Ti2O3/TiO2-Assisted Solar Photocatalytic Degradation of 4-tert-Butylphenol in Water

Colored Ti2O3 and Ti2O3/TiO2 (mTiO) catalysts were prepared by the thermal treatment method. The effects of treatment temperature on the structure, surface area, morphology and optical properties of the as-prepared samples were investigated by XRD, BET, SEM, TEM, Raman and UV–VIS spectroscopies. Phase transformation from Ti2O3 to TiO2 rutile and TiO2 anatase to TiO2 rutile increased with increasing treatment temperatures. The photocatalytic activities of thermally treated Ti2O3 and mTiO were evaluated in the photodegradation of 4-tert-butylphenol (4-t-BP) under solar light irradiation. mTiO heated at 650 °C exhibited the highest photocatalytic activity for the degradation and mineralization of 4-t-BP, being approximately 89.8% and 52.4%, respectively, after 150 min of irradiation. The effects of various water constituents, including anions (CO32−, NO3, Cl and HCO3−) and humic acid (HA), on the photocatalytic activity of mTiO-650 were evaluated. The results showed that the presence of carbonate and nitrate ions inhibited 4-t-BP photodegradation, while chloride and bicarbonate ions enhanced the photodegradation of 4-t-BP. As for HA, its effect on the degradation of 4-t-BP was dependent on the concentration. A low concentration of HA (1 mg/L) promoted the degradation of 4-t-BP from 89.8% to 92.4% by mTiO-650, but higher concentrations of HA (5 mg/L and 10 mg/L) had a negative effect.

Investigation of the Aquatic Photolytic and Photocatalytic Degradation of Citalopram

This study investigated the direct and indirect photochemical degradation of citalopram (CIT), a selective serotonin reuptake inhibitor (SSRI), under natural and artificial solar radiation. Experiments were conducted in a variety of different operating conditions including Milli-Q (MQ) water and natural waters (lake water and municipal WWT effluent), as well as in the presence of natural water constituents (organic matter, nitrate and bicarbonate). Results showed that indirect photolysis can be an important degradation process in the aquatic environment since citalopram photo-transformation in the natural waters was accelerated in comparison to MQ water both under natural and simulated solar irradiation. In addition, to investigate the decontamination of water from citalopram, TiO2-mediated photocatalytic degradation was carried out and the attention was given to mineralization and toxicity evaluation together with the identification of by-products. The photocatalytic process gave rise to the formation of transformation products, and 11 of them were identified by HPLC-HRMS, whereas the complete mineralization was almost achieved after 5 h of irradiation. The assessment of toxicity of the treated solutions was performed by Microtox bioassay (Vibrio fischeri) and in silico tests showing that citalopram photo-transformation involved the formation of harmful compounds.

Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters

Unoccupied aircraft systems (UAS, or drones) equipped with off-the-shelf multispectral sensors originally designed for terrestrial applications can also be used to derive water quality properties in coastal waters. The at-sensor total radiance a UAS measured constitutes the sum of water-leaving radiance (LW) and incident radiance reflected off the sea surface into the detector’s field of view (LSR). LW is radiance that emanates from the water and contains a spectral shape and magnitude governed by optically active water constituents interacting with downwelling irradiance while LSR is independent of water constituents and is instead governed by a given sea-state surface reflecting light; a familiar example is sun glint. Failure to accurately account for LSR can significantly influence Rrs, resulting in inaccurate water quality estimates once algorithms are applied. The objective of this paper is to evaluate the efficacy of methods that remove LSR from total UAS radiance measurements in order to derive more accurate remotely sensed retrievals of scientifically valuable in-water constituents. UAS derived radiometric measurements are evaluated against in situ hyperspectral Rrs measurements to determine the best performing method of estimating and removing surface reflected light and derived water quality estimates. It is recommended to use a pixel-based approach that exploits the high absorption of water at NIR wavelengths to estimate and remove LSR. Multiple linear regressions applied to UAS derived Rrs measurements and in situ chlorophyll a and total suspended solid concentrations resulted in 37 and 9% relative error, respectively, which is comparable to coastal water quality algorithms found in the literature. Future research could account for the high resolution and multi-angular aspect of LSR by using a combination of photogrammetry and radiometry techniques. Management implications from this research include improved water quality monitoring of coastal and inland water bodies in order to effectively track trends, identify and mitigate pollution sources, and discern potential human health risks.

Hybrid Inversion Algorithms for Retrieval of Absorption Subcomponents from Ocean Colour Remote Sensing Reflectance

Semi-analytical algorithms (SAAs) invert spectral remote sensing reflectance (Rrs(λ), sr−1) to Inherent Optical Properties (IOPs) of an aquatic medium (λ is the wavelength). Existing SAAs implement different methodologies with a range of spectral IOP models and inversion methods producing concentrations of non-water constituents. Absorption spectrum decomposition algorithms (ADAs) are a set of algorithms developed to partition anw(λ), m−1 (i.e., the light absorption coefficient without pure water absorption), into absorption subcomponents of phytoplankton (aph(λ), m−1) and coloured detrital matter (adg(λ), m−1). Despite significant developments in ADAs, their applicability to remote sensing applications is rarely studied. The present study formulates hybrid inversion approaches that combine SAAs and ADAs to derive absorption subcomponents from Rrs(λ) and explores potential alternatives to operational SAAs. Using Rrs(λ) and concurrent absorption subcomponents from four datasets covering a wide range of optical properties, three operational SAAs, i.e., Garver–Siegel–Maritorena (GSM), Quasi-Analytical Algorithm (QAA), Generalized Inherent Optical Property (GIOP) model are evaluated in deriving anw(λ) from Rrs(λ). Among these three models, QAA and GIOP models derived anw(λ) with lower errors. Among six distinctive ADAs tested in the study, the Generalized Stacked Constraints Model (GSCM) and Zhang’s model-derived absorption subcomponents achieved lower average spectral mean absolute percentage errors (MAPE) in the range of 8–38%. Four hybrid models, GIOPGSCM, GIOPZhang, QAAGSCM and QAAZhang, formulated using the SAAs and ADAs, are compared for their absorption subcomponent retrieval performance from Rrs(λ). GIOPGSCM and GIOPZhang models derived absorption subcomponents have lower errors than GIOP and QAA. Potential uncertainties associated with datasets and dependency of algorithm performance on datasets were discussed.

Radiative transfer modeling with BGC-Argo float data in the Mediterranean Sea

A radiative transfer model was parameterized and validated using Biogeochemical Argo float data acquired between 2012 and 2017 across the Mediterranean Sea. Fluorescence-derived chlorophyll a concentration, particle backscattering at 700 nm and fluorescence of colored dissolved organic matter were used to parametrize the light absorption and scattering coefficients of the optically significant water constituents (pure water, non-algal particles, colored dissolved organic matter and phytoplankton). The model was validated with in-situ downwelling irradiance profiles and irradiance-derived apparent optical properties from satellite data, such as the diffuse attenuation coefficients and remote sensing reflectance. To the authors' knowledge, this is the first time that a three-platform comparison of such kind is performed between model, floats and satellites. Results showed that by using regional parameterizations that are not only related to chlorophyll concentration and vertical distribution, the model was able to capture a more accurate spectral response in the examined wavelength range compared to chlorophyll-related (or Case 1) optical models. When using alternative models that incorporated also measurements of colored dissolved organic matter fluorescence or particulate optical backscattering, the model skill increased at all examined wavelengths. A series of upgrades, such as the inclusion of temperature and salinity data for the modification of the pure water absorption spectra, a refined pure water absorption model, as well as the correction of regional algorithms that had overestimated the pure water contribution in the blue, all contributed to improve the model performance. Finally, using a multi-spectral optical configuration enabled to estimate also the relative contribution of separate water constituents in the examined spectral range. Simulations including non-algal particles and colored dissolved organic matter performed up to 60 % and 76 % better than when considering the optical properties of pure seawater alone. Moreover, a simulation including phytoplankton absorption resulted in an error reduction of up to 43 %, especially at 412 nm and with a more uniform response at the wavelengths considered. Such studies can therefore also tackle the bio-optically anomalous nature of the Mediterranean Sea, and show that non-chlorophyll-related constituents (i.e. non-algal particles and colored dissolved organic matter) can substantially modulate the underwater light field in the blue.