Current research is focusing extensively on building Cloud based open source solutions for big geospatial data analytics in Cloud computing environments. Massive amounts of geospatially-tagged movement and micro blogging data are collected and analysed regularly. Nevertheless, movement data per se is insufficient for uncovering the possibilities for decision-informing analytics which could help in reducing the undesirable effects of climate change. For instance, answering advanced queries such as 'what are the Top-5 districts in Buenos Aires capital city in Argentina in terms of vehicle mobility data where the index of Particulate Matters PM10 is greater than 50'. Other equivalent queries are required for assisting the strategic decisions regarding health-focused smart city policies. For instance, for insightful analytics that help municipalities in designing future city infrastructures that prioritize the health of citizens. For instance, by lowering the number of vehicles that are allowed entering into highly polluted zones in peak hours of the day. In addition, this information is useful for designing mobile interactive geo-maps for city lightweight dwellers in order to inform them which streets to avoid passing-through during specific hours of a day to avoid being subjected to high-levels of vehicle-caused air-borne pollutants such as PM10. However, answering such a query would require joining real-time mobility and meteorological data. Stock versions of the current Cloud-based open-source geospatial management systems do not include intrinsic solutions for such scenarios. Future research frontiers are expected to focus on designing geospatial Cloud open-source systems which allows integration with other contextual data. In this talk we will show case some of the few available Cloud-based big spatial data management frameworks and how they can be utilized as springboards for further development so that they become mature enough to support the decisions that aim to mitigate the mobility-caused climate change problems. We will walkthrough an example that shows how we could tweak an open-source geospatial framework for answering such multi-domain queries. We will conclude the talk with short discussion of open research frontiers in this direction. For instance, because the arriving data which needs to be ingested is huge , approximate query processing should be considered a priority, thus summarizing the data (for example histograms), could be even before forwarding it through the network to a Cloud-based deployment (for example, injecting samplers on the Edge devices near the source of data).