Efficiently querying data collected from Large-area Community driven Sensor Networks (LCSNs) is a new and challenging problem. In the authors' previous papers, they proposed adaptive techniques for learning models (e.g., statistical, nonparametric, etc.) from such data, considering the fact that LCSN data is typically geo-temporally skewed. In this paper, they present a demonstration of EnviroMeter. EnviroMeter uses their adaptive model creation techniques for processing continuous queries on community-sensed environmental pollution data. Subsequently, it efficiently pushes current pollution updates to GPS-enabled Smartphones (through its Android application) or displays it via a web-interface.