Influence of building-downwash effect on urban traffic pollution

Transcription

1 Influence of building-downwash effect on urban traffic pollution G. Latini, G. Passerini & S. Tascini Dipartimento di Energetica, Università Politecnica delle Marche, Italy Abstract Building-downwash has recently captured the attention of environmental researchers since it may represent an undervalued danger in urban environments. Especially in certain meteorological conditions, buildings act as barriers triggering pollutant accumulation that will then increase concentration values. One of the main fostering conditions is the source altitude with respect to buildings while main roads represent important linear sources below building levels. In this work we highlight the influence of the building-downwash effect on pollution dispersion in an urban environment. In order to do this, we have run several simulations by the Aermod model coupled with the BPIP sub-model for building down-wash boundary conditions. The simulation area is an important street in the centre of Ancona, Italy. The period we considered comprises the most critical days of the 1998 and 1999 summers. We selected simulation days according to the values of solar radiation, wind speed and direction in the most critical combination for the buildingdownwash effect. The same simulation has been carried out with and without the downwash effect computation and interesting results have been recorded. The major pollutant concentration values in the area by increased of 20-25% when the building-downwash effect was considered. In order to overcome a lack of data and a few model limits some assumptions have been made but we consider these results as being strongly encouraging for continuing the research in this field. It clearly appears that the investigated effect is not negligible in urban environments.

2 582 Air Pollution XII 1 Introduction The analysis of pollutant emissions is, nowadays, ever more required in urban area in order to evaluate the possible environmental impact of any alteration of urban texture. The aim of this work is to highlight the influence of the building-downwash effect on pollution dispersion in an urban environment. Our study started from the environmental impact assessment due for the planning of a major road deviation in the centre of Ancona. The new branch of the road passes through a residential and hilly area of the town. For this purpose several simulations have been run to estimate the incidence of building downwash effect. Environmental engineers developed, in the last years, models able to simulate with high accuracy peculiar pollution dispersion dynamics. In fact, diffusive models grow quickly as researchers highlight further issues. AERMOD is the model with opted for. An urban area presents several peculiar aspects that must be considered to have a correct evaluation of atmospheric dispersion. In fact, the urbanization process produces radical changes of surface and atmospheric parameters in that region: increased heat storage, more surface roughness and a strong alteration of airflows. The first issue is the so-called urban heat island. The main causes are [1], [2]: the geometry of urban canyons increasing the reflection surface and the short-wave radiation absorption; heat release by buildings and vehicular traffic; building materials leading to increased heat storage and waterproofing; presence of obstacles reducing main wind speed and, consequently, heat turbulent transport. The strong increase of surface roughness is mainly due to the presence of buildings. The roughness is a very delicate parameter to estimate; in Table 1 the adopted roughness values (see Oke [3]) are reported. Table 1: Characteristic values of surface roughness in urban terrains. Terrain Type Isolated settlement (farms, villages)) Suburban Zone: -scarcely inhabited -densely inhabited Urban Zone: -densely inhabited, building with less than 5 fl. -densely inhabited, building with more than 5 fl. surface roughness z 0 (m)

3 Air Pollution XII 583 An increase of roughness influences a lager friction area and induces a lower speed velocity with respect to the rural area (at the same height). Finally, the presence of buildings and the urban geometry both trigger a, socalled, shelter effect and airflow around buildings. In other words, when the airflow encounters the obstacle (represented by a building) a higher-pressure zone is present immediately above the top (or around the sides) of the building and a consequent wake area after the building. Immediately alee the building (the cavity zone) a zone of depression is present with inverted flows. E.g. an isolated building generates a typical horseshoes-shaped flow at its sides. Nearby the ground and the alee side of the build we find a strongly reduced wind speed. In the wake-cavity zone, the turbulence triggered by the buildings traps pollutants and mix them down the ground: the building downwash effect. A fundamental role is played by the urban geometry, often parameterized by the distance-between-buildings/buildings-height ratio [4][5]. In fact, very different effects may be triggered for different values of this parameter (see figures 1a and 1b from Oke [3]). Figure 1a: Importance of H/W parameter in evaluating building downwash effect (H=height of buildings, W=distance between buildings). Figure 1b: Importance of H/W parameter in evaluating building downwash effect (H=height of buildings, W=distance between buildings). 2 The AERMOD model AERMOD is a Gaussian dispersion model that is able to calculate the concentrations of pollutants and their evolution in the atmosphere also in highly complex areas. The range of possible scenarios spreads from mountain/hilly to

4 584 Air Pollution XII urban areas considering different types of sources (volume, area and point sources). The model employs two pre-processors in order to elaborate input data: the meteorological processor, AERMET, and the orography processor, AERMAP (which is necessary to input terrain characteristics and generate a receptors grid). Furthermore, AERMOD includes the BPIP model that provides input data for urban simulations in order to evaluate the building down-wash effect. More specifically, AERMOD is a steady state model or, in other words, it considers emissions as stationary in a time lapse of an hour. The concentration distribution, in the Stable Boundary Layer (SBL), follows a Gaussian curve both in vertical direction and in horizontal direction. In the Convective Boundary Layer (CBL), the vertical distribution is described by a bi-gaussian function while the horizontal distribution yet again follows a Gaussian curve. Moreover, in the CBL, AERMOD considers the so called plume-lofting, which assumes that a mass portion of the plume, released by a lofting source, rises and remains in the upper boundary layer before being mixed down by the CBL turbulence [6], [7]. One of the best innovations, introduced by this model to characterise dispersions the PBL, is the capability of evaluating the vertical profiles of meteorological quantities such as wind, temperature, turbulence, etc., using both ground observations and upper-air observations. Surface data refer to measures at a height of 10 meters for wind direction, wind speed, temperature, and cloud coverage, which are the essential information along with albedo, Bowen ratio and surface roughness. Upper-air data include wind speed, temperature, relative humidity, pressure and geopotential height. All the previous data are elaborated by AERMET that provides all the necessary parameters to let AERMOD to extrapolate vertical profiles of meteorological quantities. 3 The simulations 3.1 The period Once AERMAP and AERMET processing have been applied, AERMOD can operate with BPIP sub-model activated in order to consider the building downwash effect [8]. To evaluate the entity of this phenomenon in the overall dispersion dynamics we considered several sample days representing different meteorological scenarios. Here, only the simulations related to the 11th of August 1998 and 1999 are presented since they are the most meaningful. Both simulations refer to an august day since the summer presents the most unfavourable conditions in terms of pollutants. The period also allowed us to neglect the home heating contribution.

5 Air Pollution XII The area The investigated area is a small, northern, portion of the town of Ancona. It has an extension around 1 square kilometre and it is mainly characterised by hilly relieves and terrain irregularities at about 100 meters above the sea level (see Figure 2). Many parameters have been already tested in order to have a good idea of meteorological regional dynamics in the Ancona area [9]. Figure 2: DEM representation of investigated area; only few of the considered buildings are displayed. 3.3 Sources We selected two pollutants as indexes for the evaluation of the environmental impact: Carbon monoxide (CO) and Nitrogen oxides (NO x ). Emissions of these chemicals are mainly due to vehicular traffic. We considered, as main emission source, the road network represented, accordingly to the EPA dispositions, as a volume source for the simulations without building downwash and as point source (stack) in the other case. For what concerns the definition and characterization of pollution sources, we considered traffic volumes as estimated in 2003 and the ANPA (Italian Environmental National Agency) emission rates for CO and NO x. 3.4 Surface characteristics While elaborating the meteorological parameters, through AERMET, we paid particular attention in selecting surface parameters, especially for roughness. We assumed values derived from a previous sensitivity analysis carried on through last years. Assumed values for albedo and surface-roughness are referred to the summer season and an urban area, while for the Bowen ratio the wet condition is considered [10] (see Table 2).

6 586 Air Pollution XII Table 2: Assumed surface characteristics. Albedo Bowen Ratio Surface Roughness Figure 3: Wind rose for 11 th of August Figure 4: Wind rose for 11 th of August 1999.

7 Air Pollution XII Meteorological data Meteorological data are very important in order to characterise the atmospheric stability and consequently conditions that foster mixing or dilution of pollutants. For what concerns pollutant transport and diffusion, wind intensity is particularly important. The scenarios we present here are related to a summer period with conditions of high insolation and high temperatures including daily maxima. Figure 3 and Figure 4 show most frequent wind directions and intensities with significant differences between them. 3.6 Receptors For all simulations a uniform Cartesian grid of receptors has been set up. The grid is made up of 441 nodes. 4 Results For each meteorological scenario, we analysed the concentration maps for CO and NO x characterised by the various emission rates. A significant pollution increasing has been recognised nearby the buildings subject to building downwash. Figure 5: CO distribution simulation for 11 th of August 1998, without building down-wash.

8 588 Air Pollution XII Figure 5 and Figure 6 show iso-concentration curves of carbon monoxide simulated with 11 th of August 1998 meteorological data, respectively without and with building downwash effect. In the first case, maximum concentration is µg/m 3 in a central receptor of the grid at X= , Y= In the second case, the maximum concentration is µg/m 3 in a very close node at X= , Y= It may be noticed as building downwash effect triggers a pollution increasing around investigated buildings and the concentration maximum point is drifted alee the buildings (the cavity zone), the same region where the turbulence responsible of building downwash is generated. Analogue results have been recorded for different meteorological scenarios. Figure 6: CO distribution simulation for 11 th of August 1998, with building down-wash. 5 Conclusions AERMOD and BPIP resulted to be useful tools for pollution dispersion analysis in urban environments too. The analysis of iso-concentration curves generated by the AERMOD simulations confirmed the following results in all the selected scenarios: maximum concentration value increased by 10-15%; concentration values nearby several buildings increased up to 70-80% Consequently, it seems evident that neglecting building downwash effect could lead to unreliable results while investigating pollution dispersion in an urban area.

9 Air Pollution XII 589 References [1] Oke, T.R., 1973, City size and the urban heat island, Atmos. Environ., 7: [2] Oke, T.R., 1982, The energetic basis of the urban heat island, J. R. Met. Soc., 108, pp [3] Oke, T.R., 1978, Boundary Layer Climates, John Wiley and Sons, New York, New York, 372pp. [4] Huber, A.H., and W.H. Snyder, 1982, Wind tunnel investigations of the effects of a rectangular-shaped building on dispersion of effluents from short adjacent stacks, Atmos. Environ., 176: [5] Briggs, G. A., 1975, Plume rise predictions in Lectures on Air Pollution and Environmental Impact Analysis, D. A. Haugen, ed., American Meteorological Society, Boston (59-111). [6] U.S. Environmental Protection Agency, 1995, User's Guide for the Industrial Source Complex (ISC3) Dispersion Models (revised) Volume I - User Instructions EPA-454/b a, U.S. Environmental Protection Agency, Research Triangle Park, NC. [7] AERMIC, 1995, Formulation of the AERMIC MODEL (AERMOD) (Draft), Regulatory DocketmAQM-95-01, AMS/EPA Regulatory Model Improvement Committee (AERMIC). [8] Schulman, L.L., J.S. Scire, 1980, Buoyant Line and Point Source (BLP) Dispersion Model User s Guide. Document P-7304B, Environmental Research and Technology, Inc., Concord, MA. [9] Cocci Grifoni, Passerini & Tascini, Application of RAMS and AERMOD models to evaluate pollutant dispersion in a coastal valley, 26 th NATO/CCMS, May 2003, Istanbul. [10] Grosch G. Thomas and Lee F. Russell Sensitivity of the AERMOD air quality model to the selection of land use parameters, Trinity Consultants, March 30, 2001.