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important sources for foods, fresh water, building materials and provide services such as water treatment and
erosion control, etc. the so called ecosystem services [7].
Originally, Moeyungyi Weland was constructed as a reservoir for water supply, irrigation and transportation
of timber. It gradually changed into a natural wetland within a century and serves as a natural wetland
wildlife sanctuary with very rich biodiversity. Thus, it is designated as an Important Bird Area (IBA) of
Asia [8], [9]. Despite valuing the valuable resources, the local people encroach to the wetland for growing
crops on the emerging land during the summer times and thus contributing the nutrients to the wetland [10].
So far, very few studies on the water quality of the Moeyungyi Wetland have been conducted. Therefore,
this study aimed to investigate the distribution of nutrients concentrations in the Moeyungyi Wetland and its
trophic states using Geographic Information Systems (GIS) techniques.
METHODOLOGY
Study Area
The study area of this research was in the Moeyungyi Wetland Wildlife Sanctuary, located in the southern
part of Bago Region in Myanmar. The average elevation of the place is about 6 m ASL, positioned at
north latitude 17° 30‟ and 17° 36‟ and between east longitudes 96° 33‟ and 96° 39‟. The Moeyungyi
Wetland is 103.59 km2 wide and the depth is approximately 5.1 m (16.7 ft) when it is measured from the
brim (during full water level time). The main sources of water for the wetland are the Bago River diverted
through Zangtu wier as well as rainfall [8]. The climate in the area is tropical with an average rainfall of
3543.05 mm per annum. The raining season in the study area is from May to October. Flooding occurs
annually during the rainy season [11]. Originally, the wetland was a constructed reservoir between 1873-
1878 during British Colonial time with the multi-purposes including flood mitigation, water supply for
irrigation and timber logging. However, the wetland slowly changed into a natural wetland with waterfowl
ecosystem. The embankment of the wetland is 2.44 m (8 ft) thick. There are 17 villages around the
wetland with approximately 12,000 households and more than 65,000 inhabitants. A vest area of
farmlands is occurring in and around the Moeyungyi Wetland [9]. The study area mapping with the
distributed sampling locations are presented in Figure 1.
Myanmar
Bago Region Moeyungyi Wetland
Figure 1 Map of Moeyungyi Wetland showing the location of the sampling points
Water Sampling and Analyses
In order to investigate the nutrients concentrations distribution in the wetland, 15 sampling points
(represented as S1 – S15), distributed throughout the wetland, were randomly selected.
The first sampling event was carried out in October 2019. Water samples were collected from the
predetermined locations in the wetland. The physio-chemical parameters such as pH, temperature, dissolved
oxygen (DO), total dissolved solid (TDS) and electrical conductivity (EC) were measured with onsite
measuring device named HORIBA U-50 and water clarity or Secchi depth (SD) were measured with Secchi
disk. The nutrient quality parameters of the water, including Chlorophyll-a (Chl-a), total phosphorous (TP)
and chemical oxygen demand (COD), were analyzed at the laboratory by acetone extraction method using
spectrophotometer, ascorbic acid method and photometric method, respectively following the Standard
Methods.
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Trophic State Index Calculation
According to Carlson‟s method, Secchi Depth (SD), concentration of Chlorophyll-a (CHL) and
concentration of total phosphorous (TP) were used to calculate the Trophic State Index (TSI) of the wetland.
The TSI of a body of water ranges from zero to one hundred (Table 1.). Trophic State Index (TSI) at various
sampling locations were calculated by using Carlson‟s method as shown below [3].
TSI (SD) = 60 – 14.41 ln (SD) (1)
TSI (CHL) = 9.81 ln (CHL) + 30.6 (2)
TSI (TP) = 14.42 ln (TP) + 4.15 (3)
The overall Carlson‟s TSI was then calculated as the average value of TSI (CHL), TSI (SD) and TSI (TP) as
follows:
TSI = (TSI (SD) +TSI (CHL) +TSI (TP))/3 (4)
Where;
TSI = Trophic State Index
ln = Natural log
SD = Secchi depth in meter
CHL = Chlorophyll-ɑ count in µg/L
TP = Total phosphorous in µg/L
Table 1 Carlson’s trophic state index values and classification of lakes
TSI Trophic Attributes
values status
< 30 Oligotrophic Clear water, oxygen throughout the year in the hypolimnion
30-40 Oligotrophic A lake will still exhibit oligotrophy, but some shallower lakes
will become anoxic during the summer
40- 50 Mesotrophic Water moderately clear, but increasing probability of anoxia
during the summer
50-60 Eutrophic Lower boundary of classical eutrophy: Decreased
transparency, warm-water fisheries only
60-70 Eutrophic Dominance of blue-green algae, algal scum probable, extensive
macrophyte problems
70-80 Eutrophic Heavy algal blooms possible throughout the summer, often
hypereutrophic
>80 Eutrophic Algal scum, summer fish kills, few macrophytes
Distribution of Nutrients Concentration using GIS
Interpolation technique using Geographic Information System is widely applied in water quality assessment
as a powerful tool. Interpolations of the selected parameters, Chl-a, COD, DO, SD, TP and TSI, were done
in order to know the spatial variation of the nutrients in the wetland by using Ordinary Kriging (OK) method
via the ArcGIS 10.1.
RESULTS AND DISCUSSION
The concentrations of physio-chemical parameters in the water samples collected from the Moeyungyi
Wetland and analyzed are shown in Table 2. The pH of any water body is an indicator of how acidic or
alkaline it is. Most of the microorganisms and vertebrate will not survive at pH below 4 (acidic) [12]. The
average pH level in the collected water samples from Moeyungyi Wetland was found to be 5.46 with the
minimum value as 4.3. Therefore, Moeyungyi Wetland can be regarded as moderately suitable for aquatic
life even though the water is acidic. In one study at Kpassa Reservoir [13], the mean pH value was recorded
as 6.54.
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Table 2 Descriptive statistics of physico-chemical parameters of water quality in Moeyungyi Wetland
Parameters Unit Average Standard Minimum Maximum
Value Deviation
pH - 5.46 0.55 4.37 6.21
Dissolved Oxygen mg/L 4.77 1.26 2.76 6.79
Temperature ֯C 33.06 1.22 33.06 35.05
Electrical Conductivity ms/cm 0.024 0.003 0.017 0.028
Total Dissolved Solid g/L 0.015 0.003 0.011 0.019
Secchi Depth m 0.49 0.20 0.25 1.04
Chemical Oxygen Demand mg/L 15.92 5.65 0.5 23.0
Total Phosphorous µg/L 19.80 12.01 9.0 46.0
Chlorophyll-a µg/L 15.40 13.26 1.25 38.02
Dissolved oxygen is an important parameter to assess the waste assimilation capacity of the water
bodies [14]. The DO concentration in the water samples from Moeyungyi Wetland ranged between 2.76
mg/L and 6.79 mg/L. The acceptable minimum concentration of DO for the most fish species is 4-5 mg/L
[15]. Thus, the average DO concentration of the Moeyungyi Wetland shows that it has a moderately suitable
water quality for living organisms in its water.
The water temperature plays an important role in the solubility of salts and gases and it can alter the physical
and chemical properties of water. It is also one of the determining factors of photosynthesis processes [16].
The water temperature of Moeyungyi Wetland was recorded to be 33.06 ± 1.26 ֯ C. According to one
research study, temperatures higher than 15˚C favor the development of microorganisms and activate
chemical reactions [17]. The water temperatures occurred in the wetland at all locations during the sampling
event were favorable for algal growth.
Electrical conductivity (EC) is the measure of the ability of a solution to carry electric current. This ability is
directly related to the concentration of ions in a solution [18]. Total dissolved solid is usually used to
calculate the EC. The average EC value in the study area was found to be 0.024 ms/cm (24 µs/cm). In a
similar study at Okhla Bird Sanctuary (a men-made wetland) in India [19], the average EC was recorded as
507.25 µs/cm. Comparatively low level of EC observed in Moeyungyi Wetland might be due to the study
period. During the study period, the study area was characterized by either no inflow into the wetland or a
very steady flow inside the wetland.
Similar to EC, total dissolved solid (TDS) is also caused by the dissolved ions in the water [18]. The
freshwaters usually have the value of TDS smaller than 1000 mg/L [20]. In Moeyungyi Wetland, the
maximum value of TDS found to be 11 mg/L and the minimum value was 0.019 mg/L. Thus, it can be said
the water in the Moeyungyi Wetland was moderately unpolluted during the study period.
Secchi Depth (SD) or transparency is measured by secchi disk. According to a study, high density of
phytoplankton and high concentration of suspended matter are the main factors which contribute to reduce
water transparency [13]. The transparency in the Moeyungyi Wetland ranged between 0.25 m and 1.04 m
with an average was 0.49 m. This was much lower than the mean SD value in a previous study (mean SD =
0.85 m) [13].
Chemical oxygen demand (COD) is an indirect indicator of the amount of organic matter in a water
body. Chemical Oxygen Demand (COD) in the Moeyungyi Wetland during the sampling event was found
to be 15.92 ± 5.65 mg/L. In the study at Okhla Bird Sanctuary, the average concentration of COD in its
water was recorded as 44.60 ± 12.07 mg/L [19]. Even though, the COD concentration in this study was not
that high as in the Okhla Bird Sanctuary, this research observed the presence of organic matters or nutrients
in the water of Moeyungyi Wetland.
Agricultural activities are considered as the major provider of nutrients mainly phosphorous and nitrogen
contents in the water body [1]. Moeyungyi Wetland has quite an agricultural activities in and around it. The
total phosphorous (TP) concentrations in the collected water samples ranged from 9 – 46 µg/L. Therefore,
the water body in the wetland was between oligotrophic condition (TP > 15 µg/L) and eutrophic conditions
(TP = 25 – 100 µg/L) according to [21].
The microalgae or phytoplankton biomass in the water of Moeyungyi Wetland was measured as
Chlorophyll-a (Chl-a) concentration. The Chlorophyll-a concentrations in the wetland ranged from 1.25 to
38.02 µg/L. It can be said that the Moeyungyi Wetland was in the mesotrophic to eutrophic conditions
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during the sampling event. In a study in Inle Lake, Myanmar the Chl-a concentration was recorded to be in
the range of 12.16 to 27 µg/L [22].
The selected parameters including Chlorophyll-a (Chl-a), chemical oxygen demand (COD), Dissolved
Oxygen (DO), Secchi Depth (SD) and Total Phosphorous (TP) and Trophic State Index (TSI) at the 15
sampling locations with the coordinates points for interpolation via ArcGIS are listed in Table 3.
Table 3 Concentrations of Selected Parameters at Various Locations in the Moeyungyi Wetland
ID Coordinates Chl-a COD DO SD TP TSI
(m) (ug/L)
Easting Northing (ug/L) (mg/L) (mg/L)
S1 242970.7 1946415 38.20 23 3.57 0.25 46.0 68.5
S2 245895.6 1945396 22.07 18 5.67 0.41 21.0 60.6
S3 247339.6 1944555 4.25 15 5.73 0.43 17.0 54.0
S4 249216.9 1945299 15.06 18 2.76 0.51 13.0 56.0
S5 252732.2 1942775 3.67 18 3.57 1.04 9.0 46.2
S6 251479.9 1939053 2.66 12 5.13 0.56 11.0 49.1
S7 251070.7 1937446 4.34 5 5.06 0.41 14.0 53.4
S8 250163.4 1937581 3.72 18 5.80 0.46 11.0 51.2
S9 248557.0 1939456 1.25 17 3.87 0.81 9.0 43.9
S10 247343.0 1940755 7.54 23 3.32 0.58 9.0 51.3
S11 245430.5 1940567 18.07 - 5.34 0.38 24.0 60.9
S12 242737.4 1937825 15.01 5 4.61 0.36 29.0 61.6
S13 242987.1 1939839 21.92 16 6.66 0.38 20.0 60.7
S14 243814.1 1942887 37.53 19 6.79 0.46 45.0 65.5
S15 246973.9 1937904 35.67 - 3.62 0.28 19.0 63.5
The interpolated maps of the selected parameters are presented in following Figures 2 - 6.
The distribution of chlorophyll-a concentration
over the study area in Moeyungyi Wetland is
presented in Figure 2. It can be seen that the
higher concentrations were observed at the
western part of the wetland. Although, there are
many communities living around the wetland,
lots of agricultural areas are adjacent to the
wetland in the western bank. Moreover, a resort
is located near S1. And, a static flow from the
west to east was observed during samples
collection. Due to those factors, the higher
concentrations were observed mainly in the
western parts of the wetland. One similar study
also reported the same trend as high values
Figure 2 Distribution of Chlorophyll-a in Moeyungyi found near resettlement area [22].
The distribution of COD in the Moeyungyi Wetland is
illustrated in Figure 3. The highest concentration was
found near S1 (under the walkway to resort and its
kitchen) and the lowest one was found near S12
(southern part) and S7 (southeast outlet of the
wetland). The highest values found at S1 could be due
to the human activities such as transportation,
agriculture and so on. However, as shown in Figure 3,
concentration of COD was more or less equally
distributed over the whole wetland (SD = 5.65). And
the lowest value or the cleanness area was still found
to be near one of the outlets of the wetland.
Figure 3 Distribution of COD Values in Moeyungyi
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Figure 4 Distribution of DO Levels in Moeyungyi The distribution of DO in the Moeyungyi
Wetland is shown in Figure 4. The high DO
concentration was found near S14 and S13 (the
open water area) and the minimum
concentration was found at S4 (next to the
northern bank) and S10 (nearby extensive
macrophytes area). The open water area are
favored with the wind blow and thus, having
high DO concentration while the area between
macrophytes have no favorable wind condition
and so having low DO values. DO was found
to be less than 4 mg/L at several locations (S1,
S4, S5, S9, S10, and S15) during the sampling
event. According to a study, fish kills can
occur in the very DO locations [15].
Figure 5 Distribution of SD in Moeyungyi Figure 6 Distribution of TP Values in Moeyungyi
The transparency of the water in the Moeyungyi Wetland was observed to be distributed as shown in Figure
5. The transparency is related to the phytoplankton biomass found in the water body [13]. Thus, the clearest
area was found at S5 (one of the main outlets at the eastern part of the wetland). The distribution of water
transparency in the wetland increased with the water flow from west to the east. It is similar to the trend
observed in the Chl-a concentration in the wetland.
The total phosphorous (TP) concentrations were interpolated as shown in Figure 6. The agricultural
activities in and near the western part of the wetland seem to contribute to the concentration of nutrients in
the wetland. According to a study, the wetland plants/macrophytes can purify the water and so, the lowest
TP concentrations were observed in the eastern part of the wetland [22]. Thus, it could be said that, the
Moeyungyi Wetland has the water purification capacity similar to other wetlands.
In addition, the calculated Trophic State Index (TSI) at different locations of the wetland were interpolated to
investigate the spatial distributions of the nutrients, as illustrated in Figure 7. As shown in Figure 7, the
results indicated that most of the area in the Moeyungyi Wetland (76%) was covered with eutrophic
condition and only less than one third of wetland had mesotrophic conditions. Out of the 76% of the
eutrophic area, 51.31 km2 was in the second level of eutrophic condition (TSI=60-70) by covering extensive
macrophyte problems and 28.25 km2 was in first level of eutrophic condition (TSI=50-60) with decreased
water transparency. The remaining area of the wetland (25.07 km2) was in mesotrophic condition (TSI=40-
50) with moderately clear water. Based on the maps, it can be said that in view of the overall nutrients
concentrations, the water quality in the wetland was decreasing along the flow from the west to east,
following the similar trends observed in some other water bodies as in the Inle Lake in Myanmar [22] and
Ganga River in India [23].
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Figure 7 Trophic States at different locations in Moeyungyi Wetland
CONCLUSIONS
The average concentrations of the investigated parameter pH, DO, temperature, EC, Chl-a, COD, DO, SD
and TP in the Moeyungyi Wetland were found to be 5.46, 4.77 mg/L, 33.06 ֯C, 0.24 ms/cm, 15.40 µg/L,
15.92 mg/L, 4.77 mg/L, 0.49 m and 19.80 µg/L, respectively. Based on these results, it appears that the
water of the Moeyungyi Wetland is polluted with high concentrations of nutrients mainly in the western parts
of the wetland. It can also be said that the water quality is not suitable for drinking purpose due to the
presence of COD and TP (according to WHO standards, COD and TP should be nil in any portable drinking
water). Moreover, due to low levels of DO and high Chl-a concentration in several parts of the wetland
seasonally, eutrophic conditions and fish kill can occur. Therefore, a recommendation could be made to
conduct another study for continuous monitoring of the wetland in order to identify the seasonal variation of
the water quality.
The trophic states of the Moeyungyi Wetland indicated the eutrophic conditions in most of the sampling
locations. Therefore, it appeared that the wetland suffered from the nutrient pollution.
Furthermore, based on the spatial variation analysis results, the hotspot area was found to be in the western
part of the wetland. In addition, the maps show that the concentrations of the investigated parameters in
general, decreased along with the water flow from west (water entering points) to the east of the wetland
(flashing point or water outlet). Therefore, it can be concluded that the Moeyungyi Wetland is still serving as
natural water purifying system for the local region. However, continuous surveillance of the water quality in
the wetland is needed for controlling the pollutants entering into the water body before it reaches beyond its
capacity of purification.
ACKNOWLEDGEMENTS
Funding for this research was provided from Norwegian Scholarship for Capacity Building Initiative for
Myanmar (CBIM-II). Thanks are due to the E Guard Environmental Services Company Limited for lending
the onsite measurement devices. Moreover, a special thank goes to Nature and Wildlife Conservation
Division, Forest Department for sharing useful data for this research.
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[14] Kumar, S., Ghosh, N.C., Singh, R.P., Sonkusare M.M., Singh, S. and Mittal, S. 2015Assessment of
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9th International Conference on Environmental Engineering, Science and Management
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Investigation of the Impact of Taung-Inn-Myount-Inn Open Dumping
Site for Municipal Solid Waste on Soil and Surface/Ground Water in
Mandalay City in Myanmar
Hsu Yee Mon1* Ranjna Jindal2 Romanee Thongdara3 and Nawatch Surinkul4
1*Master Student; 2Visiting Professor; 3Assistant Professor; 4Assistant Professor, International Masters and PhD
Programs in Environmental and Water Resources Engineering, Department of Civil and Environmental
Engineering, Faculty of Engineering, Mahidol University, Phutthamonthon Nakhonpathom, 73170 Thailand
*Phone : 0634826250, Fax : (66) 2 889 2138 Ext. 6386, E-mail : [email protected]
ABSTRACT
Inappropriate disposal of solid waste lead to contamination of the soil, air, surface/ground water and
spreading of the diseases by various vectors [1]. This study aims to investigate the impact of municipal solid
waste disposal at Taung-Inn-Myount-Inn(TIMI) dumpsite in Mandalay city, Myanmar on soil, surface water
and ground water in the surrounding area. Eight samples of soil, three of surface water and seven of ground
water were collected by random sampling method. Based on the results of geochemical analysis, the average
concentrations of pH, Pb and Hg in the soil samples were 9.5, 1.57 mg/kg and 20.03 µg/kg, respectively. Cr
was not detected in all the soil samples. Moreover, inverse distance weighting interpolation (IDW) method,
GIS was employed for obtaining the spatial variation of heavy metals in the study area. pH, electrical
conductivity(EC), salinity, temperature(T), dissolved oxygen(DO), biochemical oxygen demand(BOD5),
total suspended solid(TSS), total dissolved solid(TDS), turbidity, total alkalinity(TA), iron(Fe),
manganese(Mn) and arsenic(As) were determined in surface and ground water samples. The average
concentrations of Fe, Mn and As in the surface and ground water samples were 0.16, 0.23, 0.01 mg/L and
0.30, 0.27, 0.01 mg/L respectively. Results of geophysico-chemical investigation show that, there is no
considerable impact of leachate on the soil, surface and ground water quality in and around the TIMI
dumpsite. However, the presence of the heavy metals in soil, surface and ground water imply that the
concentrations might increase in future. Therefore, a thorough geophysico-chemical study should be carried
in and around the TIMI dumpsite area to monitor the heavy metals in order to protect the human health and
environmental pollution. Mandalay City Development Committee (MCDC) should provide safe potable
domestic water for the residents and should do sanitary landfilling to prevent further contamination of soil,
surface water and ground water as well as air.
Keywords : municipal solid waste; open dumping site; soil, surface water and ground water contamination;
geophysico-chemical methods; GIS; inversed distance weight interpolation method
INTRODUCTION
Solid wastes are the unwanted materials and usually solid forms that are produced from the activities of
people and animals. Solid waste is commonly known as trash or garbage that is consisting of everyday items
we consume and discard. The predominant components of solid wastes are food wastes, miscellaneous
inorganic wastes, yard wastes and containers product packaging from residential, institutional, commercial,
agricultural and industrial sources [2]. There is a large difference in amount and characteristics of solid waste
from country to country and even city to city [3]. Municipal solid waste (MSW) is defined as the „non-
gaseous and non-liquid waste‟ that results from the daily activities of community‟s residential and
commercial sector within a given administrative urban area [4]. Several studies indicate that the waste
components of MSW in developing countries are households wastes (55- 80%), commercial or market
wastes (10 – 30%) and with varying quantities from streets, industries, institutions among others [5]. Solid
waste disposal is considered as one of the most challenging environmental management problems. A large
amount of the generated solid waste in a town or city (MSW) is buried in landfills or disposed of in
dumpsites [6]. Most of the municipal solid waste in low-income Asian countries is dumped on land in a more
or less uncontrolled manner [7]. The most noticeable problem from the dumpsite is releasing of a bad odor
to the environment which is due to the decomposition of the wet wastes (organic wastes) [8]. Moreover,
water already present in the MSW as well as the one generated by biodegradation or precipitation, produce
leachate containing variety of inorganic and complex organic chemicals, and metals that can move vertically
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and laterally in and around the dumping area thereby causing soil, surface water and ground water
contamination [9]. Season plays an important role in the leachate formation [10]. The uncontrolled open
dumping site can cause significant impacts on the environment and human health [11].
The main objective of this study was to assess whether the one current solid waste open dumping site in
Mandalay, Myanmar is working efficiently and whether improvements to the system could be made. The
specific objectives of this study included - to investigate the impact of municipal solid waste open dumping
site at the Taung-Inn-Myount-Inn (TIMI) dumpsite in Mandalay city, Myanmar on soil, surface water and
ground water by using Geophysico-chemical Methods and to investigate the spatial-temporal distribution of
pollutants in the soil with respect to different locations in study area using Geographic Information System
(GIS).
METHODOLOGY
Location of the Study Area
Mandalay city is the second most populated city located in the central region of Myanmar with more than 1.2
million inhabitants. It lies between the geographic coordinates of latitudes 21° 51' 47'' and 21° 01' 27'' and
longitudes 96° 03' 17'' and 96° 03' 47'', (Fig. 1) [12]. Mandalay city area is 315 km2 and lies at an elevation
of around 75 m above the sea level. Mandalay city has six townships and further divided into 96 wards, 42
village tracts and 170 villages. The city is bound by the Doehtawaddy River at the south and the
Ayeyarwaddy River at the west. In Mandalay city, soil is composed of quaternary unconsolidated sediments
of the Ayeyarwaddy River and the piedmont colluvium deposits from the marhinal highlands of the Shan
Plateau. The city is one of the dry zone area of Myanmar and also one of the most climate-sensitive regions.
Mandalay city is characterized by low annual rainfall; the monsoon period is usually from May to October.
Hottest month is April, coldest is January and October is generally the wettest month of the year [13].
Fig. 1 Location of Mandalay city and study area
Source: MIMU, 2019 (map shape file) [14]
TIMI dumpsite is situated at Amarapura Township which is beside a cemetery compound. The study area is
at TIMI dumpsite and its surrounding area up to 0.5 km far from the boundary of the TIMI dumpsite. Out of
a total 42 villages in Amarapura Township, two villages fall in the study area: Myit-Laung and Sauk-Taw-
Wa villages. TIMI dumpsite receives 40% of the city wastes (392 tons of wastes) daily coming from the
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southern townships. Out of a total area 11.5 ha of the TIMI dumpsite, the existing dumpsite covers an area of
4.35 ha. MCDC installed an incinerator with a capacity is 30 tons per day at TIMI dumpsite for medical
wastes disposal. The depth of the deposited waste at the dumpsite varies over the site between 5 to 7 m on
average and is approximately 5 m above natural surface level at present. The nearest surface water is less
than 2 km away and there is an open floodplain area between the dumpsite and this watercourse. The nearest
village is approximately 1 km away from the dumpsite. The water table is only 2 to 3 m below the ground
water level as a minimum and the soil below is sandy clay with the permeability of 10-5 m per second. No
bottom liners have been installed and cover application is very infrequent. As the waste has been placed in
old pits in clay profile and this significantly reduces the possibility of leachate migration into the
groundwater table and soil. However, the comingled solid wastes are dumped at the TIMI dumpsite and can
include some household hazardous wastes such as mobile phones, battery, etc., [15].
Samples Collection
Investigations were carried out with collecting the soil, surface water and ground water samples, by random
sampling method from selected locations in and outside area of TIMI dumpsite during 31st October and 15th
December (winter season) of 2019. Phone GPS & Maps application was used to identify all the sampling
points‟ locations. Fig. 2 shows the locations of the surface water, ground water and soil sampling points in
the study area.
Fig.2 Locations of the soil, surface water and ground water sampling points in the study area
Soil Samples Collection and Analysis Methods
Soil samples from eight sampling locations (designated as S1…S8) of 0.5 kg each were collected from 3 m
below the ground level using soil boring method. The pH measurement was done using a pH meter in the
laboratory. The heavy metal: lead (Pb), mercury (Hg) and chromium (Cr) concentrations were determined
using Atomic Absorption Spectrophotometer (AAS).
Distribution of Heavy Metals Concentration using GIS: Inverse Distance Weighting (IDW) interpolation
method via the ArcGIS 10.4 was employed for obtaining the spatial distribution of Pb and Hg concentrations
in the study area. In interpolation with IDW method, a weight is attributed to the points of interpolation..
Value of the weight is dependent on the distance of the point from another point with unknown
concentration. In this method the distance between the points count, so the points of equal distance have
equal weights [16]. The weight factor is calculated with the use of the following formula:
λi = the weight of point, Di = the distance between point i and the unknown point, α = the power ten of
weight.
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Surface Water and Ground Water Samples Collection and Analysis Methods
A total number of 3 surface water samples from the sampling locations (designated as SW1…SW3) in the
water impoundment (in the rainy season) and 7 ground water samples (designated as GW1…GW7) from the
boreholes were collected into plastic containers. Water levels in the boreholes varied from 5 m to 153 m and
6 m in the water impoundment. The physico-chemical parameters measured in water samples included:
onsite parameters – pH, electrical conductivity (EC), total dissolved solid (TDS), salinity, temperature (T)
(using Palintest, Multi-Parameter); turbidity (using HACH, 2100Q Portable Turbidimeter), total suspended
solid (TSS) (using DR 3900 Spectrophotometer), iron (Fe) (using HACH Test Kit, IR-24), manganese (Mn)
(using HACH Test Kit, MN-PAN). Total alkalinity (TA), dissolved oxygen (DO), biochemical oxygen
demand (BOD5) and arsenic (As) were determined following the Standard Methods, APHA (2012) [17].
RESULTS AND DISCUSSION
Soil Geochemical Characteristics
The results of the geochemical analysis of the soil samples in the study area are shown in Table 1. Based on
the results, pH values of the soil samples varied between 8.7 to 10.3 (SD=0.60) indicating the highly-alkaline
soils found at the study area leading to tie up of nutrients in soil, which is the key to plants‟ growth. Alkaline
soils are typically found in low-rainfall areas and are difficult to be used for agricultural production. As
indicated in Table 1, the average concentrations of Pb and Hg in the soil samples were lower than the
Canadian Soil Quality Guidelines [18]. Cr was not detected in all the soil samples.
Table 1 Average concentrations of geochemical parameters in soil samples of the two sampling events
Sampling Canadian Guidelines
Points
S1 S2 S3 S4 S5 S6 S7 S8 Mean SD Min. Max. Residential
parkland
Parameters Agricultural
pH 9.9 8.9 8.7 9.8 8.9 9.3 10 10.3 9.48 0.60 8.7 10.3 6-8 6-8
Pb(mg/kg) 1.21 1.93 2.08 0.98 1.01 1.68 1.39 2.29 1.57 0.50 0.98 2.29 70 140
Hg(µg/kg) 13 14.8 14.1 7.9 24.4 37.4 18.6 30 20.03 9.87 7.9 37.4 6,600 6,600
Cr(mg/kg) ND ND ND ND ND ND ND ND - - - - 64 64
Geospatial Distribution of Heavy Metals Concentrations in Soil
Pb and Hg spatial distribution in the soil of the study area during two sampling events were plotted using
IDW interpolation method, GIS as shown in Figs. 3 (a-b) and 4 (a-b), respectively. Higher Pb concentration
was found at the eastern part of the TIMI dumpsite (at S2 & S3) in the 1st sampling event. However, the
concentration of Pb was found to be higher at the western part of the dumpsite (S6, S7 & S8) and S5 in the
2nd sampling event (Figs. 3(a) and (b)). As shown in Figs. 4 (a) and (b), the higher Hg concentration was
found at S5 and S6 in 1st sampling event. In the 2nd sampling event, higher Hg concentration was at the
western part of the dumpsite (S6, S7 & S8) and S5.
(a) at 1st sampling event (b) at 2nd sampling event
Fig. 3 Pb spatial distribution in the study area during two sampling events
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(a) at 1st sampling event (b) at 2nd sampling event
Fig. 4 Hg spatial distribution in the study area during two sampling events
Surface Water and Ground Water Physico-chemical Characteristics
The surface water and ground water in the study area is used for drinking, domestic uses, agriculture and
livestock breeding purposes. Sampling sites of three surface water samples and six ground water samples
were located around the TIMI dumpsite area; only one ground water sampling site was located in the TIMI
dumpsite area. Based on the physico-chemical analyses results, the average concentrations and standard
deviations (SD) of the analyzed parameters in the surface water and ground water samples at the selected
locations in the two sampling events (31st October and 15th December, 2019) along with the Thailand Surface
Water Quality (SWQ) Standards, Class 3 [19] and Thailand Ground Water Quality (GWQ) Standards for
drinking purposes (maximum allowable limit) [20] are presented in Table 2 and 3, respectively.
Table 2 Average concentrations of physico-chemical parameters in surface water samples of the two
sampling events
Sampling Thailand SWQ
Parameters Points SW1 SW2 SW3 Mean SD Min. Max. Standards
Class-3a*
pH 8.6 8.4 8.2 8.4 0.2 8.2 8.6 5-9
EC (µs/cm) 380.5 421.0 422.0 407.8 23.7 380.5 422.0 -
Salinity (ppm) 184.0 203.0 204.0 197.0 11.3 184.0 204.0 -
T (°C) 29.7 25.7 25.9 27.1 2.3 25.7 29.7 -
DO (mg/l) 4.1 3.2b* 3.2 b* 3.5 b* 0.5 3.2 4.1 ≥ 4.0
BOD (mg/l) 16.0 c* 12.0 c* 12.0 c* 13.3 c* 2.3 12.0 16.0 ≤ 2.0
TSS (mg/l) 77.0 72.0 75.0 74.7 2.5 72.0 77.0 -
TDS (mg/l) 263.0 299.0 300.0 287.3 21.1 263.0 300.0 -
Turbidity (N.T.U) 33.2 45.7 65.3 48.1 16.2 33.2 65.3 -
TA (mg/l) 160.0 180.0 180.0 173.3 11.5 160.0 180.0 -
Fe (mg/l) 0.175 0.150 0.150 0.158 0.014 0.150 0.175 -
Mn (mg/l) 0.225 0.200 0.250 0.225 0.025 0.200 0.250 1.0
As (mg/l) 0.008 0.005 0.005 0.006 0.001 0.005 0.008 0.01
a* “Class 3 – medium clean used for consumption but passing through ordinary treatment process and agriculture”
b* “lower than the Thailand SWQ Standards Class-3 values”
c* “higher than the Thailand SWQ Standards Class-3 values”
pH
pH is an important parameter in water quality assessment as it influences many biological and chemical
processes within a water body [21]. Average values of pH were slightly alkaline in all surface water samples
(8.2 – 8.6) and neutral in all ground water samples (7.0 – 7.6). pH values in both surface and ground water
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samples were within the recommended range of Thailand Surface Water Quality Standards for Class-3 water
body and Thailand Ground Water Quality Standards.
Electrical Conductivity (EC)
EC is a measure of total salt content in water and also an indicator of levels of inorganic constituents in water
[22]. EC ranged between 380.5 and 422.0 µS/cm in surface water samples and 517.5 and 1,643.0 µS/cm in
ground water samples.
Salinity
Salinity is the quantity of dissolved salt content in the water. The salinity was found to be in the range of
184.0 to 204.0 mg/L in surface water samples and 274.5 to 827.0 mg/L in ground water samples.
Temperature (T)
The temperature of surface water samples and ground water samples were only slightly varied ranged from
25.7 to 29.7 and 28.3 to 31.3, respectively.
Dissolved Oxygen (DO)
The amount of oxygen dissolved in water depends on the rate of aeration from the atmosphere, temperature,
air pressure and salinity [21]. The mean value of DO in surface water samples 3.5 mg/L (SD=0.5) was lower
than the Thailand SWQ Standards of 4.0 mg/L. The DO of the ground water samples varied between 3.9 and
6.7 mg/L.
Biochemical Oxygen Demand (BOD5)
BOD is used as an index for determining the amount of decomposing organic materials as well as the rate of
biological activities in water [21]. BOD5 levels in the ground water samples ranged between 2.0 and 6.0
mg/L. BOD5 levels at SW1, SW2 and SW3 were 16.0, 12.0 and 12.0 mg/L, respectively which are much
higher than the Thailand Surface Water Quality Standard of ≤ 2.0 mg/L.
Total Suspended Solids (TSS)
TSS consists of materials originating from the bed of the water body. One direct effect of TSS is the
influence on the turbidity of the receiving water body [21]. TSS concentrations in surface water samples
ranged between 72.0 to 77.0 mg/L, and in ground water samples, it ranged between 5 mg/L and 29 mg/L.
Total Dissolved Solids (TDS)
TDS is a measure of total inorganic substances dissolved in water and indicates the saline behavior of water
[22]. Water containing more than 500 mg/L of TDS is not considered desirable for drinking water supplies,
but in unavoidable cases 1500 mg/L is also allowed [23]. TDS values in all surface water samples were
263.0, 299.0 and 300.0 mg/L (lower than 500 mg/L). TDS values in ground water samples GW2, GW4 and
GW5 were less than 500 mg/L while values at GW1, GW3, GW6 and GW7, they were less than 1,500 mg/L.
Turbidity
Turbidity in water is caused by suspended and colloidal matter such as clay, silt, fine organic matter,
plankton and other microscopic organisms [21]. The turbidity in the surface water samples in the study area
ranged between 33.2 and 65.3 N.T.U. Except at GW1, GW4 and GW5 (8.3, 10.2 and 19.0 N.T.U) turbidity
in ground water samples were higher than Thailand Ground Water Quality Standard (8 N.T.U).
Total Alkalinity (TA)
Alkalinity in water is an indicator of natural salts present in water. The cause of alkalinity is the minerals,
which dissolve in water from soil [23]. TA values in surface water samples and ground water samples
ranged between 160 and 180 mg/L and 290.0 to 490.0 mg/L, respectively.
Iron (Fe)
Iron is the second most abundant metal in the earth‟s crust, of which it accounts for about 5%. Iron-bearing
in water is often indicated by orange color, causing discoloration of laundry and has an unpleasant taste,
which is apparent in drinking and food preparation. The concentration of Fe in surface water samples ranged
between 0.150 and 0.175 mg/L, and in ground water samples between 0.11 and 1.0 mg/L. Fe concentrations
in all ground water samples were lower than the Thailand Ground Water Quality Standard (1.0 mg/L).
Manganese (Mn)
Manganese is commonly found in many natural waters and may also come from various sources such as
domestic wastewater and sewage sludge disposal [8]. Mn concentrations in surface water samples ranged
between 0.2 and 0.25 mg/L which were within the Thailand SWQ Standards. While in ground water
samples, Mn concentrations were within the Thailand Ground Water Quality Standard of 0.5 mg/L except at
GW4 (0.55 mg/L).
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Arsenic (As)
Arsenic can be found naturally in certain soils and it is a metalloid. Arsenic concentration in surface water
samples was in the range of 0.005 to 0.008 mg/L, within the Thailand Surface Water Quality Standard (0.01
mg/L). In ground water samples, As concentration ranged between 0 and 0.053 mg/L, and were lower than
the Standard (0.05 mg/L), except at GW7 (0.053 mg/L).
Table 3 Average concentrations of physico-chemical parameters in ground water samples of the two
sampling events
Sampling GW GW GW GW GW GW GW Mean SD Min. Max. Thai GWQ
points 1 2 3 4 5 6 7 Standards
7.6 6.5 -9.2
Parameters 7.0 1,643
827.0 -
pH 7.4 7.6 7.2 7.2 7.1 7.5 7.3 0.2 7.0 31.3 -
6.7 -
EC(µs/cm) 816.5 517.5 1,069 1,423 1,643 918.5 803.5 1,031 388.7 517.5 6.0 -
29.0 -
Salinity 398.0 274.5 543.0 710.5 827.0 455.5 391.0 514.2 194.4 274.5 778.0 -
19.0 -
T (°C) 30.5 29.5 31.3 29.6 29.5 28.5 28.3 29.6 1.0 28.3 490.0 8
1.000 -
DO(mg/L) 4.8 6.7 4.8 4.5 4.3 3.9 6.6 5.1 1.1 3.9 0.550 1.0
0.053 0.5
BOD(mg/L) 3.0 4.0 4.0 6.0 5.0 2.0 5.0 4.1 1.3 2.0 0.05
TSS (mg/L) 9.0 11.5 5.0 29.0 17.0 6.5 21.5 14.2 8.7 5.0
TDS(mg/L) 577.0 401.5 778.0 1.01x10-6 1.17x10-6 657.0 567.0 425.8 311.8 1.1x10-6
Turbidity 8.3* 0.7 1.5 10.2* 19.0* 0.7 7.4 6.8 6.6 0.7
TA (mg/L) 334.0 290.0 394.0 414.0 490.0 368.0 330.0 374.3 65.9 290.0
Fe (mg/L) 0.51 0.110 0.110 0.110 0.150 0.110 1.00 0.300 0.342 0.110
Mn (mg/L) 0.48 0.150 0.100 0.55 0.300 0.230 0.080 0.270 0.185 0.080
As (mg/L) 0.003 0.003 Nil Nil Nil Nil 0.053 0.010 0.020 Nil
„*‟ - “higher than the maximum allowable limit of Thailand GWQ Standards for drinking purpose”
CONCLUSIONS
The soil in the study area has relatively low concentrations of heavy metals, indicating that there is no
considerable impact of the leachate on the soil quality. The surface water and ground water samples in the
study area also showed low concentrations of heavy metals: Fe, Mn and As. This is also indicating that at
present, the surface water and ground water in the study area have not been polluted by leachates from the
TIMI dumpsite. However, the surface water and ground water in the study area are not suitable for human
consumption because of the presence of heavy metals in some of the studied samples. MCDC needs to alert
the public about this dangerous condition and should provide safe potable domestic water supply for the
residents.
Based on the results of geophysico-chemical investigations at the TIMI dumpsite area, the presence of heavy
metals in soil, surface water and ground water imply that the concentrations might increase in future.
Therefore, a thorough geophysico-chemical study should be carried out in and around the TIMI dumpsite
area to protect the human health and environment. Moreover, MCDC should do sanitary landfilling of MSW
to prevent any potential contamination of soil, surface water and ground water as well as air.
ACKNOWLEDGEMENTS
The authors are indebted to Norwegian Government for providing scholarships for Myanmar students
(CBIM-2) that gave the financial support to carry out this research. The Mandalay City Development
Committee (MCDC), Myanmar is also acknowledged for allowing to conduct the research work at the study
area and providing the solid waste management data of the Mandalay city as well as the laboratory support.
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[6] Lateef, T. A., Eluwole, A. B. and Adewa, D. J. 2015. Geoelectrical Assessment of the Impact of the
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[12] Than, M. M. 2012. A Geographical Study on Seasonal Disease in Mandalay City. Mandalay
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[13] MCDC. 2017. Urban Services Business Operation Plan for Solid Waste Management, Mandalay.
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Evaluating Carbon Footprint of the Department of Environmental
Engineering, Chiang Mai University
Sarunnoud Phuphisith1* and Kanpunyanan Premchit2
1* Lecturer, Department of Environmental Engineering, Faculty of Engineering, Chiang Mai University,
Chiang Mai 50000, Thailand; 2 Undergraduate student, Department of Environmental Engineering,
Faculty of Engineering, Chiang Mai University, Chiang Mai 50000, Thailand;
* Corresponding author e-mail: [email protected]
ABSTRACT
Assessment of greenhouse gas (GHG) emission helps to quantify the amount of GHG emissions emitting
from human activities and leads to effective GHG reduction strategies. It also helps to promote a public
concern on global warming regarding human activities. The present study focused on the activities in
academic field. GHG emission was evaluated by the concept of carbon footprint for organization. The
objective of the present study was to assess the and presented the carbon footprint of the department of
Environmental Engineering, Faculty of Engineering, Chiang Mai University in academic year 2018. Three
scopes of CFO including both direct and indirect activities of the department were calculated and the results
were presented in terms of tons carbon dioxide equivalent (CO2 eq). The result showed that in the academic
year 2018 the department had the largest emission from the second scope that is the emission from electricity
consumption, at 76.41 tons CO2 eq. The second rank was the emission from the third scope; that is, the
emission from other activities namely water supply consumption, paper consumption, wastewater treatment
and solid waste management, at 0.61 tons CO2 eq. There was no emission from the first scope or the direct
emission as there was no activity occurred in that academic year. A total CF of the department was 77.02
tons CO2 eq, including three scopes. Recommendation and limitations were also discussed.
Keywords : global warming; greenhouse gas; carbon footprint for organization; Chiang Mai university
INTRODUCTION
Anthropogenic activities have been proven as one of the most significant causes of global warming.
Greenhouse gas (GHG) emissions from human activities results in increasing global temperature [1, 2]. Steps
to reduce GHG emissions of human’s activities is, therefore, necessary for the global climate crisis.
To promote the emission reduction, it is firstly to know and understand the amount of GHG
emissions due to human activities. The concept of carbon footprint (CF) has been introduced and used for
quantifying GHG emissions [3,4]. GHG inventory or CF analysis can measure the emissions from several
scales, such as from a person, an organization, an event, a product or service, a nation and a globe [5]. It is a
useful tool for the GHG management as it helps to identify important emission sources and where to achieve
GHG reductions [3]. Measurement of GHG emissions also helps to promote public awareness of GHG
emissions from human activities and their effects on global warming and climate change [6,7].
Among different scales of CF analysis, carbon footprint of an organization (CFO) is to measure
GHG emissions generated from both direct and indirect activities of the organization or company [5]. The
GHG emissions dataset of the carbon disclosure project estimated that direct emission from over 5,000
companies amounted to 35% of the global GHG emissions in 2018 [8]. Organizations are considerable
contributors to global GHG emissions, estimating CFO still limits in some particular sectors. In Thailand,
CFO has been promoted to industrial sector and local government organizations. Expansion to other sectors
is still at the beginning stage, including the educational sector [9] – [12].
Previous studies on CFO in educational sector have been done in different levels. At the university
level, Valaya Alongkorn Rajabhat University was assessed its GHG emissions from direct emissions
(scope 1) and indirect emissions from purchased electricity generation (scope 2) for one semester or five
months. The result showed that the significant emission was from scope 2, accounting for 98% of the total
GHG emissions [9]. Huachiew Chalermprakiet University, conducted CFO for one academic year, also
reported that the emissions from electricity consumption (scope 2) contributed the highest loading, at 65.37%
of the total GHG emissions covering three scopes [11]. At the faculty level, three scopes of GHG emissions
were evaluated in the faculty of Environment and Resource Studies, Mahidol University, Salaya campus for
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one academic year. The result showed that about 80% of total GHG emissions was emitted from purchased
electricity generation (scope 2), followed by scope 3 which included use of paper, solid waste management,
use of chemicals and laboratory supplies and water consumption [10]. The results from the faculty of
Engineering, Kasetsart University also showed that scope 2 emitted the largest GHG emissions [13].
The objective of the present study is to assess the CF of the department of Environmental
Engineering, Faculty of Engineering, Chiang Mai University in academic year 2018. The present study
focuses on presenting the practical method for evaluating the footprint based on readily available data.
METHODOLOGY
CF analysis was done for the Department of Environmental Engineering, Faculty of Engineering,
Chiang Mai University for the academic year 2018. The department consists two buildings: three- and two-
story. The three-story building is mainly served for classrooms, working rooms of academic staff, laboratory
staff and students, and laboratories. The two-story building is mainly for administration and laboratory
works. In the academic year 2018, there were 10 academic staff, 7 administrative and laboratory staff, and
253 undergraduate and graduate students.
The assessment included direct and indirect activities occurred in the academic year 2018, expect for
the data on solid waste generation which collected in the academic year 2019. The assessment followed the
national guideline: Thailand Greenhouse Gas Management Organization (Public Organization): TGO and the
guideline of intergovernmental panel on climate change (IPCC). The scope of assessment was done by a
control approach and the result was presented in terms of carbon dioxide equivalent (CO2 eq).
Three scopes of GHG emissions were determined including (1) direct emissions from owned or
controlled sources, (2) indirect emissions from the generation of purchased electricity and (3) indirect
emissions from other sources besides scope (2); the scope (3) refers to emissions occur in the value chain
including both upstream and downstream emissions. Details of each scope are described in Table 1. For the
direct emissions, since the department has no machine or vehicle; therefore, an activity on fuel consumption
was absent. Non-chlorofluorocarbon type of fire extinguishers are equipped at the department, so no
emission source from this activity. The most relevant in this direct emission was the emission from cooling
agents in air conditioners. The third scope or indirect emission from other sources covered water supply
consumption, paper consumption, wastewater treatment and solid waste management.
Table 1 Sources of emission and data used
Scope Emission source Evidence Unit
1 Cooling agents IV kg
2 Electricity consumption IV kWh
m3
3 Water supply consumption, IV+CAL m3
Wastewater treatment, CAL kg
Paper consumption, IV kg
Solid waste management EXP
Note: IV: Invoice from third-party; CAL: Calculation; EXP: Experiment.
Both primary and secondary data were collected and used for the calculations. Primary data
comprised amount of solid waste generated. Solid waste generated data was measured in this study every
week from October 2019 to February 2020 accounting for 20 weeks. The average weight was calculated and
used for estimating the amount of solid waste generation in one academic year which has 44 weeks.
Secondary data referred to invoice from the third-party regarding data on electricity, water supply and paper
consumption. Since there was no data on water supply consumption available for the department, only the
combined data with other departments available. Therefore, allocation of water consumption was made
considering the factors of main activities of each building and the numbers of toilets and building users. The
calculated number of water consumption then was used for estimating the amount of wastewater generation.
The total emissions were calculated by multiplying activity data with their emission factors as shown
in equation (1).
Total emissions = (activity datai x emission factori) (1)
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The emission factors were collected from TGO and IPCC (Table 2). Selection of the emission factor
considered the following information. The department purchases electricity from Provincial Electricity
Authority. Water supply comes from Chiang Mai University’s water treatment plant which produces water
supply from surface water—the university reservoir. Wastewater generated from the department is sent to
Chiang Mai University’s wastewater treatment plant which operates an activated sludge process. Solid waste
generated from the department is sent to Energy Research and Development Institute of Nakornping and be
treated by an anaerobic digestor.
Table 2 Emission factors used in this study
Activity Details Unit Emission factor Ref.
(kgCO2eq/ unit)
Cooling agent, R22 - kg 1,810 [13]
Electricity electricity grid-mix 2016-2018
Water supply produced from surface water kWh 0.5986 [14]
Writing paper (uncoated) including the processes of preparation,
production and packing m3 0.7948 [14]
Collection and average data on collection and transferring
transferring of municipal of municipal solid waste from large kg 2.1020 [14]
solid waste medium and small municipality
Anaerobic digestion at on a wet weight basis kg 0.0079 [14]
biogas facility
Activated Sludge Process must be well managed kg 0.0200 [15]
m3 0 [16]
RESULTS AND DISCUSSIONS
Inventory data of each activities were described in Table 3 and Table 4 shows the result of GHG
emission calculation. After surveying the cooling agent types being in the air conditioners, 6 of 47 items
could be identified for R22 type while the rest remained unknown. However, none of any cooling agents was
refilled during the academic year 2018. Therefore, no GHG emitted from the first scope or no direct emission
from the department. A total kilowatt-hour of 127,648.9 purchased electricity was consumed in the academic
year 2018 which accounting for 76.41 tons CO2eq emitted. Considering the first and second scopes, the
second scope or indirect emission from the generation of purchased electricity occupied the highest emission
loading, at 100%.
Table 3 Inventory data
Scope Activity Unit Inventory
no activity
1 R22 kg 127,648.9
2 Electricity kWh 343.1
m3 274.5
Water supply m3 149.7
kg 1,104.4
Wastewater treatment: Activated sludge kg
3
Paper
Solid waste management: Anaerobic digestion
Table 4 GHG emissions of the Department
Scope Activity t CO2eq t CO2eq
1 R22 0.00 Scope 1+2 Scope 1+2+3
2 Electricity 76.41
76.41
Water supply 0.27 77.02
Wastewater 0.00
3 0.31
Paper
Solid waste 0.03
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A total water usage was estimated for 343.1 m3 from the mixed data and this accounted for 0.27 tons
CO2eq emitted. The amount of wastewater generation was calculated from 80 percent of water usage, given
274.5 m3 of wastewater. Since wastewater was collected and treated by activated sludge process which
emission factor is zero; therefore, no GHG emitted from this activity. A total of 60 reams of 80gsm A4 paper
were consumed which accounted for 149.7 kg of paper. Then this amount of paper consumption resulted in
0.31 tons CO2eq emission. Solid waste generation was measured in this study every week for 20 weeks. The
average amount of solid waste generation was 25.1 kg per week. This number only came from general waste
bins that are normally sent to anaerobic digestion treatment. The amount of solid waste was estimated by
multiplying the average waste generation with 44 weeks, so a total of 1,104.4 kg solid waste could be
generated in one academic year. By the anaerobic digestion process, 0.03 tons CO2eq were emitted. Among
activities of the third scope, paper consumption was the largest emission loadings, at 50.9%, followed by
water supply consumption, at 44.1% (Figure 1). In sum, the total emissions from the third scope were 0.61
tons CO2eq or accounted for 0.79% of the total GHG emissions (Figure 2).
Figure 1 Scope 3 emissions breakdown
Figure 2 Proportion of three scopes GHG emissions
Comparing to the previous studies, similar findings were found; the emission from the generation of
purchased electricity was the most important loading of GHG emissions in educational sector [9]-[12]. Direct
comparison of the amount of GHG emissions between studies should not be done due to differences in scope
of assessment, concerned activities, emission factors used, organization size and structure, etc. One attempt
to give a picture of emission size of the organization has been done by allocating CFO to the number of
students or the number of all relevant people as shown in Table 5. Focusing only on the emissions from
scope 1 and 2, a total of 76.41 tons CO2eq was allocated to 253 students, given 0.30 tons CO2eq/ student/
year. Allocation was also made to all relevant people using the department’s building, namely academic
staff, laboratory staff and administrative staff; the number of all users was 270. Thus, the amount of emission
per user per year was 0.28 tons CO2eq.
Table 5 GHG emissions (scope 1 and 2) per user per year
University/ Faculty/ Department Ref. t CO2eq/ t CO2eq/ Year
student/year user1/year 20172
Valaya Alongkorn Rajabhat University [9] 2010
0.64 - 2010
Huachiew Chalermprakiet University [11] - 0.36
2018
Faculty of Engineering, Kasetsart University [13] 0.49 -
The department of Environmental Engineering, This study 0.30 0.28
Chiang Mai University
Note 1 : student and staff; 2 : 5 months assessment; - : not reported.
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Since this is the first time for CF analysis of the department of Environmental Engineering, so the
amount of GHG emissions reported in this study is considered as a baseline year. The results from this study
could help to identified the most importance GHG emission source; that is, electricity consumption. This
data of GHG emissions of the department activities and operation could help to support promotion on
reduction activities such as via an education approach for making all involved users aware of indirect GHG
effect also the sizes of effect from energy and water consumption. Promotion on switching to electronic
document instead of paper-based document could reduce indirect emission from paper production, but this
point has to be careful as it may increase the amount of electricity consumption instead. The current
wastewater treatment situation is fine; as zero emission was emitted from activated sludge process as far as
the process is well manage. Solid waste management situation also produced small amount of GHG emission
comparing to other activities in Scope 3. However, reducing the amount of solid waste generation could
result in decreasing indirect GHG emissions at the anaerobic digestor plant. Promotion on solid waste
generation reduction and strict waste sorting/ separation could help to achieve this point. Although plastic
bag is banned in Chiang Mai University since October 2018, shops around the university still provide plastic
bag to consumers. Thus, changing consumer behavior would be a critical step. Doing waste sorting strictly
and collect leads to effective waste management. Nowadays the department has been practicing three types
of waste sorting, namely food waste, plastic bottles waste and general waste, and only the general waste goes
to waste treatment unit. But, by surveying in this study the general waste was still not well sorted. This needs
continuous promotions and supports as well as collaborations from all related people. Although the study
found that there was no direct emission from owned or controlled sources as no activity occurred in the 2018
academic year. The survey of cooling agents used in air conditioners showed that R22 are being used and
this type of cooling agents contributes relatively high amount of GHGs—1,810 kg CO2 eq per kg of R22.
Therefore, if possible, R22 should be replaced by lower global warming impact alternatives such as R410-a,
R290, R1270 [17]-[18].
The present study evaluated the scope 3 emission by starting with the main activities of utilities
consumption, namely water consumption, paper consumption, wastewater treatment and solid waste
management. There are still other important activities occurring in the department which also indirectly emit
GHGs elsewhere, such as production of chemicals used and laboratory supplies, and employee commuting
and business travel. Such activities may be included in future work to get the more complete figure of the
department’s GHG emissions.
CONCLUSION
The CF of the Department of Environmental Engineering, Faculty of Engineering, Chiang Mai
University in the academic year 2018 was 76.41 tons CO2 eq calculated from the first and second scopes. In
fact, there was no emission from the first scope or the direct emission as there was no activity happened in
the academic year 2018. The other indirect emissions or the third scope which included consumption of
water supply and paper and generation of wastewater and solid waste would add another 0.61 tons CO2 eq to
the department’s CF, and the total CF would be 77.02 tons CO2 eq. The highest loading of emissions was
from the second scope or the indirect emission from purchased electricity.
ACKNOWLEDGEMENT
The authors would like to thank all staff of the department of Environmental Engineering and the
faculty of Engineering, Chiang Mai University for relevant data and kind support.
REFERENCE
[1] Intergovernmental Panel on Climate Change (IPCC). 2014. Climate Change 2014: Synthesis Report.
Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva,
Switzerland, 151 pp.
[2] Cook, J., Oreskes, N., Doran, P. T., Anderegg, W. R., Verheggen, B., Maibach, E. W., Carlton, J.S.,
Lewandowsky, S., Skuce, A.G., Green, S.A. & Nuccitelli, D. 2016. Consensus on consensus: a
synthesis of consensus estimates on human-caused global warming. Environmental Research
Letters, 11(4), 048002.
[3] Franchetti, M.J. and Apul, D., 2012. Carbon footprint analysis: concepts, methods, implementation, and
case studies. CRC Press.
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[4] Pandey, D., Agrawal, M. and Pandey, J.S., 2011. Carbon footprint: current methods of
estimation. Environmental monitoring and assessment, 178(1-4), pp.135-160.
[5] Gao, T., Liu, Q., Wang, J., 2013. A comparative study of carbon footprint and assessment standards.
Int. J. Low Carbon Technol. 9 (3), 237e243.
[6] Kandananond, K., 2017. The Greenhouse Gas Accounting of A Public Organization: The Case of A
Public University in Thailand. Energy Procedia, 141, pp.672-676.
[7] McKercher, B., Prideaux, B., Cheung, C. and Law, R., 2010. Achieving voluntary reductions in the
carbon footprint of tourism and climate change. Journal of sustainable tourism, 18(3), pp.297-317.
[8] The Carbon Disclosure Project, 2020. CDP Full GHG Emissions Dataset 2019 Summary. CDP.
London.
[9] Kandananond, K., 2017. The Greenhouse Gas Accounting of A Public Organization: The Case of A
Public University in Thailand. Energy Procedia, 141, pp.672-676.
[10] Aroonsrimorakot, S., Yuwaree, C., Arunlertaree, C., Hutajareorn, R. and Buadit, T., 2013. Carbon
footprint of faculty of environment and resource studies, Mahidol University, Salaya campus,
Thailand. APCBEE procedia, 5, pp.175-180.
[11] Rodtusana, I., 2013. Carbon Footprint for Organization; Huachiew Chalermprakiet University. Applied
Environmental Research, 35(2), pp.33-42.
[12] Chutima Sukanan., 2012: Carbon Footprint for Organization and Reduction of Greenhouse Gas
Emission for Faculty of Engineering, Kasetsart University. Master’s thesis. Kasetsart University,
Bangkok.
[13] Intergovernmental Panel on Climate Change (IPCC)., 2006a. 2006 IPCC guidelines for national
greenhouse gas inventories, Volume 5: Waste, Chapter 4 Biological treatment of solid waste.
Intergovernmental Panel on Climate Change. Accessed in April 2020 from https://www.ipcc-
nggip.iges.or.jp/public/2006gl/pdf/5_Volume5/V5_4_Ch4_Bio_Treat.pdf
[14] Intergovernmental Panel on Climate Change (IPCC)., 2006b. 2006 IPCC guidelines for national
greenhouse gas inventories, Volume 5: Waste, Chapter 6 Wastewater treatment and discharge.
Intergovernmental Panel on Climate Change. Accessed in April 2020 from https://www.ipcc-
nggip.iges.or.jp/public/2006gl/pdf/5_Volume5/V5_6_Ch6_Wastewater.pdf
[15] Thailand Greenhouse Gas Management Organization (Public Organization) (TGO)., 2020. Emission
factor by industry types. Accessed in April 2020 from
http://thaicarbonlabel.tgo.or.th/admin/uploadfiles/emission/ts_117a1351b6.pdf
[16] Thailand Greenhouse Gas Management Organization (Public Organization) (TGO)., 2019. Emission
factor collected from secondary data for carbon footprint for organization. Accessed on April 2020
from http://thaicarbonlabel.tgo.or.th/admin/uploadfiles/emission/ts_11335ee08a.pdf
[17] Shrivastava, A. P., & Chandrakishor, C., 2016. Evaluation of refrigerant R290 as a replacement to
R22. Int. J. Innovative Res. Sci. Eng, 2(3).
[18] Climate Change Technology Centre and Network., n.d. Shift to coolants and refrigerants with lower
GWP Accessed in April 2020 from https://www.ctc-n.org/technologies/shift-coolants-and-refrigerants-
lower-gwp
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Development of Immobilized Bacteria on Sludge Based Adsorbent for
the Post-Treatment of Septic Tank
Sirinthip Kedsana1 and Nawatch Surinkul2*
1Gradurate Student, 2Assistant Professor, Department of Civil and Environmental Engineering,
Faculty of Engineering, Mahidol University, Nakhon Pathom 73170, Thailand
*Corresponding author: Phone: (662) - 889 - 2138 Ext. 6396, Fax : (662)889-2138 Ext. 6388
E-mail : [email protected]
ABSTRACT
One of the main problems from households related to wastewater management is the contaminations to water
resources. This could affect to human health as the uses for water resource. Examples are contaminated
groundwater and surface water nearby communities. Septic tank is typical onsite wastewater treatment
system that used to treat wastewater from houses before discharging the effluent into environment. This
study applied the combination of adsorption and biodegradation processes for treating effluent of septic tank.
Many bacteria species can degrade organic and inorganic compounds which Bacillus subtilis is one of the
common uses. In the process, B. subtilis strain Y1336 was immobilized on sterile sludge based adsorbents
(SBAs) and applied into final part of septic tank to degrade the remained organics and inorganics. SBAs
immobilized with B. subtilis could reduce BOD and COD concentrations of 60.93% and 53.10%,
respectively. Concentrations of bacteria on media were ranged from 1.13×106 to 1.11×107 CFU/g. It is also
found that immobilized bacteria on SBAs can be kept for the long period in both room temperature or at 4C
with the freeze-drying condition. The experiments showed that application of immobilized bacterial
adsorbent for 6 days of hydraulic retention time can enhance the treatment performance for organics and
inorganics removals. Results show that the proposed immobilized bacterial adsorbent could be an option for
the final treatment of septic tank in order to meet the effluent standard.
Keywords : Adsorbent; Immobilization; Preservation; Freeze-drying; Bacillus subtilis; Total Dissolved Solids
INTRODUCTION
Water pollutions are resulted from rapid development of industry and community without the proper
management. It is led to severe problems of environment such as organic compounds and inorganic
compounds in water [1]. High levels of pollutants mainly organic matter in water causes increasing in
biological oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS) and total
suspended solids (TSS). These make the water unsuitable for drinking, irrigation or any other use [2].
Suspended solid and dissolved solids are usually found in water sources. Suspended solids include silt,
stirred up bottom sediment, decaying plant matter, or sewage treatment effluent. TDS concentrations in water
source is affected by many different factors such as hard water ions, fertilizer in agricultural runoff, urban
runoff, salinity from minerals or returned irrigation water, and acidic rainfall. Concentration of TDS is
indication which water source is unhealthy or polluted [3].
Septic tank or commercial package system are the common onsite sanitation system (OSS) applied to receive
and treat blackwater from the toilet. Typically, treatment process of domestic wastewater in septic tank is
biological treatment process under anaerobic condition [4]. Both of principle physical and biological
treatment are the mechanisms in septic tank system. Treatment efficiencies of 52 and 40 percent removals of
BOD of commercial package system were reported [5]. However, the effluent concentrations are still beyond
the limit of effluent standard. Bioaugmentation is the capability to remove source of pollutant by of
microorganism metabolism which mostly to use indigenous microorganisms for the treatment. Limitation of
the use of microorganisms to treat pollutants is the toxicity of chemicals. These can be effected to a
population of microorganisms and reduced efficiency in this process [6]. Immobilization is one of technique
that can support microorganisms on carrier materials. This technique could be reduced concentration of
compounds as inorganic, organic, and hazardous compounds [7-8].
This study was aimed to propose the post-treatment option which it could improve the treatment performance
of OSS. Physical treatment and biological treatment are used in this study for research organics and amount
of bacteria immobilized can be activated in septic tank system [9]. Sludge based adsorbents (SBAs) was used
as carrier material to Bacillus subtilis immobilized development. Degradation TDS characteristic of B.
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subtilis and this bacteria is typically found in septic systems [10-11]. The main objective of this study was to
follow up the bacteria (Bacillus subtilis T1336) on the immobilized media from freeze-dried of preservation
technique in the wastewater treatment system for organic removals in the septic tank.
METHODOLOGY
This study used Bacillus subtilis Y1336 as the immobilized bacteria on surface of sludge based adsorbents
(SBAs). This bacteria species is often found in wastewater and they can reduce organics and TDS in
wastewater. The SBAs was developed from water treatment sludge mixed with fly ash and calcium
carbonate (CaCO3). The pellet was approximately 9 to 10 mm diameter. The hardening was done by the hot
drying in furnace of 1,000 C for 3 hours [12].
1. Study of Morphology and Analysis of Bacillus subtilis Y1336
Strain Y1336 of commercial B. subtilis used in this study is collecting from BIONBAC powder product. It
was isolated from soil in Ping Tung, Taiwan. Culture of B. subtilis Y1336 of 0.15 g was put in 100 ml of
nutrient broth (NB) in 250 ml Erlenmeyer flask and later was shaken for 24 h on shaker of 120 revolutions
per minute (rpm) to forming active cells. 10% (v/v) of suspension bacteria was transferred into the fresh NB.
Morphology and identification of B. subtilis Y1336 were observed by inverted microscope and biochemical
tests (motility, oxidase, catalase, IMViC (indole, methyl red, voges proskauer, and citrate), carbohydrate
fermentation (dextrose, lactose, maltose, mannitol, and sucrose), and urease [13].
2. Preparation of Media, Inoculum and Immobilization
The immobilization process, sterile SBAs was cultured with 10% (v/v) inoculum of bacteria (B. subtilis
Y1336) in sterile nutrient broth (NB) at room temperature until bacteria cell is shown as exponential phase.
B. subtilis Y1336 from inoculum and immobilized bacteria was verified by biochemical tests. Scanning
electron microscope (SEM) was used to confirm the attached bacteria cells on SBAs.
3. Study of Survival rate of Immobilized Media by Freeze-drying for Long Term Storage
Number of Bacteria count was measured the colony with colony forming unit (CFU) by spread plate
technique. Freeze-drying was used in this study for easily to use and long-term storage of immobilized
bacteria on SBAs. There were 2 conditions to maintain immobilized media with freeze-dry by preserving at
room temperature and at 4C.
4. Study of the Wastewater Removal Efficiency of Immobilized Media
Immobilized media was applied in the experiment as shown in Fig.1 to treat effluent from septic tank.
Influent water was collected from effluent of septic tank. Immobilized media was filled in the reactor (5 L).
Controlled unit and immobilized bacteria media unit were tested by using 6 days of hydraulic retention time
(HRT) [14]. Influent and effluent in term of biological oxygen demand (BOD), chemical oxygen demand
(COD) and total dissolved solid (TDS) were observed by following the standard methods [15].
RESULTS AND DISCUSSIONS
1. Study of Morphology and Analysis of Bacillus subtilis Y1336
1.1 Morphology
Colony of B. subtilis Y1336 was cultured on semi-solid nutrient agar (NA). Predominant characteristic of B.
subtilis Y1336 colony was the cream to light yellow, irregular, and opaque with medium size on NA (Fig.
2(A)).
1.2 Biochemical Tests
The inoculum was used to analyze in each test of biochemical. Shape of B. subtilis Y1336 observed by
compound microscope was rod shape and ellipsoid, central, subterminal, and very slightly swollen within
sporangium (Fig. 2(B)).
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Figure 1. Experimental set up for immobilized media treatments (A) controlled and (B) immobilized
bacteria on SBAs for the post treatment of septic tank
(A) (B)
Figure 2. Bacillus subtilis Y1336 colony (A) under magnification of 1X stereo microscope, and
gram positive of B. subtilis Y1336 (B) under magnification of 100X of inverted microscope
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Table 1. Biochemical Tests of Bacillus subtilis Y1336
Biochemical Tests Bacillus subtilis Bacillus subtilis
Y1336
Shape Rod Rod
Gram’s straining ++
Motility ++
Oxidase --
Catalase ++
IMViC
Indole --
Methyl red (MR) --
Voges proskauer (VP) + +
Citrate +-
Carbohydrate fermentation
Dextrose ++
Lactose ++
Maltose ++
Mannitol ++
Sucrose ++
Urease V-
Remark: V is variable, + is positive, - is negative
Table 1 shows the biochemical tests of B. subtilis strain Y1336. It was cultured and identified from powder
product as B. subtilis Y1336. The B. subtilis Y1336 had a rod shape and gram stain positive. Mostly tests in
biochemical test showed the positive result while the citrate of IMViC test and the Urease test showed
negative results. The different result from normal strain [16] might due to characteristic of strain Y 1336.
1.3 Growth Curve
Growth of cultured inoculum of B. subtilis Y1336 in NB was measured every hour by a spectrophotometer
and resulted within a range of nearly 1.0 of spectrophotometric optical density (OD). Typical OD curve for
bacteria was measured at 600 nm (OD600). Gompertz model was used to explain the growth types and select
the value of maximum bacteria cell [17]. This model could predict the growth curve from calibration curve
while tend of bacteria number had increased with time increasing as shown in Fig. 3(A). The growth of
bacteria was increased up to maximum level in exponential phase (slow-down phase) at the time point of 6th
hour before stable in the stationary phase. This study selected the value of 0.88 at OD600 which could
estimate bacteria number at 8.53108 CFU/ml and later using in the immobilization process. However,
projection of 0.88 OD600 revealed the time point on the 23th hour for B. subtilis Y1336 growth curve (Fig.
3(B)).
(A) (B)
Figure 3. Calibration curve (A), and Growth curve (B) of Bacillus subtilis Y1336
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2. Immobilization of Bacillus subtilis Y1336 on SBAs
Cultured 10% (v/v) of bacteria with sterilized SBAs on shaker incubator, 120 rpm at room temperature was
suspension nearly 0.88 OD Rinse immobilized media with 0.85% NaCl and dried at 37C on hot air oven
before using in the next step.
2.1 Scanning Electron Microscope (SEM)
Fig. 4 shows structure of sterilized SBAs at 2000X and 3,000X by SEM technique. Surface of SBAs was
rough with the small holes. After immobilized process with B. subtilis Y1336 Fig. 5(A), surface of SBAs
was covered by bacteria as shown in Fig. 5(B). This was to confirm that B. subtilis Y1336 was immobilized
on SBAs.
(A) (B)
Figure 4. Scanning electron microscope (SEM) views of sludge based absorbents (SBAs) surface, under
magnification of 2,000X (A) and 3,000X (B)
(A) (B)
Figure 5. Scanning electron microscope (SEM) views of spores and cell of Bacillus subtilis
Y1336 (A), and immobilized B. subtilis Y1336 on SBAs, under magnification of 3,000X
2.2 Study Number of Bacillus subtilis Y1336 on Immobilized Media by Freeze-drying for Long Term
Storage
Bacteria counts on immobilized media keeping at room temperature and at 4C by freeze-drying process
weekly observed. Results of bacteria counts are shown in Fig. 6. Initiation, cell number before freeze-drying
process was 1.75107 CFU/g and after 18 weeks the number of cell were 8.72105 CFU/g and 1.59×106
CFU/g resulting to the survival rates of 81.34% and 82.76%, respectively. The results indicated that
immobilized bacteria on SBAs can be kept for the long period of time in both room temperature or at 4C
with the Freeze-drying condition.
3. Study of Application of Immobilized media
In this experiment, pH values were range from 7.93 to 8.72. Treatment results of applied immobilized media
are shown in Fig. 7-9. Initially, the effluent concentrations such as COD and TDS were fluctuated due to the
acclimatization period. The treatment performances were considered from the 75th day. Average
concentration of COD in influent was 138 mg/L. Results of the treatment show that average COD
concentration in effluent of immobilized reactor was 49 mg/L (Fig. 7(B)). In controlled reactor, it was 57
mg/L (Fig. 7(A)). Therefore, the percent removals of COD were 53.10% and 52.98% in immobilized reactor
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and controlled reactor. In this study, the ratio of BOD/COD was 0.4. Therefore, the final BOD concentration
was lower than final effluent standard of 20 mg/L.
Figure 6. Number of Bacillus subtilis Y1336 (CFU/g) on immobilized media after preserved at
room temperature and at 4C by freeze-drying process
(A) (B)
Figure 7. Comparison influent and effluent COD concentration between sterile SBAs (A) and
immobilized B. subtilis Y1336 on SBAs (B)
(A) (B)
Figure 8. Comparison of influent and effluent BOD concentration between sterile SBAs (A)
and immobilized B. subtilis Y1336 on SBAs (B)
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(A) (B)
Figure 9. Comparison of influent and effluent TDS concentrations between sterile SBAs (A) and
immobilized B. subtilis Y1336 on SBAs (B)
BOD removal was 20.48% from initial BOD of 52 mg/L in immobilized reactors (Fig.8). The TDS
concentrations were decreased to the similar range of influent after 115 days from starting experiment (Fig. 9).
It was also observed that the concentration of B. subtilis on media was ranged from 106 to 107 CFU/g. This
could enhance the treatment performance of organic and TDS removals. From the obtained data, HRT of 6
days could be used as the design criteria for the post treatment system with the immobilized media.
CONCLUSION
Comparative study of immobilized bacteria on SBAs and the adsorption media treating effluent from septic
tank showed that immobilized B. subtilis Y1336 could reduce COD and BOD more than 50% and the final
BOD concentration was lower than 20 mg/L by controlled reactor. Nevertheless, concentration of BOD in
immobilized reactor was still high because in this reactor had much amount of B. subtilis and other
microorganisms. Therefore, obtained results from immobilized reactor show the possibility to meet the
effluent standard. This study also affirms that the survival rate of B. subtilis Y1336 on SBAs is about 80%
from the freeze-drying preservation at room temperature or at 4C.
ACKNOWLEDGEMENT
This study was conducted as part of thesis research and was financial support from the Environmental and
Water Resources Engineering Program and the Faculty of Graduate Studies, Mahidol University.
REFERENCES
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[2] Shrivastava, J. R. (2012). Laboratory scale bioremediation of the yamuna water with effective
microbes (EM) technology and nanotechnology. Bioremediation & Biodegradation, 3(8), 1-5.
doi:10.4172/2155-6199.1000160
[3] Johnson, R. D. (2017). Earth Science with Vernier (22nd ed.). Beaverton: Vernier Software &
Technology.
[4] USEPA. (2002). Onsite Wastewater Treatment Systems Manuall. Office of Water, Office of
Research and Development.
[5] Eliasson, J. (2004). Septic tank effluent value. Rule development committee issue research report.
Washington State Department of Health.
[6] Sekaran, G. K. (2013). Immobilization of Bacillus sp. in mesoporous activated carbon for
degradation of sulphonated phenolic compound in wastewater. Materials Science and Engineering,
33(2), 735-745. doi:10.1016/j.msec.2012.10.026
[7] Portier, R. a. (1991, October). Immobilized microbe bioreactors for waste water treatment. Waste
management and Research, 9(5), 445-451. doi:10.1016/0734-242X(91)90075-I
[8] López A., L. N. (1997, September). The interphase technique: a simple method of cell
immobilization in gel-beads. Microbiological Methods, 30(3), 231-234. doi:10.1016/S0167-
7012(97)00071-7
[9] Martins, S. M. (2013). Immobilization of microbial cells: A promising tool for treatment of toxic
pollutants in industrial wastewater. Biotechnology, 12(28), 4412-4418. doi:10.5897/AJB12.2677
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[10] Shruthi, S. R. (2012). Bioremediation of rubber processing industry effluent by Pseudomonas sp.
Environmental Science and Technology, 2(2), 27-30.
[11] Gaikwad, G. W. (2014). Development of microbial consortia for the effective treatment of complex
wastewater. Bioremediation and Biodegradation, 5, 4. doi:10.4172/2155-6199.1000227
[12] Rattanaporn Tanjai and Nawatch Surinkul (2016) Development of low-cost absorbent from water
treatment sludge for removing organic substance” The 2nd Environmental and Natural Resource
International Conference (ENRIC 2016) 16-18 Nov 2016 Bangkok.
[13] Benson, H. (2005). Benson's microbiological applications: Laboratory manual in general
microbiology. Boston: McGraw-Hill Higher.
[14] Gungormusler, M. G. (2011). Use of ceramic based cell immobilization to produce 1,3 propane diol
from biodiesel derived waste glycerol with Klebsiella pneumoniae. Applied Microbiology, 111(5).
doi:10.1111/j.1365-2672.2011.05137.x.
[15] APHA. (2012). Standard Methods for Examination of Water and Wastewater (22nd ed.).
Washington, DC. American Plublic Health Association.
[16] Carter, G. a. (1990). Diagnostic Procedure in Veterinary Bacteriology and Mycology. (5, Ed.)
Academic Press. doi:10.1016/C2009-0-02725-1
[17] Stefanova, M. T. (1998). Agar gel immobilization of Bacillus brevis cells for production of
thermostable α-amylase. Folia Microbiologica, 43, 42–46. doi:10.1007/BF02815540
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Seasonal activity of cambium in Pinus latteri and Pinus kesiya related
to climate variables in northern Thailand
Piyarat Songtrirat1, Nathsuda Pumijumnong2* and Supaporn Buajan3
1Graduate student; 2*Supervisor and 3Researcher Address Faculty of Environment and Resource Studies,
Mahidol University, Salaya, Phutthamonthon, Nakhon Pathom, 73170, Thailand.
*Phone : +66-244-15000 (ext. 2311), Fax : 66-2441-9509, E-mail : [email protected]
ABSTRACT
Pinus latteri and Pinus kesiya at Bo Keaw Silvicultural Research Station, Chiang Mai province,
Thailand were selected to examine the influence of climate on cambial activity and wood formation. Block
samples between inner – bark and wood of five trees from both species were taken using chisel, cutter, and
hammer at monthly intervals from October 2017 to October 2018. The cambial activity was determined by
slicing with a rotary microtome at 15µm, using safranin O and fast green for staining and counting the
number of cambial cell layers. We investigate the correlation between the number of cambial cell layers,
cambial zone width and climate factors such as soil moisture (SM), total monthly rainfall (RF), relative
humidity (RH), and temperature (TEMP) using Pearson’s correlation method analysis. The results reveal that
the cambial cells of Pinus latteri and Pinus kesiya were actively formed during the rainy season from May to
October and dormant during the dry season from January to April. For Pinus latteri, we found significant
positive correlation between the number of cambial cell layers and minimum TEMP (r=0.718), RH
(r=0.710), RF (r=0.732) and SM (r=0.749). The significant correlation for cambial zone width of Pinus
latteri and climate factors were only RF (r=0.675), minimum TEMP (r=0.770) and SM (r=0.688). For Pinus
kesiya, we found significant positive correlation between the number of cambial cell layer and minimum
TEMP (r=0.733), RF (r=0.686) and SM (r=0.599). The significant correlation for cambial zone width of
Pinus kesiya and climate factors was RF (r=0.678) and minimum TEMP (r=0.768). Knowledge and
understanding of cell variation in tropical trees can indicate weather variations and factors that stimulate tree
cells to divide and stop growing. In addition, these results can provide details and a better understanding of
dendrochronology in the tropics.
Keywords: Pinus latteri; Pinus kesiya; cambial activity; rainfall; temperature; Thailand
INTRODUCTION
Forests are an important resource in terms of economy, society and environment. It is also a source
of biodiversity. It is an important source of oxygen and it helps to maintain stability in nature and
ecosystems. In the past, forest resources have been utilized by humans in response to demand without
control, resulting in a reduction of forested areas. The United Nations Food and Agriculture Organization
(FAO) has documented the trend of decreasing forest areas. In Thailand, forest areas in 1973 covered
22,170,700 ha or 43.21 % of the country. But in 2017, the forest areas were only 16,345,016 ha or 31.58 %
of the country [1].
Thailand is located in the tropical zone which has abundant rainfall, a stable temperature, high
relative humidity and a short summer season. Therefore, the forest in these areas is fertile and suitable for
plant growth. In the forest, we can find tall trees and green broad leaves. Climate has an influence on the
environment. The seasonal variations can increase or decrease the growth of trees [2]. Weather, rainfall,
temperature, relative humidity, and soil properties can all have an effect on plant growth. Since climate
change has an effect on seasonal length, temperature, humidity, and rainfall, it may affect the growth of
Pinus latteri and Pinus. Kesiya. In general, these two species grow well in areas with cold weather and fertile
soil. Tropical forests in South East Asia, have a wide variety of pine species. The distribution in natural
forests of Pinus latteri and Pinus Kesiya has been reported from Southeast Asia countries such as Myanmar,
Thailand, Laos, Cambodia, Vietnam, Philippines, and Sumatra in Indonesia. The altitudinal range of these
species is from 300-1800 m [3]. Therefore, these two pine species are found in the northern region of
Thailand. Pine has a less complex cell structure than conventional hardwoods and is classified as a wood
with annual rings [4] which is developed from cambium cells. The cambium cells are the cells between the
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bark and the wood which is a layer of cells that continues to grow and develop into xylem cells (wood).
Linasmita and Pumijumnong studied the variations of cambium in Pinus. Kesiya in Thailand and found that
cambium activity tends to be related to rainfall which may affect growth in both species of pine [5]. Studies
of Pinus sp. have shown that climate variation has an effect on the growth of trees [6]. The pine is a species
that has tree rings and have been studied for the growth of productivity and data age structure of the
forest [7]. The research about the environmental factors that affect the development of cambial activity and
its relationship to climate shown that the structure of wood was controlled by environmental factors [8].
Due to the climate change from the past to the present, it is interesting to study the cambial activity
of Pinus latteri and Pinus kesiya in order to access climate factors that affect growth and can be used as
information for implementation or management planning for forestry plantation. It can be used as a way to
reduce the impact of climate change that will occur in the future and in support of research on annual rings
(Dendrochronology).
METHODOLOGY
Study site
This study was conducted from October 2017 to October 2018 at the Bo Keaw Silvicultural
Research Station, Chiang Mai Province, Thailand. (Latitude 18° 9' N, longitude 98° 23' E and 1,049 m.
above sea level.) (Fig. 1). This region is a mixed dry dipterocarp forest with Pinus latteri and Pinus kesiya
the dominant species growing in a tropical rainforest. The total area of the Bo Keaw Silvicultural Research
Station is 838.53 hectares.
Fig.1 Map of Thailand showing the location of Mae Hong Son meteorological station (red triangle), the
Bo Keaw Silvicultural Research Station (green cycle) and the Pinus latteri and Pinus kesiya stations
High pressure areas from China frequently cover northern Thailand bringing rain and cool
temperatures. The total rainfall in this region is 1,274.3 mm. and the average temperature 26.8 ºC [9]. The
Pinus latteri station and Pinus kesiya station are approximately 400 m. from each other.
Field sample collection
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Pinus latteri and Pinus kesiya were selected for cambial activity study. The cambial samples were
removed from the trunk at breast height (1.3 m). Two samples were taken from the opposite sides of each
tree in zigzag position to avoid wounding of trees. The sample size was about 2 x 2 cm, which included bark,
cambial zone and some part of wood tissue taken from living trees by using a chisel, hammer and a knife in
monthly intervals from October 2017 to October 2018. The samples were immediately fixed in 3%
glutaraldehyde solution for disinfection and preservation.
In the lab, the specimens were then dehydrated in a graded series of ethanol at increasing
concentrations (30, 50, 70, 95 and 99 %) [10]. The specimens were then embedded in polyethyleneglycol
(PEG 1500). Cross-sections with a thickness of 15–30 µm and containing secondary xylem, cambial zone,
and phloem were cut from the PEG embedding blocks using a Leica RM 2155 rotary microtome. These
sections were stained for light microscopy (Olympus BX41) with safranine O and fast green solution in order
to differentiate lignified areas.
The cambial layers having narrow rectangular cells between the mature xylem and phloem [6] were
counted to determine the cambial activity. Samples were then dissolved in xylene and mounted in Permount.
Cambial activity measurements
The cambial activity was evaluated from anatomical characteristics of cambial. The number of
undifferentiated cambial cell layer and variation of flattened cells with thin cell wall between mature xylem
and phloem in the radial direction of transverse sections was counted at 10 randomly chosen points. The
cambial zone widths were measured along with the number of undifferentiated cambial cell layer.
Meteorological data
Data on monthly temperature (maximum, average and minimum), total monthly rainfall, and mean
monthly relative humidity from October 2017 to October 2018 were obtained from the (Mae Hong Son
(19°18´N, 97°58´E, altitude 274.2 masl.) meteorological station (Fig. 2) Soil moisture data used in this study
were measured using Soil Moisture meter (Field Scout TDR200). Soil moisture was measured at a depth of
15 cm. at least 5 points around trees sampled in both sample sites.
Fig.2 Monthly temperature (maximum, average and minimum), total monthly rainfall, and mean
monthly relative humidity from the Mae Hong Son meteorological station, soil moisture measured at
Pinus latteri and Pinus kesiya station during October 2017 to October 2018
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Data analysis
Calculations were performed to analyze the maximum, median and minimum cambial activities
rates. The correlation between soil moisture, rainfall, temperature, relative humidity and the number of cell
layers and cambial zone width in the cambial enlarging cell for 2 species was evaluated using the Pearson’s
correlation coefficient method (SPSS 21).
RESULTS AND DISCUSSIONS
Seasonal cambial dynamics
The results were seasonal cambial dynamics. The average number of cambial cell layers of
Pinus latteri was maximum in September (8.45 cell layers) and minimum in March (5.23 cell layers)
(Figure 3). The average cambial zone width was maximum in September (108.23 µm.) and minimum in
February (60.13 µm.) as shown in Figure 3. The number of cambial cell layers of Pinus kesiya was
maximum in September (7.33 cells layers) and minimum in February (4.29 cells layers). The average
cambial zone width was maximum in September (92.74 µm.) and minimum in January (52.41 µm.)
Fig.3 Average tangential layers of cambial cells (± SE) average cambial zone width (µm) (± SE) of
Pinus latteri and Pinus kesiya from October 2017 to October 2018
Pinus latteri and Pinus kesiya cambial growth activity increased from April to September with the
rainy season starting in May. Then, the cambial activity diminished in October and declined gradually
toward the start of the dry season. It was dormant and consisted of thick cambial cell layers in March (Fig.4).
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Fig.4 Cross-sectional view of cambium with adjacent xylem and phloem of Pinus kesiya in March (a)
which is dormancy period and in June (b) which is active period; of Pinus latteri in March (c) and in
June (d); CZ: Cambial zone; PH: Phloem; MX: Mature xylem; dfX; differentiated xylem; RD: Resin
duct. Scale bars (a) and (c): 50 µm, (b) and (d): 200 µm.
The relationship between cambial activity and climatic variables
The correlation coefficients (r) between the number of cambial cell layers, the cambial zone width of
Pinus latteri and Pinus kesiya and climate variables are shown in Table 1. The average minimum monthly
temperature, relative humidity, total monthly rainfall, and soil moisture has a significant positive correlation
with the number of cambia cell layers, and average minimum monthly temperature, monthly rainfall and soil
moisture has a significant positive correlation with the cambial zone width of Pinus latteri. The average
minimum monthly temperature, relative humidity and total monthly rainfall have a significant positive
correlation with the number of cambia cell layers and average minimum monthly temperature, total monthly
rainfall and soil moisture has a significant positive correlation with the cambial zone width of Pinus kesiya.
The average minimum monthly temperature has a significant positive correlation with soil moisture of Pinus
latteri and Pinus kesiya. Average relative humidity has a significant positive correlation with Pinus kesiya.
Total monthly rainfall has a significant positive correlation with the soil moisture of Pinus latteri and Pinus
kesiya. However, the total rainfall variable on cambial activity was most influential for increasing growth
increment from May to October.
Table 1 Pearson correlation coefficient (r) and the level of the p values (**Correlation is significant at
the 0.01 level; *at the 0.05 level) between climate variables, soil moisture and the number of cambial
cell layers and cambial zone width (µm) of Pinus latteri and Pinus kesiya
Pinus latteri Pinus kesiya
Climatic variables Number of Cambial Number of Cambial
Minimum monthly temperature (ºC) Cambial cell zone width Cambial cell zone width
Relative humidity (%)
Total monthly rainfall (mm) layers (µm) layers (µm)
Soil moisture (%)
0.718** 0.770** 0.733** 0.768**
0.710** 0.503 0.544* 0.298
0.732** 0.675* 0.686** 0.678*
0.749** 0.688** 0.599* 0.554
The cambial activity in Pinus latteri has a relationship with the studied variables. We found that the
number of cambial cell layers and the cambial zone width has a significant positive correlation with the soil
moisture. Pumijumnong and Wanyaphet reported that the seasonal cambial activity in Pinus latteri and
Pinus kesiya in Northern Thailand has a significant positive correlation with soil moisture. Pinus latteri and
Pinus kesiya cambial growth was higher between April and September, with the rainy season starting in
May, and then the cambial activity reduced in the dry season. The soil moisture content matched with
rainfall, although this moisture can still accumulate in the soil without rain [6]. In addition, a study of
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cambial activity of five tropical tree species in central Thailand correlated high activity with rainfall in June
of the rainy season and in March during the transition from dry to rainy season [11]. Meanwhile, the studies
in Chang Island, Thailand reported that cambial activity mostly has a significant correlation with relative
humidity and temperature [12]. Das and team reported that the dissemination of Pinus latteri in the South of
Equator, India which has an altitude similar with the study site in Northern Thailand at altitude of 30–1800
m. found that the species diversity decreased when the altitude increased [13]. Zeng and team studied the
Chinese pine (Pinus tabulaeformis) in northwestern China. It grows at altitudes between 2,200 and 2,700 m.
with a mean annual temperature lower than Thailand [14]. The geographical and ecological distributions
between Pinus latteri and Pinus kesiya in Southeast Asia was conducted by Santisuk found the Pinus latteri
spread at an altitude above 1,000 m. and decreased when the altitude increased at 1,500 m. and Pinus kesiya
spread at an altitude from 1,500 to 2,900 m. Generally, in northern Thailand, it can grow at an altitude below
1,000 m [15].
The growth of Pinus latteri and Pinus kesiya are related to the climate variables [16]. The result
from our study reported that the cambial activity started in April and during the winter season (November to
February), the cambial was dormant. This result has resembled the studies of cambial activity on branches
from woody plants in Brazilian Cerrado. The cambial activity responded to the core of rainy season (October
to February). Moreover, cambial activity has shown positively correlated with temperature and rainfall, that
is to say, the temperature and rainfall were the influential factors of cambial activity [17]. The studies of
maritime pine (Pinus pinaster Ait.) in 2010 noted that the peak number of cambium cells occurred in June
and slowly decreased in October. Then the cambial activity was still dormant during the winter and consisted
of thick cambial cell layers in July. Clearly, the temperature has an influence on cambial activity [18].
The correlation of this study showed that the average minimum monthly temperature, relative
humidity and total monthly rainfall has a significant positive correlation with number of cambia cell layers of
Pinus latteri and Pinus kesiya. This funding is related to the cambial activity in Japan where the rise in
temperature in late winter to early spring influences the growth of cambium cell in trees. The increase of
temperature in nature affects to cambial activity and decreasing temperatures might also influence cambial
growth [19]. Wang and Hamzah studied the cambial activities of three species in a tropical rainforest in
Malaysia and found the cambial activity decreased from March to April and the cambial cells had
a significant positive correlation with high relative humidity and total monthly rainfall, which is different
from this study, perhaps due to the differences in tropical zones of South East Asian [20]. The temperature
can be the factor for the growth in tropical tree species, as shown in the study of the cambium zones of two
tropical tree species in Brazil. In one study, the dry season from June to August had the highest cambial
growth in the summer and the growth was statistically the same in the spring and in autumn [21].
The influence of different environmental factors in growth may change. The temperature can be the growth
limiting factor for some tropical tree species [22]. Furthermore, studies of vessel feature of Chukrasia
tabularis in a South Asian moist tropical forest and its broad-leaved species showed a high sensitivity to
interannual climate variability [23].
In the tropical forest, environmental factors might affect tree growth in different ways. For instance,
the study of the effect of water availability and natural variation and temperature on cambial activity in
Cordiera concolor noted that the soil water content and the time of year influence cambial activity.
The temperature was a less important factor than water availability [24]. In addition, the cambial showed
dormancy during periods of drought or flooding and this factor influences cambial activity in tropical
species [25].
CONCLUSION
The study of the influence of climate variables on cambial variation of Pinus latteri and Pinus kesiya
at Bo Keaw Silvicultural Research Station, Chiang Mai province in Northern Thailand. This study mostly
showed positive significant correlation between average number of cambial cell layers and cambial zone
width of Pinus latteri and Pinus kesiya and climate factors such as minimum monthly temperature, total
relative humidity, total monthly rainfall, and soil moisture. This study should be further studied as the
cambial activity in tropical forest species in different to climatic factors, forest types, terrain, altitude and the
growth in order that important of data.
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ACKNOWLEDGEMENT
I would like to express my sincere thanks to Associate professor Dr. Nathsuda Pumijumnong for the
continuous support and encouragement of this research. I would not have achieved this far and would not
have been completed without all the support from her. In addition, I am grateful for Dr. Supaporn Buajan and
Mrs. Sineenart Preechamart for suggestions and all help in numerous. Finally, I most gratefully thank my
Faculty of Environment and Resource Studies, Mahidol University for all their support throughout this
research.
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Material Flow Analysis of Lead in Lead Acid Batteries Supply Chain
Toward Circular Economy
Wanida Suriyanon1,2,3* Napat Jakrawatana2 and Nakorn Suriyanon3
1Doctor of Engineering Program in Environmental Engineering, Faculty of Engineering,
Chiang Mai University, Chiang Mai 50200, Thailand;
2Department of Environmental Engineering, Faculty of Engineering, Chiang Mai University,
Chiang Mai 50200, Thailand;
3Department of Civil and Environmental Engineering, Faculty of Engineering, Rajamangala University of
Technology Lanna, Chiangmai 50200, Thailand;
*Phone : 0819507534, E-mail : [email protected]
ABSTRACT
In Thailand, the number of cars using conventional Internal Combustion Engine (ICE) is increasing every
year. Due to incomplete combustion by the engine, these cars produce PM 2.5 particles which are dangerous
to human health. The End of Life (EOL) batteries from these cars also cause environmental risks, if they are
not properly managed. In this study both primary and secondary data are collected and used for MFA
analysis of lead in lead acid batteries in Thailand. It was found that Thailand produced 17,841,371batteries
(equivalent to 245,140 tons of lead per year) in the year 2018. Some of these batteries were exported to
neighboring countries (equivalent to 82,798 tons of lead per year), and some were sold in country for use in
industrial factories (equivalent to 5,478 tons of lead per year) and for use in vehicles (equivalent to 155,269
tons of lead per year). The total quantity of lead in battery wastes was 160,747 tons per year. The total
quantity of lead recycled by 9 legally registered smelters in Thailand was only 86,900 tons per year. The
remaining 73,847 tons of lead in battery wastes were lost from the proper recycling system. The management
of this portion of battery wastes remains unknown. Proper recycling by legal smelters can reduce the quantity
of lead imported from other countries and decreases the demand for natural resources. The researchers
propose the following strategies to manage lead acid battery wastes. They propose that the government
should give support to smelters by offering more tax incentive measures and should support the increasing in
productivity of the smelters. The government should also cooperate with battery manufacturers to nominate
representative agency governed by governmental officers to buy EOL batteries from both small and big
waste collection and recycling shop. The government should also liberalize the investment of private sectors
or make joint investment with them in building lead smelters. These strategies will be the guideline for
management of lead in vehicle battery industry toward persistent circular economy.
Keywords : Material Flow Analysis (MFA); Lead Acid Batteries; Circular Economy
INTRODUCTION
In Thailand, batteries used in most cars are lead acid batteries which are the types of batteries used for
vehicles with Conventional Internal Combustion Engine (ICE). Thailand can produce lead acid batteries for
their own use and for export to other countries. About 80% of the cars in Thailand use lead acid batteries[1].
This type of batteries has the average life span of 2 to 3 years [2]. After the end of life (EOL), it is considered
to be a hazardous waste. The main element from battery wastes that is hazardous is lead. If not properly
managed, lead from lead acid battery wastes can contaminate the environment such as water reservoirs, the
earth and surrounding air, spreading to human, animals and plants. It can cause environmental and health
risks. Lead enters human body through respiration or digestive system. Inside the body it accumulates in the
blood, soft tissues, teeth and bones and can cause lead toxicity. It causes adverse effects on central and
peripheral nervous system, reproductive organs, kidneys, cardiovascular system and vitamin D metabolism
[3]. Also perinatal and neonatal exposures to lead can cause the decrease of neurobehavioral and visual-
motoric functions. Lead may also have carcinogenic effect. Recycling is one of the proper methods to
manage the lead wastes from EOL batteries. Used batteries are collected and sent to legally registered
smelters for proper lead recycling.
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However, for some reasons, some of used batteries might be sent to illegal smelters and are not properly
managed. At these illegal plants they employ workers to cut the batteries using axes and smelt the batteries
together with their plastic boxes and lead grids in a reverberatory furnace without any protective equipment.
The recovery efficiency of this technology is very low with high emission of pollutants. It is dangerous to
employees and the environment as well [4]. Proper battery recycling not only reduces the amount of wastes,
but also decreases the demand for natural resources.
Material flow analysis is a systematic assessment of the flows and stocks of materials within a system
defined in space and time. It is an invaluable tool used in resource management, waste management and
environmental management. It was postulated by Greek philosophers more than 200 years ago. After that,
around 40 years back, Abel Wolman introduced the term “metabolism of cities” which coined the city as a
living organism with inputs, stocks and outputs of material and energy[5]. MFA is a tool to analyze the
metabolism of materials in order to analyze material flows and stocks within a given system[6]. It can be
applied to Evaluate the importance and relevance of the flows and stocks ,Control material flows and stocks
to support certain goals such as sustainable development, Assess resource utilization and environmental
impacts, Set up long term environmental policy and resource management strategy and Understand and
control material flow of heavy metal containing batteries
In the management system for circular economy, system must be viewed holistically, at the beginning, the
middle and the end of material flow to get the highest cyclical flow (life cycle) starting from the utilization of
raw materials for battery production. This study applied Material Flow Analysis (MFA) as a tool to trace
lead flow and stock in lead acid batteries supply chain in Thailand in order to analyze the current status of
lead management and the way for improvement towards circular economy.
METHODOLOGY
This study applied Material Flow Analysis (MFA) as a tool used for systematic assessment of the flows and
stocks of materials in each unit and the whole system within a defined temporal and spatial system [7]. The
research methodology of this study could be described in 4 steps as follow:
1) Setting the scopes of the analysis study, identifying the system boundary and components
The target and the boundaries of time and space were decided. The target in this study was to establish the
material flow system of lead in lead acid battery. The time or temporal boundary was the year 2018 and the
spatial boundary was Thailand. The life cycle chain of lead in lead acid battery consisted of three stages, as
shown in Figure 1, including product manufacturing, product use and waste management. It should be noted
that there was no lead mining in this life cycle chain. In Thailand there has been no lead mining since
2001[8], because of environmental problems, such as contamination of stream water by lead ore tailings,
impacts on health and well being of people in the local area. Lead which is a raw material for battery
production came from import and recycling only. The life cycle chain of lead in lead acid battery in Thailand
was different from other studies in that there was no resource mining. Most other studies included resource
mining as a stage of the life cycle chain.
2) Data acquisition (Data collection)
Both primary and secondary data were collected.
(1) The secondary data were collected by searching the official websites of governmental offices such as the
Department of Primary Industries and Mines, the Department of Land Transport and the Office of Industrial
Economics, and the articles published in local language or Thai. The data about the quantity of lead and lead
acid battery in Thailand were gathered from the official websites of governmental offices.
(2) The primary data were collected by interviewing the customers or battery users and the battery selling
shops in Chiang Mai.
3) Schematic modeling and balance for material flow system framework
This step constructs the system to use the data collected above. When some data was not acquired, the mass
balance or so-called mass conservation, i.e. mass-in is equal to mass-out can be used to balance the materials.
Software STAN was used to do Material Flow Analysis of the real current situation.
4) Interpreting MFA result for lead in lead acid battery
In this step, the results of the above MFA for lead in lead acid battery were interpreted, to find out the
appropriate method for battery waste management system in Thai
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Energy Storage Battery shop Collection
Collector
Lead rod Mixer factor Battery factory Customer
Storage
Second Life
Legal recycling battery plant Unknown
management
Production Use Used Battery Management
Figure 1 Scope of MFA of lead inLead Acid Batteries
RESULTS AND DISCUSSIONS
It was shown in this table 1 that the total number of lead acid batteries produced in Thailand in the year
2018, was 17,841,371. Out of this number, 11,300,511 were sold in country for use in vehicles (cars and
motorcycles), 398,711 for use in industrial factories and 6,026,025 were exported to other countries. (Figure
2 shows the diagram of the material flow of lead in lead acid battery via the MFA method)
Table 1 The quantity of lead in lead acid battery in each process.
No. Process No. of Estimated Quantity of Source
battery quantity of lead(Tons
per year lead(Kg prt per year)
year)*
1 Import lead - - 161,050 Department of Primary Industries
and Mines 2018
2 Export Battery 6,026,025 82,797,584 82,798 Office of Industrial Economics
2018
3 Lead rod /mixer - - 247,950 Import Lead-Legal recycle
factory
4 Battery factory 17,841,371 245,140,438 245,140 Office of Industrial Economics
2018
4.1 Energy storage 398,711 5,478,289 5,478 Batteries sold in country
(11,699,222) – No. transportation
vehicles (11,300,511) = 398,711
4.2 Customer 11,300,511 155,269,021 155,269 Department of Land Transport
2018
4.2.1 Battery shop 8,136,368 111,793,695 111,794 72% of customer (from interview
data)
4.2.2 Collector 1,695,077 23,290,353 23,290 15% of customer (from interview
data)
4.2.3 Second life 1,469,066 20,184,973 20,185 13% of customer (from interview
data
5 Collection - - 160,747 Energy storage +Battery shop
+Second life + Collector
5.1 Legal recycling - - 86,900 Article on material flow of lead in
battery plant lead acid battery 2017
5.2 Unknown - - 73,847 Collection-Legal recycling
management
*estimation was based on “quantity of lead in lead acid battery =13.74 kg/battery
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Figure 2 diagram showing the material flow of lead in lead acid battery by MFA method.
The overall input of lead in the year 2018 was 161,050 tons. It was found that in Thailand there has been no
lead mining in Thailand since 2001[8], because of environmental problems, such as contamination of stream
water by lead ore tailings, impacts on health and well being of people in the local area. Therefore, lead
which is a raw material for battery production came from import and recycling. This amount of lead input
was totally imported in the form of lead rod from other countries. The imported and the recycled lead were,
after that, used in the production of lead acid batteries. The total amount of lead used in battery production
was 245,140 tons [9]. Data from the Department of Land Transport 2018 showed that The amount of lead in
lead acid batteries that were exported to neighboring countries was 82,798 tons. The amount of lead in
batteries that were sold in country for use in industrial factories and for use in vehicles was 5,478 and
155,269 tons respectively.
The lead acid batteries that have been used for a long time and lost their capacity to the point when no longer
suitable for use in vehicles.The primary data from interviewing with the customers showed that among the
old (EOL) batteries 72% of them were traded in for new ones in battery shops, 15% were sold to antique
shops for recycling and 13% were reused as stationary storage for photovoltaic (PV) energy in second life
application. Using these percentages of EOL batteries for calculation, it was found that the amount of lead in
lead acid batteries which were traded in for new ones in battery shops, sold to waste collection and recycling
shop and reused in second life application were 111,794 tons, 23,290 tons and 20,185 tons respectively.
From interviewing with the battery selling shops, it was found that about 30% of the old EOL batteries that
they bought were resold to antique shops, and 50% were resold to the company shops bigger than the antique
shops. There was no information about how the remaining 20% of EOL batteries were managed.
The old EOL batteries collected at battery selling shops, small antique shops and bigger company antique
shops eventually were sent to smelters for recycling. Thailand has only 9 lead smelters that are legally
registered [10]. The quantity of lead recycled by these smelters was only 86,900 tons per year. Compared
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with the lead demand of the country, the quantity of lead recovered from recycling process is still not
adequate. To get adequate quantity of lead for the production of new batteries, 161,050 tons of lead was
imported.
The total quantity of lead in lead acid battery wastes was 160,747 tons per year. The quantity of lead
recycled by the legally registered smelters was only 86,900 tons per year. It is not known how the
remaining 73,847 tons per year of lead in battery wastes were managed. This amount of lead in battery
wastes were not in the legally registered recycling system. It could be assumed that some portion of this
amount of lead was sold for battery recycling, to lead smelters which are not registered to the Department of
Industrial Works. This is because the illegal or unregistered smelters paid a higher price to the EOL batteries
than the legally registered smelters. The other portion might be sold to unregistered smelters for production
of lead rods. These lead rods would be sold to factories making ball bearings, trawl, seine, fishnet etc..
Illegal smelting that does not meet the standard can cause danger. People will have the chance to directly
expose to lead by inhalation or direct contact. Prolonged and continued exposure to lead can cause lead
toxicity. It can cause death if the body suddenly receives a large quantity of lead. People may receive lead
indirectly by drinking water or eat meat and vegetables contaminated with lead etc.
Waste and Hazardous Substances Management Bureau, Pollution Control Department [11] states that EOL
batteries are considered to be hazardous wastes from the community. Forty per cent (40%) of the battery
wastes were managed properly. Sixty per cent (60%) of them were not properly managed. The main
problem is that the garbage dumping place cannot prevent the dissemination of hazardous wastes. The
hazardous wastes are thrown away mixing with general wastes. The Waste and Hazardous Substances
Management Bureau, Pollution Control Department has therefore the policy to manage the hazardous wastes
following the environment control management plan as follows by Reduce the quantity of wastes by the 3R
principle ,namely reduce, reuse, recycle and encourage the use of materials that are friendly to environment
,Have the system to bring back hazardous wastes from used products and Promote the investment of private
sector to build smelter or the center for proper discarding hazardous wastes.
The quantity of lead imported from other countries would be reduced if the 73,847 tons of lead in battery
wastes were brought into an appropriate or proper recycling system, making the system move closer to
circular economy. Nevertheless, there must be a thorough study on how to collect and send the EOL lead
acid batteries into a correct management system.
From this study, the problem in management was found to be at the step of collection of EOL batteries
before sending them to smelters. Battery shops sold EOL batteries to small antique shops, and the small
antique shops resold them to bigger antique shops. After that it was not clear how they were managed. They
could be sold to legally registered smelters or to unregistered smelters. It is not known how many of them
were sold to legally registered smelters and how many to unregistered smelters.
The researchers propose the following strategies to manage lead acid battery wastes. They propose that the
government should give support to smelters by offering more tax incentive measures and should support the
increasing in productivity of the smelters. The government should also cooperate with battery manufacturers
to nominate representative agency governed by governmental officers to buy EOL batteries from both small
and big waste collection and recycling shop. The government should also liberalize the investment of private
sectors or make joint investment with them in building lead smelters.
CONCLUSION
In the year 2018, Thailand could produce totally 17,841,371 baterries. Lead (in the form of lead rod) which
is the raw material for battery production came from import (from other countries) and from recycling (in
country). Lead acid batteries produced in Thailand were exported to neighboring countries, used in industrial
factories and vehicles in country. About 73,847 tons of lead in battery wastes were lost from the system of
collection and sending to legally registered smelters.It is possible that this amount of lead in battery wastes
were sent to the illegal or unregistered smelters that did not meet the standard and could cause bad effects or
problems to the environment. The quantity of lead imported from other countries would be reduced if the
73,847 tones of battery wastes were brought into an appropriate or proper recycling system, making the
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system move closer to circular economy. Nevertheless, there must be a thorough study on how to collect and
send the EOL lead acid batteries into a correct management system. The proposed strategic plan for EOL
battery managements are:
(1)The government gives supports to smelters by offering more tax incentive measures
(2)Support the Increase in productivity of smelters
(3)The government cooperates with battery manufacturers to nominate representative agency governed by
governmental officers in each province to buy EOL batteries from both small and big antique shops.
(4)The government liberalizes the investment or invests jointly with the private sectors in building lead
smelters.
REFERENCE
[1] Statistics of Department of Land Transport. 2562. Department of Land Transport. Available from:
https://web.dlt.go.th/statistics/.
[2] MP battery.2562.Lead Acid Batteries. Available from : https://batterymittapap.com/.
[3] Kamila Bicanova, Zdenka Wittlingerova,Jaroslav Dvorak,Magdalena Zimova.2015.The material flows
of lead in the Czech Republic.Resources,Conservation and Recycling.98(2015)1-8.
[4] Xi Tian, Yu Gong, Yufeng Wu∗, Amma Agyeiwaa, Tieyong Zuo.2014. Management of used lead acid
battery in China: Secondary lead industry progress, policies and problems.Resources, Conservation and
Recycling.93(2014)75-84.
[5] Md Tasbirul Islam, Nazmul Huda.2019.Material flow analysis (MFA) as a strategic tool in E-waste
management:Applications, trends and future directions. Journal of Environmental
Management.224(2019)344-361.
[6] T.C. Chang, S.J. You , B.S. Yu, K.F. Yao.2008. A material flow of lithium batteries in Taiwan. Journal
of Hazardous Materials. 163 (2009) 910–915.
[7] Brunner and Rechberger.2004.Handbook of material flow analysis for environment resource and waste
engineers.
[8] Department of Primary Industries and Mines . 2019. Economic Value Assessment in the case of lead
Mining KanchanaburiProvince. Available from :
http://www.dpim.go.th/articles/article?catid=122&articleid=310
[9] MTEC. lead acid batteries . 2018.
[10] Niramon Inthanon. Travel of lead in lead acid batteries .2017 .https://www2.mtec.or.th/th/e-
magazine/admin/upload/302_56.pdf.
[11] Waste and Hazardous Substances Management Bureau, Pollution Control Department.2560. Waste and
Hazardous Substances Management.,Thailand
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Food Losses Analysis in Noodle Production
in Northern Thailand
Chuanchom Nitano1* and Napat Jakrawatana2
1* Graduate student; 2 Assistant Professor, Department of Environmental Engineering,
Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
*Phone :084-0001158, E-mail : [email protected]
ABSTRACT
This study assessed food loss in noodle production in the North of Thailand. The scope of study was the
noodle production process including grinding steaming cutting and packing process to analyze cause of loss
to find solution to reduce loss. Loss in noodle production occurred in many steps such as grinding steaming
cutting and packing. The three types of noodle production was included: fresh noodle, semi-dried noodle and
dried noodle production process. Loss in fresh noodle production is 2.02 percent the steaming process has the
highest loss of 1.38 percent. Loss in semi dried noodle production process is 5.59 percent the cutting and packing
process has the highest loss of 2.17 percent. Loss in dried noodle production process is 15.77 percent the
packing process has the highest loss of 5.09 percent. This loss information can be used with the guideline
for improvement noodle production process and to help reduce food waste in rice supply chain and support
sustainable development on food security and promote sustainable agriculture.
Keywords : Food loss and waste; Food security; Rice; Noodle production, Supply Chain
INTRODUCTION
Food security and sustainable production are important issue for achieving the Sustainable Development
Goals (SDGs.). Losses of food during production, transport, storage or so called food loss, is major concern
from both environmental and economic aspects. The study from FAO (2011) stated that food loss is as much
as one third of food production globally. Therefore, food loss is also one of the issue in food security that
should be improved. Rice is the most importance cereal in Thailand. Noodle is one of the importance food
production using rice as raw material. Loss in noodle production occur in many steps such as grinding
steaming cutting and packing. These losses have a direct impact on rice cultivation, such as the use of
resources in the cultivation. Quantity of loss in each process of noodle production are part to reduce losses
that occur and reduce resources for rice cultivation. Therefore, this study aims to study the quantity of loss in
noodle production in the North of Thailand to analyze cause of loss to find solution to reduce loss and
improvement noodle production process and to help reduce food waste in rice supply chain.
METHODOLOGY
The study quantity food loss in noodle production in 4 factories in the North of Thailand using the Food loss
and Waste Accounting and Reporting Standard (version 1.0) by Hanson et al. (2016). The steps and scope of
the study was shown in Figure 1 and Figure 2 respectively.
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Define goals and establish scope
Gather and analysis data
Calculate the average food loss
Analysis of improvement measure to
reduce food losses
Fig.1 Scope of standard for analysis
1. Define goals and establish scope: This study analyze of food loss in noodle production in the North of
Thailand.
2. Gather and analysis data: Primary data and secondary data
3. Calculate the average food loss
Loss (percent) = Quantity of loss (kg)
Quantity of rice in process (kg)
4. Analysis of improvement measure to reduce food losses: Analyze cause of loss to find solution to reduce
loss and improvement noodle production process and to help reduce food waste in rice supply chain.
This study collected data and analyze loss of noodle production process from the storage of raw materials,
production process and the packing process as shown in Figure 2.
Raw Material storage Loss Rice
- Rice Storage
Production Process Loss Flour
- Grinding
- Steaming
- Cutting
Packing Process Loss Noodle
- Quality check
- Packing
Fig.2 Scope of production process in noodle production supply chain
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RESULTS AND DISCUSSIONS
The result of losses in three types of noodle including, fresh noodle, semi-dried noodle and dried noodle
production process were shown in Figure 3-5 respectively. In fresh noodle production in figure 3, the steaming
process has the highest loss of 1.38 percent Causes of highest loss in steaming process including 1) the conveyor
belt is not in the optimum length to control the condition of flour. 2) Temperature of steaming machine is not at
the optimum temperature.
Fig.3 Quantity of loss in fresh noodle production each process
The result of loss in semi dried noodle production process showed that the cutting and packing process has the
highest loss of 2.17 percent as shown in Figure 4. Cause of highest loss is that the noodle has low moisture and
equipment for cutting was not sharp. The regularly cleaning and maintenance of cutting equipment was required.
Fig.4 Quantity of loss in semi dried noodle production each process
The result of loss in dried noodle production process showed that the packing process has the highest loss of 5.09
percent as shown in Figure 5. Cause of highest loss in packing process because the quality and size of dried
noodle are not in the same standard. The control of temperature in steaming process and control of moisture were
required to reduce loss.
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Fig.4 Quantity of loss in dried noodle production each process
The result of the total losses of three type of noodles production was summarized Table 1. And Improvement
guideline of food losses in noodle production supply chain was summarized Table 2.
Table 1 Quantity of loss in noodle production supply chain.
Type of noodle Average
(percent)
Fresh Noodle
Semi dried noodle 2.02
Dried noodle 5.59
15.77
Table 2 Improvement guideline of food losses in noodle production supply chain
Type of noodle Cause of loss Improvement guideline
Fresh Noodle 1. The conveyor belt is not the optimum length 1. Control the conveyor belt to the
2. Temperature of steaming machine is not at optimum length
Semi dried noodle the optimum temperature 2. Control temperature of steaming
machine to the optimum
Dried noodle 1. Low moisture 1. Control of moisture to the
2. Equipment for cutting was not sharp optimum
2. Regularly cleaning maintenance
1. Noodle are not in the same standard of cutting equipment
1. Control of temperature in
steaming process
2. Control of moisture in dried
process
3. Keep clean all process
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CONCLUSION
This study analyze food losses in noodles production in Northern in Thailand. The result of food loss
occurred in every type of noodle production. Dried noodles production have the highest loss of 15.77 percent
and the packing process in dried noodles has the highest loss of 5.09 percent.
REFERENCE
[1] Corrado, S., Ardente, F., Sala, S. and Saouter, E. 2016. Modelling of food loss within life cycle
assessment: From current practice toward a systematisation. 140: 847-859.
[2] Mungkung, P., Pengthamkeerati, P., Chaichana, R. Watcharothai, S. Kittiwan, K and Tapananont, S. 2018.
Lift cycle assessment of Thai organic Hom Mali rice to evaluate the climate change, Water use and
biodiversity impact. 211: 687-694.
[3] Saikhunthod, M., Peerapattana, P., 2015. Wastes reduction in production process using green
productivity: a case study in rice noodle factory.
[4] Food and Agriculture Organization of the United Nation (FAO). 2011. SAVE FOOD: Global Food
Loss and Food Waste., Germany.
[5] Department of Industrial Works. 2003. Clean Technology: Noodle production. Bangkok.
[6] Food and Drug Administration. 2009. GMP of Noodle Production. Bangkok.
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Biodegradation of PAHs by The Mixed Cultures
of Diesel Degradation Bacteria
Aphinya Fucharoen1 Pharkphum Rakruam2* and Chia-Yuan Chang3
1Graduate student, Department of Environmental Engineering, Faculty of Engineering, Chiang Mai University,
Chiang Mai 50200, Thailand; 2Assistant Professor, Department of Environmental Engineering,
Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand; 3Professor, Department of
Environmental Engineering and Science, Chia Nan University of Pharmacy and Science, Taiwan;
*Phone: 053 – 944 192, E-mail : [email protected]
ABSTRACT
Polycyclic aromatic hydrocarbons or polyaromatic hydrocarbons (PAHs) is an organic compound in
hydrocarbon group. This structure is two or more aromatic rings without heteroatoms. The most abundant
groups of aromatic compounds occurring in diesel fuels are naphthalene. Three strains of diesel-degrading
bacteria including Achromobacter insolitus, Candida spp and Xanthobactor polyaromatici yorans was
proved as high capability to degrade diesel. Thus, this study was aims to determine the optimal condition for
growth up of diesel-degrading bacteria and diesel degradation efficiency. The experiment was conduct in
batch experiment with the varied ratio of synthetic wastewater as nutrient and diesel concentration with
surfactants (N:D) at 100:0, 80:20, 60:40, 40:60, 20:80 and 0:100. Then the optimal ratio of N:D was used to
determine the effect of initial naphthalene concentration on naphthalene degradation. The different initial
concentration of naphthalene was varied in the range of 0 to 100 mg/L. The results showed that the highest
percent COD removal (100%) was found at N:D ratio 60:40 and 0:100 followed by N:D ratio 40:60 at 96.7%
and N:D ratio 80:20 and 20:80 at 97.4%, respectively. The different of nutrient added was affected the
growth of biomass. The highest biomass yield was found in N:D ratio 20:80. The growth of biomass
depended not only diesel but also glucose in nutrient. Glucose play as a cometabolism for growth up
bacteria. The initial concentration naphthalene was affected the growing of biomass and the efficiency of
naphthalene degradation. The highest naphthalene degradation efficiency (99.8%) was found at initial
napthalene concentration at 20 mg/L.
Keywords : Biodegradation, Diesel degrading bacteria, Diesel, Naphthalene,
INTRODUCTION
Sixteen PAHs are regulated by the U.S. Environmental Protection Agency (USEPA) based on their potential
human and ecological health effects including Naphthalene, Acenaphthylene, Acenaphthene, Fluorene,
Phenanthrene, Anthracene, Fluoranthene, Pyrene, Benz[a]anthracene, Chrysene, Benzo[b]fluoranthene,
Benzo[k]fluoranthene, Benzo[a]pyrene, Dibenz[a,h]anthracene, Benzo[ghi]perylene, Indeno[1,2,3-cd]pyrene
[1]. Wattayakorn, (2012) [2] reported that water sample from the estuary of Chaopraya contained 4.71 µg/g
of PAHs, the highest types of PAHS was naphthalene. Naphthalene or mothballs are toxic to human and
animals include cause of cancer, tumor, acute toxic of nerves system, hematoma system, respiration system
and digestion system.
There are many aspects of relation between diesel and naphthalene. PAHs was found in burning of coal, fuel
and part of diesel. Diesel is complex and several structure for example n-alkalines, isoand cycloalkanes,
PAHs, sulfer and aromatic compound especially naphthalene and alkylnapthalene [3]. Moreover, in the
middle distillates such as diesel fuel is identified by a variety of straight, branched, and cyclic alkanes, as
well as naphthalene, methylnaphthalenes [4].
From previous study by Singhyakaew (2015) [5], Three strains of diesel-degrading bacteria were collected
from the activated sludge process of An-ping wastewater treatment plant in Taiwan. These cultures are high
capability to degrade diesel. Moreover, the single culture of microbe is low capacity than mix culture of
bacteria because metabolize a limited scope of hydrocarbon substrates [6]. So, this study was utilized
Achromobacter insolitus, Candida spp and Xanthobactor polyaromatici yorans to degrade diesel and
naphthalene with cometabolism in metabolic process.
The process of bioremediation was influenced by some physical factors as an example temperature, pH,
oxygen, nutrient, microorganism number, consortium of microorganism, bioavailability, contaminant
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characteristics and toxic of end products [7]. The development of limiting factors can lead to more capacity
treatment technology. One important factor to limit degradation of diesel is the lack of carbon source.
Nutrient was found to stimulate growth of the pollutant degrading microorganisms and enhance their ability
to degrade contaminants [8]. Thus, the aim of this work was investigating the optimal nutrient and diesel
ratio to enhance diesel degradation and growth up of microorganism. In addition, the effect of initial
naphthalene concentration to naphthalene degradation was investigated.
METHODOLOGY
Synthetic wastewater
The synthetic wastewater was prepared based on the study of Banu et al., (2009) [9]. About 20,000 ml of
synthetic domestic wastewater was prepared by using various chemical as glucose 4.5 g, NH4Cl 4g, NaHCO3
8g, KH2PO4 0.5 g, microelement solution (MnCl2.4H2O 2 ml, ZnCl2.2H2O 2 ml, CuCl2 .2H2O 2 ml,
MgSO4.7H2O 2 ml, FeCl3.6H2O 2 ml, CaCl2.2H2O 2 ml). After that added the RO water until the final
volume was 20,000 ml. The synthetic wastewater was used as nutrient for all experiment. Synthetic
wastewater was kept in refrigerator (4๐c) until used and prepared a new one every 2 days.
Diesel solution
Diesel solution was prepared by getting the pure diesel 10 ml and mixed with 1 mL of Ethylene glycol
mono-butyl ether (EGBE), (CH2OHCH2OC4H9, 99%) and diluted with reverse osmosis water to obtained the
final volume at 1,000 ml. It was stirrer for 15 minutes for dissolve the diesel in water. At last, the 180 ml of
diesel solution was diluted again with reverse osmosis water to obtained the final volume at 1,000 ml. The
concentration of Diesel is 1.8 x10-2 (v/v) and surfactant is 1.8 x10-4(v/v). The diesel solution was prepared
every two days and keep in refrigerator 4๐c.
Mixed culture of bacteria
The mixed culture bacteria were collected from previous study of Singhyakaew (2015) [5]. The mixed
culture bacteria consist of Achromobacter insolitus, Xanthobactor polyaromatici, and Candida spp. Those
microbes were grown-up by feeding diesel with ethylene glycol mono-butyl ether (EGBT).
Experiment for determine the optimum ratio of synthetic solution (N) and diesel with surfactant (D)
for diesel degradation
A batch experiment was conducted with varied ratio of N:D in the range of 100:0 to 0:100. The experiment
was conducted with 1,000 ml working volume and start-up with 30 ml of mixed culture bacteria. The
experiment was done in aeration mode with controlled DO concentration above 2 mg/L at room temperature.
Water samples from reactor was collected and analyzed for their total COD (TCOD) and soluble COD
(SCOD) every day. In addition, the mixed liquor volatile suspended solids (MLVSS) was investigated to
study the accumulation mixed culture of diesel degradable bacteria. The experiment was operated until the
COD concentration was increasing from the previous operating day. Furthermore, various parameters
including pH, temperature, DO, and MLSS was also measured every day.
Experiment for determine the effect of initial naphthalene concentration on degradation
Two-liter volume beaker was used as reactor by added the optimal ratio of N:D from the previous
experiment. About 30 ml of mixed culture bacteria was added as seed sludge. The different initial
concentration of naphthalene which varied in the range of 0 to 100 mg/L. The experiment was done in
aeration mode with DO concentration above 2 mg/L at room temperature. Various parameters including pH,
oxidation-reduction potential (ORP), temperature, MLSS and MLVSS was measured every day. While
naphthalene concentration was measured every 2 hours by HPLC technique with fluorescence detection at
254 nm, the mobile phase was a mixture of water and acetroniitrite (70:30 v/v). Separation was carried out
with a reverse phase 5 µm c-18 column (250 x 4.6 mm) with flow rate 1 m/m.
RESULTS AND DISCUSSIONS
The optimal ratio of N:D for diesel degradation
The results in Table 1 showed that the highest percent SCOD removal was found at batch C and F (100%)
followed by batch D (96.7%) and batch B, E (97.4%), respectively. The obtained results showed that the
mixed culture bacteria used in this study was high efficiency to remove COD from wastewater. Thus, it can
9th International Conference on Environmental Engineering, Science and Management
The Heritage Chiang Rai, Thailand, May 27-29, 2020
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