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

Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.9K
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...
1.9K
Sampling Plans01:23

Sampling Plans

237
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...
237
Precipitation Processes01:12

Precipitation Processes

553
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
553
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

75
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...
75
Precipitation Gravimetry01:03

Precipitation Gravimetry

6.8K
Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
6.8K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

168
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:
168

You might also read

Related Articles

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

Sort by
Same author

Work-family conflict and intimate partner violence among Chinese female nurses: a cross-sectional study.

BMC nursing·2026
Same author

Dietary cypermethrin exposure reshapes the rumen microbiota and enriches antibiotic resistance genes: Metagenomic evidence of co-selection.

Ecotoxicology and environmental safety·2026
Same author

High-Performance Multi-Walled Carbon Nanotubes-Organic Passivated Si Solar Cells Enabled by Spatially Selective Harvesting of High-Quality Sponges.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Granulomatosis with polyangiitis initially presenting as secretory otitis media: a case report.

Frontiers in surgery·2026
Same author

Synthetic CT-enabled weekly adaptive radiotherapy for nasopharyngeal carcinoma: Optimizing plan adaptation triggers through volumetric-dosimetric monitoring.

Journal of applied clinical medical physics·2026
Same author

The Clock<sup>Δ19</sup> mutation promotes osteoarthritis via impairing SIRT3-mediated mitochondrial homeostasis in mice.

Journal of orthopaedic surgery and research·2026

Related Experiment Video

Updated: Aug 17, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K

An improved nonnegative matrix factorization with the imputation method model for pollution source apportionment

Jiashen Feng1, Tingting Duan1, Yanqing Zhou1

  • 1State Key Joint Laboratory of Environment Simulation and Pollution Control, School of the Environment, Beijing Normal University, Beijing, China.

Journal of Environmental Management
|December 14, 2022
PubMed
Summary

This study introduces a new method, Nonnegative Matrix Factorization with Imputation (NMF-IM), to accurately identify pollution sources in rivers, even with missing data from storm events.

Keywords:
Nonnegative matrix factorizationPollution source analysisRainstorm eventReceptor modelRivers

More Related Videos

Assessing the Particulate Matter Removal Abilities of Tree Leaves
05:07

Assessing the Particulate Matter Removal Abilities of Tree Leaves

Published on: October 7, 2018

6.8K
A Protocol for Conducting Rainfall Simulation to Study Soil Runoff
10:35

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

20.9K

Related Experiment Videos

Last Updated: Aug 17, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K
Assessing the Particulate Matter Removal Abilities of Tree Leaves
05:07

Assessing the Particulate Matter Removal Abilities of Tree Leaves

Published on: October 7, 2018

6.8K
A Protocol for Conducting Rainfall Simulation to Study Soil Runoff
10:35

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

20.9K

Area of Science:

  • Environmental Science
  • Water Quality Assessment
  • Pollution Source Apportionment

Background:

  • Storm events create data scarcity, hindering accurate pollution source apportionment.
  • Traditional methods struggle with incomplete datasets during extreme weather.

Purpose of the Study:

  • To develop and validate an integrated method (NMF-IM) for pollution source apportionment using incomplete data.
  • To assess the impact of antecedent dry periods on pollution source contributions.

Main Methods:

  • Integrated Nonnegative Matrix Factorization with Imputation (NMF-IM) to handle missing data.
  • Collected river and runoff samples during rainfall events in Banqiao and Nanfei River basins.
  • Quantified 16 indicators for source diagnostics using NMF-IM.

Main Results:

  • NMF-IM effectively imputed missing data and yielded source apportionment results comparable to complete data.
  • Total phosphorus (TP) showed higher concentrations and fluctuations than total nitrogen (TN).
  • Pollution source contributions varied with antecedent dry periods; treated tailwater and untreated sewage dominated short dry periods, while dust wash prevailed after longer periods.

Conclusions:

  • NMF-IM is a robust method for pollution source apportionment with missing data.
  • Effective source diagnostics require multiple indicators, with specific combinations proving most effective.
  • Pollution source contributions are dynamic and influenced by rainfall event characteristics and antecedent conditions.