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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.
2025
Climatic feedbacks and ecosystem impacts related to dust in the Arctic include direct radiative forcing (absorption and scattering), indirect radiative forcing (via clouds and cryosphere), semi-direct effects of dust on meteorological parameters, effects on atmospheric chemistry, as well as impacts on terrestrial, marine, freshwater, and cryospheric ecosystems. This review discusses our recent understanding on dust emissions and their long-range transport routes, deposition, and ecosystem effects in the Arctic. Furthermore, it demonstrates feedback mechanisms and interactions between climate change, atmospheric dust, and Arctic ecosystems.
2025
Global greenhouse gas reconciliation 2022
n this study, we provide an update on the methodology and data used by Deng et al. (2022) to compare the national greenhouse gas inventories (NGHGIs) and atmospheric inversion model ensembles contributed by international research teams coordinated by the Global Carbon Project. The comparison framework uses transparent processing of the net ecosystem exchange fluxes of carbon dioxide (CO2) from inversions to provide estimates of terrestrial carbon stock changes over managed land that can be used to evaluate NGHGIs. For methane (CH4), and nitrous oxide (N2O), we separate anthropogenic emissions from natural sources based directly on the inversion results to make them compatible with NGHGIs. Our global harmonized NGHGI database was updated with inventory data until February 2023 by compiling data from periodical United Nations Framework Convention on Climate Change (UNFCCC) inventories by Annex I countries and sporadic and less detailed emissions reports by non-Annex I countries given by national communications and biennial update reports. For the inversion data, we used an ensemble of 22 global inversions produced for the most recent assessments of the global budgets of CO2, CH4, and N2O coordinated by the Global Carbon Project with ancillary data. The CO2 inversion ensemble in this study goes through 2021, building on our previous report from 1990 to 2019, and includes three new satellite inversions compared to the previous study and an improved managed-land mask. As a result, although significant differences exist between the CO2 inversion estimates, both satellite and in situ inversions over managed lands indicate that Russia and Canada had a larger land carbon sink in recent years than reported in their NGHGIs, while the NGHGIs reported a significant upward trend of carbon sink in Russia but a downward trend in Canada. For CH4 and N2O, the results of the new inversion ensembles are extended to 2020. Rapid increases in anthropogenic CH4 emissions were observed in developing countries, with varying levels of agreement between NGHGIs and inversion results, while developed countries showed a slowly declining or stable trend in emissions. Much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites, coordinated into a global constellation, is expected in the coming years. The methodology proposed here to compare inversion results with NGHGIs can be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objectives of their pledges. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.13887128 (Deng et al., 2024).
2025
Road traffic externalities are an important consequence of land-use and transport interactions and may be especially induced by their inefficient combinations. In this study, we integrate land-use, transport and emission modelling tools (the LUTEm framework) to assess how suburban expansion vs. inward densification scenarios influence journey parameters, road network performance and traffic emissions. Case-study simulations for Warsaw (Poland) underscore the negative consequences of suburban sprawl development, which are hardly mitigated by additional land-use or transport interventions, such as rebalancing of population-workplace distribution or road capacity reductions. On the other side, compact city development lowers global traffic congestion and emissions, but can also raise the risks of traffic externalities in central city area unless complemented with further interventions such as improved public transport attractiveness. This study aims to enrich the understanding of how integrating the land-use development and transport interventions can ultimately influence travel parameters and reduce urban road traffic externalities.
2025
Poor Indoor Environmental Quality (IEQ) in schools significantly impacts students’ well-being, learning capabilities, and health. Perceived dissatisfaction rates (PD%) among students often remain high, even when indoor environmental variables appear well-controlled. This study aims to predict perceived dissatisfaction rates (PD%) across multi-domain environmental factors—thermal, acoustic, visual, and indoor air quality (IAQ)—using machine learning (ML) models. The research integrates sensor-based environmental measurements, outdoor weather data, building parameters, and 1437 student survey responses collected from three classrooms in a Norwegian school across multiple seasons. Statistical tests were used to pre-select relevant input variables, followed by the development and evaluation of multiple ML algorithms. Among the tested ML models, Random Forest (RF) demonstrated the highest predictive accuracy for PD%, outperforming multi-linear regression (MLR) and decision trees (DT), with R² values up to 0.91 for overall IEQ dissatisfaction (PDIEQ%). SHAP analysis revealed key predictors: CO₂ levels, VOCs, humidity, temperature, solar radiation, and room window orientation. IAQ, thermal comfort, and acoustic environment were the most influential factors affecting students' perceived well-being. Despite limitations as implementation in building level scale, the study demonstrates the feasibility of deploying predictive ML models under real-world constraints for improving IEQ monitoring system. The findings support practical strategies for adaptive indoor environmental management, particularly in educational settings, and provide a replicable framework for future research. Future research can expand to other climates, buildings, measurements, occupant levels, and ML training optimization.
2025
Carbonaceous aerosols (CA), composed of black carbon (BC) and organic matter (OM), significantly impact the climate. Light absorption properties of CA, particularly of BC and brown carbon (BrC), are crucial due to their contribution to global and regional warming. We present the absorption properties of BC (bAbs,BC) and BrC (bAbs,BrC) inferred using Aethalometer data from 44 European sites covering different environments (traffic (TR), urban (UB), suburban (SUB), regional background (RB) and mountain (M)). Absorption coefficients showed a clear relationship with station setting decreasing as follows: TR > UB > SUB > RB > M, with exceptions. The contribution of bAbs,BrC to total absorption (bAbs), i.e. %AbsBrC, was lower at traffic sites (11–20 %), exceeding 30 % at some SUB and RB sites. Low AAE values were observed at TR sites, due to the dominance of internal combustion emissions, and at some remote RB/M sites, likely due to the lack of proximity to BrC sources, insufficient secondary processes generating BrC or the effect of photobleaching during transport. Higher bAbs and AAE were observed in Central/Eastern Europe compared to Western/Northern Europe, due to higher coal and biomass burning emissions in the east. Seasonal analysis showed increased bAbs, bAbs,BC, bAbs,BrC in winter, with stronger %AbsBrC, leading to higher AAE. Diel cycles of bAbs,BC peaked during morning and evening rush hours, whereas bAbs,BrC, %AbsBrC, AAE, and AAEBrC peaked at night when emissions from household activities accumulated. Decade-long trends analyses demonstrated a decrease in bAbs, due to reduction of BC emissions, while bAbs,BrC and AAE increased, suggesting a shift in CA composition, with a relative increase in BrC over BC. This study provides a unique dataset to assess the BrC effects on climate and confirms that BrC can contribute significantly to UV–VIS radiation presenting highly variable absorption properties in Europe.
2025
2025
Indian Land Carbon Sink Estimated from Surface and GOSAT Observations
The carbon sink over land plays a key role in the mitigation of climate change by removing carbon dioxide (CO2) from the atmosphere. Accurately assessing the land sink capacity across regions should contribute to better future climate projections and help guide the mitigation of global emissions towards the Paris Agreement. This study estimates terrestrial CO2 fluxes over India using a high-resolution global inverse model that assimilates surface observations from the global observation network and the Indian subcontinent, airborne sampling from Brazil, and data from the Greenhouse gas Observing SATellite (GOSAT) satellite. The inverse model optimizes terrestrial biosphere fluxes and ocean-atmosphere CO2 exchanges independently, and it obtains CO2 fluxes over large land and ocean regions that are comparable to a multi-model estimate from a previous model intercomparison study. The sensitivity of optimized fluxes to the weights of the GOSAT satellite data and regional surface station data in the inverse calculations is also examined. It was found that the carbon sink over the South Asian region is reduced when the weight of the GOSAT data is reduced along with a stricter data filtering. Over India, our result shows a carbon sink of 0.040 ± 0.133 PgC yr−1 using both GOSAT and global surface data, while the sink increases to 0.147 ± 0.094 PgC yr−1 by adding data from the Indian subcontinent. This demonstrates that surface observations from the Indian subcontinent provide a significant additional constraint on the flux estimates, suggesting an increased sink over the region. Thus, this study highlights the importance of Indian sub-continental measurements in estimating the terrestrial CO2 fluxes over India. Additionally, the findings suggest that obtaining robust estimates solely using the GOSAT satellite data could be challenging since the GOSAT satellite data yield significantly varies over seasons, particularly with increased rain and cloud frequency.
2025
The complex and dynamic nature of airborne fine particulate matter (PM2.5) has hindered understanding of its chemical composition, sources, and toxic effects. In the first steps of a larger study, here, we aimed to elucidate relationships between source regions, ambient conditions, and the chemical composition in water extracts of PM2.5 samples (n = 85) collected over 16 months at an observatory in the Yellow Sea. In each extract, we quantified elements and major ions and profiled the complex mixtures of organic compounds by nontarget mass spectrometry. More than 50,000 nontarget features were detected, and by consensus of in silico tools, we assigned a molecular formula to 13,907 features. Oxygenated compounds were most prominent, followed by mixed nitrogenated/oxygenated compounds, organic sulfates, and sulfonates. Spectral matching enabled identification or structural annotation of 43 substances, and a workflow involving SIRIUS and MS-DIAL software enabled annotation of 74 unknown per- and polyfluoroalkyl substances with primary source regions in China and the Korean Peninsula. Multivariate modeling revealed seasonal variations in chemistry, attributable to the combination of warmer temperatures and maritime source regions in summer and to cooler temperatures and source regions of China in winter.
2025