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Record W2305873023 · doi:10.31357/fesympo.v20i0.2587

Chemical Characteristics of Buffer Zone Sediments And Implications on Adjoining Water in Diyawanna Lake

2015· article· en· W2305873023 on OpenAlexaboutno aff
A.A.S.D. Dias, D.T. Jayawardana

Bibliographic record

VenueProceedings of International Forestry and Environment Symposium · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsAlkalinityEnvironmental chemistryTotal dissolved solidsWater qualitySedimentSalinityOrganic matterChemistrySurface waterSurface runoffHydrology (agriculture)MineralogyEnvironmental scienceGeologyEnvironmental engineeringEcology

Abstract

fetched live from OpenAlex

Diyawanna Lake is one of the major fresh water body located in the middle of SriJayewardenapura Kotte. Most of surrounding area of the lake consists of marshlands. Duringthe recent past several reclamation and development activities going on around the lake. Inaddition, sediments from surface runoff are finally accumulating in the lake margin (bufferzone). However, there is no any study done on sediment quality and impacts of them onadjoining water, especially in the buffer zone of such a fresh water body. Therefore, main aimof this study is to investigate quality of the sediment accumulated in the buffer zone of thelake and also to study the possible impacts on lake water from the sediments due tobiogeochemical reactions in the zone. Thirty three water samples were collected during dryperiod and samples were analysed for pH, electrical conductivity (EC), oxidation reductionpotential (ORP), salinity, total dissolved solid (TDS), NO3-, PO43-, SO42-, Cl-, alkalinity,hardness, Na, K, Ca, Mg, Cd, Cr, Cu, Fe, Pb, Mn, Zn and Ni. In addition, black organicsediment samples were collected from selected locations and tested for pH, ORP, EC, organiccarbon, total organic matter, NO-3, PO4-3, Na, K, Ca, Mg, Cd, Cr, Cu, Fe, Pb, Mn, Zn and Ni.Average values of pH, EC, ORP, TDS, NO3-, PO43-, SO42-, Cl-, alkalinity, hardness, Na, K,Ca, Mg, Cu, Zn and Fe for the lake water are 7.4, 222 μS/cm, -37 mV, 222 mg/l, 0.11 ppm,15.00 ppm, 1.43 ppm, 20.00 ppm, 0.0002 ppm, 772 ppm, 16 ppm, 10 ppm, 36 ppm, 21 ppm,0.001 ppm, 0.009 ppm and 0.14 ppm respectively. However, element Cd, Pb, Mn and Niwere not detected. Piper classification for the lake water indicate CaSO4 type, which reflecttypical gypsum type of waters with impact of mine drainage due to mineral pyrite in soil.Also, sulfur emitted by the vehicles may react with water to form sulfuric acid. In addition,gypsum type of water may also due to accumulation of building materials such as cement.Therefore reason for the CaSO4 type of water may be due to several sources in the area. Inaddition, compared to WHO guidelines only Cr (0.58 ppm) present in the water isconsiderably high, this may be due to direct discharge of urban dust into the lake.Average values of pH (6.1), ORP (54 mV), EC (122 μS/cm), organic carbon (3.1%), totalorganic matter (18%), nitrate (0.93 ppm), phosphate (12 ppm), Na (3.5 g/kg), K (11 g/kg), Ca(73 mg/kg), Mg (84 mg/kg), Cd (40 mg/kg), Cr (391 mg/kg), Cu (134 mg/kg), Fe (44 g/kg),Pb (833 mg/kg), Mn (157 mg/kg), Ni (196 mg/kg) and Zn (33 g/kg) in the sediments areconsiderably different value than the water. Compare with Canadian Environment Qualityguidelines average values of Cd, Cr, Pb and Zn are higher in the sediments. This is mainlydue to accumulation of those elements from runoff water. In general, it can be concluded thatprevailing physical conditions of the lake water is controlling leaching of heavy metals fromsediments to water. Conversely, anthropogenic sources seem to be increase accumulation ofheavy metals in the buffer zone sediments.Keywords: Diyawanna Lake, Water quality, Sediment quality

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.242
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2015
Admission routes1
Has abstractyes

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