A structured prompt framework improves local LLM accuracy in environmental monitoring by transforming raw sensor data into enriched textual representations. Evaluations on indoor and outdoor datasets show local model accuracy increases from 50.9% to 81.7% indoors and from 63.7% to 89.3% outdoors with enriched prompts, while maintaining low latency near 0.22 seconds in no-chain-of-thought mode.