## 2 Related Literature
Having said that, several works in the literature have focused on simplifying the storage and presentation of such data to relief the shoulders of front-end developers from having to deal with the plain complex structures. For example, authors of [32] have focused on providing a novel mechanism for agricultural applications developers and analysts to seamlessly access point-based (tuple-by-tuple) climate data through a standardized simplified service endpoint API with no knowledge in the gridded data source complicated structures. They basically have developed a system that they term NARO (for National Agriculture and Food Research Organization) as a middleware between the presentation layer and the underlying climate data source agent, which simply translates the gridded source climate data into a corresponding point-based counterpart. Other works have taken a variant tack for studying the effects of vehicle-caused emissions on pollution levels by estimating those emissions from either vehicle movement or dedicated sensors attached to those vehicles on what is commonly known as crowdsourcing. For example, researchers in [33] have presented a novel architecture for monitoring the amounts of pollutants such as CO\({}_{2}\) that are generated by vehicle engines. Data was collected using an on-board diagnostic reader attached to each participating vehicle. They aimed at helping municipalities in Brazil to identify hotspots with elevated vehicle-caused CO\({}_{2}\) levels to delineate indicators for better urban planning and enforcing future transportation policies [34]. In addition, other works focus on interpolating pollution data at granular levels by joining downscaled data with rich historical pollution data using a novel learning-based data selection scheme [35]. Other works join pollution data coming from sources with varying temporal and spatial resolutions such as the work by [36]. Specifically, they presented a framework for the integration of air pollutions data from fixed ground stations having a sparse spatial resolution and dense temporal resolution with spatially-resolved mobile sensors pollution data that are sparse in the temporal dimension. The picture that emerges from the literature is the following: despite the availability of sparse works focusing on integrating heterogeneous mobility and contextual data (being it weather, climate, pollution, or meteorological data), there is lack of agreement on a general method that can be applied to integrate such data on various levels of details at scale with QoS guarantees. Our work presented in this paper targets at filling this gap specifically by providing a general-purpose method for enriching mobility data with environmental contextual information (such as climate and pollution data) with statistically significant guarantees on time-based and quality-based QoS constraints. To the best of our knowledge, our work presented in this paper is unique in the sense that it is the first-in-class that provides a holistic approach for explicitly combining pollution and traffic real data at scale with QoS guarantees, such as time-based and quality-based QoS constraints that could be prespecified in an SLA.