UAV-based remote sensing of turbidity in coastal environment for regulatory monitoring and assessment.

The adoption of Unmanned Aerial Vehicle (UAV) remote sensing for the regulatory monitoring of turbidity plumes induced by land reclamation operations remains a difficult task. Compared to UAV remote sensing on ambient turbidity in estuaries and rivers, such monitoring of construction-induced turbidity plumes requires significantly higher spatial resolutions and accuracy as well as wider turbidity ranges with nonlinear reflectance. In this study, a pilot-scale deployment of UAV-based hyperspectral sensing is carried out for this objective, with specific new elements developed to overcome the challenges and minimise the uncertainties involved. In particular, Machine learning (ML) models for the turbidity determination were trained by the large dataset collected to better capture the non-linearity of the relationship between the water leaving reflectance and turbidity level. The models achieve a good accuracy with a R2 score of 0.75 that is deemed acceptable in view of the uncertainties associated with construction and land reclamation work.

Effects of land use configuration, seasonality and point source on water quality in a tropical watershed: A case study of the Johor River Basin.

Land use plays a significant role in determining the spatial patterns of water quality in the Johor River Basin (JRB), Malaysia. In the recent years, there have been several occurrences of pollution in these rivers, which has generated concerns over the long-term sustainability of the water resources in the JRB. Specifically, this water resource is a shared commodity between two states, namely, Johor state of Malaysia and Singapore, a neighbouring country adjacent to Malaysia. Prior to this study, few research on the influence of land use configuration on water quality have been conducted in Johor. In addition, it is also unclear how water quality varies under different seasonality in the presence of point sources. In this study, we investigated the influence of land use and point sources from wastewater treatment plants (WWTPs) on the water quality in the JRB. Two statistical techniques - Multivariate Linear Regression (MLR) and Redundancy Analysis (RA) were undertaken to analyse the relationships between river water quality and land use configuration, as well as point sources from WWTPs under different seasonality. Water samples were collected from 49 sites within the JRB from March to December in 2019. Results showed that influence from WWTPs on water quality was greater during the dry season and less significant during the wet season. In particular, point source was highly positively correlated with ammoniacal­nitrogen (NH3-N). On the other hand, land use influence was greater than point source influence during the wet season. Residential and urban land use were important predictors for nutrients and organic matter (chemical oxygen demand); and forest land use were important sinks for heavy metals but a significant source of manganese.

A framework for assessing the adequacy of Water Quality Index - Quantifying parameter sensitivity and uncertainties in missing values distribution.

Water quality monitoring is a pillar in water resource management, but it can be resource intensive, especially for developing countries with limited resources. As such, Water Quality Indices (WQI) are developed to summarise general water quality, but efforts to assess the utility, flexibility, and practicality of WQI have been limited. In this study, we introduced an additional step to the traditional WQI development framework by introducing an adjusted form of WQI (WQIADJUSTED) to handle missing values, and capitalise on the remaining available information for the development of a WQI. A Sub-WQI was also developed to address local water quality conditions. WQI results (weighted and non-weighted) developed using different parameter optimisation methods, namely Multivariate Linear Regression and Principal Component Analysis were compared. To build upon the current framework, a new procedure was developed to assess the adequacy of WQI based on the sensitivity analysis of parameters and uncertainties associated with each parameter's missing values distribution. The number of observations needed for the development of a robust WQI was optimised with respect to user-defined acceptable change in WQI, based on Monte Carlo probabilistic simulation. The Johor River Basin (JRB), Malaysia is used as a case-study for the application of this new framework. The JRB serves as an important resource for Johor, one of the most populous state in Malaysia, and Singapore, a country south of Johor. WQIMLR performed better in explaining the general water quality than WQIPCA for weighted water quality parameters. Optimisation of sampling frequency revealed that around 130 samples will be required if a 2% change in WQI can be tolerated. The results (specific to the JRB) also revealed that total coliform is the most sensitivity parameter to missing values, and the distribution of sensitive parameters are similar for both WQINON-ADJUSTED and WQIADJUSTED.