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

Sampling Plans01:23

Sampling Plans

169
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...
169
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

4.2K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
4.2K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.2K
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...
2.2K

You might also read

Related Articles

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

Sort by
Same author

Automated machine learning assisted fluorescent sensor array based on silver nanoclusters for detection of multiple heavy metal ions.

Mikrochimica acta·2026
Same author

Case Report: Indocyanine green fluorescence imaging in complex focal nodular hyperplasia resection: report of two cases.

Frontiers in medicine·2026
Same author

Fabrication of Novel Yellow-Fluorescent Carbon Dots and Their Application for Fe³⁺ Detection.

Journal of fluorescence·2026
Same author

Functional Divergence of Mucus in Pacific Oyster (Crassostrea gigas): Insights From Integrated Proteomic and Rheological Study.

Proteomics·2026
Same author

Integrative Multiomics and Network Pharmacology Exploration of Active Components and Mechanisms of Action of Qufu Shengxin Ointment in Treating Chronic Nonhealing Wounds.

Mediators of inflammation·2026
Same author

Inter-layer edge artifact-suppressed multi-plane target amplitude synthesis for phase-only hologram generation.

Applied optics·2026

Related Experiment Video

Updated: Jun 10, 2025

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

447

3DVar sectoral emission inversion based on source apportionment and machine learning.

Congwu Huang1, Tao Niu2, Tijian Wang3

  • 1Faculty of Resources and Environmental Science, Hubei University, Wuhan, 430062, China; School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China; State Key Laboratory of Severe Weather and Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, China.

Environmental Pollution (Barking, Essex : 1987)
|October 20, 2024
PubMed
Summary

This study enhances air quality models by using machine learning for sectoral emission inversion and source apportionment. The new method significantly reduces errors in predicting particulate matter and ozone pollution.

Keywords:
3DVarCMAQMachine learningPM(2.5) and O(3)Sectoral emission inversion

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K
Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
00:09

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

13.5K

Related Experiment Videos

Last Updated: Jun 10, 2025

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

447
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K
Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
00:09

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

13.5K

Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Machine Learning Applications

Background:

  • Accurate air quality models are crucial for pollution control and forecasting.
  • Sectoral emissions are key determinants of model accuracy and source apportionment.
  • Existing methods require improvement for complex pollutant interactions.

Purpose of the Study:

  • To develop and evaluate a machine learning-based 3DVar emission inversion method for sectoral source apportionment.
  • To improve the accuracy of air quality models by incorporating sectoral emission data.
  • To enhance the inversion capabilities for nonlinear ozone-nitrogen oxides-volatile organic compounds processes.

Main Methods:

  • Utilized a machine learning-based three-dimensional variational (3DVar) emission inversion technique.
  • Developed two machine learning conversion matrices for pollutant concentration to sectoral apportionment and sectoral apportionment to emissions.
  • Integrated ozone concentration contributions from volatile organic compounds (VOCs) and nitrogen oxides (NOx) precursors.

Main Results:

  • The enhanced method demonstrated improved inversion for O3-NOx-VOCs nonlinear processes.
  • Regional errors for PM2.5 and O3 were reduced by 47% and 45%, respectively.
  • Temporal errors for PM2.5 and O3 were reduced by 44% and 16%, respectively, in the Beijing-Tianjin-Hebei region.

Conclusions:

  • The machine learning-based sectoral emission inversion method significantly improves air quality model accuracy.
  • This approach offers a more precise way to forecast pollution and inform control strategies.
  • The study highlights the effectiveness of integrating machine learning with atmospheric models for source apportionment.