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Related Concept Videos

Sampling Plans01:23

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...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
Bioremediation00:46

Bioremediation

Bioremediation is the use of prokaryotes, fungi, or plants to remove pollutants from the environment. This process has been used to remove harmful toxins in groundwater as a byproduct of agricultural run-off and also to clean up oil spills.

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Related Experiment Video

Updated: Jul 10, 2026

Microbial Control and Monitoring Strategies for Cleanroom Environments and Cellular Therapies
09:30

Microbial Control and Monitoring Strategies for Cleanroom Environments and Cellular Therapies

Published on: March 17, 2023

Siting bio-samplers in buildings.

Michael D Sohn1, David M Lorenzetti

  • 1Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. mdsohn@lbl.gov

Risk Analysis : an Official Publication of the Society for Risk Analysis
|October 26, 2007
PubMed
Summary

This study presents a probabilistic method for strategically placing biological detection samplers. The approach optimizes sampler placement to maximize detection probability across various release scenarios, improving building safety.

Area of Science:

  • Environmental Science
  • Risk Assessment
  • Sensor Technology

Background:

  • Accidental or intentional releases of biological material pose significant public health risks.
  • Effective detection relies on optimal placement of sampling equipment.
  • Uncertainty in release conditions complicates sampler siting decisions.

Purpose of the Study:

  • To develop and demonstrate a probabilistic approach for siting samplers to detect biological releases.
  • To maximize the probability of detecting releases under uncertain conditions.
  • To guide sampler network design and placement in buildings.

Main Methods:

  • Developed a probabilistic algorithm for sampler placement.
  • Considered a suite of realistic release scenarios with varying parameters (e.g., release size, location, weather).

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  • Applied the algorithm to a hypothetical 24-room commercial building to identify optimal sampler networks.

    • Main Results:

    • Identified optimal sampler networks for different sampler types and building configurations.
    • Demonstrated that sampler characteristics, particularly detection limits, significantly impact network performance.
    • The probabilistic approach effectively balances detection probability against placement constraints.

    Conclusions:

    • The probabilistic siting approach is effective for optimizing biological detection networks.
    • Sampler design, especially detection limit, is crucial for effective deployment.
    • This method can inform both sampler design priorities and practical siting strategies in buildings.