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

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

17.4K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
17.4K
Response Surface Methodology01:16

Response Surface Methodology

328
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
328
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

207
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
207
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

816
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...
816
Quantifying Heat02:46

Quantifying Heat

58.9K
Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a...
58.9K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

2.9K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
2.9K

You might also read

Related Articles

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

Sort by
Same author

Enhanced vapor wall loss of intermediate volatility alcohols at elevated relative humidity.

Aerosol science and technology : the journal of the American Association for Aerosol Research·2026
Same author

Adding spatially-resolved characterization factors for ozone formation potential in the United States Environmental Protection Agency's tool for the reduction of chemical and other impacts.

The Science of the total environment·2026
Same author

Dietary flavonoids form supramolecular assemblies, alter biochemistry, and enhance cell resilience.

Frontiers in nutrition·2025
Same author

Incineration of Perfluorooctanoic Acid Leads to Regeneration of Smaller Perfluorocarboxylic Acids.

The journal of physical chemistry. A·2025
Same author

Atmospheric oxidation of 1,3-butadiene: influence of seed aerosol acidity and relative humidity on SOA composition and the production of air toxic compounds.

Atmospheric chemistry and physics·2025
Same author

A short-duration telementoring pain management programme for Medicaid: impact on clinician outcomes.

Health education research·2025

Related Experiment Video

Updated: Oct 24, 2025

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx
07:14

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx

Published on: December 20, 2016

11.8K

Quantifying wintertime O3 and NOx formation with relevance vector machines.

David A Olson1, Theran P Riedel1, John H Offenberg1

  • 1Office of Research and Development, United States Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, United States.

Atmospheric Environment (Oxford, England : 1994)
|August 13, 2021
PubMed
Summary

This study quantifies ozone and nitrogen oxides formation using a machine learning model. The model accurately predicts concentrations and identifies key precursors influencing winter air quality.

Keywords:
dinitrogen pentoxidehydrogen peroxidemachine learningnitrous acidnitryl chloridesupport vector machine

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

563
Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.4K

Related Experiment Videos

Last Updated: Oct 24, 2025

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx
07:14

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx

Published on: December 20, 2016

11.8K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

563
Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.4K

Area of Science:

  • Atmospheric Chemistry
  • Environmental Science
  • Machine Learning Applications

Background:

  • Wintertime air quality in Utah is a concern, with ozone (O3) and nitrogen oxides (NOx) being key pollutants.
  • Previous field studies, like Olson et al. (2019), provided continuous measurements crucial for model development.

Purpose of the Study:

  • To quantify ozone (O3) and nitrogen oxides (NOx) formation using a machine learning model.
  • To assess the influence of various chemical precursors on O3 and NO2 concentrations during winter.

Main Methods:

  • Utilized a Relevance Vector Machine (RVM) machine learning model.
  • Formulated RVMs with either O3 or nitrogen dioxide (NO2) as the output variable.
  • RVMs employed sparse model formulations, using only 16-20% of the measurement data.

Main Results:

  • Achieved high prediction accuracy with RVMs: r² = 0.944 for O3 and r² = 0.931 for NO2.
  • Identified hydrogen peroxide (H2O2), dinitrogen pentoxide (N2O5), and molecular chlorine (Cl2) as O3 precursors.
  • Found that only N2O5 increases led to higher NO2 concentrations, while HNO3 and CH2O2 did not affect O3.

Conclusions:

  • RVMs are effective tools for quantifying atmospheric pollutant formation and identifying key precursors.
  • Understanding precursor-pollutant relationships is vital for winter air quality management.
  • N2O5 plays a significant role in both O3 and NO2 formation during winter conditions.