Air quality is a highly relevant issue for any developed economy. The high incidence of pollution levels and their impact on human health has attracted the attention of the machine-learning scientific community. We present a study using several machi...
Air quality is crucial for public health and the environment, which makes it important to both monitor and forecast the level of pollution. Polluted air, containing harmful substances such as particulate matter, nitrogen oxides, or ozone, can lead to...
This study addresses the urgent need for advanced digitalization tools in air pollution detection, particularly within resource-constrained municipal settings like those in Ukraine, aligning with directives such as the AAQD. The forecasting informati...
Forecasting air pollutant levels is essential in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter. Focusing the forecast on air pollution peaks is challenging and complex since the pollutant time serie...
Air pollution forecasting is essential for understanding environmental patterns and mitigating health risks, especially in urban areas. This study investigates the forecasting of criterion pollutants—
A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecast...
Exposure to air pollution will pose a serious threat to human health. Accurate air pollution forecasting can help people to reduce exposure risks and promote environmental pollution control, and it is also an extremely important part of smart city ma...
Air pollution, particularly fine (PM2.5) and coarse (PM10) particulate matter, poses significant risks to public health and environmental sustainability. This study aims to develop robust predictive and forecasting models for hourly PM concentrations...
This study explores a hybrid forecasting framework for air pollutant concentrations (PM10, PM2.5, and NO2) that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA) models with Bidirectional Long Short-Term Memory (BiLSTM) networks....
Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic for...
In this study, we evaluated the performance of an air pollution forecasting system during a scientific cruise in the South China Sea (SCS) from 9 August to 7 September 2016. The air pollution forecasting system consisted of a Lagrangian transport and...
The ever-increasing industrialization of certain areas of the planet combined with the simultaneous degradation of the natural environment are alarming phenomena, especially in the field of human health. The concentration of particulate matter with a...
The presence of pollutants in our atmosphere has become one of humanity’s greatest challenges. These pollutants, produced primarily by burning fossil fuels, are detrimental to human health, our climate and agriculture. This work proposes the us...
Since 2013, a series of air pollution prevention and control (APPC) measures have been promulgated in China for reducing the level of air pollution, which can affect regional short-term electricity power demand by changing the behavior of power users...
17 April 2018
Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environm...
Traffic flow prediction is the most critical part of any traffic management system in a smart city. It can help a driver to pick the most optimized way to their target destination. Air pollution data are often connected with traffic congestion and th...
This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory network...
In this study, a novel general multi-step ahead strategy is developed for forecasting time series of air pollutants. The values of the predictors at future moments are gathered from official weather forecast sites as independent ex-ante data. They ar...
Rapid industrialization and urban development are the main causes of air pollution, leading to daily air quality and health problems. To find significant pollutants and forecast their concentrations, in this study, we used a hybrid methodology, inclu...
15 July 2020
Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historic...
Predicting the levels of a pollutant in a given area is an open problem, mainly because historical data are typically available at certain locations, where monitoring stations are located, but not at all locations in the area. This work presents an a...
Agricultural non-point source pollution (AGNPSP) is one of the core challenges facing global water environment management. Existing research mainly focuses on post-event estimation of pollution loads and source analysis, while studies on proactive ri...
Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions...
Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffe...
The paper presents an assessment of thermal energy consumption for heating in 10 buildings made in the OWT-67N prefabricated large-panel technology from 1983 to 1986. The work covers the years 2002–2020 in three periods: before and after therma...
Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on envi...
In today’s urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in citie...
This study aims to evaluate and compare the performance of Autoencoders (AEs) and Sparse Autoencoders (SAEs) in forecasting the next-hour concentration levels of various air pollutants—specifically NO2(t + 1), PM10(t + 1), and SO2(t + 1)—...
The proliferation of pollutants affects the world’s population, mainly those who live in large cities. Neurological and cardiovascular dysfunctions have a correlation with air particulate matter concentration, among other chronic diseases. Ther...
Long-term forecasting and analysis of PM2.5, a significant air pollution source, is vital for environmental governance and sustainable development. We evaluated 10 machine learning and deep learning models using PM2.5 concentration data along with en...
2 July 2021
In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM2.5 is one of the main components of air pollution. Therefore, it is necessary to...
Particular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, l...
Background: Almaty, located in a mountain–valley basin, frequently experiences stagnant conditions that trap pollutants and cause sharp diurnal contrasts in air quality. Current forecasting systems either offer detailed physical realism at high...
Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concen...
Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging...
Accurate prediction, forecasting and interpretability of air pollutant concentrations are important for sustainable environmental management and protecting public health. An integrated artificial intelligence (AI) framework is proposed to predict, fo...
Air pollution causes millions of premature deaths per year due to its strong association with several diseases and respiratory afflictions. Consequently, air quality monitoring and forecasting systems have been deployed in large urban areas. However,...
Air pollution forecasting systems are useful tools for the reduction in human health risks and the eventual improvement of atmospheric quality on regional or urban scales. The SmartAQ (Smart Air Quality) forecasting system combines state-of-the-art m...
Air pollution has a significant impact on human health and the environment, causing cardiovascular disease, respiratory infections, lung cancer and other diseases. Understanding the behavior of air pollutants is essential for adequate decisions that...
The Nobel laureate Niels Bohr once said that: “Predictions are very difficult, especially if they are about the future”. Nonetheless, models that can forecast future COVID-19 outbreaks are receiving special attention by policymakers and h...
Particulate matter (PM) pollution is a major global concern due to its negative impact on human health. To effectively address this issue, it is crucial to have a reliable and efficient forecasting system. In this study, we propose a framework for pr...
The forecasting of river flows and pollutant concentrations is essential in supporting mitigation measures for anthropogenic and climate change effects on rivers and their environment. This paper addresses two aspects receiving little attention in th...
Lockdowns implemented during the COVID-19 pandemic were utilized to evaluate the associations between “social distancing policies” (SDPs), traffic congestion, mobility, and NO2 air pollution. Spatiotemporal linear mixed models were used on city-day d...
Accurate and stable prediction of atmospheric PM2.5 concentrations is crucial for air pollution prevention and control. Existing studies usually rely on a single model or use a single evaluation criterion in multi-model ensemble weighted forecasts, n...
To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management with...
This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city an...
An accurate forecast of fine particulate matter (PM2.5) concentration in the forthcoming days is crucial since it can be used as an early warning for the prevention of general public from hazardous PM2.5 pollution events. Though the European Copernic...
Since 2017, the operational high-resolution air quality forecasting system FORAIR_IT, developed and maintained by the Italian National Agency for New Technologies, Energy and Sustainable Economic Development, has been providing three-day forecasts of...
With increased industrialization and urbanization, sustainable smart environments are becoming more concerned with particulate matter (PM) forecasts that are based on artificial intelligence (AI) techniques. The intercorrelation between multiple poll...
Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of...
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