IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i21p12043-d669516.html
   My bibliography  Save this article

Modeling Spatial Distribution and Determinant of PM 2.5 at Micro-Level Using Geographically Weighted Regression (GWR) to Inform Sustainable Mobility Policies in Campus Based on Evidence from King Abdulaziz University, Jeddah, Saudi Arabia

Author

Listed:
  • Alok Tiwari

    (Department of Urban and Regional Planning, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Mohammed Aljoufie

    (Department of Urban and Regional Planning, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

Air pollution is fatal. Fine particles, such as PM 2.5 , in ambient air might be the cause of many physical and psychological disorders, including cognitive decline. This is why educational policymakers are adopting sustainable mobility, and other policy measures, to make their campuses carbon-neutral; however, car-dependent cities and their university campuses are still lagging behind in this area. This study attempts to model the spatial heterogeneity and determinants of PM 2.5 at the King Abdulaziz University campus in Jeddah, which is ranked first among the Saudi Arabian universities, as well as in the MENA region. We developed four OLS and GWR models of different peak and off-peak periods during weekdays in order to estimate the determinants of the PM 2.5 concentration. The number of cars, humidity, temperature, windspeed, distance from trees, and construction sites were the estimators in our analysis. Because of a lack of secondary data at a finer scale, we collected the samples of all dependent and independent variables at 51 locations on the KAU campus. Model selection was based on RSS, log-likelihood, adjusted R2, and AICc, and a modal comparison shows that the GWR variant of Model-2 outperformed the other models. The results of the GWR model demonstrate the geographical variability of the PM 2.5 concentration on the KAU campus, to which the volume of car traffic is the key contributor. Hence, we recommend using the results of this study to support the development of a car-free and zero-carbon campus at KAU; furthermore, this study could be exploited by other campuses in Saudi Arabia and the Gulf region.

Suggested Citation

  • Alok Tiwari & Mohammed Aljoufie, 2021. "Modeling Spatial Distribution and Determinant of PM 2.5 at Micro-Level Using Geographically Weighted Regression (GWR) to Inform Sustainable Mobility Policies in Campus Based on Evidence from King Abdu," Sustainability, MDPI, vol. 13(21), pages 1-14, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12043-:d:669516
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/21/12043/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/21/12043/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sarah L. Stafford, 2011. "How Green Is Your Campus? An Analysis Of The Factors That Drive Universities To Embrace Sustainability," Contemporary Economic Policy, Western Economic Association International, vol. 29(3), pages 337-356, July.
    2. Clifford M. Hurvich & Chih‐Ling Tsai, 1993. "A Corrected Akaike Information Criterion For Vector Autoregressive Model Selection," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(3), pages 271-279, May.
    3. Amanda Leigh Mascarelli, 2009. "How green is your campus?," Nature, Nature, vol. 461(7261), pages 154-155, September.
    4. Makio Ishiguro & Yosiyuki Sakamoto & Genshiro Kitagawa, 1997. "Bootstrapping Log Likelihood and EIC, an Extension of AIC," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 49(3), pages 411-434, September.
    Full references (including those not matched with items on IDEAS)

    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. Cavanaugh, Joseph E., 1997. "Unifying the derivations for the Akaike and corrected Akaike information criteria," Statistics & Probability Letters, Elsevier, vol. 33(2), pages 201-208, April.
    2. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    3. Han Lin Shang, 2023. "Sieve bootstrapping the memory parameter in long-range dependent stationary functional time series," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(3), pages 421-441, September.
    4. Kirsten Lommatzsch & Silke Tober, 2004. "The Inflation Target of the ECB: Does the Balassa-Samuelson Effect Matter?," EUI-RSCAS Working Papers 19, European University Institute (EUI), Robert Schuman Centre of Advanced Studies (RSCAS).
    5. François-Éric Racicot & Raymond Théoret, 2022. "Tracking market and non-traditional sources of risks in procyclical and countercyclical hedge fund strategies under extreme scenarios: a nonlinear VAR approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
    6. Carlos A. Medel, 2015. "Probabilidad Clásica de Sobreajuste con Criterios de Información: Estimaciones con Series Macroeconómicas Chilenas," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 30(1), pages 57-72, Abril.
    7. Daniel Grabowski & Anna Staszewska-Bystrova & Peter Winker, 2020. "Skewness-adjusted bootstrap confidence intervals and confidence bands for impulse response functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 5-32, March.
    8. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    9. Calmès, Christian & Théoret, Raymond, 2020. "Bank fee-based shocks and the U.S. business cycle," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    10. Yifei Cai & Jamel Saadaoui & Yanrui Wu, 2024. "Political relations and trade: New evidence from Australia, China, and the United States," Scottish Journal of Political Economy, Scottish Economic Society, vol. 71(3), pages 253-275, July.
    11. Nan Li & Simon S. Kwok, 2021. "Jointly determining the state dimension and lag order for Markov‐switching vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 471-491, July.
    12. Bankole Osita Awuzie & Amal Abuzeinab, 2019. "Modelling Organisational Factors Influencing Sustainable Development Implementation Performance in Higher Education Institutions: An Interpretative Structural Modelling (ISM) Approach," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    13. Jonathan E Bone & Brian Wallace & Redouan Bshary & Nichola J Raihani, 2016. "Power Asymmetries and Punishment in a Prisoner’s Dilemma with Variable Cooperative Investment," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-16, May.
    14. Daniel Fernández, 2011. "Suficiencia del capital y previsiones de la banca uruguaya por su exposición al sector industrial," Monetaria, CEMLA, vol. 0(4), pages 517-589, octubre-d.
    15. Hafidi, B. & Mkhadri, A., 2006. "A corrected Akaike criterion based on Kullback's symmetric divergence: applications in time series, multiple and multivariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1524-1550, March.
    16. Paul Gaggl, 2009. "The Role of Exchange Rate Movements for Prices in the Euro Area," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 83-103.
    17. Oscar Jorda, 2003. "Model-Free Impulse Responses," Working Papers 305, University of California, Davis, Department of Economics.
    18. Javier Pereda, 2011. "Estimación de la tasa natural de interés para Perú: un enfoque financiero," Monetaria, CEMLA, vol. 0(4), pages 429-459, octubre-d.
    19. Ogasawara, Haruhiko, 2015. "Asymptotic cumulants of some information criteria," ビジネス創造センターディスカッション・ペーパー (Discussion papers of the Center for Business Creation) 10252/5446, Otaru University of Commerce.
    20. Anna Staszewska-Bystrova, 2009. "Bootstrap Confidence Bands for Forecast Paths," Working Papers 024, COMISEF.

    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:jsusta:v:13:y:2021:i:21:p:12043-:d:669516. 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.