Relationships between on-road transport emissions and ambient particulate matter concentration in the Bangkok metropolitan region during

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1 Relationships between on-road transport emissions and ambient particulate matter concentration in the Bangkok metropolitan region Pattarapoom Phuengyaem, Agapol Junpen, Savitri Garivait * The Joint Graduate School of Energy and Environment (JGSEE), Centre of Excellence on Energy Technology and Environment (CEE), King Mongkut s University of Technology Thonburi, 126 Prachauthit Road, Bangmod, Tungkru, Bangkok, Thailand Abstract: Air pollution is a major concern not only in Thailand but all around the world, especially at the level of a megacity, which is defined as a metropolitan area with a total population in excess of ten million people. Bangkok Metropolitan Region (BMR) includes Bangkok and its five adjacent provinces (Nakhon Pathom, Pathum Thani, Nonthaburi, Samut Prakan, and Samut Sakhon) represents one of the megacities facing air pollution problems due to on-road traffic congestions. This study aims at investigating the relationships between PM 10 emissions from on-road transport and its ambient concentrations in BMR. The obtained results showed that the amount of PM 10 emitted from on-road transport over BMR during the period of was approximately 7.38 kilotones. The correlation coefficient (R 2 ) of PM10 from on-road transport emissions and ambient concentrations were 0.29 for those monitored at ambient stations and 0.66 at roadside stations, respectively. The investigation by type of vehicles showed that the highest correlation was found between PM 10 emitted from heavy duty buses and ambient concentrations recorded at ambient stations with R 2 of 0.52, followed by heavy-duty trucks (R 2 =0.26), light-duty cars (R 2 =0.16), and light-duty trucks (R 2 =0.07) respectively. For roadside stations, the analysis of emitted and monitored concentrations indicated that PM10 emitted by light-duty cars had the highest correlation with the monitored concentrations with R 2 of 0.75 followed by heavy-duty trucks (R 2 =0.68), heavy duty buses (R 2 =0.52), and light-duty trucks (R 2 =0.40) respectively. These results well demonstrate the relationships between on-road transport emissions and ambient concentrations of PM 10, which could be used to support policy-makers for formulating mitigation options in on-road transport sector at the level of megacities. Keywords: PM 10; On-road transport emission; Urban air pollution; Air quality monitoring; Bangkok Metropolitan Region (BMR) * Corresponding author. Tel.: # 4134, Fax: address: savitri_g@jgsee.kmutt.ac.th, savitrigarivait@yahoo.com, savitri.jgsee@gmail.com 1. Introduction Air pollution is a major concern not only in Thailand but all around the world, especially at the level of a megacity, which is defined as a metropolitan area with a total population in excess of ten million people. Bangkok Metropolitan Region (BMR), including Bangkok and its five adjacent provinces, namely Nakhon Pathom, Pathum Thani, Nonthaburi, Samut Prakan, and Samut Sakhon, represents one of the megacities facing air pollution problems due to on-road traffic congestions. Thailand is the top first world s most congested countries. Moreover, Bangkok is ranked the 12 th city in the world with high traffic congestion. Air pollutant emissions from road traffic includes nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbon (HC), particulate matter (PM), and other trace gases and particulate compounds from incomplete combustion (He et al., 2016). From the Thailand s Environmental Quality Report published by the Thai Pollution Control Department (PCD), the air quality in BMR has been beyond the standard especially due to exceedances of particulate matter (PCD, 2016). In addition, on-road transport has been recognized as one of the major sources of air pollution in BMR. Air pollution is of concern in urban environments, such as megacities because of the highly dense population exposure, and the large number of anthropogenic emission sources. Research studies conducted in Thailand, and particularly in Bangkok found that air pollution due to particulate matter significantly contributed to ecological and human health problems (Guo et al., 2014; Vichit-Vadakarn et al., 2008; Wong et al., 2008). In order to evaluate the contribution of on-road transport emissions on the ambient air concentrations of particulate matter in BMR, we investigated in this study the relationships between on-road transport emissions and ambient PM10 concentrations recorded at the air quality monitoring stations in BMR. 421

2 2. Materials and methods 2.1 On-road transport emissions of PM10 In this study, we used the on-road transport emission inventory developed and prepared by Cheewaphongphan et al. (2017). This dataset inventoried emissions from on-road transport in BMR, classified by vehicle and fuel types, including motorcycle (MC), light-duty vehicle by using gasoline (LD-GSL), light-duty vehicle by using middle distillate diesel (LD-MD), and heavy-duty vehicles using middle distillate diesel (HD-MD). Table 1 PM10 emissions from on-road transport sector classified by vehicle and fuel type (kilotonnes, kt) Vehicles Type* Year MC LD-GSL LDC LD-MD LDC LDT HD-MD HDT HDB Total *Vehicle types: Motocycles (MC); Light Duty (LD); Light Duty Car (LDC); Light Duty Truck (LDT); Heavy Duty (HD); Heavy Duty Truck (HDT); Heavy Duty Bus (HDB); Gasoline (GSL); Middle Distillates-Diesel (MD). Table 2 PM10 emissions from fuel consumption in on-road transport sector (kilotonnes, kt) Fuel Type Year Gasoline (GSL) Middle Distillates Diesel (MD) *Fuel types: Gasoline (GSL); Middle Distillates-Diesel (MD). 2.2 Ambient PM10 concentrations The data of ambient air pollutant concentrations during , which were reviewed and analyzed in this study, were collected from Thailand s Environmental Quality Report published by PCD (PCD, 2016). The data presented in this report were from the 27 air quality monitoring stations of BMR. There are 11 ambient air quality monitoring stations and 6 roadside stations in Bangkok, and 10 ambient air quality monitoring stations in the adjacent provinces. The detailed annual averages of ambient PM10 concentrations observed at the 27 air quality monitoring stations in BMR during are reported in Table Relationship between PM10 emitted from on-road transport and ambient PM10 concentrations in BMR In order to investigate the relationship between PM10 emitted from on-road transport and ambient PM10 concentrations in BMR, we plotted the ambient annual average concentrations data, derived from all the 27 air quality monitoring stations, as a function of the PM10 annual emissions, as well as annual emissions classified by vehicle and fuel types, then looked at their potential correlations. 422

3 Table 3 PM10 annual average concentrations (µg/m 3 ) at the 27 ambient and roadside air quality monitoring stations in BMR Station Name Year Bangkok (Area) Bansomdejchaopraya Rajabhata Bangkhuntein Highway Station Rat Burana Post office Thailand Meteorological Department Chandrakasem Klongjun NHA Huaykwang NHA Stadium Nonsi wittaya school Wat sing school Public Relations Department Badindecha School Bangkok (Roadside) Ministry of Science and Technology Department of Land Transport Chulalongkorn Hospital Wongwen 22 karakkada Thonburi Power Substation Chokchai 4 Police Station Dindang Adjacent provinces Prapradang Rehabilitation for the Disabled South Bangkok Thermal Power Plants Residence for Department of Primary Industries and Mines Samut Prakan City Hall Bangplee NHA Bangkok University Samutsakhon Highway District Samutsakhon Provincial Administration Organization Samutsakhon Wittayalai School EGAT STOU Total Ambient Station SD Total Roadside Station SD

4 3. Results and discussions 3.1 Relationship between annual PM10 emissions from on-road transportation and ambient PM10 concentrations Fig. 1 Linear regression relationship between annual PM10 emission from on-road transport and ambient PM10 concentrations measured at ambient air quality and roadside stations in BMR Fig. 1 showed the linear regression between PM10 emission from on-road transport and ambient PM10 concentrations in BMR measured at ambient air quality monitoring stations and roadside stations. We found a higher correlation coefficient between annual PM10 emissions and annual ambient PM10 concentrations measured at roadside stations (R 2 =0.67) than ambient air quality monitoring stations (R 2 =0.29). 3.2 Relationship between annual PM10 emissions from on-road transport categorized by vehicle and fuel type and ambient PM10 concentrations Fig. 2 Linear regression relationship between annual PM10 emission from motorcycles and ambient PM10 concentrations measured at ambient air quality and roadside stations in BMR 424

5 Figs. 2 to 5 showed the linear regression relationship between PM10 emission from motorcycles (MC), light duty vehicles fueled with gasoline(ldc-gsl), light duty vehicles fueled with diesel (LDC-MD; LDT-MD), heavy duty vehicles fueled with diesel (HDT-MD; HDB-MD), all vehicles fueled with gasoline, and all vehicles fueled with diesel. Overall, we observed a better correlation, between annual PM10 emissions disaggregated by vehicle and fuel types and ambient PM10 concentrations measured at roadside stations rather than ambient stations, e.g. a higher R 2 in all cases. Indeed, R 2 were in the range of for roadside stations, and for ambient stations. From Fig. 2, it was found that there was no correlation between PM10 emitted from MC and ambient PM10 concentrations. Fig. 3 Linear regression relationship between annual PM10 emission from LD-GSL and ambient PM10 concentrations measured at ambient air quality and roadside stations in BMR Figs. 3 and 4 showed a strong correlation between PM10 emissions from light-duty vehicles fueled with gasoline or diesel with ambient PM10 measured at roadside stations. Also, we observed the same order of correlation for LD-GSL (R 2 =0.69) and LD-MD (R 2 =0.64), but emissions from LD-GSL ranged between 0.10 and 0.20, those from LD-MD run between 1.20 and Fig. 4 Linear regression relationship between annual PM10 emission from LD-MD and ambient PM10 concentrations measured at ambient air quality and roadside stations in BMR 425

6 Fig. 5 Linear regression relationship between annual PM10 emission from HD-MD and ambient PM10 concentrations measured at ambient air quality and roadside stations in BMR Comparing Figs. 1 and 5, it was found that the relationships between annual PM10 emissions from on-road transport and ambient PM10 concentrations, and between annual PM10 emissions from HD- MD and ambient PM10, were quite similar, whether the ambient levels were derived from roadside or ambient stations, indicating that the BMR ambient air quality may strongly be influenced by HD-MD emissions. 3.3 Relationship between annual PM10 emissions from fuel consumed in on-road transport sector in BMR and ambient PM10 concentrations Fig. 6 Linear regression relationship between annual PM10 emission from gasoline consumed in on-road transport sector in BMR and ambient PM10 concentrations measured at ambient air quality and roadside stations in BMR From Figs. 6 and 7, it was found that there were correlations between PM10 emitted from gasoline (R 2 roadside=0.52; R 2 ambient=0.40), and from diesel (R 2 roadside=0.67; R 2 ambient=0.29) consumed in on-road transport sector and ambient PM10 concentrations. In addition, the emission decrease for both fuels, more particularly after 2011, contributed significant decrease of ambient concentrations, especially those monitored at the roadside stations. 426

7 Fig. 7 Linear regression relationship between annual PM10 emission from diesel consumed in on-road transport sector in BMR and ambient PM10 concentrations measured at ambient air quality and roadside stations in BMR 4. Conclusions This study investigated the relationships between particulate matter emissions from on-road transport sector and its ambient air concentrations in BMR. The results showed correlations between annual PM10 emissions and annual average concentrations. Stronger relationships were observed for the dataset derived from measurements at roadside air quality monitoring stations, comparatively to those at the ambient stations. In addition, it was found that annual PM10 emissions from heavy duty vehicles fueled with diesel had the strongest correlation with the ambient concentrations measured at the ambient stations, which may indicate that these emissions are the main driver of the ambient PM10 concentrations in BMR. References Cheewaphongphan, P., Junpen, A., Garivait, S. and Chatani, S Emission Inventory of On-Road Transport in Bangkok Metropolitan Region (BMR) Development from 2007 to 2015 Using the GAINS Model. The atmosphere, 8(9), 167. D'Angiola, A., Dawidowski, L.E., Gómez, D.R. and Osses, M On-road traffic emissions in a megacity. Atmospheric Environment, 44(4), He, J., Wu, L., Mao, H., Liu, H., Jing, B., Yu, Y., Ren, P., Feng, C. and Liu, X Development of a vehicle emission inventory with high temporal-spatial resolution based on NRT traffic data and its impact on air pollution in Beijing Part 2: Impact of vehicle emission on urban air quality. Atmos. Chem. Phys., 16, Ministry of Public Health Annual report. Nation Health Security Office. Pollution Control Department, Annual Thailand s Environmental Quality Reports [Online], Available at: Rutherford, D. and Ortolano, L Air quality impacts of Tokyo s on-road diesel emission regulations. Transportation Research Part D: Transport and Environment, 13(4), Vichit-Vadakan, N., Vajanapoom, N. and Ostro, B The Public Health and Air Pollution in Asia (PAPA) Project: estimating the mortality effects of particulate matter in Bangkok, Thailand. Environmental health perspectives116.9 (p. 1179). World Health Organization, Ambient (outdoor) air quality and health, [Online], Available: Available at: mediacentre/ factsheets/ fs313/eใ 427