) concentrations in 2019, accounting for 21.28% and 60.86%, respectively. In 2020, there was an improvement in water quality, with an increase in concentrations below 25.32 \u0026micro;g/l reaching 6%, a considerable increase in the class ranging from 25.33 \u0026micro;g/l to 36.72 \u0026micro;g/l with 52%, and finally an increase in the class ranging from 36.73 \u0026micro;g/l to 44.49 \u0026micro;g/l, covering a total area of 33%. Moreover, with the lockdown, the chlorophyll concentration decreased significantly in 2021 and 2022. Concentrations below 36.72 \u0026micro;g/l accounted for 93% of the total in 2021, reaching 100% in 2022. In general, SCh Lake is no exception. The dominance of the hypereutrophic ecological characteristic was observed in 2019, followed by 2020, 2021, and 2022 by the eutrophic characteristic with the dominance of blue‒green algae, algal blooms, and macrophyte issues in 2020, and hypolimnetic anoxia with possible macrophyte problems in 2021 and 2022.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3. Variations in Chl-a for 2019, 2020, 2021 and 2022\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"16%\"\u003e\n\u003cp\u003eclass\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"100%\"\u003e\n\u003cp\u003eLake of Dayet Oum Rhalez\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e10.39 \u0026ndash; 22.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e8.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e4.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e23.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e8.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e39.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e12.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e22.11 \u0026ndash; 32.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e53.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e17.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e6.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e262.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e86.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e32.77 \u0026ndash; 50.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e9.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e262.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e85.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e35.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e13.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e50.87 \u0026ndash; 61.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e64.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e21.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e30.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e10.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e41.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e15.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e61.52 \u0026ndash; 78.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e170.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e55.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e5.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e157.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e59.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"100%\"\u003e\n\u003cp\u003eLake of Dhayat Morasli\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e10.49 \u0026ndash; 27.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e5.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e88.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e65.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e76.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e55.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e27.05 \u0026ndash; 37.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e5.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e4.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e16.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e11.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e43.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e31.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e61.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e44.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e37.07 \u0026ndash; 41.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e33.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e23.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e25.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e18.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e41.86 \u0026ndash; 47.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e96.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e69.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e63.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e45.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e47.74 \u0026ndash; 66.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e28.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e20.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"100%\"\u003e\n\u003cp\u003eLake of Sidi Chahmi\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e9.781 - 25.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e6.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e5.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e40.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e15.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e96.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e25.33 \u0026ndash; 36.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e9.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e8.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e52.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e7.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e52.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e36.73 \u0026ndash; 44.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e21.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e5.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e33.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e4.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e44.49 - 53.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e9.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e60.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e6.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e53.82 - 75.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e5.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe trophic state index (Table\u0026nbsp;4 and Fig.\u0026nbsp;4) provides a quantitative measure of water quality by assessing the nutrient levels present. In 2019, 99% of DORh Lake was characterized by a high biological productivity level exceeding 50%, while less than 0.08% exhibited low nutrient levels (\u0026gt;\u0026thinsp;50%). In 2020, there was a slight improvement in water quality, with an increase in the area of biological productivity below 50% at 10% (28 ha). Unfortunately, this is accompanied by an increase in the class (\u0026gt;\u0026thinsp;60\u0026ndash;100%) at the expense of the class (50\u0026ndash;60%), with 85.45% and 4.40%, respectively. Improvement can be observed in 2021, with 11.55% for the class (\u0026lt;\u0026thinsp;30\u0026ndash;40%) and 8.13% for the class (40\u0026ndash;50%). The percentage of the class with biological activity exceeding 60% decreased to 64%. Conversely, water became uninhabitable again immediately after the lockdown, with 91% (260 ha) exhibiting biological activity levels exceeding 60% and 1.71% (4.89 ha) exhibiting levels below 40%. Overall, DORh Lake is characterized by high biological activity with a hypereutrophic water body. This indicates that the water quality is poor, even after the forced lockdown is applied.\u003c/p\u003e\n\u003cp\u003eFor DMo Lake, nutrient levels in 2019 exceeded 96% for the class above 60%, compared to 0.06% for the class below 40%. This reflects the poor state of water quality. A slight improvement was observed in 2020, with 1.99% for the 40\u0026ndash;50% class and 5.05% for the 50\u0026ndash;60% class. Following the lockdown, a significant decrease (38%) in biological activity was noted in the class above 60%, accompanied by an increase in biological activity in the class (50\u0026ndash;60%) of 59.94% (75.45 ha). Indeed, water quality will degrade again in 2022. The biological productivity increased significantly, particularly for the class (\u0026gt;\u0026thinsp;60%), with a total area of 116.97 ha (92.93%). The results revealed an improvement in water quality in 2021, transitioning from a hypereutrophic water body in 2019 and 2020 to a eutrophic water body in 2021. In general, water quality fluctuates between poor and uninhabitable areas.\u003c/p\u003e\n\u003cp\u003eThe results obtained for SCh Lake are intriguing. The nutrient levels in 2019 were divided as follows: 9.44% (ha) for the \u0026lt;\u0026thinsp;40% class, 14.08% (1.79 ha) for the 40\u0026ndash;50% class, and 11.88% (1.51 ha) for the 50\u0026ndash;60% class. The \u0026gt;\u0026thinsp;60% class represents 64% of the total class. This good water quality decreased in 2020 to yield the following results: 2.44% (0.31 ha) for the \u0026lt;\u0026thinsp;40% class, 3.31% (0.42 ha) for the 40\u0026ndash;50% class, and 80.91% (10.26 ha) for the \u0026gt;\u0026thinsp;60% class. In 2021, the lockdown imposed due to the COVID-19 pandemic significantly improved water quality, with 28.45% for classes below 40%, 35.93% for classes 40\u0026ndash;50%, 30.73% for classes 50\u0026ndash;60%, and 4.89% for classes above 60%. After the lockdown, an increase in biological productivity levels can be easily observed, particularly in the classes 50\u0026ndash;60% and \u0026gt;\u0026thinsp;60% at 64.20% (8.14 ha) and 10.88% (1.38 ha), respectively. According to the trophic scale, the water bodies transitioned from eutrophic in 2019 to hypereutrophic in 2021. Moreover, in 2021, the biological activity was considered moderate, indicating a mesotrophic water body. In terms of water surface pollution, this suggests a passable quality. However, this situation will rapidly degrade in 2022, when the water quality will become poor and the eutrophic water body will dominate.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;4. Variations in TSI for the years 2019, 2020, 2021 and 2022\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"16%\"\u003e\n\u003cp\u003eclass\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"100%\"\u003e\n\u003cp\u003eLake of Dayet Oum Rhalez\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026lt;30 \u0026ndash; 40%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e16.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e5.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e32.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e11.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e4.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e40 \u0026ndash; 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e12.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e4.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e23.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e8.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e6.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e50 \u0026ndash; 60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e92.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e32.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e12.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e4.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e46.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e16.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e13.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e4.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026gt;60 \u0026ndash; 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e192.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e67.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e243.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e85.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e182.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e64.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e260.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e91.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"100%\"\u003e\n\u003cp\u003eLake of Dhayat Morasli\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026lt;30 \u0026ndash; 40%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e40 \u0026ndash; 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e4.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.73\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e50 \u0026ndash; 60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e6.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e5.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e75.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e59.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026gt;60 \u0026ndash; 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e121.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e96.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e115.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e91.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e48.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e38.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e116.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e92.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"100%\"\u003e\n\u003cp\u003eLake of Sidi Chahmi\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026lt;30 \u0026ndash; 40%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e9.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e28.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e5.28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e40 \u0026ndash; 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e14.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e4.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e35.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e2.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e19.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e50 \u0026ndash; 60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e11.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e13.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e3.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e30.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e8.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e64.20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u0026gt;60 \u0026ndash; 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e8.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e64.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e10.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e80.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e4.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e1.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e10.88\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Secchi depth (Table\u0026nbsp;5 and Fig.\u0026nbsp;5) is used to measure the depth at which the disk is no longer visible from the water surface. This measurement serves as an indicator of water transparency and quality (Harrison \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). In 2019, 99.81% of the surface area of DORh Lake was characterized by a depth not exceeding 2 m, while depths exceeding 8 m barely accounted for 0.03%. This indicates a disastrous state of water quality (poor quality according to the trophic degree scale) with a dominance of the eutrophic mass. A marginal improvement in transparency was recorded in 2020, with 88.76% of the depths being visible below 2 m and 1% exceeding 8 m. Depths ranging from 2 m to 8 m constitute 10% of the lake. Moreover, following the enforced lockdown, a significant improvement in water clarity was observed in 2021. A considerable increase in depths exceeding 8 m at 1.4% was accompanied by an increase in depths of 2\u0026ndash;4 m and 4\u0026ndash;8 m at 32.80% and 12.75%, respectively. A total of 53.05% of the depths below 2 m decreased. The situation deteriorated in 2022, with 98% for depths below 2 m and 0.49% for depths exceeding 8 m. Generally, the dominant trophic class in DORh Lake is eutrophic, indicating high biological activity and poor water quality. However, with confinement, considerable areas transform into mesotrophic (92.75 ha) and oligotrophic (36.06 ha) areas, with water quality ranging between fair and good.\u003c/p\u003e\n\u003cp\u003eThe same trend was observed for DMo Lake, with a predominance of depths below 2 m of 99.48% in 2019 and 94.91% in 2020. This is followed by a slight improvement in depths of 2\u0026ndash;4 m, 4\u0026ndash;8 m, and 8\u0026ndash;10 m at 4.76%, 5.58%, and 0.90%, respectively. In 2021, the positive effects of the lockdown were easily identifiable, with a significant improvement in transparency in the classes ranging from 2 m to 4 m (75.89%) and between 4 m and 8 m (6.85%). However, the water quality rapidly decreased in 2022, with 99.48% of the depths below 2 m and barely 0.05% of the depths exceeding 8 m. In summary, except for 2021 (with the water body classified as mesotrophic of moderate quality), the water quality of DMo Lake is poor, and its biological activity is classified as high with a eutrophic water body.\u003c/p\u003e\n\u003cp\u003eInteresting results were obtained for SCh Lake between 2019 and 2020. In addition to classes 2\u0026ndash;4 m, depths of 0\u0026ndash;2 m, 4\u0026ndash;8 m, and 8\u0026ndash;10 m exhibited decreases in surface area, with 95.93% compared to 95.45%, 2.49% compared to 2.33%, and 0.45% compared to 0.36%, respectively. After the lockdown, water quality improved in 2021, and clarity exceeding 4 m occupied more than 10% of the water surface. However, 75% of the surface remains of inferior quality (visible at less than 2 m). In 2022, the situation is no exception, and water quality will deteriorate. A total of 97.15% of the surface is characterized by a depth not exceeding 2 m. According to these statistics, the water quality in this area is poor (eutrophic water body).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;5. Variations in SD for the years 2019, 2020, 2021 and 2022\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35.5558px;\"\u003e\n\u003ctd style=\"height: 70.5558px;\" rowspan=\"2\" width=\"16%\"\u003e\n\u003cp\u003eclass\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.5558px;\" colspan=\"2\" width=\"21%\"\u003e\n\u003cp\u003e2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.5558px;\" colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.5558px;\" colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.5558px;\" colspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003e2022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003eArea (ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003eArea (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"9\" width=\"100%\"\u003e\n\u003cp\u003eLake of Dayet Oum Rhalez\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e0 \u0026ndash; 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e284.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e99.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e251.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e88.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e150.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e53.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e278.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e98.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e2 - 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e13.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e4.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e92.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e32.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e1.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e4 \u0026ndash; 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e15.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e5.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e36.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e12.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e2.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e8 - 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.078\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e2.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e3.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e1.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e1.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"9\" width=\"100%\"\u003e\n\u003cp\u003eLake of Dhayat Morasli\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e0 \u0026ndash; 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e125.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e99.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e119.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e94.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e21.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e17.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e125.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e99.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e2 - 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e3.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e2.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e95.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e75.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e4 \u0026ndash; 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e2.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e1.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e8.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e6.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e8 - 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"9\" width=\"100%\"\u003e\n\u003cp\u003eLake of Sidi Chahmi\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e0 \u0026ndash; 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e11.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e95.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e11.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e95.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e9.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e75.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e11.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e97.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e2 - 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e1.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e1.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e1.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e13.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e4 \u0026ndash; 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e2.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e2.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e1.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e9.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e1.46\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" width=\"16%\"\u003e\n\u003cp\u003e8 - 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" width=\"10%\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe concentration of chlorophyll an in a lake is influenced by various environmental, biological, and anthropogenic factors (Huang et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e); (Benkesmia et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); (Zhang et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). These factors may vary depending on specific lake characteristics, such as proximity to urban and industrial development and the presence or absence of direct discharge into the lake. Additionally, meteorological conditions, especially precipitation, can affect chlorophyll-a concentrations through variations in water levels in the lake (Das Sarkar et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); (Han et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). They can also influence nutrient runoff from the watershed into the lake. Heavy rainfall events can lead to sudden nutrient inputs. In this study, we aimed to explore the underlying factors behind spatiotemporal variations in environmental parameters such as the chlorophyll-a concentration (Chl-a), trophic state index (TSI), and Secchi depth (SD) in DORh, DMo, and SCh lakes. To do so, we use a correlation coefficient to help identify the main causes of these variations. Our hypothesis is as follows: if the obtained correlation coefficient is high, precipitation is the primary influencing factor. Conversely, if the correlation is low, this could indicate that the confinement imposed in response to the COVID-19 pandemic is the main cause of the observed variations in the three parameters.\u003c/p\u003e\n\u003cp\u003eAccording to the results shown in Fig.\u0026nbsp;6, the effect of precipitation is weak in DMo Lake, with correlations of 16.7%, 26.22%, and 34.29% for Chl-a, TSI, and SD, respectively. Furthermore, for water bodies relatively distant from urban areas (especially DORh Lake), the effect of precipitation is significant at 81.55%, 60.19%, and 11.87%, respectively. Moreover, for SCh Lake, the effect of precipitation was relatively moderate, with 63.74%, 23.5%, and 45.02% for Chl-a, TSI, and SD, respectively. This suggests that the positive effect of confinement is recorded in DMo and to a lesser extent in SCh. This leads us to conclude that the observed pollution in DMo primarily results from untreated industrial discharge from the Es-S\u0026eacute;nia industrial zone, specifically from factories lacking water treatment facilities, which significantly contributes to the degradation of water quality. Similarly, industrial discharge from units near SCh Lake constitutes the major source of pollution. The geographical proximity of these industrial units amplifies their impact on the ecological balance of the lake. Moreover, the implementation of confinement measures was a determining factor for the sharp decrease in chlorophyll-a concentrations recorded in 2021. In response to these measures, several industrial activities were either suspended or forced to drastically reduce their production. Consequently, industrial discharge into the DMo and SCh Lakes significantly decreased, explaining the abrupt decrease in chlorophyll-a concentrations during this period.\u003c/p\u003e\n\u003cp\u003eFurthermore, DMo has been supplied with industrial discharge (estimated flow of 6000 m3/day) and is currently receiving water that has come into contact with landfills, inert waste, and drainage water from the surrounding road network (Fig.\u0026nbsp;7). In this lake, eutrophication and the presence of illegal dumps are visible signs of water pollution (Ben Bayer et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In 2009, a study conducted by the Ministry of Territorial Planning, Environment, and Tourism at three lake sites (Table\u0026nbsp;6) revealed that the water is excessively polluted with organic matter due to its COD and BOD5 values. The pH and suspended matter content also indicate water pollution. The results obtained show that even though there are no longer untreated discharges from the Es-S\u0026eacute;nia industrial zone today, the water is still excessively polluted due to the measured concentrations of cadmium, copper, and chromium. These findings are also supported by two recent studies (Mahi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); (Ben Bayer et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to (Mahi et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), physicochemical and bacteriological analyses exceed international standards, indicating a trend of lake pollution, particularly with values of 9.4 for pH, 198 mg/l for suspended matter, and 123.93 mg/l for COD.\u003c/p\u003e\n\u003cp\u003eAdditionally, the significant increase in concentrations in DORh Lake in 2021 underscores a direct correlation with behavioral changes related to the confinement measures implemented. The small agglomeration of Khedaimia, located within 500 meters of the lake, played a crucial role in this dynamic. Due to the restrictions imposed by confinement, residents were compelled to stay at home for considerable periods. This situation led to a substantial change in lifestyle, characterized by an extended time spent at home. Consequently, domestic water consumption has notably increased, affecting various aspects of daily life. Particularly affected by these changes, the quantities of household and toilet water significantly increased. Furthermore, it is essential to note that the wetland of DORh is facing the discharge of untreated industrial water but at lower rates than DMo. This is supported by a study conducted by (Aibeche et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), in which the results of physicochemical water analyses exceeded the recommended levels in Algeria, with values of 376.27 mg/l for COD, 29.70 mg/l for BOD5, and 7.8 for pH.\u003c/p\u003e\n\u003cp\u003eIn conclusion, despite the interesting results obtained and the potential of remote sensing techniques, this study has limitations that must be considered. Every remote sensing study requires ground-based measurements for validation. Unfortunately, in our case, obtaining such in situ measurements is challenging and costly, potentially introducing uncertainties. Additionally, comparing our work with other studies conducted in the same area and within a closer temporal framework can validate our findings. Moreover, it is noteworthy that the fundamental question of this study revolves around the impact of confinement on water quality, which is addressed with satisfactory results.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;6. Grid of the classification of surface water quality in Algeria and the results of the analyses (MATET \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e\n\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"21%\"\u003e\n\u003cp\u003eParameters\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"6%\"\u003e\n\u003cp\u003eUnit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"11%\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"10%\"\u003e\n\u003cp\u003eModerate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"11%\"\u003e\n\u003cp\u003epolluted\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"14%\"\u003e\n\u003cp\u003eExtremely polluted\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"5\" width=\"23%\"\u003e\n\u003cp\u003eAnalysis results\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003eSite 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003eSite 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"8%\"\u003e\n\u003cp\u003eSite 3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" width=\"100%\"\u003e\n\u003cp\u003ePhysical quality\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e6.5 \u0026ndash; 8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e6.5 \u0026ndash; 8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e8.5 \u0026ndash; 9.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u0026lt; 6.5 et \u0026gt; 9.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e9.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003e8.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e9.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eSuspended matter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003emg/l\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e0-30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e30-75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e75-100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u0026gt;100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003e358\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e258\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" width=\"100%\"\u003e\n\u003cp\u003eOrganic quality\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eDBO5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003emg/l\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e5-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e10-15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u0026gt;15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e178.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003e202.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e229.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eDCO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003emg/l\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e20-40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e40-50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u0026gt;50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e892\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003e843\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e882\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eOils and greases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003emg/l\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e25.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003e14.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e8.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\" width=\"100%\"\u003e\n\u003cp\u003eHeavy metals quality\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eArsenic (As)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003emg/l\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026gt; 0,01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e0.361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003e0.108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0.164\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eChrome (Cr+2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003emg/l\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0-0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e0.05-0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u0026gt;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003e2.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eCopper (Cu+2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003emg/l\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e0-0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0.02-0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e0.05-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u0026gt;1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e2.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003e2.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e1.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eCadmium (Cd)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003emg/l\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e0-0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u0026gt;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e0.168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003e0.987\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e0.123\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"21%\"\u003e\n\u003cp\u003eLead (Pb)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003emg/l\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"10%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u0026gt;0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"6%\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"7%\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"9%\"\u003e\n\u003cp\u003e1.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe wetlands of Oran constitute a vital ecosystem for bird repopulation, serving as nesting sites and resting spaces during their seasonal migration from north to south. However, with socioeconomic development and urban sprawl, they face serious environmental issues that threaten biodiversity. The water in wetlands becomes excessively polluted due to the measured concentrations of cadmium, copper, chrome, arsenic, and lead, as well as high levels of pH and organic matter (COD and BOD5). Two sources of pollution can be easily identified: (1) discharges of water from neighboring industrial hubs and current wastewater discharges from urban areas and (2) embankments and inert waste (demolition waste from informal settlements).\u003c/p\u003e \u003cp\u003eIn this study, Sentinel-2/MSI data were used to assess water quality and determine the impact of the COVID-19 epidemic on three wetlands: DORh, DMo, and SCh Lakes. According to three parameters (chlorophyll a (Chl-a), trophic state index (TSI), and Secchi depth (SD)), the results revealed a significant reduction in pollution levels in 2021. The average Chl-a concentration between 2019 and 2021 decreased by more than 20 \u0026micro;g/l for DMo and 19.74 \u0026micro;g/l for SCh. The TSI decreased from 5.67 to 5.32 for DORh, from 5.95 to 5.36 for DMo, and from 5.32 to 4.12 for SCh. These results are also supported by the SD values, with improvements in water transparency of 1.45 m for DORh, 1.44 m for DMo, and 0.67 m for SCh. This decrease in biological activity justifies the direct impact of the lockdown imposed in response to the COVID-19 pandemic on water quality improvement.\u003c/p\u003e \u003cp\u003eHowever, despite the improvement observed in 2021, the overall health of the three studied wetlands remains a concern. The water quality in these areas is still poor, characterized by eutrophic ecological features dominated by blue‒green algae, algal scum, and macrophyte issues. Our results, through remote sensing techniques, provide valuable support for addressing an alarming situation and developing more effective action plans, especially for these fragile ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e (the present study was not funded by any company or person)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e (No conflicts of interest)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e (not applicable)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e (Not applicable)\u003c/p\u003e\n\u003cp\u003eThere are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e The authors\u0026apos; contributions are as follows: study conception and design: AB and MLB; data collection: AB; analysis and interpretation of results: AB and MLB; and draft manuscript preparation: AB and MLB.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAibeche C, Sidhoum W, Djabeur A, Kaid-Harche A (2020) Effet des caract\u0026eacute;ristiques physico-chimiques sur la charge microbienne de l\u0026rsquo;eau des zones humides du nordouest alg\u0026eacute;rien: cas du lac T\u0026eacute;lamine et de Dayet Oum Ghellaz, Oran). 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Remote Sens 13:2498. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs13132498\u003c/span\u003e\u003cspan address=\"10.3390/rs13132498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wetlands, Chlorophyll-a, Water quality, Oran, Lockdown","lastPublishedDoi":"10.21203/rs.3.rs-4476677/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4476677/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn arid regions, water is a scarce and invaluable resource. Moreover, with urban expansions and socioeconomic changes, its quality has become a matter of significance and an indicator of environmental preservation. The objective of this study was to shed light on the impact of the COVID-19 pandemic on three wetlands in Oran, western Algeria (Lake of Dayet Oum Rhalez (DORh), Lake of Dhayat Morasli (DMo), and Lake of Sidi Chahmi (SCh)). Three parameters, namely, the chlorophyll-a concentration (Chl-a), trophic state index (TSI), and Secchi depth (SD), were selected and calculated for the period from 2019\u0026ndash;2022. The results showed that, except for DORh, the Chl-a concentration decreased from 41.73 \u0026micro;g/l to 21.01 \u0026micro;g/l for DMo and from 42.82 \u0026micro;g/l to 23.08 \u0026micro;g/l for SCh between 2019 and 2021. The TSI decreased from 5.67 to 5.32 for DORh, from 5.95 to 5.36 for DMo, and from 5.32 to 4.12 for SCh. These results are also validated by the SD values, with an improvement in water transparency from 1.16 m to 2.61 m for DORh, from 1.31 m to 2.75 m for DMo, and from 1.4 m to 2.07 m for SCh. This reduction in biological activity justifies the impact of the applied lockdown on the improvement of water quality. Additionally, despite this improvement, the overall health of the three studied wetlands remains concerning (eutrophic ecological characteristics), and water quality is often mediocre. This study, in its entirety, can contribute to better decision-making and targeted actions for the preservation of these ecosystems.\u003c/p\u003e","manuscriptTitle":"Analysis of the impact of the COVID-19 pandemic lockdown on the spatiotemporal variations in water quality in three wetland areas in Oran, western Algeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-10 14:18:03","doi":"10.21203/rs.3.rs-4476677/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d6c4473-78a7-45cd-be51-2dcd14ea7370","owner":[],"postedDate":"June 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-19T16:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-10 14:18:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4476677","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4476677","identity":"rs-4476677","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}