Sources and processes affecting concentrations of PM10 and PM2.5 particulate matter in Birmingham (U.K.)

Harrison, Roy M.; Deacon, Andrew; Jones, M.; Appleby, Robert S.

doi:10.1016/s1352-2310(97)00296-3

Cited by 299 publications

(145 citation statements)

References 11 publications

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“…A higher spatial variability was found for the coarse fractions (PM , PM , PM ). These findings are in line with previous findings [6][7][8][9]16 and did not depend on the method used to analyze the spatial variability. However, using multiple regression models to calculate mean values yielded slightly higher R 2 values as compared to the crude analyses.…”

Section: Discussionsupporting

confidence: 92%

“…Several studies in the United States and Great Britain found long-term mean PM 2.5 and PM 10 concentrations to be uniformly distributed within an urban environment (scale = 1-20 km), whereas a higher within-city variability was observed for the coarse fraction (2.5 < d < 10 µm). [6][7][8][9] However, two recent studies reported within-city spatial variability for both PM 2.5 and PM 10 in Canada and California, [10][11] but neither of these studies explicitly compared urban sites with different exposures to traffic.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Variability of Different Fractions of Particulate Matter within an Urban Environment and between Urban and Rural Sites

Röösli

Braun‐Fahrländer

Künzli

et al. 2000

Journal of the Air & Waste Management Association

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The spatial variability of different fractions of particulate matter (PM) was investigated in the city of Basel, Switzerland, based on measurements performed throughout 1997 with a mobile monitoring station at six sites and permanently recorded measurements from a fixed site. Additionally, PM 10 measurements from the following year, which were concurrently recorded at two urban and two rural sites, were compared.Generally, the spatial variability of PM 4 , PM 10 , and total suspended particulates (TSP) within this Swiss urban IMPLICATIONS It has become popular in recent years to use the concentration of PM as an indicator of air pollution exposure in epidemiologic studies. Since many of these studies assess the exposure of subjects based on one measurement per city, the accuracy of this technique will markedly affect the result of cross-sectional studies. High spatial variability of PM could result in a large non-differential misclassification of exposure that would lead to a smaller recognized health effect of air pollution.The remarkable spatial homogeneity of long-term mean PM levels clearly reduces the error of assigning data from one fixed monitoring site to all study subjects living in Basel, as was done in recent cross-sectional health studies in Switzerland (Swiss Study on Air Pollution and Lung Diseases in Adluts, Swiss Study on Childhood Allergy and Respiratory Systems). In fact, all participants lived in urban Basel, rendering PM 4 , PM 10 , and even TSP useful city-wide surrogates for long-term exposure to outdoor air pollution. environment (area = 36 km2 ) was rather limited. With the exception of one site in a street canyon next to a traffic light, traffic density had only a weak tendency to increase the levels of PM. Mean PM 10 concentration at six sites with different traffic densities was in the range of less than ±10% of the mean urban PM 10 level. However, comparing the mean PM levels on workdays to that on weekends indicated that the impact of human activities, including traffic, on ambient PM levels may be considerable.Differences in the daily PM 10 concentrations between urban and more elevated rural sites were strongly influenced by the stability of the atmosphere. In summer, when no persistent surface inversions exist, differences between urban and rural sites were rather small. It can therefore be concluded that spatial variability of annual mean PM concentration between urban and rural sites in the Basel area may more likely be caused by varying altitude than by distance to the city center.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Spatial Variability of Different Fractions of Particulate Matter within an Urban Environment and between Urban and Rural Sites

Röösli

Braun‐Fahrländer

Künzli

et al. 2000

Journal of the Air & Waste Management Association

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“…For example, Gupta et al (2006) concluded that the PM 2.5 concentration was about 62% of PM 10 concentration in the urban area of Kolkata, India. In addition, Harrison et al (1997) reported that approximately 60% of PM 10 was PM 2.5 in Birmingham, UK. Finally, Clarke et al (1999) found that 60-70% of urban PM 10 mass in city was typically in the PM 2.1 fraction, and 50% was below approximately 1.5 μm.…”

Section: Pm Concentration and Size Distributionmentioning

confidence: 99%

Comparing Physicochemical Properties of Ambient Particulate Matter of Hot Spots in a Highly Polluted Air Quality Zone

Tsai

Yuan

Hung

et al. 2010

Aerosol Air Qual. Res.

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The SURFER model is seldom used in combination with the CMB model to look for PM 10 hot spots and estimate the contribution of various sources, and this is the aim of the current study. In addition, the hot spots of ambient particulate matter (PM) and their physicochemical characteristics in a highly polluted zone in southern Taiwan were further investigated and compared. The experimental results show that PM 10 concentration in the fall was higher than in spring and summer. Moreover, northern monsoons transported suspended particles from the upwind emission sources to the sampling sites, causing an increase in secondary aerosols such as sulfate and nitrate. The contribution of secondary aerosols in rural areas such as Dai-liao and Chao-zhou (32.1% and 29.9%) and suburban areas like Ren-wu and Lin-yuan (28.7% and 29.0%) were higher than those in urban areas such as Hsiao-kang (20.3%). The higher Fe concentration in Hsiao-kang was attributed to PM emission from steel factories (6.9-7.8%). In this study, the organic carbon/elemental carbon (OC/EC) in PM 2.5 and PM 10 for the five sites were all in the order of Hsiao-kang > Ren-wu > Lin-yuan > Chao-zhou > Da-liao. In fall, farm burns are very common in both Dai-liao and Chao-zhou, and this source contributed approximately 7.3% and 6.3%, respectively, to these results. The seasonal variation of the contribution from vehicle exhausts to PM 10 (13.9-27.5%) at Hsiao-kang was always higher than those at other sites, especially in the fall. The results could provide important information for cost-effective control strategies to improve ambient air quality in hot spot areas.

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“…Motor vehicular emissions are a major source of fine particles in ambient air, especially in urban areas (Harrison et al, 1997;Kerminen et al, 1997;Ruellan et al, 2001;Harley et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Characterization of Roadside Fine Particulate Carbon and its Eight Fractions in Hong Kong

Cao

Lee

et al. 2006

Aerosol Air Qual. Res.

105

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Simultaneous measurements of PM 2.5 mass, OC and EC and eight carbon fractions were conducted in a roadside microenvironment around Hong Kong for a week in May-June 2002 to obtain the characterization of freshly emitted traffic aerosols. Traffic volume (diesel-powered, liquefied-petroleum gas and gasoline-powered vehicles), meteorological data, and sourcedominated samples were also measured. PM 2.5 samples were collected on pre-fired quartz filters with a mini-volume sampler and a portable fine-particle sampler, then analyzed for OC and EC using thermal optical reflectance (TOR) method, following the IMPROVE protocol. High levels of PM 2.5 mass (64.4 μg m -3 ), OC (16.7 μg m -3 ) and EC (17.1 μg m -3 ) observed in the roadside microenvironment were found to be well-correlated with each other. The average OC/EC ratio was 1.0, indicating that OC and EC were both primary pollutants. Marked diurnal PM 2.5 mass OC and EC concentration profiles were observed in accordance with the traffic pattern (especially for diesel vehicles). Average daytime concentrations were 1.3-1.5 times greater than nighttime values.Carbon profiles from source-dominated samples (diesel, LPG and gasoline vehicles) and diurnal variations of eight carbon fractions (OC1, OC2, OC3, OC4, EC1, EC2, EC3 and OP) demonstrated EC2 and OC2 were the major contributors to the diesel exhaust, and OC3 and OC2were the larger contributors to the LPG and gasoline exhaust. Thus, carbon fractions derived from the IMPROVE protocol could be used to identify different carbon sources.

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Sources and processes affecting concentrations of PM10 and PM2.5 particulate matter in Birmingham (U.K.)

Cited by 299 publications

References 11 publications

Spatial Variability of Different Fractions of Particulate Matter within an Urban Environment and between Urban and Rural Sites

Spatial Variability of Different Fractions of Particulate Matter within an Urban Environment and between Urban and Rural Sites

Comparing Physicochemical Properties of Ambient Particulate Matter of Hot Spots in a Highly Polluted Air Quality Zone

Characterization of Roadside Fine Particulate Carbon and its Eight Fractions in Hong Kong

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