High resolution vehicular PM10 emissions over megacity Delhi: Relative contributions of exhaust and non-exhaust sources

Journal Pre-proof High resolution vehicular PM10 emissions over megacity Delhi: Relative contributions of exhaust and non-exhaust sources

Vikas Singh, Akash Biswal, Amit P. Kesarkar, Suman Mor, Khaiwal Ravindra PII:

S0048-9697(19)34256-1

DOI:

https://doi.org/10.1016/j.scitotenv.2019.134273

Reference:

STOTEN 134273

To appear in:

Science of the Total Environment

Received date:

9 May 2019

Revised date:

27 August 2019

Accepted date:

2 September 2019

Please cite this article as: V. Singh, A. Biswal, A.P. Kesarkar, et al., High resolution vehicular PM10 emissions over megacity Delhi: Relative contributions of exhaust and non-exhaust sources, Science of the Total Environment (2018), https://doi.org/10.1016/ j.scitotenv.2019.134273

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© 2018 Published by Elsevier.

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High Resolution Vehicular PM10 Emissions over Megacity Delhi: Relative Contributions of Exhaust and Non-exhaust Sources Vikas Singh1,*, Akash Biswal1,2, Amit P. Kesarkar1, Suman Mor2, Khaiwal Ravindra3 1 National Atmospheric Research Laboratory, Gadanki, AP, India 2 Department of Environment Studies, Panjab University, Chandigarh, 160014, India 3 Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, 160012, India

Abstract: Exposure to particulate matter (PM) from traffic can cause adverse health risks. Recent studies

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project an increase in non-exhaust emissions in the future despite a reduction in exhaust

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emissions. While there is a lot of research on exhaust emissions, the challenges remain to quantify non-exhaust emissions, especially in developing countries. In this work, an approach

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has been developed, and on-road vehicular non-exhaust PM emissions are estimated due to brake wear, tyre wear, road wear and resuspension, at very high resolution (100 m2) over an

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Indian megacity Delhi. Further, the relative contribution of non-exhaust emissions to the total

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vehicular emission was also calculated. The total PM10 emissions in megacity Delhi were 31.5 Gg/year, which is mainly dominated by the non-exhaust sources. The non-exhaust emissions were found to be six times (86%) of the exhaust emission (14%). The highest contribution to

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the total vehicular PM emission comes from the cars (34%) followed by buses (23%) and heavy commercial vehicles (HCVs, 17%), which is dominated by resuspension of dust. Cars and

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buses contribute less to exhaust emissions and more to non-exhaust emissions. Majors roads are the largest contributors to the total emissions in Delhi. The emissions from HCVs, diesel cars along with the other diesel vehicles result in diesel vehicles contributing more than the petrol vehicles to both exhaust and non-exhaust emissions. As India target to reduce PM pollution under the national clean air program, the current study will be useful to plan a suitable intervention to mitigate air pollution and associated health impacts. Keywords: Brake wear, Tire wear, Road wear, Dust resuspension, Silt load, Traffic pollution Corresponding author: Dr. Vikas Singh (Email: [email protected])

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Journal Pre-proof 1 Introduction Ambient particulate matter (PM) pollution adversely affects the air quality, climate (von Schneidemesser et al., 2015) and human health (GBD 2015 Risk Factors Collaborators, 2016). Exposure to the PM pollution is the sixth leading risk factor worldwide since 1990 (GBD 2015 Risk Factors Collaborators, 2016) having both short-term (Díaz-Robles et al., 2015) and longterm (Hampel et al., 2015) health effects. The levels of ambient PM are governed by emissions from various sources such as industries, vehicular traffic, biomass burning, etc. On-road vehicular emissions are the main contributors to the total emissions (Sharma et al., 2016) especially in the urban environment near the roads (Singh et al., 2014; Beevers et al., 2013).

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On-road PM emissions can be characterized into the exhaust and non-exhaust components

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(EEA, 2016; Grigoratos et al., 2014). The exhaust PM is emitted from the tailpipe as a result of incomplete combustion of fuel inside the engine chamber. Non-exhaust PM is either

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generated from the abrasion of tyres, road, and brake wear as well as from resuspension of the dust from the road surface due to vehicle-induced turbulence (Lawrence et al., 2016; Amato,

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2018). It has been reported that non-exhaust particles can be as hazardous as exhaust particles

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(Lawrence et al., 2013; Riediker et al., 2008; Gerlofs-Nijland et al., 2007). The study of nonexhaust particle emissions is becoming more and more important because of its increasing

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share in the total vehicular emissions (Denier Van der Gon et al., 2013; Rexeis and Hausberger, 2009). It is due to the technological reduction of exhaust emission by implementing new engine standards (EURO/BHARAT) and the use of cleaner fuels (Rexeis and Hausberger, 2009).

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Denier Van der Gon et al. (2013) showed increasing share of non-exhaust from 20% to 40% excluding resuspension from road transport in the Netherlands from 1990-2009 and 1999-2010, respectively. It has been estimated that, by the end of the decade, nearly 90% of total PM emissions from road traffic will be from non-exhaust sources (EEA, 2016). The same trend is likely to follow in other parts of the world. Tyre wear (also referred as tire wear) PM emission is a complex physicochemical process, which is due to the frictional energy developed at the interface between the tyre tread and road surface (EEA, 2016). Tyres can lose up to 10% of their mass during their lifetime (Milani et al., 2004) while less than 10% of tyre wear material is expected to be emitted as PM10 under normal driving condition (Boulter et al., 2006). Brake wear emission is highly related to the type of brake used in a vehicle (drum brake or disc brake). Brake wear particles are produced when brakes are applied to reduce vehicle speed. Brake wear contribution to total traffic PM emission has been reported to be varying around 11-21% (Bukowiecki et al. 2009; Lawrence 2

Journal Pre-proof et al. 2013). The contribution of brake wear can be higher with frequent braking at congested traffic lanes and junctions (Riediker et al. 2008; Bukowiecki et al. 2009; Lawrence et al. 2013) and smaller at the highways where the braking is not frequent (Bukowiecki et al. 2009). Road wear happens due to the mass loss from the road surface due to the friction of tyres with the road surface. Road wear emissions depend upon the type of road (asphalt, Concrete), vehicle speed, and tyre pressure. As the temperature decreases, the tyres become less elastic; therefore, the road surface wears rate increases (Snilsberg et al., 2008). While the emissions to the brake wear, tyre wear and road wear can be studied using the emission factors (UK-DfT, 2018; EEA, 2016, EEA, 2013), the resuspension emissions due to

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vehicle-induced turbulence is a very complex phenomenon which is affected by several

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parameters such as the state of pavement, silt on the road, road surface humidity, vehicular weight and speed (Denby et al., 2013a, 2013b) and climate variables such as temperature,

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humidity and rainfall etc (Gulia et al., 2019; Amato et al., 2012). Traffic volume also plays an

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important role in PM emissions due to resuspension of dust, but it is possible that these emissions do not increase proportionately (Boulter et al., 2006). The AP-42 methodology by

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United state Environmental Protection Agency (USEPA, 2011, AP-42, in Chapter 13) is the widely used method to calculate road dust resuspension emission (Beltran et al., 2012, Sahu et

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al., 2011; Sharma et al., 2016). There is a strong link between the weight of vehicle and amount of silt road on a given road towards resuspension PM emission when calculated using AP-42

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methodology (TRL Non-exhaust PM summary report 2007). The emission increases significantly with either increasing the weight of the vehicle or increasing the silt on the road (Supplementary Figure S1). Silt may vary spatially for a different type of roads (Kuhns et al., 2001; TRL Non-exhaust PM summary report 2007; USEPA. 2011, Amato et al., 2012; 2017; Zhang et al., 2017; Denby et al., 2018; Gustafsson et al., 2019) because of the speed and volume of traffic, sources of silt near the road, human activities transport the road dust. Silt load is a key factor to determine the resuspension which contributes towards the non-exhaust emissions. Although AP-42 methodology is widely used, it can yield highly uncertain emission estimates because of its dependency on the data used to formulate the model (Venkatram, 2000) and the sampling of the silt load (Teng et al., 2008). Nevertheless, the AP-42 methodology can be used, with appropriate care, for assessment (Nicholson, 2001). Estimation of non-exhaust emission for a megacity like Delhi, one of the most polluted cities (WHO report, 2016), is essential for air pollution control. Earlier efforts to reduce the exhaust emissions showed no significant change in PM levels after the implementation of CNG 3

Journal Pre-proof (Ravindra et al., 2006) because of the unresolved non-exhaust emissions (Kumar et al., 2015). Moreover, the recent efforts to reduce the traffic emissions by implementing the ‘oddeven’ trail on cars has led to a marginal reduction in particulate matter levels (Kumar et al., 2017; Chowdhury et al., 2017). Therefore, the knowledge of the exhaust and non-exhaust emissions can be used to plan the pollution control strategies. The emissions estimated in and around the Delhi area by different studies vary considerably (Singh et al., 2018) but transport remains the main source (Gurjar et al., 2004, 2016; Kansal et al., 2011; Mohan et al. 2012; Sahu et al., 2011, 2015; Guttikunda and Calori, 2013; Goyal et al., 2013). However, most of the estimates (Gurjar et al., 2004; Mohan et al., 2012; Nagpure et

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al., 2013) are for exhaust emissions and does not account for the spatially resolved emissions.

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The spatially resolved emission estimate by Singh et al. (2018) is at 100m2 resolution but does not include non-exhaust components of the emissions. Sharma et al. (2016); Sindhwani et al.

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(2015); Sahu et al. (2011); Guttikunda and Calori (2013) estimated the vehicular emissions at

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2, 2, 1.67 and 1 km2 respectively including the road-dust resuspension but did not include brake, tyre and road wear in their estimations.

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To the best of our knowledge, there are only three studies by Baidya (2008); Kumari et al. (2013) and Nagpure et al. (2016), which included the most of the non-exhaust sources in their

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emission estimates for megacity Delhi but none of them included all sources. The study by Kumari et al. (2013) included tyre wear, brake wear and road surface as the source of non-

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exhaust emissions but did not include the dust resuspension. Their study shows that roadsurface wear as the prime contributor among non-exhaust sources. Baidya (2008) estimated the emissions for India for the year 2005 and included the non-exhaust sources other than road wear. A similar approach was taken by Nagpure et al. (2016) to estimate the emissions from 1991 to 2020 using a Vehicular Air Pollution Inventory (VAPI) model (Nagpure et al., 2012). They found that after 2002, the share of non-exhausts PM10 emissions is higher than exhausts. While both the studies have done detailed calculations of the non-exhaust emission, they do not include road wear emissions which are found to be a prime contributor (Kumari et al., 2013). Although three studies include most of the non-exhaust sources, the emissions are simply calculated by taking the number of registered vehicles, vehicle kilometers traveled and multiplying it by their respective emission factors. Therefore, their estimates are not spatially resolved. This approach makes it difficult to perform scenario analysis and identify hot spots for air quality management.

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Journal Pre-proof The current study estimates emissions of the particulate matter having an aerodynamic diameter below 10µm (PM10, referred as PM afterward) at a high resolution (100m2) due to exhaust and major non-exhaust sources (e.g., brake wear, tyre wear, road wear and resuspended dust). The spatial distribution of silt loading across Delhi was estimated to calculate the resuspended dust emissions. The present study also calculates the relative contribution of brake wear, tyre wear and resuspension in the total traffic-related PM emissions. As most of the emission estimates for Delhi is for the year 2010, the study provides current PM emission over megacity Delhi to assess the effectiveness of the reduction measures and plan suitable strategies

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under national clean air program (NCAP, MoEFCC, 2019). The study area, data, and methodology

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The study domain includes the megacity Delhi with an area of 1483 km2 which is situated in the North of India located at around 28°34'N and 77°12'E and has an elevation of 216 m (709

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ft) above mean sea level. Megacity Delhi has a population of 16.8 million as per the census of

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India (2011). The details of the study area, road network, vehicular counts, fuel, and age of the vehicles used in this study have been detailed in Singh et al. (2018), hence the following section

2.1 Vehicular data

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presents a brief overview of the data and methodology.

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The vehicles have been classified broadly into six categories consisting of two-wheelers (2W), three-wheelers (3W), cars (CAR), light commercial vehicles (LCV), buses (BUS) and heavy

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commercial vehicles (HCV). Vehicle category 2W includes scooters, motorbikes of all engine capacity and types. 3W includes auto, mostly CNG in Delhi. CAR includes passenger cars and taxies of all engine capacity, age, and fuel type (petrol, diesel, and CNG). LCV includes all light commercial vehicles, including light good vehicles, etc. BUS category includes all types of buses, including CNG and diesel buses. HCV includes trucks and multi-axle lorries. The average daily traffic (ADT) is one of the main parameters to calculate the on-road exhaust and non-exhaust emissions. The traffic counts data was taken from Central Road Research Institute (CRRI) and Central Pollution Control Board (CPCB) for national highways (NHs), State Highways (SHs), major and minor roads. The residential road traffic was estimated by taking households, population, and vehicles per household data from Census 2011. The detail vehicular traffic for each type of roads have been described in section 2.2 of Singh et al. (2018).

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Journal Pre-proof 2.2 Emission factors Emission factors (EF) are essential to calculate the total emissions from a vehicle. It is a unique fraction which indicates the emission rate from a source. Vehicular EF indicates how much pollutant is emitted after running of 1 km in mix driving conditions. Vehicular emission factor can be due to exhaust sources such as tailpipe emissions and non-exhaust sources such as brake, tyre, road wear, and resuspension. 2.3 Exhaust emission factor Exhaust emission factor may vary according to vehicle type, fuel type, engine capacity, age, and according to the speed of the vehicle. For this study, EFs reported in NEERI, (2008),

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developed by the Automotive Research Association of India (ARAI, 2008) for mix driving

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conditions, have been used to calculate the total on-road vehicular emissions. The details about

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exhaust EF have been reported in Singh et al. (2018). 2.4 Non-Exhaust emission factors

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The non-exhaust emission sources considered in this study are brake wear, tyre wear, road wear, and resuspension. These emission factors depend on vehicle weight, the age of the tyre,

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driving behavior, road type, road dust, and meteorology.

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2.4.1 Road wear, tyre, and brake wear emission factor: PM produced due to the mass loss from the road surface, brake pads, and tyre surface due to frictional force at the time of driving is known as wear emissions. The wear emission factors

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are not available for Indian conditions. Therefore the wear emission factors except for the 3W are taken from the UK-DfT (2018), which are based on the EMEP guidebook (EEA, 2013, EEA, 2016). The emission factors for 3W are taken as the average of the emission factors for the 2W and cars. The Road wear, tyre, and brake wear emission factors used in this study are shown in Supplementary Figure S2. 2.4.2 Particulate matter resuspension: The particulate matter resuspension emission factors have been calculated using the USEPA AP-42 latest formula (USEPA, 2011, AP-42, in Chapter 13). As per the formula (Equation 1), the emission factors depend on the vehicular weight (W), silt load (sL), and a number of rainy days (P) in a year.

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Journal Pre-proof 𝐸𝑓 = 𝐾 × (𝑠𝐿)0.91 × (𝑊)1.02 × (1 −

𝑃 ) 4∗𝑁

(1)

Where Ef = particulate emission factor (g/VKT) K = particle size multiplier for particle size range(g/VKT) sL = road surface silt loading (grams per square meter) (g/m2), and

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W = average weight (tons) of the vehicles traveling the road.

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P = Number of wet days

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N = Number of days in the average period (365 for annual)

The particle size multiplier (K) used in this study is 0.62 g/VKT, for the particles below the

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size of 10µm (USEPA, 2011). Earlier in 2003, the particle size multiplier was reported to be 4.6 g/VKT, but in the year 2006 USEPA modified the value to be 0.66 g/VKT. Later in 2011,

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AP-42 formulation was modified with K equal to 0.62 based on a larger set of measurement data (USEPA, 2011). Sahu et al. (2011) has used K equal to 4.6 g/VKT with old AP-42

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formulation, which can highly overestimate resuspension emissions. We use an average number of wet days (P) equal to 58.4 days based on the data reported by the Indian

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Meteorological Department, Delhi (IMD, Climate of Delhi at a Glance). Silt load (sL) is one of the main parameters to calculate the dust resuspension. Supplementary figure S1 presents the variation in the resuspension emission factor w.r.t. the weight of the vehicle for a given sL value. As can be observed from Figure S1, heavy load vehicles such as buses and HCVs tend to resuspend more dust than the lighter vehicle; nevertheless, the amount of resuspended PM will depend on the Silt load. Silt load: The amount of dust present per unit area of a road is called silt load (sL) and expressed as g/m2. Silt (particles less than 75 µm) present on the road is an important factor for resuspension, and it depends upon several factors including source, season, and geographical region. Silt load is a key factor which has a direct role in the calculation of the resuspension emission factor, and it can vary from road to road depending on the local conditions and location (USEPA. 2011,

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Journal Pre-proof Zhang et al., 2017, Amato et al., 2011; 2017; Kuhns et al., 2001) and across a road (Denby et al., 2018; Gustafsson et al., 2019). Studies (Zhang et al., 2017; Kuhns et al., 2001) have reported that the silt measured at the faster lanes is found to be lower than at the slower lanes. It indicates that the roads with higher ADT and faster-moving traffic are likely to have less silt because of the more resuspension which lifts the dust from the center of the road to the side of the road. The branch roads have more silt load due to the transportation of the dust due to human activities and less resuspension due to slow traffic. It is known that the resuspension will be more with fast-moving traffic (Amato et al., 2017) at the same time amount the silt on the road will be less on such roads (Zhang et al., 2017, USEPA 2011; Kuhns et al., 2001).

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Moreover, the traffic speed is inversely associated with the population density and activity in

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an urban area as the traffic congestion depends on the local population density and demand-tosupply ratio (Çolak et al., 2016). In Delhi, the congestion is also associated with street

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intersections, random pedestrian crossings, roadside parking and mix of slow and fast-moving vehicles in the populated urban areas (MoUD, 2016) resulting in more silt on the road. This

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suggests that the human population can be used as a proxy to map the silt load across the city.

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Silt load measurements are limited and cannot represent the whole of megacity Delhi. Gargava et al. (2014) estimated average silt loading to be in the range of 0.21 to 7.0 g/m2 depending on

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the road condition. We use the silt load range suggested by Gargava et al. (2014) to spatially distribute the silt load across Delhi. First, mean silt load values (0.23, 0.41, 0.61 and 2.80 g/m2

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for highways, major roads, minor roads, and residential roads respectively) are assigned based on silt load variations reported Zhang et al. (2017) and Kuhns et al. (2001). The spatial distribution of the silt load within a given type of roads has been calculated based on the normalized squared population density. Also, to account for the demand to supply ratio, we use the congestion factor of 1.5 at the busy junctions of the highways and major roads. The distribution of the silt for a different type of roads is given in Figure 1 and the mean, median, and max values are shown in Supplementary Table T1. The estimated silt load has been validated with the measurement by Pant et al. (2015) who reported the average PM10 fraction of mass loading 72.9±24.3 mg/m2 on a major road “Mathura road” which is one of the major corridors connecting to Delhi. PM10 fraction of silt load has been reported to be varying from road to road (Zhang et al., 2017; Kuhns et al., 2001). In a study by Amato et al. (2017), the PM10 fraction was found to be 15.4% in volume of the collected mass below 250 µm. In a similar study, Gustafsson et al. (2019) have shown PM10 fraction in the range of 14 to 18% of particles below 180 µm. Based on the size distribution reported by Amato et al. (2017) and 8

Journal Pre-proof Gustafsson et al. (2019), one can estimate the PM10 fraction to be around 25-30% of the silt particles. Delhi is in the Indo Gangetic Plain (IGP) geographical region that is affected by dust storms (Middleton, 1986) and has relatively higher dust flux because of the greater clay content found in their sandy loam and silt loam soil types (Desouza et al., 2015; Middleton, 1986; ICAR, 1979). The soil size distribution of loamy soil (Segal et al., 2009) indicates that the road dust in Delhi is likely to have more smaller fraction. Therefore in this study, we assume 3035% of PM10 fraction of the silt, resulting in silt value three times of PM10 fraction. Using this fraction, the corresponding silt load at “Mathura road” has been estimated to be equal to

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0.22 g/m2, which is comparable to the silt estimated in this study.

Figure 1. Box plot showing the variations in the estimated silt load at different type of roads in megacity Delhi. The red line in the middle of the box is the median silt value and the box shows the inter quartile range. The geographical distribution of the estimated silt load that has been used in the calculation of the resuspension emission factor has been shown in Figure 2. High values of the silt present near the construction sites and open grounds were ignored because they represent very few road links in Delhi. Moreover, the silt load at the edges of the road has also been ignored as very few vehicles run over the edges.

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Vehicle weight

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Figure 2. Spatial distribution of silt load over megacity Delhi.

The weight of a vehicle is an important parameter, which affects the non-exhaust emissions. The frictional force is the prime reason behind the typical non-exhaust emission from road wear, tyre wear, and brake wear emissions. Heavier vehicles induce more turbulence, which leads to more resuspension emissions (Supplementary Figure S1). In addition to induced turbulence, the heavy weighted vehicle can grind the coarser size particles to fine particles resulting in more resuspended PM emission. The weight of the vehicles can vary depending upon the model, make, engine, and load. However, in this study, we have used an average vehicle weight for 2W, 3W, Cars, Buses, HCV, and LCV are 0.175, 0.45, 1.425, 15, 20, and 7.5 tons, respectively (NEERI, 2010).

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Journal Pre-proof 2.5 Emission Calculation: A bottom-up approach has been followed to estimate both exhaust and non-exhaust emissions. The method used to calculate the exhaust emissions is presented in Singh et al. (2018). Following formula (Equation 2) has been used to calculate the non-exhaust emissions: 6

𝐸𝑖 = ∑ 𝑉𝑖,𝑗 × 𝐸𝐹𝑗,𝑘 × 𝐿𝑖

(2)

𝑗=1

Where Vi,j is the number of vehicles on the road i of category j



EFj,k is the emission factor for vehicle category j, non-exhaust emission type k. The

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

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non-exhaust type (k) include road wear, brake wear, tyre wear, and resuspension. Li is the length of the road link i

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Total non-exhaust emission TE can be calculated by taking the sum across all road link N

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(Equation 3). 𝑁

(3)

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TE = ∑ 𝐸𝑖 𝑖=1

Gridded emission inventory of PM has been estimated at 100 m2 by a weighted sum of all

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type of emissions according to the road falling in that grid. The gridded emission gEm for

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grid m can be calculated using equation 4. 𝑄

𝑔𝐸𝑚 = ∑ 𝐸𝑚,𝑖 ∗ 𝐹𝐿𝑚,𝑖

Where

(4)

𝑖=1



Q is the number of roads links falling under grid m



Em,i is the emission from the road link i in grid m



FLm,i is a fraction of the road length of road link i falling in grid m

Total emission gTE from the gridded emissions can be calculated by taking the sum across all grids M (Equation 5). 𝑀

gTE = ∑ 𝑔𝐸𝑚 𝑚=1

The calculated gTE should be equal to TE.

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(5)

Journal Pre-proof In the end, the total emission at a grid m is calculated as the sum of exhaust emission described in Singh et al. (2018) and non-exhaust emissions. 3 Result and Discussion Vehicular PM emissions of particles less than 10µm from the exhaust and non-exhaust sources have been calculated over megacity Delhi and then gridded at a very high resolution of 100 m2 using the methodology described in section 2. The exhaust emissions are the tailpipe emissions, and non-exhaust emissions include brake wear, tyre wear, road wear and resuspended dust. 3.1 The Silt load variation and spatial distribution

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The box plot of the silt load estimated for the different type of roads has been shown in Figure 1, whereas Figure 2 depicts the spatial distribution of the silt load for the entire megacity Delhi.

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The mean silt load values across all the roads have been estimated to be equal to 2.46 g/m2.

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The lowest silt load was estimated for the highways with the median value of 0.22 g/m2, and the highest silt load was found in residential lanes with median values of 1.80 g/m2, which can

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be as high as 9.9 g/m2 at some locations.The minimum, mean, median and maximum values for each type of road have been shown in Supplementary Table T1. A similar kind of silt load

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pattern has been reported by Zhang et al. (2017) and Kuhns et al. (2001). The mean silt loading reported in USEPA (2011) for the commercial/residential is found to be higher than the silt

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loading at the expressway. The California Air Resources Board’s report on Entrained Road Travel, Paved Road Dust for San Joaquin Valley (CARB, 2003 and 2016) also shows lower

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values silt load over the freeway and higher values over arterial and collector roads. The estimated spatial distribution of silt loading has been used to calculate the resuspension emissions.

3.2 Exhaust and non-exhaust emissions The spatial distribution of the total gridded vehicular emission has been shown in Figure 3. The total annual vehicular PM emission from exhaust and non-exhaust sources were found to be 31.5 Gg/year over the study area that is equivalent to the 86 Tons/day. Higher values of emissions are found on the major roads and highways for both the exhaust and non-exhaust emissions. The total exhaust and non-exhaust emissions over megacity Delhi have been estimated to be 4.5 Gg/year and 27 Gg/year respectively. Almost 86% of on-road PM emission in Delhi were due to the non-exhaust sources, whereas exhaust emission contribute to only 14% as shown in Figure 4. Among non-exhaust sources brake wear, tyre wear, road wear and 12

Journal Pre-proof resuspension emissions have been estimated to be 0.93 Gg/year, 0.60 Gg/year, 0.52 Gg/year and 25 Gg/year respectively. Among all sources of the vehicular emission, the contribution of dust resuspension alone is the highest (79%) followed by exhaust emission (14%). The contributions of the tyre wear, brake wear and road wear has been estimated to be equal to 2%, 3%, and 2% respectively. Among non-exhaust sources, most of the PM emissions were due to

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the resuspension of dust, which is 92% of the total non-exhaust emissions.

Figure 3. Gridded vehicular particulate matter emission over megacity Delhi at 100 m2 resolution.

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3.2.1 The contribution of different vehicle types

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Figure 4. Percentage contribution to total PM emissions from the exhaust and non-exhaust sources.

The contribution of different vehicle types to the exhaust and non-exhaust sources of vehicular

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emissions is shown in Figure 5 (a,b). Figure 5a shows the percentage contribution of vehicles

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type to the exhaust and non-exhaust emission sources; Figure 5b shows the percentage share of each emission source to the total emission from each vehicle category. The results show that Cars, Buses, HCVs, LCVs, 2W and 3W contributes 34% (10.8 Gg), 23% (7.4 Gg), 17% (5.5

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Gg) and 14% (4.4 Gg), 7% (2.2 Gg) and 4% (1.2 Gg) respectively to the total 31.5 Gg/year emissions. The current estimate also shows that 90% of the total vehicular emissions in Delhi

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are due to the buses, cars, HCVs and LCVs. Cars contribute to almost one-third of the total vehicular emissions. It is to be noticed that the buses and cars having only 9% and 7% contribution to the exhaust emission shows a significant contribution of 26% and 39% to the non-exhaust emission respectively. On the other hand, the contribution of LCVs, 2W, and 3W, which is 23%, 19%, and 8% respectively to the exhaust emissions, has been reduced to 12%, 5% and 3% respectively to the non-exhaust emissions. For brake wear, tyre wear and road wear emissions, it is estimated that cars contribute to half of the total emissions. ,When we see the share of individual emission sources for each vehicle type (Figure 5b), resuspension emission ,was found to be the dominant source for all categories of vehicles.

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Figure 5 (a) Percentage contribution of each category of the vehicle to the total exhaust and non-exhaust sources of emissions. (b) Percentage contribution of emission sources to the total emissions from each category of vehicles. (values less than 3% are not shown)

3.2.2 The contribution of different road types The emission may vary for different roads due to the diverse traffic, texture, and state of the pavement; and silt present on the road. The percentage contribution of each road type to the non-exhaust, exhaust, and total emissions is depicted in Supplementary Figure S3. It is estimated that 56% (17.5 Gg), 19% (6.1 Gg) and 17% (5.3 Gg) of the total emission originates at the major roads (MAJ), national highways (NAT) and minor roads (MIN) respectively. It shows that more than half of the total contribution comes from the major roads followed by national highway contributing to one-fifth of the total emission. The rural roads (RUR), state highways (STA) and residential roads (RES) all together contribute 8% of the total emissions. Generally, it is believed that the emissions should be highest at the national highways because of higher ADT, but the highest contribution to the total emission comes from major roads followed by national highways. As the emissions are directly related to the average daily traffic 15

Journal Pre-proof and length of the road, the major roads having length five times more than that of the national highway shows higher emissions. The emission per kilometer driven on a given type of the road was found highest for the national highway, i.e. two times that of the major roads. The increased share of the resuspension emissions for minor and residential roads (Supplementary Figure S3.b) is mainly due to more silt present on such roads. 3.2.3 The contribution of different fuel type Existing emissions standards suggests that on-road diesel contribute significantly to air pollution and secondary organic particle formation in urban environments (Gentner et al.,

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2017). The exhaust emission factor of diesel is eight times the emission factor of the petrol vehicle (ARAI 2008). Although the non-exhaust emission from a diesel and petrol vehicle

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having same weight is likely to be equal, their relative contribution in terms of exhaust component will differ significantly. Supplementary Figure S4 shows the relative contribution

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to the exhaust and non-exhaust emission due to all diesel, petrol, and CNG vehicles in Delhi.

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It is estimated that 58% (18.4 Gg) of the total PM emission in Delhi is due to the Diesel vehicles followed by CNG and petrol vehicles contributing 26% (8.1 Gg) and 16% (5.1 Gg) respectively

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to the total PM emissions. The percentage contributions of brake wear, tyre wear, and roads wear are found to be more in petrol vehicles than the CNG and Diesel. The diesel vehicles

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show relatively higher contribution to exhaust emissions. For the CNG vehicles, non-exhaust emissions are mainly due to the resuspension emission dominated by CNG Buses. This

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suggests that the implementation of CNG could have reduced the exhaust PM emission but not have a significant effect on PM levels (Ravindra et al., 2006) because of the resuspension emissions.

Delhi has more than 60% of petrol cars, and the remaining cars use diesel and CNG fuel. Analysis has also been done to find out the relative contribution of diesel, petrol, and CNG cars in total car emissions. It is found that 70% of total exhaust emissions in Delhi are dominated by diesel cars. However, the non-exhaust contribution of diesel cars is estimated to be only 15% (Supplementary Figure S5). On the other hand, the petrol cars contribute more to nonexhaust emissions than exhaust. Therefore, efforts to reduce the dust resuspension should be preferred than odd-even strategies (Kumar et al., 2017; Chowdhury et al., 2017) as it may lead to a reduction in PM levels mainly due to the reduction in non-exhaust emissions originating due to all vehicles.

16

Journal Pre-proof 3.2.4 Contribution among vehicle age Experimental emissions data from in-use cars have shown a clear deterioration in the emissions as cars become older, mainly because of aging of their catalytic converters and the degradation of their emission control systems (Zachariadis et al., 2001). Moreover, because of the vehicle emission control technologies and improved emission standards, the newer cars emit less than, the older cars as evident from the exhaust emission factors. However, there may be small but not significant changes in the non-exhaust emissions with the age of the vehicle. The relative contribution of the exhaust and non-exhaust PM emissions according to the age of the vehicle were also calculated for four vehicle age category. The newest vehicles have been kept in 0-5

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years, then 5-10, 10-15 years old vehicles category. The vehicles older than 15 years (15yr+)

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are kept in the fourth category. The results show that total PM emissions are dominated by the 0-5 and 5-10 years old vehicles and the contribution of 15yr+ is found to be very low because

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of less number of on-road vehicles. In exhaust, 47% of emissions are contributed by newer vehicles, mainly dominated by cars and 2Ws. However, the highest contribution of non-exhaust

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emission comes from the 5-10 years old vehicles, which are dominated by buses, cars, and HCVs. Moreover, the relative contribution in exhaust and non-exhaust does not change

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significantly with the age of the vehicle (Supplementary Figure S6). While the older vehicles (15yr+) are likely to emit more exhaust than the newer vehicles (Zachariadis et al., 2001), the

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dust resuspension from the newer and older vehicles is expected to be similar because the dynamics associated with the vehicle induced turbulence remains identical for the same vehicle

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category. Therefore, one can expect that the contribution of the older vehicles to the total emissions to be higher than the new vehicles. However, this is not seen in the total emission because of the small share of older vehicles to the total traffic. 3.3 Effect of spatially distributed silt load on resuspension emission The AP-42 methodology is sensitive to the silt values that are used to estimate the resuspension (Venkatram, 2000, Teng et al., 2008). Despite that, studies (Guttikunda and Calori, 2013; Sharma et al., 2016; ARAI, 2018) have not reported the silt values used in their calculations. Some of the studies (e.g., Sahu et al., 2011) has used constant silt value or a range of silt loads (Gargava et al., 2014) across the city to estimate the dust resuspension emissions. In this study, we wanted to show the difference in the emission using a constant silt value as compared to the spatially distributed silt values used in this paper. To compare, we have used a constant silt load equal to the mean (2.46 g/m2) and median (1.40 g/m2) of the silt load across all the roads

17

Journal Pre-proof used in the present study (Supplementary Table T1). Then we have calculated the resuspension emission with the spatially distributed silt load as described in section 2.4.2. The constant silt load equal to mean and median resulted in 146 Gg/year and 87 Gg/year of resuspension emissions respectively, which are much higher than the emission calculated with the spatially distributed silt value. This indicates that using a constant silt load value tends to overestimate the resuspension emissions. Therefore spatially distributed silt load should be preferred to model the resuspension emission across the city instead of using a constant silt

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load value.

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3.4 Comparison with other studies

The exhaust and non-exhaust emissions reported by the present study as well as earlier studies

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are shown in Table 1. The list includes the studies which estimated at-least resuspension or wear emissions along with the exhaust emission. This list does not include the studies which Nagpure et al. (2016) have estimated the

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have reported only the exhaust emissions.

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resuspension and wear emissions except for the road wear emission. Kumari et al. (2013) estimated all wear emissions but did not include the resuspension emission. The emission estimated by Sahu et al. (2011), Guttikunda and Calori (2013), Gargava et al. (2014), Sharma

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et al. (2016), ARAI (2018) and SAFAR (2018) include only resuspension emissions and do not include any type of wear emissions. The present study estimated the emissions from all non-

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exhaust sources that include resuspension, tyre wear, brake wear, and road wear emission. In order to compare the relative contribution of the non-exhaust emission uniformly across all studies, we assume the missing source of the emission based on the share reported by other studies. The studies which have not reported the road wear emission, we assume the road wear emission equal to the tyre wear emissions reported by the same study. Resuspension emissions have been kept equal to 70% of the total emission based on the median share calculated from other studies. The studies which do not report wear emission, it is assumed to be equal to ~7% of the total emissions. Significantly large variations can be seen in Table 1. It would be unfair to compare the emissions estimated using different methodologies over different domains for different years. However, assuming that the emissions do not change drastically within a few years, one can compare the emissions over the same domain. The total emission for Delhi has been reported to be varying from 24-39 Gg/year with a mean and median value of 31.8 and 32.8 Gg/year, 18

Journal Pre-proof respectively. The resuspension emission has been reported to be varying between 16.8 and 29.1 Gg/year with a mean and median value of 23.5 and 24.5 Gg/year, respectively. The variations are even higher for National Capital Region (NCR) where the total emission varies from 43 to 221 Gg/year with a mean and median value of 158 and 183 Gg/year respectively. However, it is found that the emissions calculated by the proposed methodology in this study estimated the emissions within the range of the earlier studies. Table 1. Exhaust and non-exhaust emissions (Gg/year) reported by different studies

Year

Brake Wear

Tyre Wear

Road Wear

Total Wear

Resusp ension

NonExhaust

Exhaust

Total

Kumari et al. (2013)

Delhi

2006

(Gg/year)

(Gg/year)

(Gg/year)

(Gg/year)

(Gg/year)

(Gg/year)

(Gg/year)

(Gg/year)

Gargava et al. (2014)

Delhi

2007

Singh et al. Present Study Nagpure et al. (2016)

Delhi

2010

0.93

Delhi

2011

1.4

Sharma et al. (2016)

Delhi

2014

0.37

0.41

-

1.2

16.8^

18.0^

6.0

24.0^

2.4#

28.2

30.6#

3.5

34.1#

0.6

0.52

2.1

25.0

27.0

4.5

31.5

0.4

0.4*

2.2*

18.2

20.4*

5.0

25.4*

2.5#

-p

-

0.42

-

ARAI (2018)

Delhi

2017

Sahu et al. (2011)

NCR~

re

-

of

Domain

ro

Studies

2010

-

-

-

12.2#

131.3

143.4#

30.3

173.7#

Guttikunda and Calori (2013) ARAI (2018)

NCR~

2010

-

-

-

3.0#

25.5

28.5#

14.6

43.1#

NCR

2016

-

-

-

15.5#

137.2

152.7#

68.6

221.2#

SAFAR (2018)

NCR~

2018

-

-

-

13.5#

136.0

149.5#

43.2

192.7#

-

-

-

-

31.6#

4.7

36.3#

2.8

24.0

#

26.8

12.8

39.6#

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na

lP

-

29.1

#

NCR is National capital region of Delhi (http://ncrpb.nic.in/ncrconstituent.html) 

^Resuspension emission is not reported therefore assumed to be equal to ~70% of the total emissions based on the median of other studies.



#Wear emissions not reported; therefore it is assumed to be equal to ~7% of the total emissions.



*Road wear emission is not reported therefore the road wear emission is kept equal to Tyre wear emissions reported by the same study.



~Part of NCR (Including satellite cities around Delhi)

3.5 Limitations of this study The emission estimated in this study may be affected by the uncertainty associated with traffic counts and emission factors because of the non-availability of detailed traffic activity data and country-specific emission factors. The exhaust emission factors may have uncertainties because of changing driving cycle patterns. The non-exhaust emission factors and methodology used here are for a developed country where the driving behavior and road infrastructure is different. The driving behavior, frequent braking, and traffic congestions can have a remarkable 19

Journal Pre-proof impact on the wear emissions. For example, because of traffic congestions, the frequency of braking may be more than normal in Delhi, leading to more brake, tyre wear and road wear emissions. The current estimates are based on the best available data and several assumptions based on the visual interpretation of the road type and assigning the traffic to the given roads. This study relies on the non-exhaust emission factors developed elsewhere as the studies over Delhi are very limited. The brake wear, tyre wear and road wear emission factors are from the UK-DfT (2018) based on EMEP guidebook (EEA, 2013, EEA, 2016) which may be different for Delhi.

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The resuspension emission that is based on USEPA AP-42 latest methodology, that is developed for a different region, can be highly uncertain for Delhi because of the data used in

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the formulation the model (Venkatram, 2000) is from a different region. Moreover, there will be uncertainties associated with the silt load estimation (Teng et al., 2008). While vehicular

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weight can still be standard, the actual silt present on the road may vary significantly with

re

locations and seasons. Especially the silt at some location such as near construction sites, open grounds, edges of the roads, etc. can be substantially high, which is ignored. Gustafsson et al.

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(2019) reported significant variability in road dust loading across the road with typically low levels in the wheel tracks, medium levels between tracks, and highest levels at the curbside.

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Such local spatial variability makes quantification of road dust loading uncertain without a large number of samples (Denby et al., 2018). The road soil moisture also plays an important

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role in resuspension (Denby et al., 2013b), which has not been considered except taking the number of rainy days. The dependency of resuspension on the traffic speed (Amato et al., 2017) has not been directly considered in this study, which is an important source of uncertainty. The state of the pavement of the roads may also have a large impact on the resuspension emission. A lot of research and organised dataset are required in megacities, such as Delhi, to further develop and improve the non-exhaust emission factors as a function of tyre type, brake type, road type, vehicular speed, braking frequency, road silt loading and the associated meteorological parameters such as temperature, humidity, wind speed and rainfall. It is estimated that the developing countries, including India, will add 5−10% vehicles annually. Despite the increase in the vehicular fleet, it is expected to reduce the traffic exhaust and nonexhaust emissions because of the strict local regulations and advancement in the emission reduction technologies. However, the share of non-exhaust emissions will increase (EEA, 2016). Although vehicular exhaust emissions are regulated in Delhi, the factors that derive the non-exhaust emissions, such as silt load, road, tyre and brake quality are not at present subject 20

Journal Pre-proof to regulatory scrutiny, and the non-exhaust emissions in Delhi are likely to increase in the future with an increasing number of vehicles (Nagpure et al., 2016). This study shows the importance of non-exhaust sources, which is six times that of the exhaust sources. A detailed analysis of the relative contribution presented in this paper will be useful for the researchers and policymakers to study the reduction in emissions in recent years and design emission control strategies under the national clean air program.

4

Conclusions

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Traffic-related particulate matter emissions, including exhaust and non-exhaust, were estimated at high resolution (100 m2) over megacity Delhi. It is the first study of its kind to

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estimate the spatially resolved emissions including non-exhaust sources such as brake wear,

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tyre wear, road wear and resuspension.

The annual vehicular PM emissions from both exhaust and non-exhaust sources were found to

re

be 31.5 Gg/year, out of which exhaust and non-exhaust contribution was 14% (4.5 Gg/year)

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and 86% (27 Gg/year) respectively. Among non-exhaust sources brake wear, tyre wear, road wear and resuspension emissions have been estimated to be 0.93 Gg/year, 0.60 Gg/year,

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0.52Gg/year and 25 Gg/year respectively. The resuspension emission alone contributes to 79% of the total PM emissions and contributions of the tyre wear, brake wear and road wear has been estimated as 2%, 3%, and 2% respectively. Although the emission estimated in this paper

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may have uncertainties, they are within the range of earlier reported studies. It was also observed that 90% of the total vehicular emissions are due to the cars, buses and HCVs and LCVs. Cars alone contributing to one-third of the total vehicular emissions. Cars were found to be the dominant source of the brake wear, tyre wear and road wear emissions. The analysis of contribution from different types of roads suggests that more than half of the contribution equal to 17.5 Gg/year comes from the major roads. However, the emission per km were found to be highest for the national highway, which is found to be two times that of the major roads. It is estimated that 58% of the total PM emission is due to the Diesel vehicles followed by CNG and petrol vehicles. While diesel vehicles show relatively higher contribution in exhaust emissions, the CNG vehicles show enhanced contribution in non-exhaust emissions. Petrol cars contribute more to non-exhaust emissions whereas diesel cars contribute more to exhaust

21

Journal Pre-proof emissions. It is found that the relative contribution in exhaust and non-exhaust does not change significantly with the age of the vehicle.

Acknowledgment Authors are thankful to the National Atmospheric Research Laboratory (NARL) for providing the necessary support. AB is thankful to Department of Environment Studies, Panjab University, Chandigarh for providing the necessary support and greatly acknowledge the MoES (Ministry of Earth Sciences, India) for providing fellowship as a part of PROMOTE

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project. The paper does not discuss policy issues and the conclusions drawn in the paper are

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based on the interpretation of results.

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Research Highlights

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Vehicular exhaust and non-exhaust emissions were estimated at high resolution (100 m2) over megacity Delhi.

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31.5 Gg/y of PM emissions were estimated from exhaust and non-exhaust sources for 2010 year. Non-exhaust contribute significantly (86%) to on-road traffic PM emissions.

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Dust resuspension is the main contributor (79%) to the traffic related PM emissions.

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