The potential negative impacts of organochlorine pesticides on the environment and human health continue to receive attention. In order to study the spatial distribution characteristics of organochlorine pesticides in the inland alpine region, researchers collected soil and water samples in the Huangshui River Basin of the Qinghai–Tibetan Plateau and tested them for organochlorine pesticide residues represented by dichlorodiphenyltrichloroethane (DDT) and hexachlorohexane (HCH). The study identified the sources of OCPs by component analysis. We also constructed the LEVEL III model, applicable to the Huangshui River Basin, and used it to study the migration patterns of OCPs in various environmental media. OCPs were detected at low levels in the study area environment. The results of the OCPs source analysis show that there are both historical residuals and new sources in the region. Residual DDTs may originate from the mixture of technical DDTs and dicofol, and HCHs may originate from lindane or technical HCH. DDTs are mainly stored in soil, the input and output pathways are mainly atmospheric advection input and output, and its transport behavior in the environment is mainly air–soil exchange. Carcinogens in the study area pose little threat to people exposed to contaminated soil and contaminated water, but the cancer risk to children is greater than to adults. This study is helpful to managers of regional pesticide management and control.

Chromium (Cr) contamination is widely distributed in agricultural soil and poses a threat to agricultural sustainability. Developing integrated models based on soil survey data can be an effective measure to accurately predict the spatial distribution of Cr. Focused on an agriculturally dominated area, this study presents a novel hybrid mapping model that combines land use regression (LUR) and geostatistical methods to predict Cr distribution in topsoil and examines the influence of various influencing factors on Cr content. The LUR model was first adopted to screen the influencing factors for Cr predictions. Then LUR, was combined with ordinary Kriging (OK_LUR) and geographically weighted regression Kriging (GWRK_LUR) to describe the spatial distribution of Cr. Results showed that Cr distribution was profoundly influenced by soil Cu and Zn content, the distance between the soil sampling and livestock farm, orchard areas within 100 m, and population density within 1000 m. The developed GWRK_LUR model significantly improved the prediction accuracy of the OK_LUR and LUR models (by 9% and 16%, respectively). This model provides a novel route to account for the spatial distribution of Cr in agricultural topsoil at a regional scale, which has potential application in pollution remediation.