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  • 2021wareingphd

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Health and pollution impacts in avoided and future worlds according to Earth system models

Research output: ThesisDoctoral Thesis

Published
Publication date17/07/2021
Number of pages270
QualificationPhD
Awarding Institution
Supervisors/Advisors
Thesis sponsors
  • Engineering and Physical Sciences Research Council (EPSRC)
Award date7/07/2021
Publisher
  • Lancaster University
<mark>Original language</mark>English

Abstract

Air pollution has an adverse effect on human health and, in the absence of air quality legislation, climate change may exacerbate poor air quality. The Paris Agreement global mean surface temperature change (∆GMST) goals of 1.5 °C and 2 °C above pre- industrial levels were proposed to avoid dangerous levels of climate change.
Climate change increases the frequency of warm days and heat waves, as well as increasing average temperature. Daily maximum surface temperature is highly correlated with surface ozone in many regions and the frequency of heatwaves is expected to increase with GMST. To illustrate the success of European emission controls, I compare air pollution during the European 2003 heatwave with and without air quality policy and advances in technology. If the 2003 heatwave had occurred in 2030, future emission scenarios would have further reduced air pollution and associated excess mortality, even with a higher population, highlighting the potential to reduce the health impact of future heatwaves.
When projecting heat-related mortality due to climate change, studies must consider an optimum temperature for human health, above which there is an increase in mortality rate. I use a linear regression model to estimate the percentile corresponding to the optimum temperature. I project the number of days above this temperature (warm days) for climates where ∆GMST is 1.5, 2, 3, and 4 °C. Exposure and vulnerability to warm days is higher in tropical countries and differences in impacts between regions may be underestimated by using a constant percentile globally to define an optimum temperature. This method is useful for projecting mortality in regions where there is a lack of epidemiological research.