AIMC Topic: Salinity

Clear Filters Showing 1 to 10 of 32 articles

Synergistic potential of halophytes and halophilic/halotolerant plant growth-promoting bacteria in saline soil remediation: Adaptive mechanisms, challenges, and sustainable solutions.

Microbiological research
Salinity stress poses significant challenges to agriculture, reducing productivity and limiting arable land by causing ionic and osmotic imbalances in plants, disrupting physiological processes, and leading to soil degradation over time. Halophytes a...

Spatiotemporal variations in Pearl River plume dispersion over the last decade based on VIIRS-derived sea surface salinity.

Marine pollution bulletin
A river plume indicates the dispersion and transport path of pollutants from runoff, monitoring the spatiotemporal variation of river plume distribution from space is crucial for marine environmental governance. This study focuses on the Pearl River ...

Unraveling soil salinity on potentially toxic element accumulation in coastal Phragmites australis: A novel integration of multivariate and interpretable machine-learning models.

Marine pollution bulletin
Revealing the key mechanisms influencing the behavior of potentially toxic elements (PTEs) in soil-plant systems is of great significance for environmental protection and grassland development in coastal areas. This study utilized redundancy analysis...

Enhanced nitrogen prediction and mechanistic process analysis in high-salinity wastewater treatment using interpretable machine learning approach.

Bioresource technology
This study introduces an interpretable machine learning framework to predict nitrogen removal in membrane bioreactor (MBR) treating high-salinity wastewater. By integrating Shapley additive explanations (SHAP) with Categorical Boosting (CatBoost), we...

Advanced computational approaches for predicting sunflower yield: Insights from ANN, ANFIS, and GEP in normal and salinity stress environments.

PloS one
Prediction of crop yield is essential for decision-makers to ensure food security and provides valuable information to farmers about factors affecting high yields. This research aimed to predict sunflower grain yield under normal and salinity stress ...

Driving factors of TOC concentrations in four different types of estuaries (canal, urban, agricultural, and natural estuaries) identified by machine learning technique.

Marine pollution bulletin
Mangroves are among the most significant organic carbon sinks on Earth. However, the drivers of mangrove carbon remain poorly understood due to the lack of data on organic carbon across different types of estuaries. In this study, boosted regression ...

Deep learning-based surrogates for multi-objective optimization of the groundwater abstraction schemes to manage seawater intrusion into coastal aquifers.

Journal of environmental management
Efficient optimization of pumping systems is crucial for managing salinity intrusion and ensuring groundwater sustainability in coastal aquifers. Surrogate models (SMs) are widely used in aquifer management as efficient alternatives to complex ground...

Change analysis of surface water clarity in the Persian Gulf and the Oman Sea by remote sensing data and an interpretable deep learning model.

Environmental science and pollution research international
The health of an ecosystem and the quality of water can be determined by the clarity of the water. The Persian Gulf and Oman Sea have a unique ecosystem, and monitoring their water clarity is necessary for sustainable development. Here, various crite...

Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea.

Scientific reports
Phytoplankton blooms exhibit varying patterns in timing and number of peaks within ecosystems. These differences in blooming patterns are partly explained by phytoplankton:nutrient interactions and external factors such as temperature, salinity and l...

Different pixel sizes of topographic data for prediction of soil salinity.

PloS one
Modeling techniques can be powerful predictors of soil salinity across various scales, ranging from local landscapes to global territories. This study was aimed to examine the accuracy of soil salinity prediction model integrating ANNs (artificial ne...