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Vehicle Stock Numbers and Survival Functions for On-road Exhaust Emissions Analysis in India: 1993-2018

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21 May 2024

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22 May 2024

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Abstract
In air quality management, at urban and regional levels, road transport remains the cornerstone of residential, commercial, and industrial activities, and the vehicle exhaust emissions maintain the position of one of the key contributing sources. In Indian cities, big and small, vehicle exhaust emissions and dust from vehicle movement on the roads, contribute to as much as 50% of particulate matter pollution in a year. So, having access to a vehicle exhaust emissions inventory that is reliable and replicable is critical for air quality management and this exercise starts with access to vehicle stock numbers. These numbers are typically obtained from vehicle registration databases, traffic surveys, and other governmental records, and often require time consuming data cleaning protocols before they can used for emissions and pollution baseline analysis. This paper provides a cleaned and open-access vehicle stock database, as registered and in-use fleet, for the period covering 1993 and 2018, for all India and states, along with an estimate of age-mix of the vehicles using survival functions.
Keywords: 
Subject: Environmental and Earth Sciences  -   Pollution

1. Introduction

A clean air action plan for a city, a region, or a nation starts with having access to an accountable emissions inventory covering all the known sources. A typical inventory is calculated for aerosol species like particulate matter (PM2.5, aerosols with aerodynamic size under 2.5 mm and PM10, aerosols under 10 mm), black carbon (BC), and organic carbon (OC), and gaseous species like sulphur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), volatile organic compounds (VOCs), and greenhouse gases (e.g., carbon dioxide - CO2). The known sources of emissions range from burning of fuels, such as petrol, diesel, gas, coal, biomass and waste in the sectors such as road-rail-air-water transportation, industries, residential cooking and heating, and waste management to natural emissions in the form of sea salt, dust storms, lightning, and biogenic (Crippa et al., 2023). Determining emission strengths of all these sources is a complex and challenging task (Fowler et al., 2020), as it involves documenting in space and time the dynamic fuel usage patterns for anthropogenic sources and dynamic meteorology for natural sources; along with a demand for large amounts of data from surveys and measurements (Li et al., 2017; Venkataraman et al., 2018b; Crippa et al., 2020; McDuffie et al., 2020; Crippa et al., 2021; Solazzo et al., 2021; Crippa et al., 2023). Among all the listed sectors, road transport is likely the most complicated to establish an emissions inventory (Lekaki et al., 2024). As this involves knowing not only how many vehicles are registered in the region, but also how many are on the road, how and how often are they used, how old are they, and where and when are they travelling. The last factor determines the spatial and temporal extent of the calculated total emissions.
Whether an emissions inventory is being built for a road, city, airshed, state, country, or a region, the fundamental equation follows an activity-based model using three components (a) number of vehicles (b) vehicle km travelled (c) emission factors (and fuel efficiency).
PM, BC, OC, NOx, CO, and VOC emissions are calculated using the equation.
E v , f , g , p = ∑ [ I N V v , g ∗ S f ∗ V K T v , g ∗ E F v , f , g , p ]
And SO2 and CO2 emissions are calculated using the equation.
E v , f , g , p = ∑ [ I N V v , g ∗ S f ∗ V K T v , g ∗ F E v , f , g ∗ P C f , p ]
  • v = vehicle; f = fuel; g = age group; p = pollutant
  • E = the total emissions by pollutant (p), by vehicle type (v), fuel type (f), by age group (g)
  • INV = the total number of in-use vehicles on-road by vehicle type (v) and by age (g)
  • S = the share of vehicles on-road by fuel type (g) and vehicle type (v)
  • VKT = the annual average vehicle kilometres travelled by vehicle type (v) and by age (g)
  • EF = the fleet average emission factor by vehicle type (v), fuel type (f), age group (g), and by pollutant (p)
  • FE = the fuel economy by vehicle type (v), fuel type (f), and age group (g)
  • PC = the carbon and sulphur content in the fuel.
The spatial scale of the calculations from roads to cities to regions to countries, changes the complexity of the parameters in the equations. For example, for a road segment, the vehicle usage (km) is the road length, while for a city the usage depends on the commuter travel patterns and origin-destination matrix between residential, industrial, and commercial hotspots. Unfortunately, in most of the low- and middle-income countries, this information is not always readily available in a form that can be immediately used for building an operational emissions inventory. To overcome this challenge, various data resources must be evaluated and manipulated to support the analysis.
In Indian cities, road transport emissions continue to be one of the main sources of ambient PM2.5 pollution, along with residential cooking and heating, industrial emissions including power generation and other heavy industries, open waste burning, and dust from construction and road activities. (Pant and Harrison, 2012; Guttikunda et al., 2014; UEinfo, 2019; Ganguly et al., 2020; Yadav et al., 2022). In large cities like Bengaluru, Chennai, Delhi, Kolkata, and Mumbai, the on-road vehicle exhaust emissions, and dust resuspension on the roads due to vehicle movement, collectively contributes 30-50% of the ambient PM2.5 pollution, on an annual basis. This fraction is higher for PM10 pollution, which captures most of the dust from these urban activities. For the gaseous pollutant’s NOx, CO and CO2, transport continues to the major source of emissions (Hakkim et al., 2021; Ahn et al., 2023; Lekaki et al., 2024). SO2 is the only pollutant with a decline in the total emissions with periodic improvement in the sulphur content of petrol and diesel (10ppm, as of May 2024).
One of the key inputs in building vehicle exhaust emissions inventory is the vehicle stock information, segregated into vehicle types, fuel types, and age. This is necessary for understanding the overall growth in vehicle ownership, the rate of change of newer vehicles entering the fleet and establishing a fleet average emission rate. Several studies presented an estimate of air and climate pollutants for all India and for Indian cities, but none presented an open database of vehicle stock for replication and for further calculations (van Aardenne et al., 1999; Reddy and Venkataraman, 2002; Gurjar et al., 2004; Garg et al., 2006; Singh et al., 2008; Baidya and Borken-Kleefeld, 2009; Ramachandra and Shwetmala, 2009; Goel and Guttikunda, 2015a, b; Singh et al., 2017; Venkataraman et al., 2018a; Guttikunda et al., 2019; Hakkim et al., 2021; Singh et al., 2022; Lekaki et al., 2024). All the references here use the same fundamental method of multiplying three inputs – number of vehicles, vehicle usage, and an emission factor.
For the period covering 1993 and 2018, for India and its states, this paper provides a data resource of cleaned and model ready registered vehicle stock numbers, survival functions to build in-use vehicle stock numbers, a library of calculators to build first order inputs for urban vehicle exhaust emission inventories, and an overview of the use cases for further applications.

2. Methods and Inputs

2.1. Registered Vehicle Stock Numbers

The following equation refers to the calculation of the total registered number of vehicles (RNV), based on the annual sales and/or annual new registrations information.
R N V v = ∑ [ N V v , g ]
  • RNV = the number of registered vehicles by vehicle type (v), as reported by MoRTH
  • NV = the number of new vehicles registered every year (by age (g))
RNV refers to the total number of vehicles that have been legally purchased and registered, including those in storage, inoperable, or being used infrequently. There are instances when a vehicle is re-sold and re-registered, the old registration number continues to be in the system, along with the new ownership/registration number (Mohan, 2013). In India, all the vehicles are registered for a period of 15 years. There is no prescribed retirement age for the vehicles and a provision to re-register for another 15 years can be availed, upon passing the safety and environmental checks at the regional transport office (RTO). In the National Capital Region (NCR) of Delhi, re-registration of diesel vehicles older than 10 years and petrol vehicles older than 15 years is not allowed, which are often transferred to other states for use till their end of life. Typically, when tallying the annual new registrations for RNV, summation over the past 23 years is considered, given the tendency of the vehicles to survive at various maintenance conditions (Mohan, 2013; Guttikunda and Mohan, 2014; Goel et al., 2015). This should not be construed as all vehicles under 23 years of age are plying on the roads. The Ministry of Road Transport and Highways (MoRTH) annual reports publish the total number of registered vehicles in the country by state and by city for the categories listed in Table 1 (MoRTH, 2022). For convenience, these categories are clubbed into nine broader groups – these groups are often used for emission calculations and evaluation of emission control strategies. A copy of RNV extracts from the MoRTH’s annual report is included in the Supplementary Information (SI).

2.2. In-Use Vehicle Stock Numbers

The following equation refers to the calculation of converting RNV into in-use (or on-road) number of vehicles (INV) using a survival function (SF).
I N V v , g = ∑ [ R N V v , g ∗ S F v , g ]
  • SF = the survival function by vehicle (v)
  • g = age of the vehicle (v)
INV refers to the actual number of vehicles that are on the roads, driven and used for passenger and freight transportation, and the difference between RNV and INV is significant in India (Mohan, 2013). The MoRTH annual reports refer only to RNV, and the emission calculations must use only INV.

2.3. Survival Functions

Survival functions are used to enable the transition of RNV to INV, for the emissions and pollution models to replicate real world patterns.
S F v = e − g + α v T v β v
  • SF = the survival function by vehicle (v)
  • g = age of the vehicle
  • T = characteristic service life of the vehicle (v)
  • α, β = the shape and scale functions of the SF by vehicle (v)
Survival function model presented here is an adoption of probabilistic Weibull distribution representing the probability of a vehicle to still be in-use after a certain number of years (Zachariadis et al., 1995; Baidya and Borken-Kleefeld, 2009; Goel et al., 2015; Goel et al., 2016). The shape and scale functions (α, β) are summarized in Table 2 and Figure 1 for three different scenarios – high, medium, and low survivals rates.
For Tier-1 cities like Bengaluru, Chennai, Delhi, Hyderabad, Kolkata, and Mumbai, low survival functions are applicable, meaning the turnover of the vehicles, especially personal vehicles, is the highest in the cities. Similarly, for Tier-2 (medium) cities like Bhubaneswar, Coimbatore, Indore, Kanpur, Kochi, Ludhiana, Patna, Pune, and Varanasi, medium survival function is applicable. And for national level analysis, where it is assumed that a vehicle will survive in any state or till the end of its operational life, the high survival function is applicable. A copy of survival functions for the nine broad categories under the three scenarios and estimated model ready INV information by state is included in the SI. These survival functions (Table 2) are one operational example set based on limited number fuel station surveys conducted in Indian cities (Goel et al., 2015; Goel et al., 2016; BSPCB, 2019) and must be reevaluated, to represent the vehicle ownership and usage characteristics elsewhere. The characteristic service life of the vehicles (T) is based on the prevalent restrictions on the vehicle types. For examples, in the Tier-1 cities, passenger 3-wheelers and taxis, after eight years, are required to either retire them to other cities or go through stringent maintenance checks to continue to run the vehicle in the city. Most often the aged vehicles are either run unofficially in limited capacity or transferred to smaller cities. Lack of stringent inspection and maintenance programs also lead to a gradual drop in a vehicle’s survival probability, after their heavy usage in the initial years. This is also reflected in the drop in the vehicle usage (km travelled per day) among the aged taxi, passenger 3-wheelers, and freight vehicles (Goel et al., 2015; Malik et al., 2019). For passenger vehicles, the turnover rate is higher in the big cities, which results in a sharp drop-off in the aged vehicles, irrespective of any restrictions. For example, the average turnover rate of cars is 4 to 4.5 years in Delhi (low survival) and 6 to 6.5 years in Patna (medium to high survival) (BSPCB, 2019). This also translates into a strong second-hand passenger vehicle market in India, and the assumption that at the national scale the vehicles are fully utilized in a high survival mode.

3. Open Data Resources

3.1. Vehicle Stock Numbers

Total registered number of vehicles (Table 3) in India touched 300 million in 2018 (as of March 31st, 2019 – end of India’s financial year 2018). Of this 74.4% were 2-wheelers (2Ws), 13.4% were 4-wheelers (4Ws - including jeeps, sport utility vehicles, and taxis), 0.7% were buses (including intra- and inter-city), 8.4% were freight vehicles (including light- and heavy-vehicles), and 0.7% were non-road vehicles (MoRTH, 2022). The total registered vehicles number doubled between 2011 and 2018, increased six times between 2000 and 2018, and increased 11 times between 1993 and 2018 (Figure 2).
Most of the increment comes from the 2W and 4W sectors, mostly due to lack of similar rate of interest in meeting the travel demand via mass-transit facilities in the big and medium size cities. The total bus number barely doubled since 2005, while the over demand for public transport increased at least 10 times in the same period. For example, a white paper in 1997 for Delhi’s air pollution estimated that the city needs at least 15,000 public transport buses to meet the estimated travel demand in 2000 and the same assessment was made in the clean air action plan proposed as part of the India’s national clean air programme in 2019-21, while the city continues to operate less than 4000 buses to meet the demand. The city operated 2000 buses in 1997 (CPCB, 1997; Guttikunda et al., 2023b). The public transportation demand management scenario is similar in other cities. A gradual increase in the demand for total freight vehicles is an indicator of growing economy, demand for raw and finished products, urbanization with demand more commercial amenities, and overall gross domestic product of the country (Guttikunda and Ka, 2022).
Total registered vehicle estimates by state for select years between 1993 and 2018 is presented in Table 4. This table includes 29 states and 7 union territories (including Delhi). As of 2024, there are 28 states and 8 union territories, with the state Jammu & Kashmir broken into 2 union territories with Ladakh added to the list and Dadar and Nagar Haveli and Diu and Daman combined into one union territory. In 2000, 3 new states were constituted - Chhattisgarh was cut from Madhya Pradesh, Jharkhand from Bihar, and Uttarakhand from Uttar Pradesh. In 2014, then 29th state Telangana was cut from Andhra Pradesh. The vehicle registration numbers for these new states started a year after the state’s formation. Till then, their vehicle registration tally is included in the old states. States with the largest population also host the largest fleets. As of March 31st, 2019, the Gujarat, Karnataka, Maharashtra, Madhya Pradesh, Rajasthan, Tamil Nadu, and Uttar Pradesh have more than 15 million RNVs. Delhi with less than 20 million population (1.5% of the total) has approximately 12 million registered vehicles (4% of the total). The least vehicle density per capita is in the Northeastern states of Assam, Arunachal Pradesh, Manipur, Meghalaya, Nagaland, Sikkim, and Tripura.
An estimated national average age-mix of the in-use fleet using the high survival rates is presented in Figure 3. Overall, up to 70% of the fleet tend to be under 10 years age. The past emission inventory studies indicated that ~20% of the fleet older than 10 years age hosts most of the super-emitters (aka vehicles with the highest emission rates) and account for the largest chunk of the total emissions of PM2.5, NO2, CO and VOC’s (Baidya and Borken-Kleefeld, 2009; Guttikunda and Mohan, 2014; Sadavarte and Venkataraman, 2014; Hakkim et al., 2021; Debbarma et al., 2024). This is often a symptom of poor maintenance of the older vehicles.
The evolution of vehicle standards in India is presented in Figure 4. From no norms in late 1990s to introducing Bharat Standard (BS) 6 in 2020, all the new vehicle and fuel standards were introduced in Delhi first, then the metropolitan cities, and after 2-5 years nationwide. The bifurcated standards had limited effect on reducing the overall emissions, except for in the cities with the new standards, where the passenger vehicles travelling within the city limits benefitted by emitting lesser emissions (Guttikunda and Mohan, 2014). In 2020, BS6 was introduced nationwide under “one nation – one fuel” motto. The overall fleet will take at least 5-8 years, depending on state and city, to host a fleet fraction dominant of the BS6 vehicles.
A copy of estimated in-use vehicle fleet, including age-mix using the high survival rates is included in the SI. Other sources of RNV include a new resource VAHAN, which presents analytics by state and RTO (https://analytics.parivahan.gov.in). The online database includes RNV and NV by vehicle model and fuel type. Only the data posted on the portal is public and there are no clear guidelines to access the full database at the RTO level. Annual sales information by vehicle model and fuel type can be purchased from the institutions like Society of Indian Automobile Manufacturers (SIAM).

3.2. Vehicle Exhaust Emissions Analysis Tools

The fundamental equation for building vehicle exhaust emissions is multiplying three parameters – number of vehicles, vehicle km travelled (usage) and an emission factor. When the data is scarce, this equation has versions that can be used to deduce similar emission estimates, with some accountable uncertainty (1) a method using information on total fuel consumed, share of fuel consumed in various modes, fuel efficiency of the modes, and an emission factor (b) a method using information on total passenger trips, share of trips in various modes, average trip length of various modes, and an emission factor. An example calculator of these methods is available @ https://urbanemissions.info/tools, along with resources to establish representable inputs for use in emissions analysis. These include:
  • A method to convert fleet average speeds and fleet average travel time per day into vehicle km travelled per day.
  • A method to calculate how many additional buses are required to support odd-even or an equivalent scheme (with and without fuel mix exemptions).
  • A method to calculate total fuel wasted from idling in the city and to calculate savings from traffic management.
  • A method to calculate fuel and emission benefits of shifting a share of 2-wheeler and 4-wheeler trips to buses and non-motorized transport.
  • A method to estimate vehicle exhaust emission factors using emission standards and deterioration rates.
  • An example set of survival rates based on vehicle age for nine broad vehicle categories in Table 2 (to convert yearly RNV into INV).
  • A method to spatially disaggregate (grid) the total vehicle exhaust emissions using multiple grid-level proxies as weights such as density (km per grid) of various road types, population density, landuse-landcover, and information on commercial and industrial activities.
  • A library of emission factors for aerosols and gaseous species.
The lack of a single emission factor database in India, makes it difficult to accurately estimate, validate, and evaluate the total emissions and the effectiveness of existing emission control policies. A representative sample of emission factor databases available for open use are (a) the GAINS (Greenhouse gas and Air pollution Interactions and Synergies) modelling system, which provides a repository of regional level average factors (GAINS, 2022) (b) Central Pollution Control Board (CPCB), New Delhi, India, which provides a repository of emission factors based on a limited series of tests on various vehicle types and age mixes (CPCB, 2011) (c) (Goel and Guttikunda, 2015a) which provides a database of emission factors applicable for large cities, derived from a combination of vehicle age-appropriate emission standards and deterioration rates (d) (Jaiprakash and Habib, 2018) which provides a database for emission factors for 4-wheelers based on tests using on-board testing equipment.

4. Discussion

The vehicle stock database presented here is in operational use for an all-India air quality forecasting system, along with emissions and pollution analysis for 60 cities under the Air Pollution knowledge Assessments (APnA) program for Indian cities (https://urbanemissions.info). The APnA city program is also supporting India’s National Clean Air Program (NCAP) to establish layers of information necessary for building an emissions and pollution analysis framework for 131 cities designated as non-attainment areas (CPCB, 2019; Ganguly et al., 2020; CREA, 2023; Guttikunda et al., 2023a). A localized emission inventory is necessary for establishing the emission baselines for these cities. Especially for a sector like road transport, which is linked to all the other sectors delivering raw material, finished products, and people, reliability and replicability of an inventory is important to build confidence in the overall modelling exercise. Availability of long-term open vehicle stock information is the first step in this process. Future applications of this database include (a) a long-term emissions inventory for air quality and climate management (b) evaluation of the emission mitigation strategies at urban and regional scale and (c) support to urban and national clean air action plans.
Additional databases relevant for road transport emissions analysis are available for the designated NCAP urban airsheds (with a spatial resolution of 0.01º, approximately 1km). These include: (a) gridded and geospatial shape files of road density of primary, secondary, tertiary, and other road types (b) landuse-landcover shares at the grid level covering urban, barren, agricultural, other vegetation, and water types (c) gridded urban-rural classification using built-up area extracts from satellite retrievals (d) gridded multi-year population density and (e) gridded commercial activity density as number of establishments of hotels, hospitals, industries, markets, cinema halls, malls, apartment complexes, and other buildings. These geospatial layers of information can be strategically used for spatial allocation of the total vehicle exhaust emissions calculated using the vehicle stock numbers and as inputs to land use regression models for spatial allocation of information from monitoring networks and surveys.

Data Availability Statement

All the data compiled from the MoRTH annual reports, the cleaned and model ready vehicle stock information for the period covering 1993 to 2018, and a summary of probabilistic survival functions is available @ https://doi.org/10.5281/zenodo.11214506. A copy of MS-Excel based calculators to conduct vehicle exhaust emissions analysis, to build necessary input data, and to evaluate scenarios, are available @ https://www.urbanemissions.info/tools. Additional resources are available upon request by email.

Acknowledgments

The analysis and conclusions presented herein are exclusively those of the author and do not represent the supporting organizations or the educational departments.

Conflicts of Interest

The author declares no conflict of interests.

References

  1. Ahn D, Goldberg D, Coombes T, Kleiman G and Anenberg S CO2 emissions from C40 cities: citywide emission inventories and comparisons with global gridded emission datasets. Environmental Research Letters 2023, 18, 034032.
  2. Baidya S and Borken-Kleefeld J Atmospheric emissions from road transportation in India. Energy Policy 2009, 37, 3812–3822. [CrossRef]
  3. BSPCB 2019 Comprehensive Clean Air Action Plan for the City of Patna. Bihar State Pollution Control Board with consortium partners CSTEP, ADRI and Urbanemission.
  4. CPCB 1997 White paper on pollution in Delhi (New Delhi, India: Central Pollution Control Board, Government of India).
  5. CPCB 2011 Air Quality Monitoring, Emission Inventory and Source Apportionment Study for Indian cities (New Delhi, India: Central Pollution Control Board, Government of India).
  6. CPCB 2019 National Clean Air Programme (NCAP), Portal for Regulation of Air-pollution in Non-Attainment cities (PRANA - https://prana.cpcb.gov.in. (accessed on 28 February 2023)Ministry of Environmental Forests and Climate Change, The Government of India, New Delhi, India).
  7. CREA 2023 Tracing the Hazy Air 2023. Progress Report on National Clean Air Programme (NCAP) (Centre for Research on Energy and Clean Air, New Delhi, India.
  8. Crippa M, Guizzardi D, Butler T, Keating T, Wu R, Kaminski J, Kuenen J, Kurokawa J, Chatani S and Morikawa T The HTAP_v3 emission mosaic: merging regional and global monthly emissions (2000–2018) to support air quality modelling and policies. Earth Syst. Sci. Data 2023, 15, 2667–2694. [CrossRef]
  9. Crippa M, Guizzardi D, Pisoni E, Solazzo E, Guion A, Muntean M, Florczyk A, Schiavina M, Melchiorri M and Hutfilter A F Global anthropogenic emissions in urban areas: patterns, trends, and challenges. Environmental Research Letters 2021, 16, 074033.
  10. Crippa M, Solazzo E, Huang G, Guizzardi D, Koffi E, Muntean M, Schieberle C, Friedrich R and Janssens-Maenhout G High resolution temporal profiles in the Emissions Database for Global Atmospheric Research. Scientific Data 2020, 7, 121. [CrossRef] [PubMed]
  11. Debbarma S, Raparthi N, Venkataraman C and Phuleria H C Impact of real-world traffic and super-emitters on vehicular emissions under inter-city driving conditions in Maharashtra, India. Atmospheric Pollution Research 2024, 15, 102058. [CrossRef]
  12. Fowler D, Brimblecombe P, Burrows J, Heal M R, Grennfelt P, Stevenson D S, Jowett A, Nemitz E, Coyle M, Liu X, Chang Y, Fuller G W, Sutton M A, Klimont Z, Unsworth M H and Vieno M A chronology of global air quality Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering. Sciences 2020, 378, 20190314.
  13. GAINS 2022 Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) - South Asia Program: https://iiasa.ac.at/models‐tools‐data/gains, International Institute of Applied Systems Analysis (IIASA),Laxenburg, Austria).
  14. Ganguly T, Selvaraj K L and Guttikunda S K National Clean Air Programme (NCAP) for Indian cities: Review and outlook of clean air action plans. Atmospheric Environment: X 2020, 8, 100096. [CrossRef]
  15. Garg A, Shukla P R and Kapshe M The sectoral trends of multigas emissions inventory of India. Atmospheric Environment 2006, 40, 4608–4620. [CrossRef]
  16. Goel R and Guttikunda S K Evolution of on-road vehicle exhaust emissions in Delhi. Atmospheric Environment 2015, 105, 78–90. [CrossRef]
  17. Goel R and Guttikunda S K Role of urban growth, technology, and judicial interventions on vehicle exhaust emissions in Delhi for 1991–2014 and 2014–2030 periods. Environmental Development 2015, 14, 6–21. [CrossRef]
  18. Goel R, Guttikunda S K, Mohan D and Tiwari G Benchmarking vehicle and passenger travel characteristics in Delhi for on-road emissions analysis. Travel Behaviour and Society 2015, 2, 88–101. [CrossRef]
  19. Goel R, Mohan D, Guttikunda S K and Tiwari G Assessment of motor vehicle use characteristics in three Indian cities. Transportation Research Part D: Transport and Environment 2016, 44, 254–265. [CrossRef]
  20. Gurjar B R, van Aardenne J A, Lelieveld J and Mohan M Emission estimates and trends (1990-2000) for megacity Delhi and implications. Atmospheric Environment 2004, 38, 5663–5681. [CrossRef]
  21. Guttikunda S and Ka N 2022 Evolution of India’s PM2.5 pollution between 1998 and 2020 using global reanalysis fields coupled with satellite observations and fuel consumption patterns Environmental Science: Atmospheres.
  22. Guttikunda S, Ka N, Ganguly T and Jawahar P Plugging the ambient air monitoring gaps in India’s national clean air programme (NCAP) airsheds. Atmospheric Environment 2023, 301, 119712. [CrossRef]
  23. Guttikunda S K, Dammalapati S K, Pradhan G, Krishna B, Jethwa H T and Jawahar P What Is Polluting Delhi’s Air? A Review from 1990 to 2022. Sustainability 2023, 15, 4209. [CrossRef]
  24. Guttikunda S K, Goel R and Pant P Nature of air pollution, emission sources, and management in the Indian cities. Atmospheric Environment 2014, 95, 501–510. [CrossRef]
  25. Guttikunda S K and Mohan D Re-fueling road transport for better air quality in India. Energy Policy 2014, 68, 556–561. [CrossRef]
  26. Guttikunda S K, Nishadh K A and Jawahar P Air pollution knowledge assessments (APnA) for 20 Indian cities. Urban Climate 2019, 27, 124–141. [CrossRef]
  27. Hakkim H, Kumar A, Annadate S, Sinha B and Sinha V RTEII: A new high-resolution (0.1° × 0.1°) road transport emission inventory for India of 74 speciated NMVOCs, CO, NOx, NH3, CH4, CO2, PM2.5 reveals massive overestimation of NOx and CO and missing nitromethane emissions by existing inventories. Atmospheric Environment: X 2021, 11, 100118.
  28. Jaiprakash and Habib G On-road assessment of light duty vehicles in Delhi city: Emission factors of CO, CO2 and NOX. Atmospheric Environment 2018, 174, 132–139. [CrossRef]
  29. Lekaki D, Kastori M, Papadimitriou G, Mellios G, Guizzardi D, Muntean M, Crippa M, Oreggioni G and Ntziachristos L Road transport emissions in EDGAR (Emissions Database for Global Atmospheric Research). Atmospheric Environment 2024, 324, 120422. [CrossRef]
  30. Li M, Liu H, Geng G, Hong C, Liu F, Song Y, Tong D, Zheng B, Cui H, Man H, Zhang Q and He K Anthropogenic emission inventories in China: a review. National Science Review 2017, 4, 834–866.
  31. Malik L, Tiwari G, Thakur S and Kumar A Assessment of freight vehicle characteristics and impact of future policy interventions on their emissions in Delhi. Transportation Research Part D: Transport and Environment 2019, 67, 610–627. [CrossRef]
  32. McDuffie E E, Smith S J, O’Rourke P, Tibrewal K, Venkataraman C, Marais E A, Zheng B, Crippa M, Brauer M and Martin R V A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS) Earth Syst. Sci. Data 2020, 12, 3413–3442.
  33. Mohan D Moving around in Indian cities Economic and Political Weekly 2013, 40–48.
  34. MoRTH 2022 Road Transport Yearbook and Statistics Reports for 2000 to 2020: Minister of Road Transport and Highways, the Government of India, New Delhi, India).
  35. Pant P and Harrison R M 2012 Critical review of receptor modelling for particulate matter: A case study of India Atmospheric Environment.
  36. Ramachandra T V and Shwetmala Emissions from India’s transport sector: Statewise synthesis. Atmospheric Environment 2009, 43, 5510–5517. [CrossRef]
  37. Reddy M S and Venkataraman C Inventory of aerosol and sulphur dioxide emissions from India: I--Fossil fuel combustion. Atmospheric Environment 2002, 36, 677–697. [CrossRef]
  38. Sadavarte P and Venkataraman C Trends in multi-pollutant emissions from a technology-linked inventory for India: I. Industry and transport sectors Atmospheric Environment 2014, 99, 353–364.
  39. Singh A, Gangopadhyay S, Nanda P K, Bhattacharya S, Sharma C and Bhan C Trends of greenhouse gas emissions from the road transport sector in India. Science of The Total Environment 2008, 390, 124–131. [CrossRef]
  40. Singh N, Mishra T and Banerjee R Emission inventory for road transport in India in 2020: framework and post facto policy impact assessment. Environmental Science and Pollution Research 2022, 29, 20844–20863. [CrossRef]
  41. Singh R, Sharma C and Agrawal M Emission inventory of trace gases from road transport in India. Transportation Research Part D: Transport and Environment 2017, 52, 64–72. [CrossRef]
  42. Solazzo E, Crippa M, Guizzardi D, Muntean M, Choulga M and Janssens-Maenhout G Uncertainties in the Emissions Database for Global Atmospheric Research (EDGAR) emission inventory of greenhouse gases. Atmospheric Chemistry and Physics 2021, 21, 5655–5683. [CrossRef]
  43. UEinfo 2019 Air Pollution knowledge Assessments (APnA) city program covering 50 airsheds and 60 cities in India: https://www.urbanemissions.
  44. van Aardenne J A, Carmichael G R, LevyIi H, Streets D and Hordijk L Anthropogenic NOx emissions in Asia in the period 1990-2020. Atmospheric Environment 1999, 33, 633–646. [CrossRef]
  45. Venkataraman C, Brauer M, Tibrewal K, Sadavarte P, Ma Q, Cohen A, Chaliyakunnel S, Frostad J, Klimont Z, Martin R V, Millet D B, Philip S, Walker K and Wang S Source influence on emission pathways and ambient PM2.5 pollution over India (2015-2050). Atmospheric Chemistry and Physics 2018, 18, 8017–8039.
  46. Venkataraman C, Brauer M, Tibrewal K, Sadavarte P, Ma Q, Cohen A, Chaliyakunnel S, Frostad J, Klimont Z, Martin R V, Millet D B, Philip S, Walker K and Wang S Source influence on emission pathways and ambient PM2.5 pollution over India (2015–2050) Atmos. Chem. Phys. 2018, 18, 8017–8039.
  47. Yadav S, Tripathi S N and Rupakheti M Current status of source apportionment of ambient aerosols in India Atmospheric. Environment 2022, 274, 118987.
  48. Zachariadis T, Samaras Z and Zierock K-H Dynamic modeling of vehicle populations: An engineering approach for emissions calculations. Technological Forecasting and Social Change 1995, 50, 135–149. [CrossRef]
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Figure 1. Illustration of survival functions for nine broad categories of vehicles in India under three scenarios – high, medium, and low survival rates.
Figure 1. Illustration of survival functions for nine broad categories of vehicles in India under three scenarios – high, medium, and low survival rates.
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Figure 2. Registered vehicle number growth presented as a ratio of numbers in 1993 (2W, 3W, 4W = all 2-3-4 wheelers; T = taxies; LDV = light duty vehicles; HDV = heavy duty vehicles; NRV = non-road vehicles).
Figure 2. Registered vehicle number growth presented as a ratio of numbers in 1993 (2W, 3W, 4W = all 2-3-4 wheelers; T = taxies; LDV = light duty vehicles; HDV = heavy duty vehicles; NRV = non-road vehicles).
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Figure 3. An estimated age-mix of India’s in-use vehicle fleet using the high survival rates.
Figure 3. An estimated age-mix of India’s in-use vehicle fleet using the high survival rates.
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Figure 4. Timeline of introduction of various vehicle and fuel standards in Delhi and India (source: https://dieselnet.com).
Figure 4. Timeline of introduction of various vehicle and fuel standards in Delhi and India (source: https://dieselnet.com).
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Table 1. Vehicle categories listed in the MoRTH annual reports and clubbed category for ease of analysis (2W, 3W, 4W = all 2-3-4 wheelers; T = taxies; LDV = light duty vehicles; HDV = heavy duty vehicles; NNRD = non-road vehicles).
Table 1. Vehicle categories listed in the MoRTH annual reports and clubbed category for ease of analysis (2W, 3W, 4W = all 2-3-4 wheelers; T = taxies; LDV = light duty vehicles; HDV = heavy duty vehicles; NNRD = non-road vehicles).
Clubbed category MoRTH vehicle categories
1 2W Scooters, mopeds, motorcycles
2 3W Three wheelers with three, four, and six seaters
3 4W1 Cars
4 4W2 Jeeps and other passenger sports utility vehicles
5 4WT Taxi motor cabs, maxi cabs, and others
6 BUS Omni buses, stage carriages, contract carriages, private service vehicles, and others
7 LDV Three and four-wheeler goods carriages
8 HDV Multi-axle vehicles, trucks, and lorries
9 NNRD Tractors, trailers, and other non-road vehicles
Table 2. Survival function parameters for nine broad categories of vehicles in India under three scenarios – high, medium, and low survival rates.
Table 2. Survival function parameters for nine broad categories of vehicles in India under three scenarios – high, medium, and low survival rates.
Clubbed category A β T
Low Medium High Low Medium High Low Medium High
1 2W 0.5 0.3 0.1 3.1 3.1 3.1 8 10 14
2 3W -1.0 0.0 0.0 2.0 2.0 2.0 8 12 15
3 4W1 0.0 0.0 -0.5 3.0 3.0 3.0 8 12 16
4 4W2 0.5 0.5 0.0 2.5 2.5 2.5 10 12 15
5 4WT -0.5 -0.5 -0.5 4.0 3.5 3.0 8 10 12
6 BUS -1.0 0.0 -1.0 3.2 3.0 3.0 8 12 12
7 LDV -1.0 0.0 0.0 2.5 3.0 3.0 12 16 20
8 HDV 1.0 1.0 0.0 3.8 3.8 3.8 12 16 18
9 NNRD 1.0 1.0 1.0 3.5 3.5 4.0 16 18 22
Table 3. 1993-2018 total registered vehicle fleet (in millions) in India (2W, 3W, 4W = all 2-3-4 wheelers; T = taxies; LDV = light duty vehicles; HDV = heavy duty vehicles; NRV = non-road vehicles).
Table 3. 1993-2018 total registered vehicle fleet (in millions) in India (2W, 3W, 4W = all 2-3-4 wheelers; T = taxies; LDV = light duty vehicles; HDV = heavy duty vehicles; NRV = non-road vehicles).
2W 3W 4W 4WT BUS HDV LDV NRV Total
1993 18.3 0.8 3.3 0.3 0.4 2.1 1.8 0.1 27
1994 20.8 0.9 3.5 0.4 0.4 2.3 1.9 0.2 30
1995 23.3 1.0 3.8 0.4 0.4 2.2 2.5 0.2 34
1996 25.7 1.2 4.2 0.4 0.5 2.4 2.7 0.3 37
1997 28.4 1.3 4.6 0.4 0.5 3.4 2.3 0.3 41
1998 31.3 1.5 5.0 0.5 0.5 2.6 3.2 0.3 45
1999 34.1 1.6 5.5 0.6 0.6 2.7 3.5 0.3 49
2000 38.6 2.0 6.4 0.7 0.6 2.9 3.9 0.4 56
2001 41.8 2.0 7.0 0.7 0.6 2.9 4.1 0.4 59
2002 46.8 2.2 7.7 0.8 0.8 3.2 4.5 0.4 66
2003 51.9 2.3 8.5 0.9 0.8 3.3 4.7 0.4 73
2004 58.8 2.5 9.4 0.9 0.9 3.8 5.1 0.5 82
2005 64.7 2.6 10.5 1.0 1.0 4.1 5.5 0.5 90
2006 69.1 2.8 11.6 1.1 1.4 4.3 6.2 0.6 97
2007 75.4 3.1 12.8 1.3 1.4 4.6 6.7 0.6 106
2008 82.4 3.4 14.0 1.4 1.5 4.9 7.3 0.7 115
2009 91.6 3.6 15.5 1.6 1.5 5.1 7.9 0.7 128
2010 101.9 4.0 17.5 1.8 1.6 5.5 8.6 0.8 142
2011 115.5 4.4 19.6 2.0 1.7 6.0 9.4 0.9 159
2012 133.2 4.9 22.9 2.2 1.8 6.2 10.7 1.1 183
2013 141.5 4.8 24.3 2.1 1.8 6.5 11.3 1.2 194
2014 156.7 5.2 26.9 2.3 1.8 6.9 12.3 1.3 214
2015 171.7 5.5 29.7 2.4 1.7 7.7 13.2 1.3 233
2016 187.2 5.7 32.5 2.7 1.9 7.7 14.4 1.6 254
2017 204.4 6.3 35.4 2.9 1.9 9.3 14.3 1.9 276
2018 223.0 6.9 37.3 3.1 2.1 10.0 15.3 2.2 300
2018% 74.4% 2.3% 12.4% 1.0% 0.7% 3.3% 5.1% 0.7%
Table 4. 1993-2018 (for select years) total registered vehicle fleet (in millions) by state.
Table 4. 1993-2018 (for select years) total registered vehicle fleet (in millions) by state.
1993 1995 2000 2005 2010 2015 2018
Andaman & Nicobar Islands 0.01 0.01 0.03 0.03 0.07 0.11 0.14
Andhra Pradesh 1.61 2.58 4.05 7.22 10.19 8.53 11.67
Arunachal Pradesh 0.01 0.02 0.02 0.02 0.14 0.26 0.23
Assam 0.35 0.36 0.55 0.91 1.58 2.85 3.97
Bihar 1.22 1.33 0.97 1.45 2.67 5.48 8.55
Chhattisgarh 0.86 1.54 2.77 4.81 6.38
Chandigarh 0.31 0.37 0.39 0.65 1.02 0.84 1.02
Diu and Daman 0.01 0.02 0.04 0.06 0.08 0.11 0.12
Delhi 2.28 2.68 3.55 4.50 7.24 9.94 11.40
Dadar Nagar Haveli 0.01 0.01 0.01 0.05 0.08 0.11 0.00
Goa 0.18 0.21 0.34 0.53 0.77 1.13 1.40
Gujarat 2.73 3.38 5.60 8.62 12.99 20.36 25.20
Himachal Pradesh 0.09 0.12 0.22 0.33 0.62 1.18 1.63
Haryana 0.84 1.07 1.99 3.09 5.41 8.68 11.43
Jharkhand 0.91 1.51 3.11 3.35 4.30
Jammu Kashmir 0.16 0.20 0.33 0.52 0.93 1.37 1.82
Karnataka 1.81 2.25 3.56 6.22 9.82 16.15 20.90
Kerala 0.89 1.17 2.15 3.76 5.98 10.09 13.25
Lakshadweep 0.00 0.00 0.00 0.01 0.01 0.02 0.02
Maharashtra 3.27 4.03 6.88 11.02 17.50 27.87 35.39
Meghalaya 0.04 0.04 0.06 0.10 0.18 0.29 0.37
Manipur 0.05 0.06 0.08 0.12 0.21 0.31 0.36
Madhya Pradesh 1.89 2.31 3.10 4.61 7.36 11.98 15.30
Mizoram 0.02 0.02 0.03 0.05 0.09 0.17 0.26
Nagaland 0.08 0.10 0.17 0.20 0.29 0.38 0.49
Odisha 0.54 0.66 1.11 1.94 3.34 5.83 8.28
Punjab 1.64 1.92 2.92 4.04 5.27 9.60 10.61
Puducherry 0.11 0.13 0.25 0.38 0.67 0.86 1.06
Rajasthan 1.44 1.77 2.96 4.75 7.99 13.64 17.72
Sikkim 0.01 0.01 0.01 0.02 0.04 0.05 0.07
Tamil Nadu 2.15 2.77 5.17 10.05 15.64 24.20 30.18
Tripura 0.03 0.03 0.05 0.11 0.19 0.33 0.50
Telangana 8.82 12.50
Uttarakhand 0.37 0.64 1.00 1.89 2.75
Uttar Pradesh 2.48 2.99 4.91 7.99 13.29 23.94 32.71
West Bengal 1.01 1.24 1.89 2.87 3.26 7.61 7.80
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