Bunkering for change: Knowledge preparedness on the environmental aspect of ammonia as a marine fuel.

Nitrogen cycling is essential to ecosystem functioning and the overall health of our planet. Ammonia, a nitrogen-containing product, as well as a nutrient, is promoted as a low-carbon fuel for the maritime sector, with spectacular production increase in plan. Similar to any other widespread fuels in the past, it is paramount to be prepared for the potential environmental impact of ammonia fuel.

Temporal variability and climatology of hydrodynamic, water property and water quality parameters in the West Johor Strait of Singapore.

The study presents a baseline variability and climatology study of measured hydrodynamic, water properties and some water quality parameters of West Johor Strait, Singapore at hourly-to-seasonal scales to uncover their dependency and correlation to one or more drivers. The considered parameters include, but not limited by sea surface elevation, current magnitude and direction, solar radiation and air temperature, water temperature, salinity, chlorophyll-a and turbidity. FFT (Fast Fourier Transform) analysis is carried out for the parameters to delineate relative effect of tidal and weather drivers.

ANN application for prediction of atmospheric nitrogen deposition to aquatic ecosystems.

The occurrences of increased atmospheric nitrogen deposition (ADN) in Southeast Asia during smoke haze episodes have undesired consequences on receiving aquatic ecosystems. A successful prediction of episodic ADN will allow a quantitative understanding of its possible impacts. In this study, an artificial neural network (ANN) model is used to estimate atmospheric deposition of total nitrogen (TN) and organic nitrogen (ON) concentrations to coastal aquatic ecosystems.

An ANN application for water quality forecasting.

Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies.