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Article

Fe, Rather Than Soil Organic Matter, as a Controlling Factor of Hg Distribution in Subsurface Forest Soil in an Iron Mining Area

by
Rui Qu
,
Guilin Han
*,
Man Liu
,
Kunhua Yang
,
Xiaoqiang Li
and
Jinke Liu
Institute of Earth Sciences, China University of Geosciences (Beijing), Beijing 100083, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(1), 359; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17010359
Submission received: 5 December 2019 / Revised: 2 January 2020 / Accepted: 3 January 2020 / Published: 5 January 2020
(This article belongs to the Special Issue Trace Element Exposure and Human Health)
Browse Figures
Versions Notes

Abstract

:
To identify whether the iron (Fe) mining area in the Jiulongjiang River basin (JRB) has an influence on the mercury in the forest soil, the spatial distribution patterns of mercury’s behavior on different controlling factors were analyzed, and a potential ecological risk assessment was done. A total of 107 soil samples were collected from two forest soil profiles, one profile near the Fe mining area and the other far from it. The soil near the mining area had a moderate potential ecological risk with high Fe content rich in the upper layer of soil (<70 cm), whereas soil collected far from the mining area had a low potential ecological risk. These results indicated that the rise of iron content in the soil near the mining area was beneficial to the enrichment of mercury, probably causing damage to the forest ecosystem. Both soil organic carbon (SOC) and Fe content have strong positive correlations with THg content, controlling the mercury behavior in the upper layer (<70 cm) and a lower layer (>70 cm) of soil, respectively. The high Fe content in the upper layer of soil will compete for the adsorption of mercury by SOC, leading to the poor correlation between SOC and THg.

1. Introduction

Mercury (Hg) has been identified as a global pollutant, with its high neurotoxicity causing adverse effects on the environment and human health [1,2]. The long-distance transport of Hg in the atmosphere, which is emitted by anthropogenic activities and natural sources, causes widespread contamination [3,4]. Soil, generally considered as an essential medium in the ecosystem, has motivated an increasing interest in the sink and the sources of Hg [5,6,7]. Moreover, forest soil, as an active sink of Hg, plays a critical role in global Hg cycling [8]. In a recent study, vegetation was regarded as a large reservoir of Hg in the forest [9]. Ma et al. have corroborated that the forest field had a filtering effect of Hg by comparing Hg deposition fluxes with Hg output stream and runoff. Most Hg ultimately incorporated in the forest floor due to good adsorption of Hg by organic matter and caused a specific ecological risk [10]. With rapid economic growth and iron production in China, Hg accumulation in the surrounding soil has increased [11,12]. Previous reports have studied the iron adsorption of Hg in the soil [13,14]; however, little is understood about the effect of the iron mining area for the Hg in forest soil. Some studies have corroborated that the behavior of Hg has a strong relationship with soil organic carbon (SOC) [15,16], and the forest ecosystem is a significant sink of carbon. As a consequence, the adsorption of mercury by SOC with the influence of the iron mining area in the forest soil profile is worth discussing.
The object of this study is to explore whether the iron content has an influence on the adsorption of mercury by SOC in the forest surface soil near the mining area and determine the potential ecological risk. Moreover, the vertical distribution patterns and the controlling factors of mercury were analyzed to identify the geochemical behaviors in forest soil with the influence of the mining area.

2. Materials and Methods

2.1. Study Area

The Jiulongjiang River is located in the Fujian province, in the southeast of China (24°18′ to 25°88′ N, 116°78′ to 118°03′ E) (Figure 1). The river is a total of 1723 km long, and the size of the drainage basin is 14,700 km2 [17]. The Jiulongjiang River consists of three main tributaries (Nanxi River, Xixi River, and Beixi River), which flow into the Xiamen Bay through the regional estuary. The total discharge water is approximately 75% in the catchment with the impact of a seasonal monsoonal climate from April to September [18]. The study area with the subtropical oceanic monsoon climate has an annual temperature from 19.9 °C to 21.1 °C [19]. The terrain in the upper reaches of the river is mountainous, and there is extensive cover of the forest and a few anthropogenic activities in the upper reaches, where the mean annual mean precipitation is about 2000 mm. However, most of the lower reaches have the annual mean precipitation of 1200 mm—related to rural areas, where anthropogenic wastes have been discharged in quantity due to the development of agriculture and industry [20,21]. The increasing precipitation from lower reachers to upper reachers is caused by the NE-trending fault zone and the influence of climate [22].
The Jiulongjiang River basin is an essential tectonic belt due to the interaction between the Eurasian plate and the Pacific plate [23]. With multiple magmatic activities in the Mesozoic, over 60% of the study area consists of intrusive rocks and volcanic rocks in the central and southern parts [24]. Generally, the study area is primarily dominated by carbonate and silicate rocks without obvious evaporite. The carbonate rocks are mainly distributed in the upper reaches of the Beixi River [25], while the silicate rocks are in the lower reaches of the Beixi river, the Xixi River, and the Nanxi River [20]. The area of forest in the Jiulongjiang River basin is 123,000 hm2, with a coverage rate of 77.8% [26], and both sampling sites are mixed forests, which primarily consist of evergreen broad-leaf vegetation. Many iron deposits have been found in the study area. The Makeng iron deposit, one of the largest Fe deposits in Fujian province, is a skarn-type deposit near Longyan city [27]. The main types of metal minerals in Makeng iron deposit are magnetite, and galena, sphalerite, and molybdenite [28].

2.2. Sampling Collection and Analysis Method

The sampling collection was conducted in the Jiulongjiang River basin (JRB) in January 2018. The locations of two sample sites (S1 and S2) and regional lithology of JRB are shown in Figure 1. The sample sites were both in the forest near the mainstream, and the choice of locations takes into account many factors, such as mining, land use type, and geological conditions. The mining is mainly from the large Fe deposit in Longyan city (Figure 1), where it is close to the sample sites of S2. The land-use type of S1 and S2 is forest, aiming to make a contrast between the north and south of the basin. The sampling information was documented in detail in Table 1, including the record of longitude and latitude coordinates using a global positioning system (GPS). A total of 107 soil samples were collected from soil profiles in both sampling sites, with each soil sample being about 2 kg. The interval of sampling collection was 5 cm in each soil profile. Each soil profile was dug out and the profile was layered according to the color and texture of the soil; then, soil samples were collected from bottom to top, preventing the pollution of soil samples.
After grinding the soil samples to 200 mesh, a machine Retsch MM400 (Retsch GmbH, Haan, Germany) was used to make them homogenized. The total mercury (THg) content was analyzed using an RA-915M mercury analyzer (Lumex Instrument, St.Petersburg, Russia) with a solid module. The accuracy and precision of the mercury analyzer were successfully tested by a previous study [29]. The advantages of using this method to determine the mercury content are speed and its low cost compared to other methods, such as atomic fluorescence spectrometry and inductively coupled plasma optical emission spectrometry (ICP-OES). No digestion of soil samples involved could greatly simplify the mercury test and reduce the mercury losses during pretreatment. During the determination of mercury content, the parallel random samples and standard materials (GBW07402 and GBW07405) were tested every 10 soil samples to ensure the accuracy of the analyzer. The analyzer detection and relative standard deviation (RSD) were 0.10 μg∙kg−1 and 4.8%, respectively. As for the test of soil organic carbon, 2 g soil samples were soaked for 24 h with a mixture of 1M KCl and 0.5 M HCl to remove inorganic carbon and inorganic nitrogen [30,31]; then, soil samples were treated with ultrapure water until the pH was neutral. After the pretreatment, SOC was determined by an elemental analyzer (Vario TOC cube; Elementar, Langenselbold, Germany). Besides, the soil was digested using HNO3-HF-HClO4 and the content of Fe was determined by ICP-MS (Elan DRC-e, Perkin Elmer) [32], and the pH was measured using a pH-meter (INESA Scientific Instrument Co., Ltd., Shanghai, China).

2.3. Statistical Analysis

One-way ANOVA was conducted to determine the differences among the THg, SOC, and Fe contents at the upper layer of different sampling sites; statistical significance was at the level of p < 0.05. The bivariate correlations with the Pearson correlation coefficient and two-tailed test in line regression analyses were adopted to determine the associations among the THg, SOC, and Fe contents in samples. The data analyses were processed by SPSS 18.0 (SPSS Inc., Chicago, IL, USA) and Microsoft Office 2019 (Microsoft Corporation, Redmond, Seattle, WA, USA). The figures were performed by Origin 9.0 (OriginLab Corporation, Northampton, MA, USA) and SigmaPlot 12.5 (Systat Software GmbH, Erkrath, Germany) software packages.

2.4. Potential Ecological Risk Assessment

The potential ecological risk index ( E r i ) has been an effective tool to assess the ecological risks of heavy metals. The risks caused by mercury contamination in this study area was assessed with the following equation:
E r i = T r i × C i C n i ,
where C n i represents the background value of metal n in the study area; T r i (usually 40 for mercury) represents toxic the response factor; and Ci represents the content of samples for metal i. Generally, the potential ecological risk index values were classified into five categories: (i) low potential ecological risk ( E r i < 40); (ii) moderate potential ecological risk (40 ≤ E r i < 80); (iii) considerable potential ecological risk (80 ≤ E r i < 160); (iv) high potential ecological risk (160 ≤ E r i < 320); (v) very high ecological risk ( E r i ≥ 320) [33].

3. Results and Discussion

3.1. Overview of THg in Soil Profiles

An overview of THg and other contents in both sampling sites is provided in Table 2, and the orginal data is given in Appendix A Table A1. The maximum mercury content in the studied soils is higher than the background mercury content, 81 μg∙kg−1 [34], especially in the S2 profile. Studies have compared SOC content and pH values in both soil profiles and found that they are essentially identical. Data on both profiles fluctuate largely with respect to Fe content due to the influence of anthropogenic activities and presentation of iron rust.
For the upper layer of soil profiles (<70 cm), there are significant differences in the two sampling sites (Figure 2). In general, the contents of THg, SOC, and Fe in the S2 profile are higher than that in the S1 profile, especially for the THg and Fe contents. The amount of Fe concentrates in the upper layer of the S2 profile in comparison to the S1 profile, attributed to the location of the S2 site close to the mining area. A probable explanation is that this enrichment of Fe content in the mining area is a result of high mercury content in the S2 profile with respect to the S1 profile.

3.2. THg Distribution Pattern and Controlling Factors in Soil Profiles

The vertical distribution patterns of total mercury, divided into a upper layer (<70 cm) and a lower layer (>70 cm) of soil in Figure 3, show a significant discrepancy in different forest soil profiles in the study area. Generally, the THg content of both profiles is a little higher in the surface soil (<2 cm), perhaps attributable to the cover of the plant to avoid the solar radiation [35]. The THg content is more homogenous in the S1 profile than that in the S2 profile, even though there is a significant rise of THg content at a depth of 120 cm in the S1 profile. At the depth from 70 to 110 cm, the THg content in the S1 profile slightly increases. Compared to the S1 profile, the THg content in the S2 profile shows complicated variations in the upper layer, and it decreases with the depth in the lower layer of soil. However, the soil organic carbon shows a similar variation as the content decreases with depth in the upper layer, and it becomes homogenous in the lower layer, which is an agreement with a previous study [36]. The SOC content in the S2 profile stops reducing at the end of upper layer of soil and becomes steady; thus, the division of the soil profile is conducted according to the influence of the SOC content. The vertical distribution patterns of SOC in the forest soil profiles are in agreement with the previous reports [37,38]. As for the vertical distribution of iron (Fe) content, the Fe content is higher in the S2 profile than that in the S1 profile, and decreases with the depth, while the Fe content is concentrated in a lower layer of the S1 soil profile. There is a significant increase at the end of the upper layer in the S1 soil profile with the observation of the occurrence of iron rust.
What is striking in this Figure 3 is the different THg distributions of the S1 profile and S2 profile, which have motivated interest in the controlling factors for these patterns. Several factors are known to play a role in determining mercury distribution, such as SOC content and Fe content. Soil organic carbon can enhance the adsorption of metals for soil to make the soil mercury concentrated [39,40]. The ionic mercury can be combined strongly by organic molecules, such as humic acids and fulvic acids in the presence of soil organic matter [41]. Hg tends to be complexed with S-rich varieties in organic matter, which have a high affinity for mercury, resulting in Hg accumulation in the soil [42,43]. In the upper soil, the SOC content is usually taken into consideration for the controlling factor of mercury in soil with high correlations between THg content and SOC content [44,45,46]. However, as shown in Figure 4, soil organic carbon content shows a significant correlation (R = 0.90, p < 0.01) with THg content in the upper soil of the S1 profile, and that in the S2 profile is in contrast. This discrepancy could be attributed to the decomposed of light fraction of SOC. The soil microorganisms under forest soil easily decompose the light fraction of SOC [47]; thus, the decomposed light fraction under forest cover probably has an influence on the Hg/SOC ratio, resulting in a low correlation between THg content and SOC content [46]. Moreover, the THg content in the S2 profile, where there is higher SOC content compared to that in the S1 profile, shows no significant correlation with SOC content, probably implying that there is another competitively controlling factor for mercury. It is likely the high Fe content caused by the mining area affects the adsorption for mercury from SOC. In the upper soil, the Fe content is almost double, in the S2 profile, that in the S1 profile, which may have caused the discrepancy.
Some studies have been reported wherein the vertical distribution pattern of THg content decreases with depth [48,49]. However, the vertical distribution patterns of mercury in our study area shows enrichment in the lower layer of the soil profile to some extent. The iron rust presentation in the lower layer of the S1 profile results in the high Fe content, while the Fe is concentrated in the upper layer of the S2 profile due to the influence of the mining area. The high similarity of the distribution pattern of Fe and Hg in S2 (>20 cm) indicates that the main interaction of Hg in the soil is probably with Fe oxides. Fe oxides are good adsorbents for heavy metals, exhibiting specific adsorption of mercury through ion exchange [50]. Besides, the presence of Fe is an important abiotic factor for Hg oxidation from Hg0 to Hg2+ [51], which can lead to Hg immobilization. As a high Fe content is beneficial for the adsorption for mercury in soil [52], the increase of Fe content has an influence on mercury’s distribution behavior. The correlations between Fe content and THg content have been analyzed for the lower layers of both profiles, as shown in Figure 5. The strong correlations indicate that the Fe content has contributed to a rise of mercury content in the soil profiles. These results do not rule out the other factors in the vertical distribution of mercury. However, after the SOC content decreases, the Fe becomes the dominant controlling factor on the vertical distribution of mercury, affecting the enrichment of THg in both forest profiles.

3.3. Potential Ecological Risk of Hg in Soil Profiles

In order to conduct the aim of risk evaluation in the forest soil profiles, the ecological assessment of mercury was performed, as shown in Figure 6. It is apparent that all the soil samples in S1 profiles present low potential ecological risk with their slightly low mercury contents. However, over two-thirds of soil samples present moderate potential ecological risk; some soil samples even present a considerable potential ecological risk. These results probably indicate that the extremely mercury contamination occurs in the north of JRB due to the mining, whereas the contamination is comparatively slight in the south of JRB.

4. Conclusions

This study has shown that the risk evaluation of mercury, using geochemical characteristics of mercury distribution in relation to different controlling factors in forest soil profiles in the Jiulongjiang River basin, southeast China. The mercury content tents to be enriched in the lower layer (>70 cm) due to the rise of Fe content in soil profiles. In the upper layer (<70 cm) of the soil profile, the dominant controlling factor of mercury content is SOC content, with competition from Fe. However, in the lower layer of the soil profile, the main controlling factor is Fe exclusively, as the SOC content decreases. The high Fe content near the mining area in the upper layer of soil will compete for the adsorption of mercury with SOC, leading to the poor correlation between SOC and THg. In the north of the Jiulongjiang River basin, there is a moderate potential ecological risk of mercury and even a little bit of a considerable potential ecological risk from mercury in the forest soil profile, due to the mining area of Fe. The high Fe content has probably contributed to the enrichment of mercury, causing potential contamination. However, with relatively low mercury in the south of the Jiulongjiang River basin, there is a low potential ecological risk from mercury. According to our results, the contamination caused by the mining area has a strong influence on the near ecological environment, so it should be focused on and monitored. Further study about the light fraction of SOC and the associations between species of mercury and Fe content will be conducted to verify the adsorption of Hg by Fe and explore the mechanism.

Author Contributions

Conceptualization, R.Q. and G.H.; methodology, R.Q.; software, R.Q; validation, G.H.; formal analysis, R.Q.; investigation, G.H., M.L., K.Y., X.L., and J.L.; resources, G.H.; data curation, G.H.; writing—original draft preparation, R.Q.; writing—review and editing, R.Q. and G.H.; supervision, G.H.; project administration, G.H.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (number 41661144029 and 41325010).

Acknowledgments

The authors gratefully acknowledge Yupeng Tian for his assistance with field sampling.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Data for Hg, SOM, and Fe contents in each soil layer.
Table A1. Data for Hg, SOM, and Fe contents in each soil layer.
Sampling SiteDepth (cm)Hg (μg∙kg−1)SOM (%)Fe (mg∙kg−1)
S10 37.0 1.0 9368
5 42.3 1.1 8855
10 51.3 1.0 10,750
15 32.8 0.6 8797
20 35.0 0.7 9893
25 20.8 0.4 7282
30 26.2 0.4 8612
35 33.5 0.4 11,030
40 28.2 0.3 7986
45 19.2 0.2 8871
50 23.2 0.3 10,420
55 19.5 0.2 7115
60 18.1 0.2 9315
65 15.3 0.1 8990
70 24.7 0.1 8684
75 26.0 0.2 13,090
80 28.5 0.2 14,660
85 25.6 0.1 11,560
90 25.6 0.2 14,430
95 25.8 0.2 13,800
100 24.8 0.2 11,420
105 23.2 0.1 12,910
110 26.0 0.4 14,270
115 35.6 0.2 15,040
120 79.7 0.1 12,200
125 60.0 0.3 18,860
130 79.8 0.3 22,830
135 88.6 0.2 22,460
140 79.7 0.2 18,560
145 84.9 0.2 15,490
150 68.7 0.2 21,270
155 107.1 0.2 27,550
160 63.6 0.2 16,740
165 84.4 0.2 19,750
170 55.3 0.2 15,910
175 64.3 0.2 16,810
180 77.6 0.2 19,610
S20 118.5 1.4 26,780
5 94.2 0.8 25,130
10 97.8 1.0 19,370
15 77.2 0.6 22,200
20 94.9 0.8 20,390
25 64.8 1.0 24,870
30 49.5 1.1 17,900
35 47.0 1.0 14,960
40 62.2 1.0 22,160
45 59.7 0.9 20,090
50 86.4 0.9 24,900
55 89.0 0.6 20,100
60 91.0 0.7 19,020
65 182.2 0.4 24,330
70 154.8 0.3 21,340
75 138.0 0.2 22,970
80 144.4 0.2 19,660
85 126.7 0.2 17,330
90 129.4 0.2 18,790
95 128.6 0.2 17,340
100 109.7 0.2 19,320
105 151.3 0.2 22,920
110 116.4 0.2 20,270
115 139.6 0.2 19,990
120 133.7 0.1 13,790
125 80.4 0.2 8795
130 100.2 0.2 16,300
135 117.8 0.2 19,580
140 115.5 0.3 17,170
145 116.1 0.2 19,150
150 136.1 0.2 14,320
155 113.7 0.1 13,170
160 122.2 0.2 15,740
165 137.6 0.2 13,420
170 134.3 0.2 12,790
175 117.3 0.2 14,700
180 122.2 0.1 15,680
185 109.2 0.2 14,850
190 102.4 0.2 14,800
195 109.1 0.2 14,110
200 124.9 0.2 15,390
205 123.6 0.3 12,520
210 110.2 0.2 12,190
215 116.4 0.3 13,660
220 114.6 0.2 13,400
225 97.5 0.2 12,320
230 103.4 0.2 12,180
235 104.0 0.2 12,690
240 112.0 0.2 12,900
245 102.3 0.1 13,550
250 105.3 0.2 10,860
255 102.8 0.1 10,930
260 94.5 0.2 12,760
265 93.6 0.1 13,580
270 102.5 0.2 10,130
275 102.0 0.3 11,350
280 100.3 0.2 11,640
285 82.2 0.2 9498
290 82.4 0.2 10,920
295 73.1 0.2 9759
300 70.8 0.2 8448
305 61.1 0.1 8312
310 63.3 0.2 9878
315 81.1 0.2 13,900
320 83.9 0.2 18,040
325 64.6 0.2 9580
330 63.9 0.2 10,580
335 59.9 0.2 10,170
340 52.6 0.1 10,430

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Figure 1. The mining distribution, regional lithology, and location of sampling sites in the Jiulongjiang River.
Figure 1. The mining distribution, regional lithology, and location of sampling sites in the Jiulongjiang River.
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Figure 2. The contents of THg, SOC, and Fe in each profile (0–70 cm) (o represents an outlier).
Figure 2. The contents of THg, SOC, and Fe in each profile (0–70 cm) (o represents an outlier).
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Figure 3. Vertical distribution patterns in forest soil profiles, including THg, SOC, and Fe contents.
Figure 3. Vertical distribution patterns in forest soil profiles, including THg, SOC, and Fe contents.
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Figure 4. Line regression relationships between the SOC content and THg content in the upper layers (<70 cm) of both forest soil profiles.
Figure 4. Line regression relationships between the SOC content and THg content in the upper layers (<70 cm) of both forest soil profiles.
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Figure 5. Line regression relationships between the Fe content and THg content in the lower layers (>70 cm) of both forest profiles.
Figure 5. Line regression relationships between the Fe content and THg content in the lower layers (>70 cm) of both forest profiles.
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Figure 6. The ecological assessment in S1 and S2 forest soil profiles.
Figure 6. The ecological assessment in S1 and S2 forest soil profiles.
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Table
Table 1. The information regarding the sampling sites.
Table 1. The information regarding the sampling sites.
Sampling SiteLocationMain PlantDepth (cm)Visible Characteristic of Soil
S1117°14′5″ E, 24°39′6″ NMyrtle (Rhodomyrtus tomentosa), masson pine (Pinus massoniana Lamb)0–40
40–70
70–180		Black, humus layer
Gray, silty loam
Red, silty clay loam, iron rust
S2117°25′28″ E, 25°16′21″ NPine (Pinus tabuliformis Carr)0–65
65–120
120–235
235–340		Gray–red, humus layer
red, loam
Crimson, loam
light red, loam
Table
Table 2. Statistics of the data for Hg (μg∙kg−1), soil organic carbon (SOC—%), Fe (mg∙kg−1), and pH (in unit).
Table 2. Statistics of the data for Hg (μg∙kg−1), soil organic carbon (SOC—%), Fe (mg∙kg−1), and pH (in unit).
nMinMaxMeanSD
S1Hg3715.30 107.10 44.92 25.71
SOC370.121.07 0.31 0.24
Fe377115.00 27,550.00 13,653.73 5041.15
pH374.34 5.11 4.67 0.18
S2Hg6947.00 182.20 102.46 27.84
SOC690.10 1.44 0.33 0.31
Fe698312.00 26,780.00 15,682.03 4741.00
pH694.08 4.56 4.33 0.13

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MDPI and ACS Style

Qu, R.; Han, G.; Liu, M.; Yang, K.; Li, X.; Liu, J. Fe, Rather Than Soil Organic Matter, as a Controlling Factor of Hg Distribution in Subsurface Forest Soil in an Iron Mining Area. Int. J. Environ. Res. Public Health 2020, 17, 359. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17010359

AMA Style

Qu R, Han G, Liu M, Yang K, Li X, Liu J. Fe, Rather Than Soil Organic Matter, as a Controlling Factor of Hg Distribution in Subsurface Forest Soil in an Iron Mining Area. International Journal of Environmental Research and Public Health. 2020; 17(1):359. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17010359

Chicago/Turabian Style

Qu, Rui, Guilin Han, Man Liu, Kunhua Yang, Xiaoqiang Li, and Jinke Liu. 2020. "Fe, Rather Than Soil Organic Matter, as a Controlling Factor of Hg Distribution in Subsurface Forest Soil in an Iron Mining Area" International Journal of Environmental Research and Public Health 17, no. 1: 359. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17010359

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