Trans-boundary PM10: Quantifying impact and sources during winter 2016/17 in eastern Germany

Pinxteren, Dominik van; Mothes, F.; Spindler, Gerald; Fomba, Khanneh Wadinga; Herrmann, Hartmut

doi:10.1016/j.atmosenv.2018.11.061

Cited by 16 publications

(35 citation statements)

References 36 publications

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“…For the full-year model the maximum difference in mean PM10 predictions is ∼10 g/m 3 , while in winter, the maximum difference is ∼20 g/m 3 . These numbers agree well with results from van Pinxteren et al (2019), who quantified the influence of eastern air masses on eastern Germany. They found the contribution of trans-boundary transport from eastern European countries to be 13 g/m 3 on average, depending on meteorological conditions.…”

Section: Model Sensitivity 331 Mesoscale Wind Informationsupporting

confidence: 90%

“…In addition, PM10 emissions are expected to be higher in wintertime due to combustion of solid fuels for domestic heating. This has been shown for Eastern European countries (Reizer & Juda‐Rezler, ; van Pinxteren et al, ) and likely influences PM10 concentrations in Germany. Thus, with higher locally produced or advected PM10 emissions, the accumulation effect of a low BLH is more distinct in winter than in summer, where reduced emissions are expected and an accumulation of particles is not expected (Wagner & Schäfer, ).…”

Section: Resultsmentioning

confidence: 62%

“…Results by Grange et al () also show increased values of PM10 for northern and northeastern wind directions, although to a lesser extent. In winter, the effect of particle transportation is strongest, presumably due to increased emissions from domestic heating in eastern Europe (Reizer & Juda‐Rezler, ; van Pinxteren et al, ). A slight increase in modeled PM10 at low values of instantaneous east‐west wind components is visible (see Figure ).…”

Section: Resultsmentioning

confidence: 99%

“…Air masses from continental eastern Europe tend to transport higher amounts of particles, whereas western, more maritime air tends to be cleaner due to precipitation along the trajectories of air masses. Source regions of PM10 include industrial and residential areas in Poland and the Czech Republic with heavy industries or extensive usage of solid fuels for residential heating (Beloconi et al, ; Kiesewetter et al, ; Reizer & Juda‐Rezler, ; van Pinxteren et al, ). Results by Grange et al () also show increased values of PM10 for northern and northeastern wind directions, although to a lesser extent.…”

Section: Resultsmentioning

confidence: 99%

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Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

et al. 2020

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The quantification of factors leading to harmfully high levels of particulate matter (PM) remains challenging. This study presents a novel approach using a statistical model that is trained to predict hourly concentrations of particles smaller than 10 μm (PM10) by combining satellite‐borne aerosol optical depth (AOD) with meteorological and land‐use parameters. The model is shown to accurately predict PM10 (overall R 2 = 0.77, RMSE = 7.44 μg/m 3) for measurement sites in Germany. The capability of satellite observations to map and monitor surface air pollution is assessed by investigating the relationship between AOD and PM10 in the same modeling setup. Sensitivity analyses show that important drivers of modeled PM10 include multiday mean wind flow, boundary layer height (BLH), day of year (DOY), and temperature. Different mechanisms associated with elevated PM10 concentrations are identified in winter and summer. In winter, mean predictions of PM10 concentrations >35 μg/m 3 occur when BLH is below ∼500 m. Paired with multiday easterly wind flow, mean model predictions surpass 40 μg/m 3 of PM10. In summer, PM10 concentrations seemingly are less driven by meteorology, but by emission or chemical particle formation processes, which are not included in the model. The relationship between AOD and predicted PM10 concentrations depends to a large extent on ambient meteorological conditions. Results suggest that AOD can be used to assess air quality at ground level in a machine learning approach linking it with meteorological conditions.

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Section: Model Sensitivity 331 Mesoscale Wind Informationsupporting

confidence: 90%

Section: Resultsmentioning

confidence: 62%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

et al. 2020

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“…Pawar et al (2015) utilized the TS method to assess the impacts of short-range and long-range transport (LRT) of PM 2.5 on Mohali in the northwestern Indo-Gangetic Plain. A similar method was applied to evaluate the contribution of LRT of PM 2.5 to southwestern Germany (Garg and Sinha, 2017) and eastern Germany (van Pinxteren et al, 2019). Although the TS method has been widely used, the passing frequency over some specific regions can only approximate statistics of the contributions from those regions.…”

Section: Introductionmentioning

confidence: 99%

Study on the impact of three Asian industrial regions on PM<sub>2.5</sub> in Taiwan and the process analysis during transport

et al. 2020

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Abstract. The outflow of the East Asian haze (EAH) has attracted much attention in recent years. For downstream areas, it is meaningful to understand the impact of crucial upstream sources and the process analysis during transport. This study evaluated the impact of PM2.5 from the three largest industrial regions on the Asian continent, namely the Bohai Rim industrial region (BRIR), Yangtze River Delta industrial region (YRDIR), and Pearl River Delta industrial region (PRDIR), in Taiwan and discussed the processes during transport with the help of air quality modeling. The simulation results revealed that the contributions of monthly average PM2.5 from BRIR and YRDIR were 0.7–1.1 and 1.2–1.9 µg m−3 (∼ 5 % and 7.5 % of the total concentration) in Taiwan, respectively, in January 2017. When the Asian anticyclone moved from the Asian continent to the western Pacific, e.g., on 9 January 2017, the contributions from BRIR and YRDIR to northern Taiwan could reach daily averages of 8 and 11 µg m−3. The transport of EAH from BRIR and YRDIR to low-latitude regions was horizontal advection (HADV), vertical advection (ZADV), and vertical diffusion (VDIF) over the Bohai Sea and East China Sea. Over the Taiwan Strait and the northern South China Sea, cloud processes (CLDS) were the major contribution to PM2.5 due to a high relative humidity environment. Along the transport from high-latitude regions to low-latitude regions, aerosol chemistry (AERO) and dry deposition (DDEP) were the major removal processes. When the EAH intruded into northern Taiwan, the major processes for the gains of PM2.5 in northern Taiwan were HADV and AERO. The stronger the EAH, the more the EAH could influence central and southern Taiwan. Although PRDIR is located downstream of Taiwan under northeasterly wind, the PM2.5 from PRDIR could be lifted upward above the boundary layer, allowing it to move eastwards. When the PM2.5 plume moved over Taiwan and was blocked by mountains, PM2.5 could be transported downward, via boundary layer mixing (VDIF), as it was further enhanced by the passing cold surge. In contrast, for the simulation of July 2017, the influence from the three industrial regions was almost negligible unless there was a special weather system, such as thermal lows which may have carried pollutants from PRDIR to Taiwan, but this occurrence was rare.

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Evaluation of modelled LOTOS-EUROS with observational based PM10 source attribution

Timmermans

Pinxteren

Kranenburg

et al. 2022

Atmospheric Environment: X

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Trans-boundary PM10: Quantifying impact and sources during winter 2016/17 in eastern Germany

Cited by 16 publications

References 36 publications

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

Study on the impact of three Asian industrial regions on PM<sub>2.5</sub> in Taiwan and the process analysis during transport

Evaluation of modelled LOTOS-EUROS with observational based PM10 source attribution

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Trans-boundary PM10: Quantifying impact and sources during winter 2016/17 in eastern Germany

Cited by 16 publications

References 36 publications

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

Study on the impact of three Asian industrial regions on PM&lt;sub&gt;2.5&lt;/sub&gt; in Taiwan and the process analysis during transport

Evaluation of modelled LOTOS-EUROS with observational based PM10 source attribution

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Study on the impact of three Asian industrial regions on PM<sub>2.5</sub> in Taiwan and the process analysis during transport