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Atmospheric Environment 40 (2006) 5670–5684 www.elsevier.com/locate/atmosenv
Urban-scale variability of ambient particulate matter attributes M.T. Freiman, N. Hirshel, D.M. Broday Civil and Environmental Engineering, Technion, Israel Institute of Technology, Haifa, Israel Received 10 October 2005; received in revised form 9 April 2006; accepted 13 April 2006
Abstract Real-time sampling of ambient particulate matter (PM) in the size range 0.23–10 mm and of carbonaceous matter concentrations has been carried out in a carefully designed field campaign in proximate paired neighborhoods in Haifa, Israel. The paired sites are characterized by a similar population density and neighborhood-wise socioeconomic (deprivation) index but show distinct canopy coverage. The data indicate clear sub-urban (neighborhood) scale variations in any measured PM attribute, such as concentrations, size distribution, and carbonaceous matter content. Mean ambient PM levels were comparatively higher than in other urban studies whereas carbonaceous airborne PM concentrations were lower. On top of the diurnal and seasonal variability and in spite of the significant regional effect of the semi-arid climate, local emissions and removal processes affect the PM concentrations to which people residing in urbanized regions are exposed. Analysis of possible mechanisms that could affect the observed spatial sub-urban PM differences, including local meteorology and emissions, reveal that sub-urban variability of removal processes has a major influence on ambient PM levels. Observations suggest that on top of the regional air masses which affect the city air quality and emissions from local sources, a normally unnoticed removal process, showing urban scale variability, is interception by trees and dense vegetation. In particular, the observed sub-urban variability in ambient PM concentrations is attributed, in part, to local variation of removal processes, among them the neighborhood-wise deposition on available surfaces, including canopy. r 2006 Elsevier Ltd. All rights reserved. Keywords: Urban air quality; Particulate matter; Concentration variability; Neighborhood-scale; Trees and vegetation
1. Introduction Secondary airborne pollutants have been suggested to have almost uniform long-term mean concentrations over large urban regions, of the scale of intermediate sized cities and larger (Chung and Seinfeld, 2002). This is attributed to physicochemical atmospheric processes in the gas phase, gas-toparticle conversion, and aerosol aging, which are Corresponding author. Tel.: +972 4 829 3468; fax: +972 4 822 8898. E-mail address:
[email protected] (D.M. Broday).
1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2006.04.060
presumed uniform at such scales. However, these processes are known to be highly dependent on parameters that do show spatial variability to some extent, such as temperature, relative humidity, short wavelength (solar) radiation, and the level of precursors and atmospheric seeds. These variables may vary over urban surroundings due to local emissions (industrial and traffic related), proximity to specific geographical features, such as large bodies of water, a coast line, as well as land cover variations, e.g. a marked border between a green and shaded land and the arid zone surrounding it (Burton et al., 1996). Moreover, topographical
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features can induce sub-urban meteorological variations, thus forming within-city spatial variability of secondary aerosols (Ro¨o¨sli et al., 2000; Va¨keva¨ et al., 2000; Gehrig and Buchmann, 2003). Similarly, vehicle related air pollution disperses significantly over short distances from the emission source and is, therefore, inherently inhomogeneous among different urban zones such as the downtown and city center and residential suburbs (Kendall et al., 2002). Indeed, traffic induced particulate matter (PM) and gaseous pollution show urban scale variation that depends on the traffic density and road network (Harrison et al., 2004). Suspended PM consists of fractions of distinct size fractions, compositional content, physicochemical properties, aging history, toxicity and bioavailability upon activation after deposition in the lungs. These attributes are normally used to classify specific aerosol fractions as representing direct anthropogenic sources, coming from a biogenic origin, or resulted by atmospheric processes. Semi-volatile organic compounds (SVOCs), in particular precursors such as biogenic monoterpenes and anthropogenic aromatics, account for the majority of secondary organic aerosol (SOA) formation in many urban regions worldwide (in addition to sulfates and nitrates). For example, particulate organic carbon (OC), attributable to emissions from gasoline and diesel powered vehicles (e.g., PAHs adsorbed on PM) and to vegetation (e.g., monoterpenes adsorbed on PM) constitutes 60–80% of the total particulate carbon in urban areas (Kleeman et al., 2000). Similarly, about 70% of the total particulate elemental carbon (EC) is attributed solely to diesel traffic related emissions— the major contributor to urban carbonaceous aerosols (Lowenthal et al., 1994; A˚lander et al., 2004). Urban-scale variations of particulate EC/OC levels can, therefore, reflect spatially inhomogeneous emissions of these pollutants. Urban PM, especially the finer sized particles (dpo1 mm), is known to affect cloud formation and precipitation, light scattering and atmospheric visibility, and human health due to its confirmed adverse health effects (EPA, 2004). Due to their increased surface-area to mass ratio, fine primary and secondary particles can adsorb large amounts of toxic compounds (Mantis et al., 2005). Remaining suspended in the atmosphere for extended periods and often for long distances from their sources, residents of both urban and non-urban locales are consequently exposed to elevated levels of hazardous urban PM.
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Estimates from a recent study in Britain (Powe and Willis, 2004) indicate that woodland-related air pollution reduction (including PM10) prevents ca. 65–89 deaths that would otherwise be brought forward, and precludes ca. 45–62 hospital admissions per year. Nonetheless, to date, the impact of tree coverage on urban air quality has largely been inferred by models (Nowak and Crane, 1998) rather than assessed from experimental data. Trees can capture airborne particles, especially in the fine size range (o2.5 mm), due to their large leaf area index (LAI—the ratio of total one sided leaf area to the projected area of the canopy on the ground), which corresponds to available deposition surfaces. This mechanism has been suggested to significantly reduce ambient PM levels and consequently adverse health effects (Matzka and Maher, 1999; FreerSmith et al., 2004), with fully grown trees being able to remove three times more airborne particles than lower vegetation (Beckett et al., 2000a). However, this has normally been deduced from extended measurements showing that filters placed directly beneath trees collect less deposited particles than those placed in close by open locations (Singer et al., 1996). Urban and sub-urban trees in the Chicago region (21% average tree cover) were estimated to remove 9.8 tons PM10/day (in the spring-summer leaf-season) over an area of ca. 3313 km2, thus improving the hourly average air quality by 0.4% (2.1% in heavily wooded areas) (McPherson et al., 1994). It is expected that this beneficial effect of trees will be further enhanced in urban areas that are characterized by higher canopy cover and in areas that are in close proximity to pollution sources. The objective of this work is to evaluate the urban-scale spatiotemporal variability of PM from real time measurements, and to study possible mechanisms that can explain the origin of such a variability, including local meteorology, site-specific emissions (usually, traffic related), and the possible effect of urban trees. 2. Study area and sampling sites The main anthropogenic pollution sources in Israel are located along the coastal plains of the Mediterranean Sea, alongside the most densely populated areas. In Haifa, where industry, traffic and residential areas merge, urban air quality is of vital concern. The main sources of PM in the Haifa region, apart from natural dust and secondary
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atmospheric aerosols, are emissions from industrial and traffic related combustion processes. Stationary PM sources include a 426 MW oil fired power plant, refineries with annual refining capacity yield of ca. 8 million tons of crude oil, and a large nearby petrochemical complex. Other sources include chemical, agrochemical, metallurgic, food, and detergent industries, an open quarry, and Israel’s largest commercial seaport. Possible adverse health outcomes that are attributed to high PM concentrations (morbidity, loss of life expectancy and mortality), especially among the more vulnerable sub-population groups, such as children, the elderly and people suffering from chronic respiratory disease, are therefore becoming of paramount concern and urgency in the region (Goren et al., 1990). Sampling was performed at carefully selected sites in paired residential neighborhoods in Haifa, separated by less than 1 km in aerial distance (Fig. 1). The selection criteria of the paired sites
required neighborhoods that were as similar as possible in their topography, altitude, population density, socioeconomic status profile, neighborhood-wise business and commercial activity, and neighborhood-scale traffic density (Table 1). Yet, at the same time, paired neighborhoods with intentional disparity in canopy coverage were chosen. The tree cover of each neighborhood was determined by analyzing aerial photographs (from 1998) using ERDAS 8.0 (Table 2). The neighborhoods were thereby classified as higher- and lesser-vegetated neighborhoods. Special care was taken that the sampling sites indeed represent the neighborhood rather than the site conditions. This was tested and validated by comparing measurements gathered at different sites within the same neighborhood with cross-neighborhood measurements. The measurements were carried out in local parks and open spaces within the neighborhoods, at distances of about 20–50 m from the nearest street. Morning sampling was conducted in spring 2004 at
Fig. 1. The study area and the location of the sampling sites. G. Hagana (background site) is indicated by a triangle in Mt. Carmel N.P. The aerial photos of R. Remez (left) and R. Alon (right) at the bottom demonstrate the differences in vegetation cover.
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Table 1 Profiles of the paired residential neighborhoods in which sampling took place R. Alon1
R. Remez1
Vardiya2
Romema2
Establishment
Early 1990s
Early 1950s
Late 1980s
Neighborhood area (km2) Total population Density (population/km2) Average inhabitants per household Socioeconomic status (clusterno.) Socioeconomic status (grading) Average no. of cars per household Households owning vehicles (%) Households owning 42 cars (%)
0.25 2770 11,178 3.3 18 1355 1.2 88.7 31.7
0.38 4270 11,376 2.2 15 1164 0.64 56.8 7.5
0.83 5,610 6,764 3.2 18 1360 1.21 87.1 38.2
Old: 1950–1960s. New: 1980–1990s 0.49 3630 7368 2.7 18 1319 0.98 76.0 22.3
Paired neighborhoods are denoted by identical superscripts. Data (in part) from the 1999 census.
Table 2 Attributes of the sampled neighborhoods Site
Sampling period
Environment
Vegetation type
Tree cover (%)
R. Alon1
Spring 2004 and Summer 2004
Urban, Residential
36
R. Remez1
Spring 2004 and Summer 2004
Urban, Residential
Vardiya2
Summer 2004
Urban, Residential
Romema2
Summer 2004
Urban, Residential
G. Hagana
Spring 2004 and Summer 2004
Natural wood, Background
Young and small trees, mixed vegetation, private gardens – lawns, little cover Older and large trees, natural vegetation, relatively thick tree/shrub cover Private gardens and parks, small trees and lawns Older and large trees, natural vegetation Endemic Mediterranean trees (mostly pines, some oaks)
61
41 60 95
Paired neighborhoods are denoted by identical superscripts. G. Hagana is in Mt. Carmel National Park and serves as an urban background sampling site.
a subset of the sites, whereas in summer 2004 multiple daily sampling was performed. Specifically, having only one set of instruments, the paired neighborhoods were sampled on the same day in both the morning-noon sessions (09:00–11:00 and 11:00–13:00) and the afternoon-evening sessions (16:00–18:00 and 18:00–20:00) in an alternating sampling-period routine. A semi-urban, non-inhabited location at Mt. Carmel N.P. (G. Hagana) was chosen to represent an urban-background site (Table 2).
3. Instrumentation and methodology Intensive sampling campaigns were conducted during spring 2004 (29 February–17 May) and summer 2004 (11 July–14 September) throughout the weekdays (Sunday–Thursday in Israel). Size resolved PM concentrations were measured using a multi-channel (0.23–20 mm) mini-aerosol spectrometer (Model 1.108, Grimm Aerosol Technik GmbH & Co., Germany). A 1 min time resolution was used throughout the campaigns, and particles
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of size 0.23pdpp10 mm were further analyzed. PM2.5 and PM10 data collected at nearby stations of the air-quality monitoring network (AQMN) of the Haifa District Municipalities Association for the Environment (HDMAE) served as reference for these variables, against which readings were compared. Concentrations of particulate black carbon (BC) were measured by tracking differential absorption at 880 nm every 1 min using a two-channel Aethalometer (Magee Scientific Inc., Berkeley, CA). Similarly, the relative concentration of UV (370 nm) absorbing particulate matter, which corresponds to species present in fresh diesel exhaust, was also measured. Methods for analyzing Aethalometer data are described in detail by Hansen et al. (1984). The validity of Aethalometer data relative to data obtained by other analytical methods has been discussed by Babich et al. (2000). The mass absorption efficiency of the Aethalometer was 16.6 m2 g1 (BC) and 39.5 m2 g1 (UV) and the sample flow rate was 3.6–3.7 L min1. It should be noted that BC, which resides in the fine (predominantly submicron) particulate fraction and is persistent in the air, serves as a better proxy of exposure to anthropogenic fine particulate matter than PM2.5, as the latter often underestimates exposure from traffic emissions (Fischer et al., 2000; Cyrys et al., 2003). Conventional meteorological parameters (temperature, relative humidity, pressure, wind velocity and direction) were recorded by a portable meteorological station. In addition, meteorological records from HDMAE’s AQMN stations that are in close proximity to the sampling sites were used throughout the sampling period. Wind directions at the paired sites R. Alon and R. Remez compared favorably with those of the monitoring station at Neve Shanan—a nearby residential neighborhood. Similarly, the dominating wind directions at the paired sites of Vardiya and Romema were almost identical to those measured at the monitoring station at Ahuza—another nearby AQMN residential station. Wind speeds at the sampling sites seem to be slightly calmer than in the reference sites, probably due to ground-level sampling in comparison with the 10–12 m high sampling at Neve Shanan and Ahuza, respectively. The average temperature during the summer sampling period was 25 1C and the relative humidity, as measured by the Neve Shanan monitoring station, was roughly 70%.
The data obtained during the field campaigns were carefully inspected and quality control procedures were employed. Dust storms are common during the spring and the summer seasons in Israel, and were excluded to a large extent from the data prior to analyses. Moreover, the raw data passed a carefully designed filter for removing readings believed to be erroneous. Overall, less than 1.5% of the data were declared unreliable. Statistical analyses were carried out for each site for the different sampling periods and for different particle size fractions and other measured variables. The Wilcoxon Matched Pairs Signed Rank test and Mann–Whitney U rank test were used to evaluate the significance of the differences in the devised indices and ambient levels of distinct particle size fractions, BC, and total carbon (TC ¼ BC+OC) at the paired sites. Neighborhood vehicle counts and categorization were performed during the summer campaign at the paired sites R. Remez and R. Alon. It should be emphasized that sampling was performed only during business days and that the summer campaign took place during the school holidays. These observations are related to traffic density attributes in the paired sites. 4. Results and discussion 4.1. PM size distribution A bi-modal particle size distribution (accumulation and coarse modes) has been observed at all the neighborhood sites during both sampling campaigns (spring and summer) (Fig. 2), in agreement with previous findings from urban sites (Tuch et al., 1997). The accumulation mode, which is normally attributed to coagulation and growth of both primary and secondary nucleating species, peaks in the submicron size range, between 0.23 and 0.3 mm or at smaller sizes not resolved by our instrumentation. A sharp secondary peak is evident between 0.9 and 1 mm. The coarse mode, commonly attributed to mechanically generated natural (e.g., sea salt, pollen) and anthropogenic (e.g., resuspension from road beds) emissions shows elevated readings in the alveolar mode, 2–5 mm, (Fig. 2). Normally, the urban PM2.5/PM10 ratio indicates considerable spatiotemporal variability (Brook et al., 1997; Monn, 2001; Querol et al., 2004). Nonetheless, in sub-urban and background regions comparable to Haifa this ratio is normally higher than observed in this work, indicating that the particle size
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Fig. 2. Site specific mean size-resolved concentrations as measured in the morning-noon session in the spring and summer of 2004 at (a) R. Alon, (b) R. Remez and (c) G. Hagana.
distribution in Haifa has a large contribution of coarse particles. This suggests that the coarse fraction is either affected by a background level of naturally occurring dust or that the region (or the sampling sites) is characterized by high levels of primary coarse PM emissions. These alternatives are examined below. 4.1.1. Data validity A detailed comparison revealed that PM10 readings obtained by our optical counter are normally higher than those measured by the beta attenuation gage at the Neve Shanan monitoring station. The discrepancies are, however, fairly consistent and can therefore be corrected. Measured PM2 and PM3 levels in the morning-noon and the afternoonevening at all the sites, on the other hand, are in reasonable agreement with the Neve Shanan PM2.5 data, suggesting that the aerosol mini spectrometer and the beta attenuation gage respond differently mainly to the coarse particle fraction. 4.2. Temporal variability 4.2.1. Seasonal differences Marked seasonal variation is observed between the summer and spring mean concentrations in the
paired sites R. Alon and R. Remez. In the morning, concentrations of the fine (0.23–0.4 mm) fraction are elevated in the summer in comparison with the spring, especially in R. Remez, whereas levels of the coarse mode (dp42 mm) are significantly higher during the spring (Fig. 3). PM10 levels are elevated at all sites in the spring, in particular at the background site G. Hagana in Mt. Carmel National Park (74726 mg m3), due to considerable amounts of natural PM such as Saharan dust and coarse biogenic PM (pollen), known to prevail at this period of the year. The greatest seasonal variations occur in the coarser size fraction, PM210, rather than in the fine fraction. The very low PM2/PM10 ratio, 0.26–0.32, in the spring at all sites (Fig. 3) supports this observation, in accordance with Vallius et al. (2000) and Gehrig and Buchmann (2003). The TC-to-PM0.3 ratio in the morning-noon in the paired sites R. Alon and R. Remez is significantly higher in the spring (0.68 and 0.63, respectively) than in the summer (0.45 and 0.40, respectively). This observation is attributed to the higher concentrations of fine PM in the summer as a result of increased levels of humidityinduced haze and photochemistry induced secondary PM.
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Concentration (µg m-3) Concentration (µg/m^3)
PM1
PM2
PM3
PM10
80
60
40
20
Spring morning
0.86
Summer morning
0.7
0.76
Romema(12)
Vardiya (12)
Remez (20)
Alon (20)
Hagana (8)
Romema (8)
Vardiya (8)
Remez (14)
Alon (14)
Hagana (4)
Remez (7)
Alon (6)
0
Summer evening
0.8
0.66
Fig. 3. Mean PM concentrations. The sampling campaign season, period and site are indicated on the x-axis. The number of measurement repetitions is indicated in brackets next to the site name. The correlation coefficient between PM2 and PM10 at the paired sites is indicated below the figure.
4.2.2. Diurnal trends With the decrease in atmospheric stability and the increase in the vertical mixing height, as the morning progresses towards midday, a decrease in ambient concentrations of PM and carbonaceous material, often cited in the literature, is observed at all sites both in the spring and the summer sampling campaigns. The typical diurnal variation (Fig. 4), i.e. the decline in concentrations towards noon and the increase in concentrations in the afternoon, is similar to that noted by Laakso et al. (2003). The morning and evening peaks are attributed to high volume of traffic at these hours and to the late afternoon switch between the sea and land breeze, which may cause almost stagnant conditions. 4.3. Spatial variation There are clear differences between the paired sampling neighborhoods, with R. Alon and Vardiya (the less vegetated neighborhoods, see Table 2) displaying higher morning-noon PM concentrations than (the more vegetated) R. Remez and Romema. This observation is consistent for all size fractions, both in the spring (where measurements were taken only in R. Alon and R. Remez) and the summer (Fig. 3). The Pearson correlation coefficient between PM2 and PM10 at the paired sites is moderate to
high (Fig. 3), with the greatest inter-site variation apparent for thoracic particles (dpp10 mm). For example, the difference between mean summer PM10 levels in the paired sites Vardiya and Romema was 11.8 mg m3 (18%). In general, inter-neighborhood variability in PM levels during the morning-noon sampling sessions is relatively high whereas inter-site differences in mean concentrations during the afternoon-evening sessions are rather small (Fig. 3). While PM10 levels at Vardiya are higher than at Romema (statistically significant) both in the morning and the afternoon, a mixed trend is apparent at the paired sites of R. Alon and R. Remez with higher levels of all size-fractions in R. Alon in the morning and lower levels in the evening. Significant urban-scale spatial variations of PM10 levels have been reported by Burton et al. (1996) and Janssen et al. (1999). In particular, in the San Joaquin Valley, CA, which has a comparable terrain and emissions profile to the Haifa region, PM10 spatial variation of about 20% have been noted over distances ranging from 4 to 14 km away from the monitoring stations (Blanchard et al., 1999). Spatial patterns of PM1, PM2, and PM3 were also found to vary across the city, showing significant variation among the sampled sites. During the summer, the levels of fine PM at these fractions
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PM0.3
TC
PM10 70
12
R. Remez
R. Alon
R. Alon
R. Remez 60
10
50 8 40 6 30 4 20 2
0 8:38
PM10 concentration (µg m-3)
PM0.3 and TC concentrations (µg m-3)
5677
10
11:02
13:26
15:50
18:14
0 20:38
Time Fig. 4. A characteristic diurnal time series. Concentrations measured at the paired sites R. Alon and R. Remez on 15 August 2004. The PM0.3 and the TC concentrations refer to the ordinate on the left whereas PM10 values refer to the ordinate on the right. Similar patterns were obtained regardless of the order of sites in which the alternating measurements were performed.
were all highest in Vardiya (urban site, poorly vegetated) during both the morning and the evening, followed by G. Hagana (background site, highly vegetated). Actually, the annual mean PM2.5 level at Neve Shanan monitoring station (24 mg m3), whose half-hourly data compare favorably with the data at all the sampling sites (see Section 4.1.1), were higher than those reported in many European cities (e.g., Hoek et al., 2002). The rather constant PM1 to PM2 ratio (0.84–0.90 in the summer and 0.75–0.81 in the spring) and their close correlation (rX0.94 in the summer) at all the sampling sites imply that these fractions are affected only to a minor extent by neighborhood-specific emissions (see next section), in agreement with Vallius et al. (2000). It is, therefore, possible to estimate quite reliably the concentration of any of these fractions, at any site, given the concentration of the other fraction. 4.4. Traffic-related PM concentrations The variability of PM levels among the sampled neighborhoods (Fig. 3) may reflect distinct strengths of local sources within these neighborhoods, with traffic related emissions being a major possible source. In order to pinpoint this aspect, particulate
BC/OC concentrations were measured simultaneously with the aerosol size distribution. Vehicle volume, type, driving speed, engine load, and age all affect emissions of particulate BC and OC from tail pipes (Shah et al., 2004). These emissions disperse rapidly in the ambient air due to turbulent convective mixing and therefore vehicle emissions are normally accounted for only up to about 300 m downwind of the road. This clearly suggests that spatial variation in PM concentrations and size distribution are to be expected in regions characterized by a heterogeneous network of busy roads and quiet streets, like the one found in any urbanized region. Specific to Haifa, traffic emissions are increased due to its topography, since much uphill travel is required for navigation within the city (Tartakovsky et al., 1997; Tong et al., 2000; Shah et al., 2004). It is, therefore, expected that residential proximity to major roads may, to some extent, be a proxy for increased ambient concentrations of motor-vehicle induced airborne pollutants. In order to eliminate this effect as much as possible, all the sampling sites were located in quiet residential neighborhoods with minimal commercial activity and the sampling was carried out in the main park within the neighborhood. Nonetheless, to account for emissions due to actual traffic volume, traffic
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counts were carried out during the summer sampling sessions in the paired sites R. Alon and R. Remez, with vehicles classified according to five classes (Table 3). Mean counts indicate that R. Remez (the more vegetated neighborhood) had a heavier traffic volume of all vehicle types during both the morning-noon and the afternoon-evening periods. Yet while the evening peak traffic is noted at both sites, the elevated traffic counts in R. Remez are more than two-fold that in R. Alon. The lowest summer concentrations of particulate BC and OC were observed at G. Hagana (a background site within Mt. Carmel N.P.) and are attributed to the relatively lack of heavy traffic volume. In R. Remez, the afternoon-evening vastly increased volume of private vehicles (mostly gasoline-operated) and the doubling in number of dieseloperated vehicles, known to be a major source of BC (EPA, 2004), is somehow not evident as excess BC concentrations. Yet, while the morning-noon BC-to-OC correlation coefficients at the paired sites of R. Alon and R. Remez are similar (r ¼ 0.69 and r ¼ 0.64, respectively), in the evening the coefficient remains unchanged in R. Alon (r ¼ 0.69) but increases markedly in R. Remez (r ¼ 0.89), thereby indicating the influence of the increased volume of diesel vehicles. In fact, the higher BC concentrations noted during the morning-noon session in R. Alon, contrary to the diurnal trends apparent in the vehicle counts (Table 3), may result from adverse meteorological mixing conditions under extremely stable morning conditions (including a prominent inversion), which trap PM emissions from local ground level combustion sources—both local and regional. Likewise, the slightly elevated afternoonevening PM1 concentrations in R. Remez (without a comparable increase in BC and with the TC concentrations representing 12% of the mean PM1 concentration) may represent either noncarbonaceous traffic related emissions or regional/ long-range transport of primary and secondary fine PM. It is noteworthy that due to lower diesel traffic
density in Haifa, the observed mean BC levels are generally lower than those reported in other suburban (Bre´mond et al., 1989), background (Gray et al., 1986) and urban roadside (Ruellan and Cachier, 2001) studies, although comparable to summer mean BC concentrations in Philadelphia (1.1 mg m3) and Riverside (1.6 mg m3) (Babich et al., 2000). Sub-urban variability of BC/OC concentrations, like that of PM concentrations, is noticeable. The TC content of the PM0.3 size fraction supports this observation (Fig. 5) while also indicating significantly higher TC-to-PM0.3 ratios during the morning-noon. Whereas BC and OC levels are highly correlated at each of the paired neighborhoods during the morning and the evening both in the summer (rX0.9) and the spring (r40.95), mean particulate BC concentrations in the paired neighborhoods of Vardiya and Romema are comparatively higher during summer days than in the paired neighborhoods of R. Alon and R. Remez (the differences lessen in the evening). Similarly, the BCto-OC summer morning correlation coefficients are higher in Vardiya and Romema (r ¼ 0.82) than in R. Alon and R. Remez (r ¼ 0.66). Spatiotemporal variability in BC levels is also evident on a smaller scale—between the paired neighborhoods. While in R. Remez BC concentrations were similar in the spring and the summer campaigns as well as in the morning-noon and afternoon-evening sessions (2% mean difference), morning-noon concentrations in R. Alon were elevated (35%) than the afternoon-evening readings and averaged 26% higher than in R. Remez (Fig. 5). These observations cannot be explained based on traffic counts (Table 3). Interestingly, comparing two traffic-exposed sites in Budapest, lower night BC and OC concentrations were also observed in the more vegetated site (Salma et al., 2004). Since neighborhood scale emissions of fine PM lack the power to explain the origin of the sub-urban
Table 3 Mean vehicle counts by type and time of day (h1) Site
Period
Private
Diesel
Truck
Bus
Motorcycle
R. Alon
Morning-noon Afternoon-evening
99.6 163.1
6.2 10.5
1.1 0.7
0.9 0.0
2.3 1.9
R. Remez
Morning-noon Afternoon-evening
207.3 466.1
22.7 46.8
7.3 5.4
37.7 31.2
1.7 5.5
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32 OC OC
PM1 PM1
Spring morning
TC/ PM1 TC/ PM0.3
0.21 0.68
0.17 0.63
0.14 0.40
0.12 0.32
Romema(12)
Summer evening
Summer morning
0.16 0.45
Vardiya (12)
0 Remez (20)
0 Alon (20)
8
Romema (8)
1
Vardiya (8)
16
Remez (14)
2
Alon (14)
24
7) Remez ( 7
3
PM1 Concentration (µg m-3)
BC
Alon (6)
BC, OC and TC concentrations (µg m-3)
4
0.11 0.28
0.12 0.33
0.12 0.32
0.09 0.23
0.08 0.21
Fig. 5. Mean concentrations of black carbon (BC), organic carbon (OC), total carbon (TC ¼ BC+OC), and PM1 at the paired neighborhoods in the spring and the summer of 2004. The ratio of total carbon (TC) to the finest particle fractions, TC/PM1 and TC/PM0.3, at each site is given at the bottom.
PM and BC variability, it seems that this disparity may represent either local meteorological conditions (which may affect PM dilution and dispersion) or distinct local removal processes. These explanatory variables will be examined next. 4.5. Local meteorology The seasonal and diurnal wind patterns at the sampling sites indicate the dominance of the summer Westerlies (Fig. 6). These winds advect cool and humid air masses onshore, which contain marine aerosols and residues of anthropogenic aerosols from Europe (Wanger et al., 2000; Matvev et al., 2002). Once onshore, the air is forced to ascend orographically past Mt. Carmel. The synoptic winds coincide and reinforce the Mediterranean sea breeze and the anabatic onshore winds that develop and are forced to ascend orographically upon the slopes of Mt. Carmel (Dayan and Rodnizki, 1999) and may inject pollutants into the base of the inversion layer characteristic of summer conditions. These conditions are associated with poor mixing, up to a complete stagnation within a
relatively shallow mixed layer (Ziv et al., 2004), thus increasing the surface concentrations of trapped and aged pollutants. More than half of the summer morning-noon near-ground winds at R. Alon and R. Remez were dominated by the prevailing southwesterly winds, with an increased contribution from the westsouthwest at noontime (Fig. 6). West-southwesterly winds almost exclusively dominate the wind pattern at the paired neighborhoods of Vardiya and Romema, which are located near the front of the Carmel ridge (300–500 m a.m.s.l, see Fig. 1). Mean ground-level wind speeds are high (up to 8 m s1), mainly during the summer, with morning wind speeds in the vegetation-rich neighborhoods (R. Remez and Romema) significantly lower than in the vegetation-poor neighborhoods (R. Alon and Vardiya). These findings are in agreement with Heisler (1990), who found that wind speed at 2 m a.g.l. were notably lower in a residential neighborhood with high (67%) tree cover than in a nearby twin bare neighborhood. In the late afternoon, the winds veer under the influence of katabatic winds and the land breeze (Fig. 6). The winds are calmer
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Fig. 6. Seasonal and diurnal mean winds. Circles demarcate 20% frequencies unless otherwise specified. Wind course is from the directions shown.
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than during the day but can get gusty, which has implications on local to regional aerosol transport (Jones and Harrison, 2004). The shift from sea to land breeze, combined with the change from anabatic to katabatic winds during the summer evening hours, can induce recirculation of pollutants over Haifa bay (Koch and Dayan, 1992). During spring, a shallow mixed layer accompanied by weak zonal winds driven by the offshore nearby high-pressure system that moves eastward to Israel is associated with strong (5–8 m s1) southwesterly winds. Occasionally during spring, a change in the coupling between the offshore Easterly synoptic flow and the opposing daily sea breeze causes a very stable offshore profile. Under such conditions, an increase in the concentrations of ambient pollutants at the lower atmosphere is evident (Koch and Dayan, 1992). Nonetheless, in general it is the calmer northerly and easterly sector winds that transport air masses to R. Alon and R. Remez (Fig. 6). Most important, however, is that in spite of the temporal variability in wind speed and direction, real-time data from this study suggest that the variation of different PM attributes cannot be associated with comparable variations of micrometeorological parameters. It appears that, due to their geographical proximity, the paired neighborhoods are affected by the same air masses. Moreover, as local emission sources have already been excluded from being the major cause for the observed inter-neighborhood variations in PM concentrations, it seems that these variations may result from local dissimilarity of removal processes. 4.6. Vegetation effect Although urban trees are responsible for the release of biogenic emissions that serve as precursors to secondary aerosol formation (Andreae and Crutzen, 1997), thereby offsetting some of their PM removal potential, trees have the capacity to reduce street level PM. The large trees in the older neighborhoods (R. Remez and Romema) and in the background site (G. Hagana) could, according to McPherson et al. (1994), remove an estimated 60–70 times more pollution than the smaller trees in the vegetation-poor neighborhoods—R. Alon and Vardiya. Particle uptake by trees is species specific (reflecting the tree structure and the nature of its foliage) and depends on the particle concentration and the wind speed (Beckett et al., 2000b; Freer-
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Smith et al., 2004). Endemic pine and oak are characteristic trees in the neighborhoods examined. The former, although less hardy to pollution, is a more efficient scavenger of coarse and fine PM (Singer et al., 1996). Models such as the Urban Forest Effects (UFORE) model calculated a 13% short-term improvement in PM levels in urban areas with 100% tree cover (Nowak and Crane, 1998). Although the urban scale examined in this work is relatively small and is characterized by a lower percentage of tree cover, air quality seems to be improved in neighborhoods with an increased canopy cover. Neither sophisticated models nor field research suggest that the interception capability of urban trees can be unambiguously detected, yet a clear disparity in ambient PM concentrations between neighborhoods with dense and thin tree cover has been indicated in this work. The belowcanopy (ground level) differences are stronger during the morning hours, when the stomata are open and transpiring and the atmospheric boundary sub-layer is still relatively shallow. Furthermore, the morning switch from land to sea breeze reduces the mixing height, thereby increasing the relative effect of trees in reducing airborne pollution. The removal of airborne particles by trees in the late afternoon seems to be less efficient.
5. Conclusions Different PM attributes were measured in the spring and summer of 2004 in paired nearby residential neighborhoods in Haifa, Israel, which are characterized by a similar population density and socioeconomic and business profiles but distinct canopy cover. At all the sampling sites, submicron particles (0.23–0.4 mm) were elevated in the summer whereas coarser particles (dp42 mm), which are more affected by seasonality, indicated higher concentrations in the spring. During both seasons, the PM size distribution had a significant contribution of coarse particles, which is characteristic of a semi-arid region. Still, in the summer fine particles comprised up to 50% of the PM10 mass. Clear differences in mean concentrations of PM of different sizes and of BC were observed between the paired neighborhoods, with the less vegetated neighborhoods displaying higher morning-noon concentrations of all the PM size fractions both in the spring and the summer.
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Carbonaceous aerosols are a ubiquitous component of fine PM in urban areas and can contribute up to 80% of their mass. It is generally believed that vehicle-induced emissions are the dominant source for this fraction. Yet, although particulate BC levels showed considerable sub-urban variability, the spatial and the temporal patterns could not be fully attributed, based on simultaneous traffic counts, to local traffic emissions. The dominance of the prevailing synoptic winds has been observed at all sites, with the sea and land breezes and the thermotopographically induced mountain and valley winds overlaying the general flow pattern. However neither regional nor local meteorology appear to be the reason for the observed concentration differences between the paired proximate neighborhoods. Although the benefit from urban vegetation induced air pollution reduction may be an order of magnitude smaller than from other removal mechanisms (wet and dry deposition), the role of trees in improving sub-urban air quality has been demonstrated. We assume that the main reason for this is the excess of deposition surfaces due to the large tree’s LAI. It is expected that some variation in air quality may also be present among less vegetated urban regions, such as a downtown with high-rise buildings and a zone characterized by lower buildings, due to disparity in built surfaces amenable for particle deposition. Measured mean PM10 concentrations in Haifa are higher both in the spring and in the summer than in southern California (cf. Gauderman et al., 2000), a region of comparable climate, topography and emission profile. This is attributed in part to the presence of excess dust in the air even when dust storm events are not apparent. However mean PM2 and PM3 concentrations levels in Haifa were also elevated in comparison with data from southern California, which may indicate excess anthropogenic emissions (either primary emissions or secondary PM). On the other hand, BC and OC concentrations in Haifa were lower than those reported elsewhere in urban regions. While Gauderman et al. (2000) had noted that even the relatively lower PM concentrations observed in southern California have respiratory health implications in children during their developmental years, particulate BC was suggested a better proxy of exposure to anthropogenic pollution in busy cities (Cyrys et al., 2003). Thus, while people in Haifa are exposure to higher PM levels than people living in comparable cities in Europe and the US, they are exposed to
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