Recent discussions of environmental regulatory policy have increasingly emphasized scientific transparency and interpretability as necessary criteria for studies to be considered as policy-relevant evidence. At the same time, statistical methods for estimating exposure-response curves (ERCs) to address the challenges in observational data analysis are becoming more complex and often utilizes black-box approaches. When studying the health impacts of fine particulate matter at a national level, one of the key interests of policy-makers may be how different regions contribute to ERCs as there may exist regional differences in the health impacts of PM2.5 due to different sources of fine particles and heterogeneity in the regional populations. In this paper, we propose Meta-GAM, a recently proposed method for estimating ERCs in heterogeneous data setting using a meta-analysis of generalized additive models, can be adapted to address these issues and provide interpretable, environmental policy-relevant ERCs. We apply Meta-GAM to estimate meta-analyzed region-level and national-level ERCs for PM2.5 in the Medicare population and compare results to other causal inference methods.