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<jats:p>To comply with the United Nations Sustainable Development Goals (UN SDGs), in particular with SDG 3, SDG 11, and SDG 13, a reliable air pollution prediction model must be developed to construct a sustainable, safe, and resilient city and mitigate climate change for a double win. Machine learning (ML) and deep learning (DL) models have been applied to datasets in Macau to predict the daily levels of roadside air pollution in the Macau peninsula, situated near the historical sites of Macau. Macau welcomed over 28 million tourists in 2023 as a popular tourism destination. Still, an accurate air quality forecast has not been in place for many years due to the lack of a reliable emission inventory. This work will develop a dependable air pollution prediction model for Macau, which is also the novelty of this study. The methods, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), were applied and successful in the prediction of daily air pollution levels in Macau. The prediction model was trained using the air quality and meteorological data from 2013 to 2019 and validated using the data from 2020 to 2021. The model performance was evaluated based on the root mean square error (RMSE), mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and Kendall’s tau coefficient (KTC). The RF model best predicted PM10, PM2.5, NO2, and CO concentrations with the highest PCC and KTC in a daily air pollution prediction. In addition, the SVR model had the best stability and repeatability compared to other models, with the lowest SD in RMSE, MAE, PCC, and KTC after five model runs. Therefore, the results of this study show that the RF model is more efficient and performs better than other models in the prediction of air pollution for the dataset of Macau.</jats:p>
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The convergence of air pollution control and climate change mitigation is critical in the pursuit of sustainable development. Therefore, technological innovations are pivotal in addressing the dual challenges of air pollution and global warming. This work presents an overview of technological solutions aimed at reducing air pollution and mitigating GHG emissions. While evaluating their technological strengths and limitations in real applications, this work offers a framework to promote a transition toward blue skies and net-zero emissions. This work also identifies the main sources and negative impacts of air pollution on public health and the environment. A literature overview of published articles from 1976 to 2024 showed that integrating emission reduction technologies are vital in real-word applications. More than 98% of the SO2 in the flue gas can be removed using cutting-edge desulfurization technology. SO2 is eliminated from the environment either unaltered or as sulfuric acid and sulfates. Meanwhile, thermal incinerators boast an impressive efficiency, capable of eliminating 99% of gaseous pollutants. Although existing pollution control technologies are promising to mitigate climate change, they still require further research, development, demonstration, and deployment to overcome barriers and achieve their potential. By examining the effectiveness of control technologies and proposing adaptable strategies, this work highlights the potential of integrating air quality improvement efforts with climate actions. Not only this addresses the global need for cleaner air, but also contributes to the overarching goal of climate stabilization. © The Author(s), under exclusive licence to the Institute of Chemistry, Slovak Academy of Sciences 2024.
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<jats:p>Rapid urbanization and changing climatic procedures can activate the present surface urban heat island (SUHI) effect. An SUHI was considered by temperature alterations among urban and rural surroundings. The urban zones were frequently warmer than the rural regions because of population pressure, urbanization, vegetation insufficiency, industrialization, and transportation systems. This investigation analyses the Surface-UHI (SUHI) influence in Kolkata Municipal Corporation (KMC), India. Growing land surface temperature (LST) may cause an SUHI and impact ecological conditions in urban regions. The urban thermal field variation index (UTFVI) served as a qualitative and quantitative barrier to the SUHI susceptibility. The maximum likelihood approach was used in conjunction with supervised classification techniques to identify variations in land use and land cover (LULC) over a chosen year. The outcomes designated a reduction of around 1354.86 Ha, 653.31 Ha, 2286.9 Ha, and 434.16 Ha for vegetation, bare land, grassland, and water bodies, correspondingly. Temporarily, from the years 1991–2021, the built-up area increased by 4729.23 Ha. The highest LST increased by around 7.72 °C, while the lowest LST increased by around 5.81 °C from 1991 to 2021. The vegetation index and LST showed a negative link, according to the correlation analyses; however, the built-up index showed an experimentally measured positive correlation. This inquiry will compel the administration, urban planners, and stakeholders to observe humanistic activities and thus confirm sustainable urban expansion.</jats:p>