Exploring the relationship between nitrogen dioxide and ground-level ozone by applying the joint multifractal analysis

Comparisons of Combined Oxidant Capacity and Redox-Weighted Oxidant Capacity in Their Association with Increasing Levels of COVID-19 Infection

Background: Ozone (O3) and nitrogen dioxide (NO2) are substances with oxidizing ability in the atmosphere. Only considering the impact of a single substance is not comprehensive. However, people’s understanding of “total oxidation capacity” (Ox) and “weighted average oxidation” (Oxwt) is limited. Objectives: This investigation aims to assess the impact of Ox and Oxwt on the novel coronavirus disease (COVID-19). We also compared the relationship between the different calculation methods of Ox and Oxwt and the COVID-19 infection rate. Method: We recorded confirmed COVID-19 cases and daily pollutant concentrations (O3 and NO2) in 34 provincial capital cities in China. The generalized additive model (GAM) was used to analyze the nonlinear relationship between confirmed COVID-19 cases and Ox and Oxwt. Result: Our results indicated that the correlation between Ox and COVID-19 was more sensitive than Oxwt. The hysteresis effect of Ox and Oxwt decreased with time. The most obvious statistical data was observed in Central China and South China. A 10 µg m−3 increase in mean Ox concentrations were related to a 23.1% (95%CI: 11.4%, 36.2%) increase, and a 10 µg m−3 increase in average Oxwt concentration was related to 10.7% (95%CI: 5.2%, 16.8%) increase in COVID-19. In conclusion, our research results show that Ox and Oxwt can better replace the single pollutant research on O3 and NO2, which is used as a new idea for future epidemiological research.

Coupled multifractal methods to reveal changes in nitrogen dioxide and tropospheric ozone concentrations during the COVID-19 lockdown

Due to COVID-19 pandemic, the lockdown effects on air pollution level are undeniable. Several studies around the world have detected an uneven behaviour in tropospheric ozone (O ₃) concentrations. In this work, Seville (Spain) is used as example of faced to traffic place in which the nitrogen dioxide (NO ₂) is drastically reduced (41%) while O ₃ has no significant changes. In order to evaluate the existence of differences in O ₃ behaviour that is not detected by statistical procedures, a multifractal approach was used to assess the coupled scale relationship between NO ₂ and O ₃ during the 2020 lockdown against a period reference (2017-2019). For this purpose, the two main coupled multifractal method were employed: multifractal detrended cross-correlation and joint multifractal analysis. While cross-correlation analysis did not detect differences between the cross-correlated fluctuations of NO ₂ and O ₃ in the periods analysed, the joint multifractal analysis, based on the partition function and the method of moments, found a loss of variability in O ₃ during the lockdown. This leads to a loss of multifractal characteristic of O ₃ time series. The drastically reduction of primary pollutants during the lockdown might be the responsible of the tendency to monofractality in O ₃ time series. These differences were found for a wide temporal extent ranging from 80 min to ~28 days.

Assessing Inhomogeneities in Extreme Annual Rainfall Data Series by Multifractal Approach

Testing the homogeneity in extreme rainfall data series is an important step to be performed before applying the frequency analysis method to obtain quantile values. In this work, six homogeneity tests were applied in order to check the existence of break points in extreme annual 24-h rainfall data at eight stations located in the Umbria region (Central Italy). Two are parametric tests (the standard normal homogeneity test and Buishand test) whereas the other four are non-parametric (the Pettitt, Sequential Mann–Kendal, Mann–Whitney U, and Cumulative Sum tests). No break points were detected at four of the stations analyzed. Where inhomogeneities were found, the multifractal approach was applied in order to check if they were real or not by comparing the split and whole data series. The generalized fractal dimension functions Dq and the multifractal spectra f(α) were obtained, and their main parameters were used to decide whether or not a break point existed.

Geospatial-temporal analysis of the impact of ozone on asthma rescue inhaler use

RATIONALE

Asthma is one of the most common chronic respiratory diseases in the United States. Several outdoor air pollutants have been associated with asthma morbidity. Previous studies of the effects of short-term air pollution exposure have been limited by potential exposure misclassification and limited spatial and temporal resolution of asthma outcome measures.

OBJECTIVES

We aimed to assess the association of short-term air pollutant exposure with the use of short-acting beta-2 agonists (SABA) for asthma by monitoring the time and place of occurrence with electronic medication monitors.

METHODS

In a cohort of adults and children with asthma (n = 287; 60% female), we deployed electronic medication monitors fitted to metered-dose inhalers to monitor SABA use, capturing the date, time and location of use. We assigned pollutant exposures based on each actuation's time and location (4-h mean measures for ozone and particulate matter of 2.5 µm or smaller (PM_2.5)), assessed associations using generalized linear models and explored age-specific effects.

MEASUREMENTS AND MAIN RESULTS

Ambient ozone exposure was positively associated with SABA use (p = 0.01). Age-specific associations were identified (interaction p = 0.01), with a larger increase in SABA use for children (11.3%; 95% CI: 7.0%-18.2%) than adults (8.4%; 95% CI: 6.4%-11.0%) per IQR increase of ozone (16.8 ppb).

CONCLUSIONS

These findings support existing evidence that short-term exposure to ozone can cause morbidity in individuals with asthma, and suggest that ozone exposures below the current U.S. EPA standard may be associated with increased SABA use.

Multifractal analysis of financial markets: a review

Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originated, multifractal analysis of financial markets has bloomed, forming one of the main directions of econophysics. We review the multifractal analysis methods and multifractal models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. We survey the cumulating evidence for the presence of multifractality in financial time series in different markets and at different time periods and discuss the sources of multifractality. The usefulness of multifractal analysis in quantifying market inefficiency, in supporting risk management and in developing other applications is presented. We finally discuss open problems and further directions of multifractal analysis.

Visibility graphs of ground-level ozone time series: A multifractal analysis

A recent method based on the concurrence of complex networks and multifractal analyses is applied for the first time to explore ground-level ozone behavior. Ozone time series are converted into complex networks for their posterior analysis. The searched purpose is to check the suitability of this transformation and to see whether some features of these complex networks could constitute a preliminary analysis before the more thorough multifractal formalism. Results show effectively that the exposed transformation stores the original information about the ozone dynamics and gives meaningful knowledge about the time series. Based on these results, the multifractal analysis of the complex networks is performed. Looking at the physical meaning of the multifractal properties (such as fractal dimensions and singularity spectrum), a relationship between those and the degree distribution of the complex networks is found. In addition to all the promising results, this novel connection between time series and complex networks can deal with both stationary and non-stationary time series, overcoming one of the main limitations of multifractal analysis. Therefore, this technique can be regarded as an alternative to give supplementary information within the study of complex signals.

Assessment of nitrogen oxides and ground-level ozone behavior in a dense air quality station network: Case study in the Lesser Antilles Arc

This paper presents a study on ground-level ozone (O₃), nitrogen oxides (NO_x = NO + NO₂) concentrations, and their variabilities in the ambient air of three sites of a tropical archipelago that is moderately urbanized. Statistical analysis was performed on a quite complete (>80%) set of 5 years of measurements (2008-2012). There are few studies on those pollutants and their seasonal behavior in the Caribbean area, where pollution level and cities configuration are different from megacities. Analyses are focused on pollutant variations at the scale of the day, the week, and the seasons, using hourly data. The observations show that NO_x concentrations are more elevated during the wet season, whereas O₃ concentrations are higher in the dry season. Amplitudes of ozone cycles are strongly influenced by meteorological conditions (temperature, global radiation, and wind speed) and prevailing levels of NO_x. An ozone weekend effect is detected with the highest amplitude in the city, where anthropogenic activity is the lowest during the weekend. Due to the nature and the origin of pollutants, NO_x shows higher variability than O₃ in the time series. Our results evince the need for continuous measurements of volatile organic compounds (VOCs) in order to better quantify their contribution in O₃ formation in an insular context where numerous natural sources have been identified. Implications: Statistical analyses of observed NO_x and O₃ concentrations for 5 years for a typical low industrialized site of the Caribbean area have been done. Air quality for those components is correct based on the standards of the World Health Orgaization, pollutant source spatial distributions, and level of industrialization. Observations show the same patterns as in megacities but also a strong impact of weather conditions and road traffic. Behaviors of O₃ cannot be fully explained without VOCs monitoring. Localization and type of AQS should be reconsidered to improve the accuracy of concentrations of the pollutant and better understand their behaviors.

Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces

When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multiscale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the U.S. dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the multifractal DCCA method fails.

Multifractal detrending moving-average cross-correlation analysis

There are a number of situations in which several signals are simultaneously recorded in complex systems, which exhibit long-term power-law cross correlations. The multifractal detrended cross-correlation analysis (MFDCCA) approaches can be used to quantify such cross correlations, such as the MFDCCA based on the detrended fluctuation analysis (MFXDFA) method. We develop in this work a class of MFDCCA algorithms based on the detrending moving-average analysis, called MFXDMA. The performances of the proposed MFXDMA algorithms are compared with the MFXDFA method by extensive numerical experiments on pairs of time series generated from bivariate fractional Brownian motions, two-component autoregressive fractionally integrated moving-average processes, and binomial measures, which have theoretical expressions of the multifractal nature. In all cases, the scaling exponents h(xy) extracted from the MFXDMA and MFXDFA algorithms are very close to the theoretical values. For bivariate fractional Brownian motions, the scaling exponent of the cross correlation is independent of the cross-correlation coefficient between two time series, and the MFXDFA and centered MFXDMA algorithms have comparative performances, which outperform the forward and backward MFXDMA algorithms. For two-component autoregressive fractionally integrated moving-average processes, we also find that the MFXDFA and centered MFXDMA algorithms have comparative performances, while the forward and backward MFXDMA algorithms perform slightly worse. For binomial measures, the forward MFXDMA algorithm exhibits the best performance, the centered MFXDMA algorithms performs worst, and the backward MFXDMA algorithm outperforms the MFXDFA algorithm when the moment order q<0 and underperforms when q>0. We apply these algorithms to the return time series of two stock market indexes and to their volatilities. For the returns, the centered MFXDMA algorithm gives the best estimates of h(xy)(q) since its h(xy)(2) is closest to 0.5, as expected, and the MFXDFA algorithm has the second best performance. For the volatilities, the forward and backward MFXDMA algorithms give similar results, while the centered MFXDMA and the MFXDFA algorithms fail to extract rational multifractal nature.