The coastal seas are one of the most important areas of the ocean and land. Approximately 3 billion people – about half of the world's population – live within 60 km of the coastline. At the same time, a total of 14 of the world's 17 largest cities are located along coasts. Large amounts of environmental pollution and natural disasters introduced into the coastal waters resulting in serious environmental and ecological problems. Eutrophication, hypoxia, and other adverse effects caused by anthropogenic activities are recognized as growing problems in many of the world estuaries and coastal areas from developed countries to undeveloped countries. Hence, preventing and controlling marine pollution, as well as regularly implementing monitoring programs that help understand the spatial and temporal variations in coastal water quality and environment are necessary.
Long-term ecological monitoring networks have been established in coastal areas to evaluate eutrophication and other environmental problems like harmful algal blooms (HABs), heavy metal pollution and their biomagnifications, etc, and this measurement of hydro-chemical variables and biological indicators in the coastal environment will aid better understanding of aquatic environment. These monitoring programs produce huge datasets, and it becomes really difficult to extract latent meaningful information from these datasets. To extract the latent meaningful information, multivariate statistical analysis and different biotic indices for biodiversity data are used. It may include factor analysis, cluster analysis, discriminant analysis, self-organizing maps, artificial neural network, canonical correspondence analysis, redundancy analysis and many biotic indices. These methods identify the spatial and temporal variation of water quality in coastal waters and the processes involved in it. The multivariate statistical analysis identifies different patterns in the datasets and provides meaningful underlying information which would be rather difficult just seeing the raw data.
The aim of this Research Topic is to explore the recently used or newly developed multivariate analysis or biotic indices, with an emphasis on Land-Ocean Interactions in the Coastal Zone to solve the environmental and ecological problems by multivariate statistical analysis and chemometrics.
The Research Topic welcomes studies on the chemometrics such as principal component analysis, cluster analysis, discriminant analysis, artificial neural network, different biotic indices, canonical correspondence analysis, redundancy analysis etc, but is not limited to this only.
The following subtopics will be included, but are not limited to:
1. Long-term ecological monitoring networks in coastal environment
2.Temporal and spatial variation of coastal water quality
3. Multivariate statistical analysis in coastal environment
4. Changes in the bacterial community, plankton and benthos with environmental variables
The coastal seas are one of the most important areas of the ocean and land. Approximately 3 billion people – about half of the world's population – live within 60 km of the coastline. At the same time, a total of 14 of the world's 17 largest cities are located along coasts. Large amounts of environmental pollution and natural disasters introduced into the coastal waters resulting in serious environmental and ecological problems. Eutrophication, hypoxia, and other adverse effects caused by anthropogenic activities are recognized as growing problems in many of the world estuaries and coastal areas from developed countries to undeveloped countries. Hence, preventing and controlling marine pollution, as well as regularly implementing monitoring programs that help understand the spatial and temporal variations in coastal water quality and environment are necessary.
Long-term ecological monitoring networks have been established in coastal areas to evaluate eutrophication and other environmental problems like harmful algal blooms (HABs), heavy metal pollution and their biomagnifications, etc, and this measurement of hydro-chemical variables and biological indicators in the coastal environment will aid better understanding of aquatic environment. These monitoring programs produce huge datasets, and it becomes really difficult to extract latent meaningful information from these datasets. To extract the latent meaningful information, multivariate statistical analysis and different biotic indices for biodiversity data are used. It may include factor analysis, cluster analysis, discriminant analysis, self-organizing maps, artificial neural network, canonical correspondence analysis, redundancy analysis and many biotic indices. These methods identify the spatial and temporal variation of water quality in coastal waters and the processes involved in it. The multivariate statistical analysis identifies different patterns in the datasets and provides meaningful underlying information which would be rather difficult just seeing the raw data.
The aim of this Research Topic is to explore the recently used or newly developed multivariate analysis or biotic indices, with an emphasis on Land-Ocean Interactions in the Coastal Zone to solve the environmental and ecological problems by multivariate statistical analysis and chemometrics.
The Research Topic welcomes studies on the chemometrics such as principal component analysis, cluster analysis, discriminant analysis, artificial neural network, different biotic indices, canonical correspondence analysis, redundancy analysis etc, but is not limited to this only.
The following subtopics will be included, but are not limited to:
1. Long-term ecological monitoring networks in coastal environment
2.Temporal and spatial variation of coastal water quality
3. Multivariate statistical analysis in coastal environment
4. Changes in the bacterial community, plankton and benthos with environmental variables
