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Risk assessment for the mercury polluted site near a pesticide plant in Changsha, Hunan, China
Chemosphere 169 (2017) 333e341
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Risk assessment for the mercury polluted site near a pesticide plant in Changsha, Hunan, China Haochen Dong a, Zhijia Lin b, Xiang Wan a, Liu Feng a, * a b
Department of Environmental Sciences and Engineering, Beijing University of Chemical Technology, Beijing 100029, PR China Hunan Institue of Geological Survey, Changsha, Hunan 410116, PR China
h i g h l i g h t s Total concentration and fraction distribution of mercury were characterized. Soil physicochemical properties were built-up for fractionation analysis. DGH method and model were introduced for risk assessment.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 July 2016 Received in revised form 3 November 2016 Accepted 15 November 2016
The distribution characteristics of mercury fractions at the site near a pesticide plant was investigated, with the total mercury concentrations ranging from 0.0250 to 44.3 mg kg1. The mercury bound to organic matter and residual mercury were the main fractions, and the most mobile fractions accounted for only 5.9%e9.7%, indicating a relatively low degree of potential risk. The relationships between mercury fractions and soil physicochemical properties were analysed. The results demonstrated that organic matter was one of the most important factors in soil fraction distribution, and both OM and soil pH appeared to have a significant influence on the Fe/Mn oxides of mercury. Together with the methodology of partial correlation analysis, the concept and model of delayed geochemical hazard (DGH) was introduced to reveal the potential transformation paths and chain reactions among different mercury fractions and therefore to have a better understanding of risk development. The results showed that the site may be classified as a low-risk site of mercury DGH with a probability of 10.5%, but it had an easy trend in mercury DGH development due to low critical points of DGH burst. In summary, this study provides a methodology for site risk assessment in terms of static risk and risk development. © 2016 Elsevier Ltd. All rights reserved.
Handling Editor: Martine Leermakers Keywords: Mercury Delayed geochemical hazard Risk assessment
1. Introduction Following the rapid social and economic development over the past several decades, soil pollution by mercury (Hg) has been observed worldwide (Van Straaten, 2000; Gochfeld, 2003; Gosar et al., 2006; Qiu et al., 2006; Cordy et al., 2011). For example, in the entire Lake Victoria Goldfields of Tanzania, Africa, approximately 3e4 t Hg was released annually into the atmosphere (Van Straaten, 2000). In the Amazon region, mercury has been released during gold mining operations, resulting in methylmercury exposure via the food chain (Gochfeld, 2003). Additionally, in the mining areas of Guizhou, China, mine wastes contain total Hg
* Corresponding author. E-mail address: [email protected] (L. Feng). http://dx.doi.org/10.1016/j.chemosphere.2016.11.084 0045-6535/© 2016 Elsevier Ltd. All rights reserved.
concentrations ranging from 79.0 to 710 mg kg1, with significant conversion of methyl-Hg in the study areas, causing potential health risks (Qiu et al., 2006). Mercury is a persistent pollutant and the bioaccumulation and biomagnification of toxic pollutants has a significant impact on human health and ecological environment. The health and ecological risks associated with mercury-polluted sites have been widely recognized and studied. The data from the Wuchuan mercury mining area (one of the largest mercury mining areas in Guizhou province, China) showed that people living near a mercury mining site experienced adverse health effects caused by mercury exposure. Total concentration of Hg measured in the hair of the residents near the mercury mining area reached 210 mg kg1, far beyond the value of the control group (Li et al., 2008a, 2008b). In the USA, mercury contamination with the highest concentration reaching 2.10 mg kg1 was discovered in Fort Totten by measuring
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the total concentration of Hg with a comprehensive evaluation process related to human health risk (Goldblum et al., 2006). Risk assessment based on the total amount of pollutants is an intuitive and simple method that has been applied to heavy metal contaminated sites in early stages (Agusa et al., 2005; Goldblum et al., 2006; Mieiro et al., 2009). However, as with the development of a deeper understanding of environmental behaviour and ecological effect of pollutants, the inadequacy of this method has become increasingly apparent. Since the method does not take into consideration the differences in environmental effectiveness and biological availability between various chemical forms of metals, the method often overestimates the potential risk (Goldblum et al., 2006). Speciation analysis in a bioavailability study could provide a different perspective for risk assessment. In this approach, the different chemical forms of heavy metals such as mercury are identified in order to characterize their mobility and toxicity (Issaro et al., 2009). Sequential extraction procedure (SEP) has been widely applied as a tool in the speciation analysis for metal extraction in soils, especially the Tessier SEP (Tessier et al., 1979; Issaro et al., 2009). There are five defined fractions in the order of extraction difficulty with different occurrence of forms. In 1993, European Community Bureau of Reference (BCR) certified the extractable contents of a trace metal in sediments following a three-step extraction procedure (Quevauviller et al., 1997) that has recently €kel€ become a commonly used tool for soil (sediment) studies (Ma a et al., 2011; Chakraborty et al., 2014; KerollieMustafa et al., 2015). Compared with Tessier SEP, BCR extraction has fewer steps and a weaker phenomenon of redistribution/re-adsorption that gives rise to better reproducibility. Both Tessier and BCR SEP have been widely used in previous studies of Hg in many mining areas in China, showing that the Hg concentrations in mining areas range from 0.100 to 790 mg kg1 with 60%e80% HgS as the main existing form (Horvat et al., 2003; Li et al., 2007; Qiu et al., 2013; Yang et al., 2014). Both methods have shortcomings in the extraction of mercury (Issaro et al., 2009) for example, a Hg redistribution/readsorption pattern may occur and cause incomplete extraction. According to recent studies, a new approach proposed by the ticas, Medioambientales y TecCentro de Investigaciones Energe gicas (CIEMAT) has been used for a fractionation study at the nolo n mercury mine area in Spain. The comparison between the Almade addition of Hg concentration to each fraction and the measured total Hg, ranged between 92% and 99%, and proved the feasibility of ndez-Martínez and Rucandio, 2013; the new method (Ferna ndez-Martínez et al., 2014; Fern Ferna andez-Martínez and Rucandio, 2014). The implemented SEP can also be useful in providing information about the amount of mercury available in different contaminated areas for risk assessment. A study of the n mining area found very low available merplants in the Almade cury, including water-soluble and exchangeable fractions in all n et al., 2006). Even so, Tessier SEP is still valid and samples (Milla favourable for risk assessment of heavy metal pollution (Islam et al., 2015; Rosado et al., 2016). From another point of view, soil physical and chemical properties are also an integral part of the assessment. A previous study has shown that pH provides the most useful information for estimating an element's migration that can be an important parameter for the assessment of the available mercury. Furthermore, Cation Exchange Capacity (CEC) does not improve the ability to predict the moven ment of ions through these natural soils (Korte et al., 1976). Norde (1994) studied pH and organic matter (OM) content of forest topsoil and found that the differences in soil organic matter content between the plants were small. However, these studies focus on either the chemical forms or the total concentration of mercury statically and unilaterally, and therefore, the resultant static risk
assessment of the sites overlooks the transformation between these forms in the soil. Ming et al. (2005) proposed the concept of delayed geochemical hazard (DGH) that revealed the dynamic transformation among different forms and became a tool for risk assessment. “Total releasable content of the pollutant” (TRCP) and “total concentration of active species” (TCAS) are two important concepts related to DGH. TRCP focuses on the releasable content that is equal to the total content of the pollutants in the soil (sediment) minus the content of some compounds that are quite stable with respect to activation and release in the soil (sediment) even under extreme conditions (Ming et al., 2004). TCAS is the active characteristic of the pollutant and indicates the concentration of some species in TRCP involved in much more activity under certain environmental conditions (Ming et al., 2004). Our previous study (Zheng et al., 2015) demonstrated the nonlinearity changes among different chemical fractions that occur when geochemical conditions are altered in soil by soil column tests. The results provided a possibility to establish a mathematical model to quantify the potential development of DGH. The structure and characteristics of the original model are shown in Fig. 1. The X axis indicates TRCP (C) in the soil system and the Y axis indicates TCAS (Q). The fitting curve L0 represents the trend in TCAS with the increase in TRCP; continuous input of pollutants into the soil system altered the increasing trend in TCAS. At first, an increase of TRCP (DC) resulted in a little increase in TCAS (DQ1); however, when continuous accumulation of TRCP reached a certain degree, the same increase in TRCP, DC, would give rise to a greater increase in DQ, TCAS (DQ2). This was in accordance with the non-linear mutational rising of TCAS. The previously fitted results depending on the Tessier method showed a dynamic evolution process that could be quantitatively expressed with a nonlinear polynomial and the highest degree of these polynomials are 3. Combined with the preceding digital model of DGH, we could characterize two special points: critical point of burst and burst point as follows: Critical point of burst, where the first and second derivatives are zero and convexity and concavity of the curve are altered, represents the beginning of DGH. When the first derivative reaches the maximum value and the second derivative is zero, the critical point that can be calculated and related to maximum slope, represents the most active stage of DGH (Ming et al., 2005). In a recent study (Zheng et al., 2015), a DGH model expressed with nonlinear polynomials was successfully used to assess the risk of a mercury polluted site. The mercury polluted area that is the focus of this study is located in Hunan, Changsha, near an abandoned pesticide factory (PF). Among other compounds, the main products of the factory were arsenic agents (such as calcium arsenate used in pesticides and other arsenate herbicides, bactericides, etc.), organochlorine, organophosphorus, organ nitrogen, and chlorothalonil. Due to managerial negligence during the early phases of the factory, massive discharge of pollutants to the surrounding soil caused different degrees of heavy metal pollution, including mercury and arsenic. To accurately assess the potential environmental risk of mercury pollution, total concentration of Hg and distributions based on the Tessier method were measured for a preliminary static risk assessment of the site. Meanwhile, statistical analysis techniques were used to analyse the effect of soil physical and chemical properties on mercury accumulation of various forms and elucidate the interactions among different mercury forms. Then, A DGH model was imported to characterize the dynamic evolution process of risk and provide basic data for site management and remediation. The methods used in this study provide a new concept for risk
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Fig. 1. Original digital model of DGH.
assessment of heavy metal contaminated sites. 2. Materials and methods 2.1. Soil samples Two hundred samples were gathered from the parcels of the PF located in Hunan. Soil samples were collected from topsoil (0e30 cm) of both the woodland and cropland area within 1 km2 of the factory. The locations of the sampling sites are shown in Fig. 2.
The centring of sample sites on the PF helped develop a systematic grid sampling method (with a density of 50 m 100 m) from N001 to N200 with geographic coordinates of 112 010 E 27 570 N. At each soil sampling point, 3 samples of the surface layer were mixed as one sample and packaged in plastic bags stored under dry and cold conditions prior to being transported to the laboratory. Samples were all air-dried at room temperature to minimize the loss of Hg due to volatilization. After drying, samples were sieved through a 100-mesh screen by standard Tyler sieves before chemical analysis.
Fig. 2. Study area and sampling sites in Pesticide Factory.
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2.2. Determination of soil physical and chemical properties For all samples, we first measured some physical and chemical properties including: pH, OM, CEC, and oxidation-reduction potential (ORP). For pH, OM, and CEC measurements, we followed the techniques of a previous study (Sungur et al., 2014): pH was measured by a pH-meter with 1:2.5 (soil: water ratio, w/v) suspension; we used an EC-meter to measure soil CEC of the suspension; and OM was measured using dichromate in acid medium to oxidize soil organic matter (Nelson and Sommers, 1982). ORP was determined using a depolarization method by an automatic ORP analyser (FJA-5). 2.3. Soil sample pre-treatment and analysis 2.3.1. Sample pre-treatment for total Hg The total Hg (THg) concentration of each sample was measured using an AFS instrument (AFS-3100) following the recommendations by US EPA Method 7474 (EPA, 2007). A selection process has been constructed according to the profile of the soil samples for the speciation analysis of THg. 2.3.2. Chemical fractions of Hg with Tessier method Twenty samples were selected based on the concentration gradient of THg using the following guidelines: i) Remove the samples related to the highest concentration area of mercury; they should be analysed separately. ii) Divide the rest of the samples into 20 gradients depending on the total concentration of mercury and select soil samples corresponding to each critical value. Tessier SEP was applied for analysis, as shown in Table 1. For reliability, the sum of Hg concentrations extracted in SEP would be assessed by comparing them to the total concentration of mercury measured previously. After each SEP extraction, samples were centrifuged at 4000 rpm for 10 min. The supernatants were removed to volumetric flasks and then 10 ml of preserving reagent (K2Cr2O7 þ HNO3) was added and diluted with attenuation reagent (K2Cr2O7 þ H2SO4) to 50 ml. Next, all samples were stored in refrigerator at 4 C before analysis. 2.3.3. Analysis method and conditions of AFS All samples were analysed with diluted supernatants by a double-channel atomic fluorescence spectrometer (AFS-3100, BJHG, China) under the following conditions: high-purity argon was used as a carrier gas with a flow rate of 400 mL min1; atomization height was adjusted to 10 cm; the current of hollow cathode lamp was set to 15 mA, and negative high-voltage was 270 V. Five percent HCl solution was the carrier liquid, and the mixed solution of 2% KBH and 0.5% NaOH was the reductant. Samples were analysed at least three times. Data fluctuations during measurements were generally lower than 5% and the detection limit was 0.0001 mg l1.
Table 1 Procedure of Tessier SEP. Fractions
Extracting agent and condition
HgE Water soluble and exchangeable HgC Weakly complexed and bound to carbonate HgF Bound to Fe/Mn oxides of low crystallinity HgO Bound to organic matter
MgCl2 (1 mol/L pH ¼ 7) NaAc-HAc (1 mol/L pH ¼ 5)
HgR Residual
NH2OH$HCl (0.04 mol/L) HNO3 þ H2O2 (0.02 mol/L pH ¼ 2 85 C) Aqua regia (96 C)
A section on the QC protocol was applied. We used the soil standard GBW (E) 070009 (the Institute of Geophysical and Geochemical Exploration, China) as a standard reference material. The measured average total mercury concentration of this material was 2.06 ± 0.11 mg kg1 (n ¼ 6), which is consistent with the certified value of 2.20 ± 0.40 mg kg1. Reagent blanks were also used and the result of total mercury concentration measurements was below the detection limit of AFS. 2.4. Model analysis software Statistical analyses were performed using SPSS version 22.0 for Windows. All nonlinear polynomials of the DGH model fitted by Origin 9.0 and a one-way ANOVA (analysis of variance) were applied to analyse the significance of the differences between groups when p < 0.05. Concentration distribution charts were drawn using Surfer version 8 for Windows. 3. Results and discussion 3.1. Characterization and total mercury in soil profiles The physicochemical properties of the soil samples showed that the soil in the study area was slightly acidic except for a few sampling sites. The pH ranged from 3.44 to 8.68, with moderate soil fertility (OM content ranged from 0.110% to 9.80% with an average content of 2.0%) comparable with that in the mining area (0.6%e2%) ndez-Martínez and Rucandio, 2014), relative high oxidation (Ferna resistance (ORP ranged from 491 mV to 948 mV), and moderate cation exchange capacity (CEC ranging from 7.00 cmol kg1 to 23.0 cmol kg1). Total Hg concentrations in the study area ranged from 0.0250 ± 0.0024 mg kg1 to 44.3 ± 1.7 mg kg1. According to Chinese soil environmental quality standard, there are three levels of total mercury concentration in both woodland and cropland areas: First-class level: 0.15 mg kg1, similar to the natural background value; third-class level: above 1.5 mg kg1, which is the marginal value that assures normal farming, forestry production, and plant growth. Most of the study area around the PF showed relatively lower Hg total concentrations, with 44% belonging to the first-class level, according to the soil quality criteria. Still, 7% of the study area where the THg was over 1.50 mg kg1 exceeded the third-class level. A higher concentration was observed in PF with a maximum of 44.3 mg kg1, indicating some potential risk. The average concentration of Hg in PF was 1.28 mg kg1, which is higher than the minimum concentration of Hg according to a recent study (referred to as the background value of 0.160 mg kg1) in Hunan (Wang et al., 2010). Furthermore, 17% of the samples partly exceeded the background values in European soils (maximum 0.420 mg kg1) (Gawlik and Bidoglio, 2006). Compared to the maximum permissible concentrations of potential toxic elements for agricultural soils of China (0.300 mg kg1) (Wei and Yang, 2010), about half of the samples were out of the standard range. Fig. 3 shows the distribution of total Hg in the PF. Hgcontaminated area was found at the centre-right area with concentrations reaching 44.3 mg kg1 (N149). An independent source of pollution was found near the lower left corner of the area and had a secondary concentration of THg of 40.2 mg kg1 (N186). 3.2. Distribution characteristics of different mercury fractions Fig. 4a shows the distribution of Hg in the chosen PF subsamples. The recovery rate of Hg as the sum of the fractions relative to THg varied between 71% and 116%, revealing that the method may be used to assess different chemical forms of soil. The sum of
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Fig. 3. Spatial distribution of Total Hg concentration in Pesticide Factory.
the fractions relative to THg is in good agreement at the 95% confidence level with the certified value (correlation ¼ 0.991, sig<0.05, paired-samples t-test). All subsamples had low content of HgE corresponding to the most easily available Hg content (Wallschl€ ager et al., 1998; Bloom et al., 2003) and indicating the relatively low content of the active form (maximum is 0.004 mg kg1). While HgC is susceptible to the changes in pH (Tessier et al., 1979), the content of this fraction is still low among the subsamples (maximum is 0.0300 mg kg1). The lack of HgF content in the subsamples is similar to the distribution of background soil in Hunan. HgO is the most abundant in some subsamples indicating the human factor impact (Feng and Chen, 1996). HgO and HgR are the main fractions of the selected samples that were both similar to the soils from the mining area and relevant to other mercury-contaminated industrial areas (Biester and Scholz, 1996; Lechler, 1999; Miller et al., 2013). N151 had a concentration of over 2.00 mg kg1, which exceeded the critical value of the third-class level according to its fraction, and the highest fraction of Hg in the sample was bound to the organic matter associated with anthropogenic sources (Feng and Chen, 1996). Combined with the concentration distribution, this may indicate that some man-made mercury pollution is present. Fig. 3 shows a separate analysis of the most serious areas, for which the samples N147-149 are the critical samples. Fig. 4b shows the distribution of Hg in a seriously polluted area located in PF. Appreciable amount of mobile Hg components (5.9%e9.7%) was detected in these samples; this was higher than the same amount of Hg previously detected in a mining area that had a maximum of about 1%, but similar with relevant industrial area (blow 10% of these components) (Miller et al., 2013; Chen et al., 2016).
3.3. Relationship analysis for inner connections among mercury fractions and physicochemical properties in soil Knowledge of the soil properties and interactions between the other metals is important to assess the bioavailability (Akkajit and Tongcumpou, 2010). Correlation analysis was performed to identify the relationships between the total mercury concentrations and the physicochemical properties of the soil. The correlation coefficients between THg concentration and physicochemical properties in the soil had a low value. Both pH and OM have a slight influence on mercury accumulation. To study this further, five fractions of Tessier method were analysed by both the Pearson's correlation coefficient and partial correlation with the physicochemical soil properties. Table 2 presents the correlations between the Tessier method fractions and the physicochemical soil properties. HgO had a positive relationship with OM, indicating that OM is one of the most important factors of the heavy metal fraction distribution in the soil n et al., 2006). Both OM and soil pH are factors that have a (Milla significant influence on HgF representing the Fe/Mn oxides, similar to the results of the previous studies (Simmons et al., 2009). Considering internal relations among these fractions in Tessier method, partial correlation analysis was used to control the variables and it turned out to be a clearer relationship among those parameters. 3.4. Risk development characterization and assessment Speciation analysis, as well as soil profiles, only focused on the static risk. According to previous studies, chemical speciation of Hg €ger et al., 1998; Ming et al., in the ecosystem is dynamic (Wallschla
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Fig. 4. Distribution of Hg of chosen samples in Pesticide Factory: 4a, 20 chosen samples; 4b, heavy-polluted samples in Pesticide Factory.
2004, 2005), revealing the importance of dynamic risk evolution and assessment. Delayed geochemical hazard (DGH) represents the sudden reactivation and release of long-term accumulated pollutants in soil/sediment systems due to the decreased environmental capacity or a change in physicochemical conditions that may cause an ecological and environmental hazard. A DGH model that can be quantitatively expressed with a nonlinear polynomial was used to assess and predict the potential environmental risks of Hg
exposure. In this case, fitting data to each equation were based on Tessier SEP; thus, TRCPHg should contain all the components involved in the Tessier method and be equal to the sum of the numerators of each fraction (Zheng et al., 2015). In agreement with the DGH law, the corresponding fitting equation of the site is shown in Table 3 with high correlation coefficients (R2) and large F values at p < 0.005. We calculated the second derivative of each polynomial corresponding to the concept of ‘critical point of burst’ in DGH, which means that at any stage of the chain reactions related
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Table 2 Correlation analysis between fractions of Tessier method and soil profiles (n ¼ 20). Parametera
pH
HgE HgC HgF HgO HgR
0.127 0.199 0.480b 0.258 0.126
a b c
OM 0.540 0.286 0.471b 0.629 0.337
CEC
0.273 0.018 0.398 0.645c 0.791c
0.484 0.014 0.504b 0.640c 0.771c
0.074 0.180 0.051 0.126 0.168
ORP 0.085 0.039 0.187 0.369 0.192
0.178 0.118 0.430 0.176 0.189
0.553 0.303 0.117 0.600 0.361
For each parameter, both Pearson correlation coefficient (left) and partial correlation coefficient (right) were listed. Statistically significant correlation at the selected confidence level (a ¼ 0.05). Statistically significant correlation at the selected confidence level (a ¼ 0.01).
to the Tessier method, DGH may burst once TRCPHg is higher than the critical point as calculated in Table 3. Each polynomial represented one possibility path for species transformation (related to Tessier method) when DGH occurs. The results showed low critical points of the DGH burst in the study area, indicating an easy trend to burst the risk of DGH, despite the generally low content of the active form in this area. In consideration of the availability of fractions in the Tessier method, the path HgEþCþFþOþR / HgEþCþO was used as an example. The regression equation is expressed by Eq. (1) and fitting curve could be checked in Supplementary Materials:
Y ¼ 0:1500X3 0:3491X2 þ 0:5012 n ¼ 20; R2 ¼ 0:985 (1) This is one path of DGH and we used this path to characterize the studied area, because in this path, chain reactions from HgEþCþFþOþR / HgEþCþO would lead to some mobile fractions of mercury when DGH happens. We let the second derivative of Eq.
(1) be zero. Thus, the calculated TRCPHg is equal to 0.776 mg kg1. We fitted all the potential paths of DGH and the calculated TRCPHg ranging from 0.764 to 0.810 mg kg1 presented in Table 3 to give a gist for the assessment. According the THg data, the percentage of THg beyond TRCPHg in the study area was 10.5% (ranging from 10.0% to 10.5%) classified as low-risk for both DGH. It is worth noting that the sampling point with the highest concentration of THg near PF could be more dangerous for the spurt growth of Hg concentration in these areas. That is to say, more numeric fraction transformation may occur following the DGH paths, and early steps should be taken to prevent the evolutionary hazards that may lead to DGH disasters in these areas. 4. Conclusions Based on the total Hg concentrations in the pesticide factory, although most of the area had low THg concentrations, several critical polluted areas were observed with maximum concentrations reaching 44.3 mg kg1, indicating the degree of accumulation
Table 3 Fitting equations and the corresponding digital characteristics of mercury in the studied areas. Potential DGH path
DGH models
Statistical characteristics
Y is TCASHg; X is TRCPHg (mg/Kg) HgO HgFþO HgEþO HgFþO HgFþO HgO HgO HgEþCþO HgEþFþO HgEþCþO HgFþO HgCþO HgCþO HgFþO HgCþO HgCþO HgCþO HgEþCþFþO HgCþO HgCþO HgCþO HgCþFþO HgCþFþO HgFþOþR HgOþR HgEþOþR HgCþOþR HgF HgCþF HgO
HgFþOþR HgCþFþOþR HgEþOþR HgEþCþFþOþR HgCþOþR HgFþR HgEþOþR HgCþFþOþR HgEþFþOþR HgEþCþFþOþR HgFþOþR HgCþFþOþR HgEþCþFþOþR HgEþFþR HgCþOþR HgFþOþR HgEþFþOþR HgEþCþFþOþR HgOþR HgEþOþR HgEþFþR HgEþCþFþOþR HgEþFþOþR HgEþCþFþR HgEþCþR HgEþCþFþR HgEþCþFþR HgEþF HgEþCþF HgCþOþR
* P-value of all fitting equations are less than 0.005.
Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼
3
2
0.1395X 0.3195X þ 0.4942X 0.1428X30.3301X2 þ 0.4998X 0.1432X30.3316X2 þ 0.5006X 0.145X30.3328X2 þ 0.4921X 0.1439X30.3339X2 þ 0.5021X 0.1455X30.3342X2 þ 0.4928X 0.1444X30.3354X2 þ 0.5029X 0.1465X30.3378X2 þ 0.495X 0.1466X30.3424X2 þ 0.506X 0.15X30.3491X2 þ 0.5012X 0.1507X30.3513X2 þ 0.5027X 0.1502X30.3538X2 þ 0.5122X 0.1513X30.3576X2 þ 0.5145X 0.1527X30.3579X2 þ 0.5058X 0.1532X30.3592X2 þ 0.5065X 0.1538X30.3615X2 þ 0.508X 0.1569X30.3714X2 þ 0.5132X 0.158X30.375X2 þ 0.5155X 0.1761X30.4187X2 þ 0.5093X 0.1801X30.428X2 þ 0.5073X 0.1794X30.4293X2 þ 0.515X 0.1807X30.4303X2 þ 0.5088X 0.1832X30.4416X2 þ 0.5212X 0.1874X30.4517X2 þ 0.5203X 0.1868X30.4531X2 þ 0.5274X 0.1872X30.4545X2 þ 0.5281X 0.1884X30.4553X2 þ 0.5225X 0.1889X30.4566X2 þ 0.5231X 0.1879X30.4569X2 þ 0.5297X 0.1915X30.4651X2 þ 0.5276X
The critical points of DGH burst
R2
F
TRCPHg
TCASHg
0.984 0.985 0.985 0.984 0.985 0.984 0.985 0.984 0.985 0.985 0.985 0.986 0.985 0.985 0.985 0.985 0.985 0.985 0.984 0.984 0.985 0.984 0.985 0.985 0.985 0.985 0.985 0.985 0.985 0.985
354 370 368 357 366 356 365 352 381 367 365 387 384 381 379 377 385 381 345 345 360 343 371 369 378 377 366 364 374 374
0.764 0.771 0.772 0.765 0.773 0.766 0.774 0.768 0.778 0.776 0.777 0.785 0.788 0.781 0.782 0.783 0.789 0.791 0.793 0.792 0.798 0.794 0.803 0.804 0.808 0.809 0.805 0.806 0.810 0.810
0.253 0.255 0.255 0.247 0.255 0.247 0.255 0.247 0.256 0.249 0.249 0.257 0.257 0.250 0.250 0.250 0.251 0.251 0.228 0.223 0.229 0.223 0.229 0.224 0.229 0.229 0.224 0.224 0.229 0.224
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of mercury. Tessier SEP was used to analyse the Hg fractions. Samples collected at PF have low mobile components of Hg, while Hg associated with OM and the residual are main fractions. Moreover, for samples at critical areas in PF, appreciable mobile components were extracted (ranged from 5.9% to 9.7%), providing some indication of available Hg content that could pose potential risk. The analysis of the correlations between mercury and the soil physicochemical properties showed that soil pH has a significant influence on Fe/Mn oxides; OM played an important role in soil mercury availability and fraction distribution; and partial correlation coefficients in the relationships between the fractions of Tessier method revealed the potential dynamic transformation of chemical speciation. Using the DGH model, the potential risks caused by mercury were investigated and the results indicated low critical points of DGH burst in the study area, which implies an easy trend to dynamical risk evolution. While the lower measured percentage (10.5%) indicates a low-risk of DGH for one path of the chain reactions in DGH, concentrated areas with high concentration of Hg were also observed and these may release high amounts of available Hg and could be more dangerous. Acknowledgement The authors gratefully acknowledge the financial support for this work by the Ministry of Land and Resources of P. R. China (Grant No. 201411089). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.chemosphere.2016.11.084. References Agusa, T., Kunito, T., Iwata, H., Monirith, I., Tana, T.S., Subramanian, A., Tanabe, S., 2005. Mercury contamination in human hair and fish from Cambodia: levels, specific accumulation and risk assessment. Environ. Pollut. 134, 79e86. Akkajit, P., Tongcumpou, C., 2010. Fractionation of metals in cadmium contaminated soil: relation and effect on bioavailable cadmium. Geoderma 156, 126e132. Biester, H., Scholz, C., 1996. Determination of mercury binding forms in contaminated soils: mercury pyrolysis versus sequential extractions. Environ. Sci. Technol. 31, 233e239. Bloom, N.S., Preus, E., Katon, J., Hiltner, M., 2003. Selective extractions to assess the biogeochemically relevant fractionation of inorganic mercury in sediments and soils. Anal. Chim. Acta 479, 233e248. Chakraborty, P., Babu, P.R., Vudamala, K., Ramteke, D., Chennuri, K., 2014. Mercury speciation in coastal sediments from the central east coast of India by modified BCR method. Mar. Pollut. Bull. 81, 282e288. Chen, X., Ji, H., Yang, W., Zhu, B., Ding, H., 2016. Speciation and distribution of mercury in soils around gold mines located upstream of Miyun Reservoir, Beijing, China. J. Geochem. Explor. 163, 1e9. Cordy, P., Veiga, M.M., Salih, I., Al-Saadi, S., Console, S., Garcia, O., Mesa, L.A., squez-Lo pez, P.C., Roeser, M., 2011. Mercury contamination from artisanal Vela gold mining in Antioquia, Colombia: the world's highest per capita mercury pollution. Sci. Total Environ. 410, 154e160. EPA, 2007. Mercury in sediment and tissue samples by atomic fluorescence spectrometry. United States Environmental Protection Agency, pp. 1e19. Feng, X.B., Chen, J.C., 1996. The distribution of various mercury species in soils. Acta Mineral. Sin. 16, 218e222. ndez-Martínez, R., Loredo, J., Ordo n ~ ez, A., Rucandio, I., 2014. Mercury availFerna ability by operationally defined fractionation in granulometric distributions of soils and mine wastes from an abandoned cinnabar mine. Environ. Sci. Process. Impacts 16, 1069e1075. ndez-Martínez, R., Rucandio, I., 2013. Assessment of a sequential extraction Ferna method to evaluate mercury mobility and geochemistry in solid environmental samples. Ecotoxicol. Environ. Saf. 97, 196e203. ndez-Martínez, R., Rucandio, I., 2014. Total mercury, organic mercury and Ferna n mercury mine area. mercury fractionation in soil profiles from the Almade Environ. Sci. Process. Impacts 16, 333e340. Gawlik, B., Bidoglio, G., 2006. Background Values in European Soils and Sewage Sludges. European Commission, Brussels. Gochfeld, M., 2003. Cases of mercury exposure, bioavailability, and absorption. Ecotoxicol. Environ. Saf. 56, 174e179. Goldblum, D.K., Rak, A., Ponnapalli, M.D., Clayton, C.J., 2006. The Fort Totten
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Risk assessment for the mercury polluted site near a pesticide plant in Changsha, Hunan, China Haochen Dong a, Zhijia Lin b, Xiang Wan a, Liu Feng a, * a b
Department of Environmental Sciences and Engineering, Beijing University of Chemical Technology, Beijing 100029, PR China Hunan Institue of Geological Survey, Changsha, Hunan 410116, PR China
h i g h l i g h t s Total concentration and fraction distribution of mercury were characterized. Soil physicochemical properties were built-up for fractionation analysis. DGH method and model were introduced for risk assessment.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 July 2016 Received in revised form 3 November 2016 Accepted 15 November 2016
The distribution characteristics of mercury fractions at the site near a pesticide plant was investigated, with the total mercury concentrations ranging from 0.0250 to 44.3 mg kg1. The mercury bound to organic matter and residual mercury were the main fractions, and the most mobile fractions accounted for only 5.9%e9.7%, indicating a relatively low degree of potential risk. The relationships between mercury fractions and soil physicochemical properties were analysed. The results demonstrated that organic matter was one of the most important factors in soil fraction distribution, and both OM and soil pH appeared to have a significant influence on the Fe/Mn oxides of mercury. Together with the methodology of partial correlation analysis, the concept and model of delayed geochemical hazard (DGH) was introduced to reveal the potential transformation paths and chain reactions among different mercury fractions and therefore to have a better understanding of risk development. The results showed that the site may be classified as a low-risk site of mercury DGH with a probability of 10.5%, but it had an easy trend in mercury DGH development due to low critical points of DGH burst. In summary, this study provides a methodology for site risk assessment in terms of static risk and risk development. © 2016 Elsevier Ltd. All rights reserved.
Handling Editor: Martine Leermakers Keywords: Mercury Delayed geochemical hazard Risk assessment
1. Introduction Following the rapid social and economic development over the past several decades, soil pollution by mercury (Hg) has been observed worldwide (Van Straaten, 2000; Gochfeld, 2003; Gosar et al., 2006; Qiu et al., 2006; Cordy et al., 2011). For example, in the entire Lake Victoria Goldfields of Tanzania, Africa, approximately 3e4 t Hg was released annually into the atmosphere (Van Straaten, 2000). In the Amazon region, mercury has been released during gold mining operations, resulting in methylmercury exposure via the food chain (Gochfeld, 2003). Additionally, in the mining areas of Guizhou, China, mine wastes contain total Hg
* Corresponding author. E-mail address: [email protected] (L. Feng). http://dx.doi.org/10.1016/j.chemosphere.2016.11.084 0045-6535/© 2016 Elsevier Ltd. All rights reserved.
concentrations ranging from 79.0 to 710 mg kg1, with significant conversion of methyl-Hg in the study areas, causing potential health risks (Qiu et al., 2006). Mercury is a persistent pollutant and the bioaccumulation and biomagnification of toxic pollutants has a significant impact on human health and ecological environment. The health and ecological risks associated with mercury-polluted sites have been widely recognized and studied. The data from the Wuchuan mercury mining area (one of the largest mercury mining areas in Guizhou province, China) showed that people living near a mercury mining site experienced adverse health effects caused by mercury exposure. Total concentration of Hg measured in the hair of the residents near the mercury mining area reached 210 mg kg1, far beyond the value of the control group (Li et al., 2008a, 2008b). In the USA, mercury contamination with the highest concentration reaching 2.10 mg kg1 was discovered in Fort Totten by measuring
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the total concentration of Hg with a comprehensive evaluation process related to human health risk (Goldblum et al., 2006). Risk assessment based on the total amount of pollutants is an intuitive and simple method that has been applied to heavy metal contaminated sites in early stages (Agusa et al., 2005; Goldblum et al., 2006; Mieiro et al., 2009). However, as with the development of a deeper understanding of environmental behaviour and ecological effect of pollutants, the inadequacy of this method has become increasingly apparent. Since the method does not take into consideration the differences in environmental effectiveness and biological availability between various chemical forms of metals, the method often overestimates the potential risk (Goldblum et al., 2006). Speciation analysis in a bioavailability study could provide a different perspective for risk assessment. In this approach, the different chemical forms of heavy metals such as mercury are identified in order to characterize their mobility and toxicity (Issaro et al., 2009). Sequential extraction procedure (SEP) has been widely applied as a tool in the speciation analysis for metal extraction in soils, especially the Tessier SEP (Tessier et al., 1979; Issaro et al., 2009). There are five defined fractions in the order of extraction difficulty with different occurrence of forms. In 1993, European Community Bureau of Reference (BCR) certified the extractable contents of a trace metal in sediments following a three-step extraction procedure (Quevauviller et al., 1997) that has recently €kel€ become a commonly used tool for soil (sediment) studies (Ma a et al., 2011; Chakraborty et al., 2014; KerollieMustafa et al., 2015). Compared with Tessier SEP, BCR extraction has fewer steps and a weaker phenomenon of redistribution/re-adsorption that gives rise to better reproducibility. Both Tessier and BCR SEP have been widely used in previous studies of Hg in many mining areas in China, showing that the Hg concentrations in mining areas range from 0.100 to 790 mg kg1 with 60%e80% HgS as the main existing form (Horvat et al., 2003; Li et al., 2007; Qiu et al., 2013; Yang et al., 2014). Both methods have shortcomings in the extraction of mercury (Issaro et al., 2009) for example, a Hg redistribution/readsorption pattern may occur and cause incomplete extraction. According to recent studies, a new approach proposed by the ticas, Medioambientales y TecCentro de Investigaciones Energe gicas (CIEMAT) has been used for a fractionation study at the nolo n mercury mine area in Spain. The comparison between the Almade addition of Hg concentration to each fraction and the measured total Hg, ranged between 92% and 99%, and proved the feasibility of ndez-Martínez and Rucandio, 2013; the new method (Ferna ndez-Martínez et al., 2014; Fern Ferna andez-Martínez and Rucandio, 2014). The implemented SEP can also be useful in providing information about the amount of mercury available in different contaminated areas for risk assessment. A study of the n mining area found very low available merplants in the Almade cury, including water-soluble and exchangeable fractions in all n et al., 2006). Even so, Tessier SEP is still valid and samples (Milla favourable for risk assessment of heavy metal pollution (Islam et al., 2015; Rosado et al., 2016). From another point of view, soil physical and chemical properties are also an integral part of the assessment. A previous study has shown that pH provides the most useful information for estimating an element's migration that can be an important parameter for the assessment of the available mercury. Furthermore, Cation Exchange Capacity (CEC) does not improve the ability to predict the moven ment of ions through these natural soils (Korte et al., 1976). Norde (1994) studied pH and organic matter (OM) content of forest topsoil and found that the differences in soil organic matter content between the plants were small. However, these studies focus on either the chemical forms or the total concentration of mercury statically and unilaterally, and therefore, the resultant static risk
assessment of the sites overlooks the transformation between these forms in the soil. Ming et al. (2005) proposed the concept of delayed geochemical hazard (DGH) that revealed the dynamic transformation among different forms and became a tool for risk assessment. “Total releasable content of the pollutant” (TRCP) and “total concentration of active species” (TCAS) are two important concepts related to DGH. TRCP focuses on the releasable content that is equal to the total content of the pollutants in the soil (sediment) minus the content of some compounds that are quite stable with respect to activation and release in the soil (sediment) even under extreme conditions (Ming et al., 2004). TCAS is the active characteristic of the pollutant and indicates the concentration of some species in TRCP involved in much more activity under certain environmental conditions (Ming et al., 2004). Our previous study (Zheng et al., 2015) demonstrated the nonlinearity changes among different chemical fractions that occur when geochemical conditions are altered in soil by soil column tests. The results provided a possibility to establish a mathematical model to quantify the potential development of DGH. The structure and characteristics of the original model are shown in Fig. 1. The X axis indicates TRCP (C) in the soil system and the Y axis indicates TCAS (Q). The fitting curve L0 represents the trend in TCAS with the increase in TRCP; continuous input of pollutants into the soil system altered the increasing trend in TCAS. At first, an increase of TRCP (DC) resulted in a little increase in TCAS (DQ1); however, when continuous accumulation of TRCP reached a certain degree, the same increase in TRCP, DC, would give rise to a greater increase in DQ, TCAS (DQ2). This was in accordance with the non-linear mutational rising of TCAS. The previously fitted results depending on the Tessier method showed a dynamic evolution process that could be quantitatively expressed with a nonlinear polynomial and the highest degree of these polynomials are 3. Combined with the preceding digital model of DGH, we could characterize two special points: critical point of burst and burst point as follows: Critical point of burst, where the first and second derivatives are zero and convexity and concavity of the curve are altered, represents the beginning of DGH. When the first derivative reaches the maximum value and the second derivative is zero, the critical point that can be calculated and related to maximum slope, represents the most active stage of DGH (Ming et al., 2005). In a recent study (Zheng et al., 2015), a DGH model expressed with nonlinear polynomials was successfully used to assess the risk of a mercury polluted site. The mercury polluted area that is the focus of this study is located in Hunan, Changsha, near an abandoned pesticide factory (PF). Among other compounds, the main products of the factory were arsenic agents (such as calcium arsenate used in pesticides and other arsenate herbicides, bactericides, etc.), organochlorine, organophosphorus, organ nitrogen, and chlorothalonil. Due to managerial negligence during the early phases of the factory, massive discharge of pollutants to the surrounding soil caused different degrees of heavy metal pollution, including mercury and arsenic. To accurately assess the potential environmental risk of mercury pollution, total concentration of Hg and distributions based on the Tessier method were measured for a preliminary static risk assessment of the site. Meanwhile, statistical analysis techniques were used to analyse the effect of soil physical and chemical properties on mercury accumulation of various forms and elucidate the interactions among different mercury forms. Then, A DGH model was imported to characterize the dynamic evolution process of risk and provide basic data for site management and remediation. The methods used in this study provide a new concept for risk
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Fig. 1. Original digital model of DGH.
assessment of heavy metal contaminated sites. 2. Materials and methods 2.1. Soil samples Two hundred samples were gathered from the parcels of the PF located in Hunan. Soil samples were collected from topsoil (0e30 cm) of both the woodland and cropland area within 1 km2 of the factory. The locations of the sampling sites are shown in Fig. 2.
The centring of sample sites on the PF helped develop a systematic grid sampling method (with a density of 50 m 100 m) from N001 to N200 with geographic coordinates of 112 010 E 27 570 N. At each soil sampling point, 3 samples of the surface layer were mixed as one sample and packaged in plastic bags stored under dry and cold conditions prior to being transported to the laboratory. Samples were all air-dried at room temperature to minimize the loss of Hg due to volatilization. After drying, samples were sieved through a 100-mesh screen by standard Tyler sieves before chemical analysis.
Fig. 2. Study area and sampling sites in Pesticide Factory.
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2.2. Determination of soil physical and chemical properties For all samples, we first measured some physical and chemical properties including: pH, OM, CEC, and oxidation-reduction potential (ORP). For pH, OM, and CEC measurements, we followed the techniques of a previous study (Sungur et al., 2014): pH was measured by a pH-meter with 1:2.5 (soil: water ratio, w/v) suspension; we used an EC-meter to measure soil CEC of the suspension; and OM was measured using dichromate in acid medium to oxidize soil organic matter (Nelson and Sommers, 1982). ORP was determined using a depolarization method by an automatic ORP analyser (FJA-5). 2.3. Soil sample pre-treatment and analysis 2.3.1. Sample pre-treatment for total Hg The total Hg (THg) concentration of each sample was measured using an AFS instrument (AFS-3100) following the recommendations by US EPA Method 7474 (EPA, 2007). A selection process has been constructed according to the profile of the soil samples for the speciation analysis of THg. 2.3.2. Chemical fractions of Hg with Tessier method Twenty samples were selected based on the concentration gradient of THg using the following guidelines: i) Remove the samples related to the highest concentration area of mercury; they should be analysed separately. ii) Divide the rest of the samples into 20 gradients depending on the total concentration of mercury and select soil samples corresponding to each critical value. Tessier SEP was applied for analysis, as shown in Table 1. For reliability, the sum of Hg concentrations extracted in SEP would be assessed by comparing them to the total concentration of mercury measured previously. After each SEP extraction, samples were centrifuged at 4000 rpm for 10 min. The supernatants were removed to volumetric flasks and then 10 ml of preserving reagent (K2Cr2O7 þ HNO3) was added and diluted with attenuation reagent (K2Cr2O7 þ H2SO4) to 50 ml. Next, all samples were stored in refrigerator at 4 C before analysis. 2.3.3. Analysis method and conditions of AFS All samples were analysed with diluted supernatants by a double-channel atomic fluorescence spectrometer (AFS-3100, BJHG, China) under the following conditions: high-purity argon was used as a carrier gas with a flow rate of 400 mL min1; atomization height was adjusted to 10 cm; the current of hollow cathode lamp was set to 15 mA, and negative high-voltage was 270 V. Five percent HCl solution was the carrier liquid, and the mixed solution of 2% KBH and 0.5% NaOH was the reductant. Samples were analysed at least three times. Data fluctuations during measurements were generally lower than 5% and the detection limit was 0.0001 mg l1.
Table 1 Procedure of Tessier SEP. Fractions
Extracting agent and condition
HgE Water soluble and exchangeable HgC Weakly complexed and bound to carbonate HgF Bound to Fe/Mn oxides of low crystallinity HgO Bound to organic matter
MgCl2 (1 mol/L pH ¼ 7) NaAc-HAc (1 mol/L pH ¼ 5)
HgR Residual
NH2OH$HCl (0.04 mol/L) HNO3 þ H2O2 (0.02 mol/L pH ¼ 2 85 C) Aqua regia (96 C)
A section on the QC protocol was applied. We used the soil standard GBW (E) 070009 (the Institute of Geophysical and Geochemical Exploration, China) as a standard reference material. The measured average total mercury concentration of this material was 2.06 ± 0.11 mg kg1 (n ¼ 6), which is consistent with the certified value of 2.20 ± 0.40 mg kg1. Reagent blanks were also used and the result of total mercury concentration measurements was below the detection limit of AFS. 2.4. Model analysis software Statistical analyses were performed using SPSS version 22.0 for Windows. All nonlinear polynomials of the DGH model fitted by Origin 9.0 and a one-way ANOVA (analysis of variance) were applied to analyse the significance of the differences between groups when p < 0.05. Concentration distribution charts were drawn using Surfer version 8 for Windows. 3. Results and discussion 3.1. Characterization and total mercury in soil profiles The physicochemical properties of the soil samples showed that the soil in the study area was slightly acidic except for a few sampling sites. The pH ranged from 3.44 to 8.68, with moderate soil fertility (OM content ranged from 0.110% to 9.80% with an average content of 2.0%) comparable with that in the mining area (0.6%e2%) ndez-Martínez and Rucandio, 2014), relative high oxidation (Ferna resistance (ORP ranged from 491 mV to 948 mV), and moderate cation exchange capacity (CEC ranging from 7.00 cmol kg1 to 23.0 cmol kg1). Total Hg concentrations in the study area ranged from 0.0250 ± 0.0024 mg kg1 to 44.3 ± 1.7 mg kg1. According to Chinese soil environmental quality standard, there are three levels of total mercury concentration in both woodland and cropland areas: First-class level: 0.15 mg kg1, similar to the natural background value; third-class level: above 1.5 mg kg1, which is the marginal value that assures normal farming, forestry production, and plant growth. Most of the study area around the PF showed relatively lower Hg total concentrations, with 44% belonging to the first-class level, according to the soil quality criteria. Still, 7% of the study area where the THg was over 1.50 mg kg1 exceeded the third-class level. A higher concentration was observed in PF with a maximum of 44.3 mg kg1, indicating some potential risk. The average concentration of Hg in PF was 1.28 mg kg1, which is higher than the minimum concentration of Hg according to a recent study (referred to as the background value of 0.160 mg kg1) in Hunan (Wang et al., 2010). Furthermore, 17% of the samples partly exceeded the background values in European soils (maximum 0.420 mg kg1) (Gawlik and Bidoglio, 2006). Compared to the maximum permissible concentrations of potential toxic elements for agricultural soils of China (0.300 mg kg1) (Wei and Yang, 2010), about half of the samples were out of the standard range. Fig. 3 shows the distribution of total Hg in the PF. Hgcontaminated area was found at the centre-right area with concentrations reaching 44.3 mg kg1 (N149). An independent source of pollution was found near the lower left corner of the area and had a secondary concentration of THg of 40.2 mg kg1 (N186). 3.2. Distribution characteristics of different mercury fractions Fig. 4a shows the distribution of Hg in the chosen PF subsamples. The recovery rate of Hg as the sum of the fractions relative to THg varied between 71% and 116%, revealing that the method may be used to assess different chemical forms of soil. The sum of
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Fig. 3. Spatial distribution of Total Hg concentration in Pesticide Factory.
the fractions relative to THg is in good agreement at the 95% confidence level with the certified value (correlation ¼ 0.991, sig<0.05, paired-samples t-test). All subsamples had low content of HgE corresponding to the most easily available Hg content (Wallschl€ ager et al., 1998; Bloom et al., 2003) and indicating the relatively low content of the active form (maximum is 0.004 mg kg1). While HgC is susceptible to the changes in pH (Tessier et al., 1979), the content of this fraction is still low among the subsamples (maximum is 0.0300 mg kg1). The lack of HgF content in the subsamples is similar to the distribution of background soil in Hunan. HgO is the most abundant in some subsamples indicating the human factor impact (Feng and Chen, 1996). HgO and HgR are the main fractions of the selected samples that were both similar to the soils from the mining area and relevant to other mercury-contaminated industrial areas (Biester and Scholz, 1996; Lechler, 1999; Miller et al., 2013). N151 had a concentration of over 2.00 mg kg1, which exceeded the critical value of the third-class level according to its fraction, and the highest fraction of Hg in the sample was bound to the organic matter associated with anthropogenic sources (Feng and Chen, 1996). Combined with the concentration distribution, this may indicate that some man-made mercury pollution is present. Fig. 3 shows a separate analysis of the most serious areas, for which the samples N147-149 are the critical samples. Fig. 4b shows the distribution of Hg in a seriously polluted area located in PF. Appreciable amount of mobile Hg components (5.9%e9.7%) was detected in these samples; this was higher than the same amount of Hg previously detected in a mining area that had a maximum of about 1%, but similar with relevant industrial area (blow 10% of these components) (Miller et al., 2013; Chen et al., 2016).
3.3. Relationship analysis for inner connections among mercury fractions and physicochemical properties in soil Knowledge of the soil properties and interactions between the other metals is important to assess the bioavailability (Akkajit and Tongcumpou, 2010). Correlation analysis was performed to identify the relationships between the total mercury concentrations and the physicochemical properties of the soil. The correlation coefficients between THg concentration and physicochemical properties in the soil had a low value. Both pH and OM have a slight influence on mercury accumulation. To study this further, five fractions of Tessier method were analysed by both the Pearson's correlation coefficient and partial correlation with the physicochemical soil properties. Table 2 presents the correlations between the Tessier method fractions and the physicochemical soil properties. HgO had a positive relationship with OM, indicating that OM is one of the most important factors of the heavy metal fraction distribution in the soil n et al., 2006). Both OM and soil pH are factors that have a (Milla significant influence on HgF representing the Fe/Mn oxides, similar to the results of the previous studies (Simmons et al., 2009). Considering internal relations among these fractions in Tessier method, partial correlation analysis was used to control the variables and it turned out to be a clearer relationship among those parameters. 3.4. Risk development characterization and assessment Speciation analysis, as well as soil profiles, only focused on the static risk. According to previous studies, chemical speciation of Hg €ger et al., 1998; Ming et al., in the ecosystem is dynamic (Wallschla
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Fig. 4. Distribution of Hg of chosen samples in Pesticide Factory: 4a, 20 chosen samples; 4b, heavy-polluted samples in Pesticide Factory.
2004, 2005), revealing the importance of dynamic risk evolution and assessment. Delayed geochemical hazard (DGH) represents the sudden reactivation and release of long-term accumulated pollutants in soil/sediment systems due to the decreased environmental capacity or a change in physicochemical conditions that may cause an ecological and environmental hazard. A DGH model that can be quantitatively expressed with a nonlinear polynomial was used to assess and predict the potential environmental risks of Hg
exposure. In this case, fitting data to each equation were based on Tessier SEP; thus, TRCPHg should contain all the components involved in the Tessier method and be equal to the sum of the numerators of each fraction (Zheng et al., 2015). In agreement with the DGH law, the corresponding fitting equation of the site is shown in Table 3 with high correlation coefficients (R2) and large F values at p < 0.005. We calculated the second derivative of each polynomial corresponding to the concept of ‘critical point of burst’ in DGH, which means that at any stage of the chain reactions related
H. Dong et al. / Chemosphere 169 (2017) 333e341
339
Table 2 Correlation analysis between fractions of Tessier method and soil profiles (n ¼ 20). Parametera
pH
HgE HgC HgF HgO HgR
0.127 0.199 0.480b 0.258 0.126
a b c
OM 0.540 0.286 0.471b 0.629 0.337
CEC
0.273 0.018 0.398 0.645c 0.791c
0.484 0.014 0.504b 0.640c 0.771c
0.074 0.180 0.051 0.126 0.168
ORP 0.085 0.039 0.187 0.369 0.192
0.178 0.118 0.430 0.176 0.189
0.553 0.303 0.117 0.600 0.361
For each parameter, both Pearson correlation coefficient (left) and partial correlation coefficient (right) were listed. Statistically significant correlation at the selected confidence level (a ¼ 0.05). Statistically significant correlation at the selected confidence level (a ¼ 0.01).
to the Tessier method, DGH may burst once TRCPHg is higher than the critical point as calculated in Table 3. Each polynomial represented one possibility path for species transformation (related to Tessier method) when DGH occurs. The results showed low critical points of the DGH burst in the study area, indicating an easy trend to burst the risk of DGH, despite the generally low content of the active form in this area. In consideration of the availability of fractions in the Tessier method, the path HgEþCþFþOþR / HgEþCþO was used as an example. The regression equation is expressed by Eq. (1) and fitting curve could be checked in Supplementary Materials:
Y ¼ 0:1500X3 0:3491X2 þ 0:5012 n ¼ 20; R2 ¼ 0:985 (1) This is one path of DGH and we used this path to characterize the studied area, because in this path, chain reactions from HgEþCþFþOþR / HgEþCþO would lead to some mobile fractions of mercury when DGH happens. We let the second derivative of Eq.
(1) be zero. Thus, the calculated TRCPHg is equal to 0.776 mg kg1. We fitted all the potential paths of DGH and the calculated TRCPHg ranging from 0.764 to 0.810 mg kg1 presented in Table 3 to give a gist for the assessment. According the THg data, the percentage of THg beyond TRCPHg in the study area was 10.5% (ranging from 10.0% to 10.5%) classified as low-risk for both DGH. It is worth noting that the sampling point with the highest concentration of THg near PF could be more dangerous for the spurt growth of Hg concentration in these areas. That is to say, more numeric fraction transformation may occur following the DGH paths, and early steps should be taken to prevent the evolutionary hazards that may lead to DGH disasters in these areas. 4. Conclusions Based on the total Hg concentrations in the pesticide factory, although most of the area had low THg concentrations, several critical polluted areas were observed with maximum concentrations reaching 44.3 mg kg1, indicating the degree of accumulation
Table 3 Fitting equations and the corresponding digital characteristics of mercury in the studied areas. Potential DGH path
DGH models
Statistical characteristics
Y is TCASHg; X is TRCPHg (mg/Kg) HgO HgFþO HgEþO HgFþO HgFþO HgO HgO HgEþCþO HgEþFþO HgEþCþO HgFþO HgCþO HgCþO HgFþO HgCþO HgCþO HgCþO HgEþCþFþO HgCþO HgCþO HgCþO HgCþFþO HgCþFþO HgFþOþR HgOþR HgEþOþR HgCþOþR HgF HgCþF HgO
HgFþOþR HgCþFþOþR HgEþOþR HgEþCþFþOþR HgCþOþR HgFþR HgEþOþR HgCþFþOþR HgEþFþOþR HgEþCþFþOþR HgFþOþR HgCþFþOþR HgEþCþFþOþR HgEþFþR HgCþOþR HgFþOþR HgEþFþOþR HgEþCþFþOþR HgOþR HgEþOþR HgEþFþR HgEþCþFþOþR HgEþFþOþR HgEþCþFþR HgEþCþR HgEþCþFþR HgEþCþFþR HgEþF HgEþCþF HgCþOþR
* P-value of all fitting equations are less than 0.005.
Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼ ¼
3
2
0.1395X 0.3195X þ 0.4942X 0.1428X30.3301X2 þ 0.4998X 0.1432X30.3316X2 þ 0.5006X 0.145X30.3328X2 þ 0.4921X 0.1439X30.3339X2 þ 0.5021X 0.1455X30.3342X2 þ 0.4928X 0.1444X30.3354X2 þ 0.5029X 0.1465X30.3378X2 þ 0.495X 0.1466X30.3424X2 þ 0.506X 0.15X30.3491X2 þ 0.5012X 0.1507X30.3513X2 þ 0.5027X 0.1502X30.3538X2 þ 0.5122X 0.1513X30.3576X2 þ 0.5145X 0.1527X30.3579X2 þ 0.5058X 0.1532X30.3592X2 þ 0.5065X 0.1538X30.3615X2 þ 0.508X 0.1569X30.3714X2 þ 0.5132X 0.158X30.375X2 þ 0.5155X 0.1761X30.4187X2 þ 0.5093X 0.1801X30.428X2 þ 0.5073X 0.1794X30.4293X2 þ 0.515X 0.1807X30.4303X2 þ 0.5088X 0.1832X30.4416X2 þ 0.5212X 0.1874X30.4517X2 þ 0.5203X 0.1868X30.4531X2 þ 0.5274X 0.1872X30.4545X2 þ 0.5281X 0.1884X30.4553X2 þ 0.5225X 0.1889X30.4566X2 þ 0.5231X 0.1879X30.4569X2 þ 0.5297X 0.1915X30.4651X2 þ 0.5276X
The critical points of DGH burst
R2
F
TRCPHg
TCASHg
0.984 0.985 0.985 0.984 0.985 0.984 0.985 0.984 0.985 0.985 0.985 0.986 0.985 0.985 0.985 0.985 0.985 0.985 0.984 0.984 0.985 0.984 0.985 0.985 0.985 0.985 0.985 0.985 0.985 0.985
354 370 368 357 366 356 365 352 381 367 365 387 384 381 379 377 385 381 345 345 360 343 371 369 378 377 366 364 374 374
0.764 0.771 0.772 0.765 0.773 0.766 0.774 0.768 0.778 0.776 0.777 0.785 0.788 0.781 0.782 0.783 0.789 0.791 0.793 0.792 0.798 0.794 0.803 0.804 0.808 0.809 0.805 0.806 0.810 0.810
0.253 0.255 0.255 0.247 0.255 0.247 0.255 0.247 0.256 0.249 0.249 0.257 0.257 0.250 0.250 0.250 0.251 0.251 0.228 0.223 0.229 0.223 0.229 0.224 0.229 0.229 0.224 0.224 0.229 0.224
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H. Dong et al. / Chemosphere 169 (2017) 333e341
of mercury. Tessier SEP was used to analyse the Hg fractions. Samples collected at PF have low mobile components of Hg, while Hg associated with OM and the residual are main fractions. Moreover, for samples at critical areas in PF, appreciable mobile components were extracted (ranged from 5.9% to 9.7%), providing some indication of available Hg content that could pose potential risk. The analysis of the correlations between mercury and the soil physicochemical properties showed that soil pH has a significant influence on Fe/Mn oxides; OM played an important role in soil mercury availability and fraction distribution; and partial correlation coefficients in the relationships between the fractions of Tessier method revealed the potential dynamic transformation of chemical speciation. Using the DGH model, the potential risks caused by mercury were investigated and the results indicated low critical points of DGH burst in the study area, which implies an easy trend to dynamical risk evolution. While the lower measured percentage (10.5%) indicates a low-risk of DGH for one path of the chain reactions in DGH, concentrated areas with high concentration of Hg were also observed and these may release high amounts of available Hg and could be more dangerous. Acknowledgement The authors gratefully acknowledge the financial support for this work by the Ministry of Land and Resources of P. R. China (Grant No. 201411089). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.chemosphere.2016.11.084. References Agusa, T., Kunito, T., Iwata, H., Monirith, I., Tana, T.S., Subramanian, A., Tanabe, S., 2005. Mercury contamination in human hair and fish from Cambodia: levels, specific accumulation and risk assessment. Environ. Pollut. 134, 79e86. Akkajit, P., Tongcumpou, C., 2010. Fractionation of metals in cadmium contaminated soil: relation and effect on bioavailable cadmium. Geoderma 156, 126e132. Biester, H., Scholz, C., 1996. Determination of mercury binding forms in contaminated soils: mercury pyrolysis versus sequential extractions. Environ. Sci. Technol. 31, 233e239. Bloom, N.S., Preus, E., Katon, J., Hiltner, M., 2003. Selective extractions to assess the biogeochemically relevant fractionation of inorganic mercury in sediments and soils. Anal. Chim. Acta 479, 233e248. Chakraborty, P., Babu, P.R., Vudamala, K., Ramteke, D., Chennuri, K., 2014. Mercury speciation in coastal sediments from the central east coast of India by modified BCR method. Mar. Pollut. Bull. 81, 282e288. Chen, X., Ji, H., Yang, W., Zhu, B., Ding, H., 2016. Speciation and distribution of mercury in soils around gold mines located upstream of Miyun Reservoir, Beijing, China. J. Geochem. Explor. 163, 1e9. Cordy, P., Veiga, M.M., Salih, I., Al-Saadi, S., Console, S., Garcia, O., Mesa, L.A., squez-Lo pez, P.C., Roeser, M., 2011. Mercury contamination from artisanal Vela gold mining in Antioquia, Colombia: the world's highest per capita mercury pollution. Sci. Total Environ. 410, 154e160. EPA, 2007. Mercury in sediment and tissue samples by atomic fluorescence spectrometry. United States Environmental Protection Agency, pp. 1e19. Feng, X.B., Chen, J.C., 1996. The distribution of various mercury species in soils. Acta Mineral. Sin. 16, 218e222. ndez-Martínez, R., Loredo, J., Ordo n ~ ez, A., Rucandio, I., 2014. Mercury availFerna ability by operationally defined fractionation in granulometric distributions of soils and mine wastes from an abandoned cinnabar mine. Environ. Sci. Process. Impacts 16, 1069e1075. ndez-Martínez, R., Rucandio, I., 2013. Assessment of a sequential extraction Ferna method to evaluate mercury mobility and geochemistry in solid environmental samples. Ecotoxicol. Environ. Saf. 97, 196e203. ndez-Martínez, R., Rucandio, I., 2014. Total mercury, organic mercury and Ferna n mercury mine area. mercury fractionation in soil profiles from the Almade Environ. Sci. Process. Impacts 16, 333e340. Gawlik, B., Bidoglio, G., 2006. Background Values in European Soils and Sewage Sludges. European Commission, Brussels. Gochfeld, M., 2003. Cases of mercury exposure, bioavailability, and absorption. Ecotoxicol. Environ. Saf. 56, 174e179. Goldblum, D.K., Rak, A., Ponnapalli, M.D., Clayton, C.J., 2006. The Fort Totten
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