Data analysis and preprocessing techniques for air quality prediction: a survey

Air quality prediction technology can provide effective technical means for environmental governance. In recent years, due to the strong nonlinearity of data, there has been extensive research on data analysis and preprocessing techniques. This paper aims to comprehensively summarize and analyze the...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment 2024-06, Vol.38 (6), p.2095-2117
Main Authors: Yu, Chengqing, Tan, Jing, Cheng, Yihan, Mi, Xiwei
Format: Article
Language:English
Subjects:
Air pollution
Air quality
Algorithms
Aquatic Pollution
Chemistry and Earth Sciences
Cognition & reasoning
Computational Intelligence
Computer Science
Data analysis
Data processing
Decomposition
Earth and Environmental Science
Earth Sciences
Emergency communications systems
Environment
Environmental governance
Environmental research
Feature selection
Forecasting
Interpolation
Math. Appl. in Environmental Science
Missing data
Nitrogen dioxide
Nonlinear systems
Outdoor air quality
Outliers (statistics)
Physics
Pollutants
Preprocessing
Probability Theory and Stochastic Processes
Review Paper
Risk assessment
Spatial data
Statistics for Engineering
Waste Water Technology
Water Management
Water Pollution Control
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Description
Summary:Air quality prediction technology can provide effective technical means for environmental governance. In recent years, due to the strong nonlinearity of data, there has been extensive research on data analysis and preprocessing techniques. This paper aims to comprehensively summarize and analyze the methods used in air quality forecasting, specifically focusing on four categories: data decomposition, dimensionality reduction, data correction, and spatial interpolation. Each method's purpose, characteristics, improvements, and implementation details are described in detail. The evaluation of data preprocessing methods is based on popularity, accuracy improvements, time consumption, maturity, and implementation difficulty. Among the existing methods, data decomposition and feature selection are commonly used and well-developed. However, outlier detection and spatial interpolation have limited applications and require further research. Furthermore, this paper discusses current challenges in applying these methods and future development trends, providing a valuable reference for future research.
ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-024-02693-4