"Exposure to PM2.5 has been associated with increased risk of myocardial infarction, reduced birth weights, cardiovascular and respiratory disease. Climate changes will lead to warmer air temperatures (Ta) and more extreme weather events,which are associated with increased morbidity and mortality in sensitive populations. Current epidemiological studies on the health effects of PM2.5 and Ta have many limitations.They are conducted using large geographical areas (potentially biasing the health effect risk estimates due to exposure measurement error) and are focused only in urban areas where the monitors are placed.There is also an increasing recognition that risk estimation must recognize that people are exposed to multiple risk factors simultaneously. Thus, there are huge methodological and knowledge gaps that must be filled.We need to identify the sources of heterogeneity in the short and long term exposure to air pollution and Ta effects across territories and across sub-populations as well as identify the risks associated with multi-threat exposure.To address this I aim to develop better statistical exposure assessment methods to handle the currently exposure datasets, which are misaligned in both time and space.Building on my previous work, I aim to develop and validate computationally efficient models that will allow me to more accurately estimate PM2.5 and Ta at a very high spatial (1×1 km) and temporal (daily) resolutions for Italy and France for 2000-2013. I will then make use of these generated PM2.5 and Ta estimations in a study assessing the effects of maternal exposure to PM2.5 and Ta on fetal growth. This study will dramatically advance environmental exposure assessment by producing high resolution spatio-temporaly resolved exposure models. These models will allow us to estimate both short and long term exposure effects in both urban and rural areas, reducing exposure measurement error and provide a sound epidemiological base for the magnitude of risks."