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"inference" Keyword-tagged Publications:

Title Authors PubMed ID
1 Exploiting fluctuations in gene expression to detect causal interactions between genes Joly-Smith E; Talpur MM; Allard P; Papazotos F; Potvin-Trottier L; Hilfinger A; 41401079
BIOLOGY
2 Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings Guo J; Fan W; Amayri M; Bouguila N; 39662201
ENCS
3 A Survey on Error Exponents in Distributed Hypothesis Testing: Connections with Information Theory, Interpretations, and Applications Espinosa S; Silva JF; Céspedes S; 39056958
ENCS
4 Development and performance assessment of a new opensource Bayesian inference R platform for building energy model calibration Hou D; Zhan D; Wang L; Hassan IG; Sezer N; 37936825
ENCS
5 The Convergence Model of Brain Reward Circuitry: Implications for Relief of Treatment-Resistant Depression by Deep-Brain Stimulation of the Medial Forebrain Bundle Pallikaras V; Shizgal P; 35431828
PSYCHOLOGY
6 Anterior cingulate neurons signal neutral cue pairings during sensory preconditioning Hart EE; Gardner MPH; Schoenbaum G; 34936884
PSYCHOLOGY
7 Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images Bourouis S; Alharbi A; Bouguila N; 34460578
ENCS
8 Exploring the decentralized treatment of sulfamethoxazole-contained poultry wastewater through vertical-flow multi-soil-layering systems in rural communities. Song P, Huang G, An C, Xin X, Zhang P, Chen X, Ren S, Xu Z, Yang X 33065414
ENCS
9 BENIN: Biologically enhanced network inference. Wonkap SK, Butler G 32698722
ENCS
10 Adaptive Neuro-fuzzy Inference System Trained for Sizing Semi-elliptical Notches Scanned by Eddy Currents. Mohseni E, Viens M, Xie WF 31929668
ENCS

 

Title:Exploring the decentralized treatment of sulfamethoxazole-contained poultry wastewater through vertical-flow multi-soil-layering systems in rural communities.
Authors:Song P,  Huang G,  An C,  Xin X,  Zhang P,  Chen X,  Ren S,  Xu Z,  Yang X
Link:https://www.ncbi.nlm.nih.gov/pubmed/33065414
DOI:10.1016/j.watres.2020.116480
Publication:Water research
Keywords:Interactive effect;  Microbial diversity;  Multi-soil-layering system;  Poultry wastewater;  Stepwise-cluster inference;  Sulfamethoxazole; 
PMID:33065414 Category:Water Res Date Added:2020-12-15
Dept Affiliation: ENCS
1 MOE Key Laboratory of Resources and Environmental Systems Optimization, North China Electric Power University, Beijing, 102206, China.
2 Center for Energy, Environment and Ecology Research, UR-BNU, School of Environment, Beijing Normal University, Beijing, 100875, China. Electronic address: huang@iseis.org.
3 Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Quebec, H3G 1M8, Canada.
4 Department of Civil Engineering, Memorial University of Newfoundland, St. John's, A1C 5S7, Canada.
5 Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, S4S 0A2, Canada.
6 State Key Joint Laboratory of Environmental Simulation and Pollution Control, CEEER-URBNU, College of Environment, Beijing Normal University, Beijing, 100875, China.

Description:

Exploring the decentralized treatment of sulfamethoxazole-contained poultry wastewater through vertical-flow multi-soil-layering systems in rural communities.

Water Res. 2021 Jan 01; 188:116480

Authors: Song P, Huang G, An C, Xin X, Zhang P, Chen X, Ren S, Xu Z, Yang X

Abstract

Sulfamethoxazole (SMX) is the most widely distributed sulfonamide antibiotics detected in decentralized poultry wastewater in rural communities. As an economically-feasible and eco-friendly technology for decentralized wastewater treatment in rural areas, vertical-flow multi-soil-layering (MSL) system was promising to mitigate the ecological and human health risks from SMX in such areas. The treatment of SMX-contained poultry wastewater by using MSL systems was investigated for the first time, and the main and interactive effects of related multiple variables on system performance were explored through factorial analysis, including material of permeable layer, concentration of SMX, and pH of influent. Results indicated that SMX concentration and pH of influent showed significantly negative effects on SMX removal. Medical stone used in MSL systems with larger surface area could intensify the SMX removal compared to anthracite. MSL systems showed stable performances on SMX removal with the best SMX removal efficiency more than 91%. A novel stepwise-cluster inference (SCI) model was developed for the first time to map the multivariate numeric relationships between state variables and SMX removal under discrete and nonlinear complexities. It was demonstrated that the effect of SMX in wastewater with high concentration was significant on the differentiation of soil bacteria composition in MSL systems based on microbial diversity analysis. These results can help better understand the mechanism of SMX removal in MSL systems from perspectives of factorial analysis, numeric modeling, and microbiological change.

PMID: 33065414 [PubMed - indexed for MEDLINE]




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