Introduction

The interaction between groundwater and river water has been a very interesting subject in the Cikapundung riverbank area. More statistical methods have been applied to unravel such interactions.

In this paper, generalised mixed model using R package “mgcv” was applied to hydrochemical data to compute the significant controlling parameters in the process. For this analysis, we used 295 water quality data which have been sampled in: 1997, 1998, 2007, 2011, and 2012.

Analysis

The groundwater measurements consist of 25 variables:

1. physical variables: x coordinate (x), y coordinate (y), elevation (elv) in masl, aquifer (aq), electroconductivity (EC) in µSiemens/cm, pH, hardness (hard) in parts per million (ppm), total dissolved solids (TDS) (ppm), temperature (oC), redox potential (EH), discharge (Q) (m3/sec).

2. major cations: Ca2+, Mg2+, Fe2+, Mn+, K+, Na+

3. major anions: CO32-, HCO3-, CO2, Cl-, SO42-, NO2-, NO3

-,

SiO2

We use ‘mgcv ‘package from CRAN server (R statistical software) to make several regression models and choose the best one based on the AIC value. We choose one variable as predictant (eg: EC, or CO3) and use the other variables as predictor to ses which variable has the highest control. For the analysis, we use following code as examples:Gcr10 <- gam(ec ~ te(x, y, k=k1, bs=bsm) + s(elv, k=k1, bs=bsm) + (data$aq), data=group1) plot(Gcr10, pages=1)gam.check(Gcr10)summary(Gcr10)

Figure 1 Study area

Results

The results from 14 models consistently show a weak influence of lithological parameter in the system, with a strong spatial (elevation) land use parameter. We also identify leaching and enrichment control from the river. Therefore surficial processes, in this case landuse type, play more dominant role in the groundwater system than the subsurface processes. This condition is another sign of more anthropogenic control rather than natural (lithological) control system. This model selection technique is proven to be applicable to robustly compute hydrochemical dataset.

Generalised mixed model of water quality in Cikapundung Riverbank using R

Dasapta Erwin Irawan1*, Cut Novianti Rachmi2, Prana Ugi3, Dwi Suhandoko1, Ahmad Darul1, Nurjana Joko Trilaksono1

1Institut Teknologi Bandung, 2University of Sydney

All the works were done in 2014 as a collaboration between ITB and Univ. of Sydney.

For more info please send email to erwin@fitb.itb.ac.id or mention me @dasaptaerwin

Introduction

Following the other paper discussing the application of generalised mixed model to select strong controlling variabel in the interaction between groundwater and river water, in this paper, we introduce Principal Component Analysis (PCA) using R package “pcamethods”. The package was applied to simultaneously reduce the variable dimension in the hydrochemical dataset to identify the mixing process. In this paper we used 295 water quality data which have been sampled in: 1997, 1998, 2007, 2011, and 2012.

Analysis

The groundwater measurements consist of 25 variables:

1. physical variables: x coordinate (x), y coordinate (y), elevation (elv) in masl, aquifer (aq), electroconductivity (EC) in µSiemens/cm, pH, hardness (hard) in parts per million (ppm), total dissolved solids (TDS) (ppm), temperature (oC), redox potential (EH), discharge (Q) (m3/sec).

2. major cations: Ca2+, Mg2+, Fe2+, Mn+, K+, Na+

3. major anions: CO32-, HCO3-, CO2, Cl-, SO42-, NO2-,

NO3-, SiO2

We use ‘pcamethods‘package from Bioconductor server (R statistical software) to make principal components (PCs) derived from eigenvalues. For the analysis, we use following code as examples:pca1 <- pca(group1, method = "svdImpute", scale = "uv", center = T, nPcs = 3, evalPcs = 1:3)summary(pca1)plot(sDev(pca1))

Figure 1 Study area

Results

The results show a clear differentiation of groundwater samples from river water samples in terms of physical properties, cation and anion concentration, despite the possible mixing of both waters as a function of distance to river. The role of spatial variables (x, y, elevation) and lithological type are weak in PC1 and PC2. The NO2 and NO3 concentrations as a contaminant signature are mostly correlated with river water, while groundwater shows stronger signals of surficial process (CO2, CO3, HCO3), and geology (Fe, Mg, SiO2). The Cl and SO4 variables imprinted in the groundwater are indications of possible seepage from a deeper aquifer.

PCA computation to detect water interactions in Cikapundung Riverbank using R

Dasapta Erwin Irawan1*, Cut Novianti Rachmi2, Prana Ugi3, Dwi Suhandoko1, Ahmad Darul1, Nurjana Joko Trilaksono1

1Institut Teknologi Bandung, 2University of Sydney

All the works were done in 2014 as a collaboration between ITB and Univ. of Sydney.

For more info please send email to erwin@fitb.itb.ac.id or mention me @dasaptaerwin

IntroductionPasir Impun landfill which is located at 9.6 km east side of Bandung city has been reclaimed because its capacity has reached its limit. TPA is widely total area of 35,700 m2 while the accumulation of 17,500 m2 . Now, Pasir impun has been surrounded by houses, especially in the east. In the aspect of the topography, the landfill is bordered in the east by the steep cliffs in the east. TPA altitude ranges between 700-800 m with a slope between 15-25 percent (Sudjono & Memed, 2002). According Sumargana and Sulistijo (2011), aquifer Pasir impun consists of two types, namely the free aquifer dominated by volcanic breccia lithology and confined aquifer with tuffaceous sand. Hydraulic conductivity ranges between 1.41x 10-4- 4 - 4.63 x 10-4 cm / sec.

Possibility Method• Geo Index (Aydi, 2015)• Contaminaton Factor (CF)• Pollution Load Index (PLI)• Geotechnical & hydrogeology parameters

(Anomohanran, 2015)• Physico-chemical analisis (Saadi et al, 2014)• VLF Method (very low frecuency) (Sumargana

dan Sulistijo, 2011)

StatisticChart 1.1 Research on landfill based on Scopus

Chart 1.2 Research on landfill based on Science Direct

Chart 1.3 World research on landfill plume

Chart 1.4 World research on landfill contamination identification

Conclusion Method has been used , VLF (Very Low

Frequency). Next research possibility method :

1. Geo index, Contaminaton Factor, 2. Pollution Load Index and 3. Physico-chemical analyzes.

Landfill Plume Identification : a Review

Ramadhan, F.R1., Nafisah, L.A1., Yosandian, Hazmanu1., and Irawan, D.E 2.1 Master of Hydrogeological Engineering, Faculty of Earth Sciencies Technology, ITB

2 Applied Geology, Faculty of Earth Sciences Technology, ITB

Reference (Core Paper):Anomohanran, Ochuko., 2015., Assessment of Site Suitability for Landfill Construction in Gbekele, Nigeria., Journal Geological Society of India Vol.85, June 2015, pp. 745-752.

Aydi, Abdelwaheb., 2015. Assessment of Heavy Metal Contamination Risk in Soil of Landfill of Bizerte (Tunisia) with a Focus on Application of Pollution Indicators. Environ Earth Sci (2015) 74:3019–3027.DOI:10.1007/s12665-015-4332-8

Saadi, S et. al., 2014. Geophysic and Physico-Chemical Coupled Approach of The Groundwater Contamination, Application in a Pollution by The Landfill Leachate of Oujda City (Eastern Morocco) Larhyss Journal, ISSN 1112-3680, no19, Septembre 2014, pp. 7-17

Sumargana, Lena dan Budi Sulistijo, 2011. Penggunaan Metode Very Low Frequency (VLF) untuk Pemetaan Penyebaran Kontaminan di TPA Pasir Impun, Kota Bandung. IJ-GEOSTECH, digilib.bppt.go.id diakses pada 2 Desember 2015