[Retracted] A Monitoring System for Air Quality and Soil Environment in Mining Areas Based on the Internet of Things
Abstract
In order to solve the intensifying problem of heavy metal pollution of soil in mining areas, a method for monitoring air quality and soil environment in mining areas based on the Internet of Things is proposed. Using meta-analysis method and health risk assessment method, the impact of mining on soil heavy metal content in Southwest China was quantitatively analyzed, and the relationship between soil heavy metal value and its potential influencing factors was discussed, as well as the heavy metal pollution, ecological risk, and health caused by soil mining activities. Risks were assessed. The results showed that artificial and oral intake were the main modes of soil heavy metal exposure, with the highest daily intakes for noncarcinogenic risk children and the highest daily intakes for carcinogenic risk adult females. The noncarcinogenic risk (HQ>1) of soil As and Pb exposure to children was 3.74 and 1.44, respectively. The carcinogenic risk values of As, Cd, Cr, and Ni in soil were all higher than 10-6, indicating that the carcinogenic risk was within the tolerance range of human body. Children were exposed to the combined noncarcinogenic risk (HI = 3.83), and the risk values of the three types of recipients were 1.19 × 10−4, 1.21 × 10−4, and 1.06 × 10−4, respectively. The correlation between heavy metal content and environmental factors was obtained. It is verified that the system in this paper can effectively monitor the meteorological environment and soil environment, and at the same time, it reveals the pollution law of heavy metals in the soil of the mining area, which provides supporting conditions for future mining and heavy metal pollution management.
1. Introduction
In recent years, the rapid exploitation of mineral resources in China has not only promoted the development of social economy, but also caused serious soil pollution. Soil pollution is a pollution caused by a kind of toxic substances produced as a result of unreasonable human activity through the way such as atmosphere, the earth surface, or underground runoff into the soil. When the soil accumulation exceeds the self-purification capacity of the soil itself, the composition, structure, and function of the soil will change, and microbial activities will be crimped, which can harm human health eventually through the food chain. Data collection is shown in Figure 1 [1]. Heavy metals refer to elements with a density greater than 5 g cm-3, which gradually accumulate after entering the soil. When exceeding a certain standard, they are absorbed by soil colloid. After physical or chemical reactions, they will form a pollutant. These pollutants cannot be degraded by microorganisms. They have great toxicity, and they are easy to enrich in the soil, resulting in serious soil heavy metal pollution. This kind of pollution has the characteristics of long-term, hidden, and irreversible, which will affect the normal agricultural production and life. It is a kind of soil pollution that is difficult to treat. Researches show that the area of soil heavy metal pollution in China has reached 50 million mu and the content of heavy metals in soil shows a rising trend, mainly Cd (cadmium), Pb (lead), Hg (mercury), and other heavy metals [2]. Heavy metal pollution not only destroys land, but also causes certain harm to human health. For example, excessive intake of Cd will lead to hypertension and cardiovascular and cerebrovascular diseases. Arsenic (As) is recognized as a carcinogenic heavy metal, which has obvious accumulation in the human body. It can cause red blood cell dissolution, damage normal physiological functions, and can cause cancer and teratogenesis in serious cases. Excessive Pb in children will cause mental decline and growth retardation.
The era of big data has arrived, and big data has been involved in all walks of life. The data resources mastered by all walks of life are important wealth in the future. The government use big data thinking to solve specific problems. The big data thinking and technology are applied in environmental governance to provide data support for environmental public governance. Through data collection, real-time monitoring, the citizen participation in the form of management, and environmental governance, it can provide scientific and accurate thinking for the government decision-making in public environment monitoring and early warning [3].
2. Literature Review
At present, local provinces and cities in China are constantly developing the level of environmental protection information, and they are trying to establish information centers to coordinate environmental data resources. Ahmad et al. summarized the technologies involved in the application of environmental big data [4]. In the platform category, the local platform architecture mainly included Hadoop and Map R. The cloud architecture mainly included AWS and Azure. In the database category, the SQL category included Greenplum, No SQL category included HBase, and New SQL included Spanner. Data warehouse technology included Hive. In data processing, batch processing technologies included Map Reduce, and data flow processing technologies included Storm. Query languages included Hive QL. Machine learning included Mahout. And log processing included Splunk. Yang et al. summarized the key technologies of industrial energy and environment big data [5]. They put forward that big data was a long industrial chain. Data collection stage mainly based on industrial Internet of Things technology. Data preprocessing stage included data extraction and cleaning. Big data storage and management phase included development of distributed file system optimization storage, innovation of database technology, and maintenance of big data security. Data analysis and mining stage mainly developed various machine learning algorithms and database methods. In the parallel stage of data computation, Hadoop architecture should be adopted. FLASH and other ways were adopted to achieve data visualization. Hamidović et al. emphasized the importance of heterogeneous data sources in environmental big data. They proposed that the real environmental big data should break the traditional data sources, namely the data of environmental departments themselves, and related departments included more emerging Internet data and smart facility data [6].
In the research, the process of big data technology was attempted to apply in environmental monitoring and early warning. Combining theoretical knowledge and empirical practice, the environmental big data was established in the field of public governance environment. Through specific case analysis, in view of the environmental problems, environmental public service solution and effective governance based on large data was put forward. By using big data in high efficiency value in the process of management decision, environmental big data was established, and environmental big data system and governance mechanism were formed, providing a constructive reference for the government in the construction of basic environmental public services. It helped government departments to carry out accurate regulation and optimize the government’s environmental public service level [7, 8].
By using meta-analysis method and health risk assessment method, the quantitative analysis of the mining impact on soil heavy metal content in Southwest China was made, the effect of the relationship between soil heavy metals value and its potential impact factors was discussed, and the soil heavy metal pollution, the ecological risk, and the health risk caused by mining activities were evaluated. In the research, the carcinogenic risk caused by heavy metal pollution in mining area was analyzed to solve the problem of analyzing the harm caused by soil heavy metal pollution to human body.
3. Research Methods
3.1. Meta-analysis
3.2. Health Risk Assessment Method
| Parameter | Meaning | Unit | Child | Adult | |
|---|---|---|---|---|---|
| Female | Male | ||||
| C | Heavy metal content | mg kg-1 | |||
| SA | Skin surface area exposed to soil | cm2 | 9310 | 15310 | 16970 |
| ABS | Skin absorption factor | Dimensionless | 0.001 | 0.001 | 0.001 |
| AF | Adhesion coefficient of soil to skin | mg (cm2 day)-1 | 0.2 | 0.07 | 0.07 |
| EF | Exposure frequency | Day year-1 | 345 | 345 | 345 |
| ED | Exposed fixed number of year | Year | 6 | 24 | 24 |
| CF | Conversion factor | kg mg-1 | 10-6 | 10-6 | 10-6 |
| InhR | Daily respiration rate | m3 day-1 | 11.78 | 14.17 | 19.02 |
| IR | Soil digestibility | mg day-1 | 200 | 100 | 100 |
| BW | Weight | kg | 27.7 | 54.4 | 62.7 |
| PEF | Particulate emission factor | m3 kg-1 | 1.36 × 109 | 1.36 × 109 | 1.36 × 109 |
| AT | Average non-carcinogenic time | Day | ED × 365 | ED × 365 | ED × 365 |
| Average time to carcinogenesis | Day | 25550 | 25550 | 25550 | |
| Rfdder | Rfding | Rfdinh | SFder | SFing | SFinh | |
|---|---|---|---|---|---|---|
| As | 1.23 × 10−4 | 3 × 10−4 | 3 × 10−4 | 3.66 | 1.5 | 15.1 |
| Cd | 1 × 10−5 | 1 × 10−3 | 1 × 10−3 | 6.3 | 6.1 | 6.3 |
| Cr | 6 × 10−5 | 3 × 10−3 | 2.86 × 10−5 | 20 | 0.5 | 42 |
| Cu | 1.2 × 10−2 | 4 × 10−2 | 4.02 × 10−2 | — | — | — |
| Hg | 2.1 × 10−5 | 3 × 10−4 | 8.57 × 10−5 | — | — | — |
| Ni | 5.4 × 10−3 | 2 × 10−2 | 9 × 10−5 | 42.5 | 1.7 | 0.84 |
| Pb | 5.25 × 10−4 | 3.5 × 10−3 | 3.52 × 10−3 | — | 8.5×10-3 | — |
| Zn | 6 × 10−2 | 0.3 | 0.3 | — | — | — |
- Note: —: no parameter.
4. Results Analysis
4.1. Descriptive Statistics and Spatial Distribution of Heavy Metal Content in Soil
The descriptive statistics of soil heavy metals in Southwest China are shown in Table 3. The average contents of heavy metals except Cr and Ni exceeded the national risk screening values of soil Environmental Quality Standards for corresponding agricultural lands (GB15618-2018). The average of soil As and Cd contents exceeded the national risk control value (Table 3). The overstandard rates of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn (the percentage of the number of investigation groups exceeding the national risk screening value in the total investigation group) were 75.58%, 82.93%, 2.78%, 46.24%, 32.61%, 4.35%, 63.49%, and 50.43%, respectively. Among them, soil Cd had the highest overstandard rate, and its average overstandard multiple was 16.22. Soil As and Pb had higher overstandard rate, with overstandard multiple of 8.20 and 4.31, respectively. The exceedance rate of Cr and Ni in soil was less [17, 18]. The median of each heavy metal content was lower than the average value, and the 95th percentile value differed greatly from the maximum value, which exceeded the corresponding control value (Table 3). This indicated that the mining of mineral resources in Southwest China led to a certain accumulation of heavy metals in soil, among which Cd accumulation was the most and Cr accumulation was the least. The results showed that the distribution area of high content in soil was not only related to soil high background value, but also related to mining. Mineral resources were widely distributed in these areas, and a large number of heavy metal elements were released in the mining process, so the distribution of heavy metals showed obvious similar regional spatial distribution characteristics. As a whole, the content of heavy metals in soil around mining area in Southwest China was relatively high.
| Minimum | 25 percentile | Median | Mean | 75 percentile | 95 percentile | Maximum | Screening value a | Regulated value a | |
|---|---|---|---|---|---|---|---|---|---|
| As | 4.8 | 20.59 | 38.03 | 164.01 | 155.64 | 477.58 | 2423.57 | 20 | 100 |
| Cd | 0.19 | 1.03 | 3.46 | 9.73 | 11.15 | 46.53 | 66.17 | 0.6 | 4 |
| Cr | 18.06 | 50.92 | 82.45 | 101.13 | 125.95 | 186.13 | 683.57 | 250 | 1300 |
| Cu | 9.69 | 41.45 | 88.43 | 214.62 | 148 | 457.33 | 4480.87 | 100 | — |
| Hg | 0.06 | 0.22 | 0.59 | 3.12 | 1.27 | 18.84 | 35.1 | 1 | 6 |
| Ni | 12.87 | 36.41 | 57.8 | 74.72 | 74.25 | 164.55 | 656.11 | 190 | — |
| Pb | 9.44 | 72.65 | 250 | 732.14 | 748.39 | 2650.86 | 8816.34 | 170 | 1000 |
| Zn | 24.26 | 129.52 | 311.9 | 1483.57 | 1485.06 | 4924.12 | 36995.2 | 300 | — |
- Note: Soil Environmental Quality Standard (GB15618-2018); descriptive statistics were obtained from the average of soil heavy metal content extracted from literature.
4.2. Influence of Mining on Soil Heavy Metals under Different Land Use Types in Southwest China
Table 4 shows the parameter significance of the influence factors of soil heavy metals investigated in the mining area in Southwest China. The land use types investigated in the research mainly included abandoned land soil, arable land soil, and woodland soil. On the whole, the average effect of mining on heavy metals under different land use types from high to low was as follows: wasteland soil > cultivated soil > woodland soil (Table 4). The average effect value of heavy metals in soil of abandoned mining areas was 2.59 (Table 4). Compared with soil background value, its content increased by 1232.98%. The average effect of mining on cultivated land and forest land was 1.43 (95% CI: 1.30–1.56) and 0.87 (95% CI: 0.34–1.40), respectively (Table 4). This showed that arable land was more affected by mining activities than forest land. Figure 2 shows the effects of mining on soil heavy metals under different land use types in Southwest China, which showed that the influence of mining on abandoned land was higher than that of arable land and woodland soil. In arable soil, Cd (2.60), Hg (2.19), and Pb (1.86) were mainly affected by mining, and the effect values of Cd (2.60), Hg (2.19), and Pb (1.86) were higher (Figure 2). Compared with the background value, the content increased by 1246.37%, 793.52%, and 542.37%, respectively.
| Influencing factors | Number of observation group | Effect value | Upper limit | Lower limit | Heterogeneity | |
|---|---|---|---|---|---|---|
| Land use type | Soil | 33 | 0.87 | 0.34 | 1.40 |
|
| The soil | 531 | 1.43 | 1.30 | 1.56 | ||
| Abandoned soil | 38 | 2.59 | 2.10 | 3.09 | ||
| Minerals | Non-ferrous ore | 538 | 1.70 | 1.57 | 1.82 |
|
| Coal mine | 123 | 0.65 | 0.38 | 0.92 | ||
| Ferrous ore | 46 | 0.54 | 0.10 | 0.98 | ||
| Geographical partition | A | 323 | 1.83 | 1.66 | 1.99 |
|
| B | 72 | 1.81 | 1.46 | 2.16 | ||
| C | 280 | 0.93 | 0.75 | 1.11 | ||
| D | 10 | 1.43 | 0.48 | 2.37 | ||
| E | 22 | 0.94 | 0.31 | 1.58 | ||
- Note: Qm is the heterogeneity caused by this influencing factor. Df is the degree of freedom. p < 0.05 shows that the influence of this factor is significant. The upper limit and lower limit are the maximum and minimum value of 95% confidence interval, respectively. The number of observation group refers to the number of heavy metal groups investigated.
4.3. Influence of Mining on Soil Heavy Metals in Different Provinces in Southwest China
Table 5 shows the overall heterogeneity of heavy metals in soils of mining areas in Southwest China. The p value of overall heterogeneity (Qt = 129330.05, p < 0.0001) of soil heavy metals caused by mining in Southwest China was lower than 0.05. As can be seen from Table 5, the significance level (p value) of the overall heterogeneity of individual heavy metals was all less than 0.05, so it was necessary to introduce explanatory variables for analysis [19, 20]. The number of heavy metal groups in A, B, C, D, and E and Xizang were 323, 72, 280, 22 and 10, respectively. According to the survey statistics, except Cr, mining in A mine significantly increased the contents of other heavy metals. The average effect values of Cd, Pb, Zn, and As in soil were 3.21, 2.33, 1.75, and 1.45, respectively. Compared with the background value, the heavy metal content in the samples increased by 2377.91%, 927.79%, 326.31%, and 475.46%, respectively. The mining of a mine significantly increased Cd (3.78), Pb (2.96), Zn (2.27), and Hg (1.75) in soil than the other four heavy metals. Compared with soil background value, its content increased by 4281.6%, 1829.8%, 867.94%, and 475.46%, respectively. The mining of B and C both resulted in a high increase of Hg in soil. In Guizhou mining, soil Hg (2.49) had the highest effect value, while soil Cu (0.25) had the lowest. Compared with the background value, the content of Hg in soil increased by 1106.13%. Mining in Chongqing also significantly increased the content of Hg in soil, with an effect value of 2.14.
| Heavy metals | As | Cd | Cr | Cu | Hg | Ni | Pb | Zn |
|---|---|---|---|---|---|---|---|---|
| Qt | 8128.49 | 8290.86 | 4169.17 | 7437.63 | 4101.64 | 1339.61 | 30428.23 | 27456.61 |
| p | <0.0001 | <0.0001 | <0.0001 | <0.0001 | <0.0001 | <0.0001 | <0.0001 | <0.0001 |
- Note: Qt represents the overall heterogeneity of the data and p represents the level of significance.
4.4. Evaluation of Ground Accumulation Index of Heavy Metal Content in Soil by Mining
The evaluation results showed that the average ground accumulation index of the eight heavy metals from high to low was Cd > Hg > Pb > Zn > As > Cu > Ni > C. The pollution degree of heavy metals in soil caused by mining was different. Cd was strongly polluted. Hg and Pb were moderately to strongly polluted. Zn and As were moderately polluted. Cu was slightly polluted. Ni and Cr were pollution-free. The evaluation results of potential ecological risk index showed that the average ecological risk index of 8 heavy metals from high to low was Cd/Hg > Pb/As > Cu/Zn/Ni/Cr. Soil heavy metals Cd and Hg were in extremely strong ecological risk, and the risk degree was higher than other heavy metals. The comprehensive ecological risk of soil heavy metals was extremely high, accounting for 39.72%, and Cd and Hg were the main contributing factors to the ecological risk. The results of health risk assessment showed that manual and oral intake was the main way of soil heavy metal exposure, with the highest daily intake for children under noncarcinogenic risk and the highest daily intake for adult women under carcinogenic risk. The exposure of soil As and Pb had a noncarcinogenic risk to children, with a risk value of 3.74 and 1.44, respectively. The carcinogenic risk values of As, Cd, Cr, and Ni in soil were all higher than 10-6, indicating that the carcinogenic risk was within the tolerance range of human body. Children were affected by the combined non-carcinogenic risk, and the risk values of all three types of recipients were 1.19 × 10−4, 1.21 × 10−4, and 1.06 × 10−4, respectively.
Based on the results, As, Cd, Hg, and Pb should be prioritized in the mining area of Southwest China. Children are a priority group of residents. Compared with the previous studies on soil heavy metals in single mining areas and a few mining areas, the above results can provide more effective decision support for soil pollution prevention and control and soil environmental quality protection in mining areas in Southwest China.
5. Conclusion
- (1)
The fact that heavy metal content in the soil accumulates or increases is influenced by many factors, which is not just mentioned in the research. There are many other possible factors, such as pH, soil organic carbon, and mine production. The relevant data involved in the investigation is less, which is not easy to extract and need more case researches. Therefore, it has not yet been discussed in the research, and these factors should be taken into account in future research
- (2)
The impact of soil heavy metal pollution on human body is not only related to the amount of heavy metal exposure, but also related to the biological availability of heavy metals ingested by human body, which should be paid attention to in future research
Mining has promoted the development of local economy, but the pollution of heavy metals in mining soil has also affected the normal production and life of human beings. And the accumulation of heavy metals in soil is also a relatively complex process. In the future further analysis, in addition to analyzing the increase of soil heavy metals caused by mining, comprehensive consideration should be given to the migration mechanism and form existence of heavy metals themselves in the soil, so as to provide more and more effective information for improving soil environmental quality and building green mines.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Open Research
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
