相关论文: In the Danger Zone: U-Net Driven Quantile Regressi…
Our research presents a comprehensive approach to leveraging mobile camera image data for real-time air quality assessment and recommendation. We develop a regression-based Convolutional Neural Network model and tailor it explicitly for air…
Motivated by the study of pollution trends in the city of Bergen, we introduce a flexible statistical framework for modeling multivariate air pollution data via a nonhomogeneous Hidden Semi-Markov Vector Auto-Regression. The hidden process…
Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne…
The COVID-19 pandemic considerably affects public health systems around the world. The lack of knowledge about the virus, the extension of this phenomenon, and the speed of the evolution of the infection are all factors that highlight the…
In this paper, we develop a method to estimate the infection-rate of a disease, over a region, as a field that varies in space and time. To do so, we use time-series of case-counts of symptomatic patients as observed in the areal units that…
An important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone…
Accurate and reliable forecasting models are critical for guiding public health responses and policy decisions during pandemics such as COVID-19. Retrospective evaluation of model performance is essential for improving epidemic forecasting…
The Copernicus Atmospheric Monitoring Service provides reanalysis products for atmospheric composition by combining model simulations with satellite observations. The quality of these products depends strongly on the availability of the…
The pandemic of novel SARS-CoV-2 also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as CT, X-ray, etc., plays a significant role in diagnosing the patients by presenting the visual…
Air pollution is a threat to human health, in particular since it aggravates respiratory diseases. Early COVID-19 outbreaks in Wuhan, China and Lombardy, Italy coincided with high levels of air pollution drawing attention to a potential…
Accurate and reliable air quality forecasting is essential for protecting public health, sustainable development, pollution control, and enhanced urban planning. This letter presents a novel WaveCatBoost architecture designed to forecast…
In this work we present a spatial-temporal convolutional neural network for predicting future COVID-19 related symptoms severity among a population, per region, given its past reported symptoms. This can help approximate the number of…
This paper proposes a novel approach to predict epidemiological parameters by integrating new real-time signals from various sources of information, such as novel social media-based population density maps and Air Quality data. We implement…
In air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain, and are then calibrated against measurements from monitoring stations. However, these different data sources…
Air pollution poses a serious threat to sustainable environmental conditions in the 21st century. Its importance in determining the health and living standards in urban settings is only expected to increase with time. Various factors…
The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more than 200 countries around the world, leading to a severe impact on the health and life of many people globally. As of mid-July 2020, more than 12 million…
The dose-response model has been widely used for quantifying the risk of infection of airborne diseases like COVID-19. The model has been used in the room-average analysis of infection risk and analysis using passive scalars as a proxy for…
Fine particulate matter, PM$_{2.5}$, has been documented to have adverse health effects and wildland fires are a major contributor to PM$_{2.5}$ air pollution in the US. Forecasters use numerical models to predict PM$_{2.5}$ concentrations…
Low-cost mobile sensors can be used to collect PM$_{2.5}$ concentration data throughout an entire city. However, identifying air pollution hotspots from the data is challenging due to the uneven spatial sampling, temporal variations in the…
This article presents an innovative approach for developing an efficient reduced-order model to study the dispersion of urban air pollutants. The need for real-time air quality monitoring has become increasingly important, given the rise in…