Assessment of PM2.5 and PM10 Exposure and Health Risks: A Study of Pedestrian and Two-Wheeler Transport During Peak-Traffic in Imphal, Manipur

doi:10.21203/rs.3.rs-5217315/v1

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Assessment of PM_2.5 and PM₁₀ Exposure and Health Risks: A Study of Pedestrian and Two-Wheeler Transport During Peak-Traffic in Imphal, Manipur

https://doi.org/10.21203/rs.3.rs-5217315/v1

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This study looks at the levels of PM_2.5and PM₁₀people are exposed to during busy traffic times when walking, riding two-wheelers, and at a fixed-site. Hourly average data was used to compare the amounts of particulate matter with the WHO air quality guidelines, which recommend limits of 15 µg/m³ for PM_2.5and 45 µg/m³ for PM₁₀, respectively. The results showed that particulate matter levels changed a lot between morning and evening peak hours, with higher levels on weekdays compared to weekends. Two-wheeler users had the highest exposure, with average levels of 79.72±41.87 µg/m³ for PM_2.5and 131.48±69.32 µg/m³ for PM₁₀ in the morning, and 109.15±38.63 µg/m³ for PM_2.5and 181.25±64.22 µg/m³ for PM₁₀ in the evening, mostly due to traffic emissions and the design of the vehicles. In comparison, walking and fixed-site had more steady levels of particulate matter. All transport modes went over the WHO guidelines, with two-wheeler users facing the highest exposure with exceedance factor of 6.33 and 3.50 for PM_2.5and PM₁₀, respectively. Whereas, exceedance factors of walking were 4.10 and 2.27 and for fixed-site were 4.10 and 2.32 for PM_2.5and PM₁₀, respectively. The health risks from long-term exposure to these high levels are discussed, stressing the need for actions and strategies to improve air quality in cities.

particulate matter

Air quality monitoring

traffic corridor

Imphal city

transportation modes

commuter

personal exposure.

Despite the significant advancements in industries and technology, the world continues to grapple with reducing the environmental impact of these activities. While developed nations have made strides in enhancing air quality through the adoption of various technologies and measures, developing nations still face significant challenges, leaving their populations more susceptible to the harmful effects of air pollution [1–5]. In today’s urban areas, air pollution has become a pressing environmental concern. The high density of industrial operations and traffic in these regions has a considerable impact on both ecological sustainability and residents' quality of life [6].

Air pollution is a major contributor to the risk of death from conditions such as stroke, lung cancer, lower respiratory infections, diabetes, and chronic obstructive pulmonary disease (COPD), accounting for 11.65% of global mortality [7]. In 2020, the World Health Organization (WHO) reported that approximately 3.2 million deaths annually were due to household air pollution [8]. Fine particulate matter, a key component of air pollutants, is particularly worrisome. This complex mixture includes organic particles like dust, pollen, soot, and smoke, as well as liquid droplets. PM_2.5, a type of fine particulate matter, is especially hazardous due to its small size, which allows it to penetrate deep into the lungs and enter the bloodstream, potentially causing serious health issues such as heart disease, brain damage, and respiratory problems [9–12].

Personal exposure to particulate matter can vary depending on time and location. Micro-environments indoors and in transportation sectors are in direct contact with pollution sources, while outdoor environments are indirectly affected. Air quality within vehicles is largely influenced by traffic-related emissions, more so than roadside air quality [13–14]. The decline in urban air quality is closely linked to the volume of vehicle traffic, with non-exhaust emissions such as tire wear and fuel combustion being major sources of particulate matter in traffic-heavy areas [15]. Pedestrians, particularly those at crosswalks, may face slightly higher exposure to submicron particles compared to those on the roadside, as walking or waiting at urban traffic intersections increases the risk of exposure to PM pollution from nearby traffic [16].

Some research indicates that personal exposure to particulate pollutants like PM_2.5 and PM₁₀ is similar for both pedestrians and drivers in traffic corridors. This suggests that walking, especially for short trips, may lead to higher pollutant exposure due to the longer time spent reaching a destination unless traffic levels are sufficiently reduced to lower ambient air pollution [13]. Conversely, studies on inhalation exposure suggest that individuals using open modes of transport—such as walking, motorcycling, and bicycling—are exposed to higher levels of particulate matter than those in enclosed vehicles [17–18].

This study seeks to explore the temporal and spatial variations in particulate matter concentrations and their impact on human health within Imphal's main traffic corridor, therefore, the study aims to analyse the exposure levels of individuals traveling via walking, two-wheelers, and those situated at fixed sites during peak-traffic hours, comparing them with the WHO standards. The findings could also offer a rough estimate of air quality in other parts of the city, based on similarities with the studied area. Exposure levels to PM_2.5 can vary depending on whether the area is urban, suburban, or rural, as well as by season and time of day [19–21]. This analysis may lead to the development of new strategies for managing pollution sources and reducing exposure. Additionally, it can help raise awareness among the state's population about the health risks associated with PM_2.5, enabling those with pollution-related health conditions to take precautionary measures. Stratified analyses have shown that respiratory disease mortality rates are higher among individuals who do not take protective measures on hazy days, and cardiovascular disease mortality rates are higher in older adults [13, 22]. The study’s results could also support the state’s environmental management authorities in implementing measures to safeguard public health. Furthermore, the data could be useful to healthcare providers in understanding patients' exposure histories, thereby improving the effectiveness of treatments.

2.1 Study Area

The study is carried out in a traffic-corridor located in Imphal-west, Manipur, which is a north-eastern part of India, located in the Indian Himalayan region as shown in Fig. 1. It lies between latitude 23° 50' 43.98"- 25° 42' 3.024" N and Longitude 92° 58' 52.7052" − 94° 44' 27.1464" E. The climate in Manipur varies across the state. While the western part features a tropical climate, the remainder experiences a subtropical climate marked by distinct summer, winter, and rainy seasons [23]. The average temperature in Imphal which is the capital of Manipur range from 5.8° to 25.3° Celsius during winter and 12.3° to 29.6° Celsius during summer. Imphal's climate is subtropical, influenced by its altitude. It boasts mild, dry winters spanning from November to February, followed by a lengthy, hot, and rainy season extending from April to October with an annual precipitation of 1435 mm, Imphal experiences its driest month in January, receiving only 12mm of precipitation, while July stands as the wettest month, with a rainfall of 230mm. Imphal serves as the economic capital city of Manipur, with the study area situated around the coordinates of latitude 24° 48' 37.818" E and longitude 93° 56' 11.418" N. Notably, it encompasses Ima Market, the singular market globally managed exclusively by women. This vibrant market district is renowned not only for its unique female-led operations but also for its bustling atmosphere, making it one of the most congested areas of Imphal due to the main market and the constant influx of people shopping and engaging in daily commerce.

2.2 Instrument Used

For this study, we utilized the Temtop M2000 2nd generation portable multifunctional digital meter, depicted in (Fig. 2). This lightweight device employs a laser sensor for PM_2.5 and PM₁₀ measurements. It boasts a battery life of 6–8 hours after a single charge. The device offers the flexibility to record data at various intervals. The device can operate within a temperature range of 0–50°C (32–122°F) and a humidity range of 0–90% RH. It has an accuracy of ± 10 µg/m³ and ± 15 µg/m³ for PM10. The Monitoring device was kept at a breathing height of around 1.3 m from the ground level as shown in Fig. 2.

2.3 Duration, Season and Timing of measurement

A 7-day measurement campaign was conducted for the fixed site during the last week of November 2023 starting from Monday to Sunday. The monitoring device was positioned within the study area 50 meters away from the main road at a breathing level of 1.2 meters from the ground. Data collection occurred for a duration of 1-hr during peak traffic hours: 9:00–12:00 AM in the morning and 2:00–4:00 PM in the evening. Environmental conditions such as temperature and humidity were also recorded concurrently with air quality measurements to provide comprehensive data.

Similarly, a 14-day measurement campaign took place in the 2nd and 3rd weeks of December 2023 for the walking and two-wheeler modes. The study route spanned approximately 2.5 kilometres, commencing from the West Kangla main gate Junction. Data collection sessions lasted for 1 hour during peak hours: 9:00–12:00 AM in the morning and 2:00–4:00 PM in the evening for each mode. In the walking mode, it typically takes around 45 minutes to complete one loop, an additional 1/3 of the distance was added to make one-hour monitoring duration. Conversely, for the two-wheeler mode, it took approximately 14.57 ± 3.22 minutes to complete one loop, permitting a maximum of four loops per hour depending on prevailing traffic conditions and road congestion levels. Measurements for the driving mode were conducted at speeds not exceeding 35 km/h. Data collection for walking and two-wheeler modes occurred on separate days, ensuring exposure data for a week. All data for the fixed site, walking mode, and two-wheeler mode were recorded at 1-minute intervals to ensure accuracy for the entire 1-hr monitoring duration. After the monitoring work the recorded data were downloaded daily after each session and underwent thorough quality control checks to identify and correct any anomalies.

2.4 Exposure analysis

Exposure to PM_2.5 and PM₁₀ was measured during peak-traffic hours for individuals traveling by foot, two-wheeler, and at a fixed monitoring site. Minute averaged concentrations were recorded and analysed. Data was compared with WHO guidelines, and long-term exposure risks were assessed based on existing epidemiological research linking particulate matter to health outcomes. The exceedance factors of PM_2.5 and PM₁₀ for each mode of travelled and fixed-site were calculated using the equations below:

Exceedance Factor = (Measured Concentration/WHO Guideline Value)

where, the WHO guideline value is 15 µg/m³ and 45 µg/m³, for PM_2.5 and PM₁₀, respectively.

3.1 Exposure to PM_2.5 and PM₁₀ while commuting by foot

The analysis of a 14-day measurement campaign in December 2023 reveals fluctuating concentrations of PM_2.5 And PM₁₀ during morning and evening peak hours (Figs. 3 & 4). The highest peak hour concentration of PM_2.5 concentration for the week is (121.26 ± 26.43) µg/m³ while lowest concentration is (27.12 ± 12.7) µg/m³. Whereas, the highest peak hour concentration of PM₁₀ concentration for the week is (203.90 ± 39.08) µg/m³ while lowest concentration is (43.08 ± 30.85) µg/m³.These fluctuations likely reflect variations in traffic intensity, construction activities, and meteorological conditions.

The higher concentrations observed on dry and sunny days compared to cloudy and rainy days highlight the influence of weather on pollution levels. Interestingly, concentrations peaked when traversing areas with high pollutant sources, such as traffic junctions and construction sites. This emphasizes the importance of considering localized sources of pollution when assessing personal exposure.

3.2 Exposure to PM_2.5 and PM₁₀ while commuting by two-wheeler

Data from a 14-day campaign in December 2023 reveals dynamic fluctuations in PM_2.5 and PM₁₀ concentrations during morning and evening hours (Figs. 5 & 6). The highest peak hour concentration of PM_2.5 concentration for the week is (201.16 ± 44.46) µg/m³ while lowest concentration is (38.41 ± 19.99) µg/m³. Whereas, the highest peak hour concentration of PM₁₀ for the week is (338.02 ± 75.47) µg/m³ while lowest concentration is (63.72 ± 32.13) µg/m³. Higher concentrations were observed on weekdays, particularly during rush hours, indicating the influence of traffic congestion on pollution levels. The open design of two-wheelers exposes riders directly to traffic emissions, contributing to elevated personal exposure levels. Moreover, the compact design of these vehicles may trap pollutants close to the rider, further increasing exposure.

3.3 Measured PM_2.5 and PM₁₀ concentration at fixed site

A 7-day measurement campaign in November 2023 reveals consistent PM_2.5and PM₁₀ concentrations, with the highest levels consistently recorded on Saturdays (Figs. 7 & 8). The highest peak hour concentration of PM_2.5 for the week is (214.3 ± 130.5) µg/m³ while lowest concentration is (22.84 ± 8.37) µg/m³. Whereas, the highest peak hour concentration of PM₁₀ concentration for the week is (374.9 ± 228.8) µg/m³ while lowest concentration is (38.51 ± 13.91) µg/m³. This pattern suggests the influence of weekly variations in human activities and traffic intensity on pollution levels. Notably, evening concentrations were higher than morning concentrations, indicating the accumulation of emissions throughout the day. This underscores the importance of considering diurnal variations in pollution levels when assessing personal exposure.

3.4 Comparison of personal exposure while commuting by walking, two-wheeler, and remaining in a fixed site during weekdays and weekends

Table 1 illustrate the mean particulate concentration data for weekdays and weekends across various transportation modes and at the fixed site. During weekdays, the two-wheeler mode exhibits the highest personal exposure to particulate matter in the morning, with PM_2.5 and PM₁₀ concentrations surpassing those of the walking mode by 1.28 times, 1.32 times, and 0.94 times, respectively. Furthermore, compared to the fixed site, morning particulate concentrations in the two-wheeler mode are notably elevated, with PM_2.5 concentrations being 0.49 times higher and PM₁₀ concentrations being 0.48 times higher. Similarly, in the evening, two-wheeler commuters experience significantly higher particulate exposure compared to pedestrians and fixed-site measurements.

Table 1

*Statistical summary of particulate matter during weekdays and weekend*.
`		Particulate matter PM_2.5 and PM₁₀ (µg/m³) during Week days
		Walk				Two-wheeler			Fixed site
Mor Eve		Mean		Range	SD	Mean	Range	SD	Mean	Range	SD
	PM_2.5	40.352	123.60		24.26	91.98	229.80	42.06	61.54	174.9	30.66
	PM₁₀	65.714		197.80	39.49	152.21	384.30	69.55	102.72	304.8	51.75
	PM_2.5	52.52		252.90	35.85	127.19	350.20	61.73	28.99	89.30	11.65
	PM₁₀	87.43		434.90	60.60	211.49	596.80	104.81	48.82	151.2	19.99
		Particulate matter PM_2.5 and PM₁₀ (µg/m³) during Weekend
		Walk				Two-wheeler			Fixed site
Mor Eve	PM_2.5	95.30		153.30	31.24	49.09	99.00	20.07	51.27	145.00	25.20
	PM₁₀	158.25		259.10	52.23	80.785	168.90	32.98	84.83	239.20	41.77
	PM_2.5	103.54		252.10	39.45	67.185	181.00	35.57	153.4	578.8	137.7
	PM₁₀	173.16		430.00	66.97	110.93	280.10	57.89	266.25	970.9	240.9

On weekends, contrasting patterns emerge. The walking mode demonstrates the highest personal exposure in the morning, while fixed-site measurements exhibit elevated concentrations in the evening. Specifically, in the morning, PM_2.5 and PM₁₀ concentrations during walking exceed those of the two-wheeler mode, while fixed-site measurements are marginally lower. Conversely, in the evening, fixed-site measurements for PM_2.5 and PM₁₀ exceed those of both transportation modes.

Interestingly, during morning and evening peak hours, exposure during weekdays is generally higher for two-wheeler commuters, and higher exposure in morning peak hours during weekdays at fixed-site as compare to weekend. Conversely, exposure during weekend is higher for walking commuters in both morning and evening.

The observed deviation from expected trends, with higher exposure on weekends for walking and fixed-site (evening), despite the decrease in public transportation and traffic, is attributed to poor and unstable weather conditions during the campaign period. Such conditions likely influenced atmospheric dispersion and pollutant accumulation patterns, leading to unexpected exposure variations across transportation modes and fixed sites.

3.5 Exposure risk analysis

3.5.1 Hourly average exposure for different mode of travelled and at fixed-site

This section presents the results of an exposure analysis to PM_2.5 and PM₁₀ pollutants during peak-traffic hours, comparing the levels of exposure across different modes of transport: walking, two-wheeler (TW), and fixed site (FS). The potential health risks associated with long-term exposure are assessed based on comparison with WHO air quality guidelines for PM_2.5 (15 µg/m³) and PM_2.5 (45 µg/m³). The results of the exposure analysis are summarized in Table 2, showing the hourly average levels of PM_2.5 and PM₁₀ for the two modes of transport and concentrations measured in fixed-site.

Table 2

*Hourly average exposure to PM*_2.5 *and* PM₁₀ *during peak-traffic hours*
Mode	PM_2.5 (µg/m³)	PM₁₀ (µg/m³)
Walking	61.57	102.34
Two-wheeler	94.89	157.28
Fixed-site	61.57	104.28

The data reveals that individuals walking during peak hours were exposed to an average of 61.57 µg/m³ of PM_2.5 and 102.34 µg/m³ of PM₁₀. These levels, while concerning, are notably lower than the exposure experienced by two-wheeler users, who faced the highest levels of particulate matter, with PM_2.5 concentrations averaging 94.89 µg/m³ and PM₁₀ reaching 157.28 µg/m³. In contrast, the fixed monitoring site, intended as a stationary reference point, showed PM_2.5 levels of 61.57 µg/m³ and PM₁₀ levels of 104.28 µg/m³.

These findings align with other studies that have consistently shown that individuals using active modes of transport, especially motorcyclists or two-wheeler users, face significantly higher exposure to air pollutants due to their proximity to vehicular emissions and lack of physical barriers such as those found in enclosed vehicles. For example, research by [24–26] also found that two-wheeler users experienced greater exposure to PM due to their proximity to exhaust pipes and their direct exposure to ambient air without filtration. This is further exacerbated during peak traffic hours, when traffic congestion and idling vehicles lead to elevated emissions. Additionally, personal exposure for pedestrians, though lower than two-wheeler users, still exceeded safe limits due to the cumulative effect of traffic emissions and the limited ability to avoid pollutant hotspots in urban environments [27–29].

The fixed site data, while useful as a baseline, can sometimes underestimate personal exposure, as it does not account for the dynamic environments encountered by mobile individuals. Studies by [30–32] suggest that while fixed sites provide a good overall measure of background pollution, they fail to capture the spikes in pollution encountered by individuals moving through high-traffic areas. Therefore, while the fixed site readings are comparable to pedestrian exposure, they likely underrepresent the peak exposure experienced by two-wheeler users in real-world conditions.

This comparison highlights the need for context-specific air quality interventions, as both mode of transport and the nature of the exposure (e.g., mobile vs. stationary) play critical roles in determining health risks associated with air pollution.

3.5.2 Comparison with WHO Air Quality Guidelines

The hourly exceedance of WHO PM_2.5 and PM₁₀ guidelines for assessing air pollution exposure and its health implications is calculated for each travelled mode and concentrations measured at the fixed-site. Table 3 shows the exceedance factor of these values by different travelled modes and fixed-site.

Table 3

Comparison of Exposure Levels with WHO Guidelines
Mode	Exceedance factor (PM_2.5)	Exceedance factor (PM₁₀)
Walking	4.10	2.27
Two-wheeler	6.33	3.50
Fixed-site	4.10	2.32

The results show that the exposure levels for individuals walking during peak hours were significantly higher than the World Health Organization (WHO) air quality guidelines. Specifically, PM_2.5 concentrations were 4.67 times higher than the recommended limit of 15 µg/m³, while PM₁₀ levels were 2.27 times above the recommended 45 µg/m³. This highlights that pedestrians in urban environments are exposed to harmful levels of particulate matter, which can lead to serious health implications over time.

Two-wheeler users experienced the highest exceedance, with PM_2.5 levels being 6.33 times greater than the WHO guidelines and PM₁₀ concentrations 3.50 times above the recommended limits. This dramatic exceedance is likely due to the direct exposure two-wheeler users have to vehicular emissions, as they travel in close proximity to exhaust fumes without any protective barriers. The elevated levels are consistent with studies that show how motorcyclists and scooter riders in dense traffic environments are disproportionately affected by air pollution [24–26, 33].

At the fixed-site monitoring station, PM_2.5 levels were found to be 4.10 times higher than the WHO guideline, and PM₁₀ concentrations exceeded the limit by 1.78 times. While these levels are elevated, they are lower than the exposure experienced by two-wheeler users and pedestrians. Fixed-site data generally provides a reliable background measurement of air pollution, but as supported by previous research, it often underestimates personal exposure, particularly for those in mobile environments [30–32, 34 ]. The discrepancy between fixed-site and mobile exposure data underscores the limitations of relying solely on stationary air quality monitors to assess the risks individuals face in dynamic, real-world conditions.

This analysis emphasizes the severity of air pollution exposure across different modes of transport and the urgency of addressing such public health risks. The fact that all modes of transport far exceed WHO guidelines reflects the widespread nature of air quality challenges in urban environments, particularly during peak traffic hours. Reducing these exposure levels will require targeted interventions aimed at lowering traffic emissions and improving urban air quality, particularly for the most vulnerable commuters.

This study clearly demonstrates that urban commuters, especially two-wheeler users, are exposed to extremely high concentrations of particulate matter (PM_2.5 and PM₁₀) during peak traffic hours. The recorded exposure levels exceed the WHO air quality guidelines by a wide margin, reflecting the severe air quality challenges in densely populated urban areas. Two-wheeler users, who are often exposed to exhaust emissions from nearby vehicles without any physical protection, face the greatest health risks. With exposure levels reaching 6.33 times the WHO limit for PM_2.5 and 3.50 times for PM₁₀, these commuters are particularly vulnerable to respiratory and cardiovascular health problems. Numerous studies, such as those by [24–26], have found similar trends, where the proximity of two-wheeler users to vehicle emissions makes them disproportionately susceptible to harmful pollutants. The high levels of particulate matter encountered by this group can lead to long-term health consequences, including chronic respiratory conditions and increased mortality risks.

Pedestrians are also not immune to these risks. The findings show that their exposure levels to PM_2.5 and PM₁₀ are 4.67 and 2.27 times higher than the WHO guidelines, respectively. This suggests that even individuals walking along city streets are subjected to elevated levels of pollution, often due to their proximity to high-traffic areas. Pedestrians, especially those who regularly walk during peak traffic hours, face long-term health hazards, with studies like those by [27–29] indicating that such chronic exposure can lead to an increased risk of cardiovascular diseases, lung cancer, and asthma. Despite having less exposure than two-wheeler users, the health risks for pedestrians remain considerable, particularly when walking near roads with heavy traffic.

The fixed-site monitoring station, while intended as a reference, also recorded pollution levels significantly higher than the WHO guidelines. This indicates the widespread nature of poor air quality in urban settings. Although fixed sites provide useful background data, they tend to underestimate the real-time exposure commuters face, as seen in the mobile measurements taken for this study. The consistently elevated levels across the modes of transport show that pollution is not confined to certain hotspots but is rather a pervasive issue across urban environments. This aligns with findings from [30–32 ] which showed that fixed monitoring stations often fail to capture the full extent of mobile exposure to pollution, particularly in areas with high traffic density.

These results highlight the urgent need for comprehensive mitigation strategies to tackle the high levels of particulate matter in cities, particularly for vulnerable groups such as two-wheeler users and pedestrians. One key solution is to reduce traffic emissions, as vehicles are the primary source of urban air pollution. Policies that promote cleaner, more efficient engines, electrification of transport, and stricter emission standards could drastically reduce the concentration of pollutants. Additionally, improving vehicle design, particularly for two-wheelers, to include protective features against inhaling polluted air, may offer some degree of protection. For pedestrians, creating dedicated walking lanes farther from traffic or promoting green buffers along walkways could help reduce their exposure to harmful pollutants.

Promoting the use of public transport can also be a powerful tool in reducing traffic emissions, as fewer vehicles on the road lead to lower overall pollution. Encouraging citizens to use public transportation or alternative forms of commuting like cycling can reduce the number of individual vehicles on the road, thereby lowering emissions. However, these strategies alone may not be sufficient. Fixed-site monitoring, while useful, should be supplemented with mobile exposure assessments to better understand the real-time exposure that commuters face while in transit. Mobile measurements provide a more accurate representation of the fluctuations in pollutant concentrations during specific times, such as peak traffic hours, and can guide targeted interventions more effectively.

In conclusion, long-term exposure to elevated levels of PM_2.5 and PM₁₀ poses significant public health risks, particularly in urban environments where traffic emissions dominate. These risks necessitate immediate action, both in terms of policy and urban planning, to protect vulnerable populations from the harmful effects of air pollution. Reducing overall exposure through emission control measures, infrastructure improvements, and enhanced monitoring systems should be prioritized to ensure better air quality and healthier urban populations.

Conflict of Interest:

The authors declare no competing interests.

Funding:

This research received no external funding.

Author Contribution

Chereefree KT was responsible for data collection, fieldwork and data analysis. Nongthombam Premananda Singh took the lead in writing and editing the manuscript. Both authors contributed to the final version of the article and approved the submitted manuscript.

Data Availability:

Data will be available upon request.

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You are reading this latest preprint version

Assessment of PM_2.5 and PM₁₀ Exposure and Health Risks: A Study of Pedestrian and Two-Wheeler Transport During Peak-Traffic in Imphal, Manipur

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methodology

2.1 Study Area

2.2 Instrument Used

2.3 Duration, Season and Timing of measurement

2.4 Exposure analysis

3. Results and Discussions

3.1 Exposure to PM_2.5 and PM₁₀ while commuting by foot

3.2 Exposure to PM_2.5 and PM₁₀ while commuting by two-wheeler

3.3 Measured PM_2.5 and PM₁₀ concentration at fixed site

3.5 Exposure risk analysis

3.5.1 Hourly average exposure for different mode of travelled and at fixed-site

3.5.2 Comparison with WHO Air Quality Guidelines

4. Conclusion

Declarations

Conflict of Interest:

Funding:

Author Contribution

Data Availability:

References

Additional Declarations

Status:

Version 1

Assessment of PM2.5 and PM10 Exposure and Health Risks: A Study of Pedestrian and Two-Wheeler Transport During Peak-Traffic in Imphal, Manipur

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methodology

2.1 Study Area

2.2 Instrument Used

2.3 Duration, Season and Timing of measurement

2.4 Exposure analysis

3. Results and Discussions

3.1 Exposure to PM2.5 and PM10 while commuting by foot

3.2 Exposure to PM2.5 and PM10 while commuting by two-wheeler

3.3 Measured PM2.5 and PM10 concentration at fixed site

3.5 Exposure risk analysis

3.5.1 Hourly average exposure for different mode of travelled and at fixed-site

3.5.2 Comparison with WHO Air Quality Guidelines

4. Conclusion

Declarations

Conflict of Interest:

Funding:

Author Contribution

Data Availability:

References

Additional Declarations

Status:

Version 1

Assessment of PM_2.5 and PM₁₀ Exposure and Health Risks: A Study of Pedestrian and Two-Wheeler Transport During Peak-Traffic in Imphal, Manipur

3.1 Exposure to PM_2.5 and PM₁₀ while commuting by foot

3.2 Exposure to PM_2.5 and PM₁₀ while commuting by two-wheeler

3.3 Measured PM_2.5 and PM₁₀ concentration at fixed site