Author: Irawan, DE.1, Prabowo, K.1, and Akter, F.2, Vervoort, W.2 Affiliation: 1 Faculty of Earth Sciences and Technology, Bandung Institute of Technology, Institut Teknologi Bandung, Jl. Ganesa No. 10, Bandung, 40132 2 Faculty of Agriculture and Environment, University of Sydney Biomedical Building, Australian Technology Park, NSW 2015 a)Irawan, DE: d.erwin.irawan@gmail.com Abstract: Quantitative-spatial analysis has been applied to 295 samples of shallow groundwater quality data from Bandung-Soreang Groundwater Basin (BSGwB) taken in 1997, 1998, 2007, 2010, and 2011. This paper discuss the use of variogram using geoR and generalised additive model (GAM) using mgcv R-package to identify the spatial distribution and mixing process betwee groundwater and Cikapundung river water. The variograms show significant water quality trend in north-south direction, and in the direction to the Cikapundung River. From the GAM tests using gaussian and gamma family, some significant elements can be identified: (1) geological control from Fe, Mn, Na concentration; (2) agricultural control from NO2, NO3 concentration; and (3) other surficial control from EC, CO3, CO2, SO4 concentration. Both analysis suggest the close interaction between groundwater and river water and the occurrence of mixing between both.

1. First draft
Spatial analysis of groundwater quality data
using geoR and mgcv R-package
Irawan, DE.1, Prabowo, K.1, and Akter, F.2, Vervoort, W.2
1 Faculty of Earth Sciences and Technology, Bandung Institute of Technology, Institut Teknologi Bandung,
Jl. Ganesa No. 10, Bandung, 40132
2 Faculty of Agriculture and Environment, University of Sydney
Biomedical Building, Australian Technology Park, NSW
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Institut Teknologi Bandung, 2-3 November 2014 1

2. Background
First draft
• The groundwater condition in Bandung-Soreang Groundwater
Basin (BSGwB) has been degraded over time. Indications:
• decline of water level and
• decrease of water quality.
• We need to check the occurring interactions between
groundwater (especially shallow groundwater) and surface
water.
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3. Two main objectives
• To identify:
First draft
• the intensity of mixing process in the groundwater and surface water.
• if the water quality can be used to support groundwater flow prediction.
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4. Regional setting
• Bandung city area,
Bandung-Soreang
Groundwater Basin.
• Groundwater-river water
interaction have be
mapped by Lubis (1997)
and Rochman et al. (2014)
J a v a s e a
B a n d u n g
W e s t J a v a
Lembang
fault
Isolated Effluent Influent
Cikapundung
0 4
E
S
N km
W
Maribaya Viaduct Dayeuhkolot
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First draft
B a n d u n g
C i k a p u n d u n g
w a t e r s h e d
C i t a r u m
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5. Methods1
First draft
• The dataset was composed of 295 points in total.
• The data was measured in five years (1997, 1998, 2007, 2011,
2012). For each year we have 59 data:
• 51 data from dug wells and springs.
• 8 data from Cikapundung river.
• All measurements were taken in July each year using We used
handheld instruments (Hanna Instrument).
• The dataset consists of 25 variables:
• physical variables: x coordinate (x), y coordinate (y), elevation (elv), aquifer
(aq), electroconductivity (ec), pH, hardness (hard), TDS, temperature, EH,
discharge (Q)
• cation variables: Ca, Mg, Fe, Mn, K, Na
• anion variables: CO3, HCO3, CO2, Cl, SO4, NO2, NO3, SiO2
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6. Methods2
First draft
• We introduce an statistical analysis to explore the dataset,
consists of: spatial analysis and multiple regression (generalised
additive model).
• Open source and cross-platform software:
• R3.1.1 (R Core Team, 2014) and Rstudio IDE0.98.1028 (RStudio Team, 2014).
• Add-on packages of R:
• graphical plotting using “lattice” (Sarkar, 2008),
• data manipulation with “dplyr” (Wickham and Francois, 2014),
• spatial analysis using “sp” and “geoR” (Roger S. Bivand, 2013),
• Generalised additive model using “mgcv” (Wood, 2011),
• principal component analysis using “pcaMethods” (Stacklies et al.,
2007).
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7. Methods3
First draft
• Using R, we can make the spatial and regression model with
straight forward codes. Here’s the example.
• For spatial analysis using variogram model for electrical
conductivity (EC), we used the following main code:
> ECVariogramModel <- variog(EC, trend="1st”)
> ECVariogramModel <- variog(EC,
max.dist=25000,
uvec=seq(0,25000,by=5000))
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8. Methods4
First draft
• For regression model for electrical conductivity (EC) in the function
of (x,y) coordinates, elevation, and aquifer type, we used the
following main code:
LinearModel <- gam(EC ~ te(x, y, k=k1, bs=bsm) +
s(Elevation, k=k1, bs=bsm) +
(AquiferType), data=BandungBasin))
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9. Results1: Mixing process
First draft
• All plots suggest the possible mixing process between
groundwater and river water.
• Some parameters demonstrate:
– decreasing pattern towards the river: EC, CO3, CO2, SO4, and NO2.
– increasing pattern: NO3, Fe, Mn, and Na.
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10. Results2: Mixing process
First draft
• The water quality supports effluent stream flow (groundwater
flows from aquifer to the river) from the previous water flow
mapping.
• The NO2 and NO3 reflect the influence of agricultural activities in
the upstream part.
• Fe, Mn, and Na are traces of geological control which composed
mostly of volcanic rocks.
• The other significant elements (EC, CO3, CO2, SO4) are possibly
coming from the general domestic activities.
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11. Results3: Mixing process
• Both analyses draw a possible
mixing process due to the
close interaction between both
water bodies.
• By plotting the principal
component, we can still
separate the river water
samples from groundwater
cloud.
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First draft
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12. Results2:
Proposed
water
quality
model
RW
flow
High
NO3
GW flow:
Add NO3
GW
flow
Shallow
GW flow
Cl, SO4 from deeper GW potentially
drawn up due to heavy pumping
Decreasing EC,
Na, Fe, NO2,
SO4, Mn, CO3,
CO2 due to river
dilution
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First draft
GW signature (from
lithology):
Ca, Mg, Na, K
aren’t significant
-> somehow masked?
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13. Conclusions
First draft
• Both analyses draw a possible mixing process due to the close
interaction between both water bodies. However, by plotting the
principal component, we can still separate the river water
samples from groundwater cloud.
• Considering the number of data in the training dataset, we can
use this plot to predict the water character to another water
quality dataset from the area
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14. Acknowledgement
First draft
• We wish to express our gratitude to Prof.Dr. Eddy A. Subroto as the
Dean of FEST and Prof.Dr. Lambok Hutasoit as Head of Applied
Geology Group for their continuous support for this collaborative
research between ITB and University of Sydney.
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Jour. of the Royal Statistical Soc (B) 73(1) (2011):3-36.
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15. First draft
Spatial analysis of groundwater quality data using geoR and mgcv R-package
Irawan, DE., Prabowo, K., and Akter, F., Vervoort, W
Thank you for your attention
Further questions and comments should be addressed to:
Dr. Dasapta Erwin Irawan : erwin@gc.itb.ac.id
Twitter : @dasaptaerwin
Due to the limited time to present and pages to write, the resources from this
project will be made available online by the end of this month at:
•ResearchGate: https://www.researchgate.net/profile/Dasapta_Irawan
•Academic.edu: https://itb.academia.edu/dasaptaerwin
To the extent possible under law, the author of this document have waived all
copyright and related or neighboring rights to this work. This work is published
from Sydney, Australia.
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