Multi-timescale analysis of air pollution spreaders in Chinese cities based on a transfer entropy network

Hu, He; Tan, Zhanglu; Liu, Chan; Wang, Ze; Cai, Xiaomei; Wang, Xing; Ye, Zihan; Zheng, Shuxian

doi:10.3389/fenvs.2022.970267

Cited by 3 publications

(2 citation statements)

References 63 publications

(84 reference statements)

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“…Entropy is a fundamental variable of nature that has been investigated and applied in many different areas, but its application to the study of communications, urban dimensioning, fluids, the Earth atmosphere system, medicine, biology, etc., is relatively recent [44][45][46][47]. However, its application to pollutants and the interaction with the atmosphere in the boundary layer is much more current [48,49].…”

Section: Kolmogorov Entropy (S K ) and Loss Of Information (<∆I>)mentioning

confidence: 99%

Urban Meteorology, Pollutants, Geomorphology, Fractality, and Anomalous Diffusion

Pacheco,

Mera,

Navarro

et al. 2024

Fractal Fract

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The measurements, recorded as time series (TS), of urban meteorology, including temperature (T), relative humidity (RH), wind speed (WS), and pollutants (PM10, PM2.5, and CO), in three different geographical morphologies (basin, mountain range, and coast) are analyzed through chaos theory. The parameters calculated at TS, including the Lyapunov exponent (λ > 0), the correlation dimension (DC < 5), Kolmogorov entropy (SK > 0), the Hurst exponent (0.5 < H < 1), Lempel–Ziv complexity (LZ > 0), the loss of information (<ΔI> < 0), and the fractal dimension (D), show that they are chaotic. For the different locations of data recording, CK is constructed, which is a proportion between the sum of the Kolmogorov entropies of urban meteorology and the sum of the Kolmogorov entropies of the pollutants. It is shown that, for the three morphologies studied, the numerical value of the CK quotient is compatible with the values of the exponent α of time t in the expression of anomalous diffusion applied to the diffusive behavior of atmospheric pollutants in basins, mountain ranges, and coasts. Through the Fréchet heavy tail study, it is possible to define, in each morphology, whether urban meteorology or pollutants exert the greatest influence on the diffusion processes.

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Section: Kolmogorov Entropy (S K ) and Loss Of Information (<∆I>)mentioning

confidence: 99%

Urban Meteorology, Pollutants, Geomorphology, Fractality, and Anomalous Diffusion

Pacheco,

Mera,

Navarro

et al. 2024

Fractal Fract

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“…Namely, there are differences in the evolution of air quality before and after the outbreak of COVID-19, which may lead to a change in the regularity of air quality data. Differing from regression models or networks [ 38 , 39 , 40 ], the recursive graph contains rich information for the quantitative comparison of air quality before and after the beginning of the epidemic; this paper further adopted recurrence quantification analysis to depict the complex mechanisms underlying air quality dynamics. Some indicators were used to represent the characteristic properties of recursive graphs.…”

Section: The Predictability Of the Evolution Of Aqi Trends Caused By ...mentioning

confidence: 99%

Changes in Air Quality during the Period of COVID-19 in China

Huang

et al. 2022

IJERPH

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This paper revisits the heterogeneous impacts of COVID-19 on air quality. For different types of Chinese cities, we analyzed the different degrees of improvement in the concentrations of six air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) during COVID-19 by analyzing the predictivity of air quality. Specifically, we divided the sample into three groups: cities with severe outbreaks, cities with a few confirmed cases, and cities with secondary outbreaks. Ensemble empirical mode decomposition (EEMD), recursive plots (RPs), and recursive quantitative analysis (RQA) were used to analyze these heterogeneous impacts and the predictivity of air quality. The empirical results indicated the following: (1) COVID-19 did not necessarily improve air quality due to factors such as the rebound effect of consumption, and its impacts on air quality were short-lived. After the initial outbreak, NO2, CO, and PM2.5 emissions declined for the first 1–3 months. (2) For the cities with severe epidemics, air quality was improved, but for the cities with second outbreaks, air quality was first enhanced and then deteriorated. For the cities with few confirmed cases, air quality first deteriorated and then improved. (3) COVID-19 changed the stability of the air quality sequence. The predictability of the air quality index (AQI) declined in cities with serious epidemic situations and secondary outbreaks, but for the cities with a few confirmed cases, the AQI achieved a stable state sooner. The conclusions may facilitate the analysis of differences in air quality evolution characteristics and fluctuations before and after outbreaks from a quantitative perspective.

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Evolution over Time of Urban Thermal Conditions of a City Immersed in a Basin Geography and Mitigation

Pacheco

Mera

2023

Atmosphere

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This work analyzes the evolution of an urban thermal environment using measurements, in the form of time series, of atmospheric pollutants (PM10, PM2.5, CO) and meteorological variables (temperature (T), relative humidity (RH) and magnitude of wind speed (WS)) of three periods, each of 3.25 years: 2010–2013, 2017–2020 and 2019–2022. The study region is the capital of Chile, Santiago de Chile, located in a rugged basin geography. Of the total communes that make up the capital, six communes that are at different heights from sea level were selected for this study, providing 3,074,004 data records. These communes have been subject to an intensive urban densification process. The time series are analyzed through the chaos theory, demonstrating that they are chaotic through the calculation of the parameters: Lyapunov exponent (λ > 0), correlation dimension (DC < 5), Kolmogorov entropy (SK > 0), Hurst exponent (0.5 < H < 1), Lempel–Ziv complexity (LZ > 0). Based on these parameters, the following is constructed for each commune: the CK ratio, which results from the ratio between the entropies of the meteorological variables and the entropies of the pollutants; the loss of information (<ΔI> < 0) using the Lyapunov exponent; the fractal dimension (D) using the Hurst exponent. It is verified, when comparing the three periods for the six communes, that: CK evolves declining with height, with a greater influence of pollutants; the loss of information is faster in urban meteorology; an increase in fractality. The estimation of the entropic flows, based on the Clausius equation, confirm the trend. The descriptive framework shows the weakness of the mitigation measures.

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Multi-timescale analysis of air pollution spreaders in Chinese cities based on a transfer entropy network

Cited by 3 publications

References 63 publications

Urban Meteorology, Pollutants, Geomorphology, Fractality, and Anomalous Diffusion

Urban Meteorology, Pollutants, Geomorphology, Fractality, and Anomalous Diffusion

Changes in Air Quality during the Period of COVID-19 in China

Evolution over Time of Urban Thermal Conditions of a City Immersed in a Basin Geography and Mitigation

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