Erratum to “Temporal multiscaling characteristics of particulate matter PM10 and ground-level ozone O3 concentrations in Caribbean region” [Atmos. Environ. 169 (2017) 22–35]

Plocoste, Thomas; Calif, Rudy; Jacoby-Koaly, Sandra

doi:10.1016/j.atmosenv.2017.10.042

Cited by 8 publications

(10 citation statements)

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“…In Caribbean region, air quality is highly deteriorated by the seasonal transport of African dust (Prospero et al, 2014). Contrary to urban areas highly industrialized in Europe, the United States or China, Caribbean islands exhibit low emission of particulate matter linked to anthropogenic pollution (Euphrasie-Clotilde et al, 2017;Plocoste et al, 2017Plocoste et al, , 2018. After the long-range transport from African coast to the Caribbean, i.e.…”

Section: Introductionmentioning

confidence: 99%

Multifractal characterisation of particulate matter (PM10) time series in the Caribbean basin using visibility graphs

Plocoste

Carmona-Cabezas

Jiménez‐Hornero

et al. 2021

Atmospheric Pollution Research

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Section: Introductionmentioning

confidence: 99%

Multifractal characterisation of particulate matter (PM10) time series in the Caribbean basin using visibility graphs

Plocoste

Carmona-Cabezas

Jiménez‐Hornero

et al. 2021

Atmospheric Pollution Research

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“…Windsor et al [34] examined the statistical characteristics of United Kingdom pollution time series and found evidences of high persistence and long-term memory of pollutant fluctuations up to 400 days. PM 10 and O 3 pollutants in the Caribbean region showed the multifractal nature with the significant Hurst parameter [35]. Wu et al [36] studied the long-term persistence characteristics of several air pollutants (PM 2.5 and O 3 ) during the epidemic situation of COVID-19 by using multifractal detrended fluctuation analysis (MFDFA) and they found that the concentrations of three cities (Changsha, Zhuzhou, and Xiangtan) showed strong long-term persistence characteristics and multifractal structures.…”

Section: Introductionmentioning

confidence: 99%

Memory Behaviors of Air Pollutions and Their Spatial Patterns in China

Nian

Qiao

et al. 2022

Front. Phys.

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Particulate matter (PM2.5 and PM10) and ozone (O3) are the two major air pollutants in China in recent years. The fluctuations of PM2.5, PM10 and O3 strongly depend on the weather processes and anthropogenic emission. These processes may lead to the existence of short- and long-term memory behaviors in air pollutants. Hence, here we use the autoregressive parameter a of the first-order autoregressive process [AR (1)] to characterize the short-term memory effects of pollutants. We estimate the scaling exponent α using detrended fluctuation analysis (DFA) for the long-term memory effects of air pollutants (PM2.5, PM10, and O3) in summer and winter for different cities in China. Our results show that PM2.5, PM10, and O3 have strong short-term and long-term memory characteristics both in summer and winter. Furthermore, both the short- and long-term memory effects are stronger in winter than summer for most cities associated with stronger and longer persistent weather systems in winter. In general, the scaling exponent α of PM2.5 and PM10 are smaller for northern cities than those of southern cities in China. The long-term memory patterns of O3 are stronger in northern cities and weaker in southern cities in relative to those of PM2.5 and PM10 in winter. Our results show that the short- and long-term memory behaviors of air pollutions are dominated by the weather systems with different time scales.

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“…Cordova et al 36 studied the spatio-temporal behavior of air quality in Metropolitan Lima, evaluated and predicted the concentrations using the recurrent artificial neural network LSTM, based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. It is important to notice that the concentrations have nonlinear behavior and fluctuate strongly in spatio-temporal scales 37 due to the nonlinear character of the atmospheric wind speed 38 . Consequently, to manage this strong variability, in this paper, we consider different forecasting models.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal visualization and forecasting of $${\text {PM}}_{10}$$ in the Brazilian state of Minas Gerais

Silva

López-Gonzales

Turpo-Chaparro

et al. 2023

Sci Rep

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Air pollution due to air contamination by gases, liquids, and solid particles in suspension, is a great environmental and public health concern nowadays. An important type of air pollution is particulate matter with a diameter of 10 microns or less ($${\text {PM}}_{10}$$ PM 10 ) because one of the determining factors that affect human health is the size of particles in the atmosphere due to the degree of permanence and penetration they have in the respiratory system. Therefore, it is extremely interesting to monitor and understand the behavior of $${\text {PM}}_{10}$$ PM 10 concentrations so that they do not exceed the established critical levels. In this work, we will study the $${\text {PM}}_{10}$$ PM 10 concentrations in all available monitoring stations in the Brazilian state of Minas Gerais. To better understand its behavior, we will provide a spatio-temporal visualization of the $${\text {PM}}_{10}$$ PM 10 concentrations. Besides the descriptive and visualization analysis, we consider six standard and advanced time series models that will be used to fit and forecast $${\text {PM}}_{10}$$ PM 10 concentrations, with application to three locations, one in Belo Horizonte, the Minas Gerais state capital, and the monitoring stations with the lowest and highest average $${\text {PM}}_{10}$$ PM 10 concentration levels.

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Erratum to “Temporal multiscaling characteristics of particulate matter PM10 and ground-level ozone O3 concentrations in Caribbean region” [Atmos. Environ. 169 (2017) 22–35]

Cited by 8 publications

References 0 publications

Multifractal characterisation of particulate matter (PM10) time series in the Caribbean basin using visibility graphs

Multifractal characterisation of particulate matter (PM10) time series in the Caribbean basin using visibility graphs

Memory Behaviors of Air Pollutions and Their Spatial Patterns in China

Spatio-temporal visualization and forecasting of $${\text {PM}}_{10}$$ in the Brazilian state of Minas Gerais

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