Evaluation of a year-long dispersion modelling of PM10 using the mesoscale model TAPM for Christchurch, New Zealand

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Abstract

This paper examines the utility of The Air Pollution Model (TAPM; version 2) in simulating meteorology and dispersion of PM10 for 1999 over the coastal city of Christchurch, New Zealand. Christchurch usually experiences severe degradation in air quality during austral winter. The formation of a nocturnal inversion layer and the emissions of particulate matter (PM10) mainly from solid fuel home heating appliances lead to severe smog episodes on an average of 30 nights during winter. The complex local topography surrounding the city in combination with influences from the urban areas can produce complicated boundary layer winds during quiescent weather. Simulated PM10 data are used for construction of annual exposure maps for the urban areas in order to assess the health impact of air pollutants due to chronic exposure (presented in an accompanying paper). Meteorology and PM10 dispersion results are statistically compared with the only permanent air pollution monitoring station available in order to evaluate the model's performance. Statistical measures such as the Index of Agreement (IOA) between modelled and measured data indicate that the model performs well. IOA is greater than 0.6 for meteorological variables, and various calculated skill scores place confidence in the model's performance. However, TAPM has a tendency to overestimate surface wind speed over urban areas during stagnant nocturnal conditions, resulting in quick flushing of pollutants.

Introduction

Internationally, New Zealand has a reputation for having a pristine environment with plenty of green spaces and lots of fresh, unpolluted air. However, in reality—at least as far as clean air is concerned—air pollution can be a serious problem in urbanized regions, especially during austral winter months. The coastal city of Christchurch, situated about 70 km east of the Southern Alps (172°37′ W–43°31′ S) and just north of a caldera (eroded volcanic crater) known as Banks Peninsula (Fig. 1), has a population of 300,000; occupies an area of about 140 km2, and usually experiences smog events for about 30 days each winter season when the daily-averaged concentration of PM10 exceeds the air quality guideline of 50 μgm−3 (Aberkane, 2000). The area of Banks Peninsula just south of the urban area is known locally as the Port Hills.

The modelling work presented in this paper is part of an ongoing pilot programme to assess and evaluate air pollution exposure for selected urban areas in New Zealand (see www.hapinz.org.nz). A key component of this assessment is the construction of spatially detailed, annually-averaged air pollution exposure maps using an existing dispersion model. The exposure maps are needed in order, for example, to ascertain inequality in exposure to air pollutants between different social groups (Pearce et al., accepted for publication). Hence, there was a need for a computationally efficient meteorological model with an air pollution module. To this end, The Air Pollution Model (TAPM; Hurley, 2002) has been chosen. TAPM is a PC-based mesoscale prognostic numerical model with meteorological and air pollution components. The meteorological module of TAPM predicts the local-scale circulations, such as sea breezes and slope flows, in conjunction with larger scale synoptic scale meteorological fields.

Section 2 of this manuscript will provide an overview of meteorology of Christchurch, with particular emphasis on winter time smog episodes. In section 3, the derivation of pollutant emissions inventories used as input for TAPM is described in detail, while section 4 offers information on the TAPM setup and results.

Section snippets

General

Christchurch is located in the mid-latitudes and its wind climate is largely controlled by eastward propagating high and low pressure systems and the city's geographic location relative to the Southern Alps (Sturman and Tapper, 1996). Over the Canterbury Plains, the synoptic scale wind is strongly modified by dynamic and thermal effects caused by the land–sea discontinuity, the Southern Alps and Banks Peninsula (McKendry, 1983). As shown in Fig. 2, synoptic scale westerly winds flow over and

TAPM grid setup and configuration

TAPM is a three-dimensional incompressible, non-hydrostatic, primitive equations model, which uses a terrain-following coordinate system (Hurley, 2002). For computational efficiency, it can be used in a telescoping nested configuration where higher resolution grids are successively placed inside coarser resolution grids. In addition, model solution for each grid is one way interacting—information is passed from the coarse grid downwards. The meteorological component of the model is supplied

Derivation of PM10 emissions data

Emission inventory data has been collected in Christchurch on a regular basis to monitor trends over time and to determine changes in the relative contribution of sources to emissions (NIWA, 1998, Wilton, 2001, Scott and Gunatilaka, 2003). These present emissions to the air for a “typical winter's day” for the area within the Christchurch territorial boundary and comprised the main portion of the Christchurch airshed, as defined by Sturman and Zawar-Reza (2002). The inventory contains raw

Results and discussion

Statistical measures, such as Root Mean Square Error (RMSE) and Index of Agreement (IOA) are used to evaluate TAPM's performance (RMSE=[(PO)]1/2, and IOA=1[(PO)2/(|PO|+|OO|)2]; where the Predicted (P) values by the model are compared against Observed (O) data (Willmott, 1981). The IOA is a measure of the skill of the model in predicting variations about the observed mean; a value above 0.5 is considered to be good. The modelled data, at 10 m above the ground for wind and at screen

Acknowledgements

We would like to thank the Geography support staff at the University of Canterbury for providing assistance for this project, especially James Sturman, Paul Bealing, and Matthew Faulk. The research is supported by the HRC Health and Air Pollution in New Zealand (HAPiNZ) project research grant no: 03/470.

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