Dataset "Development and optimisation of rapid analysis of weathered slag using portable XRF - Supplementary information and code"

pXRF is widely used for rapid measurement of heavy metals in soils, however, thorough evaluation of common pre-processing methods and their effectiveness is limited. This study addresses processing methods using samples collected at a high heterogenetic post-metallurgical site containing,; basic oxygen steelmaking (BOS) slag and soil; the former being an important source of potentially toxic and valuable elements. Impact of pre-treatment processes, including sieving, drying, grinding, sample vessel, and ignition on the accuracy of pXRF measurements of samples were compared against ICP-MS. Of the twelve elements detected, four showed qualitative (Cr and Fe r² ≥0.60, RSD ≤ 30%) or quantitative (Mn and Ca r² ≥0.70, RSD ≤ 20%) measurements for raw samples. This improved to six elements after pre-processing (Sr qualitative, and Pb, Cr, Mn, Ca, Fe quantitative). Sieving and grinding improved precision (average RSD fell by 7.17% and 8.37% respectively), while drying and grinding enhanced accuracy (average r2 increased by 0.03 and 0.10 respectively). This study provides the first evidence that organic matter does not significantly impact pXRF accuracy. The two distinct matrices (BOS slag and soil) on- site resulted in a bimodal concentration distribution and a negative correlation for Ti. Importantly, this research proposes that not all common pre-processing steps are necessary to generate high-quality data, thereby increasing the speed and reducing the cost of data collection. Further analysis is required to develop a methodology to generate high-quality data across all elements of interest with BOS slag, or other relevant high heterogeneity samples. The supplementary information for this study includes : the full unprocessed dataset. all code used to for statistical analysis and graph generation