Interlinking stream morphology, water quality, and re-aeration rates: A semi-empirical modeling study of Ekulu Stream, Nigeria Article Swipe
Global population explosions coupled with increase in industrial activities have resulted in a corresponding rise in effluent discharge which end up in streams and rivers. Re-aeration rate plays a vital role in the self-purification of these rivers and streams. This study was aimed at investigating the re-aeration of Ekulu Stream in Enugu metropolis and the effects of hydraulic factors and water quality parameters on its re-aeration coefficient (k2). Water and sediment samples were obtained from a 2.56 km stretch of the Stream and subjected to physico-chemical characterization. Water samples were tested for temperature, pH, total dissolved solids, total solids, electrical conductivity, dissolved oxygen and biochemical oxygen demand. Hydraulic parameters of the channel namely, slope, velocity, flow depth and width were also measured and then used to estimate the value of k2 at each sampling point based on ten selected empirical re-aeration models designated as E1 to E10. Dissolved oxygen values ranged from 2.4 to 5.4 mg/L indicating pronounced oxygen sag. The re-aeration coefficient of Ekulu Stream as computed using the various models ranged from 1.42 to 57.11 day-1 . The models of Churchill (E4), Owens et al (E8 & E9) and Bennett and Rathbun (E1 & E2) gave the highest k2 values ranging 57.11 to 18.90, 49.46 to 16.56, 47.95 to 15.40, 45.96 to 23.56 and 41.54 to 14.74 day-1 respectively. Apart from E1 and E3, all the models used in this study produced k2 values that have positive relationship with pH of water. Between 55 and 91% of the variation in the value of K2 can be explained by a linear combination of TDS and the interaction of electrical conductivity and temperature (EC*Temp). Re-aeration models incorporating only velocity and flow depth (E2, E4, E7, E8 & E9) exhibited the best linear relationship with water quality parameters with p-values of 0.012, 0.001, 0.000, 0.005 and 0.014 and R2 values of 0.72, 0.88, 0.9, 0.78 and 0.71 respectively. Water quality parameters affect the re-aeration rates of Ekulu Stream. This finding will open a new research frontier with respect to stream re-aeration and is expected to help improve re-aeration models by integrating water quality parameters or an index thereof.
Related Topics
Concepts
Effluent
STREAMS
Hydrology (agriculture)
Water quality
Environmental science
Stream flow
Biochemical oxygen demand
Sampling (signal processing)
Environmental engineering
Population
Flow (mathematics)
Sediment
Water flow
Volumetric flow rate
Oxygen
Flow conditions
Limiting oxygen concentration
Current (fluid)
Empirical modelling
Channel (broadcasting)
Surface water
Water discharge
Water level
Hydraulic retention time
Coefficient of determination
Water pollution
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.4314/njt.v44i3.12
- https://www.ajol.info/index.php/njt/article/download/311246/292753
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W7106709741
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106709741Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.4314/njt.v44i3.12Digital Object Identifier
- Title
-
Interlinking stream morphology, water quality, and re-aeration rates: A semi-empirical modeling study of Ekulu Stream, NigeriaWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-26Full publication date if available
- Authors
-
C. C. Nnaji, A AjahList of authors in order
- Landing page
-
https://doi.org/10.4314/njt.v44i3.12Publisher landing page
- PDF URL
-
https://www.ajol.info/index.php/njt/article/download/311246/292753Direct link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://www.ajol.info/index.php/njt/article/download/311246/292753Direct OA link when available
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Effluent, STREAMS, Hydrology (agriculture), Water quality, Environmental science, Stream flow, Biochemical oxygen demand, Sampling (signal processing), Environmental engineering, Population, Flow (mathematics), Sediment, Water flow, Volumetric flow rate, Oxygen, Flow conditions, Limiting oxygen concentration, Current (fluid), Empirical modelling, Channel (broadcasting), Surface water, Water discharge, Water level, Hydraulic retention time, Coefficient of determination, Water pollutionTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.plays | 27 |
| abstract_inverted_index.point | 134 |
| abstract_inverted_index.rates | 323 |
| abstract_inverted_index.study | 40, 232 |
| abstract_inverted_index.these | 35 |
| abstract_inverted_index.total | 94, 97 |
| abstract_inverted_index.using | 168 |
| abstract_inverted_index.value | 128, 253 |
| abstract_inverted_index.vital | 29 |
| abstract_inverted_index.water | 60, 294, 350 |
| abstract_inverted_index.which | 18 |
| abstract_inverted_index.width | 118 |
| abstract_inverted_index.0.000, | 302 |
| abstract_inverted_index.0.001, | 301 |
| abstract_inverted_index.0.012, | 300 |
| abstract_inverted_index.15.40, | 211 |
| abstract_inverted_index.16.56, | 208 |
| abstract_inverted_index.18.90, | 205 |
| abstract_inverted_index.Global | 0 |
| abstract_inverted_index.Stream | 49, 81, 165 |
| abstract_inverted_index.affect | 320 |
| abstract_inverted_index.linear | 261, 291 |
| abstract_inverted_index.models | 141, 171, 180, 228, 275, 347 |
| abstract_inverted_index.oxygen | 102, 105, 148, 158 |
| abstract_inverted_index.ranged | 150, 172 |
| abstract_inverted_index.rivers | 36 |
| abstract_inverted_index.slope, | 113 |
| abstract_inverted_index.stream | 338 |
| abstract_inverted_index.tested | 90 |
| abstract_inverted_index.values | 149, 201, 235, 308 |
| abstract_inverted_index.water. | 243 |
| abstract_inverted_index.Bennett | 191 |
| abstract_inverted_index.Between | 244 |
| abstract_inverted_index.Rathbun | 193 |
| abstract_inverted_index.Stream. | 326 |
| abstract_inverted_index.channel | 111 |
| abstract_inverted_index.coupled | 3 |
| abstract_inverted_index.demand. | 106 |
| abstract_inverted_index.effects | 55 |
| abstract_inverted_index.factors | 58 |
| abstract_inverted_index.finding | 328 |
| abstract_inverted_index.highest | 199 |
| abstract_inverted_index.improve | 345 |
| abstract_inverted_index.namely, | 112 |
| abstract_inverted_index.quality | 61, 295, 318, 351 |
| abstract_inverted_index.ranging | 202 |
| abstract_inverted_index.respect | 336 |
| abstract_inverted_index.rivers. | 24 |
| abstract_inverted_index.samples | 71, 88 |
| abstract_inverted_index.solids, | 96, 98 |
| abstract_inverted_index.streams | 22 |
| abstract_inverted_index.stretch | 78 |
| abstract_inverted_index.various | 170 |
| abstract_inverted_index.computed | 167 |
| abstract_inverted_index.effluent | 16 |
| abstract_inverted_index.estimate | 126 |
| abstract_inverted_index.expected | 342 |
| abstract_inverted_index.frontier | 334 |
| abstract_inverted_index.increase | 5 |
| abstract_inverted_index.measured | 121 |
| abstract_inverted_index.obtained | 73 |
| abstract_inverted_index.p-values | 298 |
| abstract_inverted_index.positive | 238 |
| abstract_inverted_index.produced | 233 |
| abstract_inverted_index.research | 333 |
| abstract_inverted_index.resulted | 10 |
| abstract_inverted_index.sampling | 133 |
| abstract_inverted_index.sediment | 70 |
| abstract_inverted_index.selected | 138 |
| abstract_inverted_index.streams. | 38 |
| abstract_inverted_index.thereof. | 356 |
| abstract_inverted_index.velocity | 278 |
| abstract_inverted_index.Churchill | 182 |
| abstract_inverted_index.Dissolved | 147 |
| abstract_inverted_index.Hydraulic | 107 |
| abstract_inverted_index.discharge | 17 |
| abstract_inverted_index.dissolved | 95, 101 |
| abstract_inverted_index.empirical | 139 |
| abstract_inverted_index.exhibited | 288 |
| abstract_inverted_index.explained | 258 |
| abstract_inverted_index.hydraulic | 57 |
| abstract_inverted_index.subjected | 83 |
| abstract_inverted_index.variation | 250 |
| abstract_inverted_index.velocity, | 114 |
| abstract_inverted_index.(EC*Temp). | 273 |
| abstract_inverted_index.activities | 8 |
| abstract_inverted_index.designated | 142 |
| abstract_inverted_index.electrical | 99, 269 |
| abstract_inverted_index.explosions | 2 |
| abstract_inverted_index.indicating | 156 |
| abstract_inverted_index.industrial | 7 |
| abstract_inverted_index.metropolis | 52 |
| abstract_inverted_index.parameters | 62, 108, 296, 319, 352 |
| abstract_inverted_index.population | 1 |
| abstract_inverted_index.pronounced | 157 |
| abstract_inverted_index.Re-aeration | 25, 274 |
| abstract_inverted_index.biochemical | 104 |
| abstract_inverted_index.coefficient | 66, 162 |
| abstract_inverted_index.combination | 262 |
| abstract_inverted_index.integrating | 349 |
| abstract_inverted_index.interaction | 267 |
| abstract_inverted_index.re-aeration | 46, 65, 140, 161, 322, 339, 346 |
| abstract_inverted_index.temperature | 272 |
| abstract_inverted_index.conductivity | 270 |
| abstract_inverted_index.relationship | 239, 292 |
| abstract_inverted_index.temperature, | 92 |
| abstract_inverted_index.conductivity, | 100 |
| abstract_inverted_index.corresponding | 13 |
| abstract_inverted_index.incorporating | 276 |
| abstract_inverted_index.investigating | 44 |
| abstract_inverted_index.respectively. | 220, 316 |
| abstract_inverted_index.physico-chemical | 85 |
| abstract_inverted_index.characterization. | 86 |
| abstract_inverted_index.self-purification | 33 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |