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Research Article

Modelling of NO2 Dispersion based on Receptor Position Due to Transport Sector in Padang City, Indonesia

Journal of Environmental Science and Technology: Volume 10 (5): 258-267, 2017

Vera Surtia Bachtiar, Purnawan , Reri Afrianita and Siti Hariani Ritonga

Abstract

Background and Objective: Nitrogen dioxide (NO2) is one of the pollutants produced by transport. NO2 concentration in the transportation sector was influenced by traffic characteristics, meteorological factors and receptor distance from the source. This study aims to model the patterns of nitrogen dioxide (NO2) dispersion in the roadside area based on the distance of receptor position from the roadway. Methodology: Model developed from the relation between the concentration of NO2 and traffic characteristics and meteorological conditions, such as traffic volume, traffic speed and wind speed. NO2 was obtained by measuring the NO2 gas at 25 points ambient air sampling conducted with a certain distance from the edge of the roadside. At the same time, was also carried out measurements of traffic characteristics and meteorological conditions. NO2 concentrations were measured by Griess Saltzman method, using impinger and a spectrophotometer. Statistical analysis was used to validated the model using t-test analysis. Results: The results show NO2 concentration varied in all sampling locations with the concentration of NO2 at point A 1 m is higher than the point B (5, 10, 25, 50 and 100 m). Regression models between NO2 with traffic characteristics and wind speed obtained from a distance of 1, 5, 10, 25, 50 and 100 m from the roadside. The model then was used to predict NO2 using the data traffic characteristics and the current wind speed measurements at 40 measurement points, for the distance of receptors were 1, 5, 10, 25, 50 and 100 m to view the NO2 dispersion. Conclusion: T-test analysis showed that the model can be used to predict dispersion of NO2.

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Vera Surtia Bachtiar, Purnawan , Reri Afrianita and Siti Hariani Ritonga, 2017. Modelling of NO2 Dispersion based on Receptor Position Due to Transport Sector in Padang City, Indonesia. Journal of Environmental Science and Technology, 10: 258-267.

DOI: 10.3923/jest.2017.258.267

URL: https://scialert.net/abstract/?doi=jest.2017.258.267

INTRODUCTION

The growth of motor vehicles on regional scale has becoming a major cause of rising levels of air pollution1. The major source of emission in the worldwide come from road transport. In Europe, road transport produces 40.5% emissions of oxides of nitrogen (NOx)2. The increase in the number of motor vehicles have also occurred in the city of Padang. Padang city serves as a center of trade, services, education, tourism, transportation and industry. Therefore, the city of Padang should be able to provide adequate services in the field of transport to improve the welfare of society. Based on data from the Central Bureau of Statistics of Padang3 the increase in the number of vehicles was quite rapid in the last 2 years which is from 392.967 units in 2013 up to 427.235 units in 2014.

The increasing number of vehicles in Padang city affected the increase the concentration of pollutants in ambient air, one of them is nitrogen dioxide (NO2). According to Pandey et al.4, NOx is most widely produced by vehicular activities. Nitrogen oxide (NO) produced from the waste transportation fuel combustion process will be oxidized in the atmosphere to form NO2. NO2 is odorless, pale yellow that interferes with visibility and can irritate the respiratory organs5. Therefore, one of the pollutants that should receive the attention was NO2 and that was a harmful gas inhalation6. Given the dangers of air pollution on health7, monitoring should be done to determine the concentration of NO2 in the city of Padang. NO2 concentration was measured by two units of impinger placed at a certain distance from the side of the road at the same time to see the NO2 dispersion. From measurements of NO2 concentration, traffic characteristics and meteorological conditions, NO2 dispersion modeling was done of the receptor distance, with a parameter of traffic characteristic and meteorological conditions. Objective of the research was to look at the relationship of NO2 concentration with traffic characteristics and meteorological factors. Furthermore, modelling the relationship of NO2 concentration with traffic characteristics and meteorological factors,based on receptor distance from source (highway), such as 1, 5, 10, 25, 50 and 100 m. So, it can be seen the dispersion of NO2 due to the traffic characteristics and meteorological factors.

MATERIALS AND METHODS

This study was performed to model NO2 dispersion in roadside area for a distance 1, 5, 10, 25, 50 and 100 m from the edge of the roadside, based on the traffic characteristics and meteorological conditions, which carried out in Padang city on March until July 2016. The concentration of dispersion describes the distribution of concentration. Data meteorological conditions necessary for the calculation were temperature (̊C) and atmospheric pressure (mm Hg) with a pocket digital appliance Weatherman, wind direction with compass and wind speed with an anemometer tool.

The sampling was conducted for primary data collection was carried out for 1 h of measurements for each sampling point using animpinger. Gries Saltzman method is the method used to determine the concentration of NO2. NO2 was then reacted with Griess Saltman reagent (absorbent) forming compounds which are pink. Measures of sampling and laboratory analysis was adapted from Indonesian National Standard 19-7119.2-2005, Indonesian Government8, on how to measure nitrogen dioxide (NO2) by Griess Saltzman method using a spectrophotometer. NO2 measurements performed at sampling point A with a distance of 1 m and point B varied at a distance of 5, 10, 20, 50 and 100 m from the edge of roadside. NO2 concentration measurement data serves to determine the NO2 concentration reduction based on the distance of receptor.

Traffic characteristics were measured by volume, vehicle speed and traffic density. Measurement of traffic characteristics are intended to determine the correlation of concentrations of NO2 obtained in the measurement. NO2 dispersion pattern was carried out based on regression models obtained. This pattern shows the spread and reduction in NO2 concentrations based on the distance of receptor.

Statistical analysis: Statistical analysis was used to validated the model using t-test analysis. T-test analysis was conducted between NO2 concentration predicted from the model and NO2 concentration resulted from the measurement. The model is valid if tvalue<tcritics.

RESULTS

Meteorological conditions: The data of the meteorological conditions at the time of measurement is required in determining the concentration of NO2. Summary data of meteorological conditions at the time of measurement of the concentration can be seen in Table 1.

Based on the Table 1 above, the temperature ranged between 24.2 and 33.4̊C with average was 28.4̊C. The average pressure obtained was 757.06 mm Hg. The average wind speed at several research sites ranged between 0.04 and 1.59 m sec–1.

Fig. 1:Traffic volume

Table 1:Meteorological conditions

Characteristics of traffic: Traffic characteristics used here such as volume, speed and traffic density. The number of vehicles passing through the sampling locations were converted into the form passenger car unit/h (pcu h–1). The traffic density was obtained from the division of traffic volume with vehicle speed.

Characteristics of traffic in research sites: It can be seen in Fig. 1-3 that the highest traffic volume amounted to 4265.15 pcu h–1 and the highest traffic density of 199.31 pcu km–1 located at Khatib Sulaiman III. The lowest traffic volumes is 673.80 pcu h–1 and the lowest traffic density of 14.91 pcu km–1 is located at Wahidin. The highest vehicle speed obtained was 46.40 km h–1 were at Lubuk Begalung and the lowest speed of 20.40 km h–1 on Khatib Sulaiman II. Variations in the number of volume, density and speed of traffic is influenced by a number of different vehicles and different measurement time at the sampling location.

Based on Fig. 4, 5 and 6 it can be seen the highest traffic volume amounted to 4285.15 pcu h–1 at Khatib Sulaiman I and highest traffic density of 108.35 pcu km–1 located on Bagindo Aziz chan I. The lowest traffic volumes of 791.40 pcu h–1 was located at Jl. Bandar Purus and traffic density as low as 13.93 pcu km–1 was located at Bandar Damar. The highest vehicle speed obtained was 57.00 km h–1 at Pattimura and the lowest speed of 38.00 km h–1 were found at Bagindo Aziz chan I.

Fig. 2:Speed of vehicle

Fig. 3:Traffic density

Fig. 4:Traffic volume

Concentration of NO2: NO2 concentration measurements were carried out for 1 h at each sampling point. NO2 concentration measurements in ambient air were performed using an two impinger for each point. A point impinger tool was placed 1 m from the side of the road, while impinger point B was placed varies with the distance of 5, 10, 25, 50 and 100 m. This different placement at point B is meant to determine the dispersion of NO2 concentrations based on the distance from the edge of the roadside. NO2 concentration measurement results from research sites and the fluctuation in concentration can be seen in Fig. 7, 8, 9, 10 and 11.

Based on Fig. 7, 8, 9, 10 and 11, NO2 concentration at each sampling location varies, the results of NO2 concentration measurement at point A 1 m at all sampling locations is higher than the point B (5, 10, 25, 50 and 100 m) from the edge of the roadside.

Fig. 5:Speed of vehicle

Fig. 6:Traffic density

Fig. 7:Comparison of NO2 concentration between point A (1 m) and point B (5 m)

The highest NO2 concentration measurement obtained was 238.64 μg Nm–3 at point A with a distance of 1 m that was located on Khatib Sulaiman III. While at point B (25 m) the concentration of NO2 gas obtained was 98.86 μg Nm–3. NO2 concentration at Khatib Sulaiman I at point A with a distance of 1 m was measured at 188.40 μg Nm–3 and point B within 100 m also was measured at 60.93 μg Nm–3.

DISCUSSION

Meteorological conditions: Based on data from Table 1, it can be seen that the temperature, pressure, wind speed and wind direction changes accordingly. Current conditions sampling is also fickle, sometimes sunny, cloudy, or rainy. Atmospheric stability can be determined from the meteorological data at the time of measurement in the field.

Fig. 8:Comparison of NO2 concentration between point A (1 m) and point B (10 m)

Fig. 9:Comparison of NO2 concentration between point A (1 m) and point B (25 m)

Fig. 10:Comparison of NO2 concentration between Point A (1 m) and point B (50 m)

Fig. 11:Comparison of NO2 concentration between point A (1 m) and point B (100 m)

The difference in temperature at different locations are due to several factors, such as the length of solar radiation, the slope of the sun and the state of the cloud. High temperatures will cause the air to be distant, so that the pollutant concentration becomes lower and lower.

Fig. 12:Comparison of NO2 concentration at point A and point B with Indonesian Government Regulation No. 41 (1999)

Fig. 13:Correlation between NO2 and the volume of vehicle at 1 m

In contrast to the cool temperatures makes the air more dense, so the concentration of pollutants in the air becomes higher9.

The air pressure is affected by altitude and temperature. The air pressure will decrease every 100 m and the higher the temperature, the pressure would be lower. Even if there is little difference, this is caused by differences in air temperature due to uneven heating of the atmosphere by sunlight10.

The higher wind speed in an area, then the mixing of pollutants from sources of emissions in the atmosphere will be even greater, so that concentrations of contaminants will dilute and pollutants will be reduced11. Wind speed also affects the distribution of pollutants, pollutant concentrations are reduced when the wind is strong therefore will distribute pollutants horizontally and vertically12. Strong wind speed will carry pollutants everywhere and pollute the air other areas13.

Analysis of NO2 concentration: Figure 7-11 shows that the concentration of NO2 was dispersed and measured up to 100 m. Dispersion of NO2 concentration can be affected by various factors such as meteorological conditions and traffic characteristics4,14-16. Several meteorological factors are temperature, pressure, wind speed and wind direction changes at each sampling site. Current conditions sampling is also unstable, sometimes sunny, sometimes cloudy or rainy. Based on research by Bachtiar et al.17, fluctuation varying meteorological conditions can affect the dispersion of pollutants in the atmosphere. Weather can affect the accuracy of the measurement results, meaning that the gas levels at the same location will be different if the weather is different.

Comparison of NO2 concentration with national air quality standards: NO2 concentration data that have been obtained is then compared with the quality standards on Indonesian Government Regulation No. 41 (1999)18, on air pollution control. The maximum concentration of NO2 pollutants in ambient air that is tolerated is 400 μg Nm–3 for time measurement of 1 h. Comparison of NO2 concentration with quality standards can be seen in Fig. 12.

Based on Fig. 12, NO2 gas concentration data both at point A and point B on the sampling location are still below the air quality standards. It shows the concentration of NO2 in Padang city is still safe and not harmful to health. The increasing number of vehicles every year, it is likely that the NO2 concentration will also increase. If the prevention is not done, environmental quality decreases.

Correlation analysis of NO2 concentration with traffic characteristics and wind speed: The volume of traffic is one of the characteristics of the traffic that has a relationship with the concentration of NO2. The correlation between the concentration of NO2 and total vehicle volume can be seen in Fig. 13.

Determination (R2) between the concentration of NO2 gas with total vehicle volume which is equal to 0.7545, so the correlation (r) at point A is 0.869 with the interpretation of the correlation is very strong as shown in Fig. 13. The very strong correlation showed that the greater the volume of traffic, the greater the concentration of NO2 produced. According to Kendrick et al.19, the relationship between the volume of traffic to the NO2 concentration is directly proportional. The greater the volume of traffic, the greater the concentration of NO2 is generated and vice versa.

(R2) determination result between the concentration of NO2 gas to vehicle speed is equal to 0.6793, so the correlation (r) at point A value of 0.824 with the interpretation of the correlation is very strong as shown in Fig. 14.

Table 2:Regression model, correlation and deviation standard
C1: Predicted NO2 concentration1 m from the edge of the roadside (μg Nm–3), C5: Predicted NO2 concentration5 m from the edge of the roadside (μg Nm–3), C10: Predicted NO2 concentration10 m from the edge of the roadside (μg Nm–3), C25: Predicted NO2 concentration25 m from the edge of the roadside(μg Nm–3), C50: Predicted NO2 concentration50 m from the edge of the roadside (μg Nm–3), C100: Predicted NO2 concentration100 m from the edge of the roadside (μg Nm–3), D: Traffic density (pcu km-1) and S: Wind speed (m sec–1)

Table 3:T-value test result

Fig. 14:Correlation between NO2 concentration and vehicle speed

Fig. 15:Correlation between NO2 concentration and traffic density

Fig. 16:Correlation between NO2 concentration and wind speed

Strong negative correlation (r) indicates the NO2 gas concentration is inversely proportional to vehicle speed. The lower the speed of traffic, the greater the concentration of NO2 gas produced. This is due to the vehicle moving at low speed will emit greater NO2. According to Jenkin20, the relationship between the speed of traffic with NO2 gas concentration is inversely proportional between 30 and 60 km h–1. The higher the speed of traffic, the smaller the concentration of NO2 is generated and vice versa.

NO2 concentrations and traffic density have a strong correlations (r = 0.834), with determination (R2) 0.6957.The relationship between NO2 concentrations and traffic density is directly proportional. As the density of traffic increase, the greater the concentration of NO2 gas produced (Fig. 15). The greater the density of traffic, the greater the concentration of NO2 gas is generated and vice versa21.

Determination (R2) between the concentration of NO2 with wind speed is equal to 0.2924, so the correlation (r) at point A is 0.541, with an interpretation of the correlation is quite strong. Such correlations indicate that the concentration is directly proportional to the wind speed (Fig. 16).

Regression models for NO2 concentrations: Regression modelling was conducted between NO2 concentration with traffic characteristics and meteorological conditions. The dependent variable which is the concentration of NO2 will be combined with the independent variable which is the density of traffic (the result of the calculation of the volume of traffic and the speed of vehicle) and wind speed. The model can be seen in Table 2.

The model obtained has a value of correlation (r) that is very strong. The model obtained is then validated using t-test method. Data can be accepted only if tvalue<tcritical. t-value test results can be seen in Table 3.

It can be seen that all models fulfill the t-test criteria, so that all models can be used to predict the value of the NO2 gas concentration. Calculation of t-test used a confidence level of 95% and α = 0.05 showed that the calculation results are in the reception area with tstat<tkritik. The predicted concentration value is at point 1, 5, 10, 25, 50 and 100 m based on the number of vehicles that passed the 40 sampling locations.

Fig. 17:Concentration of NO2 Gas at17.00-18.00

The models are showing a decrease in concentration corresponding to the distance from the edge of the roadside22. Furthermore, the model is used to predict the value of the concentration on 40 roads in Padang computed at the time of 17:00-18:00. The decreasing result of NO2 concentration during these hours can be seen in Fig. 17.

It can be seen, a drop in concentration occurred at a point 1-100 m. However, there is no decreasing at the point of 1 and 5 m. Meanwhile, the receptors within 10 m, saw a significant decrease in the concentration. This is because the distance receptors was already dispersed from the highway. The decrease of concentration between 25 and 100 m was not significant. NO2 dispersion based on distance receptors is likely influenced by other factors such as meteorological conditions and different traffic characteristics in each of the sampling location23.

CONCLUSION

Based on the results of research analysis and mapping of NO2 dispersion from transportation activities in Padang city, it can be concluded that the relationship between NO2 concentration and the traffic characteristics and meteorological conditions vary based on the number of vehicles. NO2 gas concentration relationships with meteorological conditions namely wind speed relation of NO2 concentration and traffic characteristics, vehicle speed value and total traffic density were very strong. Data concentration of NO2 t point A (1 m) and point B (5, 10, 25, 50 and 50 m) at the sampling location is still below the national air quality standard that has been set on Indonesian Government Regulation No. 41 of 1999 on the Control of Pollution Air. It showed that the concentration of NO2 in Padang city is still safe and not harmful to health.

SIGNIFICANCE STATEMENTS

This study developed models for predicting NO2 concentration based on receptor positions. Using the model help stakeholders to predict NO2 concentrations with an easy manner. This study also showed the pattern of the decreasing of NO2 concentrations toward the distance of receptors.

ACKNOWLEDGMENTS

This study was funded by Directorate General of Higher Education Ministry Research, Technology and Higher Education (KemenristekDikti) in PUPT, under contract No. 09/H.16/UPT/LPPM/2016 and PTUPT, under contract No. 059/SP2H/LT/DRPM/IV/2017.

References

Aenab, A.M., S.K. Singh and A.J. Lafta, 2013. Critical assessment of air pollution by ANOVA test and human health effects. Atmos. Environ., 71: 84-91.
CrossRefDirect Link

Bachtiar, V.S., S. Raharjo, Y. Ruslinda, F. Hayati and D.R. Komala, 2015. Mapping of ozone gas (O3) concentrations in Padang city. Procedia Eng., 125: 291-297.
CrossRefDirect Link

Bachtiar, V.S., Y. Ruslinda, D. Wangsa and E. Kurniawan, 2016. Mapping of PM10 concentrations and metal source identifications in air ambient at Surrounding area of Padang cement factory. J. Environ. Sci. Technol., 9: 390-398.
CrossRefDirect Link

Calkins, C., C. Ge, J. Wang, M. Anderson and K. Yang, 2016. Effects of meteorological conditions on sulfur dioxide air pollution in the North China plain during winters of 2006-2015. Atmos. Environ., 147: 296-309.
CrossRefDirect Link

Carslaw, D.C. and S.D. Beevers, 2005. Development of an urban inventory for road transport emissions of NO2 and comparison with estimates derived from ambient measurements. Atmos. Environ., 39: 2049-2059.
CrossRefDirect Link

Central Bureau of Statistics Padang, 2015. Padang in figures 2015. Central Bureau of Statistics Padang, Government of Indonesia.

Grundstrom, M., C. Hak, D. Chen, M. Hallquist and H. Pleijel, 2015. Variation and co-variation of PM10, particle number concentration, NOx and NO2 in the urban air-relationships with wind speed, vertical temperature gradient and weather type. Atmos. Environ., 120: 317-327.
CrossRefDirect Link

Indonesian Government, 1999. [Indonesian government regulation no. 41 of 1999 on control of air pollution]. Cabinet Secretary of the Republic of Indonesia, Jakarta. http://175.184.234.138/p3es/uploads/unduhan/Peraturan-Pemerintah-tahun-1999-041-99.pdf, (In Indonesia).

Indonesian Government, 2005. Indonesian national standard 19-7119.2-2005. Ambient air-Part2: How to test nitrogen dioxide levels (NO2) with Griess Saltzman method using spectrophotometer. BSN., Indonesia. http://sisni.bsn.go.id/index.php/sni_main/sni/detail_sni/7130.

Jenkin, M.E., 2004. Analysis of sources and partitioning of oxidant in the UK-Part 2: Contributions of nitrogen dioxide emissions and background ozone at a Kerbside location in London. Atmos. Environ., 38: 5131-5138.
CrossRefDirect Link

Katsoulis, M., K. Dimakopoulou, X. Pedeli, D. Trichopoulos, A. Gryparis, A. Trichopoulou and K. Katsouyanni, 2014. Long-term exposure to traffic-related air pollution and cardiovascular health in a Greek cohort study. Sci. Total Environ., 490: 934-940.
CrossRefDirect Link

Kenagy, H.S., C. Lin, H. Wu and M.R. Heal, 2016. Greater nitrogen dioxide concentrations at child versus adult breathing heights close to urban main road kerbside. Air Qual. Atmos. Health, 9: 589-595.
CrossRefDirect Link

Kendrick, C.M., P. Koonce and L.A. George, 2015. Diurnal and seasonal variations of NO, NO2 and PM2.5 mass as a function of traffic volumes alongside an urban arterial. Atmos. Environ., 122: 133-141.
CrossRefDirect Link

Lana, I., J. Del Ser, A. Padro, M. Velez and C. Casanova-Mateo, 2016. The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain. Atmos. Environ., 145: 424-438.
CrossRefDirect Link

Latham, S., S. Kollamthodi, P.G. Boulter, P.M. Nelson and A.J. Hickman, 2001. Assessment of primary NO2 emissions, hydrocarbon speciation and particulate sizing on a range ofroad vehicles: Project report PR/SE/353/2001. Transport Research Laboratory Limited, July 2001.

Liu, W., X. Li, Z. Chen, G. Zeng and T. Leon et al., 2015. Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China. Atmos. Environ., 116: 272-280.
CrossRefDirect Link

Monks, P.S., C. Granier, S. Fuzzi, A. Stohl and M.L. Williams et al., 2009. Atmospheric composition change-global and regional air quality. Atmos. Environ., 43: 5268-5350.
CrossRefDirect Link

Pandey, S.K., K.H. Kim, S.Y. Chung, S.J. Cho, M.Y. Kim and Z.H. Shon, 2008. Long-term study of NOx behavior at urban roadside and background locations in Seoul, Korea. Atmos. Environ., 42: 607-622.
CrossRefDirect Link

Pattinson, W., I. Longley and S. Kingham, 2014. Using mobile monitoring to visualise diurnal variation of traffic pollutants across two near-highway neighbourhoods. Atmos. Environ., 94: 782-792.
CrossRefDirect Link

Sayegh, A., J.E. Tate and K. Ropkins, 2016. Understanding how roadside concentrations of NOx are influenced by the background levels, traffic density and meteorological conditions using boosted regression trees. Atmos. Environ., 127: 163-175.
CrossRefDirect Link

Smith, L., S. Mukerjee, K. Kovalcik, E. Sams and C. Stallings et al., 2015. Near-road measurements for nitrogen dioxide and its association with traffic exposure zones. Atmos. Pollut. Res., 6: 1082-1086.
CrossRefDirect Link

Soltic, P. and M. Weilenmann, 2003. NO2/NO emissions of gasoline passenger cars and light-duty trucks with Euro-2 emission standard. Atmos. Environ., 37: 5207-5216.
CrossRefDirect Link

Venkatram, A., M. Snyder, V. Isakov and S. Kimbrough, 2013. Impact of wind direction on near-road pollutant concentrations. Atmos. Environ., 80: 248-258.
CrossRefDirect Link