A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: A case study in the Yangtze Delta, China

Abstract

It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine in conjunction with machine learning methodologies to identify and classify potentially polluting enterprises in the Yangtze Delta, China.The data were classified into 31 separate and five integrated industry types by five different machine learning approaches. Multinomial naive Bayesian methods achieved an accuracy of 86.5% and Kappa coefficient of 0.82 and were used to classify the geographic data from more than 250 000 enterprises. The relationship between the different industry classes and measurements of soil cadmium and mercury concentrations was explored using bivariate local Moran's I analysis. The analysis revealed areas where different industry classes had led to soil pollution. In the case of cadmium, elevated concentrations also occurred in some areas because of natural sources. This study provides a new approach to investigate the interaction between anthropogenic pollution and natural sources of soil heavy metals to inform pollution control and planning decisions regarding the location of industrial sites.

Estimation of Heavy Metals in Agricultural Soils Using Vis-NIR Spectroscopy with Fractional-Order Derivative and Generalized Regression Neural Network

“…Soil contamination has increased significantly worldwide with the rapid development of industrialization and urbanization [1][2][3]. Due to their contamination being covert, persistent and irreversible, heavy metals are among the most hazardous contamination types that threaten the health of animals and human beings throughout the food chain [4][5][6][7][8]. Soil heavy metals, especially mercury (Hg), chromium (Cr), lead (Pb), cadmium (Cd) and copper (Cu), tend to be accumulated in soils because of anthropogenic activities and their own easy migration properties as common industrial pollutants [9][10][11][12][13][14].…”

Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China

“…Soil is critical to many ecosystem functions, and soil degradation and pollution have gained a great deal of attention worldwide [1,2,3,4,5,6]. Contaminated soils can pose serious threats to human health through a variety of pathways including diet, inhalation and dermal contact [7,8,9,10,11,12].…”

Source and Risk Assessment of Trace Metals in Red Soils from Yunnan Province, Southwest China

“…[ 15–17 ] Based on the official data, Chinese soils are contaminated for ≈16.1% of the total area. [ 18,19 ] Furthermore, Yunnan province is one of the most metal‐polluted regions in western China, [ 20 ] where anthropogenic activities, such as agricultural production, transportation, discharge of domestic wastes, metallurgy, mining, coal combustion, metal smelting, and urban construction, threaten the soil ecosystem. [ 6,13,21–28 ] Trace metals in soils mainly derive from two sources: i) the weathering from bedrocks and ii) anthropogenic inputs.…”