Risk Assessment of Agricultural Soil Heavy Metal Pollution Under the Hybrid Intelligent Evaluation Model

Abstract

As a nationally protected land resource, farmland plays a crucial role in agriculture production and food safety, making the quality of soil and environmental health critically important. Therefore, studying the extent of soil heavy metal pollution in farmland is of great significance for understanding the growth environment of food crops and protecting agricultural land resources. This study addresses the challenge of accurately, quickly, and conveniently assessing the extent of soil heavy metal pollution across an entire research area using a limited number of soil samples. To tackle this issue, a novel soil heavy metal pollution risk hybrid intelligent evaluation model (HIEM) is proposed. The HIEM utilizes the Semi-Supervised Bayesian Regression (Semi-BR) model, trained through Bayesian Co-training, to predict the soil heavy metal content at unsampled points. It employs an improved Multiple Kernel Support Vector Machine (MKSVM) model to evaluate the pollution status of the soil. Additionally, Geographic Information System (GIS) techniques are employed for spatial analysis of the pollution situation in the research area. The study focuses on eight soil heavy metals: As, Cd, Cr, Hg, Pb, Zn, Cu, and Ni. The experimental verification of the model was conducted using field sampling data from the major agricultural areas of Huangpi and Xinzhou in Wuhan, Hubei Province, China. The experimental results show that the eastern region of Huangpi District is more severely contaminated, particularly the central area in the northeast, with moderate to high pollution levels. The hybrid intelligent evaluation model achieves an average accuracy of 96.66% in assessing single-factor pollution of the eight soil heavy metals and an overall evaluation accuracy of 97.42%. The hybrid intelligent evaluation model is able to accurately fit traditional single-factor index methods and Nemerow comprehensive pollution index method. The Geographic Information System representation reveals a consistent distribution trend of soil heavy metal pollution reflected by the hybrid intelligent evaluation model with the results obtained from single-factor index and Nemerow comprehensive pollution index evaluation, indicating the feasibility of using this evaluation method for assessing the risk of soil heavy metal pollution. The conclusion shows that the hybrid intelligent evaluation model needs at least 639 sets of sample data to achieve the highest accuracy when assessing the risk of soil heavy metal contamination in an area of about $3.7\times 10^{4}\,\,hm^{2}$ , and this paper provides a reference to solve the problem of realizing high-precision risk assessment of heavy metal contamination of agricultural soils in the case of small samples. This study is of great practical significance for soil pollution investigation, soil quality assessment and other practical work.

Keywords

• Contamination
• farmland protection
• geographic information systems
• heavy metals
• machine learning algorithms
• risk assessment