Evaluation of heavy metal (Cu, Pb, Zn) distribution in base metal mining area at Sangkaropi: implication for land use planning

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Evaluation of heavy metal (Cu, Pb, Zn) distribution in base metal mining area at Sangkaropi:

implication for land use planning

To cite this article: U R Irfan et al 2021 IOP Conf. Ser.: Earth Environ. Sci. 921 012047

View the article online for updates and enhancements.

Evaluation of heavy metal (Cu, Pb, Zn) distribution in base metal mining area at Sangkaropi: implication for land use planning

U R Irfan¹*, A Maulana¹, I Nur², M Thamrin² and M Manaf³

¹Geological Engineering Department, Universitas Hasanuddin, Gowa, Indonesia

²Mining Engineering Department, Universitas Hasanuddin, Gowa, Indonesia

³Urban and Regional Planning Study Program, Bosowa University, Makassar, Indonesia

Corresponding Author: [email protected]

Abstract. Settling-pond treatment systems have been applied in the base-metal mining area at Sangkaropi, South Sulawesi, Indonesia, before being discharged into the Koyan River through the agriculture area. This study aims to conduct a valuation of pollutant Pb, Cu and Zn in areas impacted by base-metal mining in sediment materials as a sensitive indicator for monitoring contaminants in the aquatic environment. Sediment sampling location, the first is in the upstream before the mining location, the second is located in the mining area to settling pond, and the third is in the area after settling-pond to the agriculture area. The concentrations of heavy metal were analyzed using uses AAS method. The assessment is performed based on the value of contamination degree, enrichment of heavy metal pollutant and ecological risk. The arrangement of the average frequency of heavy metal concentrations is Pb>Cu>Zn, which Cf and I-geo values have the almost same behavior. All metal in the upstream and agriculture site is classified as a low category, the Cu on the second and third sites is moderate. The assessment results from this study represent that Pb has a considerable ecological risk category value. However, polluted-Cu are categorized moderately need to be cautious, because metal enrichment comes from mineralization bedrock in the river. Therefore, the result of this study shows that geologic data, especially mineralized bedrocks associated the heavy metal concentration, should be taken into consideration in land-use planning policy in areas that have been impacted by mining and in the assessment of environmental health impacts.

1. Introduction

Public health problems can occur due to the imbalanced distribution of chemical elements in the environment resulting from anthropogenic activities. Significant toxicity from anthropogenic activities comes from mining activities which are geogenic contaminants. The Kuroko-type Volcanic Hosted Massive Sulfide (VHMS) deposits are found in Sangkaropi area, South Sulawesi [1-2]. The sulfide deposit consists of a base metal such as galena, sphalerite, and chalcopyrite. Open-pit mining in the Sangkaropi area causes the exposure of rocks containing sulfide minerals. The reaction between

sulfide minerals, oxygen, and water forms acid mine water [3], the principal environmental problem of mining activity. The sewage treatment system at the mine is a settling pond that aims to minimize the concentration of heavy metals before it flows into the Koyan River. Koyan River flows through mining areas, villages and, rice fields until Sa’dan River. The Sa’dan River Basin is used as a source of water for people in North Toraja for daily use including drinking water.

Currently, settling-ponds are experiencing siltation causing the ability to minimize the concentration of heavy metals is not optimal. This condition is due to high concentration deposition of sediment containing heavy metals flowing into the river and hence will harmful the water and soil or rock layers. Sediment deposits may be a sensitive parameter for observation contaminants in aquatic environments, such as river [4-5]. The high heavy metal content in the sediment that release into the aquatic environment will causes toxicity [6-7].

The mining of base-metal ores has caused wide distribution of metal contaminants in surface sediment.

Some studies on retention ponds suggested that concentration of heavy metal precipitation particles greatly varied depending on the nature of the metal, pH, behavior inorganic and organic ligands [8-9]. Heavy metals that accumulate in the soil are immobile so it is difficult to migrate and hence cause toxicity. They could be absorbed by plants through their roots, migrate to water or atmosphere, dan finally enter the food chain, causing health and reproduction problem in humans and animals [7-10]. Therefore, many research

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efforts have been carried out to prevent or reduce heavy metal pollution in the river and soil sediments.

Heavy metals originating from abandoned base-metal mining areas can be the main contaminants [11- 12], mainly in open mines that directly interact with surface water. For example, oxidized pyrite minerals produce acidic sulfuric acid which causes metals Al, Fe, and Mn to become soluble and potentially toxic [13]. Mining waste from the sulfide mineral group is generally extracted using aqua regia which produces high concentrations of heavy metals [14]. Heavy metals that accumulate in river sediments can be adsorbed on organic and inorganic compounds that form complex compounds. Metal mobility in water from sediment deposits that have an impact on environmental health can be minimized or prevented so the risk of exposure to toxic elements in living things can be lowered. The assessment of heavy metal pollution in aquatic ecosystems derived from the development of a sediment quality index that has been carried out by several researchers, for example by comparing the index of contamination in areas suspected of being polluted with areas that are clean or free of pollution [15]. Determination of potential contaminant toxicity to ecological risk will be compared with sediment quality guidelines (SQG) [16-17]. The objective of this research is to evaluate the allocation of heavy metals, particularly Cu, Pb, and Zn in the base-metal mining in Sangkaropi, South Sulawesi and discuss its implications for land-use planning.

2. Materials and Method 2.1 Study Area

The research was carried out around the mining location of PT. Makale Toraja Mining which is administratively located in Sangkaropi Village, Sa'dan Subdistrict, North Toraja, South Sulawesi, Indonesia. The study area is about 345.7 km to the north of the Makassar city, and 21.7 km to the north of Rantepao. The geographical position of the study area is located at coordinates of 119º57'00" to 119º57'40"

Longitude and 02º51'40" to 02º52'10" Latitude (Figure 1). The Sangkaropi area is a moderate to steep hilly area with a slope of 45% - 90%. The lithology include basalt, clay, dacitic tuff breccia, rhyolitic tuff breccias, and granite [1]. The sulfide mineral reported is galena, sphalerite, pyrite, chalcopyrite, bornite, and covellite, found in the stratiform orebody and generally covered by a thin layer of barite. PT. Makale Toraja Mining is exploiting galena in this deposit using an open pit system. Stripping the overburden and extracting the galena itself causes the exposure of rocks or soil containing sulfide minerals and when reacting with water and oxygen will form acid mine drainage.

2.2 Sampling and Chemical Analysis

The materials analyzed are taken from stream sediment samples from Koyan River deposit. A total of eleven stream sediment samples is categorized into three-zone, namely the first area located in the upstream of the mining location (01-02), the second area is from the mining area to settling pond (03-09) and the third area is the area after settling pond to the agriculture area (04,10,11). To get representative sediment samples, three samples from each location were collected at a distance of 50-200 m (Figure 1). The sample was taken at the edge and center of the river and then put into a container. Descriptions are made regarding the location, color of the sediment, grain size, the density of organic material, and minerals.

Figure 1. Location of sediment material sampling in the Koyan River and surrounding areas.

The highest accumulation of heavy metals in sediment deposits accumulates in grain size between silt and clay [18]. Therefore, the grain size is separated from the dry sample using a sieve net. To get the silt and clay fraction, the sediment rate of 100 grams was used by adding 10 ml of sodium oxalate 0.01, and 5

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ml of sodium carbonate 0.02 N and distilled water were added. The precipitate formed is then filtered, dried, and measured by weight.

Silt-clay sized sediment samples were analyzed at the Laboratory of the Center for Plantation Industries, Makassar that has been accredited by KAN (National Accreditation Committee) based on ISO 17025. To determine the concentration of elements in sediment samples used Atomic Absorption Spectrometry type Buck Scientific 205 version 3.94C.

2.3 Data Analysis

2.3.1 Sediment Quality Standard

Quality standards for heavy metal content in sediments in Indonesia have not been established and therefore the quality standards used in this study refer to the standards by the ANZECC/ARMCANZ quality guidelines from Australia and New Zealand and the Canadian Council of Ministers of the Environment [19]. The heavy metal quality standard used in this study as a comparison to analyze whether the heavy metal contained in the study site has or has not passed the specified quality standard. Other supporting parameters include the mean of world sediment [20] and the degree of contamination [21] taken as comparison with the results of previous studies.

2.3.2 Principal Component Analysis

To estimate the relationship between various forms of trace metal bonds in sulfide minerals and as a predictor of heavy metal pollution sources, we use principal component analysis (PCA) that has been widely used in heavy metal pollution research. One of the advantages of using PCA is that it can be used for all data conditions without reducing the number of original variables.

2.3.3 Sediment Contamination Evaluation

To evaluate the level of sediment contamination from heavy metals, the index that will be calculated includes contamination factor (Cf), geo-accumulation index (I-geo), and ecological risk factor (Er). The background metal value in sediment for base-metal mining area has not been established, hence we used the common regional metal value in average shale suggest by Hakanson [21] that have been used in some references to estimate ecological risk of contaminants such as heavy metal in surface sediment and soil [10, 15-16, 18, 22-25]. The following are the formulas, notations, and threshold values (Table 1-3) of each index.

= []/[] (1)

Where: Cf is the contamination factor; [C]sediment is metal concentration in sediment; [C]shale is average shale value of earth crust (Pb= 70; Zn= 175; Cu= 50).

Table 1. Degree of contamination based on Cf calculation

= _.! " (2) Cf- Value Pollution categories

< 1.0 low contamination factor 1.0 – 3.0 moderate contamination factor 3.0 – 6.0 cosiderable contamination factor

>6.0 Very high contamination factor

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Where: Igeo is the geo-accumulation index; [C]sediment is measured metal concentration in sediment; Bn is the geochemical background metal concentration (Pb= 70; Zn= 175; Cu= 50); Factor 1.5 is correction factor for the background value due to geogenic effects

Table 2. Indicator of heavy metal contamination of sediment based on I-geo value

#$ = %&_' (3)

Where: Er is the ecological risk factor; is the contamination factor; Tⁱ is the toxic response factor of the element.

Table 3. Describe ecological risk assessment on the basis of Er value

Er Value Risk category

< 40 Low potential risk factor 40 - 80 Moderate potential risk factor 80 - 160 Considerable potential risk factor 160 - 320 High potential risk factor

>320 Very high potential risk factor

3. Result and Discussion 3.1 Heavy metal Concentrations

Descriptive analytical of the concentration of heavy metal in sediments through the Koyan River around the base metal mine area in the Sangkaropi area is presented in Table 4. The average frequency of heavy metal concentrations in order is Pb>Cu>Zn, respectively 1,008.47; 201.01; and 156.77 mg/kg, which is entirely higher than the average sediment and earth's crust. These results reflect long-term mining activities have been causing a significant heavy metal release out of minerals and accumulated in sediments. Infiltration of water at sediments containing sulfide minerals causes leaching of metals Pb²⁺ , Cu²⁺ , Zn²⁺ . The release of lead, copper, and zinc is mainly caused by the presence of galena (PbS), chalcopyrite (CuFeS₂ ) and sphalerite (ZnS). Galena is further reactive in the attendance of pyrite (FeS₂ ) in the settling pond [26]. Heavy metals tend to bind organic material easily, settle and unite with the sediment so that the concentrations levels in the sediment will be higher [6].

All metal concentrations are low in agricultural area located about 1 km from the mine area, on the Koyan River branch in the east. The low Cu value in the upstream (23.07-45.17 mg/kg) and the Zn sample (39.4 mg/kg) originated from surrounding rocks and the value is within the range of sediments in the world. The drainage pattern in the Sangkaropi area, which is sub-dendritic and parallel, shows several river branches that do not pass through the mine area so that the heavy metal content in the sediment is very small. The highest concentration of Cu is 376.10 mg/kg and found in the mining area with a standard deviation of 135.56. The highest Pb and Zn metals were 1,998.56 mg/kg, standard deviation 833.58 and 435.91 mg/kg, standard deviation 126.02 was in the end settling-pond area before

I-geo Value Pollution categories

< 1 Unpolluted-moderately polluted

1.0 – 2.0 Moderately polluted

2.0 – 3.0 Moderately- stronglypolluted

3.0– 4.0 Strongly polluted

4.0 – 5.0 Strongly-extremely polluted

>5.0 Extremely polluted

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5 being drained into the river.

Refer to the standards by the ANZECC/ARMCANZ quality guidelines, concentrations of Cu and Zn in the upstream are at low levels, whereas Pb has approached the highest limit of 160.53 mg/kg. At the mining site to the settling pond, Cu and Pb concentrations exceed the highest limit, while the highest Zn concentration only in the settling pond. Cu concentration at the agriculture site bordering the settling pond decreased at site 11 which was very low at 17.10 mg/kg. However, at site 10 there was an anomaly increase in concentration caused by the deposition of sediment from the river branch which could conduct to high sediment accumulation. Pb concentration after passing through the settling-pond tends to decrease in concentration even though it is still at a value that is at risk of ecology. Average Pb concentrations were also reported to be high in gold mine tailings between 96.82 mg/kg [27] and 510 mg/kg [28]. That occurs in the form of mineral galena (PbS) in ore and this form is found when ore sulfide concentrations are high. After the settling-pond, the concentration of Zn is still below the standard level of sediment quality, and the smaller the concentration on the farther away at the agriculture site.

Table 4. Concentrations of heavy metals Cu, Pb and Zn in sediment deposits in the study area

Location Sample Heavy Metal (mg/kg dry weight)

Copper (Cu) Lead (Pb) Zinc (Zn)

1 upstream SR 01 HS 23.07 157.87 80.37

2 upstream SR 02 S1 45.37 160.53 39.24

3 Mining site SR 03 S2 238.09 1,721.14 129.61

4 Agriculture site SR 04 S3 93.43 172.27 83.63

5 Mining site SR 05 SP 376.10 1,723.29 240.70

6 Mining site SR 06 SP 344.90 1,311.96 26.40

7 Settling pond SR 07 SP 221.39 1,793.32 244.89

10 Agriculture site SR 10 SP 249.96 196.71 216.60

11 Agriculture site SR 11 HS 17.10 83.79 24.04

Mean 201.01 1,008.47 156.77

Minimum 17.10 83.79 24.04

Maximum 376.10 1,998.56 435.91

Standard Deviation 135.56 833.58 126.02

Earth crust content 50.00 14.00 75.00

Mean world sediment* 33.00 19.00 95.00

Degree of contamination** 50.00 70.00 175.00

ISQG*** Low 65.00 50.00 200.00

High 270.00 220.00 410.00

* Salomons W, Fo ¨rstner U,1984 [11]

** Hakanson, 1980 [12]

*** ANZECC, 2000 [10]

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Basic knowledge about the chemical properties and distribution of trace metals in the system is very important but difficult to understand. Statistical analysis is not only providing a fundamental assessment of the relationship between various forms of trace metal bonds but also function as predictors of trace metal pollution sources [22]. Heavy metal correlations based on Pearson correlation show a positive relationship [29]. Significant correlations imply the same source of pollution (Figure 2), a significant correlation implies the same source of pollution. Based on the results of the Pearson Correlation, it is found that the relationship between Cu, Pb, and Zn metals is 0.649 and 0.799. The biggest correlation is Pb-Cu (79.90%), Pb-Zn (64.90%) and Cu-Zn (64.40%).

Figure 2. Pearson correlation analysis of heavy metals correlation shows a positive relationship.

3.2 Assessment of Sediment Contamination

3.2.1 Calculation According to Contamination Factor

The value of contamination factor (Cf) in the upstream for Cu is about 0.461-0.907, just as in the further area agriculture with a value 0.342, as well as Zn is 0.224-0.459 which is included in the low contaminated category field. Pb shows higher value in the upstream, just as in the agriculture area ranges from 2.293-2.255, and is included in the moderate contamination class, except in the farthest area is included in the low contamination. In the mining area, Zn shows values ranging from 0.151 to 0.4545, including the uncontaminated category, while Cu value is in the range of 4.762-7.522 which belongs to the considerable contamination type. Pb represents a higher value (18.742-24.618) and classified as very high contamination. Sediments deposited in settling ponds show that Zn is plotted in the moderate category in the first and second ponds (1.160-1.399) however increasing value in the third ponds (2.491) also categorized as low-polluted. Cu value shows a moderate-polluted category from the first until the third ponds (4.427; 4.833, and 7.199, respectively).

In the agriculture area, Zn remains in the unpolluted category with values ranging from 0.137-1.237 similar to Cu in the Koyan River branch in the east (0.341-1.868). However, Cu value in the main river is higher (up to 4.999) showing considerable contamination type (Figure 3). The Cf diagram indicates that Pb pollution is high in mining into settling point site and in the upstream close to low contamination and rises slightly in the agriculture site. Before entering the mine area, Cu is included in none polluted and becomes moderate in the mining area to agriculture, and in the farthest area which is unpolluted, while Zn show no threats to the ecosystem.

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Figure 3. Value of contamination factor (CF) for heavy metals Cu, Pb and Zn

3.2.2 Calculation According to Geo-Accumulation Index (Igeo)

Geo-accumulation Index (I-geo) describes the enrichment of heavy metal concentrations above background to evaluate how many potentially heavy metals are released from the rocks or ore minerals.

In the upstream and agriculture site, all metals (Pb, Zn, Cu) are classified as an unpolluted-moderately polluted category, the Cu anomaly on site 11 is moderately polluted. The reason can be attributed to the alteration and mineralization of lithologies of fragmental volcanic breccias that form bedrock in rivers.

This shows that, although I-geo pollutant values are low in the area around the mine, it is taken care of to consider geologic data, especially mineralized rocks in land-use arrange. Metals can be more highly concentrated or secondary enrichment because of erosion in rivers over time.

In the mining area, Zn and Cu values ranging from 0.030 to 0.276 and 0.135 to 2.499 respectively which are classified as unpolluted to moderately-polluted type. However, Pb shows high values in this area, ranging from 3,761-4,934 and classified as strongly to extremely polluted. Sediments deposited in the settling pond area shows that Zn and Cu were categorized as low-polluted while Pb was classified as strongly-polluted.

Figure 4. Enrichment of heavy metal concentrations graph based on baseline levels

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The calculation of the I-geo value in the settling-pond suggests that the concentrations from the three metals increased in the third pond. This trend is interpreted due to the lowest level of the third pond which has a thicker deposit compare to others. In agriculture area, Zn and Cu are plotted in an unpolluted category with values ranging from 0.027 to 0.248 for Zn in the eastern branch of the Koyan River, and Cu ranging from 0.456-0.800 in the main river (Figure 4). These Cf and I-geo values have the same behavior as seen from the calculation formulation to get the value of Cf and Igeo which consists of two equal variables, namely the concentration or content of certain metals in the sample and certain metal content normal in nature.

3.2.3 Analysis of Ecological Risk Factor

One of the principles mining problems that underlies mining management in Indonesia is sustainable and environmental problem. Environmental risks contain elements that are uncertain to cause human and environmental harm, so it is needful to quantitatively describe heavy metals distribution that has the potential ecological risks. Approaches to estimate the possibility of ecological risk of heavy metal pollution are generally used to observe heavy metal concentrations in soils and surface sediments in aquatic environments. Heavy metal can be released into the water and has the potential to threaten the ecological health [30]. The diagram result of the potential ecological risk due to heavy metal Cu, Pb, and Zn in the research area is showed in Figure 5.

Heavy metals in sediment based on the ecological risk factor (Er) value in the upstream site shows that Cu, Zn, and Pb are around 1.709-37.610; 0.137 to 2.490; 11.276-11.466 respectively which are categorized as low potential. In the mining area and settling-pond, the values are increasing, vary from 93.711-123.092 and 122.928-142.574, which are categorized as sufficient potential. In the agriculture site after settling-ponds, the Er value is classified as a low potential area.

Figure 5. The graph that shows quantitatively the potential ecological risks caused by heavy metals Cu, Pb and Zn in the study area

There are many minerals such as galena, sphalerite, and chalcopyrite which are potential sources of toxic metals (Cu, Pb, and Zn), pollution of sediment deposits in rivers in agricultural areas is low, polluted-Cu exceptions are categorized moderately on I-geo calculations. Copper is one of the base metal ores that are commonly found in the form of sulfides such as chalcopyrite (CuFeS₂ ) and chalcocite (Cu₂ S). The ability to exist in several oxidized states in the form of ion Cu²⁺ and reduced state to form ion Cu⁺ that unstable, therefore this metal potentially toxic [31]. The evaluation results from this study represent that Pb has a considerable ecological risk category value. Heavy metals in soils are difficult to

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naturally degrade such as Pb which can be toxic even at low concentration is known to cause toxicity in people [32-33]. Even though Pb does not dissolve in water, in that it is a sulfide compound it is easily bound to human hair comprises sulfides.

Therefore, in the planning of settling-ponds, it is necessary to take into consideration the mineralogy characteristics and geochemical behavior of each metal, principally those related to conditions of varying acidity. The association of gangue minerals such as barite and carbonate can influence the behavior of metals that are potentially toxic. Suspension reactions with mineral gangue carbonate and barite [1] are responsible for the alkali character of the drainage flow so that the sediment is not too weathered and reduced metal mobility [34]. It is significant to monitor and evaluate the acidity conditions and the proportion of sediment deposits toward the settling-pond walls.

The issue of heavy metals assessment particularly Cu, Pb, and Zn will support a land-use planning policy in mining areas. The area around the mining has ecological, social, and economic functions. This assessment is very useful in environmental health impact assessment and the development of socio- economic in the community. The current and long-term impacts of mining activities need to be observed to maintain environmental quality. The heavy metal contribution to the environment will affect the productivity of agricultural and fishery products around the mining area. Sediment originating from mining activities can cause siltation which influences the quality and quantity of irrigation in rice fields around Sangkaropi area. The planning and involvement of stakeholders are needed and should begin early. Environmental monitoring programs must be carried out as soon as possible to prevent metal pollution in the short and long term.

4. Conclusion

Geo-accumulation Index (I-geo), contamination factor (Cf), and ecological risk factors (Er) have been evaluated in three areas in Sangkaropi, South Sulawesi. The average frequency of heavy metal concentrations in order is Pb>Cu>Zn with the biggest correlation is Pb-Cu. The Pb metal is classified as low polluting, while Cu and Zn are unpolluted in the up-stream area before the mining location and agriculture area. In the mining to settling-pond area, Pb has a value that is classified as strongly - extremely polluted, while Cu is classified as low - moderately polluted, and Zn is unpolluted, except the Er value is classified as low-polluted. The research is expected to support land use planning policy and socio-economic development policies by conducting environmental monitoring dan evaluation in the early stage of mining, especially in the site affected by the mining.

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Acknowledgments

The author would like to thank PT. Makale Toraja Mining which has provided opportunities for data collection and permission to publish it. Gratefully acknowledge Directorate of Research and Community Service, Directorate General of Research and Development Strengthening Ministry of Research, Technology, and Higher Education of the Republic of Indonesia for providing the research decentralization grant.

Evaluation of heavy metal (Cu, Pb, Zn) distribution in base metal mining area at Sangkaropi: implication for land use planning

Evaluation of heavy metal (Cu, Pb, Zn) distribution in base metal mining area at Sangkaropi:

implication for land use planning

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Evaluation of heavy metal (Cu, Pb, Zn) distribution in base metal mining area at Sangkaropi: implication for land use planning