IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i14p4914-d381669.html
   My bibliography  Save this article

Monitoring, Mapping, and Modeling Spatial–Temporal Patterns of PM 2.5 for Improved Understanding of Air Pollution Dynamics Using Portable Sensing Technologies

Author

Listed:
  • Ronan Hart

    (Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX 76203, USA
    These authors contributed equally to this work and should be considered the joint first author.)

  • Lu Liang

    (Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX 76203, USA
    These authors contributed equally to this work and should be considered the joint first author.)

  • Pinliang Dong

    (Department of Geography and the Environment, University of North Texas, 1155 Union Circle, Denton, TX 76203, USA)

Abstract

Fine particulate matter with an aerodynamic diameter of less than 2.5 µm (PM 2.5 ) is highly variable in space and time. In this study, the dynamics of PM 2.5 concentrations were mapped at high spatio-temporal resolutions using bicycle-based, mobile measures on a university campus. Significant diurnal and daily variations were revealed over the two-week survey, with the PM 2.5 concentration peaking during the evening rush hours. A range of predictor variables that have been proven useful in estimating the pollution level was derived from Geographic Information System, high-resolution airborne images, and Light Detection and Ranging (LiDAR) datasets. Considering the complex interplay among landscape, wind, and air pollution, variables influencing the PM 2.5 dynamics were quantified under a new wind wedge-based system that incorporates wind effects. Panel data analysis models identified eight natural and built environment variables as the most significant determinants of local-scale air quality (including four meteorological factors, distance to major roads, vegetation footprint, and building and vegetation height). The higher significance level of variables calculated using the wind wedge system as compared to the conventional circular buffer highlights the importance of incorporating the relative position of emission sources and receptors in modeling.

Suggested Citation

  • Ronan Hart & Lu Liang & Pinliang Dong, 2020. "Monitoring, Mapping, and Modeling Spatial–Temporal Patterns of PM 2.5 for Improved Understanding of Air Pollution Dynamics Using Portable Sensing Technologies," IJERPH, MDPI, vol. 17(14), pages 1-18, July.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:14:p:4914-:d:381669
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/14/4914/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/14/4914/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    2. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    3. Zhang, Chen & Ni, Zhiwei & Ni, Liping, 2015. "Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 114-123.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Asrah Heintzelman & Gabriel M. Filippelli & Max J. Moreno-Madriñan & Jeffrey S. Wilson & Lixin Wang & Gregory K. Druschel & Vijay O. Lulla, 2023. "Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments," IJERPH, MDPI, vol. 20(3), pages 1-18, January.
    2. Humphreys, Brad R. & Ruseski, Jane E., 2023. "Air quality and employee performance in teams: Evidence from the NFL," Economics & Human Biology, Elsevier, vol. 51(C).
    3. Ekaterina Alekhanova & Kate Foreman & Maya Papineau & Reid Stevens, 2023. "One Size Does Not Fit All: Co-Benefits of Congestion Pricing in the San Francisco Bay Area," Carleton Economic Papers 23-07, Carleton University, Department of Economics.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheng Hsiao, 2007. "Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 1-22, May.
    2. repec:wyi:journl:002076 is not listed on IDEAS
    3. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    4. Pulak Mishra, 2018. "Are Mergers and Acquisitions Necessarily Anti-competitive? Empirical Evidence from India’s Manufacturing Sector," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 12(3), pages 276-307, August.
    5. Sarafidis, Vasilis & Yamagata, Takashi & Robertson, Donald, 2009. "A test of cross section dependence for a linear dynamic panel model with regressors," Journal of Econometrics, Elsevier, vol. 148(2), pages 149-161, February.
    6. Puhani, Patrick A, 1999. "Public Training and Outflows from Unemployment: An Augmented Matching Function Approach on Polish Regional Data," CEPR Discussion Papers 2244, C.E.P.R. Discussion Papers.
    7. Andrés Rodríguez‐Pose & Vassilis Tselios, 2009. "Education And Income Inequality In The Regions Of The European Union," Journal of Regional Science, Wiley Blackwell, vol. 49(3), pages 411-437, August.
    8. Seo, Myung Hwan & Shin, Yongcheol, 2016. "Dynamic panels with threshold effect and endogeneity," Journal of Econometrics, Elsevier, vol. 195(2), pages 169-186.
    9. Osvaldo Lagares, 2016. "Capital, Economic Growth and Relative Income Differences in Latin America," Discussion Papers 16/03, Department of Economics, University of York.
    10. Mayer, Alexander, 2022. "On the local power of some tests of strict exogeneity in linear fixed effects models," Econometrics and Statistics, Elsevier, vol. 24(C), pages 49-74.
    11. Badi H. Baltagi, 2021. "Dynamic Panel Data Models," Springer Texts in Business and Economics, in: Econometric Analysis of Panel Data, edition 6, chapter 0, pages 187-228, Springer.
    12. Hutchison, Michael M. & Noy, Ilan, 2004. "Sudden Stops and the Mexican Wave: Currency Crises, Capital Flow Reversals and Output Loss in Emerging Markets," Santa Cruz Department of Economics, Working Paper Series qt38j2b036, Department of Economics, UC Santa Cruz.
    13. Yan Shen & Cheng Hsiao & Hiroshi Fujiki, 2005. "Aggregate vs. disaggregate data analysis-a paradox in the estimation of a money demand function of Japan under the low interest rate policy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 579-601.
    14. Puhani, Patrick A., 1999. "Estimating the effects of public training on Polish unemployment by way of the augmented matching function approach," ZEW Discussion Papers 99-38, ZEW - Leibniz Centre for European Economic Research.
    15. Hutchison, Michael M. & Noy, Ilan, 2006. "Sudden stops and the Mexican wave: Currency crises, capital flow reversals and output loss in emerging markets," Journal of Development Economics, Elsevier, vol. 79(1), pages 225-248, February.
    16. James Alm & Asmaa El-Ganainy, 2013. "Value-added taxation and consumption," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 20(1), pages 105-128, February.
    17. Aditi Bhattacharyya, 2012. "Adjustment of inputs and measurement of technical efficiency: A dynamic panel data analysis of the Egyptian manufacturing sectors," Empirical Economics, Springer, vol. 42(3), pages 863-880, June.
    18. A. Gledson de Carvalho & Francisco Anuatti-Neto & Milton Barossi-Filho & Roberto Macedo, 2003. "Costs and Benefits of Privatization: Evidence from Brazil," Research Department Publications 3149, Inter-American Development Bank, Research Department.
    19. Yongcheol Shin & Ron P Smith & Mohammad Hashem Pesaran, 1998. "Pooled Mean Group Estimation of Dynamic Heterogeneous Panels," Edinburgh School of Economics Discussion Paper Series 16, Edinburgh School of Economics, University of Edinburgh.
    20. Luise Breinlinger & Evgenia Glogova, 2002. "Determinants of Initial Public Offerings - A European Time-Series Cross-Section Analysis," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 3, pages 87-106.
    21. Suhal Kusairi & Suriyani Muhamad & M Musdholifah & Shu-Chen Chang, 2019. "Labor Market and Household Debt in Asia Pacific Countries: Dynamic Heterogeneous Panel Data Analysis," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-15, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:14:p:4914-:d:381669. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.