PROJECT SUMMARY/ABSTRACT Emergency medical services (EMS) represent a critical facet of the public health infrastructure that will be increasingly stressed by climate change, but the U.S.-wide burden on EMS systems associated with environmental exposures remains unknown. Due to inherent limitations of the administrative health data, prior works examining short-term effects of environmental exposures have assumed constant exposures over delineated locations (such as census tracts) and times (usually a day), which could lead to exposure significant misclassification and bias in the risk estimates. In this study, we will leverage a national database of 911 calls, the National Emergency Medical Services Information System (NEMSIS), to test the hypothesis that short-term air pollution and temperature exposures are associated with EMS transports. NEMSIS has information on the patient demographics as well as the location and times of EMS activations across all 50 states. This detail, size, and nationwide coverage of NEMSIS make the dataset uniquely suitable for characterizing the exposure- response relationships across multiple populations in the U.S. at varying geographic and temporal scales. Our aims are to: 1) construct a highly resolved spatiotemporal data architecture linking 911 calls with environmental exposures (fine particulate matter, nitrogen dioxide, ozone, temperature average, temperature variability), built environment, and neighborhood characteristics for years 2017-2022+; 2) evaluate the U.S.-wide short-term associations between environmental exposures and cause-specific EMS transports (all-cause, cardiovascular, respiratory, temperature-related, and injuries); and 3) examine the associations between exposures and EMS outcomes in select urban areas incorporating spatiotemporally dynamic exposure estimates derived from low- cost sensors and population mobility datasets. This proposed project evaluates the nationwide risks for EMS transports associated with air pollution and temperature exposures for the first time. This work will also allow us to measure the impact of exposure misclassification by leveraging recent advancements in exposure assessment tools and unique information in EMS data on event locations and times.