Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

729
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
729
Sampling Plans01:23

Sampling Plans

1.3K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identification of the gene cluster for the dithiolopyrrolone antibiotic holomycin in Streptomyces clavuligerus.

Proceedings of the National Academy of Sciences of the United States of America·2010
Same author

Safety evaluation of tea (Camellia sinensis (L.) O. Kuntze) flower extract: assessment of mutagenicity, and acute and subchronic toxicity in rats.

Journal of ethnopharmacology·2010
Same author

Influences of soil properties and leaching on nickel toxicity to barley root elongation.

Ecotoxicology and environmental safety·2010
Same author

Effects of CO2 insufflation on cerebrum during endoscopic thyroidectomy in a porcine model.

Surgical endoscopy·2010
Same author

Plants' use of different nitrogen forms in response to crude oil contamination.

Environmental pollution (Barking, Essex : 1987)·2010
Same author

Overexpression of p35 in Min6 pancreatic beta cells induces a stressed neuron-like apoptosis.

Journal of the neurological sciences·2010

Related Experiment Video

Updated: Apr 11, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
09:27

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

7.6K

From pollutant profiling to source attribution: An interpretable staged machine learning framework for sewer

Jia-Qiang Lv1, Yanchen Liu1, Bo Li1

  • 1State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing 100084, China.

Journal of Hazardous Materials
|April 9, 2026
PubMed
Summary

This study introduces an interpretable framework for tracking industrial pollution in sewer networks. It accurately identifies pollution sources by combining data fusion, advanced feature engineering, and transparent AI models, enhancing water security.

Keywords:
Feature engineeringIndustrial dischargeInterpretabilitySewer networksSource tracking

Related Experiment Videos

Last Updated: Apr 11, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
09:27

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

7.6K

Area of Science:

  • Environmental Science
  • Water Resource Management
  • Artificial Intelligence in Environmental Monitoring

Background:

  • Illicit industrial discharges threaten urban water security.
  • Tracking pollution sources is difficult due to data scarcity and opaque models.

Purpose of the Study:

  • Develop an interpretable framework for pollutant profiling and industrial source tracking (InF-PaT).
  • Address challenges of sparse, imbalanced pollution data.
  • Enhance transparency and trust in AI-driven environmental monitoring.

Main Methods:

  • Fused multi-source data and employed advanced feature engineering, including generative adversarial networks (GANs), Savitzky-Golay filtering, and principal component analysis (PCA).
  • Utilized a staged modeling approach with soft sensing for pollutant prediction (R² > 0.91) followed by a multilayer perceptron for source tracking (accuracy > 0.96).
  • Incorporated Shapley Additive Explanations (SHAP) for dual-level interpretability and transparent attribution.

Main Results:

  • Achieved high-accuracy soft sensing of four key pollutants.
  • Enabled precise industrial source tracking with over 96% accuracy.
  • Identified key monitoring indicators (e.g., pH) and quantified data contributions (27.98%-38.68%).
  • Provided transparent, evidence-based explanations for predictions.

Conclusions:

  • The InF-PaT framework effectively bridges the gap between high-performance prediction and regulatory trust.
  • Offers a pathway for intelligent and accountable governance of urban sewer networks.
  • Demonstrates the utility of interpretable AI in addressing critical environmental challenges.