Abstract
A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.
Highlights
Heavy metals in contaminated water and soils are an important focal point in environmental regulation and monitoring due to their human and environmental health risks [1,2]
AAS/AES techniques broadly identify heavy metal ions based on their spectral fingerprints, whereas ion-selective electrodes (ISE) techniques identify single heavy metal ions based on their specific activity with size-specific molecular cavities in an electrode material [3,4,5]
Using the above scattering data under each particle size and associated detection angle condition, we developed explicit classification functions for each heavy metal and reference cation species across all concentrations through linear discriminant analysis (LDA) based on Mahalanobis distance criteria
Summary
Heavy metals in contaminated water and soils are an important focal point in environmental regulation and monitoring due to their human and environmental health risks [1,2]. AAS/AES techniques broadly identify heavy metal ions based on their spectral fingerprints, whereas ISE techniques identify single heavy metal ions based on their specific activity with size-specific molecular cavities in an electrode material [3,4,5] These both face challenges in portable or on-site monitoring applications. In the light of these challenges, there is interest in developing portable sensing techniques that can be used to classify a wide range of heavy metal species by spectral or electrochemical fingerprint Towards these goals and other removal processes, researchers have explored the use of functional groups involved in biosorptive metal uptake, including carboxyl groups, as specific chelation agents [7,8,9,10]
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