Online spectrum auctions will play a pivotal role in restructuring the existing spectrum allocation system given growing spectrum demand from users with diverse requirements. The research in this field mainly concentrates on designing truthful mechanisms in a static or partially dynamic setting. This setting, however, does not capture the fact that spectrum becomes available according to primary users' activities, and the reserve price for this unused spectrum differs from one primary user to another. The number of secondary users with spectrum demand, their transmission deadlines, and their valuations for their transmissions also vary in time and space. Existing research cannot ensure truthful reporting of participants in presence of such diversity in demand and supply. In this work, we consider such a dynamic secondary market and present two spectrum allocation and pricing algorithms. The first algorithm requires distribution knowledge of bidders and sellers, and assesses the winning probability and payment amount of participants based on a priority function. The second algorithm determines the payment amount by exploring the interference relationships between bidders, their transmission deadlines and valuations without any distribution knowledge. We analytically prove that both these algorithms satisfy three desired auction properties - truthfulness, individual rationality, and budget balance. Finally, we present simulation results to analyze the performance of these two algorithms under different auction settings.