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Air Quality and Healthy Ageing: Predictive Modelling of Pollutants using CNN Quantum-LSTM
The concept of healthy ageing is emerging and becoming a norm to achieve a high quality of life, reducing healthcare costs and promoting longevity. Rapid growth in global population and urbanisation requires substantial efforts to ensure healthy and supportive environments to improve the quality of life, closely aligned with the principles of healthy ageing. Access to fundamental resources which include quality healthcare services, clean air, green and blue spaces plays a pivotal role in achieving this goal. Air quality, in particular, is a critical factor in achieving healthy ageing targets. However, it necessitates a global effort to develop and implement policies aimed at reducing air pollution, which has severe implications for human health including cognitive impairment and neurodegenerative diseases, while promoting healthier environments such as high quality green and blue spaces for all age groups. Such actions inevitably depend on the current status of air pollution and better predictive models to mitigate the harmful impact of emissions on planetary health and public health. In this work, we proposed a hybrid model referred as AirVCQnet, which combines the variational mode decomposition (VMD) method with a convolutional neural network (CNN) and a quantum long short-term memory (QLSTM) network for the prediction of air pollutants. The performance of the proposed model is analysed on five key pollutants including fine Particulate Matter PM2.5, Nitrogen Dioxide (NO2), Ozone (O3), PM10, and Sulphur Dioxide (SO2), sourced from air quality monitoring station in Northern Ireland, UK. The effectiveness of the proposed model is evaluated by comparing its performance with its equivalent classical counterpart using root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2). The results demonstrate the superiority of the proposed model, achieving a performance gain of up to 14% and validating its robustness, efficiency and reliability by leveraging t.
IEEE (Institute of Electrical and Electronics Engineers)
2025
Air quality and health for urban influenced populations. Commuting and spatial scale as influences on estimated exposure/health. ETC/ACC Technical paper, 2009/17
2009
2018
2016
In Europe, emissions of many air pollutants have decreased in recent decades, but there exist sites where concentrations of pollutants are still high and have become a public health problem. The air quality monitoring networks include urban stations in big cities and rural background stations. Main pollutants (SO2, NOx, CO, particulate matter) are measured automatically and reported on hourly basis, but there is very few research about air quality in small towns. The small towns are important transport nodes between cities and nowadays they are growing bigger, often being focused on seasonal tourism. In this paper, we try to understand the level of pollution in three small towns in Northern Europe, namely Otepää (Estonia), Lillehammer (Norway) and Saldus (Latvia) This research we point at seasonality of air pollution in towns related with winter sport activities, where the traffic flow increases in cold time simultaneously with heating season and higher prevalence of thermal inversions in atmospheric surface layer. Concentration peak of PM10 in Northern Europe appears in early spring, in snow thawing season and shortly after that. Even higher episodic concentrations may occur near unpaved streets in dry season. High seasonal variation of measured nitrogen dioxide concentrations was found in Lillehammer and Otepää, with remarkable contributions of traffic hotspots. This paper confirms that it is worth to study the air quality in small towns, furthermore, because air pollution levels and related public health concerns in small towns are not negligible.
Springer
2023
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2019
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