Detection of changes in the hydrobiological parameters of the Oder River during the ecological disaster in July 2022 based on multi-parameter probe tests and remote sensing methods

Absalon, Damian; Matysik, Magdalena; Woźnica, Andrzej; Janczewska, Natalia

doi:10.1016/j.ecolind.2023.110103

Cited by 19 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the regulations in the European Union (Water Framework Directive) obligating the monitoring of water quality [13], we do not have all the relevant data to determine the state of the Odra River before the catastrophic event in the summer of 2022. Fortunately, additional surveys on the Odra River in 2022 before and during the ecological disaster were conducted, which improved our knowledge of the spatial variation in the selected parameters [14,15]. Our presented paper fills some gaps in the state of knowledge on selected water quality parameters during summers in the Odra River from 2019 till the ecological disaster, supplementing this information with additional data on phenological changes in water parameters in the Odra based on data from 2015.…”

Section: Introductionmentioning

confidence: 91%

Water Quality of the Odra (Oder) River before and during the Ecological Disaster in 2022: A Warning to Water Management

Sługocki

Czerniawski

2023

Sustainability

View full text Add to dashboard Cite

Aquatic ecosystems are under pressure due to human activity. In the summer of 2022, the Odra River (Central Europe) suffered a massive death of fish and mollusks. This paper aims to show selected water quality parameters before and during the ecological disaster and find which parameters may have been crucial to the development of this disaster. We used the Kruskal–Wallis test and Spearman’s correlation to check the water parameters’ spatial and temporal diversification. In addition, non-metric multidimensional scaling was performed. The water quality parameters of the Odra system were analyzed in sections: middle Odra, lower Odra, transitional waters, and Szczecin Lagoon. Human activity has led to the formation of a system with unusual characteristics, disturbing the river’s natural continuum and related processes. The year 2022 in the middle Odra differed from the previous years in having a high water temperature (>24 °C), high ammonium concentrations (>4 mg/L), and relatively low nitrate concentrations (<6.5 mg/L). At the same time, salt pollution in the river was very high (maximum 1.4 g/L). In June and July 2022, we observed low chlorophyll a concentrations in the middle Odra (on average 2.3 µg/L and 4.4 µg/L, respectively), presumably due to salinity stress and high temperatures, suppressing freshwater phytoplankton taxa. This circumstance has created the appropriate conditions for developing euryhaline and thermophilic Prymnesium parvum. Because of decaying organisms, hypoxia occurred in the lower Odra (0.26 mg/L of dissolved oxygen in surface waters), and oxygen conditions worsened in the transitional waters (<4 mg/L). The zonal deaths of fish and mollusks result from multiple stressors induced by human activity. This disaster has proven that it is necessary to improve selected water quality parameters to reduce the risk of such disasters. The most urgent recommendations are to reduce the salt pollution of the upper section of the Odra, reduce nutrient inputs, and improve the condition of smaller rivers feeding the main course of the Odra.

show abstract

Section: Introductionmentioning

confidence: 91%

Water Quality of the Odra (Oder) River before and during the Ecological Disaster in 2022: A Warning to Water Management

Sługocki

Czerniawski

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…The spectral bands and their combinations (N/R, R/N, and B/G) have been widely used in the literature due to their excellent results in retrieving the concentration of chlorophyll [18,39,55,56]. Furthermore, spectral indices derived from the above-mentioned bands are currently the most used ones for monitoring the concentration of chlorophyll [14,39,57,58]. The normalized difference vegetation index (NDVI), obtained as the ratio between the NIR and R reflectances and their sum, is an indicator of photosynthetic activity and changes in vegetation [59,60].…”

Section: Vegetation Indices Band and Band Combinationmentioning

confidence: 99%

Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake

Rodríguez-López,

Alvarez,

Bustos Usta

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

In this study, we employ in situ, meteorological, and remote sensing data to estimate chlorophyll-a concentration at different depths in a South American freshwater ecosystem, focusing specifically on a lake in southern Chile known as Lake Maihue. For our analysis, we explored four different scenarios using three deep learning and traditional statistical models. These scenarios involved using field data (Scenario 1), meteorological variables (Scenario 2), and satellite data (Scenarios 3.1 and 3.2) to predict chlorophyll-a levels in Lake Maihue at three different depths (0, 15, and 30 m). Our choice of models included SARIMAX, DGLM, and LSTM, all of which showed promising statistical performance in predicting chlorophyll-a concentrations in this lake. Validation metrics for these models indicated their effectiveness in predicting chlorophyll levels, which serve as valuable indicators of the presence of algae in the water body. The coefficient of determination values ranged from 0.30 to 0.98, with the DGLM model showing the most favorable statistics in all scenarios tested. It is worth noting that the LSTM model yielded comparatively lower metrics, mainly due to the limitations of the available training data. The models employed, which use traditional statistical and machine learning models and meteorological and remote sensing data, have great potential for application in lakes in Chile and the rest of the world with similar characteristics. In addition, these results constitute a fundamental resource for decision-makers involved in the protection and conservation of water resource quality.

show abstract

“…Multispectral satellites like Landsat 5, 7, 8, and Sentinel-2 are now extensively used for lake eutrophication monitoring at a global scale [11]. In RS research, chlorophyll-a (Chl-a) concentration, as the key photosynthetic pigment, is widely used to map algal abundance in diverse aquatic environments, including coastal areas, lakes, and rivers, providing a valuable tool for monitoring eutrophication trends [12][13][14]. The accuracy of Chl-a estimations depends highly on the applied atmospheric correction and the selection of appropriate retrieval algorithms [15].…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Semi-Empirical Approaches to Retrieve Satellite-Derived Chlorophyll-a Concentrations from Nearshore and Offshore Waters of a Large Lake (Lake Ontario)

Shahvaran,

Kheyrollah Pour,

Van Cappellen

2024

Remote Sensing

View full text Add to dashboard Cite

Chlorophyll-a concentration (Chl-a) is commonly used as a proxy for phytoplankton abundance in surface waters of large lakes. Mapping spatial and temporal Chl-a distributions derived from multispectral satellite data is therefore increasingly popular for monitoring trends in trophic state of these important ecosystems. We evaluated products of eleven atmospheric correction processors (LEDAPS, LaSRC, Sen2Cor, ACOLITE, ATCOR, C2RCC, DOS 1, FLAASH, iCOR, Polymer, and QUAC) and 27 reflectance indexes (including band-ratio, three-band, and four-band algorithms) recommended for Chl-a concentration retrieval. These were applied to the western basin of Lake Ontario by pairing 236 satellite scenes from Landsat 5, 7, 8, and Sentinel-2 acquired between 2000 and 2022 to 600 near-synchronous and co-located in situ-measured Chl-a concentrations. The in situ data were categorized based on location, seasonality, and Carlson’s Trophic State Index (TSI). Linear regression Chl-a models were calibrated for each processing scheme plus data category. The models were compared using a range of performance metrics. Categorization of data based on trophic state yielded improved outcomes. Furthermore, Sentinel-2 and Landsat 8 data provided the best results, while Landsat 5 and 7 underperformed. A total of 28 Chl-a models were developed across the different data categorization schemes, with RMSEs ranging from 1.1 to 14.1 μg/L. ACOLITE-corrected images paired with the blue-to-green band ratio emerged as the generally best performing scheme. However, model performance was dependent on the data filtration practices and varied between satellites.

show abstract

Detection of changes in the hydrobiological parameters of the Oder River during the ecological disaster in July 2022 based on multi-parameter probe tests and remote sensing methods

Cited by 19 publications

References 24 publications

Water Quality of the Odra (Oder) River before and during the Ecological Disaster in 2022: A Warning to Water Management

Water Quality of the Odra (Oder) River before and during the Ecological Disaster in 2022: A Warning to Water Management

Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake

Comparative Evaluation of Semi-Empirical Approaches to Retrieve Satellite-Derived Chlorophyll-a Concentrations from Nearshore and Offshore Waters of a Large Lake (Lake Ontario)

Contact Info

Product

Resources

About