Influence of different complexity levels of road traffic models on air quality modelling at street scale

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period.

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Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

Algorithms ◽

10.3390/a14030076 ◽

2021 ◽

Vol 14 (3) ◽

pp. 76

Author(s):

Estrella Lucena-Sánchez ◽

Guido Sciavicco ◽

Ionel Eduard Stan

Keyword(s):

Air Quality ◽

Fundamental Problem ◽

Multivariate Time Series ◽

Quality Data ◽

Data Sets ◽

Air Quality Modelling ◽

Regression Problem ◽

Car Traffic ◽

Pollutant Concentrations ◽

Language Selection

Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.

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Sensitivity of air quality modelling to different emission inventories: a case study over Europe

Atmospheric Environment X ◽

10.1016/j.aeaoa.2021.100111 ◽

2021 ◽

pp. 100111

Author(s):

Philippe Thunis ◽

Monica Crippa ◽

Cornelis Cuvelier ◽

Diego Guizzardi ◽

Alexander de Meij ◽

...

Keyword(s):

Air Quality ◽

Emission Inventories ◽

Air Quality Modelling

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The Potential Impacts of Electric Vehicles on Urban Air Quality in Shanghai City

Sustainability ◽

10.3390/su13020496 ◽

2021 ◽

Vol 13 (2) ◽

pp. 496

Author(s):

Xiaojian Hu ◽

Nuo Chen ◽

Nan Wu ◽

Bicheng Yin

Keyword(s):

Air Quality ◽

Electric Vehicles ◽

Public Transportation ◽

Road Traffic ◽

Urban Air Quality ◽

Urban Air ◽

Vehicular Emissions ◽

Potential Reduction ◽

Efficient Measure ◽

Shanghai City

The Shanghai government has outlined plans for the new vehicles used for the public transportation, rental, sanitation, postal, and intra-city freight to be completely powered by electricity by 2020. This paper analyzed the characteristics of vehicle emissions in Shanghai in the past five years. The potential reduction in road traffic related emissions due to the promotion and application of electric vehicle in Shanghai was evaluated. The potential reduction was quantified by vehicular emissions. The vehicular emissions inventories are calculated by the COPERT IV model under the different scenarios, of which the results indicate that promoting electric vehicles is the efficient measure to control all road traffic related emissions and improve urban air quality. The results also provided basis and support for making policies to promote and manage electric vehicles.

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TNO-MACC_II emission inventory; a multi-year (2003–2009) consistent high-resolution European emission inventory for air quality modelling

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-10963-2014 ◽

2014 ◽

Vol 14 (20) ◽

pp. 10963-10976 ◽

Cited By ~ 200

Author(s):

J. J. P. Kuenen ◽

A. J. H. Visschedijk ◽

M. Jozwicka ◽

H. A. C. Denier van der Gon

Keyword(s):

Particulate Matter ◽

Air Quality ◽

High Resolution ◽

Emission Inventory ◽

Point Sources ◽

Air Quality Modelling ◽

Emission Data ◽

Data Set ◽

Spatially Distributed ◽

Reported Data

Abstract. Emissions to air are reported by countries to EMEP. The emissions data are used for country compliance checking with EU emission ceilings and associated emission reductions. The emissions data are also necessary as input for air quality modelling. The quality of these "official" emissions varies across Europe. As alternative to these official emissions, a spatially explicit high-resolution emission inventory (7 × 7 km) for UNECE-Europe for all years between 2003 and 2009 for the main air pollutants was made. The primary goal was to supply air quality modellers with the input they need. The inventory was constructed by using the reported emission national totals by sector where the quality is sufficient. The reported data were analysed by sector in detail, and completed with alternative emission estimates as needed. This resulted in a complete emission inventory for all countries. For particulate matter, for each source emissions have been split in coarse and fine particulate matter, and further disaggregated to EC, OC, SO4, Na and other minerals using fractions based on the literature. Doing this at the most detailed sectoral level in the database implies that a consistent set was obtained across Europe. This allows better comparisons with observational data which can, through feedback, help to further identify uncertain sources and/or support emission inventory improvements for this highly uncertain pollutant. The resulting emission data set was spatially distributed consistently across all countries by using proxy parameters. Point sources were spatially distributed using the specific location of the point source. The spatial distribution for the point sources was made year-specific. The TNO-MACC_II is an update of the TNO-MACC emission data set. Major updates included the time extension towards 2009, use of the latest available reported data (including updates and corrections made until early 2012) and updates in distribution maps.

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Evaluation of Regional Emission Control Based in Photochemical Air Quality Modelling

Advanced Air Pollution ◽

10.5772/17536 ◽

2011 ◽

Author(s):

Angel Rodriguez ◽

Santiago Saavedra ◽

Maria Dios ◽

Carmen Torres ◽

Jose A. ◽

...

Keyword(s):

Air Quality ◽

Emission Control ◽

Air Quality Modelling

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The role of emission reductions and the meteorological situation for air quality improvements during the COVID-19 lockdown period in central Europe

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-13931-2021 ◽

2021 ◽

Vol 21 (18) ◽

pp. 13931-13971

Author(s):

Volker Matthias ◽

Markus Quante ◽

Jan A. Arndt ◽

Ronny Badeke ◽

Lea Fink ◽

...

Keyword(s):

Air Quality ◽

Central Europe ◽

Urban Areas ◽

Road Traffic ◽

Quality Improvements ◽

Emission Data ◽

Emission Reductions ◽

Meteorological Situation ◽

Weather Situation

Abstract. The lockdown measures taken to prevent a rapid spreading of the coronavirus in Europe in spring 2020 led to large emission reductions, particularly in road traffic and aviation. Atmospheric concentrations of NO2 and PM2.5 were mostly reduced when compared to observations taken for the same time period in previous years; however, concentration reductions may not only be caused by emission reductions but also by specific weather situations. In order to identify the role of emission reductions and the meteorological situation for air quality improvements in central Europe, the meteorology chemistry transport model system COSMO-CLM/CMAQ was applied to Europe for the period 1 January to 30 June 2020. Emission data for 2020 were extrapolated from most recent reported emission data, and lockdown adjustment factors were computed from reported activity data changes, e.g. Google mobility reports. Meteorological factors were investigated through additional simulations with meteorological data from previous years. The results showed that lockdown effects varied significantly among countries and were most prominent for NO2 concentrations in urban areas with 2-week-average reductions up to 55 % in the second half of March. Ozone concentrations were less strongly influenced (up to ±15 %) and showed both increasing and decreasing concentrations due to lockdown measures. This depended strongly on the meteorological situation and on the NOx / VOC emission ratio. PM2.5 revealed 2 %–12 % reductions of 2-week-average concentrations in March and April, which is much less than a different weather situation could cause. Unusually low PM2.5 concentrations as observed in northern central Europe were only marginally caused by lockdown effects. The lockdown can be seen as a big experiment about air quality improvements that can be achieved through drastic traffic emission reductions. From this investigation, it can be concluded that NO2 concentrations can be largely reduced, but effects on annual average values are small when the measures last only a few weeks. Secondary pollutants like ozone and PM2.5 depend more strongly on weather conditions and show a limited response to emission changes in single sectors.

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Aerosols in the CALIOPE air quality modelling system: validation and analysis of PM levels, optical depths and chemical composition over Europe

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-20575-2011 ◽

2011 ◽

Vol 11 (7) ◽

pp. 20575-20629

Author(s):

S. Basart ◽

M. T. Pay ◽

O. Jorba ◽

C. Pérez ◽

P. Jiménez-Guerrero ◽

...

Keyword(s):

Air Quality ◽

Chemical Composition ◽

Air Quality Modelling ◽

Po Valley ◽

Aerosol Mass ◽

Forecasting System ◽

Bulk Parameters ◽

Quality Threshold ◽

Daily Pm10 ◽

Modelling System

Abstract. The CALIOPE high-resolution air quality modelling system is developed and applied to Europe (12 km × 12 km, 1 h). The modelled daily to seasonal aerosol variability over Europe in 2004 have been evaluated and analysed. The aerosols are estimated from two models, CMAQv4.5 (AERO4) and BSC-DREAM8b. CMAQv4.5 calculates biogenic, anthropogenic and sea salt aerosol and BSC-DREAM8b provides the natural mineral dust contribution from North African deserts. For the evaluation, we use daily PM10/PM2.5 and chemical composition data from 54 stations of the EMEP/CREATE network and coarse and fine aerosol optical depth (AOD) data from 35 stations of the AERONET sun photometer network. The model achieves daily PM10 and PM2.5 correlations of 0.57 and 0.47, respectively, and total, coarse and fine AOD correlations of 0.51, 0.63, and 0.53, respectively. The higher correlations of the PM10 and the coarse mode AOD are largely due to the accurate representation of the African dust influence in the forecasting system. Overall PM and AOD levels are underestimated. The evaluation of the chemical composition highlights underestimations of the modelled fine fractions particularly for carbonaceous matter (EC and OC) and secondary inorganic aerosols (SIA; i.e. nitrates, sulphates and ammonium). The scores of the bulk parameters are significantly improved after applying a simple model bias correction based on the chemical composition observations. SIA are dominant in the fine fractions representing up to 80 % of the aerosol budget in latitudes beyond 40° N. The highest aerosol concentrations are found over the industrialized and populated areas of the Po Valley and the Benelux regions. High values in southern Europe are linked to the transport of coarse particles from the Sahara desert which contributes up to 40 % of the total aerosol mass. Close to the surface, maxima dust seasonal concentrations (>30 μg m–3) are found between spring and early autumn. We estimate that desert dust causes daily exceedances of the PM10 European air quality threshold (50 μg m–3) in large areas south of 45° N reaching up to more than 75 days per year in the southernmost regions.

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