Introduction
The rapid increase in Earth's population that took place the last 60 years and
the changes in human practices towards a society with larger energy consumption
resulted in intensifying the atmospheric pollutant emission
. Air pollutants like
ozone (O3), carbon monoxide (CO), nitrogen oxides (NOx),
nitric acid (HNO3), and particulate black carbon (BC), organic
carbon (OC), sulfate (SO42-), and nitrate
(NO3-) aerosol components have been observed to increase, reaching
levels in the early 1980s that threaten ecosystems (leading for instance to
agricultural efficiency decrease; ) and human health
(inducing a
mortality increase; ), that increase atmospheric acidity
, and that affect climate . To limit the negative
impacts of air pollution, aggressive measures have been taken in the 1980s
to reduce human-induced emissions. In parallel, the
growing number and accuracy of air quality observations enabled monitoring of
the air quality changes and statistical association of such changes with health
issues and other environmental impacts.
Pollutant levels and seasonal variation are closely connected to emissions of
the pollutant or its precursors, as well as meteorology. Tropospheric ozone
and aerosols have largely increased since pre-industrial times as a result of
intense anthropogenic activity .
, analyzing observations and simulations, suggested that
recent air quality changes and their uncertainty are mainly associated with
emissions changes although climate warming degrades air quality. Most
anthropogenic activity takes place in the Northern Hemisphere (NH) with
87.5 % of the global population residing there; in particular 81.8 %
occurred between the Equator and 50∘ N in 2005 .
Thus, the anthropogenically induced change in pollutant levels, and thus in
air quality, since pre-industrial times is expected to be larger in this area
than in the Southern Hemisphere (SH). Anthropogenic
activity emitting NOx into the atmosphere can influence the quantity of
tropospheric ozone but, because of the ozone's nonlinear dependency on the
NOx levels , the net effect on O3 levels
requires careful evaluation. Previous studies have shown that there was a
30 % increase in the tropospheric ozone burden, which corresponds to
71 Tg, between 1890 and 1990 . This is linked to a
decrease in O3 lifetime into the troposphere by about 30 %
. report a shift of 3–6 days in the
seasonal cycle per decade for the mid-northern latitudes, where most of the
anthropogenic activity takes place, based on long continuous data records
from monitoring sites in Europe. They attributed this shift to changes in the
relative contribution of the different tropospheric O3 sources, from
the stratosphere, changes in large-scale circulation, and changes in
O3 precursor emissions and subsequent photochemical production within
the troposphere. The springtime maximum in ozone concentrations in the NH
reflects the combined effect of increased free-tropospheric photochemical
activity and stratospheric input . In that work an
increase of background ozone levels in the NH of 0.5–2 % per year for
the last 3 decades is reported, resulting to a mean concentration of
20–45 ppb for the NH for the year 2000. At Finokalia, Greece,
presented the changes in O3 seasonality due
to changes in meteorology and found a decreasing trend in ozone for the
period 1997–2004 of about 1.64 ppbyr-1
(3.1 % yr-1). At the Mauna Loa observatory in Hawaii the
40-year time series of O3 measurements shows that there is little
change during spring but there is a rise during autumn, attributed to the
weakening of the airflow from Eurasia in spring and strengthening in autumn
. performed an extended analysis on
long-term (20–40 years) time series of global surface and ozonesonde
observations. They found that the substantial tropospheric ozone increases
observed in the early 1990s, the flattening, or even the decrease in ozone
levels observed at several locations (e.g., Glacier National Park,
Minamitori-shima) during the past 10 years are the result of the restrictions
on precursor emissions. In the southern hemispheric subtropics a significant
increase has also been observed . Similar findings were
reported by by analyzing measurements from sondes,
aircraft, and surface sites. These observations support the fact that
O3 variability depends on the geographical location
and the extent of regional anthropogenic influence
. The enhancements have leveled off in the most recent
decades, most probably due to O3 precursor emissions control
.
The global modeling study performed by using the
MOZART2 model showed a 50 % increase in the ozone burden calculated since
pre-industrial times and a -6 to +43 % change projected for 2100 using
different emission scenarios. In agreement with that study, the
analysis of multi-model simulations of the future
atmosphere has shown that, depending on the anthropogenic emissions,
(particularly of NOx), the change in the tropospheric O3 burden
in 2030 compared to the 2000 levels can range between -5 % (most optimistic
scenario) and +15 % (most pessimistic scenario).
Observations also show statistically significant trends in surface levels of
atmospheric pollutants like CO . CO surface
levels were observed to increase before 1990s and decrease
in recent years due to a decrease in CO anthropogenic emissions
. MOPITT, AIRS, TES, and IASI satellite instruments have
recorded a decreasing trend in the total column of CO of about -1 % per
year in the NH and less than that in the SH from 2000 to
2011 . Similarly trends have been reported for the surface
levels of sulfate as well as the aerosol optical depth (AOD)
that provides a measure of the interaction of aerosol atmospheric column
content with radiation . Global AOD over the
ocean has been recorded by the Advanced Very High Resolution Radiometer (AVHRR)
satellite instrument to increase from 1985 to 1990 and to decrease from 1994 to
2006 . The modeling study by also shows
globally decreasing AOD trends for the period 2001–2010. Regionally the
largest decrease is calculated for eastern USA and western Europe, whereas the
eastern Chinese region shows the sharpest increasing trend. Similar results are
found in the multi-satellite study by , in which regionally
western Europe and eastern USA appear to have the fastest decreasing trends in
AOD, while central and eastern China was the fastest increasing trends in AOD. In
agreement with that study the analysis of the measurements of surface
concentrations of several aerosol species by shows
decreasing trends in the eastern USA for the period 1990–2010.
In the troposphere all major air pollutants have sufficiently long lifetimes to
be transported .
Thus, in addition to their precursor emissions and chemistry, atmospheric
circulation is controlling air pollutant levels. Changes in transport patterns
and spatial and temporal changes in anthropogenic emissions of O3
precursors were suggested as the main causes of the observed shifts in seasonal
maxima of O3 in the NH
and the increase in wintertime levels of O3 compared to earlier years
. Simulations suggest that the weakening of monsoon
circulation in past decades contributed to the observed high aerosol levels in
China , while a 6-year analysis of the Moderate Resolution
Imaging Spectroradiometer (MODIS) AOD over the Mediterranean attributed
observed AOD changes to anthropogenic emissions during summertime and to
changes in precipitation during wintertime .
Traditionally, air quality assessments are performed by comparing the
pollutants concentrations at present with those of a past year
;
however, in order to evaluate the effectiveness of the applied air quality
legislation, we need to account for the changes induced by meteorology and the
increase in anthropogenic emissions due to increases in population and energy
demand. Recently, have constructed global anthropogenic
emission scenarios assuming global stagnation of technology or global
stagnation of energy demand and for 2010 fuel mix and energy efficiency. Based
on these inventories, calculated important benefits for
human health, economy, and climate over Europe of the implementation of
European legislation and technological improvements to reduce the emissions of
air pollutants.
For the present study, a set of three different transient global
three-dimensional chemistry-transport simulations of atmospheric composition
changes over the past 3 decades has been performed and analyzed to
investigate what the air quality would have been compared to nowadays (1) if
the anthropogenic emissions per human and per major geographic region were stagnant to those in 1980 while the population increased, i.e., assuming no further air quality legislation, no changes in the
standard of living, no technological improvements, and no economy globalization
(BA1980); and (2) if the anthropogenic emissions have remained constant to those in
1980, i.e assuming stagnant per-capita anthropogenic emissions and no
population increase (AE1980). The first 30-year simulation is performed with
anthropogenic emissions for the period 1980–2010 that have been constructed, as
further explained, in order to account for population increase but not for
the globalization of the industrial activities, the technological
improvements, or the legislation applied after 1980. The second one is
performed using the same anthropogenic emissions as those of the year 1980. The base
simulation, current legislation (CL), is performed using historical anthropogenic emissions for the
period 1980–2010 that integrate the changes in industrial and technological
developments, the standard of living, and the population growth, with air
pollution abatement efforts.
Methodology – the global model setup
The model used for this work is the global three-dimensional chemistry-transport model (CTM)
TM4–ECPL , which takes into account gas and
multiphase chemistry and gas–particle partitioning
of semivolatile organics and computes the
gas-to-particle partitioning of inorganic components and the aerosol water
using the ISORROPIA II aerosol thermodynamic model in
which the dust components are neglected for the present study. TM4–ECPL low
horizontal resolution of 4∘ latitude × 6∘ longitude and
34 hybrid vertical layers to 0.1 hPa were used here, driven by ECMWF ERA-Interim
meteorology from 1980 to 2010 .
TM4–ECPL simulates the tropospheric composition and chemistry. To be
computationally efficient, the model has low vertical resolution in the
stratosphere and a very primitive representation of the full set of
stratospheric chemistry. For this reason TM4–ECPL forces the O3
concentrations in the top layers (50–10 hPa) based on the monthly mean
observations by the Microwave Scanning Radiometer (MSR) satellite for the
years 1980–2008 and Global Ozone Monitoring Experiment (GOME2) for the years
2009–2010. These data have been interpolated to the model layers by the
Royal Netherlands Meteorological Institute (KNMI) . TM4–ECPL
also calculates the stratospheric nitric acid concentration based on
O3 levels using a ratio derived from Upper Atmosphere Research
Satellite (UARS) at 10 hPa. To account for CH4 oxidation in the
stratosphere, the CH4 concentrations in the top eight layers of the
model (roughly above 17 km height) are forced to the HALOE CH4
climatology .
Since TM4–ECPL, like most CTMs, does not explicitly consider methane
emissions, it forces the surface CH4 distribution to observations using
the latitudinal monthly varying surface levels of CH4 calculated by
for the year 1984, which correspond to a global mean
surface concentration of 1.69 ppm. This surface concentration changes depending
on the simulated year and following the measured increase of CH4 in the
atmosphere. For the years between 1979 and 1989, the CH4 surface
distribution of the year 1984 is scaled to fit the observed CH4 data of
the respective year. For the years between 1990 and 2010 prescribed CH4
surface concentration files based on National Oceanic and Atmospheric
Administration (NOAA) observations are used (M. van Weele, personal
communication, 2013).
The oceanic emissions of aerosols and isoprene are calculated by the model
based on the 3 h varying meteorology and on monthly varying chlorophyll
. Other volatile organic
compound emissions from the ocean have
been taken into account in the model using the POET database
and have been kept constant from year to year
. Interannually and daily varying dust emissions
are from AeroCom Aerosol comparisons between observations and
models;extended by E. Vignati, personal communication, 2012
for years from 2000 to 2010 and have been also applied to earlier decades,
assuming the same interannual variability. Even though dust emissions are not
representative for the first years of the simulation, none of the pollutants
examined here are significantly influenced by dust. Therefore, this model
deficiency does not affect the present study. Monthly varying isoprene,
terpenes, and other biogenic volatile organic compounds for the years
1980–2010 are from the global emissions calculated by the Model of Emissions
of Gases and Aerosols from Nature (MEGANv.1) .
Lightning NOx emissions are calculated online and
soil emissions are climatological emissions from the POET database
.
Monthly anthropogenic and biomass burning emissions for the hindcast CL simulation are from the Atmospheric Chemistry and Climate
Model Intercomparison Project (ACCMIP) database until the
year 2000 and RCP6.0 projections afterwards,
also provided by ACCMIP
(http://accmip-emis.iek.fz-juelich.de/data/accmip/gridded_netcdf/accmip_interpolated/README.accmip_interpolated.txt).
As anthropogenic emissions in this paper we consider the sectors included in
the anthropogenic emissions of the ACCMIP database .
International shipping and aircraft emissions are also from the ACCMIP database
in all simulations.
Simulations performed
Three global chemistry-transport transient simulations of atmospheric
composition changes during the past 3 decades (1980–2010) were here performed
using assimilated meteorology, natural emissions, and biomass burning emissions
specific of the simulated year. All anthropogenic emissions with the exception
of shipping and aircraft emissions were different between the simulations.
Aircraft and shipping emissions were the same for all simulations. The CL simulation uses the historical emissions of the ACCMIP
database where legislation is applied to limit air pollution
. The Anthropogenic Emissions 1980 (AE1980) simulation uses
anthropogenic emissions that are constant throughout the years and equal to
those of the year 1980. This corresponds to simulations that are typically used
for comparison when evaluating the efficiency of emission control scenarios.
The Business-As-1980 (BA1980) simulation accounts for constant anthropogenic
emissions per capita and per HTAP (hemispheric transport of air pollution)
region (Fig. S1 in the Supplement), as of the year 1980, resulting in
anthropogenic emissions which follow the observed population changes (World
Development Indicators, the World Bank; http://www.worldbank.org/). In
this simulation the anthropogenic emissions neglect the air pollution
legislation applied for emission mitigation after 1980 and hence show increases
proportional to population growth since 1980. BA1980 accounts for neither the
technological improvements achieved since 1980 nor the per-capita energy
demand changes and thus not for geographic shift in industrial activities due to
globalization of production (Table S2). Finally, an extra simulation was
performed identically to the CL simulation but using the fine resolution of the
model (3∘ long × 2∘ lat) in order to investigate the effect
of the model resolution on the results.
For this study, 1-year spin-up time using the emissions and meteorology of
the year 1980, i.e., by running twice for the year 1980, has been applied. The fine-resolution simulation had not reached dynamic equilibrium after 1 year, as
needed for studying the year-to-year changes. Therefore, the year 1982 has been
used as reference year to normalize the concentrations in Fig. in
order to study relative changes in Sect. .
Construction of the BA1980 anthropogenic emissions
The BA1980 inventory assumes that land anthropogenic emissions per capita
remained constant from 1980 until now in major geographic regions, while
population and thus overall human-driven emissions changed. Advances in
technologies are thus ignored and the energy demand per capita as well as the
way energy was produced have been assumed constant with time and per region and
equal to those of 1980. To construct this anthropogenic emissions database the
ACCMIP anthropogenic emissions of the year 1980 together with global population
maps were used.
The global population for the year 1980 (not gridded) and global gridded
population maps for the period 1990–2010, available at 5-year increments,
were obtained from the United Nations (http://www.un.org/) and the World
Bank (http://www.worldbank.org/). Using a fine resolution of 1∘ × 1∘, linear interpolation between the 5-year steps was applied
per grid to produce global gridded population density maps for each year for
the period 1990–2010. The gridded population maps for the years 1980–1989
were subsequently constructed based on the gridded population distribution of
the year 1990 and a backwards extrapolation of the year-to-year change of the
total global population using a polynomial fit, thus assuming uniform
population change in all regions.
Figure S1 provides a visual representation of the 16 regions considered in
this study and corresponding to the HTAP source regions .
Population weighted emissions per species per capita per year for each HTAP
source region were calculated for the year 1980 by dividing the 1980
anthropogenic emissions by the 1980 population of each region. These
per-capita emissions were then applied on the gridded population maps to
construct the database of annually varying BA1980 anthropogenic emissions.
Based on the population density, the anthropogenic land emissions of 1980,
and the source regions as socioeconomic regions, an emission inventory has
been created that takes into account the per-capita anthropogenic emissions
of 1980, the population density increase per grid for the period 1980–2010,
and the population relocation since 1990. As a result, a new anthropogenic
emission inventory was constructed that assumes non-mitigation for
improvement of air quality after 1980 and accounts for neither technological
developments since then nor increases in energy demand per capita, is
associated with the standard of living and the globalization of the economy,
but accounts for population increase. Increase in energy demand per capita is
almost negligible in Europe and North America but is significant in
fast-developing countries, such as India and China (a factor of 2 and 3
respectively, Table S2b). Taking into account the energy demand would result
in even higher emissions globally. This also implies
that globalization of industrial activities, leading to an increase in energy
demand in developing countries disproportional to the population growth, is
not taken into account in this scenario.
Results and discussion
Emission trends
In Fig. , the annual mean anthropogenic emissions for the CL
(historical changes) and the BA1980 emission scenarios for CO,
NOx, NH3, OC, BC, and SO2 are presented,
normalized to the respective emissions of the year 1980 (fluxes for the year
1980 are shown in the histograms in the same figure) for the considered
regions: globe, Europe, North America, China, and India. During the entire
1980–2010 period the CL anthropogenic emissions of primary pollutants are lower
than the BA1980 on the global scale and regionally over North America and
Europe (Fig. shows global and regional totals, and Figs. S2 and
S3 show gridded changes of the emissions). This demonstrates that effective
emission controls and new technologies (CL emissions) have contributed to the
reduction of air pollutants emissions on a per-capita basis compared to those
in 1980, resulting in overall reductions in anthropogenic emissions by 18 % for
BC to 44 % for SOx (sum of SO2 and sulfate) globally and
between 25 % for NH3 over North America and 75 % for SOx over
Europe (Table S2a), where most development of cleaner technologies was
implemented. Energy demand per capita remained constant in the European Union
while that over North America decreased by about 10 % according to World Bank
statistics (Table S2). In contrast, regions which experienced fast
population and economic growth in the last 3 decades (such as China and
India) are calculated to have higher anthropogenic emissions under the CL
scenario than under the BA1980 scenario (by 60 % and 55 % for NOx,
respectively, and by 25 and 135 % for SOx, respectively; Table S2a).
This means that their anthropogenic emissions per capita have increased in the
last 3 decades, resulting in a “dirtier” case from what one might have
expected. It is obvious though that during the past 10 years over India, where
anthropogenic emissions of CO, BC, and OC show stability, or even decreasing
trends, efforts have been made to reduce pollution, for instance through the use of
renewable energy.
Normalized (to 1980 levels) annual mean anthropogenic emissions of
CO, NOx, NH3, OC, BC, and SO2 for
the CL (green/solid) and BA1980 (red/dashed) simulations as a function of
time. Different regions appear with different symbols: squares for the globe
(Gl), diamonds for Europe (EU), triangles for China (Ch), inverted triangles
for India (In), and circles for North America (N.A.). The histograms at the
top of each panel show the absolute emissions in 1980 (used to normalize the
emissions) for these regions that are used as reference to normalize
emissions (also shown in Table S1).
During the last 30 years the industrial sector in Asia has experienced an
explosive growth, resulting in a disproportional increase compared to the local
population growth. Globalization and cheap labor led a large fraction of the
world's industrial production to occur in the greater India and China
regions, in response to the global population and energy demand growth, not
just the local one, which explains the increased per-capita emissions over
these regions (CL compared to BA1980). Indeed, as reported by the World Bank,
the energy demand per capita has increased by factors of about 2 and 3 for
India and China, respectively, derived as the ratio of the energy demand per
capita in 2010 to that in 1980. However, when comparing CL to BA1980 emissions
for India and China by examining the ratio of the emissions in CL to those in
BA1980 (Table S2), most ratios are lower than the corresponding increase in
energy use per capita. The CL to BA1980 comparison indicates that some
improvements in air quality have been achieved in these countries during the
recent years although they cannot be seen in the trends of the CL emissions
over these regions that show emission increases. Emission mitigation can be
detected in the CL scenario by the 25–70 % reduction of all major
pollutant emissions over India and China, except SOx over India (Fig. f), compared to regional emissions estimates that account for
mean increase in energy demand (Table S2b). Ammonia emissions present a more
complicated pattern (Fig. c) as a result of the absence of any
legislation on NH3 emissions and high uncertainty
on ammonia emissions from India .
Comparison against surface measurements
TM4–ECPL simulations were performed with the different emissions scenarios.
The accuracy of the model is evaluated by comparison with available
observations around the globe (see locations in Fig. S4). Surface
observations for O3 and CO were obtained from the World Data Centre
for Greenhouse Gases (WDCGG;
http://ds.data.jma.go.jp/gmd/wdcgg/introduction.html). Surface
observations of O3, BC, and SO42- over Europe and the
USA were obtained from the European Monitoring and Evaluation Programme
(EMEP; http://www.emep.int) and the Interagency Monitoring of Protected
Visual Environments (IMPROVE;
http://vista.cira.colostate.edu/improve/),
respectively. The OC measurements are from the AeroCom Phase II database
. Monthly mean and standard deviation are calculated
from the original datasets available at various temporal resolutions. These
datasets provide adequate coverage over Europe and North America. For the rest
of the globe, the temporal and spatial variability of the measurements is
scattered, resulting in a model evaluation which is highly biased towards
North
America and Europe.
Comparison of the four simulations against observations. The dashed
line and shadowed areas indicate monthly mean surface observations and 1
standard deviation. Simulations: CL is current legislation (green); CL-fine is
current legislation in the fine resolution of the model; BA1980 is Business As
in 1980, with constant anthropogenic emission rates per capita as in 1980
(red); AE1980 is constant anthropogenic emissions as in 1980 (blue). Trends
derived from the concentrations (ψ) as a function of the year (χ)
are provided for the measurements and the four simulations inside the
frames.
Surface concentrations and trends were calculated for each of the three
scenarios to compare the computed atmospheric composition changes during the
studied period. CL simulations have been compared to global observations of
O3, CO, SO42-, BC, and OC (Figs. , , and S5–S10). These comparisons are
performed on a monthly mean basis for all stations and all years from 1980 to
2010 where data are available. Statistical analysis of the results was
conducted per model grid. For this, monthly mean of the measurements were
grouped per model grid (6∘ long. × 4∘ lat. or 3∘ long. × 2∘ lat., depending on model resolution) and they are then
averaged to derive the annual mean concentration. Model results are sampled at
the time and location of the observations, and then annual means in each grid box
are computed for model results and compared to those derived from observations.
The statistics of these comparisons are presented in Table .
Comparisons of annual average surface model results versus
observations per model grid (see Fig. S5 for station locations) for
(a) O3, (b) for CO, (c) SO42-, (d) OC, (e) BC,
and (f) NH4+. The continuous line denotes the 1 : 1 slope
and the dashed lines the 10 : 1 and 1 : 10 slopes.
Statistics of comparison of model (CL simulation) vs. observations
and fine-resolution simulation (CL-fine) vs. observations (corresponding to
pollutant concentration scatter plots shown in Figs. and S2).
No. pairs shows the number of pairs used for the comparison. Meas and
Model are means of all gridded data
that are used for the comparisons (as described in Sect. ). R
is the calculated correlation between the measured and the modeled data. NMB
stands for normalized mean bias of the model against the measurements. C
stands for the CL simulation (6∘ × 4∘) and F
stands for the CL-fine simulation (3∘ × 2∘). The
units are ppb for gases (O3, CO) and µgm-3 for
aerosols (SO42-, OC, BC, NH4+).
No. pairsa
Meas
Model
R
NMBb (%)
C
F
C
F
C
F
C
F
C
F
O3
1555
2417
30.86
31.08
34.89
36.32
0.46
0.48
13
17
CO
211
229
149.24
149.87
151.64
148.62
0.42
0.41
2
-1
SO42-
2008
3592
2.57
2.51
3.91
4.04
0.74
0.71
52
61
OC
1037
1825
3.24
2.92
1.25
1.22
0.59
0.71
-62
-57
BC
931
1728
0.29
0.28
0.28
0.29
0.59
0.50
-3
1
NH4+
626
777
0.94
0.92
2.24
2.30
0.65
0.64
140
149
a The number of pairs corresponds to the number of
grid boxes that contain measurements. Hence the larger number of pairs in the
finer resolution of the model. b Normalized mean bias is
calculated as NMB =∑(Mi-Oi)∑O, where M stands
for model and O for observations.
In the following we focus on the normalized mean bias (NMB) to evaluate the
over/underestimation of the observations by the model as well as the
correlation coefficient that provides the strength and direction of the
relationship between the model results and the observed levels of air
pollutants, while the coefficient of determination (R2) provides the
fraction of the observed air pollutant variance that is reproduced by the
model. In Table , which summarizes the statistics of Fig. , it can be seen that at the studied locations where
observations are available the model reproduces very well the mean observed
surface levels of CO (NMB between -1 and 2 % for fine and coarse model
resolution, respectively) and of BC (NMB between 1 and -3 % for fine and
coarse model resolution, respectively). It also satisfactorily reproduces the
surface O3 levels (NMB between 17 and 13 % for fine and coarse model
resolution, respectively). For sulfate the NMB is of the order of 61 and
52 %, respectively, indicating a model overestimate of the observations, while for
particulate OC, the negative NMB between -57 and -62 %, respectively, indicates
a model underestimate of the surface OC observations as also pointed out for
most AeroCom models by . The model performance is not as
good for NH4+ aerosol, which is overestimated by the model by a
factor of about 1.4 to 1.5. The model results show relatively weak correlations
with O3 and CO observations (0.4<R<0.5) and very good correlations
(R>0.5) with the other air pollutants (Table ). However, when
focusing on the model's ability to reproduce the measured patterns as
determined by the coefficient of determination (R2), the model seems to
capture nicely the observed variance of sulfates and OC with R2>0.5;
i.e., more than 50 % of the observed variability is reproduced by the model. It
also reproduces more than 40 % of the observed NH4+ variability,
50 % of that of BC, 25 % of that of O3, and about 20 % of that of CO.
We further analyze the model results to evaluate the gain in air quality
compared to an anthropogenic emissions scenario that follows population growth
and does not take into account technological advancements, changes in standard
of living, geographic shift in industrial activities to cheaper labor
countries, or emission control measures. For this, we also need to take into
account the above outlined model's ability to reproduce observed air
pollutant trends. For that, we used available monitoring stations with long
time series of measurements (Figs. and S5–S10). For each of these stations the trends from the observations and from
the corresponding values calculated by each simulation were derived. The model
reproduces the sign of the observed trend for most species and locations.
Specifically, SO42- slope direction is always captured by the
model, although the magnitude is not always well reproduced. CO trends are also
well simulated, both in direction and in magnitude for most stations.
O3 trends are generally underestimated by the model. OC, BC, and
NH4+ calculations appear to have the largest deviations from the
trends derived from observations, which likely reflects the difficulties both
in measuring carbonaceous and ammonium aerosols and in simulating their sources
and fate in the atmosphere, in particular the semivolatile character of a
large fraction of organics and of ammonium nitrate. It is worth mentioning the
clear imprint of the effect of emission control to the pollutant levels at the
stations that are mostly influenced by anthropogenic activities and are
depicted in Figs. and S5–S10. At these
stations both the BA1980 and AE1980 simulations clearly fail to capture the
pollutant levels observed the recent years.
Impact of the model resolution on the calculated results
To determine the impact of the model resolution to the calculated results, an
extra 30-year simulation was performed identical to the CL simulation but
using a finer model resolution of 3∘ long × 2∘ lat. The
calculated normalized annual mean concentrations of this simulation are
depicted by the yellow line in Fig. . Also, statistical analysis
identical to the one of the CL simulation was performed. The results of this
analysis are also provided in Table and shown in Fig. S11.
Annual mean surface concentrations for CO, NOx, O3, OC,
BC, and sulfate aerosols (rows), averaged over the globe, Europe, North
America, India, and China (columns) and normalized to the 1982 concentrations.
The difference between the blue (AE1980) and green (CL) lines is the
anticipated change in concentrations when assuming 1980 anthropogenic
emissions at any given year, while the difference between the red (BA1980)
and green (CL) lines is the calculated change in concentrations at any given
year when taking into account the increased anthropogenic emissions due to
the population growth. The yellow lines depict the annual mean surface
concentrations of the CL-fine simulation. Note the difference in scales
between species.
The statistical analysis of the comparison of the CL-fine simulation against
measured values shows a global performance very similar to that of the CL
simulation, indicating that, for the studied period and pollutants, the model
resolution has only a minor impact on the results. The largest differences in
performance between the fine and coarse grid CL simulations are found for
O3, where the calculated model mean value for the CL-fine simulation is
about 4 % higher than that of the CL simulation. Note that the differences in
the model resolution also lead to differences in the number of observational
sites per grid and in the mean of the observed concentrations that are compared
to the model results. These differences also reflect the inhomogeneity in the
spatial coverage of the observations. When using finer-resolution grids, the
largest differences in the mean concentrations (Table ), have
been computed for the aerosol components and in particular for OC (2.92 µgm-3 compared to 3.24 µgm-3, i.e., 10 % lower
compared to the coarse-resolution grid). These differences are in accordance
with the changes in the normalized mean simulated concentrations that are shown
in Fig. .
Air quality changes
The CL, BA1980, and AE1980 simulations have been performed using the same
meteorological fields, natural and biomass burning emissions, and differ only
in the anthropogenic emissions over land. Therefore, the effectiveness of the
applied mitigation policies combined with the energy consumption and the
technological advances relative to an alternative development of the society, as
described in Sect. , can be evaluated by comparison of the
computed annual mean surface concentrations of air pollutants (Figs. , , and S5–S10 and Table S3).
The results are analyzed by examining the changes in the computed
concentrations in 2010 compared to the concentrations in 1980 as well as the
percent changes between the different simulations in 2010. Focus is put on
developed areas where mitigation legislation was applied (North America and
Europe) and contrasting these with India and China that experienced rapid
growth during the last years. The AE1980 simulation shows a small variability
(maximum 20 %, usually about 5 %) in the normalized mean concentrations of
pollutants (Fig. ) that can be attributed to the climate
variability in meteorology and the natural and biomass burning emissions. In
Fig. , it is clearly seen that the climate impact on surface
concentrations of O3, OC, and BC results in increases in their levels by
about 5, 15, and 5 % respectively, in 2010 compared to 1980 on the global
scale and also over Europe, North America, and China, indicating that changes in
meteorology and climate-driven emissions induce significant variability in air
quality in these regions. The other pollutants in Fig. show
less sensitivity to meteorological and biomass burning changes.
Focusing on the CL simulation, small global decreases are computed for CO and
SO42- in 2010 (about -5 and -15 %, respectively) since 1980,
while for the other pollutants global increases are calculated (Fig. , left column). Regionally the picture is quite different, with
significant reductions in primary pollutants over Europe and North America
(except NOx) and increases over China and India. For surface CO the
environmental gain is substantial for Europe and for North America, with
simulated reductions reaching -40 and -20 % compared to the AE1980 levels
(Fig. , green lines compared to blue lines), while the large
increase in energy use in China resulted in about 20 % increase in surface CO.
The present study indicates that the gain in air quality is larger than what is
deduced by comparing CL to AE1980 (Table S3a), as is usually done, since
comparisons of the CL and BA1980 simulations reveal a higher gain in
air quality. Globally the computed surface CO levels under the CL simulation
are lower than the BA1980 ones by 22 %. Regionally the reductions achieved in
2010 are even higher (Europe -69 %, North America -44 %, China -19 %, India
-24 %). This proves that technological advances of the past years have
contributed to reduce the CO levels even in fast-developing countries. These
agree with changes in the total CO columns retrieved from satellite
observations where reductions of the total column of CO are observed for all
the studied regions for the period from 2000 to 2010 .
Another successful story is that of SO42-. Compared to the BA1980,
SO42- levels computed by the CL simulation are lower by 54 %
globally and by more than a factor of 3 over Europe and almost a factor of 2 over
North America. In contrast, in the fast-developing economies of China and India,
computed SO42- levels are higher by about 10 %. However, if we
account for the increase in energy demand per capita in these regions (by
factors of 3 for China and 2 for India), which is not taken into consideration
when constructing the BA1980 emissions database, then technological advances
seem to have limited the air pollution even in these regions (Table S2b). The
computed increase in surface SO42- levels over China until about
2007, followed by a stabilization and even a decrease in their levels, is in
general agreement with the SO2 column satellite observations
(GOME/SCIAMACHY) that show increases between 1996 and 2007 and then a decrease
in SO2 tropospheric column over China
(SCIAMACHY
Product Handbook).
For OC and BC, the global gain computed is 10 and 22 %, respectively.
Regionally, Europe shows the highest gain with a computed reduction of OC of
54 % and BC of 66 %. North America also shows high computed air quality gains
concerning OC and BC, with a 43 and 51 % reduction, respectively. For India a
gain of 20 % is computed for both pollutants, whereas for China the changes are
not significant (less than 5 % for both).
Ozone levels show the least sensitivity to the reduction policies of its
precursors' emissions, with computed global/regional changes of less than 10 %.
These model results are also supported by the small zonal changes (between
-0.2 ± 0.4 and 0.3 ± 0.4 % yr-1) in tropospheric
ozone column retrieved from SCIAMACHY observations between 2003 and 2011
. Surface NOx concentrations show increases by 13
and 28 % over India and China, respectively, in the CL compared to the BA1980
simulation while over Europe a reduction of 63 % and over North America of 29 % is
computed and the global gain is 21 %. These results are in general agreement
with the satellite observations by , according to which the
tropospheric columns of NOx are found to be rather stable or with a
decreasing trend over Europe and the USA, while they show an increasing
trend over the Asian regions they studied during the period between 1996 and
2002.