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Citation: Birim, N.G.; Turhan, C.;
Atalay, A.S.; Gokcen Akkurt, G. The
Influence of Meteorological
Parameters on PM10: A Statistical
Analysis of an Urban and Rural
Environment in Izmir/Türkiye.
Atmosphere 2023,14, 421. https://
doi.org/10.3390/atmos14030421
Academic Editors: Carla Gamelas
and Nuno Canha
Received: 29 January 2023
Revised: 15 February 2023
Accepted: 17 February 2023
Published: 21 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
atmosphere
Article
The Influence of Meteorological Parameters on PM10: A
Statistical Analysis of an Urban and Rural Environment in
Izmir/Türkiye
Necmiye Gulin Birim 1, Cihan Turhan 2, Ali Serdar Atalay 3and Gulden Gokcen Akkurt 1,*
1Department of Energy Systems Engineering, Faculty of Engineering, Izmir Institute of Technology,
35430 Izmir, Türkiye
2
Department of Energy Systems Engineering, Faculty of Engineering, Atılım University, 06830 Ankara, Türkiye
3BitNet Corporation, 34782 Istanbul, Türkiye
*Correspondence: guldengokcen@iyte.edu.tr
Abstract:
Air pollution is a substantial menace, especially in industrialized urban zones, which
affects the balance of the environment, life of vital organisms and human health. Besides the main
causes of air pollution such as dense urbanization, poor quality fuels and vehicle emissions, physical
environment characteristics play an important role on air quality. Therefore, it is vital to understand
the relationship between the characteristics of the natural environment and air quality. This study
examines the correlations between the PM
10
pollutant data and meteorological parameters such
as temperature (T
air
), relative humidity (RH), and wind speed (WS) and direction (WD) under the
European Union’s Horizon 2020 project. Two different zones (Vilayetler Evi as an urban zone and
Sasalı Natural Life Park as a rural zone) of Izmir Province in Türkiye are used as a case study and the
PM
10
data is evaluated between 1 January 2017 and 31 December 2021. A one-tailed t-test is used in
order to statistically determine the relationships between the PM
10
pollutant data and meteorological
parameters. As a further study, practical significance of the parameters is investigated via the effect
size method and the results show that the RH is found to be the most influencing parameter on the
PM10 for both zones, while Tair is found to be statistically non-significant.
Keywords: meteorological parameters; statistical analysis; effect size; PM10; air quality
1. Introduction
Industrial development, rapid urbanization and dense population have led to climate
change and global warming due to air pollution [
1
]. On the other hand, exposure to air
pollutants is of particular concern for the vulnerable residents, such as elderly, pregnant
women and young children [
2
]. Therefore, air quality in cities is monitored along with
atmospheric (airflow direction and intensity) and meteorological parameters (air pressure,
temperature, relative humidity, wind speed etc.) [
3
]. In general, the main causes of air
pollution are factories, vehicle traffic density and fossil fuel combustion processes [
4
].
However, besides anthropogenic causes of air pollution, meteorological parameters over
urban areas affect the air quality, since the movement of air pollutants is highly linked with
both horizontal and vertical movements and environmental conditions [5].
Air pollutants can be classified as primary pollutants, such as carbon monoxide (CO),
sulphur oxides (SO
x
), nitrogen oxides (NO
x
), hydrocarbons (HC) and particulate matter
(PM), and secondary pollutants based on components formed in the lower atmosphere by
chemical reactions, such as O
3
[
6
]. In general, even though the levels of the SO
x
and NO
x
are
high, PM is the most significant pollutant in Türkiye [
7
]. According to the Environmental
Protection Agency (EPA), PM is defined as a blend of the particles and droplets in the air
that consists of components such as organic compounds, metals, acids, soil and dust [
8
].
The PM
2.5
refers to particles with a diameter of 2.5
µ
m or less, while the PM
10
includes
Atmosphere 2023,14, 421. https://doi.org/10.3390/atmos14030421 https://www.mdpi.com/journal/atmosphere
Atmosphere 2023,14, 421 2 of 12
particles with a diameter of 10
µ
m or less [
8
]. The average daily limit values for PM
2.5
and
PM
10
are 25
µ
g/m
3
and 50
µ
g/m
3
, respectively, in Türkiye [
7
]. Table 1depicts the limit
values of the PM air pollutants in the regulations.
Table 1. Limit values of PM air pollutants in the regulations [9,10].
Air Pollutant Time Unit Average Limit Value (µg/m3)
TÜRK˙
IYE PM2.5 Ave. for 24 h
Annual ave.
25
-
PM10 Ave. for 24 h
annual ave.
50
40
EU PM2.5 Ave. for 24 h
annual ave.
-
20
PM10 Ave. for 24 h
annual ave.
50
40
WHO PM2.5 Ave. for 24 h
annual ave.
15
5
PM10 Ave. for 24 h
annual ave.
45
15
USA PM2.5 Ave. for 24 h
annual ave.
35
12
PM10 Ave. for 24 h
annual ave.
50
25
The annual PM
10
average concentration in 97.7% of the 175 stations in Türkiye in
2020, was above the limit value of the World Health Organization (WHO). In addition, in
66.9% of 175 stations, 24 h PM
10
levels were measured above 50
µ
g/m
3
[
11
]. While SO
x
and NO
x
are produced in industrial zones, the main source of the PM
10
is found to be
domestic heating of buildings [
12
]. On the other hand, the air quality changes significantly
depending on traffic intensity and weather conditions [12].
In the literature, there are studies which show the influence of the meteorological
parameters on air quality. Chen et al. [
13
] shows that the most influential factor on the hori-
zontal movement of the pollutants is the wind. In the literature, there are many studies on
air pollutants, specifically PM
10
and SO
2
, which are the most impactful pollutants [
13
–
21
].
For instance, Çolak et al. [
14
] investigated the relationship between SO
2
and meteorological
factors by using five different air quality measurement stations. The authors found a
positive correlation between SO
2
and pressure, while negative correlations were found
with temperature and wind speed. Ba¸sar et al. [
15
] used five-year data of PM
10
and mete-
orological parameters in Aydın/Türkiye in the correlations. The authors found a strong
correlation between all the parameters, especially in the winter season. Çelik and Kadı [
16
]
examined the relation of wind speed, relative humidity and temperature with the SO
2
and PM concentrations. The authors found moderate and weak relationships between
meteorological factors and particulate matter concentrations in Karabuk Province/Türkiye.
On the other hand, Çiçek et al. [
17
] used the PM
10
data for Ankara/Türkiye to determine
the correlations with temperature, wind speed and humidity. The authors found moderate
correlations among all parameters. In the study conducted by Buldur and Sarı [
18
], air
pollution in Isparta/Türkiye was found to be above the national limits of Türkiye. The
authors defended that climatic conditions were the most influential factor on air pollu-
tion. In a report of 2022 [
22
], Manisa/Türkiye was found in the list of provinces with the
highest levels of air pollution. The report presented that the annual PM
10
pollution in
Manisa was about two times higher than the EU limit value. Furthermore, if the wind
speed increased, the air pollution decreased in Manisa. Giri et al. [
23
] conducted a study
on the effects of meteorological conditions such as temperature, precipitation, humidity,
atmospheric pressure, and wind direction and speed, on the PM
10
concentrations in Nepal.
The study showed that PM
10
concentration had a negative relationship with precipitation
Atmosphere 2023,14, 421 3 of 12
and humidity, and a positive relationship with wind speed and atmospheric pressure.
Similarly, Barlık [
24
] found a relation between PM
10
and meteorological parameters such as
temperature, humidity, pressure, precipitation, and wind. Akbal and Unlu [
25
] proposed to
develop a hybrid deep-learning model in order to predict the PM
2.5
emissions in the capital
of Türkiye, Ankara. Five different locations which include industry and heavy traffic zones,
were chosen for the study. Convolution neural network (CNN), recurrent neural network
(RNN) and long short-term memory (LSTM) deep-learning methods were used to predict
and classify PM
2.5
values for an early warning system, by using meteorological parameters.
The authors indicated that the proposed hybrid model predicted the PM
2.5
with an accuracy
of R2of 81%.
Some of the studies found no significant correlations between air quality and me-
teorological parameters [
3
,
26
]. For instance, Ceran et al. [
26
] statistically examined the
changes in the PM
10
values according to meteorological parameters; however, the authors
found no significant correlations among these parameters in Sivas/Türkiye. Similarly,
Huebnerova et al. [
3
] found no significant correlations between the PM
10
concentrations
and meteorological data in Brno, Czech Republic. However, no study was found in the
literature which examined the relation of the PM
10
with the meteorological parameters for
the northern region of ˙
Izmir/Türkiye.
URBAN GreenUP Project [
27
], which is funded by the European Union’s Horizon
2020 research and innovation programme under grant agreement no 730426, proposes to
obtain a tailored methodology to support the co-development of renaturing urban plans,
focused on decreasing air pollution, climate change mitigation and adaptation as well as
efficient water management, and to effectively assist in the implementation of nature-based
solutions (NBSs) in urban areas. Izmir, Türkiye is one of the front-runner cities where
various NBSs were implemented. One of the NBSs implemented in the project is to install
shaded constructions and change the concrete surfaces with permeable ones in car parks, so
that the project aims to determine the improvement on the air quality, water drainage and
urban heat island effect. Two intervention sites were chosen for this implementation, one in
a highly populated urban area and the other in a rural area. To be able to evaluate the effect
of NBSs on air quality, the background data should be evaluated first. Since air quality is
highly related with meteorological conditions, background data consist of both air quality
and meteorological parameters. Air quality data was collected from air quality monitoring
stations located close to the intervention sites, for which the longest period of PM
10
data
(2017–2021) was available. This study is concentrated on the evaluation of the influence of
meteorological parameters on the PM
10
data on the intervention sites of URBAN GreenUP
Project. The meteorological parameters are temperature (T
air
), relative humidity (RH), and
wind speed (WS) and direction (WD), and were collected from meteorological stations
located at the intervention sites. Finally, the novelty of this paper is to statistically analyse
the influence of meteorological parameters on the PM10 concentrations in both urban and
rural environments of ˙
Izmir/Türkiye, with the longest period of data.
2. Materials and Methods
2.1. Study Zone
˙
Izmir is located on the west side of Türkiye, with a 12,012 km
2
surface area, and has a
hot Mediterranean/dry summer subtropical climate, which is known as a Csa type climate
according to Köppen–Geiger climate classification [
28
]. ˙
Izmir is the third largest city in
Türkiye, with a population of 4,425,789 inhabitants in 2021 [29].
In wintertime, poor meteorological conditions, especially inversion, are serious issues
in ˙
Izmir. Air pollution due to inversion (temperature reversal) is affected by industrial
pollution and fossil fuel combustion. Figure 1shows the sectoral greenhouse gas emissions
of ˙
Izmir [
30
]. The total greenhouse gas (CO
2
+ CH
4
+ NO
2
) emissions from industry
account for 31.4%, while buildings are responsible for 15%.
Atmosphere 2023,14, 421 4 of 12
Atmosphere 2023, 14, x FOR PEER REVIEW 4 of 13
İzmir is located on the west side of Türkiye, with a 12,012 km
2
surface area, and has
a hot Mediterranean/dry summer subtropical climate, which is known as a Csa type cli-
mate according to Köppen–Geiger climate classification [28]. İzmir is the third largest city
in Türkiye, with a population of 4,425,789 inhabitants in 2021 [29].
In wintertime, poor meteorological conditions, especially inversion, are serious is-
sues in İzmir. Air pollution due to inversion (temperature reversal) is affected by indus-
trial pollution and fossil fuel combustion. Figure 1 shows the sectoral greenhouse gas
emissions of İzmir [30]. The total greenhouse gas (CO
2
+ CH
4
+ NO
2
) emissions from in-
dustry account for 31.4%, while buildings are responsible for 15%.
Figure 1. Sectoral greenhouse gas emissions in İzmir [30].
The two different zones studied in this work are Vilayetler Evi and Sasalı Natural
Life Park, in İzmir (Figure 2). Vilayetler Evi zone is close to the highway on the coastline.
Therefore, it is possible for the air pollution values to be high due to intense traffic. On the
other hand, Sasalı Natural Life Park zone is a rural area and is surrounded by forest. How-
ever, Sasalı Natural Life Park is also partially surrounded by the industrial zone approxi-
mately 3 km to the west. Therefore, the industrial zone can affect this area.
Figure 1. Sectoral greenhouse gas emissions in ˙
Izmir [30].
The two different zones studied in this work are Vilayetler Evi and Sasalı Natural
Life Park, in ˙
Izmir (Figure 2). Vilayetler Evi zone is close to the highway on the coastline.
Therefore, it is possible for the air pollution values to be high due to intense traffic. On
the other hand, Sasalı Natural Life Park zone is a rural area and is surrounded by forest.
However, Sasalı Natural Life Park is also partially surrounded by the industrial zone
approximately 3 km to the west. Therefore, the industrial zone can affect this area.
Atmosphere 2023, 14, x FOR PEER REVIEW 5 of 13
(a)
(b)
Figure 2. (a) The case study area, İzmir/Türkiye, (b)Location of the Vilayetler Evi and Sasalı Natu-
ral Life Park study zones (The photos are taken from Google Earth).
2.2. Data Collection
The data were collected from the meteorological stations of Vilayetler Evi and Sasalı
Natural Life Park zones, which were installed within the scope of the European Union’s
Horizon 2020 project [27] between 1 January 2017 and 31 December 2021. However, for
the statistical analysis, the raw data were subjected to the elimination of outliers and
Figure 2. Cont.
Atmosphere 2023,14, 421 5 of 12
Atmosphere 2023, 14, x FOR PEER REVIEW 5 of 13
(a)
(b)
Figure 2. (a) The case study area, İzmir/Türkiye, (b)Location of the Vilayetler Evi and Sasalı Natu-
ral Life Park study zones (The photos are taken from Google Earth).
2.2. Data Collection
The data were collected from the meteorological stations of Vilayetler Evi and Sasalı
Natural Life Park zones, which were installed within the scope of the European Union’s
Horizon 2020 project [27] between 1 January 2017 and 31 December 2021. However, for
the statistical analysis, the raw data were subjected to the elimination of outliers and
Figure 2.
(
a
) The case study area, ˙
Izmir/Türkiye, (
b
) Location of the Vilayetler Evi and Sasalı Natural
Life Park study zones (The photos are taken from Google Earth).
2.2. Data Collection
The data were collected from the meteorological stations of Vilayetler Evi and Sasalı
Natural Life Park zones, which were installed within the scope of the European Union’s
Horizon 2020 project [
27
] between 1 January 2017 and 31 December 2021. However, for the
statistical analysis, the raw data were subjected to the elimination of outliers and meaning-
less data. These stations were selected according to urban–nature continuum principles.
A total of 21,710 data points of PM
10
concentrations and meteorological parameters were
used in this study. The stations use Model 5014i Beta Continuous Particulate Monitors [
31
]
for PM
10
measurement. The PM
10
data were collected hourly by the air quality moni-
toring stations which are close to the Vilayetler Evi and Sasalı Natural Life Park zones.
The meteorological parameters used in the study are wind speed (WS), wind direction
(WD), temperature (T
air
) and relative humidity (RH). Two HOBO RX3000 meteorological
stations [
32
] were installed in two study zones, Vilayetler Evi and Sasalı Natural Life Park
(Figure 3). The sensor specifications of the meteorological stations are given in Table 2. The
north, east, south and west directions are 0
◦
, 90
◦
, 180
◦
and 270
◦
for the WD. Meteorological
stations collect data at 10 min intervals. Since the PM
10
data are collected hourly at the air
quality monitoring stations, meteorological data are also converted to hourly averages.
Atmosphere 2023, 14, x FOR PEER REVIEW 6 of 13
meaningless data. These stations were selected according to urban–nature continuum
principles. A total of 21,710 data points of PM10 concentrations and meteorological param-
eters were used in this study. The stations use Model 5014i Beta Continuous Particulate
Monitors [31] for PM10 measurement. The PM10 data were collected hourly by the air qual-
ity monitoring stations which are close to the Vilayetler Evi and Sasalı Natural Life Park
zones. The meteorological parameters used in the study are wind speed (WS), wind di-
rection (WD), temperature (Tair) and relative humidity (RH). Two HOBO RX3000 meteor-
ological stations [32] were installed in two study zones, Vilayetler Evi and Sasalı Natural
Life Park (Figure 3). The sensor specifications of the meteorological stations are given in
Table 2. The north, east, south and west directions are 0°, 90°, 180° and 270° for the WD.
Meteorological stations collect data at 10 min intervals. Since the PM10 data are collected
hourly at the air quality monitoring stations, meteorological data are also converted to
hourly averages.
Figure 3. HOBO RX3000 meteorological station installations (Vilayetler Evi on the left, Sasalı Natu-
ral Life Park on the right).
Table 2. Sensor specifications of HOBO RX3000 meteorological station and Model 5014i Beta Con-
tinuous Particulate Monitoring Stations.
Specifications WS WD
Measurement Range 0 to 76 m/s (0 to 170 mph) 0 to 355 degrees, 5-degree dead band
Accuracy ±1.1 m/s (2.4 mph) or
±4% of reading, whichever is greater ±5 degrees
Resolution 0.5 m/s (1.1 mph) 1.4 degrees
Specifications Tair RH
Measurement Range −40 to 70°C 0 to 100% RH, −40°C to 70°C
Accuracy ±0.25°C from −40 to 0°C
±2.5% from 10% to 90% (typical) to a maximum of
±3.5% including hysteresis at 25 °C;
below 10% RH and above 90% RH ± 5% typical
Resolution 0.02°C 0.01%
Specifications Air Quality Monitoring Station (PM10)
Measurement Range 0–10,000 µg/m3
Accuracy <±5% µg/m3
Resolution 0.1 µg/m3
2.3. Statistical Analysis
Figure 3.
HOBO RX3000 meteorological station installations (Vilayetler Evi on the left, Sasalı Natural
Life Park on the right).
Atmosphere 2023,14, 421 6 of 12
Table 2.
Sensor specifications of HOBO RX3000 meteorological station and Model 5014i Beta Contin-
uous Particulate Monitoring Stations.
Specifications WS WD
Measurement Range 0 to 76 m/s (0 to 170 mph) 0 to 355 degrees, 5-degree dead band
Accuracy ±1.1 m/s (2.4 mph) or
±4% of reading, whichever is greater ±5 degrees
Resolution 0.5 m/s (1.1 mph) 1.4 degrees
Specifications Tair RH
Measurement Range −40 to 70 ◦C 0 to 100% RH, −40 ◦C to 70 ◦C
Accuracy ±0.25 ◦C from −40 to 0 ◦C
±2.5% from 10% to 90% (typical) to a maximum
of ±3.5% including hysteresis at 25 ◦C;
below 10% RH and above 90% RH ±5% typical
Resolution 0.02 ◦C 0.01%
Specifications Air Quality Monitoring Station (PM10)
Measurement Range 0–10,000 µg/m3
Accuracy <±5% µg/m3
Resolution 0.1 µg/m3
2.3. Statistical Analysis
Normality analysis of the raw data was conducted before the statistical analysis [
33
].
To this aim, the Shapiro–Wilks test [
34
,
35
] was selected in this study. If the data are
normally distributed, the authors can apply t-test statistics. Therefore, a null hypothesis is
constructed for the normality analysis as follows:
H0.“The corresponding data is normally distributed”
The significance level is selected as 0.05 for the Shapiro–Wilks test. Therefore, if
the p-value is higher than the significance level, the data are assumed to be normally
distributed.
A one-tailed t-test is applied in the context of linear regression in order to test the null
hypothesis which indicates the relation between each meteorological parameter and the
PM
10
. To this aim, the null hypothesis is constructed for each meteorological parameter as
follows:
H1.“The independent variable Tair is not significant to explain the PM10 concentrations”
H2.“The independent variable RH is not significant to explain the PM10 concentrations”
H3.“The independent variable WS is not significant to explain the PM10 concentrations”
H4.“The independent variable WD is not significant to explain the PM10 concentrations”
The null hypothesis is evaluated at the significance level of 5% and it is accepted if
the p-value is found above 0.05. On the other hand, linear regressions for the relation of
each parameter with the PM
10
concentrations, and non-linear relations which include all
meteorological parameters with the PM
10
concentrations are constructed. The linear and
non-linear regression equations for the parameters follow Equations (1) and (2).
y = a(x1) + n (1)
y = b(x1)d+ c(x2)e+ . . . . . . + n (2)
In Equations (1) and (2), a, b, c, d, e and n represent regression coefficients, while y
refers to PM10 concentrations. Moreover, x1–4 indicates meteorological parameters.
A detailed strength level for the regression model between two variables is determined
using coefficient of determination (R
2
) analysis. The coefficient of determination, for simple
Atmosphere 2023,14, 421 7 of 12
linear regressions, corresponds to the proportion of variation in the dependent variable
that can be explained by the model based on the independent variable [33,35].
Practical significance is examined in addition to the statistical significance. To this aim,
effect size (ES) statistics are used for the relationships between meteorological parameters
and the PM
10
[
36
]. The Pearson correlation coefficient (r), instead of Cohen’s d method,
was used for the examination of the effect size, since there were no two different experi-
mental groups in this study. The Pearson correlation coefficient is classified according to
Table 3[37].
Table 3. Classifications of Pearson correlation coefficient (r) values for the effect size [37].
Strength of Association Pearson’s r *
Weak 0.30 to 0.49
Moderate 0.50 to 0.70
Strong >0.70
* Can be both positive and negative: positive value represents positive correlations, while negative value of r
depicts negative correlations.
3. Results
3.1. Descriptive Data Analysis
The descriptive statistics of the data collected between 1 January 2017 and 31 December
2021 for Vilayetler Evi and Sasalı Natural Life Park are given in Table 4.
Table 4. Descriptive statistics of the collected data for each zone.
Vilayetler Evi Unit M ±SD * Range [Max; Min]
Tair ◦C 20.07 ±16.75 [1.24; 40.68]
RH % 60.6 ±21.5 [18.3; 96.0]
WS m/s 0.5 ±0.2 [0.0; 3.9]
WD Ø 188.79 ±60.14 [0.01; 355]
PM10 µg/m338 ±10 [1; 267]
Sasalı Natural Life Park
Tair ◦C 17.69 ±11.16 [−3.81; 40.52]
RH % 63.0 ±10.5 [11.0; 99.1]
WS m/s 0.6 ±0.3 [0.0; 4.9]
WD Ø 170.8 ±87.4 [0.0; 355]
PM10 µg/m333 ±8 [17; 58]
* In the table, M and SD refers to mean and standard deviation of the data, respectively.
The mean air temperature (T
air
) of the Sasalı Natural Life Park (17.69
◦
C) is lower than
the Vilayetler Evi mean (20.07
◦
C). On the other hand, the mean RH and WS are found to
be higher in the Sasalı Natural Life Park than the Vilayetler Evi. The mean WD is lower
(between south and south-east directions) in the Sasalı Natural Life Park; however, the SD
is higher than in the Vilayetler Evi, and this result shows that WD for Sasalı Natural Life
Park is more diversified.
The mean PM
10
values of Vilayetler Evi and the Sasalı Natural Life Park were 38 and
33
µ
g/m
3
, respectively. The PM
10
values are highly affected by the high traffic density in
Vilayetler Evi.
3.2. Statistic Analysis
As discussed in Section 2, the normality test was applied to the collected raw data as a
first step of the statistical analysis. Table 5depicts the Shapiro–Wilk test results on the data.
Atmosphere 2023,14, 421 8 of 12
Table 5. Shapiro–Wilk normality test results.
Distribution of Parameters p-Value
Vilayetler Evi
Tair 0.168
RH 0.176
WS 0.171
WD 0.166
PM10 0.189
Sasalı Natural Life Park
Tair 0.187
RH 0.164
WS 0.170
WD 0.106
PM10 0.108
All the p-values of the parameters are larger than the selected significance level;
therefore, one can assume that the data is normally distributed and the t-test can be applied
to the data, as a further step.
After the normality test, one-tailed t-test was conducted in the context of linear re-
gression analysis to test the significance of an independent variable (each meteorological
parameter) in a linear regression model where PM
10
is dependent variable. The collected
PM
10
and meteorological data for each study location was used and Table 6presents the
results of the linear regressions and t-test results for PM
10
concentrations in function of
each meteorological parameters for Vilayetler Evi and the Sasalı Natural Life Park.
Table 6.
Linear regression analysis and t-test results for PM
10
concentrations in function of each
meteorological parameters for each location.
Parameters Equations Standard Error t-Value p-Value R2
Vilayetler Evi
Intercept
Coefficient/Slope
Coefficient
Intercept
Coefficient/Slope
Coefficient
Intercept
Coefficient/Slope
Coefficient
Tair 0.0729x + 28.51 2856/7.29 0.01/0.01 0.947 NS/0.985 NS 0.11
RH 0.0120x + 29.33 12.7/0.05 2.31/2.34 <0.01 **/0.001 ** 0.41
WS −0.1143x + 30.1 15.4/0.05 1.96/1.94 0.024 */0.022 * 0.27
WD 0.0069x + 31.41 14.9/0.03 2.10/2.11 <0.01 **/0.001 ** 0.37
Sasalı Natural Life Park
Tair 0.0493x + 31.8 1591/2.46 0.02/0.02 0.754 NS/0.761 NS 0.16
RH 0.1189x + 32.64 10.7/0.03 3.06/3.01 <0.01 **/0.001 ** 0.51
WS −1.753x + 41.19 19.6/0.82 2.10/2.11 <0.01 **/0.005 ** 0.38
WD 0.0461x + 39.32 14.4/0.02 2.74/2.76 <0.01 **/0.001 ** 0.21
NS: not significant, *: significant at %5 level, and **: significant at 1% level.
Table 6depicts that there was a significant relation between the RH, WS and WD and
PM
10
concentrations for the Vilayetler Evi zone. The RH and WD are significant at the 1%
level, while WS is significant at the 5% level in predicting PM
10
concentrations. On the
other hand, the Tair is found to be statistically non-significant for the Vilayetler Evi zone.
Atmosphere 2023,14, 421 9 of 12
For the Sasalı Natural Life Park zone, the PM
10
values are statistically associated with
the RH, WS and WD at a 1% significance level. Similarly to the Vilayetler Evi zone, T
air
is
found to be non-significant for predicting PM10 concentrations.
Table 6also shows the linear regression results of the meterological parameters with
the PM
10
concentrations. The results indicate that the strenght of the linear regression for
T
air
is the lowest, with the R
2
of 0.11. The strenghts of the linear regression for RH are
given by an R
2
of 0.41 and 0.51 for Vilayetler Evi and Sasalı Natural Life Park, respectively.
This means that the proportion of variation in the dependent variable PM
10
that can be
explained by the independent variable RH, is 41% and 51% in each zone, respectively.
The reason for the relatively low R
2
values may be the absense of the other meterological
parmeters in the equation, since the other meterological parameters could also affect the
PM10 concentration level.
Table 7depicts the effect size control tests, in order to investigate the practical signifi-
cance of the meteorological parameters on the PM
10
concentrations. Even though p-values
of the RH, WS and WD for the Vilayetler Evi and Sasalı Natural Life Park zones show sta-
tistical significance, the effect sizes of WS are in the range of weak effect sizes, compared to
the other parameters, for both zones. This result indicates that the WS may be considered to
have a weak association with PM
10
in the study zones. On the other hand, Table 7indicates
that there are positive correlations between the RH and WD and the PM10 concentrations;
however, negative correlation is found for the WS for both zones. The result depicts that
when the WS increases, the PM
10
concentrations decrease. On the other hand, if the RH
and WD increase, the PM10 values also increase for both zones.
Table 7. Pearson correlation between meteorological parameters and PM10 concentrations.
Parameters Pearson’s r
Vilayetler Evi
Tair Null hypothesis is accepted
RH 0.60
WS −0.15
WD 0.40
Sasalı Natural Life Park
Tair Null hypothesis is accepted
RH 0.70
WS −0.41
WD 0.46
Non-linear regression for the PM
10
was also applied. Equations (3) and (4) represent non-
linear regressions for predicting the PM
10
concentrations based on the
meterological parameters.
PM10 = 2.15998WD0.355894 −0.83072WS−0.967422 + 1.9638Tair0.00298 + 1.2927RH0.8392 + 1984.7 (3)
PM10 = 0.252159WD1.0412 −5.6703WS1.68253 + 0.177812Tair1.24962 + 1.476RH0.0021 −1548.18 (4)
4. Discussion
The results of the paper are compared to the literature in this section. The effect of
the meteorological parameters on the PM
10
concentrations highly depends on the location
of the stations and selected study zones, such as urban and rural zones. In this paper,
the T
air
is found to be non-significant for the urban zone (Vilayetler Evi) and rural zone
(Sasalı Natural Life Park) in ˙
Izmir/Türkiye. However, the T
air
was found to be significant,
with a p-value of 0.019 (significance level of 5%), for urban zones in Iran, in a paper by
Fallahizadeh et al. [
38
]. In the same paper, the RH was found to be statistically non-
significant, with a p-value of 0.869. The authors concluded that dust storms are the major
Atmosphere 2023,14, 421 10 of 12
reason for these results. On the other hand, Kirešováand Guzan [
39
] found no correlation
between meteorological parameters and the PM
10
for the rural zones of Kösice, Slovakia.
One of the reasons for this result is that the authors did not take the effect of the WS into
account in the study. The authors also indicated that wood combustion in rural zones
which are surrounded with small villages increases the PM
10
concentrations without the
influence of the meteorological parameters.
In the literature, some papers [
40
,
41
] indicated that the RH is negatively correlated with
the PM
10
concentrations in different zones. For instance, Hernandez et al. [
40
] found that
when the RH increases, the PM
10
values tend to decrease for the sub-tropical climate during
winter. However, the RH is found to be positively correlated with the PM
10
concentrations
for ˙
Izmir/Türkiye in this paper. The reason for the different results in the literature may
be that the RH affects the natural deposition process of the PM
10
[
41
]. Moreover, the
effect of the RH highly changes during rainfall periods, as discussed in [
41
]. In a study of
Dung et al. [
42
], WS is found to be negatively correlated with the PM
10
in Hanoi, Vietnam,
similarly to the result of this paper.
The regression analysis in this paper showed that the accuracy of predicting the PM
10
based on the meteorological parameters was relatively low, similarly to the reference [
23
].
One of the reasons for the low R
2
values in the regression equations between meteorological
parameters and the PM
10
concentrations may be the sudden change of the PM
10
values
caused by exhaust gases from the vehicles and heavy traffic near the meteorological stations.
Limitations
This study aimed to investigate the relationship between meteorological parameters
and PM
10
concentration levels. Although the study revealed significant results, there
were some limitations, due to various reasons. For instance, this study is conducted in
˙
Izmir/Türkiye, which has a Csa climate zone classification. Regional differences should be
taken into account in further studies and data from areas with other types of climate should
be studied. Moreover, seasons and geological areas may affect the results; therefore, more
meteorological stations should be installed in order to understand seasonal and geological
variations. Finally, sudden changes in the PM
10
concentrations without any change in
meteorological parameters affected the accuracy level of the regression equations. Finally,
it is worth remembering that solar radiation is an effective parameter on the air quality and
should be included in the statistical analysis as a meteorological parameter.
5. Conclusions
The purpose of this study was to examine the influence of the meteorological param-
eters on the PM
10
concentrations, in urban and rural zones of ˙
Izmir/Türkiye, under a
European Union’s Horizon 2020 project. The meteorological parameters T
air
, RH, WS and
WD were investigated in two different zones, namely Vilayetler Evi and Sasalı Natural
Life Park. A total of 21,710 data points collected between 1 January 2017 and 31 December
2021 was used in the statistical analysis. The results showed that the RH was the most
influencing factor on the PM
10
concentrations for both zones. On the other hand, the T
air
was found to be statistically non-significant on the PM10 concentrations.
The found accuracy of the regression models to predict the PM
10
concentrations based
on the meteorological parameters is low. By integrating other factors such as precipitation,
atmospheric pressure, seasonal effects and human activities, the accuracy of the regression
analysis could be increased.
The outcome of this study allows a better understanding of the meteorological condi-
tions affecting the PM
10
concentrations, especially in Csa-type climate zones. Future studies
should address the influence of the meteorological parameters on PM
10
concentrations in
other climate zones.
Atmosphere 2023,14, 421 11 of 12
Author Contributions:
Conceptualization, N.G.B., A.S.A. and G.G.A.; methodology, N.G.B. and
A.S.A.; software, C.T.; validation, C.T. and G.G.A.; formal analysis, N.G.B.; investigation, N.G.B. and
A.S.A.; resources, G.G.A.; data curation, N.G.B., A.S.A. and C.T.; writing—original draft preparation,
N.G.B. and C.T.; writing—review and editing, C.T. and G.G.A.; visualization, C.T.; supervision, C.T.
and G.G.A.; project administration, G.G.A.; funding acquisition, G.G.A. All authors have read and
agreed to the published version of the manuscript.
Funding:
This research was funded by Urban GreenUP project which has received funding from the
European Union’s Horizon 2020 research and innovation programme under grant agreement No
730426. The APC was funded by the same programme.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
This publication has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 730426 (Urban GreenUP).
Conflicts of Interest: The authors declare no conflict of interest.
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