High-resolution (1 km) gridded dataset of premature deaths attributed to PM2.5 exposure in China (2000–2019)

Fine particulate matter (PM2.5) pollution is a major environmental risk factor contributing to multiple diseases and premature mortality. Although numerous studies have examined the health effects of PM2.5, a critical data gap remains in the availability of high-resolution, openly accessible, gridded datasets that attribute premature deaths to long-term exposure to PM2.5. Here, we constructed a high-resolution, long-term dataset of premature deaths attributable to PM2.5 in China, from 2000 to 2019, integrating multiple PM2.5 sources and two epidemiological models: the Integrated Exposure–Response (IER) model and the Global Exposure Mortality Model (GEMM). The dataset incorporates spatially explicit population distributions, baseline mortality rates, and stratification by age and sex, covering populations aged 25 years and above, and includes major disease categories, such as ischemic heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, lower respiratory infections, and other non-communicable diseases. This study outlines the analytical workflow used to generate premature death estimates and documents the key data characteristics, processing assumptions, validation procedures, and sources of uncertainty. The comprehensive dataset provides a quantitative foundation for nationwide health risk evaluation, enabling policy-relevant analyses of population vulnerability and exposure patterns. The premature death dataset is publicly available at https://doi.org/10.5281/zenodo.17568637.