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\title{Correlation between Work Duration of Gas Station Operators with Mucociliary Transport Time, Hair Pb Level, and Nasal Cytogram}
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             \author[1]{Riskiana  Djamin}

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\date{\small \em Received: 6 December 2017 Accepted: 5 January 2018 Published: 15 January 2018}

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\begin{abstract}
        


Gas station operators are groups of workers exposed to the risk of dangerous chemical compounds, particularly lead from gasoline and vehicle gas emission that are waiting for a refuelling queue or a vehicle that will depart after completing the refuelling. The research aimed at investigating the correlation between work duration of gas station operators with mucociliary transport time, hair Pb level, and nasal cytogram. The research used the analytic observational method with the cross sectional design, was conducted to the operators of gas station in Tamalanrea and in Gunung Bawakaraeng street from August 2017 until September 2017 with 30 people as total samples. Sample divided into two groups, gas station operator with work duration < 1 year for 15 people and gas station operator with work duration ? 1 year for 15 people and each sample was underwent mucociliary transport time examination, hair Pb level examination, and nasal mucosa cytogram examination. The research result indicates that the significant correlation between work duration and mucociliary transport time with p value <0.05 and OR value=11. There is a significant correlation between work duration and Pb hair level with p value <0.05 and OR value = 9.3. There is no significant correlation between work duration and eosinophil and neutrophil count with p value > 0.05. It can be concluded that the longer work duration as a gas station operator, the higher risk of mucociliary transport time disruption and risk of lead exposure will happen. There is no significant correlation between work duration with nasal cytogram.

\end{abstract}


\keywords{work duration, mucociciliary transport time, lead (Pb), nasal cytogram.}

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\let\tabcellsep& 	 	 		 
\section[{J}]{J}\par
ose and paranasalis sinus are the organs that play an important role as the front line of the body's defence in the lower air way to the microorganism and hazardous materials \hyperref[b0]{[1]}. Its effectiveness depends on the integrity of mucociliary system called the mucociliary transport system \hyperref[b1]{[2]}. It consists of ciliary cells of the respiratory epithelium, mucous -producing glands and mucous membranes N produced by goblet cells in epithelial and submucosal seromucinous glands. \hyperref[b2]{[3]}.\par
According to  {\ref Waguespack (1995)}, several conditions which affect mucociliary transport were physiologic factor, air pollution, smoking, congenital disorder, rhinitis allergy, viral infection, bacterial infection, topical drugs, systemic drugs, preservatives, and surgery. \hyperref[b3]{[4]}.\par
Gas station is one of the places where pollution and discharge of gas or waste from vehicles that contained heavy metals such as lead or Plumbum (Pb) occurs, where Pb is the most dominant pollutant in the gas station \hyperref[b4]{[5]}. Pb level in the air around the gas station is likely to be sucked thorough the air or even attached to the body tissue especially the skin (hair) \hyperref[b5]{[6]}. Analysis of lead in hair has more advantages, because hair has longer memory period and even a permanent result. Head hair grows at an average speed of half an inch (1 inch = 2.54 centimeters) per month. Therefore, three inches of human hair can tell the history of the human body for six months \hyperref[b6]{[7]}. Gas station position which near the highway makes it easier for officers to be exposed to lead pollutants from the smoke of the vehicles on the highway \hyperref[b7]{[8]}.\par
The Regional Environmental Agency has conducted a study about lead level at several points in Makassar in 2013, and the results found that the highest lead level is on the Ratulangi street with 1.319 µg/Nm 3 and the lowest is on Monginsidi street with 0,374 µg/Nm 3 \hyperref[b8]{[9]}. Based on the results of Makassar Centre for Health and Occupational Safety (COHS) study about air quality monitoring in several points of vehicle's traffic, it can be seen that in the last three years, lead is still polluting Makassar even though gasoline without lead has been applied, however the pollution remains occur. In 2012, concentration of lead in the air was 0.528 µg/Nm 3 and in 2013, concentration of lead in the air became 0.592 µg/Nm 3 \hyperref[b8]{[9]}. 
\section[{II. Materials and Method a) Location and Time}]{II. Materials and Method a) Location and Time}\par
The study was conducted at Tamalanrea gas station and gas station of Gunung Bawakaraeng street in Makassar, South Sulawesi Province, for 1 month from August 2017 until September 2017. 
\section[{lead (Pb), nasal cytogram. b) Population Dan Sample}]{lead (Pb), nasal cytogram. b) Population Dan Sample}\par
The population is Tamalanrea gas station operators and gas operators of Gunung Bawakaraeng street in Makassar. Samples are the entire affordable population that met the inclusion criteria. 
\section[{c) Design and Sampling}]{c) Design and Sampling}\par
This is an observational analytic study with cross sectional design. Samples were taken at random, every gas station operators who fulfilled the study requirement, the identity will be recorded and then anamnesis and physical examination will be taken. Each samples will undergo mucociliary transport time examination using saccharin test, hair Pb level examination using Atomic Absorption Spectrometry (AAS), and nasal cytogram examination to assess eosinophil and neutrophil. 
\section[{d) Data Analysis Technique}]{d) Data Analysis Technique}\par
The collected data is processed and presented in the form of tables and graphs. Data processing is analysed with statistical test using SPSS 22.0 version with unpaired t test and significance at the p<0.05 level. 
\section[{III.}]{III.} 
\section[{Results}]{Results}\par
An observational analytic study was conducted with cross sectional study design to determine the correlation between work duration of gas station operators with mucociliary transport time, hair Pb level, eosinophil and neutrophil count in nasal mucosa. This study was conducted in Tamalanrea gas station and in Gunung Bawakaraeng street in Makassar, South Sulawesi Province.  {\ref For}     
\section[{*Fisher test}]{*Fisher test}\par
The result of fisher test analysis shows no significant correlation between neutrophil in nasal mucosa of gas station operators in Makassar with p value=0,21 (p>0,05). There are 3 people (10\%) in work duration < 1 year with abnormal neutrophil count and 12 normal people (40\%). In work duration ? 1 year group, there are 6 people (20 \%) with abnormal neutrophil count and 9 people (30\%) with normal neutrophil count. (Table \hyperref[tab_5]{4})IV. 
\section[{Discussion}]{Discussion}\par
The major sample characteristics according to age is 15-23 years old with 23 people (76.7\%), With the plenty amount of women than men, i.e. 18 people (60\%). Munir D, 2010 stated that there was no significant difference in MCT time based on gender \hyperref[b9]{[10]}. The influence of age and gender on MCT time is still not known clearly. Some researchers say that age and gender have no effect on the speed of mucociliary transport. Prijanto, 2002 suggested that nasal mucociliary transport is related to age, i.e. older age has a slower mucociliary transport rate than younger age. This is due to the possibility that older people are more exposed to air pollution \hyperref[b10]{[11]}.\par
The result of mucociliary transport time (MTT) comparison analysis between gas station operators with work duration <1 year and ? 1 year showed a significant correlation by using unpaired t test with p value = 0.01 (p <0.05) and mean value 8.48 (SD ± 4.49) for work duration <1 year group, and in work duration ? 1 year group, the mean value was 14.68 (SD ± 3.39). In work duration <1 year group, there are 4 people (13.33\%) who experienced the disorder, this may be caused by the history of smoking and allergies. However, in the work duration ? 1 year group there are 3 normal people (10\%), this may be caused by the use of maximum protective equipment and immune system of the operators. (Figure  {\ref 1}) Fig.  {\ref 1}: Comparison of Mucociliary Transport Time (MTT) between gas station operators with work duration < 1 year and ? 1 year Smoking can affect mucociliary transport time. This is due to the ciliostatic effects of tobacco smoke. Prolongation of mucociliary transport time may be caused by a decrease in the amount of cilia or mucus viscoelasticity changes. In addition, increased mucociliary transport time was also associated with an increase in smoking duration where subjects who smoked more than 5 years had a slower mucociliary transport time \hyperref[b11]{[12]}. In the case of a very long rhinitis allergy, elongation of mucociliary transport time occurs and associated with alternation in nasal mucus flow features \hyperref[b12]{[13]}.\par
The result analysis of Pb level in gas station operator in Makassar between work duration <1 year and ? 1 year shows significant correlation by using unpaired t test with p value = 0.001 (p <0.05) and mean value 6.45 (SD ± 2.14) while mean value 10.92 (SD ± 1.89) for work duration ? 1 year group. In work duration <1 year group there is only 1 person (3.33\%) with abnormal level of Pb, this may be due to the lack use of protective equipment, food and drinks or other factors. Poisoning caused by Pb metal compounds can occur due to the metal compounds entering the body. The process entry of Pb in the body can be through several routes, such as food and drink, air, permeation or penetration of the membrane or skin layer \hyperref[b5]{[6]}.\par
In the work duration ? 1 year group there are 9 people (30\%) with normal Pb level, this may be due to the Threshold Limit Level of Pb at the gas station in Makassar below the mean value and there are 6 people (20\%) with abnormal Pb level. Gas station operators who do not use protection equipment such as masks and gloves are susceptible to exposure of lead particles that came out directly from the gas waste-pipes and inhaled gasoline vapour at a higher rate compared to operators using protective equipment \hyperref[b5]{[6]}. Fig.  {\ref 2} V. 
\section[{Conclusion}]{Conclusion}\par
There is a significant correlation between work duration of gas station operators with mucociliary transport time and Pb level. The longer the work duration of gas station operators are the higher risk to have slower mucociliary transport time and risk of lead exposure.\par
There is no significant correlation between work duration with eosinophil and neutrophil.\begin{figure}[htbp]
\noindent\textbf{11} \par 
\begin{longtable}{P{0.4473684210526315\textwidth}P{0.042382271468144044\textwidth}P{0.28725761772853187\textwidth}P{0.07299168975069252\textwidth}}
Categories\tabcellsep \multicolumn{2}{l}{Frequency (n)}\tabcellsep Percentage (\%)\\
Age 15 -25 years old 26 -35 years old\tabcellsep 23 7\tabcellsep \tabcellsep 76,7 23,3\\
Gender Men Women\tabcellsep 12 18\tabcellsep \tabcellsep 40,0 60,0\\
Work Duration < 1 year ? 1 year\tabcellsep 15 15\tabcellsep \tabcellsep 50,0 50,0\\
Smoking History Yes No\tabcellsep 10 20\tabcellsep \tabcellsep 33,3 66,7\\
Atopic History Yes No\tabcellsep 4 26\tabcellsep \tabcellsep 13,33 86,67\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Primary Data, 2017}\\
\multicolumn{2}{l}{Unpaired t test result showed a significant}\tabcellsep \multicolumn{2}{l}{disorder, 11 normal people (36.67 \%), and mean value}\\
\multicolumn{2}{l}{correlation between mucociliary transport time with work}\tabcellsep \multicolumn{2}{l}{8.48 (SD±4.49). while in the work duration ? 1 year}\\
\multicolumn{2}{l}{duration < 1 year and ? 1 year with p value=0,01}\tabcellsep \multicolumn{2}{l}{group, there are 3 normal people (10\%), 12 people}\\
\multicolumn{2}{l}{(p<0.05). There are 4 people in a group with work}\tabcellsep \multicolumn{2}{l}{(40\%) experienced the disorder and mean value 14.68}\\
\multicolumn{2}{l}{duration < 1 year (13.33\%) who experienced the}\tabcellsep \multicolumn{2}{l}{(SD±3.39). (}\end{longtable} \par
 
\caption{\label{tab_0}Table 1 )Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
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\caption{\label{tab_1}Table 2 )}\end{figure}
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\noindent\textbf{2} \par 
\begin{longtable}{P{0.27180232558139533\textwidth}P{0.22238372093023256\textwidth}P{0.04941860465116279\textwidth}P{0.024709302325581394\textwidth}P{0.07906976744186046\textwidth}P{0.13343023255813954\textwidth}P{0.034593023255813954\textwidth}P{0.034593023255813954\textwidth}}
\tabcellsep \multicolumn{4}{l}{Mucociliary Transport Time (MTT)}\tabcellsep \tabcellsep \tabcellsep \\
Categories\tabcellsep \multicolumn{2}{l}{Disorder}\tabcellsep \tabcellsep Normal\tabcellsep Mean±SD\tabcellsep OR\tabcellsep p\\
\tabcellsep n\tabcellsep \%\tabcellsep n\tabcellsep \%\tabcellsep \tabcellsep \tabcellsep \\
Work Duration < 1 Year Work Duration ? 1 Year\tabcellsep 4 12\tabcellsep 13,33 40\tabcellsep 11 3\tabcellsep 36,67 10\tabcellsep 8,48±4,49 14,21±4,07\tabcellsep 11 **\tabcellsep 0,01 *\end{longtable} \par
 
\caption{\label{tab_2}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_3}Table 3 )}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.3602729885057471\textwidth}P{0.017097701149425288\textwidth}P{0.029310344827586206\textwidth}P{0.013433908045977012\textwidth}P{0.332183908045977\textwidth}P{0.07816091954022988\textwidth}P{0.007327586206896552\textwidth}P{0.012212643678160919\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{Lead Level (Pb)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Categories\tabcellsep \multicolumn{2}{l}{Not Normal}\tabcellsep \multicolumn{2}{l}{Normal}\tabcellsep Mean±SD\tabcellsep OR\tabcellsep p\\
\tabcellsep n\tabcellsep \%\tabcellsep n\tabcellsep \%\tabcellsep \tabcellsep \tabcellsep \\
Work Duration < 1 Year Work Duration ? 1 Year\tabcellsep 1 6\tabcellsep 3,33 20\tabcellsep 14 9\tabcellsep 46,67 30\tabcellsep 6,45±2,14 10,92±1,89\tabcellsep 9 **\tabcellsep < 0,001 *\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{* Unpaired t test, ** Chi square test}\\
\multicolumn{4}{l}{Comparison of eosinophil count in nasal}\tabcellsep \multicolumn{4}{l}{(3,33\%) in work duration < 1 year group with abnormal}\\
\multicolumn{4}{l}{mucosa analysis in the gas station operators in}\tabcellsep \multicolumn{4}{l}{eosinophil count and 14 people (46,67\%) with normal}\\
\multicolumn{4}{l}{Makassar with work duration between < 1 year and ? 1}\tabcellsep \multicolumn{4}{l}{count. In work duration ? 1 year group, there are 3}\\
\multicolumn{4}{l}{year using fisher test shows no significant correlation}\tabcellsep \multicolumn{4}{l}{people (10 \%) with abnormal eosinophil count and 12}\\
\multicolumn{4}{l}{with p value p=0,29 (p>0,05). There is 1 person}\tabcellsep \multicolumn{4}{l}{people (40\%) with normal eosinophil count. (Table 4)}\end{longtable} \par
 
\caption{\label{tab_4}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.27987804878048783\textwidth}P{0.05701219512195122\textwidth}P{0.09847560975609757\textwidth}P{0.06737804878048781\textwidth}P{0.041463414634146344\textwidth}P{0.03628048780487805\textwidth}P{0.051829268292682924\textwidth}P{0.08292682926829269\textwidth}P{0.06737804878048781\textwidth}P{0.062195121951219505\textwidth}P{0.005182926829268293\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{Eosinophil}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Neutrophil}\\
Categories\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Normal}\tabcellsep \multicolumn{2}{l}{Not Normal}\tabcellsep p\tabcellsep \multicolumn{2}{l}{Normal}\tabcellsep \multicolumn{2}{l}{Not Normal}\tabcellsep p\\
\tabcellsep n\tabcellsep \%\tabcellsep n\tabcellsep \%\tabcellsep \tabcellsep n\tabcellsep \%\tabcellsep n\tabcellsep \%\\
Work Duration < 1 Year\tabcellsep 14\tabcellsep 46,67\tabcellsep 1\tabcellsep 3,33\tabcellsep \tabcellsep 12\tabcellsep 40\tabcellsep 3\tabcellsep 10\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 0,29 *\tabcellsep \tabcellsep \tabcellsep \tabcellsep 0,21 *\\
Work Duration ? 1 Year\tabcellsep 12\tabcellsep 40\tabcellsep 3\tabcellsep 10\tabcellsep \tabcellsep 9\tabcellsep 30\tabcellsep 6\tabcellsep 20\end{longtable} \par
 
\caption{\label{tab_5}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.39972972972972975\textwidth}P{0.03369369369369369\textwidth}P{0.3936036036036036\textwidth}P{0.022972972972972974\textwidth}}
15\tabcellsep \tabcellsep \\
12\tabcellsep \tabcellsep \\
9\tabcellsep \tabcellsep Normal MTT\\
\tabcellsep \tabcellsep Slower MTT\\
6\tabcellsep \tabcellsep \\
3\tabcellsep \tabcellsep \\
0\tabcellsep \tabcellsep \\
Work duration < 1 year\tabcellsep \tabcellsep Work duration > 1 year\\
18\tabcellsep \tabcellsep \\
14\tabcellsep \tabcellsep \\
11\tabcellsep \tabcellsep \\
7\tabcellsep \tabcellsep Pb Normal Pb Not Normal\\
4\tabcellsep \tabcellsep \\
0\tabcellsep \tabcellsep \\
Work duration < 1 year\tabcellsep \multicolumn{2}{l}{Work duration > 1 year}\\
\multicolumn{2}{l}{The results of fisher test analysis showed no}\tabcellsep eosinophil and p=0,21 (p>0,05)\tabcellsep for neutrophil.\\
\multicolumn{2}{l}{significant correlation of work duration with eosinophil}\tabcellsep \multicolumn{2}{l}{Zachariah 2015. suggested that there is no significant}\\
\multicolumn{2}{l}{and neutrophil count in nasal mucosa of gas station}\tabcellsep \multicolumn{2}{l}{correlation between the mass of work with eosinophil}\\
\multicolumn{2}{l}{operators in Makassar, where p=0.29 (p>0,05) for}\tabcellsep \multicolumn{2}{l}{and neutrophil. Neutrophils are short-lived cells with a}\end{longtable} \par
 
\caption{\label{tab_6}}\end{figure}
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