Fine-grained city-scale outdoor air pollution maps provide important environmental information for both city managers and residents. Installing portable sensors on vehicles (e.g. taxis, Ubers) provides a low-cost, easy-maintenance, and high-coverage approach to collecting data for air pollution estimation. However, as non-dedicated platforms, vehicles like taxis usually prefer gathering at busy areas of a city where it is more likely to pick up riders. This leaves many parts of the city unsensed or less-sensed. In addition, due to the natural changes in a city and the movements of the vehicles, the sensed and unsensed areas change over time. Consequently, challenges of air pollution estimation with data collected by non-dedicated mobile platforms are twofold: i. data coverage is sparse; ii. data coverage changes over time. Therefore, the major research question is: how can we derive accurate and robust fine-grained field (e.g. air pollution) estimation given dynamic and sparse data collected from uncontrollable mobile sensing platforms' This paper presents adaptive HMSS, an adaptive hybrid model-enabled sensing system for fine-grained air pollution estimation with dynamic and sparse data collected from uncontrollable mobile sensing platforms, which is achieved by combining the advantages of a physics guided model and a data driven model.