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Related Concept Videos

Sampling Plans01:23

Sampling Plans

810
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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Mining sequential patterns of PM2.5 pollution between 338 cities in China.

Liankui Zhang1, Guangfei Yang1, Xianneng Li1

  • 1Institute of Systems Engineering, Dalian University of Technology, No. 2 Linggong Road, Ganjingzi District, Dalian, 116024, China.

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China

Keywords:
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Area of Science:

  • Environmental Science
  • Data Science
  • Urban Planning

Background:

  • Persistent PM2.5 air pollution in Chinese urban areas necessitates targeted policies.
  • Existing macro-level studies on inter-city air pollution are insufficient for effective policy formulation.

Purpose of the Study:

  • To analyze spatial-temporal patterns of PM2.5 pollution across Chinese cities (2015-2018).
  • To identify and characterize associative relationships between urban areas regarding PM2.5 pollution.
  • To provide data for improved joint policy design for PM2.5 pollution control.

Main Methods:

  • Applied a sequential pattern mining algorithm to analyze PM2.5 data.
  • Examined patterns across three geographic granularities and ten temporal scenarios (10-100 hours).
  • Identified and assessed the severity of associative relationships between urban areas.

Main Results:

  • Revealed numerous underlying associative relationships between Chinese cities concerning PM2.5 pollution.
  • Mined patterns exhibited heterogeneity, characterized by clustering, symmetry, imbalance, decay, and stability.
  • Quantified the severity of these spatial-temporal pollution relationships across different urban granularities.

Conclusions:

  • The study provides crucial data for Chinese government decision-making at national, city, and site-specific levels.
  • Findings support the development of more effective, data-driven joint policies for PM2.5 pollution prevention and control.
  • Understanding spatial-temporal pollution patterns is key to improving urban air quality in China.