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354
Vector time series modelling of turbidity in Dublin Bay
Amin Shoari Nejad1, Gerard D McCarthy2, Brian Kelleher3
1Hamilton Institute, Insight Centre for Data Analytics, Maynooth University, Kildare, Ireland.
Journal of Applied Statistics
|October 23, 2024
Summary
This study models turbidity in Dublin Bay using a Vector Auto-Regressive Integrated Conditional Heteroskedasticity (VARICH) approach. Findings indicate wind speed significantly impacts turbidity more than dredging or dumping activities.
Area of Science:
- Environmental Science
- Water Quality Monitoring
- Time Series Analysis
Background:
- Turbidity is a key indicator of water quality.
- Human activities like dredging and dumping can alter turbidity.
- Understanding these impacts is crucial for environmental management.
Purpose of the Study:
- To model spatial and temporal turbidity variations in Dublin Bay.
- To investigate the effects of dredging and dumping on turbidity.
- To control for and compare with the influence of wind speed.
Main Methods:
- Developed a Vector Auto-Regressive Integrated Conditional Heteroskedasticity (VARICH) model.
- Utilized daily turbidity data from 2017-2018.
- Applied Bayesian credible intervals for uncertainty quantification.
Main Results:
- The VARICH model accurately reflects observed turbidity data.
- Model uncertainties are well-calibrated.
- Daily impacts of dredging and dumping on turbidity are negligible compared to wind speed.
Conclusions:
- Wind speed is the dominant factor influencing turbidity in Dublin Bay.
- The VARICH model provides a robust framework for analyzing dynamic water quality parameters.
- Human activities studied have minimal daily impact on turbidity relative to atmospheric conditions.
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