No abstract

An atmospheric chemistry model (CiTTyCAT) is used to quantify the effects of trees on urban air quality in scenarios of high photochemical pollution. The combined effects of both pollutant deposition to and emission of biogenic volatile organic compounds (BVOC) from the urban forest are considered, and the West Midlands, metropolitan area in the UK is used as a case study. While all trees can be beneficial to air quality in terms of the deposition of O3, NO2, CO, and HNO3, some trees have the potential to contribute to the formation of O3 due to the reaction of BVOC and NOx. A number of model scenarios are used to develop an urban tree air quality score (UTAQS) that ranks trees in order of their potential to improve air quality. Of the 30 species considered, pine, larch, and silver birch have the greatest potential to improve urban air quality, while oaks, willows, and poplars can worsen downwind air quality if planted in very large numbers. The UTAQS classification is designed with practitioners in mind, to help them achieve sustainable urban air quality. The UTAQS classification is applicable to all urban areas of the UK and other mid-latitude, temperate climate zones that have tree species common to those found in UK urban areas. The modeling approach used here is directly applicable to all areas of the world given the appropriate input data. It provides a tool that can help to achieve future sustainable urban air quality.

An urban land-cover classification of the 900 km 2 comprising the UK West Midland metropolitan area was generated for the purpose of facilitating stratified environmental survey and sampling. The classification grouped the 900 km 2 into eight urban land-cover classes. Input data to the classification algorithms were derived from spatial land-cover data obtained from the UK Centre for Ecology and Hydrology, and from the UK Ordnance Survey. These data provided a description of each km 2 in terms of the contributions to the land cover of 25 attributes (e.g. open land, urban, villages, motorway, etc.). The dimensionality of the land-cover dataset was reduced using principal component analysis, and eight urban classes were derived by cluster analysis using an agglomeration technique on the extracted components. The resulting urban land-cover classes reflected groupings of 1 km 2 pixels with similar urban land morphology. Uncertainties associated with this agglomerative classification were investigated in detail using fuzzy-type analyses. Our study is the first report of a quantitative investigation of uncertainty associated with a classification of this type. The resulting classification for the UK West Midland metropolitan area offers an impartial basis for a wide range of environmental and ecological surveys. The methods used can be adapted readily to other metropolitan areas where generic urban features (e.g. roads, housing density) are gridded.