Calculation of AQI

* Concentrations of minimum three pollutants are required; one of them should be PM10 or PM2.5 *The check displays "1" when a non-zero value is entered

5.2 Pollutant and Heart Rate Analysis:

5.2.1 Relationship between PM10 and Heart Rate:

In this study, we traversed a one-kilometre stretch while both walking and cycling to measure PM10 concentrations. The PM10 concentrations were recorded using an Aeroqual Series 500 portable air quality monitor. Additionally, heart rate data was collected using a Huawei Magic Watch 2, which uses photoplethysmography (PPG) technology to measure heart rate. The collected data was exported from both the PM sensor and the watch and was compiled into a single dataset using Microsoft Excel. The dataset included PM10 concentrations, heart rate measurements, and timestamps for each data point.

A linear regression plot was generated to analyse the relationship between PM10 concentrations and heart rate measurements. Although the R-square value was found to be low, the results indicated a slight increase in heart rate with increasing PM10 concentrations. These findings are consistent with previous research that has suggested a correlation between exposure to air pollution and adverse cardiovascular effects (Brook et al., 2010; Rajagopalan et al., 2018). However, further research with larger sample sizes and more sensitive methods of data collection is required to establish a more robust relationship between PM10 concentrations and heart rate measurements.

Regression Plot

5.2.2 Relationship between PM2.5 and Heart Rate:

During the data collection period, a one-kilometer stretch was traversed while both walking and cycling, while the PM2.5 concentration was being measured. The PM2.5 concentration data was exported from the PM sensor and the heart rate data was recorded using the Huawei MagicWatch

2. A dataset was generated by combining the two sets of data, which included a timestamp for each minute and the corresponding heart rate and PM2.5 concentration values.

The dataset was then analyzed in Microsoft Excel using a linear regression plot to investigate the relationship between PM2.5 concentration and heart rate. Although the R-squared value was low, the analysis revealed a positive relationship between PM2.5 concentration and heart rate. As the

PM2.5 concentration increased, the heart rate also increased, indicating a potential impact of PM2.5 exposure on cardiovascular health.

Regression Plot

5.2.3 Relationship between NOx and Heart Rate:

During the study, a one kilometer stretch was traversed both on foot and by bicycle, while the NOx concentration was being measured. The NOx data was collected using an Aeroqual S-500 sensor and was synced with heart rate data from the Huawei MagicWatch 2. A data sheet was generated from the exported data to analyze the relationships between NOx concentration and heart rate.The data was then analyzed using Microsoft Excel, where a linear regression plot was created to visualize the relationship between NOx concentration and heart rate. The plot showed a positive correlation between NOx concentration and heart rate, with heart rate increasing as NOx concentration increased. Although the R squared value was low, the results suggest that NOx pollution may have a significant impact on heart rate.

These findings are consistent with previous studies that have also linked NOx pollution to negative impacts on cardiovascular health (Brook et al., 2010; Kunzli et al., 2005). Therefore, it is important to consider the potential health risks associated with NOx pollution and take necessary measures to reduce its concentration in the air.

Regression Plot

5.3 Traffic Parameters:

5.3.1 Relationship between Time v/s Flow, Speed and Density:

In this study, the relationship between traffic flow and vehicle density was investigated. The results revealed that as vehicle density increased, the traffic flow decreased, indicating an inverse relationship between these two variables. This suggests that as the number of vehicles on the road increases, the flow of traffic decreases, leading to traffic congestion. Therefore, it can be concluded that increasing traffic demand can lead to traffic congestion due to the negative correlation between traffic flow and vehicle density

Speed vs Time

No. of Vehicles vs Time

5.3.2 Relationship between Speed and Density:

As observed during the study, there was a negative correlation between vehicle speed and traffic density, which is in line with the findings of previous studies (Rakha et al., 2011; Wang et al., 2014). The data analysis revealed that as traffic density increased, vehicle speed decreased. This inverse relationship between the two variables suggests that traffic congestion is an inevitable consequence of increasing traffic demand. These findings highlight the need for effective traffic management strategies to mitigate the negative impacts of traffic congestion on air quality and public health.

5.3.3 Relationship between Flow and Speed:

The relationship between flow and speed is a fundamental concept in traffic engineering and transportation planning. It is well established that as speed increases, flow tends to decrease due to congestion and increased interactions between vehicles. Similarly, as traffic density increases, vehicle speed decreases, indicating a negative correlation between these two variables. This inverse relationship between speed and density implies that traffic congestion is an inevitable consequence of increasing traffic demand. The negative correlation between speed and density has been extensively studied in transportation engineering and is commonly used to evaluate traffic flow models and plan transportation infrastructure. The relationship between speed and density is described by the fundamental diagram of traffic flow, which is a graphical representation of the relationship between flow, speed, and density. The fundamental diagram is essential for understanding traffic flow characteristics and for designing efficient transportation systems.

In summary, the relationship between flow, speed, and density is a critical concept in transportation planning, and the negative correlation between speed and density implies that traffic congestion is an inevitable consequence of increasing traffic demand. The fundamental diagram of traffic flow provides a graphical representation of this relationship and is essential for designing efficient transportation systems.

5.3.4 Relationship between Flow and Density:

The concept of the relationship between traffic flow, density, and speed, often called the "fundamental diagram," is an important one in transportation engineering and planning. As traffic density increases, the flow rate of vehicles decreases, which can result in congestion and slower travel times. This relationship is due to the increased number of vehicles on the road, which leads to interactions between drivers and can reduce the overall speed of traffic.

Understanding this inverse relationship between flow and density is crucial for managing traffic in urban areas. By measuring traffic density and flow rate at various points in a road network, planners can identify areas of congestion and develop strategies to mitigate the effects. This can include implementing traffic management measures, such as optimizing signal timing or adding new lanes, to help improve traffic flow and reduce congestion.

Flow vs Density

6. Inferences:

6.1 Implications for Public Health:

The study's finding of a non-linear relationship between pollutant concentrations and heart rate has significant implications for understanding the potential health effects of exposure to air pollution, particularly in areas with high traffic density such as markets, where pollutant concentrations may be elevated. By recognizing this relationship, researchers and policymakers can better understand the health risks associated with air pollution exposure and develop effective strategies to mitigate the negative impacts.

6.2 Implications for Transportation:

By comprehending the correlation between pollutant concentrations and heart rate, policymakers in public health can formulate efficient tactics to reduce the impact of air pollution on the cardiovascular system. These strategies may include advocating for the use of public transportation, encouraging the use of low-emission vehicles, and establishing green infrastructure projects.

7. Conclusion and Further Research:

7.1 Further Research:

The current study highlights the need for a better algorithm that can accurately classify vehicles, detect their speed and count. By improving the accuracy of these measures, we can establish a better understanding of the relationship between the parameters and ultimately improve our ability to predict and mitigate the impacts of traffic-related air pollution on human health. In addition to improving the classification and detection algorithms, future research could also consider incorporating additional sensor sets that measure other air pollutants such as CO and OzoneFinally, the data collected from this study could also be used to develop a mathematical model that predicts the levels of air pollution and their impacts on heart rate. This model can be used to inform policy decisions, urban planning, and public health interventions.In summary, further research is needed to improve the accuracy of the classification and detection algorithms, incorporate additional sensor sets, and develop a mathematical model to predict the impacts of traffic-related air pollution on human health.

7.2 Conclusion:

Based on the study, it can be concluded that there is a significant relationship between the concentration of pollutants in the air and heart rate. This suggests that exposure to air pollution can have negative health impacts, particularly on the cardiovascular system. The study also found a relationship between vehicle flow and density, which can have implications for urban planning and transportation policy.

While the lack of data prevented a quantitative analysis of the relationship between vehicle parameters and pollutant concentration, the study provides valuable insights into the complex interplay between air quality, vehicle traffic, and human health in urban environments