Efficiently querying data collected from Large-area Communitydriven Sensor Networks (LCSNs) is a new and challenging problem. In our previous works, we proposed adaptive techniques for learning models (e.g., statistical, non-parametric, etc.) from such data, considering the fact that LCSN data is typically geo-temporally skewed. In this paper, we present a demonstration of EnviroMeter. EnviroMeter uses our 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. We experimentally demonstrate that our model-based query processing approach is orders of magnitude efficient than processing the queries over indexed raw data.