Chongchong Qi, Tao Hu, Mengting Wu, Yong Sik Ok, Han Wang, Liyuan Chai, Zhang Lin
{"title":"Spectral-Based Machine Learning Enables Rapid and Large-Scale Adsorption Capacity Prediction of Heavy Metals in Soil","authors":"Chongchong Qi, Tao Hu, Mengting Wu, Yong Sik Ok, Han Wang, Liyuan Chai, Zhang Lin","doi":"10.1021/acsestengg.4c00325","DOIUrl":null,"url":null,"abstract":"Accurate and large-scale estimation of the soil adsorption capacity of heavy metals (HMs) is vital to tackle soil HM contamination. Here, a novel framework has been developed to evaluate the adsorption capacity of HMs in soil using visible and near-infrared spectroscopy. Soil attributes were accurately estimated without any spectral preprocessing using a combined autoencoder (AE) and deep neural network (DNN) approach. Soil HM adsorption capability was then evaluated based on spectral-derived soil attributes, using 2,416 data points on Cd(II), Pb(II), and Cr(VI). The proposed AE-DNN models offer accurate estimations of soil attributes with an average <i>R</i><sup>2</sup> of 0.811 on the independent testing sets. The trained AE-DNN models can reveal patterns typically used by experts to identify bond assignments and promote data-driven knowledge discovery. By comparison with adsorption capacity maps based on actual and estimated soil attributes, we show that the spectral-based soil adsorption capacity evaluation is statistically reliable. Our adsorption capacity maps for the EU and USA identify known soil contamination sites and undocumented areas of high contamination risk. Our framework enables rapid and large-scale prediction of the adsorption capacity of HMs in soil and showcases important guidance for further soil contamination testing, soil management, and industrial planning.","PeriodicalId":7008,"journal":{"name":"ACS ES&T engineering","volume":null,"pages":null},"PeriodicalIF":7.4000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS ES&T engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1021/acsestengg.4c00325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate and large-scale estimation of the soil adsorption capacity of heavy metals (HMs) is vital to tackle soil HM contamination. Here, a novel framework has been developed to evaluate the adsorption capacity of HMs in soil using visible and near-infrared spectroscopy. Soil attributes were accurately estimated without any spectral preprocessing using a combined autoencoder (AE) and deep neural network (DNN) approach. Soil HM adsorption capability was then evaluated based on spectral-derived soil attributes, using 2,416 data points on Cd(II), Pb(II), and Cr(VI). The proposed AE-DNN models offer accurate estimations of soil attributes with an average R2 of 0.811 on the independent testing sets. The trained AE-DNN models can reveal patterns typically used by experts to identify bond assignments and promote data-driven knowledge discovery. By comparison with adsorption capacity maps based on actual and estimated soil attributes, we show that the spectral-based soil adsorption capacity evaluation is statistically reliable. Our adsorption capacity maps for the EU and USA identify known soil contamination sites and undocumented areas of high contamination risk. Our framework enables rapid and large-scale prediction of the adsorption capacity of HMs in soil and showcases important guidance for further soil contamination testing, soil management, and industrial planning.
期刊介绍:
ACS ES&T Engineering publishes impactful research and review articles across all realms of environmental technology and engineering, employing a rigorous peer-review process. As a specialized journal, it aims to provide an international platform for research and innovation, inviting contributions on materials technologies, processes, data analytics, and engineering systems that can effectively manage, protect, and remediate air, water, and soil quality, as well as treat wastes and recover resources.
The journal encourages research that supports informed decision-making within complex engineered systems and is grounded in mechanistic science and analytics, describing intricate environmental engineering systems. It considers papers presenting novel advancements, spanning from laboratory discovery to field-based application. However, case or demonstration studies lacking significant scientific advancements and technological innovations are not within its scope.
Contributions containing experimental and/or theoretical methods, rooted in engineering principles and integrated with knowledge from other disciplines, are welcomed.