scholarly journals Prediction of source contributions to surface PM<sub>10</sub> concentrations in European cities: a case study for an episode in December 2016 using EMEP/MSC-W rv4.15 – Part.2 The local urban background contribution

2020 ◽  
Author(s):  
Matthieu Pommier
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Total Concentration ◽  
Grid Cells ◽  
Source Contribution ◽  
European Cities ◽  
Local Contribution ◽  
Concentration Changes ◽  
Definition Of ◽  
The Impact

Abstract. Despite the progress made in the latest decades, air pollution is still the primary environmental cause of premature death in Europe. The urban population risks more likely to suffer to pollution related to high concentrations of air pollutants such as in particulate matter smaller than 10 µm (PM10). Since the composition of these particulates varies with space and time, the understanding of the origin is essential to determine the most efficient control strategies. A source contribution calculation allows to provide such information and thus to determine the geographical location of the sources (e.g. city or country) responsible for the air pollution episodes. In this study, the calculations provided by the regional EMEP/MSC-W rv4.15 model in a forecast mode, with a 0.25° longitude × 0.125° latitude resolution, and based on a scenario approach, have been explored. To do so, the work has focused on event occurring between 01 and 09 December 2016. This source contribution calculation aims at quantifying over 34 European cities the Local contribution of these PM10, i.e. from the city itself, on an hourly basis. Since the methodology used in the model is based on reduced anthropogenic emissions, compared to a reference run, the choice of the percentage in the reductions has been tested by using three different values (5 %, 15 % and 50 %). The definition of the Local contribution, and thus the definition of the area defining the cities is also an important parameter. The impact of the definition of these urban areas, for the studied cities, was investigated (i.e. 1 model grid cell, 9 grid cells and the grid cells covering the definition given by the Global Administrative Area – GADM). Using a 15 % reduction in the emission and the use of larger cities for our source contribution calculation (e.g. 9 grid cells and GADM), help to reduce the non-linearity in the concentration changes. This non-linearity is observed in the mismatch between the total concentration and the sum of the concentrations from different calculated sources. When this non-linearity is observed, it impacts the NO3−, NH4+ and H2O concentrations. However, the mean non-linearity represents only less than 2 % of the total modelled PM10 calculated by the system. During the studied episode, it was found that 20 % of the predicted PM10 had a Local origin, essentially composed of primary components. 60 % of the hourly PM10 concentrations predicted by the model came from the countries in the regional domain, and they were essentially composed of NO3−. The rest of the PM10 was mainly due to natural sources. It was also shown that the Central European cities were mainly impacted by the surrounding countries while the cities located a little away from the rest of the other European countries (e.g. Oslo and Lisbon) had larger Local contribution. The usefulness of the forecasting tool has also been illustrated with an example in Paris, since the system has been able to predict a local polluted event on 02 December 2016 as documented by local authorities.

scholarly journals Prediction of source contributions to urban background PM<sub>10</sub> concentrations in European cities: a case study for an episode in December 2016 using EMEP/MSC-W rv4.15 – Part 2: The city contribution

2021 ◽  
Vol 14 (6) ◽  
pp. 4143-4158
Author(s):  
Matthieu Pommier
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Total Concentration ◽  
Grid Cells ◽  
Source Contribution ◽  
European Cities ◽  
Concentration Changes ◽  
Definition Of ◽  
The Impact ◽  
The City

Abstract. Despite the progress made in the latest decades, air pollution is still the primary environmental cause of premature death in Europe. The urban population risks more likely to suffer to pollution related to high concentrations of air pollutants, such as in particulate matter smaller than 10 µm (PM10). Since the composition of these particulates varies with space and time, the understanding of the origin is essential to determine the most efficient control strategies. A source contribution calculation allows us to provide such information and thus to determine the geographical location of the sources (e.g. city or country) responsible for the air pollution episodes. In this study, the calculations provided by the regional European Monitoring and Evaluation Programme/Meteorological Synthesizing Centre – West (EMEP/MSC-W) rv4.15 model in a forecast mode, with a 0.25∘ longitude × 0.125∘ latitude resolution, and based on a scenario approach, have been explored. To do so, the work has focused on event occurring between 1 and 9 December 2016. This source contribution calculation aims at quantifying over 34 European cities, the “city” contribution of these PM10, i.e. from the city itself, on an hourly basis. Since the methodology used in the model is based on reduced anthropogenic emissions, compared to a reference run, the choice of the percentage in the reductions has been tested by using three different values (5 %, 15 %, and 50 %). The definition of the “city” contribution, and thus the definition of the area defining the cities is also an important parameter. The impact of the definition of these urban areas, for the studied cities, was investigated (i.e. one model grid cell, nine grid cells and the grid cells covering the definition given by the global administrative area – GADM). Using a 15 % reduction in the emission and larger cities for our source contribution calculation (e.g. nine grid cells and GADM) helps to reduce the non-linearity in the concentration changes. This non-linearity is observed in the mismatch between the total concentration and the sum of the concentrations from different calculated sources. When this non-linearity is observed, it impacts the NO3-, NH4+, and H2O concentrations. However, the mean non-linearity represents only less than 2 % of the total modelled PM10 calculated by the system. During the studied episode, it was found that 20 % of the surface predicted PM10 had been from the “city”, essentially composed of primary components. In total, 60 % of the hourly PM10 concentrations predicted by the model came from the countries in the regional domain, and they were essentially composed of NO3- (by ∼ 35  %). The two other secondary inorganic aerosols are also important components of this “rest of Europe” contribution, since SO42- and NH4+ represent together almost 30 % of this contribution. The rest of the PM10 was mainly due to natural sources. It was also shown that the central European cities were mainly impacted by the surrounding countries while the cities located a bit away from the rest of the other European countries (e.g. Oslo and Lisbon) had larger “city” contributions. The usefulness of the forecasting tool has also been illustrated with an example in Paris, since the system has been able to predict the primary sources of a local polluted event on 1–2 December 2016, as documented by local authorities.

scholarly journals Prediction of source contributions to urban background PM<sub>10</sub> concentrations in European cities: a case study for an episode in December 2016 using EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0 – Part 1: The country contributions

2020 ◽  
Vol 13 (4) ◽  
pp. 1787-1807 ◽  
Author(s):  
Matthieu Pommier ◽  
Hilde Fagerli ◽  
Michael Schulz ◽  
Alvaro Valdebenito ◽  
Richard Kranenburg ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Emission Reduction ◽  
Total Concentration ◽  
Large Fraction ◽  
Mitigation Strategies ◽  
Grid Cells ◽  
Source Attribution ◽  
European Cities ◽  
Guideline Value ◽  
The Impact

Abstract. A large fraction of the urban population in Europe is exposed to particulate matter levels above the WHO guideline value. To make more effective mitigation strategies, it is important to understand the influence on particulate matter (PM) from pollutants emitted in different European nations. In this study, we evaluate a country source contribution forecasting system aimed at assessing the domestic and transboundary contributions to PM in major European cities for an episode in December 2016. The system is composed of two models (EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0), which allows the consideration of differences in the source attribution. We also compared the PM10 concentrations, and both models present satisfactory agreement in the 4 d forecasts of the surface concentrations, since the hourly concentrations can be highly correlated with in situ observations. The correlation coefficients reach values of up to 0.58 for LOTOS-EUROS and 0.50 for EMEP for the urban stations; the values are 0.58 for LOTOS-EUROS and 0.72 for EMEP for the rural stations. However, the models underpredict the highest hourly concentrations measured by the urban stations (mean underestimation of 36 %), which is to be expected given the relatively coarse model resolution used (0.25∘ longitude × 0.125∘ latitude). For the source attribution calculations, LOTOS-EUROS uses a labelling technique, while the EMEP/MSC-W model uses a scenario having reduced anthropogenic emissions, and then it is compared to a reference run where no changes are applied. Different percentages (5 %, 15 %, and 50 %) for the reduced emissions in the EMEP/MSC-W model were used to test the robustness of the methodology. The impact of the different ways to define the urban area for the studied cities was also investigated (i.e. one model grid cell, nine grid cells, and grid cells covering the definition given by the Global Administrative Areas – GADM). We found that the combination of a 15 % emission reduction and a larger domain (nine grid cells or GADM) helps to preserve the linearity between emission and concentrations changes. The nonlinearity, related to the emission reduction scenario used, is suggested by the nature of the mismatch between the total concentration and the sum of the concentrations from different calculated sources. Even limited, this nonlinearity is observed in the NO3-, NH4+, and H2O concentrations, which is related to gas–aerosol partitioning of the species. The use of a 15 % emission reduction and of a larger city domain also causes better agreement on the determination of the main country contributors between both country source calculations. Over the 34 European cities investigated, PM10 was dominated by domestic emissions for the studied episode (1–9 December 2016). The two models generally agree on the dominant external country contributor (68 % on an hourly basis) to PM10 concentrations. Overall, 75 % of the hourly predicted PM10 concentrations of both models have the same top five main country contributors. Better agreement on the dominant country contributor for primary (emitted) species (70 % is found for primary organic matter (POM) and 80 % for elemental carbon – EC) than for the inorganic secondary component of the aerosol (50 %), which is predictable due to the conceptual differences in the source attribution used by both models. The country contribution calculated by the scenario approach depends on the chemical regime, which largely impacts the secondary components, unlike the calculation using the labelling approach.

scholarly journals Prediction of source contributions to urban background PM10 concentrations in European cities: a case study for an episode in December 2016 – Part.1 The country contributions

2019 ◽  
Author(s):  
Matthieu Pommier ◽  
Hilde Fagerli ◽  
Michael Schulz ◽  
Alvaro Valdebenito ◽  
Richard Kranenburg ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Total Concentration ◽  
Large Fraction ◽  
Correlation Coefficients ◽  
Grid Cell ◽  
Primary Component ◽  
Mitigation Strategies ◽  
Grid Cells ◽  
European Cities ◽  
The Impact

Abstract. A large fraction of the urban population in Europe is exposed to particulate matter levels above the WHO guideline. To make more effective mitigation strategies, it is important to understand the influence on particulate matter (PM) from pollutants emitted in different European nations. In this study, we evaluate a source apportionment forecasting system aimed to assess the domestic and transboundary contributions to PM in major European cities for an episode in December 2016. The system is composed of two models (EMEP/MSC-W rv4.15 and LOTOS-EUROS v2.0) which allows to consider differences in the source attribution. We also compared the PM10 concentrations and both models present satisfactory agreement in the 4day-forecasts of the surface concentrations, since the hourly concentrations can be highly correlated with in-situ observations. The correlation coefficients reach values up to 0.58 for LOTOS-EUROS and 0.50 for EMEP for the urban stations; and 0.58 for LOTOS-EUROS and 0.72 for EMEP for the rural stations. However, the models under-predict the highest hourly concentrations measured by the urban stations (mean underestimation by 36 %), predictable with the relatively coarse model resolution used (0.25° longitude × 0.125° latitude). For the source receptor calculations, the EMEP/MSC-W model uses a scenario having reduced anthropogenic emissions and then it is compared to a reference run where no changes are applied. Different percentages (5 %, 15 % and 50 %) in the reduced emissions were used to test the robustness of the methodology. The impact of the different ways to define the urban area for the studied cities was also investigated (i.e. 1 model grid cell, 9 grid cells and the grid cells covering the definition given by the Global Administrative Area – GADM). We found that by combining the use of the 15 % factor and of a larger domain for the city edges (9 grid cells or GADM), it helps to reduce the impact of non-linearity on the chemistry which is seen in the mismatch between the total concentration and the sum of the concentrations from different calculated sources. Even limited, this non-linearity is observed in the NO3−, NH4+ and H2O concentrations, which is related to gas-aerosol partitioning of the species. The use of a 15 % factor and of a larger city domain also gives a better agreement in the determination of the main country contributors between both country source receptor calculations. During the studied episode, dominated by the influence of the domestic emissions for the 34 European cities investigated and occurring from December 01st to 09th 2016, the two models agree 68 % of the time (on hourly resolution) on the country, having been the dominant contributor to PM10 concentrations. 75 % of the hourly predicted PM10 concentrations by both models, have the same top 5 main country contributors. Better results are found in the determination the dominant country contributor for the primary component (70 % for POM and 80 % for EC) than for the secondary inorganic aerosols (50 %).

A Dynamic Analysis Of The Impact Of Air Pollution On The daylight Availability In An Open-plan Office In London

2021 ◽  
pp. 94-103
Author(s):  
Jiangtao Du ◽  
Steve Sharples
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Simulation Analysis ◽  
Design Stage ◽  
Design Strategies ◽  
Early Design Stage ◽  
Pollutant Deposition ◽  
Open Plan ◽  
Building Project ◽  
The Impact

The deposition of air pollutants on glazing can significantly affect the daylight transmittance of building fenestration systems in urban areas. This study presents a simulation analysis of the impact of air pollution and glazing visual transmittance on indoor daylight availability in an open-plan office in London. First, the direct links between glazing visual transmittance and daylighting conditions were developed and assessed. Second, several simple algorithms were established to estimate the loss of daylight availability due to the pollutant deposition at the external surface of vertical glazing. Finally, some conclusions and design strategies to support facade planning at the early design stage of an urban building project were developed.

scholarly journals IMPACT OF REGIONAL DISTRIBUTION AND AIR POLLUTION ON INTERNAL STRUCTURE OF MELIA AZEDARACH L. LEAVES

2021 ◽  
Vol 52 (6) ◽  
pp. 1326-1333
Author(s):  
V. Abozaid ◽  
H. Arif Abdulrahman ◽  
D. Ayoub Ibrahim
Keyword(s):  
Air Pollution ◽  
Leaf Area ◽  
Urban Areas ◽  
Regional Distribution ◽  
Industrial Area ◽  
Control Area ◽  
Melia Azedarach ◽  
Layer Width ◽  
Anatomical Features ◽  
The Impact

This study was performed to investigate the impact of air pollution on leaf area and anatomical features of Melia azedarach L. trees, in urban areas with three demographical classes: location (I) industrial area, location (II) roadside area and free parts (control area) as a location (III) of Duhok city/Kurdistan Region-Iraq, during July 2021. The results demonstrated that the leaf area of selected plants' leaves in location I had reduced with no noticeable change in the average stomata density in the three locations I, II and Ⅲ. Meanwhile, the results of the most anatomical features of the blade (blade, lower cuticle, epidermis (both upper and lower) thickness, palisade layer height and spongy parenchyma width) in addition to midrib parameters (epidermis thickness (upper and lower), collenchyma and parenchyma layer width, phloem and xylem width and pith diameter) were decreased in both locations I, II, and with well-developed anatomical features in location III.

scholarly journals Ambient Air Quality Measurement with Low-Cost Optical and Electrochemical Sensors: An Evaluation of Continuous Year-Long Operation

Environments ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 114
Author(s):  
Jiří Bílek ◽  
Ondřej Bílek ◽  
Petr Maršolek ◽  
Pavel Buček
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Sensor Network ◽  
Electrochemical Sensors ◽  
Reference Method ◽  
Ambient Air ◽  
Sensor Data ◽  
Dust Particles ◽  
Concentration Changes ◽  
The Impact

Sensor technology is attractive to the public due to its availability and ease of use. However, its usage raises numerous questions. The general trustworthiness of sensor data is widely discussed, especially with regard to accuracy, precision, and long-term signal stability. The VSB-Technical University of Ostrava has operated an air quality sensor network for more than two years, and its large sets of valid results can help in understanding the limitations of sensory measurement. Monitoring is focused on the concentrations of dust particles, NO2, and ozone to verify the impact of newly planted greenery on the reduction in air pollution. The sensor network currently covers an open field on the outskirts of Ostrava, between Liberty Ironworks and the nearby ISKO1650 monitoring station, where some of the worst air pollution levels in the Czech Republic are regularly measured. In the future, trees should be allowed to grow over the sensors, enabling assessment of the green barrier effect on air pollution. As expected, the service life of the sensors varies from 1 to 3 years; therefore, checks are necessary both prior to the measurement and regularly during operation, verifying output stability and overall performance. Results of the PMx sensory measurements correlated well with the reference method. Concentration values measured by NO2 sensors correlated poorly with the reference method, although timeline plots of concentration changes were in accordance. We suggest that a comparison of timelines should be used for air quality evaluations, rather than particular values. The results showed that the sensor measurements are not yet suitable to replace the reference methods, and dense sensor networks proved useful and robust tools for indicative air quality measurements (AQM).

scholarly journals On the long term impact of emissions from central European cities on regional air-quality

2015 ◽  
Vol 15 (22) ◽  
pp. 32101-32155 ◽  
Author(s):  
P. Huszar ◽  
M. Belda ◽  
T. Halenka
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Rural Areas ◽  
Ozone Production ◽  
Central European ◽  
Large Cities ◽  
European Cities ◽  
Regional Air Quality ◽  
Urban Emissions ◽  
The Impact

Abstract. For the purpose of qualifying and quantifying the impact of urban emission from Central European cities on the present-day regional air-quality, the regional climate model RegCM4.2 was coupled with the chemistry transport model CAMx, including two-way interactions. A series of simulations was carried out for the 2001–2010 period either with all urban emissions included (base case) or without considering urban emissions. Further, the sensitivity of ozone production to urban emissions was examined by performing reduction experiments with −20 % emission perturbation of NOx and/or NMVOC. The validation of the modeling system's air-quality related outputs using AirBase and EMEP surface measurements showed satisfactory reproduction of the monthly variation for ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2). In terms of hourly correlations, reasonable values are achieved for ozone (r around 0.5–0.8) and for NO2 (0.4–0.6), but SO2 is poorly or not correlated at all with measurements (r around 0.2–0.5). The modeled fine particulates (PM2.5) are usually underestimated, especially in winter, mainly due to underestimation of nitrates and carbonaceous aerosols. EC air-quality measures were chosen as metrics describing the cities emission impact on regional air pollution. Due to urban emissions, significant ozone titration occurs over cities while over rural areas remote from cities, ozone production is modeled, mainly in terms of number of exceedances and accumulated exceedances over the threshold of 40 ppbv. Urban NOx, SO2 and PM2.5 emissions also significantly contribute to concentrations in the cities themselves (up to 50–70 % for NOx and SO2, and up to 60 % for PM2.5), but the contribution is large over rural areas as well (10–20 %). Although air pollution over cities is largely determined by the local urban emissions, considerable (often a few tens of %) fraction of the concentration is attributable to other sources from rural areas and minor cities. Further, for the case of Prague (Czech Republic capital) it is shown that the inter-urban interference between large cities does not play an important role which means that the impact on a chosen city of emissions from all other large cities is very small. The emissions perturbation experiments showed that to achieve significant ozone reduction over cities in central Europe, the emission control strategies have to focus on the reduction of NMVOC, as reducing NOx, due to suppressed titration, leads often to increased O3. The influence over rural areas remote from cities is however always in favor of improved air-quality, i.e. both NOx and/or NMVOC reduction ends up in decreased ozone pollution, mainly in terms of exceedances.

scholarly journals Opposite Effects of Aerosols on Daytime Urban Heat Island Intensity between Summer and Winter

2020 ◽  
Author(s):  
Wenchao Han ◽  
Zhanqing Li ◽  
Fang Wu ◽  
Yuwei Zhang ◽  
Jianping Guo ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Air Pollution ◽  
Urban Heat Island ◽  
Planetary Boundary Layer ◽  
Urban Areas ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Different Seasons ◽  
Urban Heat ◽  
The Impact

Abstract. The urban heat island intensity (UHII) is the temperature difference between urban areas and their rural surroundings. It is commonly attributed to changes in the underlying surface structure caused by urbanization. Air pollution caused by aerosol particles can affect the UHII by changing the surface energy balance and atmospheric thermodynamic structure. By analyzing satellite data and ground-based observations collected from 2001 to 2010 at 35 cities in China and using the WRF-Chem model, we found that aerosols have very different effects on daytime UHII in different seasons: reducing the UHII in summer, but increasing the UHII in winter. The seasonal contrast in the spatial distribution of aerosols between the urban centers and the suburbs lead to a spatial discrepancy in aerosol radiative effect (SD-ARE). Additionally, different stability of the planetary boundary layer induced by aerosol is closely associated with a dynamic effect (DE) on the UHII. SD-ARE reduces the amount of radiation reaching the ground and changes the vertical temperature gradient, whereas DE increases the stability of the planetary boundary layer and weakens heat release and exchange between the surface and the PBL. Both effects exist under polluted conditions, but their relative roles are opposite between the two seasons. It is the joint effects of the SD-ARE and the DE that drive the UHII to behave differently in different seasons, which is confirmed by model simulations. In summer, the UHII is mainly affected by the SD-ARE, and the DE is weak, and the opposite is the case in winter. This finding sheds a new light on the impact of the interaction between urbanization-induced surface changes and air pollution on urban climate.

Air pollution tolerance index of selected plants in industrial and urban areas of Vellore district

2016 ◽  
Vol 35 (1) ◽  
Author(s):  
M. Akilan ◽  
S. Nandhakumar
Keyword(s):  
Air Pollution ◽  
Plant Species ◽  
Urban Areas ◽  
Tolerance Index ◽  
Pollution Tolerance ◽  
Air Pollution Tolerance Index ◽  
Relative Water ◽  
Leaf Tissues ◽  
The Impact ◽  
College Farm

The impact of air pollutants on the biochemical characters of the selected plant species from industrial and urban areas was studied by calculating ascorbic acid, total chlorophyll, leaf extract pH and relative water content from leaf tissues. The air pollution tolerance index (APTI) values of the selected plants of different study areas revealed that the APTI values of the plants at the College Farm recorded low compared to Arcot and Ranipet transporation and industrial areas. Among the selected plant species, higher APTI values were recorded from the industrial and urban areas. when compared to areas free from industries and transport. The four selected plant species <italic>viz</italic>. <italic>Neerium oleander, Tamarindus indicus, Azardirecta indica</italic> and <italic>Pungamia pinnata, Neerium oleander</italic> recorded higher APTI values from the industrial and transportation that revealed more tolerance than the other selected plants.The statistical results revealed that Arcot was more polluted compared to Ranipet, and the college farm recorded least polluted due to less exposure to industries, transport and urbanization.

scholarly journals Air pollution in moderately polluted urban areas: How does the definition of “neighborhood” impact exposure assessment?

2015 ◽  
Vol 206 ◽  
pp. 437-448 ◽  
Author(s):  
Quentin M. Tenailleau ◽  
Frédéric Mauny ◽  
Daniel Joly ◽  
Stéphane François ◽  
Nadine Bernard
Keyword(s):  
Air Pollution ◽  
Exposure Assessment ◽  
Urban Areas ◽  
Definition Of
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