Understanding spatiotemporal variability of in-stream water quality in urban environments – A case study of Melbourne, Australia

2019 ◽  
Vol 246 ◽  
pp. 203-213 ◽  
Author(s):  
Baiqian Shi ◽  
Peter M. Bach ◽  
Anna Lintern ◽  
Kefeng Zhang ◽  
Rhys A. Coleman ◽  
...  
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Urban Environments ◽  
Spatiotemporal Variability ◽  
Stream Water Quality

A New Developed Stream Water Quality Model: A Case Study on the River Nile *

1980 ◽  
Vol 13 (11) ◽  
pp. 385-394 ◽  
Author(s):  
M.F. Hassan ◽  
M.I. Younis ◽  
K.H. Mancy
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Water Quality Model ◽  
River Nile ◽  
Stream Water Quality ◽  
Quality Model

scholarly journals EFFECTS OF ANTHROPOGENIC ACTIVITY ON STREAM WATER QUALITY IN LANGSA, ACEH

2018 ◽  
Vol 18 (2) ◽  
Author(s):  
Helmy Akbar ◽  
Iwan Suyatna ◽  
Jailani Jailani ◽  
Singgih Afifa Putra ◽  
Fauziah Azmi
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Surface Mining ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Low Ph ◽  
Anthropogenic Activity ◽  
Stream Water Quality ◽  
Type C

Increased human activity towards the water bodies will change the condition of water quality. Case study in Langsa, Aceh, It was found that an increase in Some physical parameter (TSS) that exceeds the value determined in PP 82 of 2001 (Indonesian government standard). The high value of TSS in Station 2 and Station 3 indicates that the sediment loading to the water body is high, especially in Station 3, where the TSS concentrations far exceed the standard. Activity of type C surface mining materials tends to affect the brightness, turbidity, depth and TSS. Water conditions with low pH were also found in this study. In location studied no EPT larvae were found Keyword: Langsa, Water Quality, Stream, Total Suspended Solid, Anthropogenic Activity

Influences of urban wastewaters on the stream water quality: a case study from Gumushane Province, Turkey

2012 ◽  
Vol 185 (2) ◽  
pp. 1285-1303 ◽  
Author(s):  
Adem Bayram ◽  
Hızır Önsoy ◽  
V. Numan Bulut ◽  
Görkem Akinci
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Stream Water Quality

Impact of anthropogenic activities on urban stream water quality: a case study in Guangzhou, China

2014 ◽  
Vol 21 (23) ◽  
pp. 13412-13419 ◽  
Author(s):  
Jin-Song Liu ◽  
Ling-Chuan Guo ◽  
Xian-Lin Luo ◽  
Fan-Rong Chen ◽  
Eddy Y. Zeng
Keyword(s):  
Water Quality ◽  
Stream Water ◽  
Anthropogenic Activities ◽  
Urban Stream ◽  
Stream Water Quality

scholarly journals A data-based predictive model for spatiotemporal variability in stream water quality

2020 ◽  
Vol 24 (2) ◽  
pp. 827-847 ◽  
Author(s):  
Danlu Guo ◽  
Anna Lintern ◽  
J. Angus Webb ◽  
Dongryeol Ryu ◽  
Ulrike Bende-Michl ◽  
...  
Keyword(s):  
Water Quality ◽  
Statistical Model ◽  
Stream Water ◽  
Spatial Scales ◽  
Water Quality Monitoring ◽  
Spatiotemporal Variability ◽  
Catchment Management ◽  
Stream Water Quality ◽  
Bayesian Hierarchical ◽  
High Concentrations

Abstract. Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate–nitrite (NOx) and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution; (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit); and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.

Can the structure of a riparian forest remnant influence stream water quality? A tropical case study

Hydrobiologia ◽  
2013 ◽  
Vol 724 (1) ◽  
pp. 175-185 ◽  
Author(s):  
Janaína de F. Fernandes ◽  
Andréa L. T. de Souza ◽  
Marcel O. Tanaka
Keyword(s):  
Water Quality ◽  
Riparian Forest ◽  
Stream Water ◽  
Stream Water Quality ◽  
Forest Remnant

scholarly journals Environmental Effects of Poor Wastes Disposal on Stream Water Quality: A Case Study of Eha-Alumona in Nsukka East LGA of Enugu State, Nigeria

2019 ◽  
Vol 15 (1) ◽  
pp. 206-212
Author(s):  
C. N. Mama ◽  
C.C. Nnaji ◽  
I.P. Obe ◽  
P.C. Ihuagu ◽  
I.A. Yakubu ◽  
...  
Keyword(s):  
Water Quality ◽  
Environmental Effects ◽  
Stream Water ◽  
Stream Water Quality ◽  
Enugu State
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