For centuries, Chile has been a territory with significant mining activity, resulting in associated social benefits and impacts. One of the main challenges the country faces today is the presence of a great number of mine tailings containing heavy metals, such as Cu, Cr, Ni, Zn, Pb, As, Cd, and Fe, which make up a potential risk for the population. This study is intended to develop a methodology for determining tailings requiring urgent treatment in Chile, based on risks associated with heavy metals. Geochemical data from 530 Chilean tailings were compared to the Dutch norm and the Canadian and Australian soil quality guidelines for residential use. Additionally, criteria about residents and water bodies were used, considering a 2-km area of influence around tailings. To do this, QGIS (Böschacherstrasse 10a CH-8624 Grüt (Gossau ZH), Zurich, Switzerland), a geospatial tool, was used to geolocate each deposit, considering regions, communes, rivers, lakes, and populated areas. To evaluate potential ecological contamination risks, Hakanson’s methodology was used. Results revealed the presence of 12 critical tailings in Chile that require urgent treatment. From the 530 tailings evaluated, 195 are located at less than 2 km from a populated area and 154 at less than 2 km from a water body. In addition, 347 deposits require intervention: 30 on Cu, 30 on Cr, 13 on Zn, 69 on Pb, 138 on As, 1 on Cd, and 5 on Hg.

This article presents a comprehensive scientometric analysis of mining waste valorization, focusing on tailings utilization in construction materials from 2010 to 2024. Through examination of 1096 Web of Science publications and utilizing CiteSpace mapping and network analyses, we analyze the intellectual structure of this field. Subject category analysis reveals materials science, construction technology, and environmental engineering as the dominant disciplines, interconnected through 168 links across 64 thematic nodes. Our co-citation analysis identifies 12 major research clusters, with materials science and environmental engineering serving as primary disciplinary pillars. Keyword co-occurrence analysis of 532 nodes connected by 1181 links highlights the field’s emphasis on fly ash, concrete applications, and mechanical properties. Recent citation bursts indicate growing research focus on thermal stability, heavy metal treatment, and innovative processing methods. Through synthesizing these scientometric indicators, this review provides strategic insights for advancing sustainable construction practices through mining waste utilization. Research gaps identified include long-term durability assessment, standardization needs, and scalability challenges. By synthesizing these diverse scientometric indicators, this review provides strategic insights for researchers, industry practitioners, and policymakers, contributing to the advancement of sustainable construction practices through mining waste utilization.

This data article describes a multi‐element soil‐geochemistry dataset collected over a six‐week field campaign (2 June–9 July 2025) across the La Negra Formation outcrop in the Antofagasta Region of northern Chile. The dataset comprises 101 primary Atacama Desert soil samples, 15 systematic field duplicates and 7 outcrop rock pulps collected in the field distributed across two parallel north–south transects with an average along‐transect spacing of ~10 km between consecutive points. The transects span six sectors in the communes of Tocopilla, Antofagasta and Taltal. Every sample was prepared following the Chilean national protocol for soil studies: air‐dried, sieved to the < 2 mm fine‐earth fraction, and pulverised for analysis, so the dataset characterises the conventional fine‐earth fraction of the soils. Each sample was analysed at AGS Análisis Geoquímicos (Coquimbo, Chile; lab batch CL25‐7655) under a single, internally consistent protocol. The AGS panel covers 62 analytes per sample, reported as aqua‐regia‐extractable concentrations quantified by ICP‐MS; because aqua regia is a partial digestion, values for elements hosted in resistant silicate phases are lower bounds on total contents. Six elements (Mn, Co, Ni, Cu, Zn, Pb) were re‐analysed by atomic‐absorption spectroscopy (AAS) on the same pulps at the Laboratorio de Geoquímica, Universidad Católica del Norte (UCN, Antofagasta, Chile), providing an independent inter‐method and inter‐laboratory check on the ICP‐MS values for these environmentally relevant trace metals. Quality assurance and quality control includes per‐element duplicate , the fraction of samples above the method detection limit, an inter‐method relative‐difference table, per‐element soil‐versus‐rock comparisons and a four‐level per‐element quality flag (11 of the 62 analytes classify as high‐quality, 40 as acceptable, 6 as low‐reproducibility and 5 as low‐detection) that identifies which analytes support quantitative reuse. We describe the sampling design, the laboratory workflow and the QA/QC outcomes. The dataset is released as a set of well‐documented CSV and GeoJSON files under CC‐BY‐4.0 and provides a single‐laboratory, single‐protocol baseline of fine‐earth soil geochemistry along the two sampled transect corridors of the La Negra Formation, against which future surveys of the soils of the Atacama Desert can be compared.

Machine learning (ML) is increasingly applied to geochemical baseline estimation and anomaly detection in soils and sediments, yet the methodological conditions under which machine learning outperforms traditional approaches—and which preprocessing and validation decisions most consequentially determine that advantage—remain incompletely characterized across environmental and mineral exploration domains. A structured systematic scoping review of 146 records from the Web of Science Core Collection applied sequential filtering to yield 78 thematically eligible studies, from which 20 were prioritized through a composite index integrating age-adjusted citation impact, platform usage, and semantic relevance. Four cross-cutting findings emerge. First, performance gains in environmental applications were driven primarily by spatial model structure rather than algorithm selection: incorporating a spatial covariate derived from geographically weighted regression raised test-set explained variance from $R^2=0.80$ to $R^2=0.96$ for cadmium mobility prediction in a geochemically heterogeneous karst setting, a gain the source study supported with a held-out test set and a Monte Carlo analysis of sensitivity to data size. Second, isometric or centered log-ratio preprocessing was applied in the majority of mineral exploration studies (three of five classical and hybrid studies and four of five deep-learning studies) but in none of the seven environmental studies, representing a systematic methodological gap with direct consequences for covariate importance estimates under compositional closure. Third, Shapley additive explanations and accumulated local effects functioned as instruments of operational value, enabling element-specific anomaly threshold derivation, training sample diagnosis, and grid-cell anomaly type classification; this evidence demonstrates that the accuracy–interpretability trade-off commonly assumed in the machine learning literature is not fundamental in geochemical applications but contingent on algorithm selection. Fourth, 90% of the 20 synthesized studies (18 of 20 by study-area location—13 in China and five in Iran) were evaluated under within-domain validation designs, and the consistently high performance metrics reported should be interpreted as interpolation estimates rather than evidence of transferable predictive capability. Geographic diversification of training datasets and spatially explicit cross-regional validation are identified as structural prerequisites for regulatory-grade applicability.

Chile, the world’s leading copper producer, accumulates hundreds of tailings deposits whose battery metals were never a recovery target. We screened cobalt, nickel and residual copper across the national tailings inventory using the public geochemical survey of the Chilean National Geology and Mining Service (SERNAGEOMIN, ∼2100 surface samples), deriving representative compositions for 649 deposits integrated with the October 2025 cadastre. Each metal returns a different answer. Residual copper is ubiquitous (median 0.15%, above 0.2% in 260 deposits) and its indicative endowment of ∼24 Mt, where the impoundments are counted at their authorised capacity, or ∼20 Mt on the tonnage actually deposited, over 90% of it in twenty impoundments above 100 Mt, is the most robust target. Cobalt sits just below crustal abundance (median 14 g/t) but shows an enriched tail: 104 deposits exceed twice the crustal reference, up to 1080 g/t, concentrated in small, old deposits. A discontinuity in the source release splits the nickel determinations into two segments with medians of 70 and 7.9 g/t; chromium, vanadium and a regional soil background all place the lower segment on the rock, so nickel is reported over its 304 deposits, at a median of 7.9 g/t, one sixth of the crustal reference. Cu, Co and Ni are uncorrelated at the deposit level ($\rho \le 0.13$), so surface geochemistry gives a plant no signal on which to base joint recovery. A comparison of surface against depth over twelve paired deposits also differs by metal: copper ordering is preserved, while the same twelve pairs are too few to resolve whether surface cobalt orders the interior. Assessing the battery metals of these tailings, therefore, requires sampling at depth, and the screening names the deposits where that campaign should start.