About this Research Topic
It is critical to develop methods for processing long-term ecological monitoring data from coastal and marine areas in order to assess eutrophication and other environmental issues such as harmful algal blooms (HABs), heavy metal pollution, bioaccumulation, and the measurement of physicochemical variables and biological indicators in the coastal environment. These surveillance programmes create enormous databases, which are quite valuable for estimating the ecosystem's influence. To extract useful information, multivariate statistical analysis, neural networks, machine learning, modelling (GIS, CORMIX, MIKE, and MATLAB, among others), spatially weighted linear regression analysis, and other indices are used.
To address scientific need for massive dataset analysis and interpretation, recent technological advancements in methodology for analysing environmental variables must be enhanced. Physiochemical, plankton, nutrients, metal toxicity, benthos, microorganisms, and sediment quality are all examples of environmental quality metrics. These variables are crucial for assessing the environmental quality and ecology of the coastal and marine environments. Factor analysis, cluster analysis, discriminant analysis, self-organizing maps, artificial neural networks, canonical correspondence analysis, redundancy analysis, and numerous biotic indicators must all be used to analyse the enormous dataset. These techniques are used to determine the geographical and temporal variation of environmental parameters in coastal and marine waters, as well as the processes that cause it. The most recent technological advancements identify various patterns in datasets and deliver useful underlying data.
The purpose of this Research Topic is to investigate newly developed engineering modeling solutions, newly developed statistical tools, or a combination of methods or biotic indices in the coastal and marine sector, with a focus on terrestrial and sea interactions, in order to solve environmental and ecological problems through statistical analysis or modeling intervention.
The Research Topic welcomes research articles on multivariate statistical analysis, neural networking, machine learning, modelling (GIS, CORMIX, MIKE, and MATLAB, among others), geographically weighted linear regression analysis, PCA/FA, CCA plots, Box plots, and other biological indices, among other topics, but is not limited to this.
The following subtopics will be included, but are not limited to:
1) Estuary/coastal/marine seawater quality data
2) GIS and remotes sensing application for water quality modeling/monitoring
3) Seawater quality in and around the Fish Aggregating Devices, Open sea cage farming sites, Data buoys, Harbours, nodules mining sites, etc.
4) Microbial diversity, phytoplankton, zooplankton, and benthos with environmental variables
5) Metal toxicity study and its impact on the coastal and marine environment
Keywords: Seawater Water Quality, Marine Environment, Plankton, Modeling, Benthos, Temporal and Spatial variation, Microbial Community, Long Term Study, Anthropogenic Influences
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.