We present HIAPLLM, a privacy-preserving hybrid edge–cloud framework for real-time air-quality advisories tailored to the Indian context using Large Language Models (LLMs). At the network edge, a Raspberry Pi 4 B hosts a Gradio/Folium interface enabling users to input any Indian geocoordinates. Upon each request, the Pi issues a 5 s-timeout call to the Open-Meteo application programming interface (API), retrieving PM_2.5 (5.2–82.9 g/m^3 ), PM_10 (8.6–172.8 g/m^3 ), CO (0.10–0.80 mg/m^3 ), NO_2 (3.6–46.5 g/m^3 ), SO_2 (0.3–40.7 g/m^3 ), O_3 (35–160 g/m^3 ), aerosol optical depth (AOD: 0.14–1.10), dust loading (0–238 units), and CH_4 (1 316–1 614 ppm). These concentrations are mapped via piecewise linear breakpoints into air quality index (AQI) sub-indices and qualitative bands. The Pi then constructs a structured LLM prompt (e.g., “ PM_2.5 = 82.9 g/m^3 ; Category: Poor”) and forwards it over LAN to a laptop running ten quantized LLMs (0.6–7 B parameters) under the Ollama engine, eliminating external cloud dependency and preserving raw location privacy. We profile model load times (0.80–2.46 s) and inference latencies (6.26–94.05 s), and find a strong inverse relationship between token throughput and total latency (slope − 0.516 s per token/s, R^2=0.552 ). Multivariate analyses (Principal Component Analysis (PCA) capturing 42 PM_10 : 8.75× 10^3 , dust: 3.66× 10^3 , total_duration_s: 3.84× 10^10 ) expose extreme collinearity, addressed via dimension reduction. HIAPLLM’s runtime of O(N+P)+T_LLM and space O(N+P+m) ensures scalable, transparent, and low-cost deployment suitable for India’s connectivity-constrained, privacy-sensitive settings. Code and data are available at https://github.com/ParthaPRay/Air_Pollution_LLM_Advisor .
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