Surface wind directly affects human life, wind energy utilization, the atmospheric environment, and many other aspects. The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) megalopolis is experiencing an accelerated progress of urbanization, which may result in the change in surface roughness and atmospheric characteristics. In this study, urbanization effects on surface wind speed (SWS) in the GBA megalopolis, particularly Zhuhai, is investigated by using long-term automatic meteorological measurements, ERA5 reanalysis, and nighttime light data. Results of the analysis show that the averaged SWS has decreased significantly at a rate of −0.53 m s⁻¹ per decade over the past decades. With the help of observation-minus-reanalysis (OMR) method, which excludes the atmospheric circulation effects, we found that the decrease in SWS is mainly contributed by the increase in surface roughness, which may account for as much as 75.5% of the decrease. In other words, it is the rapid development of urbanization, rather than the change in large-scale circulation, that could be mainly responsible for the decrease over the GBA in the context of the increasing global SWS since 2010. In addition, a fan-sector method is established to quantitatively analyze the correspondences between urbanization and roughness changes. It is shown that the decrease in wind speed due to surface roughness change is significantly related to the increase in the nighttime light index (NLI) averaged over the 3 km upstream fan-sectors. Moreover, their correlation reaches to 0.36 (negative) when only accounting for the samples of NLI greater than 10. In general, the fan-sector method offers an additional option for assessing the urbanization effects on SWS.

The accuracy in predicting the typhoon track can be key to minimizing their frequent disastrous effects. This article aims to study the accuracy of typhoon trajectory prediction obtained by combining several algorithms based on current deep-learning techniques. The combination of a Convolutional Neural Network with Long Short-Term Memory (CNN+LSTM), Patch Time-Series Transformer (CNN+PatchTST) and Transformer (CNN+Transformer) were the models chosen for this work. These algorithms were tested on the best typhoon track data from the China Meteorological Administration (CMA), ERA5 data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and structured meteorological data from the Zhuhai Meteorological Bureau (ZMB) as an extension of existing studies that were based only on public data sources. The experimental results were obtained by testing two complete years of data (2021 and 2022), as an alternative to the frequent selection of a small number of typhoons in several years. Using the R-squared metric, results were obtained as significant as CNN+LSTM (0.991), CNN+PatchTST (0.989) and CNN+Transformer (0.969). CNN+LSTM without ZMB data can only obtain 0.987, i.e., 0.004 less than 0.991. Overall, our findings indicate that appropriately augmenting data near land and ocean boundaries around the coast improves typhoon track prediction.

In this study, the variations of surface wind speed (SWS) and nighttime light imageries (NLI), a proxy for urbanization, and their correspondences in Guangdong-Hong Kong-Macao Greater Bay Area (GBA) megalopolis, are firstly investigated based on observed measurements and latest reanalysis. The whole GBA presents a significant declining trend of SWS at a rate of 0.24 m s -1 decade -1 in the past decade, accompanied by an obvious increasing trend of NLI. There is a significant correlation (R = -0.81; p < 0.001) between the NLI and SWS averaged over the GBA, however with scattered positive centers, especially over the eastern part of a high level of urbanization. Then, the urbanization process is conceptualized into the Phase I (i.e., density increase) and II (i.e., height increase) for large-eddy simulations (LESs). The correspondences between SWS and buildings configurations might be varied in the different stages of urbanization. In particular, a negative relationship is confirmed in Phase I, but reverses subsequently in Phase II, followed by a surge of SWS. Accordingly, the SWS amplification under the urbanization development is mainly due to the channelized flow and deflected backflow, as well as their interactions.

With the development of the Guangdong-Hong Kong-Macao Greater Bay Area, the air quality of Macao, as an international tourism and leisure center, has drawn increasing attention. Based on the hourly observation data from the Taipa Ambient Air Quality Monitoring Station (2013–2023) in Macao, trend and correlation analyses were conducted to assess the long-term variations of six common air pollutants (O₃, PM₁₀, PM₂.₅, SO₂, NO₂ and CO) and their relationships with meteorological factors. Results show that the concentrations of PM₁₀, PM₂.₅, SO₂, NO₂, and CO have generally declined over the past decade, while the O₃ level has experienced a significant increase since 2016 and showed a marked increase in 2023, indicating that O₃ pollution is replacing primary pollutants as the dominant concern. Seasonal variation characteristics show that the concentrations of all pollutants are the lowest in summer, while the concentration of O₃ peaks in autumn and the concentrations of other pollutants are the highest in winter. Affected by photochemical reactions, O₃ shows a single peak in the afternoon, NO₂ exhibits a typical double-peak feature related to traffic emissions, and the concentrations of PM₁₀, PM₂.₅ and SO₂ are generally higher during the day and lower at night. Correlation analysis reveals a strong positive correlation between PM₂.₅ and PM₁₀, and a significant negative correlation between O₃ and NO₂. Pollutant concentrations show a positive association with air pressure but a negative relationship with relative humidity. Temperature exhibits a positive correlation with O₃ yet is negatively correlated with other pollutants. In contrast, the relationship between pollutants and wind speed remains weak. This study systematically reveals the differences in the temporal evolution of common pollutants in Macao, highlighting that while the overall air quality has improved, the risk of O₃ pollution is increasing significantly.