Related papers: A deep-learning model for predicting daily PM2.5 c…
Air pollution remains a leading global health and environmental risk, particularly in regions vulnerable to episodic air pollution spikes due to wildfires, urban haze and dust storms. Accurate forecasting of particulate matter (PM)…
The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution…
Cement production is among the largest contributors to industrial air pollution, emitting ~3 Mt NOx/year. The industry-standard mitigation approach, selective non-catalytic reduction (SNCR), exhibits low NH3 utilization efficiency,…
Ambient fine particulate matter less than 2.5 $\mu$m in aerodynamic diameter (PM$_{2.5}$) has been linked to various adverse health outcomes and has, therefore, gained interest in public health. However, the sparsity of air quality monitors…
Exposure to poor indoor air quality poses significant health risks, necessitating thorough assessment to mitigate associated dangers. This study aims to predict hourly indoor fine particulate matter (PM2.5) concentrations and investigate…
This paper presents an approach to improve the forecast of computational fluid dynamics (CFD) simulations of urban air pollution using deep learning, and most specifically adversarial training. This adversarial approach aims to reduce the…
Ambient air pollution remains a critical issue in the United Kingdom, where data on air pollution concentrations form the foundation for interventions aimed at improving air quality. However, the current air pollution monitoring station…
Urban air pollution poses significant risks to public health, environmental sustainability, and policy planning. Effective air quality management requires predictive tools that can integrate diverse datasets and communicate complex spatial…
This paper presents an engine able to forecast jointly the concentrations of the main pollutants harming people's health: nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM2.5 and PM10, which are respectively the particles whose…
Here we present a new method of estimating global variations in outdoor PM$_{2.5}$ concentrations using satellite images combined with ground-level measurements and deep convolutional neural networks. Specifically, new deep learning models…
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…
Monitoring urban air quality with high spatiotemporal resolution continues to pose significant challenges. We investigate the use of taxi fleets as mobile sensing platforms, analyzing over 100 million PM2.5 readings from more than 3,000…
This is a relevant problem because the design of most cities prioritizes the use of motorized vehicles, which has degraded air quality in recent years, having a negative effect on urban health. Modeling, predicting, and forecasting ambient…
Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the…
We introduce PM25Vision (PM25V), the largest and most comprehensive dataset to date for estimating air quality - specifically PM2.5 concentrations - from street-level images. The dataset contains over 11,114 images matched with timestamped…
Reducing air pollution, such as CO2 and PM2.5 emissions, is one of the most important issues for many countries worldwide. Selecting an environmentally friendly transport mode can be an effective approach of individuals to reduce air…
Accurately describing the distribution of CO$_2$ in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support systems to aid implementation of international climate…
Air pollution is a global hazard, and as of 2023, 94\% of the world's population is exposed to unsafe pollution levels. Surface Ozone (O3), an important pollutant, and the drivers of its trends are difficult to model, and traditional…
One way to quantify exposure to air pollution and its constituents in epidemiologic studies is to use an individual's nearest monitor. This strategy results in potential inaccuracy in the actual personal exposure, introducing bias in…
Accurate low dimension chemical kinetic models for methane are an essential component in the design of efficient gas turbine combustors. Kinetic models coupled to computational fluid dynamics (CFD) provide quick and efficient ways to test…