Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

Spatiotemporal smoothing of water quality in a complex riverine system with physical barriers

Freshwater ecosystems offer a variety of ecosystem services, and water quality is essential information for understanding their environment, biodiversity, and functioning. Interpolation by smoothing methods is a widely used approach to obtain temporal and/or spatial patterns of water quality from sampled data. However, when these methods are applied to freshwater systems, ignoring terrestrial areas that act as physical barriers may affect the structure of spatial autocorrelation and introduce bias into the estimates. In this study, we applied stochastic partial differential equation (SPDE) smoothing methods with barriers to spatial interpolation and spatiotemporal interpolation on water quality indices (chemical oxygen demand, phosphate phosphorus, and nitrite nitrogen) in a freshwater system in Japan. Then, we compared the estimation bias and accuracy with those of conventional non-barrier models. The results showed that the estimation bias of spatial interpolations of snapshot data was improved by considering physical barriers (5.8 % for (chemical oxygen demand, 22.5 % for phosphate phosphorus, and 21.6 % for nitrite nitrogen). The prediction accuracy was comparable to that of the non-barrier model. These were consistent with the expectation that accounting for physical barriers would capture realistic spatial correlations and reduce estimation bias, but would increase the variance of the estimates due to the limited information that can be gained from the neighbourhood. On the other hand, for spatiotemporal smoothing, the barrier model was comparable to the non-barrier model in terms of both estimation bias and prediction accuracy. This may be due to the availability of information in the time direction for interpolation. These results demonstrate the advantage of considering barriers when the available data are limited, such as snapshot data. SPDE smoothing methods can be widely applied to interpolation of various environmental and biological indices in river systems and are expected to be powerful tools for studying freshwater systems spatially and temporally.

Spectral and spatial kernel water quality mapping

An empirical approach through remote sensing generally produces a robust data model of water quality for inland and coastal water. Traditional regressions in water quality mapping fail because the bio-optical relationship of turbid water exhibits nonlinear and heterogeneous patterns. In addition, in situ data are generally insufficient in the water quality mapping. Mapping based on a relatively small amount of water quality samples is considered a practical issue in environmental monitoring. Learning-based algorithms that require a large amount of data are inapplicable in this case. According to the concept of Nadaraya-Watson estimator, the kernel model can estimate nonlinear and spatially varying water quality maps effectively in turbid water.Experiments indicate that the kernel estimator provides better goodness-of-fit between the observed and derived concentrations of water quality parameter, e.g., chlorophyll-a in turbid water. The kernel estimator is feasible for a relatively small size of ground observations. Approximately 30% improvement of cross-validation error was identified in this approach when compared with traditional regressions. The model offers a robust approach without further calibrations for estimating the spatial patterns of water quality by using remote sensing reflectance and a small set of observations, considering spatial and spectral information, e.g., multiple bands and band ratios.

Modelling spatial and temporal variability of water quality from different monitoring stations using mixed effects model theory

Polder-type agricultural catchments within river deltas are specific land formations which management is highly demanding from several aspects. The close contact with the coastal sea may additionally affect the quality of adjacent marine environment. This study uses the case of the Lower Neretva Valley (LNV) to test the efficiency of applying Linear Mixed Effect (LME) theory in modelling spatial and temporal variations of surface and groundwater quality within a polder-type agricultural catchment. The methodology uses linear regressive techniques while taking into account spatial and temporal autocorrelation of residuals. The objective was to assess and model the spatial and temporal variability of the quality of surface- and ground-waters, in order to predict the impact of natural processes and human activities. A dataset of physicochemical properties of surface and groundwater quality of the LNV, recorded monthly in the period 2009-2017, was used to model the spatial and temporal variations of water salinity and nitrate concentrations. The network of water quality monitoring sites covers four polders on five thousand hectares of agricultural land, including the following types of water bodies: river streams, lateral canals, pumping stations, drainage canals and groundwater. The method of data analysis, based on LME theory with correlated spatial and temporal residuals, takes also into account the heteroscedasticity of the variance associated with each type of water quality monitoring station. The two Linear Mixed Effects models proposed for the prediction of electrical conductivity and nitrate concentration in the surface waters and groundwater, proved to be efficient at adequately reproducing the heterogeneity and complexity of the study area. However, the prediction of nitrate concentration in the water was not equally satisfactory of the one of electrical conductivity due to the large variation in nutrient concentrations. To improve spatial prediction, the density of monitoring network should be increased.

A Sensing and Monitoring System for Hydrodynamic Flow Based on Imaging and Ultrasound

A built environment, that also includes infrastructures, needs to be taken under control to prevent unexpected modifications, otherwise it could react as a loose cannon. Sensing techniques and technologies can come to the rescue of built environments thanks to their capabilities to monitor appropriately. This article illustrates findings related to monitoring a channel hydrodynamic behavior by means of sensors based on imaging and ultrasound. The ultrasound approach is used here to monitor the height of the water with respect to a maximum limit. Imaging treatment is here proposed to understand the flow velocity under the area to be considered. Since these areas can be covered by trash, an enhanced version of the particle image velocimetry technique has been implemented, allowing the discrimination of trash from water flow. Even in the presence of the total area occupied by trash, it is able to detect the velocity of particles underneath. Rainfall and hydraulic levels have been included and processed to strengthen the study.