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2025
2025
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
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Open biomass burning has major impacts globally and regionally on atmospheric composition. Fire emissions include particulate matter, tropospheric ozone precursors, and greenhouse gases, as well as persistent organic pollutants, mercury, and other metals. Fire frequency, intensity, duration, and location are changing as the climate warms, and modelling these fires and their impacts is becoming more and more critical to inform climate adaptation and mitigation, as well as land management. Indeed, the air pollution from fires can reverse the progress made by emission controls on industry and transportation. At the same time, nearly all aspects of fire modelling – such as emissions, plume injection height, long-range transport, and plume chemistry – are highly uncertain. This paper outlines a multi-model, multi-pollutant, multi-regional study to improve the understanding of the uncertainties and variability in fire atmospheric science, models, and fires' impacts, in addition to providing quantitative estimates of the air pollution and radiative impacts of biomass burning. Coordinated under the auspices of the Task Force on Hemispheric Transport of Air Pollution, the international atmospheric modelling and fire science communities are working towards the common goal of improving global fire modelling and using this multi-model experiment to provide estimates of fire pollution for impact studies. This paper outlines the research needs, opportunities, and options for the fire-focused multi-model experiments and provides guidance for these modelling experiments, outputs, and analyses that are to be pursued over the next 3 to 5 years. The paper proposes a plan for delivering specific products at key points over this period to meet important milestones relevant to science and policy audiences.
2025
2025
2025
2025
2025
2025
2025
American Geophysical Union (AGU)
2025
This document provides technical details and support for the implementation of air quality monitoring under the Directive (EU) 2024/2881 of the European Parliament and of the Council of 23 October 2024 on ambient air quality and cleaner air for Europe (recast) (AAQD, Directive (EU) 2024/2881). It presents an overview of current knowledge and best practices, signposting to existing technical guidance on air quality monitoring and to sources of ongoing technical guidance development. This document does not formulate any legal provisions and as such, it does not have a legally binding value.
Publications Office of the European Union/European Commission. Directorate-General for Environment
2025
2025
2025