Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sampling Plans

Sampling Plans

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...

Extraction: Partition and Distribution Coefficients

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

One-Way ANOVA

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...

You might also read

Related Articles

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

Sort by

Same author

Fournier's Gangrene Due to Salmonella Typhimurium in a Healthy Male: A Rare Aberrant Infection.

Infectious disorders drug targets·2020

Same author

Weight-of-evidence approach to identify regionally representative sites for air-quality monitoring network: Satellite data-based analysis.

MethodsX·2020

Same author

Aerosols cause intraseasonal short-term suppression of Indian monsoon rainfall.

Scientific reports·2017

Same author

Metabolic modeling for multi-objective optimization of ethanol production in a Synechocystis mutant.

Photosynthesis research·2013

Same author

Metabolic flexibility of D-ribose producer strain of Bacillus pumilus under environmental perturbations.

Journal of industrial microbiology & biotechnology·2012

Same author

Macrocephaly-capillary malformation syndrome: three new cases.

Journal of the neurological sciences·2011

Search research articles

Related Experiment Video

Updated: Sep 21, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Generalised non-negative matrix factorisation for air pollution source apportionment.

Nirav L Lekinwala¹, Mani Bhushan²

¹Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, Maharashtra, India.

The Science of the Total Environment

|June 1, 2022

Summary

This study introduces Generalised Non-Negative Matrix Factorisation (GNMF) for air pollution source apportionment. GNMF accurately accounts for correlated errors in data, improving upon existing methods like Positive Matrix Factorisation (PMF).

Keywords:

Correlated errors Multiplicative update Projected gradient approach Receptor modeling

More Related Videos

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Published on: March 9, 2018

Related Experiment Videos

Last Updated: Sep 21, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Published on: March 9, 2018

Area of Science:

Environmental Science
Analytical Chemistry
Data Science

Background:

Source Apportionment (SA) is crucial for air pollution control policy.
Positive Matrix Factorisation (PMF) is a common SA technique but assumes uncorrelated errors.
Existing Non-Negative Matrix Factorisation (NMF) methods have restrictive assumptions on error structures.

Purpose of the Study:

To develop a novel SA method that incorporates general error covariance structures.
To address the limitation of uncorrelated error assumptions in existing SA techniques.
To introduce Generalised Non-Negative Matrix Factorisation (GNMF) for improved SA.

Main Methods:

Developed Generalised Non-Negative Matrix Factorisation (GNMF) by integrating the full error covariance matrix into the objective function.
Derived iterative update rules for factor matrices (G and F).
Extended existing NMF techniques (multiplicative and projected gradient) to ensure non-negativity in GNMF.

Main Results:

The proposed GNMF method can handle any error covariance matrix without restrictive assumptions.
GNMF subsumes various existing NMF methods as special cases.
Demonstrated the effectiveness of GNMF on a field measurement dataset for SA.

Conclusions:

GNMF offers a more robust and flexible approach to Source Apportionment compared to traditional methods.
The ability to incorporate general error structures enhances the accuracy of identifying pollution sources.
GNMF provides a valuable tool for environmental policy development by improving air pollution source identification.