PCA-Based Spatial Clustering of Heavy Metals Using Multivariate Environmental Data for Risk Mapping

PCA-Based Spatial Clustering of Heavy Metals Using Multivariate Environmental Data for Risk Mapping | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article PCA-Based Spatial Clustering of Heavy Metals Using Multivariate Environmental Data for Risk Mapping Agung Teguh Wibowo Almais, Anton Prasetyo, Tri Kustono Adi, Rif’atul Mahmudah, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7394718/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Many parameters affect the quality of seawater, one of which is the presence of heavy metal pollution in the seawater. In general, there are several parameters to measure the level of heavy metal pollution in seawater, including: solution (pH), temperature (0C), lead (Pb), Cu (Copper), Cadmium (Cd), iron (As), and Chromium (Cr). These parameters have values (data) which are the results of measurements from the laboratory. To find out where the region or region come from, it is necessary to cluster data to find out the area of heavy metal pollution. Using the PCA-Clustering technique can produce a data cluster that groups data uniquely and after going through the analysis process, the results of the data grouping show clusters of an area or areas of heavy metal pollution. Based on these results, to determine an area of heavy metal pollution, it can be used the PCA-Clustering technique because it has been proven that by using heavy metal pollution data from laboratory measurements that do not yet know the area, it is possible to cluster the area in accordance with field data. Pollution parameters Heavy metals seawater PCA-Clustering Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Heavy metal pollution in the environment is a serious threat to ecosystems and human health. Heavy metals such as lead (Pb), Cu (Copper), Cadmium (Cd), iron (As), and Chromium (Cr) are persistent, bio-accumulative, and toxic even at low concentrations [1]. The sources of pollution are diverse, including industrial, agricultural, and domestic waste [2]. Accurate monitoring and mapping of heavy metal risks is essential for effective environmental management [3], [4]. However, environmental data analysis involving many variables (multivariate) is often complex due to the high correlation between parameters and data dimensions [5]. Traditional statistical methods such as cluster analysis can result in biased interpretations if they do not pay attention to multivariate structures and spatial autocorrelations [6]. Therefore, a more sophisticated approach is needed to efficiently identify the distribution patterns of heavy metals. Principal Component Analysis (PCA) has proven to be effective in reducing the dimension of data while retaining important information [7]. In addition, in their research by Almais et. al , PCA can group a data based on its character so that the data can have a label [8]. When combined with spatial cluster analysis, PCA can group areas with similar pollution characteristics and map risk zones [9], [10]. This approach has not been widely applied in heavy metal risk mapping studies, although it has great potential to improve the accuracy of identification of contamination hotspots [11], [12]. This study aims to develop a PCA-based spatial clustering method using multivariate environmental data for heavy metal risk mapping. So that the results can be a tool to support decisions in environmental pollution mitigation based on scientific evidence [13]. In addition, this research has several contributions, including: a. Integrate PCA with spatial cluster analysis to handle heavy metal data complexity. b. Map the distribution of contamination risk based on environmental factors (e.g., soil pH, organic matter, land use). c. Provide policy recommendations based on risk zoning. The content of this article explains the following: background on using PCA for data clustering and the use of PCA in some studies on "Related Work". Then, in "Material and Method," the steps of PCA-Clustering and data acquisition are described. In the "results and discussion", it is explained that the PCA-Clustering experiment process uses the Python programming language to cluster regions based on heavy metal pollution data from laboratories and the validation of data clustering results. Finally, the "conclusions" summarize the results of the experiment and opportunities for future research. Related Work There are several conceptual references in using PCA-Clustering, as shown in Table 1 . According to Almais et. al , the data reduction technique, namely PCA, can be a modern data clustering technique if the data has many features (more than 5 features) because PCA will reduce these features, and then the reduction results can be visualized in the form of data clusters that are easier to find the information [8]. The combination of PCA with spatial cluster analysis allows for the grouping of areas with similar pollution characteristics and mapping of risk zones [14]. This approach is effective for simplifying the complexity of multivariate environmental data while preserving spatial information [15]. Table 1 Related work for PCA and Clustering Reference Topic Method Subject [12], [16] Heavy Metal Pollution PCA and GIS Identifying heavy metal pollution risk zones in urban lands [11], [17] Source Identification of Heavy Metals K-means clustering of SOM Creating geostatistical prediction maps of heavy metals and data mining Multivariate Geostatistical Detecting multivariate spatial correlations between various heavy metals in the ground [14], [18], [19] Hybrid PCA method for spatial PCA- Geodetector-MLRD Identifying the source of heavy metals (loids) in soil GWPCA Describing the spatial heterogeneity and connectivity of heavy metals SD-PCA Distinguishing metal contamination in soil based on its area. [20] Environmental Risk Analysis PCA Distinguishing between anthropogenic and environmental chemical anomalies [21] Cluster analysis for pollution and geoaccumulation index PCA Assessing heavy metal pollution from gold mining operations Ours Cluster Analysis of Heavy Metals Pollution Areas PCA-Clustering Determining heavy metal pollution areas for risk mapping The application of PCA-Clustering is still used by a few because the early emergence of PCA was not for data clustering techniques, but for techniques to reduce data that has many and large features [7]. But with the development of the times and the existence of diverse data, PCA has developed into one of the data clustering techniques that is suitable for modern data, namely data that has large features and takes a quick time to get the information. Therefore, in his research, Almais et al developed PCA for clustering data on the level of building damage after natural disasters, so that the term PCA-Clustering emerged [8]. Then, in the research of Almais et al and Purnamasari et al , PCA-Clustering to find out information on the level of damage to buildings after natural disasters using inputs in the form of images [22], [23]. Therefore, this study seeks to make regional/regional clustering using heavy metal pollution data from laboratories that do not have regional or regional labels using PCA-Clustering. On the other hand, we also try to make the results of PCA-Clustering regions or regions as a basis for creating regional clusters by knowing the characteristics of heavy metal pollution parameters that affect the area, so that it can be used as a reference by future researchers and the government. Materials and Methods There are sub-discussions in this chapter, including the first about discussing parameters and data for this study, and the second about the method for processing parameters and data in this study. Datasets and Study Areas This study uses 7 different parameters, as seen in Table 2 , which are the parameters and information in this study: Table 2 Parameters for measuring heavy metals Parameter Name Information Ph The Size of a Solution Suhu (˚C) The degree of heat or cold of an object Pb Lead Metal Code Cu Copper Metal Code Fe Iron Metal Code Cd Cadmium Metal Code Cr Chromium Metal Code The parameters contained in Table 2 are values in the form of measurement data from the chemical laboratory, these values are as shown in Table 3 . Each value in Table 3 has passed the standard measurement of the chemical laboratory without knowing the location because it is only a sample of seawater. Table 3 Parameter data for measuring heavy metals PH SUHU PB CU FE CD CR 7.35 30.9 0.02 0.002 2.564 0.014 0.163 7.25 33.2 0.06 0.005 2.779 0.035 0.241 7.2 30.1 0.14 0.025 2.39 0.052 0.307 7.23 30.2 0.12 0.25 1.859 0.048 0.325 7.58 30.3 0.24 0.034 1.208 0.1 0.452 7.98 30 0.25 0.033 1.352 0.1 0.445 7.80 30.2 0.21 0.035 1.924 0.099 0.437 7.84 29.8 0.21 0.267 1.624 0.083 0.394 Method The method uses PCA development as a clustering technique. In their research, Almais et al. developed PCA, which typically reduces data, into a moderate data clustering technique that yields very optimal results [8]. In his research, Almais et al, also applied PCA-Clustering to find out the information in an image, by using case objects to find out information about pictures of buildings affected after natural disasters [23]. Based on these studies, it can be a basis for applying PCA to cluster regions based on heavy metal pollution data from laboratory measurements, so that the results of regional clusters can reflect the unique pattern of heavy metal pollution. For more details, you can see Fig. 1 , which explains the flow of PCA-Clustering in this study. Input Using heavy metal pollution data input resulting from chemical laboratory measurements with parameters contained in Table 3 . When building a system, it requires data input that can be in the form of text, numbers, images, or even in their research, Baltrusaitis et al , the incorporation of various types of data inputs (text, numbers, images) in building machine learning [24]. Process Based on the research of Almais et al , there are several stages to generate PCA into an optimal clustering technique [8]. These stages are shown in Fig. 1 , namely data normalization, formation (n) component PCA, determination of eigenvalue and variance ratio, visualization of 2D graphs, creation of data clusters, and result. The difference is the final result, if in this study to cluster coastal areas contaminated with heavy metals. In his research, Almais et al used PCA to cluster data on the level of building damage after natural disasters. [8]. Output The output describes the final result of a process [25], [26]. The final result of this study is a cluster of a coastal area in the North Sea of East Java. So that by applying PCA-Clustering based on laboratory data, it is possible to find out the location of the grouping of areas contaminated with heavy metals based on the characteristics of the laboratory results data. Result The results chapter explains the application of the stages in Fig. 1 , the first stage explains the input, the second process, and the third output. Stage 1: There are 7 parameters to measure the level of heavy metal concentrations on the seashore; these parameters are in Table 2 , then these parameters have the value of the results of the chemical laboratory at measurements. The results of the compilation of parameters and values for each parameter can be found in Table 3 . Table 4 is the data input that will be entered into the PCA-Clustering process to find out the clustering of the area. Table 4 Data input for PCA-Clustering PH SUHU PB CU FE CD CR 7.35 30.9 0.02 0.002 2.564 0.014 0.163 7.25 33.2 0.06 0.005 2.779 0.035 0.241 7.2 30.1 0.14 0.025 2.39 0.052 0.307 7.23 30.2 0.12 0.25 1.859 0.048 0.325 7.58 30.3 0.24 0.034 1.208 0.1 0.452 7.98 30 0.25 0.033 1.352 0.1 0.445 7.80 30.2 0.21 0.035 1.924 0.099 0.437 7.84 29.8 0.21 0.267 1.624 0.083 0.394 Stage 2: Stage 2 is a process that involves data input in stage 1, then after that, it enters the data normalization process using the StandardScaler library in the Python programming language. The data normalization process produces new data from data inputs that have been normalized so that the value is balanced in each parameter. Table 5 is the result of normalizing data using StandardScaler . Table 5 Normalization Values of Data Inputs 0 1 2 3 4 5 6 -0,61145 0,303014 -1,70667 -0,77033 1,126716 -1,66946 -1,84711 -0,95352 2,533194 -1,20563 -0,74122 1,529449 -1,00008 -1,05766 -1,12456 -0,4727 -0,20355 -0,54712 0,800783 -0,4582 -0,38966 -1,02194 -0,37574 -0,45407 1,636501 -0,19387 -0,5857 -0,20748 0,175311 -0,27877 1,049054 -0,45977 -1,41331 1,071798 1,077904 1,543595 -0,56967 1,174314 -0,46948 -1,14357 1,071798 1,007056 0,927867 -0,37574 0,673274 -0,45007 -0,07212 1,039923 0,926087 1,064696 -0,76359 0,673274 1,801485 -0,63407 0,529923 0,490877 The results of data normalization are data that already have a balanced value in each of its parameters. Then the balanced value enters the fit and transform process using the fit_transforms library to calculate the data covariance matrix, decompose the eigenvalue to get the eigenvector, and project the data towards the PC that has the largest variance and an eigenvalue of more than zero (0). Figure 2 is the result of the eigenvalue and variance ratio of all PCs. Table 6 shows the numerical value of the eigenvalue and the variance ratio found on all PCs. To get the number of main components (PCs) in the data clustering process, Almais et al.'s research used the number of PCs based on an eigenvalue greater than zero (0) [8]. If based on Fig. 2 , the eigenvalue greater than 0 is on PC 1 and PC 2, then using the number of PCs as many as 2, namely PC1 and PC2. Figure 3 is a graph of the eigenvalue and variance ratio of PC1 and PC2. The numerical values of PC1 and PC2 are 5.66% and 1.28%. Table 6 Numerical value of eigenvalue and variant ratio of all PCs PC Eigenvalue (%) Variance Ratio (%) 1 5.66 70.79 2 1.28 16.02 3 0.51 6.33 4 0.38 4.73 5 0.15 1.92 6 0.01 0.18 After knowing the number of PCs, the next step is to create a cluster or data grouping based on the same characteristics in the form of 2 dimensions (2D) and 3 dimensions (3D). Figure 4 is a form of visualization of cluster results in 2D and 3D forms. Stage 3: In the last stage, it is the output stage or results that draw the results from clusters of areas contaminated with heavy metals based on chemical laboratory results. The results are visible in Fig. 5 , which is grouped into 3 dots of different colors. Based on Fig. 5 , there are 3 types of clusters, namely cluster 0 (purple), cluster 1 (green), and cluster 2 (yellow). Each point in Fig. 5 represents the data from the chemical laboratory results in Table 5 , but has gone through the data processing process using PCA-Clustering. Table 7 contains the data that contains the value of the coordinate points at each point in Fig. 5 . Discussion There are 8 chemical laboratory test results data contained in Table 3 , but these data do not have location information because the data entering the PCA-Clustering process is already shaped like Table 4 . After receiving confirmation from the data taker in the field, it turns out that the data in Table 3 contains code data and its location on each row. For the code and location data contained in Table 8 , the code and location data contained in Table 8 are the results of the measurement of the research team in the field. For the code A1 is located at the location of the Port of Pasuruan City, A2 is located on the beach stage of Pasuruan City, A3 is located at the JWMP pier of Pasuruan Regency, A4 is located at the mangrove Pateguran of JWMP Pasuruan Regency, B1 is located at the Bentar Beach of Probolinggo Regency which is 2 KM from the Beach, B2 is located at the Panntai Bentar of Probolinggo Regency which is 1 KM from the Beach, B3 is located on the edge of Probolinggo Beach, and the last location is B4 which is located on Mayangan Beach, Probolinggo Regency. What is interesting and unique about this research is not the origin of the data, but after going through the PCA-Clustering process, it turns out that data that does not have regional information can cluster based on the same region. Table 8 Code and Region/Location data Code Region/Location A1 Port of Pasuruan City A2 Pasuruan City Stage Beach A3 JWMP Pier Pasuruan Regency A4 Mangrove Strikes JWMP Pasuruan Regency B1 Bentar Beach, Probolinggo Regency (2 KM) B2 Bentar Beach, Probolinggo Regency (1 KM) B3 Bentar Probolinggo Beach Beach B4 Mayangan Beach, Probolinggo Regency Table 8 is a code and location data that can be used as a reference in validating the clustering results of PCA-Clustering, so that it can prove quantitatively if the PCA-Clustering technique can cluster an area based on heavy metal pollution parameter data from the laboratory. Integrating the data in Table 3 with the data in Table 8 is a scientific thing because the data in Table 3 has the same number of rows as the data in Table 8 . To see the results of combining Table 3 and Table 8 , there is Table 9 , which describes the code for each region, and there are heavy metal pollution parameters. While Table 10 is a combination of Table 3 and Table 8 data, which contains the code and location. Table 9 Brief data results: Integration of laboratory data with field data Code PH SUHU PB CU FE CD CR A1 7.35 30.9 0.02 0.002 2.564 0.014 0.163 A2 7.25 33.2 0.06 0.005 2.779 0.035 0.241 A3 7.2 30.1 0.14 0.025 2.39 0.052 0.307 A4 7.23 30.2 0.12 0.25 1.859 0.048 0.325 B1 7.58 30.3 0.24 0.034 1.208 0.1 0.452 B2 7.98 30 0.25 0.033 1.352 0.1 0.445 B3 7.80 30.2 0.21 0.035 1.924 0.099 0.437 B4 7.84 29.8 0.21 0.267 1.624 0.083 0.394 Table 10 Complete data Results Integration of laboratory data with field data Code Location PH SUHU PB CU FE CD CR A1 Port of Pasuruan City 7.35 30.9 0.02 0.002 2.564 0.014 0.163 A2 Pasuruan City Stage Beach 7.25 33.2 0.06 0.005 2.779 0.035 0.241 A3 JWMP Pier Pasuruan Regency 7.2 30.1 0.14 0.025 2.39 0.052 0.307 A4 Mangrove Strikes JWMP Pasuruan Regency 7.23 30.2 0.12 0.25 1.859 0.048 0.325 B1 Bentar Beach, Probolinggo Regency (2 KM) 7.58 30.3 0.24 0.034 1.208 0.1 0.452 B2 Bentar Beach, Probolinggo Regency (1 KM) 7.98 30 0.25 0.033 1.352 0.1 0.445 B3 Bentar Probolinggo Beach Beach 7.80 30.2 0.21 0.035 1.924 0.099 0.437 B4 Mayangan Beach, Probolinggo Regency 7.84 29.8 0.21 0.267 1.624 0.083 0.394 In Table 11 , it can be seen that the cluster results show that there are 3 colors, which means 3 areas that are contaminated with heavy metals. The three colors in Table 11 refer to Fig. 5 , which produces 3 different color clusters, namely yellow, purple, and green. For the yellow cluster, it turns out that after being clustered based on the area in Table 8 , it is located in the Pasuruan City area (Pasuruan City Port and Pasuruan City Stage Beach) with a cluster value of 2, while the purple cluster is clustered in the Pasuruan Regency area (Pasuruan Regency JWMP Pier, Pasuruan Regency JWMP Pateguran Mangrove) with a cluster value of 0, and the last cluster is green which clusters the Probolinggo Regency area (Bentar Beach Probolinggo Regency-2KM, Bentar Beach Probolnggo Regency-1KM, Mayangan Beach Probolinggo Regency) with a cluster value of 1. Table 11 is a combined result of Table 8 of field data, with the original data in Table 3 and the PCA-Clustering results data in Table 7 , which are the coordinate points of the results of clustering using PCA-Clustering. In his research, Liu et al carried out a powerful analytical approach to assess heavy metal pollution in the soil. This model combines the power of PCA with the spatial analysis and geographic visualization capabilities of the Geographic Information System (GIS). This integrated approach provides a comprehensive picture of pollution, helps identify pollution points, determine possible sources of heavy metals, and measure overall levels of contamination [14]. However, in this study, only one method or technique was used without combining it with the Geographic Information System (GIS), namely PCA-Clustering. With this technique, it is possible to cluster areas or locations that experience heavy metal pollution by using metal pollution data from laboratories that contain existing parameters. So by using the PCA-Clustering technique, it is possible to find out the region based on the parameters of the region without having to know the name of the region first, besides that the technique PCA-Clustering easier and lighter because it does not use all parameters to group data into a cluster that has the same structure but by reducing the parameters that is not dominant but does not eliminate information from the data. Conclusion Knowing the cluster of an area does not require using mapping techniques, but it can use data clustering techniques. In this research, it is to determine the area or region using heavy metal pollution parameter data from laboratory measurements. By using 5 parameters, namely solution (pH), temperature ( 0 C), lead (Pb), Cu (copper), cadmium (Cd), iron (As), and chromium (Cr), all of these parameters are entered into the PCA-Clustering engineering process. The process of PCA-Clustering applies parameter reduction, and then the results of the parameter reduction will enter the data clustering process by utilizing the results of data grouping from PCA. As a result, there are 3 clusters resulting from PCA-Clustering, including the first cluster is yellow with a cluster value of 2, the second cluster is purple with a cluster value of 0, and the third cluster is green with a cluster value of 1. For the data labeling process in each cluster, the results of the cluster can be used to validate the results of the field data with the data from the PCA-Clustering. Of the 3 clusters, there is labeling in each cluster between the others: cluster 2 is in the Pasuruan City area, cluster 0 is in the Pasuruan Regency area, and cluster 1 is included in the Probolinggo Regency area. Therefore, the PCA-Clustering technique is suitable for identifying clusters and labeling of data that has large parameters, because with this technique, it can reduce large parameters to smaller ones without removing information from the data. But the PCA-Clustering technique is not suitable for data that has a small number of parameters because the parameters will be lost at the data reduction stage using PCA. Declarations Competing interests The authors declare no competing interests. Funding Research Grant Program called MORA The Air Funds in 2024, with contract numbers 64/Dt.I.III/PP.05/12/2024 and 4390/LP2M/TL.00/12/2024. Author Contribution Agung Teguh Wibowo Almais: Conceptualization, Methodology, Software, Formal Analysis, Writing – Original Draft, Supervision, Funding Acquisition.Anton Prasetyo: Data Curation, Investigation, Resources, Validation.Tri Kustono Adi: Visualization, Writing – Review & Editing.Rif'atul Mahmudah: Investigation, Data Collection, Validation.Tri Harningsin: Resources, Project Administration.Adila Mujtahidah: Writing & Review. Acknowledgement The author would like to thank the research partners whose contributions indirectly facilitated the implementation of this research, especially the Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia. Their provision of facilities for brainstorming and analysis formation is invaluable in finding techniques to evaluate the damage of the sector after natural disasters using image-based inputs. 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Morency, “Multimodal Machine Learning: A Survey and Taxonomy,” IEEE Trans Pattern Anal Mach Intell , vol. 41, no. 2, pp. 423–443, Feb. 2019, doi: 10.1109/TPAMI.2018.2798607. Y. Lu, X. Xu, and L. Wang, “Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios,” J Manuf Syst , vol. 56, pp. 312–325, Jul. 2020, doi: 10.1016/j.jmsy.2020.06.010. W. M. P. van der Aalst, “Business Process Management: A Comprehensive Survey,” ISRN Software Engineering , vol. 2013, pp. 1–37, Feb. 2013, doi: 10.1155/2013/507984. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7394718","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516083045,"identity":"85977f03-fb35-4ac8-a6fd-8c4b42745610","order_by":0,"name":"Agung Teguh Wibowo Almais","email":"data:image/png;base64,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","orcid":"","institution":"Universitas Islam Negeri Maulana Malik Ibrahim Malang","correspondingAuthor":true,"prefix":"","firstName":"Agung","middleName":"Teguh Wibowo","lastName":"Almais","suffix":""},{"id":516083046,"identity":"6d077478-2924-4da3-bf92-3f3c0b00faf8","order_by":1,"name":"Anton Prasetyo","email":"","orcid":"","institution":"Universitas Islam Negeri Maulana Malik Ibrahim Malang","correspondingAuthor":false,"prefix":"","firstName":"Anton","middleName":"","lastName":"Prasetyo","suffix":""},{"id":516083047,"identity":"e93e5a8c-8050-497a-8240-ae88fa2bbb68","order_by":2,"name":"Tri Kustono Adi","email":"","orcid":"","institution":"Universitas Islam Negeri Maulana Malik Ibrahim Malang","correspondingAuthor":false,"prefix":"","firstName":"Tri","middleName":"Kustono","lastName":"Adi","suffix":""},{"id":516083048,"identity":"62c6a0c4-724c-4ec8-b771-2ec0d02591d2","order_by":3,"name":"Rif’atul Mahmudah","email":"","orcid":"","institution":"Universitas Islam Negeri Maulana Malik Ibrahim Malang","correspondingAuthor":false,"prefix":"","firstName":"Rif’atul","middleName":"","lastName":"Mahmudah","suffix":""},{"id":516083049,"identity":"e58b5f3f-f1e4-4470-b703-8fd7d7a4a644","order_by":4,"name":"Tri Harningsih","email":"","orcid":"","institution":"Sekolah Tinggi Ilmu Kesehatan Nasional","correspondingAuthor":false,"prefix":"","firstName":"Tri","middleName":"","lastName":"Harningsih","suffix":""},{"id":516083050,"identity":"0fdfecfc-ea6b-4af0-9613-72809aef1f6c","order_by":5,"name":"Adila Mujtahidah","email":"","orcid":"","institution":"Universitas Islam Negeri Maulana Malik Ibrahim Malang","correspondingAuthor":false,"prefix":"","firstName":"Adila","middleName":"","lastName":"Mujtahidah","suffix":""}],"badges":[],"createdAt":"2025-08-18 00:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7394718/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7394718/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91626689,"identity":"0980cf57-86c3-4a20-beb6-bed4c27bd666","added_by":"auto","created_at":"2025-09-18 12:17:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20011,"visible":true,"origin":"","legend":"\u003cp\u003ePCA-Clustering method flow to cluster heavy metal-contaminated areas\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7394718/v1/3dde25ada33124a71105775b.png"},{"id":91626691,"identity":"c46f1dec-f911-4e64-9ee2-79b311631e44","added_by":"auto","created_at":"2025-09-18 12:17:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28229,"visible":true,"origin":"","legend":"\u003cp\u003eEigenvalue and variant ratio graph of all PCs\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7394718/v1/00eb233ccc236cb55c194186.png"},{"id":91627880,"identity":"53564828-b754-4fb8-805b-86d90c289d0d","added_by":"auto","created_at":"2025-09-18 12:25:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30510,"visible":true,"origin":"","legend":"\u003cp\u003eOwn the value and variance ratio graph of PC1 and PC2\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7394718/v1/3ba47d92218cc2eed51ca3d1.png"},{"id":91626693,"identity":"cdf194a2-330f-4567-9023-d299cbdb1dd1","added_by":"auto","created_at":"2025-09-18 12:17:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61807,"visible":true,"origin":"","legend":"\u003cp\u003e2D and 3D visualization of data clustering results\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7394718/v1/c00479930195ad0e54e3b8ca.png"},{"id":91626695,"identity":"1912f4c0-dc4f-4d96-8f1b-67722fcb6e91","added_by":"auto","created_at":"2025-09-18 12:17:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":18042,"visible":true,"origin":"","legend":"\u003cp\u003eResults of clustering of data from chemical laboratory results\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7394718/v1/43639170c3c8f5285188b2d8.png"},{"id":107801037,"identity":"c87224b2-b14e-4a21-b07c-c09fac22d30a","added_by":"auto","created_at":"2026-04-25 18:24:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":673962,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7394718/v1/aa6f13b0-fb57-4838-9a0e-47f14f3d59c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PCA-Based Spatial Clustering of Heavy Metals Using Multivariate Environmental Data for Risk Mapping","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeavy metal pollution in the environment is a serious threat to ecosystems and human health. Heavy metals such as lead (Pb), Cu (Copper), Cadmium (Cd), iron (As), and Chromium (Cr) are persistent, bio-accumulative, and toxic even at low concentrations [1]. The sources of pollution are diverse, including industrial, agricultural, and domestic waste [2]. Accurate monitoring and mapping of heavy metal risks is essential for effective environmental management [3], [4].\u003c/p\u003e\u003cp\u003eHowever, environmental data analysis involving many variables (multivariate) is often complex due to the high correlation between parameters and data dimensions [5]. Traditional statistical methods such as cluster analysis can result in biased interpretations if they do not pay attention to multivariate structures and spatial autocorrelations [6]. Therefore, a more sophisticated approach is needed to efficiently identify the distribution patterns of heavy metals. Principal Component Analysis (PCA) has proven to be effective in reducing the dimension of data while retaining important information [7].\u003c/p\u003e\u003cp\u003eIn addition, in their research by \u003cem\u003eAlmais et. al\u003c/em\u003e, PCA can group a data based on its character so that the data can have a label [8]. When combined with spatial cluster analysis, PCA can group areas with similar pollution characteristics and map risk zones [9], [10]. This approach has not been widely applied in heavy metal risk mapping studies, although it has great potential to improve the accuracy of identification of contamination hotspots [11], [12]. This study aims to develop a PCA-based spatial clustering method using multivariate environmental data for heavy metal risk mapping.\u003c/p\u003e\u003cp\u003eSo that the results can be a tool to support decisions in environmental pollution mitigation based on scientific evidence [13]. In addition, this research has several contributions, including:\u003c/p\u003e\u003cp\u003ea. Integrate PCA with spatial cluster analysis to handle heavy metal data complexity.\u003c/p\u003e\u003cp\u003eb. Map the distribution of contamination risk based on environmental factors (e.g., soil pH, organic matter, land use).\u003c/p\u003e\u003cp\u003ec. Provide policy recommendations based on risk zoning.\u003c/p\u003e\u003cp\u003eThe content of this article explains the following: background on using PCA for data clustering and the use of PCA in some studies on \"Related Work\". Then, in \"Material and Method,\" the steps of PCA-Clustering and data acquisition are described. In the \"results and discussion\", it is explained that the PCA-Clustering experiment process uses the Python programming language to cluster regions based on heavy metal pollution data from laboratories and the validation of data clustering results. Finally, the \"conclusions\" summarize the results of the experiment and opportunities for future research.\u003c/p\u003e\n\u003ch3\u003eRelated Work\u003c/h3\u003e\n\u003cp\u003eThere are several conceptual references in using PCA-Clustering, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. According to \u003cem\u003eAlmais et. al\u003c/em\u003e, the data reduction technique, namely PCA, can be a modern data clustering technique if the data has many features (more than 5 features) because PCA will reduce these features, and then the reduction results can be visualized in the form of data clusters that are easier to find the information [8]. The combination of PCA with spatial cluster analysis allows for the grouping of areas with similar pollution characteristics and mapping of risk zones [14]. This approach is effective for simplifying the complexity of multivariate environmental data while preserving spatial information [15].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelated work for PCA and Clustering\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTopic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSubject\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[12], [16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeavy Metal Pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCA and GIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIdentifying heavy metal pollution risk zones in urban lands\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e[11], [17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSource Identification of Heavy Metals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK-means clustering of SOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCreating geostatistical prediction maps of heavy metals and data mining\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultivariate Geostatistical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetecting multivariate spatial correlations between various heavy metals in the ground\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e[14], [18], [19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHybrid PCA method for spatial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCA- Geodetector-MLRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIdentifying the source of heavy metals (loids) in soil\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGWPCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDescribing the spatial heterogeneity and connectivity of heavy metals\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD-PCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDistinguishing metal contamination in soil based on its area.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnvironmental Risk Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDistinguishing between anthropogenic and environmental chemical anomalies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[21]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCluster analysis for pollution and geoaccumulation index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAssessing heavy metal pollution from gold mining operations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOurs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCluster Analysis of Heavy Metals Pollution Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePCA-Clustering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetermining heavy metal pollution areas for risk mapping\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe application of PCA-Clustering is still used by a few because the early emergence of PCA was not for data clustering techniques, but for techniques to reduce data that has many and large features [7]. But with the development of the times and the existence of diverse data, PCA has developed into one of the data clustering techniques that is suitable for modern data, namely data that has large features and takes a quick time to get the information. Therefore, in his research, \u003cem\u003eAlmais et al\u003c/em\u003e developed PCA for clustering data on the level of building damage after natural disasters, so that the term PCA-Clustering emerged [8]. Then, in the research of \u003cem\u003eAlmais et al\u003c/em\u003e and \u003cem\u003ePurnamasari et al\u003c/em\u003e, PCA-Clustering to find out information on the level of damage to buildings after natural disasters using inputs in the form of images [22], [23].\u003c/p\u003e\u003cp\u003eTherefore, this study seeks to make regional/regional clustering using heavy metal pollution data from laboratories that do not have regional or regional labels using PCA-Clustering. On the other hand, we also try to make the results of PCA-Clustering regions or regions as a basis for creating regional clusters by knowing the characteristics of heavy metal pollution parameters that affect the area, so that it can be used as a reference by future researchers and the government.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThere are sub-discussions in this chapter, including the first about discussing parameters and data for this study, and the second about the method for processing parameters and data in this study.\u003c/p\u003e\n\u003ch3\u003eDatasets and Study Areas\u003c/h3\u003e\n\u003cp\u003eThis study uses 7 different parameters, as seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which are the parameters and information in this study:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParameters for measuring heavy metals\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInformation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe Size of a Solution\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSuhu (˚C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe degree of heat or cold of an object\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLead Metal Code\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCopper Metal Code\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIron Metal Code\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCadmium Metal Code\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChromium Metal Code\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe parameters contained in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are values in the form of measurement data from the chemical laboratory, these values are as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Each value in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e has passed the standard measurement of the chemical laboratory without knowing the location because it is only a sample of seawater.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParameter data for measuring heavy metals\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSUHU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eMethod\u003c/h3\u003e\n\u003cp\u003eThe method uses PCA development as a clustering technique. In their research, Almais et al. developed PCA, which typically reduces data, into a moderate data clustering technique that yields very optimal results [8]. In his research, Almais et al, also applied PCA-Clustering to find out the information in an image, by using case objects to find out information about pictures of buildings affected after natural disasters [23]. Based on these studies, it can be a basis for applying PCA to cluster regions based on heavy metal pollution data from laboratory measurements, so that the results of regional clusters can reflect the unique pattern of heavy metal pollution. For more details, you can see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which explains the flow of PCA-Clustering in this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInput\u003c/strong\u003e\u003cp\u003eUsing heavy metal pollution data input resulting from chemical laboratory measurements with parameters contained in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. When building a system, it requires data input that can be in the form of text, numbers, images, or even in their research, \u003cem\u003eBaltrusaitis et al\u003c/em\u003e, the incorporation of various types of data inputs (text, numbers, images) in building machine learning [24].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eProcess\u003c/strong\u003e\u003cp\u003eBased on the research of \u003cem\u003eAlmais et al\u003c/em\u003e, there are several stages to generate PCA into an optimal clustering technique [8]. These stages are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, namely data normalization, formation (n) component PCA, determination of eigenvalue and variance ratio, visualization of 2D graphs, creation of data clusters, and result. The difference is the final result, if in this study to cluster coastal areas contaminated with heavy metals. In his research, \u003cem\u003eAlmais et al\u003c/em\u003e used PCA to cluster data on the level of building damage after natural disasters. [8].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOutput\u003c/strong\u003e\u003cp\u003eThe output describes the final result of a process [25], [26]. The final result of this study is a cluster of a coastal area in the North Sea of East Java. So that by applying PCA-Clustering based on laboratory data, it is possible to find out the location of the grouping of areas contaminated with heavy metals based on the characteristics of the laboratory results data.\u003c/p\u003e\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eThe results chapter explains the application of the stages in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the first stage explains the input, the second process, and the third output.\u003c/p\u003e\n\u003ch3\u003eStage 1:\u003c/h3\u003e\n\u003cp\u003eThere are 7 parameters to measure the level of heavy metal concentrations on the seashore; these parameters are in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, then these parameters have the value of the results of the chemical laboratory at measurements. The results of the compilation of parameters and values for each parameter can be found in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is the data input that will be entered into the PCA-Clustering process to find out the clustering of the area.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData input for PCA-Clustering\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSUHU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStage 2:\u003c/h2\u003e\u003cp\u003eStage 2 is a process that involves data input in stage 1, then after that, it enters the data normalization process using the \u003cb\u003eStandardScaler\u003c/b\u003e library in the Python programming language. The data normalization process produces new data from data inputs that have been normalized so that the value is balanced in each parameter. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is the result of normalizing data using \u003cb\u003eStandardScaler\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNormalization Values of Data Inputs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e-0,61145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,303014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1,70667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,77033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,126716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1,66946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1,84711\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e-0,95352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,533194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1,20563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,74122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,529449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1,00008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1,05766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e-1,12456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0,4727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0,20355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,54712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0,800783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,4582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0,38966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e-1,02194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0,37574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0,45407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,636501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0,19387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0,5857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0,20748\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0,175311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0,27877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,049054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,45977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1,41331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,071798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,077904\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1,543595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0,56967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,174314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,46948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1,14357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,071798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,007056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0,927867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0,37574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,673274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0,45007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0,07212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,039923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,926087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1,064696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0,76359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0,673274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,801485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0,63407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0,529923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,490877\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of data normalization are data that already have a balanced value in each of its parameters. Then the balanced value enters the fit and transform process using the \u003cb\u003efit_transforms\u003c/b\u003e library to calculate the data covariance matrix, decompose the eigenvalue to get the eigenvector, and project the data towards the PC that has the largest variance and an eigenvalue of more than zero (0). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is the result of the eigenvalue and variance ratio of all PCs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the numerical value of the eigenvalue and the variance ratio found on all PCs. To get the number of main components (PCs) in the data clustering process, \u003cem\u003eAlmais et al.'s\u003c/em\u003e research used the number of PCs based on an eigenvalue greater than zero (0) [8]. If based on Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the eigenvalue greater than 0 is on PC 1 and PC 2, then using the number of PCs as many as 2, namely PC1 and PC2. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is a graph of the eigenvalue and variance ratio of PC1 and PC2. The numerical values of PC1 and PC2 are 5.66% and 1.28%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumerical value of eigenvalue and variant ratio of all PCs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEigenvalue (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariance Ratio (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter knowing the number of PCs, the next step is to create a cluster or data grouping based on the same characteristics in the form of 2 dimensions (2D) and 3 dimensions (3D). Figure\u0026nbsp;4 is a form of visualization of cluster results in 2D and 3D forms.\u003c/p\u003e\n\u003ch3\u003eStage 3:\u003c/h3\u003e\n\u003cp\u003eIn the last stage, it is the output stage or results that draw the results from clusters of areas contaminated with heavy metals based on chemical laboratory results. The results are visible in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which is grouped into 3 dots of different colors.\u003c/p\u003e\u003cp\u003eBased on Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, there are 3 types of clusters, namely cluster 0 (purple), cluster 1 (green), and cluster 2 (yellow). Each point in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e represents the data from the chemical laboratory results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, but has gone through the data processing process using PCA-Clustering. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e contains the data that contains the value of the coordinate points at each point in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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HnnEr4UKYYpNQUB1//Ef/+Gn4QhRCaao8eHMglKgPCOEEO1Eu675EEKIcjCfn/Ue2267bXKlFdbKDB8+PNd2u0IIIYToGEj5EEJ0WGzzBLZhZaE7oyAoJPRgs20x36GxBftCVGLy5Mn+OHfuXH8UQgjRfDTtSgjRoUHhYJcn25GrT58+fqtqTbcS1cBWyeGuXWPHjvU7qAkhhGguUj6EEEIIIYQQTUHTroQQQgghhBBNQcqHEEIIIYQQoilI+RBCCCGEEEI0BSkfQgghhBBCiKYg5UMIIYQQQgjRFKR8CCGEEEIIIZpCXbfanThxYvJPCCGEEEII0RkYPXp08q/t1F35qKfnhBBCiM6E2knRKJS3RKOod97StCshhBBCCCFEU5DyIYQQQgghhGgKUj6EEEIIIYQQTUHKhxBCCCGEEKIpSPkQQgghhBBCNAUpH0IIIYQQQoimIOVDCCGEEEII0RSkfAghhBBCCCGagpQPIYQQQgghRFOQ8iGEEEIIIYRoClI+hBBCCCGEEE1ByofoVIwYMcJ169atxFx//fX+3gUXXOBmzpzp/7cnDz74YIn/DjrooOROkdjfjeSUU05J/tVGHJY0g51awG88H8dPtbQ1jEIIIZpL3I5grC2xOp02Mu1+I1A7Ul+kfIhOgQmqM2bMcOeff75buXJli3nggQf8ve9973uJ7fZl77339v5Ko5nKERX35ZdfnpzVhoWFODcmTZrkr5188sn+fJ999qmp4q5HXPDejqBwCiGEqAydTbTXcN1117W045hzzjnH37N265hjjilpexpFWzvAxKpI+RCrPQiYVhlRWZ1xxhn+v3HZZZd5pWR1YPPNN2+paKlYGwWKx7HHHpucNQbiffjw4f4/6VOtEnDXXXf5eOBYC2G+EEII0bFByL/77rv9fzqx4jaQtoDrzST0k6gfUj7Eag3DrCZgjh07NlNgR6i3nngDYZheFDNM2UrDRlXMxNOhwqle1kNi10K71qODYQpYTOwfs5P2fvuf1SMTTz8LBX/cCxUP7sdhD59N82stzJs3L/lXeepZGFfmtzDcmPg8hGcsX9BwcJ9wA+GxZ/BHVhwKIYRoDtTnJuTTVjOqngbXaeuzSGtH47YinJ5Vrj3gmvmJ9oTzsD0Mn8WEhNet7bHz8P1dFSkfYrWGYVhjzJgxyb906IlHCQEKP73yGHrXGRnBUDGEUBFR6djwL5UigntYAU2fPj35V4RnDjzwQP9/9uzZ/sg1KjGbEpYGfkvr1cHfYWXLNDIbycHN0C9W8X7ta19r8S/YCAQcd9xxJe5hz8Jgz1u8EG6mq4XvqIZwxGngwIH+SEPAVCz8wDsIM+EIFSB6uEI/QzzEftZZZ/nnzZ41GoThnnvu8f/B3kM8co/wWDqQf6xxEUII0T5Qnxv77rtv8i+dcqPhae1o1vSscu0BMkLYftGWYsdmVqBQ8KzJBrRDofwQPgu0b9Yez5071x+7MlI+xGpNKDgOGTIk+VcZU1pMSaDCMiHWeimofMz93Xff3R+tUqTSoeKKwT6V6Omnn+7P8VPozqhRo/wxnhpWDQjRIbNmzUr+OTdu3Dh/xH8wbNgwfwTzb1aPEljYUV5g0KBB/njllVf6YzWYW0DlboqfNTKmLJp/qKyrUXKuvvrq5F8RU6B4j70r5rHHHvNHCw+NWLleNCGEEI2FtikU1q3daTTl2oNy7SRtuo2s22yLcIQ+Brt0iNHxB80KX0dGyofoclDRmTIQCudWeViv+bXXXuuPYMJsWGlYxRWCAkOlhX16Q6iYJk+enNxt7f1vFKbcxKMGebF4QXmhF4cRCqBhSFO20uAZnrXKmWdN2Qp7k0Jl0fw7fvx4f2wUpkTiB/xIQ1GuF00IIUTnpNb2IGzTeQ5jbSczE2JQaJAJkA2QC8opNl0FKR9itSYUsm2KU1sx4bgSae8Lpw4Z4chEo7HKjVEAKkQbAYFwzUUaoXJhQ8yhyRpNiGHIu5bnIJ7CVm/wSzgkzxS6cI6vEEKI5hK3Ec2allRrexC26cgLYXsXz0yAatrAroKUD7FaY9ODoF695nlHDaqZ5tVMmO6E4sFUp7R5rnnIO8pRb9KUt3pjCpoNr9NjlTZULoQQojnYeghIGz1oFG1tDyp16ol0pHyI1Rqm85iyQKXBtJ4sbA0CvRD2TNiDYb3utg4kXPRmwnjYI2NDtpUI3Wl0RcWaCZvuVO26kjBeiMtGKCDhcHM4cmSjTZU2DWgr5A9rWBheZ7Eg1GvUTAghRPWEIwa0YeXan3A9YVuotT0I2/RwerbIj5QPsdoTTtVhvUHca0EFw0iALQIHW6xs6zuo6EwAtkqQ9RrWG2LrO6xHJlxAXQncMaHeKqpwYTWCfr0q03j0J2s0KPa7vT/cPSz0Uz1HJKyCN7+ZwkgctWUhfozFOYSNDEPr9t/W8NhaGSGEEO1DOOWZ+tvaBoN6m7Y8bWpTSNqainAKcki59iBuJ7GHn8I2HUXJ/Mn9WneG7HKsrCMTJkxI/gnRfAoVF3vYrmIKCkRio5TYfqEySe6UcvLJJ5fYKwjPyZ0iPBfez3IntFNQXvwx9Fua/3lX/P74fRjcA+zH18Nz3mGE10NiNzDhcyGTJk1axa6ZcsTPxWnEeXw/zV9xXITuhO+w61yL45RzIZpFM9rJuM7AcA2sDOQldqvZ5A1L2v1G0Ei320pnkcHi9iErXdPaBGur4ntpbWGe9iB0J35/3P6E98PrmLiNW92od96S8iGEEEI0iUa2k6GgFAs74b1YwKoEbtmzzaLasISdN41SEOy9HRXJYKJR1DtvadqVEEIIsZrDlA+mkEBB+F5ly1CmihTa/JLpiB2VjhiW0E9CiLYh5UMIIYRYzQkF43Jz4sN1XQbz6EOTF9bKhc/Fa8OY/x7ex75dK7elaVvCEsI77N3mNzvHhOvawrBwHWUDA5yHfsJOGNY4Huw54NnwXngtfL8QXQkpH0IIIcRqTLjIdWyySUYWjBpgwIRmYCQBA1zjXjlYZMvIA4bnZsyY4Y25B2wgwciFMW7cuJYdBrO+6VNrWNJgxCQeHcGPaaAIWFjYzShUNvgydegX7Jj/LR64z3U2I+FZU0BQnsJnw/BVimMhOitSPoQQQojVmHBXu7y78MGFF17oj6FwbP8r9crbqINtTR5u1Z31LIqHCeNZO+jVGpa2wq6DKCa2m9F1ya58kLZ7knHCCSeUHO37T7ajYhrEAXHVzPAJ0ZGQ8iGEEEJ0QeybQKEQbP8RxrOgx97uDxs2zB/BFArbwjyGEQRgRCJex9HemNLFdu1M16o0qgLEg42kMNrBqI+NmGTF31FHHeWPjJyUm1ImRGdGyocQQgixGhMqD/WcylOrW1lTmwYOHJj8y6ZRYalEOEULxSHP1LPwo7FMt2LaVWjS0GiHEFI+hBBCiNUaG1GAciMW1VKroGxCfC00Kix5YDQinG5VzYJwW8sihKiMlA8hhBBiNYY1CeHC7nBRc0woUNtUo7CH3/6H60BiUEpMwQiFbluEbetAaqHWsLQVc4upVjZqkbUo3gjXgtgUNiFEZboVCln62GANTJw4MfknhBBCiDRGjx6d/KsvrFWw0QIE+HhNAVOJ6Nm3tQwoGqZEmChgu1UxdcpGPkJ3zR4Ls1kfwfMI6WluAUK9Ceahm5VoS1hC+/Z+82fon9Ae61UwthaF/yhRsTtA+DjnHsrR9773PX89ds+Ul7T4awTIYI3KW6JrU/e8VSgIdUNf1xSi/VD5E6Lj04xyWhC0kXBXMVmUs1cQqEvu4bZRUCYy78H5559fch8zadKk5G4+8oYlvs+7jfh6fA6EE7/Z9bHRV9UhfC4kLZxGHH8Y4q0RrC5tQFp88fX4kNgO8dhMwnyXlhe6GvXOW3Uf+ZDWLUT7oPInRMdH5VQ0itUtb4WjSVBQ/lbZ1jgcQWo2NgrIFMSOtjtbs6l33tKaDyGEEEII0XTCtUVM4wvXH0Fb1g+JjouUDyGEEEII0XTCLY4h/B/DOiNGI8zwFXnW3ITXTHmJr8fPcg5p19II3WPEJg1GadLs4E+7juE9do1nuiJSPoQQQgghRLsQT6vKEsiZkhVuhQxnnHGGX+gfE1/nC/SsMrCRFkZZEP7Da/aV+ph4swCmisW7sOEWGypgh+lj2DEFhE0Rzj//fP8fJk+e7B544AH/n2e6IlI+hBBCCCFEuxEK4fxnh7B6Yl/dD3dbs3fatSxFAOUEZQZMobEdzsCUDLtnH9MM17OEjB8/3u+Khv1yIz2dGSkfnQgKqw3rpZlKhZkCFD9jhcqGCdPAXtwLIERXJx72TzPliIfqMWGPYDisD1n2hBCio4MCwIiBwWhDXMc1g3jNScywYcOSf612TbFB2aD+DRWKtKlcNsKCAtJei+nbGykfnQjmToabl6Gtc27aPIWZghEXLhNyKDj2jJl9993X32OIMoQCxXWMEGJV6CmjDNmQPli5ssaJ8hMr9ZRPrh977LH+3J7B0MjF5c7sh1DmpYAIIVYn4mlVyCQm2HdkTMaK5SdMvHsXDBo0KPnXdZHy0QWgRyEs0BdeeGHyr6h4mJDDEGC8nZx97TXW5GNlRAiRn7BBPeuss5J/pR9K40jZC6Escy2cy0x5psfQGjuDBjFrtFIIIToi8fqIjkw4hQu66ihGLUj56IKEIx+meIB9mTWNc845J/lX7J1AyAmHSIUQtRHOMw6nGVx99dXJv1U5/fTTk3/OHXfccSW9a/GCTCGEWJ3IWkTeEZg1a5Y/hiPa9p+6PG2alVgVKR9dhNmzZyf/WrX1cJ1GWJDSoDcCI4RoO/PmzUv+tW4tSaNlu6pA2nC9QRm2zoLYXjikv/vuuyf/hBCiY0FHaNgZGkL9liaXxHUaz2ct7K4XYb1s7wo7a8PR63DHLNbZZoWvqyPlowtA5g93ZrBCw44LRjx8KIRoHGEDZSMcbL9ohNMcq8XcoeFWuRZCdDRsnSlCPXVd1mY4TAOP165Rp9m0LGZu8BHCcJTE6k46V0OlhOuMLOe5FsJoBu8L19ox8yOsW+kAspkg2De7KCXYI7yhDMa09a4+JVbKRyfGFphbYaKAhnPChRDNxRolGijKJeWx3AhHLVx55ZX+GK/fEkKIjoCtJTVTrq5Ku2ebeWBYZ0GHauhebMdMbC/rmsF/lIfYrTSox0M7GKvb4/BiuvpMEikfnRh6PsPMHg4TQqi5a2hQiMYTlsd4ceKQIUOSf6XrQKqB3j6erfV5IYQQotFI+ejCsFDVCOc0CiGaT9wTVu3CRewztM/wf9ixIIQQQnQkpHx0YRgSDOdKlvtQYHt87EeIrka4U1W4w1wM84VD5YSRS+YRo3iE07hUboUQQnQ0pHx0cZiKZTtK0GuaJqwwR52PDcaEC2Rt+zkhRO0w+mGLKRmNTPtQIIszH3jggRIlw9Z1oYDYuhKMplMKIYToaEj56EQglCBwGLbgvNyIBrCgiznoCDDs+hAKLxjuhVNCbKeKcPcGe66r7+AghEG5o0yEUxo5z9rZxbDFjYxKhjunmGEHFVu/hXLBtSzGjBmT/BNCCCE6Bt0KjVzdtj+aOHGiGz16dHImhGgmKn9CdHxUTkWjUN4SjaLeeUsjH0IIIYQQQoimIOVDCCGEEEII0RSkfAghhBBCCCGaQt3XfAghhBBCCCE6D/Vc89GABef7J2dCiGYyceIElT8hOjgqp6JRKG+JRlHMW1pwLoQQQgghhFjNkPIhhBBCCCGEaApSPoQQQgghhBBNQcqHEEIIIYQQoilI+RBCCCGEEEI0BSkfQgghhBBCiKYg5UMIIYQQQgjRFKR8CCGEEEIIIZqClA8hhBBCCCFEU5DyIYQQQgghhGgKUj6EEEIIIYQQTaFLKR+/+pVzI0Y4161b8XjHHcmNHCxY0Pp8JbB7wQXO7bZb8V38D+F+377Fe2ZiO0Y5t7h3yimtbpQL08yZzn3/+84dc0xyIYUHHyy6l+UX3OB5ex928UMI57zHwoefrr8+uVkG3on9mKeecu6gg1rfyX+uhcTvTLMDXKvk/ziMuEW8lIP7PGPv76qEacD/OG6zIH9YuSS+SYNy4C7vifMp77R0i01WHuS65S+ORjX5gOtmz0y5PGP20+xY2EK34nBWW4/xnrT8yXPhe0JD2Qih7FgdxDvT/J6nfIUQx9ixsGTFWVYaAXFj4UrLE4C/7HlzI61+EEII0SRW1pEJEyYUfnGy45nrrnMrzzzTrZw0qfh/+HC3kuDPmJFuPzTz57uVt9/uVn7uc8Vn0uyYmTrVrezTx60cO7b4DM/GdvAH5vzzW02avUpu7bqrW3nyya3XCRv+w25ojzByjTDjVnjPDM9ih+fxT3yfd/Au4g67/Mcux9AeccQ7cIMw4n/sXXFFqb3QEE7sYMLr+Jvnw2dxl2thuvEe7OAvu09Y7T4mdot3Yoc4tPjjyDXChDsWRgz2Q/fMYAd3sZ9lp1mmPcsf6U5cEs/EI/+z8lpoSA/ij7Tj3OLT0iTNWDkkzsPrpF1criwPhvYw5keeIU+H+anafGD53Uy5vG5u45aFOTT4Nw5DGBfV1mPl8ifxaGEMDfZDu/znHRYu3hv7P0/5Co25wfvT4gFTLo0wxIPlOc45YpfrZif2F8bCGLvXLNOR20mZ1dsob8k0yhTzVv3A1brR0ZWP8NwE7ayGL83QaPFM2j0MboWNdJqhwaPRT7sXmkpumUAQ+98EofBaeK+SQIibac9nKT+he4Qt9q/5E6EgvG4GN01wxV54D7fia5gw3LwzjgOEj/g5hBzeEV6LlS3ySCh0YcwPPB9exxD2UPhpb9Ne5c8ESeLTrlm6x+UuNMQbdkJhEYNgmBbfGNKDe2G6YXh3nHYYsx9eI8/ZO7IE47z5ALvcC6+VMzxvylOcb/PUDXF8lqvHyuVPrqWlDeGOy6opEeE1/BnaI1yVypcZzon/ND+bqZRGGO6nuR3WSZZuoR1MVpw1w0hAlGmUUd6SaZSR8lEnQ8NL45nVsKUZGra0hgxjjWXcGMaGxhQ3aCBpGNPen8ct7OBOLAwRplAIDA3vDBvmNIOblcJghnflEbx4J+FJu4f/EXiwE8etCS+hkITQhFtpApUZ3IyFOJ5JC7ulRXw9NNiJ3SOOcLOa/NNo017lz4Tp+HpWnJsxwTAWArPcI5+Qttjnfp58ikAclwf8VCnN00xaPqAMEE6upwnzoeE+YcbfaeHOUzfEJqseqzV/ogiGyiBxnhbXcdrlLV+WdnHYY5MnjUhbTBhG4jD0f611SCONBESZRhnlLZlGmXorH11uwTnzjJkXfNFFzt10k3P9+iU32sgPfuDc228X/5ebjz1smHMFQcU99phz3/iGc3vsser84zxu4e+CQOAuv7w4z9rmT59zjnOf/GRiqUHgD+ZNE46vfz25WAH8GIM7O+5YNGkQjoIA4o49tphmFsYJE5zbfPPEUgDxyH3sXXppcjHB4jMG9+++OzkpwxFHJH8K4P73vufcgQc6d+qprXPJK81x76z8+c/OFYTAVdh99/Jx+9e/Fo/bbFM8GjvvXDzGawDOO8+5H/84OckB6US6h+WB9QP4aZddWtcwZK0VSCPOB1/7WjGcxAH5lHKRlgew+8AD5ctLnrrBwL2seqwt+ZNwfOUryUkBS7/ttisejW23LR4nTy4e85avE05wrqAsuTvvbF2rEa/ByJtGl1zi3OOPtz7PWhj4yU+KR6i2DhFCCNEEEiWkLqwOWjdBNkMPWTU9g9ZjmXaPnrRw1IFeNXrlsB9P4TBD76E9F/qjGresBxJTqeeVnkRM2j0zuBP3cobG4gCDHyv1YBIu7MW9jJyHvcj4CzdDOxietx5h4iUrLq1H1ewRL+F9/MD1OL3tengtNLhL3IfXLM7JPxYuuxaGqdmmvcof4U7LV1lpaibrvuWxMG8R13ZuaV0un2K4H/aCY0gfnqWskBfC/BXnmdCk5YPQ4A7vwp20PEBYLe+lhS82+MXqgTjPYnjeTFyP1Zo/08KY5dc4DfKULxtFIS6sHONOHM5q0sie5x52wntmwuexm1WHNMuod1qmUUZ5S6ZRRiMfbaQQia7QYPnesHPPde7ii5MbbYBeN3r+Cg13Sy8rvWr0zIH1yMXQC/qnPzlXEBDcvfcWr1Xr1uzZxRGQQuPte/ey3lUvzjjDuUJj7t+5cKFzhx1W7E3M4je/KY7GxL2M9D7GoxNZ8B7eB/vvn74rzt57F9O2ILD4XlJ6jsORou9+txivjCrR+4uhh5W4p4c4i1/+0rmrrkpOEug1haOPbg0XaUmeoue4XHyI6iEde/UqpnE1XHllMY1C7rnHuYIA6kfiGC3AMJrCtZ/9LLGUQlo+CMEdetwLAv8qeYBduM46q3R0ohJpdUNIuXqs1vx57bXOnXRSclIlecrXI48Uj5/+dOtoJ2n6X/9VGs5q0mjuXOcKioUPG6PAWaM7eeoQIYQQzaFTKR8Mq9sUAzNZgupddxUFdhqstrJ0afFo00UME5bKCaMoGCgaKBFQjVs0tEOGFBWCRx8tTtlA6G60AoIwwDsvu6wocKQJR4Aihf/iKVek0ze/WVkYQ4hgSsV//3fxfQhVpNk++5RO0wjhXQgv8Ic/FI+A8IXwgUC04YbF6SjPPFO8F06lCcEu746nhS1aVDzG18eMKR7nzSseRdshD/ziF8X0rwbLH3EakV+ZJhVCPuQaAnAaWfkgje98p3i0PGD1T7WKE8R1Q0xWPVZr/iScBxyQnFRJnvK1eHHxaFO2jFGjikcLZ9404l0331xU+ogH3k888G6jljpECCFEY+lUygeNGA1QaAYOTG6mwHxtesnbis1ZHz++eIzp3Tv5kwENKz27kNctGk4a2qOOKp7jBo0xCki5Htp6YkqF+T0E/7366qqKBzAf/ZBDSpVEmxfOf5vbPW5c8WhCFEcTtm64oXgtDXp76QmN0xbhgx5Qeo2JqyeeKAp3aX6kt520SBM4mYsOWb2nPXokf7oI5LlwXr/BNdIhC3rAIY5Hy/sI14yc4U6YVxAcgXzEeRrkD3rjY0jvNL8CPesx5fJBGqZQWx5g1I+RidD/+BsIB4JxOcK6IYu4HqslfxJORijiEUpLP1vbYdi5KQ5QqXzZupHYLcPCmTeN6Hw5/vjkpADvv+KK4uiOjX7UWocIIYRoHJ1K+UBYoQEKTdyYhtATV27KTV4QEGikaTDDIX/7n9WzDtjBhEpEHrdshMR6Ew1GTOg5bAbmp7iX0hQPmzYWw3SR2CBw2D2LC8IWh4X4YfF9pTDit333TU5SQHBhYS+CSgyC2KabZgucBx9cPLJoNgT/ItTkFVQ7C5YnQ2HX/rPAOAvrZQ+FUdKNdGEqDZAX4ryCgAnY4TwNFPO0XvzPfrZ4JI1DeG+shFbKB2mQ9ym/9gwdILH/LWyEg/tZ4CeMlYcs4nqslvzJKGFaPYVdnok7QzjnetaITlr5YgE9ysONNyYXEpYsKR4tvfKmEXWAPWvYqIrVi22pQ4QQQjSIZO1HXeioi51mzCguciw09CULMAsC7yoLJLFXaCBXuY6xRYsFAWKVeyxi5Dlz0xY54p7Z4Z2FBrtkITmLK+MFkHnc4hpuYcfChL84J5xmL7TPvaywYXCH8IXvMYOb+DVeBB8vcscPFtehwf+EP7QbGp4pZsdWQzxwjWfNz7yPMFga4B+ejRenYkK3MPgZe3G8hQY/Es7Y/1wL0x338ZuFn3uhv9rDtGf5Iw2IU+Lf8kacjziP8x9xyzXS2tKuXB7FEMfEPc+m3bc8kXYPd60cWLkjzeN35skH/Oc95g7XuVbO7xjcwf9hXslTN3CN9/G85V2es3g3e5hq8ifPlotzngnd4p2x/zH4qVL54j7PWvnEDnYJk9nBH3nSiPgJw4RbliZmh+ftffYsfsiKi2YYLQqWaZRR3pJplKn3gnNcrRsdNePT6NAg0QhhrLFLa2xpvGg843v2rJk04YaGzt5D4xbvQsN93Lb7NIhpDbTZLecWxhpb8xNu00iHdjAmPIQmbOwxJhCFJmycrcHmOkfeGzfeCE6xG6FJi28zFtb4Ou8gvcwN/ofvDeMUgzsmJIXG3M+6jyGOzZ3Y8N7YPnFmcRL7qz1Me5Y/0tYE3qz8mlW2wnjETlaZMEM8YzfOw2bwR1YaY0K/pr0zbz4IywThwj/l8rgZK2tZ+Rg30+oG3LZ8bH4p984wXsvlT8LB+9LumaFsm//S3MpTvsyEbnFMq7MqpZHZIa0sjBzT8h1+xc/mVrm4aIaRgCjTKKO8JdMoU2/loxs/hQq5LkycONGNHr1/ciaEaCYTJ05Q+ROig6NyKhqF8pZoFMW8NTo5aztdbqtdIYQQQgghRPsg5UMIIYQQQgjRFKR8CCGEEEIIIZqClA8hhBBCtCtsU80WzeE3a/ioLt99YmvkrO/WCCFWP6R8CCGEECI3fBwz/HAmxj4Oa1S6nwYfqUz7/sqM4Mv2QrQFlNjddmvNlyi8KLmiuUj5EEIIIURurrmm+JFMY9Kk4kd9Q+bPL348FjN16qr3Y/igJR/JDeEjweFX9IVoC4yuHXZY8aOs559f/CAsH6TlnI+YiuYh5UMIIYQQueEr8V//unNnnlk8nzy5eEzjrrvSv6ovRLO5+27nXnrJuZ/8pKgMkzc/97niyNq99yaWRFOQ8iGEEEKIqkGIY2Tje99z7o47kosJp57q3FVXFRUVAzus32C6C8frr09u1EA4fYZ1Ikyfsd5rpnjZtBoMduOpYjYNLLwuOjcoHGF+hP/4j+Jx8eLiUTSHun9kUAghhBDZdKYPwSHwb7FF8f/jjxenSiHY9+pVHB0xUDQuusi5P/+5eO8HPyhOeWHK1t57F+3wHIpMKJWgOOyzT6k9lJgvfMG5W28tXsMO02mGDy/2ZiNgmltMr7EpX9//vnPnnuvcddc5d8wxxWuAEnPJJa3ur67oI4PVY/mL0Q/yrkin3h8Z5LvpdUOf9peRaT+j8icj0/FNZyynBcUAdWHlrrsW/3/uc6va6dPHrSwIeC3n8+cXnwntFhQFf83OMeY2R7tWUDJWnnlmqb0rrija4xjaC93nnfgjfJZrJ5/cer46G7UB1RvyXJyXZFY1xbxVPzTtSgghhBA1w4gBC9AZ+TjhBOcuvTS5kUDvMrtYMTJhU5w23LB4j2eqAbfopd5+++RCwlFHFY9//WvxCF/7WnGkJdzNCD8w4mJTtH7zG+eOO674X3QtyAPjxzv3ne8kF0TTkPIhhBBCiDZhU6xYyxHPqzdWrlzVTJ+e3KySeI5+2jtNIRk3rvWIMoIiZAuMET5X9+lWojZQPNm5LSu/isYh5UMIIYQQDSdelA5p1/Iwe3byJ6J37+RPAebws5vRlVcWe7l5xq6ddVbx3Z/+dGJZdCl+9SvnvvIVKR7thZQPIYQQQjQMRhb69HHuhz9sne4ETId69tnkJCfmVjh1Cuz/EUcUj8bxxxenaX3+80VhM7z2zW+2jo6IrgOKB+kuxaP9kPIhhBBCiDZh2+YyjSpUCozzziuu72BnLHadwvBxN1MI4Mkni0c+Bmc891zpES67rDh1ih2zeBeG/2z7G+5iBZ/8ZHGdByMitpsR11BgeL8E0K4D+YQtmRkBY8oVO6KZYcvltHwrGoOUDyGEEELUDAvIjz22+J8RBRaTI9CFsCaERel8k4Ptbp94wrmbbmoV/hH+WI8BO+3UKhR+4xvFaxyxAygYt9/u3D33FN+FQoMywTa7abDw3L7nYJx8culWwKLzQ/5hxIz8xzbModllFymizaTu3/nQHtNCtA/a412Ijo/KqWgUyluiUdT7Ox8a+RBCCCGEEEI0BSkfQgghhBBCiKYg5UMIIYQQQgjRFKR8CCGEEEIIIZpCl1I+2NuZr6+yMwfHvB83Yi9ydtfgOQxbtcVbsnHO1oHs5GHu29aDMdg1e2binUHy+NW2jTO32Mkh3KLQ4Br37F1Z9kLwD3ZjQn/xXuxV2p4uy60HH3Rut91K3SoH78GOPRPaD93CEC+kWxrY5X6595F2xBN2s8iTjqIV4svyTzXwnJU/4hs3YsK0j43lA8pQ2n0M+SEN0p93WzpngT3up+UX8kSlshxi+SotL3HN/JJlB8K6iP+4mQXxQ/jNj2EYeC5PHUOY7HkMz6S908Jm9jBpYcAPKqNCCNE56TLKBw0VeztffbVz111XvHbIIdkCqkHjdeGFxQ8XTZpU3J6PrdrYUzzk1FOLWwf+1385d+aZzi1cWNx6ME1Quvjiojvnn99qwr3O8/qVhhdeesm5+fOL//ffv1Q44Bmu8RVX9jXDjBlTvJYVdp5n67kYwvKznxW3LcTPNOrYI+xZZLnF9Wuvde5HPypumbj77kV7WQob9tlOcfz44jOE94wzWu8ddlhxz3b8NXZsMY04jwUgBJUlS4r3s0CQIv7vvju5kEGldBStkA6PPurchAnFrTjzwnN77FHMa6Q5z//3f5cKkKQ/e/6H6YDhK8YY29v/D39YNb0wbNGZtuUmwi/5auedi+/N2hcQP55wQnISgeB/443FLUF5Hv9QlrPyOVCeCE+MucW3EnCLI19u5noIyhLv4z5xxn8+sJYG/uAbCEB9g7t8xM3IU8dQXr7whdbnsUf9Z8+G5CkzKqNCCNHJYavdejFhwoTCL052PFMQ4kvOCwIvosTKgkJRcj022Cs0piXXdt3VrSwIuC3nBWFq5RVXlNqZOrXofqFhL7mO3YIAUnItNnn8ih2uhX7jP9dCv+Evrtm5mdg9M7iB/whj/FxBqSo5xy7hwx7hCu/Z/Sy34jCa3wvCQcl1DP7kXhzHZniG58NrvJdn4veYyXqXGXtnVv7Ik47NNh25/JkhzonXtHtppiA4evth+lqetjzHeZz+GNLH0h+7aXmBchqXUQxliHyblq9jgx8tv4X5hWe5RvkN7eNuQeFJ9TNhsTDH+ZNn4much+Xd6oXwnVYXxeHnWdzMyuPmVuhP/nMtfCdhD88xVn7Ca9WWGZ6Pwxua1bGMYlaHciqzehrlLZlGmWLeqh9dZuSD3sAQetbo8dtmm+RCBnwJNf7wDL2SjCQY9KzGPac77ljsgacHMIRRFD6kRK8gIwlxzzzk8evNNxePod/4zzvD3sDBg4vHsKeVEQ96ewcOTC4EMKJDz3Lax3Z+8pPkTwJ2GAWBpUuLx5BybsVhXLy4eMT/IcQPvc8FISTzg1CMgMTvsA9Kmbv1Jk86irZDviVPhOm7557F47hxxSP5Ik5/0oP0OeCA4jllNM5zcMMNxdGIEEZVHnus+MEyGzXJAv9R1hkdibn33uKxZ8/i0fjsZ4t1yPPPJxcSGE3AHHdcciGCOoCRjzCvzZpV/DiWYfUC9ZaB/yjvjEwYjC4w0njrraUjHSF56xj8RXyFoyFz5xa/Nh3S7DKjMiqEEB2TLrfgHMEb4eKii0q/rpoHhvppyBBW8n4ZNRZ4hg0rPk9jzRdbmVISNtoh5fy6aFHyJ4IpVWDzoBFCEBaYAoZbuMl0EqaRxIIV4UNQwVQDwkf8TF63EAgQ4IiT665b1T4KjE1BqWbevGHCZ72pJh1FbZCHSftQuAbLI0zBywLBn+k2lco3wmk4DYfygVDOlD2mP9k6gbQ1DNh94IHsuiBL8R01qnicPLl4NM47z7kf/zg5SeGSS4pTqaiDyGsI1BB2ChCeWOgHpjWGCgPTxCi3d96ZvZ4jbx3zne8Uj0zHolxyHUUn/tp0s8uMyqgQQnRMupzyQYOLcEEjTq9n3t4wBHfmatOA08Bbw5sF7k6f7tzppycXEuilR9hmROSKK4rHI49M90c5v5riEPuDnlDo0aN4hGuuKQpiuIVgwihCLOQjSDEnPq9SZSAA/uIXyUlCNW4xFx3FiDAisPBsCHFFPGy3XTE+Z8wo9uKSFuUECQQ71t5U6rmulWrSUbSNXr2SP1VAXvrUp5KTDCg75KUwj9hoBXnu3HOZN1RMX9YfxGubUEjKKQtDhhSPsZLBqACE4WLdBqN15ZQlRigmTSqWgZ12Kub/yy5LbgZUUrh4DjdQ4o8+uph3cRchPczDeesY3kf5Jc4ol9/+drHOif3R7DKjMiqEEB2TLqd8IEzQ0DIagHDBgsQ80JDNn18U3GnEmAoUC8ohv/mNc+ecU174RTj/05+KgoAJPSHl/GqCPQ29+YNeRxaXIlDFygV+xu9AD2UsUCBIXXppcpIT3CB84RQPqMYtekcJP8oRih09lQZCEj3fKEz2Dt5HDzBYz28MwgVKkfXINppK6SiaC+UBQTrOlzFseHDSSclJAiOCgFBuZZf0pQzS6WBlDWXhrLPKC/qMuiGQ//SnreWNPG3Tn2z6GOUWRSRr+lMIigtlBf+gEKWNyFTikUeKR6aOWj3Bu9ksI8zD1dQxTLskrCj8NjpTrn5sdplRGRVCiI5Dp1I+GJ2waRJmYiEbaGgRemksy+2oEoOggRJCbyNCcVYjhoBBr2faHPMYBCSEa3ZuSSPLrzT8t99e9Af32Gp0zpxi4xq+F8EEQYC1F/gd4Qr7++xT9CcQb9/8ZuUe0xDcpUc37nmtxS2EPNxBqEJwMX/ZOpJ4Pr0JaVnCDYpfWs9rI6mUjl2BuOyR79oDymWeskeveDwtz6Yaxcq7TTWaN6+1TqmkLJD/mC7JlCfKGyMNKNiM4FEGeQfliJFDymYl8C/rMJhmRZ1AZ0LaiEwlbDrYttsWj4ZNB7M8nLeOobyixNHhgN94Bjtpu82FNLvMqIwKIUTHoFMpHzSeNMihSVtUbbBYmvnO1WINb9p0EBriV1/NJ/wYCCmVppak+ZXGFEGGEZIpU4pCBT2S4VQvW5RrwhRHU2aYygVMx2K6RCg42vxw/qNQhCBQ4G6awFStWyG20Namc9gC+6y5/b17J38CGA1hDn8zFQ8jTzp2ZuKyl7X9bF4Q7snPcfqb8G8KQcxVV7X22mdBDz7CcTwyaetL0jotgLzJiCYjkWEeJ98DSkaodFl5o4xSVqmjEMxtqiKKMuUjdAs3ADc5NxjlOP745KQA5Y/pRIzImJDPyKGVtxCuMVoCTGGEeDqYEebhPHUMa1WISytzPEMnB+Fke+VyNLvMdPUyKoQQHYFOpXwgrNAghyYWLkJoSGk0q8Uaeno0Q0zxoPHNC25hjjoquZBBJb/SK4qwwtSCMMw8R89lCA0wvbB2naldsaGH0O6FfjPFI0u4i93BZLkVY7t6mf/xJwITgpPFOdh/vr0SguKB+yYENZO86diZicteNQp4FrjBFKow/U1oTotryiD52pTtLFiTFOcfOPjg4pGF2CGUIxutQLGK8zijdoAywP008BvTNZmaZHUEYYjdwg3ATc4NwkUZCbHRC/wHFqZQebL/pgyy8BoFgp2zQsztrE0asuoYRoviOsbiP/ZvSLPLjMqoEEJ0EJItd+tCR91jesaM4j70BaGgZd/+QgPv99uP99rHXqFhbrnOM5/7XOu++TzPc/Ge+QUhoeUdoSkIEC3fp+BYEGBK3MJt9uE3d6rxK+e4ZX7GD+F9jO3xjz/sefyeZd8MbhazR6sJ/RYavv+B+6Hd0KS5Rbh5xsKOX4ibMC4wnONXCz+G53DT7Ng1/BH7DXsWbjOEA/+EbsTGvnGAG/G9POnYHmZ12OOddCJe0/Ie1+I4J+1If8tf2OE8LV0wlgfS7pkxN+N8Ycb8aGXc3pnmZzO8s1y4zE2rC8oZ7MfxgCGPhf6wfBfnY86tvGCH/7Edy98Wr2YvfiduVKpjCFMYNp7BnTCOqy0z3MfNzlZGMfoWg0yjjPKWTKNMvb/zgat1o6NmfBpAGjEaKow1stYwhoYGikbL7pmgznMcuR83wDRw5naaMbdo+HCba7hFw0+jGLqV169cK3c/NPgXe6GbaUJEaMwPdo4/LR7SjDXyaSZ2C4OQaM8SJ2lxYYZ4MzfwA8+G4Q3DFhvshm5ZvIUmjosw/s2E9/OkY3uYjt7wxHFKPIf3SQfiMxbQw/Qn3uP7oeF+pbSgTJvQnWXIJ5bf85QXy1ehPf6bn6vJI/YcbobXyfPkZ/NXWlkwe7yvnB0MZdbycVq8Wpjy1DEm7GMfQz0ZhreaMmPvDU0c/6trGcVIQJRplFHekmmUqbfy0Y2fQgVdFyZOnOhGj94/ORNCNJOJEyeo/AnRwVE5FY1CeUs0imLeGp2ctZ0ut9WuEEIIIYQQon2Q8iGEEEIIIYRoClI+hBBCCCGEEE1ByocQQgghhBCiKUj5EEIIIYQQQjQFKR9CCCGEEEKIpiDlQwghhBBCCNEUpHwIIYQQQgghmkIDlI+Sj87KyMg0zUDadRkZmY5jhBCia1P3L5wLIYQQQgghOg/1/MJ53ZWPW779SnImhGgmh18yVOVPiA6OyqloFMpbolGQt+qpfGjNhxBCCCGEEKIpSPkQQgghhBBCNAUpH0IIIYQQQoimIOVDCCGEEEII0RSkfAghhBBCCCGagpQPIYQQQgghRFOQ8iGEEEIIIYRoClI+hBBCCCGEEE1ByocQQgghhBCiKUj5EEIIIYQQQjQFKR9CCCGEEEKIptBtZYHkf5uZOHGiu+XbryRn7c+8ZS+6f779iOu59oZu140+mVytzOIVb7nnFjzgFi1/031y2MnJ1Va4/9C8ce7FRY/686E9tnd7DDjUDVx/S39uvP+vd9zk1//inp4/3p/36r6R23/TL7hhPXfw58asJU+7Ca/+yS0uvA922HCMGzXgM26dNTfw53Ddiz9yb7z7cnJWyokjf+Z6rd3f/+ed4+f+0fut+xrru70HHuW277e/v5fFX2dc5I+fHn6aP4ZUigt4/M073JQ3bnfLP1zm37nbxoesEt9vvTenYOdvLXGWFsY88Ur4rnj2P5KzUojfE7e5IDkrhbxww0vnuqO3OHMV90ijF95+1Pufd+498GjXf93BiY0izyyYUAjnnT6NstIxhPdNfevv7pWlz3p3/3Onq5M7jePwS4Y2rfwRX5Nfv8nHB3E2ZvDxLXmwHOSD++b81udl4nHskJNWKTdg6RUSp51BGj678AH30qIp3l3SL8x/1fo1K69w/f5Xr2sph+ThXTY6eBW38pbBPOUmzHdmZ7u++5aUm8mv3eQee+O25KyUg4ee7Lbqs4f/n8ctyOMvyBuvxFu5etjKYKV6Eiq5lTeNwNI5JCuP1ZNmllPRtVDeEo2CvDV69OjkrO2scXaB5H+bmTVrlnvh7sXJWftCw7Liw/cLDdG1bkiPbQsNyhbJnfIgAL/9/mvu8bfudGt9pLvbpu+o5E4RGsobp5/nNlx3kNui926ud/eNvZDx3MJJbnivXdz6a/VKbDpvb4O1+rhPb36aF4g2Xm+ou2nGBW7Aepv75wDF485XfuUOGXaKO2DIV9xOG36iIEQ97qbOv68gsBQTGoFt2tsPeiGAsJhZZ40NXL91BhXs7eft4bfrXvpf7zfeOaL3ru6uV6503bp1yww/Ask/3rrH+ycOa6W4AASfmUumus+M+K4bvenn3ZZ9dvdx/t6/lrjBPUZ6O7jz55fOcdtvuK87fPNv+3ia8ubf3PTFj7steu3q1vzI2rnj9fm3H3L/XvnvglujS+Ji+Yfvup36f6IQx5t5eyG4ffPLl3hhatt++7gea/dL7rSm0WGbfdMLM9MXP+EFOQQfeydC1qLlbxTuH1x413ZuzjvPu6cK6bNN348V0mB9bydk/Nw/uIcK8bJln93cqE2OdB8f/KXkTmPZ+qDeTSl/5BkEPMuzr7073U2ce43Ph6RlFuTja174gdtzwOE+H6y3Zk93y8xLCnE6siRN4L45V5ek8RaFvDyikFdicPPaF88u5IkPffkgD2LfqNavWXmF96CkI8hv1msn1819xOfPl5c85bbps1eLW3nLYJ5yY8oC9QBhWrh8XqFueMwtWbHAlxHjvrlXux0LQjb+sviijpn//qvuoKFf83byupXHX5A3XvPUw3nqSajkVt40MvLmsXrTrHIquh7KW6JRkLeGDRuWnLWdTjvtit6rcj3TWdBDxnO9125t9EJeWfqc+9Sw/8+NGXS8F1Y5mnBJg2zQENL7tnWfPVt6FvETPYTz35/rz2Hawod8I2u9bdjdqf/HW3ru4PVCw37sFv/j3xcaGN5rZ38Eeg/phbQRBXrv6XV9cN4NXgGIwY+YLCrFBTw1f7wXXqxnkSMCzhvvzvLn8ERB6OndfaPC9WLvL/6iZ5Mw0mMNeeN1+Yfv+RGaOC4YmUExSIN42XjdVZUSlAr8YPGFOXjo173gSZyFoESQRgg2+B2WffC2P4Yg/ODmcVud7f0Vj6Cs7pCP/j7n917YtTxLWgHxXI6/zfo/rxxaPiAut+y9h7t79lX+3CBdRvTeuSR900YOEEZRZsjj5AnyqpU1qMWvWXnllaXPuBO3Od/nA/zD+/A75Y28a+Qtg3nKzdIVC/1InsUBdQCjAgjU5hadF5Qb85eZHmv3bRnxgDxuQR5/VROv3C9XD+etJ6GSW3nTCPLmMSGEEPVHaz6qhAY9Fiit0Xr/X8v8EXqs1ccfmSIQsmjFG27DdQYlZ84L5a+/O7NECVi64u2SHnzcD4UqoIcVwWFoj9ZeXhpUGu3Q7oD1RvgjU1JCeJ5pUAhJbQH/4zbuGUtWzC/4v1VDxl+M0oSYEDFn6fP+mDdeERJiEMDwhwlMIbwbdzdab2hypZUZi5/0xzC++E8cIsgYoRAHKEAIbn27D0yuFGG6CmlJD26aXzoDs5c+64/Dem7vj8bQguJn02bSIH8jBIa97DC4x9b+OoqEwVSeB+eNc3fMutynXxrkt1tm/tz3lGcJjdX6tVxeId/FZZBOAljx73f9EfKWwTzlBiE6BDdRBIARAKAcxeUGnl3wgNu8507JWT63II+/as0DaeStJ/OQN40gTx4TQgjRGKR81JFwFIJGEMGIxpgGjt5CpuOMGnBki+ANO/cf64/jpv/UC9EIYdMWPugF2HLQk8d8ZmtseY4e+1BIABNMTMg3nnzrbrfbxp9apbGulv02Pdb3XP515kVewLRRilDYwV9pxEJ+FmG8psE0kZ37H5SctUKcv7rshUzhlKlaaQzusY0/hgIx4B4KxpNv3eV7m8O44x692whgzPf/f1NP8IY0DwW51Z3pi4oKW6x4mcAex5lh6RwLlP3W2dQf5y2b7o/EI0Ixo4Eo13e+crkfTYrjkB52y1e/e/4MH9ccKUNGNX6tlFfKYSNu1ZTBPOUmCxTfNIXDICzvF/xRbpTAiN3K469a80AaeevJthKOiubNY0IIIRqDlI86QGPLSEXcQ05PHFOHaOB+N+27btP1t1rFDo0vU3Tocbx55sV+HjVTfyopBSgoYc+msfYa6yb/skFAw145ASYvTIVgkSbTnpgCg8BiUzAMBBxGBOLGnd5NFrRmkRWvIbhZ7G1uHQEyxs9BiMke2enVvTg6EQtL9PTCWh8pjUvSEAUD4Yz58mF4rDeYdPzYwKP8AnPSHqEKZaSzUavSuvYa6yT/0mHEiHLDlJlvbPdLt/vGh3rFJY5D0px8hTLDdCI2XWD9DWWIPBiSx6+V8koaKEz4Lx7lylMG85SbNFBgbNpfFoxcbNU7u8wYaW5V469a80BMnnqyVtLSKG8eE0II0RikfNQBdjX6xOAvJ2elMNeanj0EJXrYwvULBtMeeq29kW8ErdeR3rksuIcwX0vPIM+yzoTGt14wTYz534xkFIXt0t5+FmrTI0xvNddNYWC6DSMFWZSLV4MRINyIBSEWzrJTVjkByXq5UfgsvlHM2KEKpShWzlAoEMwIJ4vSGT0y5iz9pz9u0Xv3FkEH97GLUFUuPUU6pB097pSLMA4RiP0Iw7qbtZQB4pxee0grY+XIk1diyMMI7zZyWQuVyk0MSjIKc6Vy/8yCiT4flqOcW9X6qx7kqSerJU8aZeUxIYQQjWO1VT4YJrepLWbaA4Ropi+ljSLQaLPwE0GfBZ4sfmShZtiwIkix9eiYQV/0jeARm3/H9zr+ZcbPMht8etlr7RlkK1t65usF4WftBH6nJ9GmUIS9iAjhXMcu2+Ryb0GymDRrSlW5eA3h3bEbNpJBL245cJv4ZooKPa5sZ7z0gwVeKcqKX9wknAhJLM41bApX7F+bwpW2OF3kw4RHi8MP/v2eP8ZrMyy9Fy/PL0DmzSsxbJSQZ4QyizzlJoS6gF78SqMjNupTrtyUc6taf9WDPPVkLVSTRnEeE0II0ThWW+VjZN+9fcMYmmZDL3nfdQamNvQIATTaW/QqLrClAeQ7GTSsLAY1WPQd9tzTE3nUiP/yAjCjG2nwfDw3HeGJ3vpwXjmYcIUQzH+b2hArbUw74L/ZzwuCw8i+H0vOSqdQhMoT10/e/lI/ekA8sHtO1pSqcvEagvuMUsRuPPr6bX5kIgyj7V7Fvv4orgbxzbQd/HXslj8sKBHv+Xjk2wDlYM74Omu2Thmzef5Z8RdP4VpdYYcgiMNp+S5LiKcXHWxth2HnA9cvLspOw8qGxaGtNYjzutF9jfX8MY9fq8krBkJx2vcx8pRBI2+5Ac5fWjzF26kE0wEZacyiklt5/FVrHkgjbz1ZLVlplEWcx4QQQjSO1Vb5QOCkYQxNM0FAXn+tPpkCsvXOLk+OBr219LQb9JiH52BuIgjH0FhjP+29xEm8tsKEO3YZQmhj2lBsAEWA//Ei0kow/SX2py0ijsNuIODgz08OOyW50kqleA1BiEpTXlBE4zCyOB8QpLIUVXp9ETzpLY3n8cesKIQ5nDJmO//MWlK6gB57jJLkCc/qQOvi6lYlgvxGelocp0H4iYdYMOec6+UEVvI8yovFIYIi5yjMYV63/zYSlsev1eYVhFoE5SyhtlIZNPKWG9whn+ddCM9oXNaW03ncyuOvWvNAGnnryWqolEZpxHlMCCFE4+jUaz5s/m4s8ACN5eXPnJras8m99z8sNqb8j6FxY90Ewg+7H5lhtxbrDUSIR6i6Y9ZlLf7gHotBbYtLoBeRnj/cBN6HW/Sgpi2ipmczfD7EFszaXvu8jy8VI0AhTNMYI+TFBtgKl/9xg10pLuihRJmwcBNW1mrQkIcCPNcR7tmRiHUtLLKPBfw88RpCzyjfB4hBgIjDyBeRAUEqFDAIEwoP+YBwIHyG8+DxN/fwh6Uj/mTEJVygzDsQvOhFJ5yAnxEG+Yp3Z4E0Iz+Rr7wiXIg/y29hfBBfjCKE6UY8kLYWP8V4fKYkfkhr4hu3oZiH/+YVwhD8QBlhfZTPo4k/yHemkObxa968wrPkD9YmMJ0nzJ/4l/tQqQwaecoN1/gAKcpA+D7WqPBsDPmYHZzicgV53crjrzzxGmLlJq0ezltPGuXcyptGefOYEEKIxtBtZYHkf5uZOHFih/m0Pw2OTZ8wECwRLoCGiC8Rs2iVYX6DhojpFiE0tDayQmONgJkGowdM3TFoKFljwZQFoJFlSkTc84gQxteHmWoFCACsy0gTIhDePzP8u6n3gAaVcCPUZb0vBiER4YI53iGV4gKIRxZeI2TTa4pAyGJV5lCbIkNDj394B9Pl0kYrqolXIG5ZF8OUqTxYfgjzgF3DfXql06ZpED6ENvwP5ewCbiKU+QXRBbssgrb3NZrDLxnatPIXhjMtv5Kn+Y7C4Zt/qyT8CMgTXv2Tz+tp8YNiggCJu+RfBNCsuA7zelq+Myr5NSYtr7AeCKU5DRYrs0bCyFMGK5Ub8ve1L5zt76XBWqV4sTjxlrZTVDVu5SnPRp54tbgMCeMV8taTldzKm0bV5LFG0MxyKroWyluiUZC3Ro9O7/iuhU6rfAjR1VDDI0THR+VUNArlLdEo6q18aKtdIYQQQgghRFOQ8iGEEEIIIYRoClI+hBBCCCGEEE1ByocQQgghOgVs+MCGAuxmKYTomEj5EEIIIUTVsANZ+IHO0LDLIbvbtQfv/4vv1dT2nRjR+bFt/y2fsuOeaC5SPoQQQghRNWy7/o3tfum3zQa2Pv7Pna72R74PdfPMi1u+69Ms+DYPH6kUIg22gWdkjO9LkVd7de/vtyJnm3HRPKR8CCGEEKIm+D4KH6kN4dsrfMMHEPSE6AgwwvH3Ob/33y6y7wONGXS8P9qHUkVzkPIhhBBCiLpiwh3Tn/hgbTgly2DKS3yNkRKuM6WL/6zd4Kv0TOFCkeEe6zr4qCTP2XkecBP3eI7n8z4nOgezlz7rj8N6bu+PxtAe27mn549PzkQzqPtHBoUQQgiRTWf7EBwKAF/0D784Ty/z76Z91/9negugRPAlezuH8BpTX15/d6afrsVX8wf32NotXbHQvfHuLLfHgEPdLTN/7np338hP89q6z54FpWa6/+o9X6o/cZsLEhdbv4YfvsdGYEYN+Ixb/u/3Cu+9zC1a/mbhufOb9nX7RqOPDJbH8ilTBcM0t/wS5l9RSr0/MqgvnAvRSVDDI0THpzOWUxPqjtj8O25Yzx284oFw/8a7L7vdNz7UjdrkSG8vTSlIu8bIxA4bjmmZEmPwHtaSHLvlD5Mrrc8fPPRkt1WfPUqumZuMcPxt1v+VKCiMpKDkfHzwl9z2/fZPrq7eqA0oj+XTMK+BlI/K1Fv50LQrIYQQQrSZh1//q1ccGPF4/8Nlbu+BR7coHtXSc+0Nk3+lxOtLhvYoTqFZ+sECf0wDgXPx8je938ygeICmXgnRfKR8CCGEEKLNsMicXmUMowzshtVo2N0qDygp5rfQxKMrQojGI+VDCCGEEKslebdIZfQjzW57fYtENJ8RvXf2RzZACJmz9Hl/1JSr5iHlQwghhBBNI1QCVnz4XvKvNl5Z+pw/btF7N39Mw6ZmxdupSvHoWgzpsZ0/slGBYZscsMZINA8pH0IIIYSoCYQ3FoHD0hVv+2MWPdbq548oAfQ+T37tJr/jFLCtLgvVbQ3GMwsmpo5UMILBh+IA+5Nfv8kvau+1dn9/Dd58t7jo2txiaha7Z7GdKl+2ZoExu1/94617/QJ50TUgj7AOacobt/u8Qf4yhZRd0ETzkPIhhBBCiKpBiL/i2f/wu1rBna9c7hdzZ8FuVKYE3D37KrdF793918htZGLZB2+7a174gf/PAnHcjmFb3emLnvTv4cvU2/cbXbKonR2N2LoXcAs/wphBX/RKyvv/WuZ3NoKDh37dH0XXgXVIu218iBs3/ac+f5Efjtvq7E6z3fLqgrbaFaKToG0Whej4qJzWDooFfHr4af4oSlHeEo1CW+0KIYQQQgghVkukfAghhBCiQ2MLg1lfkrYWRAix+iDlQwghhBAdFhanMz9/+YfL/PoS/sfbpQohVh+kfAghhBCiw8L3F+KPA+qbDEKsvnTaBecMy46f+0e/60X3NdZ3ew88ym3fb//kbnnoUbnhpXOTsyJHb3FmSWXHNm33zfmt74Vh942xQ05apTJkG8CH5o1r2XmDfaTZzi1rVwX8/OzCB9xLi6Z4d9kSLvxCLFsRsq0gu4CwO8iYwceXbC9osLuH7eYBhP/Ebc4veS/v+t3z3/M9SUb8PsI45Y2/VfQ/72NbRPxFXOy/6RdW2b4wj1tsu/jYG7clZ6UcPPRkv1NKjIWD3StCv7N/+80zL07OSuHd9lVb0vr+V69r2a2Fe7tsdPAq8crWjo+/eWfZMF734o9a3Ik5ceTPUtOqnnTExYbE7z/ffsT1XHvD3F87zpvvjHLlhntspcjuOpCWdth58q273VMFO5QH7IwacGRmfntp8RSfF/iCcxp58kqMlVmEqpDQLcox+Xy7vvvWVG7yuAX4ha0oiQuzVy7tSOOpb/3dvbL0Wf+MhaGaeDWy3II88VptndseaFGwaBTKW6JR1HvB+RpnF0j+t5lZs2a5F+5enJy1HzR61730v27DdQe5T29+mhvRe1d31ytXum7duhUUhC0SW9ncN+dqt/2Go92QHtt6s0Xh+RG9dk3uFoUjtvDbc8Dh7vDNv+3WW7Onu2XmJQW7I12PtYv7mOMHBAKUhJ03OtCt8ZG1vAC04t/vu8167ujthODmtS+e7f698kPf2I/e9PP+3QYNL0LyIcNOcQcM+Yp77d3pbuLcawoK1X5uzY+sndgqNr5PFRrv0P87FhrgDdcdnNgogt8GrLeZ26zXTi32EETMLdz580vnFNzZ14dxeK9d3JQ3/+amL37cbVGIC7OH4DNzyVR32Ob/6f383r+WuL/P/b3r030TH/+Q16375l7t/Rr6acB6m7v577/qDhr6NW8n5p7Zv3Fvvveytxum7eTX/uI23WAr/+Epcwvzxruz3P6DvujWX6uXj3N2T0EY4p3d3Ee80PLykqfcNn32avEXSt+i5W8UBLCDC25s5+a883xBoLrPbdP3Y26dgnAGuDXt7Qd92oXvW2eNDVy/dQb5dGo0Wx/Uu0OUPwNBcsWH7xfy7bU+LvKUvbx5xahUbm6cfp7bYK0+vh5AKdl4vaHuphkX+HzVu/vG3g55iPdu03eUv/7qOyhMk30e2bhQRgzKNHPOZyz+hw/bngOOSO60kievxBCG22f90v8P3TRhm61ECdPC5fMKCtZjbsmKBSUfVMtTbvK6ZeX5MyO+6+Nyyz67+/SjXA8u1G8xfCvhocIzW/bZzW93+vHBX0ru5I9Xo5xbeeK12jq3veho5VR0HpS3RKMgbw0bNiw5azudUvl4YN71bvbSZ73AQY8Xjd06a67nG9FyQgDQyPXqvqH7aP8DvbCEiRvKG6b/pCC4bFRoHIv7mSNkL3z/NffUgvGF5w7w1+a+88+CAFtodNff3CskNH4ItR/++1++MQ5BkLn+xf91owcd5/bb9DgvFIVCFg34X2b8zO2y0UFuZN99/DXco4fyvQ+XljSsCA+7bfwprzCZ/03IMnCPXmJGTswOJnwn7ny48oOWMBKHfQruPFEQYIhT7OMOQtMBg7/shQtAQCGczxeEDFOM8rjFSMV2Bftb99mzxE+LVrzh1l2zR6rwgEC14t/v+d5uBCrsA/5a+yPreGE0dGutwrWZS/7hPrbJUd7e828/5EesENq4T7qQjqRH/3WHtihPCwpCHOlKOnINv//z7YfdlgWhzZTNmUuedPsNPK7FH2ZeWvR4QYDeucWtRtLRGh7ihrz3yOs3l6RPOfLkFaNSuUGof/j1vxTuHev6rLOJv4afXls2w621RnfvFnnFuX/75zkn/9KbTt5auPy1lvIMuM07eGb20udSlY88eSUEgfmh18Z5t5d9sKjEzRmLn3SHbfYt7y8MCvFLBSWMcFs9lrfc5HEL/jbrUt9xsXnPnfw510kPvggc11so7otXvOkVFeyTVkY18Qrl3II88VpNndueSEAUjUJ5SzSKeisfnXLNBwoEvV/hUPuA9Ub4I0J3OZjW9OC8ce6OWZd7d2IQaBj2D3sLYXCPrf11GnOgoQ3fD+9/uMyN6L1zclYE4eOWmT/3vbJZ08JQpGBYz+KHmIyhPbZrmU4CNPic/23W//leRPNLDIIcPfw0+AgD+CGGsNNrH2JTHOYsfd4fzV9rr7GOPxrEDVMm6F2FPG5xzldoY55d8ECLIBRCOmAQumKY3pQ2tYOe3q16t15nKkmcRjv1/7g/rvj3u/4IsVvLP3zPT/vo231gcsX5tFslvQvxSjwPLQjeIh958grkKTc91urjj0z7CkEw33CdojJIXomfJx9Sf/DxqWrJk1dCmBJGZ0EcZgg/nAbkL0YugBElyFtu8rgFdKpQR4Z1wpIV893G65U2OnR8MApEBw9xGFNNvFZyC/LEK3GRp84VQgjRvnQ65QOBG8E3biytgQ4FmBiEdxpkevERGvlaKwJ62BC/svQZfzThxei3zqb+SA9hDL2TfnpPQfCNG2SED/wLv3v+DP/VVo48Y/A1V4gFGL4MC6Zk8HVYvuBKbyVKCOtW6EmOYf49X5mlwf/7nN/7KWoI8iHmpxiEB4sDBIA0Bq5fVPQsLvK4lQbpgfBgwmcIawKYy10NLxTSdNt++yZn5WFqRwz+QVB68q273KeG/X+rCDoxryx9zs85r2RPtJI3r+QpN8Q7ygllgc4E0g+lnHUHaXkqJk2BzUuevIJfqZfSlIdyIHSXe6ZcuYmJ3WKUiJHEv868yNcJdE5AqLzgPutT6PxgXR1xjyFu0zoyYsJ4rdatPPEK5epcIYQQ7Uun3e1q7TXWTf7lh143esP5euo3tvulF+QReGgUY+Le/ixoKFn4jDsIv/FoBD29CAAoMyxgZWEyygPPxApBJSGWBe8ICcdu+UPvDgIbc6BNgDAI4yeHnexO3v5SP6+ankhGS8LGHj/5PdUjAYBeYxahQo+1itMdYoVr6Yq3/bF7kgZ53EqDHthwpMJAoWKEohqhnngnXrN6Vg3CQrqn2fvdtO96QQnhjFGUSoLWtIUPpo7aiGzy5pW85Ya8Th6nM4H023T9rSoqFbyb97HxQK1UyisI0dMWPlS1YEznCQuty5FVbmLS3KIOYXONRcvf9OvaiEvbnMGwEU9GST428Ci/KJw4RslLqyuNtHit1q08ZbBSnSuEEKJ90Va7GSDYIsgjiCK4ICzUAsIPigw9sAj5TBUxt2jY6b3deN3NWnopEXrpfYRYaagG3EGJYr0KC02zQPg5eOjX/ZQxeuoNFnbiN3qYaeAxCHzYo5cSmE6EAMjOONbAEyaEbrCpbnncSoMdtLbovXtyVoQeTRTLardZZOrNdhVGPfAXAtnO/ccmV0pBMEIwM6WOnXyyII0RovP0PotW8uSVasvN0hULffkjrzKaWalcsXMWoyOVFNVyVMor4+f8oSDUfzE5ywdlrFf3/hXzVFq5iSnnFp0HLGDH70UloHQUYs7Sf/oj77A4oh7Bfrm6Mi1eq3UrTxksV+cKIYRof1Zb5YMhdRuiN9MITBBlSlOtoMjQINKDiNBkvX0f/Ls4bcmmTxkmWC9e3vYGc68Bn/aCWzkQQFBSln6wILlSFABovBH8+KATvZAL3p/r77GAGggXUx+YpsYUL6a90NtI7yaCnk3nyONWjPVeh1NCEID+8da9Pi6rhXenTaUKQThCESs3okLaoNQRPrYQzYI0bsu0na5KnrxSTblBcO6xdl+fZ47d4n/8dEOmGmYpIOQ7RvTqkXZZeYWeeRZPVzNyR95nVC4ehYhJKzcx5dwi3lmcTscLfict4lGI5R8W10PF7xjcYxt/TKsrs+K1FrfylMGsOlcIIUT7s9oqHyP77u0bxtAADRPTM+K1HdYzb41aXkxAWOsjxSlE9LhBPNXIzm29QxrW8K79kfX80dZwZK1D6b5G0Z4tmIynD9hzJnSlwfSwclObDBa82jQpg8abqVn0NjJNi21qUVJCAQKhAUEAO0x/IfwoO/F0jjxuhTClgl7wEJQDlJtQ4bTvsTAVI0sBZbSEXvNyPdkIo2nfPMiCtUHrrJkdryz4RZAW1VMpr+QtNwi8CM5b9CpuDkHa4h4KCOkTg32E3aw8WStxXiGvMi0ozMfka+A/ykkIygLfFiFeKpFWbkIquYWyNrLvx5KzYlrYtDWeBVtPF9dHhtWVRrl4rdatkEplEOydVucKIYRof1Zb5YNGhYYxNAb34nnjphzEu1RVgoYThcN65jjS4xYLPpxzvZwiYP5hK0hAGMJtBI/Qr/bfenqtxz5UeLBDGFnQXA62qGTL2XLg1vsfvtMipKWBUML7PjnslOTKqhBXTHFgqlq5qSF53KJHMx6pIO2YchEaBCMgHjhPg7n1WSMsgOJB2PMqHrDiw/e8QpMG8cCC37g3V1RPWl7JW25shGR5cjQYMSF9QkxArjSlqRbivBLnYQzKld0L6yjChLKQV5FNKzdGHrcYJYg3krDNNCwebde9WUtKN4sgnNSBYb6vFK/VuBVTrgwalieszhVCCNH+dMrvfGyy3nA3df59Ld/AoFeNaQN7bfLplgXANEq/fu7bxb3hkz3g2RGHHlE+yMc+8zz35Jt3uwOHfLnk+wF8qIxFz/YhPYTXf7x1jzt882+17DlP7+XUt+5za32ke8u3AW57+Rdu34HHuk03aFVQeNeLix5zs5Y+7T+i9q9/r/Dz3RmJ2HvgZ70dFtLyftZW0Iiv0W0NbwfF4rDNvtnit8ufObUgcL3venbv75/BX8x332PA4f4+cO2OVy73++Sbvx549Xq3z8DPFRr70pEB7r2y5Fl326xfeKGEPfjTRg+Ipylv3u6/+M43F0JF0MjrFjBS8U5BYNmp/yeSK0UIE/EbGr5B8NzCSV5g2ypl213SeeKr17rRmx5XkobAPb4JwwfqFiyfVwjHSy3m0ddvLeSdHdyyfy12d866onBcVMgTvVvilUW9Bw7+yipuAh8/s+8pNJOOuMc76T71rXv9Bxzjby2klUHIk1fylBvKHt+y4FsPfGeEtPNl+q27C+/7WEv6cO3h1/7qv/0R5gG+I/HOBwtbFAOD68Vvy7R+VBTwd568EudhzPRFTxSef9N/QJTnAPfunf1b71boL8oH4Yq/fZNVbiCvW3znZur8ewt11BbeXzxHvbPBWn1b3OX6u/9a4uPR6kDi8P5CPcJHULkPeeI1j1t54zVvndve6FsMolEob4lGUe/vfHRbWSD532YmTpzYYT7tT48b0xvoHaUHjakIYY8fgg9bzLJolakYwHxneloReHiGYf2sqTg02hNe/ZOfYkQjymLXcNQjdItpT/TQsUNT2shI6FfsstiTtSbxe2lcEQRwk6kj7A4TCmUoRCzCBPy0c/+DVpnqwLvY2Qp/8y7us/tMLNyxpgb/0MPMFLfYHUBIYNoTcUX40tyBPG6FEG95diUC8wPT7tKUHtLh1WUvpM5vv+7FH3khMg1Gb5j3Tj6585Vfef8D8YqiU26KFmtfPjM8W7lqFIdfMrTDlD8gv5KvQ+jZtzKQVgarySt5yg0C6EPzxvlpQxDXBZRjpkBlwcLl0L14ah/+ZNoh1JJXDAs3U80Af1/7wtm+rKdxxObfWWU0IavcVOMWYUARYATF6q489VFcB1Ybr+Xcyhuv1dS57UlHK6ei86C8JRoFeWv06OK3oepBp1U+hOhqqOERouOjcioahfKWaBT1Vj601a4QQgghhBCiKUj5EEIIIYQQQjQFKR9CCCGEEEKIpiDlQwghhBBCCNEUpHwIIYQQQgghmoKUDyGEEEIIIURTkPIhhBBCCCGEaApSPoQQQgghhBBNoe4fGRRCCCGEEEJ0Hjr0F85/3f3h5EwI0Uy+unwvlT8hOjgqp6JRKG+JRkHe0hfOhRBCCCGEEKsdUj6EEEIIIYQQTUHKhxBCCCGEEKIpSPkQQgghhBBCNAUpH0IIIYQQQoimIOVDCCGEEEII0RSkfAghhBBCCCGagpQPIYQQQgghRFOQ8iGEEEIIIYRoClI+hBBCCCGEEE1ByocQQgghhBCiKXRbWSD532YmTpzoft394eSsubwzb6F76op73Cv3Pe3PN9ljC7fdCfu7/jsO8+flWL74Xff4Rbf6Z9fusa7b6eSxbsQRuyd3izz1q3vcc7+fmJyV8rH/PcYN/cQOyVkruDfz9ifca4++5P2z/yUn+uu875/XP+he+sujbsXS9/y9nU49yPUZsYm/nwbP3Hb0RW7kF/dzI7+wb3K1lQnf/p1/jxG+Lw3sQ2xn3sMvuKd/fZ9b+M9XfVxkvY+wESfvvLrQv2u37x7uNhjYN7nbyltPzXKz7n3KrT+gT6o7hOvpX9/r4wI22LSv2/U7h7qBe23lz9PIileY9qcH3PRbHvP+KucW+WXGbY97e6RfTC1pVM5fzeCry/dqt/JXC3nKXSUq5YVpf7zfp1+5vIw/5kx41k275gF32I2nJ1dLmX7zY/5+uXxFXn/i57f7sgNbfGYPt81x+6xSLvKE+64vX9riTsxh404vcZNyaPl02y+Ndlsfs7fr3mu95G5tdSN+TKtvqqkHK5UxoL55/OLbvB0gznb46gEl/s8br3nrrvZmdSunYvVBeUs0CvLW6NGjk7O2s8bZBZL/bWbWrFnuyTXnJmfNg4byvlOudL2Hb+yG7L+96zG4X4tQMmi/kW7dvj0Sm6vCs3efdJl/dv+LT3SDR2/rHv7RjQW1rJvrv8PQxJZzj53/V9/obfqxrd2A3UZ402/kILd45htu1A+PTmwVMf/Mm/yC2+bYfdxHC0LrVp8dldx1hcZ7glt/495uq6NHuZ5D+7vZf3/Gvfrg8/48i0fPvck3qrw39BfQ6C6dPd9tdtBHW/yGX7PCjSD1wg2TXY9B/fwzBu5MPvsGt9cPjnJ7fv9I13vEAPfIT25y77/9jtt01NaJreLzTxaEgb1/fKy3N//Z2e6Ji//mhh+2m1tznbUSW0Wh4YN3l3u7af4G4mm9/j193BNPfbca6MZ/87c+bnsM3rBoKaFSvCIYEY/7/ex4t8t/fsr7e8oFt/g47r35xomtolC05JW33PPXTPL+DePAqCaNKvmrWezy4eB2KX+1kLfcZZE3L4z5xZd9Xhjy8e3dE//vbz5PDNh1eGKr6M6CaXPc3EnP+/y6/Vc+ntxphbpk6Zz5bmRB4EVwf+OJGe7FcY+4zQ7+qBdy4e3prxUU+qvd0AN28HVEt49088+9+tA/3bCxO7WUizzhxq2X73jSC89WnjEI5OTjEYVyZjz0P9e7Bc/P9eHc7sQx7pnf/N299siLLXna4qnaujGrvslbD+YpY1bfWD2y5VF7uTkTn3Mv3DjZjTi8qIzljde8dVdHYHUqp2L1QnlLNAry1rBhlTvz89Ippl29PmW62+e8z7vdTj/c93Rx3P2MI/w9BOVy0OtOr5v1ttGzTU/k1Mvu8g0o0LDh/o5fP9C7bwbhlEYxhMaeHkMa5LFXneJ7AsMeOtwcuOeWvqeTnkfcoTG3nr80CMPaPYtCThooEruedliJ37J66GnMMWnQA4lfrEeUnl16UulVRTAD/P/YBTeX2CO+gbgM4X65EQz8gYAz7IAdW3o6eQYBb9GMN/y5kSde6ZHd4aufaLlOevXdelM35We3+OcN7uMvlK80qkmjSv4S6eQpd1nkiXPy7JAx27dc54hAa73nBu8mL/TZcmByJR3yEnmBdzHqAe+9tcQf4bVHXnKH3nBaSx3BCAx2CSP1k5En3AuenePDFZZnDAzad6Q/AkI4xvI87u1x5mf8KBDXoZa6Mau+qaYexD/lyhi8fOc/fBpaPYL/tzxyz5I0yhuveeouIYQQHYNOoXzQGMXCtk1jWLHkPX/M4pV7n/bCLg2f0W+7wf44e/yz/uiFkxRhfvqtU3xvXMjks//sG1Qa+dBNg0bZGkiD6RKEIQ1TFhDQ00DIQNhgGpWfBlVGcENom/aH+73gE8M7aNDXWn+d5EoRhHB465nZ/vj6Y8UG364bxCENfTWs17+XPzItK2Tp3AW+pzakUryav9Zar7s/GgigxO+SWW8mVypTTRpV8pdIJ0+5yyJPnDM1avb4Z0qUzmWvv+2V0WqJ0/2DZcu9+z2HbZRccV4wjv2CIA0fvPO+P0KecFN3xW4RDso6Iw3G3Aem+SP1k0E9xWgMIxtQbd1Yrr6pph7MA3G4YNpc/z7j3YJCF6ZRnnjNW3cJIYToGHT6BedhT2EMPWIIlbFAYg0s0yuyQMjn2bDhN0UA95izfe2oM924sT/2c8/ToNGccuEtvuFk5CKNLGUhhB4//ELP/10nXtrS6xnDGoaRx++XKrD9qyBQpWFCuMXFnPuf88dQ8ALrOa6mlxF/7HTKQV5pYfoIcUp80MtZbbyuCAS8kP7bD/HHWgWQcmlUbXqLIm0pd3njfJdvHeJ70FHKSUPr5Sdv1Qr5k/c8f+0kPwKQVo7SGLB7UWFoS7jp5aech+8kLmK3AMWMOKpEWt2Yp74JSasH88LaFPj7qVf5URXiB6Up71opi9e8dZcQQoiOQadVPmjIaJizRhRC4h6zPNBLGbttPZG9hw/wDeiRd57lpyMwpSKe4oD/7jz+/7zgTe8fC15jGMmgl6+ckIMf6AFmoewBl3/NT5dAkA97E4HGnXCm9VzCuv17+mPcUNtIis1tN/IKXpWgZ5NpIAhStx51odtop81qitf1B/T2x1jJoCcV1t6g+jSulEbVpLdYlVrKXd44R/CkPKA0koaUB8pJWyB/8h6UGtYAhaMqaZAXmfrDSFpILeFGKE8bXailHGbVjXnqm5i0ejAvvOeg353qR0AmnvZ7v6h81Nmfq/j+OF6rrbuEEEK0L51W+Xjxpkf8/OdGwY5KLGINobeRho7GmAYUQy8i19gpJwTh6LjJ5/pdYFAYWEeBgmCYshBP/ykHdsf8/Mv+faEgRiPM/GoE/SxoyPE3YbBneY7damDjnTf3x0aw7I1FfgQEIQTFKRbc88Qr01F4nt2NEK4AgdOmn9jUlmqolEbVpLeoD9XEOYonowU2JZARrEoKQznICyg0uMcoIyOJWfAehGHr3W8LlEOU31pGF9JIqxtrqW8grR6sBjakYF0IyoSNVJnSkEZavLZn3SWEEKJ6OqXyQS8604uyevnbio0qxO4z/YApDyEIR1yjBzYNGk0UBkBBABpYFpGXUxayoCGOF0ezzeaO36g83YRpRTyLkM1UFqaXmDs2xaHeIBCyYJWw2uJh3h8qIHnilXOmwnDt3pOvdLd+9kK/WJX1IyglbckLaWkEtaR3V4IpSkyLCo0phrWSN86pAxglYZoVIyQ2vY8tbtsCwjnukadwLwsE3zy9+HlgPVO8oLtW0urGWuubrHowLzz/6Ll/8fUO6TT6oi/5NBz/rd9mKolZ8doedZcQQoja6HTKBz14PYf1z9UgIkjQYxoP15uAtPEurVtyhjDlYuTnV22omcpA71savCcLFAZ6U22HGRpY3AmFNgRqYNoH5+WgB9PeR1hsSlPoHtg7LLw06ExNoYf3qLt/4HuUsRNOcRi837b+GAuRFofV9JwifCDADd5/O3/O++1bASxiNfLGK2mOYIj/mYbGeg8EENudqC3EaQS1pndXgfhH6A8NU2RqLXeQN87Z4YytcI1wel+WYFsN7JwV5oUQFOfhh+66ioBca7gpC7ZIPMR6+2O4Rl6Nyaoba61vsurBvLC+BH9aPDGy8/FLT/Jllu2PY7LiFfLUXUIIIToGnUr5oHFFuKmmJ44eRaY0hAKJrRsYMqYoFMcgMKf1prGzEoRTcwC3K/VcYof1DsB7md4RGtsek949zsvx9ovzWnaEYWF47JY9jyDH/3jxOOAfpkDQwxsu0m1ZPBusrcAucYjfqsEWin6wrHSxOIvX6eE2aolXFJv7z/ijFz7qNV0lTCNoS3p3BRC2EfpDY4JgLeUO8sY5+YddqUJ6bVbM53F+qwXcSBPwEZBRptMEZKg23ORjwpJWp9mC8bAjwP5vfsgu/miUqxtrrW+y6sG8EK6wnIP5L067SvEaQtym1V1CCCE6Bp1G+aBxYkoMU22Y7mGGNQTWIHOkF4/rBj1kYN+owA7rBuilTesxoxFnikfaPXrlEOj52JVNScBf9OTZe3iehtF6YDFMPcJNejIBtxHcQmOCE18K5xx4B1MMeIe5xaJRhBKzQ2MdumMmvBc26LhJ/Nh3FBhJCMFvxA1xhF3eaXFnYQyx+dtxby+g9CAgPHjWdS32iH+2SLWPjEGeeDV4nvhkkTHfT8gSPiy+YkEQ8qQRVOMvUUqecke8k79JCyNvnJNOjH7gLpC/WOuAwpBWdtmGF8y+wXO8nzJheZT30asevs/yCOuXGEmw+gfD85bH8oQ7hNGFsCyEEEbCw0Jt3Md//OdamE/xb7m6MW99E1KuHjTwEyatjAGjqCgw+A+wg78YHbIthbmWJ16hUt0lhBCiY9BtZYHkf5uZOHFiu3zaH4GbBaBpIKgc9NtT/X8aWnrDEUrDaQw0WlMvvcsLFAjDTCVIm+YANIRpOzIZNIYIFjYfHHust7BGmndN+u9rvLAECAr0Uma5Z+B3pkIgpNCDDLyLbx7gb8ANRjzShIUYlDDeHTbQXKPh53old7wAUBCa6LmMw2hgh2kbIfSihu4iMLEmBUEfsuK/Urxa/PA8/t/muH0yBSOzGxLGazVpVMlfzeSry/dql/JXK5XKHXHL18D7bTPIT8cz8sQ5dlgQjh3yKPmaXnwWKse95/G0orBc4E5YxqhPGH2Jp/+w7W/8AUOD0bdQCa6mvmHtEuuNsvJTGBdpYcxbN8ak1TchlerBSmXMQPFgowAra3Fa5o3Xauqu9mZ1K6di9UF5SzQK8tbo0aOTs7bTKZQPIYQaHiFWB1RORaNQ3hKNot7KR6fdalcIIYQQQgjRsZDyIYQQQgghhGgKUj6EEEIIIYQQTUHKhxBCCCGEEKIpSPkQQgghhBBCNAUpH0IIIYQQQoimIOVDCCGEEEII0RSkfAghhBBCCCGaQt0/MljPj5AIIfKj8idEx0cf4xWNQh8ZFI2i3h8ZrLvyIYQQQohsJCCKRiDlQzSKDq98KOML0T6o4RGi46NyKhqF8pZoFPVWPrTmQwghhBBCCNEUpHwIIYQQQgghmoKUDyGEEEIIIURTkPIhhBBCCCGEaApSPoQQQgghhBBNoVPvdvXWU7PcEz+/3S3856v+fIvP7OG2OW4ft8HAvv68EtP+9ICbetldyZlza/dY1x16w2mue6/1/Pnyxe+6p399r3vpL4/68w027et2/c6hbuBeW/lzmPfwC27iab9PzkrBP7udfnhyVnTvtqMvciuWvpdccW6nUw5yI7+wb3JWCuG79+Qr3QGXf83133FYcrWVV+572s28/Qm33Qn7p96Hp351j/c/79z2S6Pd1sfs3RI+4+3pr7lHz/2Lj0fCuNdZR6W6Z/4JCf1G+P55/YMt79tkjy3cTqce5PqM2MTfTwM3X7zpEffaoy/5Z46bfG5ypxXcnXHb4272+Ge8H+M4Ix4I5zuvLvTv3O27h6+SB6bf/Jibds0D3k5aOsI78xa6p664x7sHpN8OXz1glfhqLzr6Tid58lpMnjIGlLPHL77Npx/kSRsr32l5qlJeBp6f9sf7fXioG0Z+cb9VyiplZ9of7q+YZ/KUMYuLV+59umz5yRMX1daN5cpYXrdCe1nxRR557vfpW7Z/7H+PcUM/sYP/H5ZXc2v4obvWnN7NRDsSiUahvCUaRb13u+q0ygeN+d9Pvco3hGutv45744kZXoClYR971SkVhR4EzSk/u8VtvMvw5IpzvYdvXCL03PXlS12/kYNaGnYTWEZf9KUWew/9z/Vu7Z7ruvUH9PHnBkLLxy89qURwoOEF/GtkNagIA3efdJlvfNOUDwSQRTPe8I1tlnKC35bOXeD2PudY/84J3/6df9f+l5yY2CjG453H/5/b/Ywj3IgjdvdCFM+lucnzYXytvcE6/hmD8K2/cW/Xa7ON3FvPzPZxQNwcduPpiY1Splx4ixe0ECw22XOLVCXF0pl02OroUYXj4JL4QkiZevndbr8Lvuj9a26GSiRhWjTjdTdwzy3du28taVFUDht3eosAZYLfRjtt5tbr39PNuvcpLxCTv0IFsj3pyA1PnryWRp4yRl6ffPYNLWlMWj1+0a3+fQf99lRvJ8byNaQJo3nyMvUJ4SGPUF+M/9ZvvXC849cP9Ha4dteJl7qdTh7rn+Wdk/77Gi8sE27Lf3nLWBgXMPnsP7sF0+aW1CN54sLKTN66sVwZy+sW9iiLm35sa3/+wg2Tvb1QoYBbP3uhPw/rwA+Wve/L2lF3/8Cfm+Ix4vBieky/5TFfXnkO99KwOAYpH6KzorwlGkW9lY9OO+3qtUde8gImggC9azT2NE40Uq9PmZ7Yyub5ayf5XkWeNRMqHjRm9OANO2DHlgaWxp7eSIR+QPgYtO9IL5yG7iBII3SHwjR28Zv510wsCBgIwv22GZScrQp+7b/9kORsVRBwMDt89RNeeOI9e5z5GS8QcN1AWOq79aYtghdxiHn4nHH+3OCZwfttW+L3UFgjfAj3XCOeuI/AQpjTQPhDsDrod6d6u2mKB4IoAgXCHelLmMP44p2PXXCzf48JcaYoEH8hxDt2CBs96/BeQRExFkyb44U+7mMPd4iXLP+LVvLmtZg8ZQxevvMfXjC2NMbulkfu2dITH4NAzmgE6ZdGpbwMCMNDxmzfopxyRBgO30kdghBuz5KHyVvYYRTByFPG8BPPmRKGGXX25/wIyNRLW0dn88RFNXVjpTKW160ls97yZYbnMfgdlr2+yB8BxWmf8z6/Sh1Ih8XQA1oVlGVvLPIdFnYfJYd4Jo4o8zGV0lsIIURz6bTKB41S2EgCjTB88M77/pgFDRjCBUIBPeU0wDHr9e/lj/SAh9DDyAgJIJDQEMfM/vszq1xHUKHxROimZ48GMwvsIcj02XJgcqV65j4wzR8RBAzcpFeWqVqA8IcQgZAVsvHOm/vrYbzQE8wIAz22+C+GuDCByEBwSosfpkegeCDImHAXQ/zcf8Yf/fSPWDA0Xn+sKPyg9IQgvNo0Hoj98MGy5V6Y6Tlso+RKMZ7i/IT/EVJFefLktTTylDEgrcgv5FeDEawsYRPFc+Tx+62SnkalvAy8kylIYTld9vrbJe9khC1+h8UBIwSQt4xZHIbu8Z+8jBJn5ImLvHVjnjKW161Vy1jxHh0xBnGT1skw/dYpLSMmgHISwvttFOSDd5f7Y0il9BZCCNFcuuSC8wG7j0j+pUOPN3PSEY4QUpnmgUASQkNGo8x9hBQUFhQVGsZQyEoDgYbpVCFMy6KBRnCgt54pVaEAYfCeN6e+nCkM5AU/pAln9JqaMEOvJoSCHjBtCpg6BfiJxp9ncZf4QInKUqAIF3GFcLXraYclV4vgFlPFEKqYLnLtqDO9wX7oHgIFwj8wVQM7HOk9Nebc/5w/hkoEmNIWK5W8G8UHRZAe2CxhhXcQPtKrrenQFciT19LIW8ZYOwJM/yFtSFeUGpTXGO57xT1FyIW8eXmXbx3iRxO4R36mwwBCwdjyZ0yoMOQtY1lu2dQwy8vVxEUaYd2Yp4yVI62eJR6J1wfPus5PkcpKB4P0wA+V6lRA8Yrdq5TeQgghmk+XUj5oyFEqsnrTDXroESKYI828f4QFFkGagGHQ68c8bRrTW4+60K8HSOvJD0EYQKmJ/YBbNMbMa8bNFUve8yMvsdDDOhSb891W8vYErrVe9+RfOoQF/yPgHHnnWT6OEa5QHmIIP9M4EChRtOZMeDa5U8RGKxAkdvzGgX5+NvGB/dA9epWxg9DGFAzSiXhlcX+stOUNJ2mI4oNQyehUmvKEcsI7CB/pbkKfKE/eNIjJU8Zwm+l55AfShkXNTOuJ34kg+/Kd/yirMObNy9QRrMlAgSY/k+fitT82ChHnI0ZuyKshFctYwS2I8xujLbDm+sXn88ZFTFrdWE0ZCylXz7JOBYWOMoZSRJqUY/b4ZyvWqcBIkk2XNPKktxBCiObTZZQPBAAaKOsZzAsNKIIIPbcscoxh/jG9szTSNKqxghLDFJIRh+2WnKVDYznq7KO9YBPOm2b0hZ2rahXkmgF+Q3FD+EBgjIULhDYUChQt1r0wyhP2pL7x5Ex/HPLx1vn0xAcKoLmH4ENvKGterEcUu/RGQ6U0yAJ/IVCassnOXDEIpgilpDkKItNSKglQom3kKWNMt+kxqJ/PdzYiEacLins80laOSnmZ6UysJyK/oBzHo3MjP7+vz6eMIHAd490plGueqQYToFEmzB+UGxQjFIKwZz9PXISk1Y21lrFK9Sx1KUoM8YbfGQEpB4vJqQvKgUJG3ohHR6pNbyGEEM2hyygfLPDM0wOYBYtlERpCEDZYDIlAyqJHeugQpssJv/QmVpr2BTSkKDy2INN6PON1Ex0VEz7CRdshxNWYn3/Z/6d30rBpHvE0CZtegnv/Wlac1x2vebG4idOpGnADAQlhBoEyDfIQac6WvfjXRmu6MowI2RQ5M/UYFcpTxhCU2aYWQRNlgZ2wyAPsPmXKAP5jp6Zayn9aXkaJYB0G7yO/2PSwcIQEhYHrlPmbDj7H32NXNWAjimqgPBAu8hsjQOx89e4bi304w8XYeeIiJq1urLWM5alnUWIYJUIBQTnKGkWx6+WmTBEmRlriUae2pLcQQojG0iWUDwSVSnvAV4JpEeFUCRpGhI3B+2/nz3Hbto1kgWQa9FTS42k9+pXATbb4hGevnuB740PhjilCwJoUejerAX/S8xjDNeuVtcWgNu/csPNyu2lZXNt0kDSIB97FCIhhawOyBFfcszUctmg3xtLJFoPHbtlz5RQ55v2H/kqDOIS1kjTqypAXELRDs27/nv5enryWRt4yxk5GuGN5DsWd7WcRkNmlDCgrTBcKy4/5if8Iq1mk5WV61Tc7+KPJWen0sFDI5zpTKW20D2GbPG55p5oyRriY/oRbTAld8c77Pq/zTQ0jT1yEZNWNectYSLX1LLuYQdaUM6Y+MnqUBfHMtE3iOKYt6S2EEKKxdHrlgwYR4SVvg5jF4pff9N+bMKxn0HZtMegptN77GHr48/Z40rBiTPBi21+mBIWGnkNA6OF+NZg/QsHc/m9+yC7+SI8jIwCxAMI518sJ7wiOCEHlei2BMDKP37CdqeY98qI/GsQz78Q90hK3ESZCQc/+W9hshCkU7LDDPHyLuyx4XznBGOx9LE7u6pAXEAJDY0p2nryWRt4yxv+4zFm+Y+cyiMsOxhRd/g8ZUyxnaaTlZd5nbhu2SDz2r8EoDnmPb4MYtZYxlBwEbKZnhp0ZeeLCKFc35i1jRi31LNPDCGNWZwyKZ9YoMf5A8chazxGnNSZvegshhGgsa5xdIPnfZmbNmuWeXHNucta+0Dj945d3uH9/8KFXHN56+pUW8+zvxvsewTXXWcv3gN37jV+5AbsOd+sP6O2fHTf2x8WGcWAf38NHw8q88+2/PMbfhzXXXdvNmfise/Whf7oBu43w9hCoWCew2cE7u/47DE1sFsE/T/y/v7ld/vNT/r0huP/gD65zPQb3K5gN/fzsf/zfHe6j/3FwS8O8bt8e3n+hWTzzTb8mBEGPD3/FzH92tm+gew7tv4p/em++cfH+/dPc4NHbufcXLnWPnvcX13v4AN9rbfTdaqB76lf3ejd4Br/ygTA+YmbxxTz8GbdOcb1HDPD+LMbDQ26PM49sCSujPnx1+SNrruHW7dfDfbj8A58+fLBv5BdalTrcfP/td/zz9k7ce/IXd7i9f3xsyzt51+z7nnHzJr/g/Y97zK3v3rO4QxKQJmt0L6TxH+93m+y1pfvIGmt4O4tnvuH2/ekXvN+Ia/z/3vylbt0Ne7SkN9uo7hn4n3zy4riH/bml0QPf+6Pb+ZuHuI3KKGHNZJcPB3eY8heSN68xevfEJX9zww/bzcdz3jL24fsfuGeu+rvPVwiYlDXSilEGK29WZkIzZ+JzfkRgz+8f6d2GPHkZlrzyli8H+AG3yA/kM/LzVp8dldgqLnpGgCevsEZozC++vIqwnaeMAeF68x8zfTmafssUP21xwK6lwnmeuMhbN+YpY3ndIl5RqNbbqFdLvD52/s1+VznOY6gv3n1zcUlcGsTpo+feVCivPUvexzPkl01HbV2SzmbS0rs96KjlVKz+KG+JRkHeGjasfrJOp/3COXOiaXDTYCEm86GBhj78AjawsJspTkADzrQGmyYRQiP41BX3+F5IoBePaQJpvXHYYYvceG4y0LPKzlY0jDSKzOHmnVk9ggYCMb2f9OTFPaQIcjbNwGC6RgiCA8IEPYy8l9EA5rfHvZemOOA/4oNFp+H7CBvTUOhxJQ6YshRPvwjDCPSq0uudFq9A2BDmcDPtnYCbfGCNcJbzf+gW72MXLYtb4oAdeCyueBffXIj9H4aRd+F/vmeQ1TPdHnTkr9vmyWsIqAuen1vyZey8ZYxyzIYQlr/idE7DykhYLvLkZSA8KEGEx/JEHCZzv1Jeh0plzMp6Vv4MqRQXeetGqFTG8roV1qnEK3FSro5jlChtZzPyA1+NJ87TCL98H5OW3u2BvkItGoXylmgU9f7CeadVPoToaqjhEaLjo3IqGoXylmgU9VY+utR3PoQQQgghhBDth5QPIYQQQgghRFOQ8iGEEEIIIYRoClI+hBBCCCGEEE1ByocQQgghhBCiKUj5EEIIIYQQQjQFKR9CCCGEEEKIpiDlQwghhBBCCNEUpHwIIYQQQgghmoKUDyGEEELUheWL33XTb37M3fXlS921o8705tbPXuie+tU97p15C/1RiPbE8ij5UrQPUj6EEEII0Wbenv6au/uky9y0ax5w2xy3jzvyzrPccZPPdWOvOsX1Hj7A3XXipW7hP19NbAvRfFA8Fkyb4954cqZ759WFyVXRbKR8CCGEEKJNINT9/dSr/H+UjaGf2MF177WeP+fI+ccvPcmfC9FekBcH7rWV67PlwOSKaA+kfAghhBCiTTz963vdiqXvuV2/c2iL0hHTZ8Qmru/WmyZnzr311Cw35cJb3IRv/86PmjANBoMiA/MefsGf29StV+572l83sMfzNr3rof+5vuVZm+I1buyP/fm0Pz3g/2Nid4QQzUXKhxBCCCFqBoH/pb886tbusa7vVS7Hjl8/MPlX5LVHX/LPv/bIS27k5/f1bnyw7H2vIDz96/vcmJ9/2U/f2mSPLbxygcICPIPSgkLD1K4DLv+ad2vy2X/29z94d7mfVoNChBKy/oDebr8LvujW7rmum/KzW7wdIUT70G1lgeR/m5k4cWLyTwghhBBp/Lr7w8m/zgEKwb0nX+kVhP0vOTG52gqjDlMvuys5K7LTKQe5kV/Y1ysQS+cucIfdeHpypwgjFAf97lS3wcC+/hxl46aDz/HTtz72v8f4BcOMlux2+uH+PqBkPPf7ie7gP/x/Ximx96K82GiMXUNh6Wx8dflenS5vNYrOnA8aAXlr9OjRyVnbqbvyoYwvRPughkeIjk9nLKemfDCl6qDfnppcLQXlgcXojEYwStF/x2H+OsoHhEqLuZfGBpv29YoKzzHSkQbKCUpKmoBp10I/dBbUBuRHykd11Fv50LQrIYQQQtTMmut390d2skLJSIORhx6D+vn/eYV+BMPYhCMkjJ6k2UHxEEJ0XKR8CCGEEKJmmOLEiATMuO1xf6wHLDiPCa/NHv9M8q8VlB9bFyKE6JhI+RBCCCFEm9jnvM/747Q/3u/XYrQFRkZYeM6C83AkhR2sFs14w/8fvN+2fqQl3rnqn9c/6HoO2yg5E0J0RKR8CCGEEKJNMPox+qIv+f9874M59SgLBiMWLCwPQbHgGms3YoVlp5PHeuXitqMv8gvJMeO/9Vs3/NBd/f3B+2/nR1vYAYv1H7yP41rrr9OyuPztF+f5YzgSwk5a/vjucn8UXZNlr7/tjxolax+04FyIToIWGwrR8ens5RSFYs6EZ92c+58rWRDOYvR+Iwe5YQfs2LLmg29zhMSLwNnRiq+ls0idnbR2OvUgr+QY/lseV9zjRz8YKRn5xf38DloQL0hnfcgbT8xY5ZrZ7wyoDchHnO+ydmkTrdR7wXmnUz6ohGbe/oTb7oT9q9rJwnY+MKjIDr3htJKPJWGHIWX2DY8ruhB6aNjzHHvbfmm02/qYvVM/ulTJr2HFa++j1yd0i4qeYWZ7X1oFbVijgJvxtoYGvVOPX3ybfyds8Zk93A5fPaDknWGFD2l2DHoVZt37lFt/QJ/MSh53iDNrYHb77uEt2yuG5IlXes8ePfcvvseMXrG9zjpqlbjFzrQ/3J/L/0B4n792km+04p1aQohfeumy8kWj6egNT95yEUKc8vEyngPSlI+Yxd8SCMtKlh3ezzacadjuOJCn3BmxgBM3YuT/J35+u8+P5eoMI29ew90Xb3rE2yM+WWRr5ImLvPFqYJ+5/MyxJyyh0HbXly/119I4bNzpLWU5b7nLW88SB/GOSGF85U3vZiMBUTQK5S3RKKR8lAHBmfmg1W6jR4PPR4c23mV4csW53sM3LmmIacho6Pc+51jfmPIMQ8A0YOFHkxgCZhgZewz/IpzQuMZadSW/mgAx4vDdi+e3POaFCd5Hw2ngr/U37u16bbaRe+uZ2b7R5iNKsXKB8LBg2hz38p3/8I1/KKwY+Gny2Tf4DzHhH555/KJbfXhs+0QTWjbaaTO3Xv+eXrFAgEGQCPdbB4QDhrYnnvb7zB4mwjn18rtb3snXal+59+lVFL888Ypwc+fx/+d2P+MIN+KI3X04eS6MX9LtrhMv9UP62OGZSf99jRdycCsWhMwNwhf22KWBPey3V29aR2548paLGARbektNSDWBk+kdVj6J80UzXncD99zSvfvWEl8mKCuh4At8IZnyw/sNpmCQf4+6+wf+PG+5A8oL5anPlgOTKwXlY88tWhR/8hbubfqxrf35CzdM9nVIluCbN69ZGUEoD98HeeMiT7wahINpNNjf6uhRhePglnJi5cfiy7DpLhZnectd3noWyENhnb32But4t4086d0eSEAUjUJ5SzSKeisfnWrNB41m/+2HJGf5oaeR0QIERjNxA0yDNWTM9i0NOEca3LDHj4Yfs8NXP+Hv06DuceZnfGPK9ZBKfl32xiKvQJh/xl51iu+dxB0aZOCIkEGDi6CCPQQXhI0Y/MI7Q0EpBkEKAcOEHp7Z8sg9S8KIAoPAQqOOPRQOhtPT3sn9OB5D8P9jF9zs/WzvNAUGBcfIG68IM/jFBBD8iHn4nHH+HEhr4tHsILjR40sY411a6IFFKUV5wV/mxzQQMlH6xKrkTb8YBFTSBUGcZ4A0YHTBFp0aCKbcI71JT3ivIHwbKAosiMWelSkMivvQA1oVgTzlzkCZ2PW0w0rcCxWBJbPe8vmGMoAZdfbn/PVlry/yx5C8eQ2Be8G0uf7ja/H7jEpxUU28opSg0KM0oCQQDnsGFjw7x8dRGAcYGLTvSH+EvOUuTz0LpAcLjsN3hopH3vQWQgjRfLr8gnMECho8BFd6FGls06DhZMoBPf8GC5YQdo25D0zzx1DgppGld4/pVdUQ9/LR4Fvvoi2Uo2GOhRSmKiB01AJhRLBBODHoPQ3DGAsfwDsRBKrl9cem+yMKVAhCEGli5IlX/IwChOASsvHOm/vrlq70GMf+N3eZD2xgn1EpG5EpB+/GIMyJVam1XKzXv5c/MroWwggKI5NGnN8/WLbc5+VwxxvenSaoT791SsvIBOQpd4Dwi/KEMuBHFyLFBFb1V3GhK6MVIXnzGgoK5RMlwITzmDxxkTdeqevuP+OPfiQvFOxDuB6XJ54jfgbsNiK5kr/c5alngThnxNRGG2PyprcQQojm0+WVD3oEmX+OIITAy9QDGraYXb51iO99Q9hA0KSnG0JhhUYwbiSB0QQElXpA45zWqOInlCcEbXpja4E5+MAUC3oOEYoQDrOmxmCH+EDgyRJOysGCRIi3RbTRmRaFIUe8vvZI8RgKT8B0NGBKGqAopYHCE6YRoyXE9bxHXnTjxv7YL1CztI9hHjujQSKdWssFwiqCL+USIRMBnzxOmQsVGYP7COj0stPrHQu7MdgnP6S5FZNW7hix43nWFTClKE0IBhPGHzzrOj8NKXYnT17Drygo5FOmQmIHQ3yEgrpRLi7yxiujj1ZemMLE+zhS7svx+pTpPm7Cd+Ytd3nqWfyLQkj+IV4JA/bT4iGkmvQWQgjROLq88kFPIw0baxqYF01jiDBhjZ6BPaZEINwzDYGG0aYIhVQSeNoCPYQ2jSIEIR0/IUzQM8qi8lrA70znQBBinQYLZZkqkhYmBBvsIDggAJiiUAt54ixvvK61XvFLu1kQNuIoFlTo9UUBBdKWdOZrvEM+vr2fH07a8xwjZOGzKKpMTWtkuncGao0fpsqwhoc8dutRF/q1Rlkje9xHQEd4nf330t7zNGaPfzbTrZC0csdzlH+maJE3mHKHEJymnE4++8/+Hv5CmUcINvLmNRslJP/u+I0D/Zot4oUyjzISUyku8sQroxW8D4WecFI/UkYo92nhNAhjPLqQp9xBnnqWUR/8T6fIkXee5TuPqIfS4iEkb3oLIYRoLPrORwCNGg0avbQsOo1hChI9ejYtKKvXsREg3NOAp/Xa0WAjjNCrihDEOopKvZNZMLUEQYgG3XogQ2HJoPGn4acHdcWS9/z0jDR7HY2Rn9/X937Sq0vaYRDAEHZIV2AeOzCVzHqpiWMW+GKPnl0gjlnMyj3ROFiHQT4j/yPExx0DBmUAwdU6ENgFrhwsJkfgL0e5cmeQ/mN+/mUvRKf5jToFwZ26AyGZERAjb15748mZ/oh/bcoVo42E1effqOzliYty8YrQTznpt82glrDzXkYmICsN8AdKRhxfecqdUU09i1JL5xH1VVo8hORJbyGEEI1HykcKLIylUQyhYWPuOg0dwoRNW6jU21YPaHiZNpQ20hJCrx5CELx85z/8sRoQONimlmlbhJOdb4gHdptJa/xp+FFC2BoXwcJ6ZzsyCGykHb26Nx18jk8/dgcCWyC74p3i3HybsmXYBgEsGCY+WHBM+EVxJMymAplpy2iYgeDJImHimYXN5HGU6yzhF8Gd8olATfnMwnru06YwGnnLHSCYIzDH9YbBfdzBDkq9vT9PXgPKF8T+td2ewgXlRrm4qBSv/1pWXN8Sb1BhinZWOKkD0hZ05yl3UGs9a1NG0+IB8qS3EEKI5iDlIwWm7oRTAYCdaDY7+KPJWem0BRPMacDT5rBzLe7dywtuM40qr5CLkMO7atl5ibULPGtTZOi9/PilJ3lBg12usrCpDGtt0LqlZR5skXospNoCVBN08sSrLeK1tR2GnYc7ixGXTG+x0SKEQUa7LBy2biR2y2BLT3bo4f2hsG3fHGC6C+ddCeIXQTE06/bv6e/VWi4QGBE8+ZIxkC9tm1oWDpeDNQHlygBTkeiNz6LacgeMgsX1RoxtSmDTA/PkNbA1M1kK3ZrrZ083jOMiT7zaOqxwMXhIVjh5Pmv9V6VyB3nq2TSszsqKh0rpLYQQonlI+Uhh8ctv+mkPIfQ8snNMiPVW2i421oMXCgj2f/NDdvHHajABqNrF3DzHHO5qIYzWw2pYT2Ec9hATClgAWg0Ddi/uhhMKXrjFtA16iI088Yo/6eGNhSXOuW6KTAw9wLyP7woYfMcA4Ypdd0JstyP8PWTMdn5aS2gQkgC/c96VIH4RFENj04NqLRfW+27ly6A3Ps6nMTxTTrFB+Lb8F1NruePbFqz/KQd5iPxocZMnr4HtCMei9BDCiXvlevTjuMgTrwjzPIOCaOUb7H84WmGg1PB8ntGFtHIHPF+pnk2Dd+PfrHeXS28hhBDNpdMpH8wXhrSeRAQeeqSZImKwu0y4VSbTDpgLjfAUQu8cvXImNGGfLwzT4JkggR3OWahNI40d/nMt7N0zyvmVZ1moyrQM/GsGv9Jwg+02Zb2CGO6hBKS9D9i2EkJB0GAkgkbapl7gHu9EOLJtMzlnfritKcGf+IGeTIuHEIvXtB5U7NNDzocRER54n33fI9w9Km+88jVzhCXiAwgH51wP4XnssHMPApBfZB/4HcGLqWT0zFpc88zTv77P+xe7GATu0JiQxNfcORdF8qYf+YjyiB2g9x3BmjUSlo/Itwjqtv0t13mOfGl2LN2zdiAj71JGsvJrpXJHXsWfvAe/YriPQB6mO+WEZ7AP+P3xi2/zu08ZefIa4C5KLes3LH/jHuXV8nfeuMgTr8C3jyj7uGnhpHxm1WeMLoTPx/CucuUOcLdSPUu84qcwXhm1te+oxJRLbyGEEM2nU33hnAaJhjaEIX6DRsrvW598ZRcQGmjQgSkA2xy3T2rDSsPLok0ae3rnaJQRBphrbEP+YA009rLsQDm/0uCydWdW7659hZjGlx1xbP41DTQ9yWn+h3gqEPaZVx2CsMJie3MTt9hdxxpuhAeEA4sD3KC3N03YRghiClIIIwKxXS/gFRQQ3IzfZ+SNVwQNBDz8T3qyQDZ8n8V7pbiC0C2ENaZtlOsNJ38x9QqhMVZem0FH/rptnvRDqFzw/Fy/BsGuUxaeuuKeFoE7TgfcRVmwskSa862X4YfuukreMBDy03Z3ylvu4nfiTloZCOsW/E2eo35JE4Lz5rWwrMT5u5q4qBSvBnXM1Evv8m6WK3eAUsGas7Tw5S13hKFSPRvWQfgbhaeW9G4v9BVq0SiUt0SjqPcXzjuV8iFEV0YNjxAdH5VT0SiUt0SjqLfyoTUfQgghhBBCiKYg5UMIIYQQQgjRFKR8CCGEEEIIIZqClA8hhBBCCCFEU5DyIYQQQgghhGgKUj6EEEIIIYQQTUHKhxBCCCGEEKIpSPkQQgghhBBCNAUpH0IIIYQQQoimIOVDCCGEEEII0RSkfAghhBBCCCGagpQPIYQQQgghRFOQ8iGEEEIIIYRoClI+hBBCCCGEEE2h28oCyf82M3HixOSfEEIIIdL4dfeHk39C1I+vLt9LeUs0BPLW6NGjk7O2U3flQxlfiPZBDY8QHR+VU9EolLdEo6i38qFpV0IIIYQQQoimIOVDCCGEEEII0RSkfAghhBBCCCGagpQPIYQQQgghRFOQ8iGEEEIIIYRoCp1ut6tX7nvazbz9CbfdCfu7/jsOS66WZ97DL7jHL77NvfPqQn++xWf2cDt89QDXvdd6/hye+tU97rnfp28l/LH/PcYN/cQO/v/b019z0/5wv/cHpLllTPj279xrj76UnDm3yR5buP0vOTE5K2X54nfdbUdf5EZ+cT838gv7JldbqeQWzz/963vdS3951J9vsGlft+t3DnUD99rKnxvExdO/vs8t/Oerbu0e66a+764vX+rvp3HYuNPdBgP7+v+88/GLbvVxgVs7nTzWjThid3/PMH+9cu/TbsXS97y/dzr1INdnxCaJjVbeemqWu/fkK5OzIgdc/rWWdH5n3kL31BX3tMQ9bmXlA+zOuO1xn+akXyWm/ekBN/Wyu9xxk89NrhQhvadeeldL3Jfzf6PpiDudkGaz7n3KrT+gT2q+rUS5fF9N/qpHvo/zV7mynSfc5Knptzzm82CWv6rJX/grb91ndnE3risgj1uWNpRbY6dTDsoMr5XfsMwaedzKk96844mf395SP5FG2xy3T0ud1BHQjkSiUShviUZR792u1ji7QPK/zcyaNcs9uebc5Kz5IEAsnbPATb/5MTf8U7sUGv7eyZ1seGby2Te4vX98rNvz+0e6LY/ay82Z+Jx74cbJbsThrQ3bY+f/1Tdkm35sazdgtxHe9Bs5yC2e+YYb9cOjvR2Ek3u/9is34rDd3H4/O94N2m+kV1jmTHjWDR69nVtznbW8PfB+nT3fbXbQR1vcw/11+/ZIbJTy6Lk3+QYVe/13GJpcLZLHrftOudKt17+n2//iE91HC8JL360GuvHf/K0PQ4/BG3o7Fhd7/eAoHxe9Rwxwj/zkJvf+2++4TUdt7e0gDL18x5NeOLN3YRDAem++sQ87ICjcfdJlrvfwjf07B4/e1j38oxsL6m63Ev+bv/b96Re8oEHcE2eb7LXlKnFBGpAm9s4h+2/n3QXeh1u8b8j+2xfC1K9FgCIdQrdIpyWvvOWev2aSTxPirRyE+cEzr/X/t//Kx/0RLL23KuQZ0pt7781f6h47769uyMe39wJSM9nlw8HtWv5iEAQ/eHe5e7IgDJJecb7NQ1a+rzZ/tTXf877nfj+hIKhv6bY+epRbo/uaXqH513vLW+wYecJNZ8arDz7v880u//kp/64pF9zieg7t78sRVJO/fB2Qo+6zcjJv8gtum2P38XGy1WdHJXeL5HWL+Oi3zaCSOnH4obuW1HMG7514+u+9cpHmZiW38qQ35XTCt692Qw/YwbvT7SPdfB3w6kP/dMPG7pTqr/ago5VT0XlQ3hKNgrw1bFj5Tq1q6FTTrug17L/9kOQsHy/f+Q8viFhPHEL0lkfuWdKzT2O8z3mfdzt+/UAvIJtZf+PevqEznr92ku/BtN44eifpzcQtetlDXrhhstv1tMNK3MvqLUcIWLtntiBbyS0aZfww7IAdW3ppCS89notmvOHPgdEflBaLC+Jz2y+N9kIWAhUseHaOG3vVKSXvwsCgfUf6I9DbTI+u9QzjH3oqGT1AqAIEA/xldjCjzv6cF1Do7Q3B7uD9ti15Z9jr+fqU6T6Ndjv9cH+P4+5nHOHvEX8h9IISth6D+iVXskHoYSSr79abJldaef2x6d6voT94N9fee2tJcqXrQj6Ke/KroVy+z5O/6prvp83x72KEE3vkL/KEjZaGVAo3/kPB3uGrn2jpkaduwb0pP7vF5zmoJn/xvkp1H+4yukB9RxkmLGkjAnncIgyEPa4TLZ5jSC+UizTyuJUnvV975CV36A2ntbjDaA5h5DnqByGEEB2DLr/mA2VhwbS5XlAx3i007KGwSWOcphhMv3WK72EzmDoUN74mhLzxxAx/BARppjswVYoeUGs808BfGASoNPK4tV7/Xv7INJCQpXMX+J5E4B000mutv44/NwbuuaU/vvXMbH9EEIrDiFCDP+itNIgLhLzQbr/tBvvj7PHP+uPcB6b5Y2iH/zxn00wMwjb18rvdQ/9zvX9XDEJGnEYmtK1Y0jqVo1oQekYev98qYYb1Ni7Ga+gf4p8e6XX790yuiFqomO9z5K965nvKcZwHUAJQiKsFpQLWWq+7PxpDxmzv3Vwy601/Xu/8NfnsP3vFA8UpLT9XAx0t+It6ByXRFKY0sEfZ7LPlwORKKXncypPeacoPHUnwwTvv+6MQQoj2p8srH1sfs7c//v3Uq/wIBz2dTNXJWnthIAQgKJhyAZynkSZM09OKfXpA7zrx0hIBI4Red3r7ylHJLRpk5k/Tk4vwjt+nXHiL7yE0//9r2XJ/jLHe4FB5iqFXET9Yw08c4p94tMCUA3MrK7423mW4P1qvM/5luhWCE2EjDAgq5QSekHBEphrID15oSlE8gbgjbfEP8/d9vP7sFvfxS0/qUHPMV0fK5fu8+atR+Z58Qf5D4Q1HJfKyIkMQttGGUOGpV/6i3FAHEWes2bp21Jlu3Ngfe3drgbUshJ+Om8cuuNlPiUKRi8HPb059uWw8VXIrb3qXY8DurR0jQggh2pcur3wgoBz0u1P9CMjE037vFysy9SfuQYuht40GM8RGUWKhmJ7WcH42z9H7eNiNp/vFl0wtQcCIG296++m5K+eXvG7RK8g0JISQW4+60G2002Yl/ree1LghR3iAcusXUNbCESAj7k2OIb7AlAxj2etv++Oa6xd7hhG0bBrFkXee5afEIEix+LQcuIvAEqdTHgg3U/IqCZfkFRQvpn+g+LXXYvPORJ58D5XyF9Q73yOsU0+Q/3Azzrt5sPUOpmQYjLjC2hu0hqte+ctGGXsPH9BSjpgyirvxtMQ8EK9s1HDU3T/w8cvo4qT/vmaVug9lqVLnSV638qR3DHFMfaHOACGE6Dhoq90CLA5l/j+NFHPE6dU04SMLdqlh0WfIyM8X52MzVYeGE4OAwrQOejDToId1zM+/7IWcUAigd5XG1npg85DllrHsjUW+JxihHwUltEPjjFCGUGXXiQNbq7Lxzpv7Ywx2ULjCEaC8mGCPwmfxTbjxA2FIE7IQSOm5Jq183JZJpxdvesTtceZnkrPqQGhiHU0eEJaIV2AErRaBVBSpJd9Xop75HkEZwR33SPf7z/hj2TyYBtMT8cu0P97fklfoLECJB5tOZNQjf1mZIqyUIQxKAdemXVPb6IdBOR519tGrrK1AiWS3rEpKZEiWW7VA/YtSaaPbQgghOgZdXvmg0X/03L94QROhdvRFX/IN3/hv/XaVnjfDRhVi4ZiGEyGB+ck3HXyO75lfNON1f6/c1B8EIHo3eS/wXhaRI+hUS+yWwXQTFsjjpi02ZYpDKIgRBzzLdaZkMBfb3MmatsD89XDRfTUQf8Q3Chu90kwHefeNxf6dldw0gSJrYTeKCWs1auklpnd7q6NHVRSaSCcUVd5DvPrpMAWhku1E45EnUZm25PssGpHvyRe4t9t3D/d519Zw5IXn2RyBaYTklVs/e6FfLM0IKfnH8mw98xf+5H0h+INrcV1RC3Q+MMq47PVF/twUpFqUyNitWkGBzDOKLYQQorl0eeWDueXhQkYaPhp5GmR2uElj9t+f8aMcaSAkMH2A70EwlYCRlDxTf+jtpRcSaDTpqWRethn7vgXTJDgvR+gWIKgw733w/tv5c8Jq3yZh0bzBdaZw4XfCQM8o/ig3bYHn46lJCBy8P57KYgKJrekA4pspY7zzoN+e6ufD8yx785fD0sumZoXQe95zWP+aFA8gjplaE8Y/8QD8t3nybKEM9h6OTGlBQCSPiOrIm+/z5q9G5nuwMr1WME0qL5ZXeCf5n/Ue1DnsjmfUM39RB1kejgnrirZAPNqUsWevnuDXoIVpSRoCaYpSVY7QrWrqEwPlkq16rZ4QQgjRcejyygc9gpgQa+w/yFiMikCTZwEjva5MSdr7nGOTK9m8/eK8lp1ZhozZzq/fCI1tG0sPLeflCN0CW1T7wbLSha7sPhOH3bBeVwQdRoTSQLjjeYuvEEYu4vUvNsed8KXBaAUCCtMuygl9wLtRGuN3o3gwjz/NT3mJ4x6D8Gb3zP8oSnH8IewwhS8rXkU21eT7PPmrUfnesHfHIwrVQl5m+hbKTjh9sZ75i520gPIRQhhqHbkMwR2MKXqsTYnTkjQE0pT7WcRuQTX1CYoHz0rxEEKIjkmnUz5s0Wa8mBPoKaMHLtzhha0yUSZsGgaNG/fpaQu3jjVovBE2soRj5n8jRDOVgsbSL2YP7CJoMLWD91kjy9xopmXZFAXs8z80vTbbyN9jZxizl8ct6DlsIy9MPXjWdS3z04mL2eOfKfmQIuAm4bfvAdDTmgW9r/Hzhi0yZf0L8D7muPu590F84GfiFIGPdRYIKaEABszT5z5+A+/WH+73UypCiAcWiTOFhTCY4XnrJTUsvmKBBsJ4N2OCDP/N/5vsuYXvrUbJNDdIe9zM2iK2q2H5Le61BuKM/Gu94HnzPeTJX/XM95aPTHjHPfzNSEqYn41y4TbwC3nnzuP/z3+zIlZ2qs1f5eo+RgFQoPmYopUjygvuW1yGlHOL56jfwrhgiinrq6ycoPzHaUkaAmlqnQN53II86U0cEVes8WEULawDSKu4nAshhGgfuq0skPxvMxMnTmzXT/vTwMRTC5jWYNBg0cNIQx9OFaIBZNElDTEwnWLHbxyYKlTQuMU75hj2fnrkNz9kl1Q7NIDst2/+xA6jFKFglQZ+Z7oCja3Nia/GLRr1p664xwsvgFDG1LEwHlDMULrwfx4/ITSwwD0tngAhh48F4r+09yEUMNKBUETPbNY0CfyMYkJvL+4gOMZ2UbqY5pEG7jOly7C4DAnjNQ1L2zA/AW6xYJ7pdcC7dvnWIRXjrhF8dfle7Vr+Yix9Q1AuLW7Iv2yrysfnEOLTSMv3RqX8BfXK92EerGS3UrgtTPgFd5himFWG8uavSnUfEN8I73S2QFY9V8kt4p3dqKgviQtGJcqFwbB4CeOiGrcqpTdrxiyeYhhVqjSS1Sw6WjkVnQflLdEoyFujR49OztpOp1I+hOjKqOERouOjcioahfKWaBT1Vj601a4QQgghhBCiKUj5EEIIIYQQQjQFKR9CCCGEEEKIpiDlQwghhBBCCNEUpHwIIYQQQgghmoKUDyGEEEIIIURTkPIhhBBCCCGEaApSPoQQQgghhBBNQcqHEEIIIYQQoilI+RBCCCGEEEI0BSkfQgghhBBCiKYg5UMIIYQQQgjRFKR8CCGEEEIIIZqClA8hhBBCCCFEU+i2skDyv81MnDgx+SeEEEIIIYToDIwePTr513bqqnwIIYQQQgghRBaadiWEEEIIIYRoClI+hBBCCCGEEE1ByocQQgghhBCiKUj5EEIIIYQQQjQFKR9CCCGEEEKIpiDlQwghhBBCCNEUpHwIIYQQQgghmoKUDyGEEEIIIURTkPIhhBBCCCGEaApSPoQQQgghhBBNQcqHEEIIIYQQoilI+RBCCCGEEEI0BSkfQgghhBBCiKYg5UMIIYQQQgjRFKR8CCGEEEIIIZqClA8hhBBCCCFEE3Du/wekeT+JUa44vwAAAABJRU5ErkJggg==\" width=\"584\" height=\"375\"\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere are 8 chemical laboratory test results data contained in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, but these data do not have location information because the data entering the PCA-Clustering process is already shaped like Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. After receiving confirmation from the data taker in the field, it turns out that the data in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e contains code data and its location on each row. For the code and location data contained in Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the code and location data contained in Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e are the results of the measurement of the research team in the field. For the code A1 is located at the location of the Port of Pasuruan City, A2 is located on the beach stage of Pasuruan City, A3 is located at the JWMP pier of Pasuruan Regency, A4 is located at the mangrove Pateguran of JWMP Pasuruan Regency, B1 is located at the Bentar Beach of Probolinggo Regency which is 2 KM from the Beach, B2 is located at the Panntai Bentar of Probolinggo Regency which is 1 KM from the Beach, B3 is located on the edge of Probolinggo Beach, and the last location is B4 which is located on Mayangan Beach, Probolinggo Regency. What is interesting and unique about this research is not the origin of the data, but after going through the PCA-Clustering process, it turns out that data that does not have regional information can cluster based on the same region.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCode and Region/Location data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegion/Location\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePort of Pasuruan City\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePasuruan City Stage Beach\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJWMP Pier Pasuruan Regency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMangrove Strikes JWMP Pasuruan Regency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBentar Beach, Probolinggo Regency (2 KM)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBentar Beach, Probolinggo Regency (1 KM)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBentar Probolinggo Beach Beach\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMayangan Beach, Probolinggo Regency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e is a code and location data that can be used as a reference in validating the clustering results of PCA-Clustering, so that it can prove quantitatively if the PCA-Clustering technique can cluster an area based on heavy metal pollution parameter data from the laboratory. Integrating the data in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e with the data in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e is a scientific thing because the data in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e has the same number of rows as the data in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. To see the results of combining Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, there is Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, which describes the code for each region, and there are heavy metal pollution parameters. While Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e is a combination of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e data, which contains the code and location.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBrief data results: Integration of laboratory data with field data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSUHU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComplete data Results Integration of laboratory data with field data\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSUHU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePort of Pasuruan City\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePasuruan City Stage Beach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJWMP Pier Pasuruan Regency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMangrove Strikes JWMP Pasuruan Regency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBentar Beach, Probolinggo Regency (2 KM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBentar Beach, Probolinggo Regency (1 KM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBentar Probolinggo Beach Beach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMayangan Beach, Probolinggo Regency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, it can be seen that the cluster results show that there are 3 colors, which means 3 areas that are contaminated with heavy metals. The three colors in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which produces 3 different color clusters, namely yellow, purple, and green. For the yellow cluster, it turns out that after being clustered based on the area in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, it is located in the Pasuruan City area (Pasuruan City Port and Pasuruan City Stage Beach) with a cluster value of 2, while the purple cluster is clustered in the Pasuruan Regency area (Pasuruan Regency JWMP Pier, Pasuruan Regency JWMP Pateguran Mangrove) with a cluster value of 0, and the last cluster is green which clusters the Probolinggo Regency area (Bentar Beach Probolinggo Regency-2KM, Bentar Beach Probolnggo Regency-1KM, Mayangan Beach Probolinggo Regency) with a cluster value of 1.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"528\" height=\"472\"\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e is a combined result of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e of field data, with the original data in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and the PCA-Clustering results data in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, which are the coordinate points of the results of clustering using PCA-Clustering. In his research, \u003cem\u003eLiu et al\u003c/em\u003e carried out a powerful analytical approach to assess heavy metal pollution in the soil. This model combines the power of PCA with the spatial analysis and geographic visualization capabilities of the Geographic Information System (GIS). This integrated approach provides a comprehensive picture of pollution, helps identify pollution points, determine possible sources of heavy metals, and measure overall levels of contamination [14]. However, in this study, only one method or technique was used without combining it with the Geographic Information System (GIS), namely PCA-Clustering. With this technique, it is possible to cluster areas or locations that experience heavy metal pollution by using metal pollution data from laboratories that contain existing parameters. So by using the PCA-Clustering technique, it is possible to find out the region based on the parameters of the region without having to know the name of the region first, besides that the technique PCA-Clustering easier and lighter because it does not use all parameters to group data into a cluster that has the same structure but by reducing the parameters that is not dominant but does not eliminate information from the data.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eKnowing the cluster of an area does not require using mapping techniques, but it can use data clustering techniques. In this research, it is to determine the area or region using heavy metal pollution parameter data from laboratory measurements. By using 5 parameters, namely solution (pH), temperature (\u003csup\u003e0\u003c/sup\u003eC), lead (Pb), Cu (copper), cadmium (Cd), iron (As), and chromium (Cr), all of these parameters are entered into the PCA-Clustering engineering process. The process of PCA-Clustering applies parameter reduction, and then the results of the parameter reduction will enter the data clustering process by utilizing the results of data grouping from PCA. As a result, there are 3 clusters resulting from PCA-Clustering, including the first cluster is yellow with a cluster value of 2, the second cluster is purple with a cluster value of 0, and the third cluster is green with a cluster value of 1. For the data labeling process in each cluster, the results of the cluster can be used to validate the results of the field data with the data from the PCA-Clustering. Of the 3 clusters, there is labeling in each cluster between the others: cluster 2 is in the Pasuruan City area, cluster 0 is in the Pasuruan Regency area, and cluster 1 is included in the Probolinggo Regency area. Therefore, the PCA-Clustering technique is suitable for identifying clusters and labeling of data that has large parameters, because with this technique, it can reduce large parameters to smaller ones without removing information from the data. But the PCA-Clustering technique is not suitable for data that has a small number of parameters because the parameters will be lost at the data reduction stage using PCA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eResearch Grant Program called MORA The Air Funds in 2024, with contract numbers 64/Dt.I.III/PP.05/12/2024 and 4390/LP2M/TL.00/12/2024.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAgung Teguh Wibowo Almais: Conceptualization, Methodology, Software, Formal Analysis, Writing \u0026ndash; Original Draft, Supervision, Funding Acquisition.Anton Prasetyo: Data Curation, Investigation, Resources, Validation.Tri Kustono Adi: Visualization, Writing \u0026ndash; Review \u0026amp; Editing.Rif\u0026apos;atul Mahmudah: Investigation, Data Collection, Validation.Tri Harningsin: Resources, Project Administration.Adila Mujtahidah: Writing \u0026amp; Review.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe author would like to thank the research partners whose contributions indirectly facilitated the implementation of this research, especially the Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia. Their provision of facilities for brainstorming and analysis formation is invaluable in finding techniques to evaluate the damage of the sector after natural disasters using image-based inputs. In addition, the Ministry of Religion of the Republic of Indonesia has facilitated this research in the Research Grant Program called MORA The Air Funds in 2024, with contract numbers 64/Dt.I.III/PP.05/12/2024 and 4390/LP2M/TL.00/12/2024.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eW. H. Organization, \u003cem\u003eGuidelines for Drinking-water Quality FOURTH EDITION\u003c/em\u003e, 4th ed. Malta, 2011.\u003c/li\u003e\n\u003cli\u003eP. B. Tchounwou, C. G. Yedjou, A. K. Patlolla, and D. J. Sutton, \u0026ldquo;Heavy Metal Toxicity and the Environment,\u0026rdquo; 2012, pp. 133\u0026ndash;164. doi: 10.1007/978-3-7643-8340-4_6.\u003c/li\u003e\n\u003cli\u003eX. Luo, S. Yu, Y. Zhu, and X. 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P. van der Aalst, \u0026ldquo;Business Process Management: A Comprehensive Survey,\u0026rdquo; \u003cem\u003eISRN Software Engineering\u003c/em\u003e, vol. 2013, pp. 1\u0026ndash;37, Feb. 2013, doi: 10.1155/2013/507984.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pollution, parameters, Heavy metals, seawater, PCA-Clustering","lastPublishedDoi":"10.21203/rs.3.rs-7394718/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7394718/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMany parameters affect the quality of seawater, one of which is the presence of heavy metal pollution in the seawater. In general, there are several parameters to measure the level of heavy metal pollution in seawater, including: solution (pH), temperature (0C), lead (Pb), Cu (Copper), Cadmium (Cd), iron (As), and Chromium (Cr). These parameters have values (data) which are the results of measurements from the laboratory. To find out where the region or region come from, it is necessary to cluster data to find out the area of heavy metal pollution. Using the PCA-Clustering technique can produce a data cluster that groups data uniquely and after going through the analysis process, the results of the data grouping show clusters of an area or areas of heavy metal pollution. Based on these results, to determine an area of heavy metal pollution, it can be used the PCA-Clustering technique because it has been proven that by using heavy metal pollution data from laboratory measurements that do not yet know the area, it is possible to cluster the area in accordance with field data.\u003c/p\u003e","manuscriptTitle":"PCA-Based Spatial Clustering of Heavy Metals Using Multivariate Environmental Data for Risk Mapping","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 12:17:41","doi":"10.21203/rs.3.rs-7394718/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4856f199-585f-45ff-be24-173e90d44e2f","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T18:24:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 12:17:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7394718","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7394718","identity":"rs-7394718","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}