IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v7y2010i7p2866-2880d8980.html
   My bibliography  Save this article

Impact of Climate Change on Ambient Ozone Level and Mortality in Southeastern United States

Author

Listed:
  • Howard H. Chang

    (Statistical and Applied Mathematical Sciences Institute, 19 T.W. Alexander Drive Research Triangle Park, NC 27709, USA)

  • Jingwen Zhou

    (Statistics Department, North Carolina State University, Raleigh, NC 27695, USA)

  • Montserrat Fuentes

    (Statistics Department, North Carolina State University, Raleigh, NC 27695, USA)

Abstract

There is a growing interest in quantifying the health impacts of climate change. This paper examines the risks of future ozone levels on non-accidental mortality across 19 urban communities in Southeastern United States. We present a modeling framework that integrates data from climate model outputs, historical meteorology and ozone observations, and a health surveillance database. We first modeled present-day relationships between observed maximum daily 8-hour average ozone concentrations and meteorology measured during the year 2000. Future ozone concentrations for the period 2041 to 2050 were then projected using calibrated climate model output data from the North American Regional Climate Change Assessment Program. Daily community-level mortality counts for the period 1987 to 2000 were obtained from the National Mortality, Morbidity and Air Pollution Study. Controlling for temperature, dew-point temperature, and seasonality, relative risks associated with short-term exposure to ambient ozone during the summer months were estimated using a multi-site time series design. We estimated an increase of 0.43 ppb (95% PI: 0.14–0.75) in average ozone concentration during the 2040’s compared to 2000 due to climate change alone. This corresponds to a 0.01% increase in mortality rate and 45.2 (95% PI: 3.26–87.1) premature deaths in the study communities attributable to the increase in future ozone level.

Suggested Citation

  • Howard H. Chang & Jingwen Zhou & Montserrat Fuentes, 2010. "Impact of Climate Change on Ambient Ozone Level and Mortality in Southeastern United States," IJERPH, MDPI, vol. 7(7), pages 1-15, July.
    • Handle: RePEc:gam:jijerp:v:7:y:2010:i:7:p:2866-2880:d:8980
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/7/7/2866/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/7/7/2866/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. P. J. Everson & C. N. Morris, 2000. "Inference for multivariate normal hierarchical models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 399-412.
    2. Roger D. Peng & Francesca Dominici & Thomas A. Louis, 2006. "Model choice in time series studies of air pollution and mortality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 179-203, March.
    3. D. A. Stainforth & T. Aina & C. Christensen & M. Collins & N. Faull & D. J. Frame & J. A. Kettleborough & S. Knight & A. Martin & J. M. Murphy & C. Piani & D. Sexton & L. A. Smith & R. A. Spicer & A. , 2005. "Uncertainty in predictions of the climate response to rising levels of greenhouse gases," Nature, Nature, vol. 433(7024), pages 403-406, January.
    4. Sumi Hoshiko & Paul English & Daniel Smith & Roger Trent, 2010. "A simple method for estimating excess mortality due to heat waves, as applied to the 2006 California heat wave," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 55(2), pages 133-137, April.
    5. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
    6. Smith, Richard L. & Tebaldi, Claudia & Nychka, Doug & Mearns, Linda O., 2009. "Bayesian Modeling of Uncertainty in Ensembles of Climate Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 97-116.
    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. Yingli Lou & Liyin Shen & Zhenhua Huang & Ya Wu & Heng Li & Guijun Li, 2018. "Does the Effort Meet the Challenge in Promoting Low-Carbon City?—A Perspective of Global Practice," IJERPH, MDPI, vol. 15(7), pages 1-21, June.
    2. Zhiwei Xu & Perry E. Sheffield & Wenbiao Hu & Hong Su & Weiwei Yu & Xin Qi & Shilu Tong, 2012. "Climate Change and Children’s Health—A Call for Research on What Works to Protect Children," IJERPH, MDPI, vol. 9(9), pages 1-19, September.

    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. Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 2017. "Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 111-139, June.
    2. Roger D. Peng & Francesca Dominici & Leah J. Welty, 2009. "A Bayesian hierarchical distributed lag model for estimating the time course of risk of hospitalization associated with particulate matter air pollution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 3-24, February.
    3. Yunquan Zhang & Chuanhua Yu & Jin Yang & Lan Zhang & Fangfang Cui, 2017. "Diurnal Temperature Range in Relation to Daily Mortality and Years of Life Lost in Wuhan, China," IJERPH, MDPI, vol. 14(8), pages 1-11, August.
    4. Robert J. R. Elliott & Ingmar Schumacher & Cees Withagen, 2020. "Suggestions for a Covid-19 Post-Pandemic Research Agenda in Environmental Economics," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 1187-1213, August.
    5. Cui Guo & Jian Kang & Timothy D. Johnson, 2022. "A spatial Bayesian latent factor model for image‐on‐image regression," Biometrics, The International Biometric Society, vol. 78(1), pages 72-84, March.
    6. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    7. Schallaböck, Karl Otto & Fischedick, Manfred & Brouns, Bernd & Luhmann, Hans-Jochen & Merten, Frank, 2006. "Klimawirksame Emissionen des PKW-Verkehrs und Bewertung von Minderungsstrategien," Wuppertal Spezial, Wuppertal Institute for Climate, Environment and Energy, volume 34, number 34.
    8. S. Lorenz & S. Dessai & J. Paavola & P. Forster, 2015. "The communication of physical science uncertainty in European National Adaptation Strategies," Climatic Change, Springer, vol. 132(1), pages 143-155, September.
    9. Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    10. Kai Luo & Wenjing Li & Ruiming Zhang & Runkui Li & Qun Xu & Yang Cao, 2016. "Ambient Fine Particulate Matter Exposure and Risk of Cardiovascular Mortality: Adjustment of the Meteorological Factors," IJERPH, MDPI, vol. 13(11), pages 1-17, November.
    11. Christoph M. Buser & Hans R. Künsch & Alain Weber, 2010. "Biases and Uncertainty in Climate Projections," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 179-199, June.
    12. Francesca Dominici & Lianne Sheppard & Merlise Clyde, 2003. "Health Effects of Air Pollution: A Statistical Review," International Statistical Review, International Statistical Institute, vol. 71(2), pages 243-276, August.
    13. Roger Peng & Leah Welty & Aidan McDermott, 2004. "The National Morbidity, Mortality, and Air Pollution Study Database in R," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1044, Berkeley Electronic Press.
    14. Eliseev, Alexey V. & Mokhov, Igor I., 2008. "Eventual saturation of the climate–carbon cycle feedback studied with a conceptual model," Ecological Modelling, Elsevier, vol. 213(1), pages 127-132.
    15. Brian J. Reich & Shu Yang & Yawen Guan & Andrew B. Giffin & Matthew J. Miller & Ana Rappold, 2021. "A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications," International Statistical Review, International Statistical Institute, vol. 89(3), pages 605-634, December.
    16. Thomas D. Pol & Ekko C. Ierland & Silke Gabbert, 2017. "Economic analysis of adaptive strategies for flood risk management under climate change," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(2), pages 267-285, February.
    17. Andrew Finley & Sudipto Banerjee & Alan Gelfand, 2012. "Bayesian dynamic modeling for large space-time datasets using Gaussian predictive processes," Journal of Geographical Systems, Springer, vol. 14(1), pages 29-47, January.
    18. G. Brooke Anderson & Keith W. Oleson & Bryan Jones & Roger D. Peng, 2018. "Classifying heatwaves: developing health-based models to predict high-mortality versus moderate United States heatwaves," Climatic Change, Springer, vol. 146(3), pages 439-453, February.
    19. Hu, Guanyu, 2021. "Spatially varying sparsity in dynamic regression models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 23-34.
    20. Simon Gosling & Jason Lowe & Glenn McGregor & Mark Pelling & Bruce Malamud, 2009. "Associations between elevated atmospheric temperature and human mortality: a critical review of the literature," Climatic Change, Springer, vol. 92(3), pages 299-341, February.

    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:7:y:2010:i:7:p:2866-2880:d:8980. 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.