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e/iaptm 4
DUALITY OF RAINWATER IN VARIOUS LAND USE LOl;ATIONS
4.1 Introduction
India is at the threshold of a water scarcity situation. It is projected that
by the year 2025. India would become a water stressed country, hence it is
pertinent to shift the thrust of the policies from 'water development' to
'sustainable water development'. A vital element of this shift in strategy is the
increasing importance of rainwater harvesting and recharge of ground water.
Several state governments in India has introduced legislation that makes it
obligatory to incorporate roof top rainwater harvesting systems in newly
constructed buildings in urban area. In this context the quality of rainwater
becomes significant, since it depends upon many factors-the most important
being the land use pattern of the location where the rainwater is harvested.
This chapter deals with the chemical composition and the quality of rainwater
at different land use locations in Emakulam district. The variables influencing
the rainwater composition found out using statistical method are also
incorporated in this chapter.
4.2 Rainfall during the period of study
The major rainfall season for Kerala is the southwest monsoon period from
June to September. Next to the southwest monsoon, the other principal rainy
season is the northeast monsoon. Kerala receives summer showers from March to
May. The rainfall data from May 2007 to December 2008 is given in Table 4.1.
(Juah(v olr.nnvnncr in 1;/rJ(){/S land lise lo('a/ions
Table 4.1: Rainfall received in Ernakulam district during May 2007
to December 2008
Year: 2007
Month May June July Aug. Sept. Oct. Nov. Dec.
Rainfall403 721 899 372 735 424 538 12
Imml
Year: 2008
Jan. I Feb. March April T MayI
June July Aug.Month
Rainfall ;I~23 284 150 199 386 520 235(mm)
I
27
Dec.
24
Nov.Month
Rainfall (mm)
Year: 2008
_______~f_-s_5e4-:- [-:-::-'- \1-.-'--._-------
It can be seen from Table 4. I that 85% of the total rain is received
during the two monsoon seasons, ie. June to December, while the summer
rains ie. from March to May constituted 14.5% of the total rain. Qnly 23
mm of rainfall is received during January to February 2008, which
accounts for only 0.5% of the total rain. The rainfall received during the
period of study is shown in FigA.1. From the above it is quite evident that
the rainwater received during June to December is the major source for
the coming months. Any failure in the southwest monsoon or northeast
monsoon will result in scarcity of water. This will also affect the
availability of drinking water, electricity production and agriculture. All
efforts should therefore be made to plan and manage the use of water with
utmost care so that even when the monsoon fails, water scarcity is not felt.
Collection of rainwater during the rainy season both for drinking and
other purposes would hence be most useful.
59
Glla//(v o{r:llIJfl";llcr in I ";I1iOllS land lise local/oils
~ Rainfall
~ ~""
1000
800
E 600E
200
oJan May Sep Jan
Month
May Sep Jan
Fig: 4.1 Rainfall Variation during May 2007 - October 2008
In terms of water security. it may be noted that among the 35
Meteorological sub-divisions in India, Kerala receives the maximum annual
rainfall. Considering the area and population, around thirteen thousand Iitres
of water is available per head per day out of which only one or two percent is
sufficient for meeting the daily needs of a person. Thus water security in
terms of quantity especially in the high rainfall areas of Kerala, is very good.
This state is hence highly suitable for testing DRWH in terms of economic
viability and water quality vis-a-vis other alternatives for providing water
(Padma Vasudevan and Narnrat Pathak. 1999).
4.3 Chemical composition of rainwater
All the samples collected during the period of study were analyzed for
the ionic components. All the concentrations are reported in ueql I so that a
simple arithmetic procedure can be used to obtain comparisons between
various data sets. The pH value of water indicates the logarithm of reciprocal
of hydrogen ion concentration present in water. Thus pH is a measure of
60
(JlIahiy oFr;lIllHa/cr III various lnnd us: loca/iolls
hydrogen ion concentration [H' ] mmol/l n: .
is approximately equal to mg/I Il ' .
Due to the highly alkaline nature of soil 10 India, the role of HeO j
becomes very important. Since no direct method is available for the
measurement of HCO;. There are two sources of bicarbonate (HCO;) and
carbonate (CO;-) ions in rainwater. One of the atmospheric carbon dioxide
(CO,) that dissolves in rainwater to form bicarbonate ion (HCO;) and
carbonate ion (CO;-) as well as W. The other is the atmospheric aerosol
particles of calcite (CaCO,) reacting as strong bases with W to give (HCO,).
These reactions occur according to the following equations:
CO,+H,OCO,.H20,
(1)
K" = 3.4x1 0-' mol t:' atm' (at 298 K),
CO,.H 20;= He +HCO; (2)
K, = 4.6x10-7 mol r'(at 298 K).
HCO, ;= W +CO;, (3)
K 2 = 4.5x1 0-" mol r' (at 298 K),
where KJJ is Henry's coefficient; K, is CO2 , H20 dissolution coefficient; and K,
is HCO; dissolution coefficient. Then the equilibrium relations arc
KHP =[C02.H20], (4)( 1i 2
K'[C02+H,0]~[W][HCO~] (5)- .
K,[HCO;] =[W][CO;-], (6)
61
(Jllahty otruinwntcr in vnrious land usc lorntions
where Peo, is the atmospheric CO, partial pressure. Thus, in rainwater the
HCO; and CO:- concentrations following from the dissolution of CO 2 as well as
carbonate aerosol particles is
[HCO; ]~[C02.H20]K, I[H' ]~K;;r,'hK, I+[H']. . (7)
= 5.5 I [W](!lillol 1-'),
[CO;-1~[C02 .H,o]K,K2 I[W]2 =K;; I',,,, K,K, I+[H
= 0.00025 I [W]2 (umol ]'),
]2........ (8)
where the atmospheric CO, concentration is assumed to be equal to 350 ppm.
Since CO; concentration is very low, generally, it is neglected (Losno et al.,
1991: Warneck, 1988).
When pH is above 5.6 and the sample in equilibrium with atmospheric
carbon dioxide, the concentration of HCO; in mole/I is calculated as follows:
The above equation could underestimate the concentration of HCO;
(Granat, 1972).
Prior to focusing the data analysis in a multivariate way, univariate
descriptive statistics (mean and standard deviation) for the measured variables
were studied for each sampling point and the details is as given in Table 4.2. In
general, rainwater collected from the different sampling sites had a pH in the
5.62-6.00 ueql' range, while the H+ ion concentration was in 163-4.43 ueql'
range. K+ concentration varied from 4.02 ueql' for the Eloor (industrial location)
to 7.28 ueql' for Kothamangalam (rural). The corresponding values obtained for
Emakulam (urban) and Kalamassery (suburban) are 6.94 ueql' and 4.77lleqr1
respectively. It can be seen that K+ concentration is lowest in the industrial
location, slightly higher in sub urban and still higher in the urban location. K+
62
Ollah(v olr.univntcr 111 I »nous land U',C locnnons
concentration is highest in the rural location. This trend shown in the present study
is in good agreement with that reported by Kulshrestha et al. (2005) in a review of
precipitation monitoring studies in India. The trend shown in the variation of Ca2+
ion concentration is similar to that of (Kulshretra et al, 2005), with the lowest value
(37.35 ucql' for industrial, slightly higher one for urban and suburban location
with the highest value (44.1 0 ueql' ). Mg2+ concentration is lowest at Kalamassery
suburban location, while it is highest in Eloor industrial location.
Table 4.2 Descriptive Statistics of data
SamplingPH W HCO, Ca2• Mg2+ Fe2+ cu2+ Na' K' S042 Nol -
Site
Mean 5.97 2.06 10.79 38.36 39.36 1.71 2.13 24.76 7.28 0.00 0.001 ._'~ - ._','---
SO 0.49 3.05 13.67 33.61 19.53 2.45 2.37 27.42 7.73 0.00 0.00
Mean 6.00 1.98 13.35 37.82 38.01 1.29 1.56 13.67 6.94 0.00 0.002
SO 0.58 2.10 18.62 34.00 7.74 2.01 1.71 13.39 4.60 0.00 0.00.-
__3_J;:,n 5.62 4.43 6.70 37.35 43.48 1.16 2.01 23.92 4.02 0.00 0.00--_ .... - -0.58 4.08 11.01 30.10 49.46 1.57 2.17 34.25 5.08 0.00 0.00
Mean 5.99 1.63 9.21 44.10 35.67 1.86 1.62 31.38 4.77 0.00 0.014 ~--->-.-
SO 0.40 2.09 7.84 30.30 15.27 2.38 1.94 38.42 4.93 000 0.01
Mean 5.91 2.38 10.25 39.53 38.69 1.55 1.84 24.96 5.81 0.00 0.01Total
SO 0.52 3.01 13.47 31.96 24.90 2.18 2.07 30.83 5.90 0.00 0.01
1- Kothamangalam(Rural). 2- Emakulam(Urban), 3- Eloor(lndustrial), 4- Kalamassery(Suburban) Except pH, all concentrations are in ~L eql'
The relationships between various ionic species in rainwater at different
locations were determined by correlation analysis. Table 4.3 shows the Pearson's
coefficient :IT! for rainwater collected from Kothamangalam (place I). Good positive
correlation was found between Ca2+ and HCO j ' , ci+ and Na+ at 0.01 significance
level. The Pearson's coefficient table for rainwater samples at the other locations
are also given in Tables 4.4 to 4.6. Very strong positive correlation exist between
Ca2 1 and HCO j ' , Mg2+ and NO j ' , Fe2+ and ci+ at Emakulam (Place 2). NO j , has a
good positive correlation with pH and also H+ at places 3 and 4. the correlation
between Na+ and Ca2+ and Mg2+ for place 3 and that between Ca2+ and Nat, K+ was
found to be very good.
63
Ta
ble
4.3
Pe
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_J-~
.000
--
Nt
t6
t7
77
33
7
**C
orre
lati
onis
sign
ific
ant
atth
e0.
0I
leve
l(2
-tai
led)
.*
Cor
rela
tion
issi
gnif
ican
tat
the
0.05
leve
l(2
-tai
led)
.
~ ~ ""' "" s.. ~" ~ S- ~ ~ ~. 2:;' ~. :; ~ "'- ~ ~ ~ ~ ;:: ~
Ta
ble
4.6
Pe
ars
on
Co
rre
latio
nC
oe
ffic
ien
tor
rain
wa
ter
(Pla
ce4)
445
No
]·
.705
.051 8
-.9
38
1")
----
:001
--
K+
031
691
,C
u2
+-,",O;--~'~J
31I
33~33~-:ti=3----
33I
.173
--.~----:D5\
'"_
·19
2
777
28
4I
.004
3I
31
34
1"\
I1
.82
81
")
.OO
D
nu
H+
I'..g.~m'
r:or
rala
tion
•...
.,,,0
11C
orr
elat
ion
PH H.
«cea
..3
76
31
Ca2
...
34-.
173
.382
("1
34.0
34
331
14
542
166
'"-- ~- ~. ~ ;:: ~ ::' ~ ~.
~ ? :::.. ~ ~ ~ ~ ~. "~ -r;<;; ~ ".
,~'
431
569
.424
.296
4
1.00
0
.000
1414 000
1{
]0
0--+
---
404
14 221
w 14 431
947
569
14~
130
657
-~--'4 24
2
380
8u"'
360
474
I-~C-
,--
14--
--------
'I<
An~n
~~A
'160
380
02+
836
14 517
190
14077'"
~',!1
497
.363
vv
33
66
WI
OlD
14
-I"
349
277
+-_
n'5
_
33I
14-
-+
44
5
I
164
14 70
5
'51
001
376
~-----'-5-'-
'----
--:5
65.1
58----6
06--'--
05
61
--
1414
12I
I......
1--
---~I--'l-rl
-....
·w
arso
nC
orr
elat
ion
Pea
rso
nC
"rre
lati
on
Siy
.(2
·tai
lud)
N·_i
lrso
nC
n".
.IA
tin
n
Pea
rso
nC
orre
lati
on
~SiD
_IZ
-ta
i'le
d·j
----
NI
Siy
.12·
tail
lld)
f;;-
---
K.
N••
No
]·
feZ
...
Cu2
+
Mg
2+
-10
_
**C
orre
lati
onis
sign
ific
ant
atth
e0.
01le
vel
(2-t
aile
d).
*C
orre
lati
onis
sign
ific
ant
atth
e0.
05le
vel
(2-t
aile
d).
0-,
-J
()uahiF oJ"raillwalcr IiI vnrious lnnciusc lorntions
4.3.1 pH variation of rainwater with land use
Fig: 4.2 shows the presentage frequency distribution of pH of rain water
samples collected at different locations during the monsoon of the years 2007 and
2008. The reference level commonly used to compare acidic precipitation to
natural precipitation is pH 5.6, the pH that results from the equilibration of
atmospheric CO, with precipitation. It may be observed from the Fig: 4.2 that
about 45% of the total rain events at Eloor reflects that pH of rainwater is acidic,
while it is only 17% in the case of Kothamangalam. The frequency distribution of
the pH for the study period at different locations is tabulated in Table 4.7. It can
be seen that 82.5%, 70°;(" 54.2% and 87.9% of rainwater samples collected at
places 1,2, 3 and 4 respectively have pH greater that 5.6.
400 375 35.0 Place 2Place 1 30.032.5 30.0 26.7_300
~25.0233
0
~"'200 . 16.7
~20012.5 ~150 I
D~ 100:10.0 QD 0
: 10.0 I
332.5 50 0000 =-" 0,0 ! .. 0
<50 50 -5.5 55- 60 60-65 65-7.0 ) 7.0 <5.0 50- 55 5H.O 6.H5 65- 70 ) 7,0
pH pH
400 !
_300 -o
~200
375Place 3
292
208
<5.0 50-55 5.5-6.0 60-65 6.5-7.0 '7.0
pH
50.0
400 .-=300~
~200- 91~IOO- 30 .
00.'=-- 0<5.0 50-5.5
Place 439.4
333
55-60 606.5 6.5-7.0 '7.0
pH
Fig: 4.2 Frequency distribution of pH in rainwater at different land uselocations
68
(JlIa/i(Yolnnnn.ucr 1/1 vnrious Liud usc localiolJs
Table 4.7 Frequency distribution of pH of rainwater at different
land use locations
Frequency Distribution (%)
Location Kothamangalm (1) Ernakulam (2) Eloor (3) Kalamassery (4)
Land use Rural Urban Industrial Suburban
<5 5 0 8.3 3
5·5.5 12.5 30 37.5 9.1
5.5·6 32.5 26.7 29.2 39.4
6.0-6.5 --f --:I: 37.5 16.4 20.8 33.3c.-0 f-~
.--.
'" 6.5-7 10 23.3 4.2 15.2crc
ex:
>7
j ,'/-3.3 0 0
.
< 5.5 17.5 30 45.8 12.1
5.5.>=82.4.
70 54.2 87.9
The reason for the pH of 45.8% of rainwater samples of Eloor being
acidic, can be attributed to the vicinity of industries near the sampling
site. The table clearly points out that 87.9% of pH of rainwater samples of
reflects alkaline nature of rainfall.
The average H- concentration and pH rn rainwater samples at
different stations in India reported by Khemani. et.al, 1989,
Kulashrestha.et.al, 2005 and from the present study is shown in Table 4.8.
The pH values obtained in the present study are mostly in agreement
with those obtained for different land use locations at various stations in India.
69
QuaIJiy olrninvmtcr ill 1;l1ioU'·; j,'lIJd lJ.<;C locations
Table 4.8 Average pH values and H+ concentration of rain water
samples
Station pH W in u eql'
-;;; Alibag 7.2 0.063-'"..= Golaba 7.1 0.079'-'
Kalyan 5.7 2f---------.-- -
~ Ghembur 4.8 15.85-'":> Indraprastha 5.0 10"C
-= f-------- --_._-Eloor" 5.62 2.40
---
Pune 6.3 0.50
<= -----... Delhi 6.1 0.79of:::::>
Ernakularn" 6.0 1.0----_.-f--------
<=Sirur 6.7 0.20
.c ..:> .c -Vl
~ Kalamasserv" 5.99 1.02
-;;;Kothamanglam' 5.97 1.07-:>ex:
-;;; Industrial 6.1 0.794.;..~- Urban 6.4 0.398-'".=0",0.. N Sub urban 6.7 0.2--.='":;
Rural 6.5 0.316'"*present study (others as reported by Khcmam et. al, 1989)
The frequency distribution of H+ ion concentration is given in Table 4.9
and Fig: 4.3 for the various land use locations. The H+ ion concentration
corresponding to equilibrium pH of 5.6 in 2.5/-leqr 1•
70
(Juah/y oJ'raimralcr JIJ vnrions land usc locntions
1000 I 800, 70.080.0 Place 1 70.0 I Place 2
80.0 ;~600~
;:: 600 ~50.0 •c :400~ 40.0 ~300 '
267
0... 20,0 150 '::200 ' QO~00 0.0 2.5 2.5 10.0 00 3.3 0.0 00
0.0 00 . = ----~---
<3 3·5 6-9 9·12 '215 >15 <3 J·6 6·9 9·12 12 ·15 >15
H' H'
60.0 . 542 1000 879
.500 I 800 Place 4040.0 . Place 3 .~ " 600 ,:300 . 253
;200
0~ 40.0
83 8.3~ 200 . 31100 42 30
00 I 0 0 0.00
0.0 0.0 000 00 ~
<3 3·3 :·9 9-12 '2·15 ) 15 (3 3 5 6·9 9·12 12 ·15 ' 15
H' H'
Fig: 4.3 Frequency distribution of H+ in rain water at different locations
20,000
9
*15.000
""86
*" 95::t *~ 10,000 28
0
87
5.000 g * 14
60
99
S0,000
2 3 4
sampling Site
Fig: 4.4 Box and whisker plot for H+ concentrations at different location
71
(JlIa!JI.v o{rallJli-;//cr 11/ 1';//101ls !and W';(' locntions
Table 4.9 Frequency distribution of W ion concentration
Frequency distribution ('!oj
Range of W ionRural Urban Industrial Sub urban
11 eql'
<3 80 70 54.2 87.9
3-6 15 26.7 8.3 9.1
6-9 a a 25 a
9-10 a3~
4.2 3
12·15 2.5 8.3 a:--t> 15 2.5 a a-- I
The highest percentage of H+ ion concentration In the category <3
11 eql' is 87.9%, for the sub urban location, compared to 80% for the rural
location. The suburban sampling site at Kalamassery is located inside the
Cochin University campus with lot of vegetation cover.
The Box and Whisker charts are a great tool for a quick look at how
several processes compare. A box and whisker plot is a picture of how a set of
data is spread out and how much variation there is. It is sometimes called a
box plot. It does not show all the data. Instead it highlights a few of the
important features in the data. These important features are the median, the
upper (75th) quartile, the highest value, the lower (25th quartile) and the
smallest value. Box and Whisker plots are ideal for comparing multiple
processes because the centre, the spread and overall range are immediately
apparent from the chart.
The Box and Whisker plot provides a lot of information despite being a
simple tool. The length of the box provides an indication of the spread or
variation in the data. If the median is not in the centre of the box, it is an
72
(JIli/IJiy oFr:lJimafcr IiI r,/I17()[IS Ii/IJrlllsc loci/fl(}f/S
indication that the data is skewed. If the median is closer to the bottom of the
box, the data are positively skewed. If the median is closer to the top of the
box, the data is negatively skewed. FigsAA shows the box and whisker plot
according to the sampling site for different ion concentrations in ucql'.
4.3.2 Variation of Ca2+, Mg2+ and other ionic components.
Table 4.1 () gives the descriptive statistics of the entire data set. It can be
seen that the concentration of the ionic species as the following order.
Ca2~ > Mg 2+ > Na+ > HCO j - > K+ > H- > Cu2~ > Fe 2+ > NO]- thus
Ca 2- is the major contributing factor in the rainwater composition. Coarse
mode Ca aerosols are contributed by soil dust in the ambient air in India.
Therefore Ca2- aerosols seems to be a major component for neutralization of
rainwater acidity in most of the Indian site (Khemani et al., 1989, Saxena et al.
1991, Kulshrestha et. ai, 1996)
Table 4.10 Descriptive statistics of rainwater samples
Mean SO
pH 5.91 0.5238-
H' 2.38 3.0117
HC03 10.25 13.4733
Ca'· 39.53 31.9646
Mg 2• 28.35 24.4590
Fe'· 1.55 2.1786
Cu'· 1.84 2.0720
Na' 24.96 30.8316
K· 5.81 5.8960
S04 0.00 0.0000
N03 0.01 0.0068
73
(Juahly o{raiIlIJ;/lcr ill vnrious land W,;C locnnons
Fig: 4.5 to Fig: 4.12 show the frequency distribution of various
IOnIC constituents of rainwater. In case of HeOJ·, more than 75% of
rainwater samples at all locations showed concentrations below 15 ~l cql'.
More than 58% samples showed concentrations for Mg2+ below 50 ~L eql I,
where as only more than 45% samples showed concentrations for ci+below 50~l eql'. Higher concentration of all ionic components were rarely
found in the rainwater samples.
8-.----~---~---~--~_--~---___,
6
o
I
1
---r---~-
I 1I 1
1
1 -b- ~ural
i --0- ~rban
-. -1-· - c::0='P~triaJ._
i -----0-- fuburban
1 1
I 1-----1---I1
1 1---1----
I I1 1
Jan May Sep Jan May Sep JanTime (Months)
Fig: 4.5 Variation of Ca Hardness of rainwater collected from different
locations
74
(Jilahiy oJ'ra/llwalcr //1 1;1J](){J!t' l.md usc loc.uions
100.0 I
784 Place 180.0 .
~
;; 600c
~ 400
20.0 108 5.4
0,27 27 00
00 0
<15 15 -30 30- t5 45 -50 60·75 ) 75
I HCOJ
80.0 I
759Place 2
70.0I
~600 •~500 :- I:400 I~30 0 .::'200 .
69 10310.0
03.4 00 34
0.0 . . .-fl, = ='15 15- 30 30- 45 45- 60 60.75 >75
HC0 3-
1000 1000 .839818
800 Place 3 800 .Place 4
~ ~
;; 600 ~ 600 Ic
~ 400 ~ 40.0 !200 136 200
16.1
0 0.0 45 00 00 0 00 0.0 00 0000 = 00
<s ': -38 30-45 ~5 -68 50- 75 >75 "5 15·30 30.45 45 OJ 63·75 >75
HC0 3 HC03-
Fig: 4.6 Frequency distribution of HC03' in rainwater at different locations
80000-
60.000
M
0 40 000
oI
20.000
0,000
54*
2
*53
*
63sa **
11151
°10600
5'0
125 34
§ 61
~55 68 37
'867r-'
$I-- ~2
Sampling Site
Fig: 4.6 (a) Box plot for HC03' concentrations at different locations
75
(JllaIJ(v olrninvv.ncr ill 1"!/I101lS li/lld usc locntions
700 I Place 1 60.050.0 Place 2
600 .57.5
50,0 I400
~500 •~400
~400 I
~25.0 :;300I~30 0
:200 0~20 0
75 100100 00 00
100 33 0.0 33 3.3
00 D Q 00 c·· =-_. D .J::::L
<30 30·60 6:1· 90 &J ·120 120·150 > '5C <30 30·60 5HO 90·120 120 ·150 >150
Ca2• Ca2+
500 458 458 700 60.6Place 3 600 Place 4
400~ ~50 0..,300
~400 I 3030
~200 ~30 0 .
n'::100 I 4.2 42.::200
0.0 00 10.0 3.0 30 0.0 3.0
0,0 0 0 00 = = =<30 30 ·60 EG- 90 90·m jiJ·150 >15J ' 30 30· 60 60· 90 90- <20 120.· E,O >'50
Ca 2 ' Ca 2+
Fig: 4.7 Frequency distribution of Ca2+ in rainwater at different land use
locations
200,000
150,000
'0<"::L.":a 1000()(}U
50,000
0.000
53
*'"0
06
''" tza0
as
"*sz
0
~Sampling Site
Fig: 4.7 (a) Box plot for Ca2+ concentrations at different locations
76
(JuaN)" olrninsvntcr IiI v.mons Inntlnsc loriuions
700 I 60.0 Place 1
DO 0
~500 .
;;400 .
~300 i 225175
~200 I
_uJI~'OO ! 00 00 0000
<30 30 -6J 60- 90 90· 120 120 -150 >15J
Mg2t
00.0 50.? Place 2
500
~4003D.?
ll~~---":300
:200
10.000 00
00 -----_.-
<30 30 -60 60- 90 90 -120 120·150 >150
Mg2'
700 625 500 I455
424Place 3 Place 4DO 0 400 !
~500~
;;400 ;300~
:300 ~20 0 121~20,0
167 10.?
DD~100 . D100 i 00 00
4200 00 00
00 0 00<30 30 -50 60- 90 90-120 '20 -1:0 > ~ 5:, ( 30 30- 60 60-90 jO-12C 120 - ~ 50 >150
Mg2+ Mg 2
+
Fig: 4.8 Frequency distribution of Mg2+ in rainwater at different land use
locations
200.000
121
*150000
'&"::L'in 100.000:;
50.000
0.000
zSampling Site
Fig: 4.8(a) Box plot for Mg2+ concentrations at different locations
77
(jllaiJiF oFraimra/er in vnrious land lise loctnions
1000 , Place 1825
800
~~
.::: 600c
~ 40.0~ .. 15.0~ 20.0
JIL. 00 00 25 00OO~~ =
,) )-6 6-9 9-12 12-15 >'S
Fe2+
1000 87.5
800Place 3
~
.::: 60 ac
~ 400~
~ 200 8.3 42 0.0 00 0000 , L_D-r_=
,) 3·6 6-9 9·12 12·15 >15
Fe2'
10001
Place 2833
800 ' c--~ Ii~ 60.0-c
~ 40.0~
~ 200 133
D.3.3 00 00 00
00 _L . =,] 3·6 6·9 'j·12 12 -15 , 15
Fe?<
800i5.8r-r-
Place 4~600c
~
~400
~'8.2
'::200
D 30 30 00 0000 L = =
<) 3-6 6- j j·12 12·15 , 15
Fe2'
Fig: 4.9 Frequency distribution of Fe2+ in rainwater at different land use
locations
52
*14.000
12.000
87o
10.000
CT0.>
=::- 8,000
1....6.000
4.000
2.000
0.000
53
*
670
t Q,
Sampling Site
Fig: 4.9 (a): Box plot for Fe2+ concentrations at different locations
78
(Jua!Jiy olrnunvntcr 111 ";I1]OllS land lise }ocal1()J)s
1000 Place 1
77.580.0
~~
;; 600"~ 40.0~ 175~ 20.0
0 2.5 25 00 000.0 ~-'- = - - -=----_.-
<3 3·5 6-9 )·12 12-15 >15
Cu)+
800 708700 Place 3
~600 .~50 0:400
;:300 I 16.7 12.5~200
10.0 0 0 0.0 00 0000 - ~
'3 3-6 6-9 g. .2 12 - ',5 >15
Cu 2+
1000 1Place 2
633800 , rw~
~ 600c
~ 400~
~ 200 13.3
D33 00 00 00
00 - ~ ='3 3-0 :- 9 g. :2 ')·15 ' 15
Cu 2 '
1000 ,I 788 Place 4
80.0 ! -~
;; 60.0c
~ 40.0~ 182~ 200
0 30 00 00 0000 ~ =
(3 :i-6 6· ~ 9-12 12 ·15 '15Cu)+
Fig: 4.10 Frequency distribution of Cu2+ in rainwater at different land use
locations
12,000
te*
10.000
6.000
8.000
'&"::t~~
o
4.000
2.000
0.000
0'
66o
Sampling Site
57o
4
Fig: 4.10 (a): Box plot for Cu2+ concentrations at different locations
79
()uahty otrnnnvntcr III vnnous land usc loctttions
800 ~ 692 Place 1
700600
111~
~500
~400 >i;;300 231<>-200
n_~o7.7
100 0.0 000.0 ~ 0
c30 30-60 50·90 ~J. '20 120· ~50 ) 150
Na
800 75,0
700 Place 3600
~
~50 0~40 0;;300~200 125 12.5
100 0 0.0 0 00 000.0 '---~
<30 30·50 60· 90 90.120 12G-15J >150
Na
800 j 71.4 Place 2
70.060.0
~
~500 '~400 . 286;;300 ,n,<>-20,0 c
100 I 00 00 00 0.000 -- - ----- T ----~_ ..-
<30 30- 60 5G- 90 9G-120 120-150 > \50
Na
800 714700 • ~ Place 460.0
~
~500 -
~400
;;300~200 143
100 D71 71
00 00O,O~- c- O 0
<30 30 -60 6J·9'J 90 ·120 12C ·150 ) 150
Na
Fig: 4.11 Frequency distribution of Na+ in rainwater at different land uselocations
In
*140,000
120000
100.000
c-"=t• 80,000
•ZW.OOO
40,000
20.000
0,000-
tao* '"*
-
Sampling Site
Fig: 4.11(a) Box plot for Na+ concentrations at different locations
80
(JlIahl}' oimnnvntcr ill v.mons !allrlllsc locutions
60.0 518 Place 150.0
~40.0
:300
~20 0 154 154~ 77 no 77
100
.n~_~00 ,n'5 5·1J 1C·15 15·2J 2'J ·2:, )25
K'
1000 857
800- Place 3.
.; 600c
~ 400.~ 20.0 '4.3
00 00 D 00 0000
<5 5· '0 '0·'5 "·20 2:1·25 ) 25
K'
800 71.4 Place 270.0 r-r-
.600 I··.·~500
:400~300
':200 143 143
100 0 0 0.0 0.0 0.000 ~
·5 5-10 ').:5 15· 20 20·25 '25
K'
700 643
60.0 r-'Place 4
~500
',;400
~30 0
~20.0 14.3 14.3
10.0 0 07.1
0 0.0 0.00.0· ~
·5 5·;J 10·15 1\.2<0 20·25 '25
K'
Fig: 4.12 Frequency distribution of K+ in rainwater at different land uselocations
25.000
20.000
-g- 15000
a.
"10.000
5000
0000
'"o
Dee
o
Sampling Site
'"*
sa*
L
,
Fig: 4.12(a): Box plot for K+ concentrations at different locations
81
(Jl/a!J~v olrninwutcr JiJ 1;/I10llS land lise local/oJl."
05 .--~--~-~--~-~--~-~--~-,I
I I I I I I I--~--~--~-.-I-._.~--~--
I I I i I I Ii I I i I
_l __ L __ L_' __ ~ __I I I I II I I__ L _~ __
I II I
I
I--I-
II
__I_-I
I__I_
II
__ I__ ~_- ~=--~~~--6- Rural I-0-- Urban I
--<>- Industnal I I I I I:.:o:--Suburtaii --1- - T - -I--i--I--
I I I I I I I
04
0.0
~O_3
~E.~02
"'rn"0c,
0.1
Feb March April May June July Aug SeptTime (Months)
FIG: 4.13 Variation of potassium of rainwater collected fromdifferent locations
Fig: 4.13 to 4.15 shows the variation of Ca Hardness, copper
concentration and iron concentration in rainwater samples collected from
different locations.
0.25 .------,----~---~--~---~--,
0.20
'a,E..,0.15ccCo()
Ui 0.1 00.0.o()
0.05
III---,II
I I1----6- Rural I
I I Lo- Urban I- - - I - - - --, - -- -I=<F fnd"'l.fial- - -
1 1 I-G- Suburban
I I I I___ l J I L _
I I I II I I II I I I
-+---~ ---I----~---
I I I II I II I I
- - I - - - 1-'f-lrii"'*'C\-
JanSepSep Jan MayTime (Months)
May
0.00 +-~~+-~,...{j'-<)-.'l1_8_{H_O__D_D-Il_'tj_,___<>__<>_b-~_I
Jan
Fig: 4.14 Variation of Copper content present in rainwater collected fromdifferent locations
82
Q{}ai/~Y oiminnrncr IiI various land lise location»
0.4 .----~---~---~--~---~--__,
--1---I1
1
1
1 1- - "I - - - -1- - - - I - - -
1 I 1
1 I 11 1 1,- - - -j- - - - I
1
1
1 _
1
---t--1
1
I
I
---t1
1
-I:>- Rudl--0- UrbJn--0-- Industrial~SUbtrr_- - I
I 1I 11 1---1---1
1
0.3
0.0
0.2'§,Eco..:: 0.1
Jan May Sep Jan MayTime (Months)
Sep Jan
Fig: 4.15 Variation of iron in rainwater collected from different locations
4.3.3 Marine contribution
In order to estimate the marrnc and non-rnarme contributions,
different ratios like sea salt fractions and enrichment factors have been
calculated. For this, Na has been taken as reference element assuming
that all sodium is of marine origin (Kulshrestha et a., 1996). Table 4. 11
shows the ratios ofK+, ci+ and Mg2+ with respect to Nat. All ratios have
been found to be higher at all the sites than the recommended sea water
ratios. These elevated values may be due to the contribution of
anthropogenic and crustal sources. Similarly high ratios of these
components with respect to Na+ suggest a non-marine origin for these
components (Khemani, 1993; Kulshrestha et al., 1996). The sea salt
fraction (SSF) and non-sea salt fractions (NSSF) of the varIOUS
components have been calculated using the following equations
83
QUi/iIiyolrninivntcr Iii rnrious !aJ1r111sc !oca!I()JJs
% SSF = lOO(Na)(X / Na+)seaX
where X is the component of interest.
% NSSF = 100 - SSF
Enrichment factor (EF) of rainwater samples were calculated usmg the
formula
EF = (X / Na )rain(X / Na)sea
bl 4 I Sh h II · . C 2+ M 2+ K+Ta e . lows t at a rome components eg: a, g,
appear to be of non-marine contribution at all places of study. The
enrichment factors of different species calculated with respect to Na
showed that all components are enriched showing significant influence of
local sources other than marine influence at the site.
84
00
V>
Tab
le4.
11C
om
pa
riso
no
fse
aw
ate
rra
tios
with
rain
wa
ter
com
po
ne
nts
K'/N
a'C
a"/N
a'M
g"/N
a'Pl
ace
code
12
34
12
34
12
34
Seaw
aterr
atio
0.02
180.
0218
0.02
180.
218
0.04
390.
0439
0.04
390.
0439
0.22
70.
227
0.22
70.
227
Ratio
inrai
nw
ater
0.29
40.
5076
0.16
80.
152
1.54
922.
766
1.56
145
1.40
51.
0714
1.79
1.35
110.
9936
SSF%
7.41
4.29
1.29
7614
.34
2.83
371.
5871
2.811
3123
21.1
851.
268
1.68
022
.846
NSSF
92.5
995
.71
98.7
024
85.6
697
.166
398
.412
997
.189
96.8
7678
.814
98.7
3298
.319
77.1
53
EF13
.406
2328
87.
706
6.97
35.2
8963
.006
835
.53
32.0
114.
720
7.88
55.
951
4.37
7
G ~ '",;::, ~ <, ~. g ~ 2:. =2 ~ ~. "~ J, ? "- ~ rq ~ ~ ~. "~ J,
(JlI:Jhi.J' otmunvurcr in I iirious Lmci usc Ior.uions
4.3.4 Principal Component Analysis
The differences among the precipitation data were studied using
multivariate procedure - Principal Component Analysis (PCA). In order to
describe the total rainfall characteristics, PCA was applied to the complete
autoscaled data matrix. By retaining the first three principal components or
eigenvectors, 82.38% of the total variance of the total data set was
explained. This meant reducing, the dimensionality of the total data from 9
to 3 (66% reduction) loosing only the 17.62% of the information contained
in them. Selection of any additional eigenvectors did not supply relevant
additional information. The contribution of the chemical variables to each
principal component can be evaluated by studying the loadings of the
variables in the first three eigenvectors.(See Table 4.12).
Table 4.12 Rotated Component Matrix for the total data set
Eigenvector! Eigenvector 2 Eigenvector 3
W -0.306 -0.179 0.748
HC03 0.178 0.144 -0.969
Ca" 0.186 0.918 ·0.213
Mg" -0.494 0.583 0.201
Fe" 0.949 0.141 -0.191
Cu" -0.469 ·0.605 0.227
Na' 0.215 0.935 -0.033
K' 0.585 0.330 0.639
N03 0.864 0.210 ,0.164
Eigenvalue ('!oj 43.096 21.510 17.778
86
(JlIa!J~v o/ra/l1f1'aICr /11 la/ioll." lanrlusc local/c}//s____________-c-.
The interpretation was made on the basis of the different rainwater
components defined by Sequeira and Lai (1998): the sea salt base (SSB)
component, the partially neutralized acid (PNA) component, anthropogenic
activity component (AAC) and total acidity component (TAC). For a
principal component (PC), a physical interpretation of sources is possible by
comparing the elements having high correlation in a particular PC with
elements associated with known possible sources. The variables contributing
mainl y to the firsr ei F )+ NO, and HCO; . The PCI hasirst eigenvector were e-,
negative high loading for Mg2+ , Ca 2+ and H
t. This PC associated withIS
anthropogenic sources (high loadings for NO,' and Fe2+). PC2 has high
loadings for Na. Ca2- and Mg2
+ with negative loading for Cu2+ and H'. Soil
could be identified as a source for this PC2. PC3 has high loadings for H' and
K+. In this eigenvector, the potassium contribution could be considered a
vegetation or waste water related component caused by biomass burning or
waste-incineration source.
With the aim of reaching a deeper knowledge about the influence of the
sampling site localization on the composition of rainwater collected, a second
multivariate study was also conducted over the data obtained in each sampling
station. For achieving this goal, the approach used consisted of preparation of
4 autoscaled subsets of samples, one for each sampling site. Once the data
matrix has been prepared, the next step was application of PCA to each of the
4 data matrices to describe the rainwater composition for each sampling site
based on the loadings obtained. The results of PCA achieved for each of them
(the loadings for the chemical variables in the first three eigenvectors as well
as the total variance retained) are presented in Table 4,13 to Table 4.16 Three
principal components or eigenvectors have been considered to be sufficient to
represent the chemical data because in all the cases the percentage of the
explained variance was higher than 75%.
87
Oua/i(v otrninvcncr IiI vnriou, land usc locutions
Table 4.13 Rotated Component Matrix of rain water samples at Place 1
Eigenvectorl Eigenvector 2 Eigenvector 3
H+ ·0.553 0.182 0.726
"
HC03· 0.961 0.101 ·0.127.._.
Ca2+ 0.032 0.893 0.191, ..
Mg2+ 0.698 0.511 0.143
-~.".
Fe2 + I 0.852 ·0.252 0.339
Cu2+ ·0.164 ·0.114 ·0.603,
.•.
Na+ ·0.019 0.927 ·0.147
."
K+ 0.065 ·0.125 0.729
-_._--
Eigenvalue 1%) 31.658 25.351 19.992
At Kothamangalam, (place I) total three PCs have been extracted
which explain 77% of variance as shown in Table 4.13 PCI has high
loadings HCO l , Fe2+ and Mg2
+ . This PCI is attributed to terrigenous dust,
because the soil in the area is mostly laterite, which are rich in oxides of
iron. HCOl ' , suggests that atmospheric CO 2 is present in the location,
while Mg2+ concentration suggests soil as a source. PC2 has high
loadings ci+ and Na+, which again suggests soil as a source. The
variables contributing to the third eigenvectors are H+ and K+ in almot
equal loadings. This PC suggests biomass burning as a source and an acid
component.
88
(JuaiJiy olrnnnvntcr IiI I ";1/1(){JS land lise locnnons
Table 4.14 Rotated Component Matrix of rainwater samples at Place 2
Eigenvectorl Eigenvector 2 Eigenvector 3
H+ ·0.115 ·0.850 ·0.021
HC03· ·0.030 0.968 0.043
Ca2+ 00453 ·0.048 ·0.791
Mg2+ 0.187 0.139 0.695
Fe2+ 0.557 ·0.396 0.704
Cu2+ 0.769 00486 0.330
Na+ 0.831 0.159 ·0.107
K+ 0.905 ·0.153 0.087--_ ..-
Eigenvalue (%1 36.038 25.765 19.726.-....-
Similar to Kothamangalam, three PCs or eigenvectors have been
extracted for Ernakulam, (place 2) which explains 81.53% of the variance. As
given in Table 4.14, the PCI has high loadings for K+, Na+, Cu2+ and Ca2+. It
is interesting to note that PC2 has high loading for HCO) and negative
loading for H+ PC3 has high loading to Mg 2+ and Fe2+ Similar to previous
discussion, PC I and PC3 can be attributed to the waste incineration and
terrigenous dust particles, while PC2 is mostly a total acidity component
(TAC) as a source.
Table 4.15 Rotated Component Matrix of rain water samples at Place 3
Eigenvectorl Eigenvector 2 Eigenvector 3
H+ ·0.083 ·0.973 0.159
HCOl ·0.294 0.895 0.257
Ca2+ 0.681 ·00400 0.604
Mg2+ 0.965 ·0.156 0.208
Fe2+ ·00481 0.691 ·0.538
Cu2+ 0.532 0.185 0.825
Na+ 0.972 ·0.072 0.224
K+ ·0.135 0.081 ·0.966
Eigenvalue 1%1 59.548 250407 13.793
89
(Juahly olr.nnwntcr III I;U]OllS Luul nsc !rH'all()JJS
The three eigenvectors extracted for Eloor (place 3) explains 98.75% of
the variance as given in Table 4.15 Compared to other places, the total
variance retained is almost equal to 100. The variables contributing to the first
eigenvector are Na+, Ca2+ and Mg2+, which suggest soil as a source. PC2 has
loadings for Fe2+ and HCOJ ", suggesting soil and atmospheric CO2 as possible
sources respectively. PC3 has high loadings for Cu2+ and a low loading for
H+, which again suggests soil as a source due to spraying of chemicals.
Table 4.16 Rotated Component Matrix of rainwater samples at Place 4
Eigenvector1 Eigenvector 2 Eigenvector 3
H+ -0.544 -0.158 0.202----- - - --
HC03- 0.805 0.264 0.128.-_--Ca2+ 0.502 0.826 -005
- -Mg2+ 0.335 0.162 -0.853
Fe2+ 0.067 0.219 0.923- -.-. ----
Cu2+ 0.818 -0.288 0.161. -
Na+ -0.001 0.888 0.102---~~
K+ 0.710 0.315 0.148.-
Eigenvalue (%) 38.763 21.113 15.170
Similar to other sampling sites, three PCs have been extracted for
Kalamassery (place 4), which explains 75.05% of variance. As given in Table
4.16, PCI has high loadings for HCO j - , K' and Cu2+, and moderate loading for
Mg2+ while PC3 has high loading for Fe2+ and a low loading for H+. These two
PCs can be attributed to soil as a source.
From the ongoing discussion on PCA of the complete data and individual
sampling sites, the high loadings of ci+, Mg2+, Na+, and Fe2+, which are soil
oriented ions, suggests a significant influence of terrigenous dust on the
composition of rainwater. This could be due to the boom in the construction
activities in the cities as well as in country side, during the year 2007-2008. The
second major source of influence is the atmospheric CO2, which is mostly
contributed by the increase in vehicular population of Emakulam district. Waste
90
(JlI;JiJl.V o{r;uIIIJ;llcr III vnnons lnnd usc !o(';J!J()lJ.<'
incineration and biomass buming has an influence on the chemical composition of
rainwater, characterised by K+ ions concentrations especially in the urban and
suburban areas of Emakulam. It is also clear that PCA is an effective statistical
tool for identifying the sources that influence the composition of rainwater.
Table 4.17 Mean and range of parameters for free fall ratio at different land use
Parameters Rural Urban Industrial Suburban IS 1050(1991
5.76 6.03 5.66 5.94pH
(4.65·7.011 (5.04·7.081 1487·7.14) (4.90·66316.5·8.5
_... ··------rAlkalinity 10 8 20 10
(mgll as CaC031 18·121 [(6·201 [15·30) [5.201 200-- ---
0.2 0.8 1.3 08 'Turbidity [NTU) (0·0.4) (00.9) (1·41
--- ~0~81 __~- ._.
Total hardness 2.94 302 3.72
(mg/l as CaC031 (0·90) (0·9.951 11·12.941 [0.10.401 ' 300
14.21 17.06 14.29 14.82Conductivity ..
(1.4-7.011 [4.9·39.301 (2·31.80) (45·38.301
Calcium hardness 1.66 2.99 2.199 2.1675
[mg/l.) (0·61 (1-498) [0·10.951 [081.-
Sulphate Img/II 0 0 0 0 200._--~--
0.0003 0.01Nitrate Img/II 0 0
[0·0.00041 [(0.0.02)45
Iron [mglll 0.061 0.044 0.0364 0.0550.3
10·0.40) [0·0.271 10·0.1801 [0.0341
Copper (mg/ll 0.0623 0.049 0.0588 004040.05
(0·0.341 (0·0.221 [0·0.20) (0·0.201
Sodium (mg/II 0.627 0.35 0.554 0.656500
(0·0.341 [0·0.801 [0·2.41 [0·3.401
0.282 0.26 0.191 0.189Potassium [mg/II ..
(0·1.201 (0·0.601 (0·0.601 (0·0.601
Cadmium (mglll BOL BOL BOL BOL 0.01
Cobalt (mg/II BOL BOL BOL BOL 0.01
Nickel (mglll BOL BOL BOL BOL 0.02
Lead (mg/lit.1 BOL BOL BOL BOL 0.01
Mercury (mglll BOL BOL BOL BOL 0.001
0.02 0.02 0.03 0.075Zinc [mg/II
(0.01·0.031 (0.01·0.031 (0.01·0.061 (0.04·0.09)
Total coliform 230 460 45 4600
MPNIlOOml 1<3·7601 (5·11001 [0·1201 10·5701
91
()llahiy olrtnrnvntcr III vnnous lnnd u«: lorntions
Table 4.18 Mean and range of parameters for free fall rain at selected sites
Meera, V. (2007), Adeniyi & Olabanfi (2005), Sazakli et. al. (2007, Yaziz et al.(1989).
This study Trichur, lie-lie,Kefalonia.
Selanfor.Parameters
(sub-urban) Kerala' Nigeria'Island
Malaysia'Greece'
pH5.94 6.52 6.68
(7.63-8.8015.90
14.90-6.631 (6.12-6.771 (6.45-7.151 15.00-6.601
Alkalinity 10 20.89 3.2016.00-48.001 --
Imgll as CaC031 15-201 (16-241 10.50-7.001
0.8 4.3 6.3 3--
Turbidity INTUI (0-1 I (0-23.01 11.7-9.51 12.0-5.01
Total hardness 3.68 22.0 2.5124.0-74.01
Imgll as CaC031 (0-10.401 (12.0-40.01 11.7-3.91
Conductivity14.82 20.89 10.40 (56.00 13.70
(4.5-38.301 116.00-24.001 115.70·16.501 220.001 16.6033.001----,,- ----------
Calcium hardness 2.16 15.5 0.77110.62-19.21
(mglll (0-81 1<0.12-42.41 10.53·1.11-- ----- .- ._- ---, "---~-~--
Sulphate Imglll 06_50 0.50
11.00-13.001 _.(0.00-20.001 (0.00-1.271_.._'- -'---,-- - ----- - -.'.- ..._._--
Nitrate Imglll0.01 3.83 0.86
15.28-13.021100.021 10.75-11.341 10.00-2.771
---,--~
Iron Imgllit.1 0.055< 0.050 10.006-0.0401.. ..
10-0.341
Copper Imglll 0.0404<.01
1<0.006-..
0.0401..
10-0.201
Sodium Imgllit.1 0.656 0.44 0.2112.00-11.001
103.401 10.00-2.801 10.10-0.341
Potassium (mglll0.189 .. .. .. ..
10-0.601
Cadmium Imglll BOL < 0.0011< 0.0001-..0.000191
..
Cobalt Imglll BOL .. .. .. ..
Nickellmglll BOL .. .. .. ..
Lead Img/lit.1 BOL <0.010.20.. ..
10.04-0.521
Mercury Imglll BOL .. .. .. ..
0.07 0.060 1< 0.01 O·0.034
Zinc Imglll ..
0.077110.015·
10.04·0.091 10.058·0.06210.0601
Total coliform460
23910·5701 <3
MPN/100ml..
II < 3·7501a. c G.
92
(JlJ;[iIiy olruinvaucr in vnrioas land lise locutions
4.4 Quality of rainwater
As stated earlier, the quality of rainwater is important as it is the source
water in all dometic rainwater harvesting systems. The physico-chemical
characteristic and the microbiological quality of rainwater collected from the
different sampling sites were analysed. Analysis of the rainwater samples
collected during June to August 2007 showed that the concentration of
Cadmium, Cobalt, Nickel, Lead and Mercury were below the detection limits,
at all locations of study. The concentration of Zinc with in permissible limits
at all locations hence further analysis for heavy metal concentration was not
conducted. The test results are tabulated and the mean values are reported in
Table 4.17.
Comparison with IS 10500 (1991) drinking water quality guidelines
indicate some departure, for parameters pH and bacteriological quality for
rainwater collected from all locations of study. As per IS 10500 (1991), the
pH of potable water should be in the range 6.5 to 8.5 and the total coli forms
should be zero. It can be seen from table 4.17 that the pH of the rain water
samples are less than the described range and that total coli forms are present
in all the four locations. pH varied between 6.03 (for urban) to 5.66 (for
industrial), the pH at the industrial location being more acidic than the others.
This highlights the importance of rain water quality studies at different land
use locations. The copper concentration in rural and industrial location are
0.0623 mg/l and 0.0588 mg/l respectively, slightly greater than the desired
limit (0.05 mg/I). The site in the rural area is located near rubber plantation,
where pesticides in the form of copper sulphate are used seasonally. Many
industries including a pesticide factory is located within 3.5 kin radius of the
sampling site selected. This could be a possible reason for the high copper
concentrations in the rural and industrial site. However, both the rainwater
samples have concentrations within 1.5 mg/l, which is given as the permissible
93
(Juahiy o(r;/llJJJ;llcr ill vurious lnnd usc: locaIJ()JJ.'"
limit in the absence ofaltemate source, as per IS 10500 (1991).
Table 4.18 shows the concentration of different parameters in free fall
ram observed in the present study (suburban location) and that reported by
other researchers. The values are comparable except for heavy metals
concentration. In the present study, heavy metal concentration was below
detectable limits. The studies conducted by Yaziz et.a!. are much in agreement
with the present study except for heavy metals and total coliform.
The study revealed that the rainfall chemistry as well the quality were a
true representation of the characterisation of the land use pattern of the
sampling site. It can also be concluded, that raw rainwater deviates from the
standards for drinking water and hence it is advisable to treat the rainwater
before using it for potable purpose.
94