ANALYSIS AND FORECAST OF PM10 CONCENTRATION
IN A MEDIUM SIZE CITY
Papanastasiou D. K. and Melas D.
Laboratory of Atmospheric Physics, Department of Physics,
Campus Box 149, Aristotle University of Thessaloniki,
54124 Thessaloniki, Greece
E-mail: dkpapan@auth.gr; melas@auth.gr
ABSTRACT
The main objectives of this paper are to analyze the PM10 concentration levels in the urban area of Volos, which
is a medium size city in central Greece and to develop an analytical model, relating daily average value of PM10
concentration in that area with various meteorological variables and pollution, in order to predict the next day’s average
value of PM10 concentration. For these purposes, data of years 2001 until 2003 were selected and analyzed.
From the data analysis derived that the daily maximum hourly values of PM10 concentration are quite high and
that the daily average value of PM10 concentration exceeds the boundary value of 50 μg/m3 for approximately 38 % of
the total days. The daily cycle shows two maxima, one in the morning and one in the evening, usually at 09:00 and
22:00 local time. In these cases, the wind is typically from north – northwest directions. Additionally, the weekly range
of daily maximum hourly values is quite significant and it is about 25 μg/m3 from Thursday to Sunday.
The analytical model that was developed can be used to predict the next day’s average value of PM10
concentration in the atmosphere of Volos area with a satisfactory precision. The comparison that was made between the
values that are calculated by the model and those that are observed showed a quite good agreement.
KEY WORDS: PM10, air pollution prediction, regression model.
INTRODUCTION
Over the last two decades a large number of studies have been conducted around the world that
observed associations between ambient particle concentrations and excesses in daily mortality and
morbidity (Dockery et al., 1992, 1993; Scwartz and Dockery, 1992; Touloumi et al., 1994;
Katsouyianni et al., 1997). More specifically, exposure to high concentrations of PM10 in the
atmosphere is strongly associated with several health problems. These particles can affect the
respiratory system of humans and, as they are so small, they can penetrate even in the lungs. A
significant association between PM10 and medical visits to health clinics in Santiago, Chile, for
lower respiratory symptoms in children and for upper respiratory symptoms in older people was
found (Ostro et al., 1999a). A study that was conducted in Bangkok, Thailand, indicated a
statistically significant association between PM10 and mortality. A 10 μg/m3 increase in daily
PM10 concentration is associated with 1 – 2 % increase in natural mortality, a 1 – 2 % increase in
cardiovascular mortality, and 3 – 6 % increase in respiratory mortality (Ostro et al., 1999b).
European Community has established maximum thresholds of PM10 concentrations in order to
avoid their unpleasant consequences. The first Daughter Directive (1999/30/EC) under the Air
Quality Framework Directive (1996/62/EC) states that during the first stage of its implementation
by the end of 2004 the daily average value of PM10 concentration should not exceed 50 μg/m3 on
more than 35 days per year, and the annual average value of PM10 concentration should not exceed
40 μg/m3. The same Directive is warning the countries that during the second stage of its
implementation the thresholds will be stricter.
From all these that are mentioned above is obvious that it is very significant to achieve a
forecast of daily average value of PM10 concentration. When high daily average values of PM10
concentrations are predicted, the authorities might announce a public warning and an advice to
industry, so as people with respiratory problems could take some precautions and industries could
lower the emissions. A forecast for the next day’s average value of PM10 concentration provides
the ability for these actions.
1
Some efforts have been made in order to predict PM10 concentrations. A method that it is used
often is to correlate meteorological variables and concentration of other pollutants with the PM10
concentration. The relationship between pollutant concentrations and meteorological variables has
been the subject of numerous studies during the past few decades. A variety of statistical methods
have been utilized in order to develop techniques, which will enable qualitative or quantitative
short-term forecasts. The variables used in analytical modeling of pollution concentrations are
chosen to represent meteorological conditions unfavorable for pollutant dispersion and, to a certain
degree, short-term variations in emissions.
The purpose of the present study is twofold.
 To analyze the PM10 concentration levels in Volos area.
 To investigate the qualitative relation between PM10 levels and meteorological variables
and to develop an analytical model, relating daily average value of PM10 concentration in that area
with various meteorological variables and pollution, in order to predict the next day’s average value
of PM10 concentration.
DESCRIPTION OF THE VOLOS AREA AND DATA USED
The city of Volos in Greece is located at the north coast of Pagasitikos gulf, where there is a
smaller gulf, the gulf of Volos. The urban area covers an approximately 51,4 km2 area and
approximately 4 km of the coast of Pagasitikos gulf. At a distance of approximately 3 km to the
northeast of the city is mountain Pelion, which extends from the north to the southeast of the city.
Its higher peaks are at 1551 m and at 1471 m, which are to the northeast and to the east of the city
respectively and at distance of approximately 12 km from the city. Pagasitikos gulf expands to the
south of the city and it takes out to the Aegean Sea through a channel of approximately 5,5 km
width which is at 30 km southern of the city. To the northwest of the city are some hills with 500 m
mean height.
From the description above, it is derived that the city has two physical ventilation channels.
The one is the gulf and the other is that small valley that is vested by the hills that described above.
In the greater urban area of Volos live 118.564 people according to the 2001’s national
inventory, but it is believed that the habitants nowadays are much more. The roads inside the city
are generally narrow and traffic is fairly intense during some hours of the day, especially when the
shops are opened. Tourism and the port for passengers and for wares are two factors that aggravate
traffic. There are two rather small industrial areas to the west of the town and a big cement industry
to the east of the town.
The Department of Environment of the Prefecture of Magnesia selects data for some air
pollutants and also selects data for some meteorological variables. So, all the data that are used in
this analysis were taken from that station.
POLLUTION BACKGROUND IN AREA OF VOLOS
From the data analysis derived that the levels of daily average value of PM10 concentration are
very high (figure 1), according to the European Community’s Directive.
Number of days that were used in the data analysis
Number of days when the daily average value of PM10
concentration exceeded the boundary value of 50 μg/m3
Annual average value of PM10 concentration (μg/m3)
2001
296
YEAR
2002
289
2003
235
95 (32%)
116 (40%)
97 (41%)
44,57
50,33
47,90
Table 1: Comparing the results of data analysis for the daily average value of PM10 concentration
with the values that are assigned by the European Community’s Directive.
2
Table 1 shows that the days when the daily average value of PM10 concentration exceeded the
boundary value of 50 μg/m3 are much more than the 35 days per year that is assigned by the
Directive. Moreover, it can be seen that the annual average value of PM10 concentration exceeds
every year the value of 40 μg/m3 that is assigned also by the Directive.
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DAILY MAXIMUM HOURLY VALUE OF PM10 CONCENTRATION
DAILY AVERAGE VALUE OF PM10 CONCENTRATION
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JULIAN DAY
Figure 1: Daily average value of PM10
concentration (μg/m3) for years 2001 - 2003.
Figure 2: Daily maximum hourly value of PM10
concentration (μg/m3) for years 2001 - 2003
800 1000 1200 1400 1600 1800 2000 2200 2400
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PERCENTAGE (%)
PERCENTAGE (%)
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The daily maximum hourly values of PM10 concentration are also quite high (figure 2). The
higher values appear usually during winter.
The daily maximum hourly values are observed either in the morning or in the evening,
typically at 09:00 and 22:00 local time (figure 3). The morning peak is attributed to traffic and to
the morning “rush hour”, when the height of the mixing layer is still low. More research is needed
in order to explain the evening peak.
WIND DIRECTION (0)
TIME
Figure 3: % percent of the days
of years 2001 - 2003 in relation to the time
when the daily maximum hourly value
of PM10 concentration appears.
Figure 4: % percent of the days
of years 2001 - 2003 in relation to the wind
direction that the daily maximum hourly
value of PM10 concentration appears.
3
The daily maximum hourly values of PM10 concentration appear when the wind blows from
north and northwest directions (figure 4).
AVERAGE OF DAILY MAXIMUM HOURLY VALUES
OF PM10 CONCENTRATION PER DAY
MO
TU
WE
TH
FR
SA
The variance of the daily maximum
hourly value of PM10 concentration during
the week is significant (figure 5), it is about
25 μg/m3 from Thursday to Sunday and it
is caused by the traffic. There is an
increasing trend on Tuesdays and
Thursdays and a reducing trend on
Mondays, Wednesdays and during the
weekend. That is explained as shops are
opened in the afternoon on Tuesdays and
Thursdays, while they are closed in the
afternoons on Mondays and Wednesdays
and of course during the weekend after
14:00 local time on Saturdays. More
research is needed to explain the reducing
trend that appears on Fridays, as shops are
opened on Friday afternoons. During the
official vacancies the maximum hourly
values of PM10 concentration remain in
low levels.
SU
105
105
100
100
95
95
90
90
85
85
Official vacancies
80
80
75
75
MO
TU
WE
TH
FR
SA
SU
DAY OF THE WEEK
Figure 5: Weekly variance of daily maximum
hourly values of PM10 concentration (μg/m3)
for years 2001 - 2003.
PREDICTORS FOR DAILY AVERAGE PM10 LEVELS
The most important meteorological processes that influence the local pollutant concentrations
is dry and wet deposition, advection by the horizontal wind, vertical dilution within the boundary
layer accomplished mainly by turbulence and photochemical reactions with other gases, which
occur under the effect of solar radiation. It is thus very important that the meteorological variables
used in analytical modeling for forecasting pollution concentrations cover the above-mentioned
atmospheric processes. These variables are regarded as predictors for the peak pollutant levels. In
this analysis we used the horizontal wind speed and the ground temperature. Unfortunately there
weren’t data for some other variables that can be regarded as predictors for the peak pollutant level
too, such as the day-to-day air temperature change at 850 hPa, the rainfall and the total radiation
flux. Also, there weren’t data available for vehicular traffic.
The present analysis is limited to these variables, which were found to correlate significantly
with the daily average values of PM10 concentration. That’ s why variables such as the wind
direction or the relative humidity are not used. Also, because some variables weren’t correlated
linear with the daily average values of PM10 concentration, we used decimal logarithms, indexes
etc in order to achieve the best correlation between the predictors and the predictand.
Persistency of high pollution levels
Earlier studies have shown that the possibility of occurrence of pollution episodes is increased
if the previous day’s pollution levels were higher than normal. In the present study we used the
decimal logarithms of daily average value and maximum hourly value of PM10 concentration of the
day before the day that we want to predict the daily average value of PM10 concentration, as
parameters indicating the potential of a pollution episode for the next 24 hours. We used these two
values because they are correlated better with the daily average value of PM10 concentration.
Wind speed and wind direction
The basic meteorological parameters determining the horizontal transport and dispersion of air
pollutants are the mean wind speed and the wind direction. There are not any studies for Volos area
4
from which someone can derive that a specific wind direction causes an increase to the pollutants in
area. The only thing that someone can say is that the maximum hourly values of PM10
concentrations appear when the wind blows from north – northwest directions. Unfortunately, these
wind directions are not correlated significantly with the daily average values of PM10
concentration, so we didn’t use wind direction as a predictor.
We found that wind speed is correlated significantly with the daily average values of PM10
concentration when the maximum hourly value of PM10 concentration is observed. That’s why in
the present study we have used the hourly mean wind speed at 09:00 - 10:00 hours of the day that
we want to predict the daily average value of PM10 concentration, the daily mean wind speed of the
same day and the hourly mean wind speed at 21:00 - 22:00 hours of the day before.
Ground temperature
Solar radiation relates with the mixing height, as it relates with the turbulent kinetic energy,
which affects the mixing height. When the mixing layer over the urban area is shallow, pollutants
tend to accumulate in the surface air.
It is thus expected that, among other parameters, the PM10 concentration will depend upon the
intensity of the incoming solar radiation and therefore upon the altitude of the sun and the
atmospheric absorption. For these reasons, solar radiation flux should be estimated in calculations.
But, since there weren’t data for the radiation flux, the ground temperature was used so as radiation
flux could be estimated indirectly. It is obvious that when radiation flux increases, ground
temperature is higher.
Also, high air temperatures are associated with the slow moving high pressure systems, clear
and sunny skies, stagnant circulation and subsiding upper air. All these contribute to an
accumulation or an increase of PM10 concentration, so temperature can be regarded as one of the
PM10 concentration predictors.
In an older study (Van der Wal and Janssen, 2000), it is proposed that changes in temperature
can explain a significant part of the variance of PM10 concentrations. In this analysis, we found that
the difference between the maximum and the minimum value of temperature of the day that we
want to predict the daily average value of PM10 concentration and the same difference of the day
before are correlated significantly with the daily average value of PM10 concentration. So, these
two differences were used in the analytical models to introduce the relation between temperature
and PM10 concentration.
Short – term variation of emissions that affects daily variation of PM10 concentrations
It is already said that a peak of PM10 concentration is usually observed twice a day (figure 3).
In this study we used the local time in 24 hours range when the daily maximum hourly value of
PM10 concentration was observed the day before the day that we want to predict the daily average
value of PM10 concentration, in order to describe the daily variation of PM10 concentration.
Short – term variation of emissions that affects weekly variation of PM10 concentrations
The variability of the city activities, such as traffic, time schedule of the shops etc, induces a
significant short-term variation of emissions. In order to take into account this factor, in the
analytical model presented in this study an empirical index was introduced for the parameterization
of the short-term variability of emissions.
As it is already said, vehicular traffic is a very significant factor that is related with high values
of PM10 concentrations. So, we wanted to add a variable in the analytical model to introduce this
factor. Data for vehicular traffic in Volos area are not available. So, we correlated some other
pollutants, such as CO, NO, NO2 and O3, which are related directly or indirectly with traffic, with
daily average values of PM10 concentrations. We found that the average value of ozone
concentration of the day before the day that we want to predict the daily average value of PM10
concentration is correlated significantly with the daily average value of PM10 concentration. So, we
used this variable as an indirectly indicator of traffic.
5
Annual variation of PM10 concentrations
It is already said that the higher daily maximum hourly values of PM10 concentration appear
usually during winter (figure 2). In this study an expression was used to describe the eventual
annual variation of PM10 concentration, so as to achieve a consequence among the periods.
The following expression was used. Mi is a number from 1 to 12 that refers to the month.
 2  π  Mi 
Y  cos 

 12 
ESTIMATION OF THE DAILY AVERAGE VALUE OF PM10 CONCENTRATION
The quantitative estimation of the daily average value of PM10 concentration is based on an
analytical expression derived by performing multiple regression analysis.
The expression of multiple regression analysis has the following form:
y  m1  x1  m2  x 2  m3  x3  m4  x 4  m5  x 5  m6  x 6 
 m7  x7  m8  x8  m9  x 9  m10  x10  m11  x11  b
y
x1
x2
x3
x4
where:
 The decimal logarithm of the predicted daily average value
of PM10 concentration (μg/m3).
 The decimal logarithm of the daily average value of PM10 concentration (μg/m3).
 The decimal logarithm of the daily maximum hourly value
of PM10 concentration (μg/m3).
 The daily average value of ozone concentration (μg/m3).
 The hourly mean wind speed (m/s) at 09:00 - 10:00 hours (local time).
x 5  The hourly mean wind speed (m/s) at 21:00 - 22:00 hours (local time).
x 6  The daily average wind speed (m/s).
x 7  The difference between the maximum and the minimum value of temperature (0C).
x 8  The difference between the maximum and the minimum value of temperature (0C).
x 9  The local time in 24 hours range when the daily maximum hourly value
of PM10 concentration is observed.
x 10  The empirical index used to describe the weekly variation of PM10 concentration.
x 11  The value of Y.
Variables x 4 , x 6 , x 7 , x10 , x11 refer to the day that we want to predict the daily average value of
PM10 concentration and variables x1, x 2 , x 3 , x 5 , x 8 , x 9 refer to the day before.
MODEL VALIDATION
Data were split in two parts. The 66% of data was used in multiple regression analysis and the
34% of data was used for the evaluation of the analytical model. The analytical model that was
produced from the 66% of data was used to predict the daily average value of PM10 concentration
for the 34% of days. The results were compared with the daily average values of PM10
concentrations that were observed during the same days. The comparison for all days is shown in
Figure 6.
6
PREDICTED DAILY AVERAGE VALUE OF PM10 CONCENTRATION
0
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OBSERVED DAILY AVERAGE VALUE OF PM10 CONCENTRATION
Figure 6: Comparison between predicted and observed
daily average value of PM10 concentration (μg/m3).
In figure 6 the two parallel lines are in a distance of 25 μg/m3 from the diagonal, which
represents the best fit between predicted and observed values. There it can be seen that only for five
days (1.9% of the total days that are used for comparison) the variation between the predicted and
the observed values is greater than 25 μg/m3.
Additionally, the coefficient of determination R2 of the analytical expression derived by
performing multiple regression analysis is 0,61. So, it comes out the conclusion that the agreement
between observed and predicted values is generally good.
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