Prior studies have identified key factors that influence recycling intention. However, these studies rarely pay attention to the interaction of attitude and subjective norm that influences recycling intention. This study applied a conceptual model by extending the Theory of Planned Behavior (TPB) for addressing the gap. The study collected 246 responses through a street survey in Hong Kong. Findings revealed that two interaction terms (i.e., experiential attitude and subjective norm; instrumental attitude and subjective norm) influenced recycling intention. It implies that subjective norm plays a crucial role in motivating recycling behaviors. Moreover, subjective norm could increase the likelihood of recycling for people exhibiting positive experiential attitude, and motivate people who possesses limited knowledge on recycling benefits of practicing recycling behaviors. Policy implications were drawn from the findings. Limitations of the study and future research direction were also discussed.

People living in urban areas are encouraged to use urban green spaces (UGS) because of the physical, psychological and social benefits offered by the green environment to city dwellers.Prior studies have investigated the physical, socio-psychological and demographic factors in explaining the use of UGS; however, the moderating effect of social influence has rarely been examined. Based on the theory of planned behaviour, a model extending the predictors of behavioural intention was proposed in this study. Data were collected by a telephone survey conducted in Hong Kong. The results reveal that attitude, subjective norm, perceived behavioural control, and usefulness positively influence people's intention of using urban green areas. It was also proved that the interaction terms of usefulness and subjective norm, and perceived quality and subjective norm, negatively influence behavioural intention.Insightful implications for studying UGS behaviour, suggestions for urban planning and promotion of using urban green spaces are discussed.

Deep convective storms in subtropical South America are some of the most intense in the world, and the hydrological cycle plays an important role in both tropical and subtropical South America. Recent studies have suggested that the Tropical Rainfall Measuring Mission (TRMM) precipitation radar algorithm significantly underestimates surface rainfall in deep convection over land. This study investigates the range of the rain bias in storms containing four different types of extreme radar echoes: deep convective cores, deep and wide convective cores, wide convective cores, and broad stratiform regions over South America. Storms with deep convective cores show the greatest underestimation, and the bias is unrelated to their echo top height. The bias in wide convective cores relates to the echo top, indicating that storms with significant mixed phase and ice hydrometeors are similarly affected by assumptions in the TRMM algorithm. The relationship between storm type and rain bias remains similar in both subtropical and tropical regions.

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