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Subtropical monsoon climates, high-density and heterogeneous urban built environments, as well as coastal–mountainous geographical environments influence the development of urban heat island (UHI) effects in Hong Kong. For better weather control of in situ observations and spatial analysis of UHI effects, it is necessary to quantitatively understand the influence of weather conditions on UHI development in Hong Kong and establish weather-based UHI estimation models. Meteorological records of four urban stations, one rural reference station, and one wind reference station at an hourly interval during the period of 2002–2012 were collected from Hong Kong observatory. A frequency analysis of the mean values of multiple meteorological elements and UHI parameters in urban stations was conducted to examine the prevailing and critical weather conditions, as well as the associated UHI conditions in Hong Kong. Multiple Linear Regression (MLR) was used to estimate the daily maximum UHI intensity (UHImax) based on a set of meteorological elements including cloud amount, wind speed, wind direction, relative humidity, and air temperature, as well as a UHI parameter of the daily maximum UHI intensity of the previous day (UHIpre-max). The results showed that MLR-based models can explain 33% and 56% variations of the UHImax in the summer and the whole year, respectively. The relative importance of each meteorological element on UHI development differed in the summer and annual periods, and the UHImax tended to be intensified under high temperature conditions in the summer. [ABSTRACT FROM AUTHOR]

A spatial understanding of street-scale urban heat island (UHI) is essential but challenging in Hong Kong, due to its highly heterogeneous urban environment and a limited weather station monitoring network. Night-time mobile measurements were conducted during the summertime of 2014 to monitor UHI variation at local level. Three measurement routes and a total of 80 sample sites were selected according to the Local Climate Zone (LCZ) framework. The measured climatic data and urban morphology data were synergized and analyzed at LCZ scale through Geographical Information System (GIS). Stepwise Multiple Linear Regression (MLR) and Partial Least Square Regression (PLSR) were applied to quantify the connections between urban form and local UHI conditions of LCZ. Mean sky view factor, total street length, and pervious surface fraction of LCZ sites have been found to be the most explanatory variables of local UHI intensity, and over 50% of UHI variations can be explained by both statistical models of stepwise MLR and PLSR. An UHI evaluation map of urban areas in Hong Kong has been developed based on the statistical models, through which UHI hotspots have been identified. LCZ-based UHI mitigation strategies were further developed for climatic planning of Outline Zoning Plan areas. The results indicate that urban forms have significant influences on UHI development at local scale, and an optimal design of urban morphology is necessary for UHI mitigation and climate adaptation. • Mobile measurements were conducted to achieve a refined spatial understanding of UHI conditions at local scale. • Local Climate Zone (LCZ) framework was applied to standardize the mobile measurements of UHI. • Urban morphology-based UHI estimation model of LCZ was established though bivariate and multivariate analysis. • Hotspots of UHI in high-density urban areas of Hong Kong were identified. • LCZ-based planning strategies for UHI mitigation were developed. [ABSTRACT FROM AUTHOR]

This is the first study of heavy metals (As, Zn, Cd, Ni, Fe, Mn, Cu) contamination of microplastics on sandy beaches in Hong Kong. Three study sites are located in the eastern waters (Pak Lap Wan, Stanley Bay, Tung Lung Chau) and the other three in the western waters (Tai Pai Tsui, Ha Pak Nai, Shui Hau Wan). The three most abundant types of microplastics were polyethylene (42.2%), polypropylene (23.3%) and polystyrene (19.5%). The median concentration of Fe (302 mg kg−1) was the highest and followed by Zn (19.6 mg kg−1) and Mn (18.6 mg kg−1). Very low concentrations of Cu (0.89 mg kg−1), Ni (0.15 mg kg−1), As (