Search Results (189)

Air quality forecasts play a crucial role in informing the public about atmospheric pollutant levels that pose risks to human health and the environment. The accuracy of these forecasts strongly depends on the quality and resolution of the input data used in the modelling process. At HungaroMet, the Hungarian Meteorological Service, the CHIMERE chemical transport model is used to provide two-day air quality forecasts for the territory of Hungary. This study compares two configurations of the CHIMERE model: the current operational setup, which uses climatological averages from the LMDz-INCA database for boundary conditions, and a test configuration that incorporates real-time boundary conditions from the CAMS global forecast. The primary objective of this work was to assess how the use of real-time versus climatological boundary conditions affects modelled concentrations of key pollutants, including NO₂, O₃, PM₁₀, and PM_2.5. The model results were evaluated against observational data from the Hungarian Air Quality Monitoring Network using a range of statistical metrics. The results indicate that the use of real-time boundary conditions, particularly for aerosol-type pollutants, improves the accuracy of PM₁₀ forecasts. This improvement is most significant under meteorological conditions that favour the long-range transport of particulate matter, such as during Saharan dust or wildfire episodes. These findings highlight the importance of incorporating dynamic, up-to-date boundary data, especially for particulate matter forecasting—given the increasing frequency of transboundary dust events. Full article

Accurately identifying the sources of fine particulate matter (PM_2.5) pollution is crucial for pollution control and public health protection. Taking the PM_2.5 pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed the characteristics and components of PM_2.5, and quantified the contributions of meteorological conditions, regional transport, and local emissions to the summertime PM_2.5 surge in a typical Yangtze River Delta (YRD) city. Chemical composition analysis highlighted a sharp increase in nitrate ions (NO₃⁻, contributing up to 49% during peak pollution), with calcium ion (Ca²⁺) and sulfate ion (SO₄²⁻) concentrations rising to 2 times and 7.5 times those of clean periods, respectively. Results from the random forest model demonstrated that emission sources (74%) dominated this pollution episode, significantly surpassing the meteorological contribution (26%). The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) further revealed that local emissions contributed the most to PM_2.5 concentrations in Suzhou (46.3%), while external transport primarily originated from upwind cities such as Shanghai and Jiaxing. The findings indicate synergistic effects from dust sources, industrial emissions, and mobile sources. Validation using electricity consumption and key enterprise emission data confirmed that intensive local industrial activities exacerbated PM_2.5 accumulation. Recommendations include strengthening regulations on local industrial and mobile source emissions, and enhancing regional joint prevention and control mechanisms to mitigate cross-boundary transport impacts. Full article

Particulate matter 2.5 (PM_2.5) pollution poses severe threats to public health, ecosystems, and urban sustainability. With increasing industrialization and urban sprawl, accurate pollutant monitoring and effective mitigation of PM_2.5 have become global priorities. Recent advancements in machine learning (ML) have revolutionized PM_2.5 sensing by enabling high-accuracy predictions, and scalable solutions through data-driven approaches. Meanwhile, sustainable green technologies—such as urban greening, phytoremediation, and smart air purification systems—offer eco-friendly, long-term strategies to reduce PM_2.5 levels. This review, covering research publications from 2021 to 2025, systematically explores the integration of ML models with conventional sensor networks to enhance pollution forecasting, pollutant source attribution, and intelligent pollutant monitoring. The paper also highlights the convergence of ML and green technologies, including nature-based solutions and AI-driven environmental planning, to support comprehensive air quality management. In addition, the study critically examines integrated policy frameworks and lifecycle-based assessments that enable equitable, sector-specific mitigation strategies across industrial, transportation, energy, and urban planning domains. By bridging the gap between cutting-edge technology and sustainable practices, this study provides a comprehensive roadmap for researchers to combat PM_2.5 pollution. Full article

Air pollution in urban areas has increased significantly over the past few years due to industrialization and population increase. Therefore, accurate predictions are needed to minimize their impact. This paper presents a neural network-based examination for forecasting Air Quality Index (AQI) values, employing two different models: a variable-depth neural network (NN) called slideNN, and a Gated Recurrent Unit (GRU) model. Both models used past particulate matter measurements alongside local meteorological data as inputs. The slideNN variable-depth architecture consists of a set of independent neural network models, referred to as strands. Similarly, the GRU model comprises a set of independent GRU models with varying numbers of cells. Finally, both models were combined to provide a hybrid cloud-based model. This research examined the practical application of multi-strand neural networks and multi-cell recurrent neural networks in air quality forecasting, offering a hands-on case study and model evaluation for the city of Ioannina, Greece. Experimental results show that the GRU model consistently outperforms the slideNN model in terms of forecasting losses. In contrast, the hybrid GRU-NN model outperforms both GRU and slideNN, capturing additional localized information that can be exploited by combining particle concentration and microclimate monitoring services. Full article

This paper presents a novel approach for predicting Particulate Matter (PM) concentrations using mobile camera devices. In response to persistent air pollution challenges across Japan, we developed a system that utilizes cutting-edge transformer-based deep learning architectures to estimate PM values from imagery captured by smartphone cameras. Our approach employs Contrastive Language–Image Pre-Training (CLIP) as a multimodal framework to extract visual features associated with PM concentration from environmental scenes. We first developed a baseline through comparative analysis of time-series models for 1D PM signal prediction, finding that linear models, particularly NLinear, outperformed complex transformer architectures for short-term forecasting tasks. Building on these insights, we implemented a CLIP-based system for 2D image analysis that achieved a Top-1 accuracy of 0.24 and a Top-5 accuracy of 0.52 when tested on diverse smartphone-captured images. The performance evaluations on Graphics Processing Unit (GPU) and Single-Board Computer (SBC) platforms highlight a viable path toward edge deployment. Processing times of 0.29 s per image on the GPU versus 2.68 s on the SBC demonstrate the potential for scalable, real-time environmental monitoring. We consider that this research connects high-performance computing with energy-efficient hardware solutions, creating a practical framework for distributed environmental monitoring that reduces reliance on costly centralized monitoring systems. Our findings indicate that transformer-based multimodal models present a promising approach for mobile sensing applications, with opportunities for further improvement through seasonal data expansion and architectural refinements. Full article

As road traffic in Turkey is a significant source of emissions due to the increasing number of vehicles on the road, the goal of this study is to calculate greenhouse gas emissions from Turkey’s roads between 2010 and 2020, create an inventory, and estimate possible emissions until 2050. In the study, both greenhouse gases (carbon dioxide (CO₂) and nitrous oxide (N₂O) and co-emitting air pollutants that indirectly contribute to climate change (ammonia—NH₃, nitrogen oxide—NO_X, sulfur dioxide—SO₂, carbon monoxide—CO, non-methane volatile organic compounds—NMVOC, and particulate matter—PM) were investigated. The study revealed that the total number of vehicles using state roads in Turkey increased by 60% between 2010 and 2020. As a result, emissions of CO₂, N₂O, NH₃, NO_X, SO₂, CO, NMVOC, and PM increased by 29.6%, 24.2%, 0.5%, 19.9%, 9.9%, 18.2%, 21.5%, and 39.7%, respectively. When emissions were analyzed on a provincial basis, particular attention was drawn to provinces with high levels of urbanization. Based on forecast studies, the total number of vehicles registered for traffic will increase by 105% by 2050. Due to this increase, CO₂, N₂O, NH₃, NO_X, SO₂, CO, NMVOC, and PM emissions are estimated to increase by 149.17%, 151.78%, 154.39%, 138.95%, 150.97%, 153.09%, 152.09%, and 151.47%, respectively. Full article

This study aims to optimize daily forecasts of the PM2.5 concentrations in Dakar, Senegal using a long short-term memory (LSTM) neural network model. Particulate matter, aggravated by factors such as dust, traffic, and industrialization, poses a serious threat to public health, especially in developing countries. Existing models such as the Autoregressive integrated moving average (ARIMA) have limitations in capturing nonlinear relationships and complex dynamics in environmental data. Using four years of daily data collected at the Bel Air station, this study shows that the LSTM neural network model provides more accurate forecasts with a root mean square error (RMSE) of 3.2 μg/m³, whereas the RMSE for ARIMA is about 6.8 μg/m³. The LSTM model predicts reliably up to 7 days in advance, accurately reproducing extreme values, especially during dust event outbreaks and peak travel periods. Computational analysis shows that using Graphical Processing Unit and Tensor Processing Unit processors significantly reduce the execution time, improving the model efficiency while maintaining high accuracy. Overall, these results highlight the usefulness of the LSTM network for air quality prediction and its potential for public health management in Dakar. Full article

Particulate matter is a mixture of fine dust and tiny droplets of liquid suspended in the air. PM₁₀ is a pollutant composed of particles smaller than 10 µm. These particles are harmful to the respiratory system. The air quality in the region and capital Lima in the Republic of Peru has been investigated in recent years. In this context, statistical analyses of PM₁₀ data with forecast models can contribute to planning actions that can improve air quality. The objective of this work is to perform a statistical analysis of the available PM₁₀ data and evaluate the quality of time series classical models and neural networks for short-term forecasting. This study demonstrates that classical time series models, particularly ARIMA and SSA, achieve lower average forecast errors than LSTM across stations SMP, CRB, and ATE. This finding suggests that for data with seasonal patterns and relatively short time series, traditional models may be more efficient and robust. Although neural networks have the potential to capture more complex relationships and long-term dependencies, their performance may be limited by hyperparameter settings and intrinsic data characteristics. Full article

Air pollution, particularly fine particulate matter (PM_2.5), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (CMAQ) model for PM_2.5 forecasting. However, these models exhibit inherent uncertainties due to limitations in emission inventories, meteorological inputs, and model frameworks. To address these challenges, this study evaluates and compares the forecasting performance of two alternative models: Long Short-Term Memory (LSTM), a deep learning model, and Seasonal Auto Regressive Integrated Moving Average with Exogenous Variables (SARIMAX), a statistical model. The performance evaluation was focused on Seoul, South Korea, and took place over different forecast lead times (D00–D02). The results indicate that for short-term forecasts (D00), SARIMAX outperformed LSTM in all statistical metrics, particularly in detecting high PM_2.5 concentrations, with a 19.43% higher Probability of Detection (POD). However, SARIMAX exhibited a sharp performance decline in extended forecasts (D01–D02). In contrast, LSTM demonstrated relatively stable accuracy over longer lead times, effectively capturing complex PM_2.5 concentration patterns, particularly during high-concentration episodes. These findings highlight the strengths and limitations of statistical and deep learning models. While SARIMAX excels in short-term forecasting with limited training data, LSTM proves advantageous for long-term forecasting, benefiting from its ability to learn complex temporal patterns from historical data. The results suggest that an integrated air quality forecasting system combining numerical, statistical, and machine learning approaches could enhance PM_2.5 forecasting accuracy. Full article

This study presents an improved deep-learning model, termed Enhanced Time Series Mixer (E-TSMixer), for the prediction of particulate matter. By analyzing the temporal evolution of PM_2.5 concentrations from multivariate monitoring data, the model demonstrates significant prediction capabilities while maintaining consistency with observed pollutant transport characteristics in the urban boundary layer. In E-TSMixer, a fully connected output layer is proposed to enhance the predictive capability for complex spatiotemporal dependencies. The relevant data on air quality and traffic flow are fused to achieve high-precision predictions of PM_2.5 concentrations through a multivariate time-series forecasting model. An asymmetric penalty mechanism is added to dynamically optimize the loss function. Experimental results indicate that the proposed E-TSMixer model achieves higher accuracy for the prediction of PM_2.5, which significantly outperforms the traditional models. Additionally, an intelligent dual regulation of fixed and dynamic threshold model is introduced and combined with E-TSMixer for the decision-making model of the real-time adjustments of the frequency, routes, and timing of water truck operation in practice. Full article

The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report on Particulate Matter (PM) concentrations. This system leverages diverse low-cost PM sensors, enhanced by machine learning for sensor calibration, with LoRaWAN connectivity for long-range data transmission. Sensors are GPS-enabled, allowing precise geospatial mapping of collected data, which can be integrated with urban air quality forecasting models and operational forecasting systems. To achieve energy self-sufficiency, the system uses a small-scale solar-powered solution, allowing it to operate independently from the grid, making it both cost-effective and suitable for remote locations. This novel approach leverages multiple operational modes based on power availability to optimize energy efficiency and prevent downtime. By dynamically adjusting system behavior according to power conditions, it ensures continuous operation while conserving energy during periods of reduced supply. This innovative strategy significantly enhances performance and resource management, improving system reliability and sustainability. This IoT network provides localized real-time air quality data, which has significant public health benefits, especially for vulnerable populations in densely populated urban environments. The project demonstrates the synergy between IoT sensor data, machine learning-enhanced calibration, and forecasting methods, contributing to scientific understanding of microenvironments, human exposure, and public health impacts of urban air quality. In addition, this study emphasizes open source design principles, promoting transparency, data quality, and reproducibility by exploring cost-effective sensor calibration techniques and adhering to open data standards. The next iteration of the sensors will include edge processing for short-term air quality forecasts. This work underscores the transformative role of low-cost sensor networks in urban air quality monitoring, advancing equitable policy development and empowering communities to address pollution challenges. Full article

This study applied various machine learning techniques, including shrinkage methods, XGBoost, CSR, and random forest, to forecast ultrafine particulate matter (PM2.5) concentrations in Seoul, South Korea. The analysis incorporated key variables known to significantly influence PM2.5 levels, including meteorological data, coal-fired power generation, and PM2.5 concentrations in Dalian, China. Using daily data from 1 January 2018 to 30 June 2023, this study employed the Boruta algorithm, a variable selection technique based on the random forest model, to identify the most influential predictors for predicting PM2.5 concentrations. Out-of-sample multi-period forecasts were evaluated for each model using the RMSE, MAE, and Giacomini–White test to determine the most effective forecasting approach. It was found that the random forest model with the Boruta algorithm outperformed all other models, achieving improvements of 4% to 17% in the RMSE and 4% to 16.5% in the MAE across all forecast horizons. The results indicate that the random forest model and its variant incorporating the Boruta algorithm provided superior short-term forecasting performance. In particular, the Boruta algorithm highlighted the lagged variables of temperature, PM2.5 concentration, mean humidity, and Dalian PM2.5 concentration as critical factors for the accurate prediction of PM2.5 levels in Seoul. These findings underscore the utility of data-driven approaches to improve air quality forecasting and management. Full article

Breathing in fine particulate matter of diameter less than 2.5 µm (PM_2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM_2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m³ and 35.5 µg/m³) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m³ threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R² of 0.914, particularly excelling in forecasting high PM_2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM_2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins. Full article

Forecasting particulate matter with a diameter of 2.5 μm (PM_2.5) is critical due to its significant effects on both human health and the environment. While ground-based pollution measurement stations provide highly accurate PM_2.5 data, their limited number and geographic coverage present significant challenges. Recently, the use of aerosol optical depth (AOD) has emerged as a viable alternative for estimating PM_2.5 levels, offering a broader spatial coverage and higher resolution. Concurrently, long short-term memory (LSTM) models have shown considerable promise in enhancing air quality predictions, often outperforming other prediction techniques. To address these challenges, this study leverages geographic information systems (GIS), remote sensing (RS), and a hybrid LSTM architecture to predict PM_2.5 concentrations. Training LSTM models, however, is an NP-hard problem, with gradient-based methods facing limitations such as getting trapped in local minima, high computational costs, and the need for continuous objective functions. To overcome these issues, we propose integrating the novel orchard algorithm (OA) with LSTM to optimize air pollution forecasting. This paper utilizes meteorological data, topographical features, PM_2.5 pollution levels, and satellite imagery from the city of Tehran. Data preparation processes include noise reduction, spatial interpolation, and addressing missing data. The performance of the proposed OA-LSTM model is compared to five advanced machine learning (ML) algorithms. The proposed OA-LSTM model achieved the lowest root mean square error (RMSE) value of 3.01 µg/m³ and the highest coefficient of determination (R²) value of 0.88, underscoring its effectiveness compared to other models. This paper employs a binary OA method for sensitivity analysis, optimizing feature selection by minimizing prediction error while retaining critical predictors through a penalty-based objective function. The generated maps reveal higher PM_2.5 concentrations in autumn and winter compared to spring and summer, with northern and central areas showing the highest pollution levels. Full article

High dust loading significantly impacts air quality, climate, and public health. Early warning is crucial for mitigating short-term effects, and accurate dust field estimates are needed for forecasting. The Copernicus Atmosphere Monitoring Service (CAMS) offers global reanalysis datasets and forecasts of particulate matter with a diameter of under 10 μm (PM₁₀), which approximate dust, but recent studies highlight discrepancies between CAMS data and ground in-situ measurements. Since CAMS is often used for forecasting, errors in PM₁₀ fields can hinder accurate dust event forecasts, which is particularly challenging for models that use artificial intelligence (AI) due to the scarcity of dust events and limited training data. This study proposes a machine-learning approach to correct CAMS PM₁₀ fields using in-situ data to enhance AI-based dust event forecasting. A correction model that links pixel-wise errors with atmospheric and meteorological variables was taught using gradient-boosting algorithms. This model is then utilized to predict CAMS error in previously unobserved pixels across the Eastern Mediterranean, generating CAMS error fields. Our bias-corrected PM₁₀ fields are, on average, 12 μg m⁻³ more accurate, often reducing CAMS errors by significant percentages. To evaluate the contribution, we train a deep neural network to predict city-scale dust events (0–72 h) over the Balkans using PM₁₀ fields. Comparing the network’s performance when trained on both original and bias-corrected CAMS PM₁₀ fields, we show that the correction improves AI-based forecasting performance across all metrics. Full article