water matrix
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2022 ◽  
Vol 12 (2) ◽  
pp. 699
Author(s):  
Danyelle Medeiros de Araújo ◽  
Elisama V. Dos Santos ◽  
Carlos A. Martínez-Huitle ◽  
Achille De Battisti

Hydroxychloroquine (HCQ) has been extensively consumed due to the Coronavirus (COVID-19) pandemic. Therefore, it is increasingly found in different water matrices. For this reason, the concentration of HCQ in water should be monitored and the treatment of contaminated water matrices with HCQ is a key issue to overcome immediately. Thus, in this study, the development of technologies and smart water solutions to reach the Sustainable Development Goal 6 (SDG6) is the main objective. To do that, the integration of electrochemical technologies for their environmental application on HCQ detection, quantification and degradation was performed. Firstly, an electrochemical cork-graphite sensor was prepared to identify/quantify HCQ in river water matrices by differential pulse voltammetric (DPV) method. Subsequently, an HCQ-polluted river water sample was electrochemically treated with BDD electrode by applying 15, 30 and 45 mA cm−2. The HCQ decay and organic matter removal was monitored by DPV with composite sensor and chemical oxygen demand (COD) measurements, respectively. Results clearly confirmed that, on the one hand, the cork-graphite sensor exhibited good current response to quantify of HCQ in the river water matrix, with limit of detection and quantification of 1.46 mg L−1 (≈3.36 µM) and 4.42 mg L−1 (≈10.19 µM), respectively. On the other hand, the electrochemical oxidation (EO) efficiently removed HCQ from real river water sample using BDD electrodes. Complete HCQ removal was achieved at all applied current densities; whereas in terms of COD, significant removals (68%, 71% and 84% at 15, 30 and 45 mA cm−2, respectively) were achieved. Based on the achieved results, the offline integration of electrochemical SDG6 technologies in order to monitor and remove HCQ is an efficient and effective strategy.


Author(s):  
M. Roccamante ◽  
A. Ruiz-Delgado ◽  
A. Cabrera-Reina ◽  
I. Oller ◽  
S. Malato ◽  
...  

2022 ◽  
pp. 118071
Author(s):  
VM Castro-Gutierrez ◽  
L Pickering ◽  
JC Cambronero-Heinrichs ◽  
B Holden ◽  
J Haley ◽  
...  

2021 ◽  
Author(s):  
Yoko Koyama

Granular activated carbon (GAC) adsorption is frequently considered to control recalcitrant organic micropollutants (MPs) in both drinking water and wastewater. To predict full-scale GAC adsorber performance, bench- and/or pilot- scale studies are widely used. These studies have generated a wealth of MP breakthrough curves. The overarching aim of this research was to develop machine learning (ML) models from these data to predict MP breakthrough from adsorbent, adsorbate, and background water matrix properties. These models provide a simple and fast tool to predict GAC performance. To develop information for model calibration, MP breakthrough curves were collected from the peer-reviewed literature, research reports, and engineering reports. These data sets, which included results from rapid small-scale column tests (RSSCTs) and pilot/full-scale adsorbers, were analyzed to determine the bed volumes of water that could be treated until MP breakthrough reached ten percent of the influent MP concentration (BV10). The data set encompassed 43 MPs (including neutral and ionizable organic compounds), 3 GAC types by base material (18 unique GAC products), and 38 water matrices, including groundwater, surface water, and treated wastewater. Approximately 400 data sets were split into training, validation, and test sets. Seventeen candidate features, such as MP properties (Abraham parameters), background water matrix characteristics, and GAC properties, were explored in ML models to predict log-10-transformed BV10 (logBV10). BV10 values obtained from the resulting predictive model were highly correlated with experimentally determined BV10 values (coefficient of determination ~0.89 for logBV10 prediction), and the most effective model predicted BV10 with an absolute mean error of ~ 0.11 log units. Key drivers influencing BV10 prediction included the MP’s partitioning coefficient between air and hexadecane (Abraham parameter L); dissolved organic matter concentration in background water matrix; and the adsorbent’s point of zero charge (pzc). The model can be used to estimate GAC bed life and select effective GACs for the removal of MPs such as per- and polyfluoroalkyl substances (PFASs), pesticides, pharmaceuticals, and volatile organic compounds (VOCs) in a wide range of water types.


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