Fine particulate matter (PM2.5) air pollution is a global public health crisis, responsible for millions of premature deaths annually. Accurate prediction of PM2.5 concentrations is paramount for timely alerts, effective mitigation, and informed policy decisions. Chronic exposure to high levels of PM2.5 is associated with increased mortality rates from heart and lung diseases. This is especially concerning for vulnerable populations such as children, the elderly, and individuals with pre-existing health conditions. To address this challenge, accurate and timely prediction of PM2.5 levels is essential. Traditional linear models often fall short in capturing the complex interactions between multiple factors influencing air quality. Therefore, this study employs Random Forest Regression, a robust ensemble learning method, enhanced with polynomial features using meteorological data from the U.S. Environmental Protection Agency's (EPA) Air Quality System (AQS) to improve the predictive accuracy. Random Forest Regression is particularly suitable for this task due to its ability to handle high-dimensional data and capture non-linear relationships. The model's ability to capture non-linear relationships and interactions between predictors is thoroughly evaluated. We leverage the extensive AQS dataset, discuss the intricacies of feature engineering, present rigorous model validation, and explore the implications of our findings for air quality management and public health. By incorporating polynomial features, the model aims to better capture the complex interactions among variables such as temperature, humidity, wind speed, and other environmental factors that influence PM2.5 levels. This study not only highlights the effectiveness of using advanced machine learning techniques to enhance air quality predictions but also underscores the critical role of accurate forecasting in protecting public health and guiding policy decisions. Through comprehensive analysis and validation, we demonstrate the potential of this approach to provide more reliable predictions, thus supporting efforts to mitigate the adverse effects of air pollution on a global scale. As air quality continues to be a pressing global issue, innovative approaches such as the one presented in this study are essential for ensuring a healthier and more sustainable future.

Graph retrieval from a large corpus of graphs has a wide variety of applications, e.g., sentence retrieval using words and dependency parse trees for question answering, image retrieval using scene graphs, and molecule discovery from a set of existing molecular graphs. In such graph search applications, nodes, edges and associated features bear distinctive physical significance. Therefore, a unified, trainable search model that efficiently returns corpus graphs that are highly relevant to a query graph has immense potential impact. In this paper, we present an effective, feature and structure-aware, end-to-end trainable neural match scoring system for graphs. We achieve this by constructing the product graph between the query and a candidate graph in the corpus, and then conduct a family of random walks on the product graph, which are then aggregated into the match score, using a network whose parameters can be trained. Experiments show the efficacy of our method, compared to competitive baseline approaches.