Enriching patient information with environmental information such as individual exposure to air pollution or noise is a relevant procedure in health care and research. Generating this exposure information, however, is computationally intensive as the processing of large datasets with fine spatial and temporal discretisation is necessary; thereby usually exceeding hardware resources e.g. of family doctors. In an interdisciplinary research project (Healthy Urban Living) we develop an environmental modelling and information service (EMIS), consisting of these parts: a set of environmental models calculating exposure (e.g. NO2, PM10) on Dutch scale a set of algorithms to calculate exposure of individuals along their space-time paths a set a of microservices to maintain a flexible workflow in generating and executing queries The microservices architecture enables us to perform the computational intensive modelling tasks on institutional or national computing facilities, and allows lightweight client applications such as web portals to query the EMIS and thereby give health researchers straightforward access to exposure data. The presentation gives a general overview of the research project, the EMIS system architecture and outlines how open-source software tools (e.g. GDAL, PCRaster, flask, docopt, sqlalchemy and more) are used to process the spatio-temporal data sets. We additionally demonstrate use cases from the health researcher's perspective.