BACKGROUND: In recent years, anthropogenic activities have released heavy metals and polluted the aquatic environment. This study investigated the ability of the silica-stabilized magnetite (Si-M) nanocomposite materials to dispose of lead nitrate (Pb(NO)) toxicity in Nile tilapia and African catfish.

RESULTS: Preliminary toxicity tests were conducted and determined the median lethal concentration (LC) of lead nitrate (Pb(NO)) to Nile tilapia and African catfish to be 5 mg/l. The sublethal concentration, equivalent to 1/20 of the 96-hour LC Pb(NO), was selected for our experiment. Fish of each species were divided into four duplicated groups. The first group served as the control negative group, while the second group (Pb group) was exposed to 0.25 mg/l Pb(NO) (1/20 of the 96-hour LC). The third group (Si-MNPs) was exposed to silica-stabilized magnetite nanoparticles at a concentration of 1 mg/l, and the fourth group (Pb + Si-MNPs) was exposed simultaneously to Pb(NO) and Si-MNPs at the same concentrations as the second and third groups. Throughout the experimental period, no mortalities or abnormal clinical observations were recorded in any of the treated groups, except for melanosis and abnormal nervous behavior observed in some fish in the Pb group. After three weeks of sublethal exposure, we analyzed hepatorenal indices, oxidative stress parameters, and genotoxicity. Values of alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), urea, and creatinine were significantly higher in the Pb-intoxicated groups compared to the control and Pb + Si-MNPs groups in both fish species. Oxidative stress parameters showed a significant decrease in reduced glutathione (GSH) concentration, along with a significant increase in malondialdehyde (MDA) and protein carbonyl content (PCC) concentrations, as well as DNA fragmentation percentage in the Pb group. However, these values were nearly restored to control levels in the Pb + Si-MNPs groups. High lead accumulation was observed in the liver and gills of the Pb group, with the least accumulation in the muscles of tilapia and catfish in the Pb + Si-MNPs group. Histopathological analysis of tissue samples from Pb-exposed groups of tilapia and catfish revealed brain vacuolation, gill fusion, hyperplasia, and marked hepatocellular and renal necrosis, contrasting with Pb + Si-MNP group, which appeared to have an apparently normal tissue structure.

CONCLUSIONS: Our results demonstrate that Si-MNPs are safe and effective aqueous additives in reducing the toxic effects of Pb (NO) on fish tissue through the lead-chelating ability of Si-MNPs in water before being absorbed by fish.

Road construction significantly affects water resources by introducing contaminants, fragmenting habitats, and degrading water quality. This study examines the use of Remote Sensing (RS) data of Sentinel-1 (S1) and Senitnel-2 (S2) in Google Earth Engine (GEE) to do spatio-temporal analysis of turbidity in adjacent water bodies during the construction and operation of the E18 Arendal-Tvedestrand highway in southeastern Norway from 2017 to 2021. S1 radiometric data helped delineate water extents, while S2-Top of Atmosphere (TOA) multispectral data, corrected using the Modified Atmospheric correction for INland waters (MAIN), used to estimate turbidity levels. To ensure a comprehensive time series of RS data, we utilized S2-TOA data corrected with the MAIN algorithm rather than S2-Bottom Of Atmosphere (BOA) data. We validated the MAIN algorithm's accuracy against GLORIA (Global Observatory of Lake Responses to Interventions and Drivers) observations of surface water reflectance in lakes, globally. Subsequently, the corrected S2 data is used to calculate turbidity using the Novoa and Nechad retrieval algorithms and compared with GLORIA turbidity observations. Findings indicate that the MAIN algorithm adequately estimates water-leaving surface reflectance (Pearson correlation > 0.7 for wavelengths between 490 and 705 nm) and turbidity (Pearson correlation > 0.6 for both algorithms), determining Nechad as the more effective algorithm. In this regard, we used S2 corrected images with MIAN to estimate turbidity in the study area and evaluated with local gauge data and observational reports. Results indicate that the proposed framework effectively captures trends and patterns of turbidity variation in the study area. Findings verify that road construction can increase turbidity in adjacent water bodies and emphasis the employing RS data in cloud platforms like GEE can provide insights for effective long-term water quality management strategies during construction and operation phases.

Road construction significantly affects water resources by introducing contaminants, fragmenting habitats, and degrading water quality. This study examines the use of Remote Sensing (RS) data of Sentinel-1 (S1) and Senitnel-2 (S2) in Google Earth Engine (GEE) to do spatio-temporal analysis of turbidity in adjacent water bodies during the construction and operation of the E18 Arendal-Tvedestrand highway in southeastern Norway from 2017 to 2021. S1 radiometric data helped delineate water extents, while S2-Top of Atmosphere (TOA) multispectral data, corrected using the Modified Atmospheric correction for INland waters (MAIN), used to estimate turbidity levels. To ensure a comprehensive time series of RS data, we utilized S2-TOA data corrected with the MAIN algorithm rather than S2-Bottom Of Atmosphere (BOA) data. We validated the MAIN algorithm's accuracy against GLORIA (Global Observatory of Lake Responses to Interventions and Drivers) observations of surface water reflectance in lakes, globally. Subsequently, the corrected S2 data is used to calculate turbidity using the Novoa and Nechad retrieval algorithms and compared with GLORIA turbidity observations. Findings indicate that the MAIN algorithm adequately estimates water-leaving surface reflectance (Pearson correlation > 0.7 for wavelengths between 490 and 705 nm) and turbidity (Pearson correlation > 0.6 for both algorithms), determining Nechad as the more effective algorithm. In this regard, we used S2 corrected images with MIAN to estimate turbidity in the study area and evaluated with local gauge data and observational reports. Results indicate that the proposed framework effectively captures trends and patterns of turbidity variation in the study area. Findings verify that road construction can increase turbidity in adjacent water bodies and emphasis the employing RS data in cloud platforms like GEE can provide insights for effective long-term water quality management strategies during construction and operation phases.