To characterize and analyze the PM_2.5 concentrations in Hong Kong, the PM_2.5 concentrations and meteorological data were acquired from the Hong Kong Environment Protection Department (HKEPD) and the Hong Kong Observatory (HKO) respectively. In this study, PM_2.5 data recorded by Central station (22°16′54″, 114°09′29″) equipped with a TEOM Series 1400a monitor [21] are selected to represent PM_2.5 concentrations over urban areas in Hong Kong. These data are represented for the pollution in Central Business District and are considered to have higher values than suburban and rural areas. Temperature, relative humidity, pressure, and precipitation were collected from the HKO (22°18′07″, 114°10′27″), which were used to represent the meteorological conditions for Central station (Figure 1). The wind speed and wind direction were collected from Central Pier monitoring station (22°17′20″, 114°09′21″) for representing the wind conditions for Central station as geographical proximity. These data are co-located in both space and time, which serve as the basis for statistical analysis.

The MODerate Resolution Imaging Spectroradiometer (MODIS) is a sensor aboard the TERRA and AQUA Earth observation system satellites. It is a multispectral (36 spectral wavebands span over the visible light, near infrared and infrared portion of the spectrum), multi-resolution (1 km, 500 m, 250 m) sensor dedicated to the observation of the Earth. However the coarse spatial resolution (10 × 10 km) of MODIS Aerosol Optical Thickness (AOT), namely MOD04 aerosol product [22] cannot provide detailed spatial variation for local/urban scale aerosol monitoring and is inaccurate over bright urban surfaces [23], Wong et al. [23,24] developed a modified Minimum Reflectance Technique (MRT) to derive AOT over both bright and dark surfaces (e.g., urban and vegetated areas) at the relatively high resolution of 500 m, for Hong Kong and the Pearl River Delta regions.

In order to understand the interrelationship between PM_2.5 and meteorological parameters, the correlations between them were first calculated. The diurnal patterns of PM_2.5 concentration and meteorological data were also studied to understand their influences during summer and winter time. In addition, seasonal variations of PM_2.5 as well as meteorological parameters were studied. The daily concentrations (24 hour average) of PM_2.5 and meteorological parameters of 2007 and 2008 were calculated from the hourly data and then grouped into each season such as spring (March–May), summer (June–August), autumn (September–November) and winter (December–February).

In contrast to ground level PM_2.5 measurement, satellite remote sensing provides aerosol optical thickness to study urban air pollution with broad spatial coverage [25]. AOT is found to be dominated by near-surface emission except for long range dust events [26]. Recent studies have established quantitative relationships between MODIS derived AOT and PM_2.5 using linear regression models. Wang and Christopher [27] achieved a correlation coefficient of 0.7 between satellite-derived AOT at 550 nm and PM_2.5 measured at seven locations in Alabama, United States. Wong et al. [28] showed a good correlation between MODIS derived 500 m AOT and PM_2.5 (r² = 0.67), which demonstrated great potential for MODIS derived 500 m AOT as a good surrogate for PM_2.5 monitoring. In this study, we attempted to model the 2D (image) and vertical distributions of PM_2.5 which has not been done in any other study. The resulting 3D database of PM_2.5 concentrations can be used for daily air quality monitoring in environmental authority. First, the aerosol extinction profile (σ_a(z)) was modeled and the columnar AOT was divided into AOT_Δz at different elevations [29,30]. Then by utilizing the equation (PM_2.5 = 63.66 × AOT + 26.56) developed by Wong et al. [28], the PM_2.5Δz at different elevations can be derived.

By integrating the extinction coefficient profile on two different elevations z₁ and z₂, AOT_Δz between two elevations (Δz) can be computed [31] (note: Δz = z₂ − z₁): (1) AOT Δ z = ∫ Z 1 Z 2 σ a ( z ) dz

The aerosol scaling height z₀ is defined as the height of an exponential profile at which the value is decreased by 1/e from the ground level value σ_a(z₀). It describes the decreasing rate of AOT with altitude and can be calculated using Equation (2) [32,33]: (2) z 0 = AOT 550 nm / σ a ( z 0 )where the surface extinction coefficient σ_a(z₀) can be derived from the visibility (Equation (3)) [34]: (3) σ a ( z 0 ) = 3.912 / Vis ( km )

Given the surface extinction coefficient σ_a(z₀), and insignificances of the aerosol hygroscopic growth effect when relative humidity is less than 70% [35,36], the vertical extinction profile can be estimated: (4) σ a ( z ) = σ a ( z 0 ) × exp ( − z / z 0 )

The whole columnar AOT can be divided to AOT_Δz by assigning any two given heights (Δz) in Equation (1): (5) AOT Δ z = ∫ Z 1 Z 2 σ a ( z ) dz = AOT × [ exp ( − z 1 / ( z 0 ) − exp ( − z 2 / ( z 0 ) ) ]

In a similar way, visibility at any height Vis_z can be calculated from the extinction coefficient by inverting the Koschmeider equation (Equation (3)).

Finally, PM_2.5Δz concentrations at different elevations can be estimated by applying the linear regression equation (PM_2.5 = 63.66 × AOT + 26.56) developed by Wong et al. [28]: (7) PM 2.5 Δ z = 63.66 × AOT Δ z + 26.56

A program code in Matlab has been developed for data matching and converting AOT to PM_2.5Δz. Another program written in ArcEngine helps to display and visualize the data in 3D. The work flow of these programs is shown in Figure 2.

Table 1 shows the interrelationship between PM_2.5 and meteorological parameters over Hong Kong on a daily average basis. Moderate correlations were observed between PM_2.5 and temperature (TEMP), relative humidity (RH), and mean sea level pressure (MSLP), and fair correlations were observed from the other two parameters: wind speed (WS), wind direction (WD).

Figure 3 showed the diurnal trends of PM_2.5 and meteorological parameters. PM_2.5 showed a distinctive diurnal pattern while low values observed during night time (01:00–05:00). During the daytime, PM_2.5 exhibited a bimodal pattern with two marked peaks, during morning rush hours (08:00–10:00) and evening rush hours (18:00–20:00), typically when high traffic density occur. Similar observations and implications were reported by Chan and Kwok [37].

Wind direction does not show a clear diurnal pattern. Mean sea level pressure has a similar pattern to that of PM_2.5 in spite of the time lag, which displayed two clear maxima around 10:00 and 23:00. In contrast, temperature and wind speed exhibit a unimodal pattern characterized by midday maxima around 13:00. Relative humidity, however, exhibits an inverse unimodal pattern with stable overnight maximum values, which suggests the negative association with nocturnal PM_2.5 concentrations. A high relative humidity can depress the absorption of gas phase organic species into particle surface [38] and accelerate the removal of particle by dry deposition, this mechanism enhanced for hygroscopic particle [39]. Thus, PM_2.5 keeps constant at minimum values between 02:00 and 05:00. Another reason is due to less influence of anthropogenic activities on fine particulate levels during nighttime.

Despite a similar pattern observed in Figure 4 (left), summer diurnal PM_2.5 concentrations is found to be lower than in winter. On the other hand, the peak values in Figure 4 (right) are higher on weekdays compared with weekends, which may be caused by more anthropogenic activities.

Seasonal variations of PM_2.5 were obvious (Figure 5). The concentrations are higher in winter and autumn and lower in spring and summer seasons. Previous studies of roadside suspended particulates at heavily trafficked urban areas in Hong Kong conducted in 2000 [37] and 2005 [40] showed similar seasonal patterns. The mean sea level pressure exhibits a similar pattern as PM_2.5 characterized.

In order to understand the spatial pattern of PM_2.5 concentrations in the context of the built-up environment of Hong Kong, a Geographic Information Systems (GIS) prototype was developed in this study to integrate atmospheric PM_2.5 vertical profiles with 3D GIS data which are provided by the Hong Kong Lands Department. This prototype utilized ESRI ArcGIS Scene Control component to present the landscape objects including the terrain model, building polygons, AOT_Δz and PM_2.5Δz grid data in 3D space. The functionality of this system provides scene rendering using perspective view. The 3D PM_2.5Δz atmospheric layers corresponding to 500 m pixel columns were rendered using a transparent color scheme overlaid with the 3D building polygons. Since the system is aimed at the built environment within the city, only seven PM_2.5Δz atmospheric layers, each has 75 m elevation, were created. In this GIS prototype, each building is corresponding with its cadastral footprint polygon, which owns attributes including building height, number of floors and height of each floor (e.g., building height/number of floors). The PM_2.5Δz data can be related with each building by tabular linkage through the polygon-in-polygon function of the Hawths extension [41]. Therefore, any floor of a building can be related to the PM_2.5Δz concentrations and useful for direct query. The interface of this GIS prototype is shown in Figure 6 (left). Figure 6 (right) shows the query results of the PM_2.5Δz concentrations of the International Commerce Centre on 1 February 2007 (local time 10:50).