The Longitudinal Dependence of Black Carbon Concentration on Traffic Volume in an Urban Environment

Abstract The purpose of this study was to evaluate the effect of traffic volume on ambient black carbon (BC) concentration in an inner-city neighborhood “hot spot” while accounting for modifying effects of weather and time. Continuous monitoring was conducted for 12 months at the Baltimore Traffic Study site surrounded by major urban streets that together carry over 150,000 vehicles per day. Outdoor BC concentration was measured with an Aethalometer; vehicles were counted pneumatically on two nearby streets. Meteorological data were also obtained. Missing data were imputed and all data were normalized to a 5-min observational interval (n = 105,120). Time-series modeling accounted for autoregressively (AR) correlated errors. This study found that outdoor BC was positively correlated at a statistically significant level with neighborhood-level vehicle counts, which contributed at a rate of 66 ± 10 (SE) ng/m3 per 100 vehicles every 5 min. Winds from the SW-S-SE quarter were associated with the greatest increases in BC (376-612 ng/m3). These winds would have entrained BC from Baltimore’s densely trafficked central business district, as well as a nearby interstate highway. The strong influence of wind direction implicates atmospheric transport processes in determining BC exposure. Dew point, mixing height, wind speed, season, and workday were also statistically significant predictors. Background exposure to BC was estimated to be 905 ng/m3. The optimal, statistically significant representation of BC’s autocorrelation was AR([1:6]) × 288 × 2016, where the short-term AR factor (lags 1-6) indicated that BC concentrations are correlated for up to 30 min, and the AR factors for lags 288 and 2016 indicate longer-term autocorrelations at diurnal and weekly cycles, respectively. It was concluded that local exposure to BC from mobile sources is substantially modified by meteorological and temporal conditions, including atmospheric transport processes. BC concentration also demonstrates statistically significant autocorrelation at several time scales.

[1]  Ronald H. White,et al.  Socioeconomic and Racial Disparities in Cancer Risk from Air Toxics in Maryland , 2005, Environmental health perspectives.

[2]  Tami C. Bond,et al.  Calibration and Intercomparison of Filter-Based Measurements of Visible Light Absorption by Aerosols , 1999 .

[3]  Eduardo Behrentz,et al.  Characterizing the range of children's air pollutant exposure during school bus commutes , 2005, Journal of Exposure Analysis and Environmental Epidemiology.

[4]  C. Hogstedt,et al.  Myocardial infarction among professional drivers : Epidemiology , 2003 .

[5]  Yifang Zhu,et al.  Concentration and Size Distribution of Ultrafine Particles Near a Major Highway , 2002, Journal of the Air & Waste Management Association.

[6]  Nicole A. Lazar,et al.  Statistical Analysis With Missing Data , 2003, Technometrics.

[7]  Antonella Zanobetti,et al.  Diabetes Enhances Vulnerability to Particulate Air Pollution–Associated Impairment in Vascular Reactivity and Endothelial Function , 2004, Circulation.

[8]  R. H I T Z E N B E R G E R,et al.  Intercomparison of Thermal and Optical Measurement Methods for Elemental Carbon and Black Carbon at an Urban Location , 2022 .

[9]  Richard L. Verrier,et al.  Association of Air Pollution with Increased Incidence of Ventricular Tachyarrhythmias Recorded by Implanted Cardioverter Defibrillators , 2005, Environmental health perspectives.

[10]  Howard Wachtel,et al.  Distance-Weighted Traffic Density in Proximity to a Home Is a Risk Factor for Leukemia and Other Childhood Cancers , 2000, Journal of the Air & Waste Management Association.

[11]  Per Gustavsson,et al.  Myocardial Infarction Among Professional Drivers , 2003, Epidemiology.

[12]  John D. Spengler,et al.  Autocorrelation in real-time continuous monitoring of microenvironments , 2002 .

[13]  U Keil,et al.  Self-reported wheezing and allergic rhinitis in children and traffic density on street of residence. , 1994, Annals of epidemiology.

[14]  H. M Oosmu ‹ Ller,et al.  Time-Resolved Characterization of Diesel Particulate Emissions. 2. Instruments for Elemental and Organic Carbon Measurements , 2001 .

[15]  José Holguín-Veras,et al.  Elemental carbon and PM(2.5 )levels in an urban community heavily impacted by truck traffic. , 2002, Environmental health perspectives.

[16]  Pratim Biswas,et al.  Concentration gradient patterns of aerosol particles near interstate highways in the Greater Cincinnati airshed. , 2003, Journal of environmental monitoring : JEM.

[17]  Bert Brunekreef,et al.  Air Pollution from Truck Traffic and Lung Function in Children Living near Motorways , 1997, Epidemiology.

[18]  J F Collins,et al.  Time-resolved characterization of diesel particulate emissions. 2. Instruments for elemental and organic carbon measurements. , 2001, Environmental science & technology.

[19]  J Schwartz,et al.  Traffic related pollution and heart rate variability in a panel of elderly subjects , 2005, Thorax.

[20]  J. Behren,et al.  Traffic density in California: Socioeconomic and ethnic differences among potentially exposed children , 2003, Journal of Exposure Analysis and Environmental Epidemiology.

[21]  U. Baltensperger,et al.  Elemental carbon (EC) and black carbon (BC) measurements with a thermal method and an aethalometer at the high-alpine research station Jungfraujoch , 1999 .

[22]  P. Vokonas,et al.  Inflammatory markers and particulate air pollution: characterizing the pathway to disease. , 2006, International journal of epidemiology.

[23]  E. Knox,et al.  Oil combustion and childhood cancers , 2005, Journal of Epidemiology and Community Health.

[24]  P. Kinney,et al.  Airborne concentrations of PM(2.5) and diesel exhaust particles on Harlem sidewalks: a community-based pilot study. , 2000, Environmental health perspectives.

[25]  P. Hopke,et al.  Temporal variations and spatial distribution of ambient PM2.2 and PM10 concentrations in Dhaka, Bangladesh. , 2006, The Science of the total environment.

[26]  V. Kotroni,et al.  Analysis of climatological and air quality observations from Greater Athens Area , 1995 .

[27]  P. Diggle.  Time Series: A Biostatistical Introduction , 1990 .

[28]  Philip Demokritou,et al.  Measurements of PM10 and PM2.5 particle concentrations in Athens, Greece , 2003 .

[29]  T. Novakov.  The role of soot and primary oxidants in atmospheric chemistry , 1984 .

[30]  J. Chow,et al.  Nanoparticle and Ultrafine Particle Events at the Fresno Supersite , 2006, Journal of the Air & Waste Management Association.

[31]  M. Wjst,et al.  Road traffic and adverse effects on respiratory health in children. , 1993, BMJ.

[32]  J. Schwartz,et al.  Air pollution and emergency admissions in Boston, MA , 2006, Journal of Epidemiology and Community Health.

[33]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data , 2002 .

[34]  M. Schnaiter,et al.  Absorption of light by soot particles: determination of the absorption coefficient by means of aethalometers , 2003 .

[35]  Rakesh B Singh,et al.  Development of a Microscale Emission Factor Model for Particulate Matter for Predicting Real-Time Motor Vehicle Emissions , 2003, Journal of the Air & Waste Management Association.

[36]  E Rokita,et al.  Transport of traffic-related aerosols in urban areas. , 2000, The Science of the total environment.

[37]  J. Schwartz,et al.  Air Pollution and ST-Segment Depression in Elderly Subjects , 2005, Environmental health perspectives.

[38]  B. Ostro,et al.  Proximity of California public schools to busy roads. , 2003, Environmental health perspectives.

[39]  B. Glaser,et al.  Source apportionment of organic pollutants of a highway-traffic-influenced urban area in Bayreuth (Germany) using biomarker and stable carbon isotope signatures. , 2005, Environmental science & technology.

[40]  R. Yancik,et al.  Impact of changes in transportation and commuting behaviors during the 1996 Summer Olympic Games in Atlanta on air quality and childhood asthma. , 2001, JAMA.

[41]  D. Kittelson.  Engines and nanoparticles: a review , 1998 .

[42]  Trivellore E Raghunathan,et al.  What do we do with missing data? Some options for analysis of incomplete data. , 2004, Annual review of public health.

[43]  B. Brunekreef,et al.  Motor vehicle exhaust and chronic respiratory symptoms in children living near freeways. , 1997, Environmental research.

[44]  P. Paatero,et al.  PM source apportionment and health effects: 1. Intercomparison of source apportionment results , 2006, Journal of Exposure Science and Environmental Epidemiology.

[45]  L Morawska,et al.  A model for determination of motor vehicle emission factors from on-road measurements with a focus on submicrometer particles. , 2001, The Science of the total environment.

[46]  William F. Christensen,et al.  Workgroup Report: Workshop on Source Apportionment of Particulate Matter Health Effects—Intercomparison of Results and Implications , 2005, Environmental health perspectives.

[47]  J. Gibb,et al.  MARYLAND STATE HIGHWAY ADMINISTRATION , 2002 .

[48]  W. Greene,et al.  计量经济分析 = Econometric analysis , 2009 .