Assessment of Zn, Pb and Cd in Soil around an MSW Incineration Plant: Using Risk Assessment and Multivariate Statistical Techniques

Abstract

To investigate and evaluate the spatial distribution of Zn, Pb and Cd in the soil around a municipal solid waste incineration plant and its ecological risks, Zn, Pb and Cd were analyzed in soil samples around a municipal solid waste incineration plant in Chengdu, Sichuan Province, China. The results revealed that the average content of Zn and Pb did not exceed the soil environmental quality value for the risk control standard for soil contamination of agricultural land (GB15618-2018), but the average content of Cd in the soil was higher than this standard. Multivariate statistical analysis indicated that Cd was the predominant pollutant and had strong correlations with Zn and Pb. The Cd content was most impacted by human activities, which also explained that this municipal solid waste incineration plant has little effect on Zn, Pb and Cd in the surrounding soil. The geo-accumulation index decreased in the order of Cd > Zn > Pb, and the geo-accumulation index of Cd was greater than 5, indicating that the pollution level for Cd was extremely heavy. The comprehensive potential ecological risk index (RI) of various heavy metals was greater than 1200, And the potential ecological risk level of the study area was high. The contribution rate of Cd to RI was relatively large, and Cd pollution should be paid more attention to.

1. Introduction

As society progresses and the quality of life for people improves, the amount of municipal solid waste (MSW) has increased annually. At present, the main treatment methods for MSW are incineration, sanitation landfill and other treatment methods [1,2,3]. In 2017, there were 1013 harmless domestic MSW treatment plants in China, including 654 sanitation landfills and 286 MSW incineration plants [4]. By 2021, the number of harmless domestic MSW treatment plants in China had increased to 1407, with sanitation landfills reducing to 542 and MSW incineration plants increasing to 583 [5]. Incineration has been an increasingly adopted technology for MSW in China because of its characteristics of reduction, harmlessness and resource recovery [6,7,8]. The number of MSW incineration plants will continue to increase in the future. However, MSW incineration plants often lead to secondary pollution. During the incineration process, some pollutants such as heavy metals and dioxins are generated, which can directly enter the body through inhalation and skin exposure. In addition, under the effects of dry and wet sedimentation, they could enter the soil and water, thereby causing harm to human health through food chains [9,10,11]. The NIMBY effect has received increasing attention from the public.

Many studies have been reported on risk assessment of heavy metal pollution in soil around MSW incineration plants [12,13,14,15,16,17,18]. However, it is still debated whether MSW incineration plants have a significant impact on the content of heavy metal elements in the surrounding soil. Some researchers have analyzed the relationship between the content of heavy metal in the soil and distance and wind direction in order to determine whether an MSW incineration plant has an impact on the content of heavy metal in the surrounding soil. The results showed that there was no evidence that the content in the downwind direction from the incinerator was higher than other directions, and there was no evidence of any trend in the concentration as the distance from the incinerator increased. Therefore, it was considered that there was no correlation between the level of heavy metal content in the soil around MSW incineration plants and MSW incineration plants [19,20]. Some researchers believe that MSW incineration, agricultural activities and the mixture of natural and traffic sources as well as coal combustion had obviously contributed pollution to the surrounding soil [21,22,23]. Llobet monitored the content of heavy metals in the soil at the same point before and after the construction of an MSW incineration plant. The results showed the content of Cd, Cr and Pb metals did not increase significantly in this period, and even decreased, and the reason for this result was also analyzed. In this paper, Llobet also did not know whether the MSW incineration plant had an impact on the content of heavy metals in the surrounding soil [24].

Moreover, to our best of our knowledge, the above three views did not have detailed evidence and systematic analysis to illustrate the contribution of MSW incineration plants to surrounding soil contamination risk. In particular, it was rarely considered that the soil around the MSW incineration plant might also be affected by other pollution sources. Even if some researchers had taken this problem into account, it was a group analysis of the heavy metals studied, rather than a separate analysis of the effects of individual heavy metals [21,22,23].

Therefore, it was necessary to clarify whether MSW waste incineration plants had an impact on the heavy metals in the surrounding soil using multivariate statistics, and to analyze the impact of each heavy metal in detail. This study was conducive to the accurate identification of pollution sources. This study took an MSW incineration plant in Chengdu as the research object. Based on the plum-shaped sampling method, soil samples were collected in the surrounding area of the MSW incineration plant, followed by detection, data analysis and risk assessment. This research had certain theoretical and practical significances. It not only systematically analyzed whether the MSW incineration plant had a direct impact on the content of heavy metals in the surrounding soil, but also provided a new approach to similar multiple-pollution-source problems. Additionally, it also provided a positive reference for the government, enterprises and relevant decision makers in the process of MSW incineration plant site selection, which has a certain role in promoting social harmony and stability.

2. Materials and Methods

2.1. Study Area and Sample Collection

The area surrounding the MSW incineration plant in Chengdu was formerly primarily agricultural land and wasteland, boasting a relatively flat terrain. Combined with the local terrain and the Chengdu wind rose map, the northeast and southeast directions were the dominant directions, and sampling points were established within a range of 0 m to 1000 m. The distance and numbers in each direction should be similar. However, due to the complexity of the regional terrain, some actual sampling points were adjusted, and the plum-blossom type multi-point sampling method was adopted. The distribution of sampling points is shown in Figure 1.

At each sampling point, five equal samples were taken within a 5 m × 5 m area using the quartering method, resulting in a total of 1 kg of soil. The sampling depth was between 0 and 10 cm. The soil samples were then placed in polyethylene sealed bags, stored at a temperature of −4 °C, transported, and dried in the laboratory for 48 h. After screening, these samples were finally stored in clean brown bottles.

2.2. Sample Processing and Analysis

The collected samples were removed of impurities and ground through a 60-mesh nylon sieve. A 0.2 g soil sample was put into a polytetrafluoroethylene crucible, and a little water was added to wet the sample. Then, the acid system (9 mL HNO₃ + 3 mL HF + 4 mL HClO₄) was added for digestion. The programmed temperature was stabilized to 180 °C (10 min 120 °C + 20 min 150 °C + 10 min 180 °C). After digestion, all samples were cooled down to room temperature. The crucible was placed in an acid expeller at 200 °C until the contents were nearly dry; after cooling, they were transferred into a 50 mL volumetric flask for subsequent analysis. A graphite furnace absorption spectrophotometer (AA6880) was used for detection. All reagents were analytically pure. The average content of heavy metals in each soil sample was taken as the average value of three repeated analyses, and the recovery rate of soil heavy metal tests was between 80% and 120%

2.2.1. Geo-Accumulation Index Method

In 1969, German scientist Muller first proposed the geo-accumulation index method. This method was mainly used to study the quantitative method of heavy metal pollution in sediments [25].

The geo-accumulation index method was a simple and convenient method for evaluating the impact of human activities on the environment. Therefore, it has also been widely used by domestic and foreign scholars to evaluate heavy metal pollution in soil. The formula for its calculation is as follows:

$$I_{\mathrm{geo}}={\log }_2(Cn/K\cdot Bn)$$

(1)

where $K$ is the fluctuation of the background value that may be caused by rock formation movement, which is generally taken as 1.5; $Bn$ is the environmental background value of heavy metal elements (using the heavy metal background value of Chengdu [26]); $Cn$ is the measured value of a certain heavy metal’s content in the sample; the unit is $\mathrm{mg}/\mathrm{Kg}$; and $I_{\mathrm{geo}}$ is the geo-accumulation index.

Table 1. Geo-accumulation index and pollution degree classification [25].

Igeo	Level	Pollution Level
Igeo ≤ 0	0	No pollution
0 < Igeo ≤ 1	1	Mild pollution
1 < Igeo ≤ 2	2	Somewhat moderate pollution
2 < Igeo ≤ 3	3	Moderate pollution
3 < Igeo ≤ 4	4	Partial pollution
4 < Igeo ≤ 5	5	Severe pollution
5 ≤ Igeo	6	Extremely heavy pollution

lists the relationship between heavy metal pollution and the range of $I_{\mathrm{geo}}$.

2.2.2. Potential Ecological Risk Index Method

In 1980, Swedish scientists first proposed the potential ecological risk index method, which was relatively fast and simple and could use the standard method of dividing the level of pollutants and their potential ecological risk levels [27]. The potential ecological risk index method was calculated by measuring the content of pollutants in the sample, which was able to reflect heavy metal content and toxicity in the soil. The calculation formula is:

$$RI={\displaystyle \sum {}_{i=1}^n}{E}_r^i={\displaystyle \sum {}_{i=1}^n}{T}_r^i·C_s^i/C_n^i$$

(2)

$RI$ is the comprehensive potential ecological risk index of the heavy metal; $T_{\mathrm{r}}^i$ is the toxic response factor for the given substance; $C_s^i$ is the content of heavy metal in soil from the study area; $C_n^i$ is the reference value required for the calculation [26] and $E_r^i$ is the potential ecological risk coefficient of a single heavy metal.

From the formula, it can be seen that RI reflects the potential ecological risk of soil pollution based on the content and environmental background value of each heavy metal. The greater the RI value, the greater the potential ecological risk of soil pollution. Therefore, after calculating the comprehensive potential ecological risk index of the soil in the surrounding environment of the MSW incineration plant using the potential ecological risk comprehensive index, a more accurate evaluation result could be obtained for soil pollution around the MSW incineration plant. The ecological risk level is shown in

Table 2. Potential ecological risk index and grading criteria [27].

${\mathbf{E}}_{\mathbf{r}}^{\mathbf{i}}$	RI	Ecological Hazard Level
<40	<150	Low
40~80	150~300	Moderate
80~160	300~600	Considerable
160~320	600~1200	High
320	1200	Very high

.

3. Results and Discussion

3.1. The Content Distribution of Zn, Pb and Cd in Soil

The location of the MSW incineration plant on the ground was taken as the 0 point, the east–west direction was the x-axis, the north–south direction was the y-axis, and the content of heavy metal in the soil at the sampling point was determined as the z-axis. Due to the large spacing between sampling points, the data interpolation method was used to encrypt the sampling points [28]. That is, 3 × 3 grid data points were generated in the range of x and y and interpolated using the MATLAB grid spline function interpolation method. The interpolated image is shown in Figure 2.

The greater the content of pollutants, the lighter the color displayed in Figure 2. It can be seen from the spatial distribution of heavy metals Zn, Pb and Cd that there was never less than one content peak, which indicated that the study area had been affected by more than one source of pollution.

The first peak in the content of Zn, Pb and Cd was basically at the same point (x = −299.6 m, y = 49.1 m), indicating that the pollution sources were the same (Figure 2). The second peak in the content of the three figures (Figure 2a–c) was located at different points, suggesting that this area was affected by different pollution sources. Combined with the surrounding environmental analysis of the sampling points, it could be inferred that the pollution source might be the MSW incineration plant or it might not be, and there was still a lack of evidence to prove how much the MSW incineration plant contributes to Zn, Pb and Cd content in this region.

On the other hand, as shown in

Table 3. Descriptive statistics of soil Zn, Pb and Cd (mg/kg).

Name	Mean Value	Variable Coefficient (%)	Background Value [27]	Environmental Quality Risk Control Standard for Soil Contamination of Agricultural Land
				Soil Risk Screening Value (pH > 7.5)	Soil Risk Control Value (pH > 7.5)
Zn	240.63	41.43	78.8	300	-
Pb	39.59	55.03	22.1	170	1000
Cd	11.72	56.38	0.11	0.6	4

and Figure 2, most of the Cd, Zn and Pb content in the soil exceeded the soil background values. Compared with the environmental quality risk control standard for soil contamination of agricultural land (GB15618-2018), the average content of Zn and Pb in this study area met the requirements. However, the average content of Cd in most of the study area exceeded the soil risk screening value. Compared with research reports on the soil around other MSW incineration plants at home and abroad, the content levels of Zn and Cd were higher than those reported in existing studies [18,29], while the content of Pb was lower than that of some related reports.

3.2. Multivariate Statistical Analysis

3.2.1. Variation Coefficient Analysis

Statistical analysis was used in order to investigate the sources of heavy metals; the results are presented in Table 3. It can be seen from Table 3 that the coefficient of variation decreased in the order of Cd > Pb > Zn. The greater the coefficient of variation was, the stronger the influence of human activity. The coefficient of variation of Cd was the highest, at 56.38%, followed by Pb and Zn, which were 55.03% and 41.43%, respectively. This indicated that the soil was affected by heavy metals to varying degrees, with poor spatial continuity and great differentiation, and was most affected by external factors.

3.2.2. Correlation Analysis

Correlation analysis has been widely used to analyze the source of heavy metals in soil. If there is a significant positive correlation between heavy metals, it indicates that there were similar sources or geochemical behaviors, such as co-enrichment and migration, between pollutants. If there is a significant negative correlation, it shows that the sources of different heavy metal pollutants were different [30].

Table 4. Correlation coefficients of Zn, Pb and Cd in soil.

Element	Cd	Zn	Pb
Cd	1
Zn	0.450 *	1
Pb	0.519 *	0.206	1

Note: * indicated a strong correlation at the 0.05 level (two tailed).

shows the correlation analysis of heavy metals in the soil of the study area. The results showed that Cd was significantly correlated with Zn and Pb at the level of 0.05 (two-tailed). Therefore, it could be judged that there was a close relationship between Cd and Pb, and there was a close relationship between Cd and Zn. Similarly, according to the weaker correlation between Pb and Zn, it could be judged that Pb and Zn have no associated relationship. It could be concluded that more than one pollution source had an impact on Cd in the soil, otherwise Pb and Zn should also have a strong relationship.

Generally speaking, the sources of heavy metals in the soil were from heavy metal deposition in the atmosphere, such as from automobile exhaust, fossil fuel and industrial waste gas, sewage irrigation, agricultural production and solid waste dumping. Combined with the topographic map of the study area, it can be judged that the heavy metals in this area mainly came from the deposition of heavy metals in the atmosphere and agricultural activities. Possible sources of pollution included automobile exhaust from highways, agricultural activities and the MSW incineration plant. Jiao and Wei’s study found that most of the Cd in soil came from the use of phosphate fertilizer and sewage irrigation in China. Cd was the main heavy metal pollutant of highways, followed by Zn and Pb [31,32]. Most studies also showed the main heavy metal in the flue gas of incineration plants should be Zn and Pb; the content of Cd should be the least, and the heavy metals Zn and Pb should be strongly correlated with MSW incineration [33]. Li Yinghua found Pb and Zn in soil samples had good correlation. The correlation coefficient (r) was 0.982 [34]. This value was much higher than the 0.206 correlation coefficient of Zn and Pb in this study.

Therefore, it could be inferred that traffic pollution sources and agricultural activities could be an important pollution source in the study area, and the MSW waste incineration plant has no effect on Zn and Pb in the soil or the effect can be ignored. Otherwise, this result was contrary to the irrelevance of Zn and Pb.

3.2.3. Principal Component Analysis

Principal component analysis (PCA) is one of the most important and powerful methods in chemometrics as well as in many other fields. It provides the weights needed to obtain the new variable that best explains the variation in the whole dataset in a certain sense. This new variable, including the defining weights, is called the first principal component. Eigenvalues can be used to determine whether the data can be subjected to principal component analysis.

There were only three variable factors in this study, and PCA can also be used to judge their contribution rate, according to the history of PCA [35]. According to the MATLAB calculation results for eigenvalues, the eigenvalues of Zn, Pb and Cd were 0.7946, 0.9335 and 1.876, respectively. Only the eigenvalue of Cd was greater than 1, so Cd was the first principal component. The second principal component was Pb and the third principal component was Zn. Pb and Zn are recognized as highly volatile heavy metals and are also iconic elements of heavy metal pollution in MSW incineration exhaust [36]. The contents of Pb and Zn were detected, but according to Figure 2, Zn and Pb did not show a high concentration area in the dominant downwind direction. This was not consistent with the distribution of heavy metal soil pollution caused by the MSW incineration plant. Therefore, Zn and Pb in the soil were not from the MSW incineration plant, but might come from the surrounding small household factories, such as those for spraying, electroplating and welding.

The PCA was consistent with the results of coefficient of variation analysis and correlation analysis. It showed again that the heavy metals Zn, Pb and Cd in the study area were not from the MSW incineration plant.

3.3. Evaluation and Analysis

3.3.1. Geo-Accumulation Index Method

The location of the MSW incineration plant on the ground was indicated by the 0 point, with the east–west direction represented by the X-axis and the north–south direction represented by the Y-axis. The heavy metal geo-accumulation index is shown as the Z-axis in Figure 3.

According to the difference between the measured values of heavy metals and the environmental background values, the geo-accumulation index was capable of directly reflecting the level of heavy metal pollution by integrating the existing natural geological processes.

Combining Figure 3 and Table 1, the geo-accumulation index for Zn ranged between 0 and 2 (Figure 3a). In the study area, 70.26% of the soil was found to be mildly polluted (0 < I_geo ≤ 1), while 29.74% of the area was polluted at the moderately moderate level (1 < I_geo ≤ 2). Similarly, the geo-accumulation index for Pb ranged between −2.23 and 1.40 (Figure 3b). In this case, 45.67% of the study area was not polluted by Pb (I_geo < 0), and 52.74% of the area was mildly polluted (0 < I_geo ≤ 1). The remaining 1.59% of the study area was mildly polluted (1 < I_geo ≤ 2). For Cd, all the measured values were found to be greater than 5 (Figure 3c), indicating severe pollution in the study area.

Thus, from Figure 3, it can be observed that human activities have the greatest impact on soil Cd in the study area, and the heavy metal Cd was the main pollutant in the surrounding soil, which needed special attention. The result of $I_{\mathrm{geo}}$ was consistent with the findings of the correlation coefficient and principal component analysis, reinforcing the fact that traffic and agricultural sources were the main pollution sources in this region [36].

In this study, the geo-accumulation index method was employed to evaluate the risk of heavy metals in the soil around the MSW incineration plant. However, the geo-accumulation index method only conducted a simple linear regression analysis of pollution degree and environmental medium, making it unable to consider the interaction and synergistic effects between heavy metals. Consequently, a comprehensive assessment can be achieved by combining it with the potential ecological risk comprehensive index.

3.3.2. Potential Ecological Risk Index Method

The potential ecological risk comprehensive index is a more comprehensive evaluation method for the ecological risk of heavy metals in soil. It can take into account the interaction and synergistic effects of heavy metals, allowing for a better evaluation of the potential ecological risk of soil pollution.

Therefore, the RI and ${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$ of Zn, Pb and Cd in the soil of the study area were calculated (Figure 4) according to the potential ecological risk index evaluation method.

Figure 4aȓc show the ${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$ values of Zn, Pb and Cd at different points in the soil of the study area, respectively. According to the average value of the potential ecological risk index for single heavy metals, the potential ecological risk of each heavy metal was ranked as Cd > Pb > Zn. It can be seen from Figure 4a and Table 2 that the ${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$ value of Zn was less than 40 between 1 and 7, indicating that the ecological risk of Zn in this area was low. The ${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$ value of Pb was between 1 and 20, also less than 40, indicating that the risk of Pb in the study area was also low. The ${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$ value of the heavy metal Cd was greater than 1200 (Figure 4c), indicating that the ecological hazard level of Cd in the study area was high. In Figure 4d, the RI value is greater than 1200, indicating that the ecological hazard level of the whole region is high (Table 2).

Cd was the main contributor and Zn had the least contribution to RI. On one hand, it might be that the toxicity coefficient of Cd is high, and on the other hand, it might be that local human activities had the greatest impact on Cd. This is consistent with the results of the principal component analysis and geo-accumulation index. The main components of the flue gas in the MSW incineration plant were Zn and Pb [37]. Zn and Pb are iconic pollutants in MSW incineration, and there was a strong relationship between them. In this study, the main pollutant was Cd. Zn and Pb were weakly correlated, there was no associated relationship, and the potential ecological impact of Zn and Pb was weak. This also proved that the comprehensive potential ecological impact of the MSW incineration plant on surrounding heavy metal levels could be ignored.

4. Conclusions

Compared with the background values for soil in Chengdu, the contents of Cd, Pb and Zn in the soil of the study area were higher than the average background value of soil. The content of Cd in the soil exceeded the value for soil environmental quality risk control standard for soil contamination of agricultural land (GB15618-2018). The contents of Pb and Zn in the soil did not exceed the value for the soil environmental quality risk control standard for soil contamination of agricultural land (GB15618-2018).

The distribution of heavy metals in the soil around the MSW incineration plant was not related to the local dominant wind direction or distance from the MSW incineration plant. These results do not conform to the distribution characteristics of point source pollutants in MSW incineration plants.

Based on the results of principal component analysis, coefficient of variation analysis and the geo-accumulation index of the content and spatial distribution characteristics of heavy metal elements in the soil around the MSW incineration plant, the study area was affected by more than human activities. Cd was the most affected by human activities.

The result of correlation analysis showed that Zn and Pb were weakly correlated, so Zn and Pb did not come from the same pollution source, excluding the influence of the surrounding MSW incineration plant.

The potential ecological risk index showed that the potential ecological risk was high, and the main ecological risk factor was Cd, which should be paid attention to.

This study only analyzed the content of heavy metals in the soil. In the future, a more comprehensive risk assessment of the study area could be carried out by measuring the morphology and bioavailability of heavy metals in the soil, and the source of heavy metals could also be analyzed by means of isotope tracing.