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

Data Collection by Observations01:08

Data Collection by Observations

14.6K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
14.6K
Observational Studies01:11

Observational Studies

10.7K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
10.7K
Passive Filters01:27

Passive Filters

962
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
962
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

534
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
534
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

250
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
250
Active Filters01:25

Active Filters

1.3K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.3K

You might also read

Related Articles

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

Sort by
Same author

Boosting O<sub>2</sub> activation and substrate adsorption over single-atom Zr-doped Pt/CeO<sub>2</sub> for enhanced glucose oxidation to glucaric acid.

Bioresource technology·2026
Same author

Cost-effectiveness analysis of pyrotinib combined with trastuzumab and docetaxel as first-line treatment for HER2-positive metastatic breast cancer in China.

Frontiers in pharmacology·2026
Same author

Number Needed to Treat with Biologics in Type-2 Inflammation COPD: A Systematic Review and Meta-Analysis.

COPD·2026
Same author

Elimination of detrimental grain boundary segregation in Garnets.

Nature communications·2026
Same author

Atmospheric Hydrogen Variability in Flooded Areas of the Yangtze River Delta.

Environmental science & technology·2026
Same author

Electric mobility and the green transition: A spatial econometric perspective on global decarbonization.

Journal of environmental management·2026

Related Experiment Video

Updated: Jan 21, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

10.0K

Study on CO data filtering approaches based on observations at two background stations in China.

Shuo Liu1, Shuangxi Fang2, Miao Liang2

  • 1State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.

The Science of the Total Environment
|July 21, 2019
PubMed
Summary

Identifying regional carbon monoxide (CO) data requires filtering local influences. The meteorological conditions (MET) method proved most suitable for filtering CO at Chinese stations, unlike statistical or time-scale approaches.

Keywords:
Carbon monoxide (CO)Data filteringMulti-year trendObservationSeasonal variation

More Related Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

485
Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
10:28

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

6.3K

Related Experiment Videos

Last Updated: Jan 21, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

10.0K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

485
Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
10:28

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

6.3K

Area of Science:

  • Atmospheric Chemistry
  • Environmental Science
  • Climate Monitoring

Background:

  • Understanding regional atmospheric carbon monoxide (CO) characteristics necessitates reliable measurements, free from local source/sink interference.
  • Accurate CO data is crucial for regional atmospheric studies and climate change assessments.

Purpose of the Study:

  • To evaluate the applicability of three common data filtering methods for atmospheric CO measurements at two Chinese WMO/GAW regional stations.
  • To determine the most suitable method for obtaining representative CO data, minimizing local influences.

Main Methods:

  • Applied three filtering approaches: meteorological conditions (MET), robust extraction of baseline signal (REBS), and standard deviations of the running mean (SDM).
  • Analyzed atmospheric CO data from 2010/2011 to 2017 at Lin'an (LAN) and Shangdianzi (SDZ) stations in China.
  • Compared seasonal cycles and yearly CO growth rates derived from each method.

Main Results:

  • All three methods showed similar seasonal cycles at LAN but varied at SDZ.
  • Yearly CO growth rates differed significantly at SDZ: -10.6 ± 0.5 (MET), -2.2 ± 0.1 (REBS), and -23.5 ± 0.3 ppb yr⁻¹ (SDM).
  • REBS showed a slight decrease at SDZ due to data bias, while SDM potentially overestimated trends.

Conclusions:

  • The REBS method requires cautious application for CO observations, especially at stations with complex geographical and meteorological conditions.
  • The SDM method may lead to overestimation of multi-year CO trends.
  • The MET method is identified as the most suitable for filtering CO observation records at stations like SDZ, considering regional specificities.