Development of Emissions Inventory for Inland Water
Transport in Bangkok, Thailand
Final Report
Submitted to:
Climate and Clean Air Coalition
& Thailand Pollution Control Department
Submitted by:
Dr. Ekbordin Winijkul Environmental Engineering and Management
Asian Institute of Technology (AIT)
31st August 2020
Table of Content
PROJECT OVERVIEW AND KEY FINDINGS 1
CHAPTER 1
1.1
1.2
1.3
INTRODUCTION
Background
Objectives
Scope of the project
3
3
3
4
CHAPTER 2
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
METHODOLOGY
Framework of methodology
Data collection
Emission estimation
Emission estimation of all boat groups
Excel calculation tool for inland water transport emission
Emission comparison
Emission impact area from inland water transport
Emission reduction policy recommendations
5
5
7
9
14
15
16
16
16
CHAPTER 3
3.1
3.2
SUMMARY OF ACTIVITY DATA AND EMISSION
FACTORS
Activity data
Emission factors
18
18
22
CHAPTER 4
4.1
4.2
4.3
4.4
4.5
4.6
EMISSION INVENTORY RESULTS
Emission inventory results
Emission comparison
Spatial and temporal distribution of emission
Inland water transport emission impact area
Excel emission calculation template for inland water transport
Emission control strategies
24
24
36
38
44
45
46
CHAPTER 5
5.1
5.2
5.3
SUMMARY AND LIMITATIONS
Summary
Recommendations for citizen and boat operator
Limitations in emission estimation
49
49
50
50
REFERENCES
51
APPENDICES
Appendix 1
Appendix 2
Appendix 3
Survey of boat trips and information of each boat group
Emission share of different boat types
Spatial distribution of emission
53
53
66
73
1
PROJECT OVERVIEW AND KEY FINDINGS
The project “Development of emission inventory for inland water transport in Bangkok,
Thailand” aims at estimating emission for inland water transport in Bangkok, focusing on
public boats in Chao Phraya river and Saen Seap canal, and provides recommendation on
the policies to reduce emission from inland water transport in Bangkok. The EI results in
2019 were developed in this study to provide the information to Thailand Pollution Control
Department (PCD) to prepare the management plan for reducing emission from inland water
transport in Bangkok. This project was supported by the Climate and Clean Air Coalition’s
(CCAC) Solutions Center and the United Nation Environment Programme (UNEP). The
project period is October 2019 – August 2020. The team started the preliminary survey in
October 2019 and conducted the main survey during January to May 2020. The emission
inventory template and progress report were submitted to CCAC and PCD in December
2019 and May 2020, respectively. The final MS Excel emission calculation template is
transferred to PCD and CCAC together with this final report. This final report presents the
EI results for inland water transport in Bangkok and policy recommendations to reduce
emission from this sector.
The project collected activity data such as engine load factor, travelling distance, boat trips,
number of passengers, operating time during cruising and idling. The emission factors were
calculated based on NONROAD model methodology proposed by the United States
Environmental Protection Agency which incorporated the effects of engine size, age, load
factor and sulfur content in fuel. Idling emission factors were also estimated to capture
emission from boats while idling during embarking and waiting for passengers at the pier.
Seven categories with thirteen routes of Chao Phraya boats (Green flag, Orange flag, Yellow
flag, No flag, Gold flag, Blue flag and Shuttle boats), two routes of Saen Saep boats and
twenty three routes of cross river ferries were included in this study. The inventory covered
thirteen pollutants, including Hydrocarbon (HC), Carbon Monoxide (CO), Oxides of
Nitrogen (NOx), Non-methane Hydrocarbon (NMHC), Methane (CH4), Ammonia (NH3),
Nitrous Oxide (N2O), Carbon Dioxide (CO2), Sulfur Dioxide (SO2), Particulate Matter
(PM10 and PM2.5), Black Carbon (BC) and Organic Carbon (OC). Then, the emission
reduction policies were proposed to reduce emission from inland water transport.
Key findings of the project are summarized below:
In term of PM2.5, BC, and CO2, emissions from public inland water transports in 2019 were
12.1, 6.1, and 19,011 tons/year, respectively. These emissions were equivalent to the
emission of only 380 in-used buses while the total number of in-used buses in Bangkok were
estimated to be 14,148 in 2019. When considering emission per passenger, the emission per
passenger of inland water boats was 0.006 g/km-passenger which was almost the same as
the emission per passenger of buses and vans. However, when comparing the emission per
passenger with the emission from buses with different standards, this emission of inland
water boats was the same level of the emission of Euro 2 buses while the majority (about
36%) of the bus in Bangkok are Euro 3. As such, inland water boats emit more PM2.5 per
passenger per kilometer than the majority of buses in Bangkok.
The PM2.5 emission was spatially distributed in the study area, and the emission was used as
inputs to run the dispersion model with the meteorological data in 2015. The results showed
that the emission from inland water boats could contribute to a maximum of 1-4 μg m3⁄ of
24-hr average PM2.5 concentration in the distance of one kilometer away from the river or
2
canal, contributing significantly to the PM2.5 concentration and people lives along the river
and canal, and passengers taking boats for daily commute. People living along the San Seap
canal and the Chao Phraya river, especially the area close to the busy piers, should wear
mask or use air purifier in the houses during rush hours. Similarly, boat passengers should
wear mask at the piers and on the boats to reduce personal exposure to the pollutant.
Switching boat engines to Tier 4/Euro 6 with 10 ppm sulfur fuel could reduce 98% of PM2.5
emission from the current situation. Using 10 ppm sulfur fuel with the existing engines
would only reduce PM2.5 emissions by 5% from the current situation. Thus, the best policy
recommendation for PM2.5 emission reduction from boats are promoting the use of 10 ppm
sulfur and switching to Tier 4/Euro 6 engines. Use of electric motors will bring tail-pipe
emissions to zero and can significantly reduce air pollution along the river and canals. Other
recommendations include limiting the age of engines, and reducing idling through better
operations in stations and route planning. The researchers also acknowledge the potential of
inland waterways to help decongest traffic congestion in Bangkok. Expansion and
improvement of inland passenger transport could lead overall reduction of air pollution in
the city, while providing better mobility to its citizens.
This project also developed an MS Excel emission calculation template for inland water
transport which can be used to assess the emission of inland water transport for other cities.
Many major cities in Southeast Asia, and the world, are in major rivers and canals connecting
to the coast. While many inland waterways are used for freight, not many cities are looking
at passenger transport. Bangkok provides a good example in connecting road and waterway
public transport. Inland waterways have the potential to alleviate road traffic and reduce
overall emission from transport.
3
CHAPTER 1
INTRODUCTION
1.1 Background
Every year during November to March, Thailand has been facing with the problem of high
Particulate Matter with diameter less than or equal to 2.5 μm (PM2.5) concentrations in the
Bangkok Metropolitan Region or BMR. The high level of PM2.5 causes adverse effects to
people health and affect economy of Thailand, e.g. affect tourism. Thailand Pollution
Control Department (PCD) with other organizations has urged people to aware of the
problem and protect themselves during the high PM2.5 episodes. PCD also uses air quality
management tools which are emission inventory, air quality monitoring and air quality
modeling to manage air quality during the episode. However, the emission inventory which
is one of the important components in air quality management does not up-to-date and cover
all the sources in Bangkok.
Previous studies suggested that three categories of emission sources; traffic, open burning
and secondary aerosols, contributed about nearly one-third each to the PM2.5 pollution in
Bangkok. However, emission from inland water transport has not been studied and has not
been included in the previous inventories. Old engines on the boats with large amount of
black smoke emission during boat departing and embarking the ports may contribute
significantly to the total emission in Bangkok. Studying the emission from inland waterway
is, thus, necessary to better understand and manage PM2.5 emission sources in Bangkok.
To assist in the continuous effort in maintaining an up-to-date emission inventory, the
template that is easy and convenient for users and specifically for the local sources are
required, and will be developed by the end of 2019. This study will add a separate calculation
sheet to the emission inventory template that will be developed for Bangkok, focusing on
emission calculation for inland water transport in Bangkok. It will then be used to evaluate
control strategies and gives policy recommendation, preparing the policy makers for
management of the coming PM2.5 episodes.
1.2 Objectives
This study aims at estimating emission for inland water transport in Bangkok, focusing on
public boats in Chao Phraya river and Saen Seap canal. The specific objectives of this study
are indicated as follows;
1. To estimate spatial and temporal emission for inland water transport in Chao Phraya
river and Saen Saep canal;
2. To develop an excel calculation tool for inland water transport emission estimation;
3. To identify policies and measures to reduce emission from inland water transport in
Bangkok.
4
1.3 Scope of the project
The scope of this project includes:
1. The selected domains were the Chao Phraya river and Saen Saep canal in Bangkok;
2. The study focused only the Chao Phraya company’s boats and the Cross river ferries
registered in the Marine Department statistics in the Chao Phraya river, and the Saen
Saep boats in the Saen Saep canal which operated during 5.00 a.m. to 8.00 p.m.;
3. The developed emission inventory was based on the survey data in 2019 and 2020;
4. The study focused on primary pollutants which were Particulate Matter (PM2.5 &
PM10), Carbon Monoxide (CO), Black Carbon (BC), Organic Carbon (OC), Carbon
Dioxide (CO2), Methane(CH4), Non-methane hydrocarbon (NMHC), Oxides of Nitrogen
(NOx), Ammonia (NH3), Nitrous Oxide (N2O) and Sulfur Dioxide (SO2);
5. The emission calculation template was developed for the inland water transport
based on an existing Atmospheric Brown Clouds (ABC) emission inventory template
(Shrestha et al, 2013).
5
CHAPTER 2
METHODOLOGY
2.1 Framework of methodology
Figure 2.1 Framework of methodology
This project was separated into three phases; Phase 1: Study area selection; Phase 2: Data
collection; and Phase 3: Emission estimation. In Phase 1, Bangkok where Chao Phraya
express boats, Cross river ferries and Saen Saep express boats was selected for the study
area as presented in Figure 2.2.
Impact area assessment Other sectors in Bangkok
Policy
recommendation
6
Figure 2.2 (a) Thailand (b) Bangkok Metropolitan Administration (BMA)
(c) Chao Phraya river and Saen Saep canal
The methodology for data collection (Phase II) was discussed in Section 2.2 while that for
emission estimation (Phase III) was discussed in Section 2.3 and 2.4. Then, the development
of excel calculation sheet was discussed in Section 2.5. In Section 2.6, emission from this
study was compared to other study. The area of impact by emission from inland water
transport was estimated by AERMOD model and discussed in Section 2.7. Finally, policy
review and recommendation were discussed in Section 2.8.
7
2.2 Data collection
2.2.1 Survey data
As discussed in section 2.1, Chao Phraya boats, Saen Saep boats and cross river ferries were
three major groups of the boats included in this project. The survey was planned to conduct
for one week in each month for each boat group on both weekday and weekend during
different times of the day. The purpose of the survey was to collect information on the
operating time during idling and cruising condition, boat trips, travel distance and engine
load factor (LF) of each group of the boats. Then, number of trips (A) and the operating time
during idling and cruising were used to calculate activity hours (Tt) for each group of the
boat.
For the travelling distance and cruising & idling times of each boat group, this study used
GPS devices (GlobalSat Data Logger DG-100) to identify time and speed of boats in
different operating modes. In Equation 2.1, travelling hour (T) for all groups of boats in the
different routes was estimated by GPS. Also, the LF was estimated by the ratio between
actual and maximum cruising velocities of the boat obtained from the GPS, as presented in
Equation 2.2 (Browning & Bailey, 2006).
Tc = T- Ti (Equation 2.1)
𝐿𝐹 = (𝐴𝑆
𝑀𝑆)3
(Equation 2.2)
Where;
Tc: Time for cruising (hour)
Ti: Time for idling (hour)
T: Total time for one trip (hour)
LF: Load Factor
AS: Actual speed of boat (km/h)
MS: Maximum speed of boat (km/h)
The required sample size for the survey was calculated based on the statistical sampling
method as shown in Table 2.1 with the number of boats from the survey. This survey was
conducted to collect the information from different boat types as planned during the survey
period as shown in Table 2.2.
Table 2.1 Total number of boats, required sample size and number of surveys in different
boats groups
Boat type Total number of
boats under
operation
Calculated sample
size
Number of boats
from survey
Chao Phraya boats 60 52 52
Chao Phraya Tourist
Boatsa (blue flag)
4 4 4
Shuttle boatsa 8 8 8
Cross river ferries 88 73 73
Saen Saep boats 34 32 32
Total 194 169 169 a Number of boats were obtained from chaophrayariverline.com
Note: Chao Phraya Tourist boats or blue flag and Shuttle boats are subgroup of Chao Phraya boats.
8
Table 2.2 Survey period of each boat group
Boat group
2019 2020
October November December January February
Chao Phraya
boats X X X X
Cross river
ferries X X X X X
Saen Saep
boats X X X
2.2.2 Secondary data
Secondary data, such as engine age, engine technology, fuel quality, were obtained from the
Chao Phraya Express Boat Company and the Marine Department. These data were used for
emission factor estimation since the emission factors were affected by age of the engine,
engine model year (technology type), fuel quality (sulfur content) and engine power/size
(USEPA, 2010).
A summary of the required data and the sources of data in Phase 2 (data collection phase) is
presented in Table 2.3.
Table 2.3 Summary of data collection
Required Data Details Sources of data
Primary
data
Total operation time, idling
time and cruising time per
trip
Survey data
Boat trips per week Survey data
Current boat routes Survey data
Distance Survey data
Secondary data
Age of engine Chao Phraya Express Boat Company
Year of engine model Marine Department, Chao Phraya
Express Boat Company
Number of boats Marine Department Statistics, Chao
Phraya Express Boat Company and
Chao Phraya’s website
Fuel quality Chao Phraya Express Boat Company,
Private boat company
Engine power Marine Department Statistics, Chao
Phraya Express Boat Company
Maintenance program Marine Department, Chao Phraya
Express Boat Company
9
2.3 Emission estimation
Emission was calculated by Equation 2.3 for cruising and idling conditions. Emission factors
in this study were adjusted with the operating conditions of the engines, such as LF, engine
power, sulfur content in fuel, age of engine and engine technology.
𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 = [𝐴 ∗ 𝑇𝑡 ∗ 𝑃𝑜𝑤𝑒𝑟 ∗ 𝐿𝐹] ∗ [𝐸𝐹𝑎𝑑𝑗 ∗ 10−6 ] (Equation 2.3)
Where;
A: Activity (trip/month)
LF: Load factor (Unitless) Power: Average power (hp) Tt: Travelling time per trip (h)
EFadj: Adjusted Emission factor (g/hp-h)
2.3.1 Emission factors (EFs) calculation during cruising condition
Emission in this study included HC, CO, NOx, NMHC, CH4, NH3, N2O, CO2, SO2, PM10, PM2.5, BC and OC. The EFs for boat activities were calculated based on USEPA (2010) by
adjusting with local boat conditions. Thus, the developed EFs were suited for Bangkok’s
emission estimation.
In this study, all PM emissions from the boat engines were assumed to be PM10 and 97%
was assumed to be PM2.5 (USEPA, 2010). The USEPA (2010) provided different equations
for each pollutant to calculate emission factors depending on parameters affecting emission
as given in Equation 2.4, 2.5 and 2.6. These Equations were used to calculate EFs for all
boat groups at the cruising mode.
𝐸𝐹𝑎𝑑𝑗(𝐻𝐶,𝑁𝑂𝑥,𝐶𝑂)= 𝐸𝐹𝑠𝑠 ∗ 𝑇𝐴𝐹 ∗ 𝐷𝐹 (Equation 2.4)
𝐸𝐹𝑎𝑑𝑗(𝑃𝑀) = 𝐸𝐹𝑠𝑠 ∗ 𝑇𝐴𝐹 ∗ 𝐷𝐹 − 𝑆𝑃𝑀𝑎𝑑𝑗
(Equation 2.5)
𝐸𝐹𝑎𝑑𝑗(𝐶𝑂2,𝑆𝑂2) = 𝐸𝐹𝑠𝑠 ∗ 𝑇𝐴𝐹 (Equation 2.6)
Where;
SPMadj = adjustment to PM EFs to account for variations in sulfur content (g/hp-hr)
EFss = steady state emission factor (g/hp-hr)
EFadj = final emission factor used after adjustment to account for DF
TAF = transient adjustment factor (unitless)
DF = deterioration factors (unitless)
The emission factors of CO2 and SO2 were calculated based on the Brake Specific Fuel
Consumption (BSFC) which was the fraction between the rate of fuel consumption and the
power produced. The values of the BSFC of nonroad engines was discussed later (Table
2.7).
In Equation 2.4, 2.5 and 2.6, the Transient Adjustment Factor (TAF) and steady-state
emission factor (EFss) of HC, NOx, CO, CO2, SO2 and PM were required. The EFss was
obtained from the emission testing of the new engine (engine age = 0 years) of the specific
engine model year and power. Engine technology (Base/Tier 0, Tier 1, Tier 2, Tier 3 and
Tier 4) and engine power were obtained from the secondary data collection. For TAF that
10
represents the ratio between the transient and steady-state factor, it was set to 1 for boats
(USEPA, 2010).
The deterioration factors (DF) was required for calculating EFs for HC, NOx, CO and PM
in Equation 2.4 and 2.5. It represented the engine’s emission change as a function of the
technology and engine age. The DF was linked with the cumulative usage hours of the
engine which were calculated by multiplying engine age (in years) from the secondary data
collection with average activity (hour per year) from survey. Moreover, the DF was linked
with the engine load factor and the median life at full load (in hours). The equations of DF
are given in Equation 2.7 and 2.8.
𝐷𝐹 = 1 + 𝐴 ∗ (𝐴𝑔𝑒 𝐹𝑎𝑐𝑡𝑜𝑟)𝑏; 𝐹𝑜𝑟 𝑎𝑔𝑒 𝑓𝑎𝑐𝑡𝑜𝑟 ≤ 1 (Equation 2.7)
𝐷𝐹 = 1 + 𝐴; 𝐹𝑜𝑟 𝑎𝑔𝑒 𝑓𝑎𝑐𝑡𝑜𝑟 > 1 (Equation 2.8)
Where:
Age Factor = fraction of median life expanded = cumulative hours x load factor
median life at full load,in hours
A, b = constants for a given pollutant/technology type; b ≤ 1
According to USEPA (2010), there was no data of LF of boats. In this study, the propulsion
load was estimated by the Propeller Law which described the propulsion power varied by
the cube of speed as presented in Equation 2.2. This law assumed that the lower limit of the
LF was approximately 10% of the full load, and could be as low as 2% of the full load when
maneuvering at 5.8 knot (USEPA, 2009). For the boat travelling with the river current
(downstream), the actual speed should be the boat speed minus the river speed. For the boat
travelling against river current, the actual speed should be the boat speed plus the river speed.
However, the speed of the river and canal flows in Bangkok were very slow (0 to 0.94 km/h)
(Department of Drainage and Sewerage, 2020). Thus, the effects of the river and canal flows
were insignificant, and the maximum speed of boat was equal to the maximum speed
acquired from the GPS datalogger.
Median life at the full load (in hours) which was the cumulative hour at which 50% of the
engine population was removed from the fleet are listed by engine power size and engine
type (USEPA, 2010) in Table 2.4 and Table 2.5. The engine type in this study was diesel,
and the engine power information were obtained from the data collection. Thus, the median
life in hours at full load was identified.
Table 2.4 Horsepower classes for median life (USEPA, 2002)
HP Class Diesel (hp) 2-stroke (hp) 4-stroke (hp)
HP1 ≤16 ≤3 ≤6
HP2 17-25 3-16 6-16
HP3 26-50 16-25 16-25
HP4 51-100 26-50 26-50
HP5 101-175 51-100 51-100
HP6 176-300 101-175 101-175
HP7 301-600 176-250 176-250
HP8 601-750 301-600 301-600
11
Table 2.4 Horsepower classes for median life (USEPA, 2002) (Continued)
HP Class Diesel (hp) 2-stroke (hp) 4-stroke (hp)
HP9 751+ 601-750 601-750
HP10 - 751+ 751+
Table 2.5 Expected engine life in hours at full load (USEPA, 2002)
Engine
Type
HP1 HP2 HP3 HP4 HP5 HP6 HP7 HP8 HP9 HP10
Diesel 2500 2500 2500 4667 4667 4667 7000 7000 7000 -
2-stroke
Gasoline
150 200 750 - - - - - - -
4-stroke
Gasoline
200 400 750 1500 3000 3000 3000 3000 3000 3000
CNG/LPG 200 400 750 1500 3000 3000 3000 3000 3000 3000 CNG: compressed natural gas, LPG: liquefied petroleum gas
The constant A in Equation 2.7 and 2.8 can be varied in a wide range of deterioration
patterns. For example, setting A equal to 1.0 would result in emissions at the engine’s
median life being two times the emissions (DF = 1+1) of the new engine. For constant “b”,
it defined as the shape of deterioration function which can be set at any level between 0 and
1. For diesel engine, b was equal to 1. This resulted in a linear pattern of deterioration
meaning the rate of deterioration was constant throughout the median life of an engine.
Because of no information on the deterioration rate of the nonroad diesel engines, the
deterioration factors were selected based on the data derived from the highway engines. The
derivation of the constant “A” for the diesel engines based on technology types are given in
Table 2.6.
Table 2.6 Deterioration Factor for Nonroad Diesel Engines (USEPA, 2010)
Pollutant Relative Deterioration Factor (A)
Base/Tier 0 Tier 1 Tier 2 Tier 3
HC 0.047 0.036 0.034 0.027
CO 0.185 0.101 0.101 0.151
NOx 0.024 0.024 0.009 0.008
PM 0.473 0.473 0.473 0.473
For the emission factor of PM, the adjustment due to variations in fuel sulfur level (SPMadj)
was required (Equation 2.5) since the sulfur in fuel contributed to PM emission. The default
value of sulfur level used in Equation 2.9 was 0.33 weight percent (soxbas). In this project,
the actual sulfur content from the secondary data collection was used for “soxdsl” in
Equation 2.9 and 2.11.
𝑆𝑃𝑀𝑎𝑑𝑗= (𝐵𝑆𝐹𝐶 ∗ 453.6 ∗ 𝑠𝑜𝑥𝑐𝑛𝑣 ∗ 7.0 ∗ 0.01 ∗ (𝑠𝑜𝑥𝑏𝑎𝑠 − 𝑠𝑜𝑥𝑑𝑠𝑙) (Equation 2.9)
Where;
SPmadj =PM sulfur adjustment (g/hp-hr)
BSFC =in-use adjusted brake-specific fuel consumption (lb fuel/hp-hr)
453.6 = conversion from lb to grams
12
7.0 = grams PM sulfate/grams PM sulfur
Soxcnv = grams PM sulfur/grams fuel sulfur consumed = 0.02247
0.01= conversion from percent to fraction
Soxbas = default certification fuel sulfur weight percent = 0.33 weight percent
Soxdsl = episodic fuel sulfur weight percent of sulfur (specified by user)
For the term “soxcnv * 7.0”, soxcnv was the fraction of diesel sulfur that converted to PM
which was 0.02247 for all technology types, and 7.0 was the grams of sulphate PM emission
per gram sulfur. The values of BSFC were derived from the engine test result in the United
States during year 1988 to 1995 (USEPA, 2010). The BSFC of two engine size ranges are
given in Table 2.7.
Table 2.7 Average engine test results for BSFC (USEPA, 2010)
Engine (reference) BSFC (lb/hp-hr) BSFC (g/hp-hr)
Average (50 to 100 hp) 0.408 185.23
Average (≥ 100hp) 0.367 166.62 Note: If the unit of g/kWh is required, use the equation [(g/kWh) × 0.7457 = (g/hp-hr)] for conversion.
From Equation 2.6, emission factors of CO2 and SO2 were calculated by EFss and TAF. For
EFss, it was calculated from the chemical balance of carbon and sulfur in the fuel and the
exhaust gases, as shown in Equation 2.10 and 2.11. The carbon that went to the exhaust as
HC emission was subtracted to correct the amount of the unburned fuel.
𝐶𝑂2 = (𝐵𝑆𝐹𝐶 ∗ 453.6 − 𝐻𝐶) ∗ 0.87 ∗ (44
12) (Equation 2.10)
𝑆𝑂2 = (𝐵𝑆𝐹𝐶 ∗ 453.6 ∗ (1 − 𝑠𝑜𝑥𝑐𝑛𝑣) − 𝐻𝐶) ∗ 0.01 ∗ 𝑠𝑜𝑥𝑑𝑠𝑙 ∗ 2 (Equation 2.11)
Where:
SO2 and CO2 = in g/hp-hr
BSFC = the in-use adjusted fuel consumption in lb/hp-hr
453.6 = the conversion factor from pounds to grams
Soxcnv = the fraction of fuel sulfur converted to direct PM =0.02247
HC = the in-use adjusted hydrocarbon emissions in g/hp-hr
0.01= the conversion factor from weight percent to weight fraction
Soxdsl = the episodic weight percent of sulfur (specified by user)
2 = grams of SO2 formed from a gram of sulfur
2.3.2 Summary of the methodology to calculate the adjusted emission factors (EFadj)
under cruising condition
All parameters and equations for calculating the adjusted emission factors under cruising
condition are given in Table 2.8.
13
Table 2.8 Summary of the parameters and equations for EFadj
Pollutants EFss (g/hp-hr) TAF DF SPmadj
HC Based on
technology and
engine power
1
Eq2.7 & Eq2.8 -
CO Eq2.7 & Eq2.8 -
NOx Eq2.7 & Eq2.8 -
PM Eq2.7 & Eq2.8 Eq2.9
CO2 Eq2.10 - -
SO2 Eq2.11 - -
Emission estimation for BC, OC, NMHC, CH4, NH3 and N2O
For other pollutants, i.e., BC, OC, NMHC, CH4, NH3 and N2O, where USEPA (2010) does
not provide emission factors, this project used information from Winijkul (2015) which
developed EFs based on the on-road heavy duty vehicles and GAINS (2020) as shown in
Table 2.9. The ratios of BC/PM2.5 and OC/PM2.5 are also shown in Table 2.9. For NMHC,
CH4, NH3 and N2O, GAINS (2020) defined the ratios of 0.964:0.036 for NMHC:CH4 and
0.0008 and 0.0048 g/hp-hr for the EFs of NH3 and N2O, respectively.
Table 2.9 Fraction of BC and OC from PM2.5
Vehicle standard BC/PM2.5 OC/PM2.5 Sources
No standard 0.50 0.40
Winijkul (2015)
Euro I 0.65 0.26
Euro II 0.65 0.26
Euro III 0.61 0.34
Euro IV 0.83 0.16
Euro V 0.83 0.16
Euro VI 0.07 0.92
2.3.3 Emission factors during idling condition
For idling condition, the idling factor (IF) developed from the ratio between idling and
cruising emission of the on-road heavy duty diesel vehicles was calculated. Table 2.10 shows
the idling factors used in this project.
Table 2.10 Fractional adjustment of emission factor for idling condition
Pollutants
The fraction adjustment of idling factor
Idling/Cruising Sources
HC 0.84 Tong, Hung, & Cheung (2011)
CO 2.61 Park et al. (2011)
NOx 1.07 Park et al. (2011)
NMHC 0.84 -
CH4 0.84 -
NH3 1.00 *
N2O 1.00 *
14
Table 2.10 Fractional adjustment of emission factor for idling condition (Continued)
Pollutants
The fraction adjustment of idling factor
Idling/Cruising Sources
CO2 1.00 *
SO2 1.00 *
PM10 0.25 -
PM2.5 0.25 Park et al. (2011)
BC 0.25 -
OC 0.25 - * Applied equal to EFs during cruising condition under the assumption that these pollutants didn’t significantly change in
this study. IF for NMHC and CH4 were similar to HC. For PM10, BC and OC, IF were similar to PM2.5.
2.4 Emission estimation of all boat groups
Emission in this project estimated by using Equation 2.3 and 2.12 based on the EFadj
calculated for cruising and idling time separately. For emission during idling condition, the
IF was applied to the adjusted emission factors as presented in Equation 2.12. Next, emission
during cruising and idling were summed up as total emission (Equation 2.13).
Emission for cruising time (Equation 2.3):
𝐸𝑐 = [𝐴 ∗ 𝑇𝑡 ∗ 𝑃𝑜𝑤𝑒𝑟 ∗ 𝐿𝐹 ∗ 𝑃𝑜𝑤𝑒𝑟] ∗ [𝐸𝐹𝑎𝑑𝑗 ∗ 10−6 ]
Emission for idling time:
𝐸𝑖 = [𝐴 ∗ 𝑇𝑡 ∗ 𝑃𝑜𝑤𝑒𝑟 ∗ 𝐿𝐹 ∗ 𝑃𝑜𝑤𝑒𝑟] ∗ [𝐼𝐹 ∗ 𝐸𝐹𝑎𝑑𝑗 ∗ 10−6] (Equation 2.12)
Total Emission: 𝐸 = ∑(𝐸𝑐 + 𝐸𝑖) (Equation 2.13)
Where;
A: Activity (trip/month)
LF: Load factor (Unitless) Power: Average power (hp) Tt: Travelling time per trip (h)
EF: Emission factor (g/hp-h)
Et = Emission for cruising time (tons/month)
Ei = Emission for idling time (tons/month)
IF = Idling factor
EFadj = Final adjustment EFs of idling time and cruising condition (g-pollutant/hp-hr)
2.4.1 Data analysis
Data collected in Phase 2 were analyzed with the following steps:
Step 1: Number of boats and the engine powers were obtained from the Marine Department,
except for the Chao Phraya tourist boat and Shuttle boat which were collected from the
website (chaophrayariverline.com). Then, the Chao Phraya express boat was classified into
subgroup based on routes, operating time and engine power;
15
Step 2: Travelling distance, boat route and boat trip of each boat group were determined by
the survey data;
Step 3: The average hours of each trip during cruising and idling were extracted from the
GPS dataloggers;
Step 4: The EFs were calculated depending on age of engine, engine technology, LF and
sulfur content;
Step 5: The emission of boat groups was calculated and summed to total emission in
Bangkok;
Step 6: Temporal and spatial distribution of emission were estimated based on the survey
data.
2.4.2 Temporal distribution of emission
The emission in each hour of Chao Phraya boats, Saen Saep boats and Cross river ferries
were calculated based on the survey data.
2.4.3 Spatial distribution of emission
The emission per unit area was calculated. In this project, the study area was separated into
the grid cells of 500 x 500 m2. In each cell, total emission from all boats in that cell was
calculated by allocating emission to each grid cell that the boats crossed (cruising emission,
Equation 2.14) and idled (idling emission).
𝐸𝑖 = ∑𝐸𝑖
𝑁𝑔 (Equation 2.14)
Where;
𝐸𝑖 = Total emission of pollutants “i” which released by all “n” boats in grid cell
∑𝐸𝑖 = Total emission of pollutants “i” between each pier
𝑁𝑔 = Number of grid cells between each pier
The emission along each route was calculated and put in the ArcMap v.10.5 for spatial
distribution.
2.5 Excel calculation tool for inland water transport emission
Atmospheric Brown Clouds – Emission Inventory Manual (ABC-EIM) was developed in an
excel calculation or excel-based workbook sheet which can be used as a tool for compilation
and estimation of emission of the ABCs precursors. The current excel tool (ABC tool) for
inland water transport is given in Figure 2.3. User needs to fill in the blue-colored cells
which included activity data and chose the emission factor values. Currently, the excel tool
for calculating emission from inland water transport was simple and could be improved.
Thus, an excel tool for inland water transport was modified based on the calculation
methodology in this study.
16
Figure 2.3 Current excel tool (ABC tool) for inland water transport
2.6 Emission comparison
The emission inventories developed for Bangkok city such as GAINS (2020), Kim Oanh
(2020), were used to compare with the emission from this study. However, in Kim Oanh
(2020), the inland water transport emission was calculated from the estimated fuel
consumption which may not represent actual fuel consumption in the inland water transport
(both passenger and fridge transport). For GAINS (2020), total on-road emission in Bangkok
was selected. However, comparing emission calculated in this study with the previous
studies provided the confirmation of the magnitude of the emission from inland water
transport in this study.
2.7 Emission impact area from inland water transport
Since the emission from inland water transport was generated in two specific area which are
Chao Phraya river and Saen Saep canal, this study used AERMOD model to assess the area
that the emission from inland water transport contribute to the concentration in the local
area. The AERMOD model was setup and run using the meteorological data from Bangna
station (Station ID: 48453) in Bangkok in 2015. The model was run based on the spatial
distribution of PM2.5 emission developed in Section 2.4.3. However, the daily emission
which was estimated during the operating times of different routes was apportioned to 24-
hour emission as the input to the model. Then, the model was run for the maximum 24-hr
average concentration of PM2.5.
2.8 Emission reduction policy recommendation
Table 2.11 provides three scenarios of emission reduction measures which were proposed
in this study. The emission reduction from each measure was estimated using the excel tool
developed in section 2.4.4. Emission standard (Tier system) in Table 3.11 referred to
nonroad standard in the U.S. Comparing with the on-road heavy duty standard in Europe
(Euro standard), in term of PM emission, Tier 0 engine is comparable with Euro 1 engine,
and Tier 4 engine is comparable with Euro 6 engine.
17
Table 2.11: Emission Reduction Measures Investigated in This Study
Scenario 1 Scenario 2 Scenario 3
Switch boat engines to Tier 4
technology and use 10 ppm
sulfur fuel
Use engine with current
technology (Tier 0), but use
10 ppm sulfur fuel (current
was 50 ppm)
Use engine with current
technology (Tier 0), but
reducing idling time by 50%
18
CHAPTER 3
SUMMARY OF ACTIVITY DATA AND EMISSION FACTORS
3.1 Activity data
Survey and secondary data were collected from the Marine Department, Chao Phraya
Express Boat Company and Private Boat Companies (Supatra & Konsong family). The
survey data included travelling distance, current boat routes, boat trips, time during cruising
and idling, and load factor of each boat group. Secondary data were age of engine, year of
engine model, number of boats of each boat group (which were used daily) and fuel quality.
a) Secondary data collection
Number of boats and main engine power were obtained from the Chao Phraya Express Boat
Company and Marine Department. Number of boats and engine sizes used in each boat
groups are summarized in Table 3.1.
Table 3.1 Summary of number of boats and engine sizes for each engine group
Main group Subgroup Number of boats Classification criteria Engine
power(hp)
Chao Phraya
boats
No flag
60
Routes and operation
time
355
Green flag 355x2
Orange flag 355
Yellow flag 355
Gold flag 355x2
Chao
Phraya
Tourist
boat
4
247x2
Shuttle
boat 8 150x2
Saen Saep boats - 34 300-350
Cross river ferries - 88 100-450
The age of the engine and the engine model year were not much different among different
groups, as presented in Table 3.2. From the survey, all boat used on-road engines. Moreover,
secondhand engines were used in some routes of Cross river ferries and Saen Saep boats.
So, the information of both engine age and engine model were estimated by the mechanics
who did the maintenance for these boats.
19
Table 3.2 Age of engine and engine model year of each boat group
Boat Group Age of engine (year) Engine model year
Chao Phraya boats 16-20 < year 1999
Saen Saep boats 15-20 1996-2000
Cross River ferries 15-20 Before 1996/1999
The fuel used in all boats was diesel which had the sulfur content of 0.005% or 50 ppm
which was the same as the sulfur content of on-road vehicles in Thailand. For all boat groups
in this project, only the main engines were used for operation (no auxiliary engine).
b) Survey data
The total of 169 surveys were conducted during October 2019 to February 2020 to collect
the data, i.e. load factor, boat routes, traveling distance, operating time for cruising and idling
condition, and boat trips per month. Table 3.3 – 3.7 show the survey information of the cross
river ferries with low hp, Cross river ferries with high hp, Chao Phraya boats, and Saen Saep
boats, respectively.
Table 3.3 Summary of survey data of the cross river ferries (100-300 hp)
No. Routes LF
Distance
(km/round
trip)
Activity time
(min/trip) Average
Monthly
trip Cruising
time/trip
Idling
time/trip
1. Pakkret-Wat Toey 0.31 0.51 3.21 1.30 4300
2. Pakkret-Watchareewongse 0.24 0.36 3.90 1.19 4232
3. Koh Kret-Wat Sanamnuea 0.43 0.31 2.44 6.09 5276
4. Nonthaburi-
Bangsrimueng 0.23 0.56 5.25 5.15 5900
5. Thewes- Bowornmongkon 0.36 0.71 6.25 4.19 1888
6. Thewes-Karuhabodee 0.38 0.71 6.22 9.34 1248
7. Wang Lang-Tha Phrachan 0.22 0.54 4.30 7.61 2296
8. Wang Lang-Maharaj 0.25 0.51 4.66 4.77 1820
9. Wang Lang-Tha Chang 0.24 0.8 7.37 7.84 2104
10. Tha Chang-Wat Rakang 0.22 0.38 4.01 4.43 1476
11. Tha Tien-Wat Arun 0.33 0.44 4.77 9.10 2344
12. Pakklong Talad-
Kallayanimit 0.31 0.42 5.10 4.70 1508
13. Rachawongse-Dindang 0.24 0.46 4.31 8.32 2972
20
Table 3.4 Summary of survey data of the cross river ferries (100-300 hp) (continue)
No. Routes LF
Distance
(km/round
trip)
Activity time (min/trip) Average
Monthly
trip Cruising
time/trip
Idling
time/trip
14. Sri Phraya-Klongsan 0.24 0.63 4.5 6 4788
15. Oriental-Wat Suwan 0.29 0.44 5.29 4.76 2892
16. Sathorn-Pepsi 0.25 0.63 6.45 7.7 1832
17. Klong Toei-Bangkrachao 0.37 0.75 6.56 0.5 1708
18. Bangna-Taluen 0.31 0.91 6.66 6.0 736
19. Wiboonsri-Phra
Samutchedee 0.55 2.88 18.30 6.26 2776
Table 3.5 Summary of survey data of the cross river ferries (300-750 hp)
No. Routes LF Distance
(km/trip)
Activity time (min/trip) Average
Monthly
trip Cruising
time/trip
Idling
time/trip
1. Sathu Phradit-Klong
Lat Luang 0.32 1.03 8.83 3.44 1228
2 Rama 3- Klong Ladpo 0.31 0.65 5.92 7.93 1812
3. Bangnanok-
Bangnampuengnok 0.31 0.68 6.56 6.00 2040
4. Petra-Phra Pradang 0.28 0.74 7.32 7.12 3256
Table 3.6 Summary of survey data of the Chao Phraya boats (300-750 hp)
Flag
Number of
routes
Routes
LF
Distance
(km/trip)
Activity time (hr/trip) Average
monthly
trip Cruising Idling
Green
Route 1 Pakkret - Sathorn 0.29 27.94 1.28 0.23 340
Route 2 Pakkret -
Nonthaburi 0.29 9.71 0.42 0.11 80
Orange Route 3
Nonthaburi – Wat
Rajsingkorn 0.28 19.91 1.08 0.17 2516
No flag
Route 3
Nonthaburi – Wat
Rajsingkorn 0.28 19.91 1.42 0.40 176
Route 4 Nonthaburi-Wat
Soi Thong 0.28 5.03 0.42 0.12 40
21
Table 3.6 Summary of survey data of the Chao Phraya boats (300-750 hp) (continue)
Flag
Number of
routes
Routes
LF
Distance
(km/trip)
Activity time (hr/trip) Average
monthly
trip Cruising Idling
Yellow
Route 5 Nonthaburi –
Sathorn 0.29 18.14 1.00 0.11 492
Route 6 Sathorn-
Ratburana 0.29 5.26 0.25 0.07 80
Gold Route 7 Sathorn -
Prannok 0.34 5.29 0.42 0.11 1080
Blue Route 8 Phra Arthit-
Sathorn/Asiatique 0.35 9.47 0.50/0.67 0.23/0.24 1472
Shuttle
boat
Route 9
Icon-Siam-
Lhong 1919-
Rachawongse
0.26 2.25 0.38 3.96 1232
Route 10 Icon-Siam -
Sathorn 0.28 1.07 0.24 0.17 1336
Route 11 Icon-Siam -Wat
Muangkae 0.27 0.23 0.19 0.04 1760
Route 12 Icon-Siam -Sri
Phraya 0.29 0.26 0.10 0.07 2016
Table 3.7 Summary of survey data of the Saen Saep boats (300-750 hp)
No.
Routes
LF
Distance
(km/trip)
Activity
time(hr/trip)
Average
monthly trip
Cruising
Idling
1. Sriboonrueng -
Pratunam 0.14 13.25 0.61 0.21 7756
2. Phan Fa Lilat-
Pratunam 0.15 3.98 0.20 0.07 7332
From Table 3.3-3.7, the summary of the survey data were:
- The LF of different boat groups were almost the same since the average ratios were 0.29
for boats with 100-300 hp and 0.28 for boats with 300-750 hp. Therefore, the LF of 0.29
and 0.28 was used for the emission estimation for boats with 100-300 hp and 300-750
hp, respectively.
- The total of 37 routes of traveling distance, cruising and idling time of each boat route
were extracted from GPS dataloggers which included 19 routes with 100-300 hp and 4
routes with 300-750 hp of Cross river ferries, 12 routes of Chao Phraya boats and 2
routes of Saen Saep boats with 300-750 hp.
- The most frequent trips in Cross river ferries, Chao Phraya boats and Saen Saep boats
were Nonthaburi-Bangsrimuang with 5,900 trips/month, Nonthaburi-Wat Rajsingkorn
22
(orange flag) with 2,516 trips/month and Sriboonrueng-Pratunam with 7,756
trips/month.
3.2 Emission Factors
The engine LFs were extracted from the survey data, and the value of 0.29 and 0.28 were
applied to all boat groups with 100-300 hp and 300-750 hp, respectively. Since the boat
engine ages were between 15-20 years, and the engine model year was more than 20 years,
all engines were considered to be Tier 0. This assumption was based on the fact that the first
heavy duty emission standard (comparable to EURO 1/Tier 0, in term of PM emission level)
was implement in 1998 (21 years ago), and the mechanics provided the information that
some of the engines were secondhanded engines. Note that there was no emission standard
for boat in Thailand.
The EFs were calculated based on the methodology discussed in Chapter 2, and the 50 ppmS
fuel would be used in Tier 0 - Tier 2 engines, while 15 and 10 ppmS fuels would be used in
Tier 3 and Tier 4 engines, respectively. The final adjusted EFs of total thirteen pollutants of
the engines size between 100-300 hp and 300-750 hp with Tier0 - Tier4 emission standards
were calculated based on the survey and secondary data collected in this project (Table 3.8
and 3.9 for cruising and idling conditions, respectively).
23
Table 3.8 Final adjusted EFs for cruising condition (g/hp-hr)
Table 3.9 Final adjusted EFs for idling condition (g/hp-hr)
HC CO NOx NMHC CH4 NH3 N2O CO2 SO2 PM10 PM2.5 BC OC
Tier 0 0.367 0.71 3.20 8.58 0.69 0.03 0.001 0.005 528.87 0.016 0.51 0.49 0.25 0.10
Tier 1 0.367 0.33 0.85 5.68 0.32 0.01 0.001 0.005 530.01 0.016 0.24 0.24 0.15 0.04
Tier 2 0.367 0.33 0.85 4.07 0.32 0.01 0.001 0.005 530.01 0.016 0.11 0.10 0.07 0.02
Tier 3 0.367 0.19 0.87 2.51 0.18 0.01 0.001 0.005 530.46 0.005 0.14 0.14 0.08 0.03
Tier 4 0.367 0.13 0.09 0.28 0.13 0.005 0.001 0.005 530.62 0.003 0.01 0.01 0.01 0.001
Tier 0 0.367 0.71 3.20 8.58 0.69 0.03 0.001 0.005 528.87 0.016 0.51 0.49 0.25 0.10
Tier 1 0.367 0.18 1.38 5.99 0.17 0.01 0.001 0.005 530.49 0.016 0.18 0.17 0.11 0.03
Tier 2 0.367 0.17 1.14 4.24 0.16 0.01 0.001 0.005 530.51 0.016 0.08 0.08 0.05 0.01
Tier 3 0.367 0.17 1.17 2.51 0.16 0.01 0.001 0.005 530.51 0.005 0.10 0.10 0.06 0.02
Tier 4 0.367 0.13 0.12 0.28 0.13 0.005 0.001 0.005 530.62 0.003 0.01 0.01 0.01 0.001
Engine Power (hp) Technology Type BSFC (lb/hp-hr)Cruising EFadj (g/hp-hr)
100-300
300-750
HC CO NOx NMHC CH4 NH3 N2O CO2 SO2 PM10 PM2.5 BC OC
Tier 0 0.367 0.60 8.35 9.18 0.58 0.02 0.001 0.005 528.87 0.02 0.13 0.12 0.06 0.02
Tier 1 0.367 0.28 2.21 6.08 0.27 0.01 0.001 0.005 530.01 0.02 0.06 0.06 0.04 0.01
Tier 2 0.367 0.28 2.21 4.35 0.27 0.01 0.001 0.005 530.01 0.02 0.03 0.03 0.02 0.004
Tier 3 0.367 0.16 2.27 2.69 0.15 0.01 0.001 0.005 530.46 0.005 0.04 0.03 0.02 0.01
Tier 4 0.367 0.11 0.23 0.30 0.11 0.00 0.001 0.005 530.62 0.003 0.002 0.002 0.002 0.0003
Tier 0 0.367 0.60 8.35 9.18 0.58 0.02 0.001 0.005 528.87 0.02 0.13 0.12 0.06 0.02
Tier 1 0.367 0.15 3.61 6.41 0.14 0.01 0.001 0.005 530.49 0.02 0.04 0.04 0.03 0.01
Tier 2 0.367 0.14 2.97 4.53 0.14 0.01 0.001 0.005 530.51 0.02 0.02 0.02 0.01 0.003
Tier 3 0.367 0.14 3.05 2.69 0.14 0.01 0.001 0.005 530.51 0.005 0.02 0.02 0.01 0.01
Tier 4 0.367 0.11 0.30 0.30 0.11 0.00 0.001 0.005 530.62 0.003 0.002 0.002 0.002 0.0003
Idling EFadj (g/hp-hr)
100-300
300-750
Engine Power (hp) Technology Type BSFC (lb/hp-hr)
24
CHAPTER 4
EMISSION INVENTORY RESULTS
4.1 Emission inventory results
Emission inventory was developed for all thirteen pollutants for cruising and idling
conditions of the boats. The average monthly survey data (collected during five months
survey period) was multiplied with 12 months to be annual emission assuming that the boat
activities were consistent for all twelve months.
The detailed information of emission of all thirteen pollutants during cruising and idling
condition of all boat groups are provided in Appendix 2. The pollutants discussed in this
Chapter were PM2.5 and CO where the emission show significantly differences between
cruising and idling conditions. Emission of other pollutants show similar patterns to
emission of either PM2.5 or CO.
4.1.1 Total annual emissions
a) Total annual emissions of the Chao Phraya express boats in 2019
The emission was calculated for six groups of boats by the flag color (Green, Orange,
Yellow, No flag, Gold, and Blue flags) and one group with the Icon-Siam destination (shuttle
boat). The annual emission of the Chao Phraya express boat is presented in Table 4.1.
Table 4.1 Annual emission of the Chao Phraya express boat (tons/year)
Pollutant
(tons/year)
Operation Condition
Cruising Idling Total
HC 6.9 1.7 8.6
CO 31.1 23.7 54.8
NOx 82.5 26.0 108.5
NMHC 6.6 1.6 8.2
CH4 0.2 0.1 0.3
NH3 0.01 0.01 0.02
N2O 0.06 0.01 0.07
CO2 5083 1493 6576
SO2 0.17 0.05 0.22
PM10 4.9 0.4 5.3
PM2.5 4.7 0.4 5.1
BC 2.4 0.2 2.6
OC 0.9 0.1 1.0
Among the seven routes of boats, orange flag route dominated the emission which was about
1-10 times higher than emission of other flags (depending on the pollutant), followed by
Shuttle boat, Blue flag, Gold flag, Green flag, Yellow flag and No flag. The Orange flag
boats have the highest emission due to its most frequent boat trips which operated from 5.00
a.m. to 7.00 p.m.
Figure 4.1 illustrates the share of CO and PM2.5 from the seven routes of the Chao Phraya
boat (other pollutants showed similar pattern as provided in Appendix 2).
25
Figure 4.1 Emission share of CO and PM2.5 of Chao Phraya boats
Form Figure 4.1, Orange flag boats accounted for 32.2% and 34.6% of CO and PM2.5
emission, respectively. The emission share between cruising and idling of CO and PM2.5 of
the Chao Phraya boats are presented in Figure 4.2 and 4.3, respectively. The emission shares
between cruising and idling of other pollutants are provided in Appendix 2.
Figure 4.2 Emission share between cruising and idling conditions of CO for seven groups
of Chao Phraya boats
For CO emission (Figure 4.2), Orange flag boats were the main emitter with 32.3% (19.7%
cruising and 12.6% idling) of total emission, followed by 19.6% from Shuttle boats (9.5%
cruising and 10.1% idling), 16.9% from Blue flag boats (8.2% cruising and 8.7% idling),
11.4% from gold flag boats (6.8% cruising and 4.7% idling ), 9.7% from green flag boats
(6.6% cruising and 3.1% idling), 5.5% from yellow flag boats (4.1% cruising and 1.4%
idling), and 4.5% from No flag boats (1.9% cruising and 2.6% idling).
0
5
10
15
20
25
30
35
Orange flag,
32.26%
Shuttle boat,
19.62%
Blue flag,
16.95%
Gold flag,
11.44%
Green flag,
9.66%
Yellow flag,
5.52%
No flag,
4.55%
19.67%
9.5% 8.25% 6.76% 6.58% 4.09% 1.92%
12.59%
10.12%
8.7%
4.68% 3.09%
1.43%2.63%
Pe
rce
nta
ge
(%
)
CO
Cruising Idling
Green flag, 9.66%
Orange flag, 32.26%
Yellow flag,5.52%No Flag, 4.55%
Gold flag, 11.44%
Blue flag, 16.95%
Shuttle boat,
19.62%CO
Green flag, 11.41%
Orange flag, 34.62%
Yellow flag,7.08%No Flag, 3.54%
Gold flag, 11.01%
Blue flag, 14.95%
Shuttle boat, 17.39%PM2.5
26
Figure 4.3 Emission share between cruising and idling conditions of PM2.5 for seven
groups of Chao Phraya boats
In Figure 4.3, similar pattern with CO was observed. Total PM2.5 emission was primality
from Orange flag boats which contributed 34.6% (32.7% cruising and1.9% idling ), followed
by 17.4% from Shuttle boat (15.7% cruising and 1.7% idling), 15.0% from Blue flag boats
(13.6% cruising and 1.4% idling), 11.4% from Green flag boats (10.8% cruising and 0.6%
idling), 11.0% from gold flag boats (10.2% cruising and 0.8% idling), 7.1% from Yellow
flag boats (6.9% cruising and 0.2% idling) and 3.5% from No flag boats (3.1% cruising and
0.4% idling). However, the idling emission from PM2.5 was not as significant as the idling
emission from CO.
b) Total annual emissions of Saen Saep express boats in 2019
The emission of Saen Saep boats included two routes which were Nida and Golden
Mountain routes. The annual emission of the Saen Saep canal boats is presented in Table
4.2.
0
5
10
15
20
25
30
35
Orange
flag,
34.62%
Shuttle
boat,
17.39%
Blue flag,
14.95%
Green flag,
11.41%
Gold flag,
11.01%
Yellow flag,
7.08%
No flag,
3.54%
32.65% 15.74% 13.57% 10.82% 10.23% 6.88% 3.15%
1.97%
1.65%
1.38%
0.59% 0.79%
0.2%
0.39%
Pe
rce
nta
ge
(%
)
PM2.5
Cruising Idling
27
Table 4.2 Annual emission of Saen Saep boats from Nida and Golden Mountain routes
(tons/year), 2019
Pollutants
Cruising Idling
Total
emission
(ton/year)
Nida route
(Sriboonrueng-
Pratunam)
Golden
Mountain route
(Phan Falilat-
Pratunam)
Nida route
(Sriboonrueng-
Pratunam)
Golden
Mountain route
(Phan Falilat-
Pratunam)
HC 4.2 1.3 1.4 0.4 7.3
CO 18.8 5.7 19.4 5.4 49.3
NOx 50.4 15.3 18.1 6.0 89.8
NMHC 4.0 1.2 1.1 0.4 6.7
CH4 0.15 0.05 0.04 0.01 0.25
NH3 0.010 0.001 0.002 0.001 0.01
N2O 0.03 0.01 0.01 0.003 0.05
CO2 3105 945 1042 345 5437
SO2 0.10 0.03 0.03 0.01 0.17
PM10 3.0 0.9 0.2 0.1 4.2
PM2.5 2.9 0.9 0.2 0.1 4.1
BC 1.4 0.4 0.1 0.1 2.0
OC 0.6 0.2 0.1 0.1 1.0
Among two routes, Nida route (Sriboonrueng-Pratunam) dominated the total emission of
PM2.5 (76.5% of total emission) where Golden Mountain route (Phan Falilat-Pratunam)
contributed about 23.5% of total emission. PM2.5 emission mainly contributed during
cruising condition which contributed to 3.8 tons/year while emission during idling condition
was 0.3 tons/year. For CO, the emission during idling condition (24.8 tons/year) was higher
than the emission during cruising condition (24.5 tons/year). Note that Saen Saep canal had
more piers for boats to stop and idling than Chao Phraya river.
Figure 4.4 illustrates the share of total emission of CO and PM2.5 from two routes of Saen
Saep boats (other pollutants showed similar pattern as provided in Appendix 2).
Figure 4.4 Emission share of CO and PM2.5 of Saen Saep boats
From Figure 4.4, Nida route (Sriboonrueng-Pratunam) accounted for 77% of both CO and
PM2.5 emission. The emission share between cruising and idling of CO and PM2.5 of Saen
28
Saep boats are presented in Figure 4.5 and 4.6, respectively. The emission shares between
cruising and idling of other pollutants are provided in Appendix 2.
Figure 4.5 Emission share between cruising and idling conditions of CO for two routes of
Saen Saep boats
For the CO emission (Figure 4.5), Nida route (Sriboonrueng-Pratunam) was the main emitter
with 77.4% (38.1% cruising and 39.3% idling) of total emission. Golden Mountain Route
contributed 22.7% of total CO emission (11.6% cruising and 11.1% idling).
29
Figure 4.6 Emission share between cruising and idling conditions of PM2.5 for two routes
of Saen Saep boats
From Figure 4.6, cruising emission dominated the emission of PM2.5 which was different
from the case of CO emission (Figure 4.5). The total PM2.5 emission was primality from
Nida route (Sriboonrueng-Pratunam) which contributed 76.5% of total emission (70.7%
cruising and 5.9% idling).
30
c) Total annual emissions of cross river ferries in 2019
The emission was calculated from twenty-three routes of the cross river ferries. The annual emission of cross river ferries is presented in Table
4.3.
Table 4.3 Annual emission of the cross river ferries by different routes (tons/year), 2019
Routes HC CO NOx NMHC CH4 NH3 N2O CO2 SO2 PM10 PM2.5 BC OC
Pakkret-
Wat Toey 0.2 1.5 2.3 0.2 0.01 0.0001 0.001 136.0 0.004 0.1 0.1 0.04 0.01
Pakkret-
Watchareewongse 0.2 2.1 2.3 0.1 0.01 0.0001 0.001 130.7 0.004 0.03 0.03 0.02 0.004
Koh Kret-
Wat Sanamnuea 0.7 7.3 9.3 0.6 0.03 0.001 0.005 546.2 0.01 0.2 0.2 0.1 0.1
Nonthaburi-
Bangsrimueng 0.6 6.4 8.4 0.6 0.02 0.001 0.004 494.7 0.02 0.2 0.2 0.1 0.1
Thewes-Bowornmongkon 0.1 0.6 0.9 0.1 0.00 0.0001 0.001 55.4 0.002 0.04 0.03 0.01 0.001
Thewes-Karuhabodee 0.2 1.7 2.2 0.2 0.01 0.0001 0.001 129.9 0.004 0.1 0.1 0.0 0.01
Tha Phrachan-Wang
Lang 0.3 3.0 4.0 0.3 0.01 0.0001 0.003 232.5 0.01 0.1 0.1 0.1 0.02
Wang Lang-Maharaj 0.2 2.0 2.7 0.2 0.01 0.0001 0.002 160.2 0.01 0.1 0.1 0.0 0.02
Wang Lang-
Tha Chang 0.3 2.9 4.2 0.3 0.02 0.0001 0.002 251.0 0.01 0.1 0.1 0.1 0.03
Wat Rakang-
Tha Chang 0.2 1.9 2.5 0.2 0.01 0.0001 0.001 148.2 0.00 0.1 0.1 0.0 0.02
Tha Tien-
Wat Arun 0.4 4.5 5.5 0.4 0.01 0.001 0.003 321.3 0.01 0.1 0.1 0.1 0.02
Pakklong-Kallayanimit 0.1 1.3 1.9 0.1 0.01 0.0001 0.001 110.9 0.00 0.1 0.1 0.0 0.01
31
Table 4.3 Annual emission of the cross river ferries by different routes (tons/year), 2019 (continue)
Routes HC CO NOx NMHC CH4 NH3 N2O CO2 SO2 PM10 PM2.5 BC OC
Rachawongse-Dindang 0.2 2.4 3.1 0.2 0.01 0.0001 0.001 181.5 0.01 0.1 0.1 0.0 0.02
Sri Phraya-Klongsan 0.7 7.0 9.4 0.6 0.03 0.001 0.005 551.6 0.02 0.3 0.3 0.1 0.1
Oriental-
Wat Suwan 0.2 2.3 3.3 0.2 0.01 0.0001 0.002 196.9 0.01 0.1 0.1 0.1 0.02
Sathorn-Pepsi 0.3 2.8 4.0 0.3 0.02 0.0001 0.002 238.4 0.01 0.1 0.1 0.1 0.03
Sathupradit-Klong Lat
luang 0.3 3.2 4.5 0.3 0.02 0.0001 0.002 263.2 0.01 0.1 0.1 0.1 0.03
Rama 3-
Klong Latpo 0.3 3.2 4.3 0.3 0.01 0.0001 0.003 252.5 0.01 0.1 0.1 0.1 0.03
Klong Toei-Tuapai 0.0 0.1 0.2 0.0 0.00 0.0001 0.000 13.4 0.0004 0.01 0.01 0.01 0.000
Bangna-Taluen 0.1 0.7 1.0 0.1 0.00 0.0001 0.001 58.2 0.002 0.04 0.03 0.01 0.001
Bangnanok-
Bangnampuengnok 0.3 2.6 3.8 0.3 0.02 0.0001 0.002 225.0 0.01 0.1 0.1 0.1 0.03
Petra-Phra Pradang 0.4 3.9 5.6 0.4 0.02 0.001 0.003 331.7 0.01 0.2 0.2 0.1 0.04
Wiboonsri-Phra
Samutchedi 0.6 5.0 7.9 0.6 0.02 0.001 0.004 473.6 0.02 0.3 0.3 0.2 0.1
Total emission
(ton/year) 6.7 68.1 93.4 6.4 0.3 0.01 0.05 5503 0.2 2.7 2.6 1.3 0.6
Among the twenty-three routes, five routes which mainly dominated the emission were Sri Phraya-Klongsan route, Nonthaburi-Bangsrimuang,
Koh Kret-Wat Sananua, Wiboonsri-Phra Samutchedee, and Petra-Phra Pradang routes.
32
Figure 4.7 illustrates the share of CO and PM2.5 from five routes which mainly dominated
the emission of Cross river ferries. For other pollutants, the emissions were provided in
Appendix 2.
Figure 4.7 Emission share of CO and PM2.5 from five routes which mainly contributed the
total emission of cross river ferries
Form Figure 4.7, the highest emission routes were Koh Kret-Wat Sanamnua route which
accounted to 10.7% of CO emission and Wiboonsri-Phra Samutchedee route which
accounted to 10.9% of PM2.5 emission. The emission share between cruising and idling of
CO and PM2.5 of the Chao Phraya boat are presented in Figure 4.8 and 4.9, respectively.
Figure 4.8 Emission share between cruising and idling conditions of CO for five highest
emission routes of cross river ferries
Koh Kret-Wat Sanamnua, 10.65%
Sri Phraya-Klongsan, 10.30%
Nonthaburi-
Bangsrimuang, 9.36%
Wiboonsri-Phra
Samutchedee, 7.39%
Tha Tien-Wat Arun,
6.54%
Others, 55.76%
COWiboonsri-Phra Samutchedee, 10.85%
Sri Phraya-Klongsan, 9.69%
Nonthaburi-
Bangsrimuang, 8.53%
Koh Kret-Wat
Sanamnua, 8.53%
Petra-Phra Pradang,
6.59%
Others, 55.76%
PM2.5
33
For the CO emission (Figure 4.8), Koh Kret-Wat Sanamnua route was the main emitter with
10.7% (1.3% cruising and 8.8% idling) of total emission, followed by 10.3% from Sri
Phraya-Klongsan (1.3% cruising and 9.4% idling), 9.4% from Nonthaburi-Bang Srimueng
(1.3% cruising and 1.8% idling), 7.4% from Wiboonsri-Phra Samutchedee (2.2% cruising
and 5.2% idling), and 6.5% from Tha Tien-Wat Arun (0.6% cruising and 5.9% idling).
Figure 4.9 Emission share between cruising and idling conditions of PM2.5 for five highest
emission routes of cross river ferries
From Figure 4.9, cruising emission dominated the total emission, but the ratio between
cruising and idling emission were different among routes due to the times when each boat
stopped and waited at the piers. Total PM2.5 emission was primality from Wiboonsri-Phra
Samutchedee route which contributed 10.8% (8.9% cruising and 1.9% idling), followed by
9.7% from Sri Phraya-Klongsan route (6.2% cruising and 3.5% idling), 8.5% from
Nonthaburi-Bangsrimueng route (5.4% cruising and 3.1% idling ), 8.5% from Koh-Kret-
Wat Sanamnua route (5.0% cruising and 3.5% idling ), and 6.6% from Petra-Phra Pradang
route (3.9% cruising and 2.7% idling ).
34
4.1.2 Annual emission of inland water transportation in Bangkok in 2019
Table 4.4 provides emission of all thirteen pollutants for the three groups of boats in this
study.
Table 4.4 Annual emission of inland water transport in Bangkok (tons/year), 2019
Pollutants
Total emission in tons/year
Chao Phraya
boats Saen Saep boats
Cross river
ferries Total emission
HC 10.2 7.2 6.7 24.1
CO 78.4 46.4 68.1 192.9
NOx 134.4 89.8 93.4 317.6
NMHC 9.8 6.8 6.4 23
CH4 0.3 0.3 0.3 0.9
NH3 0.01 0.01 0.01 0.03
N2O 0.09 0.05 0.05 0.2
CO2 8071 5437 5503 19011
SO2 0.2 0.2 0.2 0.6
PM10 5.6 4.2 2.7 12.5
PM2.5 5.4 4.1 2.6 12.1
BC 2.7 2.0 1.3 6.1
OC 1 0.8 0.6 2.4
The total HC, CO, NOx, NMHC, CH4, NH3, N2O, CO2, SO2, PM10, PM2.5, BC, OC emissions
in 2019 from inland water transport in Bangkok were 24.1, 192.9, 317.6, 23, 0.9, 0.03, 0.2,
19011, 0.6, 12.5, 12.1, 6.1 and 2.4 tons/year, respectively. Chao Phraya boats dominated
total emissions in the domain.
PM2.5 emission of Chao Phraya boats was 5.4 tons/year, followed by Saen Saep boats (4.1
tons/year) and Cross river ferries (2.6 tons/year). For CO emission which mainly contributed
while idling, Chao Phraya boats also largely dominated the total emission with 78.4
tons/year.
Figure 4.10 Total emission share of CO and PM2.5 for Chao Phraya boats, Saen Saep boats
and cross river ferries
35
From Figure 4.10, Chao Phraya boats accounted for 40.0% and 44.9% of CO and PM2.5
emission, respectively. The emission share between cruising and idling of CO and PM2.5 of
three boat groups are presented in Figure 4.11 and 4.12, respectively.
Figure 4.11 CO emission share between cruising and idling conditions
For CO emission (Figure 4.11), Chao Phraya boats were the main emitter with 40.6% of
total emission (28.4% cruising and 12.3% idling), followed by cross river ferries with 35.3%
(6.4% cruising and 28.9% idling), and Saen Saep boats with 24.1% (12.7% cruising and
11.4% idling). When considering the ratio between cruising and idling, the ratios of CO were
2.3:1, 1.1:1 and 0.2:1 for Chao Phraya boats, Saen Saep boats, and cross river ferries,
respectively. CO emission of cross river ferries during idling was significantly higher than
the others since the cross river ferries had more routes and shorter distance between piers,
making them spending long time for idling. Moreover, some routes spent more time waiting
(idling) at pier than cruising time.
0
5
10
15
20
25
30
35
40
45
Chao Phraya boats, 40.64% Saen Saep boats, 24.06% Cross river ferries, 35.30%
28.38% 12.7% 6.43%
12.27%
11.35% 28.87%
Perc
enta
ge (
%)
CO
Cruising Idling
36
Figure 4.12 PM2.5 Emission share between cruising and idling conditions
From Figure 4.12, total PM2.5 emission was primarily from Chao Phraya boats which
contributed 44.9% (42% cruising and 2.9% idling), followed by Saen Saep boats which
contributed 33.8% (31.2% cruising and 2.6% idling), and cross river ferries which
contributed 21.3% (14.6% cruising and 6.7% idling).
The ratios between cruising and idling emission of PM2.5 were 14.4:1, 11.8:1 and 2.2:1 for
Chao Phraya boats, Saen Saep boats and cross river ferries, respectively. Cruising emission
from Chao Phraya boats was much higher than idling emission since they had the longest
traveling distance (5-24 km) among the boat groups, and some routes had the total cruising
time approximately 6 times higher than idling time for one trip.
4.2 Emission comparison
The emission in this project was compared with the emission from other studies. However,
there was no direct comparison of emission from this study with the other studies since the
scopes were not the same.
- Kim Oanh (2020): emission inventory includes not only public inland water
transport, but also all water transport (public and goods transport) in Bangkok.
- GAINS (2020): emission inventory includes all on-road transport in Bangkok.
Table 4.5 shows the comparison of the emission inventories developed for on-road mobile
source in Bangkok, i.e. GAINS (2020), emission inventory developed for all inland water
transport in Bangkok, i.e. Kim Oanh, 2020, and emission inventory developed in this project
(only public inland water transport).
0
5
10
15
20
25
30
35
40
45
Chao Phraya boats, 44.89% Saen Saep boats, 33.79% Cross river ferries, 21.32%
41.97%31.15%
14.62%
2.92%
2.64%
6.69%
Perc
enta
ge (
%)
PM2.5
Cruising Idling
37
Table 4.5 Emission comparison between this study and other studies
Pollutants GAINS (2020)
(ton/year)
Kim Oanh (2020)
(tons/year)
This study
(tons/year)
Emission year 2020 2018 2019
Sector On-road vehicles in
Bangkok
Public and goods
inland water transport
in Bangkok
Public inland water
transport in Bangkok
HC N/A 98 24
CO 67760 243 193
NOx 41470 674 317
NMHC N/A 95 23
CH4 2460 3 0.9
NH3 210 64 0.03
N2O 170 19 0.2
CO2 4,430,000 N/A 19011
SO2 90 N/A 0.6
PM10 2500 73 13
PM2.5 2180 70 12
BC 1240 28 6
OC 740 11 2 N/A=not available
The comparison of emission between Kim Oanh (2020) and this study showed that the
emission estimated in Kim Oanh (2020) was higher than the emission in this project by 1 to
5 times, except CH4 and N2O. In particular, PM2.5 and CO emission from this project were
about 20% and 80% of the PM2.5 and CO emission from Kim Oanh (2020). The major cause
of the differences was that Kim Oanh (2020) included emission from all inland water
transport (public and goods transport), but this study included only emission from public
inland water transport. Also, activity data from Kim Oanh (2020) was estimated from the
projection of total fuel consumption of inland water activities in Thailand to the fuel
consumption in Bangkok while the fuel consumption from survey was used in this study.
From Table 4.5, when comparing emission estimation in this project with the emission from
GAINS (2020) which represented total on-road emission in Bangkok, the emission from this
project was approximately 0.01-0.8% of the emission from on-road transport. Thus, it can
be concluded that emission from the inland water transports contributed less than 1% of total
on-road transport in Bangkok. However, with the limited operating routes of the inland water
transport, the emission from inland water transport was concentrated along the Chao Phraya
river and Saen Seap canal which is different from the emission from on-road vehicles which
contributed to all over Bangkok area. Thus, the impact area of emission from inland water
transport was concentrated on the local communities along the water ways, and it was
explained by the dispersion model results in Section 4.4.
To put the emission comparison on the same scale, Table 4.6 shows the emission comparison
between public boat emission in this project and on-road public transport in Bangkok in term
of grams of emission per kilometer per passenger.
38
Table 4.6 Emission comparison between this study and on-road vehicles in Bangkok (g/km-
passenger)
Pollutants Kim Oanh (2020)
(g/km-passenger)
This study
(g/km-passenger)
Emission
year 2018 2019
Sector On-road vehicles in
Bangkok Public inland water transport in Bangkok
Type Van Bus Chao Phraya
boats
Saen Saep
boats
Cross river
ferries
HC N/A N/A 0.01 0.01 0.72
CO 0.65 0.53 0.06 0.05 7.40
NOx 0.07 0.10 0.12 0.10 10.20
NMHC 0.03 0.06 0.009 0.007 0.700
CH4 0.14 0.02 0.001 0.001 0.026
NH3 0.004 0.001 0.001 0.001 0.001
N2O 0.001 0.001 0.001 0.001 0.005
CO2 16 5 7 6 602
SO2 0.001 0.005 0.001 0.001 0.018
PM10 0.003 0.007 0.006 0.005 0.296
PM2.5 0.003 0.007 0.006 0.004 0.287
BC N/A N/A 0.003 0.002 0.144
OC N/A N/A 0.001 0.001 0.057 N/A=not available
Note: Van = 13 passengers, not included driver, Bus = 65 passengers (average between 50 and 80), Chao Phraya boats and
Saen Saep boats = 70 passengers, not included driver and ticket taker (average between 60 and 80), and Cross river ferries
= 40 passengers, not included driver and ticket taker (average between 30 and 50).
From Table 4.6, the emission intensity in term of g/km-passenger of Chao Phraya boats and
Sean Saep boats were almost the same or slightly lower than those of vans and buses.
However, the emission intensity in term of g/km-passenger of Cross river ferries was much
higher than those of vans and buses due to short operating distances of the ferries. However,
when comparing the emission per passenger with the emission from buses with different
standards, this emission of inland water boats was the same level of the emission of Euro 2
buses while the majority (about 36%) of the bus in Bangkok are Euro 3. As such, inland
water boats emit more PM2.5 per passenger per kilometer than the majority of buses in
Bangkok.
4.3 Spatial and temporal distribution of emission
4.3.1 Spatial emission distribution
Spatial distribution of emission in this project was done in ArcMap for the total, cruising
and idling emission in tons/year in the resolution of 500 x 500 m2 grid map. The spatial
distribution of CO and PM2.5 emission are provided in Figure 4.13 and 4.14, respectively.
The spatial distribution of other pollutants are provided in Appendix 3.
39
-
(1) (2) (3)
Figure 4.13 CO emission from inland water transport in tons/year in a grid map of 500x500 m2:
(1) cruising condition (2) idling condition (3) total emission
40
(1) (2) (3)
Figure 4.14 PM2.5 emission from inland water transport in tons/year in a grid map of 500x500 m2:
(1) cruising condition (2) idling condition (3) total emission
41
From Figure 4.13 and 4.14, the emission distribution patterns are nearly similar between the
two pollutants. The observations were as follow:
- The emission in the red, orange and yellow grids showed high emission intensity area
from Chao Phraya river boats since these piers had most frequent trips (green flag,
orange flag, yellow flag, no flag, gold flag, blue flag boats, and four routes of shuttle
boat). Moreover, these piers were in the vicinity of the tourist routes, and these piers
were functioned as the interchange piers from Chao Phraya boats to cross river ferries
and BTS sky train to downtown. High emission intensity area of the Saen Saep canal
boats were observed in the yellow grids contributed at the piers which were at the final
destination of each route and the interchange piers.
- For the other grids with low emission, low emission intensity was observed because
some flags of the Chao Phraya boats did not operate every day, such as Green flag, No
flag and Yellow flag boats, resulting in lower emission among those piers. Also, the
green grid on the south of the river line in Figure 4.13 and 4.14 referred to emission from
the cross river ferries which showed no connection between these piers to the other parts
of the map.
When comparing emission between Chao Phraya river and Saen Saep canal, the Chao
Phraya river has significantly higher emission intensity than emission intensity of the Saen
Saep canal due to the fact that Chao Phraya river had many more boats. Although the Saen
Saen canal is located near downtown, but the Chao Phraya river has more tourist attraction
points than the Saen Saep canal.
4.3.2 Temporal emission distribution
There was no significant monthly variation observed during the survey and by reviewing the
data from the Marine Department. Thus, this project provided the temporal distribution of
emission in term of hourly emission over the survey period. Figure 4.15 and 4.16 provide
PM2.5 and CO emission of cruising and idling condition during weekday and weekend.
42
Figure 4.15 Temporal distribution of PM2.5 emission during cruising and idling condition during weekday and weekend
(Note: All four graphs were provided in different scales)
43
Figure 4.16 Temporal distribution of CO emission during cruising and idling condition during weekday and weekend
(Note: All four graphs were provided in different scales)
44
Overall, during 6.00-9.00 a.m. and 3.00-7.00 p.m. on the weekday were rush hours for
commuters, resulting in higher frequency of boat trips to accommodate more
passengers/commuters. Thus, the higher emission was also observed during these rush hours.
The boat group which dominated the PM2.5 emission was the Chao Phraya boats during
cruising condition, while the boat group which dominated the CO emission was the cross
river ferries during idling condition.
During the weekend, there was no pattern of rush hours for Chao Phraya boats. The emission
of Chao Phraya river boats started to increase around 8.00-9.00 a.m. and continued about the
same level until 6.00 p.m. This emission pattern was observed since the tourist routes which
included Blue flag boats (tourist boats), Gold flag and shuttle boats started operating for
Icon-Siam destination during the weekend. For Cross river ferries and Saen Saep canal boats,
the emission pattern showed two peaks, one in the morning and another in the afternoon
which were the same as the pattern observed during the weekday.
4.4 Inland water transport emission impact area
The AERMOD model was run and provided maximum 24-hr average PM2.5 concentration
as presented in Figure 4.17 (right).
Figure 4.17 Inland water transport emission impact area: (Left) PM2.5 emission inventory;
(Right) Max. 24-hr average PM2.5 concentration in μg m3⁄
From Figure 4.17, inland water transport in this study contributed to the maximum of 1-4
μg m3⁄ of 24-hr average PM2.5concentration in the area of 1 km away from the river and
canal while the lower concentration is expanded to 4-5 km away from the river. Thus,
Maximum 24-hr
average PM2.5
concentration in μg m3⁄
45
emission from inland water transport contribute more to local community along the water
ways and their passengers taking boats for daily commute.
4.5 Excel emission calculation template for inland water transport in Bangkok
The emission inventory template for inland water transport in an existing ABC-EIM for
Bangkok has been updated with the information collected in this study (Figure 4.18).
Figure 4.18 Example of an updated version of template for inland water calculation
The updated version added the following information to the template:
- Two engine sizes (100 < hp < 300 and 300 < hp < 750) which are commonly used for
inland water transport in Bangkok;
- Emission of all thirteen pollutants, including HC, CO, NOx, NMHC, CH4, NH3, N2O,
CO2, SO2, PM10, PM2.5, BC, OC;
- Input cell for Sulfur content in fuel which affected SO2, PM10, PM2.5, BC, OC emission;
- Emission factors for emission standards of Tier 0 to Tier 4;
- Separated calculation between cruising and idling emission.
According to the previous version of the calculation template, users were required to fill in
activity data and chose the emission factor values. When compared with an updated version,
beside activity data and emission factor values, users had more information to select, i.e. the
engine size, engine technology and fuel quality, cruising and idling condition. However, the
template, even though include lots of parameters in an updated version, it was still based on
fuel consumption as the only required input for easy update and use by different users. The
other inputs were provided as suggested values in the template which could be selected by
the users.
46
4.6 Emission control strategies
4.6.1 Reviewing the existing emission reduction measures
By reviewing the emission reduction practices obtained during the secondary data collection,
engine maintenance, i.e. having a checklist and a system repaired, were the only options
currently implemented in the Chao Phraya express boat, the Cross river ferries and the Saen
Saep boats.
Based on the national agenda for PM2.5 concentration reduction (PCD & MNRE, 2019),
emission reduction measures of the inland water transport have not been included in the
report. However, there was a list of emission reduction measures for on-road transport which
included: 1) roadside inspection; 2) lower sulfur content in fuel; and 3) regulating Euro VI
engine standard. Thus, in this study, emission reduction scenarios were developed based on
these three measures.
4.6.2 Emission reduction scenarios
This study investigated three emission reduction scenarios for inland water transport which
were:
- Scenario 1: Changing engines to Tier 4 technology and using 10 ppm sulfur fuel;
- Scenario 2: Using engine with current technology (Tier 0), but using 10 ppm sulfur fuel
(the current sulfur content in fuel was 50 ppm);
- Scenario 3: Using engine with current technology (Tier 0), but reducing idling time by
50%.
The PM2.5, CO and SO2 emission reduction from the three scenarios were provided in Table
4.7, Table 4.8 and Table 4.9, respectively.
Table 4.7 PM2.5 emission from the three emission reduction scenarios (tons/year)
Boat Groups Baseline
(2019)
S1:
Tier4+0.001%S
S2:
Tier0+0.001%S
S3: Tier 0+ 50%
Idling time
Chao Phraya
boats 5.4 0.1 5.3 5.2
Saen Saep boats 4.0 0.1 4.1 3.9
Cross river ferries 2.5 0.1 2.1 2.2
Total emission
(tons/year) 12.1 0.3 11.5 11.3
% Reduction
from baseline - 97.7% 4.5% 6.4%
From Table 4.7, PM2.5 emission of the first scenario were reduced by 97.7% from the
baseline. For the second and the third scenarios, PM2.5 emissions of Chao Phraya boats, Saen
Saep boats and cross river ferries didn’t change much from the baseline (about 5%
reduction).
47
It could be summarized that the PM2.5 emission was highly related to the engine technology.
In this scenario, replacing all Tier 0 by the cleanest engine technology (Tier 4), and using
low sulfur fuel (10 ppm) which was required for the use of Tier 4 engine, showed
dramatically reduction in PM2.5 emission. The other two scenarios, thus, do not contribute
much to the emission reduction of PM2.5 since the engines were still Tier 0 engines.
Table 4.8 CO emission from the three emission reduction scenarios (tons/year)
Boat Groups Baseline
(2019)
S1:
Tier4+0.001%S
S2:
Tier0+0.001%S
S3: Tier 0+ 50%
Idling time
Chao Phraya
boats 78.4 2.6 78.4 43.0
Saen Saep boats 46.4 1.7 46.4 35.4
Cross river ferries 68.1 7.9 68.1 39.9
Total emission
(tons/year) 192.9 12.2 192.9 118.3
% Reduction
from baseline - 93.7% 0% 38.6%
From Table 4.8, the highest emission reduction also found in scenario one (reaching 93.7%
of CO emission reduction). However, when considering the scenario three which the idling
time was reduced to 50%, CO emissions were reduced by 38.6% (from 192.9 tons/year to
118.34 tons/year). Since there was no cost involvement in this scenario, changing the current
operation practice by reducing the idling time could contribute significantly to the CO
emission reduction.
Table 4.9 SO2 Emission from the three emission reduction scenarios (tons/year)
Boat Groups Baseline
(2019)
S1:
Tier4+0.001%S
S2:
Tier0+0.001%S
S3: Tier 0+ 50%
Idling time
Chao Phraya
boats 0.2 0.06 0.2 0.2
Saen Saep boats 0.2 0.05 0.2 0.1
Cross river ferries 0.2 0.06 0.1 0.1
Total emission
(tons/year) 0.6 0.17 0.5 0.4
% Reduction
from baseline - 71.7% 16.7% 25.0%
From Table 4.18, SO2 emission of all boat groups was reduced by 71.7% (from 0.6 tons/year
to 0.17 tons/year) in the first scenario, 16.7% (from 0.6 tons/year to 0.5 tons/year) in the
second scenario, and 25% (from 0.6 tons/year to 0.45 tons/year) in the third scenario.
Moreover, reducing sulfur dioxide emission also contribute to an additional reduction of
secondary PM2.5 emission.
Although the first scenario still showed much higher emission reduction of SO2 compared
to other scenarios, but the cost associated with the first scenario was much more than the
cost of the other two scenarios. Thus, in this case, the combination of the policy between
scenario two and three which may require some investment (for fuel quality improvement),
can provide significant SO2 emission improvement.
48
4.6.3 Emission reduction policy recommendations
From the section 4.6.2, the emission reduction results from the three scenarios were analyzed
and used to provide recommendations for inland water transport emission management as
follow:
1. Immediate policies: Promoting inspection and maintenance as well as idling reduction
campaign;
2. Short-term policies (1-3 years): Using 10ppm sulfur fuel for inland water transport in
Bangkok;
3. Long-term policies (4-6 years): Limiting the ages of the engines for inland water
transport in Bangkok, and switching boat engines to Tier 4/Euro 6 or electric engines.
49
CHAPTER 5
SUMMARY AND LIMITATIONS
5.1 Summary
An emission inventory results in this project developed for the public inland water transport
activities in Bangkok in 2019. The survey of activity data which were engine load factor,
travelling distance, boat trips and operating time during cruising and idling was conducted
during October 2019 to February 2020. The emission factors were calculated based on the
methodology in USEPA (2010) which incorporated the effects of engine size, age, load
factor and sulfur content in fuel in Bangkok. The EI covered thirteen pollutants, including
HC, CO, NOx, NMHC, CH4, NH3, N2O, CO2, SO2, PM10, PM2.5, BC and OC. Thirteen routes
of Chao Phraya boats, two routes of Saen Saep boats and twenty-three routes of cross river
ferries were included in this study. Then, the emission was spatially distributed in the grid
of 500x500 m2 covering the study area. The temporal distribution of the emission was
developed in term of emission during different hours in a day while the monthly emission
was assumed to be constant. Finally, the policy recommendations were proposed based on
the three emission reduction scenarios in this study. The main conclusions of this project are
summarized below:
1. The emissions from public inland water transports in 2019 of HC, CO, NOx, NMHC,
CH4, NH3, N2O, CO2, SO2, PM10, PM2.5, BC and OC were 24.1, 192.9, 317.6, 23.0, 0.9,
0.03, 0.2, 19011, 0.6, 12.5, 12.1, 6.1 and 2.4 tons/year, respectively. Chao Phraya boats
were the major contributor to the total emission which accounted for 44.7% of PM2.5
emission and 40.7% of CO emission, followed by Saen Saep boats which contributed
33.8% and 24.1% of total PM2.5 and CO emission, and cross river ferries which
contributed 21.3% of PM2.5 emission and 35.3% of CO emission;
2. Emission inventory in this study was compared with the previous inventories, including
Kim Oanh (2020) which estimated emission from all inland water transport (passenger
and goods transport) in Bangkok. The PM2.5 and CO in this study (passenger boats only)
were estimated to be 20% and 80% of the PM2.5 and CO estimated for both inland water
passenger and good transports in Kim Oanh (2020). However, our AERMOD model
simulation showed that emission from inland water transport in this study could
contribute to the maximum of 1-4 μg m3⁄ of 24-hr average PM2.5 concentration in the
area of 1 km away from the canal and river, contributing to local community and
passengers taking boats for daily commute.
3. Spatial distribution of emission showed that emission in the Chao Phraya river was much
more than the emission in the Saen Saeb canal. The Chao Phraya river boats were the
main emission source since more boat trips were operated in the Chao Phraya, and its
piers were located in the vicinity area of tourist routes and connected to the BTS sky
train and downtown;
4. During the weekday during 6-9 a.m. and 3-7 p.m. (rush hours), the hourly emission was
higher than the hourly emission during any other times in the day. During the weekend,
overall emission was lower than the emission during weekday. Emission from the Chao
Phraya boats started to increase around 8-9 a.m. and stayed at the same level until 6 p.m.
due to the all-day operation of the Icon-Siam destination routes. For the cross river ferries
and the Saen Saep canal boats, the emission pattern peaked in the morning and afternoon,
50
the same as the pattern observed during the weekday, because some people lived in the
downtown area still commuted to work.
5. Emission inventory template in an existing AIT-EIM template for Bangkok has been
updated with the inland water transport emission calculation. The updated template
included more engine sizes which correspond to the engine sizes used in Bangkok, more
pollutants, the effects of sulfur content in fuel to emission, more engine emission
standards, and the separation of idling and cruising emission.
6. Three emission control scenarios were studied: Changing the boat engines to Tier 4 with
fuel sulfur content of 10 ppm, using current engines with fuel sulfur content of 10 ppm,
and reducing idling time to 50%. Thus, the policy recommendation were 1) Promoting
inspection and maintenance as well as idling reduction campaign; 2) Using 10ppm sulfur
fuel for inland water boats in Bangkok; and 3) Limiting boat engine ages and changing
boat engine to Tier4/Euro 6 or electric engine.
5.2 Recommendations for citizen and boat operator
1. At the busy piers with the idling time more than two minutes, the boat operator should
turn the engine off instead of idling.
2. The operator could provide better terminal operations to reduce idling time of the boats,
and reduce exposure time of the passengers.
3. Boat passengers should wear mask at the piers and on the boats to reduce personal
exposure to the pollutant.
4. People living along the San Seap canal and the Chao Phraya river, especially the area
close to the busy piers, should wear mask or use air purifier in the house during rush
hours.
5.3 Limitations in emission estimation
1. In this study, fuel consumption data was provided by the mechanics and the boat
operators. However, to get more accurate result, fuel consumption should be directly
measured by installing fuel meter on the boat engines.
2. The idling emission in this project was calculated based on the assumption that only one
boat waiting at the piers at the time. However, sometimes, there were more than one
boats waiting at the piers.
3. In this study, emission when starting the engine did not included in the calculation.
4. Emission should be measured locally for each group of boats validate the values used for
calculation to reduce uncertainties in emission estimation which will directly affect
emission factors and load factors used in the emission calculation.
51
REFERENCES
Browning, L., & Bailey, K. (2006). Current methodologies and best practices for preparing
port emission inventories [PowerPoint slides]. Retrieved from
https://www3.epa.gov/ttn/chief/conference/ei15/session1/browning_pres.pdf
Department of Drainage and Sewerage. (2020, April 9). FW.PKG.01 : จุด วัดสะพานพระราม 8.
Retrieved from http://weather.bangkok.go.th/flow/StationDetail?id=21
EMEP/EEA. (2016). EMEP/EEA air pollutant emission inventory guidebook. Copenhagen,
Denmark: European Environmental Agency.
Greenhouse Gas - Air Pollution Interactions and Synergies (GAINS). (2019). Available at
https://gains.iiasa.ac.at/gains/emissions.ASN/index.menu?open=none&switch_version
=GAINS&switch_lang=lang_en. Accessed on November 2019.
Kim Oanh. (2020, January). PCD-AIT emission inventory database workbook version 1.0.
Bangkok, Thailand: Pollution Control Department
Park , S. S., Kozawa, K., Fruin , S., Mara, S., Hsu, Y. K., Jakober, C., . . . Herner, J. (2011).
Emission Factors for High-Emitting Vehicles Based on On-Road Measurements of
Individual Vehicle Exhaust with a Mobile Measurement Platform. Air & Waste
Management Association. doi:10.1080/10473289.2011.595981
Pollution Control Department and Ministry of Natural Resources and Environment. (2019).
Plan for solving dust pollution problems in Bangkok. Bangkok: Pollution Control
Department.
Shrestha, Ram & Oanh, Nguyen Thi & Shrestha, Rajendra & Rupakheti, Maheswar &
Rajbhandari, Salony & Agustian, Didin & Kanabkaew, Thongchai & Iyngararasan,
Mylvakanam. (2013). Atmospheric Brown Clouds Emission Inventory Manual.
United State Environmental Protection Agency. (2009). Current methodologies in preparing
mobile source port-related emission inventories. Retrieved from
https://www.epa.gov/sites/production/files/2016-06/documents/2009-port-inventory-
guidance.pdf
United States Environmental Protection Agency. (2002). Media Life, Annual Activity, and
Load Factor Values for Nonroad Engine Emissions Modeling. Washington, D.C:Author.
United States Environmental Protection Agency. (2010). Exhaust and Crankcase Emission
Factors for Nonroad Engine Modeling-Compression-Ignition. Washington, D.C: Author.
Winijkul, E. (2015). Multinational emission inventories for land-based nonroad engines and
residental combustion (Doctoral dissertation, University of Illinois at Urbana-
Champaign). Retrieved from https://www.ideals.illinois.edu/handle/2142/78373.
52
APPENDICES
53
APPENDIX 1. Survey of boat trips and information of each boat group
วนัเดอืนปี (Day) (Flag) สธีง 05.00-5.59 06.00-06.5907.00-07.5908.00-08.5909.00-09.5910.00-10.5911.00-11.5912.00-12.5913.00-13.5914.00-14.5915.00-15.5916.00-16.5917.00-17.5918.00-18.5919.00-19.5920.00-20.59 รวม
ธงเขยีว (Green Flag) 5 5 1 2 2 3 1 19
ธงสม้ (Orange Flag) 1 4 5 4 3 4 3 3 3 3 3 4 4 3 1 48
ประจ ำทำง (No Flag) 1 4 2 7
ธงเหลอืง (Yellow Flag) 5 4 3 3 5 3 23
ธงทอง (Gold Flag) 3 4 4 4 4 4 4 4 4 4 3 42
ธงฟ้ำ (Blue Flag) 3 6 8 4 4 4 5 6 4 4 4 52
ไอคอนสยำม-สีพ่ระยำ
(IconSiam-Sri Phraya) 6 6 6 6 6 6 6 6 6 6 6 66
ไอคอนสยำม-วดัมว่งแค
(IconSiam-Wat Muangkae) 4 4 4 4 4 4 4 4 4 4 4 44
ไอคอนสยำม-สำทร
(IconSiam-Sathorn) 3 3 3 4 3 4 3 3 3 3 3 35
ไอคอนสยำม-ลง้1919-รำชวงศ ์
(IconSiam-Lhong1919-
Rachawongse)4 4 4 4 4 4 4 4 4 4 4 44
1 15 18 8 26 31 32 29 28 29 31 35 35 34 28 0 380
ธงเขยีว (Green Flag) 5 5 1 2 2 3 1 19
ธงสม้ (Orange Flag) 1 4 4 4 3 4 3 3 3 3 3 6 4 3 1 49
ประจ ำทำง (No Flag) 1 4 2 7
ธงเหลอืง (Yellow Flag) 5 4 3 3 5 3 23
ธงทอง (Gold Flag) 3 4 4 4 4 4 4 4 4 4 3 42
ธงฟ้ำ (Blue Flag) 3 6 4 4 4 4 5 6 4 4 4 48
ไอคอนสยำม-สีพ่ระยำ
(IconSiam-Sri Phraya) 6 6 6 6 6 6 6 6 6 6 6 66
ไอคอนสยำม-วดัมว่งแค
(IconSiam-Wat Muangkae) 4 4 4 4 4 4 4 4 4 4 4 44
ไอคอนสยำม-สำทร
(IconSiam-Sathorn) 3 3 3 3 3 4 3 3 3 3 3 34
ไอคอนสยำม-ลง้1919-รำชวงศ ์
(IconSiam-Lhong1919-
Rachawongse)4 4 4 4 4 4 4 4 4 4 4 44
1 15 17 8 26 31 28 28 28 29 31 37 35 34 28 0 376
ธงเขยีว (Green Flag) 5 5 1 2 2 3 1 19
ธงสม้ (Orange Flag) 1 4 4 4 3 4 3 3 3 3 3 6 4 3 1 49
ประจ ำทำง (No Flag) 1 4 2 7
ธงเหลอืง (Yellow Flag) 5 4 3 3 5 3 23
ธงทอง (Gold Flag) 3 4 4 4 4 4 4 4 4 4 3 42
ธงฟ้ำ (Blue Flag) 3 6 5 4 4 4 5 6 4 4 4 49
ไอคอนสยำม-สีพ่ระยำ
(IconSiam-Sri Phraya) 6 6 6 6 6 6 6 6 6 6 6 66
ไอคอนสยำม-วดัมว่งแค
(IconSiam-Wat Muangkae) 4 4 4 4 4 4 4 4 4 4 4 44
ไอคอนสยำม-สำทร
(IconSiam-Sathorn) 3 3 3 3 3 4 3 3 3 3 3 34
ไอคอนสยำม-ลง้1919-รำชวงศ ์
(IconSiam-Lhong1919-
Rachawongse)4 4 4 4 4 4 4 4 4 4 4 44
1 15 17 8 26 31 29 28 28 29 31 37 35 34 28 0 377
ธงเขยีว (Green Flag) 5 5 1 2 2 3 1 19
ธงสม้ (Orange Flag) 1 4 3 4 3 4 3 3 3 3 3 6 4 3 1 48
ประจ ำทำง (No Flag) 1 4 2 7
ธงเหลอืง (Yellow Flag) 5 4 3 3 5 3 23
ธงทอง (Gold Flag) 3 4 4 4 4 4 4 4 4 4 3 42
ธงฟ้ำ (Blue Flag) 3 3 5 4 4 4 5 6 4 4 4 46
ไอคอนสยำม-สีพ่ระยำ
(IconSiam-Sri Phraya) 6 6 6 6 6 6 6 6 6 6 6 66
ไอคอนสยำม-วดัมว่งแค
(IconSiam-Wat Muangkae) 4 4 4 4 4 4 4 4 4 4 4 44
ไอคอนสยำม-สำทร
(IconSiam-Sathorn) 3 3 3 3 3 4 3 3 3 3 3 34
ไอคอนสยำม-ลง้1919-รำชวงศ ์
(IconSiam-Lhong1919-
Rachawongse)4 4 4 4 4 4 4 4 4 4 4 44
1 15 16 8 26 28 29 28 28 29 31 37 35 34 28 0 373
ธงเขยีว (Green Flag) 5 5 1 2 2 3 1 19
ธงสม้ (Orange Flag) 1 4 4 4 3 4 3 3 3 3 3 6 4 3 1 49
ประจ ำทำง (No Flag) 1 4 2 7
ธงเหลอืง (Yellow Flag) 5 4 3 3 5 3 23
ธงทอง (Gold Flag) 3 4 4 4 4 4 4 4 4 4 3 42
ธงฟ้ำ (Blue Flag) 3 4 5 4 4 4 5 6 4 4 4 47
ไอคอนสยำม-สีพ่ระยำ
(IconSiam-Sri Phraya) 6 6 6 6 6 6 6 6 6 6 6 66
ไอคอนสยำม-วดัมว่งแค
(IconSiam-Wat Muangkae) 4 4 4 4 4 4 4 4 4 4 4 44
ไอคอนสยำม-สำทร
(IconSiam-Sathorn) 3 3 3 3 3 4 3 3 3 3 3 34
ไอคอนสยำม-ลง้1919-รำชวงศ ์
(IconSiam-Lhong1919-
Rachawongse)4 4 4 4 4 4 4 4 4 4 4 44
1 15 17 8 26 29 29 28 28 29 31 37 35 34 28 0 375
5 75 85 40 130 150 147 141 140 145 155 183 175 170 140 0 1881
ธงเขยีว (Green Flag) 0
ธงสม้ (Orange Flag) 4 5 6 5 6 6 6 6 6 6 6 6 6 2 76
ประจ ำทำง (No Flag) 0
ธงเหลอืง (Yellow Flag) 0
ธงทอง (Gold Flag) 3 4 4 4 4 4 4 4 4 4 3 42
ธงฟ้ำ (Blue Flag) 3 6 8 5 4 4 5 6 5 3 2 51
ไอคอนสยำม-สีพ่ระยำ
(IconSiam-Sri Phraya) 6 6 6 6 6 6 6 6 6 6 6 66
ไอคอนสยำม-วดัมว่งแค
(IconSiam-Wat Muangkae) 4 4 4 4 4 4 4 4 4 4 4 44
ไอคอนสยำม-สำทร
(IconSiam-Sathorn) 3 3 3 4 3 4 3 3 3 3 3 35
ไอคอนสยำม-ลง้1919-รำชวงศ ์
(IconSiam-Lhong1919-
Rachawongse)4 4 4 4 4 4 4 4 4 4 4 44
0 4 5 6 28 33 35 33 31 32 32 33 32 30 24 0 358
ธงเขยีว (Green Flag) 0
ธงสม้ (Orange Flag) 4 5 6 5 6 6 6 6 6 6 6 6 6 2 76
ประจ ำทำง (No Flag) 0
ธงเหลอืง (Yellow Flag) 0
ธงทอง (Gold Flag) 3 4 4 4 4 4 4 4 4 4 3 42
ธงฟ้ำ (Blue Flag) 3 6 8 5 4 4 5 6 5 3 2 51
ไอคอนสยำม-สีพ่ระยำ
(IconSiam-Sri Phraya) 6 6 6 6 6 6 6 6 6 6 6 66
ไอคอนสยำม-วดัมว่งแค
(IconSiam-Wat Muangkae) 4 4 4 4 4 4 4 4 4 4 4 44
ไอคอนสยำม-สำทร
(IconSiam-Sathorn) 3 3 3 3 3 4 3 3 3 3 3 34
ไอคอนสยำม-ลง้1919-รำชวงศ ์
(IconSiam-Lhong1919-
Rachawongse)4 4 4 4 4 4 4 4 4 4 4 44
0 4 5 6 28 33 35 32 31 32 32 33 32 30 24 0 357
0 8 10 12 56 66 70 65 62 64 64 66 64 60 48 0 715
รวม
รวม
รวม
วนัจันทร(์Monday)
วนัอังคำร(Tuesday)
วนัพธุ(Wednesday)
รวม
รวม
รวม
วนัพฤหัส(Thrusday)
รวมวนัรำชกำร
รวมวนัหยดุรำชกำร
วนัศกุร(์Friday)
วนัเสำร(์Saturday)
วนัอำทติย(์Sunday)
รวม
54
APPENDIX 1 (Continued)
(Day)วันที ่ 05.00-05.59 06.00-06.59 07.00-07.59 08.00-08.59 09.00-09.59 10.00-10.59 11.00-11.59 12.00-12.59 13.00-13.59 14.00-14.59 15.00-15.59 16.00-16.59 17.00-17.59 18.00-18.59 19.00-19.59 20.00-20.59 (Total)รวม
33 156 149 115 59 49 43 46
34323431374594132142
171
861
7 27 27 19 9 9 5 6 6 6 14 17 7 6 6
1634389366
169
8 30 26 17 9 6 6 6 6 6 14 18 8 7 3 170
180
6 30 30 17 9 6 6 6 6 6 14 17 8 7 3 171
2
6 29 28 19 9 8 7 8 7 8 12 22 7 8 2
8 7 8 7 8 12 19 8 6
(Total trips in one
week)รวมทัง้ส ิน้
12 12 12 12 12 14
44 46 78 107 52 40 984
6 7 9
6
6 26
77
(Total trips of
weekend) รวม
วันหยดุรำชกำร
14 17 21 14 12
6 6 6 6 6 1
14 6 172
(Sunday)วันอำทติย์
21 22 9
6 6 6 6
(Total trips of
weekday)รวมวันรำชกำร
(Saturday) วันเสำร์ 8 10 12
33
(The boat trip of inbound of Saen Saep boats) จ ำนวนเทีย่วเรอืโดยสำรคลองแสนแสบจ ำแนกตำมเวลำและวนัทีส่ ำรวจ (ขำลอ่ง) (สำยนดิำ้)(ศรบีญุเรอืง-ประตนู ำ้)(หลงัคำสงู)
(Monday) วันจันทร ์
(Tuesday) วันอังคำร
(Wednesday) วันพธุ
(Thursday)วันพฤหัสบดี
(Friday)วันศกุร์
956 8 8 58 6 6 6 6
(Day)วันที ่ 05.00-05.59 06.00-06.59 07.00-07.59 08.00-08.59 09.00-09.59 10.00-10.59 11.00-11.59 12.00-12.59 13.00-13.59 14.00-14.59 15.00-15.59 16.00-16.59 17.00-17.59 18.00-18.59 19.00-19.59 20.00-20.59 รวม
33 37 88 118 97 81 47
6 7 18 23 22 16 8
(Total trips in one
week)รวมทัง้ส ิน้808
776
23 37 41 63 52 45 45 47 45 50 100 141 119
20 29 32 53 41 33 33 34
12 12 13 12 13 12
4 6 5 10 8 5 7 6 6 7 18 20 18 18 10 148
157
3 6 6 9 9 6 6 6 6 7 18 23 22 16 11
5 7 7 12 8 6 6 6
154
158
4 7 5 9 8 8 7 8 8 8 17 27 18 16 9 159
4 3 9 13
(The boat trip of outbound of Saen Saep boats)จ ำนวนเทีย่วเรอืโดยสำรคลองแสนแสบจ ำแนกตำมเวลำและวนัทีส่ ำรวจ (ขำขึน้) (สำยนดิำ้)(ประตนู ำ้-ศรบีญุเรอืง)(หลงัคำสงู)
76
(Total trips of
weekend) รวม
วันหยดุรำชกำร
3 8 9 10 11
7 6 6 6 10 10
23 22 160
(Sunday)วันอำทติย์ 1 4 4
(Total trips of
weekday)รวมวันรำชกำร
(Saturday) วันเสำร์ 2 4 5
(Monday) วันจันทร ์
(Tuesday) วันอังคำร
(Wednesday) วันพธุ
(Thursday)วันพฤหัสบดี
87 6 6 6 6 6
23 5 6 6
8 8 7 8 7 8 17 25 17 15 9
(Friday)วันศกุร์
947 6 13 12
(Day)วันที ่ 05.00-05.59 06.00-06.59 07.00-07.59 08.00-08.59 09.00-09.59 10.00-10.59 11.00-11.59 12.00-12.59 13.00-13.59 14.00-14.59 15.00-15.59 16.00-16.59 17.00-17.59 18.00-18.59 19.00-19.59 20.00-20.59 รวม
(The boat trip of inbound of Saen Saep boats) (Golden Mountain Line)จ ำนวนเทีย่วเรอืโดยสำรคลองแสนแสบจ ำแนกตำมเวลำและวนัทีส่ ำรวจ (ขำลอ่ง) (สำยภเูขำทอง)(ประตนู ำ้-ผำ่นฟ้ำลลีำศ)
(หลงัคำเตีย้)
173
(Total trips of
weekend) รวม
วันหยดุรำชกำร
2 10 27 25 12 11 13
48 45 56 97 89 51 884(Total trips in one
week)รวมทัง้ส ิน้29 91 123 112 52 45 46
(Sunday)วันอำทติย์ 1 4 11 10 6 5 6
13 12 12 15 14 7
16 15 6 6 7 6 6 6 9 8 2 94
7 6 6 6 6 5 79
7 6 9 18 19 9 9 153(Friday)วันศกุร์ 3 14 20 19 8 6 6
154
(Thursday)วันพฤหัสบดี 6 20 20 16 9 6 6 6 6 8 14 14 8 7 146
10 15 13 9 5 149
(Wednesday) วันพธุ 4 17 21 18 8 7 5 6 7 8 21 18 7 7
6 17 17 19 7 8 8 8 7
(Monday) วันจันทร ์ 8 13 18 15 8 7 8 8 7 9
(Total trips of
weekday)รวมวันรำชกำร27 81 96 87 40 34 33 35 33 44 82 75 44 33 744
(Saturday) วันเสำร์ 1 6
14 11 11 5 1 143
(Tuesday) วันอังคำร
(Day)วันที ่ 05.00-05.59 06.00-06.59 07.00-07.59 08.00-08.59 09.00-09.59 10.00-10.59 11.00-11.59 12.00-12.59 13.00-13.59 14.00-14.59 15.00-15.59 16.00-16.59 17.00-17.59 18.00-18.59 19.00-19.59 20.00-20.59 รวม
จ ำนวนเทีย่วเรอืโดยสำรคลองแสนแสบจ ำแนกตำมเวลำและวนัทีส่ ำรวจ (ขำขึน้) (สำยภเูขำทอง)(ผำ่นฟ้ำลลีำศ-ประตนู ำ้)(หลงัคำเตีย้)
50 44 49 93 90 70 867(Total trips in one
week)รวมทัง้ส ิน้24 80 111 104 62 45 45
13 12 13 11 14 16 174
(Total trips of
weekend) รวม
วันหยดุรำชกำร
1 10 21 25 14 12 12
7 5 7 5 7 6 1 79(Sunday)วันอำทติย์ 1 4 8 10 6 6 6
6 7 6 6 7 10 4 100(Saturday) วันเสำร์ 6 13 15 8 6 6
37 32 36 82 76 54 33 726(Total trips of
weekday)รวมวันรำชกำร23 70 90 79 48 33 33
8 5 7 20 17 13 8 2 155(Friday)วันศกุร์ 3 14 20 18 9 6 5
6 6 7 14 14 10 6 2 143(Thursday)วันพฤหัสบดี 6 16 18 16 10 6 6
6 6 8 19 18 10 6 2 146(Wednesday) วันพธุ 4 12 18 15 10 6 6
8 8 7 16 14 10 6 1 147(Tuesday) วันอังคำร 5 14 18 15 10 8 7
(Monday) วันจันทร ์ 5 14 16 15 9 7 9 9 7 7 13 13 11 7 1 143
55
APPENDIX 1 (Continued)
เสน้ทำง(Routes) 05.00-5.59 06.00-06.5907.00-07.5908.00-08.5909.00-09.5910.00-10.5911.00-11.5912.00-12.5913.00-13.5914.00-14.5915.00-15.5916.00-16.5917.00-17.5918.00-18.5919.00-19.59 รวม
ปำกเกร็ด-วดัเตย
(Pakkret-Wat Toey)68 101 99 67 62 59 60 62 59 64 67 71 90 80 66 1075
ปำกเกร็ด-วชัรวีงศ ์
(Pakkret-Watchareewongse)66 91 93 66 68 60 60 65 60 65 62 81 82 70 69 1058
เกำะเกร็ด-วดัสนำมเหนือ
(Koh Kret-WatSanamnuea)40 100 114 95 90 90 90 90 90 90 90 90 90 90 70 1319
นนทบรุ-ีบำงศรเีมอืง
(Nonthaburi-Bangsrimueng)81 149 165 118 74 70 66 67 66 66 109 114 117 111 102 1475
เทเวศร-์วดับวรมงคล
(Thewes-Bowornmongkon)20 47 51 41 28 28 30 28 27 23 25 33 31 33 27 472
เทเวศร-์วดัคฤหบดี
(Thewes-Karuhabodee)0 40 36 29 23 22 20 20 16 14 18 23 21 20 19 321
ทำ่พระจันทร-์วงัหลัง
(Tha Phrachan-Wang Lang)36 38 34 36 37 44 43 39 43 38 40 42 44 31 29 574
วงัหลัง-มหำรำช
(Wang Lang-Maharaj)0 34 34 35 33 35 33 32 32 29 29 33 37 29 30 455
วงัหลัง-ทำ่ชำ้ง
(Wang Lang-Tha Chang)32 42 42 40 33 35 33 34 32 32 33 36 37 34 31 526
วดัระฆงั-ทำ่ชำ้ง
(Wat Rakang-Tha Chang)0 31 31 37 28 26 25 20 24 24 23 28 25 26 21 369
ทำ่เตยีน-วดัอรุณ
(Tha Tien-Wat Arun)18 34 44 41 39 42 46 44 41 40 43 43 40 39 32 586
ปำกคลองตลำด-วดักัลยำณมิติร
(Pakklong-Kallayanimit)26 29 32 23 25 21 22 23 21 25 25 29 26 24 26 377
รำชวงศ-์ดนิแดง
(Rachawongse-Dindang)7 53 80 72 50 43 39 40 41 38 46 53 78 64 39 743
สีพ่ระยำ-คลองสำน
(Sri Phraya-Klongsan)24 68 113 102 92 88 83 85 78 76 79 80 80 78 71 1197
โอเรยีนเตล-วดัสวุรรณ
(Oriental-Wat Suwan)0 66 79 75 50 47 41 43 43 40 50 60 66 33 30 723
สำทร-เป๊ปซี ่
(Sathorn-Pepsi)15 41 53 49 31 28 23 24 19 26 26 27 38 34 24 458
สำธปุระดษิฐ-์คลองลัดหลวง
(Sathupradit-Klong Latluang)6 28 31 31 21 18 16 20 17 16 16 20 25 24 18 307
คลองเตย-ท่ัวไป
(Klong Toei-Tua Pai)69 63 59 54 0 0 0 0 0 15 23 38 35 38 33 427
รำมำ3-คลองลัดโพธิ ์
(Rama 3-Klong Latpo)20 38 46 41 26 22 24 22 25 29 33 22 37 38 30 453
บำงนำ-ตำเลือ่น
(Bangna-Taluen)8 12 11 20 9 8 8 7 7 7 20 18 17 20 12 184
บำงนำนอก-บำงน ้ำผึง้นอก
(Bangnanok-Bangnampuengnok)26 40 63 48 26 25 25 24 27 25 34 32 45 41 29 510
เภตรำ-พระประแดง
(Petra-Phra Pradang)51 74 77 57 39 38 41 40 38 41 61 68 77 68 44 814
วบิลูยศ์ร-ีพระสมทุรเจดยี์
(Wiboonsri-Pra Samutchedee)47 71 71 53 48 31 30 23 28 28 46 50 54 58 56 694
56
APPENDIX 1 (Continued)
No. Boat
number
Boat size Engine
Passenger Long
(m.)
Width
(m.)
Depth
(m.)
Weight
(tongross) Brand
Power
(Kw) Piston
1 145 26.00 3.80 1.65 39.15 Volvo Penta 385.42 6 90
2 146 27.50 3.50 1.65 35.52 Cummin 355 6 90
3 149 26.64 4.50 1.60 47.39 Cummin 355*2 6*2 90
4 150 26.64 4.50 1.60 47.36 Cummin 355*2 6*2 90
5 152 27.70 3.90 1.30 36.90 Cummin 355 6 90
6 153 29.40 3.90 1.40 35.33 Cummin 302 6 90
7 154 27.50 3.90 1.20 40.60 Cummin 302 6 90
8 155 27.36 3.60 1.09 28.75 Cummin 355 6 90
9 156 27.12 3.60 1.48 33.92 Cummin 355 6 90
10 157 27.68 3.35 1.60 31.92 Cummin 355 6 90
11 158 28.85 3.76 1.40 36.45 Cummin 355 6 90
12 159 27.60 3.72 1.50 35.60 Cummin 355 6 90
13 160 26.82 3.64 1.60 34.76 Cummin 355 6 90
14 161 26.75 3.64 1.60 35.86 Cummin 355 6 90
15 163 28.57 3.46 1.58 34.21 Cummin 355 6 90
16 164 26.49 .90 1.37 33.71 Cummin 355 6 90
17 165 26.33 3.70 1.64 36.65 Volvo Penta 384 6 90
18 167 26.33 3.70 1.64 36.65 Cummin 355 6 90
19 168 28.86 3.56 1.60 36.24 Cummin 355 6 90
20 169 27.94 3.60 1.64 36.16 Cummin 355 6 90
21 170 29.95 3.64 1.64 40.73 Cummin 355 6 90
22 171 28.35 3.50 1.50 33.51 Cummin 355 6 90
23 172 27.65 3.68 1.64 36.92 Volvo Penta 400 6 90
24 173 28.98 3.50 1.30 31.70 Cummins 355 6 90
25 174 27.86 3.60 1.40 33.41 Cummins 355 6 90
26 175 29.48 3.40 1.60 34.70 Cummins 355 6 90
27 176 28.36 3.76 1.50 34.80 Cummins 355 6 90
28 177 28.98 3.71 1.30 32.65 Cummins 355 6 90
29 178 29.14 3.64 1.56 37.22 Cummins 355 6 90
30 179 32.90 3.52 1.50 39.18 Cummins 355 6 90
31 180 29.48 3.78 1.45 37.75 Cummins 355 6 90
32 181 28.87 3.42 1.52 33.24 Cummins 355 6 90
33 182 27.10 3.64 1.50 36.33 Cummins 355 6 90
34 183 29.71 3.48 1.45 31.03 Cummins 355 6 90
35 184 28.55 3.50 1.45 33.08 Cummins 405 6 90
36 185 29.90 3.85 1.45 42.70 Cummins 355 6 90
37 186 30.35 3.86 1.45 38.14 Cummins 355 6 90
38 187 30.15 3.86 1.45 59.67 Cummins 355 6 90
39 188 30.15 3.86 1.45 59.67 Cummins 355 6 90
40 189 30.76 3.67 1.35 35.48 Cummins 355 6 90
41 190 30.75 4.00 1.50 43.00 Volvo Penta 385.45 6 90
42 191 30.90 3.90 1.50 42.72 Cummins 355 6 90
43 192 30.00 3.80 1.50 51.66 Cummins 355 6 90
44 193 30.00 3.80 1.50 51.66 Cummins 355 6 90
45 194 30.00 3.80 1.50 51.66 Cummins 355 6 90
46 195 30.20 3.80 1.50 40.00 Cummins 355 6 90
57
APPENDIX 1 (Continued)
No. Boat
number
Boat size Engine
Passenger Long
(m.)
Width
(m.)
Depth
(m.)
Weight
(tongross) Brand
Power
(Kw) Piston
47 201 29.35 5.25 1.35 56.32 Cummins 355*2 6*2 120
48 202 29.58 5.27 1.60 58.62 Cummins 355*2 6*2 120
49 203 29.80 5.30 1.50 59.31 Cummins 355*2 6*2 120
50 204 30.52 5.26 1.40 59.12 Cummins 355*2 6*2 120
51 205 29.92 5.16 1.40 59.12 Cummins 355*2 6*2 120
52 206 29.30 5.20 1.70 59.00 Cummins 355*2 6*2 120
53 207 29.80 5.30 1.50 59.31 Cummins 355*2 6*2 120
54 208 30.52 4.90 1.50 59.70 Cummins 355*2 8*2 120
55 209 29.95 5.18 1.50 56.46 Cummins 355*2 6*2 120
56 210 29.80 4.85 1.60 59.00 Cummins 355*2 6*2 120
57 211 28.95 4.0 1.40 46.00 Volvo Penta 385*2 6*2 180
58 212 28.85 4.45 1.50 44.00 Volvo Penta 385*2 6*2 120
59 213 30.55 4.80 1.70 54.00 Cummins 355*2 6*2 120
60 214 28.86 5.20 1.60 58.00 Volvo Penta 385*2 6*2 120
58
APPENDIX 1 (Continued)
No. Station Number of
boats
Number of service boats Boat
Owner Boat name
Boat
size
Engine
name
Engine
power
(hp)
Piston Passenger Weekday Saturday Sunday
1 Pakkret – Wat Toey 5 4 3 4 เอกชน
นาทนาวี 9.29 Isuzu 169.95 6 15
สุขเกษม6 2.38 Guardner 131.86 4 12
สุขเกษม9 3.55 Isuzu 101.00 4 15
สุขเกษม11 6.31 Isuzu 100.61 4 15
สุขเกษม14 8.87 Nisson 243.43 6 20
2 Pakkret –
Wachareewongse 6 4 4 4
เทศบาลนครปากเกร็ด,
อบต. บางตะไนย ์
วชัรีวงศ ์1 12.54 Hino 187.64 4 50
วชัรีวงศ ์3 4.35 Isuzu 96.53 4 10
วชัรีวงศ ์5 13.05 Isuzu 175.39 6 20
วชัรีวงศ ์7 7.82 Isuzu 99.25 4 15
วชัรีวงศ ์8 26 Nisson 263.77 6 63
ดาวบา้นนา3 11.3 Isuzu 187.64 6 32
3 Nonthaburi – Bang Sri
Mueng 7 4 4 4 กรมเจา้ท่า
จ.ส.น.1 27.2 Guardner 152.14 6 120
จ.ส.น.5 10.29 Guardner 51.66 4 30
จ.ส.น.8 23.52 Guardner 151.04 6 100
จ.ส.น.9 27.2 Guardner 152.28 6 120
จ.ส.น.10 23.23 Guardner 150.92 6 100
จ.ส.น.11 23.49 Guardner 91.09 6 90
จ.ส.น.12 23.49 Guardner 91.09 6 90
59
APPENDIX 1 (Continued)
No. Station Number of
boats
Number of service boats Boat
Owner Boat name
Boat
size
Engine
name
Engine
power
(hp)
Piston Passenger Weekday Saturday Sunday
4 Thewes –
Bawornmongkon 3 2 1 1 เอกชน
บวรมงคล 2 8.83 Guardner 40.79 4 14
บวรมงคล 3 9.57 Guardner 76.14 5 26
ประเสริฐเจริญทรัพย ์ 16.21 Guardner 91.28 6 30
5 Thewes – Wat
Karuhabodee 2 1 1 1
กรมเจา้ท่า&เอกชน
คฤหมงคล 1 11.75 Hino 157.72 6 30
6 Tha Phrachan – Wang
Lang 5 2 2 2 สุภทัรา
สภ. 69 28.2 Guardner 183.55 6 15
สภ. 71 28.68 Guardner 152.28 4 12
สภ. 76 36.59 Cummins 354.86 4 15
สภ. 77 49.7 Cummins 354.86 4 15
สภ. 78 32 Guardner 182.46 6 20
7 Tha Maharaj – Wang
Lang 5 2 2 2 สุภทัรา
สภ. 69 28.2 Guardner 183.55 6 90
สภ. 70 28.2 Isuzu 152.28 6 90
สภ. 72 28.68 Guardner 152.28 6 90
สภ. 77 49.7 Cummins 354.86 6 90
สภ. 78 32 Guardner 182.46 6 90
60
APPENDIX 1 (Continued)
No. Station Number of
boats
Number of service boats Boat
Owner Boat name
Boat
size
Engine
name
Engine
power
(hp)
Piston Passenger Weekday Saturday Sunday
8 Tha Chang – Wang
Lang 8 2 2
2 สุภทัรา
สภ. 70 28.2 Isuzu 152.28 6 90
สภ. 71 28.68 Guardner 152.28 6 90
สภ. 72 28.68 Guardner 152.28 6 90
สภ. 73 29.59 Guardner 182.57 6 90
สภ. 74 29.59 Guardner 152.28 6 90
สภ. 75 34.09 Cummins 354.86 6 90
สภ. 76 36.51 Cummins 354.86 6 90
สภ. 78 32 Guardner 182.46 6 90
9 Tha Chang – Wat
Rakang 5 2 2 2 สุภทัรา
สภ. 78 8.87 Nisson 243.43 6 90
สภ. 69 9.29 Isuzu 169.95 6 90
สภ. 70 2.38 Guardner 131.86 4 90
61
APPENDIX 1 (Continued)
No. Station Number of
boats
Number of service boats Boat
Owner Boat name
Boat
size
Engine
name
Engine
power
(hp)
Piston Passenger Weekday Saturday Sunday
10 Tha Tien – Wat Arun 4 3 3 3 เอกชน
โพธ์ิอรุณ 12 21.11 Nisson 202.58 6 60
โพธ์ิอรุณ 15 34.09 Nisson 167.91 6 60
โพธ์ิอรุณ 18 38.1 Guardner 182.46 6 60
โพธ์ิอรุณ 20 37.31 Guardner 182.57 6 60
11 Assadang – Wat
Kalayanimit 1 1 1 1
กรมเจา้ท่า &เอกชน
กลัยาณ์ 12 16.44 Layland 203.94 6 60
12 Rachawongse –
Dindang 5 4 4 4
เอกชน&กรมเจา้ท่า
ปราณี 30.4 Guardner 87.21 6 50
ปราณี 20 31.32 Guardner 91.28 6 60
ปราณี 21 31.32 Guardner 91.28 6 60
ปราณี 44 33.11 Guardner 152.27 6 60
ปราณี 45 33.11 Guardner 152.27 6 80
62
APPENDIX 1 (Continued)
No. Station Number of
boats
Number of service boats Boat
Owner Boat name
Boat
size
Engine
name
Engine
power
(hp)
Piston Passenger Weekday Saturday Sunday
13 Sri Phraya – Klongsan 4 3 3 3 เอกชน
ปัญจทรัพย ์2 24.33 Nisson 263.77 6 63
ปัญจทรัพย ์3 30.06 Nisson 263.77 6 70
ปัญจทรัพย ์4 27.46 Nisson 263.77 6 63
ปัญจทรัพย ์8 65.27 Nisson 283.99 6 100
14 Oriental – Wat Suwan 4 3 3 2 กรมเจ่าท่า&
เอกชน
ป. บูรพา 22.99 Hino 167.23 6 45
ป. บูรพา 1 22.94 Hino 161.79 6 45
เกียรติชูชยั 19 32.62 Nisson 182.19 6 40
น าโชคชยั 10 31.25 Isuzu 161.79 6 50
15 Sathorn – Pepsi 4 2 2 2 กรมเจา้ท่า&
เอกชน
สาธร 1 33.7 Nisson 223.14 6 90
สาธร 2 33.7 Nisson 223.14 6 90
สาธร 3 52.09 Isuzu 274.64 6 110
63
APPENDIX 1 (Continued)
No. Station Number of
boats
Number of service boats Boat
Owner Boat name
Boat
size
Engine
name
Engine
power
(hp)
Piston Passenger Weekday Saturday Sunday
16 Sathuphradit – Wat
Bang Pueng 2 1 กรมเจา้ท่า ประภามณฑล 20.2 Deutz 43.51 3 30
17 Sathuphradit – Klong
Latluang 2 2 1 1 กรมเจา้ท่า
พญาภุชงค์นาคราช 1
35.3 Cummins 354.86 6 40
พญาภุชงค์นาคราช 2 54.72 Isuzu 425.99 6 40
18 Rama 3 – Klong Lat Po 2 2 2 2 เอกชน ลดัโพธ์ิ 65.56 Cummins 323.59 6 60
19 Klong Toey – Tuapai 3 2 2 - เอกชน
ช. ทรัพยส์มบูรณ์ 2
3.4 Yunmar 29.91 3 30
เกษมสาคร 2.34 Isuzu 28.55 4 30
ส. ชูเกียรติ 2.15 Isuzu 40.57 4 30
20 Bangnanok –
Bangnampuengnok 5 3 2 2 เอกชน
มนสัสกุล 1 91.96 Hino 324.57 6 120
สมบตัิมนสั 2 17.22 Hino 365.14 6 70
สมบตัิมนสั 3 53.32 Hino 318.15 6 70
สมบตัิมนสั 7 43.72 Hino 365.14 6 70
64
APPENDIX 1 (Continued)
No. Station Number of
boats
Number of service boats Boat
Owner Boat name Boat size
Engine
name
Engine
power
(hp)
Piston Passenger Weekday Saturday Sunday
21 Bangna – Ta Luen 2 1 1 1
กรมสรรพาวุธทหารเรือ &
เอกชน
สมบตัิมนสั
32.59
Guardner
182.57
6
50
คงสาคร 1 คงสาคร 1 7.15 Hino 169.03 6 16
22 Petra – Phra Phradang 6 4 4 4 เอกชน
ชลโภค 1 40.15 Nisson 304.28 6 80
ภมรธาร 1 40.15 Nisson 304.28 6 80
ธนาภพ 1 40.15 Guardner 182.57 6 80
ราชเภตรา 33.25 Guardner 182.19 6 100
ลาโภ 1 40.15 Guardner 182.57 6 80
65
APPENDIX 1 (Continued)
No. Station Number of
boats
Number of service boats Boat
Owner Boat name Boat size
Engine
name
Engine
power
(hp)
Piston Passenger Weekday Saturday Sunday
23 Wiboonsri – Phra
Samut Chedi 12 5 5 5
เอกชน&
องคก์ารบริหารส่วนจงัหวดั
สิทธิโชค 3 8.5 A.E.C 40.79 6 50
สิทธิโชค 16 15.37 Hino 141.4 6 70
สิทธิโชค 17 17.15 Hino 111.65 6 85
สิทธิโชค 18 13.98 Hino 141.4 6 73
สิทธิโชค 25 9.83 Hino 167.36 6 55
สิทธิโชค 26 14.69 Hino 141.4 6 50
สิทธิโชค 27 19.35 A.E.C 142 6 100
สิทธิโชค 30 24.16 Leyland 176.75 6 90
66
APPENDIX 2 Emission share of different boat types (%)
Pollutants Green Orange Yellow No
flag Gold Blue
Shuttle
boat
HC 10.70 33.60 6.51 3.84 11.51 15.70 18.14
CO 9.66 32.26 5.52 4.55 11.44 16.95 19.62
NOx 10.62 33.62 6.33 4.06 10.93 16.00 18.43
NMHC 10.80 33.86 6.43 4.00 10.92 15.78 18.20
CH4 10.56 34.16 6.83 4.35 12.42 15.53 16.15
NH3 13.19 13.19 12.09 4.40 13.19 21.98 21.98
N2O 14.40 31.41 4.45 3.93 14.40 17.02 14.40
CO2 10.68 33.71 6.38 4.03 10.86 15.94 18.40
SO2 9.30 32.56 5.58 6.05 13.95 13.95 18.60
PM10 11.41 34.62 7.04 3.61 11.03 15.03 17.25
PM2.5 11.41 34.62 7.08 3.54 11.01 14.95 17.39
BC 11.37 34.51 7.06 3.53 10.98 15.29 17.25
OC 11.81 34.45 7.09 3.35 10.83 14.76 17.72
67
APPENDIX 2 (Continued)
Pollutant Green (%) Orange (%) Yellow (%) No flag (%) Gold (%) Blue (%) Shuttle boat (%)
Total C I Total C I Total C I Total C I Total C I Total C I Total C I
HC 10.70 9.30 1.40 33.60 27.91 5.70 6.51 5.81 0.70 3.84 2.67 1.16 11.51 9.42 2.09 15.70 11.74 3.95 18.14 13.49 4.65
CO 9.66 6.58 3.09 32.26 19.67 12.59 5.52 4.09 1.42 4.55 1.92 2.63 11.44 6.76 4.68 16.95 8.26 8.70 19.62 9.50 10.12
NOx 10.62 8.90 1.72 33.62 26.64 6.98 6.33 5.55 0.78 4.06 2.60 1.46 10.93 8.34 2.59 16.00 11.17 4.83 18.43 12.85 5.58
NMHC 10.80 9.34 1.46 33.86 28.03 5.83 6.43 5.83 0.61 4.00 2.79 1.21 10.92 8.74 2.18 15.78 11.77 4.00 18.20 13.59 4.61
CH4 10.56 9.32 1.24 34.16 27.95 6.21 6.83 6.21 0.62 4.35 3.11 1.24 12.42 9.32 3.11 15.53 12.42 3.11 16.15 13.04 3.11
NH3 13.19 10.99 2.20 13.19 2.20 10.99 12.09 10.99 1.10 4.40 3.30 1.10 13.19 10.99 2.20 21.98 10.99 10.99 21.98 10.99 10.99
N2O 14.40 13.09 1.31 31.41 26.18 5.24 4.45 1.21 0.16 3.93 2.62 1.31 14.40 13.09 1.31 17.02 13.09 3.93 14.40 10.47 3.93
CO2 10.68 9.04 1.64 33.71 27.08 6.63 6.38 5.64 0.75 4.03 2.64 1.39 10.86 8.48 2.39 15.94 11.35 4.59 18.40 13.06 5.33
SO2 9.30 9.30 1.40 32.56 27.91 4.65 5.58 4.65 0.70 6.05 4.65 1.40 13.95 9.30 4.65 13.95 9.30 4.65 18.60 13.95 4.65
PM10 11.41 10.84 0.57 34.62 32.53 2.09 7.04 6.85 0.19 3.61 3.23 0.38 11.03 10.27 0.76 15.03 13.70 1.33 17.25 15.60 1.65
PM2.5 11.41 10.82 0.59 34.62 32.65 1.97 7.08 6.88 0.20 3.54 3.15 0.39 11.01 10.23 0.79 14.95 13.57 1.38 17.39 15.74 1.65
BC 11.37 10.98 0.39 34.51 32.55 1.96 7.06 6.67 0.39 3.53 3.14 0.39 10.98 10.20 0.78 15.29 13.73 1.57 17.25 15.69 1.57
OC 11.81 10.83 0.98 34.45 32.48 1.97 7.09 6.89 0.20 3.35 2.95 0.39 10.83 9.84 0.98 14.76 13.78 0.98 17.72 15.75 1.97
Note: C: cruising, I: idling
68
APPENDIX 2 (Continued)
Pollutant
Route 1 (%)
(Sriboonrueng-Pratunam)
Route 2 (%)
(Phan Falilat-Pratunam)
Total C I Total C I
HC 77.04 57.81 19.23 22.96 17.57 5.39
CO 77.35 38.08 39.27 22.65 11.60 11.05
NOx 76.25 56.10 20.14 23.75 17.08 6.67
NMHC 76.25 59.44 16.81 23.75 18.14 5.60
CH4 76.00 60.00 16.00 24.00 20.00 4.00
NH3 92.31 76.92 15.38 7.69 7.69 7.69
N2O 80.00 60.00 20.00 20.00 20.00 6.00
CO2 76.26 57.10 19.16 23.74 17.39 6.35
SO2 76.47 58.82 17.65 23.53 17.65 5.88
PM10 76.54 70.62 5.92 23.46 21.56 1.90
PM2.5 76.53 70.66 5.87 23.47 21.52 1.96
BC 76.47 70.59 5.88 23.53 21.57 1.96
OC 75.90 69.88 6.02 24.10 21.69 2.41
69
APPENDIX 2 (Continued)
Routes HC CO NOx NMHC CH4 NH2 N2O CO2 SO2 PM10 PM2.5 BC OC
Pakkret-Wat Toey 2.56 2.16 2.45 2.50 3.17 1.95 2.00 2.47 1.97 2.93 3.10 2.99 1.80
Pakkret-Watchareewongse 2.26 3.04 2.43 2.19 3.17 1.95 2.00 2.38 1.97 1.10 1.16 1.50 0.72
Koh Kret-Wat Sanamnuea 9.77 10.65 9.98 9.69 9.52 9.75 9.98 9.93 4.92 8.79 8.53 8.98 8.99
Nonthaburi-Bangsrimueng 8.87 9.36 9.02 8.91 6.35 9.75 7.98 8.99 9.83 8.06 8.53 8.23 8.99
Thewes-Bowornmongkon 1.05 0.91 1.00 0.94 0.63 0.39 2.00 1.01 0.98 1.47 1.16 0.97 0.18
Thewes-Karuhabodee 2.41 2.42 2.37 2.34 3.17 1.95 2.00 2.36 1.97 2.20 2.33 2.24 1.80
Tha Phrachan-Wang Lang 4.06 4.42 4.24 4.22 3.17 2.92 5.99 4.23 4.92 4.03 3.88 3.74 3.60
Wang Lang-Maharaj 2.86 2.86 2.90 2.97 3.17 2.92 3.99 2.91 4.92 2.93 3.10 2.99 3.60
Wang Lang-Tha Chang 4.66 4.29 4.53 4.53 6.35 3.90 3.99 4.56 4.92 5.13 5.04 5.24 5.39
Wat Rakang-Tha Chang 2.71 2.73 2.70 2.66 3.17 2.92 2.00 2.69 1.97 2.56 2.33 2.24 3.60
Tha Tien-Wat Arun 5.71 6.54 5.90 5.63 3.17 9.75 5.99 5.84 4.92 4.40 4.65 4.49 3.60
Pakklong-Kallayanimit 1.95 1.88 2.00 2.03 3.17 1.95 2.00 2.02 1.47 2.56 1.94 2.24 1.80
Rachawongse-Dindang 3.16 3.57 3.32 3.28 3.17 2.92 2.00 3.30 4.92 2.56 2.71 2.99 3.60
Sri Phraya-Klongsan 9.92 10.30 10.05 10.00 9.52 9.75 9.98 10.02 9.83 9.52 9.69 8.98 8.99
Oriental-Wat Suwan 3.61 3.42 3.56 3.59 3.17 2.92 3.99 3.58 4.92 4.03 3.88 3.74 3.60
Sathorn-Pepsi 4.36 4.17 4.32 4.38 6.35 2.92 3.99 4.33 4.92 4.40 4.65 4.49 5.39
Sathupradit-Klong Lat luang 4.81 4.68 4.78 4.84 6.35 2.92 3.99 4.78 4.92 4.76 5.04 5.24 5.39
Rama 3-Klong Latpo 4.51 4.67 4.60 4.69 3.17 3.90 5.99 4.59 4.92 4.40 4.65 4.49 5.39
Klong Toei-Tuapai 0.30 0.13 0.24 0.31 0.32 0.19 0.20 0.24 0.20 0.37 0.39 0.75 0.02
Bangna-Taluen 1.20 0.98 1.05 1.09 0.63 0.97 2.00 1.06 0.98 1.47 1.16 1.05 0.18
Bangnanok-Bangnampuengnok 4.21 3.76 4.06 4.22 6.35 3.90 3.99 4.09 4.92 4.76 4.65 4.49 5.39
Petra-Phra Pradang 6.17 5.67 6.00 6.09 6.35 9.75 5.99 6.03 4.92 6.96 6.59 6.73 7.19
Wiboonsri-Phra Samutchedi 8.87 7.39 8.51 8.91 6.35 9.75 7.98 8.61 9.83 10.62 10.85 11.22 10.79
70
APPENDIX 2 (Continued)
Pollutants
Koh Kret-Wat
Sanamnue (%)
Nonthaburi-Bang
Srimuang (%)
Tha Tien-Wat Arun
(%)
Sri Phraya- Klongsan
(%)
Petra-Phra Pradang
(%)
Wiboonsri-Phra
Samutchedee (%)
Total C I Total C I Total C I Total C I Total C I Total C I
HC 9.77 2.86 6.92 8.87 3.01 5.86 5.71 1.35 4.36 0.66 3.46 6.47 0.41 2.86 3.31 8.87 5.11 3.76
CO 10.65 1.26 9.38 9.36 1.31 8.05 6.54 0.57 5.96 7.01 1.54 8.75 3.86 1.25 4.42 7.39 2.23 5.15
NOx 9.98 2.46 7.52 9.02 2.56 6.46 5.90 1.11 4.79 9.38 3.03 7.02 5.6 2.45 3.55 8.51 4.37 4.13
NMHC 9.69 2.81 6.88 8.91 2.97 5.94 5.63 1.25 4.38 0.64 3.59 6.41 0.39 2.81 3.28 8.91 5.16 3.75
CH4 9.52 2.22 6.35 6.35 2.22 3.17 3.17 0.95 3.17 0.03 2.54 6.35 0.02 2.22 3.17 6.35 3.81 3.17
NH3 9.75 1.95 9.75 9.75 1.95 9.75 9.75 0.97 3.90 0.001 2.92 9.75 0.001 1.95 2.92 9.75 3.90 2.92
N2O 9.98 2.59 7.98 7.98 2.59 5.99 5.99 1.20 4.59 0.005 3.99 5.99 0.003 2.00 3.99 7.98 3.99 3.99
CO2 9.93 2.57 7.35 8.99 2.68 6.31 5.84 1.16 4.68 551.63 3.17 6.86 331.72 2.56 3.47 8.61 4.57 4.04
SO2 4.92 1.97 4.92 9.83 2.46 4.92 4.92 0.98 4.92 0.02 2.46 4.92 0.01 1.97 4.92 9.83 3.93 4.92
PM10 8.79 5.13 3.66 8.06 5.13 2.93 4.40 2.20 2.20 0.26 6.23 3.30 0.19 5.13 1.83 10.62 8.79 1.83
PM2.5 8.53 5.04 3.49 8.53 5.43 3.10 4.65 2.33 2.33 0.25 6.20 3.49 0.17 5.04 1.55 10.85 8.91 1.94
BC 8.98 5.24 3.74 8.23 5.24 2.99 4.49 2.24 2.24 0.12 5.98 2.99 0.09 5.24 1.50 11.22 8.98 2.24
OC 8.99 5.39 3.60 8.99 5.39 3.60 3.60 1.80 1.80 0.05 5.39 3.60 0.04 5.39 1.80 10.79 8.99 1.80
Note: C: cruising, I: idling
71
APPENDIX 2 (Continued)
Pollutants Chao Phraya boats Saen Saep boats Cross river ferries
HC 42.57 29.91 27.51
CO 40.64 24.05 35.30
NOx 42.33 28.27 29.39
NMHC 42.84 29.40 27.75
CH4 42.22 27.78 30.00
NH3 33.50 34.48 32.02
N2O 47.56 26.99 25.46
CO2 42.45 28.60 28.95
SO2 42.86 26.12 31.02
PM10 44.74 33.55 21.71
PM2.5 44.63 33.88 21.49
BC 44.68 33.39 21.93
OC 44.44 33.25 22.30
72
APPENDIX 2 (Continued)
Pollutants Chao Phraya boats Saen Saep boats Cross river ferries
Total C I Total C I Total C I
HC 42.57 35.58 6.99 29.91 22.55 7.36 27.51 11.05 16.47
CO 40.64 28.38 12.27 24.06 12.7 11.35 35.30 6.43 28.87
NOx 42.33 34.16 8.18 28.27 20.69 7.58 29.39 10.11 19.28
NMHC 42.84 35.73 7.11 29.40 22.81 6.59 27.75 11.01 16.74
CH4 42.22 35.56 6.67 27.78 22.22 5.56 30.00 10.00 20.00
NH3 33.50 24.63 8.87 34.48 27.09 7.39 32.02 7.39 24.63
N2O 47.56 40.73 6.82 26.99 20.37 6.62 25.46 10.18 15.27
CO2 42.45 34.60 7.86 28.60 21.30 7.30 28.95 10.39 18.55
SO2 42.86 35.10 7.76 26.12 19.59 6.53 31.02 9.80 21.22
PM10 44.74 41.82 2.92 33.55 30.93 2.62 21.71 15.03 6.68
PM2.5 44.89 41.97 2.92 33.79 31.15 2.64 21.32 14.62 6.69
BC 44.68 41.73 2.95 33.39 30.77 2.62 21.93 15.06 6.87
OC 44.44 41.36 3.08 33.25 30.41 2.84 22.30 14.60 7.70
Note: C: cruising, I: idling
73
APPENDIX 3. Spatial distribution of emission
74
APPENDIX 3 (Continued)
75
APPENDIX 3 (Continued)