Total and size-resolved particle number and black carbon concentrations near an industrial area

2015 ◽  
Vol 122 ◽  
pp. 196-205 ◽  
Author(s):  
M.P. Keuken ◽  
M. Moerman ◽  
P. Zandveld ◽  
J.S. Henzing
Keyword(s):  
Black Carbon ◽  
Particle Number ◽  
Industrial Area ◽  
Carbon Concentrations

scholarly journals Effect of Reducing Ambient Traffic-Related Air Pollution on Blood Pressure

Hypertension ◽  
2021 ◽  
Vol 77 (3) ◽  
pp. 823-832
Author(s):  
Neelakshi Hudda ◽  
Misha Eliasziw ◽  
Scott O. Hersey ◽  
Ellin Reisner ◽  
Robert D. Brook ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Air Pollution ◽  
Black Carbon ◽  
Mixed Model ◽  
Particle Number ◽  
Linear Mixed Model ◽  
High Efficiency ◽  
High Exposure ◽  
Carbon Concentrations

Exposure to traffic-related air pollution (TRAP) may contribute to increased prevalence of hypertension and elevated blood pressure (BP) for residents of near-highway neighborhoods. Relatively few studies have investigated the effects of reducing TRAP exposure on short-term changes in BP. We assessed whether reducing indoor TRAP concentrations by using stand-alone high-efficiency particulate arrestance (HEPA) filters and limiting infiltration through doors and windows effectively prevented acute (ie, over a span of hours) increases in BP. Using a 3-period crossover design, 77 participants were randomized to attend three 2-hour-long exposure sessions separated by 1-week washout periods. Each participant was exposed to high, medium, and low TRAP concentrations in a room near an interstate highway. Particle number concentrations, black carbon concentrations, and temperature were monitored continuously. Systolic BP (SBP), diastolic BP, and heart rate were measured every 10 minutes. Outcomes were analyzed with a linear mixed model. The primary outcome was the change in SBP from 20 minutes from the start of exposure. SBP increased with exposure duration, and the amount of increase was related to the magnitude of exposure. The mean change in SBP was 0.6 mm Hg for low exposure (mean particle number and black carbon concentrations, 2500 particles/cm 3 and 149 ng/m 3 ), 1.3 mm Hg for medium exposure (mean particle number and black carbon concentrations, 11 000 particles/cm 3 and 409 ng/m 3 ), and 2.8 mm Hg for high exposure (mean particle number and black carbon concentrations, 30 000 particles/cm 3 and 826 ng/m 3 ; linear trend P =0.019). There were no statistically significant differences in the secondary outcomes, diastolic BP, or heart rate. In conclusion, reducing indoor concentrations of TRAP was effective in preventing acute increases in SBP.

scholarly journals Traffic Density-Related Black Carbon Distribution: Impact of Wind in a Basin Town

2021 ◽  
Vol 18 (12) ◽  
pp. 6490
Author(s):  
Borut Jereb ◽  
Brigita Gajšek ◽  
Gregor Šipek ◽  
Špela Kovše ◽  
Matevz Obrecht
Keyword(s):  
Black Carbon ◽  
Carbon Concentration ◽  
Ground Level ◽  
Traffic Density ◽  
Carbon Distribution ◽  
Wind Speeds ◽  
Black Carbon Concentration ◽  
Low Wind Speeds ◽  
Temperature Inversions ◽  
Carbon Concentrations

Black carbon is one of the riskiest particle matter pollutants that is harmful to human health. Although it has been increasingly investigated, factors that depend on black carbon distribution and concentration are still insufficiently researched. Variables, such as traffic density, wind speeds, and ground levels can lead to substantial variations of black carbon concentrations and potential exposure, which is even riskier for people living in less-airy sites. Therefore, this paper “fills the gaps” by studying black carbon distribution variations, concentrations, and oscillations, with special emphasis on traffic density and road segments, at multiple locations, in a small city located in a basin, with frequent temperature inversions and infrequent low wind speeds. As wind speed has a significant impact on black carbon concentration trends, it is critical to present how low wind speeds influence black carbon dispersion in a basin city, and how black carbon is dependent on traffic density. Our results revealed that when the wind reached speeds of 1 ms−1, black carbon concentrations actually increased. In lengthy wind periods, when wind speeds reached 2 or 3 ms−1, black carbon concentrations decreased during rush hour and in the time of severe winter biomass burning. By observing the results, it could be concluded that black carbon persists longer in higher altitudes than near ground level. Black carbon concentration oscillations were also seen as more pronounced on main roads with higher traffic density. The more the traffic decreases and becomes steady, the more black carbon concentrations oscillate.

scholarly journals Determination of car on-road black carbon and particle number emission factors and comparison between mobile and stationary measurements

2015 ◽  
Vol 8 (1) ◽  
pp. 43-55 ◽  
Author(s):  
I. Ježek ◽  
L. Drinovec ◽  
L. Ferrero ◽  
M. Carriero ◽  
G. Močnik
Keyword(s):  
Black Carbon ◽  
Particle Number ◽  
Emission Factors ◽  
Emission Measurement ◽  
Stationary Method ◽  
Characteristic Value ◽  
Portable Emission Measurement System ◽  
Specific Distribution ◽  
Mobile Measurement

Abstract. We have used two methods for measuring emission factors (EFs) in real driving conditions on five cars in a controlled environment: the stationary method, where the investigated vehicle drives by the stationary measurement platform and the composition of the plume is measured, and the chasing method, where a mobile measurement platform drives behind the investigated vehicle. We measured EFs of black carbon and particle number concentration. The stationary method was tested for repeatability at different speeds and on a slope. The chasing method was tested on a test track and compared to the portable emission measurement system. We further developed the data processing algorithm for both methods, trying to improve consistency, determine the plume duration, limit the background influence and facilitate automatic processing of measurements. The comparison of emission factors determined by the two methods showed good agreement. EFs of a single car measured with either method have a specific distribution with a characteristic value and a long tail of super emissions. Measuring EFs at different speeds or slopes did not significantly influence the EFs of different cars; hence, we propose a new description of vehicle emissions that is not related to kinematic or engine parameters, and we rather describe the vehicle EF with a characteristic value and a super emission tail.

scholarly journals Plume-based analysis of vehicle fleet air pollutant emissions and the contribution from high emitters

2015 ◽  
Vol 8 (3) ◽  
pp. 2881-2912 ◽  
Author(s):  
J. M. Wang ◽  
C.-H. Jeong ◽  
N. Zimmerman ◽  
R. M. Healy ◽  
D. K. Wang ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Black Carbon ◽  
Time Resolution ◽  
Real World ◽  
Vehicle Emissions ◽  
Particle Number ◽  
Emission Factors ◽  
Air Pollutant ◽  
Automated Identification ◽  
Vehicle Fleet

Abstract. An automated identification and integration method has been developed to investigate in-use vehicle emissions under real-world conditions. This technique was applied to high time resolution air pollutant measurements of in-use vehicle emissions performed under real-world conditions at a near-road monitoring station in Toronto, Canada during four seasons, through month-long campaigns in 2013–2014. Based on carbon dioxide measurements, over 100 000 vehicle-related plumes were automatically identified and fuel-based emission factors for nitrogen oxides; carbon monoxide; particle number, black carbon; benzene, toluene, ethylbenzene, and xylenes (BTEX); and methanol were determined for each plume. Thus the automated identification enabled the measurement of an unprecedented number of plumes and pollutants over an extended duration. Emission factors for volatile organic compounds were also measured roadside for the first time using a proton transfer reaction time-of-flight mass spectrometer; this instrument provided the time resolution required for the plume capture technique. Mean emission factors were characteristic of the light-duty gasoline dominated vehicle fleet present at the measurement site, with mean black carbon and particle number emission factors of 35 mg kg−1 and 7.7 × 1014 kg−1, respectively. The use of the plume-by-plume analysis enabled isolation of vehicle emissions, and the elucidation of co-emitted pollutants from similar vehicle types, variability of emissions across the fleet, and the relative contribution from heavy emitters. It was found that a small proportion of the fleet (< 25%) contributed significantly to total fleet emissions; 95, 93, 76, and 75% for black carbon, carbon monoxide, BTEX, and particle number, respectively. Emission factors of a single pollutant may help classify a vehicle as a high emitter. However, regulatory strategies to more efficiently target multi-pollutants mixtures may be better developed by considering the co-emitted pollutants as well.

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