## 8 Conclusions  
The EMDI system is a novel architecture that allows the interactive integration of pollution, meteorological and mobility data at scale with QoS guarantees. Our system guarantees a statistically significant balance between time-based QoS metrics (end-end latency as discussed in Section 6.1) and quality metrics (depending on the query, either high correlation coefficient value for Top-N queries or low MAPE value for single statistics such as'mean') as discussed in Section 6.2. To recapitulate and summarize the supporting results, our system lowers the latency (a desired time-based QoS constraints) by approximately 65% and achieves a high accuracy (desired quality-based QoS constraint) as opposed to the plain baseline, as we obtain an accuracy for Top-N queries that ranges from 0.84 to 1 for Spearman and Pearson correlation coefficients, values that rely on the geo-encoding configurations. Also, for single geos-stat queries (such as'mean'), we obtain MAPE values ranging from roughly 0.00392 to 0.000195, which is statistically significant. The novel aspect of this architecture is the application of an adapted, retrofitted, and repurposed version of the filter-and-refine algorithm for performing the spatial join, which reduces the execution times that would otherwise be computationally expensive.  
Our architecture is composed of three main components: data collector, spatial join processor and data aggregator. The first component retrieves neighborhood, pollution, and mobility data from publicly accessible online databases. It also applies the required data transformations. The second component executes spatial joins and transformations with the application of \(Z\)-order curves geohash encoding and retrofitted filter-and-refine algorithms, thus speeding up the process. Data aggregator component provides the user with querying capabilities in an interactive way with the usage of Jupyter Notebooks.  
Thanks to the integration of mobility and pollution data, new insights can be derived from heterogeneous data unified under one umbrella, consequently assisting decision makers in providing informed health-aware city planning decisions [51]. In other terms, heterogeneous cross-domain data that was hardly comparable previously is efficiently unified and can be aggregated in meaningful and useful ways to be utilized by researchers, scientists, and engineers.  
Conceptualization, I.M.A.J.; methodology, I.M.A.J.; software, I.M.A.J.; validation, I.M.A.J. and L.F.; formal analysis, I.M.A.J. and L.F.; investigation, I.M.A.J. and P.B.; resources, I.M.A.J.; data curation, I.M.A.J. and P.B.; writing--original draft preparation, I.M.A.J.; writing--review and editing, I.M.A.J., L.F. and P.B.; visualization, I.M.A.J. and L.F; supervision, I.M.A.J., L.F. and P.B.; project administration, L.F. and P.B.; funding acquisition, P.B. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the OpenModel project and has received partial funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 953167.  
Particulate matter d < 10 \(\upmu\)m (PM\({}_{10}\)) CAMS global reanalysis (EAC4) data is coming from the Atmospheric Data Store (ADS) of European Centre for Medium-Range Weather Forecasts (ECMWF) and can be downloaded from the official website by filling the appropriate form at the following link: [[https://ads.atmosphere.copernicus.eu/cdsapp#](https://ads.atmosphere.copernicus.eu/cdsapp#)]([https://ads.atmosphere.copernicus.eu/cdsapp#](https://ads.atmosphere.copernicus.eu/cdsapp#))!/dataset/cams-global-reanalysis-eac4?tab=form (accessed on 5 January 2023).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.