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

Eulerian and Lagrangian Flow Descriptions01:22

Eulerian and Lagrangian Flow Descriptions

Fluid flow analysis is critical in many scientific and engineering disciplines, and two principal approaches are used to describe this flow: the Eulerian and Lagrangian methods. These methods offer different perspectives on monitoring and analyzing the motion of fluids, each with distinct advantages depending on the scenario.
The Eulerian method focuses on fixed points in space where fluid properties, such as velocity, pressure, and temperature, are observed as the fluid moves between these...
Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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 squares (OLS)...
The Kinetic Model of Gases01:24

The Kinetic Model of Gases

The kinetic model of gases explains the properties of a perfect gas using three main assumptions: molecules move in ceaseless random motion, their size is negligible compared to the distances between them, and they do not interact except during perfectly elastic collisions. The total energy of a gas is the sum of the kinetic energies of all its constituent molecules. The pressure exerted by the gas arises from the continual bombardment of the container walls by billions of colliding molecules.

You might also read

Related Articles

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

Sort by
Same author

Association between prognostic nutritional index and mortality in patients with sepsis-associated delirium: a retrospective cohort study using the MIMIC-IV database.

BMC nutrition·2026
Same author

Towards greenhouse gases mitigation for liquid pig slurry management with solid-liquid separation technologies.

Journal of environmental management·2025
Same author

Correction: Prognostic impact of type 2 diabetes mellitus and coronary microvascular dysfunction in patients undergoing rotational atherectomy during PCI.

Cardiovascular diabetology·2025
Same author

Prognostic impact of type 2 diabetes mellitus and coronary microvascular dysfunction in patients undergoing rotational atherectomy during PCI.

Cardiovascular diabetology·2025
Same author

The Value of Contrast-Enhanced Ultrasound and High-Frequency Ultrasound in Evaluating the Efficacy of Wrist Intervention for Patients With Rheumatoid Arthritis.

Journal of clinical ultrasound : JCU·2025
Same author

Impacts of O<sub>2</sub>:CH<sub>4</sub> ratios and CH<sub>4</sub> concentrations on the denitrification and CH<sub>4</sub> oxidations of a novel AME-AD system.

Environmental research·2024

Related Experiment Video

Updated: Jun 27, 2026

Design and Use of a Full Flow Sampling System (FFS) for the Quantification of Methane Emissions
08:18

Design and Use of a Full Flow Sampling System (FFS) for the Quantification of Methane Emissions

Published on: June 12, 2016

Estimating gas emissions from multiple sources using a backward Lagrangian stochastic model.

Zhiling Gao1, Raymond L Desjardins, Ronald P van Haarlem

  • 1Research Branch, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada.

Journal of the Air & Waste Management Association (1995)
|December 3, 2008
PubMed
Summary

Quantifying methane (CH4) emissions from multiple farm sources using a backward Lagrangian Stochastic (bLS) model is possible. The model accurately determines emission rates from ground-level sources, but requires specific conditions for elevated or disturbed flow scenarios.

More Related Videos

Assessment of Methane and Nitrous Oxide Fluxes from Paddy Field by Means of Static Closed Chambers Maintaining Plants Within Headspace
09:03

Assessment of Methane and Nitrous Oxide Fluxes from Paddy Field by Means of Static Closed Chambers Maintaining Plants Within Headspace

Published on: September 6, 2018

Related Experiment Videos

Last Updated: Jun 27, 2026

Design and Use of a Full Flow Sampling System (FFS) for the Quantification of Methane Emissions
08:18

Design and Use of a Full Flow Sampling System (FFS) for the Quantification of Methane Emissions

Published on: June 12, 2016

Assessment of Methane and Nitrous Oxide Fluxes from Paddy Field by Means of Static Closed Chambers Maintaining Plants Within Headspace
09:03

Assessment of Methane and Nitrous Oxide Fluxes from Paddy Field by Means of Static Closed Chambers Maintaining Plants Within Headspace

Published on: September 6, 2018

Area of Science:

  • Agricultural emissions
  • Environmental monitoring
  • Atmospheric science

Background:

  • Manure storage and livestock barns are significant agricultural sources of methane (CH4).
  • Accurate quantification of CH4 emissions from multiple on-farm sources is crucial for environmental management.
  • Inverse dispersion modeling offers a potential method for source emission quantification.

Purpose of the Study:

  • To assess the efficacy of a backward Lagrangian Stochastic (bLS) model for quantifying CH4 emissions from multiple agricultural sources.
  • To investigate the influence of source configuration, wind-flow disturbance, and source height on emission quantification accuracy.
  • To establish criteria for the reliable application of the bLS model in complex agricultural emission scenarios.

Main Methods:

  • Simulated four distinct source configurations (C1-C4) representing various on-farm emission scenarios.
  • Employed a backward Lagrangian Stochastic (bLS) model to simulate and quantify methane emissions.
  • Utilized the condition number (K) to evaluate the model's applicability and uncertainty for different source setups.

Main Results:

  • The bLS model successfully quantified emissions from multiple ground-level sources without flow obstructions (C1, C3) when the condition number (K) was below 10.
  • Emission quantification accuracy decreased for configurations with wind-flow disturbance (C2) or mixed ground-level and elevated sources (C4).
  • Flow obstruction effects were negligible when measurement sites were at least 10 times the obstruction height away from the sources.

Conclusions:

  • The bLS model shows potential for accurate discrete emission rate determination from multiple on-farm sources.
  • Reliable quantification using the bLS model is contingent upon specific conditions, particularly regarding source configuration and environmental factors.
  • Further research and validation are needed for complex scenarios involving elevated sources and wind disturbances.