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While weather stations generally capture near‐surface ambient air temperature (Ta) at a high temporal resolution to calculate daily values (i.e., daily minimum, mean, and maximum Ta), their fixed locations can limit their spatial coverage and resolution even in densely populated urban areas. As a result, data from weather stations alone may be inadequate for Ta‐related epidemiology particularly when the stations are not located in the areas of interest for human exposure assessment. To address this limitation in the Megalopolis of Central Mexico (MCM), we developed the first spatiotemporally resolved hybrid satellite‐based land use regression Ta model for the region, home to nearly 30 million people and includes Mexico City and seven more metropolitan areas. Our model predicted daily minimum, mean, and maximum Ta for the years 2003–2019. We used data from 120 weather stations and Land Surface Temperature (LST) data from NASA's MODIS instruments on the Aqua and Terra satellites on a 1 × 1 km grid. We generated a satellite‐hybrid mixed‐effects model for each year, regressing Ta measurements against land use terms, day‐specific random intercepts, and fixed and random LST slopes. We assessed model performance using 10‐fold cross‐validation at withheld stations. Across all years, the root‐mean‐square error ranged from 0.92 to 1.92 K and the R 2 ranged from .78 to .95. To demonstrate the utility of our model for health research, we evaluated the total number of days in the year 2010 when residents ≥65 years old were exposed to Ta extremes (above 30°C or below 5°C). Our model provides much needed high‐quality Ta estimates for epidemiology studies in the MCM region., Spatial pattern of the 95th percentiles of minimum (a) and maximum (b) temperature across days for each 1 km2 grid cell in the Megalopolis of Central Mexico for 2018. Temporal imputation of LST, consideration of missing data as a predictor and careful cross‐validation with detailed characterization of predictive accuracy. Application estimates population exposures to extreme temperatures for use in epidemiologic studies.

Climate change has focused attention on the effects of changing temperature, particularly the effect on human health. Thus, robust and accurate spatially and temporally resolved air temperature (Ta) data are of particular importance in the field of epidemiology and public health. However, most health studies to date have matched people to the nearest monitor. In this study, we aimed to develop a robust satellite-based spatio-temporally resolved Ta estimation model across the complex geo-climatic regions of France resulting in daily high-resolution 1 km predicted air temperature (Tap) estimations. We use a daily calibration approach using a series of processes to generate daily Tap for every day across the entire study area and period. First, we start by calibrating MODIS (Moderate Resolution Imaging Spectroradiometer) satellite-gridded surface temperature (Ts) data against Ta collected within 1 km of the Ts centroid. The calibration stage adjusted for spatio-temporal predictors, as done in environmental exposure assessment methods such as land use regressions. Second, to estimate Tap when no Ts data are available we fit a second model which uses the association of predicted grid cells Tap values (based on satellite Ts) with surrounding Ta monitors and the association with values in neighbouring grid cells. Out-of-sample tenfold cross-validation was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with available Ts and days without Ts observations (overall mean out-of-sample R2 = 0.95 for both stages). In conclusion, we demonstrate how Ts can be used reliably to predict daily Tap at high-resolution across France for use in studies looking at the effects of fine resolution Ta exposure on various health outcomes.

SummaryBackground Prenatal exposure to traffic pollution has been associated with faster infant weight gain, but implications for cardiometabolic health in later childhood are unknown. Methods Among 1418 children in Project Viva, a Boston-area pre-birth cohort, we assessed anthropometric and biochemical parameters of cardiometabolic health in early (median age 3.3 years) and mid- (median age 7.7 years) childhood. We used spatiotemporal models to estimate prenatal and early life residential PM2.5 and black carbon exposure as well as traffic density and roadway proximity. We performed linear regression analyses adjusted for sociodemographics. Results Children whose mothers lived close to a major roadway at the time of delivery had higher markers of adverse cardiometabolic risk in early and mid-childhood. For example, total fat mass was 2.1 kg (95%CI: 0.8, 3.5) higher in mid-childhood for children of mothers who lived