Poll Res-6.Pmd

EXPOSURE OF PEDESTRIANS IN SURABAYA CITY TO COARSE AND FINE PARTICULATE MATTER AND THE EFFECT OF VEGETATION CANTIKA ALMAS FILDZAH, ARIE DIPAREZA SYAFEI*, ABDU FADLI ASSOMADI, RACHMAT BOEDISANTOSO, AGUS SLAMET AND JONI HERMANA 1Department of Environmental Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia (Received 29 December, 2019; accepted 17 February, 2020)

ABSTRACT Surabaya is the second largest metropolitan city in Indonesia after Jakarta and has 4.5 million vehicles. The number of vehicles increase every year, causing increases in concentrations of TSP

(Total Suspended Particulates), PM10, PM2.5, and PM1 (Particulate Matter with aerodynamic sizes of less than 10µm, 2.5µm, and 1µm, respectively). Being exposed to these particulates continuously is

very dangerous for pedestrians ’health. In this study, concentrations of TSP, PM10, PM2.5, and PM1 were measured for 15 hours on six major roads in Surabaya. We also investigated the effect of vegetation on pedestrian particulate exposure. The measurement results showed that the concentrations of the particulates increased in the morning and evening. The highest concentrations recorded were taken on Gemblongan Street on a weekday. They were 103.2 µg/m3 3 3 3 for PM1, 219.8 µg/m for PM2.5, 503.73 µg/m for PM10, and 503.73 µg/m for TSP. Multiple linear regression analysis showed that particulate concentrations were significantly affected by the number of vehicles during the weekend and week days as well as the temperature. However, particulate concentration patterns were obviously higher during peak hours, especially in the morning. Vegetation was observed to reduce particulate exposure to pedestrian users. However, since the testing was limited to one plant species, future research could investigate plant morphology, e.g., leaf structure, that is more effective in reducing coarse and fine particulate concentrations.

KEY WORDS : Particulate Matter, Total Suspended Particulates (TSP), Plants, Vehicles

INTRODUCTION congestion during rush hour is the main factor causing air pollution in Surabaya. The pollution is Surabaya is the second largest city in Indonesia after even worse at traffic lights and bus stops (Tsang, Jakarta. Based on the 2010 population survey, 2008). According to research done in Hong Kong, on Surabaya has a population of 2.77 million, and it is busy roads near junctions, vehicles often accelerate increasing every year at a rate of 0.63% (BPS, 2017). and slow down during traffic signal cycles, which The increasing population in Surabaya City is results in increased particle emissions. Pedestrians directly proportional to the increasing means used are faced with high levels of particulate pollutants to support daily activities, such as vehicles. Based when they walk near and cross zebra zones (Hong- on 2014 data from the Department of di, 2012). Surabaya City has the third worst air Transportation, there are 4.5 million vehicles in pollution in Asia according to a survey conducted Surabaya City, and every month that total is by environmental agencies in Asia (Luki, 2019). increased by more than 17,000 vehicles. The government of Surabaya has facilitated Furthermore, the government has not been pedestrian traffic via massive sidewalk contruction offsetting these increases with additional projects. Walking can reduce congestion when infrastructure, leading to congestion. Traffic distances to destinations are short. In addition, 546 FILDZAH ET AL

mixed-use and pedestrian-oriented urban dacryorrhea, headache, fatigue, vertigo, and general environments are increasingly viewed as healthier cataptosis (Grigoropoulus, 2008). alternatives to sub urban typologies since they PM can be reduced by limiting the number of encourage active rather than automotive transport. vehicles or limiting industrial construction and One potential downside, however, is enhanced processing (Hirabayashi, 2016), but vegetation van pedestrian exposure to air pollution from the nearby can also be used to reduce PM (Nowak, 2014). This roadway environment (Bereitschaft, 2015), study investigates pedestrian exposure to ambient especially in terms of fine particulates, such as pollutant concentrations in pedestrian areas in

PM1(particulates less than 1 µm in diameter), PM2.5 Surabaya and the role of vegetation in dealing with (particulates less than 2.5 µm in diameter), the particulates. The pollutants investigated in the

coarse particulates PM10 (particulate up to 10 µm in study were particulate matters with aerodynamic

diameter) and TSP (Total Suspended Particulates). diameters of less than 1, 2.5, and 10 µm (PM1, PM2.5,

Research has shown that PM2.5 and PM10 have and PM10) and TSP across six sites in Surabaya. negative impacts on respiratory and cardiovascular health following both short-term and chronic MATERIALS AND METHODS exposure (Kelly, 2015). There is evidence linking Selection of Locations long-term exposure to PM2.5 with adverse birth outcomes, while emerging data suggest possible The measurement sites were selected based on low- effects of long-term PM exposure on diabetes, 2.5 density to high-density traffic. In other words, six neuro development, and cognitive function. TSP can measurement spots were selected based on Level of also cause various kinds of diseases in humans, Service and access to CCTV (Closed-Circuit such as coughing, shortness of breath, sneezing, Televisison). The locations are shown in the Table 1 fatigue and itchiness of the throat (Supardi, 2003). and Figure 1 below. On the other hand, PM1 has been associated with Sample Measurements sinus arrhythmias. PM1 is also absorbed easily into the blood and can cause damage to body organs. Its Particulate concentrations were measured at 5 min impacts include tachycardia, arrhythmias, dryness intervals using an Aerocet 531S Particle Mass of the rhinopharynx, dispnea, dry cough, Profiler and Counter manufactured by Metone. The

Fig. 1. Sampling procedure with plants (a) and without plants (b)

Table 1. The sampling locations Street LOS (Level Pedestrian Figure 1 Vegetation of Service) Conditions (width of the sidewalk) Jl. UripSumoharjo F 6m a No Jl. Mayjend. Sungkono E 4m b No JL. Gemblongan C 3m c No Jl. Diponegoro F 2m and 1m d Yes Jl. Dr. Moestopo E 2.5m e Yes Jl. Embong Malang A 3m f Yes EXPOSURE OF PEDESTRIANS IN SURABAYA CITY TO COARSE AND FINE PARTICULATE 547 device is able to detect fine particulates between 0.3 multiple linear regression model. First, a test was micrometer to 10 micrometers in diameter. It is also conducted to look for significant relationships possible for it to detect coarser particulates (TSP). between the variables using ANCOVA. The test The device was selected because of its portability showed that all variables were significantly and long battery life (up to 10h). Measurements different in terms of the response variables (with were carried out for 15 hours starting at 6:00 a.m. the exception of the number of diesel cars. Second, and lasting until 9:00 p.m. for six days, with a day we tested the normality of these variables. It was spent at each site. discovered that the data did not follow a linear CCTV from the Transportation Agency of relationship, thus we log-transformed the Surabaya was used to calculate the number of dependent variables. vehicles passing the sampling locations. A Kestrel 5500 was used to measure wind speed and wind RESULTS direction. Temperature and relative humidity were also measured. Each sampling was conducted Concentration Patterns during a weekday (Monday) and on the weekend The results of this study can be seen in Figures 2 to (Sunday). When sampling with plants, the croton 4. The patterns are similar in that the concentrations plants (Codiaeum variegatum) used were placed in a of TSP, PM10, PM2.5, and PM1 are higher during row with a distance between stems of ± 30 cm. In weekdays than over the weekend. This result is due this type of sampling, the measurement device was to more vehicles passing by on weekdays, thus placed 1 m behind the plants and at an altitude of ± increasing the concentration of each particulate 1.5 m. (Mulawa, 1997). On Urip Soemoharjo Street The croton plants had heights of 1.5 m-2.0 m, (without plants), the highest concentrations on a putting them at the level of the human respiratory 3 3 weekday were 65.2 µg/m for PM1, 165.1 µg/m for system. When sampling without plants, the tool was 3 3 PM2.5, 234.35 µg/m for PM10, and 255.544 µg/m for placed 1 m away from the street on a tripod with a TSP. The concentrations increased in the morning height of ± 1.5 m. Prior to placing the device, we and evening and coincided with the beginning and conducted a meteorology analysis of each site. We end of school and work activities. During the determined the dominant wind direction and speed weekend on the same street, the highest and made sure that the wind would flow to the spot 3 concentrations recorded were 77 µg/m for PM1, where we were going to place the measurement 3 3 127.7 µg/m for PM2.5, 224.79 µg/m for PM10, and device. 3 253.86 µg/m for PM10. During the weekend, the Data Analysis particulate concentrations were lower, but not significantly lower, due to the fact that this road is a Multiple linear regression was employed to find main road and also that campaign activities, which discover which variables influence concentration. refers to regional head election that taking place The variables involved included meteorological during measurements, increasing the volume of influences, such as wind speed and wind direction, motorized vehicles. The average concentrations for as well as the number of vehicles (motor use, and the weekday were 20 µg /m3 for PM , 38 µg/m3 for gasoline-fueled cars diesel cars, bus, truck), day of 1 the week, and presence of plants. The dependent variables were TSP, PM10, PM2.5, and PM1. In total, there were nine independent variables, of which six were dummies. The dummy variables in this analysis were day of the week (weekend = 0, weekday = 1), peak time in the morning (no=0, yes=1), presence of plants (using plants = 0, not using plants = 1), peak time during the day (no=0, yes=1), peak time at night (no = 0, yes = 1), and wind direction (in degree). The remaining three variables were temperature, wind speed (m/s), and traffic volume (units). The data were checked in Fig. 2. Concentration Patterns at each sites during order to make sure that they were appropriate for a weekday: a) PM1, b) PM2.5, c) PM10, and d) TSP 548 FILDZAH ET AL

3 3 PM2.5, 95 µg/m for PM10, and 113 µg/m for TSP. The measurement results for the average

The average concentrations for the weekend were24 concentrations in Figure 4 shows that the PM10 and 3 3 3 µg/m for PM1, 36 µg/m for PM2.5, 98 µg/m for PM2.5 concentrations exceed the quality standard on 3 PM10, and 112 µg/m for TSP. Based on National weekdays. The concentrations shown in Figure 4 for Regulation No. 41 of 1999 (Government Regulation Gemblongan Street (without plants) were 100 µg/ 3 3 No. 41 of 1999), the standard averages for a 24-hour m for PM2.5 and 232 µg/m for PM10. These 3 measurement period were 150 µg/m for PM10, 65 concentrations are the results of sampling in a 3 3 g/m for PM2.5, and 230 g/m for TSP. The location where the traffic is heavy, and the red traffic average concentration of every parameter was thus light causes congestion. Another source of under the permissible level both during the pollutants is the wood scrubbing activities used to weekend and weekday. make chairs. These activities increased the wood On Diponegoro Street (with plants), the highest powder at the sampling location in the morning 3 concentrations on a weekday were 104.1 µg/m for until noon. The average concentrations of PM10 and 3 3 PM1, 198.2 µg / m for PM2.5, 391.25 µg / m for PM2.5 on Moestopo Street (with plants), recorded at 3 3 3 PM10, and 458.79 µg /m for TSP.During the 174 µg/m for PM10 and 72 µg/m for PM2.5, exceed weekend, the highest concentrations were 86.2 µg/ the quality standards on weekday. In this case, the 3 3 3 m for PM1, 165.7 µg/m for PM2.5, 564.66 µg/m for sampling location was near a school; hence, traffic 3 PM10, and 677.09 µg /m for TSP. The concentrations increases on a weekday. In addition, it was also during the weekend were significantly higher due close to a hospital, increasing the traffic on to recreational visits to the Surabaya Zoo, plus some weekdays in the mornings and afternoons. In city and tourism buses stopped near the sampling summary, the study revealed that the average location. Buses and other diesel-fueled vehicles concentrations of several parameters exceed the produce the highest level of pollution even though quality standards. This finding requires serious they are fewest in number because they produces attention from the government in the form of

SO2, particulates and diesel has a greater opacity reducing particulates in the air. Particulate side value than gasoline (Goembira, 2006). effects are very dangerous for humans. Role of Vegetation and Other Factors in Particulate Exposure In this study, several things affected the

concentrations of TSP, PM10, PM2.5 and PM1 greatly. The concentration patterns change signficantly between weekend and weekday and during peak hours. The peak hour in the morning affects the concentrations of all parameters, but the peak hours

during the day and night do not. The PM1 concentration is only affected by peak hours in the morning and night. In addition, the number of Fig. 3. The total number of all types of vehicle at each site motorbikes and cars fueled by gasoline is also on weekdays and weekend known to affect the concentrations of the four

(a) (b) Fig. 4. The Average Concentration Patterns at each site on weekdays and weekend: a) PM2.5, b) PM10 EXPOSURE OF PEDESTRIANS IN SURABAYA CITY TO COARSE AND FINE PARTICULATE 549

parameters. Buses influence the PM1 and

PM2.5 concentrations significantly. In

additon, the PM1 concentration is affected 1 by trucks.

PM The peak hours during the noon and evening did not affect the concentrations

of TSP, PM10, and PM2.5 significantly. The variables that affected the concentrations

of TSP, PM10, PM2.5, and PM1 significantly were weekdays, peak hours in the morning, temperature, wind speed, motor use, and gasoline-fueled cars. In addition to these variables, the variable 2.5 buses affects the concentrations of PM1

PM and PM2.5 significantly. The concentration

of PM1 is also significantly affected by trucks. Note that the presence of plants significantly influences the

concentrations of PM10, PM2.5,and PM1, whereas wind direction affects the

concentrations of just TSP and PM1 significantly. However, higher temperature reduced the concentrations

10 of all particulates greatly, which is in agreement with other research reports (Hernandez, 2017). Finally, the presence of plants affects the concentrations of all parameters. Concentration Models 1

DISCUSSION , and PM

2.5 A major source of TSP is known to be motorized vehicles (Srivastava, 2007). , PM

10 Our findings are in agreement with this

TSP PM fact, showing as they do that the concentration of TSP increases significantly during weekdays and in the

0.4270.423morning. This 0.454 0.450 finding is also true 0.432 for 0.428 0.389 0.384 Estimate p-value Estimate p-value Estimate p-value Estimate p-value PM1, PM2.5, and PM10, suggesting that traffic volume is responsible for the increases in concentrations, especially when there are many buses and trucks. Buses and trucks emit more fine particulates. The pollutant pattern is also consistent with diurnal activities. At present, private vehicles are used more than public transportation. Hence, the number of motorized vehicles is 2 2 increasing, leading to higher particulate The Estimates of the Coefficients for the TSP, PM The Estimates of the Coefficients for TSP, concentrations. Further investigation showed that weekend:0) buses and trucks are major contributors Table 2. Table Constant(Weekday:1, Weekday/Weekend 5.148e-01 < 2e-16 *** 5.278e-01 < 2e-16 *** 6.901e+00 4.969e-01 < 2e-16 *** < 2e-16 *** 7.016e+00 4.433e-01 < 2e-16 *** < 2e-16 *** * significant at 5% level, ** 1% *** 0% level 6.531e+00 < 2e-16 *** 5.732e+00 < 2e-16 *** Peak hour (Morning)Peak hour (Noon)Peak hour (Night) (0: with plant, 1: no plant)Vegetation Temperature 5.110e-02 directionWind velocityWind Motorcycle (unit) 0.02029 * 2.215e-01Fuelcar (unit)Solarcar (unit) *** 8.80e-11 9.938e-03 9.402e-02Bus (unit) 2.753e-03 (unit)Truck 2.393e-01 2.09e-05 ***Other (unit) 0.76878Multiple R 0.93497 2.80e-12 *** 1.397e-01 -5.903e-02 -1.852e-04 -2.334e-03 2.298e-01 -7.305e-04 2.73e-08 *** -6.598e-02 1.038e-02 < 2e-16 **** 0.03597 0.945061 3.33e-09 *** 2.066e-01 < 2e-16 *** 5.90e-05 *** -7.602e-04 -6.660e-02 0.758764 2.890e-04 -1.043e-04 5.94e-16 *** 1.822e-01 -5.948e-02 5.016e-02 -8.736e-04 9.48e-08 *** < 2e-16 *** 3.39e-06 *** 2.248e-02 0.000301 *** 0.238139 3.129e-02 -5.575e-03 0.64273 < 2e-16 *** -6.971e-04 -7.718e-02 1.122e-02 0.193 -8.495e-02 1.03e-06 *** ** -4.679e-06 -1.046e-03 0.00116 * 0.01166 0.558 5.548e-05 < 2e-16 *** 5.64e-06 *** 0.23769 -7.398e-04 < 2e-16 *** 9.377e-02 3.058e-02 -3.799e-03 -6.327e-02 0.963 0.929176 -1.066e-01 8.803e-02 4.98e-06 *** 0.016061 * -1.323e-03 < 2e-16 *** 1.452e-02 0.001526 ** 1.84e-08 *** 0.086109 0.023507 * -6.190e-04 -6.333e-04 < 2e-16 *** -2.580e-04 6.111e-02 *** 0.000110 0.127141 2.562e-03* 0.011172 0.383 2.70e-08 *** 1.433e-02 0.308 7.343e-02 -1.229e-03 *** 4.23e-11 0.185 8.902e-03 0.086691 0.000473 *** -1.182e-03 0.913942 Adjusted R p-value < 2.2e-16 < 2.2e-16 < 2.2e-16 < 2.2e-16 550 FILDZAH ET AL

of TSP and PM10. However, vehicle volume was not responsible for the further deterioration of the air in line with the concentrations of fine particulates quality near roads, including the pedestrian area.

(PM1 and PM2.5), suggesting there are more factors at One reason for these results is the plant canopy. play. Due to their small diameter, their movement Ghasemian (2017), reported that the plant canopy and dispersion may be affected largely by other affects the near-road air quality significantly. A factors, such as local wind speed and temperature dense plant canopy with a leaf area density (LAD) (Wang, 2015). of 3.33-2m3 may improve the air quality by 10% by According to the mass balance calculations reducing particulates via vertical mixing and performed in (Cheng, 2011), the main components upwind deflection of the plume. However,a highly 3 in PM1 are organic carbon (OC), elemental carbon porous canopy with a LAD of1-2m degrades the

(EC), and SO4. The highest PM1 concentrations occur air quality by 15% since it reduces wind speed and in winter,they are lowest during the summer. stagnates the air pollutants within or behind the According to the study, during the summer, the canopy. Abijith (2017), also confirmed that the wind that blows comes from locations that are less presence of high-level vegetation along roads in polluted, causing the PM1 concentrations to astreet canyon configuration tends to decrease the decrease compared to what they are in other seasons air quality. In addition, the structure of the (Cheng, 2011). Hence, wind direction also vegetation can also resuspend deposits in the air determines the PM1 concentration. In the current under certain wind conditions, leading to poorerair research, wind direction and wind speed affect the quality near the road. concentration of PM1 since they were positively Despite these results, further research can correlated with it. In addition, the large number of investigate which types of plants are more powerful motorized vehicles during the morning, afternoon in capturing fine particulates by examining the and evening peak hours also increase PM1 number of stomata, the amount of epicuticular wax, concentrations. the properties of the cuticle, and leaf roughness, all Vegetation provides an additional sink for of which affected adsorptive capacities for coarse particulates (Leonard, 2016). Plants are known to particulates. In particular, the level of leaf control the concentration of pollutants in the roughness is correlated with the particulate ambient air since pollutants can accumulate in their retention rate (Zhang, 2017). However, particle leaves or waxy layers. In a study conducted by (Mo, retention rates in plants are not influenced by just 2015), a large accumulation of PM in all particle leaf morphology. The external factors wind speed sizes was found in leaves. Further, (Zhang, 2017) and rainfall also influence retention rates (Huixia, investigated the effect of leaf structure in reducing 2012). Finding a species that has a high particulate TSP. It was observed thatconiferous trees were able transfer efficiency is challenging, particularly one to capture more TSP than broad-leafed trees. In with good adaptability to the urban environment addition, urban forests also makelarge contributions and fewer negative impacts on air quality. to reducing PM10 concentrations (Bottalicoa, 2016). Vegetation which might work well in one area may Leaf shape and hair are also impotant in terms of not work well in another area with different capturing PM. Plants that have leaf hair accumulate weather and/or topology. Therefore, further greater amounts of PM than those without hair research can study not only plant morphology (leaf (Leonard, 2016). Hair on the leaves can prevent the properties and so on) but also the adaptability of PM from escaping when the leaves move and plant species to the environment. increase the surface area of the leaves (Neinhuis, 1998; Prusty, 2005; Qiu, 2009). The leaf shape that CONCLUSION absorbs the greatest amount of PM is lanceolate, which can be found, for example, on the species Based on the results of the study, the concentrations

Westringia fruticosa (Leonard, 2016). Therefore, the of PM1, PM2.5, PM10, and TSP in many pedestrian use of urban green spaces (UGSs) can reduce air locations may exceed the National Regulation No. pollution in urban areas since the plants can absorb 41 of 1999 quality standards (Government air pollution in their cells and store pollution on the Regulation No. 41 of 1999), indicating a health surfaces of their leaves (Nowak, 2006). concern. Concentrations increase during the However, there have been a few studies that have morning and evening due to the many vehicles that indicated that vegetation near the road has been pass following peak morning and afternoon traffic EXPOSURE OF PEDESTRIANS IN SURABAYA CITY TO COARSE AND FINE PARTICULATE 551 activities. Weekday average concentrations of pollution removal by green infrastructures and urban particulates are higher than those for the weekend forests in the city of Florence. In: Agriculture and due to the increased volume of private vehicles (cars Agricultural Science Procedia. 8 : 243–251. https:// and motorcycles) but especially buses and trucks, doi.org/10.1016/j.aaspro.2016.02.099. Cheng, Y., Zou, S.C., Lee, S.C., Chow, J.C., Ho, K.F., which contribute more towards coarse particulates. Watson, J.G., Han, Y.M., Zhang, R.J., Zhang, F., However, there are other factors that influence the Yau, P.S., Huang, Y., Bai, Y. and Wu, W.J. 2011. increase in pollutant concentrations, namely, Characteristics and source apportionment of PM1 temperature and wind speed. Our study also emissions at a roadside station. J. Hazard. Mater. showed moderate good abatement of particulates 195: 82–91. https://doi.org/10.1016/ on the part of the plants, suggesting their effectivity j.jhazmat.2011.08.005. to reduce particulate exposure to pedestrian users. Goembira, F. 2006. Analisis Konsentrasi dan Komposisi Partikulat (TSP, PM dan PM ) di Udara Ambien However, future research could examine different 10 2,5 plant species with varied leaf morphologyies and Kampus Universitas Andalas Limau Manis dan Sekitarnya. Universitas Andalas. http:// their abilities to capture and reduce particulates. repo.unand.ac.id/2945/. Any plant species of interest should also be strong Ghasemian, M., Amini, S. and Princevac, M. 2017. The and have good adaptability with the local climate influence of roadside solid and vegetation barriers and/or weather conditions. on near-road air quality. Atmospheric Environment. 170 : 108–117. https://doi.org/10.1016/ ACKNOWLEDGEMENTS j.atmosenv.2017.09.028. Grigoropoulos, K.N., Nastos, P.T., Ferentinos, G., The authors wish to thank the Department of Gialouris, A., Vassiliou, T., Mavroidakos, J., Avgeri, Environmental Engineering, Faculty of Civil, D., Kalabokis, V. and Saratsiotis, D. 2008. Spatial Distribution Of PM and Sinus Arrhythmias in Environmental, and Geo Engineering, Institut 1 Athens, Greece. Fresenius Environ. Bull. 17(6). Teknologi Sepuluh Nopember (ITS) for space, Hernandez, G., Berry, T.A., Wallis, S.L. and Poyner, D. administration, and facility support during the n.d. 2017. Temperature and Humidity Effects on study. We also wish to extend our appreciation and Particulate Matter Concentrations in a Sub-Tropical thanks to the Directorate of Research and Climate During Winter. 9. 102(8) : 41-49. doi: Community Services, Directorate General of 10.7763/ipcbee. Research and Development, Ministry of Research, Hirabayashi, S. and Nowak, D.J. 2016. Comprehensive Technology and Higher Education of the Republic national database of tree effects on air quality and of Indonesia for providing funding forthe project human health in the United States. Environ. Pollut. under the umbrella of the Air Quality Monitoring 215: 48–57. http://dx.doi.org/10.1016/ j.envpol.2016.04.068. project. Hong-di, H. and Wei-Zhen, L. Urban aerosol particulates on Hong Kong roadsides: Size distribution and REFERENCES concentration levels with time. 2012. Stochastic Environmental Research and Risk Assessment. Abhijith, K. V., Kumar, P., Gallagher, J., McNabola, A, 26(2) : 177–187. https://doi.org/10.1007/s00477- Baldauf, R., Pilla, F. and Pulvirenti, B. 2017. Air 011-0465-9. pollution abatement performances of green Huixia, W. 2012. Mechanisms of Plant Leaves Based on infrastructure in open road and built-up street Leaf Surface wettability on rainfall Interception and canyon environments – A review. Atmospheric Dust Capturing; Xi’an University of Architecture & Environment. 162 : 71–86. https://doi.org/10.1016/ Technology: Xi’an, China. j.atmosenv.2017.05.014. Kelly, F. J. and Fussell, J. C. 2015. Air pollution and public Badan Pusat Statistik. 2017. Surabaya dalam Angka. BPS health: emerging hazards and improved Kota Surabaya, Surabaya. understanding of risk. Environmental Geochemistry Bereitschaft, B. 2015. Pedestrian exposure to near- and Health. 37(4) : 631–649. https://doi.org/10.1007/ roadway PM in mixed-use urban corridors/: A 2.5 s10653-015-9720-1. case study of Omaha, Nebraska. Sustainable Cities Leonard, Ryan J, McArthur, Clare, Hochuli and Dieter, F. and Society. 15: 64–74. http://doi.org/10.1016/ 2016. Particulate matter deposition on roadside j.scs.2014.12.001. plants and the importance of leaf trait combinations. Bottalicoa, F., Chiricia, G., Giannettia, F., De Marcob, A., Urban Forestry & Urban Greening. 20: 249– Nocentinia, Paolettic, E., Salbitanoa, F., Sanesid, 253.http://dx.doi.org/10.1016/j.ufug.2016.09.008. G., Serenellie, C. and Travaglinia, D. 2016. Air Luki, 2009. Surabaya Peringkat Ketiga Kota Berpolusi di 552 FILDZAH ET AL

Asia.Antara news: Kepala Badan Lingkungan Hidup accumulation and leaf pigment content in vegetation Jawa Timur. https://www.antaranews.com/berita/ near the national highway at Sambalpur, Orissa, 158053/surabaya- peringkat-ketiga-kota-berpolusi- India. Ecotoxicol. Environ. Saf. 60 (2): 228–235. di-asia. 15 May 2019. http://dx.doi.org/10.1016/j.ecoenv. 2003.12.013. Mo, L., Ma, Z., Xu, Y., Sun, F., Lun, X., Liu, X., Chen, J. Qiu, Y., Guan, D., Song, W. and Huang, K. 2009. Capture and Yu, X. 2015. Assessing the Capacity of Plant of heavy metals and sulfur by foliar dust in urban Species to Accumulate Particulate Matter in Beijing, Huizhou, Guangdong Province, China. China. PLOS ONE. 10(10) https://doi.org/10.1371/ Chemosphere. 75 (4) : 447–452. journal.pone.0140664. http://dx.doi.org/10.1016/j.chemosphere.2008.12.061. Mulawa, P.A., Cadle, S. H., Knapp, K., Zweidinger, R., Srivastava, A., Gupta, S. and Jain, V.K. 2008. Source Snow, R., Lucas, R. and Goldbach, J. 1997. Effect Apportionment of Total Suspended Particulate of Ambient Temperature and E-10 fuel on Primary Matter in Coarse and Fine Size Ranges Over Delhi. Exhaust Particulate Matter Emissions from Light- Aerosol Air Qual. Res. 8 : 188–200. https://doi.org/ duty Vehicles. Environ. Sci. Technol. 3 : 1302-1307. 10.4209/aaqr.2007.09.0040. https://doi.org/10.1021/es960514r. Supardi, I. 2003. Lingkungan Hidup and Kelestariannya. Neinhuis, C. and Barthlott, W. 1998. Seasonal changes of Alumni, Bandung. leaf surface contamination in beech oak, and ginkgo Tsang, H., Kwok, R. and Miguel, A. H. 2008. Pedestrian in relation to leaf micromorphology and wettability. exposure to ultrafine particles in Hong Kong under New Phytol. 138 (1): 91–98. http://dx.doi.org/ heavy traffic conditions. Aerosol and Air Quality 10.1046/j.1469-8137.1998. 00882.x. Research. 8 (1): 19–27. https://doi.org/10.4209/ Nowak, D.J., Crane, D.E. and Stevens, J.C. 2006. Air aaqr.2007.09.0041. pollution removal by urban trees and shrubs in the Wang, J. and Ogawa, S. 2015. Effects of Meteorological

United States. Urban Forestry & Urban Greening. 4: Conditions on PM2.5 Concentrations in Nagasaki 115-123. https://doi.org/10.1016/j.ufug.2006.01.007. Japan Int. J. Environ Res. Public Health. 12 : 9089– Nowak, D.J., Hirabayashi, S., Bodine, A. and Greenfield, 9101. https://doi.org/10.3390/ijerph120809089. E. 2014. Tree and forest effects on air quality and Zhang, W., Wang, B. and Niu, X. 2017. Relationship human health in the United States. Environ. Pollut. between leaf surface characteristics and particle (Barking Essex: 1987) 193 : 119–129. http:// capturing capacities of different tree species in dx.doi.org/10.1016/j.envpol.2014.05.028. Beijing. Forests. 8 (3) : 1–12. https://doi.org/10.3390/ Prusty, B.A.K., Mishra, P.C. and Azeez, P.A. 2005. Dust f8030092.