High-Frequency Monitoring of Suspended Sediment Variations for Water Quality Evaluation at Deep Bay, Pearl River Estuary, China: Influence Factors and Implications for Sampling Strategy

Spatiotemporal analysis and prediction of water quality in Pearl River, China, using multivariate statistical techniques and data-driven model

Identifying spatiotemporal variation patterns and predicting future water quality are critical for rational and effective surface water management. In this study, an exploratory analysis and forecast workflow for water quality in Pearl River, Guangzhou, China, was established based on the 4-h interval dataset selected from 10 stations for water quality monitoring from 2019 to 2021. The multiple statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), correlation analysis (CoA), and redundancy analysis (RDA), as well as data-driven model (i.e., gated recurrent unit (GRU)), were applied for assessing and predicting the water quality in the basin. The investigated sampling stations were classified into 3 categories based on differences in water quality, i.e., low, moderate, and high pollution regions. The average water quality indexes (WQI) values ranged from 38.43 to 92.63. Nitrogen was the most dominant pollutant, with high TN concentrations of 0.81-7.67 mg/L. Surface runoff, atmospheric deposition, and anthropogenic activities were the major contributors affecting the spatiotemporal variations in water quality. The decline in river water quality during the wet season was mainly attributed to increased surface runoff and extensive human activities. Furthermore, the short-term prediction of river water quality was achieved using the GRU model. The result indicated that for both DLCK and DTJ stations, the WQI for the 5-day lead time were predicted with accuracies of 0.82; for the LXH station, the WQI for the 3-day lead time was forecasted with an accuracy of 0.83. The finding of this study will shed a light on an effective reference and systematic support for spatio-seasonal variation and prediction patterns of water quality.

Assessing the Potential of Geostationary Himawari-8 for Mapping Surface Total Suspended Solids and Its Diurnal Changes

Ocean color sensors, typically installed on polar-orbiting satellites, have been used to monitor oceanic processes for last three decades. However, their temporal resolution is not considered to be adequate for monitoring highly dynamic oceanic processes, especially when considering data gaps due to cloud contamination. The Advanced Himawari Imager (AHI) onboard the Himawari-8, a geostationary satellite operated by the Japan Meteorological Agency (JMA), acquires imagery every 10 min at 500 m to 2000 m spatial resolution. The AHI sensor with three visible, one near-infrared (NIR), and two shortwave-infrared (SWIR) bands displays good potential in monitoring oceanic processes at high temporal resolution. This study investigated and identified an appropriate atmospheric correction method for AHI data; developed a model for Total Suspended Solids (TSS) concentrations estimation using hyperspectral data and in-situ measurements of TSS; validated the model; and assessed its potential to capture diurnal changes using AHI imagery. Two image-based atmospheric correction methods, the NIR-SWIR method and the SWIR method were tested for correcting the AHI data. Then, the new model was applied to the atmospherically corrected AHI data to map TSS and its diurnal changes in the Pearl River Estuary (PRE) and neighboring coastal areas. The results indicated that the SWIR method outperformed the NIR-SWIR method, when compared to in-situ water-leaving reflectance data. The results showed a good agreement between the AHI-derived TSS and in-situ measured data with a coefficient of determination (R²) of 0.85, mean absolute error (MAE) of 3.1 mg/L, a root mean square error (RMSE) of 3.9 mg/L, and average percentage difference (APD) of 30% (TSS range 1–40 mg/L). Moreover, the diurnal variation in the turbidity front, using the Normalized Suspended Material Index (NSMI), showed the capability of AHI data to track diurnal variation in turbidity fronts, due to high TSS concentrations at high temporal frequency. The present study indicates that AHI data with high image capturing frequency can be used to map surface TSS concentrations. These TSS measurements at high frequency are not only important for monitoring the sensitive coastal areas but also for scientific understanding of the spatial and temporal variation of TSS.

Numerical Simulation of Donghu Lake Hydrodynamics and Water Quality Based on Remote Sensing and MIKE 21

Numerical simulation is an important method used in studying the evolution mechanisms of lake water quality. At the same time, lake water quality inversion technology using the characteristics of spatial optical continuity data from remote sensing satellites is constantly improving. It is, however, a research hotspot to combine the spatial and temporal advantages of both methods, in order to develop accurate simulation and prediction technology for lake water quality. This paper takes Donghu Lake in Wuhan as its research area. The spatial data from remote sensing and water quality monitoring information was used to construct a multi-source nonlinear regression fitting model (genetic algorithm (GA)-back propagation (BP) model) to invert the water quality of the lake. Based on the meteorological and hydrological data, as well as basic water quality data, a hydrodynamic model was established by using the MIKE21 model to simulate the evolution rules of water quality in Donghu Lake. Combining the advantages of the two, the best inversion results were used to provide a data supplement for optimization of the water quality simulation process, improving the accuracy and quality of the simulation. The statistical results were compared with water quality simulation results based on the data measured. The results show that the water quality simulation of chlorophyll a and nitrate nitrogen mean square errors fell to 17% and 24%, from 19% and 31% respectively, after optimization using remote sensing spatial information. The model precision was thus improved, and this is consistent with the actual pollution situation of Donghu Lake.

Assessment of Water Quality Evolution in the Pearl River Estuary (South Guangzhou) from 2008 to 2017

To control the water pollution in the Pearl River Estuary (PRE), a series of measures have been enacted in recent years. The efficacy of these measures on water quality improvement is, however, currently unknown. To evaluate the variation of water quality in response to the pollution control measures in the PRE during the last decade (2008–2017), our study conducted a long-term monitoring program of estuarine water in the representative city Guangzhou that targeted fecal coliform (F. Coli), biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), potassium permanganate index (CODMn), petroleum, total nitrogen (TN), ammonia nitrogen (NH3–N) and total phosphorus (TP). In the last decade, F. Coli, BOD5, CODCr and CODMn, petroleum and NH3–N have shown a significant reduction by 78.8%, 50.9%, 37.5%, 18.9%, 75.0% and 25.0%, respectively. In contrast, TN and TP remained stable. Water quality index calculations indicated that the water quality was elevated from the marginal–fair level to the good level, particularly after 2012. The biochemical pollutants and nutrients in the estuarine water most likely originated from the upper river due to the wastewater discharge, fecal pollution and agricultural input. The success of pollutant reduction could thus be attributed to industrial upgrading and relocation, as well as the improvement of the sewage treatment system in Guangzhou. However, efficient approaches to reduce TN pollution should be implemented in the future.

Concentration Measurement of Uniform Particles Based on Backscatter Sensing of Optical Fibers

A set of miniature optical fiber particle concentration measuring instruments is designed and applied to develop a unified expression for the concentration measurement of uniform particles in tap water. By measuring the concentrations of six uniform silicon carbide particles in the size range of 38–250 μm, the unified relationship between particle size, particle concentration, and optical scattering intensity is proposed. The unified expression is verified by the concentration measurements of silicon carbide particles with three other sizes. The results show that the measurement error is less than 10%, and the unified expression is satisfactory considering the large measuring range of 0–50 kg/m3. The effects of light intensity on the concentration measurement are discussed based on the results of 150 μm silicon carbide particles under three different light intensities. It is shown that a low light intensity can be applied for high-concentration measurement with relatively low accuracy, while a high light intensity can be adopted for low-concentration measurement with higher accuracy.