Urban tree planting initiatives are being actively promoted as a planning tool to enable urban areas to adapt to and mitigate against climate change, enhance urban sustainability and improve human health and well-being. However, opportunities for creating new areas of green space within cities are often limited and tree planting initiatives may be constrained to kerbside locations. At this scale, the net impact of trees on human health and the local environment is less clear, and generalised approaches for evaluating their impact are not well developed. In this review, we use an urban ecosystems services framework to evaluate the direct, and locally-generated, ecosystems services and disservices provided by street trees. We focus our review on the services of major importance to human health and well-being which include ‘climate regulation’, ‘air quality regulation’ and ‘aesthetics and cultural services’. These are themes that are commonly used to justify new street tree or street tree retention initiatives. We argue that current scientific understanding of the impact of street trees on human health and the urban environment has been limited by predominantly regional-scale reductionist approaches which consider vegetation generally and/or single out individual services or impacts without considering the wider synergistic impacts of street trees on urban ecosystems. This can lead planners and policymakers towards decision making based on single parameter optimisation strategies which may be problematic when a single intervention offers different outcomes and has multiple effects and potential trade-offs in different places. We suggest that a holistic approach is required to evaluate the services and disservices provided by street trees at different scales. We provide information to guide decision makers and planners in their attempts to evaluate the value of vegetation in their local setting. We show that by ensuring that the specific aim of the intervention, the scale of the desired biophysical effect and an awareness of a range of impacts guide the choice of i) tree species, ii) location and iii) density of tree placement, street trees can be an important tool for urban planners and designers in developing resilient and resourceful cities in an era of climatic change.

The relationship between environmental noise and health is poorly understood but of fundamental importance to public health. This study estimated the relationship between noise sensitivity, noise annoyance and health-related quality of life in a sample of adults residing close to the Auckland International Airport, New Zealand. A small sample (n = 105) completed surveys measuring noise sensitivity, noise annoyance, and quality of life. Noise sensitivity was associated with health-related quality of life; annoyance and sleep disturbance mediated the effects of noise sensitivity on health.

Little attention is given to applying the artificial neural network (ANN) modeling technique to understand site–specific air pollution dispersion mechanisms, the order of importance of meteorological variables in determining concentrations as well as the important time scales that influence emission patterns. In this paper, we propose a methodology for extracting the key information from routinely–available meteorological parameters and the emission pattern of sources present throughout the year (e.g. traffic emissions) to build a reliable and physically–based ANN air pollution forecasting tool. The methodology is tested by modeling NO2 concentrations at a site near a major highway in Auckland, New Zealand. The basic model consists of an ANN model for predicting NO2 concentrations using eight predictor variables: wind speed, wind direction, solar radiation, temperature, relative humidity, as well as “hour of the day”, “day of the week” and “month of the year” representing the time variations in emissions according to their corresponding time scales. Of the three input optimization techniques explored in this study, namely a genetic algorithm, forward selection, and backward elimination, the genetic algorithm technique gave predictions resulting in the smallest mean absolute error. The nature of the internal nonlinear function of the trained genetically–optimized neural network model was then extracted based on the response of the model to perturbations to individual predictor variables through sensitivity analyses. A simplified model, based on the successive removal of the least significant meteorological predictor variables, was then developed until subsequent removal resulted in a significant decrease in model performance. The developed ANN model was found to outperform a linear regression model based on the same input parameters. The proposed approach illustrates how the ANN modeling technique can be used to identify the key meteorological variables required to adequately capture the temporal variability in air pollution concentrations for a specific scenario