AI Medical Compendium Journal:
Marine pollution bulletin

Showing 1 to 10 of 64 articles

An automated field imaging microscope (AFIM) for harmful algal bloom (HAB) monitoring and aquaculture management.

Marine pollution bulletin
The monitoring of HAB is important for environmental and aquaculture management. In eutrophic waters worldwide, the development of HAB early warning systems is hampered by a lack of: (i) water quality data with sufficient spatial and temporal resolut...

Profiling of mangrove forest dynamics in the Fly River delta, Papua New Guinea.

Marine pollution bulletin
Mangrove forests (MFs), as vital ecosystems in tropical and subtropical coastal regions, play a significant role in the global carbon cycle. However, MFs are currently facing unprecedented risks of degradation due to both natural and anthropogenic fa...

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...

Evaluating marine environmental pollution using Fuzzy Analytic Hierarchy Process (FAHP): A comprehensive framework for sustainable coastal and oceanic management.

Marine pollution bulletin
Marine pollution poses a significant threat to ecosystems, biodiversity, and human health, necessitating a structured evaluation framework. This study applies the Fuzzy Analytic Hierarchy Process (FAHP) to prioritize five major marine pollution sourc...

A lightweight spatial and spectral CNN model for classifying floating marine plastic debris using hyperspectral images.

Marine pollution bulletin
Marine plastic debris poses a significant environmental threat. In order to study and combat this pollution, efficient and automated detection methods are essential. Hyperspectral imaging and deep learning provide a robust framework for classifying f...

Identifying and addressing challenges in gross pollutant trap maintenance: perspectives from the Australian stormwater industry.

Marine pollution bulletin
A common approach to removing pollution from stormwater is through the installation of gross pollutant traps (GPTs). However, GPTs are often not maintained effectively, leading to pollution accumulation and additional pollution bypassing into natural...

Integrating bioassay and machine learning data for ecological risk assessments of herbicide use on Ulva australis.

Marine pollution bulletin
Herbicide contamination of aquatic ecosystems poses a critical risk to biodiversity. Bioassays provide useful ecological insights on responses to herbicides; however, they require a model organism. Ulva australis is an ideal candidate for herbicide t...

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 ...

Seafloor debris detection using underwater images and deep learning-driven image restoration: A case study from Koh Tao, Thailand.

Marine pollution bulletin
Traditional detection and monitoring of seafloor debris present considerable challenges due to the high costs associated with underwater imaging devices and the complex environmental conditions in marine ecosystems. In response to these challenges, t...

Integrated machine learning-based optimization framework for surface water quality index comparing coastal and non-coastal cases of Guangxi, China.

Marine pollution bulletin
In this study, an optimized comprehensive water quality index (WQI) model framework is developed, which combines advanced machine learning technology to compare different types of surface water quality assessment. The proposed framework enhancement e...