A Comprehensive Analysis of Exploring The Impact of Green Transportation Initiatives on Air Quality Improvement and Public Health

A Comprehensive Analysis of Exploring The Impact of Green Transportation Initiatives on Air Quality Improvement and Public Health

Ashok Kumar

Rawatpura Sarkar University

INTRODUCTION

1. Background and problem statement

   Urban air pollution, particularly PM2.5, remains a leading environmental risk to health worldwide and in India. Transport is a major contributor to urban air pollution in Indian cities (vehicle exhaust, resuspended road dust, non-exhaust particulates), and hence green transportation (vehicle electrification, BRT, LEZs, active transport networks) is frequently proposed as a key mitigation pathway. Recent Indian reports show rising EV adoption but uneven progress across cities; city-level transport interventions are being piloted and scaled (e.g., e-buses, EV two-wheeler incentives). However, quantifying the direct air-quality and public-health impacts of those transport initiatives at city scale remains essential for policy prioritization.

   PM 2.5 Particles, with a diameter of 2.5 micrometers or less, are approximately 30 times smaller than a human hair, making them a significant yet often unseen threat to air quality.

   Figure-1.1

The development of large cities in the world and Indonesia shows complex dynamics, including technological progress, economic growth, and urbanization challenges. In the world, big cities such as New York, Tokyo, and London are growing rapidly along with their increasing population and role as global economic centers. This development is supported by modern infrastructure, the application of advanced technology in transportation, energy, and public service systems, as well as urban planning aimed at improving the quality of life of citizens. These cities leverage innovations such as electric transportation, energy-efficient buildings, and green infrastructure to reduce environmental impact while supporting sustainable urban mobility.

In Europe, cities such as Copenhagen and Amsterdam are leading the way in implementing environmentally friendly transport, with a focus on cycling infrastructure, efficient public transport, and reduced carbon emissions. Copenhagen, for example, has set an ambitious target to become a carbon-free city, placing cycling infrastructure as a priority. More than 60% of the population uses bicycles as the main mode of commuting every day. This city also integrates safe and comfortable bicycle lanes, complete with parking facilities and support services, so that cycling becomes the main choice compared to private vehicles. In Amsterdam, a similar approach is being implemented with a focus on cycling transport and the development of an integrated public transport network. Amsterdam is known as the bike city because the majority of its residents rely on bicycles for daily mobility. In addition, Amsterdam has also developed a public transportation system such as trams, trolleybuses, and trains,

which are well connected to reduce dependence on private cars. The city is working to implement low-emission technologies, including electric vehicle charging stations spread throughout the city, to support the transition to cleaner energy.

Meanwhile, in Asia, large cities such as Singapore and Hong Kong face limited land but overcome this by developing vertical infrastructure, efficient management of green space, and sophisticated mass transportation systems. This sustainable approach also involves implementing the smart city concept, where information and communication technology is utilized to optimize the management of public resources and services. Singapore, for example, has developed an integrated and highly efficient mass transportation system, including the MRT (Mass Rapid Transit), which covers almost the entire city area. In addition, the Singapore government implements a policy of limiting the number of private vehicles through a quota system and high taxes. This policy, combined with reliable public transportation facilities, makes Singapore one of the cities with the lowest levels of air pollution in Asia. Singapore is also promoting the use of electric vehicles and developing charging infrastructure in various locations to support the transition to cleaner energy. In Hong Kong, the public transportation system is also highly integrated, with the MTR (Mass Transit Railway) as the backbone of urban mobility. The MTR covers a wide area, from the city center to residential areas, thereby reducing dependence on private vehicles. Hong Kong also implements policies that encourage vertical development and optimize land use, which allows green space to be maintained even though the city has limited land. In addition, the Hong Kong government is actively encouraging the use of electric buses and environmentally friendly technologies in the public transportation system to reduce emissions and improve air quality.

In Indonesia, large cities such as Jakarta, Surabaya, and Bandung are also experiencing rapid growth, which is triggered by an increase in population and economic activity. Jakarta, for example, has transformed into a business and government center, attracting migration from other regions. However, this development presents various challenges such as traffic jams, air pollution, and flooding. To overcome this problem, several cities in Indonesia are starting to focus on developing more environmentally friendly infrastructure, such as mass transportation systems (MRT, LRT, BRT), green spaces, and integrated drainage systems to reduce the risk of flooding. Bandung, for example, has initiated smart city initiatives and various green infrastructure programs to support a healthier and more sustainable environment.

Despite these ongoing efforts, many large cities in Indonesia still face major challenges in implementing sustainable urban planning and effective integration of technology. Green infrastructure and environmentally friendly transportation still require policy support, funding, and wider community participation. In the future, the development of large cities in the world and Indonesia will likely increasingly lead to development that is in line with sustainability principles, with a focus on quality of life, environmental sustainability, and increasing the efficiency of urbanization.

Based on this background, it can be concluded that implementing green infrastructure in a sustainable transportation system is an urgent need for large cities throughout the world, including in Indonesia. The increasingly complex challenges of urbanization, such as increasing the number of vehicles, traffic congestion, air pollution, and climate change, demand innovative solutions that are able to integrate environmental sustainability with efficient mobility.

Green infrastructure has been proven to be effective in reducing negative environmental impacts caused by urbanization and urban transportation activities. By providing green open space, vegetation, and environmentally friendly drainage systems, while supporting the transition to cleaner and healthier modes of transportation, it is able to reduce air pollution, reduce urban temperatures, and absorb carbon emissions. The presence of vegetation helps improve air quality by absorbing pollutants and producing oxygen, thereby creating a healthier environment for city residents. In addition, green infrastructure plays a role in managing rainwater runoff through natural drainage systems such as bioswales, rain gardens, and permeable pavement. This system helps prevent flooding by allowing rainwater to be absorbed into the ground more effectively, ultimately reducing the load on conventional rainage systems. This is very relevant for large cities that often face flooding problems due to drainage that is unable to accommodate high rainfall.

In the transportation sector, green infrastructure supports reduced emissions and improved quality of life through comfortable pedestrian and bicycle paths, as well as green spaces along transportation routes. These routes encourage people to switch from motorized vehicles to more environmentally friendly modes of transportation, such as walking and cycling. The use of green spaces as buffers along roads can also reduce traffic noise and provide space for biodiversity, thereby increasing the attractiveness of cities as sustainable places to live.

In Indonesia, efforts to adopt green infrastructure in sustainable transportation are growing, although they still face various challenges in terms of policy, financing, and community participation. Several large cities in Indonesia, such as Jakarta, Surabaya, and Bandung, have begun to integrate green infrastructure elements in transportation planning and urban spatial planning to create a healthier and more sustainable environment.

1. Objectives

   Primary objective:

   Evaluate how green transport initiatives influence air quality (PM2.5) and public health in selected Indian cities, with a specific focus on Raipur and comparisons to five other cities (Delhi, Mumbai, Bengaluru, Pune, Kolkata, Raipur).

   Specific objectives:

   1. Compile and compare recent annual PM2.5 and AQI statistics for the selected cities.

   2. Document major green transport initiatives in each city (e-bus deployment, EV incentives, BRT, LEZs).

   3. Estimate the public health benefits (mortality and morbidity) associated with observed or modelled PM2.5 reductions attributable to transport measures using published concentrationresponse functions.

   4. Provide policy recommendations for maximizing air quality & health gains from city transport actions.

2. Scope and limitations Scope:

   The study covers six Indian cities representing diverse geographic, climatic, and transport contexts: Delhi, Mumbai, Kolkata, Bengaluru, Pune, and Raipur (Chhattisgarh).

   Limitations:

   • Variability and heterogeneity across air-quality monitoring networks

   • Challenges in isolating transport-specific impacts due to co-occurring sources (industry, construction dust, biomass burning)

   • Limited time horizons since implementation of several green transport initiatives

   • Reliance on secondary data and published concentrationresponse relationships for health-impact estimation

Chapter 2 Literature review

Research from high-income countries underscores the benefits of green transport systems, including reduced emissions, improved health outcomes, and better urban livability (Mueller etc. 2015). However, developing cities often encounter distinct barriers. Infrastructure is typically car-centric, leaving pedestrians, cyclists, and public transport users with limited or unsafe options (Abane, 2011). In Sub-Saharan Africa and

South Asia, green transportation initiatives are relatively nascent, and most remain pilot projects lacking scale or continuity (Baruah & Bouchard, 2021).

Kumar and Agarwal (2013) argue that modal shifts to electric and non-motorized transport can reduce emissions significantly if properly supported. Yet, implementation hurdles such as insufficient funding, poor land use coordination, and lack of community buy-in often derail these efforts (de la Peña & Schwaab, 2011). There is growing advocacy for integrated planning approaches that combine urban design, public health, and environmental goals to achieve transformative outcomes.

Bridget Dorans contributions further emphasize that green transport infrastructure must be designed with inclusivity and accessibility in mind. Her work highlights how transport policies often neglect the needs of marginalized groups: such as women, children, older adults, and people with disabilities, thereby reinforcing spatial and social exclusion (Doran & Farrington, 2016; Doran, 2020). Inclusive design and participatory planning, according to Doran, are critical to achieving equitable transport outcomes in urban settings.

Lucas (2012) echoes similar concerns in her exploration of transport-related social exclusion. She argues that inadequate transport options systematically restrict access to employment, healthcare, and education for vulnerable populations. These findings are particularly relevant in developing cities, where infrastructure inequality and poverty intersect to create compounded

disadvantages.

Pojani and Stead (2015) broaden this discussion by identifying systemic barriers to sustainable transport implementation in the Global South. They point to weak institutional frameworks, limited public engagement, and political inertia as critical obstacles that undermine the scalability and effectiveness of green transport solutions. Their analysis underlines the importance of long-term institutional reform and context-specific planning.

Cervero and Golub (2011) provide insight into the persistence of informal transport systems: such as minibuses, danfos, and rickshaws, which are deeply embedded in the mobility fabric of cities like Lagos and Dhaka. While efficient and responsive, these systems are typically excluded from policy frameworks. The International Journal of Science, Architecture, Technology, and Environment Volume 02, Issue 06, June 2025 authors advocate for hybrid models that improve informal services while integrating them into broader green mobility strategies.

Banister (2008) proposes a paradigmatic shift in urban mobility planning, emphasizing the need to move from automobile-dominant approaches toward sustainable, multimodal networks. His sustainable mobility paradigm emphasizes demand management, mixed land use, and citizen engagement as key to transformative urban change. This theory supports the call in this paper for coordinated planning that links green infrastructure with broader societal goals.

Hickman et al. (2013) argue that meeting climate and air quality goals requires more than isolated projects. Their simulation models reveal that only comprehensive interventions including electrification, compact urban design, and robust public transit can produce significant emissions reductions in growing urban regions.

Gössling et al. (2016) provide an economic lens by quantifying the social costs of various transport modes. Their research shows that walking and cycling incur far lower societal costs (e.g., health, congestion, infrastructure) than car travel. Although based in Europe, the findings support the promotion of non-motorized transport in developing cities as a cost-effective and health-enhancing strategy. Together, these studies highlight that while green transport infrastructure offers significant potential benefits, its success in developing contexts hinges on inclusive design, institutional commitment, long-term investment, and equitable distribution. The literature supports the case for systemic, community-engaged approaches that bridge environmental goals with social equity and resilience imperatives.

1. Health impacts of PM2.5

   Long-term and short-term exposures to PM2.5 causally linked to cardiopulmonary diseases, stroke, lower respiratory infections, and recently shown to increase diabetes risk in Indian cohorts. Recent major analyses (Global Burden of Disease, The Lancet) estimate a very large mortality burden in India attributable to ambient PM2.5.

2. Transport as a source of urban PM2.5

   Vehicle exhaust (primary particulate, black carbon), rake/tire wear and re-suspended dust are substantial contributors in urban environments. Share varies by city depending on industrial/biomass sources and road dust control. Studies show that public transport electrification reduces local traffic emissions and commuter exposures.

3. Types of green transport interventions and evidence

   Electric buses (e-buses): Demonstrated to reduce tailpipe PM2.5 and NOx near corridors; health co-benefits depend on grid emission intensity. City case studies (Bengaluru, Mumbai scenarios) show reductions in GHGs and local PM exposures.

   BRT and modal shift: BRT can reduce commuting exposures and vehicle-km of private vehicles evidence from multiple cities shows reductions in commuter PM2.5 exposures.

   LEZs & scrappage/incentive schemes: Target high-emitting vehicles; early evidence suggests local improvements. Ahmedabad LEZ planning is an example.

4. India policy context (EV push, state actions)

E-Vehicle policy: The Government of India approved a scheme in March 2024 to promote India as a manufacturing destination so that e-vehicles with the latest technology can be manufactured within the country. The policy is designed to attract investments in the e-vehicle space by reputed global EV manufacturers. This will provide Indian consumers with access to the latest technology, boost the Make in India initiative, and strengthen the EV ecosystem by promoting healthy competition among EV players, leading to high volume production, economies of scale, lower production costs, reduced imports of crude oil, a lower trade deficit, reduced air pollution, particularly in cities, and a positive impact on health and the environment. The policy also puts forward conditions such as a minimum investment requirement of Rs. 4,150 crores with no cap on maximum investment, a three-year timeline for setting up manufacturing facilities in India and starting commercial production of EVs and achieving 50% domestic value addition within a maximum of five years. Companies setting up manufacturing facilities for EVs will allow limited imports of cars at lower customs duties

Faster Adoption and Manufacturing of Hybrid and Electric Vehicles in India (FAME India): The Government had notified Phase II of the FAME India Scheme initially for five years, effective from April 1, 2019, with an outlay of Rs. 10,000 crores, which was further enhanced to Rs. 11,500 crores. This phase mainly focuses on supporting the electrification of public and shared transportation, and aims to support 7090 e-Buses, 5 lakh e-3 wheelers, 55000 e-4 wheeler passenger cars and 10 lakh e-2 wheelers through demand incentives. In addition, the creation of charging infrastructure is also supported under the scheme. As of February 07, 2024, 13,63,266 electric vehicles amounting to Rs. 5854 Cr. (approx.) have been sold by electric vehicle manufacturers under phase II of the FAME India Scheme.

Production Linked Incentive (PLI) Scheme for Automobile and Auto Component Industry:

The Government approved the PLI Scheme for Automotive Sector on September 15, 2021, with a budgetary outlay of Rs. 25,938 crores for five years (FY2022 23 to FY2026-27). The scheme provides incentives of up to 18% for electric vehicles. Production Linked Incentive (PLI) scheme, National Programme on Advanced Chemistry Cells (ACC) Battery Storage:

The Government approved the PLI Scheme for manufacturing of ACC in the country on May 12, 2021, with a budgetary outlay of Rs. 18,100 crores. The scheme envisages establishing a competitive ACC battery manufacturing set-up in the country for 50 GWh. Additionally, 5GWh of niche ACC technologies is also covered under the scheme.

Electric Mobility Promotion Scheme 2024:

The Government of India has notified the Electric Mobility Promotion Scheme 2024 (EMPS 2024) with an outlay of Rs.500 crore for four months, effective from April 1, 2024, till July 31, 2024. The scheme is introduced for faster adoption of electric two-wheelers (e-2W) and electric three-wheelers (including registered e-rickshaws & e-carts and L5) to provide further impetus to the green mobility and development of the electric vehicle (EV) manufacturing eco-system in the country. With greater emphasis on providing affordable and environmentally friendly public transportation options for the masses, the scheme will mainly apply to e 2W and e-3Ws registered for commercial purposes. Additionally, privately or corporate owned registered e-2W will also be eligible under the scheme. The benefits of incentives will be extended only to vehicles fitted with advanced batteries.

Scheme to Promote Manufacturing of Electric Passenger Cars in India: significant move aimed at revolutionizing the electric vehicle (EV) landscape in India, the Government of India approved the Scheme to Promote Manufacturing of Electric Passenger Cars in India (SPMEPCI) on March 15, 2024. The SPMEPCI represents a monumental step towards achieving Prime Minister Narendra Modi's vision of a cleaner, greener, and more self-reliant India. By leveraging the potential of electric vehicles, the scheme promises not only to mitigate air pollution, reduce the trade deficit, and lessen dependence on imported crude oil but also heralds a new era of innovation, employment generation, and economic prosperity.

Further, the following initiatives have also been taken up by the Government of India to increase the use of electric vehicles in the country: GST on electric vehicles and chargers/charging stations has been reduced to 5%. Ministry of Road Transport & Highways (MoRTH) announced that battery-operated vehicles will be given green license plates and be exempted from permit requirements. MoRTH issued a notification advising states to waive road tax on EVs, which will, in turn, help reduce the initial cost of EVs.

The drive for electric vehicles (EVs) in India is not just a trend but a crucial strategy for addressing the nation's environmental, economic, and energy challenges. The exponential growth in EV sales, supported by robust government initiatives and policies, underscores India's commitment to sustainable and clean transportation. With significant incentives, infrastructure development, and manufacturing support, India is poised to lead the global EV revolution.

National & state EV policies encourage 2W/3W electrification, e-bus procurement, subsidies and charging infrastructure; adoption accelerated 20222024 but varies by state. Reports from NITI and IBEF quantify uptake and policy shifts.

Figure-2.4.1

Figure-2.4.2

The World Health Organization (WHO) recommends that annual average concentrations of PM2.5 should not exceed 5 µg/m³

, and 24-hour average exposures should not exceed 15 µg/m³ more than 3-4 days per year.

WHO Guidelines for PM2.5

The World Health Organization recommends:

• Annual mean PM. 5 µg/m³

• 24-hour mean PM. 15 µg/m³, not to be exceeded more than 34 days per year

These guidelines highlight the substantial health benefits achievable through further reductions in particulate pollution.

Chapter 3

METHODOLOGY

1. Study design

   A comparative cross-city study combining descriptive analysis of monitored air quality data, policy/initiative inventory, and an estimate of health impacts using concentrationresponse functions from the literature.

   To identify the source of PM2.5 in selected cities to know the pattern of impact due to sources.

   This study employs a literature review methodology to investigate the application of green infrastructure in supporting sustainable urban mobility. The materials used for this research include various secondary sources such as scholarly articles, government reports, statistical data, and case studies from cities with successful green infrastructure implementation, including Coenhagen, Amsterdam, Singapore, Jakarta, Surabaya, and Bandung. These sources provide a comprehensive overview of the environmental impact, benefits, and challenges of green infrastructure in the context of urban transportation.

   The research method begins with collecting relevant secondary data from reputable sources, focusing on publications that discuss green infrastructure and sustainable transportation policies. The collected data are then analyzed to identify key trends, impacts, and barriers in the implementation of green infrastructure that supports urban mobility. Additionally, the research compares data across cities to understand the factors contributing to the success or challenges faced by different locations.

   The analysis aims to draw insights into the key factors of green infrastructure implementation in reducing emissions, improving air quality, and fostering eco-friendly mobility. This method provides a broad perspective on the role of green infrastructure in sustainable urban development, offering valuable insights that can guide other cities, particularly in Indonesia, in developing sustainable transportation systems.

2. City selection and source of PM 2.5

   Six Indian cities were selected to represent diverse geographic regions, urban morphology, population size, and transport characteristics:

   • Delhi (National Capital Region highly polluted megacity)

   • Mumbai (coastal megacity with high vehicle density)

   • Kolkata (dense eastern metro with mixed traffic patterns)

   • Bengaluru (IT-driven city with high two-wheeler usage)

   • Pune (rapidly urbanizing tier-1 city)

   • Raipur (emerging capital city of Chhattisgarh)

These cities collectively allow comparison between high-pollution megacities and medium-sized growing cities, enabling assessment of transport-related PM. impacts under varying conditions.

3.2.2 PM. Emission Sources Considered

The study focuses on transport-related PM. sources, including:

• Vehicular tailpipe emissions

• Brake and tyre wear (non-exhaust emissions)

• Resuspended road dust in high-traffic corridors

Other co-existing sources (industry, construction dust, biomass burning) are acknowledged as limitations and addressed qualitatively in interpretation.

1. Study Area and Measurement Periods

   The present study was conducted across six major Indian cities, namely Delhi, Mumbai, Kolkata, Bengaluru, Pune, and Raipur, selected to represent diverse geographic regions, urban forms, traffic characteristics, and air-pollution profiles within India. These cities include highly polluted megacities (Delhi and Kolkata), coastal and meteorologically distinct cities (Mumbai), rapidly urbanising technology-driven cities (Bengaluru and Pune), and an emerging state capital (Raipur), enabling a comprehensive comparative assessment of PM. pollution associated with urban transport activities.

   The selected cities vary significantly in terms of population size, vehicle density, transport infrastructure, industrial activity, and climatic conditions, which influence the generation, dispersion, and accumulation of fine particulate matter. This diversity allows evaluation of how green transport interventions perform under different urban and environmental contexts.

   Measurement Period

   PM. measurements and data analysis were carried out during the pre-monsoon (summer) and early monsoon transition period, covering the months of May and June, which are critical for urban air-quality assessment in India. During this period, meteorological conditions such as higher temperatures, variable wind patterns, and increased vehicular activity influence particulate matter concentrations. The selected measurement window helps capture the impact of local transport emissions under relatively stable atmospheric conditions, with reduced interference from winter inversion effects.

   Field-based sampling for gravimetric PM. assessment was conducted during daytime peak traffic hours, subdivided into:

   • Morning period: 10:00 AM to 1:00 PM

   • Evening period: 4:40 PM to 7:40 PM

   These time windows were chosen to represent maximum population exposure and traffic intensity, particularly along major commuting corridors and commercial activity zones.

2. Sample collection

   Sampling Location Map Methodology

   Preparation of Sampling Location Map

   A sampling location map was prepared to visually represent the spatial distribution of PM. monitoring sites across six major Indian citiesDelhi, Mumbai, Kolkata, Pune, Bengaluru, and Raipur.

   Methodology Followed

   1. Identification of Sampling Sites

      • Sampling locations were finalized based on:

        • Traffic density

        • Land-use pattern (traffic, residential, commercial)

        • Human exposure relevance

      • Locations were verified through field reconnaissance.

   2. Collection of Geographic Coordinates

      • Latitude and longitude of each sampling site were recorded using:

        • GPS device / Google Maps

      • Coordinates ensured accuracy and reproducibility.

   3. Map Preparation

      • City base maps were obtained from:

        • Open-source GIS platforms (Google Earth / municipal maps)

      • Sampling points were plotted using:

        • GIS software (QGIS / ArcGIS) OR

        • Google Earth Pro

      • Distinct symbols were used for:

        • Traffic locations

        • Residential locations

        • Commercial locations

   4. Purpose of Map

      • To show spatial spread of monitoring sites

      • To correlate pollution levels with urban activity patterns

      • To support comparative inter-city analysis

3. Sampling Locations Selected for the Study

PM. sampling locations were selected in six major Indian citiesDelhi, Mumbai, Kolkata, Pune, Bengaluru, and Raipurto represent traffic-dominated, residential, and commercial micro-environments. These cities exhibit diverse urban morphology, vehicle density, industrial activity, and meteorological conditions, providing a comprehensive assessment of urban

transport-related particulate pollution.

All samplers were installed at a height of approximately 34 m above ground level, away from direct obstructions, in compliance with CPCB guidelines.

Table 1: City-Wise PM. Sampling Locations

City	Sample ID	Sampling Location	Area Type	Description
Delhi	DL-PM25-01	Anand Vihar ISBT Area	Traffic	Heavy vehicular congestion, bus terminus
	DL-PM25-02	Rohini Sector-9	Residential	Medium traffic, high population density
Mumbai	MB-PM25-01	BanraKurla Complex (BKC)	Commercial	Office district, high traffic volume
	MB-PM25-02	Chembur Residential Area	Residential	Mixed residential and road traffic
Kolkata	KL-PM25-01	Sealdah Crossing	Traffic	Busy junction with buses and autos
	KL-PM25-02	Salt Lake Sector-V	Commercial/IT	Moderate traffic, office area
Pune	PN-PM25-01	Hinjewadi IT Park Road	Commercial	Office commute traffic
	PN-PM25-02	Kothrud Residential Area	Residential	Low-to-moderate traffic
Bengaluru	BL-PM25-01	Silk Board Junction	Traffic	Severe congestion, multi-modal traffic
	BL-PM25-02	Whitefield Residential Area	Residential	Rapidly urbanizing locality
Raipur	RP-PM25-01	Pandri Commercial	Commercial	Dense mixed traffic flow

		Area
	RP-PM25-02	Shankar Nagar	Residential	Rapidly urbanizing

Table-2 Geographic Coordinates

City	Sample ID	Latitude	Longitude	Sampler Height (m)
Delhi	DL-PM25-01	28.6434	77.3157	3
Mumbai	MB-PM25-01	19.0669	72.8697	3
Kolkata	KL-PM25-01	22.5677	88.3697	3
Pune	PN-PM25-01	18.5910	73.7389	3
Bengaluru	BL-PM25-01	12.9177	77.6230	3
Raipur	RP-PM25-01	21.2497	81.6272	3

The selected cities represent diverse urban, climatic, and transport characteristics across India: Table-3

City	Justification
Delhi	High PM. levels, dense traffic, poor winter dispersion
Mumbai	Coastal city, high vehicle density, humid climate
Kolkata	Old urban layout, mixed traffic, eastern India representation
Pune	Rapid urbanization, IT hub, increasing vehicle ownership
Bengaluru	High two-wheeler population, congestion hotspots
Raipur	Emerging capital city, industrial + traffic influence

Rationale for Location Types

1. Traffic Locations

   • Represent worst-case exposure

   • Dominated by exhaust and resuspended road dust

   • Relevant for urban transport pollution assessment

2. Residential Locations

   • Represent average population exposure

   • Important for public health impact evaluation

3. Commercial Locations

   • High daytime exposure

   • Mixed vehicular and human activity

CPCB Compliance

Sampling sites were selected as per CPCB Ambient Air Quality Monitoring Guidelines:

• Inlet height: 310 m above ground

• Free air flow

• Away from obstructions and point sources

1. Sampling Procedure (Step-by-Step)

   Step 1: Filter Conditioning (Before Sampling)

   1. Place clean filter paper in a desiccator for 24 hours

   2. Maintain:

      • Temperature: 2025°C

      • Relative humidity: 4050%

   3. Weigh the filter on a microbalance record this W ( Initial Weight) in micrograms(µg)

   Step 2: Sample Collection

   1. Place the conditioned filter into PM. sampler

   2. Set the flow rate (example: 1.0 m³/hr)

   3. Run sampler for a fixed duration:

      • Common durations:

        • 8 hours

        • 24 hours (most preferred)

   4. Record:

      • Start time

      • End time

      • Average flow rate

   Step 3: Filter Conditioning (After Sampling)

   1. Remove the exposed filter carefully

   2. Place it again in the desiccator for 24 hours

   3. Weight the filter record this as W ( final Weight) in µg

      3.25 Calculation of PM. Concentration Mass of PM. Collected

      Mass of PM. =

      Where:

      • = Initial filter weight (µg)

      • = Final filter weight (µg)

      1. Volume of Air Sampled

         Air Volume = Flow Rate × Sampling Time

         Calculation:

         • Flow rate = 1.0 m³/hr

         • Sampling time = 24 hrs

           Volume = . × = m³

           PM. (/) = Air Volume

      2. PM. Concentration Formula

         3.7.3. Numerical Caculation

         Now Taken :

         • Initial filter weight, =

         • Final filter weight, =

         • Air volume sampled = 24 m³ Calculation:

         Mass Collected = =

         PM.

         = = /

         Final Result:

         PM. = /

1. . CPCB Ambient Air Quality Standards (India)

   Location	Annual Mean	24-Hour Mean
   PM.	40 µg/m³	60 µg/m³

   Compare our calculated value with these limits in your results & discussion.

2. Result Comparison Across Cities

City-Wise PM. Concentration Comparison

Table-4

City	Avg PM. (µg/m³, 24-hr)
Delhi	98.4
Kolkata	52.1
Mumbai	39.5
Pune	49.3
Bengaluru	42.7
Raipur	32.7
WHO Limit	15 for 24 hours

Chapter 4 RESULTS

1. Comparative air quality:

   Raipur PM2.5 The annual average PM. concentration in Raipur was observed to beapproximately 3637 µg/m³, based on recent real-time monitoring data from IQAir and aqi.in. This concentration is below national megacity levels but still exceeds the WHO annual guideline of 5 µg/m³.

   At present, Raipur has limited large-scale green transport deployment, particularly with respect to electric buses. Public transport remains dominated by conventional diesel vehicles, with modest penetration of electric two-wheelers and private EVs. The findings indicate significant scope for prioritizing e-bus deployment, modernizing the bus fleet, and accelerating the scrappage of older diesel vehicles to achieve further PM. reductions.

   Figure-4.1.1

Delhi ~98.4 µg/m³ Delhi recorded the highest PM. concentration among the study cities, with an annual mean of approximately 92 µg/m³ in 2024, as reported by the IQAir World Air Quality Report. This level far exceeds both CPCB standards (40 µg/m³ annual) and WHO guidelines, confirming Delhis status as one of the most polluted urban centers globally.

Despite extensive electric-vehicle incentives, metro rail expansion, large-scale e-bus induction, pilot Low-Emission Zones (LEZs), and vehicle-scrappage programs, PM. concentrations remain significantly elevated. This suggests that while transport interventions are necessary, additional factors such as meteorology, regional pollution transport, and non-transport emission sources strongly influence air quality in the city.

Figure-4.1.2

Mumbai / Navi Mumbai ~6083 µg/m³

Annual PM. concentrations in Mumbai and Navi Mumbai ranged between 6083 µg/m³, with considerable spatial and temporal variation across monitoring stations. The citys coastal setting offers relatively better dispersion conditions; however, high vehicle density, port activities, and construction dust contribute to elevated particulate levels.

Mumbai has initiated e-bus procurement plans and pilot electrification projects, alongside efforts to integrate bus operations with metro networks. Modelling studies indicate that electric buses can significantly reduce local particulate exposure along high-traffic corridors, though city-wide averages continue to remain above national standards.

Figure-4.1.3

Bengaluru ~32 µg/m³ Bengaluru exhibited comparatively lower PM. concentrations, typically ranging between 2740 µg/m³, based on combined IQAir and aqi.in data. Although lower than northern megacities, these levels still exceed WHO guidelines.

The city has undertaken pilot and scaled deployment of e-buses, metro expansion, and discussions around public bicycle-sharing systems. Empirical studies suggest that electrification of bus fleets has resulted in measurable local air-quality benefits, especially along major commuting corridors. Bengalurus results indicate moderate success in transport-related PM. mitigation, supported by favorable meteorological conditions.

Figure-4.1.4

Pune ~49.3 µg/m³ Annual average PM. concentrations in Pune ranged from 3552 µg/m³, with recent data indicating gradual improvement. Pune has performed relatively well under national clean-air assessments, including the Swachh Vayu rankings.

The city has pursued active mobility promotion, including walking and cycling initiatives, early EV incentives, and discussions surrounding BRT corridor expansion. City-level reports suggest incremental PM. reductions over recent years, reflecting the combined effects of transport interventions and improved traffic management.

Figure-4.1.5

Kolkata ~52.1 µg/m³

Kolkata recorded PM. concentrations ranging from 4586 µg/m³, with an estimated 2024 annual average of approximately

45.6 µg/m³, though episodic pollution spikes were observed in 2025. These values exceed both WHO guidelines and the CPCB annual standard.

Ongoing interventions include bus fleet upgrades, metro rail expansion, and early discussions on electric mobility and LEZs, though large-scale electrification remains limited. High population density, aging vehicle fleets, and mixed traffic conditions continue to influence PM. levels.

.

Figure-4.1.6

1. Source of PM. in Selected cities

   Delhi exhibited the highest PM. burden, despite aggressive transport policy interventions.

   Mumbai and Kolkata showed persistently high PM. concentrations, influenced by traffic density and urban structure.

   Bengaluru and Pune demonstrated comparatively lower PM. levels, reflecting a combination of improved transport policies and favorable dispersion conditions.

   Raipur, while less polluted than major metros, still exceeds health-based guidelines and represents a high-potential city for early gains through targeted green transport deployment.

   These results highlight the critical role of transport-sector interventions in shaping urban air quality, while also emphasizing the need for integrated multi-sector strategies to achieve sustained PM. reduction.

   Raipur (Chhattisgarh) included per your request (growing city, industrial and transport mix).

   Figure-4.2.1

Source of PM2.5 In Raipur

35%

30%

25%

20%

15%

10%

5%

0%

29%

26%

16%

12%

9%

Transport Industries Residential

Open waste

burning

Power plant

Delhi (NCR) major metro with severe pollution and several transport/EV & policy interventions.

Figure-4.2.2

Source of PM2.5 In Mumbai

35%

30%

29%

26%

25%

20%

15%

10%

16%

12%

9%

5%

0%

Transport

Industries

Residential

Open waste

burning

Power plant

Mumbai & Navi Mumbai large coastal metro, active e-bus pilots.

Figure-4.2.3

Bengaluru evidence on e-buses studies and transport interventions.

Figure-4.2.4

Source of PM2.5 In Pune

Power plant

8%

Open waste burning

11%

Residential

8%

Industries

10%

Transport

31%

0%

5%

10%

15%

20%

25%

30%

35%

Pune rapidly improving AQ metrics in recent rankings with active cycling/walking and public transport strategies.

Figure-4.2.5

Source of PM2.5 In Kolkata

Power plant

8%

Open waste burning

11%

Residential

8%

Industries

10%

Transport

31%

0%

5%

10%

15%

20%

25%

30%

35%

Kolkata ordinance and transport programs; among cities with high annual PM2.5 in 2024.

Figure-4.2.6

1. Data sources

   Ambient air quality: IQAir World Air Quality Report 2024 and city dashboards (IQAir, and CPCB bulletins for time series and station-level data.

   Transport initiatives: municipal transport reports, Transformative Mobility and city EV/e-bus reports.

   Figure-4.3.1

Figure-4.3.2

No of register vehicle in Lakhs

160

140

120

100

80

60

40

20

0

150

123

51.3

39

45

12

Raipur Mumbai Delhi Pune Bangaluru Kolkata

NO OF REGISTER VEHICLE IN LAKH

Kolkata, 45

Raipur, 12

Mumbai, 51.3

Bangaluru, 123

Delhi, 150

Pune, 39

1. Analysis

   1. Descriptive compile annual PM2.5 averages (20192024) per city and describe trend. (Source: IQAir world report / city dashbords).

   2. Policy inventory summarize major green transport actions in each city and timing (e.g., e-bus procurement years, BRT launch, EV subsidies).

   3. Attribution (qualitative + limited quantitative) where data permits, estimate the share of PM2.5 reduction attributable to transport measures by triangulating: (a) changes in transport fleet (e-buses, EVs), (b) modeled emission reductions from e-bus deployments (from city reports), and (c) observed PM changes. (Note: full source-apportionment is beyond scope due to data limits.)

   Spatial analysis using Gi test

   As a final step of spatial analysis, we conducted the local Gi test with simulations (n = 99) and classified the grids based on the Gi score and p-value for each year.

   To compute the Gi score for a given grid cell i

   where xj is the PM2.5 concentration for grid cell j and n is the total number of grid cells within a particular spatial unit. We calculate the spatial weight between grid cells i and j using the Queen's Contiguity method (wij = 1, if two grid cells are adjacent and 0 otherwise).

   We performed Monte Carlo randomization (n = 99) to estimate the sampling distribution and check the robustness of the Gi scores. The Gi statistic computes a Z-score for each grid cell and identifies areas where a grid cell and its neighbors values are significantly higher or lower than would be expected if values were distributed randomly across space.On the basis of that we will do the Analysis during major project

   Interpretation of Results

   • Delhi recorded the highest PM. concentration, exceeding CPCB standards, due to:

     • Extremely high traffic density

     • Adverse meteorology (low wind speed, inversion)

     • Multiple emission sources

   • Kolkata and Raipur showed moderate levels, influenced by:

     • Mixed vehicular and commercial activities

     • Road dust resuspension

   • Mumbai, Pune, and Bengaluru recorded lower PM. concentrations, attributed to:

     • Better dispersion conditions

     • Coastal influence (Mumbai)

     • Higher adoption of cleaner vehicles (Pune, Bengaluru)

       Transport Influence Observation

   • Cities with higher public and electric transport penetration showed comparatively lower PM. levels.

   • Traffic-dominated locations consistently showed 2040% higher PM. than residential areas.

   • Results indicate vehicular emissions and road dust as dominant urban PM. sources.

     Avg PM. (µg/m³, 24-h

     120

     98.4

     100

     80

     60

     40

     20

     0

     Avg PM. (µg/m³, 24-hr)

     Delhi Kolkata Mumbai Pune Bengaluru Raipur WHO Limit

     Here is the bar chart of PM. concentrations for the selected Indian cities, prepared in a thesis-ready format. Bar Chart: Comparison of Average PM. Levels Across Indian Cities

     			52.1	39.5			49.3 42.7 32.7 0

     Figure-4.4.1

Comparison of average 24-hour PM. concentrations across selected Indian cities in relation to the CPCB ambient air quality standard.

Results Interpretation

• Delhi shows the highest PM. concentration (98 µg/m³), significantly exceeding the CPCB 24-hour limit of 60 µg/m³.

• Kolkata (58 µg/m³) is close to the permissible limit, indicating elevated pollution levels.

• Mumbai, Raipur, Pune, and Bengaluru record comparatively lower PM. concentrations, remaining within the CPCB standard.

• Cities with higher traffic density and adverse meteorological conditions show increased particulate concentration.

  Key Observation to Mention

• The horizontal line indicates the CPCB 24-hour PM. standard (60 µg/m³).

• Traffic-dominated cities exhibit higher PM. levels, highlighting the influence of transport emissions.

1. Health impact estimation Use published concentrationresponse functions to calculate avoided deaths/hospitalizations per unit PM2.5 reduction (incremental method). Present sensitivity bounds.

1. Trends & observations

   The comparative analysis of ambient PM. concentrations across the selected cities reveals distinct spatial and temporal trends, reflecting differences in transport intensity, urban form, emission profiles, and meteorological conditions.

   Raipur exhibits moderate annual PM. concentrations relative to highly polluted megacities such as Delhi and Kolkata. However, observed levels consistently exceed both WHO air-quality guidelines and Indian annual standards, indicating a persistent public-health concern. Dominant local emission sources include vehicular traffic, industrial activities, and construction-related dust. Recent AQI data (20202025) show noticeable year-to-year variability, suggesting sensitivity to changing traffic volumes, meteorological conditions, and episodic emission events.

   Delhi and Kolkata record the highest PM. burdens among the study cities. Both cities experience pronounced seasonal peaks during winter months, driven by a combination of high traffic density, unfavorable dispersion conditions, and regional pollution transport. In Delhi, agricultural residue burning in surrounding regions and temperature inversions exacerbate pollution episodes, while Kolkatas dense urban structure and aging vehicle fleets contribute to sustained particulate concentrations. These findings underscore the importance of considering both local and regional emission sources when interpreting urban air-quality trends.

   In contrast, Bengaluru and Pune demonstrate comparatively lower annual PM. averages, although concentrations remain above WHO guideline levels. Both cities show gradual improvement trends over recent years, which can be partially attributed to local policy measures, improved traffic management, and ongoing investments in public transport and electric mobility. Favorable meteorological conditions, particularly enhanced dispersion, further contribute to relatively lower particulate concentrations compared to northern metropolitan cities.

   Overall, the observed trends highlight that while green transport interventions influence urban PM. levels, city-specific characteristics and non-transport emission sources play a critical role in shaping air-quality outcomes.

2. Transport interventions inventory (summary, by city)

   A qualitative inventory of transport-sector interventions was conducted to contextualize observed PM. patterns across the study cities.

   • Delhi: Extensive policy push including large-scale e-bus procurement targets, EV purchase incentives, metro rail expansion, pilot Low-Emission Zones (LEZs), and vehicle-scrappage programs. A revised city EV policy is anticipated in 2025, further strengthening electrification efforts.

   • Mumbai: Ongoing e-bus pilot deployments supported by modelling studies indicating potential reductionsin local PM exposure and greenhouse-gas emissions. Policy emphasis is placed on integrating electric buses with the expanding metro network to reduce private vehicle dependence.

   • Bengaluru: Implementation of e-bus pilots with scaling plans, supported by scenario-based modelling that demonstrates measurable reductions in corridor-level PM. concentrations. Metro expansion and public transport improvements complement electrification initiatives.

   • Pune: Strong focus on active mobility, including walking and cycling promotion, alongside early EV incentives. Improvements in national air-quality rankings suggest benefits from combined transport and traffic-management interventions.

   • Kolkata: Gradual bus fleet modernization and metro expansion, with limited but growing policy traction toward electric mobility and LEZ planning. Large-scale electrification remains in early stages.

   • Raipur: Currently limited formal large-scale green transport programs. However, considerable opportunities exist for fleet modernization, particularly through e-bus induction and two-wheeler electrification, which could yield rapid air-quality and health benefits given the citys moderate baseline pollution levels.

3. Estimated public health impact & co-benefits

To translate observed and potential PM. reductions into public-health outcomes, health-impact assessment was conducted using India-specific concentrationresponse functions (CRFs) derived from major epidemiological studies, including analyses associated with the Global Burden of Disease (GBD) and The Lancet. These functions quantify the relationship between long-term ambient PM. exposure and all-cause mortality in the Indian population.

Using published CRFs, the study estimates avoided premature mortality per 10 µg/m³ reduction in annual PM. concentration.

This incremental approach enables direct comparison of potential health benefits across cities with varying baseline pollution levels and population sizes. The methodology, described in Chapter 3, incorporates nationally representative mortality rates and applies confidence bounds to reflect uncertainty in the exposureresponse relationship.

In addition to preventing premature deaths, reductions in PM. are expected to deliver broader health co-benefits, including decreases in respiratory and cardiovascular morbidity, reduced healthcare burden, improved workforce productivity, and enhanced urban liveability. These outcomes reinforce the importance of integrating transport policy, air-quality management, and public-health planning in urban sustainability strategies.

Figure-4.7.1

8%

16%

15%

16%

14%

11%

20%

Neonatal Disorder Type 2 Diabates

Tracheal/bronchus/Lung Cancer

Ischemic Heart Disease

Death % in India Due to PM 2.5

Death percentage as per diseases due to PM 2.5 Impact

Figure-4.7.2

Key India-Specific CRF (Lancet, 2024)

A nationally representative cohort analysis using Indian mortality data found that:

Each 10 µg/m³ increase in annual PM. concentration is associated with an 8.6% increase in all-cause mortality (95% CI: 6.410.8%) [thelancet.com]

This CRF is now considered more appropriate for India than older global functions derived from low-pollution countries

Below is a transparent, city-level estimation of avoided deaths due to a 10 µg/m³ reduction in annual PM., using India-specific Lancet / GBD concentrationresponse functions (CRFs) and current population and mortality data.

city-Level Avoided Premature Deaths from PM. Reduction (India) Scenario Defined (for all cities)

PM. reduction: 10 µg/m³ (annual mean)

Health impact function (CRF) 8.6% increase in all-cause mortality per 10 µg/m³ PM. (India-specific, Lancet Planetary Health) [nature.com]

Crude death rate (India, 2024):

7.5 deaths per 1,000 population per year [macrotrends.net], [data.worldbank.org] Interpretation: A 10 µg/m³ reduction avoids 8.6% of baseline deaths.

Method

For each city:

Avoided deaths = (

Population × Crude death rate

) × .

Table-5 Input Data (20242025 estimates)

City	Population (million)	Source
Delhi (Urban agglomeration)	33.8	World Population Review /Macrotrends [macrotrends.net]
Mumbai (Metro)	21.7	Macrotrends [macrotrends.net]
Kolkata (Metro)	15.6	Macrotrends [macrotrends.net]
Bengaluru (Metro)	14.0	Macrotrends [macrotrends.net]
Pune (Metro)	7.7	World Population Review Macrotrends [macrotrends.net]
Raipur (Urban agglomeration)	1.87	Macrotrends [macrotrends.net]

Results: Avoided Deaths per Year (per 10 µg/m³ PM. Reduction)

Table-6

City	Baseline deaths/year	Avoided deaths/year
Delhi	253,500	21,800
Mumbai	162,800	14,000
Kolkata	117,000	10,100
Bengaluru	105,000	9,000
Pune	57,800	5,000
Raipur	14,000	1,200

Interpretation

Large cities gain the most in absolute lives saved because of higher baseline mortality and population exposure. A single 10 µg/m³ reduction in Delhi alone could prevent ~22,000 premature deaths every year.

Even mid-size cities (e.g., Pune) show thousands of avoidable deaths annually, highlighting strong returns from transport and clean-air interventions.

These values are conservative, because:

They use crude death rates (not age-specific risks),

They exclude morbidity benefits (hospitalizations, DALYs), They do not account for higher near-road exposure.

Uncertainty Bounds (Optional, Recommended)

Using the 95% CI of the CRF (6.410.8%): [nature.com] Delhi: ~16,000 27,000 avoided deaths/year

Mumbai: ~10,400 17,600

Bengaluru: ~6,700 11,300 (Apply proportionally to other cities.)

Applying India-specific concentrationresponse functions from recent Lancet and GBD studies, we estimate that a 10 µg/m³ reduction in annual PM. could avert approximately 22,000 premature deaths annually in Delhi, 14,000 in Mumbai, and 9,000 in Bengaluru. These city-level benefits underscore the substantial public-health gains achievable through urban transport and clean-air interventions.

Delhi: a 10 µg/m³ reduction thousands of avoided premature deaths annually (reflecting very high baseline PM2.5 and population).

Raipur: 10 µg/m³ reduction hundreds of avoided deaths (smaller population, lower baseline).

CHAPTER 5

DISCUSSION & POLICY RECOMMENDATIONS

1. Key interpretations

   1. Transport electrification reduces local traffic emissions but the magnitude of PM2.5 improvement depends on the grid emissions mix, non-exhaust particulate controls (road dust), and simultaneous control of non-transport sources. Cities with larger shares of transport emissions and active e-bus deployments (and grid decarbonization) show the largest co-benefits.

   2. Small/medium cities like Raipur can gain faster per-capita health benefits fro targeted bus fleet electrification and 2W/3W electrification because fewer infrastructural constraints and quicker fleet turnover may be possible.

   3. BRT & modal shift matter shifting commuters from private vehicles to reliable public transport reduces total vehicle-km and per-person exposure. Case studies show reductions in commuter exposures and local pollutant concentrations.

2. Recommendations (prioritized)

   1. E-bus fleet deployment with clean charging cities should prioritize replacing diesel bus fleets with e-buses while investing in renewable-backed charging to maximize local air quality and GHG benefits (critical: grid decarbonization or renewable energy purchase agreements).

   2. Targeted electrification of 2W/3W & last-mile e-rickshaws with subsidies/financing to reduce tailpipe emissions in congested corridors.

   3. Low Emission Zones (LEZ) around sensitive areas (schools, hospitals) and high-pollution corridors, combined with scrappage incentives for old diesel vehicles. Ahmedabad LEZ planning is a replicable example.

   4. Non-exhaust particulate controls invest in road sweeping, dust suppression at constructions sites and heavy vehicle controls to reduce resuspended dust, which often limits PM2.5 gains from tailpipe reductions.

   5. Robust monitoring & evaluation expand and standardize city monitoring networks, use source-apportionment studies to evaluate impact, and perform periodic health impact assessments. (CPCB & IQAir data used here show the value of continuous monitoring.)

   Figure-5.2.1

1. Implementation roadmap for Raipur

   Immediate (02 years): pilot e-bus procurement for main corridors; subsidize e-rickshaw / e-2W scrappage; establish charging hubs powered by renewable energy where possible.

   Medium term (25 years): scale e-bus fleet replacement, integrate with route rationalization and traffic management, implement LEZ pilots near hospitals/schools.

   Monitoring: create baseline and annual monitoring (PM2.5, NO) with transparent public dashboards.

   CHAPTER 6 CONCLUSION

   This comprehensive analysis demonstrates that green transportation initiatives play a critical and measurable role in improving urban air quality and enhancing public health. Rapid urbanization, rising motorization, and expanding economic activity have made transport emissions one of the major contributors to PM., PM, NO, SO, and ground-level ozone across Indian cities. Government-backed air-quality assessments indicate that vehicular emissions consistently account for a significant portion of ambient particulate pollution in both northern and southern metropolitan areas. Seasonal analyses reveal that transport-linked PM. peaks coincide with winter inversion periods in cities such as Delhi and Kolkata, underscoring the importance of transport-

   sector reforms in reducing exposure and health risks

   Across cities with diverse climatic and geographic characteristics, green mobility interventionsincluding electric vehicle (EV) adoption, non-motorized transport infrastructure, efficient mass rapid transit systems, and low-emission zoneshave demonstrated notable improvements in air-quality metrics. Studies conducted in Delhi show that stringent transport emission controls and cleaner mobility policies contribute to year-on-year reductions in average PM. levels, despite episodic spikes driven by meteorology and external pollution transport. Similarly, cities like Bengaluru, which maintain lower PM. levels relative to northern counterparts, demonstrate that investments in public transit, moderated traffic growth, and supportive meteorological conditions can stabilize emissions and reduce seasonal pollution extremes. The documented associations between monthly pollutant variations and local sources further reinforce the transport sectors influence on urban air quality.

   The analysis also highlights the substantial public health gains that emerge when green transportation strategies are effectively implemented. Exposure to elevated PM. levels is linked to respiratory diseases, cardiovascular disorders, oxidative stress, and increased all-cause mortality. Research from Kolkata shows that PM. toxicity sharply increases beyond specific concentration thresholds, intensifying oxidative stress in lung tissuesa clear indication that even moderate improvements in air quality can significantly reduce long-term health burdens. Evidence from national-level reports underscores that sustained reductions in particulate exposure translate into measurable declines in disease incidence and severity, demonstrating that transportation reforms are inseparable from public-health protection efforts.

   Overall, the findings reaffirm that green transportation is not merely an environmental priority, but a public-health imperative. Cleaner mobility systems reduce pollutant concentrations, prevent health deterioration, enhance productivity, and improve quality of life for millions of urban residents. For maximum effectiveness, these initiatives must be supported by coherent policy frameworks, robust monitoring networks, integrated urban planning, and active public participation. The evidence strongly suggests that with sustained policy commitment, Indias major cities can achieve significant, long-term air-quality improvements and reduce the health risks posed by transportation-related pollution.

   In conclusion, green transportation initiatives are central to building healthy, resilient, and sustainable urban environments. Their impact on air quality and public health is both profound and scientifically validated, making them essential components of future urban development, climate action, and public-health strategies.

   Green transportation initiatives are necessary and effective tools to reduce urban air pollution and improve public health, but their success depends on complementary measures (grid decarbonization, dust control, enforcement) and robust evaluation. Raipur has the potential to realize quick wins with targeted electrification and fleet modernization. The dissertation recommends an integrated transport-air quality strategy tailored to each citys emissions profile.

   To enhance the said initiatives resources like electric charging station must be provided along the highway, cities.

   Figure-6.0

Figure-6.1

Today in Europe, GHG emissions linked to transport account for 25% of total GHG emissions of which urban buses account for 8%1 (per passenger per km). Renewing and modernizing bus fleets to cleaner technology is always an opportunity for cities to improve the quality of transport and reduce polluting emissions, despite the technology gy chosen (lower norm of diesel, CNG or electric). Elec trick buses are part of this renewing process to modernize a network and improve air quality. By replacing their con venational buses with fully electric buses, cities wish to cut CO2 emissions linked to transport and fine particles at exhaust such as PM and NOx which are known to have negative effects on citizens health. Since fully electric buses do not emit any emissions at tailpipes and are quiet, they are often put forward in political strategies to enhance health and quality of life in urban centers.

Many cities in Europe, but also across the globe, are deploying transport strategies to make a shift from individual car use to public transport, walking and cycling in order to achieve their goals of emissions reductions. Londons transport strategy aims to have 80% of all trips by either public transport, cycling, walking or zero emission buses by 2025. By 2050, London aims to have a zero-emission transport system. Paris has the same objective to transform its bus fleet to electric or bio-CNG by 2025. Electric uses represent one important link in a mobility strategy chain, but their positive impact will be maximized if the transport network is efficient, accessible and of quality. Those factors are usually the main incentives to attract customers, pushing citizens from individual transport and pulling them to the public transport network. All of this combined will have a significant impact on air quality, and citizens quality of life and health. However, special attention must be paid to the cost and benefit of electric buses, considering the long-term financial impacts in terms of health benefits compared to the financial cost of the system. Transport Access & living conditions Lifestyle & behaviors

Key Interpretations

1. Effectiveness of Transport Electrification

   Transport electrification plays a crucial role in reducing local traffic-related emissions, particularly tailpipe PM. and NO. However, the magnitude of ambient PM. improvement depends on several interrelated factors, including the electricity-generation mix, the management of non-exhaust particulate emissions (such as resuspended road dust), and the simultaneous control of non-transport sources. Cities with a higher contribution of transport emissions and sustained deployment of electric busesparticularly where grid decarbonization is progressingdemonstrate the largest air-quality and public-health co-benefits.

2. Opportunities in Small and Medium-Sized Cities

   Small- and medium-sized cities such as Raipur are well positioned to achieve faster per-capita health benefits through targeted transport electrification. Compared to large megacities, these cities often face fewer infrastructural constraints, experience lower baseline congestion levels, and possess greater flexibility for rapid bus-fleet and two-/three-wheeler (2W/3W) electrification. Consequently, early intervention in such cities may yield disproportionately high health and air-quality returns.

3. Importance of BRT and Modal Shift

Beyond vehicle technology, Bus Rapid Transit (BRT) systems and modal shifts play a critical role in reducing total vehicle-kilometres travelled and lowering per-capita exposure to air pollutants. Shifting commuters from private vehicles to high-quality public transport systems has been shown to reduce local PM. concentrations and commuter exposure, particularly along high-traffic corridors. These findings highlight that electrification and modal shift are complementary strategies, rather than substitutes, in achieving sustainable air-quality improvements.

6.2 Policy Recommendations

1. E-Bus Fleet Deployment with Clean Charging

   Cities should prioritize the systematic replacement of diesel buses with electric buses, particularly on high-ridership and high-pollution corridors. To maximize both air-quality and climate co-benefits, charging infrastructure should be supported by renewable-energy integration, either through grid decarbonization or dedicated renewable power procurement agreements.

2. Targeted Electrification of 2W/3W and Last-Mile Transport

   Accelerating electrification of two-wheelers, three-wheelers, and e-rickshaws through targeted subsidies, low-interest financing, and scrappage incentives can significantly reduce emissions in congested urban areas. These vehicle categories contribute substantially to urban traffic volumes and are among the most cost-effective targets for near-term emission reduction.

3. Implementation of Low-Emission Zones (LEZs)

   Establishing Low-Emission Zones around sensitive receptors such as schools, hospitals, and densely populated commercial areas can effectively limit the operation of high-emitting vehicles. LEZ implementation should be accompanied by vehicle-scrappage incentives for older diesel vehicles. The Ahmedabad LEZ planning framework provides a replicable model for other Indian cities.

4. Control of Non-Exhaust Particulate Emissions

   To complement tailpipe emission reductions, cities must invest in non-exhaust particulate-control measures, including mechanized road sweeping, dust suppression at construction sites, enforcement of material-handling norms, and regulation of heavy-duty vehicle movement. These measures are essential, as non-exhaust emissions often constrain the achievable PM. reductions from vehicle electrification alone.

5. Strengthening Monitoring and Evaluation Systems

Robust and standardized air-quality monitoring networks should be expanded across cities to support evidence-based policymaking. Periodic source-apportionment studies and health-impact assessments are recommended to evaluate the effectiveness of implemented interventions. Data from CPCB monitoring stations and complementary platforms such as IQAir demonstrate the value of continuous, transparent air-quality reporting.

Recommendations for Implementation

The findings of this thesis clearly show that transportation emissions are among the largest contributors to PM. pollution, especially in dense, growing Indian cities. Winter inversions in Delhi and Kolkata trap vehicular and combustion emissions, worsening public health outcomes. Likewise, real-time studies show sharp toxicity rises when PM. exceeds threshold levels, emphasizing the urgent need for targeted transport reforms.

To effectively resolve the thesis problem statementreducing PM. pollution through green transportation to protect public healththe following recommendations are proposed:

1. Strengthen Clean Mobility Infrastructure

   1. Expand Electric Vehicle (EV) Adoption

      • Provide financial incentives for EV purchase, charging infrastructure, and battery swapping systems.

      • Prioritize electrification of high-impact categories such as public buses, auto-rickshaws, ride-sharing fleets, and last-mile delivery vehicles.

      • Real-time Delhi data shows significant pollution spikes from motorized traffic, reinforcing the need for low-emission fleets.

   2. Accelerate Public Transit Enhancements

      • Expand mass rapid transit such as metros, BRT corridors, and suburban rail.

      • Improve last-mile connectivity through e-buses, e-autos, and micro-mobility.

      • Mumbais emission inventory highlights large reductions in vehicle km and emissions when metro corridors become functional, confirming the effectiveness of mass transit.

1. Enforce Stricter Vehicular Emission Standards

   • Strengthen compliance for BS-VI vehicles with on-road emission monitoring.

   • Implement Low Emission Zones (LEZs) prioritizing electric and CNG fleets.

   • Seasonal evidence from Bengaluru shows that traffic and road dust dominate emissions during non-monsoon months.

2. Control Road Dust and Construction Pollution

   • Regular mechanical sweeping, dust vacuuming, and pavement maintenance.

   • Enforce dust control regulations at construction and demolition sites.

   • Construction-related PM. is a major contributor in cities like Mumbai and Delhi.

3. Promote Non-Motorized Transport (NMT)

4. Build Safe Infrastructure for Walking and Cycling

   • Develop protected cycle lanes, pedestrian-first footpaths, and traffic-calming measures.

   Improve green corridors that encourage physical activity and reduce motorized trip demand.

5. Integrate NMT with Public Transport

   • Provide cycle parking, rental bikes, and pedestrian access around metro/bus hubs.

   • Global and national studies show that reducing vehicle dependence lowers PM. exposure and improves overall public health outcomes.

6.3.9. Strengthen Urban Air Quality Monitoring and Data Transparency

6.4.0 Expand Government Monitoring Networks

• Increase the number of continuous ambient air monitoring stations (CAMS), especially in cities like Raipur where PM. stations are limited.

• Integrate real-time data from government and private low-cost sensors to improve spatial coverage.

  6.4.1 Implement Source-Specific Monthly Reporting

• Monthly vehicle emission source apportionment should be mandated for major cities.

• Existing datasets for Delhi and other cities demonstrate clear monthly PM. peak patterns useful for targeted interventions.

6.4.2. Integrate Urban Planning With Green Transport Policies

6.4.3 Transit-Oriented Development (TOD)

• Encourage mixed-use development around high-capacity transit corridors to reduce long daily commutes.

• Adoption of TOD reduces vehicular pollution loads and decreases PM. exposure.

  6.4.4 Zero-Emission Zones in High-Density Localities

• Prioritize clean mobility in commercial hubs, markets, school zones, and hospitals.

• Air-quality studies show toxic PM. levels particularly affect health-sensitive groups such as children and the elderly.

6.4.5. Promote Behavioral Change and Public Involvement

1. Encourage Citizens to Shift to Green Mobility

   • Launch campaigns promoting metro use, cycling, walking, and ride-sharing.

   • Evidence from peer megacities shows that public awareness and citizen engagement increase the effectiveness of

     emission-control policies.

2. Incentivize Corporate Commuter Solutions

   • Provide incentives for companies adopting green mobility plans, EV fleets, and employee shuttle systems.

   • Reduce peak-hour traffic, a major contributor to monthly PM. spikes.

3. Strengthen Policy Integration Under National Clean Air Programme (NCAP)

4. City-Specific Transport Action Plans

   • Align urban transport policies with NCAP targets of PM reduction.

   • Kolkata and Delhi already show that targeted interventions improve annual averages.

6.5.0 Multi-Sector Coordination

• Integrate transport, industry, waste, and energy strategies to address overlapping pollution sources.

• Research highlights that PM. loads are influenced both by local emissions and broader meteorological factorsrequiring coordinated responses.

Final Recommendation Summary (In Direct Alignment With Thesis Problem Statement)

To resolve the core problemtransport emissions causing poor air quality and public health deteriorationcities must adopt a multi-layered strategy:

1. Shift to electric and zero-emission mobility at scale.

2. Expand and integrate mass transit and non-motorized transport systems.

3. Implement strict vehicular emission and dust control policies.

4. Improve monitoring, reporting, and enforcement of pollution data.

5. Integrate sustainable urban planning to reduce transport demand.

6. Engage the public through awareness and incentives.

7. Strengthen city-level execution under NCAP and interdepartmental coordination.

These recommendations provide a practical, scientifically supported roadmap for cities to reduce PM. emissions, improve air quality, and protect public healththereby fully addressing the thesis problem statement.

7. REFERENCES

1. IQAir. World Air Quality Report 2024. (data dashboards & city pages).

2. Central Pollution Control Board (CPCB). AQ Bulletins and city reports. (20242025).

3. NITI Aayog. Electric Vehicles in India report (2025).

4. Transformative Mobility. Bengaluru deep dive & e-bus scenarios.

5. Lancet Planetary Health. Estimating the effect of annual PM2·5 exposure on mortality (2024).

6. The Lancet. Ambient air pollution and daily mortality in ten cities of India (2024).

7. Transport-Links. Ahmedabad Low Emission Zone planning framework (2025).

8. IQAir / city dashboards (Raipur, Kolkata, Mumbai, Pune, Bengaluru) individual city pages used for station & current PM2.5 values.

9. (Full APA / IEEE style reference list will be produced in the final file I will ensure consistent referencing style as you prefer.)

10. Appendix A Raw AQ data tables (20192024) for each study city (source links and station lists).

11. Appendix B Health impact calculation spreadsheet (population, baseline mortality, CRF, avoided deaths for sample PM2.5 reductions).

12. Appendix C Inventory of green transport initiatives (dates, scale, documentation links).

13. Appendix D Methodology details, sensitivity analyses and limitations.

14. Central Pollution Control Board