The Wastewater Contamination Index: A methodology to assess the risk of wastewater contamination from satellite-derived water quality indicators

One of the major sources of pollution affecting inland and coastal waters is related to poorly treated or untreated wastewater discharge, particularly in urbanized watersheds. The excess of nutrients, organic matter, and pathogens causes an overall deterioration of water quality and impairs valuable ecosystem services. The detection of wastewater pollution is essential for the sustainable management of inland and coastal waters, and remote sensing has the capability of monitoring wastewater contamination at extended spatial scales and repeated frequencies. This study employed satellite-derived water quality indicators and spatiotemporal analysis to assess the risk of wastewater contamination in Conceição Lagoon, a coastal lagoon in Southern Brazil. Using an analytical model, three water quality indicators were derived from Level 2A Sentinel-2 MSI images: the absorption coefficients of chlorophyll-a and detritus combined with coloured dissolved organic matter, and the backscattering coefficient of suspended solids. The temporal standardized anomalies were calculated for each water quality indicator for the period of 2019–2021, and their anomalies during a known outfall event were used to evaluate spatial variation modes. The spatial mode explaining most of the variability was used to estimate weights for the water quality indicators anomalies in a linear transformation that can indicate the risk of wastewater contamination. Results showed that the wastewater spatial mode for this region was characterized by positive anomalies of backscattering coefficient of particulate matter and absorption coefficient of detritus combined with coloured dissolved organic matter, each with a relative importance of 50%. The application of this spatiotemporal analysis was formulated as the Wastewater Contamination Index. With the aid of photographic records, and additional meteorological and water quality data, the results of the index were verified for wastewater outfall events in the study area. The methodology for constructing the proposed Wastewater Contamination Index applies to other locations and can be a valuable tool for operational monitoring of wastewater contamination

Towards operational water quality monitoring with an analytical radiative transfer model

Water quality monitoring is essential for effective water resources management. Present state of the art satellite sensors provide high spatial and temporal resolution required for operational monitoring of key water quality variables in coastal and inland waters. Nonetheless, most of the retrieval models currently available require regional tuning and are therefore not applicable to other geographic areas and not useful in detecting anomalies. This study introduces an operational model to process Sentinel-2 images from any part of the world and derive water quality indicators based on the analytical solution of the two-stream radiative transfer equations. The potential of the model for monitoring distinct water bodies is demonstrated with two case studies in Brazil and Kenya. Scientific and technical improvements to the model can provide additional robustness and usability

Sentinel-2 estimated patterns of Suspended Particle Size Distribution and Turbidity in a tidal dominated estuary.

Suspended particles are important constituents of the water column that are related to many physical, geochemical, and biological processes, such as flocculation, aggregation, and sediment resuspension. Very turbid and tidal dominated coastal waters represent a challenge for estimates of Suspended Particulate Matter (SPM) and Particle Size Distribution (PSD) because of the strong gradients observed in hydrodynamics (tidal processes, wind-driven waves) and sediment dynamics. This study uses the spectrum of satellite remote sensing reflectance data from Sentinel 2A,B sensors to yield SPM and PSD in the macrotidal Seine estuary and Bay (France). Five years (2016-2021) of Sentinel 2A,B are acquired from European Space Agency archive. The satellite timeseries will be atmospherically corrected, remapped to a standard projection and cloud-masked using the ACOLITE processor. Two different in-water bio-optical algorithms will be tested (the 2SeaColor and the approach similar to Shi and Wang 2019) to retrieve non-algal particle backscattering power law slope on a pixel-by-pixel basis. Then the three‐order polynomial function in Kostadinov et al. (2009) will be applied to the timeseries of backscattering power law slope to calculate the particle size slope. The time series of particle size slope will be used to provide the first systematic quantification of satellite-derived particle size variability from satellite remote sensing in the Seine estuary and Seine Bay. Spatial and temporal patterns of SPM and PSD variability on the satellite-derived data will be compared with 10 years-long in-situ observations (PSD from a LISST sensor and SPM) along with seasonal and interannual high resolution synoptic observations

Data of the paper "The Wastewater Contamination Index: A methodology to assess the risk of wastewater contamination from satellite-derived water quality indicators"

This dataset is part of the article with DOI: https://doi.org/10.3389/fenvs.2023.1130655. In the article, the authors employed satellite-derived Water Quality Indicators and spatiotemporal analysis to assess the risk of wastewater contamination in Conceição Lagoon, a coastal lagoon in Southern Brazil. The WQIs used are satellite-derived absorption coefficients of chlorophyll-a (chla) and coloured dissolved organic matter (CDOM), and the backscattering coefficient of suspended particulate matter (SPM). The dataset comprises: 1) time series (2019-2021) of WQIs generated from the processing of Sentinel-2 Level 2A scenes with the 2SeaColor model, 2) time series (2019-2021) of the WQIs standardized anomalies (WQIs_Anomalies), and 3) time series (2019-2021) of the final Wastewater Contamination Index (WCI). The files are raster stacks in ENVI format (flat-binary raster file with an accompanying ASCII header file - *.HDR), which can be opened with most GIS software. The header file contains metadata of the rasters, including the coordinate system, pixel size, and band names (which in this case correspond to the image date in the format YYYYmmdd). More details on the methods can be found in the associated article