scholarly journals Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub>, and PM<sub>2. 5</sub> for 2001–2010

2017 ◽  
Vol 10 (4) ◽  
pp. 1767-1787 ◽  
Author(s):  
Chun Lin ◽  
Mathew R. Heal ◽  
Massimo Vieno ◽  
Ian A. MacKenzie ◽  
Ben G. Armstrong ◽  
...  
Keyword(s):  
Air Pollution ◽  
Atmospheric Chemistry ◽  
Measurement Data ◽  
Transport Processes ◽  
Daily Maximum ◽  
Model Bias ◽  
Model Framework ◽  
Urban Background ◽  
Air Pollution Epidemiology ◽  
Model Measurement

Abstract. This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km  ×  5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model. Thus the focus of the model–measurement comparison statistics presented here was on the health-relevant metrics of annual and daily means of NO2, O3, PM2. 5, and PM10 (daily maximum 8 h running mean for O3). The comparison was temporally and spatially comprehensive, covering a 10-year period (2 years for PM2. 5) and all non-roadside measurement data from the UK national reference monitor network, which applies consistent operational and QA/QC procedures for each pollutant (44, 47, 24, and 30 sites for NO2, O3, PM2. 5, and PM10, respectively). Two important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – together with root mean square error, were evaluated by site type, year, month, and day-of-week. Model–measurement statistics were generally better than, or comparable to, values that allow for realistic magnitudes of measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2, and PM2. 5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2. 5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at rural background sites (median normalized mean bias (NMB) values for daily O3 and NO2 of 8 and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB  =  −29 %) and PM2. 5 (−26 %) and a positive model bias for O3 (26 %). The directions of these biases are consistent with expectations of the effects of averaging primary emissions across the 5 km  ×  5 km model grid in urban areas, compared with monitor locations that are more influenced by these emissions (e.g. closer to traffic sources) than the grid average. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. some components of wind-blown dust. There were instances of monthly and weekday/weekend variations in the extent of model–measurement bias. Overall, the greater uniformity in temporal correlation than in bias is strongly indicative that the main driver of model–measurement differences (aside from grid versus monitor spatial representivity) was inaccuracy of model emissions – both in annual totals and in the monthly and day-of-week temporal factors applied in the model to the totals – rather than simulation of atmospheric chemistry and transport processes. Since, in general for epidemiology, capturing correlation is more important than bias, the detailed analyses presented here support the use of data from this model framework in air pollution epidemiology.

scholarly journals Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> and PM<sub>2.5</sub> for 2001–2010

2016 ◽  
Author(s):  
C. Lin ◽  
M. R. Heal ◽  
M. Vieno ◽  
I. A. MacKenzie ◽  
B. G. Armstrong ◽  
...  
Keyword(s):  
Air Pollution ◽  
Atmospheric Chemistry ◽  
Transport Processes ◽  
Urban Traffic ◽  
Model Bias ◽  
Model Framework ◽  
Urban Background ◽  
Air Pollution Epidemiology ◽  
Pm2.5 And Pm10 ◽  
Model Measurement

Abstract. This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km × 5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model. Thus the focus of the model-measurement comparison statistics presented here was on the health-relevant metrics of annual and daily means of NO2, O3, PM2.5 and PM10 (daily maximum 8-hour running mean for O3). The comparison was temporally and spatially comprehensive covering a 10-year period (2 years for PM2.5) and all measurement data from the UK national reference monitor network, which applies consistent operational and QC/QA procedures for each pollutant (60, 49, 29 and 35 sites for NO2, O3, PM2.5 and PM10, respectively). The two most important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – were evaluated by site type, year, month and day-of-week. Model-measurement correlation and bias were generally better than values found in past studies that allowed for measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2 and PM2.5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2.5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at the background sites and least bias at rural background sites (median normalised mean bias (NMB) values for daily O3 and NO2 of 8 % and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB = −29 %) and PM2.5 (−26 %) and a positive model bias for O3 (26 %). The directions of these biases are consistent with expectations of the effects of averaging primary emissions across the 5 km × 5 km model grid in urban areas, compared with monitor locations that are more influenced by these emissions than the grid average. This effect was particularly pronounced for comparison against urban traffic monitors, which are deliberately located close to strong sources of NOx and PM. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. wind-blown dust. There were instances of monthly and weekday/weekend variations in extent of model-measurement bias. Overall, the greater uniformity in temporal correlation than in bias is strongly indicative that the main driver of model-measurement differences (aside from grid vs monitor spatial representivity) was inaccuracy of model emissions (both in annual totals and in the monthly and day-of-week temporal factors applied in the model to the totals) rather than simulation of atmospheric chemistry and transport processes. Since, in general for epidemiology, capturing correlation is more important than bias, the detailed analyses presented here support the use of data from this model framework in air pollution epidemiology.

scholarly journals Evaluation of global EMEP MSC-W (rv4.34) WRF (v3.9.1.1) model surface concentrations and wet deposition of reactive N and S with measurements

2021 ◽  
Vol 14 (11) ◽  
pp. 7021-7046
Author(s):  
Yao Ge ◽  
Mathew R. Heal ◽  
David S. Stevenson ◽  
Peter Wind ◽  
Massimo Vieno
Keyword(s):  
North America ◽  
Southeast Asia ◽  
East Asia ◽  
Atmospheric Chemistry ◽  
Wet Deposition ◽  
Global Analysis ◽  
Monitoring Networks ◽  
Model Framework ◽  
Annual Total ◽  
Model Measurement

Abstract. Atmospheric pollution has many profound effects on human health, ecosystems, and the climate. Of concern are high concentrations and deposition of reactive nitrogen (Nr) species, especially of reduced N (gaseous NH3, particulate NH4+). Atmospheric chemistry and transport models (ACTMs) are crucial to understanding sources and impacts of Nr chemistry and its potential mitigation. Here we undertake the first evaluation of the global version of the EMEP MSC-W ACTM driven by WRF meteorology (1∘×1∘ resolution), with a focus on surface concentrations and wet deposition of N and S species relevant to investigation of atmospheric Nr and secondary inorganic aerosol (SIA). The model–measurement comparison is conducted both spatially and temporally, covering 10 monitoring networks worldwide. Model simulations for 2010 compared use of both HTAP and ECLIPSEE (ECLIPSE annual total with EDGAR monthly profile) emissions inventories; those for 2015 used ECLIPSEE only. Simulations of primary pollutants are somewhat sensitive to the choice of inventory in places where regional differences in primary emissions between the two inventories are apparent (e.g. China) but are much less sensitive for secondary components. For example, the difference in modelled global annual mean surface NH3 concentration using the two 2010 inventories is 18 % (HTAP: 0.26 µg m−3; ECLIPSEE: 0.31 µg m−3) but is only 3.5 % for NH4+ (HTAP: 0.316 µg m−3; ECLIPSEE: 0.305 µg m−3). Comparisons of 2010 and 2015 surface concentrations between the model and measurements demonstrate that the model captures the overall spatial and seasonal variations well for the major inorganic pollutants NH3, NO2, SO2, HNO3, NH4+, NO3-, and SO42- and their wet deposition in East Asia, Southeast Asia, Europe, and North America. The model shows better correlations with annual average measurements for networks in Southeast Asia (mean R for seven species: R7‾=0.73), Europe (R7‾=0.67), and North America (R7‾=0.63) than in East Asia (R5‾=0.35) (data for 2015), which suggests potential issues with the measurements in the latter network. Temporally, both model and measurements agree on higher NH3 concentrations in spring and summer and lower concentrations in winter. The model slightly underestimates annual total precipitation measurements (by 13 %–45 %) but agrees well with the spatial variations in precipitation in all four world regions (0.65–0.94 R range). High correlations between measured and modelled NH4+ precipitation concentrations are also observed in all regions except East Asia. For annual total wet deposition of reduced N, the greatest consistency is in North America (0.75–0.82 R range), followed by Southeast Asia (R=0.68) and Europe (R=0.61). Model–measurement bias varies between species in different networks; for example, bias for NH4+ and NO3- is largest in Europe and North America and smallest in East Asia and Southeast Asia. The greater uniformity in spatial correlations than in biases suggests that the major driver of model–measurement discrepancies (aside from differing spatial representativeness and uncertainties and biases in measurements) are shortcomings in absolute emissions rather than in modelling the atmospheric processes. The comprehensive evaluations presented in this study support the application of this model framework for global analysis of current and potential future budgets and deposition of Nr and SIA.

scholarly journals Evaluation of global EMEP MSC-W (rv4.34)-WRF (v3.9.1.1) model surface concentrations and wet deposition of reactive N and S with measurements

2021 ◽  
Author(s):  
Yao Ge ◽  
Mathew R. Heal ◽  
David S. Stevenson ◽  
Peter Wind ◽  
Massimo Vieno
Keyword(s):  
North America ◽  
Southeast Asia ◽  
East Asia ◽  
Atmospheric Chemistry ◽  
Wet Deposition ◽  
Global Analysis ◽  
Monitoring Networks ◽  
Model Framework ◽  
Annual Total ◽  
Model Measurement

Abstract. Atmospheric pollution has many profound effects on human health, ecosystems, and the climate. Of concern are high concentrations and deposition of reactive nitrogen (Nr) species, especially of reduced N (gaseous NH3, particulate NH4+). Atmospheric chemistry and transport models (ACTMs) are crucial to understanding sources and impacts of Nr chemistry and its potential mitigation. Here we undertake the first evaluation of the global version of the EMEP MSC-W ACTM driven by WRF meteorology (1° × 1° resolution), with a focus on surface concentrations and wet deposition of N and S species relevant to investigation of atmospheric Nr and secondary inorganic aerosol (SIA). The model-measurement comparison is conducted both spatially and temporally, covering 9 monitoring networks worldwide. Model simulations for 2010 compared use of both HTAP and ECLIPSEE (ECLIPSE annual total with EDGAR monthly profile) emissions inventories; those for 2015 used ECLIPSEE only. Simulations of primary pollutants are somewhat sensitive to the choice of inventory in places where regional differences in primary emissions between the two inventories are apparent (e.g. China), but much less so for secondary components. For example, the difference in modelled global annual mean surface NH3 concentration using the two 2010 inventories is 18 % (HTAP: 0.26 μg m−3; ECLIPSEE: 0.31 μg m−3) but only 3.5 % for NH4+ (HTAP: 0.316 μg m−3; ECLIPSEE: 0.305 μg m−3). Comparisons of 2010 and 2015 surface concentrations between model and measurement demonstrate that the model captures well the overall spatial and seasonal variations of the major inorganic pollutants NH3, NO2, SO2, HNO3, NH4+, NO3−, SO42−, and their wet deposition in East Asia, Southeast Asia, Europe and North America. The model shows better correlations with annual average measurements for networks in Southeast Asia (Mean R for 7 species:  = 0.73), Europe ( = 0.67) and North America ( = 0.63) than in East Asia ( = 0.35) (data for 2015), which suggests potential issues with the measurements in the latter network. Temporally, both model and measurement agree on higher NH3 concentrations in spring and summer, and lower concentrations in winter. The model slightly underestimates annual total precipitation measurements (by 13–34 %) but agrees well with the spatial variations in precipitation in all four world regions (0.65–0.78 R range). High correlations between measured and modelled NH4+ precipitation concentrations are also observed in all regions except East Asia. For annual total wet deposition of reduced N, the greatest consistency is in North America (R = 0.75), followed by Southeast Asia (R = 0.68) and Europe (R = 0.61). Model-measurement bias varies between species in different networks; for example, bias for NH4+ and NO3− is most in Europe and North America and least in East and Southeast Asia. The greater uniformity in spatial correlations than in biases suggests that the major driver of model-measurement discrepancies (aside from differing spatial representativeness and uncertainties and biases in measurements) are shortcomings in absolute emissions rather than in modelling the atmospheric processes. The comprehensive evaluations presented in this study support the application of this model framework for global analysis of current and potential future budgets and deposition of Nr and SIA.

scholarly journals Investigating the regional contributions to air pollution in Beijing: a dispersion modelling study using CO as a tracer

2020 ◽  
Vol 20 (5) ◽  
pp. 2825-2838 ◽  
Author(s):  
Marios Panagi ◽  
Zoë L. Fleming ◽  
Paul S. Monks ◽  
Matthew J. Ashfold ◽  
Oliver Wild ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Measurement Data ◽  
Industrial Sector ◽  
Transport Processes ◽  
Control Measures ◽  
Emission Inventories ◽  
Dispersion Modelling ◽  
Rapid Urbanization ◽  
Air Masses

Abstract. The rapid urbanization and industrialization of northern China in recent decades has resulted in poor air quality in major cities like Beijing. Transport of air pollution plays a key role in determining the relative influence of local emissions and regional contributions to observed air pollution. In this paper, dispersion modelling (Numerical Atmospheric Modelling Environment, NAME model) is used with emission inventories and in situ ground measurement data to track the pathways of air masses arriving in Beijing. The percentage of time the air masses spent over specific regions during their travel to Beijing is used to assess the effects of regional meteorology on carbon monoxide (CO), a good tracer of anthropogenic emissions. The NAME model is used with the MEIC (Multi-resolution Emission Inventory for China) emission inventories to determine the amount of pollution that is transported to Beijing from the immediate surrounding areas and regions further away. This approach captures the magnitude and variability of CO over Beijing and reveals that CO is strongly driven by transport processes. This study provides a more detailed understanding of relative contributions to air pollution in Beijing under different regional airflow conditions. Approximately 45 % over a 4-year average (2013–2016) of the total CO pollution that affects Beijing is transported from other regions, and about half of this contribution comes from beyond the Hebei and Tianjin regions that immediately surround Beijing. The industrial sector is the dominant emission source from the surrounding regions and contributes over 20 % of the total CO in Beijing. Finally, using PM2.5 to determine high-pollution days, three pollution classification types of pollution were identified and used to analyse the APHH winter campaign and the 4-year period. The results can inform targeted control measures to be implemented by Beijing and the surrounding provinces to tackle air quality problems that affect Beijing and China.

scholarly journals Neighbourhood-scale flow regimes and pollution transport in cities

2020 ◽  
Author(s):  
Ed Bannister ◽  
Xiaoming Cai ◽  
Jian Zhong ◽  
Rob MacKenzie
Keyword(s):  
Air Pollution ◽  
Porous Media ◽  
Urban Form ◽  
Flow Regimes ◽  
Transport Processes ◽  
Pollution Monitoring ◽  
Pollution Transport ◽  
Urban Background ◽  
Neighbourhood Scale ◽  
Quantitative Definition

&lt;p&gt;Cities intimately intermingle people and air pollution. However, is very difficult to assess the efficacy of air pollution policy. Permanent in-situ observations are usually too sparsely spaced to monitor transport processes within a city. The post-processing and maintenance costs associated with calibrated low-cost sensors remains too high for them simply to fill the gaps in permanent networks. The behaviour of pollutants around the scale of a neighbourhood (1-2km) remains particularly difficult to interpret and model. This gap in our understanding is unfortunate because neighbourhood-scale processes disperse pollutants from peaks beside busy roads to levels treated as the &amp;#8216;urban background&amp;#8217;, and may link urban pollution models with weather forecasts.&lt;/p&gt;&lt;p&gt;Urban areas can be treated as patches of porous media to which the wind adjusts by changing its mean and turbulent components. Most cities around the world are made up of lots of neighbourhoods of differing form, density and land use &amp;#8211; e.g. commercial centres interspaced with low-rise residential neighbourhoods. For cities whose urban form varies in this way, we formulated two neighbourhood-scale flow regimes, based on the size and density of the different neighbourhood patches.&lt;/p&gt;&lt;p&gt;We used large-eddy simulation to investigate how these two dynamical regimes emerge in patchy neighbourhoods, and their implications for pollution policy and research. We found that these flow regimes distribute pollutants in counter-intuitive ways, such as producing pollution &amp;#8216;hot spots&amp;#8217; in less dense patches. The flow regimes also provide: (a) a quantitative definition of the &amp;#8216;urban background&amp;#8217;, which can be used for more precisely targeted pollution monitoring; and (b) a conceptual basis for neighbourhood-scale air pollution problems and transport of fluid constituents in other porous media.&lt;/p&gt;

scholarly journals Investigating the regional contributions to air pollution in Beijing: A dispersion modelling study using CO as a tracer

2019 ◽  
Author(s):  
Marios Panagi ◽  
Zoë L. Fleming ◽  
Paul S. Monks ◽  
Matthew J. Ashfold ◽  
Oliver Wild ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Measurement Data ◽  
Industrial Sector ◽  
Transport Processes ◽  
Control Measures ◽  
Emission Inventories ◽  
Dispersion Modelling ◽  
Rapid Urbanization ◽  
Air Masses

Abstract. The rapid urbanization and industrialization of Northern China in recent decades has resulted in poor air quality in major cities like Beijing. Transport of air pollution plays a key role in determining the relative influence of local emissions and regional contributions to observed air pollution. In this paper, dispersion modelling (Numerical Atmospheric Modelling Environment, NAME model) is used with emission inventories and in-situ ground measurement data to track the pathways of air masses arriving at Beijing. The percentage of time the air masses spent over specific regions on their travel to Beijing is used to assess the effects of regional meteorology on carbon monoxide (CO), a good tracer of anthropogenic emissions. The NAME model is used with the MEIC (Multi-resolution Emission Inventory for China) emission inventories to determine the amount of pollution that is transported to Beijing from the immediate surrounding areas and regions further away. This approach captures the magnitude and variability of CO over Beijing and reveals that CO is strongly driven by transport processes. This study provides a more detailed understanding of relative contributions to air pollution in Beijing under different regional airflow conditions. Approximately 45 % over a 4 year average (2013–2016) of the total CO pollution that affects Beijing is transported from other regions, and about half of this contribution comes from beyond the Hebei and Tianjin regions that immediately surround Beijing. The industrial sector is the dominant emission source from the surrounding regions and contributes over 20 % of the total CO in Beijing. Finally, using PM2.5 to determine high pollution days, three pollution classification types of pollution were identified and used to analyse the APHH winter campaign and the 4 year period. The results can inform targeted control measures to be implemented by Beijing and the surrounding provinces to tackle air quality problems that affect Beijing and China.

scholarly journals Spectral optical properties of long-range transport Asian dust and pollution aerosols over Northeast Asia in 2007 and 2008

2010 ◽  
Vol 10 (12) ◽  
pp. 5391-5408 ◽  
Author(s):  
J. Jung ◽  
Y. J. Kim ◽  
K. Y. Lee ◽  
M. G. -Cayetano ◽  
T. Batmunkh ◽  
...  
Keyword(s):  
Optical Properties ◽  
Long Range ◽  
Atmospheric Chemistry ◽  
Light Attenuation ◽  
Measurement Data ◽  
Absorption Efficiency ◽  
Asian Dust ◽  
Urban Site ◽  
Long Range Transport ◽  
Range Transport

Abstract. As a part of the IGAC (International Global Atmospheric Chemistry) Mega-cities program, aerosol physical and optical properties were continuously measured from March 2007 to March 2008 at an urban site (37.57° N, 126.94° E) in Seoul, Korea. Spectral optical properties of long-range transported Asian dust and pollution aerosols have been investigated based on the year long measurement data. Optically measured black carbon/thermally measured elemental carbon (BC/EC) ratio showed clear monthly variation with high values in summer and low values in winter mainly due to the enhancement of light attenuation by the internal mixing of EC. Novel approach has been suggested to retrieve the spectral light absorption coefficient (babs) from Aethalometer raw data by using BC/EC ratio. Mass absorption efficiency, σabs (=babs/EC) at 550 nm was determined to be 9.0±1.3, 8.9±1.5, 9.5±2.0, and 10.3±1.7 m2 g−1 in spring, summer, fall, and winter, respectively with an annual mean of 9.4±1.8 m2 g−1. Threshold values to classify severe haze events were suggested in this study. Increasing trend of aerosol single scattering albedo (SSA) with wavelength was observed during Asian dust events while little spectral dependence of SSA was observed during long-range transport pollution (LTP) events. Satellite aerosol optical thickness (AOT) and Hysplit air mass backward trajectory analyses as well as chemical analysis were performed to characterize the dependence of spectral optical properties on aerosol type. Results from this study can provide useful information for studies on regional air quality and aerosol's effects on climate change.

scholarly journals Clean air in the Anthropocene

2017 ◽  
Vol 200 ◽  
pp. 693-703 ◽  
Author(s):  
Jos Lelieveld
Keyword(s):  
Air Pollution ◽  
Sustainable Development ◽  
Air Quality ◽  
Human Health ◽  
Atmospheric Chemistry ◽  
Clean Energy ◽  
Atmospheric Composition ◽  
Anthropogenic Sources ◽  
Years Of Life Lost ◽  
Clean Air

In atmospheric chemistry, interactions between air pollution, the biosphere and human health, often through reaction mixtures from both natural and anthropogenic sources, are of growing interest. Massive pollution emissions in the Anthropocene have transformed atmospheric composition to the extent that biogeochemical cycles, air quality and climate have changed globally and partly profoundly. It is estimated that mortality attributable to outdoor air pollution amounts to 4.33 million individuals per year, associated with 123 million years of life lost. Worldwide, air pollution is the major environmental risk factor to human health, and strict air quality standards have the potential to strongly reduce morbidity and mortality. Preserving clean air should be considered a human right, and is fundamental to many sustainable development goals of the United Nations, such as good health, climate action, sustainable cities, clean energy, and protecting life on land and in the water. It would be appropriate to adopt “clean air” as a sustainable development goal.

scholarly journals Transport, mixing and feedback of dust, biomass burning and anthropogenic pollutants in eastern Asia: a case study

2018 ◽  
Vol 18 (22) ◽  
pp. 16345-16361 ◽  
Author(s):  
Derong Zhou ◽  
Ke Ding ◽  
Xin Huang ◽  
Lixia Liu ◽  
Qiang Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Biomass Burning ◽  
Southern China ◽  
Eastern China ◽  
Measurement Data ◽  
Lower Troposphere ◽  
Climate Impacts ◽  
Eastern Asia ◽  
Pollution Episode ◽  
The Yrd

Abstract. Anthropogenic fossil fuel (FF) combustion, biomass burning (BB) and desert dust are the main sources of air pollutants around the globe but are particularly intensive and important for air quality in Asia in spring. In this study, we investigate the vertical distribution, transport characteristics, source contribution and meteorological feedback of these aerosols in a unique pollution episode that occurred in eastern Asia based on various measurement data and modeling methods. In this episode, the Yangtze River Delta (YRD) in eastern China experienced persistent air pollution, dramatically changing from secondary fine particulate pollution to dust pollution in late March 2015. The Eulerian and Lagrangian models were conducted to investigate the vertical structure, transport characteristics and mechanisms of the multi-scale, multisource and multiday air pollution episode. The regional polluted continental aerosols mainly accumulated near the surface, mixed with dust aerosol downwash from the upper planetary boundary layer (PBL) and middle–lower troposphere (MLT), and further transported by large-scale cold fronts and warm conveyor belts. BB smoke from Southeast Asia was transported by westerlies around the altitude of 3 km from southern China, was further mixed with dust and FF aerosols in eastern China and experienced long-range transport over the Pacific. These pollutants could all be transported to the YRD region and cause a structure of multilayer pollution there. These pollutants could also cause significant feedback with MLT meteorology and then enhance local anthropogenic pollution. This study highlights the importance of intensive vertical measurement in eastern China and the downwind Pacific Ocean and raises the need for quantitative understanding of environmental and climate impacts of these pollution sources.

scholarly journals Sensitivity of modeled atmospheric nitrogen species to variations in sea salt emissions in the North and Baltic Sea regions

2015 ◽  
Vol 15 (20) ◽  
pp. 29705-29745
Author(s):  
D. Neumann ◽  
V. Matthias ◽  
J. Bieser ◽  
A. Aulinger ◽  
M. Quante
Keyword(s):  
Baltic Sea ◽  
Atmospheric Chemistry ◽  
Surf Zone ◽  
Fine Particles ◽  
Measurement Data ◽  
Monitoring And Evaluation ◽  
Sea Salt ◽  
The North ◽  
A Minor ◽  
The Impact

Abstract. Coarse sea salt particles are emitted ubiquitously from the oceans' surfaces by wave breaking and bubble bursting processes. These particles impact atmospheric chemistry by affecting condensation of gas-phase species and nucleation of new fine particles, particularly in regions with high air pollution. In this study, atmospheric particle concentrations are modeled for the North and Baltic Sea regions, Northwestern Europe, using the Community Multiscale Air Quality (CMAQ) modeling system and evaluated against European Monitoring and Evaluation Programme (EMEP) measurement data. As model extension, sea salt emissions are scaled by water salinity because of low salinity in large parts of the Baltic Sea and in certain river estuaries. The resulting improvement in predicted sea salt concentrations is assessed. The contribution of surf zone emissions is separately considered. Additionally, the impact of sea salt particles on atmospheric nitrate, ammonium and sulfate concentrations is evaluated. The comparisons show that sea salt concentrations are commonly overestimated at coastal stations and partly underestimated when going inland. The introduced salinity scaling improves predicted Baltic Sea sea salt concentrations considerably. Dates of measured peak concentrations are appropriately reproduced by the model. The impact of surf zone emissions is negligible in both seas. Nevertheless, they might be relevant because surf zone emissions were cut at an upper threshold in this study. Deactivating sea salt leads to a minor increase of NH4+ and NO3- and a minor decrease of SO42- concentrations. However, the overall effect is very low and lower than the deviation from measurements. Size resolved measurements of Na+, NH4+, NO3-, and SO42- are needed for a more detailed analysis on the impact of sea salt particles.

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