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ab
cd
Figure 1 The seasonal variation of sea surface current in the Gulf of Thailand (a) under the NE
monsoon, (b) under the first inter-monsoon, (c) under the SE monsoon, (d) under the second inter-
monsoon [11]
Due to the studies of tidal constituents in The Gulf of Thailand, tidal was affected by co-oscillation tide from
the South China Sea and the tide patterns were diurnal, mixed tide with mainly diurnal, and mixed tide with
mainly semidiurnal. The Upper part of the gulf was dominated by mixed tide with predominately
semidiurnal, the middle part of the gulf was dominated by diurnal. The lower part of the gulf was the mixed
with 2 dominate patterns which were mixed with predominantly diurnal and mixed with predominantly
semidiurnal. The main tidal constituents of the Gulf of Thailand were K1, O1, M2 and S2 [13], [14], [15],
[16], [17]. Hence, the evaluation of the soluble pollutant in The Gulf of Thailand should be investigated due
to the lack of studies about soluble transport from the petroleum source. The soluble pollutant tracer might
be highly affected by seasonal currents. Due to the complexation of seasonal currents and tide, computer
modeling was used to simulate the sediment transport under the difference monsoon condition. Thus, this
study focusses on the evaluation of soluble pollution transport in The Gulf of Thailand for monitoring plan.
METHODOLOGY
This study consists of four main parts which were data collection, model set up, pollutant transport
simulation, and simulation results analysis which derives from direction and concentration. Wind
characteristics were collected from model called WAVEWATCH III which was modelled by NOAA. The
wind grid resolution was 30 second in each x and y direction and the output of model is the 10-meter winds
speed and direction in both U and V components with 3-hour intervals. Water levels were used as the data
for model simulation and divided into 2 sets. First data set was an input data for sea water level at the open
boundary which was in the time-series condition. Open boundary ends were at Tioman station in Malaysia
and Vung Tau station in Viet Nam, both data were collected from the University of Hawaii sea level center
(UHSLC). Second data set was retrieved from Thailand marine department, which were the sea water level
from 3 stations around the Gulf of Thailand; Laem Ngop station in Trat, Mae Klong station in Samut
Songkhram, and Sichon station in Nakhonsithammarat. These data were used for model calibration and
validation processes. Additionally, both data sets were a 6 years data from 2009 to 2015 and in 3-hourly
interval. The concentration of soluble pollutant assumed to be equal to 0.1 mg/l, based on mercury which
was the most concerned heavy metal substance [2]. The concentration was calculated by using the
distribution coefficient value or so-called KD coefficient due to the lacked observation data. The
coefficient was mercury water-particle partition in seawater. The highest mercury concentration that can be
contaminated in the sediment were used in calculation of soluble pollutant amount which equal to 100 mg
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mercury/kg dry weight sediment [18]. However, this value was the mercury concentration contaminated
sediment along the east coast of the gulf which was used for the substitution for the concentration at the
petroleum source due to the lack of data. According to the report from USEPA, the KD coefficient which
provided the highest soluble pollutant concentration was equal to 103 l/kg [19]. Moreover, the pollutant
concentration was assumed to discharged from the source continuously at a constant rate which equal to 1
l/s, to be consistent with the assumption, the rate was set to fix the pollutant concentration at a constant
value. In addition, the pollutant was set not to be decay in order to investigate the highest dispersion distance.
The grid which was used for simulation was manually generated by using the RGFGRID module which
included in DELFT3D. The highest grid resolution was up to 100 m near the petroleum area source region
and the lowest was up to 7 km at the outer area. Although the simulation grid covered the whole area of the
Gulf of Thailand, the focus area of this study was a petroleum sources which was located in the middle of
lower part of the gulf. The latitude of the petroleum source was at 57’ 38” N and the longitude was
15’ 41” E. The bathymetry of the Gulf of Thailand was retrieved from ETOPO and interpolated to the grid
by using QUICKIN module which was also included in DELFT3D. The sea water level in this model was
divided into 4 layers with the varied depths. The layer depths were equal to 50%, 25%, 15%, and 10% of sea
water depth from the sea water surface, respectively. The initial sea water level was set to equal to the
reference plane and there was no soluble pollutant contaminated in environment. The condition of the open
boundary was set to match with water level-time series condition and no pollutant dispersed through the
boundary. The simulation cases were divided by hydrodynamic conditions were divided into four periods;
under the northeast monsoon, under the first-inter monsoon, under the southwest monsoon, and under the
second-inter monsoon, which were varied under the monsoon condition. The Chezy number from 90 m1/2/s
to 150 m1/2/s was used to calibrating model, the calibration started from year 2009 to 2012 by using the 3-
hourly tide level which were obtained from Tioman station in Malaysia and Vung Tau station in Viet Nam.
The model accuracy was calculated by using coefficient of determination ( ) and Nash-Sutcliffe model
efficiency coefficient (NSE) which should be higher than 0.40 and 0.36. After calibration, Chezy coefficient
which provided the best result was chose to validate the model and using the data from year 2012 to 2015.
The , and NSE value were also checked for the model accuracy. The both values should also be in the
range or higher than 0.40 and 0.36, respectively. The main model parameters were showed in table 1.
Table 1 The Main Model Parameters
Parameter Value Unit
Gravity 9.81 m/s2
Water density 1,025 kg/m3
Chezy number 120 m1/2/s
Tidal constituents K1, O1, M2 and S2
Reference density 1,600 -
Specific density 2,650 kg/m3
Dry bed density 1,600 kg/m3
Median sediment diameter ( 200 kg/m3
Initial sediment layer at bed
5 mm
m
According to table 1, the Chezy number was equal to 120 m1/2/s due to calibration and validation process.
The median sediment diameter or was assumed to be equal to 200 mm which was the typical value of
sand and initial sediment layer at bed was assumed to be 5 m.
RESULTS AND DISCUSSIONS
Results were divided to 4 periods; northeast monsoon, first-inter monsoon, southwest monsoon, and second-
inter monsoon. Each period consisted of 4 sea water layers as mention before.
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Northeast Monsoon
The ebb tide transported to northwest while transported to southeast during flood tide with the
highest velocity which was about 0.3 m/s while the lowest velocity occurred during low tide and
high tide which was around 0.05 m/s. The soluble pollutant plume was transport along with the flow
currents in every sea water layers which transported to the northwest direction during ebb tide and low tide
while transported to the southeast direction during flood tide and high tide. Also, the highest plume distance
occurred during high tide whereas the lowest distance occurred during low tide. However, the plume distance
of each layer was difference. At layer 4 which also defined as discharge layer, the highest plume distance
was about 8 km. to the southeast direction while the shortest distance was 4 km. to the northwest direction.
The 0.1 mg/l soluble pollutant was diluted to 0.0004 mg/l at the source area then decreased to about 0.0002
mg/l around the platform region. The plume concentration continued to decrease to 0.0001 mg/l before the
plume concentration reduced until the concentration was undetectable. At layer 3, the farthest distance was
about 18.8 km to the southeast direction while the shortest distance was about 11.5 km to the northwest
direction. The soluble pollutant concentration decreased from layer 4 which was about 0.0001 mg/l at the
source area then decreased to about 0.00004 mg/l near the source region. The concentration reduced to
0.00001 mg/l before the plume concentration decreased to an insignificant level. At layer 2, the farthest
distance was about 19.3 km to the southeast direction while the shortest distance was about 17.6 km to the
northwest direction. The soluble pollutant concentration decreased from layer 3 which was about 0.00004
mg/l near the source area region then decreased to about 0.00002 mg/l. The concentration decreased to about
0.00001 mg/l before the plume concentration continued to decrease until its reached an insignificant level. At
layer 1, the farthest distance and the shortest distance were about 20.9 km to the southeast direction and
about 17.9 km to the northwest direction, respectively. The soluble pollutant concentration decreased from
layer 2 which was lower than 0.000025 mg/l near the source area region then decreased to about 0.00001
mg/l – 0.000015 mg/l before the plume concentration continued to decrease until reached to an undetachable
point. Figure 2 showed the soluble plume concentration and direction under northeast monsoon at layer 1.
Figure 2 The soluble pollutant concentration and direction under northeast monsoon during each tidal
phase at layer 1
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First-inter monsoon
The direction during ebb tide and flood tide and the tidal phases were similar to under the northeast
monsoon, but the velocity was slower which was around 0.1-0.15 m/s. However, the flow velocity during
low tide and high tide was higher which was around 0.1 m/s and the direction was to the west during low tide
and to the north during high tide. At layer 4, the plume transported along with the flow current. The farthest
distance occurred during the high tide which equal to 7.9 km while the shortest distance occurred during the
low tide which was 4.2 km. The soluble pollutant concentration was about 0.0004 mg/l near the source area
region then decreased to lower than 0.0002 mg/l before the plume concentration continued to decrease until
reached to an undetachable level. However, the soluble plume direction from layer 3 to layer 1 were not
clearly to be seen which seem to be transported further to the southeast direction although the still slightly
shifted to along with the flow direction. At layer 3, the farthest distance occurred during the high tide which
was about 27.9 km to the southeast direction while the shortest distance occurred during the low tide which
was 8.9 km to the northwest direction. The soluble pollutant concentration was lower than layer 4 which was
about 0.00008 mg/l around the source area region then decreased to around 0.00004 mg/l. After that, the
plume concentration was drop to 0.00001 mg/l before the concentration continued to decrease until the
plume concentration was insignificant level. At layer 2, the high tide was still provided the farthest distance
which was about 32.2 km while the low tide provided the shortest distance which was about 9.9 km. The
plume concentration was about 0.00004 mg/l around the area near the source region then decreased to around
0.00002 mg/l. The plume concentration which located at the outer area was around 0.00001 mg/l before the
concentration continued to decrease until the plume concentration was undetectable. At layer 1, the highest
distance occurred during the high tide which was about 40.2 km while the shortest distance occurred during
the low tide with the distance of 14.2 km. The plume concentration around the source region was lower than
0.000025 mg/l then dropped to about 0.000015 mg/l. After that the concentration was decreased until
undetectable. Figure 3 showed the soluble plume concentration and direction under first-inter monsoon at
layer 1.
Figure 3 The soluble pollutant concentration and direction under first-inter monsoon during each tidal
phase at layer 1
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Southwest monsoon
The flow direction during ebb tide and flood tide was similar to under the first-inter monsoon except for the
low tide and high tide which current moved to west and to southeast direction, respectively. The highest flow
velocity was about 0.3 m/s which occurred during ebb tide and the lowest was lower than 0.05 m/s, however,
the tidal phase was opposite from under the northeast monsoon which the flood tide occurred first, then
followed by the high tide, ebb, and low tide, respectively. The soluble pollutant plume transported along with
the flow current at layer 4 and layer 3 which the farthest distance occurred during low tide to northwest
direction while the shortest distance occurred during high tide to southeast direction. At layer 4, the farthest
distance was about 7.2 km while the shortest distance was about 4.6 km. The soluble pollutant concentration
was lower than 0.0004 mg/l near the source area region before reduced to lower than 0.0001 mg/l and
continued to decrease until the concentration was in insignificant level. At layer 3, the highest distance was
equal to 30.7 km whereas the shortest distance was equal to 14.5 km. The soluble pollutant concentration
was up to 0.00008 mg/l near the source area region before decreased to about 0.00004 mg/l. The plume
concentration dropped to 0.00001 mg/l until the concentration was undetectable as shown in figure 4.35.
Although the plume direction moved along with flow direction at layer 4 and layer 3, plume direction at
layer 2 and layer 1 were not clearly to be seen. The soluble pollutant plume mostly transported to the
northwest direction, however it still slightly shifted along with a flow direction via tidal phases. At layer 2,
the farthest distance still occurred during the low tide which was about 36 km while the shortest distance was
24.6 km during the high tide. The plume concentration was about 0.00004 mg/l at the area near the source
region and decreased to about 0.00002 mg/l. After that, the plume concentration was reduced to lower than
0.00001 mg/l before the concentration was dropped to an insignificant level. At layer 1, the plume
transported farthest during the low tide while the shortest was during the high tide, the distance was 39.2 km
and 26.8 km, respectively. The plume concentration near the source region was lower than 0.000035
mg/lthen decreased to around 0.00002 mg/l. After that, the concentration was decreased until undetectable.
Figure 4 showed the soluble plume concentration and direction under southwest monsoon at layer 1.
Figure 4 The soluble pollutant concentration and direction under southwest monsoon during each
tidal phase at layer 1
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Second-inter monsoon
The flow direction was similar to under the southwest monsoon. The velocity was lower to around 0.05-0.15
m/s which was similar to under first-inter monsoon as same as the flow direction. The soluble pollutant
plume of each tidal phases was hardly to be seen which mainly moved to the northwest direction. However,
the plume still slightly shifted along with a flow direction. At layer 4, the highest plume transported distance
occurred during the low tide while the lowest was during the high tide, the distance was 11 km and 3.7 km,
respectively. The soluble pollutant concentration was lower than 0.0004 mg/l near the source area region and
reduced to about 0.0002 mg/l then continued to decrease until the concentration was in insignificant level. At
layer 3, the highest plume transported distance was 41.8 km during the low tide while the lowest was about
34 km during the low tide. The soluble pollutant concentration was up to 0.00004 mg/l near the source area
region before reduced to about 0.00002 mg/l and continued to be dropped to 0.00001 mg/l until the plume
concentration was undetectable. At layer 2, the highest plume transported occurred during low tide while the
lowest occurred during high tide, the distance was 50.9 km and 37.6 km, respectively. The plume
concentration at the area near the source region was about 0.00003 mg/l and reduced to around 0.000015
mg/l before continued to be dropped to lower than 0.00001 mg/l until the plume concentration was
undetectable. At layer 1, the highest plume transported distance was 52.5 km while the shortest was 40.2 km
which occurred during low tide and high tide, respectively. The plume concentration near the source area
region was lower than 0.000025 mg/l and reduced to around 0.00001 mg/l - 0.000015 mg/l. After that, the
plume concentration dropped down to 0.00001 and continued to reduce until the concentration was
undetectable. Figure 5 showed the soluble plume concentration and direction under second-inter monsoon at
layer 1
Figure 5 The soluble pollutant concentration and direction under second-inter monsoon during each
tidal phase at layer 1
According to results, the simulated soluble pollutant plume directions were moved along with the flow
direction especially with the highest flow velocity of each period. According to the location of the source, the
flow transport direction effected to the plume direction which influenced the soluble pollutant plume to
moved further to the same direction as the flow direction. In the other words, the plume transported farthest
during the ebb tide and low tide under northeast monsoon and first-inter monsoon which was to the mouth of
the gulf. On the other hand, the plume transported farthest during the flood tide and high tide under the
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southwest monsoon and second-inter monsoon which was to inland as same as the flow direction under the
southwest monsoon and second-inter monsoon. In the part of soluble pollutant concentration, the plume
concentration around the source region was decreasing via the increasing of the plume distance. Moreover,
the plume concentration dramatically decreased when it first introduced to the environment which was about
3 times from the start, the concentration also dramatically dropped when transported from layer 4 to layer 3
which was about 1 time before it halved from the concentration at layer 3 when the plume moved to layer 2.
However, the plume concentration was slightly dropped when it transported to layer 1.
CONCLUSION
The transport of the soluble pollutant was affected by monsoon condition and the farthest distance of soluble
pollutant plume was 78 km which under the second-inter monsoon condition.
REFERENCE
[1] Department of Mineral Fuels, 2018, Origin of Petroleum, https://dmf.go.th/public/list/data/index/men-
u/611/mainmenu/611/
[2] Worakhunpiset, S. 2018. Trace Elements in Marine Sediment and Organisms in The Gulf of Thailand.
International Journal of Environmental Research and Public Health. 15(4): 1-15.
[3] Cordes, E.E., Jones, D.O.B., and Schlacher, T.A., et al. 2016. Environmental impacts of the deep-
water oil and gas industry: a review to guide management strategies. Frontiers in Environmental
Science. 4(58): 1-27.
[4] Khunkhet, S., 2018, Mercury and Arsenic concentration in marine fish tissue from the petroleum
production platform in the Gulf of Thailand, http://digital_collect.lib.buu.ac.th/dcms/files/50037792.p-
df
[5] Myrbo, A., Swain, E.B., and Engstrom, D.R., et al. 2017. Increase in Nutrients, Mercury, and
Methylmercury as a Consequence of Elevated Sulfate Reduction to Sulfide in Experimental Wetland
Mesocosms. Journal of Geophysical Research: Biogeosciences. 122(11): 2769-2785.
[6] Marine Knowledge Hub, 2018, Coastal information, http://www.mkh.in.th/index.php?option=com_co-
ntent&view=article&id=124&Itemid=222&lang=th
[7] Puchala, R.J., 2014, Morphology and origin of modern seabed features in the central basin of the Gulf
of Thailand, https://www.researchgate.net/publication/286440960_Morphology_and_origin_of_mode-
rn_seabed_features_in_the_central_basin_of_the_Gulf_of_Thailand
[8] Thai Meteorological Department, 2019, Meteorological books, https://www.tmd.go.th/info/info.php-
?FileID=52
[9] Buranapratheprat, A. and Bunpapong, M. A Two-Dimensional Hydrodynamic Model for the Gulf of
Thailand. At Proceedings of The IOC/WESTPAC Fourth International Scientific Symposium,
Okinawa, Japan on February 2-7, 1998.
[10] Snidvongs, A. and Sojisuporn, P. Numerical Simulations of The Net Current in the Gulf of Thailand
Under Different Monsoon Regimes. At Proceedings of the First Technical Seminar on Marine Fishery
Resources Survey in the South China Sea, Area I: Gulf of Thailand and Peninsular Malaysia,
Bangkok, Thailand on November 24-26, 1997.
[11] Sojisuporn, P., Morimoto, A., and Yanagi, T. 2010. Seasonal variation of sea surface current in the
Gulf of Thailand. Coastal Marine Science. 34(1): 1-9.
[12] Lorpittayakorn, P. 2015. Circulation Patterns in the Gulf of Thailand from Model. Thai Journal of
Science and Technology. 23(3): 446-465.
[13] Wyrtki, K. 1961. Physical Oceanography of the Southeast Asian waters. The University of California
Scripps Institution of Oceanography La Jolla, California.
[14] Di, Z., Yan-Bo, L., and Cheng-Kui, Z. 1994. Oceanology of China Seas Volume 1. Kluwer Academic
Publishers, Dordrecht.
[15] Fang, G., Kwok, Y., and Yu, K., et al. 1999. Numerical simulation of principal tidal constituents in the
South China Sea, Gulf of Tonkin and Gulf of Thailand. Continental Shelf Research. 19(7): 845-869.
[16] Aungsakul, K., Jaroensutasinee, M. and Jaroensutasinee, K. 2007. Numerical Study of Principal Tidal
Constituents in the Gulf of Thailand and the Andaman Sea. Walailak J Sci & Tech. 4(1): 95-109.
[17] Neill, S.P. and Hashemi, M. 2018. Fundamentals of Ocean Renewable Energy. Academic Press,
Massachusetts.
[18] Thongra-ar, W. 2008. Heavy Metals Contamination in Sediments along the Eastern Coast of the Gulf
of Thailand. Environment Asia. 1(2008): 37-45.
[19] Allison, J.D. and Allison, T.L. 2015. Partition Coefficients for Metals in Surface Water, Soil, and
Waste. USEPA, Washington, DC.
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I 042
Assessment of Impact from Biogas Leakage by Area Location of
Hazardous Atmosphere Simulation
Wilasinee Yoochatchaval1* and Wijitra Wattanapakorn2
1* Assistant Professor, Department of Environmental Engineering, Faculty of Engineering, Kasetsart University,
Bangkok 10900, Thailand; 2Graduate student, Graduate program in Safety Engineering,
Faculty of Engineering, Kasetsart University, Thailand
*Phone :(+66)92-253969, E-mail : [email protected]
ABSTRACT
The modified covered anaerobic lagoon is very popular for treatment of wastewater in many industrial
sectors owning to the cost-effective and satisfied process performance reason. However the risk of fire and
explosion are the major incident that generally impact the operators and the surrounding community. In
principle, there are four main risks existing on biogas system: deflagration, poisoning, asphyxiation, and
environmental damage due to leakage of biogas. In this research, the Area Location of Hazardous
Atmosphere (ALOHA) model had been chosen to simulate the leakage of biogas from the modified covered
anaerobic lagoon system. Also, ALOHA simulation predicted the most serious incident and the scales of
impact (threat zones). Four scenarios had been selected with respect to the previous accidents. The results of
simulation indicate the adverse impact to life and could be used as guidance to help deciding and
responding in emergency incident for the future.
Keywords : biogas; waste management; Area locations of hazardous atmospheres (ALOHA); Modified
covered anaerobic lagoon; risk assessment
INTRODUCTION
Biogas composition
Wastewater treatment technology through microbial degradation from anaerobic fermentation system with
the output of biogas is the technology giving worthy benefit on the economic, environmental, industrial and
community suitability. Biogas production from the Modified Covered Anaerobic Lagoon wastewater
treatment system is the popular system used in the wastewater treatment work in several industries. The
output from the Modified Covered Anaerobic Lagoon wastewater treatment system is biogas for electricity
generation to reduce cost of production. The biogas usually consists of the following components [1]:
Methane (CH4) 50 mol% to 70 mol%
Carbon dioxide (CO2) 30 mol% to 50 mol%
Water vapor (0 mol% to 12 mol%)
Nitrogen (0 mol% to 5 mol%)
Oxygen (0 mol% to 2 mol%)
Hydrogen sulfide (0.01 mol% to 0.4 mol% (100 to 4000 ppm(v)), and traces of ammonia, hydrogen
and higher hydrocarbons
Methane was the main production from an anaerobic wastewater treatment. Danger from the methane leak
includes fire and explosion [2]. Whenever such incident happens, it causes wide range of damage in life,
property and surrounding environment. Due to the fact that the drill on biogas leak is unable to determine to
use real gas, ALOHA method to simulate the incident of gas leaking to evaluate the damage was the option
for the factories with biogas production systems used to see the impact and damage that might happen [3].
The database CHEMSAFE [4] provides information on the safety characteristics of chemicals. The safety
characteristics of hazardous materials are the basis of primary explosion protection and of an explosion
protection document. In the case of biogas plants, these are usually the explosion limits of biogas-air
mixtures. The explosion limits for pure substances, such as methane, can be taken from data books and
databases. For more complex mixtures such as "biogas" this data is not available, so, in practice the
explosion limits of methane are frequently referred Table 1 [5]. At the lower explosion limit this is
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acceptable, because methane has a lower LEL than biogas. At the upper explosion limit, this can lead to
dangerous conditions. The upper (UEL, fuel-rich) and lower explosion limits (LEL, fuel-lean) are referred to
as the mole fractions (concentration) of the biogas in air at which flame propagation after spark ignition no
longer occurs (the last non-ignition point).
Table 1 Safety characteristics of biogas components [5,6]
Characteristic Unit Methane CO2 H2S NH3 H2
4.0
Lower explosion limit vol% 4.4 n.f. 3.9 15.4
77.0
(LEL) (mol%)
560
Upper explosion limit vol% 17.0 n.f. 50.2 33.6 0.01
(UEL) (mol%) 7
2C
Auto ignition temperature °C 595 - 270 630 T1
n.t.
Minimum ignition Energy mJ 0.29 - n.k. 14
0.07
Explosion group - 2A - 2B 2A
Temperature class - T1 - T3 T1
Acute Toxicity (LC50) ppm(V) n.t. > 8 712 7338
vol%*
Gas density rel. to Air 0.55 1.53 1.19 0.60
(n.f. = non flammable, n.t. = non toxic, n.k. = not known *) hazardous to health
In this regard, the research then used the analysis and evaluation of biogas leaking risk, impact on workers in
the factory and nearby community by ALOHA program through studying the factory in Pathumthani. At
present the production plant needs to increase the production capacity in order to meet the demand of the
market. Therefore, the risk analysis and severity assessment are required in the case of biogas leakage from
the modified anaerobic wastewater treatment system. The company then launched the project of an
additional wastewater treatment system to support power generation and to be the spare wastewater system
in order to plan the maintenance of the old system. In the past, there was the explosion incidence from
leaking wastewater treatment system as the coverage material was opened resulting in the gas leaking into
the production factory but there is still no severe incidence in case of biogas leaking.
The researcher determined the hypothesis for the worst-case scenario and the actual case scenario by
ALOHA (Areal Location of Hazardous Atmospheres). Then ALOHA program was employed to simulate the
impact from methane leaking by overlaying the simulating intensity result from ALOHA program on the
map from Google Earth showing radius scope of the chemical intensity.
METHODOLOGY
Study Area
This research investigate the modified covered anaerobic lagoon system for biodiesel wastewater treatment
located in Pathumthani province.
ALOHA Software
The model of dispersion chosen for the purpose of this research is the ALOHA model, which has been built
upon the Gaussian dispersion model of continuous, buoyant air pollution flumes [6]. Program ALOHA
model 5.4.7 developed by United States Environmental Protection Agency (EPA) and National Oceanic and
Atmospheric Administration (NOAA) was used in all scenarios. ALOHA is used widely to plan for and
respond to chemical or hazardous materials emergencies. Limitations of this software are as the followings:
very low wind seeds
very stable atmospheric conditions
wind shifts and terrain steering effects
concentration patchiness, particularly near the source
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This research applied the average maximum temperature of 32°C, referred to the meteorological data of
Pathumthani Province. Stability class for the worst case scenarios applied wind speed of minimum 5 km per
hour and 18 km per hour for actual meteorological. The Pasquill atmospheric stability classes were the most
commonly used method of categorizing the amount of atmospheric turbulent present [7]. Pasquill
categorized the atmospheric turbulence into six stability classes named A, B, C, D, E and F as referred to
Table 2. Class A is the most unstable or most turbulent class, and class F is the most stable or least turbulent
class as presented in Table 3.
Table 2 The Pasquill stability classes [7]
Stability class Definition
A Extremely unstable
B Moderately unstable
C Slightly unstable
D
E Neutral
F Slightly stable
Moderately stable
Table 3 Meteorological conditions that define the Psquill stability classes.
Wind Daytime incoming solar Nighttime cloud cover
speed (m/s)
radiation
<2
2-3 Strong Moderate Slight >50% <50%
3-5 E F
5-6 A A-B B E F
>6 D E
A-B B C D D
D D
B B-C C
C C-D D
CD D
Assumptions of the simulation scenario
Dispersion conditions for worst case scenarios with wind speed less than or equal to 1 m/s, meteorological
conditions were classified as a class B (daytime incoming solar radiation: slight) and class F (nighttime cloud
cover less than 50%). The actual case scenarios, wind speed was 5 m/s, the meteorological conditions that
defined class D (daytime incoming solar radiation: slight and nighttime cloud cover less than 50%).
According to the ALOHA’s Limitations by United States Environmental Protection Agency (EPA), ALOHA
is not suitable for modeling of the mixed gas but biogas composed with CH4, CO2, H2S and etc. For this
reason, pure methane gas was only used for calculation. The worst case was a maximum capacity storage
cover lagoon, rapture and all of the methane were instantaneously released. There are four hypothetical
situations with varied wind speed, stability class and wind direction as shown in Table 4.
Table 4 Configurations of release scenario for simulation
Scenario
Hypothetical scenario of simulation Worst case Actual case
Fixed parameters
Total amount release 1,172 kilograms 1,172 kilograms
Source height 5 meter 5 meter
Model of release
Temperature (°C) Direct source Direct source
Relative humidity 32°C 32°C
Wind direction 55% 55%
Manipulated parameters E E
Wind Speed
Atmospheric stability level (Day) 3.6 km/hr. 18 km/hr.
Atmospheric stability level (Night) B C
F D
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Input data
Methane gas is one of the most important gases for the view point of safety. Therefore, this study focused on
deflagration and dispersion of methane. Generally, the concentration of methane in biogas was variable and
could reach value up to 75% [8] independence on used material and technology. The concentration of
methane in biogas was standardly around 60 – 65%. Explosion limits for biogas was designated as
concentration of 60% of methane as lower explosion limit (LEL) and 12% as upper explosion limit (UEL)
[9]. A conservative approach was applied in modeling, i.e. the pure methane was considered.
RESULTS AND DISCUSSIONS
ALOHA model simulates the leakage of biogas and predicts threat zones. The results of flammable threat
zone toxic was simulated by ALOHA model for different four hypothetical scenarios. Scenario 1-4 should be
simulated with similar conditions. There were fixed conditions such as height of leakage hole at 5 meters,
relative humidity at 55%, cloud cover at 3 tents, temperature at 32°C.
Threat Zone of Flammable in ALOHA simulation
The worst case scenario, day time (class B), presented the flammable threat zone area of methane
concentration with the lower explosion limit (LEL) of 19 meters, 60% of methane at the lower explosion
limit of 25 meters and 10% of methane at the lower explosion limit of 60 meters as shown in Figure1.The
results exhibited the flammable area in the range of 1-60 meters that would not affect the surrounding
facilities and plants.
Figure 1 Flammable zone from worst scenario at day time.
The worst case scenario, night time (class F), presented the flammable threat zone area of methane
concentration with the lower explosion limit (LEL) of 112 meters, 60% of methane at the lower explosion
limit of 146 meters and 10% of methane at lower explosion limit of 370 meters as shown in Figure 2.The
results showed the flammable area in the range of 1-370 meters that affect the surrounding facilities and
plants. If the nearby plants have some chemical substances that can be react with methane, the damage can
be increased.
Figure 2 Flammable zone from worst scenario at night time.
For the actual scenarios, day time (class C), showed the flammable threat zone area of methane
concentration with the lower explosion limit (LEL) of 13 meters, 60% of methane at the lower explosion
limit of 16 meters and 10% of methane at the lower explosion limit of 40 meters. The results showed
flammable area in the range of 1-40 meters. Due to the effects of near-field patchiness make dispersion
predictions less reliable for the short distances, ALOHA was not drawn for the flammable threat zone. The
actual scenarios, night time (class D), showed flammable threat zone area of methane concentration with
the lower explosion limit (LEL) of 17 meters, 60% of methane at the lower explosion limit of 22 meters and
10% of methane at the lower explosion limit of 56 meters. The results showed the flammable area in the
range of 1-40 meters as shown in Figure 3. The actual scenarios for day and nighttime did not affect the
surrounding facilities and plants.
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Figure 3 Flammable zone from actual scenario at night time.
CONCLUSION
The research was the evaluation of the impact of the dispersion of biogas leakage from the modified covered
anaerobic lagoon using ALOHA program on the community at the west side of the incidence spot in biogas
producing factory for approximately 380 meters. The evaluation determined weather condition affecting the
biogas leaking in its worst case scenario, wind speed was determined at 1 m/s and the actual weather condition
determined wind speed at 5 m/s. Time was separated into 2 parts – day and night. The result found that the people
in communities who are most affected by flammable zone from the worst scenario nighttime (class F). After the
biogas leakage for 1 hour showed that for the worst case scenario, the danger occurred at night, ALOHA showed
footprints representing the watch areas with high levels of flammable at LEL, 60%LEL and 10% of LEL distance
away from the source were 112 m., 146 m. and 370m., respectively. In case of the actual case scenario, the danger
occurred at day and night, ALOHA showed footprints of flammable area did not affect the surrounding facilities
and plants. Despite the community located in the safe distance, ALOHA program was unable to stimulate the
chemical intensity spreading more than 1 hour. It could be said that those workers in the factory and people in the
community had approximately 1 hour to evacuate to the safe place. However, if there were changes such as
storage volume, leakage hole type, leakage hole size, leakage rate and meteorological condition, it can cause
of the change dispersion of methane gas. The results of the ALOHA simulation were used as a suggestion for
the emergency response plan for the case of leakage and explosion of biogas system.
REFERENCE
[1] Turner, D.B., 1970. Workbook of dispersion estimates, Department on Health Education and Welfare,
Cincinnati, Ohio, USA
[2] Schroeder V., Schalau B., Molnarne M., (2014), ExplosionProtection in Biogas and Hybrid Power
Plants, Proc.“2014 ISSST”, 2014 International Symposium onSafety Science and Technology,
Beijing, China, vol.84, 259-272.
[3] Petr Trávníček, Luboš Kotek. 2015. Risks associated with the production of biogas in Europe.
American Institute of Chemical Engineers Process Saf Prog 34: 172–178. DOI: 10.1002/prs.11734
[4] DECHEMA, BAM und PTB, CHEMSAFE® - Database for evaluated Safety Characteristics, Update
2013, STN International, Karlsruhe, http://www.dechema.de/en/chemsafe.html
[5] Bundesverband der landw. Berufsgenossenschaften e. V., Hauptstelle für Sicherheit und
Gesundheitsschutz, AU 69: “Sicherheitregeln fürlandwirtschaftlicheBiogasanlagen”, 2002
[6] Umweltbundesamt: Informationspapier zur Sicherheit bei Biogasanlagen, Juni 2006,
http://www.umweltbundesamt.de
[7] I. Barnes, M. M. Kharytonov, Simulation and Assessment of Chemical Process in a Multiphase
Evironment, NATO Science for Peace and Security Series C: Environmental Security,2008
[8] Marc J., Rogoff PhD., Francois Screve Meng., MBA. 2019. Energy from waste Technology. Waste-
To-energy (Third Edition), 2019
[9] Safety Rules for Biogas Systems. 2008. German Agricultural Occupational Health and Safety Agency
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Evaluating the Distribution of Microplastics in Patong Beach,
Phuket, Thailand
S Tongnonhin1, P Akkajit1* and D Tipmanee1
1*Assistant Professor, Prince of Songkla University, Phuket Campus, Phuket 83120, Thailand;
2Lecturer, Prince of Songkla University, Phuket Campus, Phuket 83120, Thailand;
E-mail: [email protected]
3Graduate student, Prince of Songkla University, Phuket Campus, Phuket 83120, Thailand
Corresponding author: Phone* 062-3499621, E-mail : [email protected]
ABSTRACT
Micro-plastics in the coastal area is a global problem recently as it may be harmful to aquatic
organisms. This study examined the abundance, shape and color of microplastics in Patong beach area,
Phuket province, Thailand. The sampling was done by using 0.5 x 0.5 m. of the three quadrats and collected
on April, 2019. Plastic debris was separated into 3 sizes namely micro-, meso-, and microplastic by using
mesh sieve no. 1 and 4 respectively. The results showed that both macroplastic and mesoplastics cannot be
found during the sampling, but not the microplastics. It was found that the abundance of microplastic was 80
items with a density of 320 g/m3. Black and blue were the most frequent colors of microplastics observed in
Patong beach (31%) and the fibre shape is the majority of micorplastic found in this study that accounted for
91%.
Keywords : Macro-plastic, Meso-plastic, Micro-plastic, Patong Beach.
INTRODUCTION
Phuket is one of provinces in Thailand that have built a reputation for a tourism hub, resulting in high
income from the tourism industry. Due to the island-like nature of Phuket, there are a lot of tourists, both
Thai and foreign, coming to enjoy and visit of approximately 13 million tourists a year (Phuket Provincial
Statistical Report, 2016). The problem of plastic waste is now a serious concern to be aware of and to be
solved urgently. Due to its properties of flexibility, durability , corrosive resisitance, and low cost; plastic has
been used widely wordwide and the production has been increased, Plastic waste can be classified according
its sized namely, macroplastic (> 2.5 cm), mesoplastic (5 mm to 2.5 cm), and microplastics (< 5 mm)
(National Science Museum, 2018). Microplastic is the most concern particle size due to it can act as carriers
of contaminants such as chemicals, additives or organic contaminants and that could harm human health. The
accumulation and transfer of microplastic in the coastal area can have the impact on the aquatic environment,
especially the foodchain transfer. Therefore, this research aims to determine the distribution of macroplastic,
mesoplastic and micro-plastic in Patong Beach, the most famous and popular beachof Phuket, Southern of
Thailand. The information of microplastic waste in this research is very necessary in order to be used as a
guideline for formulating policies and solving garbage problems in such specific area of Phuket.
METHODOLOG
Sample Collection and Preparation
Patong beach is selected as the study area. It is located on the central west coast where the coastal area
is used mainly for tourism activities. Beach sediment samples were collected at the intertidal zone at three
different locations of Patong beach namely, at the beginning of the beach near fishing port (P1 latitude
7°54'16.07"N longitude 98°17'48.84"E), in the middle of the beach near hotel and restauran (P2 latitude
7°53'46.48"N longitude 98°17'43.28"E) and at the end of the beach near restaurant (P3 latitude 7°53'18.92"N
longitude 98°17'30.17"E) during April, 2019 by using a quadrat of a 0.5 meter x 0.5 meter. The beach
sediments were collected at the depth of 5 centimer (Figure 1) and then kept in the containner before
trnasfering to the laboratory at Faculty of Technology and Environment, Prionce of Songkla University,
Phuket Campus for further processing.
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Figure 1 Sampling points at Patong beach (P1-P3).
Microplastic analysis
Dried sediment samples were mixed with saline solution of NaCl (density = 1.15 g/mL) for density
separation. The supernatant was then digested to remove organic substances by using 20 mL of aqueous 0.05
M Fe(II) solution (FeSO4•7H2O) and 20 mL of 30% H2O2 for five minutes at room temperature. The solution
was then left overnight and filtered using filter paper no 1. with a filtration unit and a vacuum pump. Finally,
the samples remained in the filter paper was determined for the color, shape, and quantities using a 40x
magnification microscope (AXIOSTAR, Olympus). Microplastic determination in this study was studied
according to NOAA Marine Debris Program National Oceanic and Atmospheric Administration (2015) with
some modification. For color classification, the microplastics are classified into 12 colors included
transparent, white, red, black, dark blue, blue, green, gray, brown, yellow, orange and purple according to
Marine and coastal resources research & Development institute (2014). For shape classsification, there are 7
types included pellets; foams; fragments; flakes; films; fibers; and sponges according to Marine and coastal
resources research & Development institute (2014).
RESULT AND DISCUSSION
Quantities
According to the results, macro-plastic and meso-plastic were not found in the beach sediment
samples at Patong beach, however, micro-plastic debris was observed at all the sampling point (P1-P3). The
quantities of micro-plastic debris found at all sampling stations (P1-P3) were 80 items (Table 1), however,
the majority of micro-plastic debris are mostly found at P1 (n=37) that accounted for 46% of the total micro-
plastic debris. This is probably due to the sampling location at P1 is used for tourist activities and speed boat
parking area, and these activities might affect the higher number of micro-plastics in the area. In addition, it
was found that the concentration of microplastics in the present study is higher than the Marine Resources
and Mangrove Research and Development Institute (2014) those studied micro-plastic in sediment at Chao
Laow beach area and found microplastic abundance in the sediment at 54.75 ± 20.75 and 43.41 ± 17.55
items during the rainy and dry season, respectively. In our previous study, micro-plastic debris was also
determined at the same sampling location at Patong beach during November, 2018 and the results of the
present study showed higher number of micro-plastic debris (80 items) than the previous study in 2018 as
only 38 items m-2 of microplastic debris was observed (Akkajit et al., 2019). The season might affect the
quantities of microplastic, therefore further study should be conducted to compare microplastics throughout
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the year. According to a study by Wang et al. (2020), the distribution of microplastics in the Gulf of
Thailand showed that the prevalence of microplastics is ranged from 25 to 362.5 items/kg where the
most common area is the upper part of the Gulf of Thailand (Wang et.al., 2020). In addition, Li et
al. (2019) determined the distribution of microplastics in the sediments of Poyang Lake and found
that the microplastics in the Upper Raohe near the Dexing Copper mine was 3, 153 items/ kg dw
(dry weight).
Table 1 Quantity and density (items/m3) of Micro-Plastics at Patong beach (P1-P3)
Location Quantity Density Percentage
(n) (items/m3) (%)
P1 37 46
P2 148
P3 29 36
116
14 18
56
Color
From the present study, it was found that there are 6 colors of micro-plastics found in the beach
sediment samples included black, red, dark blue, white, yellow and green (Figure 2). It was observed that the
most color of microplastics found in this study are equally in black and dark blue colors (31%). Patong beach
has a variety of tourist activities, and the color of the micro-plastic found in this study may come from tourist
acitivities or from the safeguard ropes at the sampling point P1. The results of this study is consistent with
the study of the Marine Resources and Mangrove Research and Development Institute (2014) at Chao Lao
beach those found that the blue plastic waste is mostly found during the rainy season (20%), and the black
plastics was mostly found during the dry season accounted for 29% of all total micro-plastics. However,
compared with the preliminary study of distribution and quantity of plastic-debris on beaches along the coast
at Phuket Province of Akkajit et al. (2019), blue fibers were predominant among microplastic debris found at
Kalim Beach; these are likely to have originated from pieces of rope, safeguard lines and fishing materials,
while microplastic debris found in Patong and Tri Trang Beaches were predominantly green in color (Akkajit
et al., 2019). The results of the present study is consistent with the study of Peng et al. (2017) those found
that the most common color of plastic in Changjiang River Delta is blue (25%) followed by black (16%)
color where the main activity is washing clothes (Peng et al., 2017).
Figure 2 Colors of micro-plastics at the sampling points, Patong beach, Phuket
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Shape
Shape of microplastic was determined in this study and the examples of different shapes of
microplastics at Patong beach are given in Figure 3. According to the results, there were 2 shapes of
microplastics found at Patong which are fiber and fragments. Fiber was the majority observed microplastic at
the Patong beach and this probably came from the fishing ports and fishing equipment such as net and fish
traps. The personal care products from tourist activities such as toothpaste, sunbathing and others may
contribute the microplastics into the beach and cause the fiber shape to be the mostly observed shapes of
microplastics found at Patong beach. However, analysis of the FTIR imaging data should be further
conducted in order to confirm the types and identification of microplastics. According to the study of
microplastics in the Central Adriatic Sea, 69.3% and 16.4% of the total microplastics found were fibers and
fragments, respectively (Mistri et al., 2017). The results of the present study are also similar with the study
of microplastic in sediment at the Changjiang Estuary those found 93% of microplastics are fibers (Peng et
al., 2017).
Figure 3 Examples of different shapes of microplastics at the Patong beach, Phuket Province
CONLUSION
In conclusion, 80 items of micro-plastics debris was observed at Patong beach mostly at the
beginning of the beach (P1 station) (46 %), where the location is used mainly for tourist activities. Fiber was
found as the majority shape of microplastics in this study, where, blue and dark blue color are equally found
at Patong beach (31%). The contribution of microplastic in this study may be from personal care products
and fishing materials as the area is used mainly for tourist activities. However, further studies by analysis of
the FTIR imaging data should be conducted for identification of microplastics.
REFERENCES
[1] Wang, Y., et al., (2020). Occurrence and distribution of microplastics in surface sediments from the
Gulf of Thailand. Applied Marine Pollution Bulletin 152, 110916
[2] Akkajit, P., Thongnonghin, S., Sriraksa, S., & Pumsri, S. (2019). Preliminary Study of Distribution
and Quantity of Plastic-debris on Beaches Along the Coast at Phuket Province. Applied
Environmental Research, 41(2), 54-62.
[3] Zhou, Q., et al., (2018). The distribution and morphology of microplastics in coastal soils adjacent to
the Bohai Sea and the Yellow Sea. Geoderma. 322. 201-208.
[4] Peng, G., et al., (2017). Microplastics in sediments of the the Changjiang Estuary, China. Applied
Environmental Pollution, 283-290
[5] Mistri, M., et al., (2017). Small plastic debris in sediments from the Central Adriatic Sea: Types,
occurrence and distribution. Applied Marine Pollution Bulletin 124, 435-440
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[6] Kerstin, E., et al., (2016) Microplastic pollution in lakes and lake shoreline sediment – A case study on
Lake Bolsena and Lake Chiusi (central Italy). Applied Environmental Pollution, 648-657
[7] C Khapap, 2018. Micro plastic Dark disaster inthesea. http://www.tistr.or.th/tistrblog/tistrboog
/?p=4707&fbclid=IwAR0Xy2Gc08bgMbCLmvv9WAXbUERQoN42oO2SFbXwAfQedQVyQwaI2L
N_fk. (accessed November 08, 2018).
[8] P Shawabun, 2018. Contami Nated plastics: Researchers find micro-plastics in human feces
around the world. https://www.bbc.com/thai/international-45956262 (accessed November 25, 2018).
[9] Economic base, 2017. Marine waste. http://www.thansettakij.com/content/149655 (accessed Decem
ber 15, 2018).
[10] Marine and Coastal Resources Research& Development Institute. Burapha University, Faculty of
Marine Technology, 2557. The complete report, surveying and classifying micro-plastic marine waste
samples.https://www.dmcr.go.th/upload/dt/file/file-3095-201705161494905397559.pdf (accessed
October 10, 2018).
[11] Phuket Provincial Statistical Office, n.d. Analyze and summarize the situation of tourism. http://Phu
ket.nso.go.th/index.php?option=com_content&view=article&id=373&Itemid (accessed December 15,
2018).
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I 044
Investigation on Micropollutant Degradation and Biotoxicity by using
Activated Sludge and Photocatalysis Processes Treating Landfill
Leachate under Different Hydraulic Retention Time
Pitakporn Thankulkit1, Supaporn Phanwilai2, Sivakorn Angthong3, and Jarungwit Boonnorat4,*
1 Graduated student, Department of Civil Engineering, Rajamangala University of Technology Thanyaburi
(RMUTT), Pathum Thani 12110, Thailand ; 2 Postdoctoral Fellow, Department of Environmental
Engineering, Kasetsart University, Bangkok 10900, Thailand ; 3 Assistant Professor, Department of
Industrial Engineering, Rajamangala University of Technology Thanyaburi (RMUTT), Pathum Thani 12110,
Thailand ; 4 Corresponding author, Assistant Professor, Department of Civil Engineering, Rajamangala
University of Technology Thanyaburi (RMUTT), Pathum Thani 12110, Thailand
*Phone: 02-549-3410, Fax: 02-549-3412, Email: [email protected]
ABSTRACT
The non-photo AS (AS without photocatalysis process) and the photo AS system (AS with
photocatalysis process) systems were operated with landfill leachate influent under HRT 24 and 18 h
conditions. The results show, the non-photo AS system under HRT 24, the treatment performance and
micropollutants degradation were in range of 86 – 100% and HRT 18 h were in range of 73 – 100%. For the
photo AS system under HRT 24, the treatment performance and micropollutants degradation were in range
of 93 – 100% and HRT 18 h were in range of 92 – 100%. The quantitative polymerase chain reaction (qPCR)
analysis indicates that the bacterial community including total bacteria, nitrifying bacteria (Nitrobacter and
Nitrospira) in all HRT conditions of both systems. The bacterial abundance was not significant changed. For
the biotoxicity assessment with common carp (Cyprinus carpio). Even though, the photo AS system
performed a great treatment performance and micropollutants degradation in all HRT conditions but the non-
photo AS effluent which contained low micropollutant concentrations and also the EC value that these are
some reason to present that the biological treatment system more friendly with ecological system in case
long HRT condition.
Keywords : Landfill leachate, Micropollutants, Biotoxicity, Activated sludge, Photocatalysis
INTRODUCTION
Landfill is used for final disposal of municipal solid wastes from communities. One of the main
environmental problems from landfill is the generation of landfill leachate that contained organic
compounds, inorganic compounds and also including micropollutants. It may pose serious risks to
ecosystems and human health through discharge to environment without proper treatment. Many
conventional as well as advanced treatment processes have been applied to treatment landfill leachate such as
two-stage membrane bioreactor which the one of advance biological treatment system was reported that the
treatment efficiency more over 95% when operated under hydraulic retention time (HRT) at 24 h treating the
high landfill leachate concentration (BOD, COD, TKN and micropollutants) [1,2]. The advanced oxidation
processes (AOPs) were initially proposed to treat water pollution that involving generation of hydroxyl
radicals (OH). The concept was later on extended to oxidation involving sulphate radicals too. Research on
AOPs is focused on the reduction of micropollutants or recalcitrant compounds and toxic reduction in
leachate treatment [3].
Biotoxicity assessment can be used to characterize the toxicity of wastewater and the biological
effect of its constituents. The toxicity assessment was previously carried out using a number of different
living organisms, including luminescent bacteria, aquatic vertebrates (fishes) as well as aquatic invertebrates.
Nevertheless, considerable discrepancies in the sensitivities of different test organisms have been observed
[4]. The aim of this study was the investigation of landfill leachate treatment and micropollutant degradation
with AS system and AS with photocatalysis system under different hydraulic retention time (HRT) and effect
of their effluent to aquatic animal in environment for evaluate system performance and risk assessment.
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METHODOLOGY
Two-stage activated sludge system and operations
This research was investigated with activated sludge (AS) system without and with photocatalysis
process (the non-photo AS and the photo AS systems) by using TiO2 as the materials. Two systems were
operated with acclimatized seed sludge. The total working volume of two-stage AS systems were 20 L which
anoxic and aerobic tanks were 10 L of each. Each tank (anoxic and aerobic condition) contained biomass
concentration or mixed liquor suspended solids (MLSS) in range of 5 g/L. The hydraulic retention time
(HRT) of this study was varying in two conditions were 24 h and 18 h for the non-photo AS system. For the
photo AS system, the HRTs were varying in the same period were 24 h (AS 18 h and photocatalysis process
6 h) and 18 h (AS 12 h and photocatalysis 6 h). The solid retention time (SRT) did not fix but the sludge
would drain out when the concentration over 5 g/L. For system operations, two AS systems were operated
with real landfill leachate as the influent.
Figure 1 The activated sludge system without and with photocatalysis process.
Water quality and micropollutant analysis
The water samples of this study including the influent, the waters from anoxic – aerobic tanks,
effluent. All water samples were analyzed in water quality parameters every 7 days by according to the
Standard Methods for the Examination of Water and Wastewater [5]. The micropollutants in water samples
were determined in liquid phase by use of solid phase extraction (SPE) technique and characterized by gas
chromatography-mass spectrometry (GC-MS). In the analysis, the SPE tubes (VertipakTM-C18) were used
for micropollutant extraction. The SPE tubes were cleaned with methanol (MeOH) 10 mL and follow with
RO water 10 mL and then with water sample 100 mL. The tubes were left for drying for 15 min. and eluted
with MeOH 10 mL. The elution fraction was evaporated with nitrogen gas and analyzed with GC-MS [2].
The inductively coupled plasma-optical emission spectrometry (ICP-OES) was used for heavy metals
analysis [6].
Bacterial community abundance by using quantitative polymerase chain reaction (qPCR)
The DNA extraction kit (Qiagen, Germany) used for extracted DNA of anoxic and aerobic sludge
samples of conditions 1 and 2. The qPCR technique was used to quantify in two microbial groups were (i)
total bacteria and (ii) nitrifying bacteria, that was quantified in two minor groups were ammonia oxidizing
bacteria (AOB), Nitrobacter, and Nitrospira. The quantification protocols were showed in [7].
Biotoxicity assessment
The biotoxicity was evaluated by using the common carp (Cyprinus carpio) because they present a
high sensitivity to the environs, and ubiquitous in Thailand, thus was used in this experiment for biotoxicity
assessment. The common carp (age 3 months) was obtained from Department of Fisheries of Thailand and
transported to the laboratory in plastic bags. Prior to the experiments, the fish was kept in two separate boxes
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that contained de-chlorinated tap water and was acclimate for 4 days with continuous condition. The water
was renewed every 2 days to prevent the fish wastes accumulation. In experiments, 10 common carp were
placed with the non-photo AS effluent and photo AS effluent and with 3 effluent dilution ratios of each (10,
20, and 30% (v/v)). These conditions were represented the simulation to real situation of treated water
volume discharged to the natural water resource [7].
RESULTS AND DISCUSSIONS
Treatment performance and micropollutant degradation
This study investigation of the non-photo AS and photo AS systems which parallel operated for 80
days with landfill leachate influent. Table 1, the treatment performance of non-photo AS with acclimatized
seed sludge are 84% and 86% for organic compounds (BOD and COD) of HRT 24 h and 95% and 97% for
TKN and NH3-N of HRT 24 h. Under HRT 18 h, the treatment efficiencies were 86% and 87% for organic
compounds (BOD and COD) and 91% and 97% for TKN and NH3-N. When considered the photo AS system
in the same HRT conditions found that the AS system with photocatalysis process (photo AS system)
performed the higher treatment performance when compared with non-photo AS system (Table 2). The
treatment performance of photo AS with acclimatized seed sludge are 94% and 99% for organic compounds
(BOD and COD) of HRT 24 h and 93% and 97% for TKN and NH3-N of HRT 24 h. Under HRT 18 h, the
treatment efficiencies were 94% and 90% for organic compounds (BOD and COD) and 90% and 97% for
TKN and NH3-N.
For micropollutants degradation, the micropollutants found in landfill leachate and also were
investigated of this study including bisphenol A (BPA), 2,6-di-tert-butyl-phenol (2,6-DTBP), di-butyl-
phthalate (DBP), di-(ethylhexyl)-phthalate (DEHP), and N,N-diethyl-m-toluamide (DEET). The results of
system experiment found that under HRT 24 h both systems can degrade them in range of 90 – 100%. When
both systems were changed the operation in shorter HRT condition to 18 h found that the photo AS system
performed the higher micropollutants degradation which in range of 92 – 100% whereas the non-photo AS
system were 73 – 100%. From the treatment performance and micropollutant degradation, the non-photo AS
system was efficiency remove organic compounds, nitrogen, and micropollutants under long HRT condition
(24 h) whereas the photo AS system can maintain those treatment under shorter HRT condition (18 h).
Table 1 Leachate characteristics of the non-photo AS system
Parameters Unit Inf. Non-photo AS system
HRT 24 h HRT 18 h
An. Ae.
An. Ae. Eff. Eff.
Water quality
pH - 6.3 (0.5) 6.5 (0.6) 6.5 (0.8) 7.2 (0.2) 6.6 (0.3) 6.4 (0.2) 7.1 (0.1)
NA NA 37 (6)
EC μS/cm NA NA NA 20 (4) 1500 (100) 1000 (120) 300 (25)
86%
BOD mg/L 2200 (200) 1400 (100) 900 (60) 130 (15) 3700 (240) 2700 (100) 630 (20)
87%
94% NA NA 5 (6)
91%
COD mg/L 5000 (300) 4300 (200) 3100 (100) 700 (30) NA NA 1 (1)
97%
86%
TKN mg/L 60 (8) NA NA 3 (4)
95%
NH3-N mg/L 45 (5) NA NA 1 (1)
97%
Micropollutants
BPA μg/L 25 (3) NA NA 0 (0.2) NA NA 0 (0.3)
100%
2,6-DTBP μg/L 41 (2) 100%
NA NA 0 (0.1) NA NA 3 (2)
90%
DBP μg/L 26 (5) 100%
NA NA 0 (0.1) NA NA 5 (4)
94%
DEHP μg/L 18 (5) 100%
NA NA 2 (1) NA NA 3 (6)
81%
DEET μg/L 14 (3) 95%
NA NA 1 (3) NA NA 4 (2)
73%
90%
Number of samples was 10 of each condition
NA: not analyzed
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Table 2 Leachate characteristics of the photo AS system
Parameters Unit Inf. Photo AS system
HRT 24 h (AS 18 h + Photo 6 h) HRT 18 h (AS 12 h + Photo 6 h)
An. Ae. Eff.
An. Ae. Eff.
Water quality
pH - 6.3 (0.5) 6.5 (0.3) 6.5 (0.7) 7.1 (0.2) 6.4 (0.2) 6.5 (0.3) 7.1 (0.2)
NA NA 120
EC μS/cm NA NA NA 118 1900 (100) 1400 (110) 140 (15)
94%
BOD mg/L 2200 (200) 1500 (160) 1000 (100) 20 (4) 3700 (270) 2900 (130) 500 (80)
90%
99% NA NA 6 (2)
90%
COD mg/L 5000 (300) 3700 (160) 2700 (60) 300 (20) NA NA 1 (1)
97%
94%
TKN mg/L 60 (8) NA NA 4 (3)
93%
NH3-N mg/L 45 (5) NA NA 1 (1)
97%
Micropollutants
BPA μg/L 25 (3) NA NA 0 (0.1) NA NA 0 (0.1)
100%
2,6-DTBP μg/L 41 (2) 100%
NA NA 0 (0.1) NA NA 0 (0.1)
100%
DBP μg/L 26 (5) 100%
NA NA 0 (0.2) NA NA 0 (0.3)
100%
DEHP μg/L 18 (5) 100%
NA NA 0 (0.1) NA NA 0 (0.2)
100%
DEET μg/L 14 (3) 100%
NA NA 1 (3) NA NA 1 (2)
92%
95%
Number of samples was 10 of each condition
NA: not analyzed
Bacterial community
The quantification of bacterial community in sludge of anoxic and aerobic tanks under HRT 24, 18,
and 12 h, in terms of total bacteria and nitrifying bacteria, including AOB, Nitrobacter, and Nitrospira.
Figure 2 compares total bacteria, AOB, Nitrobacter, and Nitrospira. Total bacteria in anoxic and aerobic
sludge of all HRT conditions are in range of 1.64 – 1.73 x 1010 copies/g sludge; and AOB, Nitrobacter, and
Nitrospira are 2.15 – 3.74 x 105, 4.26 – 5.15 x 104, and 5.89 – 6.24 x 104 copies/g sludge which not different
change in bacterial community when system was changed operation.
Figure 2 Bacterial community under different HRT conditions.
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Although total bacteria in all HRT conditions are insignificantly different (p>0.05) but the nitrifying
bacteria which assist heterotrophic bacteria to degrade micropollutants and enhance nitrogen treatment.
Nitrifying bacteria also promote the growth of heterotrophs [8]. Given the treatment performance and
micropollutant biodegradation in Tables 1 and 2, the abundance and composition of bacterial community in
system enhance the treatment performance and micropollutant biodegradation and were achieved under HRT
24 h but cannot maintain under shorter HRT 18 h which the bacterial abundance of total bacteria and
nitrifying bacteria of this study less than AS system of [9] and they achieved in micropollutants
biodegradation under HRT 18 h condition.
Biotoxicity assessment
Biotoxicity assessment was the one indicator that presented the toxicity of treated wastewater. This
study used the common carp (Cyprinus carpio) for experiment. The common carp was the sensitive fish with
water quality and most used for biotoxicity test in Thailand. Table 3 showed the fish mortality by using the
effluent of non-photo AS and photo AS systems under HRT 24 and 18 h conditions. The mixed ratio were
10, 20, and 30% (v/v). The common carps were cultivated under those condition for 4 days. Under condition
1, the fish mortality (%) of all mixed ratio were friendly with common carps and presented 100% of living
fishes in cultivation boxes. When considered the use of condition 2 effluent. The experiment of this
condition showed the first fish mortality when they were cultivated for 2 days and found higher dead rate
under high mixed concentration ratio at 30% (v/v).
Under HRT 24 h, the non-photo AS system more friendly to fish when compared with effluent from
photo AS system. When both systems were operated under HRT 18 h found that the rate of fish mortality of
non-photo AS systems higher than the photo AS system which found the first of fish mortality 13% under
mixed ratio 10% (v/v) whereas the photo AS system was 9% only.
From this study, the toxicity factor to fish mortality were water quality which including general
parameters, micropollutants concentration, and also the EC value. Even though, the photo AS system
perform a great treatment performance, micropollutant degradation with all HRT conditions but the non-
photo AS effluent contained low micropollutants and EC that these are some reason to present that the
biological treatment system more friendly with ecological system in case long HRT condition.
Table 3 Biotoxicity assessment
Fish mortality (%) Non-photo AS system Photo AS system
AS 18 h + photo 6 h
HRT 24 h HRT 18 h 0 AS 12 h + photo 6 h
0 5% 0
RO water 0 13% 6% 9%
13% 16% 13%
10% (v/v) 0% 25% 25%
20% (v/v) 9%
30% (v/v) 9%
CONCLUSION
The non-photo AS (AS without photocatalysis process) and the photo AS system (AS with
photocatalysis process) systems were operated with landfill leachate influent under HRT 24 and 18 h
conditions. The treatment performance of non-photo AS system were 84% of BOD, 86% of COD, 95% of
TKN, and 97% of NH3-N under HRT 24 h. For HRT 18 h, the treatment efficiencies were 86% of BOD, 87%
of COD, 91% of TKN, and 97% of NH3-N. The overall treatment performance of photo AS system showed a
greater removal in all HRT conditions; HRT 24 h were 94% of BOD, 99% of COD, 93% of TKN, and 97%
of NH3-N. For, HRT 18 h were 94% of BOD, 90% of COD, 90% of TKN, and 97% of NH3-N.
For micropollutants degradation, The micropollutant degradation under HRT 24 h of BPA, 2,6-
DTBP, DBP, DEHP, DEET were in range of 90 – 100%. When both systems were changed the operation in
shorter HRT condition to 18 h found that the photo AS system performed the higher micropollutants
degradation which in range of 92 – 100% whereas the non-photo AS system were 73 – 100%. From the
treatment performance and micropollutant degradation, the non-photo AS system was efficiency remove
organic compounds, nitrogen, and micropollutants under long HRT condition (24 h) whereas the photo AS
system can maintain those treatment under shorter HRT condition (18 h).
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The bacterial community including total bacteria, nitrifying bacteria (Nitrobacter and Nitrospira) in
all HRT sludge conditions of both systems. The bacterial abundance was not significant changed. For the
biotoxicity assessment with common carp (Cyprinus carpio). Even though, the photo AS system performed a
great treatment performance and micropollutants degradation in all HRT conditions but the non-photo AS
effluent which contained low micropollutant concentrations and also the EC value that these are some reason
to present that the biological treatment system more friendly with ecological system in case long HRT
condition.
ACKNOWLEDGMENT
The authors would like to express deep gratitude to the Thailand Research Fund (TRF) and the
Office of the Higher Education Commission (OHEC) for research grant number MRG6180118.
REFERENCES
[1] Tadkaew, N., Hai, F.I., McDonald, J.A., Khan, S.J., Nghiem, L.D., 2011. Removal of trace organics
by MBR treatment: the role of molecular properties. Water Res. 45, 2439-2451.
[2] Boonnorat, J., Chiemchaisri, C., Chiemchaisri, W., Yamamoto, K., 2014. Removals of phenolic
compounds and phthalic acid esters in landfill leachate by microbial sludge of two-stage membrane
bioreactor. J. Hazard Mater. 277, 93-101.
[3] Gautam, P., Kumar, S., Lokhandwala, S., 2019. Advanced oxidation processes for treatment of
leachate from hazardous waste landfill: A critical review. J. Cleaner Prod. 237, 117639.
[4] Kjeldsen, P., Barlaz, M.A., Rooker, A.P., Buan, A., Ledin, A., Christensen, T.H., 2002. Present and
long-term composition of MSW landfill leachate: a review. Crit. Rev. Environ. Sci. Technol. 32,
297-336.
[5] APHA, 2005. Standard Methods for the Examination of Water and Wastewater, twenty-first ed.
American Public Health Association, Washington, DC, USA.
[6] A, D., Oka, M., Fujii, Y., Soda, S., Ishigaki, T., Machimura, T., Ike, M., 2017. Removal of heavy
metals from synthetic landfill leachate in lab-scale vertical flow constructed wetlands. Sci. Total
Environ. 584-585, 742-750.
[7] Boonnorat, J., Boonapatcharoen, N., Prachanurak, P., Honda, R., Phanwilai, S., 2017. Toxic
compounds biodegradation and toxicity of high strength wastewater treated under elevated nitrogen
concentration in the activated sludge and membrane bioreactor systems. Sci. Total Environ. 592,
252-261.
[8] Kanyatrakul, A., Prakhongsak, A., Honda, R., Phanwilai, S., Treesubsuntorn, C., Boonnorat, J.,
2020. Effect of leachate effluent from activated sludge and membrane bioreactor systems with
acclimatized sludge on plant seed germination. Sci. Total Environ. 724, 138275.
[9] Boonnorat, J., Kanyatrakul, A., Prakhongsak, A., Honda, R., Panichnumsin, P., Boonapatcharoen,
N., 2019. Effect of hydraulic retention time on micropollutant biodegradation in activated sludge
system augmented with acclimatized sludge treating low-micropollutants wastewater. Chemosphere
230, 606-615.
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I 046
Assessment of Drinking Water Quality Through Turbidity and
Microbial Indicators in Stored Rainwater of Flood-prone
Communities, Nakhon Si Thammarat Province, Thailand
Teeraphat Attavinijtrakarn1, Athit Phetrak2, Jenyuk Lohwacharin3, Suthirat Kittipongvises4,
Nutta Taneepanichskul5, and Wandee Sirichokchatchawan6*
1Graduate student, College of Public Health Sciences, Chulalongkorn University, Bangkok 10330, Thailand;
2Lecturer, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand; 3Lecture, Department of
Environment Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand;
4Assistant Professor, Environmental Research Institute, Chulalongkorn University, Bangkok 10330, Thailand;
5Assistant Professor, College of Public Health Sciences, Chulalongkorn University, Bangkok 10330, Thailand;
6*Lecture, College of Public Health Sciences, Chulalongkorn University, Bangkok 10330, Thailand;
*Corresponding author: Tel: 02-2188042 E-mail: [email protected]
ABSTRACT
In 2011, severe flooding spread along the Mekong and Chao Phraya river across several parts of
Thailand. One of the most affected provinces is Nakhon Si Thammarat. During floods, the affected
communities might face water shortage and have to rely on flood water for domestic activities such as
bathing, cooking or drinking. Ingestion and utilization with flood water contaminated with bacteria,
pollutants, and sewages can causes negative health consequences such as diarrhea and skin infection. The
residents those previously affected by floods are often stored rainwater which is a container or big jar used to
collect and store rainwater runoff, typically from rooftops via pipes, for drinking in case of the water
shortage during floods. A cross sectional study was conducted through purposive selection in two flood-
prone districts of Nakhon Si Thammarat province, namely Pak Phanang and Chian Yai districts. Stored
rainwater samples were collected by random walk selection from eight households those previously
experienced flooding events. To assess the water quality, turbidity (NTU) and microbial indicators including
total coliform/E.coli were assessed. Water samples was collected from the household directly from the stored
rainwater container using sterilized bottles. The samples were kept on ice and tested with 3M Petrifilm. The
total coliform and E. coli counts were performed after 24 hours and 48 hours of incubation. The samples
from Pak Phanang district showed higher NTU and colony forming unit (CFU) compared with the samples
from Chian Yai district. We also noticed that from the same sample, when the NTU was high, CFU was also
high. Nevertheless, further study should be conducted in order to assess the association between turbidity and
microbial concentration, and the association with the health consequences from drinking the stored
rainwater.
Keywords : stored rainwater; drinking water quality; turbidity; microbial indicators; flood-prone
communities; Nakhon Si Thammarat province
INTRODUCTION
Nowadays, global warming and climate changes are presumed to intensify and increase frequency of
many extreme weather events. It is estimated that these events will cost nearly three-hundred thousand lives
and more than two billion US dollars between 2030 and 2050 [1]. Amongst the natural disasters, floods have
the tendency to increase in both frequency and severity. The consequences may include lacking safe water
for consumption and utilization leading to higher risk of getting water-borne diseases. Furthermore, the most
affected population are those with poorer resources, especially on health facilities and infrastructure. Asia
and Africa are the top two regions that had been strike and affected by flood disasters [2]. For instance,
South Asia was hit by torrential seasonal monsoons in 2017 between June and September. The event caused
widespread flooding in Bangladesh, India, India, Nepal and Pakistan. It took about 2,000 lives and affected
more than 41 million people [3].
In 2011, severe flooding spread along the Mekong and Chao Phraya river across several parts of
Thailand. One of the most affected provinces is Nakhon Si Thammarat. This flooding event has been
described as the worst flooding in the history of Thailand. The impacts of floods are not only in your asset
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but also health and quality of life. Many researches indicate that either physical (infection, illness and
injuries) or mental health (insomnia, anxiety, depression and post-traumatic disorder) or both may be getting
bad consequences during and after flood. The stress can decrease your quality of life and wellbeing
concurrently. There are many risks and causes of illness throughout the period of flooding. During floods, the
affected communities might face water shortage and have to rely on flood water for domestic activities such
as bathing, cooking or even drinking. Ingestion and utilization with flood water contaminated with bacteria,
pollutants, and sewages can causes negative health consequences such as diarrhea and skin infection [4].
Since floods can cause great damages on water supply resulting in acute water shortage, residents in flood-
prone communities are often adapt and prepare for the flooding events in advance [5]. From our preliminary
visit to flood-prone communities in Nakhon Si Thammarat province, we found that residents those
previously affected by floods are often stored the rainwater in an earthen jar for drinking and cooking in case
of the water shortage during floods.
The essential point is that the contamination of organic and inorganic pathogen will fold in the water
system during the flood. Thus, using microbial indicators to estimate the hygiene of water is based on
achieving E. coli concentration to achieve a 99.9% reduction, present a containing less than 3 x 103 spores
of Clostridium perfringens per gram and absence of Salmonella spp. in 50 grams. The pathogens can carry
through flood flow to source of waters, and infection will occur via ingestion or utilization of contaminated
water [6]. E. coli are common bacteria living in the gastrointestinal organ of humans, animals, and their
feces. They are also found in soil and plant material. Since these bacteria are familiar with the environment;
they are commonly used as an indicator for water quality assessment [7].
Therefore, this study aims to assess drinking water quality using turbidity (NTU) and microbial
indicators (total coliform and Escherichia coli) of stored rainwater in previously flooded communities of
Nakhon Si Thammarat province. Since turbidity is an effective indicator of both total suspended solids (TSS)
and E. coli., which also increase human health risk causing a great public health concern [8]. The result of
this study will serve as an initiate step towards an appropriate management on drinking water quality and
storage in flood-prone communities.
METHODOLOGY
A cross sectional study was conducted in Nakhon Si Thammarat province, specifically at Pak
Phanang and Chain Yai district in Thailand. The province has been affected by many natural disasters
including floods. It is also considered as flood-prone area in Thailand. Geographical characteristics of
Nakhon Si Thammrat province are varied according to the area, which are mainly separated into
mountainous and plain areas. Both Pak Phanang and Chain Yai districts falls into plain area situated near the
Gulf of Thailand. They are the flood-prone districts that have been experienced many flooding events.
(Figure 1).
Chain Yai district
Pak Phanang district
Figure 1 Study area and point of water sampling
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Study population
In this study, the households that had ever experienced floods in the flood-prone communities in Pak
Phanang and Chian Yai districts, Nakhon Si Thammarat province, Thailand were recruited as study
population. The inclusion criteria included living in Pak Phanang district or Chian Yai district, Nakhon Si
Thammarat province, Thailand more than one year, had previous flood experience while living in the
mentioned district, aged 18 years old and above, and able to communicate in Thai language. The study
excluded those who were unwilling to participate in this study.
Sampling technique
The multi-stage sampling technique was employed in this study [9]. Firstly, Nakhon Si Thammarat
province was purposively selected from all the provinces in Thailand since the province was recently faced
with the major flood disaster, and also one of the flood-prone provinces. Next, Pak Phanang and Chain Yai
districts were purposively selected for being flood-prone communities. In summary, both the province and
districts were selected because they were flood-prone areas with history of many flooding events in the past.
Later, a random walk sampling technique [10] was applied for Pak Phanang and Chian Yai districts (Figure
2). In this study, the primary healthcare center of both districts was selected at the starting location for a
random walk sampling. Then, a direction of travel was determined by spinning a pen. The direction that the
head of the pen pointing at was the direction for the random walk sampling. The household from the
determined direction closest to the selected primary healthcare center was selected as the first participant for
this study. After the first household, the next nearest household was selected for the second interview and
repeated until all the samples were reached.
Figure 2 Sampling frame
Data collection
Stored rainwater samples were collected from the households those previously experienced flooding
events in Pak Phanang and Chain Yai district in Nakhon Si Thammarat province, Thailand. First, the
members of the selected household were asked to indicate the container that stored the rainwater for
consumption. The stored rainwater in the context of this study implied to rainwater collected in a big earthen
jar from the runoff of rainwater typically from rooftops collected via pipes or roof tiles as shown in Figure 3.
Then, the stored rainwater was collected through water tap connected to the earthen jar (if have) or using
clean water bowl. The collected rainwater samples were first immediately assessed for turbidity (NTU) using
the portable turbidity meter, HACH turbidity meter- model 2100Q [11]. Each sample was measured with
repetition three times. Second, another 50 mL of the samples were stored in the sterile bottle and kept on ice
for assessment of microbial indicators. Microbial indicators, namely total coliform/E.coli were assessed
using 3M Petrifilm according to the manufacturer instruction [12]. Briefly, 3M Petrifilm was placed on a flat
and level surface. The petrifilm cover was lifted, and 1 mL of the sample was pipetted perpendicular to the
inoculation surface onto the center of bottom film. Then, the petrifilm cover was slowly dropped to avoid air
bubbles and left undisturbed for one minute to permit the gel to form. Later, the petrifilms were incubated at
37 °C. The total coliform and E. coli counts were performed after 24 hours and 48 hours of incubation,
respectively. In addition, face-to-face interview was also conducted using a developed structured
questionnaire to ensure that the stored rainwater was for consumption.
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Figure 3 Examples of the containers and the stored rainwater
RESULTS AND DISCUSSIONS
From interviewing with the household members, we found that stored rainwater was mostly used for
drinking and cooking activities during flooding events. Rainwater was mostly stored in a big earthen jar as
shown in Figure 3, which collected via roof tiles or pipe. After measuring the turbidity, the results indicated
that turbidity of the stored rainwater samples from Pak Phanang district were greater than the samples
collected from Chian Yai district (Figure 4). The highest turbidity was found in Pak Phanang district from
house number 5, shown in Figure 4 as Pak Phanag-H5, with average 1.67 NTU. Interestingly, the lowest
turbidity was also found in Pak Phanang district from house number 4 with average 0.22 NTU. Inorganic or
organic suspended solid substances are normally the cause of turbidity in rainwater [13, 14]. It was reported
that the high level of turbidity in rainwater were the results from physical, chemical, and/or biological
pollutants such as bird waste, chemical used for coating roof tiles, and microorganisms in air, respectively
[15, 16].
Figure 4 Turbidity (NTU) of the stored rainwater samples
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Figure 5 Total coliform (CFU/mL) of the stored rainwater samples
Figure 5 shows the colony forming unit per milliliter (CFU/mL) of total coliform bacteria from the
stored rainwater samples collected from Pak Phanang and Chian Yai districts, Nakhon Si Thammarat
province. The highest concentration of total coliform bacteria was found in the sample from Pak Phanang
district for both 24 and 48 hours of incubation, 180 CFU/mL and 235 CFU/mL, respectively. Whereas the
lowest concentration of total coliform bacteria was found in the sample from Chian Yai district for both 24
and 48 hours of incubation, 1 CFU/mL and 5 CFU/mL, respectively. Moreover, the average of total coliform
bacteria concentrations after 24 hours of incubation were greater in the samples from Pak Phanang district
than Chian Yai district, 84 CFU/mL and 18 CFU/mL, respectively. Similar trend was also observed after 48
hours of incubation from both districts, with the average concentration of total coliform bacteria in Pak
Phanang district 128 CFU/mL and 43 CFU/mL from Chian Yai district. Interestingly, there was no E. coli
presented in any of the collected stored rainwater samples.
From the interview and observation, we found that the process of collecting rainwater might
contribute to the different of the turbidity and microbial concentration between the samples from two
districts. In Chian Yai district, rainwater was transported and collected through pipe (some are polyvinyl
chloride pipe). Whereas in Pak Phanang district, rainwater was transported and collected using old roof tiles
as shown in Figure 3. Nevertheless, since drinking water should have a turbidity of 5 NTU or less, and no E.
coli detected as of the guidelines by World Health Organization, all samples could be considered as passing
WHO guidelines for water intended for drinking [17, 18]. In addition, we also noticed that the samples with
high turbidity (NTU), also had higher total coliform bacterial concentration. Therefore, there might be some
correlation between turbidity and total coliform bacterial concentration. Comparable finding from previous study
indicated that turbidity was significantly correlated with the concentration of E. coli and Enterococci spp. [19].
CONCLUSION
This study has presented the turbidity and total coliform bacterial contamination in stored rainwater used for
drinking and cooking activities during floods in flood-prone communities, Nakhon Si Thammarat province,
Thailand. The samples from Pak Phanang district showed higher NTU and colony forming unit (CFU) compared
with the samples from Chian Yai district. Nevertheless, further study should be conducted in order to assess the
association between turbidity and microbial concentration, and the association with the health consequences from
drinking the stored rainwater.
ACKNOWLEDGEMENT
This work is supported by the Thailand Science Research and Innovation (TSRI), project number
SRI6230303.
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REFERENCE
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topics/climate-change#tab=tab_1.
[2] Ritchie, H. and Roser, M. 2020. Natural Disasters. Retrieved 7 April 2020 from:
https://ourworldindata.org/natural-disasters.
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risk. Retrieved 7 April 2020 from: https://www.unicef.org/media/media_100719.html
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situation in Poland. Oceanologia. 56(2): 385-407.
[6] Stelma, G. N. 2018. Use of bacterial spores in monitoring water quality and treatment. Journal of
water and health. 16(4): 491-500.
[7] Stevens, D. 2017. Microbe Profile: Escherichia coli O157: H7 – notorious relative of the
microbiologist’s workhorse. Edinburgh Research Explorer.
[8] Huey, G. M., & Meyer, M. L. 2010. Turbidity as an indicator of water quality in diverse watersheds of
the Upper Pecos River Basin. Water. 2(2): 273-284.
[9] Moskowitz, H., Plante, R., and Tang, K. 1986. Multistage Multiattribute Acceptance sampling in
serial production systems. IIE Transactions. 18(2): 130-137.
[10] Wingfield-Digby, P.K. 2010. Rapid assessment sampling in emergency situations. Thailand: UNICEF.
[11] Hach company. 2017. 2100Q and 2100Qis User Manual Edition 4.
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2020 from: https://multimedia.3m.com/mws/media/729710O/product-instructions-3m-petrifilm-
coliform-count-plate.pdf
[13] Sillanpää M., Ncibi M. C., Matilainen A., and Vepsäläinen M. 2018. Removal of natural organic
matter in drinking water treatment by coagulation: A comprehensive review. Chemosphere. 190:54–
71.
[14] Ratnoji, S. S., and Singh, N. 2014. A study of coconut shell-activated carbon for filtration and its
comparison with sand filtration. International Journal of Renewable Energy and Environmental
Engineering. 2(3): 201-204.
[15] Mielke H. W., Patterson C. 2018. Dynamic geochemistry of tetraethyl lead dust during the 20th
century: getting the lead in, out, and translational beyond. International Journal of Environmental
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[16] Tiwari S., Tripathi I. P., Gandhi M., Gramoday C., and Tiwari H. 2013. Effects of lead on
environment. International Journal of Emerging Research in Management and Technology. 2(6)
[17] World Health Organization. N.d. Fact sheet 2.33 Turbidity measurement. Retrieved 7 April 2020
from: https://www.who.int/water_sanitation_health/hygiene/emergencies/fs2_33.pdf
[18] World Health Organization. 1997. Guidelines for drinking-water quality. Retrieved 7 April 2020 from:
https://www.who.int/water_sanitation_health/dwq/2edvol3a.pdf
[19] Huey, G. M., & Meyer, M. L. 2010. Turbidity as an indicator of water quality in diverse watersheds of
the Upper Pecos River Basin. Water. 2(2): 273-284.
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I 048
Experimental Study on Hydrogenation of Metal Hydride
in Storage Tank
Thanasak Chumwisoot1* and Roongrojana Songprakorp2
1* PhD student, Division of Energy Management Technology, School of Energy, Environment and Materials, King
Mongkut’s University of Technology Thonburi, 126 Bangmod, Thungkru, Bangkok, 10140, Thailand,
2 Lecturer, Division of Energy Technology, School of Energy, Environment and Materials, King Mongkut’s
University of Technology Thonburi, 126 Bangmod, Thungkru, Bangkok, 10140, Thailand
*Phone : 08-1286-9097, Fax : 02-470-8617, E-mail: [email protected]
ABSTRACT
Despite thermal management to improve metal hydrides (MHs) storage performance, a new approach in
physical mixing is introduced to increase hydrogen sorption in MHs. This work aims to experimentally study
the desorption behavior of metal hydride tank filled with LaNi5 metal hydride powder during discharge mode
under ultrasonic treatments. The results of the study show that the same amount of time to charge hydrogen
into metal hydride tank at atmospheric pressure and room temperature with and without applied ultrasonic, a
60% of charging time with applied ultrasonic and a 100% of charging time without ultrasonic have same
desorption rate. While hydrogen is more desorbed at an 80% of charging time with ultrasonic treatment than
a full charging time of the same treatment.
Keywords : Hydrogen storage; Absorption; Metal hydride and Ultrasonic
INTRODUCTION
Metal Hydrides (MHs) are one of the promising candidates for many mobile and stationary hydrogen storage
applications. The main advantages of metal hydrides over storage techniques include high volume density of
hydrogen, relatively low operating pressure. However, hydrogenation and dehydrogenation of MHs involve
heat released and absorbed. Therefore, thermal management techniques must be deployed to handle the heat
when using the MHs. So far, there have been numerous works on heat transfer techniques to improve the
MH system performance. The physical mixing of metal and hydrogen gas was adopted in hydrogen charging
process by Wang (2014) [1]. The physical mixing allows heat removal and acceleration of the kinetics of the
hydrogenation process. The experiments were conducted with and without mixing to improve hydrogen
absorption rate. The additional heat transfer equipment or heat exchanger with metal hydride storage tank
was studied by Wang (2012)[3]. Macdonald (2007) [7] examined the dynamic of metal hydride storage
system through the experimental and simulation approaches. The heat transfer in metal hydride storage tank
can be improved by install the heat pipe. It is heat exchanger. It is useless energy for operation refer to
Mellouli (2009) [11]. However, the heat transfer rate can be improved by adding aluminum foam into metal
hydride storage refer to Chung (2013)[12].
To utilize hydrogen from the metal hydride storage at room temperature is still hurdled unless novel
approaches with no heat involvement are possible.
Recent works of Belkhiria (2019) [5], Shafiee (2017) [8] and Chang (2017) [9] showed that an external
magnetic field applied to chemical reaction can spin dipole moment of molecules of metal and improve the
desorption rate. The dipole moment of metal hydride can be arranged by external electric field. Hydrogen
gas is easy to take an absorption reaction with metal. Wei (2017) [2] pointed out that an ultrasonic energy
applied to chemical reaction or sonication can mixing many compounds by vibration and circulation.
The ultrasonic is inaudible acoustic wave that have a higher frequency than the human ear can hear (more
than 20 kHz). It can reduce hydrogen charging time in metal hydride. Therefore, the metal hydride powder
can be mixed with hydrogen gas, fast chemical reaction and fast release heat of reaction by ultrasonic wave.
The ultrasonic can increase speed of hydrogen atom and metal hydride atom to chemisorption. The heat from
an exothermic reaction can speed up transfer to the wall container because the metallic atom moving from
ultrasonic force.
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This study used metal hydride lanthanum-nickel hydride (LaNi5) of Sigma Aldrich supply. The lanthanum-
nickel hydride (LaNi5) have good specific characteristic to operate at pressure and temperature close to
ambient condition. However, hydrogen in metal hydride rely on the metal lattice but not easy to rush
hydrogen absorption into metal. In this study, metal hydride powder (LaNi5) mixing hydrogen gas under
ultrasonic condition for decrease absorption rate time. The absorption rate to charge hydrogen gas into the
metal hydride tank by ultrasonic shacking is also investigated and discussed
The fuel cell car used the hydrogen gas to produce the electrical energy for run electrical motors. If we can a
lot storage volume of hydrogen gas for run it then the car can use it than the fossil fuel. It is reduced the air
pollution in environmental affect to global warming pollution. The metal hydride is main strategy for choose
because safety and easy to use.
METHODOLOGY
The ultrasonics can facililate mixing metal powder and hydrogen molecules. Increasing heat transfer rate of
MHs can help improve charging efficiency.
According to Sastri (1998)[10], Lanthanum-Nickel Hydride (LaNi5) can absorb 1.3% by weight of hydrogen
gas at atmospheric pressure and room temperature. Classified as a feature that makes this alloy powder easy
to use and cheap when compared to other metals for storing hydrogen gas. However, while charging
hydrogen into metal hydride tank, the absorption process takes place and releases heat as an exothermic
reaction. Inversely, heat is needed during the desorption process when the stored hydrogen is discharged.
The high grade of sigma aldrich LaNi5 metal hydride powder and hydrogen gas 99.999% purity are used in
this study. The MH powder is loaded into a stainless-steel tube with stainless- steel mesh. The absorption and
desorption is studied by using test station with automatically controller software at Suranaree University of
Technology test. The test station has three type K thermocoples, two pressure transducer (OMEGA
Engineering) and a stainless-steeel equipment test of Swagelok for example tube, valve, regulator, storage
tank, feed tank etc refer to Dansirima (2019) [4]. The ultrasonic source was 40 kHz 50 W.
Fig. 1 shows the schematic diagram of the experimental setup for the study. For MH tank test station
assembly, metal hydride (MH) storage tank is assembled in a glove box and the ultrasonic power source is
installed under the MH tank. During the test, the initial pressure and temperature of the MH tank is set at 10
bar and 25 ºC. The experiment is conducted in controlled temperature room. Hydrogen gas pressure and flow
are controlled by gas regulator. The 40 kHz 50 W ultrasonic generator is in contacted with the metal hydride
storage tank. The ultrasonic wave is fed directly to the target and vibration takes place on metal hydride
powder covered with the hydrogen gas fed from the external hydrogen tank. The experimental data, pressure
and temperature, is recorded in PC computer.
Figure 1 Experimental setup
The absorption(charging) times are 60 min., 48 min., and 36 min. for 100%, 80% and 60% of fully charging
times, respectively. The vaccum time is 10 min and pressure transducer is kept constant.
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RESULTS AND DISCUSSIONS
The experimental setup was see from figure 1. The result is a measure pressure different by the low-pressure
transducer and temperature by the thermocouple. The results of the hydrogen desorption (H/M) and time (sec)
betaween with and without ultrasonic apply at absorption see from figure 2. The hydrogen absorption can measure
by pressure transducer and the mass of metal can measure by 3 digit of load cell weight. Before experimental test,
we are prepare metal hydride activation by high pressure and high temperature. The efficeientcy of metal hydride
storage depend on method of activation.
When we are apply hydrogen and ultrasonic with hydrogen absorption at about 60 % and 80% of time. The results
of the hydrogen desorption rate 60% of time and a normal charge 100% are equals.
The results in figure 2 shows kinetics of desorption reaction of metal hydride powder. The ultrasonic can increase
kinetic due to fast absorption time rate. When we apply ultrasonic the quantum spins in metal hydride change
effect to hydrogen atom move into the metal crystalline matrix. The ultrasonic improves mass transfer of
absorpbed hydrogen.
In figure 2, the slopes of absorption curves are kinetics of desorption reaction at different treatments. The higher
slope the more hydrogen gas absorbed into MH powder. The kinetic of absorption reaction can be used as an
index in terms of hydrogen charging time and hydrogen stored capacity. Additionally, the temperature of
surroundings and bed of metal hydride tank is of important for absorption reaction.
The ulrasonic wave can help accerelating an absorption reaction. Therefore, it is possible to increase rate of
reaction between the molecules of metal hydride and the molecules of hydrogen.
Figure 2 % hydrogen desorption with time
CONCLUSION
The hydrogen charging time can be reduced by applying ultrasonic. The ultrasonic has potential to be used
with metal hydride storage tank at the charging process for reduce charging time. The absorption rate of
100% normal charging time and the absorption rate of 60% charging time with ultrasonic have same pattern.
ACKNOWLEDGEMENT
This research is financial supported by Nation Research Council of Thailand under the FY2016 Thesis Grant
for Master Degree Student or Doctoral Degree Student. The test station and equipment are supported by
School of chemistry, Institute of Science, Suranaree University of Technology.
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REFERENCE
[1] Wang H, Prasad AK, Advani SG. Accelerating hydrogen absorption in a metal hydride storage tank by
physical mixing. Inter journal of hydrogen energy 2014;39: 11035-11046.
[2] Wei R, L. X, Yang M, Xu J. Effect of ultrasonic vibration treatment on solid-state reactions between
Fe2O3 and CaO. Ultrasonic Sonochemistry 2017, 38: 281-288.
[3] Wang H, Prasad AK, Advani SG. Hydrogen storage system based on hydride material incorporating a
helical-coil heat exchanger. Inter journal of hydrogen energy 2012, 37:14292-14299
[4] Dansirima P, Thiangviriya S, Plerdsranoy P, Utke O, Utke R. Small hydrogen storage tank filled with
2LiBH4–MgH2 nanoconfined in activated carbon: Reaction mechanisms and performances. Inter
journal of hydrogen energy 2019;44:10752-10762
[5] Belkhiria S, Briki C, Dhaou MH, Jemni A. Experimental study of metal-hydrogen reactor’s behavior
under the action of an external magnetostatic field during absorption and desorption. Inter journal of
hydrogen energy 2019;xxx:xxxx-xxxx
[6] Briki C, Rango P, Belkhiria S, Dhaou MH, Jemni A. Measurements of expansion of LaNi5 compacted
powder during hydrogen absorption/desorption cycles and their influences on the reactor wall. Inter
journal of hydrogen energy 2019;44:13647-13654
[7] MacDonald BD, Rowe AM. Experimental and numerical analysis of dynamic metal hydride hydrogen
storagesystems. Journal of Power Sources 2007;174:282-293
[8] Shafiee S, McCay MH, Kuravi S The efffect of magnetic field on thermal-reaction kinetics of
paramagnetic metal hydride storage bed. Applied sciences 2017;7:1006
[9] Chang, R. and Goldgby, K.A. 2017. Chemistry. McGraw-Hill Education., New York.
[10] Sastri, M.V.C, Viswanathan, B and Srinivasa Murthy, S 1998. Metal Hydride. Narosa Publishing
House., New Delhi.
[11] Mellouli S, Dhaou H, Aski F and Nasrallah SB Hydrogen storage in metal hydride tanks equipped
with metal foam heat exchange. Inter journal of hydrogen energy 2009, 34:9393-9401
[12] Chung CA, Yang S, Yang C, Hsu C and Chiu P Experimental study on the hydrogen charge and
discharge rates of metal hydride tanks using heat pipes to enhance heat transfer. Applied Energy
2013;103:581-587
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I 049
Composition and Functional Responses of Microbial Community to
Temperature and Substrate in Anaerobic Digestion Process
Mujalin K. Pholchan1* Koontida Chalermsan2 Piyanuch Niamsup3 and
Srikanjana Poonpat Poonnoi4
*1Lecturer, Environmental Technology, Faculty of Science, Maejo University, Chiang Mai 50290, Thailand;
2 Student, Environmental Technology, Faculty of Science, Maejo University, Chiang Mai 50290, Thailand;
3 Assis.t professor, Biotechnology, Faculty of Science, Maejo University, Chiang Mai 50290, Thailand;
4Assoc. professor, Faculty of Engineering and agroindustry, Maejo University, Chiang Mai 50290, Thailand;
*Phone : 0856220285, Fax : 053-873540, E-mail : [email protected]
ABSTRACT
This study investigated the impact of two important key factors including temperature and substrate
on microbial community in the anaerobic co-digestion process of septage and longan peel waste by
temperature phased anaerobic digesters (TPAD). Denaturing gradient gel electrophoresis (DGGE) and
metagenomics sequencing was used to analyzed microbial community structures. The DGGE and cluster
analysis results clearly indicated that substrate and temperature strongly influence the structure of bacterial
populations. Significant differences of microbial communities were observed from both TPADs digesters.
Also, co digestion with longan associated with the changes of bacterial community structure in TPAD
system. It was found that Firmicutes Bacteroidetes Cloacimonetes Tenericutes and Proteobacteria were
most dominant bacterial phyla in TPAD systems. High number of Firmicutes and Tenericutes were detected
from mesophilic tank, while Bacteroidetes and Cloacimonetes were found from thermophilic reactor.
Moreover, each of the digesters harbored distinct yet dynamic microbial populations, and some of the
methanogens were significantly correlated with methane productions. Methanosarcina and
Methanothermobacter appeared to be the most dominant methanogenic genera in both digesters operated
with different temperatures. The microbiological findings may help understand the metabolism that
underpins the anaerobic processes within each of the two digesters of TPAD systems co-digesting septage
and agricultural waste.
Keyword: Anaerobic digester, Temperature Phased Anaerobic Digestion (TPAD), PCR-DGGE
technique, Microbial community
INTRODUCTION
Anaerobic digestion and its designs including Temperature Phased Anaerobic Digestion (TPAD)
have been developed and applied for various types of waste and wastewater including agricultural waste for
a source of renewable energy [1-2]. In recent years, the implementation of anaerobic co-digestion has gained
a lot of interests due to energy self efficiency and sustainable waste management [3]. Septage is usually
removed from septic tank by vacuum trucks and transport to a distant treatment plant, however, most of the
septage is not well treated and mismanaged leading to environmental problems in the country. Consequently,
it requires a suitable method for managing and treatment. Considering a large amount of waste and nutrient-
rich characteristic, septage has the potential to be used as substrate to produce biogas. Besides, mixing
different wastes with septage has been applied for enhancing biogas production. It is due to the supply of
missing and imbalance nutrients by the co-substrate and positive synergisms established in the digestion
process that support the growth of microorganisms involved hydrolysis and methanogenesis. Previous
research have studied using of septage for anaerobic digestion (AD) as treatment and generated renewable
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energy as co-digestion with landfill leachate [4], food waste [5], and microalgae [6]. In some cases, septage
is used as an alternative fertilizer in agriculture and aquaculture without any prior treatment [7].
However, the performance of the anaerobic digestion process (AD) relies on a combination of
physical, chemical and biological processes in which microorganisms play an important role. However, the
recent system designs based on the information of microbial community composition still have a limited
number and remain unclear due to lacking of sufficient detailed knowledge in understanding of the microbial
ecology of the system. There are still much left to be known concerning the underlying mechanisms linking
operating conditions such as temperature and substrate of AD systems to microbial community structure and
function. Therefore, understanding of the complex microbial communities in the AD and their responses to
environmental changes might provide the valuable information that can be used to optimize the AD system
[8]. Since 99% of bacterial strains cannot be culture-grown on media, assessing the microbial diversity using
molecular techniques have more advantages than the conventional method due to it is rapid, less laborious,
more sensitive and specific [9]. Previously, many studies have been conducted on microbial communities in
the temperature phased anaerobic digestion (TPAD) as denaturing gradient gel electrophoresis (DGGE) Yu
and co-workers [10] used denaturing gradient gel electrophoresis (DGGE) for the determination of archaeal
community and found Methanobacteria and Methanosarcina both mesophilic and thermophilic process.
Additionally, Hameed and teams [11] determined the microbial communities in TPAD of municipal
wastewater sludge and its bacterial community was dominated by Firmicutes, Bacteriodetes and
Proteobacteria while archaeal community was dominated by Methanimicrobia and Methanobacteria. It is
evidenced that core microbial groups have different growth conditions, physiology and stress tolerance
which also varies among waste and system condition. Also imbalance among these organisms due to the
disturbances which varied from case to case could cause mulfunction of such system. Hence successful
TPAD treating different types of waste requires the study to elucidate the effect of common operating
condition on change of microbial community.
This study aimed to investigate the effect of temperature and substrate on the structure of microbial
communities involved in anaerobic co-digestion process between septage and longan peel waste using
molecular techniques based on DGGE and metagenomic sequencing. This finding could provide more
information of the system and could be applied for further enhancment of the AD system treating agricultural
waste.
METHODOLOGY
1. Anaerobic co-digestion process and sludge samplings
Laboratory scale of temperature phase anaerobic digestion systems (TPAD) with two reactors
operated at 55°C (thermophilic digester) and 35 °C (mesophilic digester) were conducted by Thunyaluk [12]
as shown in Fig 1. Longan peeling waste obtained from dried longan production process in Lumphun
province was used as a co-substrate mixing with septage. Microbial sludge were sampling from untreated
septage (ST) and outlet points of both digestors (LP55 and LP35 for thermophilc and mesophilic digester
respectively) every week. They were then preserved by mixing with 70%w/w ethanol with the ratio of 1:1.
and stored at -20˚C for molecular analysis using PCR-DGGE technique and metagenomic sequencing.
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Figure 1 Diagrammatic of temperature phase anaerobic digestion reactor
2. Microbial community analysis
Total DNA was extracted from the sludge sample using the nucleospin® soil (Bio-rad laboratories,
USA) following the manufacturer’s instructions. The DNA samples were then sent for metagenomic
sequencing analysis which was performed on Illumina HiSeq platform by Novogene Biological Information
Technology Co. (Tianjin, China). The relationship of microbial diversity is applied in analyzing the
complexity of species diversity for a sample calculated with QIIME (Version 1.7.0), displayed with R
software (Version 2.15.3) and generate the Venn and Flower diagram. Unweighted Pair-group Method with
Arithmetic Means (UPGMA). Changes of archaea and bacterial communities were analyzed by PCR-DGGE
as described by Manatsawat [13] and Pholcahn et al [14]. The variable V3 region of 16S rDNA was
amplified by using primers targeted to conserved regions of the 16S rDNA gene using specific primers set of
341F/Uni518R [15] and 344F/Arc522R [16]. The amplification conditions used in this study were modified
from Hogg and Lehane [17] and conducted using T100 thermal cycler (Bio-rad laboratories, USA). Once the
PCR product were stored at -20 ˚C. DGGE analysis of PCR product was performed on DCodeTM system
(Bio-Rad laboratories, USA). DGGE images were analyzed via Gene tools analysis software version 3.02.00
of SynGene Genius system (SynGene, UK). Dominant DNA bands representing different bacteria and
archaea sequences were excised and purified by RBC TA Cloning Vector Kit (RBC BIOSCIENCE, Taiwan)
prior to sequencing (First base, Malaysia) for future study.
RESULTS AND DISCUSSIONS
1. Performance of TPAD for waste treatment and biogas production
The system performance of anaerobic co-digestion between septage with longan peeling waste are
shown in Table 1. The results showed that higher biogas production was found from thermophilic digester,
while no significant differences (P ≥ 0.05) of methane contents was found from thermophilic digester with
approximately 39.60-56.10% and 40.20-57.80% for thermophilic and mesophilic, respectively. Interestingly,
it was found that methane production throughout the operating period from both digesters co- digesting with
septage and longan peel waste seemed to be more stable than the one with sole substrate (explained
elsewhere). This possibly could be the result of more complex substances and varieties of substrate
composition of using longan peel waste as co-substrate. Some studies suggested that the compositions of the
substrates are important for achieving stable processes [18], while low availability of the substrates for
microorganisms can be another factor for biomethane production reduction [19]
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Table 1 Summary of TPAD reactor performance of anaerobic co-digestion of septage and longan peeling
waste
STLP
Parameters Thermophilic Mesophilic
pH digester digester
Temperature (°C) 7.43 ± 0.28 7.31 ± 0.21
VFA (mgCH3COOH/l) 53.62 ± 1.25 35.83 ± 0.87
COD removal (% of feed) 2,020.44 ± 4.88 1,959.10 ± 3.62
TS removal (% of feed) 80.37 ± 1.29 80.96 ± 1.57
VS removal (% of feed) 70.61 ± 1.47 62.86 ± 1.30
Biogas production (ml/day) 76.51 ± 1.30 66.35 ± 1.07
Total biogas production (ml) 10,803.96 ± 41.17 1,730.18 ± 13.99
1,761,046.21 282,019.76
2. Effect of temperture and substrate on relative microbial abundance
The relative abundances of bacterial 16S rRNA transcripts from metagenomic sequencing analysis
revealed some differences from the community composition at the DNA level (Fig. 2). Firmicutes
Bacteroidetes Cloacimonetes Tenericutes and Proteobacteria were identified as top dominant phyla within
the bacterial community from both digesters and also untreated septage, while their relative abundances
varied among samples. This indicated the effect of substrate charateristics and operating condition on
microbial comunity structure. Firmicutes Bacteroidetes and Proteobacteria were dominated in untreated
septage which also appeared in the different abundance ratio in the thermophilic and mesophilic reactors.
However, there were some new dominance species harbored in this system. It was found that anaerobic co-
digestion decreased the level of Bacteroidetes while increased the level of Firmicutes and relative abundance
of Tenericute and Cloacimonetes. This could involve the availability of substances, the metabolic pathway in
the biodegradation process and also the operating condition. The results also revealed that high percentage of
Firmicutes and Tenericutes were detected from mesophilic reactor, while high ratio of Firmicutes
Bacteroidetes and Cloacimonetes were found from thermophilic reactor. Firmicutes Bacteroidetes and
Proteobacteria have been identified as the main phyla in various AD [20]. Most of members in Firmicutes
phylum are syntrophic bacteria that can degrade various VFAs. This coincidences with high percentage of
VFA removal in all digesters obtained from this study (50-80%). In addition, Proteobacteria are also
important microbes in anaerobic digestion process as they are well-known for glucose, propionate, butyrate,
and acetate-utilizing microorganisms [21]. However, all detected bacteria was also able to conduct
acidogenesis, the second step in the decomposition of organic matter. Cloacimonetes was decreased only in
the mesophilic digester of STLP. However, the relative compositions of these bacteria in digester were
variable among different stages of operation which may be associated with the wastewater constituents.
According to [22], however, the abundance of phylum Cloacimonetes was linked to lower methane
production in the reactors fed with protein-rich substrates. This could explian lower methane production
from the digester.
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Figure 2 Relative abundance of difference bacteria phylum from TPAD systems at steady state.
It was found that methanogen communities were unique and diverse between digesters operated with
different temperature and substrate variation due to the metabolic pathway occuring in each digesters (Fig.
3). The result showed that the euryarchaeota phylum was the most dominant orders and
Methanothermobacter Methanosarcina Methanosaeta and Methanobacterium were the top four predominant
methanogen genera found from both mesophilic and thermophilic digesters. Some of the methanogens
occurring in the system were significantly correlated with methane productions. Some studies suggested that
Methanobacterium and Methanothermobacter has been found to be able to grow under high and medium
range of temperture. These species use H2/CO2 and formate for methane production. [23-24]. Interestingly,
Woesearchaeota was only found from the thermophilic digester. This species, known as haloarchaea, is
distantly related to nitrogen-phosphate remover [25]. This probably linked to large amount of nitrogen-
phosphate contained in longan peel in the thermophilic system and may impact on the biological process. In
addition, Methanosaeta was found to have more population than Methanosarcina in septage, while
Methanosaeta was detected less than Methanosarcina in TPAD systems co-digested between septage and
longan peel. This is because Methanosarcina generally have higher growth rates and tolerances against high
concentrations of VFA than Methanosaeta [26] . From the result, it is likely that some organisms might have
only one specific function while some can perform multiple functions such as both hydrolysis and
fermentation.
Figure 3 Krona diagram of archaea genera in TPAD system.
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3. Effect of temperture and substrate on microbial communities structure
Clustering analysis of bacterial communities from DGGE profile and % relative abundance of
dominance species (Fig 4 and 5) showed that thermophilic and mesophilic digester harbored different
bacterial communities. There appeared the shifts in the microbial community structure during the operational
period. The whole bacterial community and metanogenic community in both digesters formed differeent
groups corresponding to differeent phase of operation. It was also found that bacterial and archaea
community structures were diverse and distinct between digesters due to the variation of substrate and its
intermediates and temperature. This coincides with some works reported that substrate variation and
temperature had the effect on microbial community structures. [27]. Archaea community seemed to be less
diverse than bacterial community and the community structure of microbial community in thermophilic
reactor showed more stable (Fig 5). This was supported by the Shannon index (H’) values which showed that
bacterial diversity was higher and have higher similarity than archaea community for both digesters (2.278±
0.0605 and 2.268± 0.1775 for thermophilic and mesophilic of bacterial community and 1.402±0.092 and
1.589 ±0.1415 for thermophilic and mesophilic of archaea community). This can be the result of high variety
of substrates and intermediate products in the mesophilic phase and there are many different species have the
growth condition under this temperature.
Figure 4 Bacterial DGGE banding pattens from TPAD systems
Figure 5 The relative abundance of microbial communities from TPAD system obtained from DGGE
profiles (a) bacterial communities (b) Archaea communities
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CONCLUSION
This study proved that substrate and temperture drive the dynamics of key microbial population and
its correlation with hydrolytic and metanogenic functionality of TPAD systems. Each digester harbored
distinctive microbial populations, some of which were significantly correlated with the TPAD system
performance. The results indicated that Methanothermobacter and Methanosarcina were the most important
methanogenic bacteria, while Firmicutes Bacteroidetes Cloacimonetes Tenericutes and Proteobacteria were
most dominant bacterial phyla in TPAD systems. High number of Firmicutes and Tenericutes were detected
from mesophilic tank, while Bacteroidetes and Cloacimonetes were found from thermophilic reactor.
ACKNOWLEDGEMENT
This work was partially supported by Thailand national research funding number MJU1-61-039 , Maejo
University’s disciple scholarship and STEM Workforce from NSTDA.
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[19] Wang M., Sahu K.A., Björn R.., Park C. Anaerobic co-digestion of microalgae Chlorella sp. and
waste activated sludge.Bioresour. Technol 2013; 152: 585–590.
[20] Zhang, C., Q. Yuan, and Y. Lu, Inhibitory effects of ammonia on syntrophic propionate oxidation in
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9th International Conference on Environmental Engineering, Science and Management
The Heritage Chiang Rai, Thailand, May 27-29, 2020
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Development of Activated Carbons from Rubber Wood Using
Microwave Induced Phosphoric Acid Activation
Panatda Klingklay1, Kittipong Kunchariyakun2, Weerawut Chaiwat3, Suthatip Sinyoung1*
1 Graduate student ; 1* Assistant professor, Environmental Engineering Program, Department of Civil
Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, Thailand
2School of Engineering and Resources, Walailuk University, Nakhornsithammarat, Thailand
3Division of Engineering, Mahidol University Kanchanaburi Campus, Kanchanaburi, Thailand
Phone : 080-0658953, E-mail : [email protected]
ABSTRACT
This research was to synthesize activated carbon from rubber wood using microwave induced phosphoric
acid (H3PO4) activation. To determine the optimum carbonized temperature, varying 400, 600 and 800 °C for
1 hour in N2 atmosphere, the TGA technique was applied. Results indicated that the carbonized temperature
of 600 °C exhibited the highest fixed carbon of 76.43%, which was applied to utilize in the activation
process. In the activation process, the carbonized samples were activated using 40% phosphoric acid with
microwave at 600, 800 and 1000 watts for 90 seconds. The obtained activated carbons were characterized by
BET, Fourier Transform Infrared Spectrometer (FT-IR) and Field Emission Scanning Electron Microscope
(FE-SEM) techniques. Results found that the increase in microwave power has directly effect on surface
area, total pore volume and average pore size. The surface areas exhibited as 406.18, 725.94 and 981.25
m2/g, for 600 800 and 1000 watts, respectively. Therefore, this finding will be a fundamental knowledge for
development of rubber wood as industrial activation carbon using microwave induced phosphoric acid
(H3PO4) in the future.
Keywords : Activated carbon, Activation, Phosphoric acid, Microwave
INTRODUCTION
Activated carbon (AC) is a carbon-rich material with a large surface area and pore volume. The main its
ability is that able to absorb various substances and allow to store their molecules in the internal surface. The
applications of AC are used as an adsorbent for colors, odors, various contaminants and organic compounds
in both liquid and gas forms [1]. The performance of AC is typically dependent on its surface areas and total
pore volumes, which are vary on raw materials and methods to synthesize. There are basically two processes
to produce activated carbons: chemical activation process and physical activation process [2], including both
processes together. Regarding activated by chemical process, KOH, H3PO4 [3] and ZnCl2 [4], have been
mostly the chemical reagents for this purpose, due to help bite and remove dirt from the pores of activated
carbon. Among these chemical reagents, H3PO4 pronounced as an excellent activated precursor, which is
able to give greater porosity, and H3PO4 can be reused. and use low energy [5].
Recently, new approach for activation process was generated from microwave power, which could reduce
time and energy requirement, compared to the conventional process [6], and the traditional thermal heating
method [7]. The microwave method led to the development of relatively higher surface areas than
impregnation via conventional heating [6].
Agricultural by-products, such as rice husk [8], bamboo [9] and rubber wood [10], are wildly utilized as
activated carbon, due to easily to find and their value added. In Southern of Thailand, rubber wood is one
fifth economic plant. After cutting and sawing processes, the sawdust is a by-products from these processes,
which is utilized as fuel or dumped in landfill. Therefore, this research aims to develop rubber wood sawdust
as activated carbon (AC) for its value added. The chemical regent, phosphoric acid (H3PO4), coupled with
the microwave energy were also applied to activated source in this study.
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METHODOLOGY
Rubber wood Ground and Washed/Drying Carbonization at
sawdust screene by sieve in oven at 110°C 400 600 and 800°C
No.60 for 24 hours. for 1 hour.
*Optimum carbonized temperature
Activated Carbon Washed/Drying Activated by Immeresd in
* Activation process in oven at 110°C microwave at 600, 40%H3PO4 solution
800, and 1000 watts
for 24 hours. for 24 hours.
for 90 seconds.
Fig. 1. Flow diagram for conversion of activated carbons from rubber wood
Conversion of activated carbons from rubber wood is shown as flow diagram in Figure 1.
1. Preparation of rubber wood sawdust
The preparation of activated carbon from rubber wood sawdust involved three stages. First, the rubber
wood sawdust was ground and screened thourgh sieve No.60. Then, powdered samples were washed with
distrilled water to remove undesirable contaminations, such as soil and clay, and dried in electric oven at
110 °C for 24 hours.
2. Carbonization of rubber wood sawdust
The powdered samples were carbonized under N2 gas to achieve the desired temperature at 400 600 and
800 °C for 1 hour, with the heating rate of 10 °C/min. The amounts of volatile, ash and fixed carbon in their
cabonized samples were then determined using thermogravimetric analysis (TGA) Model TGA8000.
Charcoal with high fixed carbon will be used for further activation.
3. Microwave of activation
Activation process, the optimum carbonized sample was immeresd in 40% H3PO4 solution for 24 hours.
After that, it was activated by microwave radiation at 600, 800, and 1000 watts for 90 seconds. The activated
samples were wased with DI water until a constant pH obtained, and then dried at 110 °C for 24 hours. The
properties of AC were exmined by measuring their porosities and pore size distributions by Surface Area
and Porosity Analyzer Model ASAP2460 using the Brunner–Emmet–Teller (BET) technique. The funtional
structure of activated carbons were determined by Fourier Transform Infrared Spectrometer (FT-IR) Model
Bruker/Tensor27 method. While, the mophology of samples were examined using Field Emission Scanning
Electron Microscope (FE-SEM) Model Apreo.
RESULTS AND DISCUSSIONS
Cabonization process
To determine the optimum carbonized temperature, thermogravimetric analysis (TGA) was applied in terms of
ash, volatile and fixed carbon, as given in Table 1. Results exhibited that the percentage of volatiles decreased
with increasing temperature, whereas increased in ash. This was due to the increasing temperature give more the
energy to break the bonds within the structure of the raw material. While, the fixed carbon of sample at 600 °C
revealed the highest value. Although, the fixed carbon at 800 C has value nearly at 600 °C, but it used relative
high enery. Thus, this indicated that the optimum carbonized condition in this research is 600 °C for 1 hour, and
then was used in the activation process (Figure 2) in the next step.
9th International Conference on Environmental Engineering, Science and Management
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Table 1. Proximate analysis (%wt. dry basis)
Temperature Ash % Fixed Carbon
(˚C) 4.32 64.03
4.67 Volatile 76.43
400 6.96 31.65 75.07
600 18.91
800 17.98
Fig. 2. Analysis results of activated carbon at temperature at 400, 600 and 800 ˚C
using Thermogravimetric Analysis (TGA) technique.
Activation process
After carbonized at 600 °C, the samples were activated using 40% H3PO4 solution with microwave radiation
at 600, 800, and 1000 watts for 90 sec. Results found that the activated carbon without activation the surface
area, total pore volume and average pore size are 1.41 m2/g, 0.01 cm3/g and 35.69 nm, respectively, as given
in Table 2. The activation was able to improve the surface area and total pore volume, especially microwave
with H3PO4. The increase in microwave powers had positively effect on surface area and total pore volume.
This is because microwave energy can directly eliminate the undesirable matters that attached internal and
external surface of AC [11]. In this research, the activation by microwave at 1000 watts with H3PO4
represented the highest surface area and total pore volume as 981.25 m2/g, 0.37 cm3/g, respectively.
Table 2. Summary of BET surface area, total pore volume, pore volume of prepared activated carbon
at the differents condition.
Samples Surface area Total pore Pore size
(m2/g) volume (nm)
600 ๐C (cm3/g)
600 ๐C+40%H3PO4 1.41 0.01 35.69
600 ๐C+40%H3PO4+600 watt 212.16 0.12 12.41
600 ๐C+40%H3PO4+800 watt 406.18 0.19 3.14
600 ๐C+40%H3PO4+1000 watt 725.94 0.28 2.82
981.25 0.37 2.32
9th International Conference on Environmental Engineering, Science and Management
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Regarding FTIR results (Fig. 3 and Table 3), the carbonized sample at 600°C shows that broad band at 3650-
3200 cm-1, which is assigned to O–H stretching of hydroxyl groups from carboxyls, phenols or alcohols and
adsorbed water. Subsequent, the wave number positions in the range 1780-1520, 1520-1300, and 1200-1130
cm-1 were caused by bondings C=O, C=C and C–O, respectively. The activation by microwave with H3PO4
resulted to the intensity loss at 1520-1300 cm-1. This indicated that the microwave energy not only eliminates
undesirable matters, but also destroys carbon structures in the samples.
Fig. 3. FT-IR spectra of activated carbon (a) without activation,
(b) activation with 40%H3PO4, (c) activation with 40%H3PO4 and microwave at 600 watts,
(d) activation with 40%H3PO4 and microwave at 800 watts and
(e) activation with 40%H3PO4 and microwave at 1000 watts
Table 3. Summary of waveform numbers that occur among function groups And the bond of position,
wave number range of activated carbon from rubber wood which is carbonized at temperatures of
400, 600 and 800 ˚C.
Wave number Functional Wave number positions
group
range (carbonization)
(cm-1)
400 ˚C 600 ˚C 800 ˚C
3452 *
3650 - 3000 O-H 3452 *** 3452 ** 2922 *
1630 *
2950 - 2970 C-H 2922 ** - 1440 *
1780 - 1520 C=O 1593 ** 1585 *** -
1520 - 1300 C=C 1440 ** 1440 *** -
1350 - 1330 C-H 1317 *** -
1200 - 1130 C-O - 1166 ***
*** high ** medium * low
Fig. 4 shows the morphology of sample with/without activation, which were carried out by FE-SEM
technique. Results shows that the sample without activation and activation with 40%H3PO4 have a relatively
smooth surface appear quite porous with pore size approximately 1.5 to 4 µm, as illustrated in Fig. 4a and
4b. The activation by microwaves with H3PO4 lead to become more rough surface with distribution of
macropore and mesopore, as given in Fig. 4c-e. In addition, it also found that the pore size seem to reduece
from 2 to 4 µm to 0.5 to 2 µm, when microwave engergy increased from 800 watts to 1000 watts. This
finding agreed with BET results.
9th International Conference on Environmental Engineering, Science and Management
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- 281 - (b)
(a) (d)
(c)
(e)
Fig. 4. FE-SEM images of activated carbon (a) without activation,
(b) activation with 40%H3PO4, (c) activation with 40%H3PO4 and microwave at 600 watts,
(d) activation with 40%H3PO4 and microwave at 800 watts and
(e) activation with 40%H3PO4 and microwave at 1000 watts
CONCLUSION
The conclusion could be drawn as follows.
• The optimum carbonized temperature for sawdust rubber wood was 600 °C for 1 hour.
• The activation with phosphoric acid and microwave energy could be attributed to surface area and total
pore volume improvement.
• The increase in microwave energy had directly effect on surface area, total pore volume and average pore
size.
REFERENCE
[1] Liou, T. H., & Wu, S. J. (2009). Characteristics of microporous/mesoporous carbons prepared from
rice husk under base-and acid-treated conditions. Journal of hazardous materials, 171(1-3), 693-703.
[2] Bansode, R.R., Losso, J.N., Marshall, W.E., Rao, RM. and Portier, RJ. (2004). Pecan shell-based
granular activated carbon for treatment of chemical oxygen demand (COD) in municipal wastewater.
Bioresource Technology, 94, 129-135
[3] Wu, Y., Jin, X. J., & Zhang, J. (2013). Characteristics of nitrogen-enriched activated carbon prepared
from waste medium density fiberboard by potassium hydroxide. Journal of wood science, 59(2), 133-
140.
9th International Conference on Environmental Engineering, Science and Management
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[4] Wang, T., Tan, S., & Liang, C. (2009). Preparation and characterization of activated carbon from
wood via microwave-induced ZnCl2 activation. Carbon, 47(7), 1880-1883.
[5] Yakout, S. M., and Sharaf, G. (2012). Characterization of activated carbon prepared by phosphoric
acid activation of olive stones. Arabian Journal of Chemistry 9(2): S1155–S1162.
[6] Alslaibi, T. M., Abustan, I., Ahmad, M. A., & Foul, A. A. (2013). A review: production of activated
carbon from agricultural byproducts via conventional and microwave heating. Journal of Chemical
Technology & Biotechnology, 88(7), 1183-1190.
[7] Hesas, R. H., Daud, W. M. A. W., Sahu, J. N., & Arami-Niya, A. (2013). The effects of a microwave
heating method on the production of activated carbon from agricultural waste: A review. Journal of
Analytical and Applied pyrolysis, 100, 1-11.
[8] Zakari, Z., Buniran, S., & Ishak, M. I. (2010, December). Nanopores activated carbon rice husk.
In 2010 International Conference on Enabling Science and Nanotechnology (ESciNano) (pp. 1-2).
IEEE.
[9] Liu, Q. S., Zheng, T., Wang, P., & Guo, L. (2010). Preparation and characterization of activated
carbon from bamboo by microwave-induced phosphoric acid activation. Industrial Crops and
Products, 31(2), 233-238.
[10] Mazlan, M.A.F., Uemura, Y., Yusup, S., Elhassan, F., Uddin, A., Hiwada, A., and Demiya, M. (2016).
Activated carbon from rubber wood sawdust by carbon dioxide activation. Procedia engineering, 148,
530-537.
[11] Yuen, F. K., & Hameed, B. H. (2009). Recent developments in the preparation and regeneration of
activated carbons by microwaves. Advances in colloid and interface science, 149(1-2), 19-27.
9th International Conference on Environmental Engineering, Science and Management
The Heritage Chiang Rai, Thailand, May 27-29, 2020
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Environmental Health Needs Assessment for Local Government
Flood Response in Nakhon Si Thammarat, Thailand
Jariya Matrongduang1, Nutta Taneepanichskul2*, Wandee Sirichokchatchawan2,
Suthirat Kittipongvises3 , Athit Phetrak4 and Jenyuk Lohwacharin5
1*Graduate student, College of Public Health Sciences, Chulalongkorn University (CPHS), Thailand;
2 Lecturer, College of Public Health Sciences, Chulalongkorn University (CPHS) 11th floor Institute
Building 3, Soi Chulalongkorn 62, Phyathai Road, Pathumwan Bangkok 10330, Thailand
3 Assistant Professor, Environmental Research Institute Chulalongkorn University (ERIC) 2nd Floor,
Institute Building 2, Phayathai Rd., Pathumwan, Bangkok, Thailand;
4 Assistant Professor, Department: Social and Environmental Medicine, Faculty of Tropical Medicine,
Mahidol University Ratchawithi Rd, Ratchathewi Bangkok, Thailand;
5 Lecturer, Environmental Engineering Department, Faculty of Engineering, Chulalongkorn University,
Phayathai Rd., Pathumwan, Bangkok, Thailand;
Corresponding author: Tel*: 089-206-6534 e-mail : [email protected]
ABSTRACT
Flooding is considered as one of disaster that affect more people than any other natural disaster. The
effects of flooding are extremely dangerous in developing countries due to low level flood protection.
Initiatives in the development of response to floods need to create responses and support from stakeholders
to identify causes, decision makers and local solutions [1]. In Thailand, each department has a flood response
plan but there is still no collaboration as expected. However, there’s limited studies on environmental health
needs assessment for local government flood response in Thailand. Therefore, the results of this study will
get flood action plan that responds to people's needs and use to recommend action plan for local government
organization through population approach. Further there have protocol joint operations when flooding
occurs.
This study conducts by cross-sectional study in Pak Phanang and Chian Yai districts, Nakhon Si
Thammarat province, Thailand. By face-to-face interview with a structured questionnaire. Need assessment
was analyzed by using modified Priority Need Index. Higher modified PNI score indicate a required need for
flood response from local government than lower modified PNI. The result showed for flood warning
information, the highest modified PNI is Provincial Waterworks Authority denote people in community want
to get more flood warning from Provincial Waterworks Authority. And for Water Consumption Quality
Information, Meteorological Department showed the highest PNI score. There may suggested that people in
the community required information about water quality during flood from Meteorological Department. The
result of the research shows the needs of people in the area and may be useful to local government
organization in writing an flooding emergency response action plan.
Keywords : environmental health need assessment; floods response; action plan; priority need index; local
government organization
INTRODUCTION
Flood is considered as one of disaster that affect more people than other natural disaster. Asia is the
most flood-affected region accounting for flood-related fatalities nearly 50% in the last quarter of the 20th
century [2]. For instance, the prolonged flood 2011 in Thailand caused more than 800 deaths and extensive
damage and losses. Nakhon Si Thammarat prone to flood during the monsoon every year.
Handling with disaster requires a team with multidisciplinary approach. Emergency prevention,
preparedness, response and recovery mitigation will help to reduce impacts from disaster. Also, flexibility
and offensive health systems that anticipate needs and challenges are more likely to reduce risks and respond
effectively during emergencies. Local government plays its essential role during flood. However, limited
studies provide a gap between current situation and desirable situation for flood response among affected
population.
9th International Conference on Environmental Engineering, Science and Management
The Heritage Chiang Rai, Thailand, May 27-29, 2020
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In Thailand: The Cabinet has approved the National Disaster Prevention and Mitigation Plan 2015 on
March 31, 2015 and assigned the Ministry, Department of Organizations and government agencies,
provinces, districts, local administrative organizations, private sectors and other sectors to act as according to
the National Disaster Prevention and Mitigation Plan 2015 and to enable the risk management of disaster-
related areas. Also drive the disaster prevention and mitigation action plans of local administrative
organizations into action and include them in the local development plan.
In the year 2019, the Department of Local Administration issued a written order to the provincial
governors in every province. The subject is : Preparation, prevention and resolution for floods and landslides
to set guidelines for assistance and preparation for prevention and solution of flooding and landslides for
local administrative organizations. The preparation guidelines consist of 4 main topics as follows
1. Prepare before disaster consists of determining a budget sufficient to face potential disaster,
publicizing the people in risk areas to prepare for the situation likes flash flood or mudslides, survey related
materials and equipment to be ready for operation immediately in case of floods such as water pump, heavy
equipment, etc. Arrange drills for disaster prevention and mitigation plans and require community-based
warning volunteers, take action to eliminate water obstacles such as dredging drains and procuring water-
supporting areas. Adding, monitor weather reports, rainfall, and river levels from relevant agencies in order
to prepare for the situation appropriately.
2. Actions during the disaster consists of the following details. The Provincial Local Administration
Office shall be the coordination center of the local government organization in providing assistance to the
victims in their area as appropriate. The local government organization shall use the prepared budget to help
the people who has suffered first. Providing clean water to help people in consumption, the local government
organization can provide clean water as well as produce clean water for the public free of charge. And if
government agencies, government organizations, civil society organizations provide clean water or consumer
products to distribute to the public request local administrative organizations to facilitate the work of
government agencies and various organizations in the operation. Accelerate the evacuation of affected
people or expect to experience disaster in the life and property of the people first.
3. Operations after the disaster details as follows. Accelerate the survey of damage from floods
including problems and needs of people in various fields in order to provide immediate assistance under their
authority. If the budget is not enough, request support from the Disaster / Disaster Relief Committee.
Coordinate with the Provincial Disaster Prevention and Mitigation Office and agencies involved in helping
people in the area at all times and to prevent duplication. Cleaning and storing sewage Solid waste from
flooding in order to be clean and prevent the occurrence of an epidemic then repairing public utilities to be
able to return to work quickly. As well as coordinating with local health service units and village health
volunteers to provide services to people in the event of disease that comes with flooding and other health
services. Enjoin the civil defense disaster relief volunteers to look after government property, people and
victims. And rehabilitation and promote careers for victims by coordinating with relevant agencies within the
province or area of responsibility.
4. Performance report, the local government organization shall report the result of the operation to
prevent and solve the flooding problem, flash flood and landslides to the Department of Local
Administration [3].
In Thailand; the readiness to handle floods in term of public health, despite having a clear structure and
chain of command but the point is unclear is the content of the management plan and participation of various
departments such as community and private sectors. The Department of Disease Control does not have a plan
to support flooding, there is only a warning to provincial agencies to take action against disease. Department
of Health has a plan for the preparation of materials and equipment to solve environmental health problems
and staff development for environmental health workers. National Institute of Emergency Medicine has an
action plan but still lack clear boundaries of responsibility and coverage of services. Also, rehearsing the
plan is not enough "Coordination and command are still redundant" from the records of the Department of
Disease Control. Or "coordinating information from other agencies is difficult because when this happens
each unit has a lot of missions no ones can provide true information in real situations "from the Department
of Health's lesson record [4].
Environmental Health Needs Assessment conduct to determine needs of an affected area. Needs
assessment using Priority Need Index (PNI) approach is a tool for ranking needs by finding the differential
value between desirable characteristics (I – importance) and current characteristics (D-degree of success), by
determining the needs at the real level. Because of unclear structure of local government flood response plan
9th International Conference on Environmental Engineering, Science and Management
The Heritage Chiang Rai, Thailand, May 27-29, 2020
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in Thailand, it would be better to understand current and desirable response of local government response
during flood. In addition, there is not management plan and participation during flood composing of various
departments [4]. Therefore, this study aimed to access needs of flood response from different local
government among flood-effected population in Nakhon Si Thammarat, Thailand. The results will provide
needs of local government flood response among affected population which will support a future action plan
for flood response in Thailand.
METHODOLOGY
A cross-sectional study was conducted in Pak Phanang and Chian Yai districts, Nakhon Si Thammarat
province, Thailand. We included head of each household with age 18 years old and above in flood-affected
area who lived in the area more than 1 year and had been afftected by flood at least once during stay period.
We estimated to collect 150 households. Households will be selected by snowball sampling.
Face-to-face interview with a structured questionnaire was done in affected community in Pak Phanang
and Chian Yai districts. A set of questionnaires composed of general characteristic of respondents, health
symptoms during last flood. Current and desirable characteristics of local government flood response were
provided by 5-likert scale. The first (1) scale indicated no response or support from that organization. The
last scale (5) indicated response or support from that organization.
Need assessment was analyzed by using modified Priority Need Index (modified PNI) equation [5] as
following;
modified PNI = Desirable (I) - Current (D)
Current (D)
Higher modified PNI score indicate a required need for flood response from local government than
lower modified PNI. Local government with high modified PNI score should be firstly prioritized for
providing flood response.
RESULTS AND DISCUSSIONS
Table 1 shows general characteristics of participants including; average age of respondent was 57.47 ±
13.65 years old. Most of them were female (72.67%) with primary school level (65.56%) and farmer
(34.44%). During last 10 years, most of them reported flood-affected around 6 – 10 times.
Table 1 General Characteristic of participants
Age (years) Mean ± SD Number (n=151) Percentages
Gender Male 57.47 ± 13.65
Education levels Female 41 27.33
Below primary school 110 72.67
Occupational Primary school 13 8.61
Secondary school 99 65.56
Flood-affected during last Diploma 32 21.19
10 years (times) Bachelor's degree or higher 1 0.66
Farmer 6 3.98
Merchant 52 34.44
General career 19 12.58
Housewife / butler 22 14.57
Private sector staff 23 15.24
Government officer 1 0.66
Unemployed 3 1.99
Fishing 15 9.93
Other 14 9.27
1 time 2 1.32
3-5 times 10 6.62
6-10 times 40 26.50
>10 times 84 55.63
>15 times 14 9.27
2 1.32
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