Quantitative Health Impact Assessment (QHIA) is a formalized method designed to illustrate the impact of a given health determinant on health, based on the current state of scientific knowledge and on local data and future scenarios. It has been widely applied to air pollution and has more recently been extended to other environmental determinants such as noise. This pilot study proposes reproducible methods for conducting HIA of the presence of green spaces, physical activity induced by active mobility, heat, ambient air pollution, and noise. It compares the current situation for these determinants with theoretical scenarios of how these determinants might evolve. The main objective was to test the relevance and feasibility of such EQIS. Particular attention was paid to the consistency of the approaches used for each determinant, in order to obtain consistent estimates of the expected benefits under different scenarios of how these determinants might evolve. The method was tested with the European Metropolis of Lille, Montpellier Méditerranée Métropole, and the Rouen Normandy Metropolis, drawing on existing data for these regions. Regular exchanges with the metropolitan areas made it possible to refine the protocol, identify available data and useful contextual information, and clarify the scenarios. This collaborative process aimed to best meet the expectations of the metropolitan areas while adhering to the methodological constraints of the HIA. For all determinants, the EQIS are relevant and feasible, but with points of attention concerning mainly the construction of scenarios and the choice of exposure-risk relationships. These points are particularly sensitive for the EQIS on green spaces and active mobility, where significant assumptions are involved in translating the scenarios into equations. For air pollution and noise, the choice of exposure-risk relationships remains the primary source of uncertainty. For heat, the scale of analysis is the most limiting factor for developing scenarios. Furthermore, it was not possible to test intervention scenarios that simultaneously influence multiple determinants because the necessary environmental data were not available. The results highlight that interventions aimed at increasing the number of urban green spaces, promoting active mobility, improving air quality, and reducing noise and heat in cities would result in significant health benefits in terms of mortality, morbidity, healthcare utilization, and discomfort. These benefits would be more pronounced for the most disadvantaged populations. The ÉQIS appears to be a relevant tool for better accounting for the health consequences of urban environmental determinants. To advance future ÉQIS studies, research should focus on developing exposure-risk relationships tailored to the French context. These relationships should utilize environmental metrics that facilitate the development of scenarios useful for local action. Furthermore, the production of cross-cutting environmental data must be promoted more systematically, for example by modeling the impacts of a mobility policy in terms of modal share as well as noise, air pollution, the urban heat island effect, and opportunities for greening. Such data would facilitate the implementation of HEIS and a systemic approach to urban health.