scholarly journals Supplementary material to "Temporal and spatial analysis of ozone concentrations in Europe based on time scale decomposition and a multi-clustering approach"

Author(s):  
Eirini Boleti ◽  
Christoph Hueglin ◽  
Stuart K. Grange ◽  
André S. H. Prévôt ◽  
Satoshi Takahama
2019 ◽  
Author(s):  
Eirini Boleti ◽  
Christoph Hueglin ◽  
Stuart K. Grange ◽  
André S. H. Prévôt ◽  
Satoshi Takahama

Abstract. Air quality measures that were implemented in Europe in the 1990s resulted in reductions of ozone precursors concentrations. In this study, the effect of these reductions on ozone is investigated by analyzing surface measurements of ozone for the time period between 2000 and 2015. Using a non-parametric time scale decomposition methodology, the long-term, seasonal and short-term variation of ozone observations were extracted. A clustering algorithm was applied to the different time scale variations, leading to a classification of sites across Europe based on the temporal characteristics of ozone. The clustering based on the long-term variation resulted in a site type classification, while a regional classification was obtained based on the seasonal and short-term variations. Long-term trends of de-seasonalized mean and meteo-adjusted peak ozone concentrations were calculated across large parts of Europe for the time period 2000–2015. A multi-dimensional scheme was used for a detailed trend analysis, based on the identified clusters, which reflect precursor emissions and meteorological influence either on the inter-annual or the short-term time scale. Decreasing mean ozone concentrations at rural sites and increasing or stabilizing at urban sites were observed. At the same time downward trends for peak ozone concentrations were detected for all site types. The effect of hemispheric transport of ozone can be seen either in regions affected by synoptic patterns in the northern Atlantic or at sites located at remote high altitude locations. In addition, a reduction of the amplitude in the seasonal cycle of ozone was observed, and a shift in the occurrence of the seasonal maximum towards earlier time of the year. Finally, a reduced sensitivity of ozone to temperature was identified. It was concluded that long-term trends of mean and peak ozone concentrations are mostly controlled by precursors emissions changes, while seasonal cycle trends and changes in the sensitivity of ozone to temperature are driven by regional climatic conditions.


Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 617
Author(s):  
Jianpeng Ma ◽  
Shi Zhuo ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guangzhu Zhang

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.


2021 ◽  
Author(s):  
Kellyn Kessiene de Sousa Cavalcante ◽  
Jarier de Oliveira Moreno ◽  
Reagan Nzundu Boigny ◽  
Francisco Roger Aguiar Cavalcante ◽  
Caroline Mary Gurgel Dias Florêncio ◽  
...  

2021 ◽  
Author(s):  
Guangyao Zhang ◽  
Yuqi Wang ◽  
Weixi Xie ◽  
Han Du ◽  
Chunlin Jiang ◽  
...  

2021 ◽  
pp. 107754632098596
Author(s):  
Mingyue Yu

Intrinsic time-scale decomposition and graph signal processing are combined to effectively identify a rotor–stator rubbing fault. The vibration signal is decomposed into mutually independent rotational components, and then, the Laplacian energy index is obtained by the graph signal of the autocorrelation function of rotational components, and the signal is reconstructed by an autocorrelation function of each proper rotation (PR) component relative to smaller Laplacian energy index (less complexity). Finally, characteristics are extracted from rotor–stator rubbing faults in an aeroengine according to square demodulation spectrum of a reconstructed signal. To validate the effectiveness of the algorithm, a comparative analysis is made among traditional intrinsic time-scale decomposition algorithm, combination of intrinsic time-scale decomposition and autocorrelation function, and the proposed intrinsic time-scale decomposition–graph signal processing algorithm. Comparative result shows that the proposed intrinsic time-scale decomposition–graph signal processing algorithm is more precise and effective than the traditional intrinsic time-scale decomposition and intrinsic time-scale decomposition and autocorrelation function algorithms in extracting characteristic frequency and frequency multiplication of rotor–stator rubbing faults and can greatly reduce the number of noise components irrelevant to faults.


Sign in / Sign up

Export Citation Format

Share Document