The EDGARv4.3.1 (Emissions Database for Global Atmospheric Research) global
anthropogenic emissions inventory of gaseous (SO
Overview of historical European Union (in blue) and international (in red) air quality regulations. UNECE/CLTRAP covers all European countries, USA, Canada, Belarus, Russia, Turkey, Israel, Ukraine, and central Asian states.
In the last few decades, air quality issues have gained worldwide importance
due to the fast pace of industrialization in many countries (Fenger, 2009).
Air pollution negatively affects human health (Pope and Dockery, 2006;
Anderson et al., 2012; Lelieveld et al., 2015), influences climate,
visibility and ecosystems (Monks et al., 2009; IPCC, 2013), and therefore
has significant effects on human life and the environment. It is crucial to
understand the impacts of anthropogenic air pollutants which are released
into the atmosphere by large and small-scale combustion, industrial
processes, transportation, waste disposal, agriculture and forest and
land-use change. Emission inventories have been developed in order to
quantify total and sector-specific emissions at the country, regional and
global levels, e.g., EMEP (European Monitoring and Evaluation Programme,
Schematic of the considered emission scenarios: REF(1970),
REF(2010), STAG_ENERGY and STAG_TECH. The
In Sect. 2.1 an overview of the data and assumptions used to develop the two emission scenarios is provided, addressing the EDGAR v4.3.1 bottom up emissions data set for the REF (reference), STAG_TECH and STAG_ENERGY emissions. This is followed by a short description of the TM5-FASST model used here to screen the impact of the considered emission scenarios on pollutant concentrations, human health and crop yields in Sect. 2.2.
EDGARv4.3.1 (Emissions Database for Global Atmospheric Research version v4.3.1;
In 2010, the EU had 27
member states and is therefore defined as such in this study. Not including the petrochemical industry with oil
production and refining.
Emission scenario assumptions. AD (activity data), EF (emission
factors), RED (reduction factor), EFF (efficiency factor), TECH (technology), EOP (end-of-pipe) and substance
STAG_TECH: we modeled the STAG_TECH scenario
by assuming for the three sectors of interest constant 1970 emission factors
to all technologies present in the emission database globally (specified
mainly regionally, but few also globally). We further assumed in the
STAG_TECH scenario no implementation of end-of-pipe (EOP)
control measures in Europe. Other regional standards regulating end-of-pipe
control, e.g., US power plant standards were not changed, because they fell
outside the European scope of this study. Thus in STAG_TECH,
European energy production is generated by the power plants of 1970 without
additional end-of-pipe measures as shown in Eq. 2 (in other words the
emission reduction RED equals the lower limit This is the
technological default of fly ash not passing through the stack.
Aside of the American
continent (North, Central, South), which implements mainly US Tier
standards, other countries show a considerable share of EURO standards even
outside Europe, in particular but not only Japan, Korea, etc.
STAG_ENERGY: the STAG_ENERGY scenario was
modeled by assuming that the three sectors of interest consumed the same
amount of energy (TJ) as in 1970, but with the 2010 fuel mix, energy
efficiency (EFF), technologies, and end-of-pipe abatements, as shown in Eq. (3). Since the fuel market is to a large extent global, this scenario was
implemented in all countries for the three selected sectors. All power
plants, vehicles and industries with the reference 2010 emissions standards
consume coal, gas and oil with the 2010 share but at the 1970 energy level (in
TJ). In addition to the calibration per sector of the energy consumption
level (in TJ), we evaluated the change in energy efficiency by fuel type,
sector and region. For the power generation sector we scaled for each
country the “main activity producer electricity plants (TJ)” with the 1970
over 2010 ratio of the “electricity output of main activity producer
electricity plants (GWh)” from IEA (2014). For the road transport sector we
scaled the “fuel consumption for road transport” with a factor composed of
the 1970 over 2010 road transport fuel consumption ratio multiplied with the
1970 over 2010 fuel efficiency ratio. The latter was calculated with the
macro-regional averaged values of petrol and diesel economies (L 100 km
In the ACPD version of this paper (Crippa et al., 2015), another scenario called STAG_FUEL discussed the combined impact of stagnation of fuel-mix and fuel amount. However, in revising the manuscript we decided to focus on the consumption of energy (TJ) instead of fuel because we consider the fuel mix and efficiency choices as exogenous variables just as the technology progress and end-of-pipe measures. When considering a stagnation of fuel with constant fuel mix and energy since 1970, due to the remaining contributions of relatively dirty fuel, this scenario results in higher emissions than STAG_ENERGY. This scenario is here not further discussed, since the interpretation of results is not adding much to the STAG_ENERGY scenario. The interested reader is referred to the corresponding ACPD paper.
The TM5-FASST model (Fast Scenario Screening Tool, version
v4.2.0_2014) is a linearized source–receptor model derived
at a receptor resolution of 1
For each source region, the TM5-FASST model requires as input the annual
emissions of primary PM
In Fig. 3, we first compare the global reference emission levels in 1970 (REF(1970)) and 2010 (REF(2010)), and then the two retrospective scenarios (STAG_TECH and STAG_ENERGY) to the REF(2010). With the scenarios we focus on the three selected sectors: power industry (labeled “power”), non-power industrial combustion of the manufacturing industry (“industry”) and road transport (“road”). We then evaluate the changes at the EU level in Fig. 4. Note that in our evaluations we consider the year 2010 as the reference year.
Effect of air quality abatements (STAG_TECH
scenario) on annual power generation emissions in Europe (EU27), SO
Global SO
When comparing the STAG_TECH to the REF(2010) case, we find a
global reduction of road emissions by 8.5 times for SO
Figure 5 presents the evolution over the years 1970–2010 of SO
For the STAG_ENERGY scenario, we observe the lowest emissions
both at global and European scales. With this scenario, in 2010 pollutant
emissions would vary between 10 and 30 % of the REF(2010) emission
values both at the European and global scales (refer to Figs. 3b and 4b as
well as to Table S1.2). Compared to REF(1970), the STAG_ENERGY
scenario shows around 95 % lower SO
Change between 1970 and 2010 of energy content of major fuels
(TJ yr
The primary emissions of industrial activities, including all manufacturing
activities, are SO
Effect of air quality abatements (STAG_TECH
scenario) on annual road transport emissions in Europe (EU27), SO
The largest effects of technology changes and end-of-pipe control measures
are observed in the road sector in the EU. The fuel quality directive
reduced SO
Regional contributions to PM
Figure 9 shows the spatial (0.1
Hotspots of avoided emissions due to progressive implementation
of air pollution policy and better technology in Europe: difference of
STAG_TECH and REF emissions in 2010 (t yr
Power, industry and road emissions data from the considered scenarios have
been used in the TM5-FASST model to derive the corresponding population
weighted-average PM
Relative change between the scenarios (STAG_ENERGY, STAG_TECH and REF(1970)) and the
reference case (REF(2010)): regional change in
Modeled surface delta PM
The interplay of European air quality policies and technological advancement
to reduce anthropogenic emissions in Europe and the world over the last 40
years has been investigated. Our analysis looks back to 1970, when the first
European air quality directive was introduced and compares with 2010, the
last year with reliable statistical data availability. In addition to our
reference EDGARv4.3.1 reference emissions, we introduced two retrospective
scenarios in order to analyze separately the impact of concurrent factors on
2010 emission levels. Specifically, the STAG_ENERGY scenario
evaluates the change in energy consumption, energy efficiency and fuel
shift, and the STAG_TECH scenario evaluates the change in technologies
with implementation of abatement measures. The two scenarios present a range
of emissions, lower and higher than the reference case for the year 2010,
which assess the specific role and impact of EU legislation on air quality,
of technology development and of fuel use. The story told by these scenarios
can be informative for designing multi-pollutant abatement policies in
emerging economies. Here we focus on the emissions of the most relevant
pollutants (SO
The authors acknowledge support of the EU FP7 project PEGASOS and the valuable comments to the manuscript provided by S. Galmarini (JRC). The paper has greatly benefitted from the insights and suggestions provided by reviewer Rob Maas and an anonymous reviewer. Edited by: A. Hofzumahaus