Air pollution seasons in urban moderate climate areas through big data analytics

Zareba, Mateusz; Weglinska, Elzbieta; Danek, Tomasz

doi:10.1038/s41598-024-52733-w

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Based on this data, the relatively small impact of transportation on the overall PM2.5 trend in Krakow is evident. The trend in PM2.5 concentrations is dominated by the combustion of solid fuels, mainly coal for heating homes and water, and is strongly influenced by annual seasons, as confirmed by various analyses [38] including big data analyses [39]. It should be emphasized that these studies focus solely on the analysis of suspended particulate matter in the air and do not consider other health-detrimental volatile compounds, the occurrence of which may be dominated by transportation.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Techniques for Spatio-Temporal Air Pollution Prediction to Drive Sustainable Urban Development in the Era of Energy and Data Transformation

Zareba,

Cogiel,

Danek

et al. 2024

Energies

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Sustainable urban development in the era of energy and digital transformation is crucial from a societal perspective. Utilizing modern techniques for analyzing large datasets, including machine learning and artificial intelligence, enables a deeper understanding of historical data and the efficient prediction of future events based on data from IoT sensors. This study conducted a multidimensional historical analysis of air pollution to investigate the impacts of energy transformation and environmental policy and to determine the long-term environmental implications of certain actions. Additionally, machine learning (ML) techniques were employed for air pollution prediction, taking spatial factors into account. By utilizing multiple low-cost air sensors categorized as IoT devices, this study incorporated data from various locations and assessed the influence of neighboring sensors on predictions. Different ML approaches were analyzed, including regression models, deep neural networks, and ensemble learning. The possibility of implementing such predictions in publicly accessible IT mobile systems was explored. The research was conducted in Krakow, Poland, a UNESCO-listed city that has had long struggle with air pollution. Krakow is also at the forefront of implementing policies to prohibit the use of solid fuels for heating and establishing clean transport zones. The research showed that population growth within the city does not have a negative impact on PMx concentrations, and transitioning from coal-based to sustainable energy sources emerges as the primary factor in improving air quality, especially for PMx, while the impact of transportation remains less relevant. The best results for predicting rare smog events can be achieved using linear ML models. Implementing actions based on this research can significantly contribute to building a smart city that takes into account the impact of air pollution on quality of life.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Techniques for Spatio-Temporal Air Pollution Prediction to Drive Sustainable Urban Development in the Era of Energy and Data Transformation

Zareba,

Cogiel,

Danek

et al. 2024

Energies

Self Cite

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Analyzing Pocket Park Locations and Pedestrian Accident Rates Using Generative Adversarial Networks

Li,

Gao,

Zheng

et al. 2024

Preprint

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Evaluation of Emission Factors for Particulate Matter and NO2 from Road Transport in Sofia, Bulgaria

Velizarova,

Dimitrova,

Hristov

et al. 2024

Atmosphere

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Traffic-related air pollution has a significant impact on the concentration of particulate matter (PM) and nitrogen oxides (NOx) in urban areas, but there are many uncertainties associated with the modeling of PM concentration due to non-exhaust emissions. Bulgarian weather, road surfaces and traffic conditions differ significantly from the UK’s and other EU countries’ averages, which underpin many assumptions in established models. The hypothesis is that the emission factors differ from those used to calculate traffic emissions using the EMIT model. The objective of this work is to adjust the emissions for PM and the relationship between the fractions of NOx and PM using the hourly mean concentrations from road transport and urban background automatic air quality stations in Sofia, Bulgaria. Various already-published and newly developed methods are applied to local observations to derive functions and relations that better represent Bulgarian road and traffic conditions. The ADMS-Urban model is validated and evaluated by comparing pollutant concentrations from simulations using original and adjusted emissions, showing an improvement in results after applying functions and relationships derived from local observations. This work is part of our efforts to improve air quality modeling in urban areas in Bulgaria.

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Air pollution seasons in urban moderate climate areas through big data analytics

Cited by 5 publications

References 29 publications

Machine Learning Techniques for Spatio-Temporal Air Pollution Prediction to Drive Sustainable Urban Development in the Era of Energy and Data Transformation

Machine Learning Techniques for Spatio-Temporal Air Pollution Prediction to Drive Sustainable Urban Development in the Era of Energy and Data Transformation

Analyzing Pocket Park Locations and Pedestrian Accident Rates Using Generative Adversarial Networks

Evaluation of Emission Factors for Particulate Matter and NO2 from Road Transport in Sofia, Bulgaria

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