Atmospheric Environment 35 (2001) 1351}1363
Long-term trends of primary and secondary pollutant
concentrations in Switzerland and their response
to emission controls and economic changes
Jerome Kuebler *, Hubert van den Bergh , Armistead G. Russell
Swiss Federal Institute of Technology, CH-1015 Lausanne, Switzerland
Georgia Institute of Technology, Atlanta, GA 30332-0512, USA
Received 19 April 2000; received in revised form 20 July 2000; accepted 25 July 2000
Abstract
A detrending technique is developed for short-term and yearly variations in order to identify long-term trends in
primary and secondary pollutants. In this approach, seasonal and weekly variations are removed by using a mean year;
the residual meteorological short-term variation is removed by using a multiple linear regression model. This methodology is employed to detrend ozone (O ), NO , VOC and CO concentrations in Switzerland. We show that primary
V
pollutants (NO ,VOC and CO) at urban and sub-urban stations show a downward trend over the last decade which
V
correlates well with the reductions in the estimated Swiss emissions. In spite of these large decreases achieved in precursor
emissions, summer peak ozone concentrations do not show any statistically signi"cant trend over the last decade.
Application of this method to ozone concentrations measured at the Jungfraujoch (3580 m a.s.l.) also shows no trend over
the last 10 years. Detrended summer ozone correlates well with European Union gross national product and industrial
production growth rates. These results suggest that if substantial reductions in summer peak ozone in Switzerland are
desired, emissions reduction strategies must be part of control program involving a much larger region. 2001 Elsevier
Science Ltd. All rights reserved.
Keywords: Detrending; Ozone and primary pollutants trends; Emissions control policies; Economic impacts on air quality
1. Introduction
The Swiss Plateau, embedded between the Jura and
the Alps, frequently has ozone (O ) levels exceeding the
Swiss ozone standard of 120 lg m\ (60 ppb) with peak
values well above 200 lg m\ (EidgenoK ssische Komission fuK r Lufthygiene, 1993). In 1986 the Swiss Government imposed emissions reduction measures in order to
improve air quality. Now, more than 10 years later, it is
of interest to demonstrate the e!ectiveness of the controls, both in their ability to lower the emission of the
ozone precursors (nitrogen oxides and volatile organics)
* Correspondence address: DGR-LPAS, EPFL, CH-1015
Lausanne, Switzerland.
E-mail address: jerome.kuebler@ep#.ch (J. Kuebler).
and to quantify the impact of the emission changes
on ambient O levels. However, meteorological con
ditions also play an important role in determining the
peak ground level O concentrations. Thus, the e!ect
of variations in meteorological conditions can mask
the dependence of the long-term trends in O on precur
sors emissions. In Europe and in the US, several statistical studies have been done in order to remove
meteorological e!ects from measured pollutant time
series. One class of methods was developed to identify
and remove weekend/weekday #uctuations (BroK nimann
and Neu, 1997; Altshuler and Arcado, 1995). Another set
correlated long-term (e.g., seasonally) "ltered pollutants
trends and meteorology (Rao and Zurbenko, 1994;
Flaum and Rao, 1996). Finally, Gardner and Dorling
(1999) used neural networks to remove meteorological
variability.
1352-2310/01/$ - see front matter 2001 Elsevier Science Ltd. All rights reserved.
PII: S 1 3 5 2 - 2 3 1 0 ( 0 0 ) 0 0 4 0 1 - 5
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J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
Here, a di!erent approach to detrending is developed
that is applicable to primary and secondary pollutants
taking into account the di!erent time scale phenomenon
(e.g., weekly, seasonal, inter-annual variation) and shortterm meteorologically in#uenced phenomenon. What is
going to be called a pollutant's `detrendeda time series in
this article consists of a time series independent of these
seasonal, weekly and short-term meteorologically induced patterns. The extracted long-term trends in CO,
NO and VOC are compared to the evolution in Swiss
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emissions estimates. The yearly evolution of both primary and secondary species are also compared to gross
national product (GNP) and industrial production
growth of European countries, as a means of capturing
regional emission changes and their impact on local and
regional O . The relationship between the evolution of
primary pollutants and O on a multiple year time scale
is also analysed for later comparison with modelling
results.
2. Method
Assessing the impact of control strategies on pollutant
concentrations is approached by analysing the long-term
trend in observed pollutant concentrations, after removing the impact of short-term emission variations and
meteorological #uctuations. This requires several years
of air quality and meteorological data.
The Swiss Agency for Environment, Forests and Landscape (SAEFL) provided the atmospheric chemical data
from the Swiss National Air Pollution Monitoring Network (NABEL) for the 1985}1998 period at 12 stations
(Fig. 1). Half-hourly measurements were used to extract
the daily maximum concentrations of O and mean
concentrations of NO , VOC and CO for the period
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between 11:00 a.m. and 06:30 p.m. The late morning/afternoon period is chosen to reduce the impact on
the daily average of very high morning concentrations
Fig. 1. Location of the NABEL monitoring sites.
(which may occur during periods of reduced vertical
mixing) (BroK nimann and Neu, 1997). The Daily maximum O is calculated as the mean value of the four
highest half-hourly values for that day. O and NO
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measurements were available at all 12 stations, whereas
CO was only measured at the urban stations and VOC at
two urban stations.
Meteorological data originate from the ANETZ (Automatisches Netz der Schweizerischen Meteorologischen
Anstalt) database from Swiss Meteorological Institute,
and NABEL meteorological measurements. The meteorological parameters used include the global solar
radiation, temperature, and wind speed. The daily solar
radiation value is calculated as the sum of hourly sums
(W h m\) over 24 h, where temperature (3C) and wind
speed (m s\) are the mean of hourly mean values between 11:00 am and 3:00 p.m. (CET, standard time). The
four hour period is chosen since it is believed that winds
during this time period have a more signi"cant impact on
photochemistry and afternoon O levels (Neu et al.,
1994).
The approach generally used to assess control strategy
e!ectiveness via time-series analysis involves splitting the
time series into components with di!ering characteristic
time periods. Here, we assume that a pollutant time series
can be represented as
C(t)"LT(t)#S(t)#=(t)#STM(t)#white noise(t), (1)
where C(t) is the original time series, LT(t) is the longterm trend component, S(t) is the seasonal variation
(period: 365 days) due to meteorological and emissions
variations over that period, =(t) is the weekly variation
primarily due to emissions cycles and STM(t) is the
short-term variation due meteorological and emission
variations (period &1 day). The `white noisea component is the residual that is not captured in the other
components. The long-term trend should be primarily
due to policy (e.g., implementation and in"ltration of
control strategies) and economic changes impacting
emissions, but can also re#ect a longer term meteorological trend as well. An example of a longer term trend that
could impact O levels would be global warming or
landuse changes that impact the radiative balance and
biogenic emissions in the area. The decomposition of the
raw time series into di!erent components will allow us to
examine the characteristics of each process separately to
understand the di!erent deterministic and stochastic processes in#uencing the data. Most of the focus, here, will
be in identifying the long-term trends of both O and its
precursors.
Meteorological e!ects have a major impact on three
components in the expansion: the seasonal variation (S)
(solar radiation, temperature), short-term variation due
to more local meteorological #uctuations (STM) which
represent either a departure from the `expected valuea
J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
for that day of the year (e.g., clouds for solar radiation or
a cold front for temperature) or a short-term stochastic
e!ect (e.g., for wind speed). The short-term #ucuations
may contribute to the white noise as well. The short-term
variation can be isolated at least partially in the time
series by performing multi-variable regression between
pollutant time series and meteorological parameters; seasonal variation can be removed by using a `mean yeara.
However, it is expected that both a long-term trend (due
to meteorological and emissions changes) exists. The
long-term trend has to be estimated and removed from
the time series before calculating the mean year, in order
to avoid introducing a bias. In our study, we will extract
this estimate of the long-term trend (LT(t)) using the
Kolmogorov}Zurbenko "lter KZ
(Zurbenko, 1991).
KN
The KZ "lter is a low-pass "lter produced by repeated
KN
iterations of a simple moving average. Each iteration of
the moving average is de"ned by
1 I
>"
C ,
R m
G>H
(\I
(2)
where m"2k#1 and then the > becomes the input for
R
the second pass and so on. The length and the number of
iterations (m and p, respectively) are user speci"ed. The
KZ "lter separation technique can be applied to time
series containing missing data, whereas other separation
techniques like the wavelet transform (Lau and Weng,
1995) or the anomaly technique (Wilks, 1995) require
special treatment of the data. Eskridge et al. (1997) discussed the relative strength of these three "ltering techniques. Since all the points are equally weighted in the
KZ "lter method, no biases are introduced by missing
data. However, the wavelet transform does not equally
weight all of the data points, so missing data may, in fact,
introduce a bias to the "ltered data (Milanchus et al.,
1997). As missing data is very frequent in ambient concentration time series, we choose the Kolmogorov}Zurbenko "lter.
C*2(t)"KZ
(C(t)).
"
(3)
C*2(t) (Fig. 2) is an estimate of the long-term trend
obtained by "ltering the raw time series with a length of
365 days and 3 passes, resulting in a cut-o! frequency of
(365 days ;3)"1.7 year.
A normalised time series *C,(t) is then calculated by
subtracting the long-term trend estimate to the raw time
series and dividing the result by the long-term trend.
C(t)!C*2(t)
*C,(t)"
.
C*2(t)
(4)
*C,(t) represents a normalised time series free of longterm variation. This transformation (normalisation) is
similar to doing a log transformation of the variables and
takes into account the reduction of emissions on primary
1353
compounds, which would be expected to impact the
magnitude of the #uctuations.
In other studies (Rao and Zurbenko, 1994; Flaum and
Rao, 1996; Graf-Jaccotet and Jaunin, 1998; Gardner and
Dorling, 1999) solar radiation and/or temperature were
used as surrogates of the seasonal variation in order to
remove the annual pattern from the pollutant time series.
That approach has the disadvantage that it aggregates
the annual cycle of seasons in#uencing meteorological
variables like temperature and solar (summer is warmer
than winter) and their variation around the `expected
valuea at a speci"c time of the year governing the shortterm meteorologically induced phenomena. Here, these
two phenomena are separated by using the calculation of
a `mean yeara and removing the in#uence of yearly and
weekly variations. As discussed below, the approach accounts for the annual behaviour where a week of January
will not show the same variation as a week in August.
Both seasonal and weekly variations were removed using
a mean year of 52 weeks of 7 days built from *C,(t) as
follows:
1 , *C,(tH#(52;7)(i!1)),
(5)
*C,+(tH)"
N
G
where N
is the number of years in the raw time series.
*C,+(t) (Fig. 3) shows a clear seasonal cycle and a strong
autocorrelation with a maximum for a seven day lag,
which represents the weekly cycle in emissions and its
e!ect on O concentrations. This approach will also
capture the e!ect of holidays that occur on a speci"c day
(as opposed to dates) of the year (e.g., Thanksgiving in the
US occurs on the fourth Thursday in November). Then
an estimate of the mean time series is created using the
estimated long-term trend and the mean year:
CM (t)"C*2(t) (1#*C,+(t!(52;7)(i!1))),
(6)
i"1!N ,
where CM (t) contains the long-term (LT), seasonal (S) and
weekly (W) variation. Next, the combined STM(t) and
white noise is found by subtracting this from the original
time series.
r(t)"C(t)!CM (t),
(7)
where r(t) is then used as the dependent variable in
a linear regression with the meteorological parameters as
the independent variables, STM(t) being the best "t:
STM(t)"b#a Met (t)#2#a Met (t).
(8)
L
L
This provides a method to estimate the impact of
short-term variations from weather e!ects on pollutant
concentrations: The meteorological variables used in the
detrending are the excess solar radiation, excess
temperature and wind speed. Excess solar radiation and
excess temperature O#(t) are calculated by removing the
"ltered mean year (O+(t)) solar radiation or temperature
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J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
Fig. 2. Filtered time series of (a) ozone, (b) NO , (c) VOC and (d) CO in DuK bendorf.
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from the observation for the corresponding day:
O#(t)"O(t)!O+(t!(365(i!1))) i"1!N ,
(9)
where
, O(tH#365(i#1)) t*"1!365 (10)
G
and O(t) is the observed temperature or solar radiation
and O+(t) is the mean year of the same parameter, as
calculated by (Eq. (10)). O+(t) is smoothed using a local
regression model with a smoothing parameter of 0.5 and
a locally "tted polynomial of degree 2 (Venables and
Ripley, 1998) in order to remove most of the short-term
and stochastic variation in the mean expected meteorological value. The removal of the mean year from both
variables facilitates removing the seasonal component
from the meteorological parameter time series, keeping
O+(tH)"
N
1
the short-term variation only. It also reduces the correlation between the parameters with a strong seasonal variation such as solar radiation or temperature. This is
important because in a multivariable linear regression
the explanatory parameters should be as independent as
possible. The correlation between the selected meteorological variables for the DuK bendorf site show very low
correlation between wind speed and excess temperature
(R"0.001) and solar radiation (R"0.0005). Excess
solar radiation and temperature remain somewhat correlated (R"0.24), but much less so then when using the
raw data.
These explanatory parameters were chosen in part on
physical grounds. The temperature provides an integrating factor of conditions leading to O formation as it
takes into account the warming of the air in prior days,
and it impacts biogenic and evaporative emissions and
leads to more rapid photochemistry. In part, this can
J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
1355
Fig. 3. Mean year (see text for de"nition) (a) ozone, (b) NO , (c) VOC and (d) CO in DuK bendorf.
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account for the build-up of photochemical pollution on
a regional scale. The solar radiation is a more immediate
driver of photochemical reactions and integrates indirectly other factors like cloud cover and rain. Wind
speed a!ects the dilution and transport of the emissions.
We explored using other variables, e.g., relative humidity, pressure, dew point temperature and total daily rainfall, but either the correlation with air quality data was
extremely low or the dependence on the `maina meteorological variables (solar radiation, temperature and wind
speed) was very high and adding another variable did not
improve the meteorological model performance signi"cantly. Other meteorological models were tested, e.g.,
a weighted linear regression with a sigmoid-weighting
function to try to capture the high values. However, as
will be discussed below, if we only compute the summer
season time series, the weighting function did not improve the relationship. For O the meteorological model
was "t with the summer season days only (May}October), while the whole year was used to "t the primary
pollutants meteorological model for long-term detrending.
After "nding STM(t), we can build a time series, C+'(t),
free of short-term meteorological variations that were
explained by the meteorological model:
C+'(t)"C(t)!STM(t)"LT(t)#S(t)
#=(t)#white noise(t),
(11)
where C+'(t) will still contain some short-term meteorological in#uences that were not captured in the development of the simple linear regression model, which remain
in the white noise.
Seasonal S(t) and weekly =(t) variation can now be
removed by subtracting a mean year of C+'(t) as calculated in Eq. (5) in order to obtain a residual time series
containing only long-term trend LT(t) and white noise.
C "C(t)!STM(t)!S(t)!=(t)
"LT(t)#white noise.
(12)
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J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
Fig. 4. Ozone time series C(t) separated in "ve components, (a) short-term meteorologically induced variation, STM(t); (b) seasonal, S(t);
(c) weekly, W(t); (d) long term, LT(t); and (e) white noise.
A graphical representation of Eq. (12) is shown in
Fig. 4. C (t) represents the long-term variation and the
white noise around the raw time-series mean value,
which can be added now. The desired long-term metric
can now be extracted from C for O . As explained
below, it was chosen to extract the seasonal 90th percentile of each year. For the O data at DuK bendorf, LT(t)
contributes approximately 3% of the total variance,
STM(t) 47%, S(¹)#=(¹) 13% and the white noise
26%. Note that the variance of the sum is equal to the
sum of the variances plus twice the sum of the covariances, which is why the sum is not exactly equal to 100%.
A summer season O time series reconstructed from
STM(t)#S(¹)#=(¹)#LT(t) is plotted against the
original time series in Fig. 5. The correlation coe$cient
R between the original and the reconstructed time series
is 0.85. The remaining variance can be attributed to white
noise that expresses stochastic processes in#uencing air
quality.
3. Results and analysis
The above technique was applied to measurements of
O , NO , VOC and CO, respectively, at eleven NABEL
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stations throughout Switzerland for periods from 7 to 14
years (depending upon data availability).
On the Swiss Plateau high O episodes are only ex
perienced during the summer season and detrending was
applied to the time series for all pollutants using the
May}September period. For O , the metric used was the
90th percentile since this value includes the 15 worst days
of each year and contains most of the exceedences when
local production in#uenced by local emissions signi"cantly impacts O levels. On the other hand, the metric
used for the primary pollutants was the mean value as it
represents the annual evolution of emissions responsible
for summer smog. The choice of using the mean value or
the 90th percentile makes little di!erence in the outcome
of the detrending, as they show very similar behaviour.
J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
1357
Fig. 5. Original and statistically reconstructed summer ozone values at DuK bendorf.
However, detrending the whole year or just the summer
time series does show very di!erent trends, especially for
primary pollutants at urban stations. The urban stations
are, by de"nition, located near the emissions sources
where the ambient concentrations of primary pollutants
are very sensitive to immediate dilution which is in#uenced by the mixed layer height and wind speed. Furthermore, the high variability of low mixing heights
(which is not measured) during winter is not completely
captured by the meteorological model. The associated
remaining #uctuations thus increase white noise amplitude in the residual time series leading to large variations
in primary pollutant concentrations. Using only summer
season (May}September) time series allows lowering the
mixing height variability, for the strong summer solar
radiation provides good vertical mixing, stabilising the
observed concentrations.
Fig. 6a shows the 90th percentile summer O level for
eleven NABEL stations, and Figs. 6b}d show the summer mean values for NO , VOC and CO. The results are
V
divided into urban and rural sites.
Looking "rst at the primary pollutants, it is seen that
at the urban stations, NO , CO and VOC levels present
V
a clear downward trend at all sites. The NO decrease
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reaches about 50% in ZuK rich over a 12-year period and
over 40% in DuK bendorf over a 10-year period, though
a smaller decrease (13% over 9 years) is observed in
Lugano. CO concentrations show an even more dramatic decrease ranging from more than 20 to 50% over
a 10-year period at some stations; though others, like
Basel whose monitor is located in a park and is not
directly exposed to tra$c, show virtually no change.
VOC levels in ZuK rich and DuK bendorf show a clear downward trend of around 50% over a 12-year period. Rural
stations do not present any clear trend in NO , with the
V
exception of Magadino (located south of the Alps) and
Sion (a rural station is located near a major highway)
which present downward trends. Sion also presents
a clear downward trend for CO, indicating the large
in#uence from the nearby highway. At the other rural
sites, the low NO levels (5}10 ppb) indicate little imV
mediate in#uence from urban activities.
In contrast to the urban primary pollutants, there is
very little, if any, downward trend in O at either the
urban or rural sites in spite of the reductions in
O precursors. It is worth noting that the stations of
Lugano and Magadino show the highest levels of O ,
NO and CO (in Lugano). These two stations are located
V
in Ticino, south of the Alps, close to the Italian border,
and are heavily in#uenced by pollutants coming from the
Milan area (SAEFL, 1998).
In order to further assess the impact on O of trans
ported pollutants versus local production, the rural stations were split into two categories: those located below
1000 m, which are in#uenced by local emissions, and
those above that elevation. The higher elevation sites are
more impacted by regional pollutant transport, as they
are somewhat removed from local emissions. The rural
stations located below the altitude of 1000 m show slightly lower O levels and higher levels of NO than the ones
V
located above 1000 m, e.g., Chaumont and Rigi, suggesting that the high-altitude stations are good indicators of
regional background O coming from longer range
transport, while the lower altitude stations are more
in#uenced by the local Swiss emissions. This behaviour
can also be con"rmed by the station sensitivity to meteorological variables. If the meteorological model shows
a high correlation with the measured concentrations, the
1358
J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
Fig. 6. Detrended 90th percentile summer O and detrended mean summer values of NO , VOC and CO for rural stations above and
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under 1000 m a.s.l. and rural stations located within agglomerations and at city-centre.
O concentration at the station is more in#uenced by
local conditions; i.e. non-negligible amounts of O are
locally produced; on the other hand, if the correlation is
low, long-range transport of O is likely to be the main
factor determining the concentration.
The short-term meteorological regression parameters
as described in Eq. (8) for O , NO , VOC and CO at all
V
the stations are listed in Table 1. These correlations could
seem low compared to those found in the other studies
quoted above, but they do not represent the same relation between meteorology and ambient pollutant levels.
In our case the relationship is only based on the meteorological short-term variations around the expected value
and not on the full annual variation (seasonal and short
term) which presents logically higher correlations. However, the correlation's are relatively high for O (&0.5)
and, as expected, solar radiation and temperature both
enhance O formation, as wind strength decreases O .
The correlations for NO are lower than for O , but still
V
signi"cant. In this case, wind speed is also a surrogate for
dilution, leading to decreased NO , solar radiation was
V
expected to play the role of a surrogate for turbulence
leading to enhanced dilution, but the correlation do not
show any signi"cant in#uence. In the same way as that
for O , the NO correlation coe$cients are directly
V
related to the station's locations: the more remote
the stations, the smaller are the correlations. The rural
remote station of Chaumont shows smaller correlation
than LaK geren, which is a rural station located near
a densely populated region. The same principle works for
urban stations located in a park (ZuK rich) and near a road
in a very industrial area (DuK bendorf). VOC shows the
lowest correlation with meteorology at both ZuK rich and
DuK bendorf; the correlation with temperature was expected to be a little bit higher as evaporative emissions
are strongly temperature dependent. Either this dependence is already taken into account in the seasonal variation, or the VOC vapour-capturing devices at the "lling
stations and on-board vehicles are e!ective as VOC
evaporation is very sensitive to temperature. The
measurement technique (FID detection of total and
non-methane hydrocarbons) may also limit the ability to
detect a trend as the VOC concentration is obtained by
subtracting methane from total hydrocarbon concentrations. This di!erence is calculated based on large numbers and thus implies a large uncertainty, on the other
hand, the monitor measures a large number of di!erent
species and is not speci"c; for these reasons the results do
only have a relative meaning (SAEFL, 1998).
The detrending technique was also applied to the
NABEL station at the Jungfraujoch observatory station
(3580 m a.s.l.) in the Swiss Alps. This station is located
in the lower free troposphere for most of the year, except
for some days in summer when convection becomes an
244.07
203.02
197.40
152.65
113.97
0.17
0.13
0.23
0.15
0.08
!38.26
!78.74
!28.94
!28.17
!16.50
0.00
0.00
0.02
0.01
0.00
0.00 !0.96
0.00 7.66
0.00 !5.35
0.09 8.93
0.03 2.45
!0.009
!0.036
!0.003
!0.055
!0.023
0.17
0.21
0.16
0.17
0.08
0.08
0.15
0.08
0.19
0.18
0.05
a
R a
R
a
R
b
23.89
23.10
21.12
10.54
14.22
1.08
1.58
3.67
8.79
6.10
7.41
0.17
0.20
0.16
0.12
0.08
0.04
0.09
0.07
0.19
0.17
0.04
!3.73
!3.36
!8.38
!1.99
!2.07
!0.35
!0.73
!0.98
!3.84
!2.31
!1.65
0.00
0.00
0.00
0.02
0.00
0.01
0.06
0.01
0.01
0.01
0.00
!0.077
!0.095
0.654
0.509
0.264
!0.146
!0.288
0.206
!0.239
!0.106
!0.488
0.00
0.00
0.00
0.09
0.02
0.01
0.00
0.00
0.00
0.00
0.00
!1.45
!1.54
!1.68
!2.85
0.47
0.30
!0.34
!0.73
!1.29
!1.07
!1.96
0.52
0.49
0.48
0.42
0.43
0.45
0.48
0.55
0.52
0.45
0.28
1.82
1.78
1.58
4.05
1.75
1.81
1.68
1.82
1.59
1.68
2.44
0.42
0.41
0.42
0.22
0.28
0.28
0.36
0.39
0.42
0.31
0.19
R
0.05 3.71
0.07 3.74
0.04 3.36
0.00 6.59
0.01 !2.36
0.00 !0.98
0.02 0.99
0.10 2.20
0.03 2.66
0.05 2.63
0.00 4.12
0.59
0.55
0.53
0.45
0.47
0.49
0.50
0.58
0.56
0.48
0.29
0.000
0.000
!0.002
!0.004
!0.002
0.000
!0.000
0.039
!0.000
0.001
0.001
R
a
R
a
R
a
R
a
R
a
a
Temp
SR
0.003
0.003
0.003
0.002
0.001
0.000
0.001
0.002
0.002
0.002
0.002
Duchbendorf
Basel
Zurich
Lugano
Sion
Chaumont
Rigi
Laegeren
Taenikon
Payerne
Magadino
WS
Temp
SR
R
b
WS
Temp
SR
R
b
WS
!0.004 0.00 1.41 0.00 !12.50 0.11 80.11 0.12
!0.014 0.00 5.52 0.01 !23.07 0.07 57.99 0.10
0.18
0.15
0.20
0.20
0.09
a
R
a
R
a
R
b
WS
Temp
SR
R
VOC
CO
NO
V
Ozone
Table 1
Regression parameters for the meteorological model as described in Eq. (8), including correlation for each individual meteorological parameter and for the complete regression
R
J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
1359
e$cient dynamical process (Baltensperger et al., 1997;
Lugauer et al., 1998). It also experiences stratospheric
intrusions (Stohl et al., 2000). These intrusions were removed from the raw half hour time series by applying
a k$3p "lter, where k is the mean and p is the standard
deviation of the distribution (Zanis et al., 1999). The
meteorological detrending results in a very low correlation (&0.08), which con"rms that measurements at this
station indeed re#ect regional background O values, as
they are almost independent from local meteorology. The
detrended O (Fig. 7) results con"rm the Zanis et al.
(1999) "ndings, showing that there was no statistically
signi"cant linear trend at Jungfraujoch. The summer
90th percentile shows the same behaviour as at lower
altitude stations with the exception of 1991 and 1996,
which anticorrelate. The Davos NABEL station (1640 m
a.s.l.) also shows low O levels in 1991, whereas the
Zugspitze station (2960 m a.s.l.) in the German Alps does
not. However, Zanis et al. (1999) detected a September
1991 trend discontinuity and explained it as induced by
natural variability. As no remarkable meteorological effect was detected in the meteorological observations (e.g.,
resulting from the Pinatubo eruption in June 1991 which
increased stratospheric aerosol concentrations and decreased solar radiation to the surface) some measurement
artefacts may explain this decrease. On the other hand,
the 1996 increase may result from a very convective
summer leading to increased mixing with the lower
troposphere `pumpinga pollutants from lower atmospheric layers. This is con"rmed by the increased NO
and SO concentrations in 1996 and increased `close
lightninga observations indicative of convective conditions at the Jungfraujoch, and which also can form NO .
V
The meteorological detrending can be used, in part, to
investigate if there has been a long-term trend in the
meteorology leading to increased or decreased O forma
tion. This was investigated by using the linearized meteorological detrending relationships, along with the
observed meteorology, to predict the excess O due to
meteorological #uctuations at each site, and then averaging over all sites. The resulting time series does not
show any signi"cant long-term trend over the last 15
years; corroborating what was found at Jungfraujoch
where no statistically signi"cant trend could be found in
the observations of free tropospheric O . These results
suggest that the meteorological parameters governing
O formation do not show any long-term trend over the
last decade.
It is of particular interest that all stations in 1993 and
1996 show a decrease in detrended O . When examining
the gross national product growth rate (GNP) and industrial production growth rate in Switzerland (Swiss Federal Statistical O$ce) and in the European Union (EU)
countries (EUROSTAT) since 1990 (Fig. 8) it is seen that
there were also economic downturn impacting Switzerland and much of Europe. Gardner and Dorling (1999)
1360
J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
Fig. 7. Detrended 90th percentile summer O and unprocessed mean summer NO and SO at the Jungfrauchoch. Detrended 90th
percentile summer O averaged over 11 low-altitude NABEL stations.
Fig. 8. Normalised 90th percentile of summer O averaged over urban, low rural ((1000 m), high rural ('1000 m) and southern
Switzerland (Ticino) locations compared to annual Swiss (CH) and European Union (EU) gross national product (GNP) and industrial
production growth rates.
also observed the 1993 dip in meteorologically adjusted
O trends at four British stations, while at the same
time the EU GNP and industrial production show a decrease on this year. This decrease in economic activity
almost certainly had an in#uence on regional emissions
(e.g., NO at Sion, Magadino and Lugano) levels
V
leading to the decrease of regional O detected in the
processed observations. This decrease in primary pollutants emissions is visible in ambient concentrations of
NO at the stations of Sion, Magadino, Lugano and
V
ZuK rich. This along with the rather #at response in O
to decreased NO and VOC levels, suggests that local
V
reductions in O precursors have relatively little
impact on higher O levels in Switzerland. Thus any
e!ective strategy to reduce high O levels throughout
Switzerland must be part of controls over a much larger
region.
This latter statement can be further assessed when
investigating the detrended O levels response to pri
mary pollutants detrended evolution. A regression analysis of whole year average detrended O levels against the
corresponding VOC and NO at Zurich and DuK bendorf
V
was performed and showed strong anticorrelation with
NO , and a weak (non-signi"cant at DuK bendorf) positive
V
correlation with VOC. This suggests, at least for the
average O (which is relatively low), that NO stripping
V
of O and the OH radical is reducing O locally. This
can be also observed at Lugano, but none of the other
stations show any signi"cant anti or correlation with
NO or VOC. While the average O levels may be
V
inhibited by increases in NO , this may not be true of
V
higher O levels, e.g., those above the 120 lg m\ stan
dard. To investigate this, the 90th percentile, summertime
O levels above the standard were analysed using the
method above. Little positive or negative trend, again, is
found, and in this case, there is no statistically signi"cant
correlation with either VOC, CO or NO observations.
V
This suggests that local reductions in O precursors have
had little impact on high O levels. However, the conti
nental economic slowdown in 1993 and 1996, and the
corresponding emissions reductions, did reduce high O .
Previous model studies (Kuebler et al., 1996; Clappier et
al., 1997; Perego et al., 1999) also showed that photooxidant levels over the Swiss Plateau were not very
J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
responsive to emissions control within the range of those
achieved to date.
The evolution of the primary pollutant concentrations
was compared to trends estimated by the Swiss emissions
inventory (Table 2). The decreases in emissions over the
1361
1985}1998 period is estimated at 30% for NO , 42% for
V
VOC and 55% for CO, and are mainly attributed
to tra$c and industrial source emission reductions.
Fig. 9 shows the comparison of normalised processed
observations of NO , VOC and CO versus estimated
V
Table 2
Swiss emissions estimates (SAEFL, 1995) for NO , VOC and CO. The total emission amount and the relative contribution of each major
V
source are speci"ed for each pollutant and for each year. The last column lists the decrease of total emissions during the 1985}1998
period
Pollutant
Source
1985
1990
1995
1998
Decrease
1985}1998 (%)
NO
V
Total (t yr\)
Tra$c (%)
Industry (%)
Agriculture (%)
Home (%)
Total (t yr\)
Tra$c (%)
Industry (%)
Agriculture (%)
Home (%)
Total (t yr\)
Tra$c (%)
Industry (%)
Agriculture (%)
Home (%)
178,850
69
21
5
6
324,300
39
50
5
5
990,200
76
9
6
10
165,600
65
23
6
8
291,600
31
58
5
6
706,500
72
10
6
11
135,630
61
25
7
7
210,500
24
60
7
9
509,800
63
13
9
16
124,554
60
25
8
7
187,520
20
61
8
10
448,360
58
14
11
18
!30
!39
!14
19
!23
!42
!70
!29
!4
5
!55
!66
!32
!11
!16
Main sources contributions
VOC
Main sources contributions
CO
Main sources contributions
Fig. 9. Normalised mean value of processed full year NO , VOC and CO observations averaged over urban, low rural ((1000 m), high
V
rural ('1000 m) and southern Switzerland (Ticino) locations compared to estimated tra$c and industrial emissions (after accounting
for yearly fossil fuel consumption by source sector).
1362
J. Kuebler et al. / Atmospheric Environment 35 (2001) 1351}1363
tra$c and industrial emissions "tted with fossil fuel consumption (Swiss Federal O$ce of Energy, 1998, 1999).
"rst at the urban stations, it is seen that the inventory, if
not accurate in magnitude, captures the trend well.
A multiple regression using the detrended primary pollutant levels as the dependent variable and the tra$c and
industry emissions estimates as the independent variables
was performed for all the urban stations, as well as for
Magadino and Sion. A T-test with a 5% con"dence level,
suggests that ambient concentrations of NO , CO and
V
VOC are independent of industrial emissions at all the
urban and suburban stations, with Sion (located at the
edge of a highway) showing the highest correlation with
tra$c emissions estimates. The T-test performed at the
rural stations shows that the lower altitude sites correlate
with total NO emissions estimate, but could not resolve
V
which sources (tra$c, industry, housing, and agriculture)
dominate. Finally, high-altitude rural stations do not
show any correlation with the NO total emissions estiV
mate, except LaK geren, located above the densely populated Limmat Valley.
The detrending method developed here, by capturing
short-term responses to meteorological variables, has
other application. These features can be used, not only to
detrend time series, but also to forecast short-term
O levels by using relatively simple forecasted meteoro
logical parameters like cloud cover, temperature and
wind speed (e.g., Kahn, 2000; Thomas, 2000).
4. Conclusions
A new detrending approach for air pollutant concentrations was applied to identify air quality trends in
Switzerland. The use of a mean year to remove the
seasonal and weekly variations improved the power of
the approach over competing methods and allowed the
use of meteorological variables only to detrend the
short-term variation.
Detrended primary pollutant observations at sub-urban and urban stations suggest that the Swiss inventory
is correctly capturing the reduction in emissions, though
rural stations do not detect any signi"cant trend over the
last decade. However, these reductions, which are substantial, are having little impact on detrended summer
high O levels, where little response can be found in the
mean annual levels.
The meteorological model, used to detect if any trend
was to be found in meteorological variables governing
O formation, suggests that no trend due to global
change could be detected in the excess O response to
meteorological #uctuations. The Jungfraujoch (3580 m
a.s.l.) station, which is representative for upper boundary
layer/lower free troposphere behaviour, also shows no
statistically signi"cant trend in the detrended summer
O levels.
Finally more economically-oriented parameters, like
the Swiss and mean European Union (EU) gross national
product (GNP) and industrial production growth rate
correlate with high O levels, suggesting that decreases in
high O levels are most likely if the Swiss emissions
reductions are part of a wider set of (continental) controls.
Acknowledgements
This work was supported by the Swiss National Fund
for Scienti"c Research and the Ecole Polytechnique Federale de Lausanne. The authors are grateful to SAEFL
and specially to Dr. P. Filliger for providing air quality
and meteorological data and also to the Swiss Meteorological Institute for providing the ANETZ meteorological data.
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