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Urban environments face two challenging problems that are parallel in nature but yet with compelling potential synergistic interactions; urban heat island (UHI) and air pollution. We explore these interactions using in-situ temperature and air pollution data collected from 13 monitoring stations for nine years. Through regression analysis and analysis of variance (ANOVA) tests, we found that carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and particulate matter (PM) show positive correlations with UHI intensity (UHII). At the same time, Ozone (O 3 ) was negatively correlated with UHII. Moreover, there was a substantial seasonal effect on the strength of the correlations between UHI and air pollution, with some air pollutants showing strong associations with UHI during certain seasons (i.e., winter and autumn). The strongest interactions were observed for NO 2 (R² = 0.176) and PM10 (R² = 0.596) during the wintertime and for SO 2 (R² = 0.849), CO (R² = 0.346), PM2.5   (R² = 0.695) and O 3 (R² = 0.512) during autumn. Understanding such interactions is essential for urban climate studies and our study provides a basis for scientific discussions on integrative mitigation strategies for both UHI and air pollution in Seoul city., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The urban heat island is a vastly documented climatological phenomenon, but when it comes to coastal cities, close to desert areas, its analysis becomes extremely challenging, given the high temporal variability and spatial heterogeneity. The strong dependency on the synoptic weather conditions, rather than on city-specific, constant features, hinders the identification of recurrent patterns, leading conventional predicting algorithms to fail. In this paper, an advanced artificial intelligence technique based on long short-term memory (LSTM) model is applied to gain insight and predict the highly fluctuating heat island intensity (UHII) in the city of Sydney, Australia, governed by the dualistic system of cool sea breeze from the ocean and hot western winds from the vast desert biome inlands. Hourly measurements of temperature, collected for a period of 18 years (1999-2017) from 8 different sites in a 50 km radius from the coastline, were used to train (80%) and test (20%) the model. Other inputs included date, time, and previously computed UHII, feedbacked to the model with an optimized time step of six hours. A second set of models integrated wind speed at the reference station to account for the sea breeze effect. The R 2 ranged between 0.770 and 0.932 for the training dataset and between 0.841 and 0.924 for the testing dataset, with the best performance attained right in correspondence of the city hot spots. Unexpectedly, very little benefit (0.06-0.43%) was achieved by including the sea breeze among the input variables. Overall, this study is insightful of a rather rare climatological case at the watershed between maritime and desertic typicality. We proved that accurate UHII predictions can be achieved by learning from long-term air temperature records, provided that an appropriate predicting architecture is utilized., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier B.V. All rights reserved.)