Publicação
Spatial and multivariate statistics in assessing water quality in the North Sea
| Resumo: | The Southern North Sea region plays a vital role in both the economy and society of the surrounding countries. Analyzing the quality of your water is a critical process that involves an assessment of physical, chemical, and biological parameters, essential to guarantee environmental sustainability and the health of local communities and marine ecosystems. Using Multivariate and Spatial Statistics methods, this study seeks to identify spatial patterns and autocorrelations to assess water quality in that region. The data set used was taken on a scientific cruise carried out in December 2020 aboard the RV Meteor vessel, led by a team of German researchers. The raw data went through pretreatment guided by the Data Quality Control protocol of SeaDataNet, an international oceanography project aimed at making European maritime data available. Spike and gradient tests were performed, in addition to data standardization and imputation through inverse distance weighting interpolation. For a better understanding of the scientific area, the data were aggregated by zones for certain analyses and were sometimes considered globally. An exploratory spatial data analysis (ESDA) was carried out to summarize its main characteristics. A reduction in the dimensionality of the original data was carried out through principal component analysis as an auxiliary tool for spatial analysis. The Spatial autocorrelation is analyzed by calculating global and local Moran’s I Statistics. The outcomes indicate a significant spatial autocorrelation for all variables considered in the freshwater areas and a notable range flattening of the variables in the open sea areas, which possibly caused the lack of significant spatial autocorrelation in those areas. |
|---|---|
| Autores principais: | Ody, Christopher |
| Outros Autores: | Ramos, M. Rosário; Carolino, Elisabete |
| Assunto: | Exploratory spatial data analysis Principal components Spatial correlation Water quality Multivariate statistics North Sea |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Instituto Politécnico de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Científico do Instituto Politécnico de Lisboa |
| Resumo: | The Southern North Sea region plays a vital role in both the economy and society of the surrounding countries. Analyzing the quality of your water is a critical process that involves an assessment of physical, chemical, and biological parameters, essential to guarantee environmental sustainability and the health of local communities and marine ecosystems. Using Multivariate and Spatial Statistics methods, this study seeks to identify spatial patterns and autocorrelations to assess water quality in that region. The data set used was taken on a scientific cruise carried out in December 2020 aboard the RV Meteor vessel, led by a team of German researchers. The raw data went through pretreatment guided by the Data Quality Control protocol of SeaDataNet, an international oceanography project aimed at making European maritime data available. Spike and gradient tests were performed, in addition to data standardization and imputation through inverse distance weighting interpolation. For a better understanding of the scientific area, the data were aggregated by zones for certain analyses and were sometimes considered globally. An exploratory spatial data analysis (ESDA) was carried out to summarize its main characteristics. A reduction in the dimensionality of the original data was carried out through principal component analysis as an auxiliary tool for spatial analysis. The Spatial autocorrelation is analyzed by calculating global and local Moran’s I Statistics. The outcomes indicate a significant spatial autocorrelation for all variables considered in the freshwater areas and a notable range flattening of the variables in the open sea areas, which possibly caused the lack of significant spatial autocorrelation in those areas. |
|---|