Source apportionment of soil heavy metals with PMF model and Pb isotopes in an intermountain basin of Tianshan Mountains, China

The plots of I_geo, PERI, and PLI of HMs in the topsoil of the tourist area of Sayram Lake (Fig. 5) reveal the degree of HM pollution and eco-risk in this study area on the one hand and, on the other hand, indicate the direction for the relevant agencies to target soil environmental protection and HM pollution prevention and control measures. In this study, the I_geo results showed that Cd was the most highly enriched HM, and Pb, Zn, Cd, and Ni were slightly enriched in a few sample sites. The unnatural accumulation of these elements is usually closely associated with human activities in the area³⁴. Tourism is the main economic activity in the district, and published studies have reported that tourism infrastructure construction (e.g., roads, buildings, etc.) and tourism wastes (e.g., plastic bags, batteries, hotel wastewater) release Cd into the soil³⁵. Additionally, the accumulation of Pb, Zn, Cu and Ni in soils is usually associated with traffic emissions³⁶. The PERI showed that the study area was at low risk overall, with only point ss04 exhibiting medium risk; however, this result was caused by the abnormally high Cd concentration value (Fig. 4) at point ss04 (Cd (concentration): 1.08 mg/kg, Cd (background): 0.34 mg/kg). This anomalous concentration value has a large influence on the PERI calculated based on the measured concentration, the background value and the toxicity coefficient. Therefore, references to this point can be appropriately removed when considering eco-risk. The PLI of each sampling point was greater than 1 and less than 2, which means that the area was in a moderately contaminated state. In general, the degree of soil HM contamination in this area was low; however, due to HM toxicity, bioaccumulation, and persistence³⁷, the HM contamination of this area still requires sustained attention.

Correlation analysis is an efficient way to reveal correlations among HMs through Pearson correlation coefficients, and HMs with significant correlations may originate from the same source³⁸. As shown in Table S5, the elemental pairs Cd-Cu (p < 0.01), Cd-Ni (p < 0.01), Cd-Zn (p < 0.05), Ni-Cu (p < 0.01), Pb‒Zn (p < 0.01), Cr-Ni (p < 0.01), Cr-Pb (p < 0.05), Cr-Zn (p < 0.05), Zn-Ni (p < 0.05), Zn-Cu (p < 0.01) were significantly and positively correlated. These results suggest that the elemental group Cd-Cu-Ni-Zn can be considered the same source. The significant correlation of Pb‒Zn implies that they have homologous characteristics, and published studies have reported that Pb and Zn are usually enriched by traffic emissions²⁰. However, Cr, Ni, Pb and Zn may also share similar sources, and this result is probably caused by mixed sources of HMs, since natural sources, human activities in the study area, or atmospheric deposition may contribute to HM concentrations. Conversely, Cd showed no correlation with Cr and Pb, which indicated that they were controlled by different sources. Therefore, the PMF model was employed to further identify the sources of HMs and to quantify the contribution of each source to each HM.

The HM concentration data file and uncertainty data file were used as input data according to the EPA PMF5.0 user’s guide. To ensure the accuracy and reasonableness of the model results, the factors were set to 2, 3 and 4, and the random starting seed number of the default model was run 100 times. It was determined that the explanatory rate of the model was best with 3 factors. In addition, the coefficient of determination R² for all observed and predicted values of HMs was greater than 0.7, with a minimum value of 0.74 and a maximum value of 0.99. Bootstrapping (BS) and displacement of factor elements (DISP) were employed to evaluate the bias and uncertainty of the PMF results³⁹. The results showed that over 90% of the base factors were reproduced in the BS model, and no factor swaps were observed in the DISP model within the lowest maximum permitted change in Q (dQ_max). Therefore, it is reasonable and valid for the PMF model to explain the information contained in the original data using 3 factors.

The PMF model source apportionment results are shown in Fig. 6, where Factor 1 has high loading values for Pb and Zn (39.7% and 32.0%, respectively), implying that they were influenced by the same sources. The mean concentrations of Pb and Zn exceeded the background values for the tourist area of Sayram Lake, while the CVs were 0.26 and 0.2, respectively, which were medium variation levels, indicating that they might be influenced by human activities⁴⁰. In addition, the I_geo indicated the presence of slight level of Pb and Zn contamination at some sampling points in the tourist area of Sayram Lake, which suggests the existence of external sources of Pb and Zn. Published studies have shown that Pb emissions from vehicle exhaust account for two-thirds of total global Pb emissions⁴¹; despite the global ban on the production and use of leaded gasoline since 2000, vehicle exhaust emissions are still considered to be the main cause of Pb accumulation in soils⁴². Additionally, the wear and tear of brakes, bearings and tires of automobiles also promote the release of Pb and Zn into the soil environment⁴³. The soil samples collected in this study were located in the tourist area of Sayram Lake, where tourist visits reach values of 2,530,000 per year and may contribute significantly to local traffic. The frequent vehicle traffic on the G30 highway, emissions from vehicle exhaust and the wear and tear of parts may promote the deposition of Pb and Zn in the topsoil. Therefore, Factor 1 was inferred to be a traffic source.

Factor 2 was dominated by Cr, Cu, Ni, Pb, and Zn, with loadings of 52.2%, 45.8%, 48.9%, 37.1%, and 36.1%, respectively. The average concentration of Cr was lower than the background value, and the average concentrations of Cu and Ni slightly exceeded the local background values. Furthermore, they have small coefficients of variation, indicating low influence from external pollution sources. Notably, Pb and Zn showed high loadings in both Factor 1 and Factor 2, indicating that Pb and Zn were from mixed sources and were controlled by both Factor 1 and Factor 2. Previous studies have shown that Cr, Cu and Ni in soils commonly originate from the soil parent materials and are controlled by the geological background material and soil-forming processes⁴⁴. Many studies also support this view^45,46. Therefore, Factor 2 was inferred to be a natural source.

Factor 3 was mainly characterized by Cd, Ni, Cu and Zn, with loadings of 71.1%, 31.0%, 29.9% and 31.9%, respectively. In this study, the mean concentration of Cd was 2.8 times the local soil background value, with a CV of 0.58, which was a strong level of variation in relation to the mean, indicating that Cd may be influenced by anthropogenic sources. According to previous studies, the accumulation of Cd in soils is usually associated with agricultural activities (sewage irrigation, pesticide spraying and fertilizer use)⁴⁷, plating of automotive lubricants and brake pads⁴⁸, coal combustion⁴⁴ and industrial ore smelting⁴⁹. In addition, researchers have found that tourism activities can also increase levels of HMs in soils. In a study conducted by Wang et al.⁵⁰ on the effect of tourism activities on soil quality in the scenic area of Mount. Tai, Shandong Province, it was found that tourism activities accelerated the accumulation of HMs, with the average content of Cd exceeding the background value by 36.2%. Enrichment of HMs in soils under tourism loading conditions was also reported in another study, where local soils were highly contaminated with Cd⁴. An investigation of the study area showed that there were no mineral smelting activities or agricultural activities in the basin. The enrichment of Cd may originate from HM-containing wastes from tourism activities (batteries, plastics, wastewater, etc.) and atmospheric deposition (e.g., deposition from the wear and tear from motor vehicles and combustion of coal for heating). Therefore, Factor 3 can be considered a mixed effect of the above sources.

Figure S1 shows the contribution of three factors (F1: traffic source, F2: natural source, F3: tourist waste and atmospheric deposition) to HMs. The contributions of the three factors to Pb and Zn in the soil in a descending order were F1 ≈ F2 > F3. Cr, Cu and Ni were mainly influenced by F2. Cd was controlled by F3 (71.1%). Overall, it seems that the HMs of the topsoil in the tourist area of Sayram Lake were influenced by multiple sources, with the greatest contribution from F2 (38.5%), followed by F3 (34.3%) and F1 (27.2%). Notably, F3 has the greatest capacity to release Cd into the soil environment, and F3 control should be considered a priority.

To validate the results of the PMF model, the sources of Pb in the soil were further analyzed using the Pb isotope method. The Pb isotope ratio results confirmed that the Pb present in the topsoil of the tourist area of Sayram Lake was influenced by anthropogenic sources. As shown in Fig. 7, the Pb isotope ratios in soil samples from the tourist area of Sayram Lake were plotted with those in other related environments (²⁰⁶Pb/²⁰⁷Pb vs. ²⁰⁸Pb/²⁰⁶Pb). The Pb isotope ratio data of relevant environments mainly include Pb‒Zn ores from the Tianshan Mountains in Xinjiang⁵¹, vehicle exhaust emissions in China^52,53, urban road dust in Xinjiang⁵⁴ and dustfall in the Tianshan Mountains⁵⁵. Figure 7 shows that there were significant differences in the Pb isotope ratios of different regions. The sampling points near G30 had higher ²⁰⁶Pb/²⁰⁷Pb ratios and lower ²⁰⁸Pb/²⁰⁶Pb ratios. The sampling points near side roads had lower ²⁰⁶Pb/²⁰⁷Pb ratios and higher ²⁰⁸Pb/²⁰⁶Pb ratios. The Pb isotope ratios at the sampling sites located near the G30 main road were similar to those of vehicle exhaust emissions, urban road dust and dustfall, and the results indicated that Pb enrichment was strongly related to traffic emissions. Increased human activity has accelerated the accumulation of HMs in the environment, and isotopic methods can identify the sources of HMs based on the similarities they exhibit¹⁴. By integrating the Pb isotope data in each environment, it was determined that the Pb in the soil of the tourist area of Sayram Lake was partially derived from traffic emissions. This result validated the plausibility of factor 1 in the PMF model being a traffic source.

A comparison with other tourist areas (Table 1) yielded two distinctive findings: Pb, Zn, Cd and Cu are common pollutants that can easily accumulate in soils in tourism areas; the enrichment of HMs is strongly related to the type of local land use and human activities. Differences in human activities and the geographic contexts of each area lead to differences in the influencing factors and enrichment levels of HMs⁵⁶. To safeguard the soil environmental, HMs that are easily enriched in tourist areas should be highlighted for monitoring, and anthropogenic sources around tourist areas should be reasonably controlled.

The material composition and structure of the soil influence the concentration of HMs in soils. To investigate the effect of soil composition and structure on HMs under natural conditions, data including whole-rock composition, grain size, soil organic carbon and loss-on-ignition were used for redundancy analysis with HMs. Figure 8(a) reveals the relationship between SiO₂, LOI₁₀₀₀, Al₂O₃, CaO, Na₂O, TiO₂, SO₃, P₂O₅, MnO, K₂O, Fe₂O₃, MgO, SOC and Cd, Cr, Cu, Ni, Pb and Zn. The results showed that the content of HMs was related to SiO₂, Al₂O₃, TiO₂, P₂O₅, MnO, K₂O, Fe₂O₃ and SOC. Among them, Pb was most closely related to Al₂O₃ and SOC, and previous studies have shown that soil organic matter has a strong adsorption capacity for HMs⁵⁷. Zn, Cr, Cu and Ni were closely related to Fe–Mn minerals and silicates, which was similar to the results of Ma et al.⁵⁸. Additionally, this also indicated that HMs were weakly affected by carbonate and sulfate. The relationship between HMs and grain size (Fig. 8b) showed that HMs tended to be stored in soil particles with grain sizes < 32 µm. Among them, Cd, Ni, Cu and Cr were associated with clay (< 4 µm) and silt (16–32 µm), while Pb and Zn were associated with a fine-silty (4–16 µm) grade.

The findings revealed that the enrichment of Cd, Pb and Zn in the topsoil of the tourist area of Sayram Lake was influenced by anthropogenic sources. The results of the contamination and eco-risk evaluation suggest that the potential risks posed by the high I_geo and Eⁱ_r values of Cd should be treated seriously by relevant authorities. Intensive visitors and traffic will promote the release of HMs that accumulate in the topsoil to contaminate the soil environment and weaken its functions. Tourism quotas have been suggested to reduce soil contamination and degradation¹⁷. However, for traffic emissions of HMs beyond individual or regional control (e.g., lead in gasoline, car brakes, brake pads and tires), authorities may change their approach and undertake soil remediation. For example, the hyperaccumulation function of plants can be used to reduce HMs in soils or appropriate amounts of soil conditioners can be added⁵⁹ to reduce the polluting capacity of HMs by agglomerating them through adsorption and complexation effects.

In this study, the concentration characteristics, potential sources, contamination levels and eco-risk assessment of HMs were analyzed by collecting topsoil samples from the tourist area of Sayram Lake, but some limitations exist. First, a small number of topsoil samples were collected in this study; therefore, they might not adequately reflect the environmental status of the study area and reveal the spatial distribution characteristics of HMs. In addition, the objects of this study only involved Cd, Cr, Cu, Ni, Pb and Zn in the soil, and the absence of studies on other HMs (e.g., Hg, Co, W, etc.) and metalloids (As) has limitations for gaining an overall understanding of the soil environment in this study area. Additionally, HM source apportionment analysis should be combined with multiple source analysis methods to avoid contingency and subjectivity of the results. For example, quantitative analysis methods such as PCA-MLR and CMB can be combined to investigate potential sources and contributions of HMs, and isotope tracing methods for Cd, Cu and Zn can be used to reveal the sources of HMs more accurately. It is worth noting that there are some differences in the pollution evaluation results of the PERI and PLI methods that were caused by the different evaluation standards and thresholds adopted by the different methods. Therefore, the regional applicability of pollution evaluation methods is challenging, and subsequent studies on the toxicological effects of HMs in the region could be carried out to determine the pollution evaluation criteria and thresholds suitable for this region.

Source: Ecology - nature.com