Digital mapping of heavy metals in urban soils: A review and research challenges

Abstract

Heavy metal pollution in urban soil is one of the global environmental problems, and monitoring its spatial distribution can provide basic data for assessing and managing the ecological and environmental problems. Due to the strong human activities in the urban space and the high heterogeneity and fragmentation of the landscape, it is of great significance to explore how to characterize the diversity in human activities within the city and to establish a paradigm for mapping spatial distribution of heavy metal in urban soils. This paper reviewed the theory of soil-landscape model, the data sources for extracting environmental variables, and the geo-statistical methods for establishing regression models. Furthermore, receptor models for tracing heavy metals were discussed. We suggested that using multi-source heterogeneous geographic data (geological data, soil type data, meteorological data, terrain data, time series Landsat images) to extract environmental covariates. In particular, integrating social perception data (point of interest, Tencent user data) and remote sensing image data to extract the urban functional district types that reflect differences in human activities. GeoDetector is recommended to select key environmental covariates that spatially coupling with urban soil heavy metals, and combined with receptor models to trace the sources of heavy metals. Machine learning algorithm is a good choice to construct the estimation models by cooperating with key environmental variables, and to verify the general transferability of the models by using the local weighted and ‘spiking’ modeling strategies. The newly introduced methods and ideas may be expected to provide technical methods for the monitoring of heavy metal pollution in urban soils, and to further expand the application of digital soil mapping theories and methods in urban soil mapping.