Biomagnetic monitoring combined with support vector machine: a new opportunity for predicting particle-bound-heavy metals

Qian’Ying Dai,Xin Qian,Meng Yang,Huiming Li,Mengfan Zhou,Fengying Li

doi:10.1038/s41598-020-65677-8

Abstract

Biomagnetic monitoring includes fast and simple methods to estimate airborne heavy metals. Leaves of Osmanthus fragrans Lour and Ligustrum lucidum Ait were collected simultaneously with PM10 from a mega-city of China during one year. Magnetic properties of leaves and metal concentrations in PM10 were analyzed. Metal concentrations were estimated using leaf magnetic properties and meteorological factors as input variables in support vector machine (SVM) models. The mean concentrations of many metals were highest in winter and lowest in summer. Hazard index for potentially toxic metals was 5.77, a level considered unsafe. The combined carcinogenic risk was higher than precautionary value (10−4). Ferrimagnetic minerals were dominant magnetic minerals in leaves. Principal component analysis indicated iron & steel industry and soil dust were the common sources for many metals and magnetic minerals on leaves. However, the poor simulation results obtained with multiple linear regression confirmed strong nonlinear relationships between metal concentrations and leaf magnetic properties. SVM models including leaf magnetic variables as inputs yielded better simulation results for all elements. Simulations were promising for Ti, Cd and Zn, whereas relatively poor for Ni. Our study demonstrates the feasibility of prediction of airborne heavy metals based on biomagnetic monitoring of tree leaves.

Highlights

Biomagnetic monitoring includes fast and simple methods to estimate airborne heavy metals
The annual mean PM10 concentration in the study area was 84 μg/m3, which was slightly higher than the annual limit of 70 μg/m3 set by the National Ambient Air Quality Standard (NAAQS) and much higher than the annual guideline value of 20 μg/m3 proposed by the World Health Organization (WHO)
The concentrations of atmospheric pollutants including PM10, PM2.5, SO2, NO2 and CO were higher in winter mainly because of the emissions of domestic heating systems and the unfavorable meteorological conditions, such as low wind speed and low temperature, which enhance the accumulation of air pollutants[44]

Summary

Introduction

Biomagnetic monitoring includes fast and simple methods to estimate airborne heavy metals. Coarse PM is usually deposited on leaf surfaces whereas finer PM is trapped in leaf waxes[11] With their wide distribution in urban areas and ease of sampling, tree leaves have been used to investigate the spatial and temporal patterns of atmospheric pollutants, including PM12,13, heavy metals[10,14,15], polycyclic aromatic hydrocarbons[16,17], and nitrogen oxides[18,19]. Several reports have focused on the relationships between leaf magnetic properties and metal concentrations in leaf samples[10,20,21,22,23] or in deposited atmospheric dust[23,24] Their methods cannot be used to directly analyze actual. The potential of statistical models combined with leaf magnetic properties to predict atmospheric heavy metals has yet to be fully explored

Methods

Results

Conclusion