Editorial: Chemometric approach to distribution, source apportionment, ecological and health risk of trace pollutants

Intensive technological development has led to tremendous pressure on the quality of the environment, which is reflected in its pollution by toxic trace pollutants. The modern approach to studying risks from these pollutants includes the integration of analytical chemistry, environmental science, mathematics, statistics, and toxicology. A dramatic increase in data generated by modern analytical instruments in studies on trace pollutants in the environment imposes a quite complex task to extract meaningful information from these data. Thus, the use of chemometrics became irreplaceable. Principal component analysis (PCA) (Radomirović et al., 2020; Trujillo-González et al., 2022a), hierarchical cluster analysis (HCA) (Lučić et al., 2022), positive matrix factorization (PMF) (Radomirović et al., 2021), geostatistical analysis (GIS) (TrujilloGonzález et al., 2022b); (Miletić et al., 2022), Monte Carlo simulation (MCS) (Seyednejad and Ghiasi, 2022), numerical modeling (Wang et al., 2019), machine learning and artificial intelligence modeling (ANN) (Egbueri, 2021; Agbasi and Egbueri, 2022) have been frequently used chemometric methods for trace pollutants data evaluation. Since there is great competitivity or complementarity between these methods, it is common to use several methods together in a single study. Therefore, a comprehensive approach is needed in this area where different research can be incorporated, covering a range of chemometric methods and their combinations for various pollutants in the environment. This Research Topic addressed recent advances in the application of chemometrics in studies on the occurrence, distribution, and fate of trace pollutants in different media (soil, OPEN ACCESS