In this study, an evaluation of the Urban Heat Island (UHI) and Park Cool Island (PCI) intensities in Aracaju, North-Eastern Brazil, was performed. The basis of our evaluation is a 2-year dataset from the urban climatological network installed with the principles and concepts defined for urban areas related to climatic scales, sitting and exposure, urban morphology, and metadata. The current findings update UHI intensities in Aracaju refuting the trend registered in previous studies. On average, the UHI was more intense in the cool season (1.3 °C) than in hot season (0.5 °C), which was caused by wind speed decrease. In relation to the PCI, mitigation of high air temperatures of 1.5–2 °C on average was registered in the city. However, the urban park is not always cooler than the surrounding built environment. Consistent long-term monitoring in the cities is very important to provide more accurate climatic information about the UHI and PCI to be applied in urban planning properly, e.g., to provide pleasant thermal comfort in urban spaces.

The common idea that the sea breeze is a phenomenon that is always beneficial to the thermal from point of view is questioned in this study, since the sea breeze front (SBF) development is related with warmer days in the Sergipe region, located in the northeastern coast of Brazil. For this reason, the first climatology of the SBF and its potential implications on meteorological conditions were presented by using remote sensing (GOES-13 satellite image) and surface meteorological data. Results showed daily performance of the SBF: onset at 12:00 UTC and cessation at 19:00 UTC corresponding to a duration of 7 h, mean strength of 3.6 ms⁻¹, and maximum inland penetration of 94 km from coast. In addition, we also observed SBF passage is related with an average increase in solar radiation (84 W m⁻²), temperature (1 °C), and a decrease in relative humidity (10%) and dew point temperature (1 °C). We hypothesize that, in certain circumstances, the SBF development is not beneficial for human thermal comfort in the Sergipe region.

Characterizing the behaviour of the sea breeze phenomenon is the foremost factor in the reduction in the heat stress and the achievement of the pleasant environment in coastal cities globally. However, this seminal study shows that the Sea Breeze Front (SBF) development can be related to an increase in outdoor thermal discomfort in a northeastern Brazilian city during summer. We explored the relationship between SBF and thermal comfort conditions using in situ meteorological observations, the SBF identification method, local climate zones (LCZs) classification, and the Physiological Equivalent Temperature (PET) thermal comfort index. SBF days and Non-SBF days were characterized in terms of weather conditions, combining meteorological data and technical bulletins. SBF days included hot and sunny days associated with the centre of the Upper Tropospheric Cyclonic Vortices (UTCV). In contrast, Non-SBF days were observed in UTCV’s periphery because of cloudy sky and rainfall. The results showed that the mean temperature and PET in the SBF days were 2.0 °C and 3.8 °C higher, respectively, compared to Non-SBF days in all LCZ sites. The highest PET, of 40.0 °C, was found on SBF days. Our findings suggest that SBF development could be an aggravating factor for increasing heat stress of the people living in the northeastern coast of the Brazilian city, after SBF passage.

Currently, cities have aroused the interest of researchers due the local climate change caused by the surface urban heat island (SUHI) effect. The impact of anthropogenic land use and cover changes has led to more frequent intense SUHI, with direct consequences on urban quality of life. Therefore, this research aims at analyzing the influences of natural and anthropogenic variables on the seasonality and spatial SUHI intensity in a Brazilian city, using remote sensing data and analysis of several physical parameters. Results show that the city of São Carlos has an SUHI mosaic and surface urban freshness island (SUFI). On average, 86% of the urban area presented a SUHI, whilst most SUFIs are located near watercourses, parks, slopes and valley bottoms, revealing the effects of green areas and relief on creation of microclimates. The SUHI showed significant seasonal variability.

Artificial Intelligence (AI) tools based on Machine learning (ML) have demonstrated their potential in modeling climate-related phenomena. However, their application to quantifying greenhouse gas emissions in cities remains under-researched. Here, we introduce a ML-based bottom-up framework to predict hourly CO₂ emissions from vehicular traffic at fine spatial resolution (30 × 30 m). Using data-driven algorithms, traffic counts, spatio-temporal features, and meteorological data, our model predicted hourly traffic flow, average speed, and CO₂ emissions for passenger cars (PC) and heavy-duty trucks (HDT) at the street scale in Berlin. Even with limited traffic information, the model effectively generalized to new road segments. For PC, the Relative Mean Difference (RMD) was +16% on average. For HDT, RMD was 19% for traffic flow and 2.6% for average speed. We modeled seven years of hourly CO₂ emissions from 2015 to 2022 and identified major highways as hotspots for PC emissions, with peak values reaching 1.639 kgCO₂ m⁻² d⁻¹. We also analyzed the impact of COVID-19 lockdown and individual policy stringency on traffic CO₂ emissions. During the lockdown period (March 15 to 1 June 2020), weekend emissions dropped substantially by 25% (−18.3 tCO₂ day⁻¹), with stay-at-home requirements, workplace closures, and school closures contributing significantly to this reduction. The continuation of these measures resulted in sustained reductions in traffic flow and CO₂ emissions throughout 2020 and 2022. These results highlight the effectiveness of ML models in quantifying vehicle traffic CO₂ emissions at a high spatial resolution. Our ML-based bottom-up approach offers a useful tool for urban climate research, especially in areas lacking detailed CO₂ emissions data.