Suitability of Chemcatcher® passive sampling in monitoring organotin compounds at a wastewater treatment plant

2016 ◽  
Vol 2 (4) ◽  
pp. 769-778 ◽  
Author(s):  
H. Ahkola ◽  
J. Juntunen ◽  
K. Krogerus ◽  
T. Huttula ◽  
S. Herve ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Passive Sampling ◽  
Municipal Wastewater ◽  
Treatment Plant ◽  
Organotin Compounds

Municipal wastewater contains a number of harmful chemicals whose concentrations can fluctuate dramatically.

scholarly journals Assessment of Microplastics in a Municipal Wastewater Treatment Plant with Tertiary Treatment: Removal Efficiencies and Loading per Day into the Environment

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1339
Author(s):  
Javier Bayo ◽  
Sonia Olmos ◽  
Joaquín López-Castellanos
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Municipal Wastewater ◽  
Removal Rate ◽  
Treatment Plant ◽  
Source Control ◽  
Municipal Wastewater Treatment ◽  
Tertiary Treatment ◽  
Stable Performance ◽  
Wastewater Samples

This study investigates the removal of microplastics from wastewater in an urban wastewater treatment plant located in Southeast Spain, including an oxidation ditch, rapid sand filtration, and ultraviolet disinfection. A total of 146.73 L of wastewater samples from influent and effluent were processed, following a density separation methodology, visual classification under a stereomicroscope, and FTIR analysis for polymer identification. Microplastics proved to be 72.41% of total microparticles collected, with a global removal rate of 64.26% after the tertiary treatment and within the average retention for European WWTPs. Three different shapes were identified: i.e., microfiber (79.65%), film (11.26%), and fragment (9.09%), without the identification of microbeads despite the proximity to a plastic compounding factory. Fibers were less efficiently removed (56.16%) than particulate microplastics (90.03%), suggesting that tertiary treatments clearly discriminate between forms, and reporting a daily emission of 1.6 × 107 microplastics to the environment. Year variability in microplastic burden was cushioned at the effluent, reporting a stable performance of the sewage plant. Eight different polymer families were identified, LDPE film being the most abundant form, with 10 different colors and sizes mainly between 1–2 mm. Future efforts should be dedicated to source control, plastic waste management, improvement of legislation, and specific microplastic-targeted treatment units, especially for microfiber removal.

scholarly journals Energy Valorisation of Wastewater Treatment Plant Sludge through Thermal Gasification: An Economic Perspective

Proceedings ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 3
Author(s):  
Luis F. Carmo-Calado ◽  
Roberta Mota-Panizio ◽  
Gonçalo Lourinho ◽  
Octávio Alves ◽  
I. Gato ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Municipal Wastewater ◽  
Treatment Plant ◽  
Electricity Production ◽  
Baseline Scenario ◽  
Municipal Wastewater Treatment ◽  
Plant Processing ◽  
Sludge Drying ◽  
Calorific Power

The technical-economic analysis was carried out for the production of sludge-derived fuel from a municipal wastewater treatment plant (WWTP). The baseline for the analysis consists of a sludge drying plant, processing 6 m3 of sludge per day and producing a total of about 1 m3 of combustible material with 8% of moisture and a higher calorific power of 18.702 MJ/kg. The transformation of biofuel into energy translates into an electricity production of about 108 kW per 100 kg of sludge. The project in the baseline scenario demonstrated feasibility with a payback time of about six years.

Impact of a wastewater treatment plant on Cryptosporidium oocysts and Giardia cysts occurring in a surface water

2000 ◽  
Vol 41 (7) ◽  
pp. 31-37 ◽  
Author(s):  
E. Carraro ◽  
E. Fea ◽  
S. Salva ◽  
G. Gilli
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Municipal Wastewater ◽  
Treatment Plant ◽  
Municipal Wastewater Treatment ◽  
Mean Values ◽  
Cryptosporidium Oocysts ◽  
The Impact ◽  
Receiving Water ◽  
Giardia Cysts

The aim of this study was to assess the impact of a municipal wastewater treatment plant (MWTP) on the occurrence of Cryptosporidium oocysts and Giardia cysts in the receiving water. All MWTP effluent samples were Giardia and Cryptosporidium contaminated, although low mean values were found for both parasites (0.21±0.06 oocysts/L; 1.39±0.51 cysts/L). Otherwise, in the raw sewage a greater concentration was detected (4.5±0.3 oocysts/L; 53.6±6.8 cysts/L). The major occurrence of Giardia over Cryptosporidium, both in the influent and in the effluent of the MWTP, is probably related to the human sewage contribution to the wastewater. Data on protozoa contamination of the receiving water body demonstrated similar concentrations in the samples collected before (0.21±0.07 oocysts/L; 1.31±0.38 cysts/L) and after (0.17±0.09 oocysts/L and 1.01±1.05 cysts/L) the plant effluent discharge. The results of this study suggest that the MWTP has no impact related to Giardia and Cryptosporidium river water contamination, and underline the need for investigation into the effectiveness of these protozoa removal by less technologically advanced MWTPs which are the most widespread and could probably show a lower ability to reduce protozoa.

Improving the efficiency of a wastewater treatment plant by fuzzy control and neural networks

2001 ◽  
Vol 43 (11) ◽  
pp. 189-196 ◽  
Author(s):  
M. Bongards
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Municipal Wastewater ◽  
Fuzzy Controller ◽  
Control Strategies ◽  
Treatment Plant ◽  
Municipal Wastewater Treatment ◽  
Controlled System ◽  
Measurement Results ◽  
Predictive Controller

One of the main problems in operating a wastewater treatment plant is the purification of the excess water from dewatering and pressing of sludge. Because of a high load of organic material and of nitrogen it has to be buffered and treated together with the inflowing wastewater. Different control strategies are discussed. A combination of neural network for predicting outflow values one hour in advance and fuzzy controller for dosing the sludge water are presented. This design allows the construction of a highly non-linear predictive controller adapted to the behaviour of the controlled system with a relatively simple and easy to optimise fuzzy controller. Measurement results of its operation on a municipal wastewater treatment plant of 60,000 inhabitant equivalents are presented and discussed. In several months of operation the system has proved very reliable and robust tool for improving the system's efficiency.

Integrated Wastewater Treatment Plant Performance Evaluation Using Artificial Neural Networks

1999 ◽  
Vol 40 (7) ◽  
pp. 55-65 ◽  
Author(s):  
Mohamed F. Hamoda ◽  
Ibrahim A. Al-Ghusain ◽  
Ahmed H. Hassan
Keyword(s):  
Neural Networks ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Wastewater Treatment Plant ◽  
Municipal Wastewater ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Plant Performance ◽  
Municipal Wastewater Treatment ◽  
Artificial Neural

Proper operation of municipal wastewater treatment plants is important in producing an effluent which meets quality requirements of regulatory agencies and in minimizing detrimental effects on the environment. This paper examined plant dynamics and modeling techniques with emphasis placed on the digital computing technology of Artificial Neural Networks (ANN). A backpropagation model was developed to model the municipal wastewater treatment plant at Ardiya, Kuwait City, Kuwait. Results obtained prove that Neural Networks present a versatile tool in modeling full-scale operational wastewater treatment plants and provide an alternative methodology for predicting the performance of treatment plants. The overall suspended solids (TSS) and organic pollutants (BOD) removal efficiencies achieved at Ardiya plant over a period of 16 months were 94.6 and 97.3 percent, respectively. Plant performance was adequately predicted using the backpropagation ANN model. The correlation coefficients between the predicted and actual effluent data using the best model was 0.72 for TSS compared to 0.74 for BOD. The best ANN structure does not necessarily mean the most number of hidden layers.

Sign in / Sign up

Export Citation Format

Share Document