Skip to main content
Log in

Multifractal detrended fluctuation analysis of particulate matter and atmospheric variables at different time scales

  • Original Paper
  • Published:
Meteorology and Atmospheric Physics Aims and scope Submit manuscript

Abstract

Anthropogenic and natural aerosol emissions poses a threat to human and animal health. Particulate matter has a complex relationship with atmospheric parameters. In this study, the multifractal detrended fluctuation analysis was used to investigate complexity in particulate matter and atmospheric parameters at five small time steps (6, 8, 10, 12, and 15 min) at a tropical location. The study was carried out at annual and monthly scale. Multifractal strengths in the range \(0.21-0.32\), \(0.16-0.28\), \(0.15-0.26\), \(0.40-0.68\), \(0.41-0.71\), and \(0.12-0.23\) were obtained for PM1, PM2.5, PM10, temperature, humidity, and pressure respectively at the annual scale. At all time steps, multifractality of particulate matter was observed to decrease with increasing particle size. Multifractality in atmospheric parameters were found to reduce with increasing time steps. The monthly analysis suggests the influence of seasonal transitions on multifractality of particulate matter.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Availability of data and materials

Data used in this work is publicly available at www.purpleair.com

References

  • Adejuwon JO, Odekunle TO (2006) Variability and the severity of the “little dry season’’ in Southwestern Nigeria. J Climate 193:483–493

    Article  Google Scholar 

  • Agier L, Deroubaix A, Martiny N, Yaka P, Djibo A, Broutin H (2013) Seasonality of meningitis in Africa and climate forcing: aerosols stand out. J Royal Soc Interface 10(79):20120814

    Article  Google Scholar 

  • Akinsusi J, Ogunjo S, Fuwape I (2022) Nonlinear dynamics and multifractal analysis of minimum-maximum temperature and solar radiation over lagos state, nigeria. Acta Geophysica 70:2171–2178

    Article  Google Scholar 

  • Bi J, Carmona N, Blanco MN, Gassett AJ, Seto E, Szpiro AA, Sheppard L (2022) Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection. Environm Int 158:106897

    Article  Google Scholar 

  • Cárdenas-Moreno P, Moreno-Torres L, Lovallo M, Telesca L, Ramírez-Rojas A (2021) Spectral, multifractal and informational analysis of PM10 time series measured in Mexico city metropolitan area. Physica A Statist Mechan Appl 565:125545

    Article  Google Scholar 

  • Chi R, Li H, Wang Q, Zhai Q, Wang D, Wu M et al (2019) Association of emergency room visits for respiratory diseases with sources of ambient PM2. 5. J Environm Sci 86:154–163

    Article  Google Scholar 

  • Dajuma A, Ogunjobi KO, Vogel H, Knippertz P, Silué S, N’Datchoh ET, Vogel B (2019) Cloud-venting induced downward mixing of the central african biomass burning plume during the west Africa summer monsoon. Atmos Chem Phys Discuss https://doi. org/10.5194/acp-2019-617

  • Gao S, Wang Y, Huang Y, Zhou Q, Lu Z, Shi X, Liu Y (2016) Spatial statistics of atmospheric particulate matter in China. Atmos Environm 134:162–167

    Article  Google Scholar 

  • Gautam R, Hsu N, Lau KM, Kafatos M (2009) Aerosol and rainfall variability over the Indian monsoon region: distributions, trends and coupling. Annal Geophys Annal Geophys 27:3691–3703

    Article  Google Scholar 

  • Gläser G, Wernli H, Kerkweg A, Teubler F (2015) The transatlantic dust transport from north Africa to the Americas-its characteristics and source regions. J Geophys Res Atmos 120(21):11–231

    Article  Google Scholar 

  • He HD, Pan W, Lu WZ, Xue Y, Peng GH (2016) Multifractal property and long-range cross-correlation behavior of particulate matters at urban traffic intersection in shanghai. Stochast Environm Res Risk Assessm 30(5):1515–1525

    Article  Google Scholar 

  • Helmert J, Heinold B, Tegen I, Hellmuth O, Wendisch M (2007) On the direct and semidirect effects of Saharan dust over Europe: A modeling study. J Geophys Res Atmos 112:D13

    Article  Google Scholar 

  • Inapakurthi RK, Miriyala SS, Mitra K (2021) Deep learning based dynamic behavior modelling and prediction of particulate matter in air. Chem Eng J 426:131221

    Article  Google Scholar 

  • Kantelhardt JW, Zschiegner SA, Koscielny-Bunde E, Havlin S, Bunde A, Stanley HE (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Physica A Statist Mechan Appl 316(1–4):87–114

    Article  Google Scholar 

  • Koren I, Kaufman YJ, Washington R, Todd MC, Rudich Y, Martins JV, Rosenfeld D (2006) The bodélé depression: a single spot in the sahara that provides most of the mineral dust to the amazon forest. Environm Res Lett 1(1):014005

    Article  Google Scholar 

  • Krzyszczak J, Baranowski P, Zubik M, Hoffmann H (2017) Temporal scale influence on multifractal properties of agro-meteorological time series. Agric Forest Meteorol 239:223–235

    Article  Google Scholar 

  • Li X, Mauzerall DL, Bergin MH (2020) Global reduction of solar power generation efficiency due to aerosols and panel soiling. Nat Sustain 3(9):720–727

    Article  Google Scholar 

  • Liu Q, Huang Z, Hu Z, Dong Q, Li S (2022). Long-Range Transport and Evolution of Saharan Dust Over East Asia From 2007 to 2020. J Geophys Res Atmos 12718e2022JD036974. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022JD036974 e2022JD036974 2022JD036974 https://arxiv.org/abs/https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022JD036974https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022JD036974 https://doi.org/10.1029/2022JD036974

  • Lu T, Wan Y, Bechle M, Presto A, Hankey S (2019) External validation of national land use regression models for PM 2. 5 using a low-cost sensor network. Environm Epidemiol 3:251

    Article  Google Scholar 

  • Manimaran P, Narayana A (2018) Multifractal detrended cross-correlation analysis on air pollutants of university of Hyderabad campus, India. Physica A Statist Mechan Appl 502:228–235

    Article  Google Scholar 

  • Merbitz H, Fritz S, Schneider C (2012) Mobile measurements and regression modeling of the spatial particulate matter variability in an urban area. Sci Total Environm 438:389–403

    Article  Google Scholar 

  • Ogunjo ST (2021) Multifractal properties of meteorological drought at different time scales in a tropical location. Fluctuat Noise Lett 20(01):2150007

    Article  Google Scholar 

  • Ogunjo ST (2023) The impact of the 2007–2008 global financial crisis on the multifractality of the Nigerian stock exchange. SN Busi Econom 3(1):31

    Article  Google Scholar 

  • Ogunjo ST, Fuwape I, Babatunde Rabiu A, Oluyamo SS (2021) Multifractal analysis of air and soil temperatures. Chaos: An Interdisciplin J Nonlinear Sci 31(3):033110

    Article  Google Scholar 

  • Ogunjo ST, Olaniyan O, Olusegun C, Kayode F, Okoh D, Jenkins G (2022) The role of meteorological variables and aerosols in the transmission of COVID-19 during harmattan season. GeoHealth 6(2):e2021GH000521

    Article  Google Scholar 

  • Ogunjo ST, Rabiu A, Fuwape I, Obafaye A (2021) Evolution of dynamical complexities in geospace as captured by dust over four solar cycles 1964–2008. J Geophys Res Space Phys 126 (4): e2020JA027873

  • Olayiwola AM, Olaitan AA (2019) Spatial preference of Urban residential location in Oshogbo, Nigeria. Ghana J Geography 11(1):140–158

    Google Scholar 

  • Pinker R, Pandithurai G, Holben B, Dubovik O, Aro T (2001) A dust outbreak episode in Sub-Sahel West Africa. J Geophys Res Atmos 106(D19):22923–22930

    Article  Google Scholar 

  • Plocoste T, Calif R, Jacoby-Koaly S (2017) Temporal multiscaling characteristics of particulate matter pm10 and ground-level ozone o3 concentrations in caribbean region. Atmos Environm 169:22–35

    Article  Google Scholar 

  • Plocoste T, Carmona-Cabezas R, Jiménez-Hornero FJ, de Ravé EG, Calif R (2021) Multifractal characterization of particulate matter (pm10) time series in the Caribbean basin using visibility graphs. Atmos Pollut Res 12(1):100–110

    Article  Google Scholar 

  • Plocoste T, Pavón-Domínguez P (2020) Temporal scaling study of particulate matter (PM10) and solar radiation influences on air temperature in the caribbean basin using a 3d joint multifractal analysis. Atmos Environm 222:117115

    Article  Google Scholar 

  • Rap A, Scott CE, Spracklen DV, Bellouin N, Forster PM, Carslaw KS, Mann G (2013) Natural aerosol direct and indirect radiative effects. Geophys Res Lett 40(12):3297–3301

    Article  Google Scholar 

  • Wang X, Li T, Ikhumhen HO, Sá RM (2022) Spatio-temporal variability and persistence of PM2. 5 concentrations in china using trend analysis methods and hurst exponent. Atmos Pollut Res 13(1):101274

    Article  Google Scholar 

  • Xue Y, Pan W, Lu WZ, He HD (2015) Multifractal nature of particulate matters (PMS) in Hong Kong urban air. Sci Total Environm 532:744–751

    Article  Google Scholar 

  • Zhang C, Ni Z, Ni L (2015) Multifractal detrended cross-correlation analysis between pm2. 5 and meteorological factors. Physica A Statist Mechan Appl 438:114–123

    Article  Google Scholar 

  • Zhang C, Ni Z, Ni L, Li J, Zhou L (2016) Asymmetric multifractal detrending moving average analysis in time series of PM2. 5 concentration. Physica A Statist Mechan Appl 457:322–330

    Article  Google Scholar 

  • Zunino L, Tabak BM, Figliola A, Pérez DG, Garavaglia M, Rosso OA (2008) A multifractal approach for stock market inefficiency. Physica A Statist Mechan Appl 387(26):6558–6566

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the Centre for Atmospheric Research and their partners for promoting high standards of atmospheric observatory practice as well as the Federal Government of Nigeria for continuous funding of the Nigerian Space programme (www.carnasrda.com). Furthermore, I. Fuwape and S. T. Ogunjo acknowledge a R & D grant from the Centre for Atmospheric Research, National Space Research and Development Agency, Federal Ministry of Science and Technology, Anyigba, Nigeria.

Funding

I. Fuwape and S. T. Ogunjo received a R & D grant from the Centre for Atmospheric Research, National Space Research and Development Agency, Federal Ministry of Science and Technology, Anyigba, Nigeria.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Samuel Ogunjo.

Ethics declarations

Conflict of interest

Authors declare no conflict of interest.

Additional information

Responsible Editor: Clemens Simmer, Ph.D.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fuwape, I., Ogunjo, S., Akinsusi, J. et al. Multifractal detrended fluctuation analysis of particulate matter and atmospheric variables at different time scales. Meteorol Atmos Phys 135, 27 (2023). https://doi.org/10.1007/s00703-023-00971-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00703-023-00971-4

Navigation