Measurements of Particle Number and Mass Concentrations and Size Distributions in a Tunnel Environment

Abstract. The German Ultrafine Aerosol Network (GUAN) is a cooperative atmospheric observation network, which aims at improving the scientific understanding of aerosol-related effects in the troposphere. The network addresses research questions dedicated to both climate- and health-related effects. GUAN's core activity has been the continuous collection of tropospheric particle number size distributions and black carbon mass concentrations at 17 observation sites in Germany. These sites cover various environmental settings including urban traffic, urban background, rural background, and Alpine mountains. In association with partner projects, GUAN has implemented a high degree of harmonisation of instrumentation, operating procedures, and data evaluation procedures. The quality of the measurement data is assured by laboratory intercomparisons as well as on-site comparisons with reference instruments. This paper describes the measurement sites, instrumentation, quality assurance, and data evaluation procedures in the network as well as the EBAS repository, where the data sets can be obtained (doi:10.5072/guan).

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Size-segregated particle number and mass concentrations from different emission sources in urban Beijing

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-12721-2020 ◽

2020 ◽

Vol 20 (21) ◽

pp. 12721-12740

Author(s):

Jing Cai ◽

Biwu Chu ◽

Lei Yao ◽

Chao Yan ◽

Liine M. Heikkinen ◽

...

Keyword(s):

Particle Size ◽

Particulate Matter ◽

Source Apportionment ◽

Particle Number ◽

Fine Particles ◽

Size Distributions ◽

Vehicular Emissions ◽

Particle Size Distributions ◽

Chemical Fingerprints ◽

Mass Concentrations

Abstract. Although secondary particulate matter is reported to be the main contributor of PM2.5 during haze in Chinese megacities, primary particle emissions also affect particle concentrations. In order to improve estimates of the contribution of primary sources to the particle number and mass concentrations, we performed source apportionment analyses using both chemical fingerprints and particle size distributions measured at the same site in urban Beijing from April to July 2018. Both methods resolved factors related to primary emissions, including vehicular emissions and cooking emissions, which together make up 76 % and 24 % of total particle number and organic aerosol (OA) mass, respectively. Similar source types, including particles related to vehicular emissions (1.6±1.1 µg m−3; 2.4±1.8×103 cm−3 and 5.5±2.8×103 cm−3 for two traffic-related components), cooking emissions (2.6±1.9 µg m−3 and 5.5±3.3×103 cm−3) and secondary aerosols (51±41 µg m−3 and 4.2±3.0×103 cm−3), were resolved by both methods. Converted mass concentrations from particle size distributions components were comparable with those from chemical fingerprints. Size distribution source apportionment separated vehicular emissions into a component with a mode diameter of 20 nm (“traffic-ultrafine”) and a component with a mode diameter of 100 nm (“traffic-fine”). Consistent with similar day- and nighttime diesel vehicle PM2.5 emissions estimated for the Beijing area, traffic-fine particles, hydrocarbon-like OA (HOA, traffic-related factor resulting from source apportionment using chemical fingerprints) and black carbon (BC) showed similar diurnal patterns, with higher concentrations during the night and morning than during the afternoon when the boundary layer is higher. Traffic-ultrafine particles showed the highest concentrations during the rush-hour period, suggesting a prominent role of local gasoline vehicle emissions. In the absence of new particle formation, our results show that vehicular-related emissions (14 % and 30 % for ultrafine and fine particles, respectively) and cooking-activity-related emissions (32 %) dominate the particle number concentration, while secondary particulate matter (over 80 %) governs PM2.5 mass during the non-heating season in Beijing.

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Contribution of residential wood combustion and other sources to hourly winter aerosol in Northern Sweden determined by positive matrix factorization

Atmospheric Chemistry and Physics ◽

10.5194/acp-8-3639-2008 ◽

2008 ◽

Vol 8 (13) ◽

pp. 3639-3653 ◽

Cited By ~ 33

Author(s):

P. Krecl ◽

E. Hedberg Larsson ◽

J. Ström ◽

C. Johansson

Keyword(s):

Matrix Factorization ◽

Particle Number ◽

Winter Season ◽

Positive Matrix Factorization ◽

Small Towns ◽

Size Distributions ◽

Northern Sweden ◽

Wood Combustion ◽

Positive Matrix ◽

Mass Concentrations

Abstract. The combined effect of residential wood combustion (RWC) emissions with stable atmospheric conditions, which frequently occurs in Northern Sweden during wintertime, can deteriorate the air quality even in small towns. To estimate the contribution of RWC to the total atmospheric aerosol loading, positive matrix factorization (PMF) was applied to hourly mean particle number size distributions measured in a residential area in Lycksele during winter 2005/2006. The sources were identified based on the particle number size distribution profiles of the PMF factors, the diurnal contributions patterns estimated by PMF for both weekends and weekdays, and correlation of the modeled particle number concentration per factor with measured aerosol mass concentrations (PM10, PM1, and light-absorbing carbon MLAC) Through these analyses, the factors were identified as local traffic (factor 1), local RWC (factor 2), and local RWC plus long-range transport (LRT) of aerosols (factor 3). In some occasions, the PMF model could not separate the contributions of local RWC from background concentrations since their particle number size distributions partially overlapped. As a consequence, we report the contribution of RWC as a range of values, being the minimum determined by factor 2 and the possible maximum as the contributions of both factors 2 and 3. A multiple linear regression (MLR) of observed PM10, PM1, total particle number, and MLAC concentrations is carried out to determine the source contribution to these aerosol variables. The results reveal RWC is an important source of atmospheric particles in the size range 25–606 nm (44–57%), PM10 (36–82%), PM1 (31–83%), and MLAC (40–76%) mass concentrations in the winter season. The contribution from RWC is especially large on weekends between 18:00 LT and midnight whereas local traffic emissions show similar contributions every day.

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Size segregated particle number and mass emissions in urban Beijing

10.5194/acp-2020-248 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jing Cai ◽

Biwu Chu ◽

Lei Yao ◽

Chao Yan ◽

Liine M. Heikkinen ◽

...

Keyword(s):

Particle Size ◽

Particulate Matter ◽

Source Apportionment ◽

Particle Number ◽

Related Factor ◽

Size Distributions ◽

Vehicular Emissions ◽

Particle Size Distributions ◽

Chemical Fingerprints ◽

Mass Concentrations

Abstract. Although secondary particulate matter is reported to be the main contributor of PM2.5 during haze in Chinese megacities, primary particle emissions also affect particle concentrations. In order to improve estimates of the contribution of primary sources to the particle number and mass concentrations, we performed source apportionment analyses using both chemical fingerprints and particle size distributions measured at the same site in urban Beijing from April to July 2018. Both methods resolved factors related to primary emissions, including vehicular emissions and cooking emissions, which together make up 76 % and 24 % of total particle number and organic aerosol (OA) mass, respectively. Similar source-types, including particles related to vehicular emissions (1.6 ± 1.1 μg m−3; 2.4 ± 1.8 × 103 cm−3 and 5.5 ± 2.8 × 103 cm−3 for two traffic-related components), cooking emissions (2.6 ± 1.9 μg m−3 and 5.5 ± 3.3 × 103 cm−3) and secondary aerosols (51 ± 41 μg m−3 and 4.2 ± 3.0 × 103 cm−3) were resolved by both methods. Converted mass concentrations from particle size distributions components were comparable with those from chemical fingerprints. Size distribution source apportionment separated vehicular emissions into a component with a mode diameter of 20 nm (Traffic-ultrafine) and a component with a mode diameter of 100 nm (Traffic-fine). Consistent with similar day and night-time diesel vehicle PM2.5 emissions estimated for the Beijing area, Traffic-fine, hydrocarbon-like OA (HOA, traffic-related factor resulting from source apportionment using chemical fingerprints), and black carbon (BC) showed similar diurnal patterns, with higher concentrations during the night and morning than during the afternoon when the boundary layer is higher. Traffic-ultrafine particles showed the highest concentrations during the rush-hour period, suggesting a prominent role of local gasoline vehicle emissions. In the absence of new-particle formation, our results show that vehicular (14 % and 30 % for ultrafine and fine particles, respectively) and cooking (32 %) emissions dominate the particle number concentration while secondary particulate matter (over 80 %) governs PM2.5 mass during the non-heating season in Beijing.

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Variability of black carbon mass concentrations, sub-micrometer particle number concentrations and size distributions: results of the German Ultrafine Aerosol Network ranging from city street to High Alpine locations

Atmospheric Environment ◽

10.1016/j.atmosenv.2018.12.029 ◽

2019 ◽

Vol 202 ◽

pp. 256-268 ◽

Cited By ~ 11

Author(s):

J. Sun ◽

W. Birmili ◽

M. Hermann ◽

T. Tuch ◽

K. Weinhold ◽

...

Keyword(s):

Black Carbon ◽

Particle Number ◽

Size Distributions ◽

City Street ◽

Micrometer Particle ◽

Carbon Mass ◽

Ultrafine Aerosol ◽

Mass Concentrations

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Long-term observations of tropospheric particle number size distributions and equivalent black carbon mass concentrations in the German Ultrafine Aerosol Network (GUAN)

Earth System Science Data Discussions ◽

10.5194/essdd-8-935-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 935-993 ◽

Cited By ~ 1

Author(s):

W. Birmili ◽

K. Weinhold ◽

M. Merkel ◽

F. Rasch ◽

A. Sonntag ◽

...

Keyword(s):

Black Carbon ◽

Particle Number ◽

Measurement Data ◽

Urban Traffic ◽

Data Evaluation ◽

Size Distributions ◽

Evaluation Procedures ◽

Carbon Mass ◽

Ultrafine Aerosol ◽

Mass Concentrations

Abstract. The German Ultrafine Aerosol Network (GUAN) is a cooperative atmospheric observation network, which aims at improving the scientific understanding of aerosol-related effects in the troposphere. The network addresses research questions dedicated to both, climate and health related effects. GUAN's core activity has been the continuous collection of tropospheric particle number size distributions and black carbon mass concentrations at seventeen observation sites in Germany. These sites cover various environmental settings including urban traffic, urban background, rural background, and Alpine mountains. In association with partner projects, GUAN has implemented a high degree of harmonisation of instrumentation, operating procedures, and data evaluation procedures. The quality of the measurement data is assured by laboratory intercomparisons as well as on-site comparisons with reference instruments. This paper describes the measurement sites, instrumentation, quality assurance and data evaluation procedures in the network as well as the EBAS repository, where the data sets can be obtained (doi:10.5072/guan).

Download Full-text

Contribution of residential wood combustion to hourly winter aerosol in Northern Sweden determined by positive matrix factorization

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-8-5725-2008 ◽

2008 ◽

Vol 8 (2) ◽

pp. 5725-5760 ◽

Cited By ~ 2

Author(s):

P. Krecl ◽

E. Hedberg Larsson ◽

J. Ström ◽

C. Johansson

Keyword(s):

Matrix Factorization ◽

Particle Number ◽

Winter Season ◽

Positive Matrix Factorization ◽

Small Towns ◽

Size Distributions ◽

Northern Sweden ◽

Wood Combustion ◽

Positive Matrix ◽

Mass Concentrations

Abstract. The combined effect of residential wood combustion (RWC) emissions with stable atmospheric conditions, which is a frequent occurrence in Northern Sweden during wintertime, can deteriorate the air quality even in small towns. To estimate the contribution of RWC to the total atmospheric aerosol loading, the positive matrix factorization (PMF) method was applied to hourly mean particle number size distributions measured in a residential area in Lycksele during winter 2005/2006. The sources were identified based on the particle number size distribution profiles of the PMF factors, the diurnal contributions patterns estimated by PMF for both weekends and weekdays, and correlation of the modeled particle number concentration per factor with measured aerosol mass concentrations (PM10, PM1, and light-absorbing carbon MLAC). Through these analyses, the factors were identified as local traffic (factor 1), local RWC (factor 2), and local RWC plus long-range transport (LRT) of aerosols (factor 3). In some occasions, it was difficult to detach the contributions of local RWC from background concentrations since their particle number size distributions partially overlapped and the model was not able to separate these two sources. As a consequence, we report the contribution of RWC as a range of values, being the minimum determined by factor 2 and the possible maximum as the contributions of both factors 2 and 3. A multiple linear regression (MLR) of observed PM10, PM1, total particle number, and MLAC concentrations is carried out to determine the source contribution to these aerosol variables. The results reveal RWC is an important source of atmospheric particles in the size range 25–606 nm (44–57%), PM10 (36–82%), PM1 (31–83%), and MLAC (40–76%) mass concentrations in the winter season. The contribution from RWC is especially large on weekends between 18:00 LT and midnight whereas local traffic emissions show similar contributions every day.

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Cluster analysis of atmospheric particle number size distributions at a rural site downwind of Seoul, Korea

Atmospheric Pollution Research ◽

10.1016/j.apr.2021.101086 ◽

2021 ◽

pp. 101086

Author(s):

Yonghwan Lee ◽

Yongjoo Choi ◽

Hyungjun An ◽

Jisoo Park ◽

Young Sung Ghim

Keyword(s):

Cluster Analysis ◽

Particle Number ◽

Rural Site ◽

Size Distributions ◽

Atmospheric Particle

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Review of Respirable Coal Mine Dust Characterization for Mass Concentration, Size Distribution and Chemical Composition

Minerals ◽

10.3390/min11040426 ◽

2021 ◽

Vol 11 (4) ◽

pp. 426

Author(s):

Behrooz Abbasi ◽

Xiaoliang Wang ◽

Judith C. Chow ◽

John G. Watson ◽

Bijan Peik ◽

...

Keyword(s):

Mass Spectrometry ◽

Particle Size ◽

Real Time ◽

Coal Mine ◽

Energy Dispersive ◽

Size Distributions ◽

X Ray ◽

Membrane Filters ◽

Mine Dust ◽

Mass Concentrations

Respirable coal mine dust (RCMD) exposure is associated with black lung and silicosis diseases in underground miners. Although only RCMD mass and silica concentrations are regulated, it is possible that particle size, surface area, and other chemical constituents also contribute to its adverse health effects. This review summarizes measurement technologies for RCMD mass concentrations, morphology, size distributions, and chemical compositions, with examples from published efforts where these methods have been applied. Some state-of-the-art technologies presented in this paper have not been certified as intrinsically safe, and caution should be exerted for their use in explosive environments. RCMD mass concentrations are most often obtained by filter sampling followed by gravimetric analysis, but recent requirements for real-time monitoring by continuous personal dust monitors (CPDM) enable quicker exposure risk assessments. Emerging low-cost photometers provide an opportunity for a wider deployment of real-time exposure assessment. Particle size distributions can be determined by microscopy, cascade impactors, aerodynamic spectrometers, optical particle counters, and electrical mobility analyzers, each with unique advantages and limitations. Different filter media are required to collect integrated samples over working shifts for comprehensive chemical analysis. Teflon membrane filters are used for mass by gravimetry, elements by energy dispersive X-ray fluorescence, rare-earth elements by inductively coupled plasma-mass spectrometry and mineralogy by X-ray diffraction. Quartz fiber filters are analyzed for organic, elemental, and brown carbon by thermal/optical methods and non-polar organics by thermal desorption-gas chromatography-mass spectrometry. Polycarbonate-membrane filters are analyzed for morphology and elements by scanning electron microscopy (SEM) with energy dispersive X-ray, and quartz content by Fourier-transform infrared spectroscopy and Raman spectroscopy.

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