Machine-learning models for on-site estimation of background concentrations of arsenic in soils using soil formation factors

摘要

Taking into account great spatial heterogeneity in soil environments is essential to carrying out an accurate soil contamination assessment at the regional scale. Although there are numerous methods for distinguishing between natural and anthropogenic element contents, few studies focus on on-site determination methods, with few site-specific and sensitive references available. In this study, site background concentration is estimated as an on-site reference for soil contamination assessment.