Large Language Models (LLMs) are increasingly deployed on edge devices to address privacy and latency concerns in modern Web applications. While numerous studies focus on inference frameworks, the critical problem of tuning runtime configurations remains largely underexplored. This endeavor is particularly challenging on edge devices due to severe budget limitations and the dynamic variability of system resources.

Approximate nearest neighbor search (ANNS) has become vital to modern AI infrastructure, particularly in retrieval-augmented generation (RAG) applications. Numerous in-browser ANNS engines have emerged to seamlessly integrate with popular LLM-based web applications, while addressing privacy protection and challenges of heterogeneous device deployments. However, web browsers present unique challenges for ANNS, including computational limitations, external storage access issues, and memory utilization constraints, which state-of-the-art (SOTA) solutions fail to address comprehensively.

PM2.5 refers to fine particulate matter less than 2.5 micrometers in diameter. PM2.5 is a common air pollutant. It is capable of entering the respiratory system, and is associated with a variety of health issues such as asthma and other diseases. Pedestrians are at risk of exposure to traffic-related PM2.5 due in part to increased numbers of vehicles in city settings and their associated exhaust fumes - a key contributor to PM2.5. In this paper, we present a framework to minimise PM2.5 exposure for pedestrians by helping them avoid areas with high PM2.5 concentration levels. Specifically we predict the concentration levels through an XGBoost model and background concentration levels from official air quality monitoring stations around Melbourne. We factor in real-time, portable, air quality monitoring devices, weather conditions and real-time traffic flow information. The coefficient of determination (R2), root mean squared error (RMSE) and the mean average error (MAE) for the XGBoost model achieves 0.71, 1.98 and 1.1 respectively. The Dijkstra algorithm is then applied to generate the minimum PM2.5 exposure of routes with alternative routes suggested trading off distance and PM2.5 exposure. Compared with the shortest route, experiments show that PM2.5 exposure can be decreased by 11 - 15% with only a marginal increase in route length.