Modelling the Impact of Road Dust on Air Pollution: A Sustainable System Dynamics Approach

. Road dust contributes significantly to air pollution by releasing fine particulate matter (PM) and other pollutants into the air, which can cause respiratory and cardiovascular problems and premature death. This dust is generated through the wear and tear of vehicle tires and road surfaces, as well as the accumulation of dirt and debris on the road, primarily from construction activities and cargo trucks carrying building materials. Wind, weather conditions, and vehicle movement play crucial roles in the distribution and concentration of these particles in the air. To address this issue, this paper focuses on identifying various variables that are connected to road dust operations and their interrelationships with air pollution variables, representing the dynamic pattern of the entire system. The paper proposes the establishment of a sustainable causal-loop model using system dynamics (SD) modeling in Vensim, connecting feedback mechanisms to effectively control the road dust concentration. Additionally, the paper suggests different policy interventions applied to the whole system to achieve optimized results. In the future, this research aims to convert and simulate the causal-loop model to a stock-flow model and compare the effectiveness of different policy interventions to further reduce road dust contributing to air pollution.


INTRODUCTION
* Corresponding author: sarah14khan@gmail.com;ghanghaskaushal@gmail.comRoad dust is a major cause of air pollution, releasing various types of particulate matter and pollutants into the air, which can result in respiratory and cardiovascular issues.The World Health Organization (WHO) estimates that outdoor air pollution is responsible for around 4.2 million premature deaths worldwide each year, and road dust is a significant contributor to this problem [1].The composition of road dust varies depending on local factors, including climate, geography, and traffic patterns.However, common components of road dust include soil, mineral dust, brake and tire wear, and other debris.These particles can range in size from less than 2.5 micrometres (PM2.5) to less than 10 micrometres (PM10), which are small enough to penetrate deeply into the lungs and cause health problems.
In India, the percentage contribution of road dust to total PM pollution varies by location and season.Nevertheless, research has indicated that road dust is a significant source of PM pollution in numerous Indian cities.According to a report by the Central Pollution Control Board (CPCB), road dust accounts for the largest percentage of PM10 emissions in Indian cities, at 45% of total emissions.The report also indicates that road dust contributes to 10.9% of PM2.5 emissions in Indian cities [2][3][4][5][6][7].These statistics demonstrate the considerable impact that road dust can have on air quality in India, particularly in urban areas with concentrated traffic and construction activities.The contribution of road dust to air pollution in an urban region is presented in figure 1 as a pie chart.
In order to comprehend the intricate behavior of air pollution caused by road dust, it is essential to examine the parameters of road dust operations and their associations with other parameters.These parameters are linked to one another through a feedback loop, and analyzing the response of these loops to modifications in one or more parameters is crucial to reduce road dust operations and control air pollution.
This paper aims to identify the inter-relationships between various parameters associated with road dust emissions and develop a Causal Loop Diagram (CLD) using the SD approach to comprehend the behavior of these relationships.The purpose of this paper is to establish a qualitative model that links road dust operations to air pollution [8][9][10][11][12].

MATERIALS AND METHODOLOGY
The purpose of this study is to determine the relationship between road dust and air pollution.Rather than quantifying all parameters, the focus is on identifying specific parameters that need to be optimized in order to reduce pollution.The study follows the steps outlined below: • Identification of parameters involved in road dust operations that are directly or indirectly linked to air pollution in general.• Establishing a causal relationship between these parameters using feedback loops and creating a CLD diagram.• Examining the nature of these loops to determine if they are positive or negative.
Positive loops encourage the system to grow exponentially, while negative loops balance the system.• Targeting the parameters in the loop system that are responsible for driving the pollution rate.
part of the experimental program, concrete cubes and beams were casted with and without coconut shells.The ingredients for concrete include Portland cement, sand, coarse aggregates, coconut shells, and water.Compressive strength was evaluated using the cubes, while flexural strength was evaluated using the beams.

CAUSAL LOOP MODEL
The process of System Dynamics Modelling involves two steps.The first step is a qualitative modelling approach that establishes the inter-relationship between different variables through a Causal Loop Diagram (CLD).The second step is a quantitative modelling approach that employs mathematical expressions to establish links between different variables [3,4].The CLD is developed by linking key variables in a system and expressing the causal relationship between them.It represents the qualitative aspects of the model, where variables are connected in a feedback loop, and their interdependencies can either have a positive or negative effect on each other.A positive effect means that an increase or decrease in one variable leads to a corresponding increase or decrease in another variable, respectively, while a negative effect opposes the growth and tries to maintain dynamic equilibrium in the system.These interdependencies can either create a self-reinforcing (positive) loop or a balancing (negative) loop.In this paper, a CLD of air pollution due to road dust operations is developed, and it is linked with population model [5,[13][14].The parameters identified for the model and their function types are presented in Table 1.

POLICY IMPLICATIONS
The study selected two sub-systems connected to road dust via exogenous and endogenous variables, as shown in Fig 2 .These sub-systems are linked together in mostly negative feedback loops, indicating a balancing system.Fig 3 displays various policies that have been directly linked to the variables and can impact one or more of them.Implementing these policies can help reduce road dust pollution and mitigate air pollution.Examples of existing policies include using a water sprinkling system to control road dust diffusion and transportation, using covered vehicles for transport, and prohibiting grinding and cutting of building material in the open.Exhaust ventilation systems, wet dust suppression, and enclosure construction can be used to control dust released from construction activities.Although this study did not consider some techniques due to the complexity of modelling them, such techniques include covering unpaved surfaces with gravel, increasing the moisture content of the road surface, binding particles together, and avoiding road-side storage of construction material [5].However, these type of techniques are more valuable for concrete or green technology as well [6][7][8].turbulence generated by vehicles or wind [10].These emissions from non-exhaust sources are a significant contributor to the concentration of atmospheric PM pollutants in urban and industrial settings, leading to violations of air quality standards.Road dust emissions are influenced by a range of factors, including traffic volume and composition, driving speed, road surface materials, as well as meteorological factors such as temperature, humidity, rainfall, and wind conditions.The measures required to reduce road dust emissions can vary from one region to another due to differences in traffic and climatic conditions.Preventative strategies, such as paving access to unpaved areas or restricting road traffic, aim to prevent the accumulation of particles on the road surface, while mitigating measures focus on the removal or binding of the already deposited particles [4].
The recognition of the dynamic behavior of variables and their interrelationships with each other in a system is essential.This study focuses on identifying the parameters associated with road dust and linking them to other subsystems in a real-world scenario.The population and pollution subsystems that are part of road dust operations are combined to understand their dependencies using a CLD model.This model illustrates how feedback loops work, either leading to system expansion resulting in catastrophe or reinforcing the system, leading to maintaining equilibrium.Additionally, the model highlights the need for policies to target specific variables instead of applying them to the entire system.The future scope of this research is to convert the qualitative model into a quantitative one by linking variables through mathematical expressions.To achieve this, secondary data is collected through literature and expert opinions.This will assist policy makers and other stakeholders in effective planning and management [9][10][11][12].

Fig. 3 .
Fig. 3. Policy intervention applied to CLD of Air Pollution System

Table 1 :
Parameters selected for CLD Model The key variables selected are Population and Pollution Load connected to Road Dust.There are 11 loops of population as the key variable in the model.Loop 1 has 2 parameters: only population and birth (Reinforcing Loop).Loop 2 also has only 2 parameters: population and death (Reinforcing Loop).Loop 3 has 5 parameters: Population, Vegetation Cover, Road Dust, Pollution Load, Health Impact and Death (Reinforcing Loop).Loop 4 has 5 parameters: Population, Urbanization, Road Dust, Pollution Load, Health Impact and Death (Balancing Loop).Loop 5 has 5 parameters: Population, Land Use Pattern, Road Dust, Pollution Load, Health Impact and Death (Balancing Loop).Loop 6 has 6 parameters: Population, Vegetation Cover, Soil erosion, Road Dust, Pollution Load, Health Impact and Death (Reinforcing Loop).Loop 7 has 6 parameters: Population, Urbanization, Road Transport, Road Dust, Pollution Load, Health Impact and Death (Balancing Loop).Loop 8 has 6 parameters: Population, Land Use Pattern, Vegetation Cover, Road Dust, Pollution Load, Health Impact and Death (Reinforcing Loop).Loop 9 has 6 parameters: Population, Urbanization, Construction Activities, Road Dust, Pollution Load, Health Impact and Death (Balancing Loop).Loop 10 has 6 parameters: Population, Urbanization, Industries/Factories, Road Dust, Pollution Load, Health Impact and Death (Balancing Loop).Loop 11 has 7 parameters: