ArticleInternational journal of environmental research and public health2020
Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 37 citations in OpenAlex.
- Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series.Entropy (Basel, Switzerland) · 2022Article
- The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.Sensors (Basel, Switzerland) · 2021Review
- Assessment of the Construction of a Climate Resilient City: An Empirical Study Based on the Difference in Differences Model.International journal of environmental research and public health · 2021Article
- Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy.Entropy (Basel, Switzerland) · 2021Article
- Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis.Computational intelligence and neuroscience · 2021Article
- An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.
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