Analysis of exceedances in the daily PM&amp;lt;sub&amp;gt;10&amp;lt;/sub&amp;gt; mass concentration (50 μg m&amp;lt;sup&amp;gt;−3&amp;lt;/sup&amp;gt;) at a roadside station in Leipzig, Germany

Engler, C.; Birmili, W.; Spindler, Gerald; Wiedensohler, Alfred

doi:10.5194/acp-12-10107-2012

Cited by 34 publications

(8 citation statements)

References 54 publications

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“…S9 of the Supplement). Dry deposition is generally largest over regions with large ambient NH 3 concentrations over the Netherlands and Belgium, as also reported in the literature (Flechard et al, 2011). We also predict high nitrogen dry deposition around the Po Valley in northern Italy.…”

Section: Nitrogen Depositionsupporting

confidence: 87%

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A model study on changes of European and Swiss particulate matter, ozone and nitrogen deposition between 1990 and 2020 due to the revised Gothenburg protocol

Aksoyoğlu¹,

Keller²,

Ciarelli³

et al. 2014

Preprint

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Abstract. We report a study of changes in air quality due to emission reductions using the chemical transport model CAMx. The model domain includes all of Europe with a nested domain over Switzerland. The model simulations were performed for 1990 (the reference year for the Gothenburg Protocol), 2005 (the reference year for the revised Gothenburg Protocol), 2006 (for model validation) and 2020 (the target year for the revised Gothenburg Protocol) using three emission scenarios prepared by IIASA/GAINS. Changes in ozone, particulate matter and nitrogen deposition are the central theme of the study. The relative changes in the annual average PM2.5 concentrations between 1990 and 2005 were reproduced very well. Both model results and observations show that annual mean concentrations of PM2.5 decreased by about 20–50% in Europe. Simulations using the baseline scenario (BL 2020) suggest that PM2.5 concentrations in 2020 will be about 30% lower than those in 2005. The largest predicted decrease in PM2.5, based on the MTFR (Maximum Technically Feasible Reduction) scenario, was about 60% and was located mainly in the eastern part of Europe. In the case of ozone, both model results and measurements show an increase in the mean ozone mixing ratios between 1990 and 2005. The observations, however, suggest a larger increase, indicating the importance of background ozone levels. Although emission reductions caused a decrease in peak ozone values, ozone levels in polluted regions increased due to reduced titration with nitric oxide (NO). This caused a change in the frequency distribution of ozone. Model simulations using emission scenarios for 2020 suggest that annual average ozone mixing ratios will continue to increase. Changes in the levels of the damage indicators AOT40 for forests and SOMO35 are reported as well. The model results suggest that nitrogen deposition decreased by 10–30% in the eastern part of Europe since 1990, while it increased by about 20% in the Iberian Peninsula. The decrease is mainly due to the deposition of oxidized nitrogen species, whereas deposition of reduced nitrogen compounds increased. In Switzerland, nitrogen deposition is larger in the northern part of the Alps where ammonia emissions are the highest. Applying the baseline scenario, we found that the deposition of oxidized nitrogen compounds will have decreased by a further 40% by 2020, whereas deposition of reduced species will continue to increase. This will lead to a 10–20% decrease in the total nitrogen deposition in most of the model domain, with a 10% increase in the eastern part of Europe.

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Section: Nitrogen Depositionsupporting

confidence: 87%

“…rope, which are 120 µg m −3 maximum daily 8 h mean for O 3 and 50 µg m −3 daily mean for PM 10 (Engler et al, 2012;Hettelingh et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A model study on changes of European and Swiss particulate matter, ozone and nitrogen deposition between 1990 and 2020 due to the revised Gothenburg protocol

Aksoyoğlu¹,

Keller²,

Ciarelli³

et al. 2014

Preprint

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“…Among all GUAN sites, Leipzig-Mitte exhibits the greatest exposure to traffic-related pollutants. PM 10 mass and ultrafine particle number concentrations were discussed by Engler et al (2012) and Ma and Birmili (2015), respectively. Leipzig-Mitte was added to GUAN for the purpose of monitoring possible changes in ultrafine particle number and eBC mass concentrations along with the introduction of the low-emission zone (Umweltzone) in Leipzig (Rasch et al, 2013;Löschau et al, 2015).…”

Section: Leipzig-mittementioning

confidence: 99%

“…Some studies, however, have suggested that the massbased metrics might not be the most favourable parameter to characterise PM-induced health effects (HEI Review Panel on Ultrafine Particles, 2013). Some epidemiological studies have associated health endpoints with the number of ultrafine particles or the particle surface area rather than particle mass (Ibald-Mulli et al, 2002;Franck et al, 2011;Rückerl et al, 2011). Ultrafine particles are ubiquitous in urban atmospheres (Kumar et al, 2014), and their ability to penetrate deep into the human body after inhalation has been forwarded as a rationale for their adverse health effects.…”

Section: Introductionmentioning

confidence: 99%

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

Birmili

Weinhold

Rasch

et al. 2016

Earth Syst. Sci. Data

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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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“…Hence, an understanding of the finer aerosol concentration in the atmosphere is important. Even though PM10 is of major concern in worldwide environmental issues, such as visibility degradation and air quality assessment (Engler et al 2012), PM2.5 is mostly responsible for health hazards (Jin et al 2006). Fine (PM2.5) particles have longer residence times in the atmosphere and are composed of higher fractions of organic compounds compared to PM10 (Jaenicke 1984).…”

Section: Introductionmentioning

confidence: 99%

Particulate Matter Concentrations in the Southern Tip of India: Temporal Variation, Meteorological Influences, and Source Identification

et al. 2017

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Objective This study investigates the temporal variability of particulate matter (PM10 and PM2.5) concentrations at an inland tropical coastal site in Thiruvananthapuram (8.5°N, 76.9°E), which is in the province of Kerala in the southern part of India. Method Continuous measurements of PM10 and PM2.5 were carried out with high resolution datasets from March 2014 to February 2016. Variations of PM10 and PM2.5 concentrations were statistically analyzed. The conditional bivariate probability function analysis, coherence wavelet transform and HYSPLIT back trajectory model are used for this study. Results The annual mean mass concentrations of PM10 and PM2.5 were 44.5 ± 14.6 and 27.7 ± 10 lg m , respectively, for PM10 and PM2.5. Both concentrations exhibited higher values during winter and lower values during monsoon. The coarse particle concentration (PM10-PM2.5) during the study period was 17.38 ± 3.64 lg m . Diurnal analysis revealed that the concentrations were higher during morning (*08:00 IST) and evening (*20:00 IST) and low during 12:00 IST-17:00 IST. The anthropogenic activities, the diurnal variation of boundary-layer height and the effects of meteorological parameters are the major elements for these variations. The effects of meteorological parameters on particulate matter were studied, and a negative correlation was observed with wind speed, rainfall, and relative humidity. The relationship between particulate matter and rainfall was investigated by applying coherence wavelet transform. The bivariate analysis showed that high PM2.5 concentrations were associated with low wind speeds indicating the presence of local pollutants. The highest concentrations were found for PM10 with moderate to strong winds, which represent the presence of local emissions. as well as long-range transport. The conditional bivariate probability function analysis was performed to identify the probability of source contributions to PM concentrations at different wind speed and direction. The results indicated that the highest probability to get high concentrations of PM from south-southwest direction with wind speed \5 ms -1. HYSPLIT model back-trajectories of high PM10 episodes show two dominant streamlines, one originating from the Bay of Bengal region and another from Middle East Asia.

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Analysis of exceedances in the daily PM<sub>10</sub> mass concentration (50 μg m<sup>−3</sup>) at a roadside station in Leipzig, Germany

Cited by 34 publications

References 54 publications

A model study on changes of European and Swiss particulate matter, ozone and nitrogen deposition between 1990 and 2020 due to the revised Gothenburg protocol

A model study on changes of European and Swiss particulate matter, ozone and nitrogen deposition between 1990 and 2020 due to the revised Gothenburg protocol

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

Particulate Matter Concentrations in the Southern Tip of India: Temporal Variation, Meteorological Influences, and Source Identification

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