Scholarly article on topic 'Exposure to in-vehicle respirable particulate matter in passenger vehicles under different ventilation conditions and seasons'

Exposure to in-vehicle respirable particulate matter in passenger vehicles under different ventilation conditions and seasons Academic research paper on "Earth and related environmental sciences"

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Abstract of research paper on Earth and related environmental sciences, author of scientific article — Suresh Jain

Abstract This study presents the in-vehicle particulate matter (PM) concentration in a number of passenger vehicles under various ventilation modes, land use land cover (LULC) in different seasons in megacity Delhi, India. In-vehicle monitoring was conducted in buses, cars and autos (three-wheeler) using air-conditioned (AC) and Non-AC during peak and off-peak hours. The site selected is a ∼15 km long stretch from Punjabi Bagh to Safdarjung Hospital, based on diversity in LULC, availability of vehicles and heavy traffic flow along the direction of travelling. In-vehicle PM was measured using GRIMM aerosol spectrometer and categorised in three classes (PM1, PM2.5 and PM10). The study found that concentration of PM1, PM2.5 and PM10 were significantly (p ≤ 0.05) higher in winters as compared to summers. It was observed that PM concentration was significantly (p ≤ 0.05) higher in Non-AC travel modes compared to AC modes. PM concentrations were high near industrial and commercial areas and during traffic congestion showing the influence of LULC. It is also important to highlight that PM1, PM2.5 and PM10 concentrations were significantly (p ≤ 0.05) higher in case of taxis (cars) compared to personal cars which varied from 2.5 to 3.5 times higher in case of AC mode and ∼1.5 times in case of Non-AC mode. Exposures to PM concentration were highest in case of Non-AC bus compared AC-Bus, Non-AC cars, autos and AC-cars. PM concentrations in case of autos and Non-AC cars were almost comparable without any significant (p > 0.05) difference. Regression analysis showed significant correlation between ambient and in-vehicle concentration for PM2.5. Regional deposition fractions were calculated using International Commission on Radiological Protection model to show the deposition in head air-pass, trachea-bronchial and alveolar regions. It was found that deposition of PM1 was highest in the alveolar region.

Academic research paper on topic "Exposure to in-vehicle respirable particulate matter in passenger vehicles under different ventilation conditions and seasons"

Accepted Manuscript

Exposure to in-vehicle respirable particulate matter in passenger vehicles under different ventilation conditions and seasons

Suresh Jain

PII: S2468-2039(16)30059-0

DOI: 10.1016/j.serj.2016.08.006

Reference: SERJ 72

To appear in: Sustainable Environment Research

Received Date: 18 June 2016 Revised Date: 23 July 2016 Accepted Date: 29 August 2016

Please cite this article as: Jain S, Exposure to in-vehicle respirable particulate matter in passenger vehicles under different ventilation conditions and seasons, Sustainable Environment Research (2017), doi: 10.1016/j.serj.2016.08.006.

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Windows - open and closed Air condition - with and without re-circulation Land use land cover - LULC Number of people

Age and type of vehicle Mechanical or traffic induced abrasion Traffic intersections Seasons - winter and summer

Received 26 June 2016

Received in revised form 23 July 2016

Accepted 29 August 2016

Exposure to in-vehicle respirable particulate matter in passenger vehicles under different ventilation conditions and seasons

Suresh Jain

Department of Natural Resources TERI University New Delhi 110070, India Department of Energy and Environment TERI University New Delhi 110070, India

Keywords: In-vehicle concentration, Regression analysis, Air pollution, Deposition fraction; Land use land cover

Corresponding author

Email: sureshjain_in@yahoo.com

ABSTRACT

This study presents the in-vehicle particulate matter (PM) concentration in a number of passenger vehicles under various ventilation modes, land use land cover (LULC) in different seasons in megacity Delhi, India. In-vehicle monitoring was conducted in buses, cars and autos (three-wheeler) using air-conditioned (AC) and Non-AC during peak and off-peak hours. The site selected is a ~15 km long stretch from Punjabi Bagh to Safdarjung Hospital, based on diversity in LULC, availability of vehicles and heavy traffic flow along the direction of travelling. In-vehicle PM was measured using GRIMM aerosol spectrometer and categorised in three classes (PM1, PM2.5 and PM10). The study found that concentration of PM1, PM25 and PM10 were significantly (p < 0.05) higher in winters as compared to summers. It was observed that PM concentration was significantly (p < 0.05) higher in Non-AC travel modes compared to AC modes. PM concentrations were high near industrial and commercial areas and during traffic congestion showing the influence of LULC. It is also important to highlight that PM1, PM25 and PM10 concentrations were significantly (p < 0.05) higher in case of taxis (cars) compared to personal cars which varied from 2.5 to 3.5 times higher in case of AC mode and ~1.5 times in case of Non-AC mode. Exposures to PM concentration were highest in case of Non-AC bus compared AC-Bus, Non-AC cars, autos and AC-cars. PM concentrations in case of autos and Non-AC cars were almost comparable without any significant (p > 0.05) difference. Regression analysis showed significant correlation between ambient and in-vehicle concentration for PM2.5. Regional deposition fractions were calculated using International Commission on Radiological Protection model to show the deposition in head air-pass, trachea-bronchial and alveolar regions. It was found that deposition of PM1 was highest in the alveolar region.

1. Introduction

The expansion of industries, growth of cities and concentrated human activities are leading to alarming increase in air pollution levels in all metro cities in the world [1-2]. The major anthropogenic categories such as industrial, vehicular and domestic sources have increased the air pollution levels in urban areas, which is one of the main cause of adverse health effects on human beings including cardiovascular and respiratory diseases [3-5]. World Health Organization (WHO) identified ambient air pollution as one of the 10 major risk factors contributing to global health burden [6]; causing ~16% of premature deaths [7]. Vehicular emissions have been estimated as the major source of particulate matter (PM) [8-11]. PM from vehicular exhaust poses the greatest threat to human health. The fine particulate matter (PM2.5 and PM1) are more harmful to human health than coarser particles (PM10) as they penetrate deep into the lungs and also act as carrier of toxic substances such as diesel engine exhaust emissions [12]. Studies have also demonstrated that, for the same amount of PM mass deposited in the lung, toxicity tends to increase as particle size decreases [13,14]. As a person spends about 70-90 min in travelling per day [15,16], the in-vehicle microenvironment is a potentially significant contributor, accounting to 10-20% of total daily average PM exposure. Short-term exposure to traffic-related PM concentrations during commuting has been shown to have a variety of negative health effects [17-20]. It aggravates existing pulmonary and cardiovascular diseases, whereas long-term exposure to increase in-vehicle PM concentrations is associated with high risk of premature death in elderly [21].

Personal exposure to PM in various transport micro-environments such as car, bus, train, taxi and trucks is higher than in indoor locations and at urban monitoring sites [15,22-25]. A number of factors have been identified which influence the exposure of human beings to in-vehicle PM concentrations such as peak and lean hours, wind speed, relative humidity (RH), driving lane, roadway type, congestion level, seasonal changes,

vehicle type, vehicle emission rate, status of windows, mode of operation, use of AC, duration of time spent inside the vehicle and undiluted emissions from exhaust of leading vehicles [4,26-30]. Srimuruganandam and Nagendra [31] have observed high PM concentration in weekdays during peak hours when there is high traffic flow and comparatively lower concentrations during afternoon. Moreover, the highest PM concentrations were measured while being in traffic congestions or when behind a heavy diesel-driven vehicle [32]. Fruin et al. [33] observed that in-vehicle black carbon concentrations were highest during afternoon hours when traffic was dominated by diesel running heavy duty vehicles with low exhaust pipe location. Kaur et al. [11] and Chan et al. [24] found highest average ultrafine PM concentration in buses because of slight elevation from the ground. The physical condition of buses, sources inside and outside buses, and re-suspension by passenger activity are also significant factors associated with higher levels of commuters' exposure to PM concentrations. The PM concentration shows a positive correlation with the numbers of passengers on the bus during the trip [34] as well as opening and closing of doors/windows. Gómez-Perales et al. [35] examined minibus and bus occupant exposures in Mexico, and found that average PM2.5 exposure in morning was higher than in evening for both types of buses because of increased wind speed during evening rush hours which disperses the pollutants. Chan et al. [24] assessed PM2.5 exposure levels in Non-AC and AC double-decker and single-decker buses and reported that levels in AC buses were approximately half of that observed in Non-AC counterparts. Similar results were presented by Apte et al. [22] who observed lower concentration inside cars with windows closed, and AC compared to auto-rickshaw and open window cars due to reduced air exchange rates and air filter. Therefore, the present study aims to investigate the occupant exposure to in-vehicle PM concentrations in

different transportation modes during peak and non-peak hours under various ventilations modes, land use land cover (LULC) and seasons in Delhi. 2. Methodology

2.1. Site description

The Delhi is among the most populated and polluted megacities (eleventh as per WHO database in 2016) of Asia [2]. Road transport is the only mode for transportation of goods and people, leading to high traffic density with an average speed of mere 20 km h-1 [36]. Although, it is mandatory for all public vehicles like buses, taxis and autos (three-wheelers) to use compressed natural gas (CNG) as fuel, still the PM10 and PM25 concentration in Delhi is among the highest in the world [22]. The road network of total road length is about 31183 km constituting 26459 km of Municipal Corporation of Delhi, 1290 km of New Delhi Municipal Corporation, 360 km of national highway and 3180 km of other roads [37]. The total registered vehicles are 7.8 million, constituting 2.5 cars, 4.96 two-wheelers, 0.087 auto rickshaw, 0.07 taxis, 0.04 buses and 0.14 million goods vehicles.

One of the road networks was considered for the study with a completely different land use patterns. This corridor is a Ring Road which commences from Punjabi Bagh in the west Delhi and ends at Safdarjung Hospital in the South Delhi, including Rajouri Garden, Maya Puri, Naraina, Delhi Cantonment, Dhaula Kaun, Moti Bagh, Bhikaji Cama Place and Sarojini Nagar, covering a total distance of ~15 km. This corridor has all types of land use consisting of marble market, industrial area, green area, business hub, hospitals, flyovers, grid separators and metro construction as shown in Fig. 1 and Table 1. Goods vehicles are allowed on this stretch from 11:00 am to 4:00 pm.

2.2. Modes of travelling and monitoring instruments

The study was conducted for measuring in-vehicle PM concentrations in different vehicle modes under different ventilation conditions on alternate days. The monitoring

was carried out in low floor AC-Bus and Non-AC buses, AC and Non-AC cars, and Auto (three-wheelers) along a ~15 km long stretch from Punjabi Bagh to Safdarjung Hospital with diversity in LULC and direction of heavy flow of traffic (Fig. 1). The in-vehicle air samples were collected during peak (morning 8.30-10.00 am and evening 5:30-8:30 pm) and off-peak hours (11:00 am to 3:00 pm). In order to have consistency in the in-vehicle monitoring the same route with instrument at the identical position was considered (Fig. 2). The time taken to cover the stretch in different transit modes varies from 25-50 min based on the traffic conditions and the mode of transport. This shows the impact of PM concentration on human health during exposure for different time durations. In-vehicle monitoring was conducted in May and June (2012 and 2013) representative for summer season and December 2012 and January 2013 representative for winter season. Five samples were taken from each mode in summer and winter seasons, including weekdays and weekends. One sample includes monitoring during morning, afternoon and evening hours.

The GRIMM dusts monitor (Model 1.108) was used to monitor aerosol concentrations. It is a portable light scattering dust monitor that works on the principle of scattering of light. Signals are scattered by the particles passing through, which are then transferred to recipient-diode. A pulse height analyser classifies the transmitted signals into 15 different channels of different size ranges from 0.30 to 15 |im. The measurements

were taken as particle count (count L- ) and then converted into mass (^g m- ). The data were converted into mass using "Urban Environment" density factor comparable to EPA result [31]. The time interval was chosen to be 6 sec for each sample to understand the variations in PM concentrations at various locations due to change in LULC. Instrument was placed at the breathing level, i.e., 0.5 m above the seat using a stool and positioned in such a way so that it causes least interruption to commuters.

2.3. Statistical analysis

The two-way ANOVA and t-test was used for statistical analysis along with Box and Whisker plots. Box-and-whiskerplots were used to observe the variation across different modes of transport under different ventilation settings [26]. Real time average graphs were used to observe the influence of LULC on PM concentration. The two-way ANOVA and paired t-test were primarily used to check the variation in PM concentration with the duration of time [38] and seasons. The cases where mean values were not significantly different, the p-value comes out to be greater than 0.05, i.e., with the chosen confidence interval, the difference is significant. The analysis was carried out using a spreadsheet and SPSS (TERI University licence). 3. Results and Discussion

3.1. Variation in PM concentration in different transit modes

This section explains the variation in PM concentration along the stretch during peak (morning and evening) and off-peak (afternoon) hours in different modes of transport. The selected route primarily caters semi-residential, commercial, industrial, green belt, semi-commercial and sensitive areas. The route also encounters considerable passenger activity and traffic flow during peak and off-peak hours since it is the ring road stretch and connects all the major roads in Delhi. Figures 3a-3f shows the distribution of PMU PM25 and PM10 concentrations in the form of Box-whisker plots.

Among all transport modes, AC and Non-AC buses have the highest PM concentrations compared to AC and Non-AC cars and autos. The highest PM10 concentrations were observed at Punjabi Bagh (2313, 2743 |ig m- ), Marble market (2527,

3645 |ig m- ), Naraina industrial area (3162, 3873 |ig m- ) and Safdarjung hospital (2102,

2377 |ig m- ) in AC and Non-AC Buses, respectively. In Case of Non-AC buses, PMb PM25 and PM10 concentrations were almost 1.5 times more as compared to AC-Buses. The

higher PM concentrations in Non-AC buses may be due to penetration of particles through open windows from outdoor environment. It is important to note that different LULC along with opening and closing of doors, embarking and disembarking of people at bus terminals results in higher PM concentrations in buses. Similar results have been reported by Tsai et al. [29] that AC-Bus has less PM concentrations compared to Non-AC buses but higher than cars. Tartakovsky et al. [20] have found similar results for buses in that periodic opening and closing of doors and embarking and disembarking of passengers makes ventilation modes irrelevant and results in higher concentrations in buses (6 to 8 times higher) compared to passenger cars. Further it has been observed that PM concentrations were higher during morning and evening peak hours compared to afternoon hours due to less congestion of vehicles (Fig. 3). Similar results have been reported by Srimuruganandam and Nagendra [31] for urban areas in India, where they found that PM concentrations were higher in morning hours compared to afternoon hours.

The PM1, PM25 and PM10 concentrations were significantly (p < 0.05) higher in case of Non-AC cars compared to AC-cars (Fig. 3a-3f). In case of AC-cars, fluctuations in PM concentrations were less compared to Non-AC cars due to use of AC with circulation which results in reduction of infiltration of outdoor PM particles; however, reduction in PM concentration depends on air filter cleaning and quality [4,26]. In case of AC-cars, it has been observed that initially, concentrations in car cabins were higher, and gradually it decreases with the passage of time, which shows the effect of AC on PM concentrations. Similar trend has been observed by Apte et al. [22] in their study conducted in Delhi for cars and autos. In our case, the highest PM10, PM25, PM1

concentrations observed were 389, 339 and 290 |ig m- at the starting point of Punjabi

Bagh; 177, 172 and 158 |ig m- , respectively, at Safdarjung hospital which is the end point of sampling. In case of Non-AC cars, the highest PM10, PM2.5, PM1 concentrations were

observed at Marble market (735; 590 and 482 |ig m- ), Naraina industrial area (701, 589

and 507 |ig m- ) and Dhaula Kaun (633, 463 and 379 |ig m- ), which connects to various road networks in all directions and therefore, outdoor vehicular emissions and resuspension of particles contributes to in-vehicle PM concentrations. Similar results have also reported by Apte et al. [22] and Tsai et al. [29] that AC-cars have less PM concentrations compared to Non-AC cars due to infiltration of outdoor air inside the vehicle cabin through windows. It is important to highlight that when Non-AC car was just

behind the bus during traffic jam, it resulted in episodic PM10 concentration 2100 |ig mon a particular day of monitoring, which was the highest during entire monitoring in case of Non-AC cars. This shows that vehicle position on-road and type of vehicle in front of you also affect the in-vehicle concentrations.

In case of autos (three-wheelers), the average PM1, PM2 5 and PM10 concentrations were almost comparable to Non-AC cars without any significant difference (p = 0.98);

where concentrations vary from 30-59, 124-181 and 418-625 |ig m- , respectively, during summer months. Autos show the highest PM10 concentrations at Marble market (1754 |ig

m- ) and 2115 |ig m- at Naraina industrial area. This shows the influence of LULC and outdoor environment on PM concentrations. Similar results have been reported by Apte et al. [22] for auto rickshaws and cars in Delhi. They did not find any significant difference

in average PM25 concentrations 170 ± 17 for autos and 170 ± 47 |ig m- for Non-AC cars. 3.2. Effect of seasons on in-vehicle PM concentrations

This section explains the variation in PM concentration in summer and winter seasons during peak (morning and evening) and off-peak (afternoon) hours in different modes of transport. The average PM1, PM25 and PM10 concentrations during summer and winter seasons are presented in Figs. 3 and 4. Overall, there is a significant (p = 0.04) difference in PM concentrations in summer and winters for all transport modes. In winter, PM1

concentrations varied from 5 to 11 times higher compare to summer season in all types of transport modes. Higher differences were observed in case of AC-cars and then autos, Non-AC car, AC and Non-AC buses in decreasing order. This may be attributed to unstable atmospheric conditions in summers resulting in high dilution of PM concentrations compared to winter season. Similar trend was observed for PM2.5 and PM10 concentrations except Non-AC car for PM10 concentrations, which were higher in summer compared to winter. This may be attributed to episodic conditions and traffic congestion as explained in previous section when the car was just behind other vehicles. 3.3. Effect of car type on in-vehicle PM concentrations

This section explains the variation in PM concentration in taxis (cars used for taxis, 13 yr old) and private cars (personal cars, 1-3 yr old) during peak (average of morning and evening) and non-peak (afternoon) hours as shown in Fig. 5. In case of AC-cars, PM concentrations were significantly (p = 0.001) higher in taxi compared to personal cars. Personal cars have almost 1.5 to 3.5 times less PM concentrations compared to taxis; higher differences were observed in case of AC compared to Non-AC ventilation modes. This may be due to the reason that taxis are not maintained properly (vehicle service) compared to personal cars where people do timely service, and cleaning of air filters more regularly compared to taxis. Another reason may be that taxis run on CNG, while personal cars on petrol. Two-way ANNOVA test shows statistical significant difference in PM1 concentrations between AC and Non-AC cars (p = 0.01) and taxis and personal cars (p = 0.007). Similar trends were observed for PM25 (p = 0.05) and PM10 (p = 0.05). However, in case of afternoon peak hours, the difference in PM concentrations between taxi and personal car was not significant; where there was almost no difference in PM1 and PM2.5 concentrations during AC ventilation mode compared to Non-AC ventilation mode. However, PM concentrations were almost 1.5 times higher in the taxi compared to the

personal cars. Therefore, it is important findings for this study which first time highlighted that exposure will be more in taxis compared to personal cars.

3.4. PM concentration in three-wheelers (autos) and two-wheelers

The PM1, PM25 and PM10 concentrations were measured for three days for two-wheelers in summer season without going into comprehensive monitoring as done for other modes of travel just to compare the results with autos (three-wheelers) as shown in Fig. 6. There was no significant (p > 0.5) difference in PM concentration in both modes of transport. Figure 7 shows real time morning peak hours PM concentrations for two-wheelers and three-wheelers together; this did not show much difference in PM concentrations. It may be due to the reason that three-wheeler is almost open and directly in contact with outdoor environment likes two-wheelers; therefore, PM concentrations are comparable. It has been observed that sub-fine to coarse (PM1/PM10) and fine/coarse (PM2.5/PM10) ratios are almost same 0.06, 0.31 and 0.07 and 0.28 for two-wheeler and three-wheeler, respectively. The PM1/PM10 and PM25/PM10 ratios were very low, which shows that ambient factors (such as mechanical or traffic-induced abrasion, re-suspension of particles, and vehicular emissions from the surrounding area) have more influence on in-vehicle PM concentrations, as both vehicles are almost in contact with outdoor environment [29]. Similar trend was also observed in case of two-wheelers where higher PM concentrations were in the morning peak hours compared to evening and then afternoon hours. Like in case of Non-AC cars and autos, often people were exposed to very high episodic conditions when two-wheeler positions were just behind a bus or car.

3.5. In-vehicle vs. ambient PM2 5 concentration

Ambient PM25 concentration monitored by Delhi Pollution Control Committee (DPCC) was used for comparing the in-vehicle PM concentration. The data were compared for two monitoring stations at Punjabi Bagh (starting point of monitoring) and

Safdarjung hospital (end point of monitoring) along the corridor. The analysis was carried out for each transit mode. It has been observed that in-vehicle PM concentration is more than ambient concentration which is many folds higher than the prescribed National Ambient Air Quality Standards by Central Pollution Control Board, India. In order to investigate the impacts of temperature, RH and ambient PM concentration on in-vehicle PM concentration, regression analysis was performed. It was observed from the analysis that out of all the factors, ambient PM concentration had the most significant impact with a R values ranging from 0.78 to 0.99 in different transit modes (Table 2). It was also seen

that temperature and RH did not show any significant impact with R < 0.5 and negative

values. The data had a high R and adjusted R values with low standard deviation. The in-vehicle PM25 concentration was monitored at Punjabi Bagh and Safdarjung regularly during sampling at a particular time. The ambient air concentrations (obtained from DPCC) were also available for the same time at 1 min interval and meteorological data (obtained from India Agricultural Research Institute were available on daily average basis. It was observed from the results that in-vehicle PM concentration is a function of ambient concentration with a high degree of correlation. A maximum value of R = 0.99 were observed in many vehicles with the minimum

R2 = 0.82

in Non-AC buses.

3.6. Deposition fraction

Regional deposition of particles was calculated for this study to assess the potential hazard of inhaled particles in head air pass region, tracheobronchial region and alveolar region using International Commission on Radiological Protection model [39]. It was observed that value of the deposition fractions of PM10 was the highest, i.e., 0.96 in head air pass region while lowest of 0.19 in the alveolar region. The deposition of fine particles was higher in the alveolar region as shown in Fig. 8. It is observed that time taken during travelling in buses is nearly an hour and therefore, exposure to fine particles in winters are

more in case of buses compared with other modes of travel. The concentration of these particles is higher than other size ranges. Therefore, the damage is more. This can be concluded from this analysis that the highest personal exposure while travelling is in case of Non-AC buses compared to AC-Buses, Non-AC cars, autos, two-wheelers and AC-Cars in decreasing order and more in winters compared to summer season. 4. Conclusions

This study presents the result from air quality research in a micro-environment to characterize the PM concentrations inside different transit modes using experimental and statistical analysis. In-vehicle PM concentrations were measured in a number of passenger vehicles under various ventilation modes, LULC in different seasons. The monitoring was conducted in buses, cars and autos using AC and Non-AC ventilation modes during peak and off-peak hours. In case of Non-AC buses, PM1, PM2.5 and PM10 concentrations were almost 1.5 times more as compared to AC-Buses. The higher PM concentrations in Non-AC buses may be attributed to penetration of particles through open windows from outdoor environment. Further, the PM1, PM25 and PM10 concentrations were significantly (p < 0.05) higher in case of Non-AC cars compared to AC-Cars due to use of AC with circulation which results in reduction of infiltration of outdoor PM concentrations. Overall, there was a significant (p = 0.04) difference in PM concentrations in summer and winters for all transport modes. In winter, PM1 concentrations varied from 5 to 11 times higher compare to summer season in all types of transport modes. Higher differences were observed in case of AC-Car and then autos, Non-AC cars, AC and Non-AC buses in decreasing order. Most of the time, peaks in PM concentration were observed near the commercial and industrial areas along the stretch, during high passenger vehicle activity and in traffic congestion areas. Further, window position and travel during peak and off-peak hours affect the in-vehicle PM concentrations significantly. It is essential to highlight

that there was no significant (p > 0.5) difference in PM concentration in two-wheelers, autos and Non-AC cars. The deposition fraction supported the fact that finer particles penetrate deep inside the lungs and results in various health disorders. It was observed that ambient concentration strongly influenced the in-vehicle concentration and in-vehicle concentration was consistently higher than ambient environment during commuting. Acknowledgements

The author would also like to acknowledge the contribution of Ms. Surbhi Kharbanda and Ms. Karishma Vohra during data collection. References

[1] Aggarwal P, Jain S. Energy demand and CO2 emissions from urban on-road transport

in Delhi: current and future projections under various policy measures. J Clean Prod 2016;128:48-61.

[2] Kumar P, Jain S, Gurjar BR, Sharma P, Khare M, Morawska L, et al. New directions:

can a "blue sky" return to Indian megacities? Atmos Environ 2013;71:198-201.

[3] Aggarwal P, Jain S. Impact of air pollutants from surface transport sources on human

health: a modeling and epidemiological approach. Environ Int 2015;83:146-57.

[4] Muala A, Sehlstedt M, Bion A, Osterlund C, Bosson JA, Behndig AF, et al. Assessment of the capacity of vehicle cabin air inlet filters to reduce diesel exhaust-induced symptoms in human volunteers. Environ Health 2014;13:1-14.

[5] Vlachokostas C, Achillas C, Slini T, Moussiopoulos N, Banias G, Dimitrakis I. Willingness to pay for reducing the risk of premature mortality attributed to air pollution: a contingent valuation study for Greece. Atmos Pollut Res 2011;2:275-82.

[6] Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H. A comparative

risk assessment of burden of disease and injury attributable to 67 risk factors and risk

factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2224-60.

[7] WHO. Burden of Disease Data by Region. Geneva, Switzerland: World Health Organization; 2015. http://apps.who.int/gho/data/node.main.156?lang=en/.

[8] Jain S, Aggarwal P, Sharma P, Kumar P. Vehicular exhaust emissions under current

and alternative future policy measures for megacity Delhi, India. J Trans Health 2016 (doi.org/10.1016/j.jth.2016.06.005) (in press).

[9] Jain S, Khare M. Adaptive neuro-fuzzy modeling for prediction of ambient CO concentration at urban intersections and roadways. Air Qual Atmos Health 2010;3:203-12.

[10] Jain S, Khare M. Urban air quality in mega cities: a case study of Delhi City using vulnerability analysis. Environ Monit Assess 2008;136:257-65.

[11] Kaur S, Nieuwenhuijsen MJ, Colvile RN. Fine particulate matter and carbon monoxide exposure concentrations in urban street transport microenvironments. Atmos Environ 2007;41:4781-810.

[12] McEntee JC, Ogneva-Himmelberger Y. Diesel particulate matter, lung cancer, and asthma incidences along major traffic corridors in MA, USA: a GIS analysis. Health Place 2008;14:817-28.

[13] Oberdörster G. Pulmonary effects of inhaled ultrafine particles. Int Arch Occ Env Hea 2001;74:1-8.

[14] Kim S, Shen S, Sioutas C, Zhu YF, Hinds WC. Size distribution and diurnal and seasonal trends of ultrafine particles in source and receptor sites of the Los Angeles basin. J Air Waste Manage Assoc 2002;52:297-307.

[15] Matz CJ, Stieb DM, Davis K, Egyed M, Rose A, Chou B, et al. Effects of age, season, gender and urban-rural status on time-activity: Canadian human activity pattern survey 2 (CHAPS 2). Int J Environ Res Public Health 2014;11:2108-24.

[16] Schäfer A, Victor DG. The future mobility of the world population. Transport Res A-Pol 2000;34:171-205.

[17] Knibbs LD, Cole-Hunter T, Morawska L. A review of commuter exposure to ultrafine particles and its health effects. Atmos Environ 2011;45:2611-22.

[18] Peters A, von Klot S, Heier M, Trentinaglia I, Hormann A, Wichmann HE, et al. Exposure to traffic and the onset of myocardial infarction. N Engl J Med 2004;351:1721-30.

[19] Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, et al. Long-term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med 2007;356:447-58.

[20] Tartakovsky L, Baibikov V, Czerwinski J, Gutman M, Kasper M, Popescu D, et al. In-vehicle particle air pollution and its mitigation. Atmos Environ 2013;64:320-8.

[21] Pope CA, Dockery DW. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manage Assoc 2006;56:709-42.

[22] Apte JS, Kirchstetter TW, Reich AH, Deshpande SJ, Kaushik G, Chel A, et al. Concentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, India. Atmos Environ 2011;45:4470-80.

[23] Gulliver J, Briggs DJ. Personal exposure to particulate air pollution in transport microenvironments. Atmos Environ 2004;38:1-8.

[24] Chan LY, Lau WL, Zou SC, Cao ZX, Lai SC. Exposure level of carbon monoxide and respirable suspended particulate in public transportation modes while commuting in urban, area of Guangzhou, China. Atmos Environ 2002;36:5831-40.

[25] Adams HS, Nieuwenhuijsen MJ, Colvile RN, McMullen MAS, Khandelwal P. Fine particle (PM2.5) personal exposure levels in transport microenvironments, London, UK. Sci Total Environ 2001;279:29-44.

[26] Bigazzi AY, Figliozzi MA. Impacts of freeway traffic conditions on in-vehicle exposure to ultrafine particulate matter. Atmos Environ 2012;60:495-503.

[27] Hudda N, Kostenidou E, Sioutas C, Delfino RJ, Fruin SA. Vehicle and driving characteristics that influence in-cabin particle number concentrations. Environ Sci Technol 2011;45:8691-7.

[28] Vijayan A, Kumar A. Experimental and Statistical Analyses to characterize in-vehicle fine particulate matter behavior inside public transit buses operating on B20-grade biodiesel fuel. Atmos Environ 2010;44:4209-18.

[29] Tsai DH, Wu YH, Chan CC. Comparisons of commuter's exposure to particulate matters while using different transportation modes. Sci Total Environ 2008;405:71-7.

[30] Alm S, Jantunen MJ, Vartiainen M. Urban commuter exposure to particulate matter and carbon monoxide inside an automobile. J Expo Anal Environ Epidemiol 1999;9:237-44.

[31] Srimuruganandam B, Nagendra SMS. Characteristics of particulate matter and heterogeneous traffic in the urban area of India. Atmos Environ 2011;45:3091-102.

[32] Weijers EP, Khlystov AY, Kos GPA, Erisman JW. Variability of particulate matter concentrations along roads and motorways determined by a moving measurement unit. Atmos Environ 2004;38:2993-3002.

[33] Fruin SA, Winer AM, Rodes CE. Black carbon concentrations in California vehicles and estimation of in-vehicle diesel exhaust particulate matter exposures. Atmos Environ 2004;38:4123-33.

[34] Song WW, Ashmore MR, Terry AC. The influence of passenger activities on exposure to particles inside buses. Atmos Environ 2009;43:6271-8.

[35] Gómez-Perales JE, Colvile RN, Fernández-Bremauntz AA, Gutiérrez-Avedoy V, Páramo-Figueroa VH, Blanco-Jiménez S, et al. Bus, minibus, metro inter-comparison of commuters' exposure to air pollution in Mexico City. Atmos Environ 2007;41:890-901.

[36] Gurjar BR, van Aardenne JA, Lelieveld J, Mohan M. Emission estimates and trends (1990-2000) for megacity Delhi and implications. Atmos Environ 2004;38:5663-81.

[37] Jain S, Aggarwal P, Kumar P, Singhal S, Sharma P. Identifying public preferences using multi-criteria decision making for assessing the shift of urban commuters from private to public transport: a case study of Delhi. Transport Res F-Traf 2014;24:60-70.

[38] de Nazelle A, Fruin S, Westerdahl D, Martinez D, Ripoll A, Kubesch N, et al. A travel mode comparison of commuters' exposures to air pollutants in Barcelona. Atmos Environ 2012;59:151-9.

[39] ICRP. Human Respiratory Tract Model for Radiological Protection. International Commission on Radiological Protection Publication 66. Ann ICRP 1994;24:1-3.

Table 1

Characteristics and LULC of Ring road

Area Area Location Characteristics affecting PM

(Latitude & Longitude) concentration

Punjabi Bagh 28.6681° N, 77.1365° E Semi commercial activities*, traffic junction**, cremation ground**

Rajori Garden 28.6502° N, 77.1251° E Commercial activities***

Mayapuri Industrial area**

Naraina 28.6263° N, 77.1343° E Industrial area***

Delhi Not provided due to Green belt***

cantonment security reasons

Moti Bagh 28.5786° N, 77.1760° E Residential area***

Bhikajikama 28.5693° N, 77.1896° E Semi-commercial activities**,

Palace traffic congestion***

Safdarjung 28.5695° N, 77.2061° E Silence zone***

Hospital

Strength of activities affecting PM concentration: *low, **medium, ***high

Table 2

Regression analysis between in-vehicle and ambient PM2.5 concentrations

Transit Mode Junction R2 Adjusted R2 Standard error

AC-Bus Punjabi Bagh 0.99 0.99 0.16

Safdarjung 0.93 0.86 2.25

Non-AC Bus Punjabi Bagh 0.99 0.99 0.18

Safdarjung 0.82 0.64 4.94

AC-Car Punjabi Bagh 0.97 0.94 3.15

Safdarjung 0.99 0.99 4.75

Non-AC Car Punjabi Bagh 0.99 0.99 1.62

Safdarjung 0.99 0.99 3.30

Auto Punjabi Bagh 0.99 0.99 6.02

Safdarjung 0.85 0.69 1.08

DELHI ROAD STRETCH

Legends

i School [h] Hospitals ( ) Junctions

-Joining Roads

Settlements Drainage Network Railway Line Green Area

Road Network Industrial Area

Small Roads

JUNCTIONS:

1. Punjabi Bagh (Start)

2. E.S.I Hospital

3. Rajori Garden

4. Raja Garden Marble Mkt.

5. Maya Puri Crossing

6. Naraina Industrial Area

7. Brar Square

8. Delhi Cantt

9. Dhaula Kaun

10. Moti Bagh

11. Bikaji Cama Place

12. Safdarjung Hospital (End)

Fig. 1. Land Use Land Cover Map for monitoring routes.

Fig. 2. Monitoring location inside the different vehicles.

~1-1-1-1-1-1-1-1-1-1-1-1-1-1-r

PM1 PM2.5 PM10PM1 PM,J PM10PM, PM2.5 PM10PM, PM,J PM10PM1PM2.5 PM10 PM1 PM2.5PM10 PM1 PM2.5PM10 PM1 PM2.5PM10 PM1 PM2.5PM10 PM1 PM2.5PM10

AC-Bus Non-AC Bus AC-Car Non-AC Car Auto AC-Bus Non-AC Bus AC-Car Non-AC Car Auto

Fig. 3. Box and Whisker plots showing distribution of PM (^g m- ) concentration in various modes of travel during peak and non-peak hours and seasons.

PMj PM2 5PM10 AC-Bus

PM1 PM2 5PM10 Non-AC Bus

PM2.5 PM10 PM1

AC-Car

PM25 PM10 PM 1

Non-AC Car

PM2.5 PM10 Auto

Fig. 4. Average (peak and non-peak hours) PM concentrations (^g m-3) during summer and winter seasons.

AC-Car Non-AC Car

Fig. 5. Average PM Concentrations (^g m-3) in Taxi and Private (personal) cars.

PM1 PM2.5 PM10

Morning

PM1 PM2.5 PM10

Evening

PMi PM2.5 PM10

Afternoon

Fig. 6. Average (peak and non-peak hours) PM concentrations (^g m- ) in two-wheelers in summer.

Time (morning peak hours)

Fig. 7. Real time PM concentrations (^g m- ) during morning peaks hours in two-wheelers and three-wheelers (autos).

§ 0.06

2.5 5.0 7.5

PM size range (^m)

Fig. 8. Deposition fraction of particles in different lung regions.

Research highlights

• In-vehicle PM concentration were measured in a number of passenger vehicles in Delhi

• Effects of ventilation modes, land use land cover, different seasons have been assessed

• PM concentrations were significantly higher in winters than summers

• PM concentration was significantly higher in Non-AC travel modes compared to AC modes

• Exposure in two-wheelers, three-wheelers and Non-AC cars with open windows are almost same