ALBERT

Description: Understanding the health impacts of particulate matter (PM) requires spatiotemporally continuous exposure estimates. We developed a multi-stage ensemble model that estimates daily mean PM2.5 and PM10 at 1 km spatial resolution across France from 2000 to 2019. First, we alleviated the sparsity of PM2.5 monitors by imputing PM2.5 at more common PM10 monitors. We also imputed missing satellite aerosol optical depth (AOD) based on modelled AOD from atmospheric reanalyses. Next, we trained three base learners (mixed models, Gaussian Markov random fields, and random forests) to predict daily PM concentrations based on AOD, meteorology, and other variables. Finally, we generated ensemble predictions using a generalized additive model with spatiotemporally varying weights that exploit the strengths and weaknesses of each base learner. The Gaussian Markov random field dominated the ensemble, outperforming mixed models and random forests at most locations on most days. Rigorous cross-validation showed that the ensemble predictions were quite accurate, with mean absolute error (MAE) of 2.72 μg/m3 and R2 of 0.76 for PM2.5; PM10 MAE was 4.26 μg/m3 and R2 0.71. Our predictions are available to improve epidemiological studies of acute and chronic PM exposure in urban and rural France.

Description: Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely on daily Ta metrics, rather than hourly ones, making them insufficient for intra-day assessment of health exposure. In this study, we present a three-stage machine learning-based ensemble model to estimate hourly Ta at a high spatial resolution of 1 × 1 km2, incorporating remotely sensed surface skin temperature (Ts) from geostationary satellites, reanalysis synoptic variables, and observations from weather stations, as well as auxiliary geospatial variables, which account for spatio-temporal variability of Ta. The Stage 1 model gap-fills hourly Ts at 4 × 4 km2 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which are subsequently fed into the Stage 2 model to estimate hourly Ta at the same spatio-temporal resolution. The Stage 3 model downscales the residuals between estimated and measured Ta to a grid of 1 × 1 km2, taking into account additionally the monthly diurnal pattern of Ts derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In each stage, the ensemble model synergizes estimates from the constituent base learners—random forest (RF) and extreme gradient boosting (XGBoost)—by applying a geographically weighted generalized additive model (GAM), which allows the weights of results from individual models to vary over space and time. Demonstrated for Israel for the period 2004–2017, the proposed ensemble model outperformed each of the two base learners. It also attained excellent five-fold cross-validated performance, with overall root mean square error (RMSE) of 0.8 and 0.9 °C, mean absolute error (MAE) of 0.6 and 0.7 °C, and R2 of 0.95 and 0.98 in Stage 1 and Stage 2, respectively. The Stage 3 model for downscaling Ta residuals to 1 km MODIS grids achieved overall RMSE of 0.3 °C, MAE of 0.5 °C, and R2 of 0.63. The generated hourly 1 × 1 km2 Ta thus serves as a foundation for monitoring and assessing human health exposure to temperature extremes at a larger geographical scale, helping to further minimize exposure misclassification in epidemiological studies.

Description: Rising global temperatures over the last decades have increased heat exposure among populations worldwide. An accurate estimate of the resulting impacts on human health demands temporally explicit and spatially resolved monitoring of near‐surface air temperature (Ta). Neither ground‐based nor satellite‐borne observations can achieve this individually, but the combination of the two provides synergistic opportunities. In this study, we propose a two‐stage machine learning‐based hybrid model to estimate 1 × 1 km2 gridded intra‐daily Ta from surface skin temperature (Ts) across the complex terrain of Israel during 2004–2016. We first applied a random forest (RF) regression model to impute missing Ts from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites, integrating Ts from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite and synoptic variables from European Centre for Medium‐Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. The imputed Ts are in turn fed into the Stage 2 RF‐based model to estimate Ta at the satellite overpass hours of each day. We evaluated the model's performance applying out‐of‐sample fivefold cross validation. Both stages of the hybrid model perform very well with out‐of‐sample fivefold cross validated R2 of 0.99 and 0.96, MAE of 0.42°C and 1.12°C, and RMSE of 0.65°C and 1.58°C (Stage 1: imputation of Ts, and Stage 2: estimation of Ta from Ts, respectively). The newly proposed model provides excellent computationally efficient estimation of near‐surface air temperature at high resolution in both space and time, which helps further minimize exposure misclassification in epidemiological studies.