an investigation of particulate emission using chemical ...... · of emissions at receptor a were...

บ ท ค ว า ม จ า ก ง า น วิ จั ย

Journal of Safety and Health : Vol. 7 No. 25 May-August 2014 25

Research Article

Abstract This study aimed to investigate the source

of particulate emission contribution at Na-Phra-Lan Subdistrict, Chalermphrakiat District, Saraburi Province, Thailand which is surrounded by cement plants and quarries by using the receptor model (Chemical Mass Balance model, CMB 8.2). This model requires data input from emission sources and receptors. The gravimetric and chemical com-position of particulate matter (PM10) emissions were analyzed to determine the PM10 source profiles. The samples of particulate matter at Na-Phra-Lan Subdistrict, Chalermphrakiat District of three receptors (1) Khung-Khow-Khew village (receptor A)

represented industrial area, residential area and heavy traffic area, (2) Na-Phra-Lan village (receptor B) represented industrial area, residential area and heavy traffic area and (3) Ban Nhong-Jan village (receptor C) represented agricultural area using a Minivol Air Samples were collected and analyzed to determine the chemical composition. They were collected on teflon and quartz fiber filters for analyzing of elemental composition, water soluble ions and carbon species by using X-ray fluores-cence, ion chromatography and organic elemental analysis. The results showed that the significant sources contribution at Na-Phra-Lan Subdistrict, Chalermphrakiat District were white cement plants,

An Investigation of Particulate Emission Using Chemical

Composition Analysis MethodChuennadda Chulamanee*

Pramuk Osiri**

Preecha Loosereewanich**

Supat Wangwongwatana*

Panwadee Suwattiga***

* Pollution Control Department, Ministry of Natural Resources and Environment, Bangkok, 10400, Thailand

** Department of Occupational Health and Safety, Faculty of Public Health, Mahidol University, Bangkok, 10400, Thailand

*** Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, 10800, Thailand

26 Journal of Safety and Health : Vol. 7 No. 25 May-August 2014

ว า ร ส า ร ค ว า ม ป ล อ ด ภั ย แ ล ะ สุ ข ภ า พ

ปีที่ 7 ฉบับที่ 25 ประจำาเดือนพฤษภาคม-สิงหาคม 2557

quarry and crushing plants, portland cement plants, biomass burning, diesel vehicle and motorcycle at 71.87%, 13.62%, 9.72%, 1.84%, 1.22% and 0.15% respectively. Unknown sources were 1.58%. The summary of dominant chemical composition found in Na-Phra-Lan Subdistrict, Chalermphrakiat District, Saraburi Province were Ca, S, Si, Fe, SO+

4 and Ca2+

at 25.11%, 13.68%, 9.61%, 5.82%, 5.78% and 4.01% respectively. The cement groups found Ca, Si, S and Fe as major chemical components at 58.03%, 18.6%, 13.50% and 3.61% respectively. It can be concluded that cement groups were the major source contribu-tors in the study area.

Keywords : Chemical mass balance/Chemical composition/Particulate matter/Emission source contributor/Cement plant/Saraburi province

1. IntroductionNa-Phra-Lan Subdistrict, Chalermphrakiat

District, Saraburi Province, Thailand has experienced a high level of air pollution, especially particulate matter less than 10 micrometers (PM10) for many decades. This study aimed to investigate the source of particulate emission contribution at Na-Phra-Lan Subdistrict using the chemical composition analysis method and Chemical Mass Balance model (CMB) (Coulter, 2004). The multiple emission sources in this area were appropriately 40 quarry and rock crushing plants with air pollution stacks and without air pollution stack, three portland cement plants with air pollution stacks, two white cement plants with air pollution stacks and more than ten white cement plants without air pollution stack. The other sources were mobile sources, other industries and biomass burning (Pollution Control Department, 2004).

The activities of quarrying and rock-crushing in this area have been suspicious to be the emission sources of PM10. Although there were several attempts to solve this problem, the PM10 level

continued to exceed the average 24-hour ambient air quality standard (120 µg/m3) (Pollution Control Department, 2004). Possibly, the PM10 might not only be contributed from the quarry and crushing plants, but might also be contributed by other sources such as cement plants, automobile exhaust, unpaved road dust and open-burning in cultivated areas. Thus, to determine the emission source of the ambient pollution in receptor locations, the source apportionment model technique was used to identify the pollution contributor in the study area (Vega et al., 1997; Vega et al., 2001; Arpa Wangkiat et al., 2001; Fraser et al., 2003; Chan et al., 2005; Hagler et al., 2006).

2. Material and methodsThe study performed PM10 sampling in ambient

air at three receptor locations and emission sources using a Minivol Air Sampler to discover the chemical composition. There were two types of sampling filters, teflon and quartz. The particulates on teflon filter was analyzed to determine the metallic and non-metallic elements included aluminum (Al), barium (Ba), calcium (Ca), chlorine (Cl), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), sodium (Na), phosphorus (P), sulfur (S), silicon (Si), titanium (Ti), vanadium (V), and zinc (Zn) by x-ray fluorescence (XRF). The sampled quartz filter was divided into two parts, one part for analyzing of organic carbon (OC) and elemental carbon (EC) by organic elemental analyzer (OEA). The other part was put into deionized water to dissolve the collected PM10 for analyzing of the soluble ionic species such as SO2-

4 , NO-3 , Cl-, Na and

K by the Ion Chromatography method (Judith et al., 2003; Pollution Control Department, 2005, 2006).

The ambient air sampling was done at three receptor sites : 1) Khung-Khow-Khew Village (receptor A) represented industrial area, residential



Research Article

area and heavy traffic area, 2) Na-Phra-Lan Village (receptor B) represented industrial area, residential area and heavy traffic area and 3) Ban Nhong-Jan Village (receptor C) represented agricultural area. The receptor A was 400 meters away from Phahol Yothin road surrounded by quarry and crushing plants. The receptor B was near Phahol Yothin road surrounded by cement plants, quarry and crushing plants. The receptor C was an agricultural area; corn

Fig. 1 Study area : Chalermphrakiat District

The emission sources of fine particulate matter in this area were quarry and crushing plants, cement plants, automobile exhaust and open-burning in cultivated areas. The PM10 source stacks sampling (US.EPA, 1997) was done at the portland cement plants for two samples, white cement plants for two samples and quarry and crushing plants for one sample (rock was a raw material assumed to have similar composition). The secondary data from literature reviews of source profiles of automobile exhaust gases and biomass burning were also studied.

The quality assurance and quality control of chemical composition analysis were mass balance between total mass of PM10 and total mass of chemical components. The criterion of the total mass

of chemical components of this study was ±15% of the PM10 total mass.

3. Results3.1 The PM10 concentration at receptor sites

The results of 24-hour PM10 concentra-tions of three receptor sites in the rainy and dry season periods at Na-Phra-Lan Subdistrict area were 22.49-289.21 µg/m3. The maximum concentration was 289.21 µg/m3 at receptor A. The minimum concentration was 22.49 µg/m3 at receptor C. The number of samples for 24-hour PM10 concentration exceeded the air quality standard for 45.3% of the total samples (29/64 samples) at receptor A and 63.3% (38/60 samples) at receptor B.

field, rice paddy field and sun flower field. The location of three receptor sites and emission sources with or without air pollution stack are shown in Fig. 1. All samplers were calibrated with a primary flow meter before and after sampling. The sampling period was from June 2005 to March 2006. The weather conditions during sampling were dry and mainly prevailing winds blowing from the north.




Fig. 2 The 24-hour PM10 concentration at receptor A

Fig. 3 The 24-hour PM10 concentration at receptor B

Fig. 4 The 24-hour PM10 concentration at receptor C

The 24-hour PM10 concentration at receptor A from June 2005 to March 2006 was two times higher than the air quality standard during dry season, October to January, and the maximum concentration was 289.21 µg/m3 in November as shown in Fig. 2. The PM10 concentration at receptor B

was also two times higher than the air quality standard and the maximum concentration was 272.53 µg/m3 (Fig. 3). This was in contrast withreceptor C representing the agricultural area in which the PM10 concentration complied with the air quality standard (Fig. 4).



Research Article

3.2 The characteristic and chemical compo-

sition of PM10 at receptor sites

The chemical composition analysis of 24-hour PM10 in ambient air at the three receptor sites for 28 chemical species were determined; sodium (Na), magnesium (Mg), aluminum (Al), silicon (Si), phosphorus (P), titanium (Ti), vanadium (V), chromium (Cr), iron (Fe), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), cobalt (Co), copper (Cu), zinc (Zn), barium (Ba), manganese (Mn), organic carbon (OC), elemental carbon (EC), soluble sulfate ion (SO2 -

4 ),

soluble nitrate ion (NO-3), soluble chloride ion (Cl-),

soluble sodium ion (Na+), soluble potassium ion (K+), soluble ammonium ion (NH+

4), soluble calcium ion (Ca2+) and soluble magnesium ion (Mg2+).

The results of the PM10 chemical compo-sition analysis for three receptor sites (15 samples from receptor A, 10 samples from receptor B and 24 samples from receptor C) revealed that the chemical composition of these sample were Ca, S, Si, Fe and Ca+ etc, as show in Fig. 5.

Fig. 5 The chemical composition at receptor sites




The analysis results of PM10 chemical composition of receptor A were similar to receptor B especially Ca, Si, S, Fe and Ca2+. Both Ca and Si elements were high at receptor A (38.67% and 8.17%) and receptor B (27.26% and 9.46%). Na+ and SO2-

4 at receptor C were the highest percent of chemical composition, 9.55% and 8.23%, respectively. Na+,

SO2-4, Cl-, K and S at receptor C were higher than

receptor A and receptor B. Organic and Elemental Carbon (OC and EC) at three receptor sites were similar.

3.3 The emission inventory at Na-Phra-Lan

Subdistrict, Chalermphrakiat District

Three types of emission sources were stationary sources, mobile sources and area sources. The stationary sources in this study were portland cement plants, white cement plants and quarry and crushing plants with air pollution stacks. Quarry and crushing plants without air pollution stacks and biomass burning were classified as the area sources.

The emission rates of these sources were calculated by using the emission factors of US.EPA AP-42. It showed that a mobile source was the highest emission rate (42%) followed by quarry plant 35% (quarry with stack 1%, quarry without stack and no

air pollution control device 34%), white cement plant 14%, portland cement plant 6% and biomass burning 3% (Pollution Control Department, 2004).

3.4 The chemical composition of PM10 from

emission sources

The chemical composition of PM10 of two emission sources, portland cement and white cement were characterized, but quarry and crushing plants used secondary data from the US.EPA. Source profiles of diesel engines, gasoline engines, motorcycle, and biomass burning were explored from source apportionment of fine particulate matter in a Samutprakan Province report (Pollution Control Department, 2005).

The chemical composition of PM10 from emission sources were analyzed as shown in Fig. 6. The major proportion of specie at the portland and white cement plants were Ca. The Si species of portland cement and quarry plant were higher than white cement plant. OC and EC were the majority of the emissions in combustion process. The proportion between OC and EC from biomass burning were not different while the proportion of EC from diesel engines was higher than OC but OC from gasoline engines/ motorcycles was higher than EC.



Research Article

Fig. 6 The chemical composition of source profiles




Portland cement had Mg, P, K+, NH+4 and

Ca2+ as the marker species while white cement showed Cr, Fe, NO-

3 and Na+ as the marker species. Al, Si, K, Ca and EC were the marker species for quarry and crushing which were also found in portland cement and white cement. The marker species of biomass burning were K, OC and EC. Diesel engine had Co as the marker species while gasoline engines had SO2-

4 as the marker species. The marker species for motorcycles were Cu, Zn and Ba.

3.5 Source contribution of PM10 in Na-Phra-

Lan Subdistrict, Chalermphrakiat District

Source contribution of PM10 was investigated by using the receptor model (U.S.EPA.CMB 8.2). The model used the chemical and physical characteristics of particulate matter measured at various emission sources and receptors to estimate

source contributions to those receptors. The results from the CMB model showed that the major sources of emissions at receptor A were from white cement 60.07%, quarry and crushing plant 30.61%, portland cement 6.31%, motorcycles 0.46% and diesel engines 0.08%. An unknown source contribution was 2.47% (Fig. 7). It can be observed that the number of quarry and crushing plant was the largest and should have been the main contributor instead of the second contributor. This can be explained by two reasons that most of the quarry and crushing plants had no air pollution stack and had lower emission rates compared to the white cement plants. The PM10

emission rate of white cement, portland cement and quarry and crushing (with stack) were 67%, 26% and 7% consecutively. It might be a cause of limiting the dispersion of fine particles by the distance.

Fig. 7 Source contribution at receptor A (%)



Research Article

The major source contributions at receptor B were white cement 68.84%, portland cement 17.33%, quarry and crushing plant 10.25% and diesel engines 3.59% (Fig. 8) The significant source contributions at receptor C were white cement, biomass burning and portland cement at 86.69%, 5.53% and 5.51%, respectively. An unidentified source was 2.27% (Fig. 9). Then it can be concluded that the emission source contributions for receptor A and B were white cement, quarry and crushing plant and portland cement. The emission of quarry and crushing plants at receptor B was

smaller than at receptor A due to the number of plants and the geographic location of these two receptors (Fig. 7-8). Quarry and crushing plants were not the emission source contributor at recep-tor C because there was no quarry and crushing plants within 2 kilometers from receptor C. The emission contribution of diesel engine at receptor B was higher than receptor A and C because receptor B was near by the Phahol Yothin road. The biomass burning emission was found only at the receptor C (Fig. 7-9).

Fig. 8 Source contribution at receptor B (%)

Fig. 9 Source contribution at receptor C (%)




The overall results of three receptors representing the studied area showed that the PM10

source contributions in Na-Phra-Lan Subdistrict, Chalermphrakiat District, Saraburi Province were from white cement plants, quarry and crushing

plants, portland cement plant, biomass burning diesel engines and motorcycles at 71.87%, 13.62%, 9.72%, 1.84%, 1.22% and 0.15% respectively as shown in Fig. 10. It also shows 1.58% from an unidentified source.

4. ConclusionThis study analyzed the chemical composition

of PM10 from three receptors. These data were input to CMB model to determine the PM10 source contribution. The result of PM10 chemical composition analysis of 3 receptor sites in Na-Phra-Lan, Chalermphrakiat District, Saraburi Province found that the PM10 chemical composition analysis of Khung-Khow-Khew village (receptor A) area was similar to Na-Phra-Lan area village (receptor B) especially Ca, Si, S, Fe and Ca2+. Both of Ca and Si element were high in atmosphere of receptor A and receptor B at 38.67% and 8.17% for receptor A, 27.26% and 9.46% for receptor B respectively. The study also found Ca and Si were 9.40% and 11.20% at Ban Nhong-Jan village (receptor C).

These findings compared to the emission sources and found that Ca was the major proportion of specie at the portland and white cement plants.

The Si element of portland cement and quarry plant were higher than white cement plant. The study found no Si in the chemical composition of source profiles from the other emission sources.

This can be concluded that majority contribu-tor for all receptor sites were white cement, quarry and crushing and portland cement except receptor C. It found no quarry and crushing as one of emis-sion source contributor at the receptor C. However, vehicles such as diesel engine and motorcycle were some contribution to receptor A and receptor B, while receptor C found biomass burning as the third of emission source contribution in the area.

A usage of the model result without other information to point out sources apportionment, especially emission inventory of study area, can lead to establish an inappropriate policy to combat air pollution in the study area. Emission inventory data can be a supporter to confirm CMB model whether

Fig.10 Source contribution at Chalermphrakiat District (%)



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results of this model corporate with number of emis-sion sources in the area. Within sources apportion-ment, emission inventory should be recommended and done together with CMB model.

The limitation of CMB model, as mentioned earlier, CMB model uses statistical technique by comparing chemical species between emission sources and receptors to specify what emission sources play a major role to air quality over atmosphere. In fact, pollutants dispersion depends on several factors, particularly in meteorological data, emission stack height and distance between emission sources and receptors. These factors can effect to ground level concentration of particle in this study. In order to determine whether which emission sources is a real major contributor, disper-sion model and meteorological model are useful in conjunction with CMB analysis to determine where contributions might have come from which are very useful to identify more accuracy and improve their disadvantage.

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an investigation of particulate emission using chemical ...... · of emissions at receptor a were...

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