Editorial: Advanced geochemical mapping and geochemical background/baseline: an environmental perspective

The chemical compositions of different environment media, particularly the distribution of potentially toxic elements (PTEs), reflect a complex interplay between natural (geogenic)are primarily influenced by natural factorsprocesses, including parent lithology, weathering intensity, and geochemical cycling, and a wide range of anthropogenic inputsyet their concentrations are increasing modified by a wide range of anthropogenic activities, such as industrial discharge, mining, agriculture, urbanization, etc. However, the central challenge in contemporary geochemical research is not simply recognizing this dual influence, but the difficulty in reliably distinguish themDistinguishing between natural and anthropogenic contributions of chemical elements, particularly the potentially toxic elements (PTEs) has become a crucial challenge in environmental pollution studies (Reimann and Garett, 2005). Over the past few decades, geochemical background (GB) values, which were originally introduced mainly for mineral exploration, have become a cornerstone scientific tool in environmental research for defining pollution and identifying the source of contamination distinguishing natural (geogenic) variability from anthropogenic influences (Reimann et al., 2005;Reimann and Caritat, 2005;Reimann and Caritat, 2017;Salomão et al., 2019Salomão et al., , 2020;;Sahoo et al., 2020; Dung et al., 2013;Guo et al., 2025)) with the widespread application in environmental pollution research (Reimann and Caritat, 2017;Salomão et al., 2019Salomão et al., , 2020;;Sahoo et al., 2020). However, despite decades of research, the determination of GB values for PTEs remains methodological inconsistent, conceptually ambiguous, and strongly dependent on spatial scale (Reimann and Garett 2005;Reimann and Caritat, 2017;Souza-Filho et al., 2022).In essenceprinciple, GB values, which originally developed for mineral exploration, represent the natural concentration range of elements in a given environmental medium, such as soil, sediments or water that is consistently present in, under pristine or minimal anthropogenic disturbed conditions and has not been influenced by localized human activities, providing a reference for distinguishing natural variability from anthropogenic sources (Galuszka, 2007;Reimann and de Caritat, 2017;Barrio-Parra et al., 2025); as such, these values provide a basic for distinguishing natural (geogenic) variability from anthropogenic influences (Reimann et al., 2005;Reimann and Caritat, 2005;Dung et al., 2013;Guo et al., 2025).AdditionallyOn the other hand, geochemical baseline, which sometimes often used interchangeably with GB in the scientific literatures, has a distinct concept (Reimann and Garrett, 2005), which. refers A geochemical baseline to the current state of element concentrations in soils at refers to a specifiedc time period and location, encompassing incorporating both natural processes and diffuse anthropogenic influencesinputs (Reimann and Garrett, 2005;Duran et al., 2022). Both concepts Together, these values provide a reference framework for various environmental applications, including source apportionment, anomalies detection and the quantification of contamination levels for effective environmental monitoring and management (Salomão et al., 2019;Meloni et al., 2023). Moreover, GB values also essential for developing environmental quality guidelines, which often ignored in national regulatory standards (Dung et al., 2013;Salomão et al., 2020), as well as for land-use planning and resource mapping (Berdie et al., 2025;Matschullat et al., 2012). However, despite these advantages, their inconsistent definition and application across studies contribute to significant uncertainty and challenges in environmental evaluation (Reimann and Garrette, 2005;Galuszka and Migaszewsk, 2011;Mascarenhas et al., 2025). Moreover, GB values essential for developing environmental quality guidelines, which often ignored in national regulatory standards (Dung et al., 2013), as well as for land-use planning and resource mapping (Berdie et al., 2025;Matschullat et al., 2012).The lack of conceptual clarity is emerged by methodological heterogeneity and there is no agreement over the best and most widely accepted methods for establishing GB (Kichinska and Turek, 2017). A wide range of approaches, such as mean ± SD, median ± 2MAD, box-whisker diagram, iterative 2, cumulative distribution function, and Tucker Inner Fence (TIF) employed to derive GB and reference values, which obtained based on the sample statistics (Matschullat et al., 2000;Reimann and de Caritat, 2017;Ander et al., 2013;Dung et al., 2013;Souza-Filho et al., 2022;Mascarenhas et al., 2025). However, these methods often yielding significantly different results for same region (Sahoo et al., 2020;Salomão et al., 2020). Also, some of these methods have limitations in limited sample size and skewed data distribution (Barrio-Parra et al., 2025). Therefore, methodology for establishing GB is remained open for discussion (Matschullat et al. 2000;Reimann, Garrett 2005;Kicinska and Turek, 2017). Such discrepancies are further amplified in geochemically heterogenous systems, where lithological variability, mineral deposits, weathering/pedogenic process, and land-use intensity create multiple overlapping geochemical populations (Reimann et al., 2005;Palmer et al., 2021). As a consequence, contamination assessment of PTEs in such environmental system However, determining GB values is not straightforward and can be a complex task, particularly in regions with heterogeneous geology and a history of contamination (Reimann et al., 2005;Palmer et al., 2021). If GB values are not accurately established, they may lead to an overestimation overestimate of contamination risks in metal-rich lithologies or an underestimation underestimate of pollution in geochemically depleted regions. Despite these limitations, the robustness and precision of methods make it valuable approaches for establishing GB values (Mascarenhas et al., 2025). Advances in geochemical mapping, geostatistics, and geospatial technologies have significantly improved the resolution and interpretation of GB-derived data (Souza-Filho et al., 2022). The integration of high-density sampling, advanced GIS techniques and highresolution spatial data of lithology, mineralogy, soil type, land use and land cover patterns enables more robust delineation of GB values, distinguish geochemical provinces and enhances spatial resolution for better identification of Therefore, the development of site-specific or region-specific GB values is crucial to avoid such misinterpretations (Dung et al., 2013;Sahoo et al., 2020;Fornasaro et al., 2025). Furthermore, the reliability of GB values is further enhanced when they estimated using advance statistical methods and integrated with advanced GIS techniques and high-resolution spatial interpolation of lithology, land use and land cover, mineralogy. Such integration enhances spatial resolution, accuracy, and analytical capabilities, enabling for more precise delineation of sampling points with naturally elevated concentrations and those affected byversus anthropogenic inputs signals (Meloni et al., 2013;Palmer et al., 2021;Salomão et al., 2020;Sahoo et al., 2020). Establishing a robust natural GB is therefore crucial to avoid misclassifying geogenically enriched areas as contamination. MoreoverMore recently, the integration of robust geostatistical/multivariate methods and AI/integration of machine learning (ML) and artificial intelligence (AI) techniques to regional geochemical provinces and background values have emerged as a powerful way forward for modeling complex, non-linear relationships in geochemical data and predicting the source and distribution of PTEs enhances source apportionment studies (Berdie et al., 2025). While, these techniques offer significant potential in geochemical studies, their methodological application still requires critical evaluation and careful integration within a coherent conceptual framework in addressing environmental problems effectively Consequently, there is a growing need for a deeper understanding of these methods or advanced techniques in conjunction with GB and geochemical mapping to address environmental problems more effectively.Against this backdrop, there is a pressing need to move towards a more standardized, scalable and critically informed framework for GB determination and geochemical mapping. In this context, the Research Topic entitled "Advanced Geochemical Mapping and Geochemical Background/Baseline: An Environmental Perspective" published in Frontiers in Soil Science and Frontiers in Environmental Science brings together recent collections (including invited latest original research articles and review papers) aimed at addressing recent advances in this field. The main aim of this Research Topic was to explores 'what is new and what lies ahead in the field. The schematic framework (Figure 1) illustrates how geochemical background serves as the central integrative components linking is next' in this topic, thereby advancing the knowledge necessary to support improved environmental assessments and resource management. The topic of particular interest to this topic include a range interrelated sub-themes (Figure 1), including the cutting-edge methodologies for defining geochemical background/baseline in diverse environmental or geological compartments, high-resolution multi-elemental analysis, integration of GIS and other geospatial technologies, robust geostatistical and multivariate compositional statistical approachesdata analysis, and AI/ML models for key environmental outcomes, including source apportionments challenges, establish site-specific GB framework, contamination risk prediction, policy development, land use planning and mineral exploration and highlights the interconnected nature of geological, geomorphological and land use factors in shaping geochemical variability. the development of scalable sampling strategies from local to regional and continental scales , and the application of these dataset to environmental risk evaluation and the development of environmental quality guidelines. 2025) studied geochemical mapping with mineralogical data and grainscale sedimentology to explore the interaction of geochemical and physical properties in complex river system. Their findings demonstrate that physical properties like grain size should be explicitly considered while planning river flood control and engineering assessment to avoid misinterpretation of geochemical signals.Another significant advancement highlighted in this Research Topic is the The increasing application of AI and advanced machine learningML models in geochemical prediction and risk assessment of contaminants. play a very crucial role in water geochemistry and contaminant prediction even with limited hydrogeochemical data. In this regard, Kerketta et al. (2024) studied the role of different machine learning models such as Random Forest, eXtreme gradient boosting (XGBoost), extreme learning machine (ELM), multi-layer perceptron (ELM, and MLP) in predicting fluoride (F⁻) concentrations in shallow aquifers of Punjab based on basic water quality variables. The model identified that TDS along with Na⁺, Cl⁻, and Ca²⁺ as key predictors of F⁻ risk in groundwater. This work demonstrates that, advanced ML techniques can effectively map contamination risk and aid in sustainable management of groundwater resources. Ghosh et al. (2025) integrate ML models with geochemical data to predict potassium (K) dynamics in four major Indian soils (red, alluvial, calcareous, black) in both natural and flooded conditions. RF models efficiently predict K availability and uptake, highlighting ML methods can be used as a powerful tool for optimizing fertilizer management. Finally, the inclusion of a reviewThe review by Asare et al. (2023) demonstrates on the plant-soilmicrobe interactions in PTE dynamics extends the scope of GB research beyond abiotic control, emphasizing the role of biological process in controlling metal mobility and bioavailability.knowledge on As, Cd, Pb, and Zn hyperaccumulation by plants and soil microbiome responses. Although this work is related to broader soil ecology, it connects to geochemical baselines by explaining how biotic processes modulate PTE distributions and mobilities. Taken together, Overall, all these studies collectively demonstrated that advancing geochemical advance methods in geochemical background research requires not only improved statistical and analytical tools, but also a conceptual shift towards integrative multidisciplinary approaches.studies integrated with geochemical mapping, multivariate statistics and ML models to effectively analyze environmental contamination. This is fundamental for advancing our understanding of better application of GB values in diverse environmental studies.Despite substantial several researches advances in geochemical background research, its definition and the importance of geochemical background and baseline values, their definition and application remain methodologically inconsistent and conceptually ambiguous, limiting its reliability in many environmental assessmentstudies. A central challenge lies in defining a "natural" geochemical background, which ideally requires Some of the major challenges in GB/baseline studies include: Difficulty in assessment of GB in complex geochemical heterogeneity system: The most significant challenge is that finding natural pristine environment condition, that is completely unaffected by human activity; such conditions are increasingly rare or non-existent, and that "natural" itself is a contested construct (Reimann and Garrett, 2005). Thus, the "natural background" is an idealized reference condition that must be statistically estimated or modeled than directly observed., which basely exit. Furthermore, there is still difficulty in defining GB levels in geochemically heterogenous systems, where wide variations in areas with significant spatial heterogeneity of elemental distributions due to complex parent materialslithology, mineralogical compositions, local weathering conditions, soil formation processes and land-use intensities generate multiple. The presence of multiple overlapping populations in geochemical data (Johnson et al., 2005;Ionita et al., 2025;Sako et al., 2021). In such contexts, it will be quite difficult to give a clear distinction between geogenic and anthropogenic contributions. from multiple sources complicates background determination, requiring advanced statistical methods like the Expectation-Maximization (EM) algorithm to separate these populations (Sun et al., 2023). Besides lithological heterogeneity, distribution of PTEs in various environmental matrices such as soils, sediments, and water bodies are influenced by factors like local weathering conditions, mineralogical diversity, grain size, and hydrological processes (Johnson et al., 2005;Ionita et al., 2025;Sako et al., 2021). In addition Moreover, the mobility and bioavailability of PTEs are controlled by complex geochemical reactions, redox conditions, pH, organic matter content, and microbial interactions, further challenging the accurate estimation and interpretation of background characterization values (Peel et al., 2022;Mehta and Kocar, 2019).A second major limitation arises from Methodological methodological constraints including lack of standardized sampling design, sampling size and analytical protocols for multielements analysis (e.g., aqua regia extraction versus aqua regia plus HF versus lithium tetraborate fusion methods), and data treatment approaches, which lead to significant inconsistencies across studies.analytical approaches impacts the reliability of environmental assessments and the comparability of data from different surveys. In analytical protocol, some studies apply aqua regia extraction, others use lithium tetraborate fusion method, while studies also extract bioavailable fractions. These differences hinder the comparability of datasets. As a result, this would be a great challenge in integrating different datasets in defining geochemical backgrounds at a national or global scale for diverse environmental media such as soils, sediments, stream waters (Wang and Zhang, 2012;Gałuszka and Migaszewski, 2011;Johnson et al., 2005;De Vivo et al., 2008). Also, geochemical mapping and background estimation requires spatial modeling based on sparce, heterogenous and potentially inaccurate data sets. Hence, it is vital for uncertainty quantification to provide accurate and reliable results for well-informed decision-making (Wang and Zuo, 2024). Moreover, while multivariate statistical methods and GIS-based mapping are widely used, there is a lack of standardized protocol for data handing and uncertainty quantification, leading to divergent interpretations the geochemical databases that can support consistent baseline/background derivation and comparative mapping across regions. Also, the complexity in defining truly representative baselines across scales from local to continental. This highlights the complexity in defining truly representative baselines across scales from local to continental, thus a challenge echoed in foundational geochemical background discussions.Finally, a critical yet underexplored challenge is the translation of GB values into Application to actionable environmental guidelines and policy making frameworks. Existing environmental guidelines often often rely on fixed threshold values that ignore the geological context and conflict with natural background levels (Dung et al., 2013), resulting in the .: Distinguishing natural variability of heavy metal concentrations influenced by geological factors from anthropogenic metal concentrations is crucial for setting a benchmark for contamination assessment and potential misclassification of naturally elevated element concentrations as pollution or, conversely, the underestimation of anthropogenic impacts in depleted systems (Dung et al., 2013). However, national regulatory threshold values often ignore geological context and conflict with natural background levels (Dung et al., 2013). Therefore, transforming scientifically rigorous GB or baselines into actionable environmental guidelines and intervention values remains a policy and scientific translation challenge.Considering Addressing the above-mentioned several challenges requires a shift towards integrated, standardized, site-specific and future directions should focus on multi-disciplinary and research approaches site-specific approaches that combine geochemical mapping, robust statistics, advanced modeling, GIS spatial mapping and emerging data-driven AI/ML technologiesintegration of AI and machine learning with geochemical data. Future research This should focus on the development of robust framework for GB determination that explicitly include standardized protocol, geological/mineralogical context, uncertainty, and spatial variability. following specific strategic directions. Standardized Geochemical Baseline Protocols: Developing Establishing a standardized protocols for GB and baseline calculation, including sampling protocol, analytical technique, the use of robust statistics, compositional data analysis, and uncertainty can comparability across studies and the development of regional to global geochemical to the reliability of background estimation in complex of robust GB in the of geology and site-specific GB and baselines site-specific geological and mineralogical is crucial to avoid misinterpretation of natural anomalies as pollution et al., 2025). Furthermore, integration of GB with would be to anthropogenic and sources of in Also, incorporating geochemical models that redox and processes of PTEs can understanding of their mobility and environmental risks (Peel et al., 2022;Mehta and Kocar, geochemical mapping following represent towards are several regions that are still sampling and following will spatial variability and establishing effective GB across multiple However, such should scale background for a Also, background sampling would significantly with scale and regional and continental (e.g., national geochemical mapping continental provide the for all sub-themes by high-density integration of AI and with geochemical data with AI and machine learning and The new should focus on statistical with ML AI can predict a for a region with specific for a future research advanced ML models and learning to and the natural with the latest AI techniques data and machine learning and models can offer significant potential for advancing methodological and and geochemical and mapping. These complex and and geochemical and complex and non-linear relationships between and which can better spatial and patterns of element distributions (Berdie et al., 2025). This also contribute lead to of global geochemical databases for and developing monitoring systems for environmental Random and be to integrate soil, and land-use addressing anthropogenic such as and geochemical baselines integrated geochemical and geospatial with AI is critical for sustainable environmental et al., 2021). However, approaches, their application must be used as a tool within a broader methodological the development of Geochemical geochemical data or and is As several researches are and increasingly generate geochemical the of a standardized protocols and essential for would the of baseline geochemical and the integration of local studies into research at continental and global frameworks. Finally, there is a between regulatory and natural geochemical natural background values the of pollution assessment and can effectively the and of et al., 2024). Hence, the future should also focus on scales that will and improved environmental monitoring and The lack of geochemical was explicitly In integrating local studies into continental baselines and advanced into policy remain key environmental and policy based on GB values, which often ignored in national environmental quality guidelines. GB values the of pollution providing a more reliable for pollution risk evaluation and the scientific of environmental Thus, future environmental quality should integrate background values along with than for which offer ignore geological as a it overestimate or underestimate the contamination scientific baselines with regulatory standards with and will be crucial for the broader environmental of geochemical the contributions of a of the articles that accepted in this Research which together present a the of methodological and integrated approaches in advances geochemical mapping and background The findings studies that site-specific GB values are essential for reliable in contamination risk particularly in geochemically heterogenous and influenced environment than on national or reference Furthermore, the integration of multivariate statistics with advance geochemical mapping and ML approaches a way forward to address complex contamination and source apportionment This to Furthermore, the determination of GB using advanced statistical methods integrated with geospatial mapping, land use and land cover data and lithological is essential for accurately identifying the presence of multiple populations in geochemical data and for distinguishing anthropogenic from natural variability in heterogenous the integration of machine learning and GIS-based modeling is leading the forward by