1
|
Liu Y, Molinari S, Dalconi MC, Valentini L, Bellotto MP, Ferrari G, Pellay R, Rilievo G, Vianello F, Salviulo G, Chen Q, Artioli G. Mechanistic insights into Pb and sulfates retention in ordinary Portland cement and aluminous cement: Assessing the contributions from binders and solid waste. JOURNAL OF HAZARDOUS MATERIALS 2023; 458:131849. [PMID: 37393826 DOI: 10.1016/j.jhazmat.2023.131849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/31/2023] [Accepted: 06/12/2023] [Indexed: 07/04/2023]
Abstract
Identifying immobilization mechanisms of potentially toxic elements (PTEs) is of paramount importance in the field application of solidification/stabilization. Traditionally, demanding and extensive experiments are required to better access the underlying retention mechanisms, which are usually challenging to quantify and clarify precisely. Herein, we present a geochemical model with parametric fitting techniques to reveal the solidification/stabilization of Pb-rich pyrite ash through conventional (ordinary Portland cement) and alternative (calcium aluminate cement) binders. We found that ettringite and calcium silicate hydrates exhibit strong affinities for Pb at alkaline conditions. When the hydration products are unable to stabilize all the soluble Pb in the system, part of the soluble Pb may be immobilized as Pb(OH)2. At acidic and neutral conditions, hematite from pyrite ash and newly-formed ferrihydrite are the main controlling factors of Pb, coupled with anglesite and cerussite precipitation. Thus, this work provides a much-needed complement to this widely-applied solid waste remediation technique for the development of more sustainable mixture formulations.
Collapse
Affiliation(s)
- Yikai Liu
- Department of Geosciences and CIRCe Centre, University of Padua, via G. Gradenigo 6, 35129 Padua, Italy
| | - Simone Molinari
- Department of Geosciences and CIRCe Centre, University of Padua, via G. Gradenigo 6, 35129 Padua, Italy.
| | - Maria Chiara Dalconi
- Department of Geosciences and CIRCe Centre, University of Padua, via G. Gradenigo 6, 35129 Padua, Italy
| | - Luca Valentini
- Department of Geosciences and CIRCe Centre, University of Padua, via G. Gradenigo 6, 35129 Padua, Italy
| | | | | | | | - Graziano Rilievo
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Fabio Vianello
- Department of Comparative Biomedicine and Food Science, University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - Gabriella Salviulo
- Department of Geosciences and CIRCe Centre, University of Padua, via G. Gradenigo 6, 35129 Padua, Italy
| | - Qiusong Chen
- Sinosteel Maanshan General Institute of Mining Research Co., Ltd., Maanshan 24300, China; School of Resources and Safety Engineering, Central South University, Lushan South Road 932, 410083 Hunan, China
| | - Gilberto Artioli
- Department of Geosciences and CIRCe Centre, University of Padua, via G. Gradenigo 6, 35129 Padua, Italy
| |
Collapse
|
2
|
Han SC, Chang E, Zechel S, Bok F, Zavarin M. Application of community data to surface complexation modeling framework development: Iron oxide protolysis. J Colloid Interface Sci 2023; 648:1015-1024. [PMID: 37343488 DOI: 10.1016/j.jcis.2023.06.054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/16/2023] [Accepted: 06/09/2023] [Indexed: 06/23/2023]
Abstract
This study presents a comprehensive community data-driven surface complexation modeling framework for simulating potentiometric titration of mineral surfaces. Compiled community data for ferrihydrite, goethite, hematite, and magnetite are fit to produce representative protolysis constants that can reproduce potentiometric titration data collected from multiple literature sources. Using this framework, the impact of surface complexation model type and surface site density (SSD) on the fit quality and protolysis constants can be readily evaluated. For example, the non-electrostatic model yielded a poor data fit compared to diffuse double layer model and constant capacitance models due to the absence of known surface charge effects. Regardless of the choice of iron oxide mineral, pKa1 decreased with increasing SSD while the opposite tendency was observed for pKa2. This newly developed framework demonstrates a method to reconcile community data-wide potentiometric titration data using Findable, Accessible, Interoperable, Reusable data principles to produce mineral protolysis constants that improve robustness of surface complexation models for applications in metal sorption and reactive transport modeling. The framework is readily expandable (as community data increase) and extensible (as the number of minerals increase). The framework provides a path forward for developing self-consistent, comprehensive, and updateable surface complexation databases for surface complexation and reactive transport modeling.
Collapse
Affiliation(s)
- Sol-Chan Han
- Seaborg Institute, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, United States
| | - Elliot Chang
- Seaborg Institute, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, United States
| | - Susanne Zechel
- Institute of Resource Ecology, Actinide Thermodynamics Department (FWOA), Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden, Germany
| | - Frank Bok
- Institute of Resource Ecology, Actinide Thermodynamics Department (FWOA), Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden, Germany
| | - Mavrik Zavarin
- Seaborg Institute, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, United States.
| |
Collapse
|
3
|
Zhang W, Huang W, Tan J, Huang D, Ma J, Wu B. Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives. CHEMOSPHERE 2023; 311:137044. [PMID: 36330979 DOI: 10.1016/j.chemosphere.2022.137044] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.
Collapse
Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Dawei Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Jun Ma
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
| |
Collapse
|
4
|
Surface complexation model theory application in NaOL and CTAB collector adsorption differences of diaspore and kaolinite flotation. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2022.121288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
5
|
Meray A, Sturla S, Siddiquee MR, Serata R, Uhlemann S, Gonzalez-Raymat H, Denham M, Upadhyay H, Lagos LE, Eddy-Dilek C, Wainwright HM. PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:5973-5983. [PMID: 35427133 PMCID: PMC9069689 DOI: 10.1021/acs.est.1c07440] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 03/08/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
In this study, we have developed a comprehensive machine learning (ML) framework for long-term groundwater contamination monitoring as the Python package PyLEnM (Python for Long-term Environmental Monitoring). PyLEnM aims to establish the seamless data-to-ML pipeline with various utility functions, such as quality assurance and quality control (QA/QC), coincident/colocated data identification, the automated ingestion and processing of publicly available spatial data layers, and novel data summarization/visualization. The key ML innovations include (1) time series/multianalyte clustering to find the well groups that have similar groundwater dynamics and to inform spatial interpolation and well optimization, (2) the automated model selection and parameter tuning, comparing multiple regression models for spatial interpolation, (3) the proxy-based spatial interpolation method by including spatial data layers or in situ measurable variables as predictors for contaminant concentrations and groundwater levels, and (4) the new well optimization algorithm to identify the most effective subset of wells for maintaining the spatial interpolation ability for long-term monitoring. We demonstrate our methodology using the monitoring data at the Savannah River Site F-Area. Through this open-source PyLEnM package, we aim to improve the transparency of data analytics at contaminated sites, empowering concerned citizens as well as improving public relations.
Collapse
Affiliation(s)
- Aurelien
O. Meray
- Applied
Research Center, Florida International University, 10555 W Flagler Street, Miami, Florida 33174, United States
| | - Savannah Sturla
- Department
of Environmental Science, Policy, and Management, University of California Berkeley, Mulford Hall, 2521 Hearst Avenue, Berkeley, California 94709, United States
| | - Masudur R. Siddiquee
- Applied
Research Center, Florida International University, 10555 W Flagler Street, Miami, Florida 33174, United States
| | - Rebecca Serata
- Department
of Civil and Environmental Engineering, University of California Berkeley, Davis Hall, 2521 Hearst Avenue, Berkeley, California 94709, United States
| | - Sebastian Uhlemann
- Climate
and Ecosystem Sciences Division, Lawrence
Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley 94704, United States
| | - Hansell Gonzalez-Raymat
- Savannah
River National Laboratory, Savannah River Site, Aiken, South Carolina 29808, United States
| | - Miles Denham
- Panoramic
Environmental Consulting, LLC, P.O. Box
906, Aiken, South Carolina 29802, United States
| | - Himanshu Upadhyay
- Applied
Research Center, Florida International University, 10555 W Flagler Street, Miami, Florida 33174, United States
| | - Leonel E. Lagos
- Applied
Research Center, Florida International University, 10555 W Flagler Street, Miami, Florida 33174, United States
| | - Carol Eddy-Dilek
- Savannah
River National Laboratory, Savannah River Site, Aiken, South Carolina 29808, United States
| | - Haruko M. Wainwright
- Climate
and Ecosystem Sciences Division, Lawrence
Berkeley National Laboratory, 1 Cyclotron Road, MS 74R-316C, Berkeley 94704, United States
- Department
of Nuclear Science & Engineering, Massachusetts
Institute of Technology, Cambridge, Massachusetts 02139, USA
| |
Collapse
|