This piece was originally published in The Hindu Business Line. 

 The post-Diwali air quality index (AQI) is being widely discussed in newspaper reports, and with the exception of 2022, the day after Diwali 2024 had the lowest 24-hour average AQI for that day since 2015. In the last 2–3 months, Delhi enjoyed its best air quality of the year and once saw an AQI close to 53. It was similar at this time last year, when Delhi had a few months of good AQI and clear skies. This is all the result of multiple initiatives by agencies, including the environment and transport departments of the Government of Delhi, the Commission for Air Quality Management, and more.  

This article intends to look at some the transport-specific initiatives, including the Delhi Electric Vehicles policy, which was released in August 2020 and is currently extended till March 2025. This was a major milestone in the city’s strategy to combat air quality issues. Indeed, the city has over time created and executed several actions to improve air quality, and one of the key strategies has been electrification of the transport sector. As Delhi paves its way towards EV policy 2.0, let’s look at its electrification journey through the lens of public transport service improvement. 

Delhi’s EV policy set a 50% electrification target for new public transport vehicles and covered new stage-carriage buses procured for the city’s fleet, including for last-mile connectivity services. 

The transition to electric started with public buses, and in a significant move under the Faster Adoption and Manufacturing of Electric Vehicles (FAME) II scheme, Delhi procured and deployed 400 electric buses (e-buses) in 2022 at a competitive rate of ₹43/km. These buses have now been operational for over 2 years and each 12 m e-bus can operate for 120 km on a single charge, which contributes to a significant reduction in carbon footprint. A study estimated that replacing Delhi’s entire existing bus fleet with new e-buses could reduce total pollutant emissions by nearly 75%.

In addition to deploying e-buses, the Delhi Government floated tenders for e-scooter and e-cycle adoption in January 2024, to diversify the transport options available in the city. Additionally, pursuant to an initiative focusing on gender equity introduced in April 2022, the Delhi Government issued permits for 3,500 e-autos and 500 of these were earmarked for female-driven e-autos. Training programs for female drivers are underway and interdepartmental coordination is helping to streamline the process of bringing onboard women-owned e-autos.

Building infrastructure

Delhi’s commitment to building infrastructure for electric vehicles is also evident in the efforts of Delhi Transco, which issued tenders to set up charging and battery-swapping facilities across the city. Companies like Sun Mobility have already begun setting up these facilities and users are given subsidized rates, to help ensure the affordability and accessibility of e-mobility solutions. Another outcome of these tenders is that, in collaboration with the Delhi Metro Rail Corporation and the Transport Department, Sun Mobility has launched three-wheeler-e-rickshaw services to improve last-mile connectivity.

Public transport services that operate at high frequency and on shorter routes are needed. A major initiative is the Mohalla bus scheme, which was formulated to create neighbourhood-level connectivity using the e-bus fleet. The e-buses procured under the Mohalla bus scheme are set to be deployed soon.

Even though Delhi’s EV policy envisioned only 50% of all new stage-carriage procurement to be electric, all buses deployed by the Delhi Government since January 2024 are electric. This year alone, from January to August 2024, 595 e-buses were deployed in Delhi. Delhi’s efforts to promote electric mobility not only improve sustainability but also offer long-term economic and health benefits by reducing fuel costs and improving air quality for residents. 

Although inherently challenging, the quantification of vehicle emissions has evolved considerably in recent decades and now extends well beyond the original lab-based measurements. Here we’ll explain a new development in the field from the University of York and the International Council on Clean Transportation, which partnered to create a technique that simplifies point sampling, an approach to measuring on-road emissions. We find that it can be adopted widely—by any city seeking to better understand transport emissions—and provides valuable new information about how much different vehicles are contributing to ambient air pollution.

The need for such a technique arises from the challenges inherent in measuring vehicle emissions: Millions of individual sources of emissions move in space and time and come from numerous generations of fuel, vehicle, and aftertreatment technologies. There are also environmental influences such as ambient temperature and road gradient.

One inescapable requirement is that numerous vehicles should be measured to achieve a representative sample and ensure that robust conclusions can be drawn from the data. This is especially true when characterizing the emissions of individual vehicle manufacturers or even specific vehicle models. Use of remote sensing technology is an attractive way to measure emissions from vehicles operating in real-world conditions because it records exhaust emissions of passing vehicles by shooting lights across vehicle exhaust plumes to measure the light absorptions of pollutants of interest. The TRUE Initiative uses such techniques extensively in many cities around the world.

While remote sensing has many advantages for measuring common pollutants such as nitrogen oxide, nitrogen dioxide (NO₂), and carbon monoxide, it’s not as well suited to measuring other pollutants in vehicle exhausts. Individual hydrocarbons that have harmful health impacts, including benzene and toluene, are typically not available, for instance, and neither are particulate metrics like particle number (PN) and black carbon (BC).

An alternative unobtrusive approach to measuring on-road emissions from traffic is point sampling, a technique where fast response instruments located curbside measure the dispersing plumes of passing vehicles. With point sampling, pollutants are extracted from the plume and rerouted to analyzers. Point sampling allows users to expand the number of measurable pollutants by using dedicated instruments that specialize in specific gaseous and particulate pollutants. As carbon dioxide (CO₂) is measured simultaneously with the air pollutants of interest, pollutant ratios can be determined (such as nitrogen oxides [NO_x]/CO₂) and from these, different emission factors can be calculated.

That said, point sampling brings its own challenges. For most roadside locations, individual vehicles are not conveniently separated from one another to allow individual plumes to be accurately measured as the vehicle passes; this can easily result in data loss of over 70%. A quiet location with low vehicle traffic is necessary, but simultaneously not very useful for achieving a large sample size. Conversely, a busier location quickly runs into the problem of overlapping plumes that mix with each other.

To find another way of approaching this problem, we developed an adapted analysis approach that generates disaggregated vehicle emissions information from data with plume overlap. Figure 1 shows an example of vehicle passes at a location in York (UK) over a 15-minute period. It’s clear there is no period where a single plume exists in isolation from others. The new analysis technique uses regression to relate roadside concentrations of different pollutants to the amount of plume that’s expected on average. Each time a vehicle of a particular type passes (denoted by the colors), an average plume profile is virtually added to the time series. The new technique is called plume regression and it not only greatly simplifies point sampling, but also provides valuable new information. The methodology behind plume regression is described in more detail in a paper published in Environmental Science & Technology earlier this month.

Figure 1. A 15-minute period of vehicle passes made at a location in York, with the vertical lines on the x-axis showing the time a vehicle passes the point sampling instruments

We applied this plume regression approach to several contrasting data sets and pollutants, including NO_x, NO₂, and ammonia in York, and NO_x, PN, BC, and 10 individual volatile organic compounds (VOCs) in Milan as part of the CARES project. We compared the NO_x emission factors derived from the plume regression with remote sensing data, and they showed very good agreement. Figure 2 shows an example of fuel-specific emission factors for NO_x from around 24,000 measurements in Milan. These results reflect the expected emission differences across fuel types and the reduction in emissions from older to newer Euro emission standards.

Figure 2. Fuel-specific NO_x emission factors (g/kg) based on the plume regression approach using data from the CARES project in Milan

In addition to providing emission factors, the new plume regression approach provides valuable information on concentration source apportionment, or how much of the pollutant concentration measured at the roadside is coming from different types of vehicles. For example, vehicle emission measurements alone cannot tell us how much of measured NO₂ at the roadside can be attributed to Euro 5 diesel cars. That requires more advanced air quality modeling to predict near-road concentrations, and it’s a challenging task in complex urban environments.

The information from the plume regression approach is highly valuable to those interested in reducing the roadside concentrations of important air pollutants. An example from Milan is shown in Figure 3, and the area of each rectangle represents the contribution made to roadside NO_x concentrations by different fuels and vehicle technologies. Observe that the bulk of NO_x is from diesel vehicles (blue) and a much smaller contribution is from gasoline (green), LPG (yellow), and CNG (purple). The major contribution is from diesel pre-RDE Euro 6 passenger cars and Euro 5 passenger cars; this is due to both their number and high real-world emissions.

Figure 3. Absolute concentration contribution to roadside NO_x at a roadside location in Milan, and the size of each area shows the total contribution to NO_x concentrations by fuel and vehicle types

This new plume regression approach could be widely adopted around the world. It’s basically the same as the ambient measurements already made at thousands of roadside sites that provide hourly pollution concentrations, but uses fast response instruments and adds a measure of CO₂, the byproduct of vehicle fuel combustion. Because of this, point sampling combined with the plume regression approach can enhance the existing capability of roadside monitoring sites. By quantifying vehicle emissions and estimating how much different vehicles are contributing to ambient air pollution concentration, the new method unlocks a large potential for cities to better understand transport emissions and develop data-driven policies to reduce one of the main sources of air pollution.

We are grateful to Naomi Farren and Sam Wilson for carrying out the measurements and to Kaylin Lee and Mallery Crowe for co-authoring the publication for Environmental Science & Technology. We also thank Ricardo for supporting the Ph.D. of Sam Wilson and Markus Knoll at the Technical University Graz and for sharing additional measurements from the Milan CARES campaign.

Presentations:

• Real-world motor vehicle exhaust emissions in Delhi and Gurugram using remote sensing ​
• Low-emission zone as a policy measure to reduce exhaust emissions​: A case study of Pimpri-Chinchwad.

Winters in Delhi NCR have become synonymous with deteriorated air quality. As temperatures begin to dip, the air quality in the national capital also declines triggering the implementation of restrictive measures under Graded Response Action Plan (GRAP).  

Among several sources of pollution, transportation consistently emerges as a major factor in Delhi’s persistent air pollution crisis. The ICCT’s recent study  measured tailpipe emissions from over 100,000 vehicles in the NCR through remote sensing technology, reveals that exhaust emissions especially from commercial vehicles are substantially higher in real-world conditions. One of the key findings from the report was that while CNG is regarded as a cleaner fuel due to lower particulate matter emissions, it showed high NO_x emissions. While emergency measures like the GRAP are essential, these findings emphasise the need for more targeted and permanent interventions to reduce emissions from road transport.  

Low Emission Zones or LEZs, the geographically defined areas where polluting motorized vehicles are restricted can offer 70-80% reductions in particulate matter (PM) and NO_x emissions in cities. Another ICCT’s study offers the roadmap for the introduction of low-emission zones (LEZs), evaluating the potential reduction in emissions as a case study in Indian city of Pimpri-Chinchwad, Maharashtra.  

Under the India ZEV Alliance initiative, this webinar by the ICCT in partnership with ET Auto will shed light on technologies that can assist in accurate and real-time monitoring of vehicular emissions and discuss policy interventions that can effectively abate them and help in better air quality.