scholarly journals Effect of the restricted use of phosphate detergent and upgraded wastewater-treatment facilities of water quality in the Chattahoochee River near Atlanta, Georgia

1994 ◽  
Author(s):  
D.J. Wangsness ◽  
E.A. Frick ◽  
G.R. Buell ◽  
J.C. DeVivo
Keyword(s):  
Water Quality ◽  
Wastewater Treatment ◽  
Treatment Facilities ◽  
Restricted Use ◽  
Phosphate Detergent ◽  
Chattahoochee River

scholarly journals Metagenomics of Wastewater Influent from Southern California Wastewater Treatment Facilities in the Era of COVID-19

2020 ◽  
Vol 9 (41) ◽  
Author(s):  
Jason A. Rothman ◽  
Theresa B. Loveless ◽  
Madison L. Griffith ◽  
Joshua A. Steele ◽  
John F. Griffith ◽  
...  
Keyword(s):  
Water Quality ◽  
Wastewater Treatment ◽  
Nucleic Acids ◽  
Southern California ◽  
Taxonomic Group ◽  
Microbial Water Quality ◽  
Treatment Facilities ◽  
Wastewater Samples ◽  
Influent Wastewater

ABSTRACT Sequencing wastewater may be useful for detecting pathogens and assaying microbial water quality. We concentrated, extracted, and sequenced nucleic acids from 17 composite influent wastewater samples spanning seven southern California wastewater treatment facilities in May 2020. Bacteria were the most proportionally abundant taxonomic group present, followed by viruses and archaea.

scholarly journals Analysis of Water Quality Characteristics in Domestic Wastewater Treatment Facilities.

2000 ◽  
Vol 23 (9) ◽  
pp. 584-589
Author(s):  
Takeshi YAHASHI ◽  
Yousuke FURUKAWA ◽  
Takao WATANABE ◽  
Hideaki OHMORI ◽  
Yoshio INOUE
Keyword(s):  
Water Quality ◽  
Wastewater Treatment ◽  
Domestic Wastewater ◽  
Quality Characteristics ◽  
Domestic Wastewater Treatment ◽  
Treatment Facilities

Assessment of alternative wastewater treatment approaches in guangzhou, China

1999 ◽  
Vol 39 (5) ◽  
pp. 227-234 ◽  
Author(s):  
Y. X. Tao ◽  
P. Hills
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Large Scale ◽  
Surface Water Quality ◽  
Water Shortage ◽  
Analytic Hierarchy ◽  
Treatment Facilities ◽  
The Pearl River ◽  
The City

The city of Guangzhou, in Guangdong Province, China, faces major problems in the development, implementation and application of a comprehensive water pollution control strategy. Since the 1980s, discharges of industrial wastewater and domestic sewage have increased dramatically, resulting in a continuing deterioration in the quality of urban water bodies. The Pearl River, which passes through the city, is now seriously polluted and water quality in many sections fails to attain even the lowest national surface water quality standards. The 19 urban creeks in the city are also badly polluted, and Guangzhou is facing a water shortage due to pollution of its supply sources. This paper overviews the water pollution problem of Guangzhou. Existing approaches to wastewater treatment are evaluated using an Analytic Hierarchy Process (AHP) and a form of cost-effectiveness analysis. Problems associated with different approaches are explored. Options to improve the effectiveness of wastewater treatment are proposed. The paper seeks to demonstrate that in Guangzhou, the most effective approach to wastewater treatment involves the use of small and medium-scale treatment facilities. This is in contrast to the situation in many other parts of the world where greater emphasis has been placed on the development of large-scale facilities for wastewater collection, transport and treatment.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

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