Long-term health risk of climate change associated surface PM2.5 concentration variation: Multiple ACCMIP model data under different emission scenarios (RCP26, 45, 60, 85) and population scenarios (SSP1, SSP2, SSP3)
Date of Award
Master of Science
Joshua S. Fu
Qiang He, Kimberley Carter
In this study, multiple models from Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) are utilized to derive the global burden of disease of chronic obstructive pulmonary disease (COPD), lung cancer (LNC), and lower respiratory infection (LRI) derived from surface PM2.5 elevation. Various Representative Concentration Pathways and Shared Socio-economic Pathways scenarios are compared as well as various models to deduct the impact from various scenarios. The time series variation and seasonal variation are also illustrated in this study. Multi-model ensemble was conducted to reduce the deviation in model projection output.
Projection shows increase in the population normalized relative risk for COPD, LNC, and LRI disease between 2030 and 2000 for developed countries and northern Africa for various population scenarios using the average value for different emission scenarios from GISS-E2-R model output. MIROC-CHEM actually predicts a much lower normalized PM concentration or relative risk level than GISS-E2-R model. Black carbon adjusted relative risk results show that LNC derived relative risk might be much more sensitive for black carbon compared with COPD or LRI. LAC region is more sensitive to black carbon surface pollution compared with other regions for LRI relative risk.
Dust storms in the Saharan regions can be a major contribution to risk elevation in Saharan and adjacent regions. Australia dust storm in the central Australia can also be responsible to the exposure at coastal areas. The emission increase in East Asia since 1980s can be responsible to the widespread risk elevation in recent years.
Zhu, Xiufen, "Long-term health risk of climate change associated surface PM2.5 concentration variation: Multiple ACCMIP model data under different emission scenarios (RCP26, 45, 60, 85) and population scenarios (SSP1, SSP2, SSP3). " Master's Thesis, University of Tennessee, 2016.