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Based on the 19 year observation from 1998 to 2016 at the Tsuan Wan and Central/Western District monitoring stations in Hong Kong, the aim of this paper was to assess the wet deposition pathway of Benzo(a)pyrene (BaP) on a large time-scale. In order to achieve this goal, multi-fractal detrended cross-correlation analysis (MF-DCCA) was used to characterize the long-term cross-correlations behaviors and multi-fractal temporal scaling properties between BaP (or PM2.5) and precipitation. The results showed that the relationships between BaP and precipitation (or PM2.5) displayed long-term cross-correlation at the time-scale ranging from one month to one year; no cross-correlation between each other was observed in longer temporal scaling regimes (greater than one year). These results correspond to the atmospheric circulation of the Asian monsoon system and are explained in detail. Similar dynamic processes of the wet deposition of BaP and PM2.5 suggested that the main removal process of atmospheric BaP was rainfall deposits of PM2.5-bound BaP. Furthermore, cross-correlations between BaP (or PM2.5) and precipitation at the long time-scale have a multi-fractal nature and long-term persistent power-law decaying behavior. The temporal evolutions of the multi-fractality were investigated by the approach of a sliding window. Based on the evolution curves of multi-fractal parameters, the wet deposition pathway of PM2.5-bound BaP is discussed. Finally, the contribution degree of wet deposition to PM2.5-bound BaP was derived from the coefficient of determination. It was demonstrated that about 45% and 60% of atmospheric BaP removal can be attributed to the wet deposition pathway of PM2.5-bound BaP for the Tsuan Wan and Central/Western District areas, respectively. The findings in this paper are of great significance for further study on the removal mechanism of atmospheric BaP in the future. The MF-DCCA method provides a novel approach to assessing the geochemical cycle dynamics of BaP. [ABSTRACT FROM AUTHOR]

The spatiotemporal distributions and physical–optical properties of aerosols are of great scientific significance for the study of climate change and atmospheric environment. What are the characteristics of aerosols in constant high humidity? Continuous wet weather (CWW) is a special weather phenomenon that occurs frequently during the late winter and early spring in South China. In this study, the CALIPSO satellite data and the ERA5 and MERRA-2 reanalysis data are used to analyze the aerosol optical properties of a total of 68 CWW processes from 2012 to 2021 in the Guangdong–Hong Kong–Macau Greater Bay Area (GBA). We attempt to explore the variations in meteorological conditions and physical–optical properties of aerosols during the before-stage, wet-stage, and after-stage under different humidity levels. The results show that the prevailing wind direction is northeasterly and that the temperature and humidity are lower under the influence of cold high pressure in the before-stage. Moreover, the high aerosol optical depth (AOD) mainly results from regional transport. During the wet-stage, clean ocean airflow causes AOD to remain at a low level, whereas temperature and humidity increase significantly. The wet-stage ends with coldness when it is controlled by cold high pressure again. The atmospheric circulation in the after-stage is similar to that in the before-stage. However, a remarkable feature is that there is a temperature and humidity inversion layer, which results in a significant increase in AOD. This study reveals the physical–optical properties of aerosols during the three stages and the influence mechanism of meteorological factors on aerosols, which can provide a scientific basis for the study of CWW in the future. [ABSTRACT FROM AUTHOR]

Storm separation is a key step when carrying out storm transposition analysis for Probable Maximum Precipitation (PMP) estimation in mountainous areas. The World Meteorological Organization (WMO) has recommended the step-duration-orographic-intensification-factor (SDOIF) method since 2009 as an effective storm separation technique to identify the amounts of precipitation caused by topography from those caused by atmospheric dynamics. The orographic intensification factors (OIFs) are usually developed based on annual maximum rainfall series under such assumption that the mechanism of annual maximum rainfalls is close to that of the PMP-level rainfall. In this paper, an alternative storm separation technique using rainfall quantiles, instead of annual maximum rainfalls, with rare return periods estimated via Regional L-moments Analysis (RLMA) to calculate the OIFs is proposed. Based on Taiwan's historical 4- and 24-h precipitation data, comparisons of the OIFs obtained from annual maximum rainfalls with that from extreme rainfall quantiles at different return periods, as well as the PMP estimates of Hong Kong from transposing the different corresponding separated nonorographic rainfalls, were conducted. The results show that the OIFs obtained from rainfall quantiles with certain rare probabilities are more stable and reasonable in terms of stability and spatial distribution pattern. [ABSTRACT FROM AUTHOR]

• A multi-model and multi-method ensemble approach for wind resource projection is proposed. • Future wind potential in Hong Kong is assessed for the first time. • Robustness of the climate change signal is evaluated. • An increase in wind power density during summer (+23.7 %) and a decrease during autumn (–15.1 %) in 2081–2100 are projected for SSP5-8.5. Harvesting renewable wind energy is among the most cost-efficient means of reducing carbon emissions and achieving carbon neutrality. However, wind resource is susceptible to climate change impacts since the global temperature increase will reshape atmospheric circulation patterns. To facilitate the evaluation of fine-scale wind energy potential under climate variability, this paper proposes and validates a multi-model and multi-method ensemble wind resource projection approach. Then this approach is utilized to investigate the future variation of wind resources in Hong Kong based on the combined use of global climate models from Coupled Model Intercomparison Project Phase 6 and long-term observations from meteorological stations. It is found that there is a significant increase in future wind resources during summer, while a remarkable decline is projected during autumn. Nevertheless, the variations of wind resources in winter and spring are relatively insignificant. The outcomes of this study are expected to offer a framework for fine-scale wind resource assessment under climate change, and facilitate the economic and risk assessments of future wind farm projects. [ABSTRACT FROM AUTHOR]