Background: Industrial activities contribute to poor air quality either directly or through background concentrations, bringing to fore health issues regarding the health effects of the release of malodourous air pollutants. Methods: This research focused on the effects of exposure of air pollutants from industrial facilities and traffic on school children by using selected airway inflammation biomarker, cysteinyl leukotrienes (CysLTs), in sputum. Questionnaires adapted from the American Thoracic Society (ATS) and the International Study of Asthma and Allergies in Childhood (ISAAC) were used to compile respiratory symptoms, history of exposure, and demographic data. Results: CysLTs level measured by using enzyme.linked immunosorbent assay (ELISA) was higher in the exposed group (0.402 ± 0.389 ng/mL) than in the comparative group (0.191 ± 0.231 ng/mL). A strong, significant correlation was established between sulfur dioxide (SO2) (r = 0.924, P < 0.001) and particulate matter 2.5 (PM2.5) (r = 0.242, P = 0.014), with the levels of CysLTs among school children in exposed group. This study reveals that SO2 is the most significant factor that influenced CysLTs levels among school children at P less than 0.001. Conclusion: CysLTs are proven to be reliable biomarkers of airway inflammation in healthy children, whereas sputum method is proven to be a reliable, safe and noninvasive procedure for school children with their reproducibility and sensitivity as portrayed in this study. Thus, the findings provide fundamental aspects relevant to future interventions to healthy children living near an industrial area from the environmental scope.
Keywords: Cysteinyl leukotrienes, NO2, PM10, PM2.5, school children, SO2
|How to cite this article:
Suhaimi NF, Jalaludin J, Bakar SA. Cysteinyl leukotrienes as biomarkers of effect in linking exposure to air pollutants and respiratory inflammation among school children. Ann Trop Med Public Health 2017;10:423-31
|How to cite this URL:
Suhaimi NF, Jalaludin J, Bakar SA. Cysteinyl leukotrienes as biomarkers of effect in linking exposure to air pollutants and respiratory inflammation among school children. Ann Trop Med Public Health [serial online] 2017 [cited 2020 Jul 8];10:423-31. Available from: https://www.atmph.org/text.asp?2017/10/2/423/208691
Extreme growth of industrialization contributes to air pollution problems in the nearby areas. Particulate matter is a type of air pollutant that comes in numerous sizes and compositions, as well as vary in the compounds attached to them. Two common groups of particulate matters include those with up to 10 µm aerodynamic diameter (PM10) and those with up to 2.5 µm aerodynamic diameter (PM2.5). Air pollutants in the form of gases include sulfur dioxide (SO2) and oxides of nitrogen (NOx). SO2 is formed when sulfur or compounds that contain sulfur are burned in the air, whereas nitrogen dioxide (NO2) is formed when nitrogen oxide (NO) combines with oxygen (O2) in the air.
Numerous epidemiologic and toxicologic studies conducted worldwide have shown various health effects associated with short‐term and long‐term exposure to air pollutants.,,, The deposition of air pollutants depends on their sizes, whereby certain minute pollutants could reach the gas exchange region. For example, particulate matter could trigger inflammatory responses via oxidative and toxic compound imported on their surface, which will cause alveolar activation and acute inflammation.
Continuous exposure to a polluted environment may cause health impacts such as throat irritation and breathing difficulty to begin with, whereas serious health problems could develop later especially for the high‐risk group, predominantly in children. Residents living near industrial areas are also considered as high‐risk group, as they are exposed to high levels of air pollution around these areas. It is a concern that school children in this area are continuously exposed to industrial air pollutants, which may slowly impair their physical growth and biologic development.
Biomarkers have been utilized within clinical research studies of asthma to organize samples of the population for further study. Some of the biomarkers have been validated to be used as asthma biomarkers; most of the new biomarkers are still emerging with inconclusive applicability and lack of standardization. The administration of CysLTs in airway inflammation study is also relatively new in Malaysia. CysLTs are biomarkers of effect for this study, which means they will show a measurable biochemical or other variation in an organism, that can be recognized as associated with an established or possible health problem, depending on the magnitude of exposure.
|Materials and Methods|
This cross‐sectional comparative study was conducted in an industrial area and a comparative area in Kemaman, Terengganu. Terengganu is located on the East Coast of Peninsular Malaysia. Primary schools with 5 km from industrial sites were considered as exposed schools, whereas schools in Kemaman that were located at a distance of more than 5 km from an industrial site with less traffic were considered as comparative schools.
Groups of primary school children in the exposed and comparative areas of Kemaman, Terengganu, were recruited in this study. According to the enrolment statistics by Terengganu State Education Department in 2015, there were 122,102 enrolments in primary schools in Terengganu, out of which 21,103 were from 41 government primary schools in Kemaman district, locally known as Sekolah Kebangsaan. Respondents in this study were all grade 5 students ages between 10 and 11 years in 2015, so the sampling cohort included all male and female students of grade 5 studying at selected primary schools in Kemaman District in year 2015. The location of their schools was used as the strata. These children must have their parents or guardians consent to participate in the study. A list of registered school children was obtained from the school teachers of respective primary schools.
The current study comprises 204 respondents who had exposure monitoring in schools and biomarker collection. Inclusion criteria for selection of respondents included primary school children aged between 10 and 11 years during school year 2015, Malaysian citizen, Malay ethnic, and free from history of doctor‐diagnosed chronic respiratory diseases or allergies. Exclusion criteria for the selection of respondents included primary school children not registered in the selected schools, whose parents refuse to fill in the questionnaires completely, and who experienced symptoms of upper respiratory tract infections such as cough, sore throat, runny nose, and nasal congestion during sampling period of sputum samples. Those who fulfilled the inclusion criteria were recruited through simple random sampling based on the inclusion criteria listed above.
Parents or guardians of participating children received the questionnaires adapted from ISAAC and ATS, which were translated to Malay for those parents who did not understand English. The adapted questionnaires were pretested on at least 10% of the respondents prior to data collection. This was done to assess the understanding of the respondents of the questionnaires. The questionnaires were distributed by the respective school teachers to all school children present at the schools. It was highly recommended that respondents’ parents fill the questionnaires, which provided the information on sociodemographic and socioeconomic background of the respondents, their home and school environment, and their respiratory health, whether they had recurring respiratory symptoms or not. Moreover, consent forms were also given to be read and signed by the parents or guardians of the respondents. All respondents were given a choice to continue participating in the study or to withdraw at any time.
The measurements at schools were conducted for 4 h during the morning school session. Classrooms were chosen as the air sampling location because they were deemed as the most frequently area visited by the children. Measurements of indoor PM10 and PM2.5 in the buildings of schools were taken by using DustTrak DRX Aerosol Monitor 8534. The instruments were placed at 1.0 m above the floor at the back of the classroom. Data were collected in real‐time in each sample location, which was easily done with the incorporation of data logger. Both SO2 and NO2 were measured by using LaMotte Air Sampling Pump with model 7714 for SO2 and model 7690 for NO2, which applied colorimetric method. The concentration of the gases was determined after the air was absorbed into the absorption solution through an impinger in a bubbler tube at a flow rate of 1 L/min for each gas. All of these instruments were placed away from sources that generate heat, school children, windows, and doors to minimize other sources that could contribute to the detection of these air pollutants.
Recruited children had to undergo spirometry first before being further selected to perform sputum induction for biomarker collection. Sputum induction was performed by an ultrasonic nebulizer (Model CUN60, Citizens Systems Japan Co. Ltd.) using 4.5% hypertonic saline in the school infirmaries. Sputum samples were collected during school hours, concurrently with pollutants exposure monitoring in classrooms. Sputum collection took place before the recess session, since sputum specimens are best if coughed up first thing in the morning. Samples collected in specimen bottles were kept in an ice box at about 4°C before being transported to the Environmental Chemistry Laboratory. The fresh sputum samples were stored at –20°C for 3-5 days to temporarily preserve the samples before being processed further. Then the processing of sputum samples took place in the Chemical Pathology Laboratory, UPM. One milliliter of each fresh sputum sample was diluted with 1 mL of normal saline. Then, the specimen mixture was centrifuged at 4°C for 30 min at 17,000g. The supernatant was stored at –80°C before being used for measuring the concentration of selected biomarker, CysLTs, using an ELISA.
Antigenicity of CysLTs
The sputum samples taken from the respondents were assessed quantitatively for concentration of CysLTs using ELISA according to the manufacturer’s instruction. The ELISA kit (Human CysLTR1 Kit, Elabscience Biotechnology Co. Ltd.) used in the in vitro quantitative determination of human cysteinyl leukotriene receptor 1 (CysLTR1) applied the method of sandwich ELISA. The 96‐well plates in the ELISA kit were precoated with an antibody that was specific to CysLTR1, which binded all of the CysLTs monoclonal antibody added to the well. Then, a biotinylated detection antibody that was specific for CysLTR1 was added to each well containing the samples, followed by the addition of Avidin‐Horseradish Peroxidase (HRP). Products with distinct color, blue, were produced proportionately to the amount of CysLTs tracer bound to the wells from the samples obtained. This means that the intensity of this color is inversely proportional to the amount of CysLTs captured in the plate. The enzyme–substrate reaction was stopped by the addition of sulfuric acid, which changed the color of the solution to yellow. The optical density (OD) of the samples was measured at a wavelength of 450 ± 2 nm, where the OD values were proportional to the concentration of CysLTR1. The CysLTR1 concentration was calculated by comparing the OD of the samples to the standard curve.
Data collected were analyzed using the Statistical Package for Social Science Version 22 (SPSS Ver. 22). Descriptive analysis was carried out to convey the important aspects of the data collected, including screening and organizing the data. Data normality of continuous variables was determined based on Shapiro Wilks. The normally distributed data were analyzed using parametric tests, while the data not distributed normally were analyzed using nonparametric tests such as Mann–Whitney U‐test. Mean and standard deviation were reported for the data that were normally distributed, whereas median and interquartile range were reported for the data that were not distributed normally.
Continuous data such as the concentrations of air pollutants and CysLTs were categorized into high or low as the cutoff point based on the mean value for normally distributed data, or median value for non‐normally distributed data. Meanwhile, categorical data such as respiratory symptoms were categorized into yes or no to indicate their presence or absence, respectively, on the basis of the returned questionnaires.
In addition, Spearman’s rho test was performed to determine any correlation between two continuous variables being measured in this study. Multiple linear regressions were performed when the dependent and independent variables were continuous variables. Meanwhile, multiple logistic regressions were performed when there were dichotomous, ordinal, or continuous variables.
Of the 645 questionnaires delivered, only 568 were returned. The response rate for both locations regardless of whether the parents or guardians agreed to participate or not was approximately 88%. According to the location analysis, 294 out of 331 (89%) questionnaires were returned among the exposed group, whereas 274 out of 314 (87%) questionnaires were returned among the comparative group. A total of 204 of 341 respondents proceeded with exposure monitoring in schools and biomarker collection.
Sociodemographic and socioeconomic information
Forty‐eight boys (47.1%) and 54 girls (52.9%) from the exposed area participated in biomarker investigation, which was carried out concurrently with exposure monitoring in schools. As for the comparative area, 54 boys (52.9%) and 48 girls (47.1%) participated in the same procedure. The distribution of sociodemographic and socioeconomic background is shown in [Table 1] and [Table 2], respectively. There was no significant difference in parents’ education level, types of residences, and all variables of socioeconomic background for both groups at P less than 0.05.
|Table 1: Comparison of Sociodemographic Background
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|Table 2: Comparison of Socioeconomic Background
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Comparison of air pollutants in children’s classrooms
[Table 3] shows that the concentrations of air pollutants were higher among the exposed group for each pollutant than among the comparative group.
|Table 3: Comparison of Air Polutants in Children’s Classrooms
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Concentration of CysLTs levels
Human CysLTs ELISA kit was specifically used for determining the CysLTs concentration in sputum samples obtained from the respondents. Biomarker collection was carried out concurrently with monitoring the exposure during school session, before recess time. The results in [Table 4] show that the CysLTs level was higher among the exposed group than among the comparative group.
|Table 4: CysLTs Level
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Correlation between the concentrations of air pollutants and CysLTs levels
[Table 5] shows the relationship between concentrations of air pollutants at schools with the levels of CysLTs among the school children. A significant correlation was established between SO2 and PM2.5 in schools with the levels of CysLTs among school children in the exposed group. There is a strong linear relationship between SO2 in schools and the level of CysLTs in the exposed group (r = 0.924, P < 0.001), with r2 = 0.854 for the exposed group. Nevertheless, there is a poor linear relationship between PM2.5 in schools and the level of CysLTs in the exposed group (r = 0.242, P = 0.014), with r2 = 0.059 for the exposed group. On the other hand, there is a poor linear relationship between PM10 and NO2 in schools in both groups, as well as PM2.5 and SO2 in the comparative group with the levels of CysLTs among respondents; the correlation is not significant.
|Table 5: Correlation between Air Pollutants Concentration in Classrooms with CysLTs Levels
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Factors influencing the levels of CysLTs
Different indoor and outdoor activities contribute to levels of air pollutants in schools and residences. Multiple linear regressions were performed to determine the factors that are significantly associated with the levels of CysLTs among school children after controlling the confounders. [Table 6] shows four variables that represent factors at schools that are significantly associated with the levels of CysLTs among school children when they were statistically analyzed at multivariate level. Results from multivariate level analysis using multiple linear regressions reveal that SO2 is the most significant factor in schools that influenced CysLTs levels among school children. There is a significant direct linear relationship between SO2 in schools and CysLTs (P < 0.001). An increase of 1 µg/m3 in SO2 in the schools causes in an increase of 0.001 ng in CysLTs among the school children. A variance of 72.6% in CysLTs can be explained by SO2 in schools.
|Table 6: Factors Influencing the Levels of CysLTs
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Among the respondents given the questionnaires, the gender distribution in both areas was of almost equal frequency, with more participants, irrespective of the gender, from the exposed area (188 respondents) than from the comparative area (153 respondents). The selection of school children did not entirely represent the distribution of school children in Malaysia because only Malays were included in the study. This was because the national primary schools in both areas have either a large majority or a total population of only Malay school children. Selecting one ethnicity only in this study was also to reduce the variation in the expression level among the respondents, which could affect the concentration level of biomarker. Coe et al. found that genetic factors accounted for some of the differences in interleukin 6 (IL‐6) between Japanese and Americans. They conducted a study to understand IL‐6 as a key factor in the physiology of inflammation. Moreover, only school children who were free from doctor‐diagnosed respiratory illnesses and upper respiratory tract infections were selected to be involved in biomarker collection. This screening was done to ensure only healthy respondents were recruited because respiratory illnesses and infections could have been the confounding factors while studying the concentration of biomarker in this study. However, age was matched between the two areas, whereby all of the school children involved were 11 years old according to year.
Education level of parents is important to understand the questions posed in the questionnaires. In term of parents’ education level, there was no significant difference among fathers and it is observed that more than half of the parents from both groups completed secondary education. When looking at the socioeconomic background, it is found that there is no difference in the median of total income, dwellers, rooms, and crowding ratio and only a slight difference in the median of household income per person in each house for both groups. Thus, it can be said that respondents in both groups were living in almost the same sociodemographic and socioeconomic conditions. Nazariah et al. had a different finding, wherein the socioeconomic background for total income, total dwellers, and total rooms between the school children in urban and rural area were significantly different at P less than 0.05.
The target indoor air pollutants were measured in classrooms to identify the magnitude of exposure to the school children. As determined by the median, the exposed group’s exposures to PM10 in the classrooms were 2.2 times higher than the comparative group’s exposures. Nevertheless, it is still under threshold limit by the Malaysia Ambient Air Quality Standard (MAAQS) for 24‐h at 150 μg/m3 as determined by the Department of Environment Malaysia. In addition, the exposed group’s exposures to PM2.5 in classrooms were significantly 1.9 times higher than the comparative group’s exposures. The concentration of PM2.5 in the classrooms in exposed area exceeded the permissible limit by MAAQS for 24‐h at 75 μg/m3. Meanwhile, the concentration of PM2.5 in the classrooms in comparative area had not exceeded the permissible limit by MAAQS. The exposed group’s exposures to SO2 in classrooms were 2.7 times higher than the comparative group’s exposures. Nevertheless, these are still under the threshold limit by MAAQS for 1‐h at 350 μg/m3. In addition, the exposed group’s exposures to NO2 in classrooms were 9.7 times higher than the comparative group’s exposures. However, the concentration of NO2 in classrooms did not exceed the permissible limit by MAAQS for 1‐h at 320 μg/m3.
High concentration of air pollutants in classrooms may be caused by few outdoor and indoor sources. Particulate matters are emitted from transportation, industrial activities, and natural sources. SO2 and NO2 are emitted from industrial activities or automobile exhausts. PM2.5 may remain suspended in the air for a long period, while PM10 may remain suspended in the air for several days and can spread by winds over long distances from the original source. Distances of schools and residences from main roads and industrial facilities play a role in determining the concentration of air pollutants. Motor vehicles release particulate matters either from burning of fuels or from wear of tires on roads. Development of industrial activities in exposed area increases with the development of road networks, thus increasing the number of vehicles and their occurrences to travel on the roads.
Some of the outdoor sources of PM10 and PM2.5 include natural sources, which are harder to control such as pollen, forest fires, and sea salt. As for the outdoor sources of human activities, PM10 is commonly released from roadways, construction, and agricultural activities, whereas PM2.5 is commonly released from industrial processes and motor vehicles. In term of distance from industries, which is a crucial factor in this study, schools located within 5‐km radius of industrial facilities were chosen to be among the exposed group, whereas schools located greater than 5‐km radius of industrial facilities were chosen to be among the comparative group. Air pollutants from industries and power plants work in the same manner as the pollutants from traffic emissions, as they may easily penetrate indoors via windows, doors, and any holes in the buildings. Furthermore, these stationary sources are also localized sources of air pollutants. Hence, this study is performed to assess whether residents living near these industries and power plants have more exposure and health effects from the exposure.
Findings from this study are in accordance with those of a local study by Jalaludin et al., who carried out measurements of PM10, PM2.5, and NO2 in classrooms in primary schools in Malaysia to compare the exposure between urban and rural areas. Similar methodology had been used to measure the particulate matters and NO2. They reported that the mean concentration of PM10 in classrooms of urban group (87.04 ± 16.35 µg/m3) was 1.5 times higher than the mean concentration of PM10 in classrooms of rural group (56.76 ± 6.70 µg/m3). Besides, PM2.5 in the classrooms of urban group (50.72 ± 10.65 µg/m3) was 1.8 times higher than the mean concentration of PM2.5 in the classrooms of rural group (28.36 ± 4.41 µg/m3). Moreover, NO2 in the classrooms of urban group (0.121 ± 0.038 µg/m3) was 3.8 times higher than the mean concentration of NO2 in the classrooms of rural group (0.032 ± 0.006 µg/m3).
Biomarker is an entity that expresses the condition of a biologic organism and an organism’s function in a variety of biologic processes. Biomarkers usually indicate actual exposure through the concentration of pollutants that have entered into the human body. Nevertheless, the concentration of a biomarker in a body does not necessarily reflect the exposure level to air pollutants. There are many other factors that can have the same effects, which are also studied in this study as controlled confounders. As determined by the median, the exposed group’s level of CysLTs was 2.1 times higher than the comparative group’s level of CysLTs with a significant difference. This could reflect the pathophysiology of respiratory inflammation that is affected by inhalation of air pollutants.
CysLTs involve chemical mediators, specifically lipid mediators that are founding factors in inflammation. The processes involved in respiratory inflammation may be explained in a cascading effect. When environmental stimuli such as air pollutants enter the respiratory tract, these foreign entities are recognized as a threat to the body. The immune system activates and recruits inflammatory cells, which then infiltrate the lungs and secrete lipid mediators, include CysLTs. This process results in structural changes in the respiratory tract such as airway remodeling due to smooth muscle contraction, and this happens within minutes of stimulation. Beck‐Speier et al. performed an in vivo study with allergic mice and in vitro study with primary rat alveolar macrophages to find the underlying mechanism that explains the exposure to environmental particles and its susceptibility to asthmatic patients. Both above‐mentioned studies found that an increase in the release of endogenous lipid mediators following particles exposure affected the pro‐ and anti‐inflammatory balance of the mediators.
Air pollutants have been shown to trigger respiratory symptoms of asthmatic patients among healthy respondents; [13,18] thus, a similar concept is applied in this study to understand the relationship between leukotrienes level in healthy respondents who are exposed to air pollutants and the concentration of the air pollutants. Wan et al. found that the CysLTs level in exhaled breath condensate samples were almost similar in asthmatic children (0.072 ng/15 min) and healthy children (0.091 ng/15 min) in Taiwan. They claimed that the healthy children could have been exposed to environmental pollutants, which may cause mild, persistent airway inflammation, and therefore show slightly higher concentration of CysLTs. Besides, a cross‐sectional comparative study of 7–12‐year‐old asthmatic and healthy children found significantly higher levels of CysLTs in the exhaled breath condensate samples of asthmatic children compared with healthy children (77.3 ± 21.6 pg/mL vs. 60.3 ± 26.8 pg/mL at P = 0.0005). The respondents live in Leon, a city in Mexico, that is claimed to be highly polluted with PM10. The comparative children in this study showed higher levels of CysLTs (0.191 ± 0.231 ng/mL) compared with healthy children, which was probably due to different approach taken in obtaining the biologic samples, as well as sample processing.
On the other hand, the results show that there is a significant correlation between the concentration of PM2.5 and SO2 in schools and the level of CysLTs. The correlation established suggests that PM2.5 and SO2 from schools in the exposed group stimulated the production of CysLTs among the respondents. There is a poor linear relationship between PM2.5 in schools and the level of CysLTs in exposed group (r = 0.242, P = 0.014). Jalaludin et al., who performed the study among primary school children in Malaysia, reported fair linear correlations between indoor PM2.5 (r = 0.493, P < 0.001) with tumor necrosis factor alpha (TNF‐α) in the urban school area, as well as between indoor PM2.5 (r = 0.356, P < 0.001) with TNF‐α in the rural school area. Furthermore, Nazariah et al., who conducted similar study among primary school children in Malaysia, reported fair linear correlations between indoor PM2.5 (r = 0.506, P < 0.001) with IL‐6 in the urban school area, as well as between indoor PM2.5 (r = 0.519, P < 0.001) with IL‐6 in the rural school area.
There is a strong linear relationship between SO2 in schools and level of CysLTs in the exposed group (r = 0.924, P < 0.001). A retrospective analysis in controlled human exposure study was done by Thompson et al. among 45 adults aged 18–40 years old who were discovered to have an increase in IL‐6 that positively correlated with SO2 investigated, with a 4‐day moving average that showed a 0.25 increment of standard deviation (SD) in IL‐6 per interquartile range (IQR) of SO2 (95% confidence interval [CI] = 0.06–0.43). The positive correlation between IL‐6 and SO2 was reduced with moving averages longer than 6 days. SO2 data were considered at the time when blood was drawn for IL‐6 samples along with 2‐h to 7‐day moving averages. This may depict the later effects of SO2 exposure with a longer exposure to induce inflammatory responses, but not more than 6 days.
Besides, a study conducted by Liu et al. among asthmatic children aged 9–14 years old collected the biomarkers in their study from exhaled breath condensate samples. Thiobarbituric acid reactive substances (TBARS), one of the biomarkers, were positively correlated with same‐day, 2‐day average and 3‐day average SO2, same‐day and 3‐day average NO2, also same‐day, lag 1 day, 2‐day average, and 3‐day average PM2.5. Meanwhile, 8‐isoprostane, the other biomarker was positively associated with same‐day SO2. Elevated concentration of TBARS and 8‐isoprostane among asthmatic children exposed to higher concentration of SO2, NO2, and PM2.5 suggest the visible effects of ambient air pollutants on airway inflammation.
From multiple linear regression model, the results showed that the most significant factor that was associated with the level of CysLTs is SO2 in schools. These analyses were carried out after taking into considerations other factors that could influence the level of CysLTs such as socioeconomic and sociodemographic factors. For the schools, there was a significant direct linear relationship between SO2 in schools and CysLTs (P < 0.001). With an increase of 1 µg/m3 in SO2 in schools, CysLTs among school children increased by 0.001 ng. A 72.6% variance in CysLTs could be explained by SO2 in schools.
Two previous studies suggested that exposure to SO2 has a great impact on respiratory health, which is supported by the fact that SO2 can penetrate into the alveolar regions in the lungs, enter blood stream, and then leave via urine., Industries that release SO2 from their processes include petroleum refineries, mineral ores smelting plants, factories that process coal, fertilizer factories, and sulfuric acid manufacturers. Kemaman is considered as the hub of the petrochemical industry, which could be the main contributor to the high concentration of SO2 in the vicinity. Other than petrochemical industry, Kemaman also has heavy industries such steel industries, which also perform processes that emit SO2 in the ambient air. In addition, SO2 could be released from a large bitumen refinery that conducts industrial processes involving petroleum and petrochemical products.
Limitation of this study includes the age of the respondents. This was unintentional because the study was originally proposed to be conducted among 11‐ and 12‐year‐old children. This specific age may cause limitation among the respondents because 12‐year‐old children were not allowed by the Ministry of Education to join the study. Therefore, the study was only representative of grade 5 children in both areas.
Findings from this study strongly suggest that school children in the exposed area of Kemaman, who were exposed to a higher level of air pollutants (PM10, PM2.5, SO2, and NO2), had higher risk of developing inflammatory responses. Hence, these significantly influenced the level of biomarker CysLTs, indicating airway inflammation. SO2 in schools was the most significant factor that played a role in the secretion of CysLTs as inflammatory mediator in the lungs. Nevertheless, the definite causal agents are difficult to be elucidated because there were several limitations in the nature of this study, which were predominantly result of the design of the study itself. For the nature of a cross‐sectional study, the exposure and the health outcome were concurrently assessed. Even though the data in the study showed that certain relationships were established between the exposure and the outcome, the proof of evidence was insufficient. The true measure of association could only be determined if the entire population participates and precise data on exposure and outcome are gathered. Despite these limitations, CysLTs are reliable biomarkers of airway inflammation in healthy children, whereas sputum method is proven to be a reliable, safe, and noninvasive procedure for school children with their reproducibility and sensitivity as portrayed in this study.
This study was funded Ministry of Education Malaysia under Fundamental Research Grant Scheme (project code: 04‐01‐14‐1449FR).
NFS: Involved in the design, implementation and analysis of the study. She is also the lead author of this article. JJ: Involved in the initial design of the study, contributed to the interpretation of the results of the article, provided advice and input on the statistical analysis. She is also the recipient for the research fund for this study and the corresponding author of this article. SAB: Provided advice and input in the analysis of biomarkers. She is also involved in writing the article. All authors read and approved the final manuscript.
Ethics and consent
The study was presented to the ethics committee for research involving human subjects of Universiti Putra Malaysia with reference number of FPSK (EXP14) P106. The respondents were asked to participate in the study on a voluntary basis with permission from parents or guardians. Consent forms were given to be read and signed by parents or guardians. All respondents were given a choice to continue participating in the study or to pull out at any time when they choose to do so. The information about respondents involved in this research remains confidential.
We wish to extend our special gratitude to all respondents, their parents and guardians, management of schools, and laboratory staffs of Faculty of Medicine and Health Sciences, UPM for their willingness and supports to engage in this research. We also thank Ir. Dr. Mohd. Habir Ibrahim from TATIUC who shared useful information that assisted the study. We are especially indebted to Ministry of Education Malaysia for funding this project under Fundamental Research Grant Scheme (project code: 04‐01‐14‐1449FR).
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
Sopian NA, Jalaludin J, Mohd Tamrin SB. Risk of respiratory health impairment among susceptible population living near petrochemical industry ‐ A review article. Iran J Public Health 2016;45(Suppl 1):9‐16.
Hussin FS, Jalaludin J. Association of PM10 and PM2.5 exposure with respiratory health of the children living near palm oil mill, Dengkil. MJPHM 2016;16(Suppl 2):20-6.
Choo PC, Jalaludin J, Hamdon TR, Adam NM. Preschools’ indoor air quality and respiratory health symptoms among preschoolers in Selangor. Proc Environ Sci 2015;30:303-8.
Wesley AD, Jalaludin J. Indoor air pollutant exposure and eosinophil cationic protein as an upper airway inflammatory biomarker among preschool children. Proc Environ Sci 2015;30:297-302.
Vattanasit U, Navasumrit P, Khadka MB, Kanitwithayanun J, Promvijit J, Autrup H, Ruchirawat M. Oxidative DNA damage and inflammatory responses in cultured human cells and in humans exposed to traffic‐related particles. Int J Hyg Environ Health 2014;217(1):23-33.
Suhami NF, Jalaludin J. Biomarker as a research tool in linking exposure to air particles and respiratory health. Bio Med Res Int 2015; 2015:Article ID 962853.
World Health Organization. Biomarkers and human biomonitoring. 2011. Available from: http://www.who.int/ceh/capacity/biomarkers.pdf. [Last accessed on 2016 Apr 02].
Jabatan Pendidikan Negeri Terengganu. Maklumat Asas Pendidikan. 2015. Available from: http://jpnterengganu.moe.gov.my/bm/index.php/jabatan-pendidikan‐negeri‐terengganu. [Last accessed on 2016 Jan 15].
Jabatan Pendidikan Negeri Terengganu. Maklumat Asas Pendidikan: PPD Kemaman. 2015. Available from: http://jpnterengganu.moe.gov.my/bm/index.php/ppd‐kemaman. [Last accessed on 2016 Jan 15].
Colbeck I, Nasir ZA, Ali Z. Characteristics of indoor/outdoor particulate pollution in urban and rural residential environment of Pakistan. Indoor Air. 2010;20:40-51.
Baumann U, Göcke K, Gewecke B, Freihorst J, von Specht BU. Assessment of pulmonary antibodies with induced sputum and bronchoalveolar lavage induced by nasal vaccination against Pseudomonas aeruginosa: A clinical Phase I/II Study. Respir Med 2007;81:57.
Coe CL, Love GD, Karasawa M, Kawakami N, Kitayama S, Markus HR, Tracy RP, Ryff CD. Population differences in proinflammatory biology: Japanese have healthier profiles than Americans. Brain Behav Immun 2011;25:494-502.
Nazariah SSN, Juliana J, Abdah MA. Interleukin‐6 via sputum induction as biomarker of inflammation for indoor particulate matter among primary school children in Klang Valley, Malaysia. GJHS 2013;5:93-105.
Pidwirny M. Human alteration of the atmosphere. In: Understanding physical geography. Chapter 10. British Columbia: Our Planet Earth Publishing; 2014.
Jalaludin J, Syed Noh SN, Suhaimi NF, Md Akim A. Tumor necrosis factor‐alpha as biomarkers of exposure to indoor pollutants among primary school children in Klang Valley. AJAS 2014;11:1616-1630.
Ishmael FT. The inflammatory response in the pathogenesis of asthma. JAOA 2011;111:S11-S17.
Beck‐Speier I, Karg I, Behrendt H, Stoeger T, Alessandrini F. Ultrafine particles affect the balance of endogenous pro‐ and anti‐inflammatory lipid mediators in the lung: In‐vitro and in‐vivo studies. Part Fibre Toxicol 2012;9:27.
Ayuni NA, Juliana J, Ibrahim MH. Exposure to PM10 and NO2 and association with respiratory health among primary school children living near petrochemical industry area at Kertih, Terengganu. JOMB 2014;3:282-7.
Wan GH, Yan DC, Tseng HY, Tung TH, Lin SJ, Lin YW. Cysteinyl leukotriene levels correlate with 8‐isoprostane levels in exhaled breath condensates of atopic and healthy children. Pediatr Res 2013;74:584-91.
Linares Segovia B, Cortés Sandoval G, Amador Licona N,Guízar Mendoza JM, Núñez Lemus E, Rocha Amador DO, et al. Parameters of lung inflammation in asthmatic as compared to healthy children in a contaminated city. BMC Pulmon Med 2014;14 (111):1-7.
Thompson AM, Zanobetti A, Silverman F, Schwartz J, Coull B. Baseline repeated measures from controlled human exposure studies: Associations between ambient air pollution exposure and the systemic inflammatory biomarkers IL‐6 and fibrinogen. EHP 2010;118:120-4.
Liu L, Poon R, Chen L, Frescure AM, Montuschi P, Ciabattoni G, et al. Acute effects of air pollution on pulmonary function, airway inflammation, and oxidative stress in asthmatic children. EHP 2009;117:668-74.
Labelle R, Brand A, Buteau S, Smargiassi A. Hospitalizations for respiratory problems and exposure to industrial emissions in children. Environ Pollut 2015;4:77-85.
Haddam N, Samira S, Dumont X, Taleb A, Haufroid V, Lison D, Bernard A. Lung epithelium injury biomarkers in workers exposed to sulphur dioxide in a non‐ferrous smelter. Biomarkers 2009;14:292-8.
Source of Support: None, Conflict of Interest: None
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]