## 2 Related Literature  
Several works from the relevant state-of the-art have focused on integrating traffic and georeferenced mobility data with other sources of enrichment contextual data such as weather and pollution data over temporal and locational dimensions, aiming at characterizing the correlations between various parameters affecting human and vehicle mobility patterns. Example scenarios include the estimation of exposure to vehicle-caused air pollutant [7; 15; 16; 17]. Also, utilizing taxi trajectory data to monitor city-wide air pollution [18; 19]. In addition to urban vehicle emission pollution prediction [20; 21; 22; 23; 24; 25]. Visualization and interactive exploratory data analysis is another challenge that is linked to such scenarios comprising heterogeneous smart city data [26]. For example, Authors in [27] have developed SWIM (for Ship and Weather Information Monitoring) tool for an interactive visualization of ship movement data combined with weather data. They basically aimed at assisting the decision making in selecting routes for ship's future trips in a way that avoids hazards that nature could bring to moving ships. Combining traffic and pollution data has attracted several stances in the recent literature. For example, within the consortium of the TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project, authors of [28; 29] presented a system that aimed at understanding the correlation between vehicle traffic and urban air quality. They basically designed a system for providing real-time and predicted air quality values for many cities in Europe by deploying cost-effective air quality monitoring sensors, in addition to other tasks such as data capturing and integration from heterogeneous sources, thus offering a unified umbrella for joint analytics of urban air quality based on traffic flows. In the same vein, authors of [30] have designed IMP (short for Integrated Monitoring Platform) for generating unified database views that aim at the combined monitoring of same-location vehicle traffic and corresponding air pollution. They have tested their framework in the city of Florence in Italy, and thanks to the unified view, the quantification of the relationship between vehicle traffic and related air pollution is concluded and explained by applying linear regression models and Artificial Neural Networks (ANN). They have focused on combining air quality data (specifically NO2, CO and CO2) with metrological (wind speed, temperature, and relative humidity) and vehicle traffic data (traffic flow and vehicle speed) to achieve this goal. Also, a recent study by [10] focused on unleashing a statistical pattern that characterizes emissions and uncovers the correlations between emissions and human mobility in terms of human exposure to emission's pollutants at road-network level. They have applied the nearest-neighbors algorithm to assign trajectory points to roads. However, it is not clear how their spatial join method performs in terms of time-based QoS constraints (i.e., latency and throughput) given big data at the scale of millions of tuples. Additionally, a recent study by [31] has integrated various heterogeneous data from the city of Zaragoza in Spain related to traffic, meteorology and pollution to estimate the traffic flow impact on the concentrations of atmospheric pollutants such as PM\({}_{10}\) and NO\({}_{2}\). When it comes to analyzing environmental data together with mobility data, complications do not occur solely because of the lack of the appropriate ability to join heterogeneous data. It is also caused by the complicated structural data representations of environmental data such as climate and weather data. For example, developers typically must deal with the logistical complexities of the complicated hierarchical data structure of the gridded climate data sources (such as GRIB and NetCDF files).