Arif Susanto, Dasapta Erwin Irawan, Farhan Hudaya, Mochammad Rifky Rizqullah, Enggal Estuaji, Dita Aprilia PUtra, Dziki Hilmawan, Fadhlan Rahmany Binadzier, and Fachrul Arief
Faculty of Earth Sciences and Technology,
Institut Teknologi Bandung
Abstract
Pangalengan Plateau is a volcanic area which is famous as one of the centers of tea plantations in Indonesia. The Majority of land use is in the form of plantations, but this does not rule out the possibility of anthropogenic contamination to groundwater.
As many as 30 groundwater quality data have been collected from springs, dug wells, and drilled wells. We measured temperature, EC, pH, and the content of major elements (Ca, Na, Mg, K, SO4, HCO3, Cl, NO2, and NO3), NH4, and organic components (EColi and total coliform).
Multivariable statistical analysis was used to identify possible anthropogenic influences in the groundwater. We apply Principal Component Analysis and Distance Mapping Analysis with Python programming language (Orange Data Mining).
We managed to identify the prominent components of NO2, NO3, and NH4 in groundwater. It is suspected that this condition is caused by the influence of fertilizers, plant chemicals, plantation, and domestic waste that flows on the surface of the soil and rivers in the area. These substances then ssep into the soil layers and then reach the groundwater zone.
The influence of domestic waste is also marked by the emergence of EColi bacteria and high coliform values.
Keywords: Pangalengan, anthropogenic, water quality, domestic waste, plantation waste
Topic: Interdisciplinary geosciences
Type: Oral Presentation
1. Introduction
1.1 Pangalengan in a glance
Pangalengan is an area located in West Java, Indonesia. It is known for its beautiful scenery, including tea plantations, mountains, and waterfalls. The area is also famous for its dairy industry, producing high-quality milk, yogurt, and cheese. Pangalengan has a cool climate due to its high elevation, making it a popular destination for tourists seeking a respite from the heat of the lowlands. The town has several attractions, including hot springs, golf courses, and cultural sites such as the Pangalengan Traditional House. Overall, Pangalengan offers a unique combination of natural beauty and cultural richness that makes it a popular tourist destination in West Java.
Geothermal resources in West Java are believed to be significant, and the Indonesian government has identified the region as having high potential for the development of geothermal energy. There are several geothermal power plants located in West Java, including the Wayang Windu Geothermal Power Plant in Pangalengan, which has a capacity of around 227 MW. The Wayang Windu Geothermal Power Plant is operated by PT Pertamina Geothermal Energy and supplies electricity to the Java-Bali power grid.
1.2 Geothermal contamination to groundwater
Geothermal energy is a renewable energy source that is derived from the Earth’s heat. Although it is a clean energy source, it can still have negative impacts on the environment, particularly on water quality. Here are three things to know about geothermal contamination to groundwater:
- Chemical Contamination: Geothermal projects can release chemicals such as boron, arsenic, and mercury into the environment. These chemicals can seep into groundwater and contaminate it. High levels of these chemicals can be harmful to human health and the environment.
- Thermal Pollution: Geothermal projects can also generate thermal pollution, which is the release of heated water into the environment. This heated water can raise the temperature of nearby water sources and cause harm to aquatic life. It can also change the chemical makeup of the water and make it unsuitable for certain uses.
- Induced Seismicity: Geothermal projects involve drilling into the Earth’s crust, which can trigger seismic activity. This can cause earthquakes and other ground disturbances that can damage the surrounding environment and impact water quality.
1.3 Anthropogenic contamination
Anthropogenic contaminations refer to the pollution of groundwater caused by human activities. Here are three things to know about anthropogenic contaminations and their effects on groundwater quality:
- Industrial activities: Industries are one of the major sources of anthropogenic contaminants of groundwater. The chemicals used in industrial processes, such as solvents, heavy metals, and petroleum products, can leak into the soil and eventually contaminate the groundwater.
- Agricultural activities: Agricultural practices that involve the use of fertilizers, pesticides, and herbicides can also lead to groundwater contamination. These chemicals can seep into the ground and enter the groundwater, making it unsafe for human consumption.
- Waste disposal: Poor waste disposal practices, such as improper dumping of trash and hazardous waste, can also lead to groundwater contamination. The chemicals from the waste can seep into the soil and eventually enter the groundwater, posing a significant risk to human health.
2. Materials and methods
2.1 Data
The data consists of 15 water quality samples (Table 1).
| Code | Code_2 | Type | Aquifer | Turbidity_NTU | Color_TCU | Odour | Temp_C | Taste | EC | TDS | pH | Hardness_mgl | Ca_mgl | Mg_mgl | Fe3_mgl | Mn_mgl | K_mgl | Na_mgl | Li_mgl | NH4_mgl | CO3_mgl | HCO3_mgl | Cl_mgl | SO4_mgl | NO2_mgl | NO3_mgl | Ecoli_100ml | Tot_coli_100ml |
| CSK | csk | dug well | volcanic-soil | 0.26 | 0 | 0 | 24.8 | 0 | 177 | 119 | 5.02 | 64.2 | 12.5 | 8 | 0.041 | 0.134 | 4.74 | 13.95 | 0 | 0 | 0 | 16.9 | 16 | 5.94 | 0.01 | 63.8 | 0 | 0 |
| Bor1 | bor1 | well bore | volcanic-rock | 18.7 | 32.1 | 0 | 25.5 | 0 | 146 | 98 | 6.02 | 48 | 10.6 | 5.2 | 1.283 | 0.359 | 4.6 | 12.72 | 0 | 1.9 | 0 | 47.1 | 17.9 | 5.21 | 0.26 | 20.54 | 11 | 2400 |
| MAD BBS1 | bbs1 | spring | volcanic-rock | 5.3 | 0 | 0 | 23.5 | 0 | 230 | 153 | 5.71 | 75.1 | 17.1 | 7.8 | 2.327 | 1.277 | 7.94 | 15.97 | 0 | 0.6 | 0 | 88.3 | 24.7 | 7.6 | 0.05 | 3.5 | 240 | 0 |
| MAD BBS2 | bbs2 | spring | volcanic-soil | 1.8 | 0 | 0 | 23.5 | 1 | 163 | 112 | 6.11 | 40.97 | 10.12 | 3.76 | 0.49 | 0.69 | 1.61 | 16.53 | 0 | 0.2 | 0 | 28.9 | 28 | 3.7 | 0 | 12.3 | 0 | 0 |
| MAD BBS3 | bbs3 | spring | volcanic-soil | 11.7 | 0 | 0 | 24.1 | 0 | 163 | 109 | 6.43 | 57.6 | 14.6 | 5.1 | 0.426 | 0.439 | 3.532 | 16.126 | 0 | 0.1 | 0 | 27.7 | 31.4 | 12.7 | 0.04 | 21.3 | 0 | 14 |
| MAD BBS4 | bbs4 | spring | volcanic-soil | 9.9 | 0 | 0 | 24 | 0 | 149 | 99 | 6.41 | 41.5 | 11.6 | 3.1 | 0.948 | 0.143 | 0.419 | 15.77 | 0 | 0.1 | 0 | 24 | 26.6 | 4 | 0.03 | 19.5 | 240 | 0 |
| MAD BBS5 | bbs5 | spring | volcanic-rock | 0.3 | 5.6 | 0 | 25,1 | 0 | 880 | 587 | 5.45 | 140.1 | 32.7 | 14.1 | 0.872 | 2.622 | 4.082 | 140.175 | 0 | 0.5 | 0 | 24.9 | 266.8 | 0 | 0.01 | 71.08 | 0 | 31 |
| MAD PMJ | pmj | spring | volcanic-soil | 0.2 | 1.3 | 0 | 22.2 | 0 | 87 | 58 | 5.60 | 40.9 | 10.9 | 3.3 | 0 | 0.014 | 0 | 9.766 | 0 | 0 | 0 | 26.8 | 7.2 | 0 | 0 | 30.7 | 0 | 5 |
| MAD LBG1 | lbg1 | spring | volcanic-soil | 0.4 | 0 | 0 | 22 | 0 | 98 | 68 | 6.72 | 38.33 | 9.03 | 3.78 | 0 | 0 | 3.54 | 8.36 | 0 | 0.1 | 0 | 54.2 | 6.2 | 0.4 | 0 | 3.4 | 0 | 0 |
| MAD PSG2 | psg2 | spring | volcanic-soil | 0.5 | 0 | 0 | 23 | 1 | 188 | 128 | 5.28 | 43.27 | 11.14 | 3.7 | 0.12 | 0.15 | 1.77 | 16.24 | 0 | 0.1 | 0 | 9.2 | 16 | 1.2 | 0 | 64.3 | 0 | 0 |
| MAD CIS1 | cis1 | spring | volcanic-soil | 0 | 0 | 0 | 24 | 1 | 125 | 84 | 5.08 | 32.68 | 8.24 | 2.9 | 0.52 | 0.09 | 1.45 | 9.71 | 0 | 0 | 0 | 10.4 | 13.6 | 3.1 | 0 | 35.4 | 0 | 0 |
| MAD1 CNY | cny1 | spring | volcanic-soil | 0.60 | 0 | 0 | 23 | 0 | 98 | 68 | 6.92 | 45.40 | 13.90 | 2.60 | 0 | 0.01 | 2.30 | 9.70 | 0 | 0.50 | 0 | 76.30 | 3.50 | 0 | 0 | 0.30 | 0 | 0 |
| BAK-CNY | cny2 | dug well | volcanic-soil | 6.10 | 24.00 | 0 | 22 | 0 | 91 | 60 | 6.80 | 42.10 | 15.80 | 0.60 | 0.86 | 0.24 | 1.60 | 9.20 | 0 | 0.30 | 0 | 69.50 | 5.10 | 1.40 | 0 | 0 | 0 | 0 |
| MAD BLK1 | blk1 | spring | volcanic-soil | 0.50 | 2.50 | 0 | 22.20 | 0 | 109 | 73 | 6.25 | 39.40 | 7.90 | 4.80 | 0.096 | 0.036 | 0 | 13.083 | 0 | 0 | 0 | 45.10 | 4.70 | 28.20 | 0 | 1.50 | 1 | 8 |
| MAD BLK4 | blk2 | spring | volcanic-soil | 1.60 | 0.60 | 0 | 22.80 | 0 | 120 | 80 | 4.74 | 44.40 | 11.30 | 3.90 | 0.271 | 0.302 | 0 | 13.230 | 0 | 0 | 0 | 12.70 | 5.40 | 48.60 | 0 | 0.60 | 0 | 0 |
2.2 Methods
Multivariable statistical analysis is a type of statistical method used to analyze data that involves more than one variable. It is often used in scientific research, social sciences, and business to identify patterns and relationships among multiple variables.
The main objective of multivariable statistical analysis is to explore the relationships between two or more variables and to determine the nature and strength of those relationships. The analysis can help identify important predictors of a particular outcome or dependent variable.
There are several methods used in multivariable statistical analysis, including multiple regression analysis, factor analysis, principal component analysis, and cluster analysis. These methods allow researchers to examine the relationships among multiple variables and to identify any underlying factors or groupings within the data.
Multivariable statistical analysis is a powerful tool for understanding complex data sets and can provide valuable insights into the relationships between variables. However, it requires a good understanding of statistical concepts and methods and should be used carefully to avoid drawing incorrect conclusions.
We used Orange Data Mining application to run both analyses by applying the following canvas.
2. Analyses and discussions
2.1 Principal components analysis
PCA is a technique used to transform high-dimensional data into a smaller set of variables called principal components. These principal components are linear combinations of the original variables that capture the most important patterns and variations in the data.
PCA is often used for data exploration and visualization, as it can help identify important relationships among variables and highlight any underlying structures or patterns in the data. It can also be used for data compression, as the new set of principal components can often represent the data with fewer variables than the original dataset.
PCA is a type of unsupervised learning, meaning that it does not require any prior knowledge of the data labels or categories. It is a powerful tool for exploratory data analysis, but it requires a good understanding of statistical concepts and careful interpretation of the results to avoid drawing incorrect conclusions.
2.2 Cluster analysis
Cluster analysis is a technique used in data analysis to identify groups of data points that are similar to each other. Here are three key things to know about cluster analysis:
- Objective: The main objective of cluster analysis is to group similar data points together and to separate dissimilar data points. This can help in identifying patterns, relationships, and trends in the data.
- Types of Clustering: There are two main types of clustering: Hierarchical clustering and Partitioning clustering. Hierarchical clustering involves creating a tree-like structure of clusters, while partitioning clustering involves dividing the data into non-overlapping groups.
- Applications: Cluster analysis has a wide range of applications in various fields such as marketing, biology, image processing, and social science. It can be used for customer segmentation, pattern recognition, gene expression analysis, and identifying social networks.
4. Remarks
- Indications of anthropogenic contamination (E-coli and Total Coliform).
- Surficial contamination reaches deeper aquifer? or inappropriate construction?
- Sources: chemical fertilizers, cattle farm wastes, domestic wastes (septic tanks, etc)