Lead source apportionment and climatic impacts in rural environmental justice mining communities.

Lead Source Apportionment and Climatic Impacts in Rural Environmental Justice Mining Communities - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Environ Res . Author manuscript; available in PMC: 2026 Apr 15. Published in final edited form as: Environ Res. 2025 Feb 14;271:121130. doi: 10.1016/j.envres.2025.121130 Search in PMC Search in PubMed View in NLM Catalog Add to search Lead Source Apportionment and Climatic Impacts in Rural Environmental Justice Mining Communities Zain Alabdain Alqattan Zain Alabdain Alqattan 1 Department of Environmental Science, College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ, USA Find articles by Zain Alabdain Alqattan 1 , Alexandra Trahan Alexandra Trahan 1 Department of Environmental Science, College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ, USA Find articles by Alexandra Trahan 1 , God’sgift N Chukwuonye God’sgift N Chukwuonye 1 Department of Environmental Science, College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ, USA Find articles by God’sgift N Chukwuonye 1 , Miriam Jones Miriam Jones 1 Department of Environmental Science, College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ, USA Find articles by Miriam Jones 1 , Mónica D Ramírez-Andreotta Mónica D Ramírez-Andreotta 1 Department of Environmental Science, College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ, USA 2 Division of Community, Environment & Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA Find articles by Mónica D Ramírez-Andreotta 1, 2, * Author information Article notes Copyright and License information 1 Department of Environmental Science, College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ, USA 2 Division of Community, Environment & Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA Author Contributions Zain Alabdain Alqattan: Investigation, Formal analysis, Validation, Visualization, Writing – original draft, Writing – review & editing, methodology. Alexandra Trahan: Investigation, Writing – review & editing. Godsgift N. Chukwuonye: Investigation, Writing – review & editing, methodology. Miriam Jones: Investigation, Writing – review & editing. Mónica D. Ramírez-Andreotta: Conceptualization, Methodology, Investigation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition. * Corresponding Author: mdramire@arizona.edu , University of Arizona, 1177 E Fourth Street, Rm. 429, Tucson, AZ 85721, USA, Fax: 520-621-1647 Issue date 2025 Apr 15. PMC Copyright notice PMCID: PMC11895395  NIHMSID: NIHMS2059489  PMID: 39956423 The publisher's version of this article is available at Environ Res Abstract After a sequence of natural disasters in Gila County, Arizona, USA environmental justice (EJ), mining areas, community members raised concerns about metal(loid)s exposure and origin. To address these concerns, non-residential sediments (0–2cm, 2–15cm, and 15–30cm), household soil (0–2cm), and indoor and outdoor dust samples were analyzed for metal(loid)s concentration and Pb isotopes via inductively coupled plasma mass spectrometry. To identify the potential sources of Pb, 37 studies were considered, and 21 different Pb isotopic ratios were documented and compared. Spearman’s correlation and principal component analysis were used to investigate the co-occurrence of metal(loid)s associated with Pb. The results demonstrated a clear association (p <0.05) between Pb and mining activity in households and non-residential locations as well as a co-occurrence with As, Cd, Cu, Mo, Sb, and Zn at 0–2cm and in non-residential with As, Cd, and Zn at 2–15cm and 15–30cm. The outdoor household dust was impacted by a mixture of Pb sources and was associated with metal(loid)s coming from mining, wildfire, lead based-paint and landfill, whereas indoor Pb dust was associated mainly with metal(loid)s coming from geogenic sources. Further, 66% of town/city sediment samples across depth and 53.8% of outdoor dust samples were aligned with mining fingerprint and 30.1% of outdoor dust and 25% of household soil samples were aligned with the wildfire Pb isotopic ratio/fingerprint. The Positive Matrix Factorization model illustrates flood’s ability to remobilize metal(loid)s from neighboring mine sites to the households’ locations. Currently there is no established Pb isotopic ratio composition for wildfires in Arizona; this study lays the foundation for understanding the complex relationship between the myriads of lead sources in our environment, wildfires, and flooding. Keywords: Copper mining, soil/sediment, dust, lead source apportionment, metal(loid)s, climatic events Graphical Abstract: 1. Introduction Compound events are the simultaneous occurrence of multiple extreme climate events ( Ridder et al. 2020 , Zscheischler et al. 2020 , Zscheischler et al. 2018 ) and are expected to cause nearly 250,000 deaths per year between 2030 to 2050 ( Ghanbari et al. 2023 , World Health Organization, 2023a ). To build resiliencies, the interaction of multiple climate drivers and the accumulative impacts needs to be understood ( Qian et al. 2023 ); especially when considering how compound events may increase exposure to environmental pollutants from legacy and current/active sources. Wildfires and flooding that occur in industrialized, urbanized, or near resource extraction, can emit and/or (re)mobilize contaminants in the environment, e.g., metal(loid)s from legacy mine sites ( Zscheischler et al. 2018 , Qian et al. 2023 , Wang & Kanter, 2014 , Murphy, 2020 , Poudyal et al. 2019 ), highlighting the complexity and environmental health threats posed by multivariate extremes ( Zscheischler et al. 2018 , Pu et al. 2022 , Lopez et al. 2023 ). Climatic events in arid and semi-arid areas exacerbate soil erosion and land degradation in mining region ( Ju et al. 2024 ) and could redistribute historic anthropogenic metal(loid)s such as lead (Pb), leading to heightened concern, as there is no safe level of lead exposure ( World Health Organization, 2023b ). For instance, exposure to low Pb concentrations can disturb the human nervous system, mainly and cause learning disabilities in children ( Collin et al. 2022 ). Lead is ubiquitous and has a long history of being released from various industrial activities, e.g., mining and fossil fuel combustion prior 1990s ( Collin et al. 2022 ). Lead can also enter the environment from lead-based paints found in residential households built before 1980’s or from the disposal of waste products in municipal landfills ( Collin et al. 2022 , U.S.EPA, 2024a , CDC, 2024 ). Therefore, historical Pb is often found in different environmental media (i.e. soil and dust) and acts as a long-term source of present Pb in residential areas ( Wang et al. 2022 ). Accordingly, more attention is being devoted to characterizing Pb concentrations in the various environmental media and determining where it might come from after extreme climatic events ( Baieta et al. 2022 , Levin et al. 2020 , Lazăr et al. 2024 , Iordache et al. 2022 ). Lead isotopes can be used to distinguish between geogenic and anthropogenic Pb and can be source tracked using Pb isotopic composition ( Kristensen et al. 2014 , Wang & Kanter, 2014 ). Lead has four stable isotopes, 208 Pb, 207 Pb, 206 Pb, and 204 Pb, with the last being a non-radiogenic isotope ( Laycock et al. 2022 , Murphy et al. 2020 ). The change in these isotopic compositions is explicit to the geological formation of a region and consequently, they do not form because of industrial processes ( Murphy, 2020 ). Therefore, the isotope signatures are distinctive and could be used to discriminate Pb from anthropogenic and natural sources in different environmental media ( Kristensen et al. 2014 , Wang & Kanter, 2014 , Laycock et al. 2022 , Murphy et al. 2020 ). Further, the occurrence of historic and/or current anthropogenic Pb is generally associated with other anthropogenic metal(loid)s i.e., Sb, Cd, As, Zn or with lithogenic source metals, i.e., Al and Fe, and differ based on the Pb origin ( Barbieri, 2016 ; Wang et al. 2022 ). Both active and legacy mine sites are notable anthropogenic sources of metal(loid)s, generating massive waste sites, which can lead to the destruction to natural habitat, and reduced soil, water and air quality ( Haddaway et al. 2019 , Li et al. 2021 , Schneider et al. 2019 ). Arizona has over 60,000 mining sites that played a vital role for the state’s economy over the past century ( BLM, n.d. , Tuck & Arizona. Department of Mineral Resources, 1953 ). The increase in mining activities comes with a cost of releasing potential toxic metal(loid)s into the neighboring communities, particularly, in disaster prone regions ( Navarro et al. 2008 , Bulovic et al. 2024 , Murphy et al. 2020 ). A large body of literature has described Pb exposure pathways and outcomes, however knowledge gaps exist, and efforts are needed to further understand Pb source apportionment ( Batuman et al. 2014 , Kumar, 2018 , U.S.EPA, 2024a , Laycock et al. 2022 ) after natural disasters. Our understanding of how Pb presence, concentration and exposures may be worsened after a wildfire is limited, ultimately jeopardizing local communities’ health. Here, we quantify the Pb pollution magnitude and origin after natural disasters, especially in impacted regions lacking pre-established metal(loid)s data ( Isley & Taylor, 2020 ). This community-engaged research project evaluated the post-wildfire and flooding geohazards and accompanied potential movement of contaminants, and associated resiliencies (Rethinking Wildfires Flood and Health, n.d.). To incorporate the diversity of community experiences/voices and inform data report back processes, the “Cobre Valley Community Environmental Board” was established. Frequently asked by the residents: “What was in the sediment and debris left in our community and homes? and where did these contaminants come from?” Based on the above, the research objectives of this study are: Evaluate community-wide Pb concentrations after extreme climatic events in non-residential soil and sediment, household surface soils, and indoor and outdoor dust Conduct a review of the literature and determine Pb sources in the community using 208 Pb/ 206 Pb and 206 Pb/ 207 Pb isotopic ratios Determine the association and co-occurrence of Pb with metal(loid)s using the Spearman’s correlation and principal component analysis (PCA) to further support Pb origin(s) Assess the impact of flooding on Pb and metal(loid) concentrations in household areas using a Positive Matrix Factorization model (PMF) 2.1. Study Area The study areas are Globe, Miami, Icehouse Canyon, and Claypool in Gila County, Arizona, USA. This region is part of Arizona’s Copper Triangle that accounts for 13.6% of the total historic copper production in the United States ( Briggs, 2023 ). Miami and Globe are the primary communities, with an estimated total population of 16,738 ( U.S. Census Bureau, 2022 , USGS, 1997 , Fig.1 ). Both towns have a higher poverty rate and lower higher education attainment compared to the national rates, at 16.7% and 15.3%, respectively ( U.S. Census Bureau, 2022 , Shrider & Creamer, 2023 ). The large porphyry copper deposits have made this region a hub for mining since 1876 ( Briggs,2023 , City of Globe, 2023 ), increasing the number of legacy/active mines that neighbor local communities, making these environmental justice (EJ) communities. Figure 1. Open in a new tab Map of the study area highlighting anthropogenic/potential contamination sources, telegraph wildfire boundary, and flood depth. The active and legacy mines data were extracted from Richardson et al 2019 , USGS, 2024 , and GSA, n.d. ; chemical plants and other point sources were identified from U.S.EPA, 2015 , ArcGis, 2018 , and U.S.EPA, 2024d . The households and non-residential sampling locations in A) Miami, B) Claypool, C) Globe, and D) Icehouse canyon, Arizona, USA are indicated on the inset maps. Sample location color represents the lead concentration (mg −1 kg −1 ). To protect the households’ privacy, the samples’ locations (i.e., icon) were enlarged. The Telegraph fire began on June 4, 2021, and at the time, it was one of the largest wildfires in Arizona, USA covering over 180,000 acres across different land uses ( Forest Service, 2021 , Fig.1 ). The fire left a massive burn scar that stretched approximately 8 kilometers southeast of Superior, Arizona, USA (33.290° N, 111.1142° W), approximately 1 kilometer southwest of Miami, Arizona, USA (33.3992° N, 110.8687° W). The fire was close to other towns in Gila County, Arizona, approximately 3 kilometers away from Globe (33.3942° N, 110.7865° W), Icehouse Canyon (33.3817° N, 110.7668° W) and approximately 2 kilometers away from Claypool (33.4095° N, 110.8411° W). Due to the wildfire’s fast movement and burn distribution, the state declared an emergency (AZ Governor, 2021 ). Following the Telegraph fire, Arizona faced a heavy monsoon (June 2021 to August 2021), which was ranked the 7 th wettest in Arizona history since 1901 ( NOAA, 2024 ). The lack of vegetative coverage and the drought season impacted soil conditions, allowing torrential rains to flow rapidly over the land rather than infiltrate the land surface. This led to sudden and frequent flash floods in the region, loss of homes, and the destruction of public areas. 2.2. Household Study Design Thirty-seven households’ participants were trained and instructed on how to collect surface soil and dust samples. To assist with recruitment, training, and retention, two “promotoras” (community health workers) were hired and worked closely with the research team and households’ participants to provide support and insure the sampling efficiency. In addition, two group trainings were hosted in October 2022. Samples were collected between October 2022 and February 2023 (an additional sample set collected in December 2023). Participants were given a sample collection kit and instruction manual written in English and Spanish to facilitate sample collection. As described below, a community member and/or promotora collected household dust and soil samples and upon completion, samples were sealed in individual zip locks and transported inside a cooler over ice to the designated sample drop-off location Miami Town Hall, Miami, Arizona. Upon drop-off on pre-designated days, a chain of custody form was completed, and University of Arizona (UArizona) personnel retrieved samples. All samples were transported to the UArizona in a cooler with ice. 2.2.1. Household Surface Soil Collection Participants were instructed to collect a surface soil sample from an area around their home that they use and/or was impacted by flooding. Participants were instructed to collect a composite soil sample. Once the sampling location was selected, the participant divided the area into 6 sampling spots within a 1m-by-1m grid. Participants then loosened the soil, removed debris, i.e., vegetation, rocks, and collected the top 2 cm of each of the six soil spots. Surface soils were then homogenized in a one-gallon bucket. Each homogenized bulk sample was poured into a labeled brown paper bag and placed in a 1-gallon Ziploc bag. To minimize contamination during sample preparation, participants were given pre-cleaned/metal trace-free equipment and instructed to use of nitrile gloves and wear a N-95 mask. 2.2.2. Household Dust Sample Collection To collect indoor and outdoor dust samples from households, participants were instructed to fill out the labels before sampling. The participant then selected an area on the floor of the house with a hard and flat surface and away from the entryway. Then the participant followed the provided instructions on how to properly collect the dust sample, which is intended for housing and urban locations and follows the ASTM E1728–03 method ( ASTM, 2003 , Huerta et al.,2023 , Rodríguez-Chávez et al., 2021 ). Once the participant was done collecting the indoor sample, a field blank indoor dust sample was collected where participants followed the same sampling procedure without wiping any surface. For the outdoor dust and associated blank sample, the same procedure was followed; however, the selected sampling area was on the porch/deck. A total of 37 participants submitted indoor dust, outdoor dust, and associated field blank samples. 2.3. Non-residential Sediment Sample Collection Based on local knowledge, the Gila County Assessor Map ( Gila County Assessor, n.d. ), and a 10-year flood velocity and flood depth map ( JE Fuller Hydrology and Geomorphology, Inc, 2022 ), 78 accessible public/non-residential locations were identified in September 2022. Then samples were collected between October 2022 and November 2022 along Miami and Globe washes and public areas in or near the flood zone in Globe, Icehouse Canyon and Claypool, Arizona ( Fig.1 ). Like the household’s soil samples, the sampling area was standardized to a 1m × 1m grid divided into six sampling spots to collect a composite sample. In each sampling location, three samples were collected at the following depths: 0–2 cm, 2–15 cm, and 15–30 cm below ground surface. A hand auger (2–3/4”, Telescoping, AMS) was used to sample the middle (2–15 cm) and lower layer (15–30 cm). At each depth, soil was collected from six sampling spots in the grid and homogenized into a 1-gallon bucket. The homogenized sample was poured in brown paper bags and placed inside 1-gallon plastic bags (Ziploc). Sampling equipment was wiped twice using 70% ethanol and a Kim Wipe before/in between each sampling. Soil/sediment samples (N=234) were transported on ice to the University of Arizona for further processing. Using the aforementioned sampling process and depths, control soil samples were taken from eight regional areas that, to the best of our knowledge, were not impacted by the wildfires or flooding (N=24). In the field, a photo and geolocation cache were collected at sampling locations and coordinates were recorded in the field by google maps. To understand the Pb isotopic composition of the burned soil, the research team received approval from the U.S. Forest Service in September 2023 to sample the wildfire-impacted areas. Samples (N=3, <15 cm depth below ground surface) were collected from three locations within the wildfire perimeter. Lastly, using whirl-pak bags, five surface slag samples from Globe and Hayden, AZ were collected in January 2020 were collected and brought to the University of Arizona laboratory. 2.4. Elemental Analysis 2.4.1. Soil, Sediment, Ash, and Slag Sample Soil, sediment, ash, and slag bulk samples were prepared and digested following the procedures outlined by Manjón et al. (2020) . All samples were analyzed via inductively coupled plasma mass spectrometry (Agilent 7700x ICP-MS) for Beryllium (Be), Aluminum (Al), Vanadium (V), Chromium (Cr), Manganese (Mn), Iron (Fe), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Arsenic (As), Selenium (Se), Molybdenum (Mo), Silver (Ag), Cadmium (Cd), Tin (Sn), Antimony (Sb), Barium (Ba), and Lead (Pb). The quality assurance/control (QA/QC) is based on the United States Environmental Protection Agency (USEPA) protocol in Method 6020 ( U.S. EPA, 2014 ). The average percent recovery and the relative standard deviations (RSD) were calculated for all the metal(loid)s in soil and sediment using NIST SRM 2711a Montana II soil ( Table S1 ). The obtained mean percent recovery for soil and sediments were within the EPA’s acceptable range ( Dayton & Whitacre, 2009 , U.S. EPA, 2007 , U.S. EPA, n.d. ). 2.4.2. Ghost Wipes Ghost Wipes collected by residents were weighed and air dried in 50 ml metal-free vials to a constant mass, while being covered with Kim wipes to minimize any contamination. The ghost wipe digestion, calibration, and the QA/QC were in accordance with National Institute for Occupational Safety and Health (NIOSH) method NMAM 7302 ( O’Connor & Ronnee, 2020 ). QA/QC samples were prepared from certified reference material (NIST SRM 2584). To calculate the elemental concentration, the dust weight was calculated as the following: D u s t W e i g h t = S a m p l e m a s s ( d r y g h o s t w i p e ) ( g ) - m a s s o f b l a n k g h o s t w i p e ( g ) (1) Then the unit was reported in u g sq −2 by using the following equation: u g L * D i l u t i o n F a c t o r * Digestion mass ( L ) Surface Area ( sq.ft ) (2) And the final unit was reported in u g g −1 by using the following equation: u g L * D i l u t i o n F a c t o r * Digestion mass ( L ) Dust Weight ( g ) (3) The average percent recovery and the RSD was completed using NIST SRM 2584 ( Table S1 ). 2.4.3. Lead Isotopes Composition and Anthropogenic/Natural source The lead Isotope compositions were analyzed for 30% of the total number of samples (N Non-residential, Sediment =85, N Households, Soil =12, N Households, Dust =13) under the same operational conditions of ICP-MS described above for the elemental concentration. The external calibration standard NIST SRM 981 was used to analyze, validate, and calibrate lead ratio. The analytical precision of lead isotopic compositions for 206 Pb/ 207 Pb and 208 Pb/ 206 Pb were <0.5%. To study the anthropogenic source apportionment of lead, 206 Pb/ 207 Pb and 208 Pb/ 206 Pb were calculated, since these ratios are widely used throughout literature. Previously identified Pb source apportionments data were obtained to then evaluate and confirm the sources of lead in the partnering EJ communities ( Table 1 ). A detailed flowchart describing the decision-making approach is presented in Figure S1 . The inclusion criteria were as follows: 1) reliable analytical technique and precision (<5%), 2) regional sources such as coal or western/southwestern aerosol and atmospheric lead from regional smelter/tailing; 3) wildfires; 4) mineral ore from the partnering community; 5) other commonly known sources of Pb in the environment, i.e., landfill, Pb-based paint, cement, and leaded gasoline; and 6) lithogenic sources. If the identified lead fingerprint did not match with any sample, then it was excluded, except for Hayden, Arizona, due to its proximity to the study area. This effort follows a rigorous decision-making flowchart, however, it is not a literature review ( Fig S1 ). As a result, a total of 37 studies were reviewed and based on the decision flow chart, only 21 were included. Table 1. Pb isotopic ratios included in this study from different anthropogenic and natural sources in the environment. Location Potential Source * Study Summary Analytical Technique(s) Analytical Precision Inclusion Rationale for this current study United States ( Díaz-Somoano et al 2009 ) Coal Ten samples from different coal deposits in the United States analyzed by the Instituto Nacional del Carbon sample bank in Oviedo, Spain. ICP-Q-MS ICP-MC-MS - Isotope ratio precision was 0.02% as determined by repeat measurements of the NIST SRM 981 Pb standard. -The black mesa reservation coal production - 10% of Arizona’s electricity ( EIA, 2024 ) North America, coal providences United States ( Chow, & Earl, 1972 ) Coal Coal samples from various North American coal providence. Samples represent different coal depositions over geologic time scales in North America. These samples were significantly different from leaded gasoline samples; therefore, they exhibited a distinct source of lead pollution. The Isotopic lead composition of 143 coal samples in North America were determined and compared against the isotopic composition of leaded gasoline. A mass spectrometer (MS) with an electron multiplier - 90 % confidence interval was assigned to the lead isotope ratios with precision between 0.1 to 0.3% -The black mesa reservation coal production - 10% of Arizona’s electricity - Leaded gasoline was identified in Arizona as one major source for lead exposure in the past ( Dale et al 2000 ) Leaded Gasoline Pasadena and San Diego, California, United States ( Chow et al 1965 ) Leaded Gasoline The isotopic ratio of lead was determined from the antiknock gasoline and the aerosols of the Los Angeles basin. MS Gas mass spectrometry (GMS) - 90 % confidence interval was assigned to the lead isotope ratios with precision between 0.1 to 0.3% - A gas source instrument with errors of about 1% for Pb 206 , Pb 207 and Pb 208 and of about 5 % for Pb 204 . - Leaded gasoline was identified in Arizona as one major source for lead exposure in the past ( ADEQ, n.d ) Aerosol - The study area might be impacted by toxics release to air from industrial facilities which might pose potential exposure risk on human as documented by EPA EJ screen ( U.S.EPA, 2015 ). United States ( Bollhofer & Rosman, 2001 ) Atmospheric Lead Isotopic composition and concentration of Pb were analyzed from various aerosols samples collected in the Northern Hemisphere. The atmospheric lead isotope ratios of the western United States were used in this study. Multi-collector thermal ionization mass spectrometer - Using NIST SRM 981, the isotopic ratios were within (0.12 ± 0.05) % per mass unit for measurements on the Faraday Cups -Metallic dust from legacy and active mine site. - The study area is impacted by toxics release to air from industrial facilities which might pose potential exposure risk on human as documented by EPA EJ screen (U.S.EPA, 2018). Hayden and Winkelman, Arizona, United States ( Csavina et al 2014b ) Atmospheric Lead Isotopic lead as source apportionment of metal and metalloid contaminants from ASARCO Hayden Plant. ICP-MS - Using NIST SRM 981, the analytical precision of lead isotopic ratios was under 0.5% -Metallic dust from legacy and active mine site. Mining - Legacy and active mine, tailing, and slag. Arizona, United States ( Bouse et al 1999 ) Mining Lead Isotopes compositions of sulfides minerals from mining districts in Arizona. ICP-MS The precision of lead isotopic ratios was < 5% and the uncertainty was < 10%. -Legacy and active mine, tailing, and slag. Boston, Massachusetts, United States ( Rabinowitz et al 1987 ) Lead- based Paint Exterior/interior paint samples with high lead concentration were collected from three case studies. Each case was linked to children that were hospitalized for lead poisoning. High resolution mass spectrometry (HRMS) - The selected samples had a mean standard deviation <0.05% (based on the intrasample variability) -The presence of older housing, which often, but not always, indicates the presence of lead paint, and therefore the possibility of exposure ( U.S.EPA, 2015 ). Hillsdale, New Jersey, United States ( Jaeger, et al 1998 ) Lead- based Paint Interior/exterior paint samples were collected from a pre-1950 single family house as part of inspection for lead done by homebuyers. Multi-collector thermal ionization mass spectrometer - Using NIST SRM 981, the analytical precision of lead isotopic ratios was under 0.5% -The presence of older housing, which often, but not always, indicates the presence of lead paint, and therefore the possibility of exposure ( U.S.EPA, 2015 ). Watsonville and Santa Cruz, California, United States ( Gwiazda & Smith, 2000 ) Lead- based Paint The sampling process was in accordance with the U.S. Housing and Urban Development guidelines. Samples were obtained from three case studies, and each represents a different source of lead such deteriorated exterior lead-based paint and lead- lacquer on woodwork inside the house. ICP-MS - External measurement precision is < 0.2% -The presence of older housing, which often, but not always, indicates the presence of lead paint, and therefore the possibility of exposure ( U.S.EPA, 2015 ). Kagel Canyon, California, United States ( Odigie & Flegal, 2014 ) Wildfires The study investigated the forest fire’s remobilization of industrial contaminants deposited in the Angeles National Forest. Ash and unburned soil samples were collected from different locations within the burned perimeter of the 2012 Williams Wildfire site. HR ICP-MS - Using NIST SRM 981, the analytical precisions for 208Pb/207Pb, 206Pb/207Pb, and 207Pb/204Pb were 0.07%, 0.04%, and 0.09%, respectively. - Representative of a Wildfire Santa Barbara, California, United States ( Odigie & Flegal, 2011 ) Wildfires Lead Isotope composition from ash samples collected from the 2009 Jesusita Fire in Southern California. Thermal ionization mass spectrometer - Using NIST SRM 981, the analytical precisions for 208Pb/207Pb, 206Pb/207Pb, and 207Pb/204Pb were 0.22%, 0.38%, and 0.30%, respectively. - Representative of a Wildfire Blacktown, Earlwood, and Rozelle, Australia ( Isley & Taylor, 2020 ) Wildfires Examined atmospheric trace element concentrations and Pb isotopic compositions associated with before, during, and after four large-scale wildfires. Q-ICP-MS - Using NIST SRM 981, each sample was correct for mass fractionation with the lowest lead ratio precision at 0.16% and highest at 0.45%. - Representative of a Wildfire Arizona, United States ( Wooden and Dewitt, 1991 , Bryant et al 2001 ) Lithogenic Lead isotopic composition of central and southeastern crust in Arizona. Single-collector ICP-MS - Lead isotopic ratios were corrected for a mass fractionation of 0.12% derived based on empirical corrections determined for NBS-981 and NBS-982. Alos, the isotopic ratios are reproducible at ± 0.1% with 95% confidence interval - AZ sampling region Beijing, China ( Widory, et al 2010 ) Cement Pb isotopic composition emitted from cement factories was linked to atmospheric aerosols pollution (e.g. PM 2.5 ) and total suspended particles. MC-ICP-MS - Using NIST SRM 981, the uncertainty level was less than 0.1% -Representative of a concrete plant Shanghai, China ( Tan et al 2006 ) Cement PM 10 particulates were studied to define the source apportionment of lead pollution in Shanghai. The study found that 3% of lead-contaminated particles in the atmosphere were of cement origin. ICP-MS - Using NIST SRM 981, the analytical precisions was < 0.1% -Representative of a concrete plant France ( Vilomet et al 2003 ) Cement The study compares the lead isotope composition from a landfill to the groundwater to understand the contaminant plume distribution within a shallow aquifer. Other regional aerosol sources were measured like a cement plant. Thermal ionization mass spectrometry - Using NIST SRM 981, analytical error was less than 0.5% -Representative of a concrete plant Municipal landfill - Representative of a landfill United States, ( Wang et al 2019 ) Ash Ash samples were collected from 12 U.S. coal-fired power plants. Coal originally from Appalachian, Illinois, and Powder River Basins. ICP-MS - Using NIST SRM 981, the analytical precisions for 208 Pb/ 204 Pb, 207 Pb/ 204 Pb , 206 Pb/ 204 Pb, 208 Pb/ 206 Pb, and 206 Pb/ 207 Pb were 0.0109, 0.0032, 0.0026, 0.0003, and 0.0001, respectively Excluded Open in a new tab * Term used in isotopic plots ( Figs. 3 and 4 ). 2.5. Data Analysis The lead enrichment factor (EF) was obtained to investigate the anthropogenic contribution to soil and sediment. The mean EF was plotted by depth for non-residential and households’ locations ( Fig.2 ). The EF is ranked from no enrichment at EF<2 to extremely high enrichment at EF >40 ( Sutherland, 2000 ). Iron was selected as a reference element due to its reliability to assess the magnitude of natural and anthropologic elements in soil and sediment ( Matys Grygar & Popelka, 2016 , Bern, 2019 , Schiff & Weisberg, 1999 , Zeider et al., 2021): E F = ( P b F e ) sample ( P b F e ) Baseline (4) Figure 2. Open in a new tab (A) Mean lead enrichment factor and standard deviation in soil samples across three depths. Shaded background depicts enrichment ranges with green indicating no enrichment (0>EF<2), yellow, minimal enrichment (2>EF<5), pink, moderate enrichment (5>EF<20), and orange indicating significant enrichment (20>EF<40); (B) Bar plot of the mean Pb concentrations in households and non-residential sites over soil and sediment depth. The Kruskal-Wallis significant is indicated by the letter. Bars with the same letter indicate no significant difference, while bars with different letter(s) indicate a significant difference in mean Pb concentration. (C) A consolidated table comparing Pb mean concentration by site and highlighting major statistically differences based on Kruskal-Wallis. The P b and F e concentrations of the control sample in this study were used as the baseline concentration. The Spearman’s correlation (ρ, p < 0.05) was used to study the association between metal(loid)s and Pb concentration ( Table S2 – S5 , Table 2 ) at each location over soil/sediment depth ( Table S6 – S8 ) and dust sampling area ( Table S9 – S10 ). Based on their location, non- residential data points from Claypool and Icehouse Canyon were combined to Miami and Globe, respectively. Spearman’s correlation was also employed in the indoor and outdoor dust samples for the same purpose. In addition, ρ between the elemental concentration in households were tested between soil and outdoor dust, soil and indoor dust, and outdoor and indoor dust. The grading standard of the (ρ) ranks from no correlation at ρ = 0 to a very strong correlation at ρ≥0.7 ( DasÃ, 2005 ). Moreover, a non-parametric Kruskal-Wallis test (p ≤ 0.05) was used to determine differences between mean Pb concentration in soil and sediments by depth ( Fig 2 ). Table 2. pd concentration Min-Max [median, mean] across study environmental media and their alignment with lead source apportionment from previous studies. Environment al Media Soil and Sediment Location ╱ Depth Pb concentration alignment with lead source apportionment Pb concentration across entire study 0–2 cm 2–15 cm 15– 30 cm 0–2 cm 2–15 cm 15– 30 cm Miami 6.32 –23.24 [12.05,12.93] 7.63–103.3 [22.38,26.78] 10.48 – 87.38 [23.1,35.43] 6.32 : 32.50 [13.66,15.46] 7.63 – 103.3 [22.38,25.46] 10.48 – 223.4 [25.89,43.65] Globe 27.13 – 36.75 [31.45,31.70] 27.75 – 34.67 [28.97,30.09] 21.14 – 30.19 [28.64,27.15] 25.13 – 69.50 [31.45,36.90] 27.75 – 98.70 [32.41,43.01] 21.14 – 103.1 [29.67,40.43] IceHouse Canyon 91.54 * 161.1 * 117.2 * 13.39 – 91.54 [17.27,40.73] 23.50 – 161.1 [27.6,70.73] 21.66 – 117.2 [21.69,53.51] Control - - - 9.65 – 39.34 [23.39,23.94] 9.12 – 41.93 [26.53,26.03] 10.83 – 38.02 [27.82,26.12] Households 81.58 – 386.86 [196.3,221.6] - - 29.11 – 916.7 [189.8,265.5] - - Environment al Media Dust Location Area Pb concentration in samples aligned with lead source apportionment Pb concentration across entire study Households Indoor 0.10 – 41.84 [5.26,13.291 0.097 – 41.84 [2.58,9.88] Outdoor 1.07 – 308.6 [10, 48.781 1.07 – 308.6 [10,43.39] Open in a new tab * Only one sample was selected. To further evaluate the association between anthropogenic versus lithogenic Pb sources and metal(loid)s co-occurrence, a principal component analysis (PCA) was performed on soil, sediment, and dust sample. For this analysis, the metal(loid)s concentrations were log transformed, then the varimax rotation was applied. To ensure internal consistency, a data subset with high impact was validated against each principal component (PC) and then the Cronbach’s alpha (α) was calculated to ensure the values fall within the acceptable threshold of ≤0.7. Then, the source appointment results were integrated across environmental media along with households’ flooding data to be integrated into the multivariate analysis. The PC dimensions of 60% or more within the elemental content were extracted for the interpretation, here eigenvalue of each PC was divided by the sum of all eigenvalues across all components, and this is also known as “proportion of variance explained”. Statistical analyses were completed using R (v 4.1.1). 2.6. Positive Matrix Factorization model of the households’ samples and proximity to pollution source To investigate the contribution of flooding on metal(loid)s mobility, specifically P b from local industrial sources the USEPA positive matrix factorization (PMF) model 5.0 ( U.S.EPA, 2014 ) was employed. The model is defined based on two factor matrices, a residue matrix and objective function Q as follows: X i j = ∑ k = 1 p g i k f k j + e i j (5) Q = ∑ i = 1 n ∑ j = 1 m ( e i j u i j ) 2 (6) Where equation (5) represents the concentration of the j th metal(loid)s in the i th sample, g i k is the contribution of the source k to sample i (mg/ kg), f k j is a matrix of metal(loid)s profiles via k sources of the j th elemental component. The matrix of residuals is indicated by “ e i j ” and it is adjusted to the minimum value of Q to ensure a better PMF solution ( U.S.EPA, 2014 , Rodenburg et al. 2020 ). The uncertainty of the j th element concentration u i j and detection method limit (MDL) were utilized based on equation 7 . I f c ≤ M D L , u i j = 5 6 × M D L , o t h e r w i s e , u i j = ( E r r o r f r a c t i o n × c ) 2 + ( 0.5 × M D L ) 2 (7) Here, c is the elemental concentration. Further, the PMF bias, variability, and error were estimated via Bootstrap (BS). To examine the data fit in the PMF model, the capacity of the multivariate analysis was tested to converge a stable solution as recommended by the EPA guideline ( U.S.EPA, 2014 ). The proximity between sources of contamination relative to each household’s sample was obtained from Google Earth Pro (v 7.3) using the ruler tool. The distance values and flood data were integrated into the PMF model to investigate the association between proximity from a point source and lead contamination in households’ locations. 3.1. Pb Concentrations and Enrichment by Environmental Media 3.1.1. Soil, Sediment, and Pb enrichment The descriptive statistics of Pb by location and depth are presented in Table 2 . Lead mean concentration data and major findings based on sampling site and location are presented in Figure 2 . In a previous project investigating the quality of rooftop harvested rainwater (see: https://projectharvest.arizona.edu/ ), the mean Pb concentration in soils not irrigated with rooftop harvested rainwater (at depth 0–15 cm) was 44.43 mg kg −1 (Palawat and Ramírez-Andreotta, unpublished results) compared to 265.5 mg kg −1 in this current study. Although the Project Harvest samples provide a point of comparison for the general Globe/Miami, AZ area (most samples were from Globe), it is important to note that they were not from the same locations, depth, and were collected between 2017–2020. Pb EF were calculated across the three depths for both non-residential and surface households’ samples ( Fig. 2 ). Generally, the mean Pb EF in household surface soil was greater than in non-residential surface samples (0–2 cm). This could be explained by the myriad of past Pb deposition coming from industrial emissions and other potential Pb sources i.e. legacy of historical contamination such in mining or leaded gasoline. In the USA, it is unsurprising to find Pb in residential soil, where it was estimated that one in four households has higher Pb concentration than soil guidance levels ( Filippelli et al. 2024 ). The Miami non-residential sample mean EF values were <2 across sediment depth. Globe (15–30 cm) and Icehouse Canyon (2–15cm) had a mean EF value of >2 and both locations had EF <2 at the other depths. The greater EF values could be explained by prolonged accumulation from certain human activities and differ for each sediment depth ( Zhang et al. 2015 , Zhang et al. 2021 , Ciszewski et al. 2016 ). 3.1.2. Dust Indoor and outdoor mean Pb dust concentrations were statistically different (p≤0.05), 9.88 mg kg −1 and 43.39 mg kg −1 , respectively ( Table S10 ). While studies have identified anthropogenic sources of Pb that contribute to indoor Pb dust ( Andrade et al. 2023 , Naimabadi et al. 2022 , Wang et al. 2023 ), findings also highlight how the pollution source can influence the magnitude difference between indoor and outdoor Pb dust concentration ( Chu et al. 2023 ). For example an analysis of outdoor dust in residential areas depicted the contribution of different industrial emissions on elevating Pb exposure risk ( Fry et al. 2020 ). In this context, the magnitude of Pb concentration in settled outdoor dust could result from the collective accumulation of Pb coming from different anthropogenic sources ( Jordanova et al. 2012 ). 3.2. Source Apportionment and Spatial Distribution of Pb 3.2.1. Source Identification of “Sediments” in Non-residential To understand the sources of lead by location, lead source apportionment and percent alignment were completed for each location ( Tables S11 ). Samples collected in Miami and Globe had a 66% alignment with the mining fingerprint. In Miami, the number of non-residential samples matching the mining source apportionment in 2–15 cm (n=14) were higher than 0–2 cm (n=10) and 15–30 cm (n=12) ( Fig 3 ). Of these samples, nine locations matched the mining fingerprint across all three sediment depths. The remaining Miami samples marginally matched different anthropogenic fingerprints (at less than 4%). Two samples came from different lead sources. Only one Miami sample fell outside the defined lead fingerprints and matched the lead isotopic composition of Arizona’s crust. Samples impacted by multiple anthropogenic fingerprints (i.e., mixing effect) can be considered more stable (i.e., higher 208 Pb/ 206 Pb and lower 206 Pb/ 207 Pb ratios) than samples matched only one Pb source. Today, Pb pollution inherently represents a mixture of sources and the isotope compositions of modern anthropogenic Pb will most likely have that mixed trend (Koffman et al., 2012, Yu et al., 2016 , Bollhöfer et al., 2002 ). The growth of the non-radioactive Pb isotopes reflect the decay time of U and Th, meanwhile, legacy Pb sources, i.e., lead-based paint and leaded gasoline are observed in relatively older Pb ore than those extracted by mining in Arizona ( Longman et al., 2018 , Dunlap et al., 2000 , Sherrell et al., 1992 , Fig 3 ). Figure 3. Open in a new tab Lead isotope plot ( 206 Pb/ 207 Pb: 208 Pb/ 206 Pb) of soil samples from Miami, Globe, Icehouse Canyon, Control and Households across all depths. The plot includes the lead isotopic composition of the anthropogenic sources ( Table 1 ). Hayden isotopic composition did not match any of the soil samples however, it was included as it matches the isotopic composition of Arizona’s copper mining district, confirming the origin apportionment from mining operations. In Icehouse Canyon, wildfires and coal were the primary sources of lead (22%), with one location matching a different anthropogenic fingerprint at 0–2 cm and 15– 30 cm depths. The abrupt change in radiogenic lead isotope ratio over sediment depth depicts different times of Pb accumulation and reflects Pb enrichment from different anthropogenic sources ( Farmer et al., 1996 , Bindler et al., 2001 , Chillrud et al . 2004 ). For instance, we observed this trend in one location in Icehouse Canyon, where the enriched Pb in 2–15cm ( Fig. 2 ) was attributed to mining while the mixing effect of multiple anthropogenic sources ( Fig. 3 ) had a lower Pb enrichment in 0–2 cm and 15–30 cm depths ( Fig. 2 ), proving that the formation of sediments profile was sensitive to temporal behaviors ( Kemp et al., 2012 ). Such a phenomenon was not observed in Globe, instead, mining was the predominant source of Pb over sediment depth, ascribing that the deposition of Pb occurred at one time or through a constant release of Pb from the neighboring mining sites over time. 3.2.2. Source Identification of “Soil” in Households and Control Control and households’ samples had the lowest Pb isotopic ratios among all samples ( Fig 3 ). Lead from control sites was not aligned with any of the defined Pb source apportionments. On the contrary, one household sample was within the California wildfire isotopic ratio, and another aligned with Arizona’s crust, and only two samples were within the range of the Telegraph wildfire. Interestingly, the households’ samples did not match the AZ copper mining fingerprint. Rabinowitz (2005) attributed the deviation in Pb isotopic compositions to: 1) lead coming from different geological aged ore and 2) challenges associated with capturing Pb isotope ratios within a single mining district, particularly, when the region is understudied. Typically, soil data in rural marginalized communities are underrepresented, adding to the environmental injustices observed in areas adjacent to pollution sources ( Jones et al., 2022 , Malone & Shakya,2024 , Alqattan et al., 2024 ). Even though we were not able to get the ore from the mining sites in Miami and Globe, our team implemented an additional step to investigate where the Pb might come from ( section 3.6 ). 3.2.3. Source Identification of “Dust” in Households The lead isotopic compositions varied distinctively between indoor and outdoor dust samples ( Fig. 4 ). Lead source apportionment and percent alignment were completed for Dust ( Table S12 ). With regards to indoor dust, the AZ copper mining fingerprint was the only lead source apportionment that matched (15.4%). Still, one sample fell within the Hayden slag. Contrastingly, outdoor dust Pb samples were mainly impacted by AZ copper mining (53.8%) followed by wildfires (30.1%), lead-based paint (23.1%), leaded gasoline (23.1%), landfill (7.7%), and western aerosols (7.7%). Despite the scatter in the outdoor isotopic composition, three samples were influenced by multiple anthropogenic sources of Pb. Out of the matched samples with mining fingerprints, two of these dust samples also matched the geogenic source, i.e., the Arizona crust. Unlike Pb isotopic compositions in households’ soils, Pb in dust seemed to come from older-age Pb ores. In the United States, accumulated outdoor dust in older houses, particularly, near smelter and mining sites could come from multiple sources of Pb that differ in their isotopic composition from those in residential soils, which are impacted by continuous atmospheric deposition ( Tu et al . 2020 , Sowers et al . 2024 , Gulson et al . 1995 ). Figure 4. Open in a new tab Lead isotope plot ( 206 Pb/ 207 Pb: 208 Pb/ 206 Pb) of both indoor and outdoor household dust samples. The plot includes the lead isotopic composition of the anthropogenic sources* ( Table 1 ). Hayden isotopic composition did not match any of the dust samples however, it was included as it matches the isotopic composition of Arizona’s copper mining district, confirming the origin apportionment from mining operations. 3.3. Exploring the Relationship/Co-occurrence between Pb with Metal(loid)s 3.3.1. Correlation of Pb and Other Metal(loid)s in Soil and Sediment To understand whether there was a co-occurrence between Pb and other metals by sample depth, a Spearman correlation was conducted, see supplemental tables S6 – S8 . Generally, Miami exhibited a higher number of significant positive correlations between Pb and other metal(loid)s, apart from Ag, in 0–2cm depth ( Table S6 ). Further, the correlation increased for eleven metal(loid)s in 2–15 cm, moderate for Ag; strong for Sn, Sb, Ba; and very strong for Be, Co, Cu, As, Cd, Se, Mo ( Table S7 ). The significant ascending correlation provides insights into the metal(loid)s source apportionment and reflects the historical accumulation in the sediments, particularly, the metal(loid)s associated with historical mining ( Nag & Cummins, 2021 , Li et al . 2018 , Zhao & Marriott, 2013 ). The correlation result aligns with our findings discussed earlier about Miami being impacted mainly by mining (i.e., 66% matched mining fingerprint). Yet, some heavy metals such Ni, Mn and Co might come from multiple origins and differ in their correlation with Pb ( Wang et al. 2022 , Nag & Cummins, 2021 ). In samples obtained from Miami at 15–30 cm, an increase in Sb correlation was observed. Sb was generally associated with mining activities and wastes, especially when it co-occurred with As, Pb, and Zn ( Bolan et al . 2022 , Zhou et al . 2019 ). Over control soil depth, only Sb in 2–15 cm and 15– 30 cm had a significant correlation with Pb ( Table S7 & S8 ). In Globe and Icehouse canyon, the significant correlation with Pb at 0–2 cm is shown in Table S6 and can ranked as following: moderate for Be, Al, V, Sb, and Ba (ρ:0.4–0.59); a strong correlation for Mo, Ag, and Sn (ρ:0.6–0.79); and a very strong correlation for Cu, Zn, As, and Cd (ρ> 0.8). Here, it is evident that lead in the surface layer was mainly associated with anthropogenically-sourced metal(loid)s, i.e., Cd, As, Zn, and Cu and to less extent with lithogenically-sourced elements like Al and with V and Ba from varying and mixed sources ( Wang et al. 2022 , Zhao et al. 2021 , Barbieri, 2016 ). Notably, the correlation of Be, V, Fe, Se, Ag, and Ba with Pb increased at 15–30 cm in comparison to 2–15 cm while Cu, Zn, Cd, and Sb maintained their correlation rank/strength ( Table S7 – S8 ). The correlation between Pb with lithogenic origin elements seemed to be less at lower layers of sediments (15–30 cm), which can relate to the elemental dispersion processes from sorting the fluvial sediment ( Ciszewski & Grygar, 2016 ). Lastly, As maintained a very strong correlation (ρ> 0.8) with Pb over the sediment depth. Since the correlations between Pb and the anthropogenically-sourced metal(loid)s changed minimally by depth in Globe/Icehouse canyon area, this could indicate that these metal(loid)s were released concomitantly with Pb. In households’ soil samples, positive correlations between Pb and metal(loid)s can be grouped as Zn and Cd (ρ> 0.8) > Cu, As, Ag, and Sb (ρ:0.6–0.79) > Be, Se, Mo, and Ba (ρ:0.4–0.59) 3.3.2. Correlation of Pb within Dust Table S5 shows a descriptive statistical summary of metal(loid)s concentrations for the indoor and outdoor dust in households’ locations. Stronger correlations between Pb and other metal(loid)s were observed in the outdoor dust ( Table S9 ), with the exception of (Zn, Se, Ag, and Sn). Conversely, indoor dust correlations were ranked as Cd (ρ:0.6–0.79) > Be, Fe, As, Mo, and Sb (ρ:0.4–0.59) > Se, Mn, and Sb (ρ:0.2–0.39). The results delineate that outdoor dust was impacted primarily by anthropogenic activities whereas Pb in indoor dust comes from geogenic and mixed sources. 3.3.3. Metal(loid)s Correlations in Household Surface Soil and Indoor/Outdoor Dust The spearman correlations of the metal(loid)s were obtained between indoor and outdoor dust samples ( Table S10 ). Generally, there was no clear correlation of Pb between the two groups, indicating that Pb in dust could come from a different source/s across the two sampling area. However, other metals(loid)s had a different range of correlation and can be ranked as Al and Mn (strong)> Be, V, Cr, Fe, Co, and Se (moderate) > Ni, As, Ba (weak), which were most likely linked to geogenic sources. When assessing the co-occurrence of metal(loid)s between indoor dust versus household soil samples, a weak positive correlation was observed for V and Mn, a moderate negative correlation was observed for Cu, and a non-significant correlation was observed for Pb ( Table S13 ). This was expected since household dust and soil can be impacted by a mixture of sources, i.e., anthropogenic and geogenic. In addition, all metal(loid)s had a non-significant correlation (p≥0.05) between the outdoor dust and household surface soil samples ( Table S14 ). This means they were most likely not coming from the same origin. Generally, indoor and outdoor household dust arises from different sources and disperses into the atmosphere via regional aeolian erosion, which can be representative of crustal materials, or human activities, such as smelting and fossil fuel combustion ( Somsunun et al. 2023 , Prabhakar et al. 2014 , Csavina et al. 2014a ). Climate change is another major factor that can exacerbate and increase aeolian erosion due to drought and wildfires, both of which have the tendency to degrade soil and increase the formation of dust ( U.S.EPA, 2024b , Liang et al. 2021 , Arcusa et al. 2020 ). In addition, Arizona’s “monsoon season” will increase the amount of aeolian and anthropogenic dust, enriching the surrounding area with metal(loid)s ( Prabhakar et al. 2014 , White et al. 2023 , Sprigg et al. 2014 , Dingle et al. 2021 ). Therefore, dust settled in residential areas exhibits a wide range of particle sizes linked to different source apportionment ( Li et al. 2019 , Doyi et al. 2020 ). Csavina et al. (2014b) investigated the prevalence of different dust particle sizes and their origin near a mining/smelting area. The study observed that coarse and fine particles with similar lead isotopic composition represented the background source of lead in the area. In our case study, the telegraph wildfire burned part of the Tonto National Park (33.6494° N, −111.1088° W), where Al, Mn, and Fe (i.e., crustal origins) were significantly correlated to fine soil, i.e., <2.5 μm ( Prabhakar et al. 2014 ). Fine dust particles are more likely to penetrate indoor households ( Jordanova et al. 2012 ). This aligns with our findings, where indoor dust consists of lithogenic sources in the region and is associated with the same group of elements found in Tonto National Forest. Consequently, we hypothesized that coarse dust particles coming from lithogenic origin have settled outdoors due to gravitational force. Our results were also consistent with other studies conducted in a similar arid climate, where Al, Co, Cr, Fe, Mn, Ni in dust were generated mainly from a lithogenic source ( Al-Swadi et al. 2022 ). Another study found that Be, V, Cr, Mn, Co, Ni, As are highly elucidated by natural sources ( Jin et al. 2019 ). Therefore, as shown previously, the correlation between the lithogenic metal(loid)s in indoor and outdoor dust was linked to complex interaction of variables around and within the households’ areas. 3.4. Principal Component Analysis 3.4.1. Soil and Sediment To reaffirm lead sources in this study, the principal component analysis (PCA) merges the metal(loid)s co-occurrence data with the lead isotopic fingerprints. The PCA from each depth showed common associations and varying trends among metal(loid) concentrations ( Fig. 5A – C ). The relationship magnitude of each parameter with the principal component is presented by an arrow. For instance, Pb concentration had the highest PC1 loading in 0–2 cm and was associated with Cu, As, Cd, Zn and to a lesser extent Mo, Sb, Ag, and Sn. This co-occurrence pattern was driven mainly by the “flooded” households’ samples followed by the non-residential samples that aligned with the “mining” fingerprint. Further, samples impacted by wildfire seemed to be associated with the same group of metal(loid)s impacted by mining. Wildfire has a long history of releasing metal(loid)s associated with anthropogenic activities in impacted areas; for instance, when compared to pre-fire conditions, Pb and Zn concentrations associated with legacy mining were significantly higher ( Rust et al. 2022 ). Conversely, the “other” (i.e. samples that did not match any of the defined lead isotopic compositions from the literature) non-residential samples seemed to be distributed between PC1 and PC2, and primarily impacting the PC2 loadings for Fe, V, Co, Ni and secondarily to Cr, Mn, and Al concentrations. The “unflooded” household’s samples were mostly impacting the PC2 loadings ( Fig. 5a ). Figure 5. Open in a new tab Principal component analysis (PCA) highlighting the relationship between metal(loid)s from: (A) 0–2 cm; (B) 2–15 cm; (C) 15–30 cm. Each point shows a different sample location. Non-residential area samples are color coded based on the anthropogenic contributions found from the isotopic plot ( Figure 3 ). Any sample that did not match the identified anthropogenic fingerprints in table 1 were identified as others. The households’ samples were divided based on whether their property experienced any flooding. Wildfire impacted samples from both areas are outlined in red. Internal consistency was tested with Cronbach’s alpha (α), all the value fall within the acceptable threshold of ≥ 0.7. When considering sample depth in non-residential, the co-occurrence between Pb and Cu, As, Cd, Zn, Mo, Sb, Ag, and Sn concentrations were less at 2–15cm and 15–30cm than 0–2 cm. Still, mining was the major variable impacting the association observed within the non-residential samples. While the impact of wildfire was minimal at 2–15 cm, it was found to align with samples impacted by mining and associated with Pb, Ag, Cd, Zn, and As ( Fig. 5b ). In summary, the Pb association with other metal(loid)s diverged over the sediment depth but continues to resemble the wildfire and mining Pb fingerprints. 3.4.2. Dust The dust PCA loadings indicate a greater association among Mn, Al, Be, Cr, V, As, Mo, Sb, Sn ( Fig 6 ). Interestingly, Cr was highly associated with two outdoor dust samples. Both samples shared a primary source of lead “leaded based paint”. Old houses in our study were suspected to contain substantial amounts of lead from deteriorated paint ( U.S.EPA, 2015 ), often, lead chromate was used as a color pigment in the past ( Wexler, 2014 , O’Connor et al. 2018 , Rasmussen et al. 2013 , Wani et al. 2015 ). Herein, the Cr addition in the leaded paint might influence the observed link between the lead source and Cr concentration. In addition, the metal(loid)s co-occurrence corresponded to the outdoor dust coming from various anthropogenic and to a lesser extent from the “other” non-residential samples. Figure 6. Open in a new tab PCA of the relationship between the analyzed metal(loids) in dust samples, sample location, and anthropogenic source. Each point illustrates a different sample location; a circle represents the indoor dust while a triangle represents an outdoor sample. Samples were colored-coded based on the anthropogenic contributions in Figure 4 . Internal consistency was tested with Cronbach’s alpha (α), here α is within the acceptable threshold of ≥ 0.7. Pb co-existence with Zn, Ni, Cu, Se, Co, Fe, and Cd was observed. Two samples impacted by lead sources lean toward Ni in PC2; leaded gasoline could be the reason for such a pattern, where fossil fuel combustion is generally associated with Ni in dust while Zn, Se, and Cu could link to smelting as found in Tucson and Phoenix area ( Prabhakar et al. 2014 ). Since the dust PCA was mainly impacted by anthropogenic sources, indoor dust samples did not exhibit a clear association to the metal(loid)s. However, indoor dust clustered together, proving the hypothesis that if metal(loid)s in outdoor dust were coming from different sources than indoor, then the two groups will not correlate. Further, the PCA is largely influenced by the high correlation within the manifest variables and captures the latent collective effect/interaction between them, therefore, the enriched metal(loid)s (i.e. anthropogenic-metal(loid)s) will have a higher impact on stratifying PCs within the analysis. 3.5. Spatial and Temporal Factors Influencing Metal(loid)s Concentration in Non-residential Areas Historic mining’s main footprint is the metal(loid) inputs into the ecosystem ( Beier et al. 2022 , Carvalho, 2017 , Dinis −120 et al. 2020 ). After flooding, legacy mines waste can relocate and leach into the sediments ( Qiao et al. 2023 , Nascimento et al. 2023 ). Therefore, sediments can be a sink of metal(loid)s ( Beier et al. 2022 , Huang et al. 2020 ) and by increasing the number of industries in a region, the concentration of the contaminants is expected to increase. This phenomenon is apparent when comparing Pb concentration between Globe and Miami. Even though Miami and Globe were equally impacted by mining, Globe had a higher content of anthropogenic metal(loid)s linked to mining fingerprints than Miami, except for Cu ( Fig. 3 , Fig. 5a – c ). Figure S2 illustrated the watersheds, surface water trajectories, and the topography of the region, highlighting the mine tailings elevation and steep gradients near sampling locations. Here, mine tailing surface runoff via rainfall or/and flooding could explain the associated metal(loid)s with Pb ( Qiao et al. 2023 , Buscaroli et al. 2021 ); deteriorating water quality ( Wei et al. 2018 ); and changing the metal(loid)s concentration in the sediments. Non-residential areas that were highly affected by mid-high to very high flooding, i.e., Miami, had a lower Pb concentration when compared to other locations. The rain and floods observed in the wet season could be the main driver for pollutant mobilization ( Weber et al. 2023 ). Here, the water flow intensified the sediment transport and as the current velocity drops, flood sediments along with the carried contaminates are deposited ( Balaban et al. 2019 ). Therefore, areas affected by high water discharges have lower sediment concentration or/and are impacted by a mixture of uncontaminated and contaminated sediments, which could result in metal(loid) dilution ( Weber et al. 2023 , Ciszewski & Grygar, 2016 ). 3.6. Metal(loid)s Origin in Flooded Households A PMF model was used to determine the impact of flooding on Pb and metal(loid) concentrations. The Pb concentration between flooded and unflooded location was compared across all locations ( Fig S3 ), where households samples came up significantly different (p≤0.05) between the two groups. The potential sources of metal(loid)s in households’ and control sites were apportioned into 3 factors based on the minimum and stable objective function Q in the PMF model ( Fig 7a ). The contribution of factor 1 was high (≥35%) for V, Mn, Fe, Cr, Ni, Co, Al, and Ba and slightly impacted Cd, Sn, Be, Zn, Pb, and As (< 20%). Therefore, based on our previous results, this factor is likely to represent a geogenic source. Concurrently, there was no direct contribution observed between factor 1 and Ag, Cu, and Mo. On the other hand, factor 2 implies a different contribution pattern between the analyzed metal(loid)s which can be ranked based on the contribution rate as “Be, Al, and Co (≥35%)” > “Ni, Cr, Mo, As, Se, Fe, and Ag (≥20%)” > “Ba, Cu, Mn, V, Sb, Zn, and Cd (< 20%)”. Factor 2 did not account for any contribution to Pb and Sn. Hence, the second factor reflects a mixed source of lithogenic and anthropogenic. Further, a higher contribution weight (≥35%) was observed for Cd, Sn, Sb, Zn, Pb, Cu, Ag, Mo, As, Ba, Se, and Be in factor 3; meanwhile, factor 3 was slightly characterized by Fe, Cr, Ni, Mn, Co, Al, and V (< 20%). Based on our findings, this factor is primarily derived from anthropogenic sources. Figure 7. Open in a new tab Household metal(loid)s source apportionment. (A) The source contribution of the households’ soil sample extracted from the PMF are illustrated by factor 1 (geogenic), factor 2 (mixed source), factor 3 (anthropogenic). (B) Proximity and flooding status with each household (HS) and control (CTRL) sample’s factor profile “fingerprint”. Blue background represents flooded locations while white background represents unflooded locations. In Figure 7b , the proximity of resource extraction to flooded and unflooded households’ samples and control samples were evaluated. The PMF factor profile uses the “geogenic”, “anthropogenic” and “mixed” fingerprints. Flooded household samples closer to resource extraction activities (<0.9 km) were highly impacted by anthropogenic sources, whereas samples located further away from the mining sites (> 1.95km) were characterized primarily by factor 1 or factor 2. Moreover, the fingerprint’s contribution in unflooded samples had a different pattern than the flooded samples. The closer the unflooded samples were to the mining sites, the higher the contribution to factor 2 (i.e., mix sources), while samples further away (>2.5 km) were influenced by geogenic origin. 3.7. Compound Event and Marginalized Communities The consolidated effects of wildfire and flooding on elevated Pb concentrations in households’ are evident and 25% of the household soil samples were aligned with the wildfire Pb isotopic ratio/fingerprint ( Fig. 5a ).The PMF model results suggest the potential role of flooding in mobilizing the metal(loid)s from mining sites near households’ areas ( Fig. 7 ). When comparing PCA and PMF findings, 50% of the samples were associated with anthropogenic metal(loid)s, hence, confirming the possibility of the metal(loid)s redistribution as result of the flooding event. Floods are considered agents of contaminant dispersal, causing sedimentation to residential settings ( Foulds. et al 2014 ). In Germany, flooded sediments were enriched with Zn, Pb, Cd, Cu, and As and all were above the referenced soil exceedance levels ( Weber et al. 2023 ). The elevated concentration of the later metal(loid)s can pose serious risk on human health, e.g., Weber et al. (2023) observed that inhalation and ingestion of Pb from flooded sediments had exceeded the tolerable human daily intake. Similar patterns of having higher detection levels of As and Pb were observed in flooded residential areas at 97.9% and 100%, respectively in a study conducted in a flood-prone rural region of Robeson County, North Carolina, USA ( Cutts et al. 2024 ). In this respect, our study findings are similar to previous studies, further confirming the dangers brought on from compounded events, especially around Pb concentration and exposures in local communities. To further contextualize our study findings, we compared the soil and dust (indoor and outdoor) to available screening levels. For instance, the U.S EPA has recently established a Dust-Lead Reportable Level for Indoor Floors at 5 μg sq ft −1 ( U.S EPA, 2024e ); 30.6% of the indoor dust samples in our study exceeded this value. The outdoor dust samples were compared to the US HUD Dust-Lead Clearance Action Levels - Porch Floors at 40 μg sq ft −1 ( U.S. HUD, 2017 ); 14% exceeded this value. When comparing surface soil Pb concentrations to the revised USEPA regional screening levels for lead in residential soil ( U.S.EPA, 2024c ), we observed the following: 1) four samples exceeded 100 mg kg −1 , a value which should be used when there are multiple sources of lead exposure; 2) six samples exceeded 200 mg kg −1 , a value which should be used when soil is the sole Pb exposure. Hence, we observed that the compound event consequently drives the risk of Pb exposure in vulnerable rural households’ locations, especially when they coincide with the occurrence of legacy and active mines ( Rust et al. 2022 ). Our findings from the PMF did not fully align with the lead isotopic composition of mining in the region, however, the study faced three primary limitations: (1) samples were collected a year after the flash flood and wildfire event; (2) Multiple rainfall seasons occurred between the time of wildfire/flood event and sampling period; and (3) absence of established, pre-fire/flood soil geochemical data in the region. 4. Conclusion The goal of the study was to provide a holistic understanding of lead sources and exposures in rural mining communities after extreme climate events. This study highlights mining as a major contributor for Pb concentration in both non-residential and household locations. In addition, metal(loid)s observed in non-residential locations were correlated to regional anthropogenic sources. In household locations, 50% of the soil samples were impacted by flooding alone and 25% of the soil samples were impacted by both wildfire and flooding. When comparing indoor and outdoor dust, Pb in outdoor dust was associated with anthropogenic (e.g., mining at 53.8%) and wildfire at 30.1%, while metal(loid)s in indoor dust were associated mainly with lithogenic origin and only two samples were sourced tracked to the AZ copper mine fingerprint (15.4%). Unfortunately, after two decades of suffering from extreme flooding events and losing almost 100,000 lives worldwide, we still face challenges and struggle to identify solutions in rural communities ( Jamshed et al. 2021 ). Our study region is known to supply an enormous amount of copper to the world and these areas continue to be disenfranchised even though they play a pivotal role in providing the necessities of life ( Jones et al. 2022 , lark et al. 2022 ). In addition, there is a disproportionate use of resources, with more going to urban areas when it is known that rural areas are marginalized and have limited infrastructural development ( Alqattan et al. 2024 , lark et al. 2022 , Cutts et al. 2024 ). The abundance of resource extraction and other anthropogenic activities, combined with the lack of infrastructure, make this area incredibly vulnerable to extreme climate events. This research addressed these challenges by cooperating with the local EJ communities and conducting a community-engaged multiple media (i.e., soil, sediments, and dust) research project. We hope that this research highlights the challenges being faced in rural, mining, EJ communities in Arizona and beyond and improves resiliency against metal(loid)s exposure and compound event burdens. Supplementary Material 1 NIHMS2059489-supplement-1.docx (1.4MB, docx) Highlights. Mining was the predominant source of Pb in Arizona rural, mining areas Pb was associated with the anthropogenic metal(loid)s: As, Cd, Cu, Mo, Sb, and Zn 25% of Pb in household soils was associated with wildfire isotope fingerprints Indoor and outdoor dust had different Pb sources Flooding remobilizes Pb and metal(loid)s to households, increases the community’s exposure Acknowledgments We extend our gratitude to the Cobre Valley Community Engagement Advisory Board (CV-CEAB) for their expertise, involvement, and guidance throughout the project. We also thank all the participants from Globe/Miami for their engagement, time, and for sharing valuable information with us. Working with you has been an honor. The success of this project can largely be attributed to the support provided by our undergraduate research assistants—Gabriella Parra, Lev Brikmanis, Camille Tinerella, and Alexlis Rivera-Jinel—along with laboratory managers Alexandra Trahan and Julienne Foscenca. We also wish to recognize the contributions of William Hamilton Borkan for their assistance with the geospatial representation. The recruitment of participants was led by our two promoters, Miriam Jones and Britta Crone, to whom we are profoundly thankful. Finally, our sincere appreciation goes to the Miami Arizona town manager, Mr. Alexis Rivera, for his enthusiastic support. We acknowledge that the University of Arizona is located on the original homelands of indigenous nations, including the Tohono O’odham and the Pascua Yaqui, among others, which have stewarded this Land since time immemorial. We recognize the significant responsibility we share to honor and respect the ancestral and contemporary caretakers of this region. We extend our recognition to all the tribal communities that have been historically connected with the lands in the Globe/Miami Arizona area. With this acknowledgment, we pay respect to the land itself and the people who have cared for it throughout the generations. Financial Support Research reported in this publication was supported by the National Institute of Environmental Health Sciences of the National Institute of Health under Award Numbers R21ES034591 and P42 ES004940 as well as the University of Arizona College of Agriculture, Life, and Environmental Sciences’ Innovation Venture Investment. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health. Footnotes Statements and declarations Competing Interests The authors declare that they have no conflicts of interest. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. The corresponding author is working to make the data available in a public repository. References ADEQ. (n.d.). Lead (Pb) Pollution. Arizona Department of Environmental Quality. Retrieved July 1, 2024, from https://www.azdeq.gov/lead-pb-pollution [ Google Scholar ] Al-Swadi HA, Usman ARA, Al-Farraj AS, Al-Wabel MI, Ahmad M, & Al-Faraj A (2022). 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