Detection of outliers in air pollution from Shijiazhuang using functional data analysis

Tao Yang; Xiaoping Shi

doi:10.1117/12.2692369

23 August 2023 Detection of outliers in air pollution from Shijiazhuang using functional data analysis

Tao Yang, Xiaoping Shi

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127841E (2023) https://doi.org/10.1117/12.2692369
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

Air pollution seriously affects the production and life of human society, causing weather and climate to exhibit anomalies. The high variability of such data makes it difficult for traditional methods to accurately detect anomalous values. In order to solve such problems, this paper generalizes the 𝐿_∞ depth function according to its easy computational characteristics on high-dimensional data and applies the depth function to the detection of outliers in function data analysis. On simulated data, weighted 𝐿^∞ function depth is more robust in estimating the mean function compared to other function depths, with the smallest mean integrated squared error in estimating the mean function, and TPR is more efficient in identifying outliers. This approach is used to identify outliers in the data on air pollution in Shijiazhuang City, and it is compared with the typical boxplot method and other outlier detection techniques based on depth functions. The results show that the weight 𝐿_∞ method can identify the most polluted months of 𝑃𝑀₁₀, 𝑃𝑀_2.5, 𝑆𝑂₂ and 𝑁𝑂₂, confirming the effectiveness of our proposed method. It provides a scientific basis for the improvement of weather regulation and control measures for heavy pollution in Shijiazhuang city.

Citation Download Citation

Tao Yang and Xiaoping Shi "Detection of outliers in air pollution from Shijiazhuang using functional data analysis", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127841E (23 August 2023); https://doi.org/10.1117/12.2692369

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KEYWORDS

Air contamination

Data analysis

Air quality

Fermium

Frequency modulation

Model-based design

Pollution

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