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Table 2 Prevalence of symptom during flood

Symptoms Number of participants who
have symptom (Percentages)
Not have
Conjunctivitis 18 (11.92%)
Diarrhea 20 (13.24%)
Skin disease (dermatitis, Athlete's foot) 25 (16.56%)
Respiratory disease (Common cold, pneumonia) 111 (73.50%)
Leptospirosis 75 (49.67%)
Mosquito-borne disease (DHF, Malaria) 20 (13.25%)
Mental health impact 33 (21.85%)
69 (45.70%)

Table 2 reported the highest prevalence of symptoms during flood was skin symptoms (73.50%)
followed by respiratory symptoms (49.67%) and mental health (45.70%). Only 13.24% and 16.56% of
respondents reported eye symptoms and diarrhea. Adding, there had prevalence of mosquito-borne disease
during floods equal 21.85%.

Table 3 Need assessment of Local Government during Flood

Local government organization modified
PNI score
Flood Warning Information
Local Administrative Organization 0.143
Provincial Public Health Office 0.266
Disaster Prevention and Mitigation Department 0.365
Provincial Waterworks Authority 0.488
Meteorological Department 0.360
Royal Irrigation Department 0.399
Private Sectors 0.293
Water Consumption Quality Information
Local Administrative Organization 0.155
Provincial Public Health Office 0.347
Disaster Prevention and Mitigation Department 0.376
Provincial Waterworks Authority 0.406
Meteorological Department 0.485
Royal Irrigation Department 0.463
Private Sectore 0.186

Table 3 shows modified PNI score of local government according to warning information about
flood and water supply quality response during flood. For flood warning information, Provincial Waterworks
Authority (modified PNI = 0.488) showed the highest modified PNI score while Local Administrative
Organization (modified PNI = 0.143) indicated the lowest score of PNI. Therefore, Provincial Waterworks
Authority may have to mitigate warning information to local people during flood. For Water Consumption
Quality Information, Meteorological Department (modified PNI = 0.485) showed the highest PNI score
followed by Royal Irrigation Department (modified PNI = 0.463) and Provincial Waterworks Authority
(modified PNI = 0.406). The lowest PNI score was Local Administrative Organization (modified PNI =
0.155). PNI score may suggested that people in the community required information about water quality
during flood from Meteorological Department.

CONCLUSION
This research presented that people would like to get less further information about flood warning and

water quality from Local Administrative Organization. However, flood-affected community suggested that
Provincial Waterworks Authority should provide more flood warning information to the community while
Water Consumption Quality Information should be provided by Meteorological Department.

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ACKNOWLEDGEMENT

This work is supported by the Thailand Science Research and Innovation (TSRI), project number
SRI6230303.
REFERENCE

[1] Diagne, K. (2007). Governance and natural disasters: addressing flooding in Saint Louis, Senegal. Environment
and Urbanization, 19(2), 552-562. doi:10.1177/0956247807082836

[2] Doocy, S., Daniels, A., Murray, S., & Kirsch, T. D. (2013). The human impact of floods: a historical review of
events 1980-2009 and systematic literature review. PLoS currents, 5,

[3] Chulacharoen, S. (2019). Preparation, prevention and resolution for floods and landslides 2019. (Mth
0810.4/W3001). Thailand: Department of Local Administration.

[4] Suriyawongphisarn, P. (2012). Readiness to cope. The Great Flood 2011: Lessons from experience, Health
Systems Research Institute. 1: 18.

[5] Wongwanich, Suvimol. 2015. Needs Assessment. 3rd ed. Bangkok. Chulalongkorn University Printing

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I 056

Assessment of Biomethane Potential (BMP)

from Different Kinds of Maize Residue Component

Nuttawan Suebnanta1, Rotjapun Nirunsin2*, Tanate Chaichana2 and Thapana Cheunbarn3

1Graduate student; 2Assistant Professor, Department of Renewable Energy Engineering, School of
Renewable Energy, Maejo University Chiang Mai 50290, Thailand 3Assistant Professor, Faculty of Science,

Maejo University Chiang Mai 50290, Thailand.
*Phone: 091-479-7152, Fax: 053-875-599, E-mail: [email protected]

ABSTRACT
The air pollution caused by burning agricultural waste has been a major problem in the countryside for a
long time, because it is considered to be the source of harmful fine particles such as PM 2.5. Biogas system
is an attractive technology which converts waste to energy to be used as fuel. The objective of this research
was to study the biomethane potential from four different kinds of maize residue component, consisting of
the stalks, leaves, husks, and cobs, fermented with pig manure sludge. Biogas production experiment was
conducted in glass bottles (1,000 ml) with 400 ml working volume. Then, 6 g VS L−1 of the inoculum was
added in the bottles with substrate to inoculum ratio of 70:30 g VS L−1 (S/I ratio 2.3). All experiments were
done under mesophilic condition (35 ± 2˚C) with 40 days retention time and all of them were replicated three
times. The VS/TS ratios of stalk, leaves, husks and cobs, indicated bio-digestibility of the digesters, which
reached high value of 0.875, 0.884, 0.940 and 0.940, respectively.The highest cumulative biogas productions
from leaves, husks, cobs and stalks were of 8,550, 6,615, 5,882 and 753 ml, accordingly. In addition, the
highest daily biogas yield from the substrates such as leaves, husks, cobs and stalks were 214, 165 and 147
and 19 ml/day, respectively. The average of methane yield of stalk, leaves, barks and cobs in 40 days were
0.0004, 0.3075, 0.2373 and 0.1985 m3/kg VSadded, and the average of mathane percentage were 11.7, 50.66,
47.11 and 44.97 respectively. This research found that the maize leaves have the highest cumulative biogas
productionof 8,550 ml the daily biogas yield 214 ml/day and the highest methane percentage of 50.66.
Therefore, the leaves have the highest potential for biogas production.

Keywords : biomethane potential; maize residue; biogas production; agricultural wastes

INTRODUCTION
Energy has become an essential element in our daily life, howover, this energy is depleting due to

overconsumption by human. Therefore, the alternative energy is investigated such as solar energy, wind

energy and biogas production [1]. The biogas production is the most attractive alternative energy due to its
continuous production. Biogas production in Thailand could be produced from wastewater from industrial
plants, agricultural waste, animal farms and agricultural wastes, etc. Agricultural waste materials such as
wood waste, grass, bagasse, rice husk, corn husk and corn cobs, could be used as the source of raw materials

for biogas production [1]. Those agricultural wastes, especially corn cobs or maize residues, have the

potential to transform into an energy source in terms of quantity and quality. In the past, farmers have always
used a burning method to eliminate them because it is easier and faster than other methods, however, at the
present, burning is concerned as it the main cause of pm 2.5 problems in Thailand.The method that is
commonly used to produce biogas is biological process via anaerobic fermentation. The anaerobic
fermentation consists of four steps [1] which are hydrolysis, acidogenesis, acetogenesis and methanogenesis,
respectively. In hydrolysis, a large molecule (carbohydrate, protein and lipid) are broken down into small
molecules (sugar, amino acid and fatty acid). The second step is acidogenesis, where small molecule are
further converted to organic acid, for example, acetic acid, propionic acid, butyric acid, valeric acid and
lactic acid. Most organic acids are fermented and acetic acid is the main product under acetogenesis. Finally,
the methane gas (CH4) is produced under methanogenesis step by methanogens [2].

Therefore, the objective of this research is to study the biomethane potential from four different kinds
of maize residue components consisting of the stalks, leaves, husks, and cobs for investigate proper condition
of biogas production from maize residue and to generate useful results for better alternative solutions to the
farmers in eliminating maize residues instead of burning and as a means to reduce energy costs.

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METHODOLOGY

1. Inoculum and Substrate Preparation

The inoculum in this study was pig manure sludge [3] in anaerobic digester tank, collected from a biogas
plant in Chiang Mai, Thailand. The characteristics of inoculum was shown in Table 1.

Table 1. The characteristics of inoculum. Unit Value
mg/l 75,702
Characteristic mg/l 56,937
Total Solid (TS)
Volatile Solid (VS)

In this study, substrates were four different kinds of maize residues such as stalks, leaves, husks and cobs.

They were grinded with a blender at 14,000 rpm for 15 minutes into small pieces about 1-5 mm in length [4].

After that the biomass was pre-acidified at ambient temperature for 72 hours to enhance hydrolysis,
acidogenesis and subsequently methanogenesis [2]. It was kept at below 4 ˚C before use. The characteristics

of the studied maize residues were shown in Table 2.

A.Corn Stalks B. Corn Leaves

C. Corn husks D. Corn Cobs

Figure 1. The Maize residues (A.corn stalks, B.corn leaves C.corn husks and D.corn cobs)
after pre-acidify.

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Table 2. The characteristics of maize residues used in the experiment.

Maize residues

Stalks Leaves Husks Cobs

Proximate Analysis 99,698 273,627 269,997 380,621
TS (mg/L) 87,270 241,976 253,748 357,613
VS (mg/L) 0.875 0.884 0.940 0.940
VS/TS ratios 9.83 12.26 11.02 9.24
MC (%wt.) 72.92 67.23 73.72 73.16
VM (%wt.) 15.77 11.89 13.90 16.04
FC (%wt.) 1.48 8.62 1.36 1.57
Ash (%wt.)
Ultimate Analysis 42.827 37.843 41.850 43.163
C (%wt.) 5.917 5.655 6.053 6.033
H (%wt.) 0.340 1.473 0.218 0.523
N (%wt.) ˂0.01 0.083 ˂0.01 ˂0.01
S (%wt.) 43.654 41.365 45.566 44.620
O (%wt.) 3,366 2,981 3,356
HHV (kcal/kg) 3,244

2. Biomethane Potential (BMP Test)

For this part, the setting up of BMP test was shown as figure 2 and described as the following. Initially,
four different kinds of maize residues components (stalks, leaves, husks and cobs) were added in glass
bottles (1,000 ml) with 400 ml of working volume. After that, 6 g VS L−1 of the inoculum was added in
the bottles [5] with the ratio substrate to inoculum of 70:30 g VS L−1 (S/I ratio 2.3) and then nutrient
solution was mixed into experimental bottles until total volume reached 400 ml. Next, the alkalinity of the
samples was adjusted to pH value of approximately 7.00 with sodium bicarbonate (NaHCO3). Finally, the
residual oxygen was removed by the addition of nitrogen gas (N2) [4,5]. Then it was sealed with a septum
and a rubber cap. All experiments were done under mesophilic condition (35 ± 2˚C) [5] for 40 days
retention time and all of them were replicated for three times.

Figure 2. Biomethane Potential (BMP Test)

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3. Analytical Methods
The total solids (TS), volatile solids (VS), COD and total alkalinity of the maize residues and inoculum were
measured according to the APHA standard methods [6]. All parameter analysis was triplicated to ensure the
precision.

Quantity of biogas productions was determined via measurement of gas pressure using a Digital
Manometer (DM9200) and Biogas analyzer (Gas data version GFM406) was used to measure for gas
compositions.

RESULT AND DISCUSSION

1. The characteristics of substrate and inoculum
Characteristics of maize residues were determined by using proximate and ultimate analysis. The results
were shown in table 2, the VS/TS ratios of stalks, leaves, husks and cobs indicated the bio-digestibility of
each digester bottles that had high value of 0.875, 0.884, 0.940 and 0.940, respectively. Basically,
a substrate with VS/TS ratio over 0.80 was considered as the potential feedstock for anaerobic digestion [7].
The ultimate analysis showed percentage of chemical elemental compositions. Carbon (C) amount is major
elemental composition that showed potential of substrate . The percentage of carbon composition in the
stalks , leaves, husks and cobs were 42.827, 37.843, 41.850 and 43.163, respectively. All values were in the
range of 37.80 - 45.00 %. Solid waste which had a high percentage of carbon validated sufficient potential to
be substrate in anaerobic digestion process. Hence, the proximate and ultimate result analysis of the maize
residues guarantees that all four different kinds of maize residues could be used as the substrate in biogas
production [8].
The important operating parameter such as COD, TS, and VS removal efficiency were study to indicate the
reactor performance and were shown in the table 3, The final VS removal efficiency of stalks, leaves, husks
and cobs were 26.60%, 87.59%, 86.77% and 86.43% respectively. The initial pH ranges of 5.80 to 7.10 as
observed in Table 3 also suggested the better performance of anaerobic digestion of maize residues and pig
manure sludge in biogas production [9], the final pH ranges of 4.71 to 6.45 also reported that
methanogenesis in anaerobic digester occured efficiency at pH 6.5 – 8.2 while hydrolysis and acidogenesis
had high efficiency for operating at pH 5.5 and 6.5 [9,10]. So, the appropriate value of pH ranges of
substrates enhanced biogas production.

Table 3. Biogas and Methane Yields of Different Substrates in Biomethane Potential Assays.

Maize residues

Parameter Stalks Leaves Husks Cobs
0.0873 0.4262
Biogas yield (m3/kg VSadded) 0.0004 0.6195 0.4793 0.1985
Methane yield (m3/kg VSadded) 11.70 44.97
CH4 (%) 5.84 0.3075 0.2373
Initial pH 4.71 6.73
Final pH 26.60 50.66 47.11 6.17
VS removal (%) 57.35 86.43
COD removal (%) 6.23 7.07 67.98

6.07 6.45

87.59 86.77

85.16 75.17

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2. Biomethane potential
Cumulative biogas production and methane percentage of BMP test from different kinds of maize residue
components fermented for 40 days were shown in Figure 3 (A) and (B), respectively. The highest cumulative
biogas productions from leaves, husks, cobs and stalks are of 8,550, 6,615, 5,882 and 753 ml, respectively.
All the data increased exponentially in the initial phase of anaerobic digestion, which corresponded to the
hydrolysis, acidogenesis and acetogenesis stages that converted rapidly to biodegradable organic matter,
namely acetic acid [11] and then methanogens created methane from the final products of acetogenesis as
well as from some of the intermediate products from hydrolysis and acidogenesis in the final stage of
anaerobic digestion or Methanogenesis. However, when the experiment exceeded 30 days, the reaction rates
started to be stable as the easily degradable components depleted [1]. In addition, the highest daily biogas
yield from the substrates such as leaves, husks, cobs and stalks were 214, 165 and 147 and 19 ml/day,
respectively. In which the daily biogas yields correlate positively to the cumulative biogas productions [12].

(A)

(B)
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(C)
Figure 3. Cumulative biogas production (A) Methane percentage (B) and Dally biogas yields

(C) of maize residue components in biomethane potential test.
The average of methane yield from maize residues such as stalks, leaves, husks and cobs in 40 days were
0.0004, 0.3075, 0.2373, and 0.1985 m3/kg VSadded, respectively. The average methane percentages in 40 days
were 11.7, 50.66, 47.11 and 44.97 respectively. Due to the differences in chemical composition, the stalks,
leaves, husks and cobs of maize residue, resulted in highly different performance in the anaerobic digestion
via biomethane potential [12]. The maize leaves had the highest methane yield and methane percentage, but
the maize stalk had lowest cumulative biogas production, methane yield and methane percentage [11],
because stalks had higher lignin and cellulose content, they degraded slower than the leaves, husks and cobs.
The leaves had the highest degradation rate because of its high content of soluble components and
hemicellulose.
CONCLUSION
This research work presented the biomethane potential from four different kinds of maize residue
components such as stalks, leaves, husks and cobs, with substrate to inoculum ratio of 70:30 gVS L−1.This
research found that the maize leaves had the highest cumulative biogas production of 8,550 ml, the daily
biogas yield of 214 ml/day and highest methane percentage of 50.66. Therefore, the leaves have the highest
potential for biogas production.
ACKNOWLEDGEMENT
The authors would like to gratefully acknowledge the following supporters of this research; School of
Renewable Energy at Maejo University, Graduate School at Maejo University and National Research
Council of Thailand (NRCT). We likewise greatly appreciate the critical and constructive comments from
the anonymous reviewers, which have helped improve this manuscript.

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REFERENCES
[1] Chanokphon Wan. 2015. Biogas production from combined fermentation of pretreatment corn plants

together with glycerol waste. National Academic Conference, Kasetsart University. The 9th
Kamphaengsaen Campus 1837.
[2] Chen, C., Zheng, D., Liu, Deng, L and et. 2015. Continuous Dry Fermentation of Swine Manure for Biogas
Production. Waste Management. 38: 436-442.
[3] Feodorov, V. Modern Technol.ogies of Treatment and Stabilization for Sewage Sludge from Water
Treatment Plant. Agric. Agric. Sci. Procedia 2016, 10, 417–430.
[4] Jameson Filer, Huihuang H. Ding and et. 2019.Biochemical Methane Potential (BMP) Assay Method for
Anaerobic Digestion Research. School of Engineering, University of Guelph, 50 Stone Road E., Guelph,
ON N1G 2W1, Canada. Water 2019, 11, 921.
[5] Wanqin, Z., Quanyuan, W. and et.,2014. Batch Anaerobic Co-Digestion of Pig Manure with
Dewatered Sewage Sludge Under Mesophilic Conditions. Applied Energy. 128, 175–183.
[6] American Public Health Association, American Water WorksAssociation, and Water Environment
Federation: Standard Methods for the Examination of Water and Wastewater,23th ed.;:Washingtion,
DC, 2017.
[7] Yu, H., and Fang, P. 2002. Acidogenesis Of Dairy Wastewater at Various pH Levels. Water Science and
Technology. 45 (10), 201–206.
[8] Yeqing Li et. Al. 2013 .Evaluating Methane Production from Anaerobic Mono- and Codigestion of
Kitchen Waste, Corn Stover, and Chicken Manure. Energy & Fuels.
[9] Akindele OKEWALE, Kamoru BABAYEMI, Olusola ADESINA. 2018. Biogas Production from
Anaerobic Co–Digestion of Corn Cobs, Pig and Poultry Droppings. ABUAD Journal of Engineering
Research and Development (AJERD) ISSN: 2645-2685. Volume 1, Issue 2, 273-282.
[10] Eze J. I.1,2* and Ojike O.1, 2012.Anaerobic production of biogas from maize wastes.
International.Journal of the Physical Sciences Vol. 7(6), pp. 982 - 987, DOI: 10.5897/IJPS11.1519.
Academic Journals. ISSN 1992 – 1950.
[11] E. K. Tetteh4 and et. 2017. Biochemical Methane Potential (BMP) of Miscanthus Fuscus for
Anaerobic Digestion. International Journal of Scientific and Research Publications, Volume 7, Issue
12, December 2017 .ISSN 2250-3153.
[12] Haipeng Xu a,b 2019. Methane production from the anaerobic digestion of substrates from corn Stover
Differences between the stem bark, stem pith, and leaves. Science of the Total Environment 694
133641.

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I 057

Development of Proactive and Holistic Air Quality Management
Approach for Factories

Sudjit Karuchit1*, Nares Chuersuwan2, Nirun Kongritti3, and Tananchai Wannasook4

1*Assistant Professor, School of Environmental Engineering, Institute of Engineering, Suranaree University of
Technology, Nakhon Ratchasima 30000, Thailand; 2Associate Professor, School of Environmental Health,

Institute of Public Health, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand; 3Assistant
Professor, Environmental Science Program, Faculty of Science and Technology, Nakhon Ratchasima Rajabhat

University, Nakhon Ratchasima 30000, Thailand; 4Director, Region Environmental Office 16,
Ministry of Natural Resources and Environment, Songkhla 90000, Thailand
*Phone : 044224456, Fax : 044224451, E-mail : [email protected]

ABSTRACT
This paper’s goal is to present the proactive and holistic air quality management (PHAM) approach and its
implementation results from 3 small- and medium-sized factory case studies. The approach consists of 7
steps: 1) survey and data collection, 2) questionnaire survey in communities, 3) emission inventory
development, 4) pollution monitoring, 5) air quality modeling, 6) health risk assessment, and 7) air quality
management plan. Each step can be applied according to the circumstance of the individual factory. The
approach developed and the case study results are useful for factories interested in adopting the approach to
achieve the goal of good environmental practice and sustainable co-existence with communities.

Keywords: air pollution; air quality management; proactive; holistic; factory

INTRODUCTION
Industrial air pollution is a major environmental problem that is widespread and still ongoing. Particulate
matters, gaseous pollutants, and odor emitted from the factories’ operations often cause health effects and
bring down the quality of life of the people living nearby. Part of the reason is that the mindset of the factory
owners is limited to the “end-of-pipe” treatment – only focus on the stack discharge and do nothing more.

This mindset is no longer sufficient or appropriate. Instead, air quality management of a factory should be
proactive – it should not only conform to the law but also do more to protect the people and the environment.
It should also be holistic – consider all inputs, outputs, and emissions across the boundary, the manufacturing
processes, and the interaction with surrounding communities and the environment (Fig.1). These concepts
are in line with the “Green Industry” concept, which states that the industry should have sustainable and
environmental-friendly development and corporate responsibility [1].

This paper presents the results of a research project which attempted to bring the concepts into real
practice [2]. The objectives of this paper are to present (1) the proactive and holistic air quality management
(PHAM) approach for small- and medium-sized factories, and (2) partial results of the implementation of the
approach with 3 case studies.

METHODOLOGY
There were 2 major parts of the research. The first part was the development of the PHAM approach. At the
beginning of the research activities, a draft approach was developed based on literature reviews and
discussions among the research team. This draft approach was then used with the 3 case studies in the second
part of the research. Once the implementation was finished, the approach was evaluated and the final version
of the PHAM approach was concluded.

The second part was the implementation of the approach with case studies. It began by creating a shortlist of
potential factories based on factory type and size, air pollutants emitted, and recommendations from local
environmental officials. Subsequently, more specific criteria were used and factory visits were carried out.
Three factories in Muang district of Nakhon Ratchasima province, Thailand, were finally selected to
participate in the project. Factory 1 is a medium-sized tapioca starch factory, Factory 2 is a medium-sized

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particle board furniture factory, and Factory 3 is a small-sized animal feed factory. The research team then
formed a working team with the environmental staffs of each factory and carried out the implementation of
the PHAM approach.

Figure 1 Holistic Air Quality Management Diagram for Factories

RESULTS AND DISCUSSIONS
The PHAM Approach
This section explains the summary of the PHAM approach developed in the research. The final PHAM approach
has 7 steps: (1) field survey and data collection, (2) questionnaire survey of nearby communities, (3) emission
inventory, (4) pollution monitoring, (5) air quality modeling, (6) health risk assessment, and (7) air quality
management plan (Fig.2).
At the beginning, the factory which is going to implement the approach should form a “working team”
consists of environmental staffs and other related staffs, e.g. from production and occupational health and
safety department. In the first step of the approach, the team starts by collecting secondary data such as
factory maps, details of buildings, production processes, environmental management practice, air pollution
control systems, and environmental monitoring data. A factory survey and staff interviews are also necessary
for understanding the overall condition of the existing factory operation and related problems.
The second step of the approach involves a study of the surrounding communities. The working team should
go out and visit the nearby residential area to see the existing condition and to collect relevant data. Data
gathering can be done via questionnaire surveys, interviews, or environmental samplings. By actively
looking for air pollution problems experienced by the communities and their opinions toward the factory,
valuable information can be obtained and used in subsequent steps.
In the third step, an emission inventory of the factory should be developed. Emission sources can be
categorized into 4 types: point source, line source, area source, and fugitive source. The detail of the
emission sources should include source type, number of sources, pollutant type, physical property (e.g. area,
length, stack height and diameter), and relevant data such as production rate, fuel type, fuel consumption
rate, etc. For small- and medium-sized factories, the amount of emission or emission rate can generally be
estimated from 2 approaches: (1) use appropriate emission factors with activity data, or (2) use concentration
values and effluent gas flow rates obtained from available stack sampling results.

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Air pollution monitoring in the fourth step requires air sampling devices and corresponding technical skills.
Therefore, this step is usually performed by hiring environmental service companies. A sampling of air
quality can be categorized into 3 types: (1) stack sampling, (2) ambient sampling, and (3) personal sampling.
The working team should consider the necessity of air samplings by taking into account the degree of the
problems, the community’s opinion, and the available budget. Details of the sampling including the type of
pollutants, sampling location, and sampling frequency should also be carefully considered.

Figure 2 The PHAM Approach

The fifth and sixth steps of the PHAM approach are air quality modeling and health risk assessment,
respectively. Compared to the preceding first 4 steps, these two are more advance and less likely to be
necessary for small factories. However, medium and large factories have the potential to perform these steps
and show their environmental commitment to the surrounding communities.

Two commonly accepted air quality models, SCREEN3 and AERMOD, can be used for prediction of
ground-level air pollution concentration around the factory area. The SCREEN3 air quality model – a
freeware that requires minimal input data – can be used for estimating worst-case scenarios and to find
possible maximum ground-level concentrations and their coordinates. Factory’s staffs with an environmental
science background can learn to use the model from its manual and available tutorial resources. The
AERMOD model, on the other hand, is more complex and can only be used by trained technical staffs, e.g.
from consulting companies or academic institutes. It is usually not necessary unless an in-depth investigation
of the environmental impact on the surrounding area is needed, or the factory management intend to use the
model as a tool for advance planning [3], [4].

Health risk assessment consists of 4 steps: (1) hazard identification, (2) dose-response relationship, (3)
exposure assessment, and (4) risk characterization. Again, the working team needs to evaluate the necessity
of this step by taking into account the findings from previous steps. For example, if there are complaints
from people living in the area about health problems related to air pollution, and the monitoring or modeling
indicates a significant level of corresponding air pollutant concentrations, the risk assessment should be
carried out. If a factory has staffs with environmental health background, a full-scale health risk assessment
can be undertaken. Otherwise, the assessment can be done in a simplified approach – just for screening
purposes. On the other hand, small factories with minimal air pollution impact can safely skip this step.

For the final step, the development of air quality management plan, all air pollution aspects found from the
analysis in steps 3-6 should be identified and prioritized based on their significance and urgency. The plan
for the aspects can also be divided into a different period, e.g. short-, medium-, and long-term plans. For

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each problem, the working team should examine all available options and select the optimum ones based on
environmental, technical, and economical consideration.
Implementation of PHAM Approach – Case Studies
This section explains partial results of the case study implementation – the full details can be found in the
original research report [2]. For all 3 factories, the first 4 steps were performed since they were essential to
air pollution management. For each factory, relevant data were gathered from staff interviews, documents,
and field surveys in step 1, and a questionnaire survey was carried out in the nearby area in step 2. Fig.3
shows examples from Factory 2 such as a map of the factory and surrounding residences, its particle-board
furniture production process, the cyclone air pollution control system, and an interview of a resident by a
working team staff.

Figure 3 Examples of Step 1 and 2 From Factory 2 (a = aerial map of the factory with markings of the
questionnaire respondents around 1 km. radius, b = production processes, c = air pollution control
devices, and d = resident interview)

It is worth noting that, in the case of Factory 1, the majority of the 35 respondents who lived in the 1 km.
radius of the factory indicated that there was frequent, medium- to high-level dust problems during the day-
time. The odor problem was also significant and usually occurred after the rain. The overall opinion score of
the respondents toward the factory was 2.9 out of 5, which reflects a certain level of disapproving viewpoint.
The survey and interview of the other 2 factories, though, shown no major concern of industrial air pollution
problem.

Next, an emission inventory was compiled for each factory, which consisted of air pollution sources and
types, pollutant types, and emission rates. An example of a partial inventory of Factory 1 is shown in Table
1. There were 15 individual air pollution sources listed in the inventory but only the ones with significance
rating “high” and “medium” were considered in later steps. Subsequently, the types of air sampling were
considered and selected to perform. Only Factory 1 had all 3 types of sampling – source, ambient, and
personal – while the other two had only ambient and personal sampling. Fig.4 and Fig.5 show the ambient
sampling within the factory area and the personal sampling of workers of the 3 factories, respectively. For all
case studies, some monitoring data were from the factories' regular monitoring and some were primary data
of this research.

In the air quality modeling steps, different methods were selected for use with each factory on the basis of
the findings from the first 4 steps. Since Factory 1 has various point- and area-sources and multiple
pollutants, and the community had shown concern for dust and odor problems, so the AERMOD model was
considered necessary. Factory 2 has only one pollutant of concern and no apparent complaint from the

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public, so the SCREEN3 model was deemed adequate. Factory 3 did not require modeling work because its
operation had low emission and impact.

Table 1 Partial Emission Inventory of Factory 1

Source Type Pollutant Significance
Stack 1-3 (Boiler) Point TSP, NO2, SO2 High
Stack 4-5 (Hot Air Oven) Point High
Stack 6-7 (Generator) Point TSP
Stack 8 (Flare) Point TSP, NO2, SO2 Medium
Internal Roads Line Low
Raw Material Receiving Area 1-2 Area NO2, SO2 Low
Stockpile Yard Area TSP Low
Wastewater Pond 1-3 Area High
TSP, Odor High
TSP

H2S, CH4, Odor

Figure 4 Examples of Ambient Sampling (a = stockpile area of Factory 1, b = working yard of Factory
2, and c = entrance area of Factory 3)

Figure 5 Examples of Personal Sampling of Workers (a = starch packing staff at Factory 1, b =
particle board cutting staff at Factory 2, and c = operating staff at Factory 3)

Fig.6 shows examples of the air quality modeling step for Factory 1. The study area defined in AERMOD
was 3 × 3 km2, with 3 receptor locations: 1) in the factory area, 2) at the factory’s fence, and 3) at a nearby
residential area (Fig.6 a). The model was used first to estimate present air pollutant levels at the 3 locations,
and subsequently to predict the new levels as a result of air quality management options in the final step. The
model’s isopleth map of the existing 24-hr average concentration of SO2 showed that most high

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concentration areas are within the factory’s boundary (Fig.6 b). As for Factory 2, the SCREEN3 model
estimated that the maximum 24-hr average and 1-yr average values of total suspended particulate (TSP)
concentration were many times lower than the corresponding ambient standards.

Based on the modeling results and other findings, only Factory 1 was deemed necessary for the health risk
assessment in the sixth step. The risk from exposure to dust via the inhalation route was considered, and the
Hazard Quotient (HQ) was used for characterizing the risk. A simplified process of risk assessment was
carried out and the results suggested that the outdoor workers had significant risk (HQ = 1.1) while indoor
workers and people living near the factory did not (Table 2).

Figure 6 Air Quality Modeling using AERMOD for Factory 1 (a = factory boundary and locations of 3
receptors, b = concentration isopleth of 24-hr average SO2 concentration levels)

Table 2 Risk Characterization of Dust Exposure in Factory 1

Group Concentration Concentration Standards Hazard
Estimation Method (microgram/m3) (microgram/m3) Quotient
People living near the factory
(Receptor 3 location) Sampling 17.83 120 0.15
Outdoor workers (Receptor 1 Modeling 1.41 120 0.01
location) Sampling 133.41 120 1.11
Indoor workers Modeling 60.74 120 0.51
Sampling 0.27 5 0.05

In the final step, all results from earlier steps were used to formulate the air quality management plan for
each factory. For Factory 1, the ambient dust and SO2 levels at the receptors were considered significant and
high priority issues. The improvement can be done at the tapioca stockpile area, the starch drying process,
and the hot-air generators. Options for improvement at the tapioca stockpile area were: (1) spraying water at
the stockpiles – using full-cone spray nozzles to regularly spray water to the stockpiles; (2) surrounding the
stockpile area with tree walls – planting 3-5 meter-high trees surrounding the area in 3 layers; and (3)
organizing the stockpiles in a specific pattern – an arrangement in such pattern as flat-topped oval piles.
Options for improvement at the starch drying process were adding control devices: a cyclone system or a bag
filter system. Options for improvement of the hot-air generators were installment of one, two, or three
economizers to the hot-air generators to reduce fuel used and SO2 emission.

Consequently, the suitability of the options was evaluated from the technical, economical, and environmental
standpoints. Based on the evaluation results, the most appropriate option for reducing dust levels was the
installment of a bag filter system as a control device at the starch drying process, with the highest total score.
As for SO2 reduction, the best option was to install economizers at all 3 hot-air generators, with the highest

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total score. The estimation by AERMOD showed that such improvement can reduce ambient dust and SO2
concentration levels at the receptors up to 18% and 30%, respectively [3], [4].

For Factory 2 and 3, since prior findings did not point toward any substantial or pressing air pollution issues,
only guidelines for their air quality management practice were developed. They covered 5 main areas: (1)
good housekeeping practice, (2) storage, transfer, and transport of raw materials and products, (3)
manufacturing process and pollution control systems, (4) staff training, and (5) building a good relationship
with the community.

CONCLUSION
The proactive and holistic air quality management (PHAM) approach has been developed and tested by
implementation with 3 case-study factories. The final PHAM approach has 7 steps: (1) field survey and data
collection, (2) questionnaire survey of nearby communities, (3) emission inventory, (4) pollution monitoring, (5)
air quality modeling, (6) health risk assessment, and (7) air quality management plan. The first 2 steps are for
gathering existing data from within the factory’s boundary and from the surrounding communities. This
information is the basis for planning and undertaking step 3 through 6, and the corresponding results are used for
developing the plan in the final step. In each step, the working team needs to consider appropriate actions to
take according to the circumstance of the individual factory. The approach can be widely adopted and is not
dependent on the readiness of existing data.

The results of this research can be beneficial to other factories interested in adopting the approach to achieve
the goal of good air quality management practice. Successful environmental management of a factory leads
to sustainable co-existence with its surrounding communities. However, the achievement depends greatly on
the mindset of the factory’s owner and the determination of the environmental staffs.

ACKNOWLEDGEMENT
This work was funded by Suranaree University of Technology.

REFERENCE
[1] Industrial Environment Technology Promotion Division. 2019. Green Industry Manual. Department of

Industrial Work, Ministry of Industry.
[2] Karuchit, S., Chuersuwan, N., Kongritti, N., and Wannasook, T. 2018. Air Quality Management

Approach for Small and Medium Factories based on Green Industry Concept. Final Report, Suranaree
University of Technology.
[3] Sukkasem, P., Karuchit, S. and Chuersuwan, N. 2015. Air Quality Model as a Management Tool: Case
Study of a Starch Factory in Thailand. The 4th International Symposium on Engineering, Energy and
Environments, Thammasat University, Pattaya Campus, Thailand, 8-10 November 2015.
[4] Karuchit, S. and Sukkasem, P. 2018. Application of AERMOD model with clean technology
principles for industrial air pollution reduction. The 3rd International Conference on Engineering
Science and Innovative Technology (ESIT2018), Phang-Nga, Thailand, April 19 – 22, 2018.

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I 058

Adsorption of Mercury by Metal-Organic Frameworks
in Saline Phase

Korfun Borisutsawad1*, Chalita Ratanatawanate2 and Patiparn Punyapalakul3,4,5

1Graduate student, International Postgraduate Program in Hazardous Substance and Environmental
Management (IP-HSM), Graduated School, Chulalongkorn University, Bangkok 10330, Thailand;
2Researcher, National Nanotechnology Center (NANOTEC), National Science and Technology
Development Agency, Pathum Thani, Thailand;
3 Center of Excellence on Hazardous Substance Management (HSM),
Chulalongkorn University, Bangkok 10330, Thailand
4Associate Professor, Department of Environmental Engineering, Faculty of Engineering,
Chulalongkorn University, Bangkok, Thailand;
5Research unit Control of Emerging Micropollutants in Environment,
Chulalongkorn University, Bangkok, 10330, Thailand
*Phone : +6686-836-3436, E-mail : [email protected]

ABSTRACT
Mercury contamination in the marine ecosystem causes negative impacts to aquatic organisms and

biodiversity. Thus, adsorption technology has attracted great attention in mercury removal in seawater. In

this study, HKUST-1-SH and ZIF-8-SH, the thiol-functionalized metal-organic frameworks (MOFs) are
investigated for the feasibility of mercury (Hg2+) removal in synthetic seawater. Dithioglycol was used as the

grafting agent to introduce thiol-functionalization onto HKUST-1 and ZIF-8. The structure and property of

HKUST-1-SH and ZIF-8-SH were characterized by the x-ray diffraction (XRD), scanning electron

microscopy (SEM), nitrogen adsorption isotherms and elemental analyzer. The kinetics, isotherms, stability,
and reusability of the Hg2+ adsorption process in seawater were studied. The batch adsorption results of thiol-
functionalized MOFs revealed that the Hg2+ concentration was rapidly declined in the first minute and reach

the equilibrium within 60 minutes when the initial concentration was at 0.1 mg/L, and HKUST-1-SH showed

higher adsorption rate. The kinetic data were fitted with the pseudo-second-order model; moreover, the

isothermal data were best fitted with the Langmuir model. It indicates that the adsorption process and mode
are monolayer and chemisorption. Both HKUST-1-SH and ZIF-8-SH can be reused without Hg2+ desorption

and still maintained the adsorption efficiency over 90% after four and three consecutive cycles, respectively.

Although there are the structural changes in both HKUST-1-SH and ZIF-8-SH after using in saline, but these

thiol-functionalized MOFs still exhibited excellent performance for mercury removal due to the remaining

sulfur followed the Pearson acid-base concept.

Keywords: adsorption; mercury; metal-organic frameworks; saline; thiol-functionalization

INTRODUCTION
Marine pollution becomes an urgent issue to the worldwide population due to higher demand for

natural resources, resulting in the spread of mercury into the aquatic environment. Mercury, one of the most
toxic heavy metals, can cause bioaccumulation and biomagnification along food chains. Therefore, various
studies search for the appropriate methods to prevent the dispersion of mercury in freshwater and saline
water. Several techniques have been used for mercury removals such as precipitation, ion exchange,
electrochemical processes. Still, there are unavoidable limitations from these techniques, such as high cost,
low efficiency, sensitivity to environmental conditions, the formation of by-products [1]. Among these
methods, adsorption is widely investigated due to its many advantages compared with other methods such as
simplicity, flexibility, and cost-effectiveness.

Adsorption is a common technique for metal ions removal in aqueous phases, which can be applied
by various kinds of adsorbents; activated carbon, biochar, porous silica, and metal-organic frameworks
(MOFs). MOFs have been developed for mercury adsorption because of its various functionalities and
tunable pore size. Functional groups of MOFs can be modified to increase the mercury selectivity and
removal efficiency by grafting with a thiol group (-SH) on the metal complex node. Ke et al. [2] presented a
novel copper-based MOF called HKUST-1-SH to target mercury in freshwater. The results after adsorption
for 2 hours showed that HKUST-1-SH had removal efficiency over 90% with the initial concentration of

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0.081 mg/L. The maximum adsorption capacity of Hg is up to 714.29 mg/g. However, the adsorption
capacity of Hg, stability, and reusability in the saline phase still needed to be studied. Jian et al. [3] reported
the synthesis of zeolitic imidazolate frameworks (ZIF-8) at room temperature. ZIF-8 is one of the interesting
MOFs because of its high surface area, unimodal micropores, abundant functionalities, exceptional chemical
robustness, and thermally stable stability [3]. However, thiol modified ZIF-8 (ZIF-8-SH) has not been
investigated on the application of Hg adsorption in the saline phase. Various researchers studied the effects
of total dissolved solids (TDS) to adsorption performance, Behjati et al. [4] reported the increase of TDS
concentration had no adverse impact on the Hg (II) adsorption capacity due to the remaining of thiol groups.

In this study, HKUST-1-SH and ZIF-8-SH have been chosen to adsorb Hg (II) at low concentration
to investigated their Hg capacity, stability, and reusability in saline water, with the expectation that thiol-
functionalized MOFs can enhance the adsorption of Hg in the saline water.

METHODOLOGY
1. Synthesis of adsorbents
1.1 HKUST-1-SH

HKUST-1 was first prepared by dissolved 6.05 g of Cu(NO3)2.2.5H2O into 100 mL deionized water,
followed by the addition of a 200 mL solution of 2.73 g of benzene-1,3,5-tricarboxylic acid. The mixture was
stirred for 30 minutes. The blue suspension was taken to wash with methanol for 24 hours at room
temperature and then dried overnight at 70°C. To prepare the thiol functionalized HKUST-1, 1 g of HKUST-
1 was added into 90 mL of ethanol with 10 mL of dithioglycol and stirred for 24 hours at room temperature.
The result of green powder was washed with ethanol several time and then dried overnight at 70°C.
1.2 ZIF-8-SH

ZIF-8 was first prepared by dissolved 6.585 g of Zn(OAc)2 in 300 mL of deionized water and mixed
with 49.2 g of 2-methylimidazole and 300 mL deionized water. Then stirred for 1 hour and separated the
precipitate by centrifugation and wash with deionized water for 3 times. The obtained products were dried
overnight at 70°C. To functionalized the thiol group to ZIF-8, 1 g of ZIF-8 was added into 90 mL of ethanol
followed by 10 mL of dithioglycol and stirred for 24 hours at room temperature. The precipitation was
separated by centrifugation and washed with ethanol 3 times. Then ZIF-8-SH is obtained after dried at room
temperature.
2. Characterization of adsorbents

The adsorbents were characterized by powder X-ray diffraction (XRD), the morphology and particle
size of MOFs were examined by a field emission scanning electron microscope (FESEM) and Brunauer–
Emmett–Teller (BET) surface area obtained from nitrogen adsorption isotherms analysis. The ratio of -SH of
adsorbents was measured by CHNS analyzer. The concentration of Hg (II) was determined by the mercury
analyzer, and the concentration of Cu2+ and Zn2+ were determined by ICP-OES.
3. Adsorption experiments

The adsorption experiments were studied under a batch experiment. The stock solution of Hg (II) was
prepared in 1% HNO3. The synthetic seawater was prepared at 35,000 ppm of salinity. The adsorbent was
separated by filtration through 0.45 µm nylon filter, and the remaining Hg2+ concentration was analyzed by
mercury analyzer. The Hg (II) removal was conducted in the saline phase at 25°C and pH was controlled
between 5-8. pH and ORP were measured during the experiment.
3.1 Adsorption kinetic

Adsorption kinetics was studied for 2 hours by using 200 mg of adsorbents in 200 mL solution at room
temperature, and the initial concentration of mercury was set at 0.1 mg/L.
3.2 Adsorption isotherm

The initial concentration of mercury was varied from 0.6-45 mg/L in 20 mL solution; then samples
were shaken at 300 rpm for 1 hour.
3.3 Stability test

Three sets of 200 mg of adsorbents were shaken in 200 mL of synthetic seawater at 300 rpm for 1 hour.
Then, Cu2+ and Zn2+ released were measured by ICP-OES, and adsorbents properties were investigated by
XRD, SEM-EDS, and BET surface area.
3.4 Reusability

In order to verify the reusability of HKUST-1-SH and ZIF-8-SH, the adsorption procedures were
retaken without using any eluents to desorb Hg from used adsorbents. Four and three cycles of Hg adsorption
were repeated for HKUST-1-SH and ZIF-8-SH, respectively.

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RESULTS AND DISCUSSIONS
1. Characterization
1.1 XRD measurements

The preparation of dithioglycol grafted HKUST-1 and ZIF-8 samples were confirmed by X-ray
diffraction, as shown in figure 1. All of the diffraction peaks of both after the modification with dithioglycol
are well-matched with their pristine. It can be seen that HKUST-1-SH and ZIF-8-SH exhibited main
diffraction peaks at 2θ values of 7.9°, 10.7° and 12.7° and 7.4°, 12.7° and 18.0°, respectively. As a result, all
functionalized HKUST-1 and ZIF-8 by thiol group are well-developed. However, the intensity of the
diffraction peaks of both HKUST-SH and ZIF-8-SH were reduced comparing with the pristine MOFs, which
might be caused by the grafting of dithiogylcol at the metal complex node.

a) b)

ZIF-8

Figure 1 XRD patterns of (a) HKUST-1-SH compare to HKUST-1 and (b) ZIF-8-SH compare to ZIF-8

1.2 Porosity by nitrogen adsorption isotherms
The physical properties of the samples are listed in table 1. All of the surface area and pore volumes

of the synthesized HKUST-1-SH and ZIF-8-SH have a significant loss compare to their pristine MOFs
except the pore volume of HKUST-1-SH which is slightly increased (1185, 1492 and 1571 m2/g and 0.76,
0.75 and 0.60 cm3/g, respectively [1],[5],[6]. Therefore, the addition of the thiol group affects the surface
area, pore-volume, and pore size. Especially for the porosity of MOFs, dithiogylcol is supposed to interact at
the metal node of Cu and Zn of HKUST-1 and ZIF-8, respectively. Hence the increase of metal complex
node size can be expected and resulted in the enlargement of pore size from micro-scale to meso-scale
diameters.

Table 1 The surface area, pore volume and pore size of HKUST-1-SH and ZIF-8-SH by BET analyzer

Adsorbent Surface area Pore volume Pore size
(m2/g) (cm3/g)
HKUST-1-SH 409 0.765 (nm)
ZIF-8-SH 490 0.613 9.2
9.0

1.3 SEM measurements
The surface morphologies of the functionalized samples were characterized by scanning electron

microscopy (SEM) as shown in Figure 2. The SEM images demonstrated the presence of MOFs crystals with
unique particle morphology. In figure 2a, SEM image of HKUST-1-SH presents the rough octahedral shape
due to partial decomposition of the framework from dithioglycol grafting. Whereas, the shape of ZIF-8-SH
particles as shown in figure 2b exhibits the same truncated rhombic dodecahedron as pristine ZIF-8.

a) b)

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a)

Figure 2 SEM image of (a) HKUST-1-SH and (b) SEM image of ZIF-8-SH

1.4 Elemental analysis
The amount of sulfur in the synthesized adsorbents was evaluated by CHNS elemental analyzer to

confirm the thiol-functionalization on the adsorbents, as shown in table 2. Therefore, the -SH loading of
HKUST-1-SH and ZIF-8-SH is 15.9% and 36.81%, respectively. The higher sulfur contents of the adsorbent
will provide a higher adsorption capacity toward Hg2+ ions, followed by the Hard-Soft-Acid-Base theory.

Table 2 Elemental analysis of HKUST-1-SH and ZIF-8-SH by CHNS analyzer

Adsorbents Carbon (C) Percent (%w/w) Sulphur (S)
19.79 Hydrogen (H) Nitrogen (N) 15.92
HKUST-1-SH 18.17 36.81
ZIF-8-SH 3.33 0.00
2.88 2.52

2. Adsorption experiments

2.1 Adsorption kinetic
The effect of contact time on the removal of Hg was studied in the range of 1 second to 2 hours by

using 1 g/L of adsorbent at room temperature. As can be seen in figure 3, the equilibrium achieved within 60
minutes at 80% and 93% for HKUST-1-SH and ZIF-8-SH at the initial concentration of 0.1 mg/L,

suggesting that these thiol-functionalized MOFs have both high adsorption capacity and efficiency for
mercury removal from seawater. The adsorbed Hg (II) was calculated using equation (1), and the rate
constant for pseudo-first-order and pseudo-second-order could be obtained from equation (2) and (3),
respectively. In table 3, the r2 value of 1.00 and 0.9998 suggested the adsorption kinetics were all fitted well
with the pseudo-second-order model; the adsorption rate is controlled by the chemisorption. In
chemisorption, the chemical bonding occurs between adsorbate and substrate reveals that the Hg2+ ions can
bind to the active sites on the surface of the adsorbent.

( 0- e) m (1)

qt qe 1-ek1t (2)
(3)
t 1t
qt k2q2e qe

Where C0 and Ce are concentration (mg/L) at initial and equilibrium, respectively. V (liter) is the volume of
adsorption testing. m (g) is the mass of adsorbent. k1 (1/min) is the rate constant of pseudo-first-order, k2
(g/mg.min) is the rate constant of pseudo-second-order, qe (mg/g) is the amount of Hg2+ adsorbed at
equilibrium and qt (mg/g) is the amount of Hg2+ adsorbed at time t (min).

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0.07

Adsorption capacity (mg/g) 0.06 HKUST-1-SH ZIF-8-SH

0.05

0.04

0.03

0.02

0.01

0
0 30 60 90 120
Time (min)

Figure 3 The effect of reaction time on the adsorption amount of Hg (II) of HKUST-1-SH
and ZIF-8-SH

Table 3 Kinetic parameters for Hg2+ removal of the synthesized adsorbents in sea water

Adsorbents Pseudo first order Pseudo second order
qe k1 r2 qe k2 r2
HKUST-1-SH 0.04 0.09 0.6846 0.42 45.20 1.0000
ZIF-8-SH 0.66 0.77 0.8124 0.05 1244.06 0.9998

2.2 Adsorption isotherm
The adsorption capacity for Hg (II) removal was studied by varying the initial concentration from

0.6 to 45 mg/L by using 0.5 g/L of the adsorbent for 1 hour at room temperature. The results in figure 4 show

Hg adsorption capacity of HKUST-1-SH and ZIF-8-SH compare with PAC; it can be seen that ZIF-8-SH has

the highest adsorption capacity. As in table 4, three isotherms have been investigated, including Linear,
Langmuir, and Freundlich adsorption isotherms by using equation (4) to (6). Based on r2 value presented in
table 4, it was found that HKUST-1-SH and ZIF-8-SH were best fitted with the Langmuir isotherm (r2 =

0.9954 and 0.9843, respectively) indicated that both adsorbents were homogeneous and monolayer formation

at the outer surface.

qe = KpCe (4)
(5)
qe = qm e (6)
1e

qe = KFCe1/n

Where qe is the equilibrium adsorption capacity (mg/g), Qm is the maximum adsorption capacity (mg/g), Ce
is the equilibrium concentration (mg/L), Kp is the linear constant, KL is the Langmuir constant, KF is the
Freundlich constant and n is the adsorption intensity.

Table 4 Isotherm parameters for Hg2+ removal of the synthesized adsorbents in sea water

Adsorbents Linear Langmuir Freundlich
Kp r2 n KF r2
qm KL r2

HKUST-1-SH 85.1 0.9667 18.2 7.13 0.9954 1.89 13.3 0.8371

ZIF-8-SH 907.2 0.8475 94.3 106 0.9843 1.89 445.1 0.9244

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Figure 4 Hg (II) adsorption isotherms of HKUST-1-SH, ZIF-8-SH and PAC

2.3 Stability
2.3.1 Crystalline structure by XRD

From XRD patterns in figure 5, it was clear that all crystalline structures were collapsed due to their
change in the pattern of virgin and after used in saline for 1 hour.

a) b)

Figure 5 The change in crystalline structure after used in sea water of (a) HKUST-1-SH
and (b) ZIF-8-SH.

2.3.2 Porosity by nitrogen adsorption isotherms

Table 5 shows the changes in surface area, pore-volume, and pore size of HKUST-1-SH and ZIF-8-

SH before and after used in saline for 1 hour by BET analyzer. The Brunauer-Emmett-Teller surface area of
HKUST-1-SH after mixing in saline water exhibited a decrease from 409 m2/g to 6.1 m2/g. In comparison,
the surface area of ZIF-8-SH was decreased from 490 m2/g to 167 m2/g imply that both MOFs structure are

definitely changed, and the HKUST-1-SH showed the extremely collapse of the structure.

Table 5 The change in physical properties of HKUST-1-SH and ZIF-8-SH before and after using in
seawater by BET analyzer

Adsorbent Surface area Pore volume Pore size (nm)
(m2/g) (cm3/g)
HKUST-1-SH Virgin Saline
ZIF-8-SH Virgin Saline Virgin Saline 9.2 16.8
9.0 13.3
409 6.1 0.765 0.072

490 167 0.613 0.372

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2.3.3 Morphology and elemental contents by SEM-EDS
The morphologies of HKUST-1-SH and ZIF-8-SH after mixing in seawater for 1 hour were

observed using a scanning electron microscope and energy-dispersive x-ray spectroscopy (SEM-EDS) to
inspect their change in surface appearance and morphological information. In figure 6a, SEM images of used
HKUST-1-SH show the complete change in its shape due to its low salt tolerance contents, as in table 6. As
shown in figure 6b, SEM images of used ZIF-8-SH show a little change in its shape. The elemental contents
measurement by EDS in table 6 is investigated to see the incidence before and after using MOFs in a high
salinity solution. Therefore, sodium and chloride ions in seawater have potential effects on the structural
changes of HKUST-1-SH and ZIF-8-SH.

a) b)

c) d)

Figure 6 Comparison of SEM images for stability test of virgin HKUST-1-SH (a) and ZIF-8-SH (c)
and used HKUST-1-SH (b) and ZIF-8-SH (d).

Table 6 Elemental contents before and after used in seawater of HKUST-1-SH and ZIF-8-SH.

Elemental contents (%) HKUST-1-SH ZIF-8-SH

Sodium (Na) Virgin Saline Virgin Saline
Chloride (Cl) 0.0 17.4 6.5 2.8
Nitrogen (N) 0.0 43.7 0.0
1.4 0.3 34.8 7.5
Sulfur (S) 34.3 20.6 14.3 8.0
Zinc (Zn) 0.0 0.0 13.8
Copper (Cu) 25.0 14.9 0.0 34.1
44.4

0.0

2.3.4 Metal release

The stability test of HKUST-1-SH and ZIF-8-SH after mixing 1 hour in saline water reported the
leakage of Cu2+ from HKUST-1-SH at 84.4 ± 4.7 mg/g, while the leakage of Zn2+ from ZIF-8-SH was only

1.64 ± 0.3 mg/g.

3.4 Reusability
Reuse of HKUST-1-SH and ZIF-8-SH without Hg desorption was done and showed stable

adsorption performances (as figure 7), after four consecutive cycles of HKUST-1-SH, the removal
percentage of Hg were not decreased (initial concentration was 5 mg/L). The HKUST-1-SH adsorbent still

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kept excellent adsorption performance because the Hg/S ratio was still low. Likewise, the reusability of ZIF-
8-SH for a third time also kept the adsorption performance.

120 HKUST-1-SH ZIF-8-SH
100 90.0 89.6
97.8 98.3 94.3 99.25 98.6
80
Removal efficiency (%)
60
40

20

0 2 Cycle 3 4
1

Figure 7 Reusability of HKUST-1-SH after four consecutive cycles and ZIF-8-SH after three cycles

CONCLUSION
HKUST-1-SH and ZIF-8-SH have both high adsorption capacity and high adsorption efficiency for

Hg (II) removal in saline even at low concentrations. Also, these MOFs have excellent reusability, which
might relate to the low Hg/S ratio. But the crystalline structure of HKUST-1-SH was fully collapsed in saline
while ZIF-8-SH had a higher tolerance to saline.

ACKNOWLEDGEMENT
This work was carried out as a part of the research program in “ ontrol of hazardous contaminants

in raw water resources for water scarcity resilience” granted by the enter of Excellence on Hazardous

Substance Management (HSM). Supports from by the National Nanotechnology Center (NANOTEC),

National Science and Technology Development Agency (NSTDA), Ministry of Science and Technology,

Thailand, through its program of Research Network NANOTEC (RNN) are also acknowledged. This work

was also partially supported by Thailand Research Fund (TRF) under the International Research Network:

Functional Porous Materials for Catalysis and Adsorption (No. IRN61W003).

REFERENCES
[1] Bao et al. 2017. Highly effective removal of mercury and lead ions from wastewater by

mercaptoamine-functionalised silica-coated magnetic nano-adsorbents: Behaviours and mechanisms.
Applied Surface Science. 393: 457-466.
[2] Ke et al. 2011. Thiol-functionalization of metal-organic framework by a facile coordination-based
postsynthetic strategy and enhanced removal of Hg2+ from water. Hazardous Materials. 196: 36-43.
[3] Jian et al. 2015. Water-based synthesis of zeolitic imidazolate framework-8 with high morphology
level at room temperature. Royal Society of Chemistry Advances. 5: 48433-48441.
[4] Behjati, M., Baghdadi, M., and Karbassi, A. 2018. Removal of mercury from contaminated saline
wasters using dithiocarbamate functionalized-magnetic nanocomposite. Environmental Management.
213: 66-78.
[5] Piscopo et al. 2016. Positive effect of the fluorine moiety on the oxygen storage capacity of UiO-66
metal–organic frameworks. New Journal of Chemistry 40: 8220-8224.
[6] Li, S., Zhang, Z., and Huang, Y. 2017. Zeolitic imidazolate framework-8 derived nanoporous carbon
as aneffective and recyclable adsorbent for removal of ciprofloxacin antibiotics from water. Hazardous
Materials. 321: 711-719.

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Factors Affecting on Household Willingness to Participate in
Municipal Solid Waste Management in Yangon, Mandalay and

Nay Pyi Taw Cities of Myanmar

Nwe Ni Win1, Ampai Thongteeraparp2, Kobkaew Manomaipiboon1 and Achara Ussawarujikulchai1

1* Faculty of Environment and Resource Studies, Mahidol University, Salaya 73170, Thailand;
2* Department of Statistics, Faculty of Science, Kasetsart University, Pahonyothin 10900, Thailand;

1* Faculty of Environment and Resource Studies, Mahidol University, Salaya 73170, Thailand;
1* Faculty of Environment and Resource Studies, Mahidol University, Salaya 73170, Thailand

Corresponding author: Tel*:082 455 4488, Email: [email protected]

ABSTRACT
Municipal solid waste management is one of the emerging problems in the developing countries and
Myanmar is no exception. Improper waste management is caused by unplanned urbanization, and lack of social
awareness and community involvement and inadequate resources. World Bank estimated that global waste
generation will increase from 1.3 billion tons in 2012 to 2.2 billion tons by 2025. According to IGES 2017
report, Yangon, Mandalay and Nay Pyi Taw cities are responsible for 55% of country’s waste generation. So,
effectively managing municipal solid waste in these cities can solve half of the country’s waste management
problem. The research aims to study municipal solid waste management knowledge of the households in three
cities and the factors affecting on willingness to participate in municipal solid waste management in these three
cities. Questionnaire was applied to assess knowledge, attitude and willingness to participate of the respondents.
Pearson’s correlation, independent sample t-test and Anova were applied for analyzing the factors affecting on
willingness to participate in municipal solid waste management. The study found that the respondents’ overall
knowledge, attitude and willingness to participate on municipal solid waste management were high. Education
and waste tax were found as the socioeconomic factors affecting on willingness to participate in Nay Pyi Taw.
Waste separation habit was found as influencing factor in Yangon and Mandalay. Knowledge and attitude were
the two factors mainly affecting on willingness to participate in municipal solid waste management. Therefore,
knowledge of the respondents on municipal solid waste management should be promoted by sharing step by step
guide for different waste disposal methods.

Keywords: Municipal solid waste, Myanmar, knowledge, willingness to participate, Pearson’s correlation

INTRODUCTION
Solid waste management is one of the essential systems for human society. Although developed countries are
improving waste management technologies, developing countries are still facing problems in waste
management (1). Inadequate solid waste management is caused by the unplanned invasion of the city,
extreme weather conditions, and absence of social awareness/community involvement, inadequate resources
which includes improper equipment and no funds (2). Improper solid waste management can impact to the
environment and human health such as spreading infectious diseases, pollution to land and water, blocking
sewage system and loss of biodiversity (3). Enough rules and regulations, awareness and participation by the
residents, adequate structure are needed to achieve effective waste management system (4) & (5).
Participation of the people is important for successful implementation of solid waste management (6).
People's participation can be achieved through the high knowledge and awareness level. People knowledge is
associated with the environmental responsible behavior (7). Lack of people awareness and education
program about solid waste management are hindrance to perform community-based solid waste management
approach in developing countries (6).

Total population of Myanmar is 51,486,253 in 2014 which is 46% more compared to 1983
census report. Myanmar population is continuously growing since 2.7 million in 1872 to 10.5 million in 1901
and 13.2 million in 1921, 28.9 million in 1973, 35.3 million in 1983 and 51.5 million in 2014 (8). Waste
management in Myanmar is becoming a problem due to its growing population and it is also another problem
for environment as the waste cause pollution to soil, flooding and pollution to freshwater sources. According
to observation, potential environmental and public health impacts related to waste management in Myanmar
are flooding, clogging of sewer system with potential to outbreak of cholera, pollution to surface water and
ecosystem, pollution to drinking water and barrier in irrigation system, creating vector breeding sites and
plastic pollution in ocean water (9). In 2012, World Bank estimated municipal solid waste generation in

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Myanmar was 5,616 tons with per capita generation rate of 0.44 kg/day and the expected per capita
increment of 0.85 kg/day and total waste generation rate of 21,012 tons/day in 2025. Waste composition for
Yangon in 2013 was 76% organic waste, 10% plastic, 4% paper and textile and 10% other wastes which
includes woods, rubber and leather, metal and glass and crockery and stones (10). Estimation for municipal
solid waste management of Myanmar by Netherland Enterprise Agencies, in Myanmar waste scoping
mission report (2017), was waste generation in Myanmar is 20,000 tons/day with 0.8 kg per capita per day
and expected to increase to 1 kg per capita per day in 2025. Waste composition of waste in Myanmar was
estimated as organic material (77%), plastics (13%), paper (7%) and other (3%). The three major cities,
Yangon, Mandalay and Nay Pyi Taw generated 1981 tons/day, 955 tons/day and 160 tons/day, respectively
and it was 55% of the total waste generation in Myanmar (11). So, effectively managing waste in the three
cities can manage half of the total waste of the country. The research aimed to study solid waste management
knowledge of the residents in Yangon, Manadalay and Nay Pyi Taw and to study the factors affecting on
willingness to participate of residents in municipal solid waste management. In Myanmar, a case study for
promoting people participation in solid waste management in Bagan clarified that people participation in
solid waste management is low as a result of lack of knowledge about the people's role and participation in
solid waste management, and about the harmful effect of their waste disposal behavior (11).

METHODOLOGY
The study assessed municipal solid waste management knowledge, attitude and willingness to participate of
residents by questionnaire. The questions included four parts; general information, knowledge, attitude and
willingness to participate. General questions included age, gender, education, occupation, income and waste
related questions such as waste separation in house and type of waste separated, waste collection frequency
and waste tax. Knowledge questions included waste separation at source, greenhouse gas emission (GHG)
and reduction from municipal solid waste management, waste treatment methods which can reduce GHG
emission, different waste treatment methods, waste type suitable for different waste treatment methods,
benefits of different treatment methods, impacts of open dumping and open burning on environment and
human health. The attitude questions included the importance of waste management on environment,
responsibility sharing by residents for waste management, attitude of residents on open burning, open
dumping and burying of residents, waste separation, recycling and composting and time consuming on waste
separation, recycling and composting and residents attitude on participation in municipal solid waste
management. The sample sizes of houses for the city were calculated by Yamane (12) and the sample
households were 100 for each city. The data collected were coded and analyzed by Pearson’s correlation,
independence sample t-test and Anova to find out the factors affecting on willingness to participate in
municipal solid waste management (13).

RESULTS AND DISCUSSIONS
The results for general information were presented by descriptive statistic as shown in table 1.

Table 1 Socioeconomic and waste related data in Yangon, Mandalay and Nay Pyi Taw

Yangon Mandalay Nay Pyi Taw

Variables Mean Mean Mean

Age 35.97 46.66 47.25

Number of family member 4 55

Income (MMK) 699420 383408 322810

Waste collection tax 532 284 202.36

Category Group/Level Yangon Mandalay Nay Pyi Taw

Male 54 29 43

Gender Female 46 71 57

Education Basic education 26 75 65

Higher education 74 25 35

High paid 39 34 45

Occupation Low wage 26 66

Other 35 60 49

Waste Yes 56 64 81

separation No 44 36 19

habit

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Type of waste Wet/dry waste 33 81
separated Recycle waste 23 57 80
No separation 44 35 19
Waste Daily collection 40 35 33
collection time Weekly collection 35 54 60
No service or not 25 11 7
remember time

Knowledge level was examined by ―Yes and No‖ question and coded as ―1 and 0‖. Mean score

of knowledge in Yangon, Mandalay and Nay Pyi Taw was 38.76, 35.3 and 33.16, respectively. For waste
separation at source, ―Yes, No‖ question was constructed and the same question type was also applied for

different waste disposal methods. The results were shown in percentage. For the waste types in different

disposal methods, multiple choice question was applied and total score was 6. Total score for the benefits of

each treatment methods was 4 and the average score is shown in table 2. Open burning and open dumping
impacts were first assessed by ―Yes, No‖ questions and the benefits were then chosen by multiple choice

question. Total score for impacts of open burning and open dumping was 7. Regarding to knowledge of

greenhouse gas (GHG), global warming and GHG emission from municipal solid waste management and
GHG reduction from municipal solid waste management, ―Yes,No‖ question was utilized and the same

question type was applied for GHG reduction from 3Rs, composting and source separation. Knowledge score

for each level is shown in table 2.

Table 2 Knowledge score of respondents on municipal solid waste management in Yangon, Mandalay
and Nay Pyi Taw

Knowledge question Yangon Score Nay Pyi Taw
78% Mandalay 54%
Waste separation at source
Waste treatment methods 64% 31%
Landfill 73% 84%
Composting 85% 52% 50%
Incineration 62% 87% 88%
3 Rs 70% 52% 20%
Anaerobic Digestion 25% 81%
31% 0.84
Anaerobic digestion Waste type for different disposal methods 4.48
Recycling 2.83 1.4 4.29
Composting 3.94 4.45
4.62 4.20 0.60
Anaerobic digestion 2.43
Recycling Benefits for each methods 0.87 2.25
Composting 1.73 2.41
2.45 2.45
2.59

Open burning impacts on human health and environment 98% 98% 93%
Open dumping impacts on human health and environment 100% 99% 98%
Effects of open burning 4.97 5.23 5.02
Effects of open dumping 5.44 5.99 5.98

GHG 44% 45% 37%
Global warming 85% 59% 57%
GHG emission from MSWM 40% 32% 26%
GHG reduction from MSWM 40% 24% 26%
GHG reduction from 3Rs 53% 23% 17%
GHG reduction from composting 61% 27% 21%
GHG reduction from source separation 52% 28% 15%

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To examine attitude level of respondent, 17 questions was applied by Likert’s scale. Average score for
attitude level in Yangon, Mandalay and Nay Pyi Taw was 65.92, 70.26 and 67.57. The questions included
waste management as an important issue for environment, and the question related to responsibility of waste
management were residents are responsible for waste management, municipality is responsible for waste
management, both residents and municipal should share responsibility. The rest of the questions were
applied to assess the attitude of residents on waste management and willingness to participate. The question
were waste should be disposed by open dumping, burying and open burning, households should separate
waste at source, waste separation is time consuming, waste separation by municipal staff is harmful and
costly, amount of waste generated should be reduced, recycling is important for waste management,
households should do waste composting, home composting is time consuming, compost products can be
used as soil conditioner, composting and recycling are beneficial to environment and participation in
municipal solid waste management is troublesome for residents. The average score for each attitude question
is shown in table 3.

Table 3 Attitude score of residents on municipal solid waste management in Yangon, Mandalay and

Nay Pyi Taw

Questions Average score

Yangon Mandalay Nay Pyi Taw

Waste management is important for environment 4.44 4.73 4.85

Residents should take responsibility for waste management 3.83 2.98 3.91

Municipality should take responsibility for waste 2.76 3.39 2.30

management

Both residents and municipality should share responsibility 4.31 4.70 4.82

for waste management

Waste should be disposed by open dumping 4.10 4.59 4.38

Waste should be disposed by burrying 4.05 4.39 4.08

Waste should be disposed by open burning 4.14 4.65 3.46

Households should separate waste at source 4.10 4.29 4.29

Waste separation at home is time consuming 3.22 4.15 3.40

Waste separation by municipal staff is harmful and costly 3.92 4.30 4.05

Amount of waste generated should be reduced 3.99 3.62 4.13

Recycling is important for waste management 4.16 4.35 4.23

Household should do waste composting 4.06 4.01 4.04

Composting is time consuming 3.06 3.52 3.18

Compost products can be used as soil conditioner 4.27 4.60 4.57

Composting and recycling are beneficial to environment 4.15 4.40 4.40

Participation in municipal solid waste management is 3.36 3.59 3.48

troublesome for residents

For willingness to participate in municipal solid waste management, the questionnaire was constructed by 9
questions. Mean score of willingness to participate in three cities was 8.05 in Yangon, 8.62 in Mandalay and
9.14 in Nay Pyi Taw. The questions for assessing willingness to participate in municipal solid waste
management were separated by two parts; community level and household level and ―Yes, No‖ question was
applied. The questions were if the participants are willingness to participate in municipal solid waste
management, which waste treatment, they preferred to participate in community level and household level.
In community level, waste treatment methods were source reduction, recycling, composting, waste
separation and anaerobic digestion. For household level, waste treatment methods included waste reduction,
reusing, home composting, and waste separation for composting and recycling. The other question included
if municipality should do all stages of waste management and if the households are willing to pay for waste
management. The average score for each question is shown in table 4.

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Table 4 Average score of willingness to participate in municipal solid waste management in Yangon,

Mandalay and Nay Pyi Taw

Questions Average score

Yangon Mandalay Nay Pyi Taw

Willing to participate in municipal solid waste management 91% 89% 89%

Community level

Source reduction 68% 43% 66%

Recycling 48% 62% 73%

Composting 56% 47% 53%

Waste separation 59% 78% 83%

Anaerobic digestion 8% 8% 11%

Household level

Waste reduction 86% 89% 86%

Reusing 735 86% 84%

Home composting 40% 43% 58%

Waste separation for community composting 70% 73% 89%

Waste separation for recycling 82% 84% 91%

Municipality should do all stages of waste management 39% 73% 38%

Willing to pay for waste management 77% 87% 90%

For the factors affecting on willingness to participate, Pearson’s correlation, independent sample t-test and
Anova was applied for finding the relationship between socioeconomic data, knowledge, attitude and
willingness to participate with significant level of 0.05. The analysis results revealed relationship between
education and willingness to participate and reversed relationship between waste tax and willingness to
participate in Nay Pyi Taw and the relationship between waste separation habit and willingness to participate
in both Yangon and Mandalay. The expected relationship between knowledge and willingness to participate
and attitude and willingness to participate was found in all three cities. The relationship between
socioeconomic factors and willingness to participate, knowledge and willingness to participate and attitude
and willingness to participate is shown in table 5.

Table 5 Association of willingness to participate with socioeconomic factors, knowledge and attitude

Variables Yangon Mandalay Nay Pyi Taw

Age r value p value r value p value r value p value
Income
Family member 0.143 0.155 -0.185 0.066 0.064 0.528
Collection tax
Knowledge 0.108 0.283 -0.015 0.882 -0.031 0.763
Attitude
Variables -0.118 0.241 -0.035 0.731 0.005 0.963

Gender -0.069 0.494 0.050 0.622 -0.261 0.009
Education
Residential area 0.339 0.001 0.407 0.000 0.346 0.000
Waste separation habit
Variables 0.498 0.000 0.559 0.000 0.508 0.000

Occupation Yangon Mandalay Nay Pyi Taw
Household type
t value p value t value p value t value p value
Waste collection time
0.351 0.436 -0.329 0.743 1.645 0.103

0.335 0.738 1.535 0.128 2.670 0.009

-0.941 0.349 0.234 0.816 -0.057 0.955

2.032 0.045 2.515 0.015 1.346 0.181

Yangon Mandalay Nay Pyi Taw

F value p value F value p value F value p value

1.724 0.184 0.986 0.377 0.312 0.733

0.308 0.736 0.241 0.786 0.304 0.738

0.921 0.402 0.010 0.990 1.324 0.271

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The relationship between education and willingness to participate was found only in Nay Pyi Taw as local
NGOs in Yangon and Mandalay are active and collaborate with Municipalities for educating about waste
management. Waste tax also had relationship with willingness to participate in Nay Pyi Taw as Nay Pyi Taw
is the capital city and all of the government offices are residing in the city and the government staff seems to
belief that the staff don’t need to pay tax and that caused relationship between waste tax and willingness to
participate. Waste separation habit had relationship with willingness to participate due the separated waste
collection by the Municipality in Yangon and actively education by local NGOs in both cities. The expected
and hypothesized relationship was found between attitude and willingness to participate and knowledge and
willingness to participate. The results showed that higher education is one of the propelling factors for
willingness to participate. However, the relationship was not found in Yangon and Mandalay although the
cities also had more higher educated participants in the study. The lack of relationship between education
and willingness to participate in two cities was due to the participation by local NGOs in educating to
residents and the cities also have higher local participation waste collection. So, the results in three cities
showed that knowledge sharing affected on willingness to participate than education. Waste separation habit
had relationship with willingness to participate in Yangon and Mandalay as the community already had the
knowledge to separate waste and they are now practicing them. So, the community was already active and
they are willing to participate in waste management. The relationship between knowledge and willingness to
participate and attitude and willingness to participate was found in all cities. The study also found out that
the residents had high knowledge and attitude level and willingness to participate in municipal solid waste
management. Therefore, the policy makers and municipalities should focus on the home practice guideline of
each waste treatment methods.

CONCLUSION
The study was conducted to examine the knowledge of residents on municipal solid waste management and
to find out the factors affecting on willingness to participate in municipal solid waste management in
Yangon, Mandalay and Nay Pyi Taw cities of Myanmar. The study found out that the residents had high
level of knowledge, attitude and willingness to participate in municipal solid waste management. Among the
socioeconomic factors, education, waste tax and waste separation habit were the factors affecting on
willingness to participate in municipal solid waste management. Knowledge and attitude were the two
dominant factors in willingness to participate. Hence, the authority should promote the knowledge and
attitude of residents by providing home practice guideline of different waste disposal methods in the cities.

ACKNOWLEDGEMENT
The author would like to thank to the authority of Yangon, Mandalay and Nay Pyi Taw and to all the
participants.

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[7] Hines, J. M., Hungerford, H. R., & Tomera, A. N. (1987). Analysis and synthesis of research on

responsible environmental behavior: A meta-analysis. The Journal of environmental education, 18(2),
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in Myanmar; Current Status, Key Challenges and Recommendations for National and City Waste
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[12] Minn, Z., Srisontisuk, S., & Laohasiriwong, W. (2010). Promoting People's Participation in Solid
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I 061

Comparison of MPs Contamination between Downstream and
Upstream Sites: A Case Study of Lower Chao Phraya River, Thailand

Khattiya Ounjai1 Suwanna Kitpati Boontanon2* Shuhei Tanaka3 and Shigeo Fujii4

1 Graduate Student (Double Degree Program); 2*Associate Professor, Department of Civil and Environmental
Engineering, Faculty of Engineering, Mahidol University, Salaya, Nakhonpathom 73170, Thailand.

1 Graduate Student (Double Degree Program) 2*,3Associate Professor and 4Professor, Graduate School of
Global Environmental Studies, Kyoto University, Yoshida, Sakyo-Ku, Kyoto, 606-8501, Japan

*Phone : +66-2-8892138 ext. 6390, Fax : +66-2-8892138 ext. 6388, E-mail : [email protected]

ABSTRACT
Microplastics (MPs) were ubiquitously found and distributed globally in the environments especially marine
environment. Majority of MPs were discharged from land to the sea. However, most of the studies focused
on the marine environment and few reports were studied MPs in the freshwater environment especially from
Asia continent which was reported to be main contributor for MPs pollution. Thailand as one of the top
countries that was estimated to produce mismanaged plastic waste to the ocean is expected to discharge MPs
to the river and the sea. This study investigated MPs pollution in Chao Phraya river, major river in Thailand,
by focus on lower Chao Phraya river at two sites: upstream at Pathum Thani province and downstream at
Bangkok Metropolis to identify concentration and characteristics of MPs in surface water and evaluate
impact of urbanization by comparing MPs results from two different sites. MPs were sampled on surface
water by manta trawl. Results of MPs concentration were 4.0 and 22.9 MPs particles/m3 at upstream and
downstream sites, respectively. FT-IR results reveal that majority of MPs were polyethylene and
polypropylene which are common materials for single use plastics. In addition, comparison results showed
that downstream site had higher MPs concentration and composition types which indicate higher MPs inputs
from several sources which clearly showed effects of urbanization at Bangkok. More investigation about
MPs sources and seasonal variation are recommended for future study. While, results of MPs size
distributions at both sizes showed similar trend toward smaller size and 8 percent of MPs found at
downstream site were below 335 µm which is mesh site of manta net used in this study. Thus, investigation
of smaller MPs also recommended in the future.

Keywords : Microplastics; Freshwater environment; Thailand; Chao Phraya River

INTRODUCTION
Plastics are man-made materials that are used worldwide due to their durability and resistance. Global
production and consumption of plastics were rapidly growth since their first production in 1950. Sixty
percent of all plastics production (between 1950 to 2015) or 4,900 million metric tons of plastics were
estimated to be discarded and accumulated in the environments [1]. Plastics are major marine litter in seas
and oceans. Therefore, plastic pollution are long-term threat and challenge to be solved worldwide. Large
amount of marine plastic pollution was derived from land-based sources which were estimated to be 4.8 to
12.7 million metric tons of plastic waste discharged from land to the sea, annually [2]. Aside from marine
plastics pollution, in recent years, concerns over another plastic pollution or so called “microplastics” were
arose. Microplastics referred to plastics particles that smaller than 5 millimeters which are classified by their
sources into primary microplastics and secondary microplastics [3]. Primary microplastics typically refers to
intentionally produced microplastics such as plastics pellets used for industrial feedstocks, microplastics used
as exfoliating agent in personal care and cosmetics products, and synthetic fibers that could be released from
domestic washing of synthetic clothes while secondary microplastics which were dominant group found in
the environment was generally referred to small pieces of plastics unintentionally degraded by weathering
processes such as oxidation, photodegradation and biodegradation [3]. Microplastics are emerging pollutants
that pose environmental problems to organisms especially aquatic organisms as microplastics can absorb
organic contaminants, heavy metals as well as pathogens from the environments into organisms [4].

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As mentioned earlier, majority of plastic waste were inputted from land-based sources to marine
environment with estimation of 1.15 to 2.41 million metric tons of plastic waste that were discharged from
rivers to the oceans and Asia continent were estimated to be main contributor which account for 67 percent
of global estimation [5]. While Thailand, one of Southeast Asia countries, was estimated as the sixth place
for countries with mismanaged plastics waste with 0.15-0.41 million metric tons of plastic waste/year that
were expected to be discharged to the ocean [6]. However, microplastics data in Thailand is still insufficient
and more details are needed to understand MPs pollution and use for future prevention of MPs. In this study,
we focused on Chao Phraya river, major river in Thailand as Chao Phraya river basin covers 30 percent of
land area in country and supplies water resources for irrigation, electricity generation, industrial use,
domestic water use, navigation, and river integrity in Thailand [7]. In this study, we aim to investigate the
MPs concentration in Chao Phraya river and to evaluate impact of urbanization by comparing MPs data from
two sampling sites: downstream site at Bangkok Metropolis and upstream site at Pathum Thani province.

METHODOLOGY
Study area
Chao Phraya river originated in middle part of Thailand. The river length is around 365 km. Chao Phraya
river flows through heart of Bangkok Metropolis and eventually flows south to the Gulf of Thailand. The
river plays important roles for people in Bangkok as water supply, drainage, transportation, and water
recreation. However, due to urbanization in Bangkok Metropolis and increase of industrial sites, water
quality in Chao Phraya river became deteriorated which greatly affects social and economic aspects for
people [7]. Bangkok Metropolis is capital city of Thailand with population over 9 million and several
industries include plastic industries also included [8]. In addition, over 0.6 million plastic bags were
estimated to be used daily in Bangkok in 2010 [9].

This study focuses on Chao Phraya river at two different locations 1) downstream at Bangkok Metropolis
(Latitude 13.671648, Longitude 100.545654) to investigate MPs concentration at capital city with high
consumption of plastics and solid waste generation which might result in large amount of microplastics
discharge to Chao Phraya river. To evaluate impact of urbanization in Bangkok, another location for MPs
sampling was selected at 2) upstream location of Chao Phraya river, Pathum Thani province at Samlae water
supply pumping station (Latitude 14.042385, Longitude 100.554446). This pumping station is located 41
kilometers away from Bangkok and supply raw water from Chao Phraya river for water treatment plant in
Bangkok with capacity of 3.8 million m3/day [10]. The locations of both sites are demonstrated in Figure 1.

Figure 1 Map of the sampling sites at upstream and downstream
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MPs Sampling
MPs were sampled from surface water at two sites of lower Chao Phraya river in July 2019 by using
Microplastics Manta net, Hydrobios company, Germany (opening of 30 cm high x 15 cm wide with the mesh
size of 335 µm). Samples from both locations were collected by trawling Manta net on the surface of the
river outside of wake zone beside a research boat for 15 minutes with trawling speed between 4.9-11.9 kph
depend on wave and weather condition. Furthermore, Sampling were done against direction of river flow
from downstream toward upstream and MPs sampling were operated during low tide time that river water
flow to the sea to avoid tidal effects and intrusion of MPs from seawater. Filter water volume were calculated
from Mechanical Flow Meter Model 438 110 (Hydrobios, Germany) that was installed at mouth of the manta
net. Total filter water volumes were 45 and 51 m3 at upstream and downstream site, respectively. Filtered
samples were washed from manta net bag with DI water and kept in glass bottle before transfer to laboratory
for further analysis.

MPs Purification
In laboratory, collected samples were washed with deionized water through stack of stainless sieves (mesh
size 5 mm, 1 mm, 0.515 mm, and 0.108 mm). Particles that larger than 5 millimeters were discarded.
Samples were then separated into three size ranges base on sieves’ mesh sizes: 5-1mm, 1-0.515 mm, and
0.515-0.108 mm. After that, samples were purified with 200 mL 30% hydrogen peroxide and incubated at
55°C for 3 days to digest organic matter such as plankton that was also filtered and mixed with MPs samples.
After that, MPs were separated from other inorganic substances by density separation method. 5.3 M NaI
(density 1.52 g/cm3) were used for density separation in this study. All MPs and particles floated on
supernatant were recovered for further analysis. In addition, to ensure that all purification processes
especially digestion process did not affect size of MPs. The protocol had been tested before with standard
Polypropylene pellets (average size = 4.3 mm) and Polyethylene pellets (average size = 440 µm), which were
represented highest and smallest size of MPs in this study. Results showed no physical observation and size
changes after purification processes.

MPs size measurement and Identification
Total particles remained in each size ranges after purification processes were weighted and 25 % by weight
of each size ranges were randomly taken as representatives of sample population for visualization and
identification processes. Representative particles were then observed under Trinocular Zoom
Stereomicroscope (Iris, model SZM45-B8L-T, Thailand). Image of each particles was taken with Moticam
5+ camera and used for particle size measurement using image processing program: Motic Image Plus 3.0
program. After that, all particles were identified their chemical components by using Nicolet 6700 FT-IR
Spectrometer, diamond ATR mode to determine MPs and identify different types of MPs components.

RESULTS AND DISCUSSIONS
MPs Concentration
83.7% and 97.6% of all particles from upstream and downstream that obtained from purification processes were
confirmed to be MPs by FT-IR results. MPs concentration at downstream site was calculated to be 22.9
particles/m3 which is almost five times higher than MPs concentration founded at upstream site: 4.0 particles/ m3.
MPs concentration found in this study were also higher than previous study at Bangkok area that reported MPs
concentration at 7 particles/m3 [11] and other rivers: rivers in Greater Paris and Danube river [12] but similar
concentration was reported at Saigon river, Vietnam [13]. Thus, MPs concentration were most likely higher in
Asia countries as expected from estimation in [5].

Shape of MPs
Observation of MPs under stereomicroscope revealed different shapes of MPs as shown in Figure 2. MPs were
catagorized base on their appearances into 5 groups; fragment, sheet or film, foam, fiber and bead or pellet which
were commonly classified in MPs researches [14].

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1 mm c)

1 mm 1 mm 1 mm
d) e)

1 mm 1 mm

Figure 2 Examples of MPs in different shapes;
a) fragment b) sheet (left) and film (right) c) foam d) fiber and e) bead

While Figure 3 shows results of MPs in different shapes at both upstream and downstream sites. More than half
of results were fragment types followed by sheet or film and foam types which to be expected as majority of MPs,
which were resulted from degradation of larger plastics pieces. Thus, fragment, sheet or film, and foam were
likely to degrade from plastic bag, plastic packaging other plastics materials in daily use while fiber might indicate
MPs from primary source: domestic washing of synthetic cloths. Bead or pellet also indicated MPs from primary
sources. Althrough beads and pellets have been referred interchangeably but beads usually refer to MPs used for
exfoliating purpose in personal care and cosmetics products where as pellets refer to pre-production or feed stock
pellets used for manufacture of plastic products [14]. Comparison shows difference of MPs shape distribution
between upstream and downstram sites. Although fragment types are dominant in both sites. Sheet/film type and
foam type are significantly increase at downstream. Sheet/film type indicates consumer uses such as plastic bags
and wrappers while foam type indicates food containers and protective packaging [14]. Thus, increase in different
shapes of MPs at downstream site demonstrates more input of plastic waste from use of several types of plastics in
human activities which corresponding to high population and high consumption of plastics especially single-use
plastics in Bangkok due to urbanization.

Shape of MPs Fiber 1% 0.081% Bead/Pellet

Fiber
Foam 4% 4% 1% Bead/Pellet

Sheet/Film 12% Foam
18%

Fragment Sheet/Film Fragment
80% 28% 53%

Upstream Downstream

Figure 3 Distributions of shape of MPs at upstream and downstream sites

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Difference in MPs composition

FT-IR results revealed chemical components of each MPs particle. The results from upstream and downstream
point were used for calculation of MPs concentration in different polymer types in particles/10 m3 of surface water

at upstream and downstream point as shown in Figure 4.

80 78.1 Upstream
Downstream
70 64.4
60

MPs particles/10 m3 50

40 35.9

30 24.4

20

10 8.9 7.2 9.4 11.2
2.9
0.9 2.2 3.1 1.1 1.7
Other
0

Polyethylene Polypropylene PP/PE blend Polystyrene Polyurethane Paraffin wax Cellophane

(PE) (PP) (PS) (PU)

Figure 4 MPs concentration in different polymer types at upstream and downstream locations

Majority of composition were Polyethylene (PE) and Polypropylene (PP) follow by PP/PE blend which to be
expected as these components are main materials for plastic packaging and single-use plastics. While, Polystyrene
(PS) indicates foam types MPs which commonly used for food and protective packaging. Overall, downstream
location was founded higher concentration of MPs in all polymer types compare to upstream location which
demonstrates higher MPs pollution from different sources to Chao Phraya river at downstream location.
Furthermore, Polyurethane (PU) and Paraffin wax were detected only at downstream site. Polyurethane in the
form of flexible foam was popular materials used in home furnishings thus, detection of PU indicated MPs
pollution from furniture waste to the river at downstream point which shows contamination of large or bulky solid
waste to Chao Phraya river in Bangkok. Paraffin wax, which is one of petroleum waxes, also commonly used for
coatings of several products such as food products, packaging, personal care and home care products [15]. In
consequence, MPs at downstream not only vary widely in shapes but also in components of MPs which probably
results from higher consumption and disposal of plastics products in Bangkok.

Size distributions of MPs at upstream and downstream sites.
Size distributions of MPs in both downstream and upstream site are demonstrated in frequency percentage as
shown in Figure 5. Compare to upstream, MPs in downstream site were broader in their size ranges from large
particles at 5,000 µm to very smaller size of 200 µm while MPs in upstream were ranged between 4,400 to 400
µm. While gray lines show cumulative frequency of MPs size distribution, almost 50% cumulative frequency of
MPs in both upstream and downstream locations were in size range below 1200 µm which indicates high
abundance of MPs toward smaller sizes. MPs that smaller than 400 µm were detected only at downstream site
location. Overall, size distributions of both downstream and upstream location were increased toward smaller size
range and 50% cumulative frequency of MPs in both sites were smaller than 1,200 µm. Thus, in term of quantity,
majority of MPs found in lower Chao Phraya river were ranged in mini-microplastics range (<1mm). Moreover,
mesh size of manta net used for MPs survey in this study is 335 µm but eight percent of MPs resulted in smaller
size than survey mesh size with minimum size of 164 µm.

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Figure 5 Size distributions of MPs at upstream and downstream locations
Corresponding to literature review from [16] which reviewed that almost 80% of current MPs survey focus for
MPs with size range ≥300 µm (indicated by mesh size of manta net or plankton net used for MPs survey) and the
author also surveyed MPs used for personal care and cosmetics. Results revealed almost 95% of MPs in these
products were less than 300 µm [16]. Thus, depending on focused size of sampling, we might underestimate
signifcant amount of MPs in the environment and smaller size investigation is recommended for future survey.
CONCLUSION
At the downstream point of lower Chao Phraya river, located in Bangkok city representing the urban area, the
results reveal MPs contamination at 22.9 particles/m3 which is five times higher than concentration at upstream
point in Pathum Thani province, where the water supply pumping station is located. Comparison between
downstream and upstream sites, the result also shows higher polymer types of MPs at downstream which more
input of plastic wastes from different sources at downstream location. While size distributions of both sites show
similar trend of MPs increase toward smaller size range but distribution at downstream was broader and smaller
detected MPs compare to upstream. In conclusion, urbanization are affected MPs contamination between
upstream and downstream location in several aspects; concentration, polymer types, and size distribution. Thus,
we recommended future study to focus on MPs contamination data regarding location and season to provide
source tracking data and seasonal variation as well as recommend investigation for abundance of smaller size MPs
in the future.
ACKNOWLEDGEMENT
This work had been supported from The 60th Year Supreme Reign of His Majesty King Bhumibol Adulyadej
Scholarship, the Faculty of Graduate Studies, Mahidol University and On-site Laboratory Initiative of
Graduate School of Global Environmental Studies, Kyoto University.

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REFERENCE
[1] Geyer R, Jambeck JR, Law KL. Production, use, and fate of all plastics ever made. 2017;(July):25–9.
[2] Haward M. Plastic pollution of the world’s seas and oceans as a contemporary challenge in ocean

governance. Nat Commun [Internet]. 2018;9(1):9–11. Available from:
http://dx.doi.org/10.1038/s41467-018-03104-3
[3] Crawford CB, Quinn B. Microplastics, standardisation and spatial distribution. Microplastic Pollut.
2016;101–30.
[4] Alimba CG, Faggio C. Microplastics in the marine environment: Current trends in environmental
pollution and mechanisms of toxicological profile. Environ Toxicol Pharmacol.
2019;68(February):61–74.
[5] Lebreton LCM, Van Der Zwet J, Damsteeg JW, Slat B, Andrady A, Reisser J. River plastic emissions
to the world’s oceans. Nat Commun [Internet]. 2017; 8:1–10. Available from:
http://dx.doi.org/10.1038/ncomms15611
[6] Jambeck JR, Geyer R, Wilcox C, Siegler TR, Perryman M, Andrady A, et al. the Ocean.
2015;347(6223).
[7] World Water Assessment Programme. UN World Water Development Report 1: Water for People,
Water for Life; Chapter 16: Chao Phraya River Basin, Thailand; Berghahn Books: New York, NY,
USA, 2003; pp. 387–400.
[8] Johansson E. and Ericsson H. E. 2018. Quantification for the Flow of Microplastic Particles in Urban
Environment: A Case of the Chao Phraya River, Bangkok Thailand. A Minor Field Study.
[9] Lee, Lynette. 2010. Thailand fight addiction to plastics bags. The Guardian. Accessed [online]:
https://www.theguardian.com/environment/2010/jun/28/thailand-plastic-bags.
[10] Metropolitan Waterworks Authority. 2010. Water Treatment and Transmission. Accessed on 6 April
2020: http://www.mwa.co.th/ewtadmin/ewt/mwa_internet_eng/ewt_news.php?nid=296
[11] A. T. Ta and S. Babel. 2019. Microplastic contamination in freshwater environment: A case study in
the Chao Phraya River, Bangkok. The international conference on Sustainable Design and Climate
Change Adaption. 58th Vietnam Journal of Construction. p. 69-72
[12] Li, J., Liu, H., and Paul Chen, J. (2018). Microplastics in freshwater systems: A review on occurrence,
environmental effects, and methods for microplastics detection. Water Research, 137, 362–374.
[13] Lahens, L., Strady, E., Kieu-Le, T.-C., Dris, R., Boukerma, K., Rinnert, E., … Tassin, B. (2018).
Macroplastic and microplastic contamination assessment of a tropical river (Saigon River, Vietnam)
transversed by a developing megacity. Environmental Pollution, 236, 661–671.
[14] Helm PA. Improving microplastics source apportionment: A role for microplastic morphology and
taxonomy? Anal Methods. 2017;9(9):1328–31.
[15] Suaria G, Aliani S, Merlino S, Abbate M. The occurrence of paraffin and other petroleum waxes in the
marine environment: A review of the current legislative framework and shipping operational practices.
Front Mar Sci. 2018;5(MAR):1–10.
[16] Conkle JL, Báez Del Valle CD, Turner JW. Are We Underestimating Microplastic Contamination in
Aquatic Environments? Environ Manage [Internet]. 2018;61(1):1–8. Available from:
http://dx.doi.org/10.1007/s00267-017-0947-8.

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I 062

Municipal Solid Waste Quantity and Composition Evaluation for
Assessment of Wasteaware Indicators in Special Economic Zone

(SEZ), Chiang Rai Province

Chaloemphan Kaewkanta1*, Yanasinee Suma2 and Numfon Eaktasang3

1* Graduate student, Faculty of Public Health, Thammasat University,
2 Lecturer, Faculty of Public Health, Thammasat University (Lampang Campus), 3 Lecturer,

Faculty of Public Health, Thammasat University (Rangsit Campus)
*Phone : +66 811645945, E-mail : [email protected] or [email protected]

ABSTRACT
Municipal solid waste (MSW) is global problems in terms of social inclusion, environmental

hazards, economic sustainability, and human health with both developed and developing nations. This study
was to evaluate waste composition in special economic zone area in Chaing Rai province. Methodology was
quartering method the first of composition assessment, a part of ‘wasteaware benchmark indicator’ (WBI) in
special economic zone, Chiang Rai province.

From the results, waste compositions in special economic zone were tree types: recycled waste
(48.89%), organic waste (47.95%), and general waste (3.16%). Moreover, over all the recycled waste should
be five compositions: paper and cardboard, plastic bottle, glass, metal and rubber waste. The highest paper
and cardboard (box paper and newspaper), plastics bottle including PETs bottle, glass (beverage and soybean
glass bottles), metals and rubber. However, the outcome of this study can be used to support decision-
marking, further development for promote 3Rs campaign, as well as provide inputs of future research. It also
highlights the recommendation for improvement in existing system to achieve the goal of sustainable solid
waste management considering technology, institutional and financial factors.

Keywords: municipal solid waste management; wasteaware indicators; waste composition; waste
evaluation; quartering method

INTRODUCTION
Municipal solid waste (MSW) is significantly problem in both developed and developing

countries [1]. MSW leads to disrupt sustainable development and sustainable economic growth including
social, economic, environmental dimension and healthy environment [2,3]. According to the United Nations
report, population is expected to reach 8.6 billion in 2030, and growth up to 9.8 billion in 2050. Moreover,
approximately 90 % of this expected population is from Asian countries [1].

The inappropriate MSW has become problem in developing countries such as India, Sri Lanka,
Pakistan, Ethiopia, Philippines and Thailand. Only 50-90 % of total MSW was effectively collected and 30-
60 % of total MSW was improper collected and disposed by open dumping [4].

MSW of Thailand in 2018 was 27.73 billion tons per year; waste generation rate per capita was
about 1.13 kg/capita/day. Growing urbanization, populations, consumptions, socioeconomic status, travel
support and lifestyles have greatly accelerated the rate of MSW generation [5]. However, MSW was properly
managed more than 11.69 million tons per year, 8.5 billion ton-year of waste recovery and useful and 7.17
billion tons per year of inappropriate disposal such as open dumping and open burning. Therefore, the Thai
government has announced MSW management as a national agenda and promoted to reduce waste at the
source. However, there is lack of some MSW data in Thailand, especially, in Special Economic Zone (SEZ)
areas.

SEZ areas in Chiang rai province cover 21 sub-districts in the 3 districts of Chiang rai which consists
of Chiang Saen, Chiang Khong, and Mae Sai with the total area of 916.2 km2 [6]. Moreover, Chiang rai SEZ
has 6 permanent borders by crossing 3 borders with Myanmar and 3 borders with the Lao PDR.
Nevertheless, the government reported that waste generation rate in Chiang rai was 400,000 tons per year.
About 50 % of MSW was unsuitable disposed, including open dumping and open burning in these areas [7].

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As it is like the outpost city which is a shopping center linking between Myanmar and is a place to attract
more tourists, resulting in the higher waste generation.

The previous study investigated to assess MSW management system by Wasteaware benchmark
indicators (WBI) [2,8,9,10]. WBI is based on the Integrated Sustainable Solid Waste Management (ISWM)
framework for the comprehensive performance measurement of both physical components and governance
aspects of MSW management while considering all relevant stakeholders [8,10]. The waste composition is
one of key information in WBI. To achieve the more realistic analysis, this study was to evaluate waste
composition in SEZ area in Chiang Rai province.

METHODOLOGY
The study area, Chiang Saen district located in Chiang Rai province, Northern Thailand. It quiet

small city and peaceful, near Meakong river and closed to famous ‘Golden Triangle’ cross-border with Laos
and Myanmar. It extends for about 80 km along from Chiang Rai urban and 554 km from Bangkok.
Moreover, Tambon (Sub-district) Administrative Organization (TAO), governorate including 6 sub-districts,
72 villages, and population in Chiang Saen District showed in Figure 1 and Table 1, respectively.

Ban Saeo Sub-district

Chiang Saen District, Chiang Rai province

Figure 1 The location of Chiang Saen district in Ban-Saeo sub-district and covering 15 villages

Table 1. The population in Chiang Saen district. [16]

Sub-district Population Percentage of city population (%) Villages
10
Wiang 10,807 20.80 13
15
Pa Sak 8,337 16.05 14
12
Ban Saeo 11,444 22.03 8
72
Si Dun Mun 8,120 15.63

Mae Ngoen 8,463 16.29

Yonok 4,777 9.20

Total 51,948 100.00

Source: (National Statistical Office, 2019) [16]

To determine MSW composition, samples of MSW were collected by TAO. The sampling of waste
composition was performed during weekdays for a month and one time per week in February, 2020. At the
community’s landfill site, the waste was weighed using a scale with sensitivity of 0.01 kg, and recorded on a
sampling sheet. The bags were divided by weight; light (< 10 kg), medium (10-20 kg) and heavy (> 20 kg).
These waste sample bags were randomly sampled to collect a combined sample weight of 100 kg.

The 100 kg of waste was then characterized using the quartering method [11].

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Figure 2 Quartering technique in disposal site
Moisture content of samples was measured by weighing a waste sample as discarded which
represented its wet weight, drying it to a constant weight at a temperature not exceeding 105 ºC using hot air
oven and calculating the weight change. This weight loss is then expressed as a percentage which
represented a moisture content [12], as shown on Figure 3.

Figure 3 Moisture content and bulk density of waste analysis
Bulk density was determined by filling a known volume container and mass with a waste sample and
then weighing the loaded container, (the container was constantly shaken during filling). Then, the density is
calculated by dividing the net weight of the waste sample (weight of loaded container minus weight of empty
container) by the container’s volume and expressed as kg/m3 [12].

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RESULTS AND DISCUSSIONS
Moisture content and bulk density of MSW

The moisture content of MSW was 46.50 %, which mostly comprised of organic waste that contains a
high level of moisture. Moreover, bulk density of MSW was 0.44 kg/L or 440 kg/m3, which affects the
transportation of waste to the disposal sites in that it may take up the space of the waste container during the
transportation.
MSW generation and waste composition

The MSW generation was approximately 2 tons/d or 0.4 kg capita-1 d-1. This result was below the
national average waste generation rate, reported as 1.14 kg capita-1 d-1 [13].

Figure 4 Waste compositions in Ban-Saeo sub-district

Figure 5 Waste composition in Ban-Saeo sub-district
From Figure 4, the recycled waste represented the largest component, accounting for 48.89 % by
weight of the total weight. The other categories of waste including organic waste and general waste,
accounted for 47.95 %, and 3.16 %, respectively. Organic waste, such as food waste and garden waste, was
difficult to measure the exactly amount due to mixing and decomposition. Surprisingly, this finding point to
the absence of provisions for proper segregation and allowed recycled waste to landfill. Furthermore, the
household hazardous waste and infectious or otherwise hazardous waste was not found.

Figure 6 Recycled waste compositions in Ban-Saeo sub-district
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According to Figure 5 and Figure 6, overall the recycled waste was found including five
compositions: paper and cardboard, plastic bottle, glass, metal and rubber waste. The highest paper and
cardboard (box paper and newspaper), plastics bottle including PETs bottle, glass (beverage and soybean
glass bottles), metals and rubber according to the percentage by weight of 38.11 %, 32.34 %. 27.97 %,
1.05 % and 0.52 %, respectively. Waste compositions in this study were similarity with Suma Y, et al.
(2019); SWEEPNet (2014); Muhammad A, et al (2018) [11, 14, 15]. The most compositions were organic
waste included food waste and garden waste accounting 42.79%, 56.00 % in Egypt and 56.60 % in Karbala,
Iraq. Whereas, this study was not parallel in Wilson DC (2015) which found that the highest percentage by
weight was organic waste (35.10%) and paper and cardboard and plastic was found 21% and 6 %,
respectively [8].

CONCLUSION
The amount of recyclable waste by weight from the quartering method was the largest amount. The

followed waste was organic waste and general waste, respectively. There was not found hazardous waste.
This finding points to the absence of provisions for proper segregation and waste separation promotion.
Local government organization (LGO) should promote 3Rs campaign and suggest the community to
compost organic waste for reducing the amount of waste, generating community income and decreasing the
environmental impact. However, these findings will be used further as information on WBI assessment to
consider the performance of both physical components and governance aspects of MSW management in this
SEZ area.

REFERENCE
[1] United Nations. World Urbanization Prospects: The 2018 Revision [key facts]. Economic & Social

Affairs. 2018, 2020 January 15.
[2] Chanhthamixay B, Vassanadumrongdee S, Kittipongvises S. Assessing the Sustainability Level of

Municipal Solid Waste Management in Bangkok, Thailand by Wasteaware Benchmarking Indicators.
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[3] Hoornweg D, Bhada-Tata P. What a Waste: A Global Review of Solid Waste Management. Urban
Development & Local Government Unit: World Bank Group; 2012.
[4] World Bank. What a Waste : A Global Review of Solid Waste Management. Goverment EAL; 2012.
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[8] Wilson DC, Rodic L, Cowing MJ, Velis CA, Whiteman AD, Scheinberg A, et al.
‘Wasteaware’benchmark indicators for integrated sustainable waste management in cities. Waste
Management. 2015;35:329-42.
[9] Oduro-Appiah K, Scheinberg A, Mensah A, Afful A, Boadu HK, de Vries NJWM, et al. Assessment
of the municipal solid waste management system in Accra, Ghana: A ‘Wasteaware’benchmark
indicator approach. Waste Management & Research. 2017;35(11):1149-58.
[10] Ali M, Geng Y, Robins D, Cooper D, Roberts W, Vogtländer J. Improvement of waste management
practices in a fast expanding sub-megacity in Pakistan, on the basis of qualitative and quantitative
indicators. Waste Management. 2019;85:253-263.
[11] Suma Y., Pasukphun N., Hongthong A., Keawdounglek V., Laor P., Apidechkul T., et al. Waste
Composition Evaluation for Solid Waste Management Guideline in Highland Rural Tourist Area in
Thailand. Applied Environmental Research. 2019;41(2):13-26.
[12] Waleed M., Sh. Alabdraba, Haneen A. K. AL-Qaraghully. Composition of Domestic Solid Waste and
The Determination of itsDensity &Moisture Content: A case study for Tikrit City, Iraq. International
Review of Civil Engineering (I.RE.C.E.), Vol. 4, N. 2 March 2013

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[13] Pollution Control Department. Pollution Control Department’s Thailand pollution report in

2016, 2017. [Online] Available
from:http://infofile.pcd.go.th/mgt/Thailand_state_pollution2017%20Thai.pdf?CFID=1444046&CFTO
KEN=65882008[Accessed 28 March 2018]
[14] Muhammad A., Rafid al-Khaddar., Patryk K., David J., Ali A. Benchmarking of the Current Solid
Waste Management System in Karbala, Iraq, Using Wasteaware Benchmark Indicators. World
Environmental and Water Resources Congress 2018; 2018 May 31.
[15] SWEEPNet. Country Report On The Solid Waste Management In Egypt, The Regional Solid Waste
Exchange of Information and Expertise network in Mashreq and Maghreb countries; 2014.
[16] National Statistical Office. Reported Population and Households Statistics in 2019. [Accessed 20
January 2020]. Retrieved from
stat.bora.dopa.go.th/stat/statnew/statTDD/views/showProvinceData.php

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I 066

Health impacts of life cycle particulate formation from private
vehicles and public buses in Bangkok

Phatcharakorn Sakpheng1 Ittipol Paw-armart2 Witsanu Attavanich3 Sirima Panyametheekul4
Jitti Mungkalasiri5 Shabbir H. Gheewala6,7 and Trakarn Prapaspongsa1*

1 *Department of Civil and Environmental Engineering, Faculty of Engineering,
Mahidol University;2Automotive Emission Laboratory, Pollution Control Department;
3Department of Economics, Faculty of Economics, Kasetsart University; 4Faculty of Engineering,
Chulalongkorn University;5National Metal and Materials Technology Center (MTEC), National Science and
Technology Development Agency (NSTDA);6The Joint Graduate School of Energy and Environment

(JGSEE), King Mongkut’s University of Technology Thonburi;
7Centre of Excellence on Energy Technology and Environment, PERDO:
Tel* : (+66) 2 889 2138, Fax : (+66) 2 441 9731, e-mail : [email protected]

ABSTRACT
Fine particulate matter (PM2.5) is a major cause of premature deaths worldwide. Transportation is considered
as one of the most significant sources of PM2.5emissions. This research aims to estimate the PM2.5 emission
levels and their health impacts caused by private vehicles and public buses in Bangkok. The primary PM2.5
emissions were estimated by using the EMEP/EEA air pollutant emission inventory guidebook 2016. The
health impacts were assessed by applying the UNEP / SETAC based model (PM2.5 intake fraction model, and
PM2.5 effect factor model). The functional unit used in the study is one passenger- kilometer (pkm)
representing the transport of one passenger over a distance of one kilometer. The results showed that van and
pick-up types of vehicles had the highest PM2.5 emissions and health impacts per pkm, followed by
passenger cars, motorcycles, and buses. Modal shift from private vehicles to public buses could, therefore,
reduce the emissions and their impacts. The analyses of the PM2.5 emissions based on the statistics of a
cumulative number of cars classified by fuel type; and the health impacts from the PM2.5 emissions showed
continuously reducing emission trends from 2014 to 2018. Nonetheless, the accumulation of old vehicles
with less stringent emission standards resulted in a limited potential reduction in emissions and health
impacts. When considering only the technologies and fuels of newly registered cars and excluding the old
cars accumulated from previous years, the emission reduction became more obvious. Hence, the controls and
technical improvements for the old cars as well as the introduction of stricter emission standards for the new
cars are important tools to effectively reduce PM2.5 emissions and their related health impacts.

Keywords: Fine particulate matter; Life cycle impact assessment (LCIA); Human health effects; Global
guidance; Intake fraction; Effect Factor

INTRODUCTION
Primary and secondary fine particulate matter (PM2.5) emissions have become a serious environmental
problem as their exposure can cause a series of health effects. Many studies from the World Health
Organization (WHO) indicate the relationship between the primary and secondary PM2.5 emissions level and
adverse effects on our body. PM2.5 might be a universal carrier for toxic substances such as carcinogens and
heavy metals transporting them from the skin into organ systems. As a result, the PM2.5 inflames and
constricts the blood vessels leading to mortal cardiovascular symptoms including high blood pressure, faster
pulse, coronary artery disease, and ischemic heart disease [1]. Additionally, the situation of this pollution in
the Bangkok and Metropolitan Region (BMR) monitored by the Pollution Control Department (PCD)
reported that the emissions (24-hour average) from 2016 to 2019 in the BMR were beyond the standard (at
50 µg/m3) at the beginning of the year (January-April) and the end of the year (December) annually.
Interestingly, two major reasons for the issue are meteorological conditions and the combustion products
which commonly come from the burning of fossil fuels and biomass. Reportedly, the main source of this
emission is from vehicles, 11.6 million vehicles in total (calculated by the cumulating of registered vehicles
in Bangkok and metropolitan area, on April 30, 2019; more than 10.10 million and 1.2 million, respectively)
[2]. Moreover, Nawahda et al. (2013) [3] conducted a study on the effects of reducing the concentration of
PM2.5 on premature deaths in Japan between 2006 and 2009 by measuring data from the 1,843 air quality

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monitoring stations in Japan. The results showed that improvements in the quality of the air in the study
period associated with the reduction of premature deaths level, especially in vulnerable and the elderly
groups. Likewise, Martinez et al. (2018) [4], whose studied about the relationship between particulate matter
management policies and the economic value of death, found that, in 2012, the long-term effects of the PM2.5
(at the level of 49.2 µg/m3) created 1,199 premature deaths, representing an economic impact between
22,000-57,000 million baht (570-1,470 million euros). Besides this, there were 547 and 937 cases of patients
admitted to the hospitals due to coronary heart diseases and respiratory diseases, respectively. Thus, they
believed that decreasing the PM2.5 levels into the European standard (25 µg/m3) would reduce the number of
deaths by 45 percent. Furthurmore, if the PM2.5 levels did not exceed the World Health Organization
standard (10 µg/m3), the rate of mortality would be reduced by 77 percent. Both of the policies would also
reduce the number of patients sent to the hospital due to coronary heart disease and many respiratory
diseases. In addition, Blumberg et al., (2003) [5] studied on a cost-benefit analysis comparing with the
United States Environmental Protection Agency (US EPA) implementation; using automobile standards at
Tier 2, and the Heavy-duty Engine and Vehicle Standards that reduced the sulfur in the fuel in parallel with
the vehicle exhaust standards. Their criterion of benefits calculated from only monetary factors as non-
monetary such as illness, loss of visibility, and agricultural damage made difficult to measure. The result
showed that a lower of the premature deaths regarding the reduction of particulate matter extractor is the
main benefit of these policies, especially for the Heavy-duty Engine standards. While, a restriction on the
amount of NOx and SOx exposure from the Tier 2 car standard, which causes secondary particulate matter
pollution, also made an equally positive effect on this situation. For predictive analysis, they found that the
actual damages will have been reduced until 2030 and they would save up money from these policies over 86
billion US dollars. Hence, the objectives of this study are to estimate primary fine particulate matter
formation from private vehicles and coaches in Bangkok and to study on their health impacts. The expected
outcome from this research is an estimation of the PM2.5 emissions from each type of vehicle and
recommendations on the appropriate type of technology for reducing these impacts, which can be a guideline
for future policy planning.

METHODOLOGY
Emission Inventory
Total emission is calculated from the Tier 2 emission as described in the EMEP/EEA [6], shown in equation
1. The relevant variables consist of the Emission Factor which also is applied from EMEP/EEA [6], the
number of vehicles uses [7], and the average number of passengers [8] from each vehicle category as shown
in Table 2. For characterization, this study conducted in two types of transportation including private
vehicles and public coaches in Bangkok. The private vehicle types considered in this study consist of
passenger cars, vans & pickups and motorcycles. While the public bus type contains only fixed-route buses.
We studied the cumulative number of cars and the newly registered cars classified by fuel type from 2014 to
2018. The cumulative number of the cars in each year is calculated from the addition of the newly registered
cars and the cumulative number of last year. Thus, we can compare the PM2.5 formations from various
resources including the annual cumulative number of cars within the range of study and the number of newly
registered cars in 2018.

Emission = Activity Data × Emission Factor (1)

Health Impact Assessment
Health impact assessment from the primary and secondary PM2.5 emissions is based on the Global Guidance
for Life Cycle Impact Assessment Indicators Volume 1; in the part of health impacts of fine particulate
matter [9] showing relevant indicators as in equations (2) and (3). This study uses the health impact
assessment model of UNEP / SETAC including the PM2.5 intake Fraction model [10], Effect Factor model
[11] and Characterization Factors model [9]. Characterization Factors are the results of joining the study of
two variables, intake fractions (iF) and Effect factor (EF). The characterization factors are in a unit of
DALY/kg emitted. Intake fraction (iF) is obtained by combining two variables; fate factor and exposure
factor. Effect factor (EF) is obtained by combining two variables; exposure-response slope and severity
factor. The human health impacts will be in a unit of Disability-adjusted life year (DALY).

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IS = CF × Emission (2)

Where; IS: Impact Score (DALY/pkm), CF: characterization factor (DALY/ kg emitted)

CF = iF × EF (3)

Where; iF: intake fraction (kg inhaled / kg emitted), CF: characterization factor (DALY/ kg emitted), EF:
Effect factor (DALY/kg intake)

The table below shows the parameters from the study of equations (2) and (3). The result demonstrated that
the parameters related to the Impact Score (impact/functional unit) are Characterization factors (DALY/ kg
emitted) as presented in Table 1 and emission PM2.5 (g/pkm) as presented in Table 2. The parameters related
to the Characterization factors (DALY/ kg emitted) are intake fraction from the PM2.5 intake fraction model
[10], effect factor from PM2.5 effect factor model [11], as presented in Table 1.

Table 1 iF from PM2.5 intake fraction model [10], Effect Factor from PM2.5 effect factor model [11] and
Characterization Factors from equation (3) [9]

Cities iF (kg inhaled / kg emitted) EF (DALY/kg intake) CF (DALY/ kg emitted)
Bangkok 5.00E-05 3.92E+01 1.96E-03

RESULTS AND DISCUSSIONS
Emission Inventory
In this study, the fundamental information for calculating emission PM2.5 (g/pkm) consists of the type of
vehicles, type of technology, type of fuel, Emission Factor of the PM2.5, a number of vehicles, and an
occupancy rate (person), as shown in Table 2. the PM2.5 emissions from the transportation in Bangkok are
calculated from 2014 to 2018. The calculation is a top-down approach where vehicle data and numbers
obtained from the Department of Land Transport are coupled with emission factor data from EMEP / EEA
air pollutant emission inventory guidebook 2019 [6] and PRTR release estimation manual for motor vehicles
in Thailand [12]. The accumulation number of cars classified by fuel type showed a downward trend from
2014 to 2018. When comparing the amount of PM2.5 formation causing from the newly registered cars and
the cumulative number of cars in 2018, the result shows that the newly registered cars created less amount of
PM2.5 than the cumulative number of cars. When observing the type of newly registered vehicles in 2018, the
significant exposure was Van & Pick up, following by Passenger car (P.C.), and Motorcycle (M.C.),
respectively. Consequently, we suggest that restricted management of this pollution caused by Van & Pick
up, Passenger car (P.C.), and Motorcycle (M.C.) integrated with the higher technology standards can create a
better air quality.

Table 2 Life Cycle Inventory fine particulate matter formation from private vehicles and public buses
in Bangkok and total Emission PM2.5 (g/pkm)

Type of Technology Fuel Number of Total number PM2.5 Total PM2.5
vehicle Emission vehicles of vehicles Emission (g/pkm)
Year Factor of (Unit) (Unit) (g/pkm)
PM2.5 (g/vkm)
Occupancy rate
(person)

Passenger car (P.C.) Pre-Euro Petrol 0.0022 688,086 19% 3,541,270 3.66E-04 1.13E-03 1.54E-02
[a] Euro I Diesel 0.0022 286,702 8% 1.15 1.53E-04 1.43E-02
2014 Euro II 0.0022 401,383 11% 2.14E-04
Euro III 0.0011 860,107 24% 2.29E-04
Euro IV 0.0011 630,745 18% 1.68E-04
0.2209 161,578 4% 8.64E-03
Pre-Euro 0.0842 67,324 2% 1.37E-03
Euro I 0.0548 94,254 3% 1.25E-03
Euro II

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Type of
vehicle
Year
Technology

Fuel
Emission
Factor of
PM2.5 (g/vkm)
Number of
vehicles

(Unit)
Total number

of vehicles
(Unit)

Occupancy rate
(person)
PM2.5
Emission
(g/pkm)

Total PM2.5
(g/pkm)

Passenger car (P.C.) [a] Euro III LPG 0.0391 201,973 6% 4,760,392 1.91E-03 3.67E-07 1.42E-02
2018 Euro IV CNG 0.0314 148,113 4% 1.15 1.13E-03
Pre-Euro Petrol 0.0022 223.68 0% 1.19E-07 1.92E-08
P.C. [d] Euro I 0.0022 0% 348,728 4.96E-08 1.02E-03 1.00E-
2018N[e] Euro II Diesel 0.0022 93.20 0% 1.15 6.95E-08 02
Euro III 0.0011 130.48 0% 7.45E-08 1.31E-02
Van & Pick up[b] Euro IV LPG 0.0011 279.60 0% 1,188,975 5.46E-08 5.44E-02
2014 Euro IV CNG 0.0011 205.04 0% 1.20 1.92E-08 2.78E-07
Pre-Euro Petrol 0.0022 14% 2.77E-04
Van & 2018 Euro I Diesel 0.0022 72 6% 1,532,013 1.15E-04 2.55E-08 4.95E-02
Euro II CNG 0.0022 688,086 8% 1.20 1.61E-04 6.27E-04
Pick Euro III Petrol 0.0011 286,702 18% 1.73E-04 9.41E-03
up[b] Euro IV 0.0011 401,383 30% 2.90E-04 1.51E-07
Pre-Euro Diesel 0.2209 860,107 3% 6.52E-03 1.01E-04
Van & Euro I 0.0842 1,440,863 1% 95,763 1.04E-03 95,763
Pick Euro II Petrol 0.0548 161,578 2% 1.20 9.43E-04 5.43E-02
up[d] Euro III 0.0391 67,324 4% 1.44E-03
2018N[e] Euro IV Diesel 0.0314 94,254 12% 3.20E-03 8.57E-05
Pre-Euro Petrol 0.0022 201,973 0% 8.99E-08
Euro I 0.0022 557,058 0% 3.75E-08 4.94E-02
Euro II 0.0022 0% 5.24E-08
Euro III 0.0011 224 0% 5.62E-08 1.57E-05
93 3.35E-02
Euro IV 0.0011 130 0% 4.20E-08
280
Euro IV 0.0011 0% 2.55E-08
0.0011 209 66% 6.27E-04
Euro IV 0.0314 34% 9.41E-03
127
Pre-Euro 0.0011 228,517 0% 1.51E-07
Euro I 120,156
Euro II 0.0023 0% 4.16E-07
Euro III 0.0023 55 0% 6.23E-07
Euro IV 0.0023 0% 2.08E-06
0.0011 258 9% 8.05E-05
Pre-Euro 0.0011 387 2% 1.69E-05
Euro I 0.356 1,289 0% 5.32E-04
Euro II 0.117 104,422 0% 2.62E-04
Euro III 0.117 21,916 1% 8.74E-04
Euro IV 0.0783 2,132 73% 4.74E-02
0.0409 3,198 15% 5.20E-03
Pre-Euro 0.0023 10,660 0% 3.23E-07
Euro I 0.0023 863,487 0% 4.84E-07
Euro II 0.0023 181,226 0% 1.61E-06
Euro III 0.0011 258 7% 6.25E-05
Euro IV 0.0011 387 2% 2.08E-05
0.356 1,289 0% 4.13E-04
Pre-Euro 0.117 104,422 0% 2.04E-04
Euro I 0.117 34,809 1% 6.78E-04
Euro II 0.0783 2,132 56% 3.68E-02
Euro III 0.0409 3,198 33% 1.14E-02
Euro IV 0.0011 10,660 2% 1.57E-05
863,487 3.35E-02
Euro IV 511,371 98%
1,641

Diesel 0.0409 94,122

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Type of
vehicle
Year
Technology

Fuel
Emission
Factor of
PM2.5 (g/vkm)
Number of
vehicles

(Unit)
Total number

of vehicles
(Unit)

Occupancy rate
(person)
PM2.5
Emission
(g/pkm)

Total PM2.5
(g/pkm)

Motorcycle (M.C.) [c] ECE 4000 Petrol 0.014 31,677 1% 1.27E-04
2014 Petrol 0.014 63,353 2% 2.55E-04
ECE 4001 0.014 63,353 2% 2.55E-04
3,167,670CO ≤ 13
1.100.014190,0606%5.19E-03
g/km
5.19E-03HC ≤ 50.014316,76710%7.64E-04
0.0035 2,502,459 79%
Motorcycle (M.C.) [c] g/km 0.014 1% 4,984,051 1.27E-03 4.46E-03
2018 CO ≤ 4.5 0.014 31,677 1% 1.10 2.51E-03 4.46E-03
0.014 63,353 1% 8.09E-05
g/km 63,353 1.62E-04
0.014 4% 1.62E-04
HC+NOx 190,060
≤ 3 g/km 0.014 6% 4.85E-04
CO ≤ 3.5 0.0035 316,767 87%
4,318,840 8.09E-04
g/km 2.76E-03

HC+NOx 3.18E-03
≤ 2 g/km

Euro III

ECE 4000

ECE 4001
CO ≤ 13

g/km
HC ≤ 5

g/km
CO ≤ 4.5

g/km

HC+NOx
≤ 3 g/km
CO ≤ 3.5

g/km

HC+NOx
≤ 2 g/km

Euro III

M.C. [d] Euro III Petrol 0.035 472,718 100% 472,718
2018N[e] 1.10

Bus[d] Pre-Euro Petrol 0 2,004 8% 23,924 0.00E+00 0.00E+00 1.62E-03
2014 Euro I 0 1,055 4% 25.10 0.00E+00
Euro II Diesel 0 3,796 16% 0.00E+00 1.53E-03
Bus[d] Euro III CNG 0 3,691 15% 27,739 0.00E+00 9.32E-05 1.50E-03
2018 Petrol 0.333 1,479 6% 25.10 8.20E-04 0.00E+00
Pre-Euro 0.129 778 3% 1.67E-04
Euro I Diesel 0.061 2,802 12% 2.85E-04 1.41E-03
Euro II CNG 0.056 2,724 11% 2.54E-04
Euro III 0.01 5,595 23% 9.32E-05 9.20E-05
Euro III 0 2,004 7% 0.00E+00
0 1,055 4% 0.00E+00
Pre-Euro 0 3,796 14% 0.00E+00
Euro I 0 5,565 20% 0.00E+00
Euro II 0.333 1,479 5% 7.07E-04
Euro III 0.129 778 3% 1.44E-04
0.061 2,802 10% 2.46E-04
Pre-Euro 0.056 3,857 14% 3.10E-04
Euro I 0.01 6,403 23% 9.20E-05
Euro II
Euro III
Euro III

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Type of Technology Fuel Number of Total number PM2.5 Total PM2.5
vehicle Emission vehicles of vehicles Emission (g/pkm)
Year Factor of (Unit) (Unit) (g/pkm)
PM2.5 (g/vkm)
Occupancy rate
(person)

Bus[d] Euro III Petrol 0 353 35% 1,007 0.00E+00 0.00E+00 7.73E-
2018N[e] Diesel 0.0566 279 28% 25.10 6.25E-04 6.25E-04 04

CNG 0.01 375 37% 1.48E-04 1.48E-04

[a] Passenger car (P.C.) did not include LPG. (Focus the accumulated number of vehicles in the year 2014 - 2018)
[b] Van & Pick up did not include LPG and CNG. (Focus the accumulated number of vehicles in the year 2014 - 2018)
[c] Motorcycle (M.C.) did not include Diesel, LPG, and CNG. (Focus the accumulated number of vehicles)
[d] Bus was not included LPG. (Focus the accumulated number of vehicles in the year 2014 - 2018)
[e] Focus the only number of newly registered cars statistics classified by fuel type in the year 2018 (2018N)

Health Impact Assessment
The assessment on human health impacts of life cycle particulate formation in Figure 1 shows a slightly
downward trend of the health impact Score (DALY/pkm) causing the cumulative number of cars collected
from 2014 to 2018. When comparing the number of newly registered and cumulative cars in 2018, the health
impact from the number of the newly registered car was lower than the cumulative number in this year.
While, when considering on the influential type of transportation, we found that Van & Pick up provided the
most harmful air quality, following by Passenger car (P.C.), and Motorcycle (M.C.), respectively.

Figure 2 Comparing the human health impacts of life cycle particulate formation from private vehicles
and public buses in Bangkok

9th International Conference on Environmental Engineering, Science and Management
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