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The correlations between particulate matter concentrations, planetary boundary layer height and meteorological parameters

THAI NGUYEN UNIVERSITY
UNIVERSITY OF AGRICULTURE AND FORESTRY

DO MINH HONG

THE CORRELATIONS BETWEEN PARTICULATE MATTER
CONCENTRATIONS, PLANETARY BOUNDARY LAYER HEIGHT AND
METEOROLOGICAL PARAMETERS

BACHELOR THESIS
Study Mode: Full-time
Major

: Environmental Science and Management

Faculty

: International Training and Development Center

Batch


: 2012-2016

Thai Nguyen, 05/12/2016


Thai Nguyen University of Agriculture and Forestry
Degree program

Bachelor of Environmental Science and Management

Full name

DO MINH HONG

Student ID

DTN1253150038

Thesis title

The correlations between particulate matter concentration,
planetary boundary layer height and meteorological
parameters

Supervisor

Ph.D., Associate Professor Tang-Huang Lin (National
Central University, Taiwan)
MSc. Nguyen Van Hieu (Thai Nguyen University of
Agriculture and Forestry, Vietnam)

Abstract:
In this study, data describing PM10 concentrations, planetary boundary layer
height, atmospheric temperature, relative humidity and wind speed in 2015 were
analyzed and correlated for the further application to the air quality assessment in
Taoyuan city, Taiwan. PM10 data were collected from an air quality station in urban
area. The characteristics of PM10 concentrations were explored, and it's relationwith
meteorological parameters were examinedaccordingly. The

studied


area is

characterized by low wind speed and humidity, with mild to warm winter and hot
summer. Daily mass concentration of PM10 ranged from 10 to 104 µg/m3, which was
under the limit of national air quality standards (125 µg/m3). The highest level of PM10

i


was observed duringwinter, while the lowest loading was during summer.Pearson
analysis revealed strong negative correlations between PM10 and temperature,
humidity and wind speed (>4 m/s) with the correlation coefficient of -0.84, -0.92, and 0.86, respectively. Although, there was a weak correlation (-0.48) between PM10 and
planetary boundary layer height for all observations, the relations during an interval
near surface are significant (almost more than -0.8) indicating the impact of weather
system.
Keywords

Particulate matter, PM10, planetary boundary layer, wind
speed, Taiwan

Number of pages

38

Date of submission

December 2016

ii


ACKNOWLEDGEMENTS
First and foremost,I would like to thank myadvisorAssoc. Prof. Tang-Huang
Linfor being supportive, guiding and understanding during a difficult time.You have
set an example of excellence as a researcher, mentor, instructor, who spent endless
hours proofreading my research papers and giving me excellent suggestions which
resulted in improved versions of documents.
I would like to thank my advisor MSc. Nguyen Van Hieufor his constant
enthusiasm and encouragement.
I would also like to thank members of "Environmental Remote Sensing
Laboratory": Kuo-En Chang,Wei-HungLien, Yi-Ling Chang,Yuan-Hsiang Chang,
Tsung-Ting Lee and Sheng-Kai Zeng.I am very grateful to all of you for your support
and kindness.
Finally, I take this opportunity to record my sense of gratitude to my families
and friends who encourage and backing me unceasingly.
Thai Nguyen, 05/12/2016
Author

Do Minh Hong

iii


TABLE OF CONTENT
LIST OF FIGURES .......................................................................................................1
LIST OF TABLES ........................................................................................................2
LIST OF ABBREVIATION..........................................................................................3
PART I. INTRODUCTION ..........................................................................................4
1.1.

Research rationale .....................................................................................4

1.2.

Research's objectives .................................................................................5

1.3.

Research questions ....................................................................................5

1.4.

Limitations ................................................................................................5

PART II. LITERATURE REVIEW ..............................................................................6
2.1.

Particulate Matter ......................................................................................6

2.1.1. Particulate Matter..........................................................................................6
2.1.2. Factors that affect particulate matter ............................................................8
2.2.

Planetary Boundary Layer.........................................................................9

2.3.

Taiwan Air Quality Monitoring Network ...............................................10

2.3.1. TAQMN Background .................................................................................10
2.3.2. TAQMN Goal .............................................................................................10
2.4.

Global Modeling and Assimilation Office ..............................................11

2.4.1. GMAO Mission ..........................................................................................11
2.4.2. GMAO Data Products.................................................................................12
2.5.

Matlab......................................................................................................13

2.6.

Pearson's Correlation Coefficient............................................................14

PART III. MATERIALS AND METHODS ...............................................................16

iv


3.1.

Description of the Study Area .................................................................18

3.2.

Data and Equipment ................................................................................19

3.3.

Methodology ...........................................................................................19

PART IV. RESULTS AND DISCUSSION ................................................................22
4.1.

Statistics of the variables ........................................................................22

4.2.

Relationship between the variables ........................................................29

4.2.1. Relationship between PM10 and planetary boundary layer .........................29
4.2.2. Relationship between PM10 and meteorological parameters ......................30
PART V. CONCLUSION ...........................................................................................33
REFERENCES ............................................................................................................34

v


was observed duringwinter, while the lowest loading was during summer.Pearson
analysis revealed strong negative correlations between PM10 and temperature,
humidity and wind speed (>4 m/s) with the correlation coefficient of -0.84, -0.92, and 0.86, respectively. Although, there was a weak correlation (-0.48) between PM10 and
planetary boundary layer height for all observations, the relations during an interval
near surface are significant (almost more than -0.8) indicating the impact of weather
system.
Keywords

Particulate matter, PM10, planetary boundary layer, wind
speed, Taiwan

Number of pages

38

Date of submission

December 2016

ii


LIST OF TABLES
Page
Table 1. Brief description of GMAO data products

13

Table 2. Monthly means of meteorological elements in Taoyuan in 2015

23

Table 3. Correlation of particulate matter and planetary boundary layer heightin

30

2015
Table 4. Correlation of particulate matter and meteorological parameters in

31

2015

2


LIST OF ABBREVIATION

EOS

Earth Observing System

EPA

Environmental Protection Agency

GEOS

Goddard Earth Observing System

GMAO

Global Modeling and Assimilation Office

NASA

National Aeronautics and Space Administration

PBL

Planetary Boundary Layer

PBLH

Planetary Boundary Layer Height

PM

Particulate matter

TAQMN

Taiwan Air Quality Monitoring Network

3


PART I. INTRODUCTION
1.1.

Research rationale
Particulate matters are complex pollutants of different sizes, shapes and origins

suspended in the atmosphere. Those with aerodynamic size not greater than 10 µm in
diameter are collectively referred to as PM10. Due to their small sizes, PM10 can be
inhaled readily and can penetrate deep into the human body. Hence respiratory health
effects on people can be observed when they are exposed at elevated concentrations.
Studies indicated that an increase in daily mean PM10 concentrations might cause an
increase in daily mortality and hospital admissions (Bell et al., 2008; Pope & Dockery,
1992).
Meteorology is a major factor in ambient PM concentrations since dispersion
processes, removal mechanisms, and chemical formation of atmospheric particles
depend on parameters. The meteorological parameters such as wind speed (WS),
temperature (T), relative humidity (RH), and planetary boundary layer height (PBLH)
etc. are expected to have important effects on PM10 variation. For the reason, some
studies carried out in urban areas have investigated the relationship between
meteorological variables and PM concentration(Galindo et al., 2011; Hien et al., 2002;
Wai, 2005). In addition, planetary boundary layer has a significant effect on the air
pollutants, especiallythe particulate matters near surface(Quan et al., 2013; Rigby et
al., 2006).
For the case of Taoyuan city, it's a special municipality in northwestern Taiwan,
which is prosperous in commerce and industry. Due to trade prosperity in recent years
and the proliferation of job opportunities, Taoyuan has developed into a major

4


economic district in Northern Taiwan. Air traffic at Taiwan Taoyuan Internation
Airport has increased steadily. The emissions produced from larger energy
consumption and from increasing local traffic volume might generate more
particulates which impose stresses on the atmospheric environment. Understanding
PM10 behavior and the relationship with meteorological variables is an essential issue
related to the environmental assessment. Thus, having this project conducted “The
correlations between particulate matter concentration, planetary boundary layer
height and meteorological parameters".
1.2.

Research's objectives
The objective of this study is to explore the influence of meteorological

parameters onPM10concentrationsin Taoyuan city, Taiwanduring 2015.PM10 and
meteorological parameters data were collected from an ambient air quality station in
Taoyuan; planetary boundary layer height data were obtained from the online outputs
provided by GMAOin 2015. These data have been analyzed to assess ambient PM10
levels, diurnal and monthly variation, and inter-correlations of the variables.
1.3.

Research questions

1. What is the content of PM10 in Taoyuan city?
2. How does planetary boundary layer and meteorological parameters effect on
concentrations of PM10in Taoyuan city?
1.4.

Limitations
The analysis conducted so far is limited because due toPM10 and meteorological

parameters data was collected in one stationonly; it might limit the representative of
results in this study.

5


ACKNOWLEDGEMENTS
First and foremost,I would like to thank myadvisorAssoc. Prof. Tang-Huang
Linfor being supportive, guiding and understanding during a difficult time.You have
set an example of excellence as a researcher, mentor, instructor, who spent endless
hours proofreading my research papers and giving me excellent suggestions which
resulted in improved versions of documents.
I would like to thank my advisor MSc. Nguyen Van Hieufor his constant
enthusiasm and encouragement.
I would also like to thank members of "Environmental Remote Sensing
Laboratory": Kuo-En Chang,Wei-HungLien, Yi-Ling Chang,Yuan-Hsiang Chang,
Tsung-Ting Lee and Sheng-Kai Zeng.I am very grateful to all of you for your support
and kindness.
Finally, I take this opportunity to record my sense of gratitude to my families
and friends who encourage and backing me unceasingly.
Thai Nguyen, 05/12/2016
Author

Do Minh Hong

iii


Studies illustrate that the main PM10 sources were mineral dust, emissions
derived from power generation, vehicle exhausts, marine aerosol, soil and sea salt
particles (Kavouras et al., 2001; Rodríguez et al., 2004). Meteorological parameters
play a significant role in transport, diffusion and natural cleansing in the atmosphere.
The air pollution cycle consist of three phases: release of air pollutant at the sources,
transport and diffusion in the atmosphere, and reception by people, plants and animals
(Goel & Trivedy, 1998). One main problem is that particulate pollution may remain in
the atmosphere for some time depending on the size and the amount of precipitation
that occurs. For example, winds can carry PM10 great distances before they finally
reach the surface.
Particle pollution contains microscopic solids or liquid droplets that are so small
that they can get deep into the lungs and cause serious health problems. The size of
particles is directly linked to their potential for causing health problems. Small
particles less than 10 micrometers in diameter pose the greatest problems, because
they can get deep into your lungs, and some may even get into your bloodstream.
Studies indicated that an increase in daily mean PM10 concentrations might cause an
increase in daily mortality and hospital admissions (Bell et al., 2008; Pope& Dockery,
1992).
EPA found particulate matters levels are usually high during north-east monsoon
in autumn and winter, especially for PM10. EPA has set up Air Quality Standards
which provides information to people in the website. To be specific, size equivalent to

7


less than 10 microns of suspended particles (PM10) standard of the average of 24 hours
is 125 µg/m3 and annual average is 65 µg/m3.
2.1.2. Factors that affect particulate matter
There are many factors affect particulate matter that can decrease or increase
theconcentration of particulate matter. Most natural aerosol sources are controlled by
climatic parameters like wind, moisture, and temperature. The transport and removal
of particulate matter is highly sensitive to winds and precipitation. Removal of
particulate matter from the atmosphere occurs mainly by wet deposition (in which
atmospheric pollutants mix with water vapor and fall as precipitation) (NRC, 2005a).
The wind speed plays a role in diluting pollution. When vast quantities of
pollutants are spewed into the air, the wind speed determines how quickly the
pollutants mix with the surrounding air and, of course, how fast they move away from
their source. Strong winds tend to lower the concentration of particulate matters by
spreading them apart as they move downstream. Moreover, the stronger the wind, the
more turbulent the air. Turbulent air produces swirling eddies that dilute the particulate
matters by mixing them with the cleaner surrounding air. Hence, when the wind dies
down, particulate matters are not readily dispersed and tend to become more
concentrated(Ahrens, 2014).
Particulate matter chemistry is affected by changes in temperature. Temperature
is one of the most important meteorological variables influencing air quality in urban
atmospheres because it affects gas and heterogeneous chemical reaction rates and gasto-particle partitioning. The net effect that increased temperature has on airborne
particle concentrations is a balance between increased production rates for secondary

8


particulate matter (which increases particulate concentrations) and increased
equilibrium vapor pressures for semi-volatile particulate compounds (which decreases
particulate concentrations). Increase temperatures may either increase or decrease the
concentration of semi-volatile secondary reaction products, such as ammonium nitrate,
depending on ambient conditions. Regions with relatively warm initial temperatures
(>17 °C) may experience a reduction in particulate ammonium nitrate concentrations
as temperature increases, while regions with relatively cool initial temperatures (<17
°C) may experience minor reductions or even small increases in particulate ammonium
nitrate concentrations as temperature increases (Gray, 2009).
2.2.

Planetary Boundary Layer
The planetary boundary layer (PBL) is the lowest layer of the troposphere where

wind is influenced by friction (Fig.2). The thickness (depth) of the PBL is not constant
and it is dependent on many factor. At night and in the cool season the PBL tends to be
lower in thickness while during the day and in the warm season it tends to have a
higher thickness. The two reasons for this are the wind speed and thickness of the air
as a function of temperature. Strong wind speeds allow for more convective mixing.
This convective mixing will cause the PBL to expand. At night, the PBL contracts due
to a reduction of rising thermals from the surface. Cold air is denser than warm air,
therefore the PBL will tend to be shallower in the cool season (Tan, 2014).
Because air pollution concentrations are generally emitted from surface, and
strongly constrained in the PBL, the air pollutants are significantly higher in the PBL
than the rest of the atmosphere (Geng et al., 2009; Hayden et al., 1997).

9


Fig.2. Planetary Boundary Layer (source: http://shodor.org/)
The evolution of the PBL height plays important roles for the long-range
transport, and regulates the diurnal variability of air pollutants in large cities (Ying et
al., 2009). Thus, better understanding of the evolution of PBL is an essential issue for
the interpretation of atmospheric constituents (Bright & Mullen, 2002).
2.3.

Taiwan Air Quality Monitoring Network

2.3.1. TAQMN Background
Currently, the Taiwan Air Quality Monitoring Network (TAQMN) have 76 air
quality monitoring stations, including 60 general stations , 5 industrial stations, 2
national park stations (1 station simultaneous as the general station), 4 background
stations (2 stations simultaneous as the general station), 6 traffic stations and 2 other
stations. The monitoring air quality data is real-time presented.
2.3.2. TAQMN Goal
The main goals of Taiwan Air Quality Monitoring Network (TAQMN) are: air
quality monitoring data is the major basis of air quality protection and air pollution
control. To have an effective control on air quality relies on the long-term operation
and well-maintained monitoring system. To acquire the high-quality, complete,
representative and reliable monitoring data, the monitoring operation need the
10


TABLE OF CONTENT
LIST OF FIGURES .......................................................................................................1
LIST OF TABLES ........................................................................................................2
LIST OF ABBREVIATION..........................................................................................3
PART I. INTRODUCTION ..........................................................................................4
1.1.

Research rationale .....................................................................................4

1.2.

Research's objectives .................................................................................5

1.3.

Research questions ....................................................................................5

1.4.

Limitations ................................................................................................5

PART II. LITERATURE REVIEW ..............................................................................6
2.1.

Particulate Matter ......................................................................................6

2.1.1. Particulate Matter..........................................................................................6
2.1.2. Factors that affect particulate matter ............................................................8
2.2.

Planetary Boundary Layer.........................................................................9

2.3.

Taiwan Air Quality Monitoring Network ...............................................10

2.3.1. TAQMN Background .................................................................................10
2.3.2. TAQMN Goal .............................................................................................10
2.4.

Global Modeling and Assimilation Office ..............................................11

2.4.1. GMAO Mission ..........................................................................................11
2.4.2. GMAO Data Products.................................................................................12
2.5.

Matlab......................................................................................................13

2.6.

Pearson's Correlation Coefficient............................................................14

PART III. MATERIALS AND METHODS ...............................................................16

iv


GMAO members perform research, develop models and assimilation systems,
and produce quasi-operational products in support of NASA's missions. The "Goddard
Earth Observing System" (GEOS) family of models is used for applications across a
wide range of spatial scales, from kilometers to many tens of kilometers.
Originally formed to support NASA's "Earth Observing System" (EOS) mission,
GMAO's role has evolved to include newer space- and aircraft-based observations.
Modeling in the GMAO has adopted the Earth System Modeling Framework,
which promotes a modular structure that allows model components to be connected
together in a relatively straightforward manner. This approach promotes structured
programming using modules or component models to treat specific physical, chemical,
or biological processes. Used carefully, The Earth System Modeling Framework
allows for proper treatment of coupling among different processes, such as the indirect
and direct affects of aerosols on clouds and the terrestrial radiation balance (“GMAO
Mission,” 2015).
2.4.2. GMAO Data Products
GMAO generates and distributes a number of products that either make extensive
use of NASA's satellite observations, provide support to satellite missions and field
campaigns, or help with the planning for new missions. These products also support
researchers funded by NASA and others.
All products are experimental and are intended for use by NASA investigators
and scientific researchers.

12


Table 1.Brief description of GMAO data products
Product

Brief Description
Analyses and forecasts produced in real time, using the most

GEOS-5 FP
recent validated GEOS-5 system
Analyses produced for Instrument Teams, using a stable version
GEOS-5 FP-IT
of GEOS-5
Seasonal Forecasts

Ocean analyses and nine-month atmosphere-ocean forecasts

MERRA-2

A reanalysis of the period 1979 to the present, including aerosols
A "Nature Run" using a high-resolution version of GEOS-5,

7km-G5NR
spanning two years and including aerosols and carbon gases
Soil moisture and carbon fluxes produced as part of the SMAP
SMAP L4
mission

2.5.

Matlab
The MATLAB platform is optimized for solving engineering and scientific

problems. The matrix-based MATLAB language is the world’s most natural way to
express computational mathematics. Built-in graphics make it easy to visualize and
gain insights from data. A vast library of prebuilt toolboxes lets you get started right
away with algorithms essential to your domain. The desktop environment invites
experimentation, exploration, and discovery. These MATLAB tools and capabilities
are all rigorously tested and designed to work together.
13


MATLAB provides a desktop environment tuned for iterative engineering and
scientific workflows. Integrated tools support simultaneous exploration of data and
programs, letting you evaluate more ideas in less time. You can use MATLAB in a
wide range of applications, including signal and image processing, communications,
control design, test and measurement, financial modeling and analysis, and
computational biology. For a million engineers and scientists in industry and
academia, MATLAB is the language of technical computing(“MATLAB R2012a,”
2016).In this study, Matlab is used for processing the HDF4 files from GMAO and
analysis data.
2.6.

Pearson's Correlation Coefficient
The modeling of the relationship between a response variable and a set of

explanatory variables is one of the most widely used of all statistical techniques. We
refer to this type of modeling as regression analysis. A regression model provides the
user with a functional relationship between the response variable andexplanatory
variables that allow the user to determine which of the explanatory variables have an
effect on the response. The regression model allows the user to explore what happens
to the response variable for specified changes in the explanatory variables.
The Pearson correlation coefficient is a measure of the strength of a linear
association between two variables and is denoted by r. Basically, a Pearsoncorrelation
attempts to draw a line of best fit through the data of two variables, and the Pearson
correlation coefficient, r, indicates how far away all these data points are to this line of
best fit (how well the data points fit this new model/line of best fit).In a sample it is
denoted by r and is by design constrained as follows:

14


-1 ≤r ≤ 1
A value of 0 indicates that there is no association between the two variables. A
value greater than 0 indicates a positive association; that is, as the value of one
variable increases, so does the value of the other variable. A value less than 0 indicates
a negative association; that is, as the value of one variable increases, the value of the
other variable decreases.The closer the value is to 1 or –1, the stronger the linear
correlation.
Pearson's correlation coefficient
Pearson's correlation coefficient when applied to a sample is commonly
represented by the letter r and may be referred to as the sample correlation coefficient
or the sample Pearson correlation coefficient. We can obtain a formula for r by
substituting estimates of the covariance and variances based on a sample into the
formula above. So if we have one dataset {x1,...,xn} containing n values and another
dataset {y1,...,yn} containing n values then that formula for r is:

=





(

(

− ̅ )(

− ̅)





(

)

− )

where:



,

,

are defined as above

̅= ∑

(the sample mean); and analogously for

Pearson's correlation and least squares regression analysis
The square of the sample correlation coefficient is typically denoted r2 and is a
special case of the coefficient of determination. In this case, it estimates the fraction of
the variance in Y that is explained by X in a simple linear regression. So if we have the
15


observed dataset {y1,...yn} and the fitted dataset {f1,...fn}, and we denote the fitted
dataset {f1,...fn} with {ŷ1,...ŷn}, then as a starting point the total variation in the Yi
around their average value can be decomposed as follows:
( − ) =
where the

( − ) +

( − )

are the fitted values from the regression analysis. This can be

rearranged to give

∑( − ) + ∑( − )
+
∑( − )
∑( − )

1=

The two summands above are the fraction of variance in Y that is explained by X
(right) and that is unexplained by X (left).
Next, we apply a property of least square regression models, that the sample
covariance between

− is zero. Thus, the sample correlation coefficient

and

between the observed and fitted response values in the regression can be written
(calculation is under expectation, assumes Gaussian statistics)

,
=
=

∑ ( − )( − )

∑( − ) ∙∑( − )

∑( −

∑[
=

=

+

− )( − )

∑( − ) ∙∑( − )




+



∑( − ) ∙∑( − )


]



∑( − ) ∙∑( − )

16


3.1.

Description of the Study Area .................................................................18

3.2.

Data and Equipment ................................................................................19

3.3.

Methodology ...........................................................................................19

PART IV. RESULTS AND DISCUSSION ................................................................22
4.1.

Statistics of the variables ........................................................................22

4.2.

Relationship between the variables ........................................................29

4.2.1. Relationship between PM10 and planetary boundary layer .........................29
4.2.2. Relationship between PM10 and meteorological parameters ......................30
PART V. CONCLUSION ...........................................................................................33
REFERENCES ............................................................................................................34

v


PART III.MATERIALS AND METHODS
3.1.

Description of the Study Area
Taoyuan is a special municipality in northwestern Taiwan, neighboring New

Taipei, Hsinchu County, and Yilan County. Taoyuan is located approximately 40 km
southwest of Taipei, in northern Taiwan, and occupies 1,221 km2. Its shape has a long
and narrow southeast-to-northwest trend, with the southeast in the Xueshan Range and
the far end on the shores of the Taiwan Strait.
Taoyuan developed from a satellite city of Taipei metropolitan area to be the
fourth-largest metropolitan area, and fifth-largest populated city in Taiwan. Since
commuting to the Taipei metropolitan area is easy, Taoyuan has seen the fastest
population growth of all cities in Taiwan with a population of 2,116,988 people
(2016).
Taoyuan, at the edge of the Greater Taipei Region, made some structural and
life-style changes within its society. Due to trade prosperity in recent years and the
proliferation of job opportunities, Taoyuan has developed into a major economic
district in Northern Taiwan. The population has been increasing ever since. Large
number of passengers and freight transport has made Taoyuan International Airport
the most important gateway of Taiwan to the outside world. Also, Taoyuan offers
major sea routes to Southeast and Northeast sea transportation. Taoyuan has
transformed from an agricultural based city into an economical metropolitan city.
Taoyuan has a humid subtropical climate, with mild to warm winters and hot wet
summers, typical of northern Taiwan (“History About Taoyuan,” 2014).

18


3.2.

Data and Equipment

Fig.4.Map of Taiwan showing the study area
Three measurement data sets were used in this study: PM10, satellite, and
meteorology data (from January 1, 2015 to December 31, 2015). The hourly PM10 data
andmeteorological data (relative humidity, temperature, wind speed) were obtained
fromTaoyuan station (longitude: 121.32, latitude: 24.99)in Taoyuan city, which is a
part of the Taiwan Air Quality Monitoring Network (see http://taqm.epa.gov.tw for
more

information)

developed

by

the

Taiwan

Environmental

Protection

Administration.PM10 was measured by the ambient air dust concentration monitor
VEREWA F701, withavailability > 95% and detection limit of<1 µg/m³.The
measuring principle of the F701 ambient dust monitor is based on the absorption of the
beta rays (electrons) emitted by a radioactive emitter through particles collected from
19


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