36

3. PHYSICO-CHEMICAL PARAMETERS OF BRINE IN

VARIOUS PONDS OF PUTHALAM SALTWORKS

3.1. INTRODUCTION

Solar saltworks are the extreme environments for excellence. Solar

evaporation is the most common and oldest method of salt production. It has been

practiced for centuries along sea coasts in many countries. Solar evaporation is

projected to account for an increasing share of global salt production through 2013.

Usually solar saltpans are fed with seawater via pumping. As seawater flows from

pond to pond, its concentration rises continuously through natural evaporation. The

evaporation of brine is achieved by exposure to solar radiation and with the help of

the climate of the area, especially rainfall, temperature, wind, humidity and

duration of sunshine. So a salinity (concentration) vector is created throughout the

ponds with a simultaneous and continuous reduction of the volume of seawater.

This is the physico-chemical process of salt production (Korovessis and Lekkas,

2009). Temporal patterns of interaction between salinity and water depth can be

important determinants of the biological community of the saline system

(Campbell, 1995; Davis, 2009) and the hydrological activity determines the quality

and quantity of salt production in the solar saltworks (Rahaman and Jeyalakshmi,

2009a; Reginald and Banu, 2009). High salinity, physical impermanence,

physico-chemical instability, low concentration of oxygen, high temperature, high

concentration of calcium and magnesium and low productivity had been the most

characteristic features of the solar saltworks (Sundararaj et al., 2006).

37

Seawater contains more dissolved salts than all types of freshwater (Gale

and Thomson, 2006). The ratios of solutes differ dramatically. Seawater contains

about 2.8 times the bicarbonate than river water based on molarity, the percentage

of bicarbonate in seawater as a ratio of all dissolved ions is far lower than in river

water. Sodium and chlorine have very long residence times, while calcium (vital

for carbonate formation) tends to precipitate much more quickly (Pinet and Paul,

1996). Various salts are precipitate successively at different saturation degrees

during the evaporation process. The sequence of the precipitation of dissolved salts

is in direct relation to the chemical composition of solutions and to their

physico-chemical parameters, to the solubility products of the minerals under

consideration and to their kinetics of precipitation (Amdouni, 2006).

Solar salterns contain rich and varied communities of phototrophic

microorganisms along the saltern gradient, and the photosynthetic primary

production largely determines the properties of the saltern system (Oren, 2009).

Evaporation, the chief process involved in the salt production increases with rise in

air and water temperature (Rose, 2007). Water evaporation is promoted in a natural

way on every solar saltworks which depending upon the characteristics of the

surrounding atmosphere (air temperature, air velocity, moisture and solar

irradiation), as well as on the brine conditions (temperature, density and salt

concentration). Also the quality of the brine depends on the location of water

source.

Temperature plays an important role than salinity in regulating

photosynthesis and oxygen consumption of microbial mat (Wieland and Kuhl,

38

2006). A chain of organism is developed in the evaporating ponds along the

physico – chemical process constituting the biological process of solar salt

production process. This process mainly depends on the quality of feeding

seawater, the prevailing conditions of ponds, such as brine temperature, depth,

concentrations and turbidity, the control of the physico-chemical process during

salt production and the overall design of the solar saltworks (Korovessis and

Lekkas, 2009). The precipitation of the halite occurs only when the solution

becomes almost ten times more concentrated than the initial seawater. During the

massive precipitation of halite, the fall of Na+ concentration is more marked than

that of Cl–. The chloride ion, whose concentration continues to rise in the brines,

serves as a compensatory ion for K+ and Mg++ (Amdouni, 2006). The gypsum

deposit appears only in ponds where brines are about 3.5 times more concentrated

than the initial seawater.

Physico-chemical disturbances can affect the quality and quantity of salt

(Coleman and White, 1992). Climate and soil parameters need to be taken into

consideration when determining the technique parameters such as brine water

depth, brine protection area and fresh water drainage rate. It needs to stress that

this physical process is balanced with biological community existed in brine water

ecosystem (Zhiling and Guangyu, 2009). Consequences include fast destruction of

the salinity gradient, replacement of desired biological systems by problematic

organisms, destruction of the benthic community and releases of organic substances

to the brine (Davis and Giordano, 1996; Magana et al., 2005).

39

Light play an important role for the growth of benthic microalgae. The

planktonic community provides organic nutrients for entire saltworks and giving

colours to the brine which is turn helps in increasing the evaporation and allows

light to reach pond floor (Reginald, 2003; Davis, 2006).

Estuarine and coastal areas are complex and dynamic aquatic environment

(Morris et al., 1995). The physico-chemical parameters of the Puthalam saltworks

were carried out during the study period. To understand water ecosystems, study of

the physico-chemical parameters and biological relations are very essential.

Physico-chemical characteristics may play an important role in the rate of microbial

attachment to the surfaces. The bacterial attachment and biofilm formation are

different aqueous systems affected by season. This may be due to the temperature

or water or other seasonally affected parameters (Kokare et al., 2009).

Algal biomass and the accumulated detritus and organic matter on and

within the sediment and exploited by opportunistic herbivores and deposit feeders

tolerent to organic enrichment (Evagelopoulos et al., 2009). Combined nitrogen

and phosphate can be present in quantities insufficient to establish and maintain

communities favourable to salt production, or these nutrients may be present in

excessive concentrations. A biological system maintained at a desired condition

allows economic and continuous production of high quality salt at design capacity

(Davis, 2000; Moosvi, 2006; Kavakli et al., 2006).

The algal species Dunaliella salina improves evaporation, cleans the brine

from organic substances, resulting in growth of clear and large salt crystals, i.e.

improved salt quality (Reginald and Diana, 2008). Water is the most abiotic

40

component of all aquatic ecosystem and while studying the distribution of

phytoplankton the knowledge of the physico-chemical quality of water becomes

very important. Seasonal variation in physico-chemical characters of water

prevailing in this saltworks has not been studied in detail. Therefore it was thought

to undertake studies on physico-chemical quality of water samples (brine) in

Puthalam saltworks.

The research was aimed for the qualitative analysis of

physico-chemical parameters in different ponds of Puthalam saltworks due to

monthly variation.

41

3.2. MATERIALS AND METHODS

3.2.1. Study sites and sampling

The present study was carried out for a period of two years extending from

March 2009 to February 2011. The saltworks chosen for the study was divided into

reservoirs, condensers and crystallizers. The reservoir receives water directly from

the sub-soil brine. The water was allowed to stand in the reservoir for a period of

time for the salinity to increase, then it was pumped from the reservoir to the

condenser for evaporation and after a few days the concentrated brine transferred

into the crystallizer, where the water was retained for a period of few days for

crystallization. The saltpans are constructed and separated by mud ridges. The

pans which are roughly rectangular in shape are in different dimensions, about

9 – 12 m in width and 13 – 15 m in length. The series of ponds is a constant flow

system with ponds maintaining a stable hypersaline environment (Coleman, 2009).

The following physico-chemical parameters were studied by water samples (brine)

collected in the early morning hours from different pond systems viz. reservoir

ponds, condenser ponds and crystallizer ponds at Sri Sankara Allom Salt Factory,

Puthalam saltworks of Kanyakumari District. The samples were collected in

pre-cleaned polyethylene cans and labeled individually with details. Preservation

and transportation of the water samples brought to the laboratory for the estimation

of relevant water quality parameters. Each can was used for particular purpose

which is biological oxygen demand (BOD). The samples were analyzed for ten

different parameters as per standard methods. The data recorded weekly were

taken average and presented for monthly during the study period.

42

3.2.2. Analysis of physico-chemical parameters

3.2.2.1. Rainfall

The data on total rainfall of every month, commencing from March 2009 to

February 2011 were obtained from Meteorological department of the Collectorate,

Nagercoil, Kanyakumari District, Tamil Nadu.

3.2.2.2. Atmospheric temperature

During the study period the atmospheric temperature was measured on the

site using a 110oC thermometer.

3.2.2.3. Brine temperature

Brine temperature of all ponds was measured in the field by immersing a

110oC thermometer in proper depth.

3.2.2.4. pH

pH of the brine samples were recorded with Hi-Indicator pH paper

(pH range 2 to 10.5).

3.2.2.5. Depth

A calibrated measuring tape weighed at one end was used to measure brine

depth of the pond systems.

3.2.2.6. Salinity (Brine density)

The salinity of water in all the ponds were estimated with a hand salinity

Refractometer (News. 100 Thanka Sanjiro Co. Ltd., Japan., 1 ppt sensitivity).

43

3.2.2.7. Estimation of Biological Oxygen Demand (BOD – Winkler’s method)

Special reagents

a. Manganous sulphate reagent (Winkler A solution)

Dissolved 480 g of manganous sulphate tetrahydrate (MnSO4, 4H2O or

400 g of manganese sulphate dihydrate, MnSO4, 2H2O or 365 g of manganous

sulphate monohydrate, MnSO4, H2O) in distilled water and made the volume to 1

litre.

b. Alkaline iodide solution (Winkler B solution)

500 g of sodium hydroxide was diluted in 500 ml of distilled water.

Dissolved 300 g of potassium iodide in 450 ml of distilled water and the two

solutions were mixed.

c. Standard thiosulphate solution

Approximately 0.01 N thiosulphate solution was prepared by using 2.9 g of

sodium thiosulphate per litre.

d. Starch indicator solution

0.1 – 0.2% of starch indicator solution was prepared by 2 g of soluble starch

was suspended in 300-400 ml of distilled water.

e. 0.01 N iodate solution

This solution was prepared using exactly 0.3567 g of KIO3 dissolved in

1 litre of distilled water.

44

Procedure

The BOD bottle was opened and added 1 ml manganous sulphate (A)

reagent and 1ml of alkaline iodide solution (B). Restored the bottle immediately

and mixed the contents thoroughly by shaking until the precipitated manganous –

manganic hydroxide to disperse evenly. No air bubbles should be trapped in the

bottle. 1 ml of concentrated (sp. gr. 1.84) sulphuric acid was added to this and

re-stoppered the bottle and mixed so that all the precipitate dissolved. No air was

allowed in the bottle.

For acidification, 50 ml of solution should be transferred into a specially

painted conical flask by a pipette within an hour. It has to be titrated at once with

standard 0.01N thiosulphate solution until very pale straw colour remains. 5 ml of

starch indicator was added and concluded the titration. Disappearance of blue

colour was the end point.

f. Determination of the factor ‘f’

Brine water was filled in 300 ml bottle and added 1 ml of concentrated

sulphuric acid followed by 1 ml of alkaline iodide solution and mixed thoroughly.

Finally added 1 ml of manganous sulphate solution and mixed again.

Approximately 50 ml of aliquots was withdrawn into the titration flasks. One or

two flasks were used for blank determination and added 5 ml of iodate solution and

titrated against thiosulphate solution within 2-5 minutes. If ‘V’ is the titration in

millimeter the

f = for the 0.5 N thiosulphate (or)

1.00 V

45

f = V00.5

for the 0.01 N thiosulphate

The mean value of ‘f’ should be found from three replicates.

Calculation

Mg – at O2 / l = 0.1006 x f x V

When a 50 ml aliquot is taken from a 300 ml BOD bottle or

Mg- at O2 / litre = 2-Y

Y x

X5

x f x V

When a x – ml aliquot is taken from 7ml bottle.

Calculate f factor as mentioned above

AlO2 / litre = 11.20 x mg – at O2 / litre

3.2.2.8. Estimation of Total Dissolved Solids (Gravimetric method)

Principle

A known volume of water is evaporated to dryness and the quantity of the

dissolved solids present in the water is estimated gravimetrically.

Procedure

There had been pipetted out 100 ml of water into a clean dry potash basin,

the accurate weight of which has been taken by drying in a steam oven for an hour,

cooling in a desiccator and weighing in a chemical balance. The water was

evaporated in the basin to dryness over a water bath. Wiped the outside basin,

dried in an air oven at 105ºC for an hour to remove moisture, cooled and weighed.

46

The difference between the weights is the weight of total dissolved solid, which is

expressed as ppm.

Calculation

Volume of water taken = 100 ml

Weight of empty basin = A g

Weight of basin + residue = B g

Therefore, weight of total soluble salts = B – A

This is present in 100 ml of water

Therefore, the total dissolved solid content of water (ppm) = x 106

3.2.2.9. Estimation of chloride (Ewing, 1976)

Principle

The chloride present in the water is precipitated as silver chloride by

titration with standard silver nitrate solution using potassium chromate as the

indicator. After all the chloride is precipitated, the excess of silver nitrate combines

with potassium chromate indicator to form flesh red precipitate of silver chromate.

Procedure

Pipetted out 50 ml of the water into a porcelain dish. A few drops of

potassium chromate indicator was added and titrated against 0.1 N AgNO3 till the

flesh red precipitate of silver chromate appears. From the amount of 0.1 N AgNO3

consumed, the chloride content was calculated.

Calculation

Volume of water taken = 50 ml

(B – A) 100

47

Volume of 0.1 N AgNO3 used = A ml

1 ml of 0.1 N AgNO3 = 0.00355 g of Cl

Therefore, A ml of 0.1 N AgNO3 = 0.00355 x A

This is present in 50 ml of water

Therefore, the amount of chloride in water sample (ppm)

= x 106

In terms of m.e. / litre = x 1000 x 5.35

1000

3.2.2.10. Estimation of sulphate (Gravimetric method)

Sulphate in the water sample can be estimated by number of methods. They

include gravimetric, turbidimetric and volumetric methods. Gravimetric method is

used when the sulphate quantity is high in the sample. Volumetric method is used

when it is very low amount and turbidimetric method is used when medium range

of sulphate is present.

Gravimetric method

Principle

The sulphate in the water sample is precipitated as barium sulphate by the

addition of barium chloride in hydrochloric acid medium. The precipitate is

filtered, washed free of chloride, ignited and weighed as barium sulphate.

Procedure

Pipetted out 50 ml of the water sample into a clean 250 ml beaker. 10 ml of

HCl and 1 gm of solid ammonium chloride had been added. This was heated to

0.00355 x A 50

0.00355 x A 50

48

boiling and added 10 ml of 10% barium chloride solution drop by drop with

constant stirring and continued boiling for another 2 to 3 minutes. The precipitate

was allowed to settled and tested for completion of precipitation by adding a small

amount of barium chloride solution through the sides of the beaker. If any turbidity

is noticed, added sufficient quantity of barium chloride solution and digested for

half an hour to promote granulation of precipitate.

Filtered through whatman no. 42 filter paper and washed with boiling water

till the filtrate runs free of chloride (Test with silver nitrate solution). Then

transferred the filter paper along with the precipitate to a weighed silica basin and

dry it in hot air oven. Ignited over a low flame initially, taking care to ash the filter

paper completely and then ignited strongly over a rose head flame to constant

weight. From the weight of barium sulphate obtained, the sulphate content of the

sample was calculated.

Calculation

Volume of water sample used = 50 ml

Weight of empty silica crucible = A ml

Weight of crucible + BaSO4 precipitate = B ml

Weight of BaSO4 = (B-A) g

233 g of BaSO4 contains 96 g of SO4

Therefore, (B-A) g of BaSO4 will contain = 33.233

96 x (B – A) g of SO4

This is present in 50 ml of sample

Therefore, the amount of sulphate (ppm) = 3.233

96 x (B – A) x 106

49

In terms of m.e / litre = 3.233

96 x (B – A) x

501000

x 48

1000

3.2.2.11. Estimation of sodium (Flame photometry method)

Principle

Sodium emits a bright yellow colour when excited in the flame. The

intensity of emission is proportional to the concentration of sodium in the sample,

which is measured by flame photometer.

Procedure

The flame photometer was standardized before feeding the water. Set

galvanometer to zero using zero ppm sodium. Then by using the solution of 100

ppm sodium, adjusted the meter reading to 100. Then feed the water sample in the

flame photometer and noted the meter readings. From the standard curve of Na, the

concentration of Na (ppm) in the water sample was noted.

Preparation of standard curve

Dissolved 0.254 g of sodium chloride in distilled water in one litre

volumetric flask and made up the volume to the mark. This gives 100 ppm sodium

solution. From this, prepared a series of working standards (10, 20, 30, 40, 50, 60,

70, 80 and 90 ppm) by making (10, 20, 30, 40, 50, 60, 70, 80 and 90 ml of 100

ppm) sodium solution to 100 ml with distilled water. The flame photometer was

reading for the above sodium solutions and plotted the readings against the

corresponding concentrations of sodium. A standard curve for the concentration of

sodium in water samples were found by using these readings.

50

Calculation

Concentration of sodium from the standard curve = A ppm

Therefore, amount of sodium in m.e./litre = 23A

3.2.2.12. Estimation of calcium (Ewing, 1976)

Principle

Calcium is precipitated as calcium oxalate by the addition of ammonium

oxalate solution in acetic acid medium. The precipitate is washed free of chloride

and dissolved in sulphuric acid and titrated against 0.1 N potassium permanganate.

Procedure

Pipetted out 50 ml of the water sample into a 250 ml beaker. Added 5 ml of

HCl and boiled it. Then added about 1 to 2 g of solid NH4Cl. Two drops of methyl

red indicator followed by ammonium hydroxide (yellow colour). Then added

acetic acid drop by drop till slight red colour develops. Boiled it and added 10 ml

of saturated ammonium oxalate solution. Boiled and digested on a sand bath for

about half an hour, allowed to cool and tested the completion of the precipitation.

Filtered through No.40 filter paper. Washed the precipitate with luke-warm water

until free of chloride. Reserved the filtrate and washings for the estimation of

magnesium.

Pierced the filter paper by a glass rod, washed down all the precipitate on

filter paper with a jet of hot water collecting them in the same beaker in which the

precipitate was made. Added 10 ml of warm H2SO4 and on the filter paper and

washed the paper again with the jet of hot water. Warmed the contents of the

51

beaker at 65oC and titrated against 0.1 N KMnO4 till a pink colour appeared.

Towards the end of the titration, added the filter paper and continued the titration

till a faint pink colour developed which was stable for a minute or two. Don’t pulp

the filter paper while titrating.

Calculation

Volume of water sample = 50 ml

Volume of 0.1 N KMnO4 used = A ml

1 ml of 0.1 KMnO4 = 0.002 g of Ca

Therefore, A ml of 0.1 N KMnO4 = 0.002 x A

Therefore, Ca present in ppm = 50002.0

x A x 106

In terms of m.e. / litre = 50002.0

x A x 1000 x 20

1000

3.2.2.13. Estimation of iron (Laitinen and Harris, 1975 )

Principle

The iron in the water sample is reduced to ferrous form by adding dilute

sulphuric acid and zinc granules. The ferrous iron is oxidized to ferric form by

titration with standard potassium permanganate. Using the volume of standard

potassium permanganate consumed, the content of iron is estimated.

Procedure

Pipetted out 25 ml of the water sample into a porcelain basin and

evaporated to dryness over a water bath. When completely dried, about 1 to 2 ml

of concentrated H2SO4 was added and again evaporated almost to dryness. The

52

residue must become white showing that all the iron has been converted into ferric

sulphate. The residue was transferred to a 250 ml conical flask using hot water and

then added 40 ml of H2SO4 (1 : 4) and few zinc granules. The mouth of the conical

flask was covered with a funnel. Warmed if necessary to start the reaction and

allowed to stand for at least half an hour for complete reduction. Test was done for

the complete reduction using ammonium thiocyanate solution (2 %) taken on a

porcelain tile against a drop of the solution. There should not be any blood red

colour. This was filtered into a 250 ml conical flask containing a pinch of sodium

carbonate through glass wool and washed the original flask and funnel with hot

water and collected the washings in the 250 ml conical flask. Then titrated it

immediately against 0.1 N KMnO4. Appearance of permanent pink colour indicated

the end point.

Calculation

Volume of water sample taken = 25 ml

Volume of 0.1 N KMnO4 used = V ml

1 ml of 0.1 N KMnO4 = 0.0056 g Fe

Therefore, ‘V’ ml of 0.1N KMnO4 = 0.0056 x V

This is present in 25 ml Therefore, amount of Fe (ppm) = x 1000 x

In terms of m.e. / litre = 25

V x 0.056 x 1000 x

561000

0.056 x V 25

106 5

53

3.2.2.14. Estimation of magnesium (Laitinen and Harris, 1975)

Principle

Magnesium is precipitated in ammonia medium as magnesium ammonium

phosphate by the addition of disodium hydrogen phosphate. The precipitate is

filtered, washed with dilute ammonia free of chloride, dried and weighed as

magnesium pyrophosphate.

Procedure

The filtrate obtained was taken from the estimation of calcium and reduced

the volume to about 100 ml by evaporation. A pinch of solid ammonium chloride

and ammonium hydroxide solution (1 : 4) was added till the medium turns to

alkaline. Then added 10 to 15 ml of freshly prepared 10% disodium hydrogen

phosphate solution and stirred well. This was left for overnight to complete the

precipitation. The precipitate is magnesium ammonium phosphate.

Filtered through Whatman No.42 filter paper. Washed with dilute ammonia

(1:7) till free of chloride and transferred all the precipitate to filter. Dried the filter

in a hot air oven and ignited in a weighed crucible till the residue becomes white.

Then cooled in a desiccator and weighted as magnesium pyrophosphate.

Calculation

Volume of water sample = 50 ml

Weight of empty crucible = A ml

Weight of crucible + residue = B ml

Weight of magnesium pyrophosphate = (B – A) g

54

1 molecule of Mg2P2O7 contains 2 mg atoms

i.e. 222 g of Mg2P2O7 = 2 x 12 = 24 g of Mg

(B-A) g of Mg2P2O7 = (B – A) x 22224

g of Mg

This is present in 50 ml

Therefore, in ppm = (B - A) x 22224

x

In terms of m.e./litre = (B - A) x 22224

x 1000 x 12

1000

3.2.2.15. Estimation of potassium (Flame photometry method)

Principle

Potassium emits a lilac colour when excited in the flame. The intensity of

emission is proportional to the concentrations of potassium in the sample, which is

read through a flame photometer.

Procedure

The flame photometer was standardized before feeding the water sample.

The galvanometer was set to zero using distilled water. Then by using the solution

of 100 ppm potassium, the meter was adjusted to read 100. Then feed the water

sample in the flame photometer and noted the meter readings. From the standard

curve for potassium, the concentration of potassium (ppm) in the water sample was

found out.

106 50

55

Preparation of standard curve

Dissolved 0.1907 g of KCl in distilled water in a 1000 ml volumetric flask

and the volume was made to the mark. This gives 100 ppm potassium solution.

From this, working standards of potassium (10, 20, 30, 40, 50, 60, 70, 80 and 90

ppm) was prepared by making up 10, 20, 30, 40, 50, 60, 70, 80 and 90 ml of 100

ppm potassium solution to 100 ml. The flame photometer reading was

corresponding concentration of potassium. This was used as a standard curve for

finding out the concentration of potassium in the water samples.

Calculation

Concentration of K from the standard curve = A ppm

In terms of m.e. / litre = 39A

56

3.3. RESULTS

The physico-chemical patameters are considered as the most important

principles in the identification of the nature, quality and type of water for any

aquatic ecosystem. So the physico-chemical parameters of the brine (water)

samples in the various ponds of the Puthalam saltworks during the investigation

period March 2009 to February 2011 were recorded and described below.

3.3.1. Rainfall

The variation in the total rainfall recorded during different months of the

study period (from March 2009 to February 2011) is presented in Table 3.1. The

rainfall data are based on the report of the Meteorological section of Collector’s

Office, Nagercoil, Kanyakumari District, Tamil Nadu. The recorded data ranged

from 11.3 mm to 358.50 mm in the first year study (from March 2009 to February

2010). According to it there was a maximum rainfall of 358.50 mm was recorded

in the month of November and the minimum rainfall of 11.3 mm was recorded in

the month of December. There was no rainfall in the month of February.

In the second year study (from March 2010 to February 2011) the recorded

rainfall data ranged from 0.2 mm to 452.9 mm. The maximum rainfall (452.90 mm)

was registered in the month of November as same as 2009. But the minimum

rainfall (0.2 mm) was recorded in the month of March. There was no rain in the

month of February 2011, as same as 2010 (Fig. 3.1).

57

3.3.2. Atmospheric temperature

The mean monthly variation of the atmospheric temperature in Puthalam

saltworks was recorded during the study period and presented in Table 3.2. The

maximum atmospheric temperature recorded was 30.66ºC for the month of May

2009 and the minimum temperature observed was 24.2ºC during November 2009.

The year-wise mean of 27.43 ± 3.23ºC was registered in the first year study period.

According to the data the maximum atmospheric temperature of 30.16ºC

was observed in the month of March, 2010 and the minimum of 24.26ºC was

recorded in the month of November 2010. The year-wise mean of 27.21 ± 2.95ºC

was calculated for the second year of study (Fig. 3.2).

From the recorded data, it is clearly stated that the atmospheric temperature

in Puthalam saltworks was almost same during the both years of study.

3.3.3. Brine temperature

Mean monthly variation of brine temperature was recorded in the various

ponds (reservoir, condenser and crystallizer) during the study period (first year) is

shown in Table 3.3 and Fig. 3.3 The temperature of the brine samples was found to

be the highest in the reservoir pond was 28.62ºC during February. The lowest brine

temperature was 22.67ºC during November were recorded and the annual mean

brine temperature was 25.65 ± 2.98ºC. Condenser pond showed the maximum

brine temperature of 30.2ºC and the minimum of 24.47ºC. Here the mean brine

temperature of 27.34 ± 2.87ºC was observed. Similarly, the water samples of the

58

crystallizer pond attained the maximum brine temperature of 32.47ºC and the

minimum of 25.5ºC along the mean value of 28.98 ± 3.49ºC were recorded.

Statistical analysis (two-way ANOVA) for the data on the brine

temperature, as a function of sampling ponds and months showed that the variation

between ponds and months were statistically significant (F = 56.9486;

P < 0.05 and F = 13.7756; P < 0.05) during the first year study.

The data on mean monthly variation of brine temperature in the different

ponds of Puthalam saltworks under the study in the second year is presented in

Table 3.4 and Fig. 3.4. Here the highest brine temperature was recorded in the

reservoir pond was 27.3ºC. At the same time, the lowest brine temperature was

19.3ºC with the mean brine temperature of 23.3 ± 4ºC were recorded. In the

condenser pond, the samples reached maximum brine temperature of 29.01ºC and

the minimum of 22ºC, but the mean brine temperature observed was 25.51 ±

3.50ºC. The crystallizer pond attained the highest brine temperature of 30.2ºC and

the lowest of 22.97ºC. At the same time, the mean brine temperature 26.59 ±

3.61ºC was registered.

Two way ANOVA (analysis of variance) test conducted for the data on

brine temperature as a function of sampling ponds and months revealed that the

variation between ponds and months were statistically significant (F = 42.4116;

P < 0.05 and F = 17.6445; P < 0.05) during the second year study.

59

3.3.4. pH

Table 3.5 and Fig. 3.5 shows the average pH of the brine samples in the

different ponds during the first year study. The pH values were found to be the

maximum of 6.92 and the minimum of 6.12 with the mean value of 6.52 ± 0.4 pH

in the reservoir pond. The condenser pond reached the high pH value of 8.73 and

the low pH value of 6.93 with the mean pH value of 7.83 ± 0.9 were observed. The

maximum pH value of 8.89 and the minimum of 7.19 was registered in the

crystallizer pond with the annual mean pH of 8.04 ± 0.85.

In the first year study, the statistical analysis by two way ANOVA for the

data on brine pH as a function of sampling ponds and months showed that the

variation between ponds were statistically significant (F = 46.87075; P < 0.05) and

the variation between the months was not statistically significant (F = 2.037396;

P < 0.05).

Data on the brine pH of all the ponds in the time of second year was

recorded and presented in Table 3.6 and Fig. 3.6. Reservoir pond showed the

maximum and minimum brine pH of 6.89 and 6.06 respectively. The mean brine

pH in the reservoir pond was 6.48 ± 0.42. Meanwhile, the maximum of 6.88 and

the minimum of 8.03 with the mean brine pH of 7.46 ± 0.58 was recorded in the

condenser pond. In the crystallizer pond, the highest pH 8.76 was registered and

the lowest pH 7.13 was observed along the mean pH 7.95 ± 0.82.

Statistical analysis (two way ANOVA) for the data on brine pH as a

function of sampling ponds and months showed that the variation between the

60

ponds were statistically significant (F = 83.27678; P < 0.05) but the variation

between the months was not statistically significant (F = 2.093874; P < 0.05)

during the second year study.

3.3.5. Depth of the ponds

The data on the depth of the various ponds in the Puthalam saltworks was

recorded during the first year is given in Table 3.7 and Fig. 3.7. The reservoir pond

showed the maximum depth of 69.2 cm and the minimum of 54.3 cm with the year-

wise mean of 61.75 ± 7.45 cm. The average monthly depth in the condenser pond

was registered the maximum depth of 14.6 cm and the minimum depth of 10.6 cm

along the mean value of 12.6 ± 2 cm. The depth of the brine samples of the

crystallizer pond attained the maximum, minimum and the mean value of 6.5, 4.4

and 5.45 ± 1.05 cm respectively.

The two way ANOVA test conducted for the data on depth as a function of

sampling ponds and months showed the variation between ponds were statistically

significant (F = 2125.085; P < 0.05) but the variation between months was not

statistically significant (F = 1.475729; P < 0.05) during the first year study.

The monthly variation of the depth in the sampling ponds of Puthalam

saltworks during the second year study was presented in Table 3.8 and Fig. 3.8

The reservoir pond reached the maximum value of 69.7 cm and the minimum value

of 54.5 cm with a mean value of 62.1 ± 7.6 cm. Condenser pond showed the

maximum, minimum and mean value of 13.9, 10.1 and 12.0 ± 1.9 cm depth

respectively. Crystallizer pond registered the maximum depth of 5.2 cm, minimum

61

of 3.3 cm and a mean value of 4.2 ± 0.95 cm. Among the ponds, the depth of the

crystallizer pond was low when compared with other ponds.

It is inferred from the results of the two way ANOVA test for the data on

depth as a function of sampling ponds and months showed that the variation

between ponds were statistically significant (F = 1263.658; P < 0.05) but the

variation due to months was not statistically significant (F = 0.975735; P < 0.05)

during the second year study.

3.3.6. Salinity (Brine density)

Table 3.9 and Fig. 3.9 represent the mean monthly variation in brine density

(ppt) recorded in the various ponds of the Puthalam saltworks during the first year

study. The samples attained the maximum salinity of 60.4 ppt and the minimum

salinity of 42.7 ppt was observed in reservoir pond. Likewise, in the condenser

pond the maximum of 141.5 ppt and the minimum of 120 ppt was observed with

the mean brine salinity of 130.75 ± 10.75 ppt. In the crystallizer pond, the highest

salinity was 205.5 ppt and the lowest salinity was 183.75 ppt were observed. At

the same time the mean salinity was 194.63 ± 10.88 ppt.

Two way ANOVA, analysis of variance revealed that the variation of

salinity between ponds and the variation between months were statistically

significant (F = 1560.517; P < 0.05 and F = 2.116575; P < 0.05).

The data on the monthly variation in salinity (ppt) of various ponds during

the second year study are given in Table 3.10 and Fig. 3.10. In the reservoir pond,

the highest salinity 66.52 ppt was noticed and the lowest salinity was 38.01 ppt.

62

Following this, the condenser pond showed the maximum, minimum and the mean

salinity of 128.1, 105.32 and 116.71 ± 11.39 ppt respectively. In the crystallizer

pond, the maximum salinity of 205 ppt, and the minimum of 175.25 ppt and the

mean salinity recorded was 190.13 ± 14.88 ppt.

Two way analysis of variance for the data on brine salinity as a function of

sampling ponds and months showed that the variation between ponds and months

were statistically significant (F= 3273.987; P < 0.05 and F = 13.40923; P < 0.05)

during the second year study.

3.3.7. Biological oxygen demand (BOD)

The biological oxygen demand level for twelve months of the study period

(March 2009 to February 2010) in all the ponds of Puthalam saltworks is presented

in Table 3.11 and Fig. 3.11. In all the different ponds the BOD level was varied.

For instance, the reservoir pond showed the maximum of 11.64 and minimum of

8.35 mg/l with the mean BOD level of 9.99 ± 1.64 mg/l. Similarly, the condenser

pond recorded the maximum of 16.43 mg/l and minimum of 12.13 mg/l with the

mean value of 14.28 ± 2.15 mg/l. The BOD level of the brine samples showed the

highest value of 21.15 and lowest of 15.61 along the mean value of 18.38 ± 2.76

mg/l were noticed in the crystallizer pond.

The statistical analysis (two way ANOVA) for the data on biological

oxygen demand in the brine as a function of sampling ponds and months showed

that the variation between ponds and months were statistically significant

(F = 275.5834; P < 0.05 and F = 4.516973; P < 0.05) in the first year investigation.

63

Table 3.12 and Fig. 3.12 represent the data on the monthly variation of

biological oxygen demand in the brine samples of various ponds in Puthalam

saltworks during the study period from March 2010 to February 2011. The

reservoir pond showed the maximum of 10.61 mg/l, minimum of 6.01 mg/l and

mean of 8.31 ± 2.29 mg/l. Likewise, the condenser pond showed the highest of

13.03 mg/l, lowest of 9.01 mg/l and a mean of 11.02 ± 2 mg/l of BOD. The

crystallizer pond expressed a maximum biological oxygen demand of 18.75 mg/l,

minimum level of 12.60 mg/l with the mean level of 15.67 ± 3.07 mg/l.

The two way ANOVA for the data on BOD as a function of sampling ponds

and months showed that the variation between ponds and months were statistically

significant (F = 223.8827; P < 0.05 and F = 10.05913; P < 0.05) during the study

period of March 2010 to February 2011.

3.3.8. Total Dissolved Solids (TDS)

The data on the mean monthly variation of total dissolved solids (ppm/100

ml) in the brine samples of different ponds during the first year study were

observed and shown in Table 3.13 and Fig. 3.13 for the first year study. High value

of total dissolved solids in the reservoir pond was 4.89 ppm/100 ml and a low value

of 1.63 ppm/100 ml was observed. Also, the mean value of 3.26 ± 1.62 ppm/100

ml was recorded. Following the reservoir, the condenser pond had the peak value

of 8.82 ppm/100 ml, the low value of 4.07 ppm/100 ml and the mean value of 6.45

± 2.37 ppm/100ml were noticed. Likewise, in the crystallizer pond the maximum,

minimum and the mean value of 10.50, 5.53 and 8.01 ± 2.48 ppm/100 ml total

dissolved solids observed respectively.

64

From the results of the statistical analysis the total dissolved solids in brine

samples as a function of sampling ponds and months showed that the variation

between ponds and months were statistically significant (F= 119.1517; P < 0.05

and F= 5.982365; P < 0.05) during the study period.

Table 3.14 and Fig. 3.14 depicts the results on the monthly variation of TDS

in different ponds during the investigation period. Maximum of 2.43 ppm/100 ml

and the minimum of 1.04 ppm/100 ml along the mean value of 1.71 ± 0.69

ppm/100 ml total dissolved solids were found in the reservoir pond. The brine

samples of condenser pond registered the maximum value of 7.97 ppm/100 ml and

the minimum of 3.09 ppm/100 ml with the mean value of 5.53 ± 2.43 ppm/100 ml

TDS. Similarly, in the crystallizer pond the highest total dissolved solids value of

9.44 ppm/100 ml and the lowest of 6.41 ppm/100 ml was observed for the mean

value of 7.92 ± 1.51 ppm/100 ml.

For the second year, the two way ANOVA test was conducted for the data

on the total dissolved solids as a function of sampling ponds and months showed

that the variation between ponds and months were statistically significant

(F = 115.058; P < 0.05 and F = 3.91682; P < 0.05).

3.3.9. Chloride content

Chloride content in the brine samples of various ponds of Puthalam

saltworks during the first year investigation is provided in Table 3.15 and Fig. 3.15.

Among the tested samples, the maximum of 2.96, minimum of 1.54 and the mean

value of 2.25 ± 0.70 ppm/l chloride content was observed in reservoir pond. The

condenser pond possessed the highest chloride concentration of 4.96 ppm/l in the

65

month of March and the lowest chloride concentration of 3.60 ppm/l in the month

of August with the mean chloride content of 4.28 ± 0.68 ppm/l were recorded. The

chloride content of the brine samples in crystallizer ponds registered the maximum,

minimum and the mean value of 5.92, 4.61, 5.26 ± 0.65 ppm/l respectively.

The two-way analysis of variance for the data on chloride content of brine

samples showed that the variation between ponds and the variation between months

were statistically significant (F = 185.8784; P < 0.05 and F = 3.001843; P < 0.05).

The results on the chloride content in the brine samples of various ponds of

Puthalam saltworks were estimated and given in Table 3.16 and Fig. 3.16. The

chloride content was maximum of 2.84 ppm/l and the minimum of 1.56 ppm/l in

reservoir pond with the year-wise mean of 2.20 ± 0.64 ppm/l were calculated. In

the condenser pond, the highest, lowest and the mean value of 4.90, 3.57 and 4.23 ±

0.66 ppm/l of chloride content was noticed respectively. The samples resulted with

the maximum chloride content of 5.99 ppm/l, the minimum of 4.41 ppm/l and the

mean value of 5.20 ± 0.78 ppm/l were recorded in the crystallizer pond.

Results of the two way ANOVA test conducted for the data of the second

year study on chloride content as a function of sampling ponds and months showed

that the variation between ponds were statistically significant (F = 139.7373;

P < 0.05) but the variation between months were not statistically significant

(F = 1.141513; P < 0.05).

66

3.3.10. Sulphate content

The recorded sulphate content of the brine samples in different ponds of

Puthalam saltworks during the first year is provided in Table 3.17 and Fig. 3.17.

The reservoir pond expressed the maximum sulphate content of 0.067 ppm/l and

the minimum of 0.051 ppm/l along the mean value of 0.059 ± 0.008 ppm/l. The

condenser pond registered the highest, lowest and mean value of 0.171, 0.162 and

0.166 ± 0.007 ppm/l sulphate content. Likewise, the maximum of 0.215 ppm/l, the

minimum of 0.185 ppm/l and the mean level of 0.2 ± 0.015 ppm/l sulphate content

were recorded in the brine samples of crystallizer.

Statistical analysis (two way ANOVA) for the first year data on sulphate

content as a function of sampling ponds and months showed that the variation

between ponds were statistically significant (F = 1154. 017; P < 0.05) but the

variation between months were statistically not significant (F = 1.808528;

P < 0.05).

The data on the monthly fluctuation of the sulphate content in the

experimental ponds during the second year is given in the Table 3.18 and

Fig. 3.18. Reservoir pond showed the maximum, minimum and mean value of

0.089, 0.051 and 0.07 ± 0.019 ppm/l sulphate content respectively. In the

condenser pond, the highest sulphate level of 0.196 ppm/l and the lowest level of

0.125 ppm/l were recorded. Meanwhile, the mean level of the sulphate content was

0.16 ± 0.035 ppm/l. The sulphate content of the brine samples showed the highest

value of 0.298 ppm/l and the lowest value of 0.222 ppm/l with the mean value of

0.26 ± 0.038 ppm/l in the crystallizer pond.

67

The two way analysis of variance for the data on sulphate content of the

brine samples as a function of sampling ponds and months showed that the

variation between ponds and months were statistically significant (F = 569.7121;

P < 0.05 and F = 5.959617; P < 0.05) during the second year study.

3.3.11. Sodium content

The results on the variation in sodium content of the various ponds of

Puthalam saltworks during the first year study is provided in Table 3.19 and

Fig. 3.19. The samples in the reservoir pond showed the maximum sodium content

of 1.11 ppm/l and the minimum of 0.42 ppm/l along the mean level of 0.76 ± 0.34

ppm/l. In the condenser pond the sodium content showed the highest of 2.66 ppm/l

and lowest of 1.46 ppm/l with the mean value of 2.06 ± 0.59 ppm/l. Likewise, the

crystallizer pond showed the maximum, minimum and the mean sodium content of

3.56, 2.25 and 2.90 ± 0.65 ppm/l respectively.

Result of the ANOVA test conducted for the data on sodium content of the

brine samples as function of sampling ponds and months showed that the variation

between ponds and months were statistically significant (F = 236.7946; P < 0.05

and F = 4.350909; P < 0.05) during the first year investigation.

The sodium content in the brine samples collected from the various ponds

of Puthalam saltworks for every month of the second year study are shown in the

Table 3.20 and Fig. 3.20. The sodium content of the reservoir pond proved that the

maximum of 0.77 ppm/l and the minimum of 0.48 ppm/l with the mean value of

0.62 ± 0.14 ppm/l. In the condenser pond, the greatest, smallest and mean sodium

content was 2.03, 0.71 and 1.37 ± 0.66 ppm/l respectively. Similarly, the

68

crystallizer pond showed the highest sodium content of 2.76 and the minimum of

1.53 with the mean sodium content of 2.15 ± 0.61 ppm/l were observed.

From the statistical analysis (two way ANOVA) it is inferred that the data

on sodium content of the brine samples as a function of sampling ponds are months

showed that the variation between ponds and months were statistically significant

(F = 62.86229; P < 0.05 and F = 3.049863; P < 0.05) on the second year study.

3.3.12. Calcium content

Table 3.21 and Fig. 3.21 provides the data on the monthly variation of

calcium content recorded in the brine samples of different ponds under the first

year study. The maximum value of 0.07 ppm/l and the minimum of 0.02 ppm/l

calcium content with the mean calcium content of 0.05 ± 0.02 ppm/l was observed

in reservoid pond. Also, the condenser pond registered the high value of 0.12 and

the low value of 0.07 ppm/l with the mean value of 0.09 ± 0.02 ppm/l calcium

content. The brine samples of the crystallizer pond clearly proved that the

maximum calcium content of 0.16 ppm/l and the minimum calcium content of

0.10 ppm/l along the mean value of 0.13 ± 0.02 ppm/l were noticed during

investigation. Among the ponds, the calcium content was more in the crystallizer

pond when compared with other ponds.

From the result of the statistical analysis (two way ANOVA) the calcium

content of brine samples between the sampling ponds and months showed that the

variation between ponds were statistically significant (F = 52.23997; P < 0.05) and

the variation between months were not statistically significant (F = 0.849422;

P < 0.05).

69

The data on the monthly variation in the calcium content of brine samples in

Puthalam saltworks during March 2010 to February 2011 were recorded and

presented in the Table 3.22 and Fig. 3.22. The reservoir pond had the minimum of

0.04 ppm/l in the month of December 2010 and the maximum of 0.12 ppm/l in the

month of February 2011 with the year-wise mean of 0.08 ± 0.04 ppm/l. In the

period of investigation, the highest calcium content observed in the condenser

samples was 0.18 ppm/l, whereas the lowest calcium content of 0.09 ppm/l was

recorded with a mean of 0.13 ± 0.04 ppm/l. The maximum of 0.27, minimum of

0.12 and the mean value of 0.20 ± 0.07 ppm/l calcium content were noticed in the

crystallizer pond.

Results on the two way ANOVA test conducted for the data on the calcium

content of the brine samples as a function of sampling ponds and months showed

that the variation between ponds and months were statistically significant

(F = 39.84883; P < 0.05 and F = 2.733082; P < 0.05) during the second year.

3.3.13. Iron content

The iron content of the brine samples in the respective ponds of Puthalam

saltworks was studied and presented in Table 3.23 and Fig. 3.23. The calcium

content present in the brine of reservoir pond ranged from 0.01 to 0.12 ppm/l. The

mean calcium content was 0.06 ± 0.05 ppm/l. Similarly, the brine samples of the

condenser pond fluctuated between 0.06 and 0.16 with the mean of 0.11 ± 0.05

ppm/l. In the crystallizer pond the maximum and the minimum iron content of 0.19

and 0.10 ppm/l along the mean value of 0.15 ± 0.04 ppm/l were recorded.

70

Statistical analysis of the two way ANOVA for the data on iron content of

the brine samples as a function of sampling ponds and months showed that the

variation between ponds and months were statistically significant (F = 61.13341;

P < 0.05 and F = 7.366142; P < 0.05) during the first year.

The values of iron content in the brine samples of various ponds of

Puthalam saltworks during the second year study was recorded and tabulated in

Table 3.24 and Fig. 3.24. The amount of iron content in the brine samples of the

reservoir pond ranged between 0.10 to 0.18 ppm/l along the mean of 0.14 ± 0.04

ppm/l. In the condenser pond, a maximum of 0.28, minimum of 0.15 and the mean

value of 0.21 ± 0.06 ppm/l of iron content were noticed. Similarly the brine

samples of crystallizer pond had the highest iron content of 0.34 ppm/l whereas the

lowest of 0.22 ppm/l was observed. At the same time mean iron content was 0.28 ±

0.06 ppm/l.

The two way ANOVA test conducted for the data of the variation in iron

content of the brine samples as a function of sampling ponds and months were

observed that the variation between ponds were statistically significant

(F = 51.1074; P < 0.05) but the variation between months were not statistically

significant (F = 1.551472; P < 0.05) during the second year study.

3.3.14. Magnesium content

The monthly variation in the magnesium content in different ponds of

Puthalam saltworks was conducted and presented in Table 3.25 and Fig. 3.25. The

brine samples in the reservoir pond expressed maximum of 0.18, minimum of 0.05

with the mean value of 0.11 ± 0.06 ppm/l. The tested brine samples of the

71

condenser pond registered the maximum magnesium content of 0.59 ppm/l,

minimum magnesium content of 0.14 ppm/l and the mean magnesium content of

0.37 ± 0.22 ppm/l. In the crystallizer pond, the magnesium content with the

maximum value of 0.89, minimum of 0.46 along the mean value of 0.67 ± 0.21

ppm/l were recorded.

The two-way analysis of variance for the data on magnesium content of the

brine samples as a function of sampling ponds and months showed that the

variation between ponds and months were statistically significant (F = 112.1862;

P < 0.05 and F = 5.234352; P < 0.05) in the first year study.

Table 3.26 and Fig. 3.26 represents the data on monthly variation of

magnesium content in the brine samples of Puthalam saltworks. The magnesium

content in the reservoir pond showed the highest value of 0.24, lowest value of 0.15

with the mean value of 0.20 ± 0.04 ppm/l. In the condenser pond, maximum of

0.48, minimum of 0.31 with the mean value of 0.39 ± 0.08 ppm/l magnesium

content were recorded. The crystallizer pond had the maximum of 0.59, minimum

of 0.41 along the mean value of 0.50 ± 0.09 ppm/l magnesium content.

The results of two-way ANOVA test revealed that the data on magnesium

content of the brine samples as a function of sampling ponds and months showed

that the variation between ponds and months were statistically significant

(F = 159.9743; P < 0.05 and F = 3.223526; P < 0.05) during the second year.

72

3.3.15. Potassium content

The data on potassium content in the brine samples from the different ponds

of Puthalam saltworks was observed during the first year study. The results

showed that the reservoir pond had a maximum of 0.18, minimum of 0.09 and the

mean value of 0.14 ± 0.04 ppm/l. The condenser pond expressed the potassium

ranged from 0.31 to 0.12 with the mean value of 0.21 ± 0.09 ppm/l. The potassium

content registered the highest value of 0.42, the lowest value of 0.28 with the mean

of 0.35 ± 0.07 ppm/l (Table 3.27 and Fig. 3.27) in the crystallizer pond.

Results of the two way ANOVA test conducted for the data on potassium

content of the brine samples as a function of sampling ponds and months showed

that the variation between ponds and months were statistically significant

(F = 123.5165; P < 0.05 and F = 6.025474; P < 0.05).

The data on potassium content in various ponds of Puthalam saltworks

during the second year investigation is given in the Table 3.28 and Fig. 3.28. In the

reservoir pond the maximum value recorded was 0.14 and the minimum was 0.04

with a mean value of 0.09 ± 0.04 ppm/l. Following this, the condenser pond

showed highest potassium content of 0.26 and minimum potassium content of 0.10

along the mean value of 0.18 ± 0.07 ppm/l. The brine samples recorded the

maximum of 0.34, minimum of 0.19 and the mean value of 0.26 ± 0.07 ppm/l

potassium content in the crystallizer pond.

It is inferred from the results of the two way ANOVA test conducted for the

data on potassium content of brine samples as a function of sampling ponds and

months showed that the variation between ponds and months were statistically

73

significant (F = 117.377; P < 0.05 and F = 8.834485; P < 0.05) during the second

year study investigation.

74

3.4. DISCUSSION

Solar salt production process is a semi-agricultural operation involving the

physical process evaporation, in extensive open areas; climatic conditions have an

important role to play within the solar saltpans. Among the different climatic

factors, rainfall and atmospheric temperature are the two crucial elements that

influence the solar evaporation process. As evaporation proceeds, enormous

changes in the physical parameters like pH, salinity and brine temperature as well

as changes in the concentration of chemical constituents like chloride, sulphate,

sodium, potassium, magnesium, iron and calcium take place. Hence the results

obtained during the investigation period were discussed and proved as follows.

In the present study, it was observed that the highest rainfall of 358.50 mm

in the year 2009; 452.90 mm in 2010 were recorded during November in both

years. The rainfall not only diluted the brine of the different stages of salt

production process but also greatly affected the salt production process. In this

period, the salt production process was affected in Puthalam saltworks. Monsoon

makes vital changes in water quality that affects the hydrochemistry of any water

body (Santhosh et al., 2006). There was no rainfall in the month of February in

both years (2010 and 2011).

Atmospheric temperature is the intensity aspect of heat energy fall into the

earth from the sun by solar radiation. It indirectly modifies the effects of other

ecological agents and is a universal influence. The difference in atmospheric

temperature determines the solar heat storage of water (Lal, 2005).

75

As rainfall and atmospheric temperature are directly related, the highest

monthly atmospheric temperature monitored in the saltworks was 30.66 ± 0.57 ºC

in the month of May, 2009 and was 30.16 ± 0.94ºC in March, 2010. It is clearly

revealed that this high temperature was with reference to the summer season in this

month. Similarly the atmospheric temperature fell down to 24.2ºC in the month of

November for both years and this low temperature was due to high rainfall.

Contradictory to the above statement, there was not much notable decrease of

temperature in other months even there were rainfalls.

The role of light and temperature is considered as two important factors in

salt manufacture. Light has very significant role on the growth and internal

composition of marine algae. The effects of varying light intensity range from the

seasonal slowing/acceleration of growth rates in marine ecosystems, or marine

microalgae sinking through the water column and out of the photic zone due to

light attenuation (Barnes and Mann, 1999). The growth pattern of algae too will

change due to changes in light and temperature. Evaporation is one of the most

important factors affecting salinity (Singh, 1992). Temperature positively affects

salinity as higher temperature promotes evaporation in water bodies concentrating

salts.

The quality of surface water is a very sensitive issue. Temperature has been

identified as the primary abiotic factor controlling physiological, biochemical and

biological activities of water body (Delince, 1992). Lower temperatures are likely

to reduce metabolism and growth. Analysis of the brine temperature fluctuations

indicated four distinct seasons in the ecosystem. The results recorded in the study

76

showed temperature was significantly higher in dry months are influenced by the

intensity of solar radiation, evaporation (Abowei, 2010; Sankar et al., 2010) and

the observed low values of rainy seasons that was in October, November and

December for both years. It is understood that low temperature was with reference

to the heavy rainfall. The present investigation is in accordance with the earlier

reports (Chidambarathanu, 1998; Reginald, 2003; Sekar, 2010; Pradeep et al.,

2012).

The present study revealed that the pH values of the investigated pond

reservoir lies on the alkaline range for both years showed almost same. But the pH

range increased from reservoir to condenser and pH concentration was near neutral

throughout the study period. It was within the range (pH 6.93 to 8.73) as reported

by Antoine and Al-Saadi (1982). The increase in the pH of water samples from

reservoir to crystallizer through condenser was due to the increase in the

concentration of iron oxide and calcium carbonate. But the recorded data on pH

variation in relation with the increasing salinity that is from reservoir to crystallizer,

the pH was also increasing. This fluctuation in pH range, observed at various

ponds of Puthalam saltworks parallels the other works (Ibrahim et al., 2009;

Touliabah et al., 2010; Govindasamy et al., 2012). Most of the natural seawaters

are generally alkaline due to the presence of sufficient quantities of carbonate.

Alkaline state of pH might be due to the chemical buffering and release of

bicarbonate and carbonate ions or salts (Sharma and Gupta, 2004). Examination of

the pH values of the water samples revealed that from condenser to crystallizer

ponds the pH crossed the alkaline to acidic range. It was due to the greater

77

concentration of magnesium sulphate and magnesium chloride in the crystallizer

and in the water samples (Rose, 2007). Krumgalz et al. (1980) and El-Din (1990)

reported that the seasonal variation in pH was mainly affected by temperature,

salinity, carbonate and bicarbonate system, rather than the photosynthetic activity

of the primary producers.

The depth of the various ponds of Puthalam saltworks had a direct relation

with temperature. The depth of the ponds showed a great decrease from reservoir

to crystallizer. It leads the higher evaporation rate in condenser and crystallizer

than reservoir. The shallow salt pits have quicker evaporation rate, high salinity

and high temperature. These parameters have a very good relationship with the

depth of the ponds. The mean depth values of reservoir, condenser and crystallizer

ponds were 61.75, 12.6 and 5.4 cm respectively during first year and 62.1, 12.0 and

4.2 cm respectively in second year. 15 to 40 cm of brine height is the best practice

in solar salt production (Garcia, 1993; Lartey, 1997). The variations in atmospheric

temperature due to several other intrinsic chemical parameters that acquire during

brine concentration and salt crystallization. The halophilic bacteria are found

abundant in the salterns, especially more in crystallizer ponds, and their growth

patterns are directly related to the change in light, temperature and salinity. They

enhance the evaporation rate and crystallization of salt (Jones et al., 1981).

Salinity has been viewed as one of the most important variables influencing

the utilization of organisms in estuaries (Marshall and Elliot, 1998) and affecting

species richness in continental water bodies (Lancaster and Scudder, 1987;

Williams et al., 1990; Derry et al., 2003). Salinity was adversely related to the

78

phytoplankton may increase due to evaporation thus a positive correlation between

salinity and temperature were expressed (Kaya et al., 2010). The salinity was

found to be high during summer season and a sharp decline during the winter

season at both the years. The higher values could be attributed to low amount of

rainfall, higher rate of evaporation as suggested by Sridhar et al. (2006), Sankar

et al. (2010) and Prasanna and Ranjan (2010). The lowest values were attributed to

the heavy rainfall moderately reduced the salinity. These differences are produced

by variations in the physical parameters of brine like temperature, amount of

precipitation, atmospheric pressure etc. (Gordon, 1972; Lal, 2005; Dahesht

et al., 2010). The fluctuation in salinity plays a key role in establishing the

distribution and dynamics of the chemical water quality. It has a strong influence

on the distribution of biological species (Ueda et al., 2000).

Temperature and salinity affect the dissolution of oxygen (Saravanakumar

et al., 2008). Dissolved oxygen is an important parameter for survival of aquatic

life. Low dissolved oxygen of the ecosystem is indication of the presence of

organic matter resulting in higher biological oxygen demand (BOD). BOD

depends on temperature, extent of biochemical activities, concentration of organic

matter and such other related factors. In the present investigation, maximum value

of BOD was recorded in Season IV. The highest value of BOD may be as a result

of mixing rain water in the saltpans and hence increase in dissolved oxygen and

decrease in salinity which also raises the biological affinity at elevated temperature

in the brine and consequently raise the BOD values in Season IV and which was

low in Season I (summer). These results are in accordance with the reports of

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Prasanna and Ranjan (2010) and Hassan et al. (2010). The high level of BOD is

also being due to the decomposition of detritus plankton and organic matter

whereby oxygen becomes consumes and CO2 is produced. This result agrees with

the fact that oxygen solubility decreases with increasing temperature and salinity

(Bhownick and Singh, 1985; Abdo, 2005; Calliaria et al., 2005; Touliabah et al.,

2010).

Dissolved matter in water is a useful parameter describing the chemical

constituents of the water and can be considered as a general of edaphic relations

that contribute to productivity within the water body (Goher, 2002). Positive

loading of salinity, total hardness, conductance, TDS (total dissolved solids) are the

common phenomenon in an estuarine environment (Panigrahi et al., 2007) whereas

positive loading of NH3 and BOD supports decomposition of organic materials by

the microbial organisms within the ecosystem. The values of TDS were found to

increase with an increase in salinity. The TDS was maximum in Season I and

minimum in Season IV. From the present study the TDS value is higher in summer

than the rainy and winter seasons. The obvious decrease in TDS during winter is

mainly due to the decrease in temperature that consequently reduces the

evaporation rate. Meanwhile, the higher values recorded during summer may be

due to the elevation of the water temperature which lead had to the increase in the

evaporation rates and the accumulation of the dissolved salts in water. These results

are also in conformity with the results obtained by Abdo (2005) and Prasanna and

Ranjan (2010).

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Brine samples naturally contain number of dissolved inorganic constituents.

The major cations are calcium, magnesium, sodium and potassium. The anions are

chloride, sulphate, carbonate and bicarbonate. The present investigation showed

that the chloride content in the brine sample gradually increased from reservoir to

crystallizer during the study period. The chloride ion concentration rise in the

brines serves as a compensatory ion for K+ and Mg++ (Amdouni, 2006). Though

the crystallizer pond discharged chloride as sodium chloride, the concentration of

chloride continued to increase, in the subsequent bittern stage. It was due to factors

like the incomplete crystallization of sodium chloride and the presence of highly

soluble potassium chloride and magnesium chloride in the bittern and large amount

of chloride was found in the crystallizer ponds, as evidenced by Elkins (1968).

From the observation, the values got culminated during Season I and II and also

witnessed the minimum chloride content observed in rainy season. This result is in

agreement with the reports of Nair (2000) and Rose (2007).

Apart from chloride, sulphate is a major anion in brine samples. The

sulphate content of the brine increased from the source to the bittern stage. High

salinity values contain high concentration of HCO–, Mg++ and SO4– – (Oren and

Shilo, 1982). Sulphate gets eliminated as gypsum CaSO4. 2H2O and CaSO4 in the

condenser stage. During evaporation the sulphate concentration increases as

MgSO4 and K2SO4 than the deposition as gypsum and anhydrous calcium sulphate.

Highest values of sulphate observed in season I for both years study and the lowest

values were observed in season III for the first year and season IV for the second

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year due to rainfall in the study period, which same as the findings of Hutchington

(1988) and Abdel-Satar (2005).

Sodium is the principal cation of the brine samples, which concentration

increased from the source (reservoir) to the crystallizer through the condenser were

noticed in this study. Almost all of the sodium in brines takes place in the

formation of halite (salt) while there remain some important quantities of chloride

in the residual solutions (Amdouni, 2006). In the crystallizer pond, sodium

separated as sodium chloride, decreasing its concentration in the subsequent bittern

stage. About 72 to 76 percentage of the total salt had crystallized between 25.4 and

28ºBe and the remaining magnesium brine had been left out liquor, bittern. The

highest values of sodium observed in season IV in the first year and season II for

second year but the lowest values observed in November for two year’s study were

similar with the report of Ramkumar et al. (2010).

Variation in the concentration of sodium, potassium, calcium and

magnesium to be only through evaporate loss of water from the ecosystem. The

calcium content was always found lesser than magnesium (Sundararaj et al., 2006).

Calcium is one of the most abundant elements in natural waters imparting hardness

(Harsha et al., 2006). The calcium content in the brine samples was comparatively

lower than other ions. Trace amount of calcium may attribute to the excessive

organic matter coupled with calcium in high saline concentrating ponds that leads

to deposition of this mineral (Davis, 1990). Bass-Becking (1931) found that

cyanobacteria were sensitive to increase calcium and magnesium concentrations at

higher salinities. The relative ionic proportion for the various elements varies with

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salinity. This variability in ionic proportion with increasing salinity was reported

by Bayly and Williams (1996).

In the present study, the chemical constituent iron showed no relation to

different seasons (Ewing, 1976; Reginald, 2003). Their concentration was almost

same throughout the study period. Moreover, the level of their respective

percentage increased when salinity increased, i.e., from reservoir to crystallizer, the

concentration of the iron are increasing. It plays important role in metabolism and

growth (Pawar, 2010).

Magnesium is a major constituent that affects salt quality. Hardness of

water mainly depends on the presence of dissolved calcium and magnesium salts.

Magnesium hardness was found to increase with salinity. The results explained

that the magnesium content in the brine has increased step by step and reached its

maximum in the crystallizer pond. During the period, the maximum magnesium

content was present in Season I but the minimum magnesium content was

expressed in Season III in 2009. The same result was also expressed in the year

2010. This is due to the fact that during the course of evaporation, magnesium

chloride and magnesium sulphate stay in the solution until the brine reaches salinity

of 300 ppt. Dilution due to heavy rainfall minimized the concentration of

magnesium (Hutchington, 1988). Nissenbaum (1975) reported that the inhibitory

effect of high magnesium and calcium concentrations may be the cause of very low

species diversity occurring in the hypersaline Dead Sea.

Potassium salts are highly soluble, no potassium salt is found to be

crystallized before the bittern stage. The potassium content was on the increase

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from the source of the bittern through the reservoir, condenser and crystallizer.

During the investigation period, the potassium reached the maximum value in

September and minimum was observed in November and December due to dilution

by rainfall in both years, which is in agreement with an earlier record (Rose, 2007).

As the salinity increased due to solar evaporation in saltwork ecosystem, the

relative proportions of the ions in solution also change and organisms may exhibit

sensitivity to the relative proportions of ions such as K+, Ca2+, Na+, Mg2+ (Nixon,

1970). High potassium concentration is due to evaporation of gypsum deposit, and

sulphate release considerable range of potassium to brine.

Physical phenomena of evaporation and precipitation of low and high

solubility salts are intimately linked to biological processes that occur in every

pond of a solar saltworks (Herrmann et al., 1973; Krumbein, 1985). A chain of

organism is developed into evaporating pond systems constituting the biological

process of solar salt production process. The process depends on the quality of

feeding seawater, the prevailing conditions on the ponds such as brine temperature,

depth, turbidity and concentration, the control of the physico-chemical process

during salt production and overall design of the saltworks (Davis, 1980; McArthur,

1980).

Physico-chemical characteristics of pH, nutrient levels, ionic strength,

temperature etc. may play an important role in the rate of microbial attachment to

the surfaces. Crystallizer and condenser ponds often display a bright red

contaminations as they harbor a large number of pigmented microorganisms (Oren,

2002; 2009a) found at high salinity range (Rodriguez-Valera et al., 1981). The

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various physico-chemical parameters were studied indicate well defined differences

between the brine samples of saltpan system from the present study. The

investigation provides a baseline information regarding the physico-chemical

parameters and it is a useful tool for the future ecological assessment and

monitoring of these solar salt ecosystem.