Impacts of Insufficient Observations on the Monitoring of Short- and Long-Term Suspended Solids Variations in Highly Dynamic Waters, and Implications for an Optimal Observation Strategy

Coastal water regions represent some of the most fragile ecosystems, exposed to both climate change and human activities. While remote sensing provides unprecedented amounts of data for water quality monitoring on regional to global scales, the performance of satellite observations is frequently impeded by revisiting intervals and unfavorable conditions, such as cloud coverage and sun glint. Therefore, it is crucial to evaluate the impacts of varied sampling strategies (time and frequency) and insufficient observations on the monitoring of short-term and long-term tendencies of water quality parameters, such as suspended solids (SS), in highly dynamic coastal waters. Taking advantage of the first high-frequency in situ SS dataset (at 30 min sampling intervals from 2007 to 2008), collected in Deep Bay, China, this paper presents a quantitative analysis of the influences of sampling strategies on the monitoring of SS, in terms of sampling frequency and time of day. Dramatic variations of SS were observed, with standard deviation coefficients of 48.9% and 54.1%, at two fixed stations; in addition, significant uncertainties were revealed, with the average absolute percent difference of approximately 13%, related to sampling frequency and time, using nonlinear optimization and random simulation methods. For a sampling frequency of less than two observations per day, the relative error of SS was higher than 50%, and stabilized at approximately 10%, when at least four or five samplings were conducted per day. The optimal recommended sampling times for SS were at around 9:00, 12:00, 14:00, and 16:00 in Deep Bay. The “pseudo” MODIS SS dataset was obtained from high-frequency in situ SS measurements at 10:30 and 14:00, masked by the temporal gap distribution of MODIS coverage to avoid uncertainties propagated from atmospheric correction and SS models. Noteworthy uncertainties of daily observations from the Terra/Aqua MODIS were found, with mean relative errors of 19.2% and 17.8%, respectively, whereas at the monthly level, the mean relative error of Terra/Aqua MODIS observations was approximately 10.7% (standard deviation of 8.4%). Sensitivity analysis between MODIS coverage and SS relative errors indicated that temporal coverage (the percentage of valid MODIS observations for a month) of more than 70% is required to obtain high-precision SS measurements at a 5% error level. Furthermore, approximately 20% of relative errors were found with the coverage of 30%, which was the average coverage of satellite observations over global coastal waters. These results highlight the need for high-frequency measurements of geostationary satellites like GOCI and multi-source ocean color sensors to capture the dynamic process of coastal waters in both the short and long term

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

Suspended sediment (SS) is an important water quality indicator of coastal and estuarine ecosystems. Field measurement and satellite remote sensing are the most common approaches for water quality monitoring. However, the efficiency and precision of both methods are typically affected by their sampling strategy (time and interval), especially in highly dynamic coastal and estuarine waters, because only limited measurements are available to analyze the short-term variations or the long-term trends of SS. Dramatic variations of SS were observed, with standard deviation coefficients of 48.9% and 54.1%, at two fixed stations in Deep Bay, China. Therefore, it is crucial to resolve the temporal variations of SS and its main influencing factors, and thus to develop an improved sampling strategy for estuarine ecosystems. Based on two years of continuous high-frequency measurements of SS and concurrent tidal and meteorological data, we demonstrated that the tide is the dominant factor influencing the SS variation among tide, wind (speed and direction), and rainfall in Deep Bay, China. For the monitoring of maximum suspended sediment concentration (SSC), the recommended optimum sampling time coincides with the occurrence of the ebb tides, whereas multiple sampling times are recommended for monitoring of minimum SSC. Although variations of SS are also affected by other factors, the recommended sampling strategy could capture the maximum and minimum SSC variations exactly more than 85% days in a year on average in Deep Bay. This study provides a baseline of SS variation and direct sampling strategy guidance for future SS monitoring and could be extended to other coastal or estuarine waters with similar climatological/tidal exposures

Current status and historical trends of organochlorine pesticides in the ecosystem of Deep Bay, South China

To characterize the current status and historical trends in organochlorine pesticides (OCPs) contamination in Deep Bay, an important water body between Hong Kong and mainland China with a Ramsar mangrove wetland (Maipo), samples from seawater, suspended particulate matter (SPM), surface sediment, sediment core and fish were collected to determine the OCPs concentrations. Sediment core dating was accomplished using the Pb-210 method. The average concentrations of DDTs, HCHs and chlordanes in water were 1.96, 0.71, 0.81 ng l(-1), while in SPM were 36.5, 2.5, 35.7 ng g(-1) dry weight, in surface sediment were 20.2, 0.50, 2.4 ng g(-1) dry weight, and in fish were 125.4, 0.43, 13.1 ng g(-1) wet weight, respectively. DDTs concentrations in various matrices of Deep Bay were intermediate compared with those in other areas. Temporal trends of the targeted OCPs levels in sediment core generally increased from 1948 to 2004, with the highest levels in top or sub-surface sediment. Both DDT composition and historical trends indicated an ongoing fresh DDT input. A positive relationship between the bioconcentration factor (BCF) of target chemicals and the corresponding octanol-water partition coefficient (K-ow), and between the biota-sediment accumulation factors (BSAF) and the K-ow were observed in the Bay. The risk assessment indicated that there were potential ecological and human health risks for the target OCPs in Deep Bay. (C) 2009 Elsevier Ltd. All rights reserved

Contamination by perfluoroalkyl substances and microbial community structure in Pearl River Delta sediments

Environmental microbiota play essential roles in the maintenance of many biogeochemical processes, including nutrient cycling and pollutant degradation. They are also highly susceptible to changes in environmental stressors, with environmental pollutants being key disruptors of microbial dynamics. In the present study, a scientific cruise was launched on July 2017 around Pearl River Delta, a suitable studying site for perfluoroalkyl substances (PFASs) in the wake of the severe PFAS pollution. Surface sediment samples were collected from 18 representative stations to assess PEAS accumulation and profile microbial community. PFAS concentrations ranged from 24.2 to 181.4 pg/g dry weight in sediment, and perfluorooctanesulfonic acid (PFOS) was the dominant homologue. The concentrations of PFAS homologues in the current study were much lower than those reported in previous studies, implying effective management and control of pollution from PFAS-related industries. 165 rRNA gene amplicon sequencing revealed that Proteobacteria was the dominant phylum, while nitrogen-metabolizing Nitrosopumilus and sulfate-reducing Desulfococcus genera were the most abundant. Variations in microbial communities among sampling stations were mainly due to the differences in abundances of Escherichia, Nitrosopumilus, and Desulfocaccus. The outbreak of Escherichia bacteria at specific coastal stations potentially indicated the discharge of fecal matter into the marine environment. Dissolved oxygen (DO) in bottom seawater significantly influenced the structure of microbial communities in the sediment, while current study failed to observe significant effects from PEAS pollutants. Positive correlations were found between DO and sulfate-reducing bacteria in Desulfococcus and GOUTA19 genera. Overall, this study explored relationships between environmental variables (e.g., PFAS pollutants) and sediment bacteria. Biogeochemical parameters significantly influenced the structure and composition of microbial communities in sediment. (C) 2018 Elsevier Ltd. All rights reserved