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A Novel Approach to Understanding Delhi’s Complex Air Pollution Problem

Shuvabrata Chakraborty (fpm17010@iiml.ac.in) and Samir K Srivastava (samir@iiml.ac.in) are with the Indian Institute of Management, Lucknow.

 

With rising concerns about the steep increase in air pollution in the National Capital Territory of Delhi, several factors—particularly motorised transportation, construction, and stubble burning in neighbouring states—are being identified as contributing to this hazard. However, in order to make effective policy decisions, there is a need for a holistic approach that identifies the root causes of the problem. The use of system dynamics simulation offers a novel systems thinking approach to understand Delhi’s air pollution, taking into account the dynamic nature of the air pollution system as well as the complex interdependencies among the various factors and sources of air pollution.

The authors express special gratitude to the anonymous referee for the useful inputs and constructive feedback which helped in improving the quality of the paper significantly.
 

The National Capital Territory (NCT) of Delhi has seen rapid growth in its industrial, transportation, and housing sectors over the past decades. The population of Delhi has increased from 1.378 crore in 2001 to 1.678 crore in 2011, a decadal growth of 21.2% against a national growth rate of 17.7%. The total area of Delhi is 1,483 square kilometres (km2). Its population density was recorded as 11,320 persons per km2 in 2011, compared to a density of 9,340 persons per km2 in 2001 (Directorate of Economics and Statistics 2017). This rapid growth in population, along with the increased rate of industrialisation and urbanisation and the rise in motorised transport, have resulted in an increased concentration of various air pollutants such as nitrogen oxides, sulphur dioxide, suspended particulate matter, respirable suspended particulate matter (RSPM/PM10), carbon monoxide, ozone, lead, benzene, ­hydrocarbons, and the like (Goyal et al 2006).

PM10 includes all particulate matter (PM) with an aero­dynamic diameter less than 10 micrometres (µm). These particles have the highest tendency to penetrate human lungs and may cause aggregated respiratory and cardiovascular diseases, often leading to premature deaths (Seaton et al 1995). PM2.5, included within the PM10 volume, refers to the particles that are less than 2.5 µm in aerodynamic diameter. More than 95% of combustion-related emissions from petrol, diesel, and natural gas constitute PM2.5, and thus PM10 as well. Apart from emissions, a good percentage of PM10 pollution also comes from dust sources such as construction sites, vehicular movements on poorly built and broken roads, and seasonal dust storms. Nearly 80% of the dust commonly found on roads falls between the range of 2.5 µm and 10 µm and is consequently ­accounted within the PM10 measurement. PM10 was the only size fraction measured in India until 2009, when PM2.5 was added to the criteria.

These factors led us to select PM10 as the broad topic of our investigation, wherein emissions and dust are the primary contributing factors (UrbanEmissions.info 2016). The focus on PM10 as a major pollutant can also be explained using the ­Exceedance Factor metric, which is defined as the ratio of the annual mean concentration of a pollutant and its respective standard as per the National Ambient Air Quality Standards (NAAQS) specified by the Central Pollution Control Board (CPCB). Based on the Exceedance Factor, air pollution is categorised to be within critical, high, moderate, or low pollution ranges. Figure 1 (p 33), based on a study from the ENVIS Centre on Control of Pollution of Water, Air and Noise, shows the state of pollution in Delhi for nitrogen oxides, sulphur dioxide, and PM10 where PM10 has exceeded the critical level consistently since 2009 and hence demands immediate attention. According to the same report, the anthropogenic sources of PM10 primarily include vehicular emissions and soil and road dust, apart from biomass burning, solid waste burning and construction activities. Figure 2 depicts the annual average ambient PM10 concentrations in Delhi (across all monitoring stations) from 2001 to 2015.

When compared with the World Health Organization guideline for PM10 concentration, which is at 20 micrograms per cubic metre of air (µg/m3), or the NAAQS (2009) limit of 60 µg/m3, the situation is clearly critical in Delhi, with PM pollutant levels far above the safe limits. Moreover, pollution levels are worse in the winter months because of increased emissions from heating and due to meteorological conditions, and the concentrations are ­almost double the annual averages (Guttikunda and Gurjar 2012).

On 7 November 2017, around the festival of Diwali, the air quality in Delhi dropped to the “severe” category. The government, referring to Delhi as a “gas chamber,” called for primary schools to remain closed and issued health advisories for high-risk groups, including children and the elderly, to avoid outdoor activities. Apart from severe health effects, Delhi smog has detrimental consequences on the safe driving of vehicles and trains and the safe take-off and landing of flights because of the resulting drastic drop in visibility. The horrible events that unfolded on the Yamuna Expressway on 8 November 2017, when more than 20 vehicles were damaged and several passengers injured, is an example of the results of such conditions. Even sport has not been spared from the impact of pollution. On 3 December 2017, during a bilateral test cricket match between India and Sri Lanka, play was stopped several times when the visiting Sri Lankan players were forced to wear anti-pollution masks to beat the “very poor” to “severe” Air Quality Index (AQI).

With the debate around the “main” culprit behind ever-­increasing pollution coming into focus time and again, the ­authorities have considered a range of measures, including the odd-even rule and banning construction activities on a trial basis. The results have been inconclusive. A number of studies and news reports have contemplated the various sources of the smog, including the stubble burning in neighbouring states, burning of firecrackers during Diwali, and meteorological conditions such as the lack of wind (Gurjar et al 2004; Mohan et al 2007; Sahu et al 2011). However, the extant literature fails to consider the dynamic nature of the air pollution system as well as the complex interdependencies among various factors.

Over the years, the most notable measures taken by the government to control the burgeoning air pollution include the switching of more than 1 lakh vehicles to CNG (compressed natural gas) in the early 2000s, the public transport revamp during the 2010 Commonwealth Games, the implementation of the prestigious metro project and its subsequent expansions, and the conversion of coal-based power plants within Delhi to gas-based ones. However, the benefits reaped from these actions (Kathuria 2005) have been gradually outdone by factors such as the increase in population density, lack of adequate public transport, the ­increasing number of passenger vehicles on the roads, the ­increase in construction activities, and the increase in the movement of construction material and debris by trucks passing through the city (Guttikunda 2012).

An important factor contributing to the crisis is the massive dieselisation of the car segment owing to the misuse of fuel tax policy to keep diesel prices cheaper (Roychowdhury 2017). Heavy-duty commercial vehicles, which are banned from ­entering the city limits during the daytime, are another important factor. Although both combustion and dust sources contribute to PM generation, a report released by the Centre for Science and Environment suggests that diesel PM is far more harmful than other particles like wind-blown dust. The report cites results from several studies to explain the hazards of diesel exhaust to human health. Heavy-duty vehicles are among the biggest consumers of diesel fuel in India. While they comprise just 5% of the total vehicles in India, they are responsible for as much as 66% of the PM load from the on-road transport sector and 74% of the total particulate load in the country (Roychowdhury and Nasim 2016). Further, the emission factors developed by the Automotive Research Association of ­India show that PM emissions from bigger engines are significantly higher than from smaller engines. The situation is further aggravated by the ill-maintained and ageing fleet of trucks which barely meet outdated emissions standards. India lags behind in targeted compliance to Bharat Stage (BS)–IV emission standards even as the government is contemplating skipping BS–V and implementing BS–VI norms by April 2020, given the severity of fuel emissions.

Due to the PM concentration and traffic congestion caused by heavy-duty trucks, the Delhi government has imposed a ban on their entry, idle-parking, and plying within city limits during a specified time interval of the day, which is revised from time to time depending on the AQI. The banned hours mainly cover the span of the day during which commuter traffic is fast. The real-time air quality data available from Delhi Pollution Control Committee shows a trend of pollution peaking during the night when the city faces enormous truck ­traffic. Therefore, even though the overall vehicle traffic falls and the weather stays cooler, pollution remains elevated in the night and the city does not get a chance to clean up. Guttikunda (2012) explains that these vehicles, which primarily carry raw and finished products, construction debris, sand, and bricks, result in more emissions at night and this pollution tends to linger even after the trucks have stopped operating in the morning. Another important factor is the debris and sand that is often transported without any covers, adding to the silt landing on the roads. All this calls for a serious intervention in the management of freight and the movement of waste.

Objective and Scope

In light of the above discussion, the need of the hour is to contemplate whether we are asking the right questions and taking the right measures to tackle the deteriorating air quality in Delhi. This paper is an attempt along this direction. Specifically, it seeks to evaluate the effectiveness of policies such as the odd–even rule.

The diverse entities that constitute the connected system of air pollution cause it to behave dynamically. The essence of a system lies in the interconnectedness of its entities and its ­effective understanding requires a holistic approach in order to avoid the twin perils of shifting a problem “there” by fixing it “here” and causing a bigger problem “later” by fixing it “now.” Breaking the system into parts breaks the system itself and consequently its system-level characteristics are lost and become difficult to trace. The system dynamics (SD) approach uses causal loop diagrams (CLD) to capture the cause-and-­effect relationships among the different connected entities of a complex system. Such elaborate and extensive visual depictions of connected systems help decision-makers avoid poor decisions that have the potential to backfire and result in policy resistance. In SD, after CLD development, the evolution of the system over time is studied using simulation of a computer model. Thus, system dynamics aptly captures the ripple effect of a system across time and space. In this study, we have used Vensim Personal Learning Edition (PLE) to develop and simulate an SD model.

While a number of factors that add to Delhi’s air pollution have been discussed in the introductory section, we intend to focus broadly on the influence of vehicular emissions and road/construction dust on PM (PM10/RSPM) concentration. Subsequently, in the modelling process, the scope has further been narrowed to study the effect of vehicular emissions on PM generation. We have considered three categories of vehicles, namely passenger cars, two-wheelers, and heavy and light commercial vehicles, and seek to ascertain their relative influence on PM generation.

Methodology

The model proposed in this study is in line with the principles of SD, which was developed in the 1950s by Jay Forrester at the Massachusetts Institute of Technology to deal with large-scale systems of high complexities. It is particularly useful for examining the dynamic characteristic of systems and investigating the overall behaviour of those systems through analysing ­interactions among the elements involved. The interactions among the elements are depicted by feedback loops—either positive (same effect) or negative (opposite effect) loops—­using which one can trace, demonstrate, and analyse how a change in any specific element would influence the overall ­behaviour of the system. Systems thinking and SD simulation have been widely used for studying complex interactive issues related to diverse sectors such as airport terminal capacity planning, wind energy sustainability, supply chain risk assessments, impact of subsidy policies, impact of waste disposal fees, self-sufficient city development, and so on. It has also been used for successful policy formulation in countries like China, Singapore, South Africa, South Korea, the United States (US), and ­Taiwan on issues as diverse as carbon reduction, industrial ­restructuring, allocation of wireless spectrum for mobile communications, logistics industry development, sustainable water resources management, and transition to a green economy. The detailed methodology of this paper is depicted as a flow chart in Figure 3.

Data collection: The SD model in this paper seeks to study the relative effects of three different classes of vehicles, that is, passenger cars, two-wheelers, and heavy and light commercial vehicles. To simulate the developed stock-flow model, data for the following variables was collected:

(i) Data on registered vehicles over a period of 20 years with respect to each of the three vehicle categories was collected from the “Statistical Abstract of Delhi 2016,” which is brought out by the Directorate of Economics and Statistics (2017). The data is further supplemented by results from the “Economic Survey of Delhi 2017–18,” released by the Department of Planning (2018).

(ii) Data pertaining to PM generated per vehicle per unit of time for all the categories have been referred from Goyal et al (2013).

(iii) Data related to the scrap rates of the vehicles have been ­ascertained by contacting four randomly selected scrap ­dealers from the Mayapuri scrap market.

Causal loop diagrams: Figure 4 depicts the initial conceptual map or CLD developed in Vensim PLE. It highlights the broad conceptual framework that includes the interaction of two ­important factors—road dust and vehicular emission—­towards the particulate air pollution of Delhi. The different feedback loops identified are shown in Table 1.

In the loops identified, “S” indicates the same effect while “O” indicates opposite effect. Parallel lines indicate time ­delays. All the loops understandably start and terminate at “air pollution,” which is what we want to measure finally.

Vehicles primarily contribute to air pollution via two means: fuel emissions and road dust. In addition to the constant ­vehicular movement, dust, in general, has its origin at construction sites and in seasonal dust storms. In order to build a robust SD model, quantitative measures for construction ­activities—and the dust generated by them—as well as road dust generated due to vehicular movement need to be ascertained based on empirical data. The available literature includes studies pertaining to the computation of dust contributions from vehicular movements (Etyemezian et al 2003) as well as construction activities (Gangolells et al 2009). However, given the relative severity of the emissions from the urban transport system (Lytton et al 2016), we consider a part of the initial CLD (without any loss of generality) that is comprised of the effects of vehicular emission on PM generation for our final system dyna­mics analysis. We do this because of the high and increasing contribution of vehicular sources to the overall air pollution level, as shown in Table 2. The stock and flow model for this reduced CLD was developed in Vensim PLE and simulated to study the relative PM contributions of different cate­gories of vehicles.

Stock and Flow Model

Based on the CLD envisaged, the stock and flow diagram has been developed using Vensim PLE. It consists of four stocks or state variables: PM, passenger cars, commercial vehicles, and two-wheelers. The corresponding rate variables include PM generated and PM mitigated; passenger cars addition and passenger cars scrap; commercial vehicles addition and commercial vehicles scrap; and two-wheelers addition and two-wheelers scrap.

Apart from the different vehicle addition and scrap rates, the auxiliary variables include PM per passenger car per year, PM per two-wheeler per year, PM per light commercial vehicle per year, and PM per heavy commercial vehicle per year. These parameters of emission tonnes per time units have been calculated based on the results of Goyal et al (2013), which are ­reproduced in the second column of Table 3. They have estimated the emission values using the International Vehicle Emissions (IVE) model because of its suitability to Delhi’s context. The IVE model is a computer-based model customised for motor vehicles emission estimations in deve­loping countries and has also been successfully implemented in cities such as Shanghai and Beijing. It considers the various modes of driving, meteorological conditions, and emission factors of different pollutants with respect to differently fuelled vehicles.

It is important to note that the relative contributions of buses and three-wheelers are small and negligible, hence ignored in our simulation. The fourth column of Table 3 shows the calculated values for the four categories of vehicles that have been assumed constant in our model. Here, PM-generated per vehicle per year is calculated based on a rounded approximation of the number of vehicles registered: 40 lakh for two-wheelers, 21 lakh for passenger cars, and 1.5 lakh for commercial vehicles as per registered motor vehicles data obtained from the “Statistical Abstract of Delhi 2016.” The ratio of heavy commercial vehicles has never exceeded 10% of the total commercial vehicles and hence, a conservative estimate of 5% heavy commercial vehicles and 95% light commercial vehicles has been assumed in the simulation.

A large number of vehicles registered in Delhi ply in the NCR areas and those registered in NCR ply in Delhi, according to the “Economic Survey of Delhi 2017–18.” The report says that the transport department is trying to estimate the actual number of vehicles in Delhi by taking into account several factors, such as vehicles that have outlived their life due to any account, transferred to and from other states, and so on (Department of Planning 2018). The Centre for Science and Environment commissioned a study during June–July 2015, carried out by V R Techniche Consultants to accurately estimate the number of commercial vehicles entering and leaving Delhi at all key toll points. The survey revealed that 52,146 commercial vehicles (excluding taxis) enter Delhi daily. The total commercial light and heavy-duty trucks entering and leaving the city per day number 1,15,945. Although, it is difficult to ascertain whether the same vehicle that has entered Delhi has also left Delhi the same day or the amount of distance/time a vehicle has travelled through the city before coming to a halt, these vehicles do have an imprint on Delhi’s air and add to particulate emissions. Moreover, in terms of PM contribution, Delhi’s own vehicles are responsible for 61% of the particulate load from the transport sector; non-Delhi trucks are responsible for 29% and non-Delhi others for 10% (Roychowdhury and Nasim 2016). Therefore, in the absence of concrete data regarding the number of vehicles actually plying the city roads, the scope of simulation in this paper is limited to the vehicles registered in Delhi, the segment which contributes to nearly three-quarters of the PM load in the capital.

Although the emissions estimations are made based on 2009 vehicle emissions data, it can safely be assumed that the emission quality of passenger cars and two-wheelers on the road have improved over the years, given their innovation, purchase, exchange, and scrap rates. On the other hand, the same factors for commercial vehicles have improved little (Goel and Guttikunda 2015). Therefore, a conservative assumption that these estimates remain constant since 2009 is made in this study. However, the scope to check the robustness of the model with respect to the variation in the above assu­mption has also been incorporated through sensitivity analysis.

The number of vehicles has been approximated to grow linearly (based on high R square values of linear regression analysis) as per registered vehicle data over a period of 20 years for each type of vehicle (Figure 5). Similarly, the scrap rates for the respective vehicle types have been ascertained based on discussion with a few scrap dealers. The slider option provided by the synthesim simulation mode in Vensim PLE has been used so that the model behaviour with respect to each parameter variations can be observed dynamically. The range and unit of variations for each parameter have been fixed subjectively based on historical data. The simulation has been run for a period of 10 years, with 2013 as the base year.

Model Testing and Validation

The developed SD model was validated using the set of tests outlined in Sterman (2000). First, the CLD was checked for its consistency with the research problem at hand. The initial CLD encompasses all the factors that are related to air pollution in Delhi. The cause and effect relations clearly indicate that the factors considered are relevant to the problem. The derived CLD used for the final SD simulation run takes into account the vehicle types which contribute significantly to the PM concentration while excluding the types with marginal emission shares. Second, the stock-flow diagram was checked for its consistency with the envisaged CLD. The addition of vehicles and their respective emissions percentages that contribute to pollution have been incorporated. The quantitative equations have been checked using the check model function of the ­Vensim software. Third, the dimensional consistency of the model was checked using the units check function of Vensim. Finally, the model was subjected to the Extreme Conditions Test, in which the behaviour of the model is investigated under ­certain extreme conditions. Typically, extreme values are assi­gned to specific variables and then the generated system ­behaviour is compared to the understood real system behaviour. The particulate matter per passenger car per year was subjected to extreme values to observe the system behaviour and it was found that the validation results were consistent with the fact that poor emission quality results in increased pollution, thereby showing that the model developed is robust and can be used for in-depth simulation analysis. The above four tests, although not exhaustive, comprise the core of a range of validation tests prescribed for SD models (Qudrat-­Ullah and Seong 2010).

Sensitivity analysis was carried out to check the robustness of the model with respect to the uncertainty in the assumptions made. Sensitivity analysis identifies whether conclusions change in ways important to the purpose when assumptions are varied over the plausible range of uncertainty (Sterman 2000). Three types of sensitivity that any model can show are numerical, behaviour mode, and policy sensitivity. The primary assumptions in this model are the use of emission estimates from Goyal et al (2013) which are based on emissions data from 2009. The variation in the numerical values of results caused by changes in assumptions indicates that all models ­exhibit numerical sensitivity. Behaviour mode sensitivity exists when a change in assumptions changes the behaviour patterns of the model. To check for the same, the slider options were used to vary the four emission rate per vehicle per year parameters over their respective ascertained range of uncertainties. No change in the growth pattern of the output ­parameters was detected, thus proving behavioural robustness. Policy sensitivity exists when changes in assumptions reverse the original impact or the desirability of the policy. In our model, when the emissions for passenger cars and two-wheelers were increased to the maximum of their respective ranges, while keeping those of the light and heavy-duty commercial vehicles to a minimum, the model still behaved in the same manner, thereby reinforcing the robustness of the assumptions.

Simulation Results and Analysis

After checking the model for its robustness, various simulation runs pertaining to two important scenarios were carried out.

Scenario 1—effect of vehicle addition rate on PM level: For the base run, the passenger car addition rate, two-wheeler addition rate, and commercial vehicle addition rate were set at 8%, 6%, and 5% respectively. First, the passenger car addition rate was increased from 8% to 15% (Figure 6.1). Second, the two-wheeler addition rate was increased from 6% to 15% (Figure 6.2). Lastly, the commercial vehicle addition rate was increased from 5% to 15% (Figure 6.3). The simulation results show that there is no significant impact of passenger cars addition rate or the two-wheelers addition rate on PM level as compared to that of the commercial vehicles. In Figure 6.3, the three lines almost coincide. A noticeable exponential growth is observed when the commercial vehicles ­addition rate is increased. In other words, the addition of passenger cars or two-wheelers to Delhi’s roads is not of much concern compared to the addition of the commercial vehicles. So, commercial vehicles, although relatively very less in numbers, have a much higher potential to affect air pollution.

Scenario 2—effect of emission level improvements in commercial vehicles: Improvement in emission quality of vehicles can be achieved by reducing the tonnes of PM generated by them per time unit. Consequently, when the heavy commercial vehicle emission was improved by 10%, the corresponding change in PM levels was as shown in Figure 7.1 (p 38). In the same vein, if the emission for light commercial vehicles improves by 50%, the corresponding change is shown in Figure 7.2.

It is important to note here that to obtain a noticeable reduction in PM growth, the particulate matter per light commercial vehicle per year had to be reduced by 50%, but greater overall reduction was obtained by only 10% reduction in particulate matter per heavy commercial vehicle per year. This shows that even with a fractional share (around 5%) of the total commercial vehicles, the heavy commercial vehicles contribute significantly to air pollution. Additionally, when the emission rates were improved for the two-wheelers and the passenger cars, there was hardly any shift in the PM curve.

Therefore, an improvement in the emission quality of commercial vehicles, and more specifically, of heavy commercial vehicles through innovations in emission technology along with desired policy changes to encourage the same is the need of the hour.

 

Recommendations and Conclusions

Based on the above simulation results, the validity and effectiveness of much talked about policies such as the odd-even rule can be questioned. The odd-even rule, which was implemented twice for 15 days each during January and April 2016, was put in contention in November 2017 when the AQI level hovered above the “severe” level for most of the day. The odd-even rule brings down traffic congestion considerably. However, as it applies mainly to private passenger cars and two-wheelers, its effectiveness in curbing pollution levels has been a matter of debate (Mohan et al 2017). The simulation here, in its limited scope, suggests that the odd-even rule will not be of much consequence in reducing air pollution in NCT as long as the menace of diesel-powered heavy commercial trucks is not curbed. Further, stubble burning in neighbouring states needs to be tackled more effectively.

In the light of the simulation results, commercial vehicles, particularly the heavy ones, demand more urgent attention as far as the PM pollution in NCT is concerned. It is important here to highlight some key policy actions taken by the government ­recently in this regard. The most notable among them is the 135-km-long six-lane Eastern Peripheral Expressway (EPE) or Kundli–Ghaziabad–Palwal Expressway, touted as India’s first smart and green highway, which was completed in a record span of 17 months and inaugurated in May 2018. The 136-­km-long Western Peripheral Expressway (WPE) or Kundli–Manesar–Palwal Expressway was inaugurated in November 2018. Together, they are expected to divert a major chunk of vehicle traffic which is not destined for Delhi, thereby decongesting the national capital and improving its AQI. Although reports from the National Highways Authority of India suggest that the EPE has not witnessed the amount of usage expected, efforts are on to improve its ­visibility and viability for drivers through measures like the introduction of more interchanges and signage (Shrangi 2018).

In an attempt to encourage truckers, especially those who are not destined for Delhi, to take alternative highways, the Supreme Court had imposed an Environment Compensation Charge in 2015 on each truck that enters Delhi, and also ­restricted pre-2006 trucks from entering Delhi. Further, the Delhi Traffic Police has charted a Graded Response Action Plan to implement stringent actions based on the AQI of the city. A new category called “Severe” or “Emergency” has been introduced for times when special measures are recommended. These measures could include stopping the entry of trucks into Delhi (except those with essential commodities), stopping construction activities, and the appointment of a task force to take decisions on any additional steps, including shutting of schools.

The menace of stubble and cracker burning has also kept the authorities on their toes. In March 2018, the central government announced a `1,152 crore crop residue management scheme over two years for the farmers of Punjab, Haryana, Delhi and Uttar Pradesh (Times of India 2018). Efforts are on to incentivise the distribution of in situ crop management machines called happy seeders, as well as to sensitise farmers about the ill-effects of crop burning. Crop burning makes the soil less fertile and pushes the farmer to employ more fertilisers, water, and power for the same land area (Economic Times 2018). Cultivation of mushroom, producing biofuels, and making bio-concretes which can be used for buildings are some of the alternative solutions to burning stubble (Swarajya 2018). A recent spike in the AQI of Delhi, which has been attributed to stubble burning in the neighbouring states, has sparked a call for severe graded penalties and even the recommendation of non-procurement from areas where farmers burn crops, in ­addition to the recommendation of incentivising viable alternatives (Economic Times 2018).

Nevertheless, any number of preventive measures will fall flat if not implemented effectively. Reports suggesting non-compliance of Pollution under Control norms, ineffective ­Environmental Compensation Charge collection posts, and the plying of banned vehicles on city roads emerge regularly. Hence, in order to effectively implement corrective measures, strict monitoring at the ground level is of utmost importance. The ban on the entry of commercial vehicles during the daytime needs to be rigorously implemented and monitored. The National Green Tribunal ruling banning vehicles older than 15 years needs to be enforced with immediate effect. Towards this end, the government needs to incentivise the scrapping of old vehicles, particularly commercial ones. Public–private partnership can play an important role here. Automobile manufacturing companies need to work on and promote emission technology improvements such as the diesel particulate filter for heavy commercial vehicles. Roll-on/roll-off using rail wagons to transmit heavy commercial vehicles through NCT can also be a good option. Lastly, the rapid rate of urbanisation has led to deforestation and the depletion of green cover. Preservation and ­increasing of green cover through increased awareness and active efforts from the administration could be vital for protecting the city’s lungs.

Evolving effective decisions and policies in a complex and dynamically evolving environment requires systems thinking to expand our mental boundaries and encompass the intricately-linked components that are spread across time and space. This study provides us a glimpse of the benefits that can be derived through a systems thinking approach using SD models for simulation. In the context of Delhi’s air pollution, it is important to look at the situation in its entirety to get to the root of the problem and avoid any policy resistance. This paper attempts a holistic approach to identify the root causes of the problem for making effective policy decisions. It is expected to pave a new direction towards effective decision-making in ­India, riding on the robustness of systems thinking and SD simulation, which have been used for policy formulation in countries like China, Singapore, South Africa, South Korea, the US, and Taiwan.

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Updated On : 6th Sep, 2019

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