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Intake fraction of nonreactive motor vehicle exhaust in Hong Kong
Atmospheric Environment 44 (2010) 1913e1918
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Intake fraction of nonreactive motor vehicle exhaust in Hong Kong Zhiwen Luo a, Yuguo Li a, *, William W Nazaroff b a b
Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 July 2009 Received in revised form 10 February 2010 Accepted 12 February 2010
The intake fraction (iF) of nonreactive constituents of exhaust from mobile vehicles in the urban area of Hong Kong is investigated using available monitoring data for carbon monoxide (CO) as a tracer of opportunity. Correcting for regional transport of carbon monoxide into Hong Kong, the annual-average iF for nonreactive motor vehicle emissions within the city is estimated to be around 270 per million. This estimated iF is much higher than values previously reported for vehicle emissions in US urban areas, Helsinki and Beijing, and somewhat lower than those reported for a densely populated street canyon in downtown Manhattan, New York City, or for emissions into indoor environments. The reported differences in intake fractions in various cities mainly result from the differences in local population densities. Our analysis highlights the importance of accounting for the influence of upwind transport of pollutants when using ambient data to estimate iF for an urban area. For vehicle exhaust in Hong Kong, it is found that the in/near vehicle microenvironment contributes similarly to the indoor home environment when accounting for the overall iF for children and adults. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Intake fraction Vehicle emission Regional pollutant transport Carbon monoxide Exposure
1. Introduction Motor vehicle emissions are a major and ubiquitous source of urban air pollutants, including particulate matter, benzene, carbon monoxide, nitric oxide and polycyclic aromatic hydrocarbons (Chan and Yao, 2008). The ambient pollution level associated with vehicle emissions can become especially elevated in urban areas because of factors that include heavy traffic volumes (Lu and Wang, 2008), unfavourable meteorological conditions (Cheng and Lam, 1998), and deep street canyons (Fernando et al., 2001). Exposure to vehicular pollutants can contribute to adverse health effects, especially for sensitive groups like children and the elderly (Wong et al., 2001, 2002). Various approaches are being developed and implemented to mitigate pollution levels associated with motor vehicles in urban areas. Intake fraction, which describes the source-to-receptor relationship for an air pollution source, is defined as the fraction of total emissions that is inhaled by a receptor population (Bennett et al., 2002). For example, an intake fraction of 1 in a million (106) indicates that 1 g of pollution is inhaled by an exposed population per tonne of pollution emitted from the source. This sourceoriented metric of the emissions-to-intake relationship has been
* Corresponding author. Tel.: þ852 2859 2625; fax: þ852 2858 5415. E-mail address: [email protected] (Y. Li). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.02.016
applied to characterize exposure conditions in both indoor and outdoor environments (Marshall et al., 2005; Nazaroff, 2008; Wang et al., 2006). For urban vehicle emissions, iF estimates have been made from the scale of urban street canyons (Zhou and Levy, 2008) to the entire land of the continental United States (Greco et al., 2007). Vehicular iF studies were recently published for Finland (Loh et al., 2009) and Beijing (Du et al., 2007). In aggregate, these studies show that for mobile sources in typical US and European urban areas, the iF is generally on the order of (5e50) 106. By contrast, for pollutants emitted indoors, the iF is commonly on the order of (2e20) 103 (Lai et al., 2000). For a typical street canyon in midtown Manhattan, New York, Zhou and Levy (2008) obtained an iF estimate of about 3000 per million which is within the range commonly reported for indoor emission sources. They attributed this unusually high iF for a release to ambient air to the high population density and poor ventilation conditions in street canyons. Hong Kong is one of the world's most densely populated cities. It has a small land area of 1100 km2, of which 21% has been developed as urban area. The land population density was 6460 km2 in 2008, while the district with the highest population density is Kwun Tong, with 51,600 km2 (Hong Kong Census and Statistics Department, 2008). In 2002, there were 275 vehicles per kilometer of roadway in Hong Kong, compared with 33 in US and 93 in California (Hong Kong Traffic Department, 2002). Because of the high traffic density and high population density, we are motivated in this study
Z. Luo et al. / Atmospheric Environment 44 (2010) 1913e1918
to characterize the intake fraction for primary pollutants from motor vehicles in Hong Kong. The goal is to better understand the exposure consequences of emissions in this city. The paper is organized as follows. In Section 2, the methods employed and input data utilized are presented for estimating the iF for primary, nonreactive pollutants emitted from motor vehicles in Hong Kong. Section 3 discusses the annual-average iF results for Hong Kong, as well as its apportionment among age-group subpopulations. Conclusions from this study are presented in Section 4. 2. Methods
180 Roadway Other
160 CO Emission Rate (1000 tonnes/y)
1914
140 120 100 80 60 40 20
2.1. Intake fraction definition 0
For the emission of a specific pollutant, following Marshall et al. (2003), the iF can be expressed as the ratio of two terms: (a) the aggregate inhalation intake of a pollutant by an exposed population that can be attributed to emissions from a particular source or source class, divided by (b) the total emissions from that source. Mathematically, we may write N P M P
iF ¼
Population Intake ¼ Total Emissions
i
Pi Brij Dtij Cej
j
E
(1)
where N is the number of population groups defined in the study and M is the number of different microenvironments considered. For the present study N ¼ 3 and M ¼ 4. In equation (1), E is the total emissions over the time period considered (kg); Pi is the total number of people exposed in the ith population group; Brij is the average volumetric breathing rate for individuals in the ith population group (m3 s1) in the microenvironment j; Dtij is the time spent in the microenvironment j for people group of i (s); and Cej is the exposure concentration attributable to urban emissions in microenvironment j (kg m3). The value of the dimensionless iF is bracketed by the range of 0e1 where 0 means that no emissions are inhaled while 1 indicates that all emissions are inhaled. For the present study, our goal is to estimate annual-average intake fractions of primary, nonreactive vehicle emissions for the city of Hong Kong. We consider cumulative exposure within the city by all persons to all vehicle emissions that occur within the city, using carbon monoxide as a marker compound. Using this tracer of convenience, the determination of iF requires information about CO emissions, population size, breathing rate and the ambient CO exposure concentration, which will be addressed later. There are several approaches to estimate exposure concentrations for motor vehicle emissions, including complex atmospheric dispersion models, one-compartment box models, empirical emissionseconcentrations relationships and ambient monitoring data. In the present study, we estimate iF based on measured ambient CO data across the Hong Kong territory, with microenvironmental adjustments based on published literature, and correcting for contributions to ambient concentrations that are a consequence of regional transport, rather than local emissions.
1990
1992
1994
1996
1998
2000
2002
2004
2006
Year
Fig. 1. Annual CO emission inventory in Hong Kong, 1990e2006 (HKEPD, 2006).
vehicle emission standards, switching to LPG vehicles, and promoting better vehicle maintenance and eco-driving habits. However, ambient concentrations have not followed the same trend as emissions. Increased regional transport of CO from outside of Hong Kong might offset to some extent the effect of reducing vehicle emissions within the city. 2.3. Population size and breathing rate The government census data in 1996, 2001 and 2006 are shown in Table 1. We use linear interpolation from these data to estimate population data for other years. Children (0e18 y) account for 21.2% of the population, adult (19e60 y) comprise 63.3%, and elderly people (>60 y) make up 15.5%, according to the 2004 census data. We use these proportions to represent the populations of different age groups. In this paper, breathing rate refers to the volume of air inhaled by a person per unit time. Marshall et al. (2003, 2005) used an estimate of 12.2 m3 d1 to represent the population-average breathing rate. Higher breathing rates are often used for risk assessment purposes so as to provide an extra measure of protection in risk management. Loh et al. (2009) used different breathing rates according to different activities, ranging from a light-activity breathing rate, 9.6 m3 d1, to a moderate-activity breathing rate, 38.4 m3 d1. Li and Hao (2003) adopted the value of 20 m3 d1 for Chinese people in Hunan. Du et al. (2007) used 24 m3 d1 for Beijing. People spend time in different microenvironments, which can have exposure concentrations that differ from the monitoring station measurements. For pollutants emitted from motor vehicles, exposure levels tend to be higher than monitoring station measurements for microenvironments that are in or near vehicles. Also, in different microenvironments, people may tend to participate in different levels of activities, which affects their breathing rate. For example, at home, a major activity is resting, while at outdoors, people may tend to be more active, being involved in walking, running or sports. Keeping these circumstances in mind,
2.2. CO emissions in Hong Kong The annual CO emission inventory data from 1990 to 2006, as shown in Fig. 1, were provided by Hong Kong Environmental Protection Department (HKEPD) (HKEPD, 2006). Road transport contributes more than 80% of cumulative CO emissions in Hong Kong. The absolute rate of CO emissions has decreased by about 60% since 1995, reflecting the impact of the government's regulations on reducing vehicle emissions through measures such as tightening
Table 1 Population data (1000s) for Hong Kong. Districts
1996
2001
2006
Hong Kong Island Kowloon New Territories Marine Total
1313 1988 2907 10 6218
1335 2024 3343 6 6708
1268 2020 3574 3 6864
Z. Luo et al. / Atmospheric Environment 44 (2010) 1913e1918
and acknowledging the limits imposed by insufficient empirical data, we divide the whole urban environment into four microenvironmental categories: indoors at home, other indoor locations, in or near vehicles, and other outdoor locations away from vehicles. Loh et al. (2009) adopted 20 m3 d1 for the average breathing rate in the traffic microenvironment in Finland; however, we assume that it is somewhat lower in Hong Kong. Most vehicular travel in Hong Kong is by means of public transportation, e.g. subway, bus or tram. People would mostly wait in the bus stop or sit in the bus with lower activities as compared with walking or bicycling. Breathing rates not only significantly depend on the level of activities but also vary among individuals of different ages. Children and seniors have lower average breathing rates than adults. Allan et al. (2008) made a detailed investigation of the breathing rate for use in health risk assessments, considering different age groups and levels of activities. Within their framework, the breathing rates for different age groups in different microenvironments in Hong Kong are estimated and shown in Table 2. It should be noted that the estimates from Allan et al. (2008) are somewhat higher than those from Layton (1993) as the researchers employed different approaches. 2.4. Timeeactivity patterns Chau et al. (2002) determined detailed timeeactivity patterns in four microenvironments for different age groups in Hong Kong by means of telephone interviews. A summary of the results is presented in Fig. 2. Overall, the population spends the most time at home (>60%) and about 10% of time outdoors. Adults spend more time in daily transportation compared with children and the elderly, while the time spent at home is highest in the subgroup of older people. By combining this information with estimated average breathing rates (Table 2), we can also estimate the total population-average daily breathing rate to be around 14.1 m3 d1 per person in Hong Kong. This result is w15% higher than that from Layton (1993). 2.5. Ambient CO levels and exposure concentrations 2.5.1. Ambient CO concentration In Hong Kong, the Environmental Protection Department (EPD) has continuously monitored hourly ambient air pollution levels at 14 fixed monitoring stations since 1983. Six of these stations record carbon monoxide concentration levels; see Fig. 3. Three stations are located along urban roadsides with heavy traffic (Central, Causeway Bay, and Mong Kok), and the other three are sited at the building roof level to represent the background concentrations (Tsuen Wan in the urban residential area, Tung Chung in the new town and Tap Mum in the remote rural area). The data of Mong Kok are not included here due to their unavailability from 1999 to 2001. 2.5.2. Exposure concentration for CO in different microenvironments People may be exposed to different CO concentrations in different microenvironments. Because of the influence of proximity, levels are normally higher in or near vehicles and lower away
Table 2 Breathing rates for various age groups and microenvironments. Breathing rate (m3 d1)
Indoor at home
Other indoor locations
In or near a vehicle
Other outdoor locations
Children Adults Elderly
12.5 13.8 13.1
14 15.5 14.8
14 15.5 14.8
18.7 20.5 19.5
1915
from vehicles. Following Marshall et al. (2003), we have estimated the exposure concentration as follows:
Cej ¼ fCamb þ gej 1 Camb
(2)
where Cej is the exposure concentration (mg m3) attributable to the mobile emissions in microenvironment j, i.e., indoors at home, other indoor locations, in or near vehicles, and other outdoor locations away from vehicles, respectively; Camb is the ambient concentration; f is the fraction of ambient concentrations attributable to motor vehicles; and gej is the ratio of attributable concentration in microenvironment j to the ambient concentration. The parameter f can be decomposed into two multiplicative terms: f1 the fraction of ambient concentration attributable to local emissions and f2 the fraction of local emissions constituted by mobile sources. In prior studies, f was estimated to be 80% in SoCAB (Marshall et al., 2003) and 84% in Beijing (Fu and Yuan, 2001); these researchers assumed that all ambient concentrations in the sites studied are only due to local emissions ðf1 ¼ 1Þ. However, this assumption is not valid in Hong Kong. The regional transport of pollution from outside of Hong Kong plays an important role in the urban air quality (Fung et al., 2005). Lo et al. (2006) estimated that 78% of respirable suspended particles (RSP) in Hong Kong were contributed by regional transport for calm-weather situations; the estimate was 71% for more typical meteorology. However, there has been no published estimate of the apportionment of CO in Hong Kong between local sources and regional transport. To address this limitation, we investigated the influence of regional CO transport by applying a simple wind-direction-filtering method. The principle applied in this estimate is that the concentration at the rural station with few vehicle sources upwind (Crural) can be regarded as representing the regional background concentration for the downwind urban station, where CO concentration is termed as Curban. Therefore the fraction of ambient concentration attributable to regional transport upwind can be obtained directly by Crural/Curban, weighted by the frequencies of the occurrence of different wind directions for a specific year. The fraction of ambient concentration attributable to local emissions is estimated as
f1 ¼ 1 Crural =Curban
(3)
In our study, wind data from the WGL weather station were acquired. WGL is free of the influence of the urban area and thus can be regarded as the best site for representing the background wind. Wind-direction data were grouped into four categories, i.e., north-to-east, east-to-south, south-to-west, and west-to-north, as shown in Fig. 3. The frequency of occurrences is summarized in Table 3. As the monitoring station TM is located to the north-east of the urban area, which is characterized as a rural site with limited traffic, it is reasonable to regard TM as the background/upwind site when the north-to-east winds prevail. Hence, we can directly estimate the fraction of regional CO transport during the condition of north-to-east winds by comparing the CO concentration at TM with the urban CO concentration averaged over the other stations, as shown in Table 4. An increasing trend of the influence of regional transport during these wind conditions is obvious, rising from about 50% in 1999 to more than 85% in 2006. An abnormally low value (29%) in 2001 may be influenced by the incompleteness of the data set (162 out of 790 CO concentration measurements in the year 2001 are not available). Instead of relying on this incomplete data set, we estimate a percentage contribution (60%) for year 2001 by interpolating between 2000 and 2002. For the other wind directions, we assume that the same result would apply for a west-tonorth wind. (There is no appropriate monitoring station for direct
1916
Z. Luo et al. / Atmospheric Environment 44 (2010) 1913e1918
In/near vehicles
5.5%
Other outdoor locations
3.9% 7.1%
Indoors at home
Other indoor locations
4.7%
61.7%
8.6% 59.5%
3.2% 71.6%
28.9%
16.7%
28.7% Children
Adults
Elderly
Fig. 2. Timeeactivity patterns for different age groups in Hong Kong (Chau et al., 2002).
evaluation in west-to-north wind case). Note (Table 3) that the north-to-east winds occur more than half of the total time in a year, whereas the west-to-north winds occur only about 10% of the time. Therefore, the lack of background CO data for the west-to-north winds is expected to be of little influence. For times when the wind is coming from south (east-to-south and south-to-west winds), the regional contribution of CO is set to be zero since, in this case, the wind is mainly blowing from the ocean. Therefore, by considering the frequencies of the occurrence of different wind-direction groups, we estimate the annual total ratio of regional CO transport from outside Hong Kong for 1999e2006, as is summarized in Table 4. This result substantiates the impression of an increasing role over time during the past decade played by regional transport of CO from outside Hong Kong. We use the same value of 84% for f2 as in
Beijing, as the small variation between two very different urban areas (SoCAB in the US and Beijing, China) suggests that uncertainty in f2 contributes little to the uncertainty in iF estimates. By this means, the yearly averaged f values are calculated. The results, shown in Table 3, indicate a declining trend over time. The value of the proximity enhancement parameter, g, can vary among different microenvironments. We have followed the approach of Marshall et al. (2003), using g ¼ 4.0 for in/near vehicles and g ¼ 1.0 for all other microenvironments. 3. Results and discussion We approximated the urban level of carbon monoxide as the arithmetic average of the measurements at the four monitoring
Fig. 3. Locations of CO monitoring stations (TM e Tap Mum, TW e Tsuen Wan, TC e Tung Chung, C e Central, CWB e Causeway Bay) and one weather station (WGL e Waglan Island) in Hong Kong, and the wind-direction classification (green arrows). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Z. Luo et al. / Atmospheric Environment 44 (2010) 1913e1918 Table 3 Frequency of the occurrence of different wind directions.
North-east wind East-south wind South-west wind West-north wind
1999 48.6% 17.3% 19.9% 14.2%
2000
2001
2002
2003
2004
2005
2006
59.4% 59.6% 54.4% 54.9% 49.9% 53.2% 52.2% 13.8% 19.2% 17.9% 17.7% 25.4% 22.9% 22.8% 15.2% 12.3% 14.5% 16.4% 13.5% 16.9% 18.3% 11.6% 8.9% 13.2% 10.9% 11.2% 7.0% 6.7%
stations in the urban area. Station TM was excluded as it represents a rural condition. Strictly speaking, owing to the lack of high spatial resolution, this approach produces a rough estimate that could be refined in the future using an approach as in Marshall et al. (2003) in the event that more detailed population density maps and data from more CO monitoring stations were available. Given the estimated average CO concentration and emission data, and using equations (1)e(3), the annual-average intake fractions from 1999 to 2006 can be evaluated. Fig. 4 presents the results and shows an increasing trend over time, as would be expected given the temporal trend of increasing population in Hong Kong. The magnitude of iF fluctuates between 200 and 300 per million over the eight-year evaluation period with an average value of 270 per million. To be clear, this result indicates that w270 g carbon monoxide is inhaled for every million grams of that pollutant emitted from motor vehicles in Hong Kong. This same value is expected for any other primary species that is emitted from motor vehicles and that is effectively nonreactive, so that it is conserved on the urban residence time scale. In previous research, intake fractions for primary motor vehicle emissions have been reported to be w14 per million in typical US urban areas, w25 per million in Helsinki and w3000 per million in street canyons of Manhattan. By comparison, for important indoor emissions such as particles from environmental tobacco smoke in residences, iF values were reported to be in the range of w1000e2000 per million (Klepeis and Nazaroff, 2006). Our results for vehicle emissions in Hong Kong exceed the range of values previously reported for cities and are below those for typical indoor environments and for the Manhattan street canyon. There are several influential factors contributing to the diversity of iF values reported, including urban form, meteorological parameters, and so on, amongst which the population density is probably the most important. According to the box model presented by Marshall et al. (2005), all else being equal, iF is proportional to the linear population density, which is the population density (population per land area) multiplied by a characteristic length scale for the urban environment. The average population density in SoCAB was reported to be 860 km2 in Marshall et al. (2003), while Zhou and Levy (2008) estimated the population density in the Manhattan street canyon to be 70,000 km2 at night and 400,000 km2 during the day. The value for Hong Kong at around 6000 km2 is between the values in these two environments, and so it is not surprising that the iF values estimated for Hong Kong are also intermediate between the results for these other locations. One of the unique features of our analysis is that it explicitly accounts for the effect of upwind emissions and regional transport
In/near vehicles
Indoors at home
Other outdoor locations
Other indoor locations
350 300 Intake Fraction (per million)
Frequency
1917
250 200 150 100 50 0
1999
2000
2001
2002 2003 Year
2004
2005
2006
Fig. 4. Annual-average intake fraction for CO from motor vehicle exhaust in Hong Kong, apportioned by exposure location.
of CO. We stress that when exposure to air pollution is assessed for an urban area, it is very important to consider the extent to which that area is under the influence of regional pollutant transport. In the present case, had we ignored regional transport of CO into Hong Kong, the iF would have been overestimated at w400 per million. The iF can be allocated into contributions from exposures to different population age groups and in different microenvironments; results of such an allocation are displayed in Fig. 5. Being the largest subpopulation, adults experience about 60% of the overall iF for motor vehicle emissions in Hong Kong. For children and adults, it is interesting that the contributions from the microenvironments in/near vehicle and indoors at home are comparable, while the exposure in other outdoor environments makes the smallest contribution. Exposure to motor vehicle emissions in outdoor environments while in or near mobile sources is thus estimated to be similar to that occurring in indoor environments at home, even though people spend more than 60% of their time indoors, while the proportion of time spent in transportation is only 7%. An implication is that controlling and improving air quality in transportation microenvironments is comparably important as improving indoor environmental quality, at least with respect to protecting a highly urbanized population, such as Hong Kong's against direct emissions from vehicles. However, for the elderly, their exposure is more affected by the other indoor and outdoor environments than by the microenvironment in and near vehicles. Although our results appear reasonable in comparison to what is already known about emissions-exposure relationships, it should be noted that limitations and uncertainties for the present study arise from each step of estimating iF. The monitoring stations for CO are not sufficient to represent well the exposure conditions for the whole urban area of Hong Kong. We assumed, in effect, that the population is evenly distributed with respect to these monitoring stations, but in
Table 4 Contributions to urban CO concentration in Hong Kong owing to regional transport and local mobile emissions.a Percentage of CO contribution
1999
2000
2001
2002
2003
2004
2005
2006
Regional transport from north-east wind Regional transport from all wind directions
57% 36% 64% 54%
50% 36% 64% 54%
60% 41% 59% 49%
71% 48% 52% 43%
92% 60% 40% 33%
87% 53% 47% 39%
85% 51% 49% 41%
88% 53% 47% 40%
f1 f
a Note: f is the fraction of ambient concentrations attributable to local emissions from motor vehicles; f1 is the fraction of ambient concentrations attributable to local emissions.
1918
Z. Luo et al. / Atmospheric Environment 44 (2010) 1913e1918
No. HKU 7145/07E) and a CIBSE HK Branch grant. We thank Hong Kong Environmental Protection Department and Hong Kong Observatory for provision of the air pollution and weather data sets. We also thank an anonymous reviewer for his/her valuable comments and suggestions to improve the paper.
200
Intake Fraction (per million)
180 160 140 120
References
100 80 60 40 20 0 Children
Adults
Elderly
In/near vehicles
Indoors at home
Other outdoor locations
Other indoor locations
Fig. 5. Contributions to the intake fraction for primary pollutants from motor vehicles in Hong Kong, apportioned among different age groups. The intake fraction for the entire population would be represented by the sum of the three bars plotted.
the real situation, there are spatial and temporal distributions of population within the urban area. The breathing rates used were based on the study of American people. Possible differences between American and Asian populations, e.g. in body size and activity levels, introduces further uncertainty. Moreover, insufficient CO data for rural sites in relation to different wind directions also adds some uncertainty when applying the wind-filter method. Another important uncertainty might be due to the emission inventory, which was calculated by EPD Hong Kong using the EMFAC-HK model. Previous studies have shown that the uncertainty derived from EMFAC can be as high as 30% (Marshall et al., 2003). 4. Conclusions For carbon monoxide emitted by motor vehicles in the urban area of Hong Kong, we calculate an annual intake fraction of 270 per million, based on the monitored CO data distributed over the Hong Kong territory. The results are much higher than those in other cities, indicating a higher population exposure risk, per unit emissions, to vehicular pollutants emitted into the urban area of Hong Kong. The estimated high iF value results from the combination of various factors including the compact and high-rise urban morphology, high population density and typical meteorological conditions. One important contribution to iF estimation methodologies is that the transport of pollutants from upwind areas should be considered in some cases, like Hong Kong. The wind-filtering approach that we employed to estimate the portion of upwind transport to the total ambient pollution concentration may also be applied to other settings. The in/near vehicle microenvironment should be more carefully studied in future work owing to its high contribution to the overall iF value. Our study suggests that opportunities may exist to improve urban air quality in Hong Kong by reducing the concentrations in near-road microenvironments by means of better ventilation or filtration in addition to the traditional approach of implementing effective emission controls. Acknowledgements The work described was supported by the Research Grants Council of Hong Kong Special Administrative Region, China (Project
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Intake fraction of nonreactive motor vehicle exhaust in Hong Kong Zhiwen Luo a, Yuguo Li a, *, William W Nazaroff b a b
Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 July 2009 Received in revised form 10 February 2010 Accepted 12 February 2010
The intake fraction (iF) of nonreactive constituents of exhaust from mobile vehicles in the urban area of Hong Kong is investigated using available monitoring data for carbon monoxide (CO) as a tracer of opportunity. Correcting for regional transport of carbon monoxide into Hong Kong, the annual-average iF for nonreactive motor vehicle emissions within the city is estimated to be around 270 per million. This estimated iF is much higher than values previously reported for vehicle emissions in US urban areas, Helsinki and Beijing, and somewhat lower than those reported for a densely populated street canyon in downtown Manhattan, New York City, or for emissions into indoor environments. The reported differences in intake fractions in various cities mainly result from the differences in local population densities. Our analysis highlights the importance of accounting for the influence of upwind transport of pollutants when using ambient data to estimate iF for an urban area. For vehicle exhaust in Hong Kong, it is found that the in/near vehicle microenvironment contributes similarly to the indoor home environment when accounting for the overall iF for children and adults. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Intake fraction Vehicle emission Regional pollutant transport Carbon monoxide Exposure
1. Introduction Motor vehicle emissions are a major and ubiquitous source of urban air pollutants, including particulate matter, benzene, carbon monoxide, nitric oxide and polycyclic aromatic hydrocarbons (Chan and Yao, 2008). The ambient pollution level associated with vehicle emissions can become especially elevated in urban areas because of factors that include heavy traffic volumes (Lu and Wang, 2008), unfavourable meteorological conditions (Cheng and Lam, 1998), and deep street canyons (Fernando et al., 2001). Exposure to vehicular pollutants can contribute to adverse health effects, especially for sensitive groups like children and the elderly (Wong et al., 2001, 2002). Various approaches are being developed and implemented to mitigate pollution levels associated with motor vehicles in urban areas. Intake fraction, which describes the source-to-receptor relationship for an air pollution source, is defined as the fraction of total emissions that is inhaled by a receptor population (Bennett et al., 2002). For example, an intake fraction of 1 in a million (106) indicates that 1 g of pollution is inhaled by an exposed population per tonne of pollution emitted from the source. This sourceoriented metric of the emissions-to-intake relationship has been
* Corresponding author. Tel.: þ852 2859 2625; fax: þ852 2858 5415. E-mail address: [email protected] (Y. Li). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.02.016
applied to characterize exposure conditions in both indoor and outdoor environments (Marshall et al., 2005; Nazaroff, 2008; Wang et al., 2006). For urban vehicle emissions, iF estimates have been made from the scale of urban street canyons (Zhou and Levy, 2008) to the entire land of the continental United States (Greco et al., 2007). Vehicular iF studies were recently published for Finland (Loh et al., 2009) and Beijing (Du et al., 2007). In aggregate, these studies show that for mobile sources in typical US and European urban areas, the iF is generally on the order of (5e50) 106. By contrast, for pollutants emitted indoors, the iF is commonly on the order of (2e20) 103 (Lai et al., 2000). For a typical street canyon in midtown Manhattan, New York, Zhou and Levy (2008) obtained an iF estimate of about 3000 per million which is within the range commonly reported for indoor emission sources. They attributed this unusually high iF for a release to ambient air to the high population density and poor ventilation conditions in street canyons. Hong Kong is one of the world's most densely populated cities. It has a small land area of 1100 km2, of which 21% has been developed as urban area. The land population density was 6460 km2 in 2008, while the district with the highest population density is Kwun Tong, with 51,600 km2 (Hong Kong Census and Statistics Department, 2008). In 2002, there were 275 vehicles per kilometer of roadway in Hong Kong, compared with 33 in US and 93 in California (Hong Kong Traffic Department, 2002). Because of the high traffic density and high population density, we are motivated in this study
Z. Luo et al. / Atmospheric Environment 44 (2010) 1913e1918
to characterize the intake fraction for primary pollutants from motor vehicles in Hong Kong. The goal is to better understand the exposure consequences of emissions in this city. The paper is organized as follows. In Section 2, the methods employed and input data utilized are presented for estimating the iF for primary, nonreactive pollutants emitted from motor vehicles in Hong Kong. Section 3 discusses the annual-average iF results for Hong Kong, as well as its apportionment among age-group subpopulations. Conclusions from this study are presented in Section 4. 2. Methods
180 Roadway Other
160 CO Emission Rate (1000 tonnes/y)
1914
140 120 100 80 60 40 20
2.1. Intake fraction definition 0
For the emission of a specific pollutant, following Marshall et al. (2003), the iF can be expressed as the ratio of two terms: (a) the aggregate inhalation intake of a pollutant by an exposed population that can be attributed to emissions from a particular source or source class, divided by (b) the total emissions from that source. Mathematically, we may write N P M P
iF ¼
Population Intake ¼ Total Emissions
i
Pi Brij Dtij Cej
j
E
(1)
where N is the number of population groups defined in the study and M is the number of different microenvironments considered. For the present study N ¼ 3 and M ¼ 4. In equation (1), E is the total emissions over the time period considered (kg); Pi is the total number of people exposed in the ith population group; Brij is the average volumetric breathing rate for individuals in the ith population group (m3 s1) in the microenvironment j; Dtij is the time spent in the microenvironment j for people group of i (s); and Cej is the exposure concentration attributable to urban emissions in microenvironment j (kg m3). The value of the dimensionless iF is bracketed by the range of 0e1 where 0 means that no emissions are inhaled while 1 indicates that all emissions are inhaled. For the present study, our goal is to estimate annual-average intake fractions of primary, nonreactive vehicle emissions for the city of Hong Kong. We consider cumulative exposure within the city by all persons to all vehicle emissions that occur within the city, using carbon monoxide as a marker compound. Using this tracer of convenience, the determination of iF requires information about CO emissions, population size, breathing rate and the ambient CO exposure concentration, which will be addressed later. There are several approaches to estimate exposure concentrations for motor vehicle emissions, including complex atmospheric dispersion models, one-compartment box models, empirical emissionseconcentrations relationships and ambient monitoring data. In the present study, we estimate iF based on measured ambient CO data across the Hong Kong territory, with microenvironmental adjustments based on published literature, and correcting for contributions to ambient concentrations that are a consequence of regional transport, rather than local emissions.
1990
1992
1994
1996
1998
2000
2002
2004
2006
Year
Fig. 1. Annual CO emission inventory in Hong Kong, 1990e2006 (HKEPD, 2006).
vehicle emission standards, switching to LPG vehicles, and promoting better vehicle maintenance and eco-driving habits. However, ambient concentrations have not followed the same trend as emissions. Increased regional transport of CO from outside of Hong Kong might offset to some extent the effect of reducing vehicle emissions within the city. 2.3. Population size and breathing rate The government census data in 1996, 2001 and 2006 are shown in Table 1. We use linear interpolation from these data to estimate population data for other years. Children (0e18 y) account for 21.2% of the population, adult (19e60 y) comprise 63.3%, and elderly people (>60 y) make up 15.5%, according to the 2004 census data. We use these proportions to represent the populations of different age groups. In this paper, breathing rate refers to the volume of air inhaled by a person per unit time. Marshall et al. (2003, 2005) used an estimate of 12.2 m3 d1 to represent the population-average breathing rate. Higher breathing rates are often used for risk assessment purposes so as to provide an extra measure of protection in risk management. Loh et al. (2009) used different breathing rates according to different activities, ranging from a light-activity breathing rate, 9.6 m3 d1, to a moderate-activity breathing rate, 38.4 m3 d1. Li and Hao (2003) adopted the value of 20 m3 d1 for Chinese people in Hunan. Du et al. (2007) used 24 m3 d1 for Beijing. People spend time in different microenvironments, which can have exposure concentrations that differ from the monitoring station measurements. For pollutants emitted from motor vehicles, exposure levels tend to be higher than monitoring station measurements for microenvironments that are in or near vehicles. Also, in different microenvironments, people may tend to participate in different levels of activities, which affects their breathing rate. For example, at home, a major activity is resting, while at outdoors, people may tend to be more active, being involved in walking, running or sports. Keeping these circumstances in mind,
2.2. CO emissions in Hong Kong The annual CO emission inventory data from 1990 to 2006, as shown in Fig. 1, were provided by Hong Kong Environmental Protection Department (HKEPD) (HKEPD, 2006). Road transport contributes more than 80% of cumulative CO emissions in Hong Kong. The absolute rate of CO emissions has decreased by about 60% since 1995, reflecting the impact of the government's regulations on reducing vehicle emissions through measures such as tightening
Table 1 Population data (1000s) for Hong Kong. Districts
1996
2001
2006
Hong Kong Island Kowloon New Territories Marine Total
1313 1988 2907 10 6218
1335 2024 3343 6 6708
1268 2020 3574 3 6864
Z. Luo et al. / Atmospheric Environment 44 (2010) 1913e1918
and acknowledging the limits imposed by insufficient empirical data, we divide the whole urban environment into four microenvironmental categories: indoors at home, other indoor locations, in or near vehicles, and other outdoor locations away from vehicles. Loh et al. (2009) adopted 20 m3 d1 for the average breathing rate in the traffic microenvironment in Finland; however, we assume that it is somewhat lower in Hong Kong. Most vehicular travel in Hong Kong is by means of public transportation, e.g. subway, bus or tram. People would mostly wait in the bus stop or sit in the bus with lower activities as compared with walking or bicycling. Breathing rates not only significantly depend on the level of activities but also vary among individuals of different ages. Children and seniors have lower average breathing rates than adults. Allan et al. (2008) made a detailed investigation of the breathing rate for use in health risk assessments, considering different age groups and levels of activities. Within their framework, the breathing rates for different age groups in different microenvironments in Hong Kong are estimated and shown in Table 2. It should be noted that the estimates from Allan et al. (2008) are somewhat higher than those from Layton (1993) as the researchers employed different approaches. 2.4. Timeeactivity patterns Chau et al. (2002) determined detailed timeeactivity patterns in four microenvironments for different age groups in Hong Kong by means of telephone interviews. A summary of the results is presented in Fig. 2. Overall, the population spends the most time at home (>60%) and about 10% of time outdoors. Adults spend more time in daily transportation compared with children and the elderly, while the time spent at home is highest in the subgroup of older people. By combining this information with estimated average breathing rates (Table 2), we can also estimate the total population-average daily breathing rate to be around 14.1 m3 d1 per person in Hong Kong. This result is w15% higher than that from Layton (1993). 2.5. Ambient CO levels and exposure concentrations 2.5.1. Ambient CO concentration In Hong Kong, the Environmental Protection Department (EPD) has continuously monitored hourly ambient air pollution levels at 14 fixed monitoring stations since 1983. Six of these stations record carbon monoxide concentration levels; see Fig. 3. Three stations are located along urban roadsides with heavy traffic (Central, Causeway Bay, and Mong Kok), and the other three are sited at the building roof level to represent the background concentrations (Tsuen Wan in the urban residential area, Tung Chung in the new town and Tap Mum in the remote rural area). The data of Mong Kok are not included here due to their unavailability from 1999 to 2001. 2.5.2. Exposure concentration for CO in different microenvironments People may be exposed to different CO concentrations in different microenvironments. Because of the influence of proximity, levels are normally higher in or near vehicles and lower away
Table 2 Breathing rates for various age groups and microenvironments. Breathing rate (m3 d1)
Indoor at home
Other indoor locations
In or near a vehicle
Other outdoor locations
Children Adults Elderly
12.5 13.8 13.1
14 15.5 14.8
14 15.5 14.8
18.7 20.5 19.5
1915
from vehicles. Following Marshall et al. (2003), we have estimated the exposure concentration as follows:
Cej ¼ fCamb þ gej 1 Camb
(2)
where Cej is the exposure concentration (mg m3) attributable to the mobile emissions in microenvironment j, i.e., indoors at home, other indoor locations, in or near vehicles, and other outdoor locations away from vehicles, respectively; Camb is the ambient concentration; f is the fraction of ambient concentrations attributable to motor vehicles; and gej is the ratio of attributable concentration in microenvironment j to the ambient concentration. The parameter f can be decomposed into two multiplicative terms: f1 the fraction of ambient concentration attributable to local emissions and f2 the fraction of local emissions constituted by mobile sources. In prior studies, f was estimated to be 80% in SoCAB (Marshall et al., 2003) and 84% in Beijing (Fu and Yuan, 2001); these researchers assumed that all ambient concentrations in the sites studied are only due to local emissions ðf1 ¼ 1Þ. However, this assumption is not valid in Hong Kong. The regional transport of pollution from outside of Hong Kong plays an important role in the urban air quality (Fung et al., 2005). Lo et al. (2006) estimated that 78% of respirable suspended particles (RSP) in Hong Kong were contributed by regional transport for calm-weather situations; the estimate was 71% for more typical meteorology. However, there has been no published estimate of the apportionment of CO in Hong Kong between local sources and regional transport. To address this limitation, we investigated the influence of regional CO transport by applying a simple wind-direction-filtering method. The principle applied in this estimate is that the concentration at the rural station with few vehicle sources upwind (Crural) can be regarded as representing the regional background concentration for the downwind urban station, where CO concentration is termed as Curban. Therefore the fraction of ambient concentration attributable to regional transport upwind can be obtained directly by Crural/Curban, weighted by the frequencies of the occurrence of different wind directions for a specific year. The fraction of ambient concentration attributable to local emissions is estimated as
f1 ¼ 1 Crural =Curban
(3)
In our study, wind data from the WGL weather station were acquired. WGL is free of the influence of the urban area and thus can be regarded as the best site for representing the background wind. Wind-direction data were grouped into four categories, i.e., north-to-east, east-to-south, south-to-west, and west-to-north, as shown in Fig. 3. The frequency of occurrences is summarized in Table 3. As the monitoring station TM is located to the north-east of the urban area, which is characterized as a rural site with limited traffic, it is reasonable to regard TM as the background/upwind site when the north-to-east winds prevail. Hence, we can directly estimate the fraction of regional CO transport during the condition of north-to-east winds by comparing the CO concentration at TM with the urban CO concentration averaged over the other stations, as shown in Table 4. An increasing trend of the influence of regional transport during these wind conditions is obvious, rising from about 50% in 1999 to more than 85% in 2006. An abnormally low value (29%) in 2001 may be influenced by the incompleteness of the data set (162 out of 790 CO concentration measurements in the year 2001 are not available). Instead of relying on this incomplete data set, we estimate a percentage contribution (60%) for year 2001 by interpolating between 2000 and 2002. For the other wind directions, we assume that the same result would apply for a west-tonorth wind. (There is no appropriate monitoring station for direct
1916
Z. Luo et al. / Atmospheric Environment 44 (2010) 1913e1918
In/near vehicles
5.5%
Other outdoor locations
3.9% 7.1%
Indoors at home
Other indoor locations
4.7%
61.7%
8.6% 59.5%
3.2% 71.6%
28.9%
16.7%
28.7% Children
Adults
Elderly
Fig. 2. Timeeactivity patterns for different age groups in Hong Kong (Chau et al., 2002).
evaluation in west-to-north wind case). Note (Table 3) that the north-to-east winds occur more than half of the total time in a year, whereas the west-to-north winds occur only about 10% of the time. Therefore, the lack of background CO data for the west-to-north winds is expected to be of little influence. For times when the wind is coming from south (east-to-south and south-to-west winds), the regional contribution of CO is set to be zero since, in this case, the wind is mainly blowing from the ocean. Therefore, by considering the frequencies of the occurrence of different wind-direction groups, we estimate the annual total ratio of regional CO transport from outside Hong Kong for 1999e2006, as is summarized in Table 4. This result substantiates the impression of an increasing role over time during the past decade played by regional transport of CO from outside Hong Kong. We use the same value of 84% for f2 as in
Beijing, as the small variation between two very different urban areas (SoCAB in the US and Beijing, China) suggests that uncertainty in f2 contributes little to the uncertainty in iF estimates. By this means, the yearly averaged f values are calculated. The results, shown in Table 3, indicate a declining trend over time. The value of the proximity enhancement parameter, g, can vary among different microenvironments. We have followed the approach of Marshall et al. (2003), using g ¼ 4.0 for in/near vehicles and g ¼ 1.0 for all other microenvironments. 3. Results and discussion We approximated the urban level of carbon monoxide as the arithmetic average of the measurements at the four monitoring
Fig. 3. Locations of CO monitoring stations (TM e Tap Mum, TW e Tsuen Wan, TC e Tung Chung, C e Central, CWB e Causeway Bay) and one weather station (WGL e Waglan Island) in Hong Kong, and the wind-direction classification (green arrows). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Z. Luo et al. / Atmospheric Environment 44 (2010) 1913e1918 Table 3 Frequency of the occurrence of different wind directions.
North-east wind East-south wind South-west wind West-north wind
1999 48.6% 17.3% 19.9% 14.2%
2000
2001
2002
2003
2004
2005
2006
59.4% 59.6% 54.4% 54.9% 49.9% 53.2% 52.2% 13.8% 19.2% 17.9% 17.7% 25.4% 22.9% 22.8% 15.2% 12.3% 14.5% 16.4% 13.5% 16.9% 18.3% 11.6% 8.9% 13.2% 10.9% 11.2% 7.0% 6.7%
stations in the urban area. Station TM was excluded as it represents a rural condition. Strictly speaking, owing to the lack of high spatial resolution, this approach produces a rough estimate that could be refined in the future using an approach as in Marshall et al. (2003) in the event that more detailed population density maps and data from more CO monitoring stations were available. Given the estimated average CO concentration and emission data, and using equations (1)e(3), the annual-average intake fractions from 1999 to 2006 can be evaluated. Fig. 4 presents the results and shows an increasing trend over time, as would be expected given the temporal trend of increasing population in Hong Kong. The magnitude of iF fluctuates between 200 and 300 per million over the eight-year evaluation period with an average value of 270 per million. To be clear, this result indicates that w270 g carbon monoxide is inhaled for every million grams of that pollutant emitted from motor vehicles in Hong Kong. This same value is expected for any other primary species that is emitted from motor vehicles and that is effectively nonreactive, so that it is conserved on the urban residence time scale. In previous research, intake fractions for primary motor vehicle emissions have been reported to be w14 per million in typical US urban areas, w25 per million in Helsinki and w3000 per million in street canyons of Manhattan. By comparison, for important indoor emissions such as particles from environmental tobacco smoke in residences, iF values were reported to be in the range of w1000e2000 per million (Klepeis and Nazaroff, 2006). Our results for vehicle emissions in Hong Kong exceed the range of values previously reported for cities and are below those for typical indoor environments and for the Manhattan street canyon. There are several influential factors contributing to the diversity of iF values reported, including urban form, meteorological parameters, and so on, amongst which the population density is probably the most important. According to the box model presented by Marshall et al. (2005), all else being equal, iF is proportional to the linear population density, which is the population density (population per land area) multiplied by a characteristic length scale for the urban environment. The average population density in SoCAB was reported to be 860 km2 in Marshall et al. (2003), while Zhou and Levy (2008) estimated the population density in the Manhattan street canyon to be 70,000 km2 at night and 400,000 km2 during the day. The value for Hong Kong at around 6000 km2 is between the values in these two environments, and so it is not surprising that the iF values estimated for Hong Kong are also intermediate between the results for these other locations. One of the unique features of our analysis is that it explicitly accounts for the effect of upwind emissions and regional transport
In/near vehicles
Indoors at home
Other outdoor locations
Other indoor locations
350 300 Intake Fraction (per million)
Frequency
1917
250 200 150 100 50 0
1999
2000
2001
2002 2003 Year
2004
2005
2006
Fig. 4. Annual-average intake fraction for CO from motor vehicle exhaust in Hong Kong, apportioned by exposure location.
of CO. We stress that when exposure to air pollution is assessed for an urban area, it is very important to consider the extent to which that area is under the influence of regional pollutant transport. In the present case, had we ignored regional transport of CO into Hong Kong, the iF would have been overestimated at w400 per million. The iF can be allocated into contributions from exposures to different population age groups and in different microenvironments; results of such an allocation are displayed in Fig. 5. Being the largest subpopulation, adults experience about 60% of the overall iF for motor vehicle emissions in Hong Kong. For children and adults, it is interesting that the contributions from the microenvironments in/near vehicle and indoors at home are comparable, while the exposure in other outdoor environments makes the smallest contribution. Exposure to motor vehicle emissions in outdoor environments while in or near mobile sources is thus estimated to be similar to that occurring in indoor environments at home, even though people spend more than 60% of their time indoors, while the proportion of time spent in transportation is only 7%. An implication is that controlling and improving air quality in transportation microenvironments is comparably important as improving indoor environmental quality, at least with respect to protecting a highly urbanized population, such as Hong Kong's against direct emissions from vehicles. However, for the elderly, their exposure is more affected by the other indoor and outdoor environments than by the microenvironment in and near vehicles. Although our results appear reasonable in comparison to what is already known about emissions-exposure relationships, it should be noted that limitations and uncertainties for the present study arise from each step of estimating iF. The monitoring stations for CO are not sufficient to represent well the exposure conditions for the whole urban area of Hong Kong. We assumed, in effect, that the population is evenly distributed with respect to these monitoring stations, but in
Table 4 Contributions to urban CO concentration in Hong Kong owing to regional transport and local mobile emissions.a Percentage of CO contribution
1999
2000
2001
2002
2003
2004
2005
2006
Regional transport from north-east wind Regional transport from all wind directions
57% 36% 64% 54%
50% 36% 64% 54%
60% 41% 59% 49%
71% 48% 52% 43%
92% 60% 40% 33%
87% 53% 47% 39%
85% 51% 49% 41%
88% 53% 47% 40%
f1 f
a Note: f is the fraction of ambient concentrations attributable to local emissions from motor vehicles; f1 is the fraction of ambient concentrations attributable to local emissions.
1918
Z. Luo et al. / Atmospheric Environment 44 (2010) 1913e1918
No. HKU 7145/07E) and a CIBSE HK Branch grant. We thank Hong Kong Environmental Protection Department and Hong Kong Observatory for provision of the air pollution and weather data sets. We also thank an anonymous reviewer for his/her valuable comments and suggestions to improve the paper.
200
Intake Fraction (per million)
180 160 140 120
References
100 80 60 40 20 0 Children
Adults
Elderly
In/near vehicles
Indoors at home
Other outdoor locations
Other indoor locations
Fig. 5. Contributions to the intake fraction for primary pollutants from motor vehicles in Hong Kong, apportioned among different age groups. The intake fraction for the entire population would be represented by the sum of the three bars plotted.
the real situation, there are spatial and temporal distributions of population within the urban area. The breathing rates used were based on the study of American people. Possible differences between American and Asian populations, e.g. in body size and activity levels, introduces further uncertainty. Moreover, insufficient CO data for rural sites in relation to different wind directions also adds some uncertainty when applying the wind-filter method. Another important uncertainty might be due to the emission inventory, which was calculated by EPD Hong Kong using the EMFAC-HK model. Previous studies have shown that the uncertainty derived from EMFAC can be as high as 30% (Marshall et al., 2003). 4. Conclusions For carbon monoxide emitted by motor vehicles in the urban area of Hong Kong, we calculate an annual intake fraction of 270 per million, based on the monitored CO data distributed over the Hong Kong territory. The results are much higher than those in other cities, indicating a higher population exposure risk, per unit emissions, to vehicular pollutants emitted into the urban area of Hong Kong. The estimated high iF value results from the combination of various factors including the compact and high-rise urban morphology, high population density and typical meteorological conditions. One important contribution to iF estimation methodologies is that the transport of pollutants from upwind areas should be considered in some cases, like Hong Kong. The wind-filtering approach that we employed to estimate the portion of upwind transport to the total ambient pollution concentration may also be applied to other settings. The in/near vehicle microenvironment should be more carefully studied in future work owing to its high contribution to the overall iF value. Our study suggests that opportunities may exist to improve urban air quality in Hong Kong by reducing the concentrations in near-road microenvironments by means of better ventilation or filtration in addition to the traditional approach of implementing effective emission controls. Acknowledgements The work described was supported by the Research Grants Council of Hong Kong Special Administrative Region, China (Project
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