The Impact of Traffic Emission on Air Quality in an Urban Environment ()
1. Introduction
The air quality of urban environments has become more important in recent years. Control of air quality affected by traffic emission is vital for human health. Vehicular emissions are one of the major sources of air pollutants in the urban environment.
In recent years, many researchers have investigated pollutant emissions at various locations in Saudi Arabia. [1] found that in Jeddah, the most significant source of air pollution is the automobile. The number of automobiles in Jeddah jumped from less than 30,000 in 1973 to approximately 1,075,000 in 1992. This rate of increase roughly applies to most Saudi cities. Studies on air pollution have been made in Makkah, Saudi Arabia, focusing on the central area near the Haram Mosque and other important religious sites (i.e., Mina and Arafat). These studies show high concentrations of atmospheric air pollutants, in excess of standards. This is attributed to traffic emission during the Hajj season, when about three million people gather in these limited areas [2-10]. Also, there are many studies assessing air quality inside tunnels near the Haram Mosque, which also show very high concentrations surpassing standards [11-13].
Although several studies of pollutant emission around the Haram area in Makkah have been done, there is a lack of air quality data in that area. The objective of this study is to measure existing air quality and model pollutant dispersion within and around Haram, to improve both.
2. Methodology
2.1. Experimental Study
2.1.1. Site Description
Three sites were selected for air quality monitoring, to represent the study area and surroundings. Figure 1 shows locations of monitoring stations at the three sites. Two of these are close to the Haram Mosque. One is located near the Tawheed Hotel, about 400 meters west of the Kabaa, and the other is in front of Gesr Al-Nadwa gate, 200 meters from the north side of the Kabaa. The third site is at the end of the Shameyah area, approximately 600 meters from the north side from the Kabaa. Site selection reflected requirements of the air quality model, prevailing meteorological parameters, and locations of potentially affected sensitive receivers (both human and physical) in the area, for original and future
Figure 1. Locations of the mobile stations around Haram.
building scenarios.
Site selection was also based on traffic density. Station (S1) was chosen at the highest possible traffic density point, whereas station (S2) is in a zone with no traffic allowed. Station (S3) is in a relatively moderate traffic density area, at the border of the Shameyah area. In this way, effects of external sources of pollution, either permanent or temporary, can be measured.
2.1.2. Monitoring Stations
The current status of air quality within the study domain was observed by onsite ground level measurements. Monitoring was done simultaneously at all sites during May. The following parameters of national standards promulgated by the Presidency of Meteorology and Environment (PME) were measured: 1) nitrogen dioxide (NO2), 2) ozone (O3), 3) sulfur dioxide (SO2), 4) hydrogen sulfide (H2S), 4) carbon monoxide (CO), 5) nonmethane volatile organic compounds (NMVOC), and 6) particulates (PM10, PM2.5). Observed data of ground level concentration at the monitoring stations were used to validate simulated values from the air dispersion model.
2.2. Numerical Model
2.2.1. Model Description
The ISC-AERMOD dispersion model (Industrial Source Complex—AMS/EPA Regulatory Model) is an advanced, new generation model developed by the US EPA, and serves as a complete replacement for the ISC3. It is designed to predict pollutant concentration, as well as the extent of deposits over short-range (out to 50 kilometers) dispersion of air pollutant emissions from an industrial source complex. ISC-AERMOD features an integrated Geographical Information System (GIS), as well as intuitive data analysis interface tools that enable modeled objects and results to interact and be displayed alongside a variety of geophysical data.
The ISC-AERMOD is a steady-state plume model, in the sense that it assumes that concentrations over various distances during a modeled hour are governed by the temporally averaged meteorology of that hour. The steady state assumption yields useful results since statistics of concentration distribution are of primary concern, rather than specific concentrations at particular times and locations. ISC-AERMOD has been designed to handle computation of pollutant impacts in both flat and complex terrain within the same modeling framework.
In the stable boundary layer (SBL), concentration distribution is assumed Gaussian in both the vertical and horizontal. In the convective boundary layer (CBL), the horizontal distribution is assumed Gaussian, but the vertical distribution is described with a bi-Gaussian probability density function (pdf). The model also tracks any plume mass that penetrates an elevated stable layer, and allows it to reenter the boundary layer when and if appropriate.
Calculations of the AERMOD model were performed on a rectangular grid for the Haram area. All emission sources, such as traffic as line sources and power plants as point sources, are located on the selected rectangular grid. Coordinates extend a distance of 4 km in the positive direction of x, and a distance of 4 km in the positive direction of y. Grid base elements are squares, with side dimensions 500 m. The uniform square grids include 289 receptors. Figure 2 describes the study grid and locations of example pollutant sources.
2.2.2. Meteorological Data
Meteorological parameter data inputs for this modeling were obtained from the surface weather observatory station at King Abdulaziz International Airport (Figure 1), and were assumed representative of meteorological data for the entire city of Makkah. A detailed analysis of the meteorological data such as ceiling height, wind speed, wind direction, air temperature, total cloud opacity and total cloud amount has been made for the year 2009.
A fair estimate of pollutant dispersion in the atmosphere is based on the frequency distribution of wind direction, as well as wind speed. Figure 3 illustrates the hourly wind rose diagram for 2009. The prevailing wind direction was from the north, and comprised about 41.7% of all hourly wind directions. About 19.8% of the time, wind speed was 1.54 m/s; 14.9% of the time it was 3.09 m/s; during 48% of the time, wind was calm.
Temperature fluctuation around the Haram Mosque in Makkah is great. This is ascribed to the city being affected by thermal stability. The minimum recorded temperature for 2008 was 21˚C in January, and the maximum was 51˚C in July.
Pasquill classifies atmospheric stability according to
Figure 3. Hourly wind data for Makah during a year.
six classes. They are: A—extremely unstable, B—moderately unstable, C—slightly unstable, D—neutral, E— slightly stable and F—moderately stable. This classification was compiled according to wind speed, cloud cover and solar insolation, following [14] table. The mixing height is defined as the height above the surface through which relatively vigorous vertical mixing occurs [15], and it is determined using the Holzworth technique [16]. In the present study, meteorological data of wind speed, wind direction, temperature, atmospheric stability, mixing height, and other parameters have been used in the air dispersion model.
3. Results and Discussion
3.1. Model Validation
Results of the air quality model were validated using the three mobile stations (S1), (S2), and (S3) near the Haram area, as indicated previously in Figure 1. Stations monitored the various pollutants during the final weeks of May 2009. A comparison between monitored and model data is shown in Table 1.
Figure 4 shows a graphic comparison between monitored and modeled data. CO predictions for the three stations (3234, 4792, 1269 µg/m3) were generally close to monitored values (3406, 4206, 549 g/m3). The results indicate good agreement between calculated concentrations and observed values, which demonstrate satisfactory model performance. NOX predictions at stations (S2) and (S3) (172 and 109 µg/m3, respectively) were higher than measurements (65 and 108 µg/m3, respectively). Since the area was experiencing temporary heavy construction that was not included in the prediction model, the stations recorded higher concentrations of PM10 and PM2.5 particulates than corresponding modeled values.