Optimal real-time collection of a variety of environmental parameters from several environmental data sources, still remains a challenge in the selection process. As environmental web services now have access to a wider range of environmental data sources, the quality of these services can vary, even if they offer the same functionality. This competition among providers means that environmental data may differ in quality. Due to this competition, different environmental data sources compete to provide these functionally equivalent services with different levels of quality: the quality of services (QoS), as well as, the quality of the data sources themselves and their data (QoDS). Therefore, we present an approach to satisfy the need of ranking and selecting the optimal services. Our contribution is an automated knowledge-driven approach that relies on the ELECTRE III MCDM (Multi-Criteria Decision-Making) method and on quality-aware service selection, to optimally select services.