Skip to main content
SpringerLink
Log in

Analysis of spatial pattern of aerosol optical depth and affecting factors using spatial autocorrelation and spatial autoregressive model

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Previous studies neglected the implicit information in the spatial pattern of the aerosol optical depth (AOD), such as spatial configuration characteristics, spatial heterogeneity and spatial dependence. The understanding of AOD is helpful in accurately estimating the environmental and climatic effects from aerosols. In this study, a method of spatial autocorrelation statistics from geostatistics was applied to investigate the spatial pattern of AOD in Hubei province in central China during the period 2003–2008. Spatial autoregressive models were used to quantize the correlations between AOD and affecting factors, such as elevation, forest coverage and population density. In addition, the difference between the standard linear regression model and the spatial models was discussed. The results were as follows: the spatial pattern of AOD in Hubei province shows significant spatial autocorrelation, indicating that AODs are clustered such that higher AODs tend to be surrounded by higher AOD neighbors, while lower AODs are surrounded by lower AOD neighbors, and the spatial autocorrelation scale of the AOD over Hubei is approximately 400 km. The high–high zone is mainly distributed in the Wuhan city circle and the Jianghan plain areas, while the low–low zones are mainly located in the middle and high mountain areas of northwest Hubei province. The overall degree and pattern of spatial autocorrelation do not change largely from 2003 to 2008, which indicates a stable spatial configuration of AOD. A significant negative spatial autocorrelation exists between AOD and elevation, which may suggest that AOD and elevation have an inverse spatial distribution, and the same applies for forest coverage. Population density and the AOD show a significant positive spatial autocorrelation, which may imply that they have similar spatial distribution, while industrial production and AOD do not show an obvious positive spatial autocorrelation. Spatial autoregressive models show better predictive ability and stability than the standard linear regression model because the spatial autocorrelation of the AOD is taken into consideration.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Anselin L (1995) Local indicators of spatial association-LISA. Geogr Anal 27(2):93–115

    Article  Google Scholar 

  • Anselin L (1996) The Moran scatterplot as an ESDA tool to assess local instability in spatial association. Spat Anal Perspect GIS 111:111–125

    Google Scholar 

  • Anselin L (2013) Spatial econometrics: methods and models (vol 4). Springer Science and Business Media, Berlin

    Google Scholar 

  • Anselin L, Syabri I, Smirnov O (2002) Visualizing multivariate spatial correlation with dynamically linked windows. Urbana 51:61801

    Google Scholar 

  • Anselin L, Syabri I, Kho Y (2006) GeoDa: an introduction to spatial data analysis. Geogr Anal 38(1):5–22

    Article  Google Scholar 

  • Basu S, Thibodeau TG (1998) Analysis of spatial autocorrelation in house prices. J Real Estate Finance Econ 17(1):61–85

    Article  Google Scholar 

  • Cliff AD, Ord JK (1973) Spatial autocorrelation. Pion, London

    Google Scholar 

  • Cliff AD, Ord JK (1981) Spatial processes: models and applications. Pion, London

    Google Scholar 

  • Conway D, Li CQ, Wolch J, Kahle C, Jerrett M (2010) A spatial autocorrelation approach for examining the effects of Urban greenspace on residential property values. J Real Estate Finance Econ 41(2):150–169

    Article  Google Scholar 

  • Dale MRT, Fortin MJ (2002) Spatial autocorrelation and statistical tests in ecology. Ecoscience 9(2):162–167

    Google Scholar 

  • Getis A (2007) Reflections on spatial autocorrelation. Reg Sci Urban Econ 37(4):491–496

    Article  Google Scholar 

  • Glick B (1979) The spatial autocorrelation of cancer mortality. Soc Sci Med D Med Geogr 13(2):123–130

    Google Scholar 

  • Guo Y, Hong S, Zhuang Y, Feng N (2010) Temporal variation and spatial distribution of atmospheric aerosols over Hubei province. Geomat Inf Sci Wuhan Univ 35(11):1381–1385 (in Chinese)

    Google Scholar 

  • Guo YJ, Hong S, Feng N, Zhuang YH, Zhang L (2012) Spatial distributions and temporal variations of atmospheric aerosols and the affecting factors: a case study for a region in central China. Int J Remote Sens 33(12):3672–3692

    Article  Google Scholar 

  • Havard S, Deguen S, Zmirou-Navier D, Schillinger C, Bard D (2009) Traffic-related air pollution and socioeconomic status: a spatial autocorrelation study to assess environmental equity on a small-area scale. Epidemiology 20(2):223–230

    Article  Google Scholar 

  • Herber A, Thomason LW, Gernandt H, Leiterer U, Nagel D, Schulz KH, Kaptur J, Albrecht T, Notholt J (2002) Continuous day and night aerosol optical depth observations in the Arctic between 1991 and 1999. J Geophys Res Atmos (1984–2012) 107(D10):AAC 6-1–AAC 6-13

    Article  Google Scholar 

  • Hubert LJ, Golledge RG, Costanzo CM (1981) Generalized procedures for evaluating spatial autocorrelation. Geogr Anal 13(3):224–233

    Article  Google Scholar 

  • Kaufman YJ, Tanre D, Boucher O (2002) A satellite view of aerosols in the climate system. Nature 419(6903):215–223

    Article  Google Scholar 

  • Kazadzis S, Bais A, Amiridis V, Balis D, Meleti C, Kouremeti N, Zerefos CS, Rapsomanikis S, Petrakakis M, Kelesis A, Tzoumaka P, Kelektsoglou K (2007) Nine years of UV aerosol optical depth measurements at Thessaloniki Greece. Atmos Chem Phys 7(8):2091–2101

    Article  Google Scholar 

  • Koenig WD (1999) Spatial autocorrelation of ecological phenomena. Trends Ecol Evol 14(1):22–26

    Article  Google Scholar 

  • LeSage JP (1999) The theory and practice of spatial econometrics, vol 28. University of Toledo, Toledo, p 33

    Google Scholar 

  • Li CC, Mao JT, Lau KHA, Chen JC, Yuan ZB, Liu XY, Zhu AH, Liu GQ (2003) Characteristics of distribution and seasonal variation of aerosol optical depth in eastern China with MODIS products. Chin Sci Bull 48(22):2488–2495

    Google Scholar 

  • Li B, Yuan H, Feng N, Tao S (2010) Spatial and temporal variations of aerosol optical depth in China during the period from 2003 to 2006. Int J Remote Sens 31(7):1801–1817

    Article  Google Scholar 

  • Lianou M, Chalbot MC, Kavouras IG, Kotronarou A, Karakatsani A, Analytis A, Katsouyanni K, Puustinen A, Hameri K, Vallius M, Pekkanen J, Meddings C, Harrison RM, Ayres JG, ten Brick H, Kos G, Meliefste K, de Hartog J, Hoek G (2011) Temporal variations of atmospheric aerosol in four European urban areas. Environ Sci Pollut Res 18(7):1202–1212

    Article  Google Scholar 

  • Luo YF, Lu DR, Zhou XJ, Li WL, He Q (2001) Characteristics of the spatial distribution and yearly variation of aerosol optical depth over China in last 30 years. J Geophys Res Atmos (1984–2012) 106(D13):14501–14513

    Article  Google Scholar 

  • McFiggans G, Artaxo P, Baltensperger U, Coe H, Facchini MC, Feingold G, Fuzzi S, Gysel M, Laaksonen A, Lohmann U, Mentel TF, Murphy DM, O’Dowd CD, Snider JR, Weingartner E (2006) The effect of physical and chemical aerosol properties on warm cloud droplet activation. Atmos Chem Phys 6(9):2593–2649

    Article  Google Scholar 

  • McMurry PH (2000) A review of atmospheric aerosol measurements. Atmos Environ 34(12):1959–1999

    Article  Google Scholar 

  • O’Dowd CD, Aalto P, Hmeri K, Kulmala M, Hoffmann T (2002) Aerosol formation: atmospheric particles from organic vapours. Nature 416(6880):497–498

    Article  Google Scholar 

  • Overmars KP, de Koning GHJ, Veldkamp A (2003) Spatial autocorrelation in multi-scale land use models. Ecol Model 164(2):257–270

    Article  Google Scholar 

  • Pant V, Siingh D, Kamra AK (2011) Size distribution of atmospheric aerosols at Maitri Antarctica. Atmos Environ 45(29):5138–5149

    Article  Google Scholar 

  • Parmar RS, Satsangi GS, Kumari M, Lakhani A, Srivastava SS, Prakash S (2001) Study of size distribution of atmospheric aerosol at Agra. Atmos Environ 35(4):693–702

    Article  Google Scholar 

  • Peterson EE, Merton AA, Theobald DM, Urquhart NS (2006) Patterns of spatial autocorrelation in stream water chemistry. Environ Monit Assess 131(1–3):571–596

    Article  Google Scholar 

  • Ping JL, Green CJ, Zartman RE, Bronson KF (2004) Exploring spatial dependence of cotton yield using global and local autocorrelation statistics. Field Crops Res 89(2):219–236

    Article  Google Scholar 

  • Plakhina IN, Pankratova NV, Makhotkina EL (2009) Variations in the atmospheric aerosol optical depth from the data obtained at the Russian actinometric network in 1976–2006. Izv Atmos Oceani Phys 45(4):456–466

    Article  Google Scholar 

  • Polissar AV, Hopke PK, Poirot RL (2001) Atmospheric aerosol over Vermont: chemical composition and sources. Environ Sci Technol 35(23):4604–4621

    Article  Google Scholar 

  • Pöschl U (2005) Atmospheric aerosols: composition, transformation, climate and health effects. Angew Chem Int Ed 44(46):7520–7540

    Article  Google Scholar 

  • Qiu JH, Yang LQ (2000) Variation characteristics of atmospheric aerosol optical depths and visibility in North China during 1980–1994. Atmos Environ 34(4):603–609

    Article  Google Scholar 

  • Ramachandran S, Kedia S, Srivastava R (2012) Aerosol optical depth trends over different regions of India. Atmos Environ 49:338–347

    Article  Google Scholar 

  • Sakerin SM, Kabanov DM (2002) Spatial inhomogeneities and the spectral behavior of atmospheric aerosol optical depth over the Atlantic Ocean. J Atmos Sci 59(3):484–500

    Article  Google Scholar 

  • Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econo Geogr 46(2):234–240

    Article  Google Scholar 

  • Tsai PJ, Lin ML, Chu CM, Perng CH (2009) Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006. BMC Public Health 9(1):464

    Article  Google Scholar 

Download references

Acknowledgments

This research was funded by Open Research Fund Program of Key Laboratory of Digital Mapping and Land Information Application Engineering, NASG (No. GCWD201404) and Open Research Fund Program of Henan Province Engineering Laboratory of Information Technology of Emergency Platform (No. YJ2013003), the Doctoral Foundation from Zhengzhou University of Light Industry and the National Natural Science Foundation of China (No. 61403349). We are grateful to Professor Hong and Dr. Guo for their valuable comments. The authors would also like to thank the anonymous reviewers for their suggestions and comments.

Author information

Authors and Affiliations

  1. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Dongfeng Road 5, Zhengzhou, 450000, People’s Republic of China

    Wang hua, Zhu fubao & Zhang weiwei

  2. School of Resources and Environment, North China University of Water Resources and Electric Power, Beihuan Road 36, Zhengzhou, 450000, People’s Republic of China

    Zhang junfeng

Authors
  1. Wang hua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhang junfeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhu fubao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhang weiwei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wang hua.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

hua, W., junfeng, Z., fubao, Z. et al. Analysis of spatial pattern of aerosol optical depth and affecting factors using spatial autocorrelation and spatial autoregressive model. Environ Earth Sci 75, 822 (2016). https://doi.org/10.1007/s12665-016-5656-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-016-5656-8

Keywords

Search

Navigation