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Related Concept Videos

Sampling Plans01:23

Sampling Plans

169
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...
169

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Air pollution data: A dataset gathered through a crowd sensing platform.

Slave Temkov1, Pance Cavkovski1, Petre Lameski1

  • 1Ss Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, North Macedonia.

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Summary
This summary is machine-generated.

This study presents a comprehensive air pollution dataset from Skopje, North Macedonia, collected via crowd sensing. The data aids in understanding pollution trends and informing public health strategies.

Keywords:
Air qualityCrowd sensingIoT platformPollutionSensor networks

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Area of Science:

  • Environmental Science
  • Data Science
  • Urban Planning

Background:

  • Air quality monitoring is crucial for public health.
  • Existing datasets may lack spatial and temporal resolution.
  • Crowd sensing offers a novel approach to data collection.

Purpose of the Study:

  • To introduce a high-resolution dataset for air pollution and noise monitoring.
  • To provide a resource for studying urban environmental factors.
  • To support research on air quality and public health.

Main Methods:

  • Utilized a crowd sensing Internet of Things (IoT) platform.
  • Collected real-time data on particulate matter (PM2.5, PM10), gases (NO2, O3, CO), meteorological parameters, and noise levels.
  • Data gathered across multiple urban locations in Skopje, North Macedonia, from 2018 to 2024.

Main Results:

  • An extensive dataset with high spatial and temporal resolution was compiled.
  • The dataset includes diverse environmental parameters, offering a holistic view of urban pollution.
  • Data spans multiple years, enabling trend analysis.

Conclusions:

  • The dataset is a valuable resource for pollution research and forecasting.
  • It supports the assessment of urban planning impacts on air quality.
  • Facilitates data-driven decision-making for improved public health and environmental policies.