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 METEOROLO

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

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

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

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

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

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

LIST OF FIGURES

Page Fig.1 PM graphic showing size compared to hair and grain of sand 6

Fig.3 Taiwan Air Quality Monitoring Network site 11

Fig.7 Diurnal variations of PBLH (m) seasonal mean in Taoyuan in 2015 23 Fig.8 Daily mean values of PM10 concentrations in Taoyuan station in 2015 24 Fig.9 Variation of monthly PM10 concentrations in Taoyuan station in 2015 25 Fig.10 Time series of particulate matter concentrations in Taoyuan in 2015 26 Fig.11 Time series for planetary boundary layer height and meteorological

variables (wind speed, relative humidity and temperature) in Taoyuan in

one year

27

Fig.12 Diurnal variations of parameter in Taoyuan for the studied period (a)

PM10, (b) planetary boundary layer height, (c) wind speed, and (d)

relative humidity and temperature

28

Fig.13 Relationships between PM10 and planetary boundary layer height 29 Fig.14 Relationships between PM10 and meteorological parameters 32

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

Planetary Boundary Layer Height Particulate matter

Taiwan Air Quality Monitoring Network

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

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 PM10levels, 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

PART II LITERATURE REVIEW

2.1 Particulate Matter

2.1.1 Particulate Matter

According to the US Environmental Protection Agency (EPA), "Particulate matter" or PM is the term for a mixture of solid particles and liquid droplets found in the air Some particles, such as dust, dirt, soot, or smoke, are large or dark enough to

be seen with the naked eye Others are so small they can only be detected using an electron microscope

Particulate matterincludes "inhalable coarse particles", with diameters larger than 2.5 micrometers and smaller than 10 micrometers and "fine particles", with diameters that are 2.5 micrometers and smaller, they are the standard concentrations used in EPA How small is 2.5 micrometers? Think about a single hair from your head The average human hair is about 70 micrometers in diameter – making it 30 times larger than the largest fine particle (US EPA, n.d.)

Fig.1 PM graphic showing size compared to hair and grain of sand

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

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 gas-to-particle partitioning The net effect that increased temperature has on airborne particle concentrations is a balance between increased production rates for secondary

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)

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

comprehensive support process The operation of the monitoring station includes the space configuration, the type of equipment, maintenance and quality assurance, and so

on All processes have to be well done, to get the qualified monitoring data

2015)

Fig.3 Taiwan Air Quality Monitoring Network site

2.4 Global Modeling and Assimilation Office

2.4.1 GMAO Mission

The Global Modeling and Assimilation Office (GMAO) is a unique organization that uses computer models and data as

program of Earth Observations

comprehensive support process The operation of the monitoring station includes the

type of equipment, maintenance and quality assurance, and so

on All processes have to be well done, to get the qualified monitoring data

Taiwan Air Quality Monitoring Network site (source: http://taqm.epa.gov.tw/)

Global Modeling and Assimilation Office

The Global Modeling and Assimilation Office (GMAO) is a unique organization that uses computer models and data assimilation techniques to enhance NASA's program of Earth Observations

comprehensive support process The operation of the monitoring station includes the

type of equipment, maintenance and quality assurance, and so

on All processes have to be well done, to get the qualified monitoring data (TAQMN,

(source: http://taqm.epa.gov.tw/)

The Global Modeling and Assimilation Office (GMAO) is a unique organization

similation techniques to enhance NASA's

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

Table 1.Brief description of GMAO data products

Seasonal Forecasts Ocean analyses and nine-month atmosphere-ocean forecasts

MERRA-2 A reanalysis of the period 1979 to the present, including aerosols

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:

-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

Pearson's correlation and least squares regression analysis

The square of the sample correlation coefficient is typically denoted r 2 and is a special case of the coefficient of determination In this case, it estimates the fraction of

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 Yiaround their average value can be decomposed as follows: