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Sampling Plans

Sampling Plans

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...

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Related Experiment Video

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Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

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Exploring Regional Reduction Pathways for Human Exposure to Fine Particulate Matter (PM_2.5) Using a Traffic

Ahmad Bin Thaneya¹, Arpad Horvath¹

¹Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States.

Environmental Science & Technology

|November 13, 2023

Summary

This study models fine particulate matter (PM2.5) exposure from traffic in Chicago. Rerouting vehicles to minimize pollution exposure can reduce damages but increases travel time, highlighting trade-offs in urban planning.

Keywords:

air quality exposure human health traffic assignment transportation

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Published on: May 22, 2020

Area of Science:

Environmental Science
Transportation Engineering
Public Health

Background:

Fine particulate matter (PM2.5) from vehicle emissions poses significant health risks.
Accurate quantification of PM2.5 exposure is crucial for developing effective mitigation strategies.

Purpose of the Study:

To develop and apply an exposure-based traffic assignment (TA) model for Chicago.
To quantify PM2.5 exposure and associated damages from various vehicle types.
To compare baseline travel equilibrium with pollution-optimized routing scenarios.

Main Methods:

Utilized an exposure-based TA model incorporating travel demand data.
Considered emissions from light-duty vehicles, heavy-duty trucks, public transit, and EV charging.
Compared user equilibrium (UET) with system optimal (SOI) scenarios based on pollutant intake.

Main Results:

Baseline PM2.5 exposure damages estimated at $3.7B-$8.3B/year.
SOI reduced total damages by 8.2% and benefited high-impact populations (10-20% reduction).
SOI increased travel time by 66%; bi-objective optimization and other strategies showed 1-40% exposure reduction.

Conclusions:

Exposure-based TA modeling effectively quantifies PM2.5 impacts and evaluates mitigation strategies.
Reducing PM2.5 exposure involves trade-offs with travel time, necessitating balanced optimization.
Combined strategies, including cleaner fleets and increased public transit, can achieve over 50% exposure reduction.