Innovative Equation for Determining the Geochemical Background Values of Chromium Based on Major Components in Rock–Soil–Sediment Systems

The geochemical background value of chromium (Cr) serves as a pivotal factor in environmental assessments and mineral exploration endeavors. Traditionally, geochemical background values have been determined using statistical parameters derived from dataset analysis, though this method may possess inherent limitations. In this study, we introduce a novel equation to calculate the geochemical background value of Cr, based on the premise that major elements can effectively delineate the geochemical background for trace elements. Using a dataset encompassing 791 abundance records from rocks, soils, and sediments, we derived and fitted the equation. Our findings revealed that when treating these 791 records as representative of background samples, approximately 95% of the records lie within a logarithmic error of ±0.2 from the calculated values. Based on this observation, we propose adopting a logarithmic error threshold of ±0.2 as a criterion for identifying background samples. To further validate the equation, we tested background samples from diverse regions with varying degrees of weathering. Among the 138 samples analyzed, only two exhibited background values with a logarithmic error exceeding ±0.2 from the measured values, although all samples fell within a ±0.3 error margin. We subsequently applied this equation to the orthopyroxenite and dunite zones within the Pados-Tundra complex, located in the western Kola Peninsula, Russia. According to the ±0.2 logarithmic error criterion, mildly altered orthopyroxenite samples could be deemed as background Cr samples, where certain altered orthopyroxenite samples exhibited a logarithmic error in the ±0.2–±0.3 range. In contrast, altered and mineralized samples in the dunite zone showed a significant deviation beyond the ±0.2 error range, indicating that they should be classified as anomalous Cr samples. This innovative methodology offers a refined approach for background determination and anomaly identification, thereby enhancing mineral exploration and Cr environmental quality assessments.