Chapter 14 biowaste for defluoridation and statistical investigation of nalgo...Ratnakaram Venkata Nadh
Physicochemical parameters of ground water in Nalgonda District of Telangana (erstwhile a part of Andhra Pradesh), India were measured and analysed by statistical investigation. Activated carbons were prepared from bio-waste materials viz., Citrous nobilis, Bombax malabaricum, Pithacelobium dulce and Citrous limon sheaths (CNC, BMC, PDLC and CLC respectively) and were utilized as adsorbents for defluordization of the collected water samples, which were found to be satisfactory.
What are quality parameters and what is their usefulness? They are criteria used to measure success in relation to a set of goals that have to do with production performance, efficiency, effectiveness and user satisfaction
Analysis of groundwater quality of visnagar taluka, mehasana district gujaratvishvam Pancholi
Ground water is the principal source of drinking water in our country and indispensable source of our life. The quality of water is of vital concern for mankind, since it is directly linked to human welfare. The present work investigated various physiochemical parameters of villages of Visnagar taluka of Mehsana district, Gujarat. Because of north Gujarat is affected by various water quality parameters like fluoride is high in many parts of north Gujarat. A total of 50 water samples will be collected from the tube wells for post-monsoon season and analyzed for the various physiochemical parameters like pH, electrical conductivity (EC), nitrate (NO3-), magnesium (Mg2+), Calcium (Ca2+), hardness, and alkalinity, sulphates (SO42-), chloride (Cl-), sodium (Na+), potassium (K+), Fluoride (F-) and total dissolved solids (TDS). The result were compared with standards prescribed by IS: 10500(2012). It was found that the ground water contaminated at 16 sampling sites namely Khadalpur, Chhogala, Sunshi, Denap, Jetalvasana, Tarabh, Visnagar Rural, Bhalak, Kamalpur (GOT), Kamalpur (KHA), Kansa, Magaroda, Pudgam, Sadutala, Thalota, Vadu while other 34 sampling sites showed physiochemical parameters within the water quality standards and quality of water is good so it is fit for drinking uses.
Chapter 14 biowaste for defluoridation and statistical investigation of nalgo...Ratnakaram Venkata Nadh
Physicochemical parameters of ground water in Nalgonda District of Telangana (erstwhile a part of Andhra Pradesh), India were measured and analysed by statistical investigation. Activated carbons were prepared from bio-waste materials viz., Citrous nobilis, Bombax malabaricum, Pithacelobium dulce and Citrous limon sheaths (CNC, BMC, PDLC and CLC respectively) and were utilized as adsorbents for defluordization of the collected water samples, which were found to be satisfactory.
What are quality parameters and what is their usefulness? They are criteria used to measure success in relation to a set of goals that have to do with production performance, efficiency, effectiveness and user satisfaction
Analysis of groundwater quality of visnagar taluka, mehasana district gujaratvishvam Pancholi
Ground water is the principal source of drinking water in our country and indispensable source of our life. The quality of water is of vital concern for mankind, since it is directly linked to human welfare. The present work investigated various physiochemical parameters of villages of Visnagar taluka of Mehsana district, Gujarat. Because of north Gujarat is affected by various water quality parameters like fluoride is high in many parts of north Gujarat. A total of 50 water samples will be collected from the tube wells for post-monsoon season and analyzed for the various physiochemical parameters like pH, electrical conductivity (EC), nitrate (NO3-), magnesium (Mg2+), Calcium (Ca2+), hardness, and alkalinity, sulphates (SO42-), chloride (Cl-), sodium (Na+), potassium (K+), Fluoride (F-) and total dissolved solids (TDS). The result were compared with standards prescribed by IS: 10500(2012). It was found that the ground water contaminated at 16 sampling sites namely Khadalpur, Chhogala, Sunshi, Denap, Jetalvasana, Tarabh, Visnagar Rural, Bhalak, Kamalpur (GOT), Kamalpur (KHA), Kansa, Magaroda, Pudgam, Sadutala, Thalota, Vadu while other 34 sampling sites showed physiochemical parameters within the water quality standards and quality of water is good so it is fit for drinking uses.
Study on Drinking Water Quality at Shirokhali Village in Kachua, BangladeshSifat Islam
Bangladesh is a developing country with high density of population. But according to UNICEF (1991) about 25% of the population in the developing countries have no access to safe water. Ensuring the safety of drinking water is, therefore, a growing problem. This problem is mostly acute in the south-western part of Bangladesh because of salinity intrusion and arsenic contamination. Notwithstanding microbial dangers, the safety of drinking water in Bangladesh is also threatened by chemical contamination. Shirokhali, a village of Dhoakhali Union at Kachua upazilla, Bagerhat is selected as study area. The inhabitants of this village mainly use RWHs and tubewells water. Very few uses the pond and khal water for drinking purposes. There were 17 samples collected including 1khal, 1 government pond, 1 personal pond, 4 RWHs and 10 tubewells from different locations of the study area. Both Physicochemical analyses including pH, temperature, EC, TDS, salinity, hardness, Turbidity, DO, BOD, Na+, K+, Ca2+, Mg2+, Fe2+, arsenic Cl-, HCO3-, SO42-, PO43-,NO3- etc. and microbial analyses including faecal coliforms, total coliforms and E.coli were performed. The analyses was done by following the standard methods. It is found in the result that, microbial contamination in the water E.coli contamination is low in the tubewells water but high in some RWH systems established there because of low treatment and lack of knowledge. There is also found total coliforms and faecal coliforms in tubewells water which exceeds WHO standards. After the observing the physico-chemical parameter there found high volume of salinity, turbidity and hardness, Cl-, and HCO3-.The result of correlation analysis of the water quality parameters represented that there is no negative relationship. But pH and SO43- has no significant correlation with other parameters.The physicochemical analysis also showed that the collected waters were not suitable for drinking. Arsenic test was done and all the tubewells were free from contamination. But according to the key informant information (DPHE) got that there were arsenic contamination in the area was tested in the year 2003. Proper management and knowledge can be helpful to enrich the existing water quality deterioration.
Limitations of the Study:-
While conducting this research I have faced some problems which might limit the research result. They are-
There were some problems with flame photometer and spectrophotometer reading. Sometimes the instruments provide unreliable reading. So, there may be some error with the result.
In the case of coliform test, contamination may have occurred for some samples by air.
Time and money are the most valuable limitations for this research. If there is suitable time and money for performing this research this will be a great work for further management.
Classification either on quality or type based for groundwater can offer great advantages especially in regional groundwater management. It provides a short, quick processing, interpretation for a lot of complete hydro-chemical data sets and concise presentation of the results. There is a demonstrable need for a quality assurance, with the advanced usage of world's largest fresh water storage i.e Ground water. Its getting depleted over the years and the quality of the same degrading with a rapid pace. Ground water Quality is assessed mainly by the chemical analysis of samples. The data obtained from the chemical analysis is key for the further classification, analysis, correlation etc. Graphical and Numerical interpretation of the data is the main source for Hydro-chemical studies. In this paper we test the performance of the many available graphical and statistical methodologies used to classify water samples including: Collins bar diagram, Stiff pattern diagram, Schoeller plot, Piper diagram, Durov's Double Triangular Diagram, Gibbs's Diagram, Stuyfzand Classification. This paper explains various models which classify, correlate etc., summarizing the water quality data. The basic graphs and diagrams in each category are explained by sample diagrams. In addition to the diagrams an overall characterization of hydro-chemical facies of the water can be carried out by using plots which represents a water type and hardness domain. The combination of graphical and statistical techniques provides a consistent and objective means to classify large numbers of samples while retaining the ease of classic graphical presentation.
L’OLIGOTROFITZACIÓ CULTURAL DELS RIUS OCCIDENTALS I ELS CANVIS A L’ECOSISTEMA FLUVIAL: EL CAS DE L’EBRE.
Seminari Dept Ecologia - UB per Carles Ibañez (IRTA-Sant Carles de la Ràpita) el 18/01/2013
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While conducting this research I have faced some problems which might limit the research result. They are-
There were some problems with flame photometer and spectrophotometer reading. Sometimes the instruments provide unreliable reading. So, there may be some error with the result.
In the case of coliform test, contamination may have occurred for some samples by air.
Time and money are the most valuable limitations for this research. If there is suitable time and money for performing this research this will be a great work for further management.
Classification either on quality or type based for groundwater can offer great advantages especially in regional groundwater management. It provides a short, quick processing, interpretation for a lot of complete hydro-chemical data sets and concise presentation of the results. There is a demonstrable need for a quality assurance, with the advanced usage of world's largest fresh water storage i.e Ground water. Its getting depleted over the years and the quality of the same degrading with a rapid pace. Ground water Quality is assessed mainly by the chemical analysis of samples. The data obtained from the chemical analysis is key for the further classification, analysis, correlation etc. Graphical and Numerical interpretation of the data is the main source for Hydro-chemical studies. In this paper we test the performance of the many available graphical and statistical methodologies used to classify water samples including: Collins bar diagram, Stiff pattern diagram, Schoeller plot, Piper diagram, Durov's Double Triangular Diagram, Gibbs's Diagram, Stuyfzand Classification. This paper explains various models which classify, correlate etc., summarizing the water quality data. The basic graphs and diagrams in each category are explained by sample diagrams. In addition to the diagrams an overall characterization of hydro-chemical facies of the water can be carried out by using plots which represents a water type and hardness domain. The combination of graphical and statistical techniques provides a consistent and objective means to classify large numbers of samples while retaining the ease of classic graphical presentation.
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Introduction
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Humble Origins
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1. Morphometry and Water Quality of Selected
Lakes at Ramaroshan Wetland Area,
Western Part of Nepal.
1
Presenter: Tarka Bahadur Chalaune
M.Sc 4th Semester
Roll No: 894
Date: 7/20/2021
Supervisor: Prof. Chhatra Mani
Sharma, PhD
3. Introduction
3
Lakes are important features as they support and regulate the service of soil
formation, groundwater recharge, biodiversity and tourism (MEA, 2005).
Both natural and anthropogenic factors are responsible for deterioration of water
quality (Simeonov et al., 2003; Zhu et al., 2008) and influencing morphometry of
lakes (Arthur & Sageman, 1994).
The morphometry of lake determines future supply of water for various ecosystem
functioning (Shiklomano & Rodda, 2003).
The lake morphometry is also important in determining the type of vegetation, water
clarity, even types of birds and other wildlife (Stefandis & Papastergiadou, 2012).
The different studies showed that Himalayan freshwater lakes are under threat of
eutrophication, toxic heavy metals, sedimentation and siltation, etc. (Sharma et al.,
2005; Ravikumar et al., 2013).
4. According to Department of Forestry (2017), out of 7 lakes, 4 lakes are in
good condition whereas remaining three are degrading in Ramaroshan.
No study has been published yet regarding the morphometry and water
quality in these lakes, so this study may act as baseline as.
The present study will not only be important from the scientific point of
view but also management of lake and future development of high-
altitude lake ecosystems from the ecological and economic point of view.
4
Rationale
5. Objectives
5
The general objective of the present study is to investigate the
morphometry and water quality of selected lakes in
Ramaroshan watershed.
Specific objectives
To assess the physicochemical parameters of lake waters
To investigate the morphometry of lakes
6. Materials and Methods
6
Figure 1: Study area map of Ramaroshan Lake
Study area
Ramaroshan area is
renowned for 12 lakes and
18 grassland
42 km away from
headquarter of Achham
district, Mangalsen.
Altitude = 1401 to 3794 m
asl
Annual rainfall= 1790.8 mm
(DHM, 2020)
Coordinates 29°13′55.95″N
latitude and 81°28′9.67″E
longitude.
7. Materials and Methods
Water quality data collection and analysis techniques
Water samples were collected during winter (Jan 2020) following the standard
procedure as described in Sharma et al. (2012).
The analysis procedure were followed from APHA (2005).
7
On site parameters Methods
pH, Temperature, TDS (Total dissolved
solids), EC (Electrical conductivity)
Multiprobe meter
(HANNA)
DO (Dissolved oxygen) DO meter
Major anions
Bicarbonate (HCO3
-), Carbonate (CO3
-),
Chloride (Cl-)
Titration
Major cations
Calcium(Ca2+), Magnesium (Mg2+) Titration
Figure 2 : Onsite measurement
Table 1: Methods for on-site parameters testing
8. 8
Laboratory analysis (CDES) Device
Major anions
Phosphate (PO4
3-), Sulphate (SO4
2-),
Nitrate (NO3
-)
Spectrophotometer
Major cations
Sodium (Na+), Potassium (K+), Flamephotometer
Ammonia (NH4
+) Spectrophotometer
Materials and Methods
Data analysis
Ionic relationship with the
water quality parameters
Piper plot (Piper, 1944)
Water Quality Index (WQI)
(Amadi & Akobundu, 2011)
Multivariate statistical
analysis
Principal Component
Analysis (PCA)
Cluster analysis (CA)
Table 2: Methods of parameters testing in laboratory
Figure 3: Lab analysis
9. 9
Materials and Methods
Name of the
Lakes
Number of
samples
(size of lakes)
Method Device
• Jingale
• Batula
• Mathillo
Dhaune
149
42
26
Line transect method
(i.e N shape)
• An echo-sounder (depth
measuring gauge; plastimo
echotest II)
• GPS point (Garmin Venture SC).
Morphometry data collection and analysis techniques
Figure 4: Data collection for bathymetry
ArcGIS and surfer tools used to analyze
digital depth sounder data and GPS points for
calculating volume, area and prepared
bathymetry map of lakes .
Table 3: Data collection method for the bathymetry
Study of surface area change of lakes
Satellite image of year 1990, 2005 and 2020 from
Landsat 5, 7 and 8 was used, respectively.
10. Results
Jingale Lake
The bathymetry map showed that
Maximum depth of lake 44 m, with
area 20.92 ha and volume 2692769m3
The lake has a narrow end and wider
at the middle part but the entire lake
can’t be observed in a single view
from a point
Batula Lake
The figure showed that maximum
depth of lake 15 m with area 4.68 ha
and volume 375399.1m3
The outlet of Jingale Lake is the main
inlet for Batula Lake
10
Results of Bathymetry Survey
Figure 5: Bathymetry map of Jingale lake
Figure 6: Bathymetry map of Batula Lake
11. 11
Results
Figure 7: Bathymetry map of Mathilo Dhaune
Mathilo Dhaune
The bathymetry map showed that
Maximum depth of lake 9.5 m, with
area 2.37 ha and volume 140515.9
m3
In the lake littoral zone was devoid of
vegetation around 15 m periphery
and covered by boulders rocks
12. Temporal change of Lake area from
RLCA
12
Results
Lakes
Year A.D Change in
area (ha)
1990 2005 2020
Jingale 19.98 20.29 20.92 0.94
Batula 3.62 3.7 4.68 1.06
Mathilo
Dhaune 2.17 2.51 2.52 0.4
Jingale Lake
Batula Lake
Mathilo Dhaune
Lake
Table 4: Temporal area change of lakes
Figure 8: Outline map of lakes showing change in
area
The ordering of the three lakes' increments
was Batula Lake > Jingale Lake > Mathillo
Dhaune, respectively.
13. 13
Parameter Max Min Stdv Mean
NDWQS,
(2063) WHO (2011)
pH 7.57 6.26 0.31 6.86 6.5-8.5 6.5-8.5
EC 193 25 36.71 77.41 1500 1500
TDS 100 12 19.02 40.02 1000 1000
Temp 15 3.8 2.84 8.52
DO 9.72 7.4 0.69 8.36
K+ 3.6 0.08 0.77 1.34 10*
Na+ 6.1 5.1 0.23 5.6 200
Turbidity 14 0.1 2.07 0.85
NO3
- 0.07 0.04 0.008 0.04 50 10
PO4
2- 0.24 0.1 0.027 0.158 1
NH4
+ 0.29 0.064 0.065 0.12
SO4
2- 3.36 0.05 0.62 1.04 250 250
Cl- 21.3 2.83 3.91 11.83 250 250
TH 110 16 19.03 44.11 500 500
MgH 36 4 6.38 14
CaH 86 8 16.91 29.69
Ca2+ 34.4 34.4 3.2 6.76 200 75*
Mg2+ 8.784 0.97 1.55 3.41 30*
Total Alk 145 10 23.98 43.58 120
Free CO2 6.6 2.2 1.61 3.32
Table 5: Summary of physicochemical parameters
Note: Units of all the variables are mg/L, except pH, WT (℃), and EC (µS/cm)
CaH= Calcium Hardness, MgH= Magnesium Hardness
Mean concentration of
parameters was under the
guidelines of WHO and
NDWQS
Mean concentration of
major ions shows the
order as: Ca2+>Na+>
Mg2+> K+> NH4
+ for
cations and
HCO3
->Cl->SO4
2->PO4
2-
>NO3
- for anions
General physicochemical parameters of lake waters
Results
14. 14
PC1, PC2 and PC3 shows high % of the total
variance i.e 30.52 %, 15.79% and 11.87%
showing controlling factors.
Strong loading of EC, TDS, TH, CaH, and Ca2+
with moderate loading of HCO3
- is seen in PC1
Similarly, PC2 showed strong loading of PO4
2-
and NO3
- followed by PC3 such as MgH and
Mg2+
Principal Component Analysis Table 6: Summary of PCA showing component matrix
Figure 10: PCA plot showing component
Parameters 1 2 3 4 5 6
pH -0.29 -0.20 -0.40 0.39 -0.32 -0.45
EC 0.92 0.15 -0.20 0.08 0.13 -0.04
TDS 0.92 0.15 -0.21 0.08 0.13 -0.04
Tempr -0.29 0.04 0.08 -0.02 0.82 0.18
DO 0.13 -0.18 -0.04 0.49 0.17 0.51
K+ 0.30 0.13 -0.16 -0.60 -0.36 0.11
Na+ -0.21 -0.41 0.12 -0.50 0.35 -0.29
Turbidity -0.32 0.20 -0.08 0.33 -0.30 0.56
NO3
- -0.15 0.85 0.38 0.08 0.01 0.00
PO4
2- -0.13 0.82 0.46 0.05 0.15 -0.11
NH3 -0.27 0.71 0.39 0.01 -0.22 -0.25
SO4
2- 0.27 -0.48 0.31 0.40 0.01 -0.30
Cl- 0.16 -0.24 0.30 -0.46 -0.23 0.44
TH 0.97 0.04 0.15 0.05 0.00 0.01
MgH 0.38 -0.42 0.77 0.10 -0.12 -0.01
CaH 0.93 0.23 -0.12 0.00 0.06 -0.01
Ca2+ 0.93 0.24 -0.11 -0.01 0.07 0.00
Mg2+ 0.41 -0.41 0.77 0.08 -0.11 0.00
total Alk 0.70 0.10 -0.17 -0.05 -0.08 -0.09
Eigen value 5.80 3.00 2.26 1.53 1.33 1.26
% of
Variance 30.52 15.79 11.87 8.03 7.03 6.65
Cumulative
% 30.52 46.31 58.18 66.21 73.23 79.89
Results
15. Piper plot
15
Results
Most of the samples from
class 4 are mixed of Ca-
Mg-Cl type and from class
1 are mixed of Ca- HCO3
type.
Ca and Mg are the
dominant cations.
HCO3 is dominant anion.
Figure 11: Piper plot showing water type of RLCA
16. Cluster analysis
16
Results
43 samples are grouped
into distinct 3 clusters.
Cluster 1 is composed
of 62.79% of total
samples, similarly
cluster 2 &3 composed
32.56 % and 4.66%
concentration of
parameters in samples,
clusters can be ordered
as Cluster 2< Cluster 1<
Cluster 3
Cluster 2 is least
polluted than other
clusters.
1 2 3
Figure 12: Hierarchical cluster analysis of RLCA
17. 17
Water Quality Index
Results
WQI
value Rating of water quality Grading
0-25 Excellent A
26-50 Good B
51-75 Poor C
76-100 Very poor D
Above
100
Unsuitable for drinking
purpose E
Mean WQI for lakes = 27
Out of 9 lakes, 3 lakes had grade A
water quality value 25
Lama Daha lake was slightly more
polluted than other lakes
Figure 13: Chart showing WQI for studied lakes of
RLCA
Table 7: Categories of WQI (Broan et
al.,1972)
18. Comparative major ions analysis
Table 8 : Comparison among different major ions concentration of RLCA and previous
studies from mid-hill and high altitude lakes in Nepal.
18
In comparison, of major ions with other lakes, Na+ is found to be higher RLCA than
other lakes.
Lakes Na+ Mg2+ K+ Ca2+ Cl- SO4
2- HCO3
- Reference
Ramaroshan
lakes 5.6 ± 0.23
3.41 ±
1.55
1.34 ±
0.77 6.76 ± 3.2
11.83 ±
3.91
1.04 ±
0.62
43.58 ±
23.98 Present study
Rara Lake
0.35 ±
0.19
5.89 ±
3.65
0.80 ±
0.51
9.17 ±
2.67
0.10 ±
0.05
0.14 ±
0.09 54.02 ± 23.5 (Gurung et al. 2018)
Gokyo Lakes 0.9 ± 0.22 0.4 ± 0.03 0.6 ± 0.06
5.13 ±
0.91 0.2 ± 0.05
4.3 ±
1.15 17 ± 2.24
(Lacoul & Freedman,
2007)
Pach Pokhari
0.31 ±
0.24 0.2 ± 0.11
0.22 ±
012 1 ± 0.4 3.5 ± 1.39 4.2 ± 1.3 13.9 ± 13 (Raut et al. 2015)
Gosaikunda
Lake 0.5 ± 5.44 1.30 ± 0.7 0.3 ± 0.07 3.5 ± 2.05 20.50 ± 14
3.94 ±
2.4 17.5 ± 3.3 (Raut et al. 2012)
Langtang
Valley 1.9 ± 1.3 1.64 ± 1.3 2.08 ± 1.5
10.77 ±
5.53 8.24 ± 2.3 5 ± 5.31 36.63 ± 15.2 (Tuladhar et al. 2015)
Results
19. Bathymetry of Lakes
Jingale Lake is the deepest lake at RLCA among the three lakes followed by
Batula lakes and Mathillo Dhaune Lakes.
The deeper lakes are suitable because they have a higher percentage of large-
bodied animals, longer-lived species, and more predators (Goetz et al., 2014).
Temporal change of lake area from RLCA
Lake area increment is likely due to the artificial check wall in the periphery
area of the Jingale and Batula Lake, Similarly in case of Mathillo Dhaune
there were no outlets, and receives massive inlet from Batula Lake.
Similar findings were also reported in Rupa Lake Pokhara Where surface area
increased during 1988 -2013, however, Phewa and Begnas Lakes were
shrinking at the same time (Thakuri et al., 2021).
19
Discussion
20. Physicochemical parameters
The range of water quality parameters observed within the guidelines provided
by NDWQS (2063) and WHO (2011)). Similar findings were also reported in
nearby high altitude Rara Lake & Gokyo (Gurung et al., 2018; Sharma et al.,
2011), but, slightly less concentration was observed in Phewa Lake (Khadak
&Ramanathan, 2020)
PCA
PCA1, PCA2 & PCA 3 explained having strong loading factors shows they are
contributed by natural sources from the carbonate weathering and lithology of
the study area (Amadi, 2011). Similarly, rest of the PCAs refer the contribution
from the organic matter in the water.
20
Discussion
21. Piper Plot
58% of samples belong to Ca-Mg-Cl mixed type whereas 41% to Ca-HCO3
type. The results suggested that the carbonate weathering in the RLCA. Similar
finding also reported in nearly located Rara Lake (Gurung et al., 2018)
The class 1 and 4 indicates dominance of earth alkaline metal such as Ca+Mg
>Na+K & acidic anions metal such as SO4+Cl > Na+K which could be related
to the geology
Cluster analysis
The cluster 1 and 3 included slightly polluted sites than cluster 2, might be due
to high human disturbance, high wildlife recreational activities, presence of
algal blooms and anthropic activities
This sampling site clusters together due to the higher concentration of the EC,
TDS, SO4
2- and TH.
21
Discussion
22. Water Quality Index
The WQI of Lama Daha Lake was more polluted than others due to high
concentration of sulphate, TDS and nitrate.
The agricultural runoff and domestic grazing around Lama Daha Lake may
be reason for the increase in these ions.
Comparative major ions analysis
High concentration of Na+ in studied area than other lakes might be due to
the silicate weathering from surrounding rocks and the ion exchange process
in water bodies (Kumar et al., 2019; Mallick, 2017).
22
Discussion
23. Conclusion
Jingale is the largest and deepest lake than Batula and Mathilo Dhaune
lakes.
Area of the studied lakes has increased from 1990 to 2020 A.D due to
artificial check wall.
The physico-chemical parameters of the lake waters were within the
guidelines of WHO and NDWQS.
Calcium and bicarbonate were the dominant cation and anion
WQI of RLCA is belonged to good category
Factor loading identifies high influence of carbonate type of lithology
which is also supported by piper plot.
23
24. Enhanced collaborative management of wetlands resources for conservation
and sustainable livelihoods because there are no organizations working for
these issues, except tourism board.
Wetland biodiversity conservation values integrated into national policy and
planning framework is needed.
24
Recommendations
25. References
M E.A. (2005). Ecosystems and Human Well-being: Synthesis (Washington: Island Press).
Sharma, C.M., Sharma, S., Bajracharya, R.M., Gurung, S., Jüttner, I., Kang, S., & Li, Q. (2012). First results on
bathymetry and limnology of high-altitude lakes in the Gokyo Valley, Sagarmatha (Everest) National Park,
Nepal. Limnology, 13(1), 181-192.
MoFSC. (2003). National Wetland Policy 2003 (2059). Ministry of Forestry and Soil Conservation, Nepal.
Amadi, A.N., (2011). Assessing the effects of Aladimma dumpsite on soil and groundwater using water quality index
and factor analysis. Australian Journal of Basic and Applied Sciences, 5(11), 763-770.
APHA, (2005). Standard Method for the Examination of Water and Waste water, American Public Health
Association, American Water Works Association, Water Environment Federation, Washington D.C.
Gurung, S., Gurung, A., Sharma, C. M., Jüttner, I., Tripathee, L., Bajracharya, R. M., ... & Kang, S. (2018).
Hydrochemistry of Lake Rara: A high mountain lake in western Nepal. Lakes & Reservoirs: Research &
Management, 23(2), 87-97.
Piper, A.M., (1944). A graphic procedure in geochemical interpretation of water analyses. Trans Am Geophys Union;
25: 914– 923.
Khadak, U.K., & Ramanath, A.L. (2021) Hydrochemical analysis of Phewa Lake: A Lesser Himalaya lake in the
Pokhara Valley, Nepal. Environment and Natural Resources Journal, 19(1), 68-83.
Thakuri, S., Lama, F., Malla, R., Khadka, N., Ghimire, N. P., & Salerno, F. (2021). Lake watershed dynamics and
bathymetry modeling of Rara and Begnas lakes in Nepal. Earth, 2(2), 272-286.
GoN. (2063). National Drinking Quality Standards and Directives, 2005 (p. 22). Ministry of Physical Planning and
Works. Government of Nepal.
25
28. I would like to express my sincere gratitude to Prof. Chhatra Mani Sharma
PhD.
I would also like to thank Mr. Jiban sharma, Ms. Alina Dangol and Mr Sagar
Rokaya for assisting the field data collection and Mr. Ramesh basnet for
laboratory analysis.
Central Department of Environmental Science (CDES-TU) for research Grant
University Grants Commission (UGC) for partial research Grant
Higher Education Reform Project (HERP-DLI-7B) for Seed Grant to the
supervisor
28
Acknowledgement;