Existing Artificial Olfaction (AO) primarily serves two tasks: Odor Classification (OC) and Odor Source Localization (OSL). Both tasks w.r.t. indoor event detection scenarios are studied either using a single electronic nose (e-nose) mounted on the ceiling or mobile robot(s) equipped with e-noses. However, they are not compatible with smart home scenarios due to diverse obstacles (e.g., chairs and tables) and the need for natural interaction. In this paper, we explore the feasibility and usability of a Conversational Interfaces for Artificial Olfaction (CIAO) system using Large Language Models (LLMs) in Smart Home. We made the first olfaction-oriented corpus for LLM evaluation, as well as an olfaction dataset via a self-developed olfactory sensory network. We train the dedicated models for OSL and OC using the dataset and integrate them into a tool within the MCP (Model Context Protocol) server. Five commercial LLMs are used as MCP clients for experiments and validation. Our experimental results indicate that our CIAO system is technically feasible and applicable. Besides, we observe that ChatGPT-4o relatively outperforms in terms of both answer quality and overall LLM usability in pervasive IoT scenarios. Qwen-Plus, in contrast, appears to be a promising solution for robot-compatible applications. To our knowledge, this work is the first effort to bring forward conversational interfaces for AO, enabling multi-turn conversations with contexts beyond one-off question answering. Our codes and partial corpus are available at https://github.com/HokyeeJau/CIAO.