Can toxins persist in house dust and soil years after an episodic fire event?

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Myriam Ziou, Amanda Jane Wheeler, Bo Strandberg, Grant Williamson, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5612625/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Communities exposed to smoke and ash from severe industrial fires often express concern that chemicals from the fire episode pose an ongoing risk to their health by persisting in and around the home environment. While previous studies have utilised house dust and soil samples to estimate exposure to contaminants resulting from fire and industrial emissions up to five years post-event, the evidence for persistence is limited. This study aimed to investigate if evidence of contamination attributable to a mine fire episode (Latrobe Valley, Victoria, Australia) could be observed in those medium three years later. Methods In 2017, eighty-five participants in a birth cohort study established post-fire in the Latrobe Valley provided indoor vacuum dust and garden soil samples. The samples were analysed for a suite of polycyclic aromatic hydrocarbons and chemical elements, including barium and magnesium, which had been previously identified as markers of fire emissions in roof cavity dust. The spatial distribution of these elements and compounds was compared with the distribution of smoke and ash from the 2014 fire, after accounting for housing characteristics. Results There was no evidence of persistent contamination of soil or indoor dust samples that could be attributable to this severe fire and pollution episode three years previously. These findings can be helpful in reassuring affected communities about the risk of long-term persistence of potentially harmful substances. Conclusions Household soil and dust may be more useful for understanding exposures from contemporaneous or persistent pollution sources such as road networks or industrial facilities. Fire emissions House dust Soil Metals PAH Exposure Figures Figure 1 Figure 2 Figure 3 1. Introduction In 2014, a major open pit coal mine fire event (the Hazelwood coal mine fire) occurred near the town of Morwell, in the Latrobe Valley of Victoria, Australia. This fire burned for 45 days before being declared under control, and caused severe smoke pollution over Morwell and surrounding localities (Luhar et al., 2020 ). Previously, in 2015, dust samples collected in roof spaces of homes impacted by the Hazelwood coal mine fire were used to demonstrate that there was a spatial pattern in exposures to the smoke emissions. Homes closer to the fire had increased concentrations of chemical markers barium (Ba) and magnesium (Mg) (Wheeler et al., 2020 ), which was consistent with Victorian Environment Protection Authority (EPA Victoria, 2015) air and ash sampling conducted during the fire event (Fisher et al., 2015 , Reisen et al., 2017 ). The Hazelwood coal mine fire had a deep impact on the Latrobe Valley community, impact that led to significant health, environmental, and economic concerns. Members of the community expressed a strong desire for governmental responses that included environmental cleanup and health monitoring. Residents demanded the establishment of air quality monitoring stations and criticized the inadequacy of existing systems. Many struggled to obtain information on air quality and felt abandoned by authorities as they attempted to protect their businesses and health (Teague et al., 2014). A specific ongoing concern raised by members of the community in the years following the fire was support for the clean-up had been inadequate at the time, and that chemical contaminants from smoke and ash from the mine fire were persisting in their homes and affecting their health (Teague et al., 2014). There is increasing evidence that house dust and soil can provide an indication of exposure to long-term air pollution sources (Gillings et al., 2022 , Gul et al., 2023 , Sajn et al., 2023 ). Both media (with dust referring to particles with a diameter < 300 µm deposited on floors and other indoor surfaces (Rasmussen et al., 2013 )) can be analysed for a suite of environmental contaminants including metals, elements and polycyclic aromatic hydrocarbons (PAH). The patterns of these contaminants can be used as chemical markers to identify exposure to specific emission sources (Gillings et al., 2022 , Gul et al., 2023 , Rasmussen et al., 2013 , Sajn et al., 2023 , Wheeler et al., 2020 ). Studies that have collected roof space dust, indoor house dust and soil samples from households in proximity to industrial settings or road networks suggest that there are associations between pollutants measured using these three media (Gillings et al., 2022 , Sajn et al., 2023 ). This suggests that these media have capacity to act as exposure proxies for long-term pollution. Only one study has evaluated persistence of PAH pollutants in household dust samples (Whitehead et al., 2013 ). They collected samples of household dust from homes at two different time points, analysed them for PAH markers, and concluded that if the dust samples were collected within five years, it may be feasible to detect chemicals attributable to area and mobile sources. In 2017, we invited participants from the Early Life Follow-up (ELF) study, a birth cohort established following the Hazelwood coal mine fire, to provide indoor dust, along with soil samples from their outdoor spaces, to assess if markers of mine fire emissions were clearly detectable in homes soil or dust 3 years after the event. In this paper, we aimed to: 1) Assess the spatial patterns of chemicals, including those known to be associated with the mine fire emissions, in the house dust and soil samples provided by the study participants. 2) Determine whether any spatial patterns are associated with mine fire exposure. 3) Determine whether housing characteristics are associated with patterns in chemical constituents of residential house dust and soil. 4) On the basis of our findings, assess the suitability of using residential house dust and soil samples to assign exposures to a one-off acute smoke event, such as an open-pit coal mine fire. 5) Assess the extent to which the public can be confidently reassured that there is a low risk of contaminants persisting in and around their homes from this one-off acute smoke event. 2. Methods Details of participant recruitment for the overall ELF study cohort are described in detail by Melody et al. ( 2021 ). Briefly, the cohort consisted of children who were either in utero, less than two years of age, or not exposed to the coal mine fire emissions. A total of 571 infants and their families enrolled and were followed up for clinical testing of cardiovascular, respiratory and allergic outcomes. For this study, families in the cohort were approached in 2017 to provide the research team with (i) the contents of their vacuums after completing a whole of house vacuum using a new empty vacuum collection bag and (ii) soil samples from their outdoor space. To support sampling, we mailed out labelled resealable plastic bags (one each for the dust and soil), a plastic tablespoon to collect a spoonful of surface soil from four locations around the outdoor space, a short survey on the vacuum model and relevant sampling details, and a pre-paid return envelope. A total of 85 families completed the collection. Sample processing and pollutant analyses followed the protocols described in Wheeler et al. ( 2020 ). Briefly, samples were stored at -20°C until processed. The soil samples were dried prior to processing in an oven at 30°C. Half of the samples were placed in a 150 µm plastic mesh then agitated for 10 minutes using a mechanical shaker (Endecotts, EFL300, USA) to sieve the larger content which was then discarded. Dust and soil samples were then milled (Retsch MM400, USA) at 30 l/s with five agate balls for 2 minutes and 50 seconds to ensure that the samples were homogenous. Amber glass vials were used to store 2 g of dust and soil for further analyses. Elemental analysis utilised EPA method 3052 to digest the samples, and inductively coupled plasma mass spectrometry (ICP-MS) for most elements, following US EPA Method 200.8 (US EPA, 1994b). A small subset of elements i.e. sodium (Na), Mg, calcium (Ca), potassium (K), iron (Fe), aluminium (Al) and sulphur (S) - were analysed using inductively coupled plasma atomic emission spectroscopy (ICP-OES) following US EPA Method 200.7 (US EPA, 1994a), as they were present in concentrations too high to be suitable for ICP-MS analysis. PAH concentrations were analysed using high resolution gas chromatography/low-resolution mass spectrometry (HRGC/LRMS). Methods followed Jorgensen et al. ( 2013 ) and all 16 US EPA priority PAHs were measured. Details of the methodology is available in Wheeler et al. ( 2020 ). 2.1 Data cleaning Dust and soil samples were received from 85 houses. A total of four houses were excluded as they had not returned the accompanying survey, which led to a final sample of 81 houses. Additionally, four houses did not provide either (i) a dust sample or (ii) a soil sample, and were thus also excluded from the respective statistical analyses (two from the dust, two from the soil). Four elements, i.e. cadmium (Cd), molybdenum (Mo), Na and antimony (Sb), were below the lower limit of detection in > 20% of soil samples and were thus not considered for statistical analyses. Both soil and dust samples from each participant were needed to simultaneously evaluate any residual evidence of the mine fire contamination. As a result of this, PAHs were also excluded from both soil and dust statistical analyses, due to a quarter (n = 19) of the participants that had sent a dust sample providing a sample too small to allow for their measurement, and 8 out of the 16 PAHs measured below the lower limit of detection in the soil samples. 2.2 Statistical analysis We summarised each element measured and their interdependency using a range of descriptive statistics. For each of the dust and soil samples, we then used univariable linear regression to evaluate any associations between mine fire smoke exposure and the soil and dust concentrations of eight key elements selected a priori. Specifically, we investigated the relationship between Al, Ba, Ca, cobalt (Co), Fe, Mg, manganese (Mn) and S, and (i) exposure to fire-related fine particulate matter (PM 2.5 ) as estimated by an atmospheric model (Luhar et al., 2020 ); and (ii) distance to the mine. These chemical elements were selected for this analysis as they increased by at least 10-fold in PM 10 at the peak of the fire compared to background ambient levels and were previously identified as potential chemical markers of the fire emissions (Wheeler et al., 2020 ). The relationship between markers of traffic (chromium (Cr), Fe, Mn and Mo) selected based on the literature (Bari et al., 2015 ) and distance to the highway was also assessed using the same approach. Box-Cox transformation (Box and Cox, 1964 ) was applied to the dependent variables to satisfy the assumptions of normality in linear regression models. Multivariate analysis was performed to evaluate any correlations and patterns between all chemical elements measured in the dust and soil. First, a Mantel test was conducted to evaluate Spearman’s rank correlations between distance matrices of the concentrations and geographical house distances using 10,000 permutations (Legendre and Legendre, 2012 ). Distance matrices were estimated using Euclidean distances between z-scores of the concentrations and Haversine distances (Sinnott, 1984 ) between house locations. Principal components analysis (PCA) was then employed for dimensionality reduction on the log-transformed z-scores (Xue et al., 2011 ). The number of principal components (PC) retained for each analysis was determined using Horn’s parallel analysis (Zwick and Velicer, 1986 , Horn, 1965 ). The envfit function of the vegan package (Oksanen et al., 2022 ) was employed to test associations between the extracted PC as explanatory variables and air pollution exposure, geographical and housing variables as response variables, using 10,000 permutations. Finally, we performed a hierarchical cluster using Ward’s method with Manhattan distance (Templ et al., 2008 ) to identify patterns of similarity between elements. All analyses were performed in R version 4.3.2 (R Core Team, 2023 ), using the packages vegan (Oksanen et al., 2022 ), geosphere (Hijmans, 2022 ), EFAtools (Steiner and Grieder, 2020 ), and cluster (Maechler et al., 2023 ). Informed consent was obtained from all individual participants included in the study. 3. Results 3.1 Description of the dust and soil samples and housing characteristics Zinc (Zn), Ba, Mn, copper (Cu), and Cr were the chemical elements with the highest concentrations in the dust samples. In the soil samples, the five most abundant elements were Ba, Mn, Zn, lead (Pb) and vanadium (V) (Table 1). In general, concentration distributions were right-skewed, and ranges were wide across homes (Table 1). Table 2 compares the average concentrations found in our samples to comparable studies from various locations. When comparing Ba and Mg concentrations, the two elements that were associated with the mine fire emissions in the roof space study (Wheeler et al., 2020), our average concentrations in the dust and soil were slightly higher for Ba and substantially lower for Mg (Table 2). Table 1 – Summary statistics of elemental/PAH concentrations (mg/kg) present in indoor dust samples and outdoor soil samples from 81 houses. (A) Dust Element n Mean SD Min Q1 Median Q3 Max Range Al 79 11.6 5.85 0.452 8.16 9.84 14.5 35.0 34.6 As 79 8.40 5.33 0.936 4.35 7.14 11.1 30.5 29.6 Ba 79 547 389 94.2 289 450 659 2140 2046 Be 79 0.262 0.182 0.0394 0.148 0.219 0.302 1.05 1.01 Ca 79 33.7 29.6 2.66 19.1 25.3 37.0 194 192 Cd 79 1.38 1.57 0.130 0.703 0.934 1.45 11.4 11.3 Co 79 7.19 5.92 1.58 3.98 5.50 8.56 41.8 40.2 Cr 79 83.5 88.5 10.4 40.0 57.4 81.8 623 613 Cu 79 165 124 37.3 100 140 179 736 699 Fe 79 16.3 10.7 2.88 8.89 13.4 18.9 53.4 50.5 K 79 5.82 3.90 0.00879 4.25 5.29 6.69 35.4 35.3 Li 79 4.46 1.88 0.894 3.20 4.19 5.62 10.1 9.17 Mg 79 5.78 3.65 0.190 3.72 4.86 6.49 22.4 22.2 Mn 79 266 287 56.2 134 190 300 2314 2258 Mo 79 3.21 6.51 -0.723 0.384 1.10 3.39 38.9 39.6 Na 79 17.6 31.0 0.180 7.47 9.76 16.4 258 258 Ni 79 63.7 53.5 8.15 34.1 46.7 80.3 381 373 Pb 79 64.8 86.4 6.18 25.6 37.5 62.0 488 482 S 79 9.15 6.10 1.21 5.73 8.00 10.1 46.8 45.6 Sb 79 7.60 17.6 1.40 3.67 4.72 6.26 159 158 Se 79 1.29 1.51 0.0382 0.661 1.01 1.42 12.6 12.6 V 79 21.8 13.8 3.32 13.7 17.9 26.1 69.3 66.0 Zn 79 984 470 265 668 915 1227 2855 2590 (B) Soil Element n Mean SD Min Q1 Median Q3 Max Range Al 79 3.20 1.40 1.02 2.45 2.95 3.72 8.91 7.89 As 79 5.45 5.50 0.665 2.46 3.71 5.89 38.6 37.9 Ba 79 178 85.6 70.3 123 168 219 617 546 Be 79 0.288 0.167 0.0827 0.179 0.239 0.345 0.955 0.872 Ca 79 1.59 1.16 -0.0279 0.909 1.32 1.90 8.68 8.71 Co 79 3.44 1.82 1.21 2.28 3.11 4.30 11.2 10.0 Cr 79 23.5 19.7 7.75 14.8 20.2 26.2 174 166 Cu 79 21.4 12.7 5.06 13.1 19.0 26.7 74.3 69.2 Fe 79 2.83 1.33 1.15 2.06 2.47 3.23 10.6 9.44 K 79 0.272 0.170 0.0820 0.154 0.237 0.320 1.15 1.07 Li 79 5.04 1.95 1.69 3.80 4.75 5.91 11.9 10.2 Mg 79 0.392 0.397 0.130 0.234 0.323 0.403 3.35 3.22 Mn 79 156 91.5 51.1 86.4 124 197 420 369 Ni 79 10.1 7.48 2.98 5.90 8.36 11.8 60.4 57.5 Pb 79 45.3 21.7 19.6 30.5 39.5 51.6 140 120 S 79 0.290 0.230 0.0586 0.175 0.218 0.316 1.62 1.56 Se 79 0.882 0.400 0.288 0.632 0.827 1.02 2.81 2.52 V 79 26.1 12.8 12.4 18.2 23.2 30.3 95.4 83.0 Zn 79 116 82.7 14.1 61.5 95.7 149 444 430 Table 2 – Average elemental concentrations (mg/kg) in samples from comparative studies conducted in Australia and around the world. Study (Wheeler et al., 2020)* Isley et al. (2022) Gillings et al. (2022) Gillings et al. (2022) Rasmussen et al. (2013) Dingle et al. (2021) Gul et al. (2023) Current study Current study Loca-tion Latrobe Valley, Austra-lia Austra-lia a Broken Hill, Austra-lia Broken Hill, Austra- lia 13 cities, Canada Fort McMurray, Canada Ankara, Turkey Latrobe Valley, Australia Latrobe Valley, Australia Source Roof dust Indoor dust Indoor dust Front yard soil Indoor dust Indoor dust Indoor dust Indoor dust Outdoor soil Al 17000 -- -- -- -- 16000 -- 11.6 3.20 As 6.87 31.9 41 36 13.1 13 6.00 8.40 5.45 Ba 154 -- -- -- -- -- -- 547 178 Be 0.119 -- -- -- -- -- -- 0.262 0.288 Ca 30800 -- -- -- -- 45000 -- 33.7 1.59 Cd -- -- -- -- 6.0 11 0.60 1.38 -- Co 5.17 -- -- -- -- 5.4 2.90 7.19 3.44 Cr 26.9 105 -- -- 117 92 26.9 83.5 23.5 Cu 107 232 197 36 279 1900 101 165 21.4 Fe 24500 -- -- -- -- 26000 -- 16.3 2.83 K 2.89 -- -- -- -- 4600 -- 5.82 0.272 Li 4.11 -- -- -- -- -- -- 4.46 5.04 Mg 8070 -- -- -- -- 7000 -- 5.78 0.392 Mn 333 336 1418 2396 -- 250 78.9 266 156 Mo 1.23 -- -- -- -- 8.3 2.40 3.21 -- Na 5980 -- -- -- -- 41000 -- 17.6 -- Ni 25.1 49.4 -- -- 102 60 38.2 63.7 10.1 Pb 423 305 783 635 210 4500 32.5 64.8 45.3 S 10200 -- -- -- -- -- -- 9.15 0.290 Sb 2.16 -- -- -- -- -- 2.40 7.60 -- Se 7.86 -- -- -- -- 1.6 0.90 1.29 0.882 V 20.7 -- -- -- -- 15 27.3 21.8 26.1 Zn 5140 1680 2243 1396 833 14000 298 984 116 Notes: *Wheeler et al. (2020) is a previous study from our group that had investigated roof dust as a potential marker of exposure to the Hazelwood coal mine fire. a Isley et al. (2022) included samples from 35 countries in their study, but we report here the mean concentrations for Australia. Table 3 – Univariable linear regression between markers of exposure to the mine fire (fire-related PM 2.5 and distance to the mine) and elements elevated at least 10 times during that period, and between distance to the highway and traffic-related elements. A Box-Cox (power) transformation was applied to the outcomes to improve normality and back-transformed β coefficients at the mean of the predictor variables are presented. Fire-related PM 2.5 (per increase of 10 µg/m 3 ) Dust Soil Element β (95%CI) R 2 p β (95%CI) R 2 p Al 0.04 (-1.86, 1.95) < 0.01 0.96 -0.23 (-0.58, 0.11) 0.02 0.21 Ba -93.42 (-171.04, -15.81) 0.05 0.04 -1.06 (-23.55, 21.44) < 0.01 0.93 Ca -0.73 (-6.31, 4.85) < 0.01 0.80 -0.02 (-0.32, 0.29) < 0.01 0.92 Co -1.00 (-1.91, -0.09) 0.05 0.06 -0.19 (-0.61, 0.22) < 0.01 0.39 Fe -2.02 (-4.36, 0.32) 0.03 0.12 0.04 (-0.27, 0.36) < 0.01 0.79 Mg 0.01 (-1.00, 1.03) < 0.01 0.98 0.02 (-0.03, 0.07) < 0.01 0.47 Mn -28.17 (-60.18, 3.83) 0.03 0.12 -5.97 (-28.5, 16.55) < 0.01 0.61 S 0.34 (-1.10, 1.77) < 0.01 0.64 0.01 (-0.04, 0.05) < 0.01 0.70 (B) Distance to mine (per increase of 1 km) Dust Soil Element β (95%CI) R 2 p β (95%CI) R 2 p Al 0.00 (-0.27, 0.27) <0.01 0.99 0.03 (-0.03, 0.09) 0.01 0.29 Ba 13.13 (-1.20, 27.46) 0.04 0.07 0.28 (-3.12, 3.67) <0.01 0.87 Ca 0.32 (-0.50, 1.14) <0.01 0.44 0.42 (-0.03, 0.06) <0.01 0.42 Co 0.15 (-0.02, 0.32) 0.04 0.08 0.03 (-0.04, 0.10) <0.01 0.45 Fe 0.37 (-0.03, 0.77) 0.04 0.07 0.01 (-0.04, 0.05) <0.01 0.74 Mg 0.06 (-0.08, 0.21) <0.01 0.40 0.00 (0.00, 0.01) <0.01 0.49 Mn 5.86 (-0.01, 11.73) 0.05 0.05 0.61 (-2.98, 4.19) <0.01 0.74 S -0.05 (-0.24, 0.15) <0.01 0.65 0.00 (-0.01, 0.00) <0.01 0.68 (C) Distance to highway (per increase of 1 km) Dust Soil Element β (95%CI) R 2 p β (95%CI) R 2 p Cr 2.55 (-0.55, 5.66) 0.03 0.10 0.91 (0.19, 1.62) 0.08 0.01 Fe 0.76 (0.13, 1.39) 0.07 0.02 0.04 (-0.03, 0.11) 0.02 0.27 Mn 14.60 (5.19, 24.01) 0.12 <0.01 7.23 (1.40, 13.07) 0.08 0.01 Mo 0.04 (-0.11, 0.20) <0.01 0.57 - - - Note : Minimal discrepancies can exist between p-values and estimated 95% confidence intervals, due to the back-transformation of coefficients obtained with the Box-Cox transformation being achieved with the Delta Method, which performs numerical approximations. We calculated Spearman correlation coefficients between each pair of elements in both the soil and the dust (Fig. 1, numerical values available in Table S1). Within dust, all elements were mainly positively correlated with each other, with the exception of S which had a weak negative correlation with most elements. Overall, Al, beryllium (Be), Co, Fe, Mn, selenium (Se) and V showed strong positive correlations (ρ > 0.7) with each other. Within soil, all pairwise correlations were positive, with the exception of weak negative correlations between Al and Zn, arsenic (As) and Pb, Cu and Fe, and lithium (Li) and Zn. Overall, Co, Cr, Mn, nickel (Ni) and V showed strong positive correlations between each other. When comparing elements in dust and in soil, there were no strong correlations between any of the elements. However, Al and Be concentrations in soil showed overall the stronger positive correlations with the elements contained in dust, while Ca showed the strongest negative correlations with elements contained in dust. Housing characteristics of the 81 participating houses are summarised in Table S2. 3.2 Univariate analysis Neither of the elements evaluated as potential chemical markers of mine fire exposure were positively associated with average fire-related PM 2.5 . Unexpectedly, Ba concentrations in dust were significantly inversely associated with the average fire-related PM 2.5 exposure at the participants’ addresses (Table 3A), i.e. the relationship was in the opposite direction to the one we had previously identified in roof samples (Wheeler et al., 2020). When investigating distance to the mine as a proxy for exposure to the fire, Mn concentrations were positively associated with distance to the mine, with houses farther from the mine having higher concentrations of Mn in their dust (Table 3B). When analysing the relationship between elements associated with traffic emissions and distance to the highway, positive significant associations were found with Fe in dust, Cr in soil, and Mn in both (Table 3C). In other words, the concentrations of these elemental markers of traffic emissions were higher in houses farther from the highway, which was unexpected, see Table S2 for details on distances. 3.3 Multivariate analyses The Mantel test found a significant correlation between the dust and soil compositions (Mantel’s r = 0.169, p.=0.04). This suggests that similar dust elemental compositions were more likely to be found in the houses with similar soil elemental compositions. Dust composition was also significantly correlated to geographical distances between the houses (Mantel’s r = 0.12, p.=0.02), indicating higher resemblance between dust elemental compositions in houses of closer proximity. However, there was no evidence of a correlation between soil composition patterns and geographical distances between the houses (Mantel’s r = 0.05, p = 0.18). Based on Horn’s parallel analysis (Zwick and Velicer, 1986, Horn, 1965), two meaningful PC were retained in the dust PCA and three in the soil PCA. Neither the dust nor soil PCA showed evidence of an association between the retained PC and fire-related PM 2.5 (dust: R 2 = 0.03, p = 0.37; soil: R 2 = 0.04, p = 0.37) or distance to the mine (dust: R 2 = 0.03, p = 0.35; soil: R 2 < 0.01, p = 0.89) based on the envfit analysis (Table S3). This suggests that overall patterns in multivariate composition are not associated with distance to the mine or estimated PM 2.5 exposure. However, the presence of a wood heater in the house was associated with the two ordination axes for dust samples (R 2 = 0.11, p < 0.01), mainly with the first PC, which explained 44% of the total variance in dust (Fig. 2A, Table S3). In accordance with what was observed when estimating pairwise correlations, PC 1 showed positive overall correlations between concentrations of most elements. Thus, houses with a wood heater tended to have higher overall elemental concentrations, with only S projecting slightly negatively on that axis. In the soil PCA, distance to the highway (R 2 = 0.14, p = 0.02) was correlated with the ordination, mainly with the first and third PC (Fig. 2B, Table S3). As with the dust, the first soil PC showed that elemental concentrations were predominantly positively correlated with each other. Therefore, houses farther from the highway tended to have higher elemental concentrations in their soil. Distance to the highway also had a negative loading on PC3, which correlated positively with Cr, Li and V, and negatively with Ca, Pb and S. Hierarchical cluster analysis applied to the dust samples (Fig. 3A) identified groupings coherent with the key PCA trends. For example, Na, K, Mg, Ca and S cluster together on the right side of the dendrogram, suggesting these elements share similar concentration patterns, but we are unclear of the specific sources. This is consistent with the PCA, where these elements form a distinct group towards the top and centre-right of the ordination. Other clusters included Cu, Zn and Ba. Cu and Zn concentrations are particularly closely related (Fig. 3A), these two elements are commonly found together in alloys. Pb, Cd and Sb form another cluster, with Pb and Cd particularly closely related. Again, the elements in these clusters fall close to each other on the PCA plot. When we applied hierarchical clustering to the soil samples (Fig. 3B), one cluster included Cr, Mn, V, Co and Ni, all of which had negative loadings on PC1 in the ordination (implying that concentrations in this group of elements were positively associated with distance to major highway). Our results also show some groupings that were consistent between indoor and outdoor samples, such as (1) K, Mg, Ca and S, (2) Cu, Zn and Ba, and (3) Mn and Co. The clusters identified also supported a lack of patterns that would be expected to be associated with mine fire or traffic exposure. 4. Discussion Our study’s findings support the notion that community exposures (from air, soil and dust) during the Latrobe Valley coal mine fire were acute and confined to the time of the event. The patterns of the chemical markers previously associated with the mine fire were not evident in the participant’s house dust or soil samples. This conclusion aligns with outcomes from studies both in Australia and internationally, which underscore the temporary nature of contaminants in acute industrial or environmental events. For example, studies from Canada and Turkey have demonstrated that while post-event samples from chronic pollution sources like roadways show consistent contamination (Gillings et al., 2022 ; Gul et al., 2023 ), episodic events such as the 2016 Fort McMurray wildfires in Canada revealed minimal residual contamination in house dust and soil just months after the event (Dingle et al., 2021). Additionally, the response of the Latrobe Valley community, including significant protests and calls for environmental remediation, highlights the importance of assessing both immediate and long-term risks in regions impacted by acute pollution events (EPA Victoria, 2015; Reisen et al., 2017 ). International studies from Greece and North Macedonia show similar community responses to pollution events linked to mining or industrial emissions, where concerns for lasting contamination often persist despite short-lived pollutant exposures (Konadu et al., 2023; Sajn et al., 2023 ). Such reactions emphasize the need for accurate data on contamination levels over time. Our findings contribute to this global body of knowledge, confirming minimal post-event exposure risks, thus enabling rural health providers to focus on immediate, event-based impacts. This reassurance supports public health strategies aligned with rural health priorities: addressing, mitigating, and clearly communicating episodic risks to build confidence in environmental safety within impacted communities. In our study, none of our statistical methods suggested an association between potential chemical markers of the (i) fire or (ii) traffic emissions, and proximity to the mine or highway respectively. Indeed, the only elements associated with distance to highway were associated in an inverse direction. This was unexpected and suggests that the ability of these media to represent exposure patterns may be contingent and complex. Our results could potentially indicate that homes which are closer to sources of pollution keep doors and windows closed to reduce exposures. Homeowners may also clean their homes more frequently. Alternatively, a separate, unrelated source or combination of sources such as house age, building materials or underlying geology could be responsible for the observed spatial patterns. With respect to the lack of an association between elemental composition and distance to major highway, we note that the traffic volumes on the major highway through Morwell are relatively low, thus the lack of association could reflect a weak emission signal. Comparing the concentrations of key elements in the house dust and soil collected for this study with the roof space dust samples collected by Wheeler et al. ( 2020 ) yields some interesting observations. When comparing with the concentrations observed in the roof dust, concentrations of the mine fire chemical markers, Ba and Mg, were orders of magnitude lower in both the house dust and soil samples we collected at a later time point. Potential reasons for this could be the longer time between the mine fire and the collection of samples (one year for the roof dust samples, three years for this study’s house dust and soil samples). With respect to the house dust, it is reasonable to hypothesise that vacuum dust samples are more complex exposure repositories than roof dust samples due to the regular disturbance and turnover of indoor house dust (for example, through regular cleaning). In contrast, roof dust is rarely disturbed. With respect to soils, the lack of a signal may reflect the washing away of mine fire related contaminants by the rain and/or wind. We note that VIC EPA soil sampling found no evidence of soil contamination several months after the fire (EPA Victoria, 2015); combined with our results this suggests that soil is unlikely to retain exposure signals long enough to act as a useful post-hoc exposure proxy. Table 2 compares our house dust results with a range of previous house dust studies that reflect scenarios where homes were impacted by long-term emissions which continued to emit contaminants. This results in a continuous source of pollutants that can be entrained or infiltrate into homes. Overall, compared to the other studies, concentrations in our house dust samples were lower for available comparable elements. When considering these data, we note comparing results between countries is challenging in that occupant behaviours may differ and building codes may differ. Other differences between studies could also be a result of the study design. We note that the Isley et al. ( 2022 ) samples were submitted by study participants who were concerned about local pollutant sources, as a result, these sample concentrations could be skewed higher than is typical for other Australian homes. The Rasmussen et al. ( 2013 ) study could be most representative of typical homes found in Canada as they were not associated with any specific sources or emissions. Still, most of the elements that were available for comparison were elevated compared to our study, it is unclear why Canadian homes would have higher levels of contaminants. Unlike other studies which we have reviewed, where the patterns of elemental composition dust and soil samples were highly correlated with each other, our study demonstrated relatively weak associations in patterns between the two media. This could be a result of low concentrations of the elements of interest, resulting in a lack of clarity of patterns. Alternatively in our study context, there could have been less soil infiltration into homes, or other factors that had a strong influence on the vacuum dust composition, noting that our analysis of housing characteristics suggested only wood heater use had a substantial relationship with house dust composition. Study limitations include the small number of samples provided from the entire ELF cohort; they may not be representative of all homes in the region affected by the mine fire. Further, only one sample per home was provided, using a method with relatively low standardisation, potentially impacting the quality of the samples. Although we note that other studies have used this approach previously and have demonstrated that there is relatively little variation in samples collected within the same home (Rasmussen et al., 2013 ). 5. Conclusions It was reassuring that the homes did not have evidence of ongoing exposures through either the house dust or soil to the mine fire emissions. Our findings suggest that house dust and soil may have substantial limitations in their ability to act as post-hoc proxies for exposure to short-term air pollution events, such as fires. Undisturbed settings in roof spaces may be better sample reservoirs for understanding the range of impacts spatially to short or medium-term events after a disaster. Our findings confirm minimal post-event exposure risks within living spaces of homes to the mine fire emissions three years after the fire. Declarations Funding Funding provided by the University of Tasmania. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions MZ - Methodology, Writing - original draft, Writing - review & editing, Software, Formal analysis. AW - Data curation, Formal analysis, Methodology, Conceptualization, Investigation, Validation, Funding acquisition, Project administration, Resources, Supervision, Writing - original draft, Writing - review & editing. BS - Formal analysis, Investigation, Methodology, Writing - review & editing. GW - Methodology, Formal analysis, Writing - review & editing. KC - Methodology, Writing - review & editing, Formal analysis. MD – Data curation, Project administration, Writing - review & editing. PA - Conceptualization, Methodology, Formal analysis, Writing - review & editing. AH - Conceptualization, Formal analysis, Funding acquisition, Methodology, Investigation, Writing - review & editing. GZ - Formal analysis, Methodology, Writing - review & editing. MBM - Writing - review & editing, Formal analysis. FJ - Conceptualization, Methodology, Investigation, Formal analysis, Supervision, Funding acquisition, Resources, Writing - original draft, Writing - review & editing. PJ - Conceptualization, Methodology, Investigation, Formal analysis, Supervision, Resources, Writing - review & editing, Writing - original draft. Ethics approval Ethical approval was provided by the University of Tasmania Human Research Ethics Committee #H0015236. Data Availability The datasets generated during and/or analysed during the current study are not publicly available due to ongoing analyses of the birth cohort dataset but are available from the corresponding author on reasonable request. References BARI, M. A., KINDZIERSKI, W. B., WALLACE, L. A., WHEELER, A. J., MACNEILL, M. & HEROUX, M. E. 2015. Indoor and Outdoor Levels and Sources of Submicron Particles (PM1) at Homes in Edmonton, Canada. Environ Sci Technol, 49 , 6419-29. BOX, G. E. P. & COX, D. R. 1964. An Analysis of Transformations. Journal of the Royal Statistical Society Series B-Statistical Methodology, 26 , 211-252. EPA VICTORIA 2015. Hazelwood Recovery Program water, soil and ash assessment – Morwell and surrounds, February 2014 – May 2015. Carlton, VIC. FISHER, G. W., TORRE, P. & MARSHALL, A. 2015. Hazelwood Open-Cut Coal Mine Fire Air Quality and Climate Change, 49 , 23-27. GILLINGS, M. M., FRY, K. L., MORRISON, A. L. & TAYLOR, M. P. 2022. Spatial distribution and composition of mine dispersed trace metals in residential soil and house dust: Implications for exposure assessment and human health. Environ Pollut, 293 , 118462. GUL, H. K., GULLU, G., BABAEI, P., NIKRAVAN, A., KURT-KARAKUS, P. B. & SALIHOGLU, G. 2023. Assessment of house dust trace elements and human exposure in Ankara, Turkey. Environ Sci Pollut Res Int, 30 , 7718-7735. HIJMANS, R. J. 2022. geosphere: Spherical Trigonometry. 1.5-18 ed. HORN, J. L. 1965. A Rationale and Test for the Number of Factors in Factor Analysis. Psychometrika, 30 , 179-85. ISLEY, C. F., FRY, K. L., LIU, X., FILIPPELLI, G. M., ENTWISTLE, J. A., MARTIN, A. P., KAH, M., MEZA-FIGUEROA, D., SHUKLE, J. T., JABEEN, K., FAMUYIWA, A. O., WU, L., SHARIFI-SOLTANI, N., DOYI, I. N. Y., ARGYRAKI, A., HO, K. F., DONG, C., GUNKEL-GRILLON, P., AELION, C. M. & TAYLOR, M. P. 2022. International Analysis of Sources and Human Health Risk Associated with Trace Metal Contaminants in Residential Indoor Dust. Environ Sci Technol, 56 , 1053-1068. JORGENSEN, R. B., STRANDBERG, B., SJAASTAD, A. K., JOHANSEN, A. & SVENDSEN, K. 2013. Simulated restaurant cook exposure to emissions of PAHs, mutagenic aldehydes, and particles from frying bacon. J Occup Environ Hyg, 10 , 122-31. LEGENDRE, P. & LEGENDRE, L. 2012. Numerical Ecology , Elsevier. LUHAR, A. K., EMMERSON, K. M., REISEN, F., WILLIAMSON, G. J. & COPE, M. E. 2020. Modelling smoke distribution in the vicinity of a large and prolonged fire from an open-cut coal mine. Atmospheric Environment, 229 , 117471. MAECHLER, M., ROUSSEEUW, P., STRUYF, A., HUBERT, M. & HORNIK, K. 2023. cluster: Cluster Analysis Basics and Extensions. 2.1.6 ed. MELODY, S. M., WHEELER, A. J., DALTON, M., WILLIAMSON, G. J., NEGISHI, K., WILLIS, G., SHAO, J., ZHAO, B., CHAPPELL, K., WILLS, K., REEVES, M., EMMERSON, K. M., FORD, J., DENNEKAMP, M., FOONG, R. E., ABRAMSON, M. J., IKIN, J., WALKER, J., VENN, A., DHARMAGE, S., HALL, G., ZOSKY, G. & JOHNSTON, F. 2021. Cohort Profile: The Hazelwood Health Study Latrobe Early Life Follow-Up (ELF) Study. Int J Epidemiol, 49 , 1779-1780. OKSANEN, J., SIMPSON, G., BLANCHET, F., KINDT, R., LEGENDRE, P., MINCHIN, P., O'HARA, R., SOLYMOS, P., STEVENS, M., SZOECS, E., WAGNER, H., BARBOUR, M., BEDWARD, M., BOLKER, B., BORCARD, D., CARVALHO, G., CHIRICO, M., DE CACERES, M., DURAND, S., EVANGELISTA, H., FITZJOHN, R., FRIENDLY, M., FURNEAUX, B., HANNIGAN, G., HILL, M., LAHTI, L., MCGLINN, D., OUELLETTE, M., RIBEIRO CUNHA, E., SMITH, T., STIER, A., TER BRAAK, C. & WEEDON, J. 2022. vegan: Community Ecology Package. 2.6-4 ed. R CORE TEAM 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. RASMUSSEN, P. E., LEVESQUE, C., CHENIER, M., GARDNER, H. D., JONES-OTAZO, H. & PETROVIC, S. 2013. Canadian House Dust Study: population-based concentrations, loads and loading rates of arsenic, cadmium, chromium, copper, nickel, lead, and zinc inside urban homes. Sci Total Environ, 443 , 520-9. REISEN, F., GILLETT, R., CHOI, J., FISHER, G. & TORRE, P. 2017. Characteristics of an open-cut coal mine fire pollution event. Atmospheric Environment, 151 , 140-151. SAJN, R., PANCEVSKI, Z., FRONTASYEVA, M. & STAFILOV, T. 2023. Levels and distribution of chemical elements in house dust from the area of an abandoned Pb-Zn smelter in North Macedonia. J Environ Sci Health A Tox Hazard Subst Environ Eng, 58 , 1-12. SINNOTT, R. W. 1984. Virtues of the Haversine. Sky and Telescope, 68 , 159-159. STEINER, M. & GRIEDER, S. 2020. EFAtools: An R package with fast and flexible implementations of exploratory factor analysis tools. Journal of Open Source Software, 5. TEAGUE, B., CATFORD, J. & PETERING, S. 2014. Hazelwood Mine Fire Inquiry Report. TEMPL, M., FILZMOSER, P. & REIMANN, C. 2008. Cluster analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, 23 , 2198-2213. US EPA 1994a. Method 200.7: Determination of Metals and Trace Elements in Water and Wastes by Inductively Coupled Plasma-Atomic Emission Spectrometry. Revision 4.4 ed. Cincinnati, OH. US EPA 1994b. Method 200.8: Determination of Trace Elements in Waters and Wastes by Inductively Coupled Plasma-Mass Spectrometry. Revision 5.4 ed. Cincinnati, OH. WHEELER, A. J., JONES, P. J., REISEN, F., MELODY, S. M., WILLIAMSON, G., STRANDBERG, B., HINWOOD, A., ALMERUD, P., BLIZZARD, L., CHAPPELL, K., FISHER, G., TORRE, P., ZOSKY, G. R., COPE, M. & JOHNSTON, F. H. 2020. Roof cavity dust as an exposure proxy for extreme air pollution events. Chemosphere, 244 , 125537. WHITEHEAD, T. P., METAYER, C., PETREAS, M., DOES, M., BUFFLER, P. A. & RAPPAPORT, S. M. 2013. Polycyclic aromatic hydrocarbons in residential dust: sources of variability. Environ Health Perspect, 121 , 543-50. XUE, J. H., LEE, C., WAKEHAM, S. G. & ARMSTRONG, R. A. 2011. Using principal components analysis (PCA) with cluster analysis to study the organic geochemistry of sinking particles in the ocean. Organic Geochemistry, 42 , 356-367. ZWICK, W. R. & VELICER, W. F. 1986. Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99 , 432-442. Supplementary Files ELFSoilanddustpaperExposureandHealthSI.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5612625","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402031353,"identity":"a6e55e7a-d71b-468f-a6fd-6de1499f20ed","order_by":0,"name":"Myriam Ziou","email":"","orcid":"","institution":"University of Tasmania Menzies Research Institute: University of Tasmania Menzies Institute for Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Myriam","middleName":"","lastName":"Ziou","suffix":""},{"id":402031354,"identity":"d75d7d6c-c185-40af-b7cc-3c9433d37ed3","order_by":1,"name":"Amanda Jane Wheeler","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACNhDBw8CQuIGBgfEAA4MNAwMzAS38SFoYgFrSCGuRbEDVcpiwwwyuHX724E0NQ+J29uYDBz7uOS8v38778AFDDW69BrfTzA3nHGPI3dlzLOHgjGe3DTccZjc2YDiGT0uCmTQPG0Puhhs5Bod5DtxOMGBmY5NgYMOtxf52+jdpnn9ALffffzj858C5BPlmkJZ/+GzJMZPmbWOo33CDB+j3AwcSGA4DtTC24dVSJjm3T6J4w5k0g4M9B5KBfmFjNkjsS8ejJX2bxJtvNokbjh9++ODHATt5+f5jjA8+fLPGqQUKJND4CYQ0jIJRMApGwSjACwDUdl1BX4zh4QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9288-8163","institution":"CSIRO Environment Business Unit","correspondingAuthor":true,"prefix":"","firstName":"Amanda","middleName":"Jane","lastName":"Wheeler","suffix":""},{"id":402031355,"identity":"ec7634c9-5176-4a89-b50f-9c13f554f1ae","order_by":2,"name":"Bo Strandberg","email":"","orcid":"","institution":"Sahlgrenska University Hospital Occupational Therapy: Sahlgrenska universitetssjukhuset Arbetsterapi Sahlgrenska","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Strandberg","suffix":""},{"id":402031356,"identity":"3af028bc-d5b2-42af-a59e-0a6b74feb500","order_by":3,"name":"Grant Williamson","email":"","orcid":"","institution":"University of Tasmania","correspondingAuthor":false,"prefix":"","firstName":"Grant","middleName":"","lastName":"Williamson","suffix":""},{"id":402031357,"identity":"f38ff4eb-40a0-4178-a047-04905fb19275","order_by":4,"name":"Katherine Chappell","email":"","orcid":"","institution":"University of Tasmania","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Chappell","suffix":""},{"id":402031358,"identity":"c5e2d323-637b-4873-bd5d-22c323696c98","order_by":5,"name":"Marita Dalton","email":"","orcid":"","institution":"University of Tasmania Menzies Research Institute: University of Tasmania Menzies Institute for Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Marita","middleName":"","lastName":"Dalton","suffix":""},{"id":402031359,"identity":"977e04df-2a7c-4a95-83b3-0ec14b74c28d","order_by":6,"name":"Pernilla Almerud","email":"","orcid":"","institution":"University of Gothenburg: Goteborgs Universitet","correspondingAuthor":false,"prefix":"","firstName":"Pernilla","middleName":"","lastName":"Almerud","suffix":""},{"id":402031360,"identity":"7b403c82-849c-4421-b1d2-fc087be5ef15","order_by":7,"name":"Andrea Hinwood","email":"","orcid":"","institution":"EPA Victoria: Environmental Protection Authority Victoria","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Hinwood","suffix":""},{"id":402031361,"identity":"aeef107e-9133-4bd1-9ac2-101e56747797","order_by":8,"name":"Graeme R. Zosky","email":"","orcid":"","institution":"University of Tasmania Menzies Institute for Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Graeme","middleName":"R.","lastName":"Zosky","suffix":""},{"id":402031362,"identity":"2ac731d8-a9d8-46ff-8eef-8369c2cf0eac","order_by":9,"name":"Maite L. Berasaluce Morgado","email":"","orcid":"","institution":"University of Tasmania Menzies Institute for Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Maite","middleName":"L. Berasaluce","lastName":"Morgado","suffix":""},{"id":402031363,"identity":"cec1ea03-d825-4348-b416-9ac7dde6783e","order_by":10,"name":"Fay H. Johnston","email":"","orcid":"","institution":"University of Tasmania Menzies Institute for Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Fay","middleName":"H.","lastName":"Johnston","suffix":""},{"id":402031364,"identity":"c4e64197-8a17-4b38-bab9-c363d49da666","order_by":11,"name":"Penelope Jones","email":"","orcid":"","institution":"University of Tasmania Menzies Institute for Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Penelope","middleName":"","lastName":"Jones","suffix":""}],"badges":[],"createdAt":"2024-12-10 03:09:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5612625/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5612625/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74075197,"identity":"539b1c92-8837-4d10-8af3-417d16ce9fc0","added_by":"auto","created_at":"2025-01-17 13:34:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":361643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman’s pairwise rank correlation coefficients between dust and/or soil elemental concentrations from the samples collected in the participants’ residences. \u003c/strong\u003eNumerical values visually represented in this figure are presented in Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e \u003cstrong\u003e\u0026nbsp;Dust (B)\u003c/strong\u003e \u003cstrong\u003eSoil (C)\u003c/strong\u003e \u003cstrong\u003eDust (rows) and soil (columns)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5612625/v1/4b7d4cfda4219b026aaaddc3.png"},{"id":74077240,"identity":"a7d85388-c9ba-462c-b531-115c4851775b","added_by":"auto","created_at":"2025-01-17 13:50:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1078469,"visible":true,"origin":"","legend":"\u003cp\u003ePCA results presented on biplots. The first two PCs were retained in the dust analysis and the first three in the soil analysis, based on Horn’s parallel analysis (Zwick and Velicer, 1986, Horn, 1965). The black dots represent the houses (samples), and the elements (variables) are presented in red. Significant results obtained when regressing environmental variables on the ordination with the function \u003cem\u003eenvfit\u003c/em\u003e detailed in Oksanen et al. (2022) are also presented. For the dust analysis, averages of houses with and without a wood heater (a categorical variable) are shown in blue and ellipses are drawn around these centroids in the colours presented in the legend. Houses (points) are also coloured according to their respective categories. For the soil analysis, the coordinates of the blue vector for distance to the highway (a continuous variable) are proportional to the regression coefficients for each axis, which indicates correlation with the PC.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e(A)\u003c/strong\u003e \u003cstrong\u003eDust (PC1 vs PC2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B-1) Soil (PC1 vs PC2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B-2) Soil (PC1 vs PC3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B-3) Soil (PC2 vs PC3)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5612625/v1/bdaf5b72e1fa0149ce3babd1.png"},{"id":74075201,"identity":"ee19f91e-09cc-4800-8822-5202396daedd","added_by":"auto","created_at":"2025-01-17 13:34:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":351102,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDendrogram presenting similarities between the between-house distribution of elements analysed in the dust (panel A) and soil (panel B) samples. The dendrogram was produced using hierarchical clustering, using Ward’s method and Manhattan distance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e \u003cstrong\u003eDust\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e \u003cstrong\u003eSoil\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5612625/v1/b081e771b75c38aec3cb4127.png"},{"id":79565567,"identity":"adbc4ac1-9225-4dd0-bf4d-f3db22ac61d9","added_by":"auto","created_at":"2025-03-31 09:27:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3343992,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5612625/v1/5beed85c-4fef-4004-99b2-034d8b223244.pdf"},{"id":74075198,"identity":"f0c72a6a-ad93-4333-880d-d3ac35f05884","added_by":"auto","created_at":"2025-01-17 13:34:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":54252,"visible":true,"origin":"","legend":"","description":"","filename":"ELFSoilanddustpaperExposureandHealthSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-5612625/v1/805ef789e76a85b273f3ce38.docx"}],"financialInterests":"","formattedTitle":"Can toxins persist in house dust and soil years after an episodic fire event?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn 2014, a major open pit coal mine fire event (the Hazelwood coal mine fire) occurred near the town of Morwell, in the Latrobe Valley of Victoria, Australia. This fire burned for 45 days before being declared under control, and caused severe smoke pollution over Morwell and surrounding localities (Luhar et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Previously, in 2015, dust samples collected in roof spaces of homes impacted by the Hazelwood coal mine fire were used to demonstrate that there was a spatial pattern in exposures to the smoke emissions. Homes closer to the fire had increased concentrations of chemical markers barium (Ba) and magnesium (Mg) (Wheeler et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), which was consistent with Victorian Environment Protection Authority (EPA Victoria, 2015) air and ash sampling conducted during the fire event (Fisher et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e, Reisen et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe Hazelwood coal mine fire had a deep impact on the Latrobe Valley community, impact that led to significant health, environmental, and economic concerns. Members of the community expressed a strong desire for governmental responses that included environmental cleanup and health monitoring. Residents demanded the establishment of air quality monitoring stations and criticized the inadequacy of existing systems. Many struggled to obtain information on air quality and felt abandoned by authorities as they attempted to protect their businesses and health (Teague et al., 2014).\u003c/p\u003e\n\u003cp\u003eA specific ongoing concern raised by members of the community in the years following the fire was support for the clean-up had been inadequate at the time, and that chemical contaminants from smoke and ash from the mine fire were persisting in their homes and affecting their health (Teague et al., 2014).\u003c/p\u003e\n\u003cp\u003eThere is increasing evidence that house dust and soil can provide an indication of exposure to long-term air pollution sources (Gillings et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e, Gul et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e, Sajn et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Both media (with dust referring to particles with a diameter\u0026thinsp;\u0026lt;\u0026thinsp;300 \u0026micro;m deposited on floors and other indoor surfaces (Rasmussen et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e)) can be analysed for a suite of environmental contaminants including metals, elements and polycyclic aromatic hydrocarbons (PAH). The patterns of these contaminants can be used as chemical markers to identify exposure to specific emission sources (Gillings et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e, Gul et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e, Rasmussen et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e, Sajn et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e, Wheeler et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eStudies that have collected roof space dust, indoor house dust and soil samples from households in proximity to industrial settings or road networks suggest that there are associations between pollutants measured using these three media (Gillings et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e, Sajn et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This suggests that these media have capacity to act as exposure proxies for long-term pollution. Only one study has evaluated persistence of PAH pollutants in household dust samples (Whitehead et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). They collected samples of household dust from homes at two different time points, analysed them for PAH markers, and concluded that if the dust samples were collected within five years, it may be feasible to detect chemicals attributable to area and mobile sources.\u003c/p\u003e\n\u003cp\u003eIn 2017, we invited participants from the Early Life Follow-up (ELF) study, a birth cohort established following the Hazelwood coal mine fire, to provide indoor dust, along with soil samples from their outdoor spaces, to assess if markers of mine fire emissions were clearly detectable in homes soil or dust 3 years after the event.\u003c/p\u003e\n\u003cp\u003eIn this paper, we aimed to:\u003c/p\u003e\n\u003cp\u003e1)\u0026nbsp; \u0026nbsp;Assess the spatial patterns of chemicals, including those known to be associated with the mine fire emissions, in the house dust and soil samples provided by the study participants.\u003c/p\u003e\n\u003cp\u003e2)\u0026nbsp; \u0026nbsp;Determine whether any spatial patterns are associated with mine fire exposure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3)\u0026nbsp; \u0026nbsp;Determine whether housing characteristics are associated with patterns in chemical constituents of residential house dust and soil.\u003c/p\u003e\n\u003cp\u003e4)\u0026nbsp; \u0026nbsp;On the basis of our findings, assess the suitability of using residential house dust and soil samples to assign exposures to a one-off acute smoke event, such as an open-pit coal mine fire.\u003c/p\u003e\n\u003cp\u003e5) \u0026nbsp; Assess the extent to which the public can be confidently reassured that there is a low risk of contaminants persisting in and around their homes from this one-off acute smoke event.\u003c/p\u003e\n"},{"header":"2. Methods","content":"\u003cp\u003eDetails of participant recruitment for the overall ELF study cohort are described in detail by Melody et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Briefly, the cohort consisted of children who were either in utero, less than two years of age, or not exposed to the coal mine fire emissions. A total of 571 infants and their families enrolled and were followed up for clinical testing of cardiovascular, respiratory and allergic outcomes. For this study, families in the cohort were approached in 2017 to provide the research team with (i) the contents of their vacuums after completing a whole of house vacuum using a new empty vacuum collection bag and (ii) soil samples from their outdoor space. To support sampling, we mailed out labelled resealable plastic bags (one each for the dust and soil), a plastic tablespoon to collect a spoonful of surface soil from four locations around the outdoor space, a short survey on the vacuum model and relevant sampling details, and a pre-paid return envelope. A total of 85 families completed the collection.\u003c/p\u003e \u003cp\u003eSample processing and pollutant analyses followed the protocols described in Wheeler et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Briefly, samples were stored at -20\u0026deg;C until processed. The soil samples were dried prior to processing in an oven at 30\u0026deg;C. Half of the samples were placed in a 150 \u0026micro;m plastic mesh then agitated for 10 minutes using a mechanical shaker (Endecotts, EFL300, USA) to sieve the larger content which was then discarded. Dust and soil samples were then milled (Retsch MM400, USA) at 30 l/s with five agate balls for 2 minutes and 50 seconds to ensure that the samples were homogenous. Amber glass vials were used to store 2 g of dust and soil for further analyses.\u003c/p\u003e \u003cp\u003eElemental analysis utilised EPA method 3052 to digest the samples, and inductively coupled plasma mass spectrometry (ICP-MS) for most elements, following US EPA Method 200.8 (US EPA, 1994b). A small subset of elements \u003cem\u003ei.e.\u003c/em\u003e sodium (Na), Mg, calcium (Ca), potassium (K), iron (Fe), aluminium (Al) and sulphur (S) - were analysed using inductively coupled plasma atomic emission spectroscopy (ICP-OES) following US EPA Method 200.7 (US EPA, 1994a), as they were present in concentrations too high to be suitable for ICP-MS analysis.\u003c/p\u003e \u003cp\u003ePAH concentrations were analysed using high resolution gas chromatography/low-resolution mass spectrometry (HRGC/LRMS). Methods followed Jorgensen et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and all 16 US EPA priority PAHs were measured. Details of the methodology is available in Wheeler et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data cleaning\u003c/h2\u003e \u003cp\u003eDust and soil samples were received from 85 houses. A total of four houses were excluded as they had not returned the accompanying survey, which led to a final sample of 81 houses. Additionally, four houses did not provide either (i) a dust sample or (ii) a soil sample, and were thus also excluded from the respective statistical analyses (two from the dust, two from the soil).\u003c/p\u003e \u003cp\u003eFour elements, \u003cem\u003ei.e.\u003c/em\u003e cadmium (Cd), molybdenum (Mo), Na and antimony (Sb), were below the lower limit of detection in \u0026gt;\u0026thinsp;20% of soil samples and were thus not considered for statistical analyses. Both soil and dust samples from each participant were needed to simultaneously evaluate any residual evidence of the mine fire contamination. As a result of this, PAHs were also excluded from both soil and dust statistical analyses, due to a quarter (n\u0026thinsp;=\u0026thinsp;19) of the participants that had sent a dust sample providing a sample too small to allow for their measurement, and 8 out of the 16 PAHs measured below the lower limit of detection in the soil samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe summarised each element measured and their interdependency using a range of descriptive statistics. For each of the dust and soil samples, we then used univariable linear regression to evaluate any associations between mine fire smoke exposure and the soil and dust concentrations of eight key elements selected a priori. Specifically, we investigated the relationship between Al, Ba, Ca, cobalt (Co), Fe, Mg, manganese (Mn) and S, and (i) exposure to fire-related fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) as estimated by an atmospheric model (Luhar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); and (ii) distance to the mine. These chemical elements were selected for this analysis as they increased by at least 10-fold in PM\u003csub\u003e10\u003c/sub\u003e at the peak of the fire compared to background ambient levels and were previously identified as potential chemical markers of the fire emissions (Wheeler et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The relationship between markers of traffic (chromium (Cr), Fe, Mn and Mo) selected based on the literature (Bari et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and distance to the highway was also assessed using the same approach. Box-Cox transformation (Box and Cox, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1964\u003c/span\u003e) was applied to the dependent variables to satisfy the assumptions of normality in linear regression models.\u003c/p\u003e \u003cp\u003eMultivariate analysis was performed to evaluate any correlations and patterns between all chemical elements measured in the dust and soil. First, a Mantel test was conducted to evaluate Spearman\u0026rsquo;s rank correlations between distance matrices of the concentrations and geographical house distances using 10,000 permutations (Legendre and Legendre, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Distance matrices were estimated using Euclidean distances between z-scores of the concentrations and Haversine distances (Sinnott, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) between house locations.\u003c/p\u003e \u003cp\u003ePrincipal components analysis (PCA) was then employed for dimensionality reduction on the log-transformed z-scores (Xue et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The number of principal components (PC) retained for each analysis was determined using Horn\u0026rsquo;s parallel analysis (Zwick and Velicer, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1986\u003c/span\u003e, Horn, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1965\u003c/span\u003e). The \u003cem\u003eenvfit\u003c/em\u003e function of the \u003cem\u003evegan\u003c/em\u003e package (Oksanen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was employed to test associations between the extracted PC as explanatory variables and air pollution exposure, geographical and housing variables as response variables, using 10,000 permutations. Finally, we performed a hierarchical cluster using Ward\u0026rsquo;s method with Manhattan distance (Templ et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) to identify patterns of similarity between elements.\u003c/p\u003e \u003cp\u003eAll analyses were performed in R version 4.3.2 (R Core Team, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), using the packages \u003cem\u003evegan\u003c/em\u003e (Oksanen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u003cem\u003egeosphere\u003c/em\u003e (Hijmans, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u003cem\u003eEFAtools\u003c/em\u003e (Steiner and Grieder, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and \u003cem\u003ecluster\u003c/em\u003e (Maechler et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003ewas obtained from all individual participants included in the study.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.1 Description of the dust and soil samples and housing characteristics\u003c/h2\u003e\n \u003cp\u003eZinc (Zn), Ba, Mn, copper (Cu), and Cr were the chemical elements with the highest concentrations in the dust samples. In the soil samples, the five most abundant elements were Ba, Mn, Zn, lead (Pb) and vanadium (V) (Table\u0026nbsp;1). In general, concentration distributions were right-skewed, and ranges were wide across homes (Table\u0026nbsp;1). Table\u0026nbsp;2 compares the average concentrations found in our samples to comparable studies from various locations. When comparing Ba and Mg concentrations, the two elements that were associated with the mine fire emissions in the roof space study (Wheeler et al., 2020), our average concentrations in the dust and soil were slightly higher for Ba and substantially lower for Mg (Table\u0026nbsp;2).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e– Summary statistics of elemental/PAH concentrations (mg/kg) present in indoor dust samples and outdoor soil samples from 81 houses.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(A) Dust\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e(B) Soil\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eElement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eZn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003cdiv align=\"left\"\u003e\u003cstrong\u003eTable 2 – Average elemental concentrations (mg/kg) in samples from comparative studies conducted in Australia and around the world.\u0026nbsp;\u003c/strong\u003e\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(Wheeler et al., 2020)*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIsley et al. (2022)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGillings et al. (2022)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGillings et al. (2022)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRasmussen et al. (2013)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDingle et al. (2021)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGul et al. (2023)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCurrent study\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCurrent study\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLoca-tion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLatrobe Valley, Austra-lia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAustra-lia\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBroken Hill, Austra-lia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBroken Hill, Austra- lia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e13 cities, Canada\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFort McMurray, Canada\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnkara, Turkey\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLatrobe Valley, Australia\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLatrobe Valley, Australia\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRoof dust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndoor dust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndoor dust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFront yard soil\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndoor dust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndoor dust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndoor dust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIndoor dust\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOutdoor soil\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e11.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e3.20\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e8.40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5.45\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e547\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e178\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.262\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.288\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e33.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1.59\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1.38\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e--\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e7.19\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e3.44\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e83.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e23.5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e165\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e21.4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e16.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e2.83\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5.82\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.272\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e4.46\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5.04\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5.78\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.392\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e266\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e156\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e3.21\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e--\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e17.6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e--\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e63.7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e10.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e64.8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e45.3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e9.15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.290\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSb\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e7.60\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e--\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e1.29\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e0.882\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e21.8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e26.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e984\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e116\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eNotes: *Wheeler et al. (2020) is a previous study from our group that had investigated roof dust as a potential marker of exposure to the Hazelwood coal mine fire. \u003csup\u003ea\u003c/sup\u003e Isley et al. (2022) included samples from 35 countries in their study, but we report here the mean concentrations for Australia.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e– Univariable linear regression between markers of exposure to the mine fire (fire-related PM\u003csub\u003e2.5\u003c/sub\u003e and distance to the mine) and elements elevated at least 10 times during that period, and between distance to the highway and traffic-related elements. A Box-Cox (power) transformation was applied to the outcomes to improve normality and back-transformed β coefficients at the mean of the predictor variables are presented. Fire-related PM\u003csub\u003e2.5\u003c/sub\u003e (per increase of 10 µg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eβ (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eβ (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04 (-1.86, 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.23 (-0.58, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-93.42 (-171.04, -15.81)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.06 (-23.55, 21.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.73 (-6.31, 4.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02 (-0.32, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.00 (-1.91, -0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.19 (-0.61, 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.02 (-4.36, 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04 (-0.27, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01 (-1.00, 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02 (-0.03, 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-28.17 (-60.18, 3.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.97 (-28.5, 16.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34 (-1.10, 1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01 (-0.04, 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e(B) Distance to mine (per increase of 1 km)\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eElement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eβ (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eβ (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00 (-0.27, 0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03 (-0.03, 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.13 (-1.20, 27.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28 (-3.12, 3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.32 (-0.50, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42 (-0.03, 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15 (-0.02, 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03 (-0.04, 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.37 (-0.03, 0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01 (-0.04, 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06 (-0.08, 0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00 (0.00, 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.86 (-0.01, 11.73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61 (-2.98, 4.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.05 (-0.24, 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00 (-0.01, 0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003e(C) Distance to highway (per increase of 1 km)\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eElement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eβ (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eβ (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.55 (-0.55, 5.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.91 (0.19, 1.62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.76 (0.13, 1.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04 (-0.03, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.60 (5.19, 24.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.23 (1.40, 13.07)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04 (-0.11, 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eNote : Minimal discrepancies can exist between p-values and estimated 95% confidence intervals, due to the back-transformation of coefficients obtained with the Box-Cox transformation being achieved with the Delta Method, which performs numerical approximations.\u003c/p\u003e\n \u003cp\u003eWe calculated Spearman correlation coefficients between each pair of elements in both the soil and the dust (Fig. 1, numerical values available in Table S1). Within dust, all elements were mainly positively correlated with each other, with the exception of S which had a weak negative correlation with most elements. Overall, Al, beryllium (Be), Co, Fe, Mn, selenium (Se) and V showed strong positive correlations (ρ \u0026gt; 0.7) with each other. Within soil, all pairwise correlations were positive, with the exception of weak negative correlations between Al and Zn, arsenic (As) and Pb, Cu and Fe, and lithium (Li) and Zn. Overall, Co, Cr, Mn, nickel (Ni) and V showed strong positive correlations between each other. When comparing elements in dust and in soil, there were no strong correlations between any of the elements. However, Al and Be concentrations in soil showed overall the stronger positive correlations with the elements contained in dust, while Ca showed the strongest negative correlations with elements contained in dust.\u003c/p\u003e\n \u003cp\u003eHousing characteristics of the 81 participating houses are summarised in Table S2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.2 Univariate analysis\u003c/h2\u003e\n \u003cp\u003eNeither of the elements evaluated as potential chemical markers of mine fire exposure were positively associated with average fire-related PM\u003csub\u003e2.5\u003c/sub\u003e. Unexpectedly, Ba concentrations in dust were significantly inversely associated with the average fire-related PM\u003csub\u003e2.5\u003c/sub\u003e exposure at the participants’ addresses (Table\u0026nbsp;3A), i.e. the relationship was in the opposite direction to the one we had previously identified in roof samples (Wheeler et al., 2020). When investigating distance to the mine as a proxy for exposure to the fire, Mn concentrations were positively associated with distance to the mine, with houses farther from the mine having higher concentrations of Mn in their dust (Table\u0026nbsp;3B).\u003c/p\u003e\n \u003cp\u003eWhen analysing the relationship between elements associated with traffic emissions and distance to the highway, positive significant associations were found with Fe in dust, Cr in soil, and Mn in both (Table\u0026nbsp;3C). In other words, the concentrations of these elemental markers of traffic emissions were higher in houses farther from the highway, which was unexpected, see Table S2 for details on distances.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.3 Multivariate analyses\u003c/h2\u003e\n \u003cp\u003eThe Mantel test found a significant correlation between the dust and soil compositions (Mantel’s \u003cem\u003er\u003c/em\u003e = 0.169, p.=0.04). This suggests that similar dust elemental compositions were more likely to be found in the houses with similar soil elemental compositions. Dust composition was also significantly correlated to geographical distances between the houses (Mantel’s \u003cem\u003er\u003c/em\u003e = 0.12, p.=0.02), indicating higher resemblance between dust elemental compositions in houses of closer proximity. However, there was no evidence of a correlation between soil composition patterns and geographical distances between the houses (Mantel’s \u003cem\u003er\u003c/em\u003e = 0.05, p = 0.18).\u003c/p\u003e\n \u003cp\u003eBased on Horn’s parallel analysis (Zwick and Velicer, 1986, Horn, 1965), two meaningful PC were retained in the dust PCA and three in the soil PCA. Neither the dust nor soil PCA showed evidence of an association between the retained PC and fire-related PM\u003csub\u003e2.5\u003c/sub\u003e (dust: R\u003csup\u003e2\u003c/sup\u003e = 0.03, p = 0.37; soil: R\u003csup\u003e2\u003c/sup\u003e = 0.04, p = 0.37) or distance to the mine (dust: R\u003csup\u003e2\u003c/sup\u003e = 0.03, p = 0.35; soil: R\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.01, p = 0.89) based on the \u003cem\u003eenvfit\u003c/em\u003e analysis (Table S3). This suggests that overall patterns in multivariate composition are not associated with distance to the mine or estimated PM\u003csub\u003e2.5\u003c/sub\u003e exposure. However, the presence of a wood heater in the house was associated with the two ordination axes for dust samples (R\u003csup\u003e2\u003c/sup\u003e = 0.11, p \u0026lt; 0.01), mainly with the first PC, which explained 44% of the total variance in dust (Fig.\u0026nbsp;2A, Table S3). In accordance with what was observed when estimating pairwise correlations, PC 1 showed positive overall correlations between concentrations of most elements. Thus, houses with a wood heater tended to have higher overall elemental concentrations, with only S projecting slightly negatively on that axis.\u003c/p\u003e\n \u003cp\u003eIn the soil PCA, distance to the highway (R\u003csup\u003e2\u003c/sup\u003e = 0.14, p = 0.02) was correlated with the ordination, mainly with the first and third PC (Fig.\u0026nbsp;2B, Table S3). As with the dust, the first soil PC showed that elemental concentrations were predominantly positively correlated with each other. Therefore, houses farther from the highway tended to have higher elemental concentrations in their soil. Distance to the highway also had a negative loading on PC3, which correlated positively with Cr, Li and V, and negatively with Ca, Pb and S.\u003c/p\u003e\n \u003cp\u003eHierarchical cluster analysis applied to the dust samples (Fig.\u0026nbsp;3A) identified groupings coherent with the key PCA trends. For example, Na, K, Mg, Ca and S cluster together on the right side of the dendrogram, suggesting these elements share similar concentration patterns, but we are unclear of the specific sources. This is consistent with the PCA, where these elements form a distinct group towards the top and centre-right of the ordination. Other clusters included Cu, Zn and Ba. Cu and Zn concentrations are particularly closely related (Fig.\u0026nbsp;3A), these two elements are commonly found together in alloys. Pb, Cd and Sb form another cluster, with Pb and Cd particularly closely related. Again, the elements in these clusters fall close to each other on the PCA plot.\u003c/p\u003e\n \u003cp\u003eWhen we applied hierarchical clustering to the soil samples (Fig.\u0026nbsp;3B), one cluster included Cr, Mn, V, Co and Ni, all of which had negative loadings on PC1 in the ordination (implying that concentrations in this group of elements were positively associated with distance to major highway). Our results also show some groupings that were consistent between indoor and outdoor samples, such as (1) K, Mg, Ca and S, (2) Cu, Zn and Ba, and (3) Mn and Co. The clusters identified also supported a lack of patterns that would be expected to be associated with mine fire or traffic exposure.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study\u0026rsquo;s findings support the notion that community exposures (from air, soil and dust) during the Latrobe Valley coal mine fire were acute and confined to the time of the event. The patterns of the chemical markers previously associated with the mine fire were not evident in the participant\u0026rsquo;s house dust or soil samples. This conclusion aligns with outcomes from studies both in Australia and internationally, which underscore the temporary nature of contaminants in acute industrial or environmental events. For example, studies from Canada and Turkey have demonstrated that while post-event samples from chronic pollution sources like roadways show consistent contamination (Gillings et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gul et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), episodic events such as the 2016 Fort McMurray wildfires in Canada revealed minimal residual contamination in house dust and soil just months after the event (Dingle et al., 2021).\u003c/p\u003e \u003cp\u003eAdditionally, the response of the Latrobe Valley community, including significant protests and calls for environmental remediation, highlights the importance of assessing both immediate and long-term risks in regions impacted by acute pollution events (EPA Victoria, 2015; Reisen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). International studies from Greece and North Macedonia show similar community responses to pollution events linked to mining or industrial emissions, where concerns for lasting contamination often persist despite short-lived pollutant exposures (Konadu et al., 2023; Sajn et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such reactions emphasize the need for accurate data on contamination levels over time. Our findings contribute to this global body of knowledge, confirming minimal post-event exposure risks, thus enabling rural health providers to focus on immediate, event-based impacts. This reassurance supports public health strategies aligned with rural health priorities: addressing, mitigating, and clearly communicating episodic risks to build confidence in environmental safety within impacted communities.\u003c/p\u003e \u003cp\u003eIn our study, none of our statistical methods suggested an association between potential chemical markers of the (i) fire or (ii) traffic emissions, and proximity to the mine or highway respectively. Indeed, the only elements associated with distance to highway were associated in an inverse direction. This was unexpected and suggests that the ability of these media to represent exposure patterns may be contingent and complex. Our results could potentially indicate that homes which are closer to sources of pollution keep doors and windows closed to reduce exposures. Homeowners may also clean their homes more frequently. Alternatively, a separate, unrelated source or combination of sources such as house age, building materials or underlying geology could be responsible for the observed spatial patterns. With respect to the lack of an association between elemental composition and distance to major highway, we note that the traffic volumes on the major highway through Morwell are relatively low, thus the lack of association could reflect a weak emission signal.\u003c/p\u003e \u003cp\u003eComparing the concentrations of key elements in the house dust and soil collected for this study with the roof space dust samples collected by Wheeler et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) yields some interesting observations. When comparing with the concentrations observed in the roof dust, concentrations of the mine fire chemical markers, Ba and Mg, were orders of magnitude lower in both the house dust and soil samples we collected at a later time point. Potential reasons for this could be the longer time between the mine fire and the collection of samples (one year for the roof dust samples, three years for this study\u0026rsquo;s house dust and soil samples). With respect to the house dust, it is reasonable to hypothesise that vacuum dust samples are more complex exposure repositories than roof dust samples due to the regular disturbance and turnover of indoor house dust (for example, through regular cleaning). In contrast, roof dust is rarely disturbed. With respect to soils, the lack of a signal may reflect the washing away of mine fire related contaminants by the rain and/or wind. We note that VIC EPA soil sampling found no evidence of soil contamination several months after the fire (EPA Victoria, 2015); combined with our results this suggests that soil is unlikely to retain exposure signals long enough to act as a useful post-hoc exposure proxy.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e compares our house dust results with a range of previous house dust studies that reflect scenarios where homes were impacted by long-term emissions which continued to emit contaminants. This results in a continuous source of pollutants that can be entrained or infiltrate into homes. Overall, compared to the other studies, concentrations in our house dust samples were lower for available comparable elements. When considering these data, we note comparing results between countries is challenging in that occupant behaviours may differ and building codes may differ.\u003c/p\u003e \u003cp\u003eOther differences between studies could also be a result of the study design. We note that the Isley et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) samples were submitted by study participants who were concerned about local pollutant sources, as a result, these sample concentrations could be skewed higher than is typical for other Australian homes. The Rasmussen et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) study could be most representative of typical homes found in Canada as they were not associated with any specific sources or emissions. Still, most of the elements that were available for comparison were elevated compared to our study, it is unclear why Canadian homes would have higher levels of contaminants.\u003c/p\u003e \u003cp\u003eUnlike other studies which we have reviewed, where the patterns of elemental composition dust and soil samples were highly correlated with each other, our study demonstrated relatively weak associations in patterns between the two media. This could be a result of low concentrations of the elements of interest, resulting in a lack of clarity of patterns. Alternatively in our study context, there could have been less soil infiltration into homes, or other factors that had a strong influence on the vacuum dust composition, noting that our analysis of housing characteristics suggested only wood heater use had a substantial relationship with house dust composition.\u003c/p\u003e \u003cp\u003eStudy limitations include the small number of samples provided from the entire ELF cohort; they may not be representative of all homes in the region affected by the mine fire. Further, only one sample per home was provided, using a method with relatively low standardisation, potentially impacting the quality of the samples. Although we note that other studies have used this approach previously and have demonstrated that there is relatively little variation in samples collected within the same home (Rasmussen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIt was reassuring that the homes did not have evidence of ongoing exposures through either the house dust or soil to the mine fire emissions. Our findings suggest that house dust and soil may have substantial limitations in their ability to act as post-hoc proxies for exposure to short-term air pollution events, such as fires. Undisturbed settings in roof spaces may be better sample reservoirs for understanding the range of impacts spatially to short or medium-term events after a disaster. Our findings confirm minimal post-event exposure risks within living spaces of homes to the mine fire emissions three years after the fire.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding provided by the University of Tasmania.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMZ - Methodology, Writing - original draft, Writing - review \u0026amp; editing, Software, Formal analysis. AW - Data curation, Formal analysis, Methodology, Conceptualization, Investigation, Validation, Funding acquisition, Project administration, Resources, Supervision, Writing - original draft, Writing - review \u0026amp; editing. BS - Formal analysis, Investigation, Methodology, Writing - review \u0026amp; editing. GW - Methodology, Formal analysis, Writing - review \u0026amp; editing. KC - Methodology, Writing - review \u0026amp; editing, Formal analysis. MD \u0026ndash; Data curation, Project administration, Writing - review \u0026amp; editing. PA - Conceptualization, Methodology, Formal analysis, Writing - review \u0026amp; editing. AH - Conceptualization, Formal analysis, Funding acquisition, Methodology, Investigation, Writing - review \u0026amp; editing. GZ - Formal analysis, Methodology, Writing - review \u0026amp; editing. MBM - Writing - review \u0026amp; editing, Formal analysis. FJ - Conceptualization, Methodology, Investigation, Formal analysis, Supervision, Funding acquisition, Resources, Writing - original draft, Writing - review \u0026amp; editing. PJ - Conceptualization, Methodology, Investigation, Formal analysis, Supervision, Resources, Writing - review \u0026amp; editing, Writing - original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was provided by the University of Tasmania Human Research Ethics Committee #H0015236.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are not publicly available due to ongoing analyses of the birth cohort dataset but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBARI, M. A., KINDZIERSKI, W. B., WALLACE, L. A., WHEELER, A. J., MACNEILL, M. \u0026amp; HEROUX, M. E. 2015. 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Comparison of five rules for determining the number of components to retain. \u003cem\u003ePsychological Bulletin,\u003c/em\u003e 99\u003cstrong\u003e,\u003c/strong\u003e 432-442.\u003c/li\u003e\n\u003c/ol\u003e "}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fire emissions, House dust, Soil, Metals, PAH, Exposure","lastPublishedDoi":"10.21203/rs.3.rs-5612625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5612625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eCommunities exposed to smoke and ash from severe industrial fires often express concern that chemicals from the fire episode pose an ongoing risk to their health by persisting in and around the home environment. While previous studies have utilised house dust and soil samples to estimate exposure to contaminants resulting from fire and industrial emissions up to five years post-event, the evidence for persistence is limited. This study aimed to investigate if evidence of contamination attributable to a mine fire episode (Latrobe Valley, Victoria, Australia) could be observed in those medium three years later.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn 2017, eighty-five participants in a birth cohort study established post-fire in the Latrobe Valley provided indoor vacuum dust and garden soil samples. The samples were analysed for a suite of polycyclic aromatic hydrocarbons and chemical elements, including barium and magnesium, which had been previously identified as markers of fire emissions in roof cavity dust. The spatial distribution of these elements and compounds was compared with the distribution of smoke and ash from the 2014 fire, after accounting for housing characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere was no evidence of persistent contamination of soil or indoor dust samples that could be attributable to this severe fire and pollution episode three years previously. These findings can be helpful in reassuring affected communities about the risk of long-term persistence of potentially harmful substances.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHousehold soil and dust may be more useful for understanding exposures from contemporaneous or persistent pollution sources such as road networks or industrial facilities.\u003c/p\u003e","manuscriptTitle":"Can toxins persist in house dust and soil years after an episodic fire event?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-17 13:34:50","doi":"10.21203/rs.3.rs-5612625/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41ff140a-7948-45ff-bbde-3ff94bd67401","owner":[],"postedDate":"January 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-31T09:19:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-17 13:34:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5612625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5612625","identity":"rs-5612625","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}