Related papers: HVAQ: A High-Resolution Vision-Based Air Quality D…
Urban Visual Pollution (UVP) has emerged as a critical concern, yet research on automatic detection and application remains fragmented. This scoping review maps the existing deep learning-based approaches for detecting, classifying, and…
Over the past few years, researchers have presented many different applications for convolutional neural networks, including those for the detection and recognition of objects from images. The desire to understand our own nature has always…
This study addresses the critical challenge of modeling and mapping urban air quality to ascertain pollutant concentrations in unmonitored locations. The advent of low-cost sensors, particularly those deployed in vehicular networks,…
In recent years, the world has become increasingly concerned with air pollution. Particularly in the global north, countries are implementing systems to monitor air pollution on a large scale to aid decision-making. Such efforts are…
We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in the observed data as possible, while also ensuring the…
The ever-accelerating process of globalization enables more than half the population to live in cities. Thus, the air quality in cities exerts critical influence on the health status of more and more urban residents. In this article, based…
The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With…
Conventional methods for water pollutant detection, such as chemical assays and optical spectroscopy, are often invasive, expensive, and unsuitable for real-time, portable monitoring. In this paper, we introduce VibraWave, a novel…
The increasing prevalence of marine pollution during the past few decades motivated recent research to help ease the situation. Typical water quality assessment requires continuous monitoring of water and sediments at remote locations with…
The formation of aerosol particles in the atmosphere impacts air quality and climate change, but many of the organic molecules involved remain unknown. Machine learning could aid in identifying these compounds through accelerated analysis…
The level of air quality in urban centres is affected by emission of several pollutants, mainly coming from the vehicles flowing in their road networks. This is a well known phenomenon that influences the quality of life of people. Despite…
With the intensification of global climate change, accurate prediction of air quality indicators, especially PM2.5 concentration, has become increasingly important in fields such as environmental protection, public health, and urban…
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…
Measuring gases for air quality monitoring is a challenging task that claims a lot of time of observation and large numbers of sensors. The aim of this project is to develop a partially autonomous unmanned aerial vehicle (UAV) equipped with…
With the increase of global economic activities and high energy demand, many countries have raised concerns about air pollution. However, air quality prediction is a challenging issue due to the complex interaction of many factors. In this…
Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent…
Urban air quality is a major concern today. Concentrations of pollutants, such as nitrogen dioxide, must be monitored to ensure that they do not exceed hazardous thresholds. For this reason, scarse reference stations, which are generally…
Low-cost air pollution sensors, offering hyper-local characterization of pollutant concentrations, are becoming increasingly prevalent in environmental and public health research. However, low-cost air pollution data can be noisy, biased by…
Air pollution has long been a serious environmental health challenge, especially in metropolitan cities, where air pollutant concentrations are exacerbated by the street canyon effect and high building density. Whilst accurately monitoring…
The widespread adoption of Internet of Things (IoT) technologies has significantly advanced environmental monitoring (EM) by enabling cost-effective and scalable sensing solutions. Concurrently, machine learning (ML) and artificial…