Changes in apparent temperature and PM_2.5 around the Beijing–Tianjin megalopolis under greenhouse gas and stratospheric aerosol intervention scenarios

Abstract

Apparent temperature (AP) and ground-level aerosol pollution (PM2.5) are important factors in human health, particularly in rapidly growing urban centers in the developing world. We quantify how changes in apparent temperature – that is, a combination of 2 m air temperature, relative humidity, surface wind speed, and PM2.5 concentrations – that depend on the same meteorological factors along with future industrial emission policy may impact people in the greater Beijing region. Four Earth system model (ESM) simulations of the modest greenhouse emissions RCP4.5 (Representative Concentration Pathway), the “business-as-usual” RCP8.5, and the stratospheric aerosol intervention G4 geoengineering scenarios are downscaled using both a 10 km resolution dynamic model (Weather Research and Forecasting, WRF) and a statistical approach (Inter-Sectoral Impact Model Intercomparison Project – ISIMIP). We use multiple linear regression models to simulate changes in PM2.5 and the contributions meteorological factors make in controlling seasonal AP and PM2.5. WRF produces warmer winters and cooler summers than ISIMIP both now and in the future. These differences mean that estimates of numbers of days with extreme apparent temperatures vary systematically with downscaling method, as well as between climate models and scenarios. Air temperature changes dominate differences in apparent temperatures between future scenarios even more than they do at present because the reductions in humidity expected under solar geoengineering are overwhelmed by rising vapor pressure due to rising temperatures and the lower wind speeds expected in the region in all future scenarios. Compared with the 2010s, the PM2.5 concentration is projected to decrease by 5.4 µg m−3 in the Beijing–Tianjin province under the G4 scenario during the 2060s from the WRF downscaling but decrease by 7.6 µg m−3 using ISIMIP. The relative risk of five diseases decreases by 1.1 %–6.7 % in G4, RCP4.5, and RCP8.5 using ISIMIP but has a smaller decrease (0.7 %–5.2 %) using WRF. Temperature and humidity differences between scenarios change the relative risk of disease from PM2.5 such that G4 results in 1 %–3 % higher health risks than RCP4.5. Urban centers see larger rises in extreme apparent temperatures than rural surroundings due to differences in land surface type, and since these are also the most densely populated, health impacts will be dominated by the larger rises in apparent temperatures in these urban areas.