Research in the field of sediment geochemistry suggests potential linkages between catchment processes (land use), internal phosphorus (P) loading and lake water quality, but evidence is still poorly quantified due to a limited amount of data. Here we address the issues based on a comprehensive data set from 27 lakes in southern Finland. Specifically, we aimed at: 1) elucidating factors behind spatial variations in sediment geochemistry; 2) assessing the impact of diagenetic transformation on sediment P regeneration across lakes based on the changes in the vertical distribution of sediment components; 3) exploring the role of the sediment P forms in internal P loading (IL), and 4) determining the impact of IL on lake water quality. The relationship between sediment P concentration and field area percentage (FA%) was statistically significant in (mainly eutrophic) lakes with catchments that included more than 10 % of fields. We found that sediment iron-bound P (Fe-P) increased with increasing FA%, which agrees with the high expected losses from the cultivated areas. Additionally, populated areas increased the pool of sediment Fe-P. Internal P loading was significantly positively related to both sediment Fe-P and sediment organic P (Org-P). However, Org-P was not significant (as the third predictor) in models that had a trophic state variable as the first predictor and Fe-P as the second predictor. Further, the vertical profiles of sediment components indicated a role of diagenetic transformations in the long-term sediment P release, especially in lakes with deeper maximum depth and longer water residence time. Finally, IL was significantly positively correlated to water quality variables including phytoplankton biomass, its proportion of cyanobacteria, chlorophyll a concentration and trophic state index. Our findings suggest that reduction of P losses from the field and populated areas will decrease internal P loads and increase water quality through a reduced pool of Fe-P.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.watres.2024.122157 | DOI Listing |
Int Microbiol
December 2024
Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia.
The present research work is concerned with the production and optimization of the dopa-oxidase enzyme by using pre-grown mycelia of Aspergillus oryzae. Different strains of A. oryzae were collected and isolated from various soil samples.
View Article and Find Full Text PDFSci Rep
December 2024
Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, USA.
Groundwater monitoring is a crucial part of groundwater remediation that produces data from various strategically placed wells to maintain a water quality standard. Using the United States Department of Energy's Hanford 100-HRD area well data, recurrent neural networks are trained in the form of one-dimensional Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and Dual-stage Attention-based LSTM (DA-LSTM) networks to reduce monitoring costs and increase data sampling responsiveness that is subject to laboratory analysis delays, with the best network being DA-LSTM achieving an R score of 0.82.
View Article and Find Full Text PDFSci Rep
December 2024
College of Water Conservancy and Architectural Engineering, Shihezi University, Shihezi, 832000, Xinjiang, China.
Heavy metal contamination of drinking water, primarily driven by industrial activities, represents a critical challenge, with implications for human health and environmental safety. Gujranwala is an industrial and thickly populated city. The current study aimed to assess and compare heavy metal contamination levels in drinking water from five industrial areas and evaluate their potential impacts on human health.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Civil, Construction and Environmental Engineering (Dept 2470), North Dakota State University, PO Box 6050, Fargo, ND, 58108-6050, USA.
A precise streamflow forecast is crucial in hydrology for flood alerts, water quantity and quality management, and disaster preparedness. Machine learning (ML) techniques are commonly employed for hydrological prediction; however, they still face certain drawbacks, such as the need to optimize the appropriate predictors, the ability of the models to generalize across different time horizons, and the analysis of high-dimensional time series. This research aims to address these specific drawbacks by developing a novel framework for streamflow forecasting.
View Article and Find Full Text PDFJ Public Health Manag Pract
October 2024
Author Affiliations: Public Health - Seattle and King County, Washington.
Context: Most major urban areas in the US, including Seattle and King County, have a long-standing lack of public restrooms, handwashing stations, and drinking water, presenting public health risks.
Objective: To aid decision-makers in expanding access, we review available information regarding successful hygiene programs in urban settings to identify shared characteristics and costs.
Design: We reviewed 10 journal articles, 49 news articles, and 54 pieces of gray literature including reports, white papers, and online resources describing real-world hygiene, sanitation, and drinking water programs in US and global urban settings.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!