Background PM10 atmosphere: In the seek of a multifractal characterization using complex networks

Abstract In the literature, several epidemiological studies have already associated respiratory and cardiovascular diseases to acute exposure of mineral dust. However, frail people are also sensitive to chronic exposure to particulate matter with an aerodynamic diameter 10 μ m or less ( P M 10 ). Consequently, it is crucial to better understand P M 10 fluctuations at all scales. This study investigates P M 10 background atmosphere in the Caribbean area according to African dust seasonality with complex network framework. For that purpose, the regular Visibility Graph (VG) and the new Upside-Down Visibility Graph (UDVG) are used for a multifractal analysis. Firstly, concentration vs degree (v-k) plots highlighted that high degree values (hubs behavior) are related to the highest P M 10 concentrations in VG while hubs is associated to the lowest concentrations in UDVG, i.e. probably the background atmosphere. Then, the degree distribution analysis showed that VG and UDVG difference is reduced for high dust season contrary to the low one. As regards the multifractal analysis, the multifractal degree is higher for the low season in VG while it is higher for the high season in UDVG. The degree distribution behavior and the opposite trend in multifractal degree for UDVG are due to the increase of P M 10 background atmosphere during the high season, i.e. from May to September. To sum up, UDGV is an efficient tool to perform noise fluctuations analysis in environmental time series where low concentrations play an important role as well.

[1]  Gurmukh Singh,et al.  Multifractal analysis of multiparticle emission data in the framework of visibility graph and sandbox algorithm , 2018 .

[2]  Bras,et al.  Multifractal analysis: Pitfalls of standard procedures and alternatives. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[3]  F. J. Caniego,et al.  Multifractal scaling of soil spatial variability , 2005 .

[4]  S. Fitzwater,et al.  The case for iron , 1991 .

[5]  Francesc Sagués,et al.  Two representations in multifractal analysis , 1995 .

[6]  A. B. Ariza-Villaverde,et al.  Joint multifractal description of the relationship between wind patterns and land surface air temperature , 2011 .

[7]  T. Nawrot,et al.  Long-Term Exposure to Particulate Matter Air Pollution Is a Risk Factor for Stroke: Meta-Analytical Evidence , 2015, Stroke.

[8]  Luciano Telesca,et al.  Visibility graph analysis of wind speed records measured in central Argentina , 2012 .

[9]  E. Bacry,et al.  Multifractal formalism for fractal signals: The structure-function approach versus the wavelet-transform modulus-maxima method. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  C. Toledano,et al.  Assessment of a new detection threshold for PM10 concentrations linked to African dust events in the Caribbean Basin , 2020 .

[11]  M. Legrand,et al.  Transport of Saharan dust over the Caribbean Islands: Study of an event , 2005 .

[12]  P. Pavón-Domínguez,et al.  Temporal scaling study of particulate matter (PM10) and solar radiation influences on air temperature in the Caribbean basin using a 3D joint multifractal analysis , 2020 .

[13]  T J Woodruff,et al.  The relationship between selected causes of postneonatal infant mortality and particulate air pollution in the United States. , 1997, Environmental health perspectives.

[14]  R. Calif,et al.  The statistical behavior of PM10 events over guadeloupean archipelago: Stationarity, modelling and extreme events , 2020 .

[15]  Joseph M. Prospero,et al.  CALIPSO-Derived Three-Dimensional Structure of Aerosol over the Atlantic Basin and Adjacent Continents , 2012 .

[16]  A. Turner,et al.  From Isovists to Visibility Graphs: A Methodology for the Analysis of Architectural Space , 2001 .

[17]  Peter J. Lamb,et al.  African Droughts and Dust Transport to the Caribbean: Climate Change Implications , 2003, Science.

[18]  K. Schepanski Transport of Mineral Dust and Its Impact on Climate , 2018 .

[19]  R. Vecchi,et al.  Hourly elemental composition and sources identification of fine and coarse PM10 particulate matter in four Italian towns , 2003 .

[20]  Yusef Omidi Khaniabadi,et al.  An investigation of particulate matter and relevant cardiovascular risks in Abadan and Khorramshahr in 2014–2016 , 2019 .

[21]  S. H. Melfi,et al.  Validation of the Saharan dust plume conceptual model using lidar, meteosat, and ECMWF Data , 1999 .

[22]  R. Calif,et al.  Temporal multiscaling characteristics of particulate matter PM10 and ground-level ozone O3 concentrations in Caribbean region , 2017 .

[23]  L. Olsen,et al.  A Multifractal Formalism , 1995 .

[24]  Zu-Guo Yu,et al.  Determination of multifractal dimensions of complex networks by means of the sandbox algorithm. , 2014, Chaos.

[25]  D. Ceburnis,et al.  Transfer of labile organic matter and microbes from the ocean surface to the marine aerosol: an experimental approach , 2017, Scientific Reports.

[26]  V. M. Karyampudi,et al.  Analysis and Numerical Simulations of the Saharan Air Layer and Its Effect on Easterly Wave Disturbances , 1988 .

[27]  Elena Sánchez-López,et al.  Improving graph-based detection of singular events for photochemical smog agents. , 2020, Chemosphere.

[28]  E. Bacry,et al.  Singularity spectrum of fractal signals from wavelet analysis: Exact results , 1993 .

[29]  Lucas Lacasa,et al.  Description of stochastic and chaotic series using visibility graphs. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Chung-Kung Lee,et al.  Multifractal Characteristics in Air Pollutant Concentration Time Series , 2002 .

[31]  Gunjan Soni,et al.  Signed visibility graphs of time series and their application to brain networks , 2019 .

[32]  Anastasia K Paschalidou,et al.  Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management , 2011, Environmental science and pollution research international.

[33]  Yu Chen,et al.  Structural and Mechanistic Analysis of Drosophila melanogaster Agmatine N-Acetyltransferase, an Enzyme that Catalyzes the Formation of N-Acetylagmatine , 2017, Scientific Reports.

[34]  T. Carlson SYNOPTIC HISTORIES OF THREE AFRICAN DISTURBANCES THAT DEVELOPED INTO ATLANTIC HURRICANES , 1969 .

[35]  T. Painter,et al.  Impact of disturbed desert soils on duration of mountain snow cover , 2007 .

[36]  J. Viel,et al.  Impact of Saharan Dust on Severe Small for Gestational Births in the Caribbean. , 2020, The American journal of tropical medicine and hygiene.

[37]  V. Cachorro,et al.  Impact of long-range transport over the Atlantic Ocean on Saharan dust optical and microphysical properties based on AERONET data , 2018, Atmospheric Chemistry and Physics.

[38]  Lucas Lacasa,et al.  Network structure of multivariate time series , 2014, Scientific Reports.

[39]  Gilbert Cadelis,et al.  [Exacerbations of asthma in Guadeloupe (French West Indies) and volcanic eruption in Montserrat (70 km from Guadeloupe)]. , 2013, Revue des maladies respiratoires.

[40]  F. J. Jiménez-Hornero,et al.  Multifractal characterisation of particulate matter (PM10) time series in the Caribbean basin using visibility graphs , 2020, Atmospheric Pollution Research.

[41]  Joseph M. Prospero,et al.  Characterizing the annual cycle of African dust transport to the Caribbean Basin and South America and its impact on the environment and air quality , 2014 .

[42]  N. Mahowald,et al.  The size distribution of desert dust aerosols and its impact on the Earth system , 2014 .

[43]  Ling Sh,et al.  Particulate matter air pollution exposure: role in the development and exacerbation of chronic obstructive pulmonary disease , 2009 .

[44]  A. B. Ariza-Villaverde,et al.  Can complex networks describe the urban and rural tropospheric O3 dynamics? , 2019, Chemosphere.

[45]  Zuhan Liu,et al.  A time–scaling property of air pollution indices: a case study of Shanghai, China , 2015 .

[46]  Kuang-Ling Yang,et al.  Spatial and seasonal variation of PM10 mass concentrations in Taiwan , 2002 .

[47]  H. Schellnhuber,et al.  Efficient box-counting determination of generalized fractal dimensions. , 1990, Physical review. A, Atomic, molecular, and optical physics.

[48]  J. Prospero,et al.  African dust outbreaks: A satellite perspective of temporal and spatial variability over the tropical Atlantic Ocean , 2010 .

[49]  M. Bell,et al.  Particulate Matter and Risk of Hospital Admission in the Kathmandu Valley, Nepal: A Case-Crossover Study , 2017, American journal of epidemiology.

[50]  Thomas Plocoste,et al.  Evidence of the effect of an urban heat island on air quality near a landfill , 2014 .

[51]  J. Molinié,et al.  Short-Term Effects of the Particulate Pollutants Contained in Saharan Dust on the Visits of Children to the Emergency Department due to Asthmatic Conditions in Guadeloupe (French Archipelago of the Caribbean) , 2014, PloS one.

[52]  Kelly K. Caylor,et al.  Spatial patterns of soil nutrients in two southern African savannas , 2008 .

[53]  R. Calif,et al.  Multi-scale time dependent correlation between synchronous measurements of ground-level ozone and meteorological parameters in the Caribbean Basin , 2019, Atmospheric Environment.

[54]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.

[55]  Yong Wang,et al.  Multifractal behavior of an air pollutant time series and the relevance to the predictability. , 2017, Environmental pollution.

[56]  Georgios Grivas,et al.  Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece , 2006 .

[57]  S. Monjoly,et al.  Assessment of nitrogen oxides and ground-level ozone behavior in a dense air quality station network: Case study in the Lesser Antilles Arc , 2018, Journal of the Air & Waste Management Association.

[58]  C. Tsallis,et al.  Nonextensivity and Multifractality in Low-Dimensional Dissipative Systems , 1997, cond-mat/9709226.

[59]  R. Calif,et al.  Investigation of local correlations between particulate matter (PM10) and air temperature in the Caribbean basin using Ensemble Empirical Mode Decomposition , 2020 .

[60]  Rudy Calif,et al.  Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm , 2014 .

[61]  X. Querol,et al.  Geochemical variations in aeolian mineral particles from the Sahara-Sahel Dust Corridor. , 2006, Chemosphere.

[62]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[63]  Toby N. Carlson,et al.  Vertical and areal distribution of Saharan dust over the western equatorial north Atlantic Ocean , 1972 .

[64]  N. Mahowald,et al.  Global Iron Connections Between Desert Dust, Ocean Biogeochemistry, and Climate , 2005, Science.

[65]  V. Anh,et al.  Multifractality and Laplace spectrum of horizontal visibility graphs constructed from fractional Brownian motions , 2016, 1602.05280.

[66]  M. Jury Caribbean Air Chemistry and Dispersion Conditions , 2017 .

[67]  Robert W. Burpee The Origin and Structure of Easterly Waves in the Lower Troposphere of North Africa , 1971 .

[68]  Shaun Lovejoy,et al.  Multifractal analysis of phytoplankton biomass and temperature in the ocean , 1996 .

[69]  R. Calif,et al.  Spectral Observations of PM10 Fluctuations in the Hilbert Space , 2019, Functional Calculus.

[70]  B. Mandelbrot Intermittent turbulence in self-similar cascades: divergence of high moments and dimension of the carrier , 1974, Journal of Fluid Mechanics.

[71]  T. Vicsek,et al.  Multifractal Geometry of Diffusion-Limited Aggregates , 1990 .

[72]  Adolfo Posadas,et al.  Multifractal Characterization of Soil Particle-Size Distributions , 2001 .

[73]  Jensen,et al.  Erratum: Fractal measures and their singularities: The characterization of strange sets , 1986, Physical review. A, General physics.

[74]  Ambient air pollution, smog episodes and mortality in Jinan, China , 2017, Scientific Reports.

[75]  P. Pavón-Domínguez,et al.  Multifractal detrended cross-correlation analysis of wind speed and solar radiation. , 2020, Chaos.

[76]  Elliot Saltzman,et al.  A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time Series in Ecological Science , 2013 .

[77]  A. Kravchenko,et al.  Multifractal analysis of soil spatial variability , 1999 .

[78]  J. Stuut,et al.  Particle size traces modern Saharan dust transport and deposition across the equatorial North Atlantic , 2016 .

[79]  Schreiber,et al.  Multifractal wave functions at the Anderson transition. , 1991, Physical review letters.

[80]  M. Benedetti,et al.  Influence of atmospheric deposits and secondary minerals on Li isotopes budget in a highly weathered catchment, Guadeloupe (Lesser Antilles) , 2015 .

[82]  D. Harte Multifractals: Theory and Applications , 2001 .

[83]  J. C. Nuño,et al.  The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion , 2009, 0901.0888.

[84]  Yongxiang Huang,et al.  Intermittency study of high frequency global solar radiation sequences under a tropical climate , 2013 .

[85]  Spatial statistics of atmospheric particulate matter in China , 2016, 1809.09998.

[86]  J Schwartz,et al.  Short term fluctuations in air pollution and hospital admissions of the elderly for respiratory disease. , 1995, Thorax.

[87]  Lucas Lacasa,et al.  Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks , 2017, bioRxiv.

[88]  C. Yu,et al.  The temporal variations of PM10 concentration in Taipei: a fractal approach , 2004 .

[89]  G. Goudarzi,et al.  Temporal profile of PM10 and associated health effects in one of the most polluted cities of the world (Ahvaz, Iran) between 2009 and 2014. , 2016, Aeolian research.

[90]  Toby N. Carlson,et al.  Saharan air outbreaks over the tropical North Atlantic , 1980 .

[91]  Eduardo Gutiérrez de Ravé,et al.  Visibility graphs of ground-level ozone time series: A multifractal analysis. , 2019, The Science of the total environment.

[92]  T. Vicsek,et al.  Determination of fractal dimensions for geometrical multifractals , 1989 .

[93]  Jensen,et al.  Direct determination of the f( alpha ) singularity spectrum and its application to fully developed turbulence. , 1989, Physical review. A, General physics.

[94]  P. Baranowski,et al.  Multifractal analysis of meteorological time series to assess climate impacts , 2015 .

[95]  Xiuhua Guo,et al.  Short-term PM10 and emergency department admissions for selective cardiovascular and respiratory diseases in Beijing, China. , 2019, The Science of the total environment.

[96]  D. Schertzer,et al.  Multifractal Analysis and Simulation of the Global Meteorological Network , 1994 .