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

Assessment of PM2.5 and PM10 Exposure and Health Risks: A Study of Pedestrian and Two-Wheeler Transport During Peak-Traffic in Imphal, Manipur | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Assessment of PM 2.5 and PM 10 Exposure and Health Risks: A Study of Pedestrian and Two-Wheeler Transport During Peak-Traffic in Imphal, Manipur K T Cheerfree, Nongthombam Premananda Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5217315/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study looks at the levels of PM 2.5 and PM 10 people 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 2.5 and 45 µg/m³ for PM 10 , 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 2.5 and 131.48±69.32 µg/m³ for PM 10 in the morning, and 109.15±38.63 µg/m³ for PM 2.5 and 181.25±64.22 µg/m³ for PM 10 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 2.5 and PM 10 , respectively. Whereas, exceedance factors of walking were 4.10 and 2.27 and for fixed-site were 4.10 and 2.32 for PM 2.5 and PM 10 , 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. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction 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 PM 10 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. Methodology 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 PM 10 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 PM 10 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 PM 10 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 PM 10 , respectively. 3. Results and Discussions 3.1 Exposure to PM 2.5 and PM 10 while commuting by foot The analysis of a 14-day measurement campaign in December 2023 reveals fluctuating concentrations of PM 2.5 And PM 10 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/m 3 while lowest concentration is (27.12 ± 12.7) µg/m 3 . Whereas, the highest peak hour concentration of PM 10 concentration for the week is (203.90 ± 39.08) µg/m 3 while lowest concentration is (43.08 ± 30.85) µg/m 3 .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 PM 10 while commuting by two-wheeler Data from a 14-day campaign in December 2023 reveals dynamic fluctuations in PM 2.5 and PM 10 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/m 3 while lowest concentration is (38.41 ± 19.99) µg/m 3 . Whereas, the highest peak hour concentration of PM 10 for the week is (338.02 ± 75.47) µg/m 3 while lowest concentration is (63.72 ± 32.13) µg/m 3 . 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 PM 10 concentration at fixed site A 7-day measurement campaign in November 2023 reveals consistent PM 2.5 and PM 10 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/m 3 while lowest concentration is (22.84 ± 8.37) µg/m 3 . Whereas, the highest peak hour concentration of PM 10 concentration for the week is (374.9 ± 228.8) µg/m 3 while lowest concentration is (38.51 ± 13.91) µg/m 3 . 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 PM 10 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 PM 10 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 PM 10 (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 PM 10 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 PM 10 87.43 434.90 60.60 211.49 596.80 104.81 48.82 151.2 19.99 Particulate matter PM 2.5 and PM 10 (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 PM 10 158.25 259.10 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 PM 10 173.16 430.00 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 PM 10 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 PM 10 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 PM 10 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 PM 10 for the two modes of transport and concentrations measured in fixed-site. Table 2 Hourly average exposure to PM 2.5 and PM 10 during peak-traffic hours Mode PM 2.5 (µg/m³) PM 10 (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 PM 10 . 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 PM 10 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 PM 10 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 PM 10 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 (PM 10 ) 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 PM 10 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 PM 10 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 PM 10 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. 4. Conclusion This study clearly demonstrates that urban commuters, especially two-wheeler users, are exposed to extremely high concentrations of particulate matter (PM 2.5 and PM 10 ) 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 PM 10 , 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 PM 10 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 PM 10 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. Declarations 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. 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Relationships between Meteorological and Particulate Matter Concentrations (PM 2.5 and PM 10 ) during the Haze Period in Urban and Rural Areas, Northern Thailand. Air, Soil and Water Research. 2022;15. https://doi:10.1177/11786221221117264 Jhon Wesly Sitanggang, Elvi Sunarsih, Hamzah Hasyim (2023). Literature Review: Analysis of Exposure of Vehicle Emission Gases (Co, No 2 , So 2 , PM 2.5 , and PM 10 ) To Public Health Risks. Journal Social research. https://doi.org/10.55324/josr.v2i7.1142 Z Yang , J Liu , J Yang , L Li , T Xiao , M Zhou , C.-Q Ou (2024). Haze weather and mortality in China from 2014 to 2020: Definitions, vulnerability, and effect modification by haze characteristics. Journal of Hazardous Materials Volume 466. https://doi.org/10.1016/j.jhazmat.2024.133561 Dikshit, K.R., Dikshit, J.K. (2014). Weather and Climate of North-East India. In: North-East India: Land, People and Economy. Advances in Asian Human-Environmental Research . 149-173 https://doi.org/10.1007/978-94-007-7055-3_6 Goel, R., Gani, S., Guttikunda, S.K., Wilson, D., Tiwari, G., (2015). On-road PM 2.5 Pollution Exposure in Multiple Transport Microenvironments in Delhi . Atmospheric Environment , Volume 123, Part A, December 2015, Pages 129-138. https://doi.org/10.1016/j.atmosenv.2015.10.037 Ramos, C.A., Wolterbeek, H.T. & Almeida, S.M (2016). Air pollutant exposure and inhaled dose during urban commuting: a comparison between cycling and motorized modes. Air Qual Atmos Health 9, 867–879 https://doi.org/10.1007/s11869-015-0389-5 Magda Cepeda, Josje Schoufour, Rosanne Freak-Poli, Chantal M Koolhaas, Klodian Dhana, Wichor M Bramer, Oscar H Franco (2016). Levels of ambient air pollution according to mode of transport: a systematic review. The Lancet Public Health . Volume 2, Issue 1, January 2017, Pages 23-34. https://doi.org/10.1016/S2468-2667(16)30021-4 Anja Ilenič , Alenka Mauko Pranjić, Nina Zupančič, Radmila Milačič, Janez Ščančar (2023). Fine particulate matter (PM2.5) exposure assessment among active daily commuters to induce behaviour change to reduce air pollution. Science of The Total Environment , Volume 912, 20 February 2024, 169117. https://doi.org/10.1016/j.scitotenv.2023.169117 Patra, S.S., Vanajakshi, L.D. (2021). Analysis of the Near-road Fine Particulate Exposure to Pedestrians at Varying Heights. Aerosol Air Qual . Res. 21, 210104. https://doi.org/10.4209/aaqr.210104 Daniela Dias and Oxana Tchepel (2018). Spatial and Temporal Dynamics in Air Pollution Exposure Assessment. Int J Environ Res Public Health . 2018 Mar; 15(3): 558 https://doi.org/10.3390/ijerph15030558 Kaur S, Nieuwenhuijsen M, Colvile R. (2005). Personal exposure of street canyon intersection users to PM 2.5 , ultrafine particle counts and carbon monoxide in central London, UK. Atmos Environ 39:3629-3641. https://doi.org/10.1016/j.atmosenv.2005.02.046 Marquet, O., Tello-Barsocchini, J., Couto-Trigo, D. et al (2023). Comparison of static and dynamic exposures to air pollution, noise, and greenness among seniors living in compact-city environments. Int J Health Geogr 22, 3 (2023). https://doi.org/10.1186/s12942-023-00325-8 Bereitschaft, Bradley (2015). Pedestrian exposure to near-roadway PM2.5 in mixed-use urban corridors: A case study of Omaha, Nebraska. Geography and Geology Faculty Publications . https://doi.org/10.1016/j.scs.2014.12.001 L. Boniardi, F. Borghi, S. Straccini, G. Fanti, D. Campagnolo, L. Campo, L. Olgiati, S. Lioi, A. Cattaneo, A. Spinazzè, D.M. Cavallo, S. Fustinoni (2021). Commuting by car, public transport, and bike: exposure assessment and estimation of the inhaled dose of multiple airborne pollutants . Atmospheric Environment , Volume 262. https://doi.org/10.1016/j.atmosenv.2021.118613 A. McCreddin, M.S. Alam, A. McNabola (2015). Modelling personal exposure to particulate air pollution: An assessment of time-integrated activity modelling, Monte Carlo simulation & artificial neural network approaches. International Journal of Hygiene and Environmental Health , Volume 218, Issue 1. https://doi.org/10.1016/j.ijheh.2014.08.004 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5217315","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":366914214,"identity":"6759ed0a-a1e8-41ea-b56e-bc58828309b3","order_by":0,"name":"K T Cheerfree","email":"","orcid":"","institution":"Manipur Institute of Technology, Manipur University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"K","middleName":"T","lastName":"Cheerfree","suffix":""},{"id":366914215,"identity":"13f6a2be-a67c-4be1-91ff-a32275d77856","order_by":1,"name":"Nongthombam Premananda Singh","email":"data:image/png;base64,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","orcid":"","institution":"Manipur Institute of Technology, Manipur University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Nongthombam","middleName":"Premananda","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2024-10-07 10:23:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-5217315/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5217315/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67319796,"identity":"33506049-08a4-4544-9553-dea8f5e9f346","added_by":"auto","created_at":"2024-10-23 15:23:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":277599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMap showing the study location\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5217315/v1/dce9d9aae55972a08e0c61b7.png"},{"id":67319820,"identity":"dd2f2fae-ffce-44b7-bfa9-fbf1d658b18c","added_by":"auto","created_at":"2024-10-23 15:23:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122344,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMonitoring Device (Temtp M2000 2\u003c/em\u003e\u003csup\u003e\u003cem\u003end\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e Generation)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5217315/v1/91c2980b265a96924f6e6077.png"},{"id":67319823,"identity":"1cc2d12c-3dc8-4c10-bf90-878ca1ada704","added_by":"auto","created_at":"2024-10-23 15:23:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53597,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLevel of Particulate matter (PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003eand PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) exposure during morning and evening traffic peak hours while commuting by walk\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5217315/v1/d6aa5fde66a96b9f733c72ca.png"},{"id":67319817,"identity":"f8eb280b-eb3e-4a8a-a263-810756c34c6b","added_by":"auto","created_at":"2024-10-23 15:23:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123225,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVariation in level of particulate matter (PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003eand PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) exposure during peak-traffic hours while commuting by walk\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5217315/v1/f8b93462ec14daa9e4f5ac98.png"},{"id":67320517,"identity":"5b9c5a95-04e6-489a-a610-4bbb62153c91","added_by":"auto","created_at":"2024-10-23 15:31:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLevel of Particulate matter (PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003eand PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) exposure during morning and evening traffic peak hours while commuting by two-wheeler\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5217315/v1/0109d70c1255842571dd0667.png"},{"id":67319819,"identity":"981633de-fbc3-4290-8b8f-1a004b8862bc","added_by":"auto","created_at":"2024-10-23 15:23:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":103817,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVariation in level of particulate matter (PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003eand PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) exposure during peak-traffic hours while commuting by two-wheeler\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5217315/v1/b964a6572c125c89031fb146.png"},{"id":67319821,"identity":"7bd8d621-a308-4969-ae9c-6177dad67ab7","added_by":"auto","created_at":"2024-10-23 15:23:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":88740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLevel of Particulate matter (PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003eand PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) exposure during morning and evening traffic peak hours at Fixed Site (FS)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5217315/v1/b10c93c8fa7b2e6a2307c02d.png"},{"id":67319822,"identity":"0b3f0679-af2b-475e-b5f3-6003543cc2d2","added_by":"auto","created_at":"2024-10-23 15:23:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":116507,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVariation in level particulate matter (PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003eand PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) exposure during peak-traffic hours at fixed site (FS)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5217315/v1/33084a85e2ef4ef80b91fd2e.png"},{"id":73823530,"identity":"38bb2c47-58b3-48f1-b702-75f1927f8b97","added_by":"auto","created_at":"2025-01-15 05:01:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1749349,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5217315/v1/6dc9cae3-52ae-4672-94cb-20cce4669be6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssessment of PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e and PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e Exposure and Health Risks: A Study of Pedestrian and Two-Wheeler Transport During Peak-Traffic in Imphal, Manipur\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDespite 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 [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In today\u0026rsquo;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 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAir 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 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In 2020, the World Health Organization (WHO) reported that approximately 3.2\u0026nbsp;million deaths annually were due to household air pollution [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. 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\u003csub\u003e2.5\u003c/sub\u003e, 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 [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePersonal 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 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome research indicates that personal exposure to particulate pollutants like PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e 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 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Conversely, studies on inhalation exposure suggest that individuals using open modes of transport\u0026mdash;such as walking, motorcycling, and bicycling\u0026mdash;are exposed to higher levels of particulate matter than those in enclosed vehicles [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis 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\u003csub\u003e2.5\u003c/sub\u003e can vary depending on whether the area is urban, suburban, or rural, as well as by season and time of day [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. 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\u003csub\u003e2.5\u003c/sub\u003e, 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 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The study\u0026rsquo;s results could also support the state\u0026rsquo;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.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u003cem\u003eStudy Area\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It lies between latitude 23\u0026deg; 50' 43.98\"- 25\u0026deg; 42' 3.024\" N and Longitude 92\u0026deg; 58' 52.7052\" \u0026minus;\u0026thinsp;94\u0026deg; 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 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The average temperature in Imphal which is the capital of Manipur range from 5.8\u0026deg; to 25.3\u0026deg; Celsius during winter and 12.3\u0026deg; to 29.6\u0026deg; 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\u0026deg; 48' 37.818\" E and longitude 93\u0026deg; 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cem\u003eInstrument Used\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eFor this study, we utilized the Temtop M2000 2nd generation portable multifunctional digital meter, depicted in (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This lightweight device employs a laser sensor for PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e measurements. It boasts a battery life of 6\u0026ndash;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\u0026ndash;50\u0026deg;C (32\u0026ndash;122\u0026deg;F) and a humidity range of 0\u0026ndash;90% RH. It has an accuracy of \u0026plusmn;\u0026thinsp;10 \u0026micro;g/m\u0026sup3; and \u0026plusmn;\u0026thinsp;15 \u0026micro;g/m\u0026sup3; for PM10. The Monitoring device was kept at a breathing height of around 1.3 m from the ground level as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 \u003cem\u003eDuration, Season and Timing of measurement\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eA 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\u0026ndash;12:00 AM in the morning and 2:00\u0026ndash;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.\u003c/p\u003e \u003cp\u003eSimilarly, 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\u0026ndash;12:00 AM in the morning and 2:00\u0026ndash;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\u0026thinsp;\u0026plusmn;\u0026thinsp;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 \u003cem\u003eExposure analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eExposure to PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e 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\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e for each mode of travelled and fixed-site were calculated using the equations below:\u003c/p\u003e \u003cp\u003e Exceedance Factor = (Measured Concentration/WHO Guideline Value)\u003c/p\u003e \u003cp\u003ewhere, the WHO guideline value is 15 \u0026micro;g/m\u0026sup3; and 45 \u0026micro;g/m\u0026sup3;, for PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussions","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cem\u003eExposure to PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eand PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sub\u003e \u003cem\u003ewhile commuting by foot\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe analysis of a 14-day measurement campaign in December 2023 reveals fluctuating concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e And PM\u003csub\u003e10\u003c/sub\u003e during morning and evening peak hours (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The highest peak hour concentration of PM\u003csub\u003e2.5\u003c/sub\u003e concentration for the week is (121.26\u0026thinsp;\u0026plusmn;\u0026thinsp;26.43) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e while lowest concentration is (27.12\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. Whereas, the highest peak hour concentration of PM\u003csub\u003e10\u003c/sub\u003e concentration for the week is (203.90\u0026thinsp;\u0026plusmn;\u0026thinsp;39.08) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e while lowest concentration is (43.08\u0026thinsp;\u0026plusmn;\u0026thinsp;30.85) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e.These fluctuations likely reflect variations in traffic intensity, construction activities, and meteorological conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 \u003cem\u003eExposure to PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eand PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sub\u003e \u003cem\u003ewhile commuting by two-wheeler\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eData from a 14-day campaign in December 2023 reveals dynamic fluctuations in PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e concentrations during morning and evening hours (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The highest peak hour concentration of PM\u003csub\u003e2.5\u003c/sub\u003e concentration for the week is (201.16\u0026thinsp;\u0026plusmn;\u0026thinsp;44.46) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e while lowest concentration is (38.41\u0026thinsp;\u0026plusmn;\u0026thinsp;19.99) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. Whereas, the highest peak hour concentration of PM\u003csub\u003e10\u003c/sub\u003e for the week is (338.02\u0026thinsp;\u0026plusmn;\u0026thinsp;75.47) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e while lowest concentration is (63.72\u0026thinsp;\u0026plusmn;\u0026thinsp;32.13) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cem\u003eMeasured PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eand PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sub\u003e \u003cem\u003econcentration at fixed site\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eA 7-day measurement campaign in November 2023 reveals consistent PM\u003csub\u003e2.5\u003c/sub\u003eand PM\u003csub\u003e10\u003c/sub\u003e concentrations, with the highest levels consistently recorded on Saturdays (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The highest peak hour concentration of PM\u003csub\u003e2.5\u003c/sub\u003e for the week is (214.3\u0026thinsp;\u0026plusmn;\u0026thinsp;130.5) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e while lowest concentration is (22.84\u0026thinsp;\u0026plusmn;\u0026thinsp;8.37) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. Whereas, the highest peak hour concentration of PM\u003csub\u003e10\u003c/sub\u003e concentration for the week is (374.9\u0026thinsp;\u0026plusmn;\u0026thinsp;228.8) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e while lowest concentration is (38.51\u0026thinsp;\u0026plusmn;\u0026thinsp;13.91) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.4 \u003cem\u003eComparison of personal exposure while commuting by walking, two-wheeler, and remaining in a fixed site during weekdays and weekends\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e 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\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e 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\u003csub\u003e2.5\u003c/sub\u003e concentrations being 0.49 times higher and PM\u003csub\u003e10\u003c/sub\u003e 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.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eStatistical summary of particulate matter during weekdays and weekend\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e`\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c12\" namest=\"c3\"\u003e \u003cp\u003eParticulate matter PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e (\u0026micro;g/m\u0026sup3;) during Week days\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eWalk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eTwo-wheeler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eFixed site\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMor\u003c/p\u003e \u003cp\u003eEve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e123.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e229.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e61.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e174.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e30.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e65.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e152.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e384.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e102.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e304.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e51.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e52.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e252.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e127.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e350.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e89.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e87.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e434.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e211.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e596.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e104.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e151.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e19.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c12\" namest=\"c3\"\u003e \u003cp\u003eParticulate matter PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e (\u0026micro;g/m\u0026sup3;) during Weekend\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eWalk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eTwo-wheeler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eFixed site\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMor\u003c/p\u003e \u003cp\u003eEve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e145.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e25.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e158.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e259.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e168.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e84.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e239.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e41.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e103.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e252.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e181.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e153.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e578.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e137.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e173.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e430.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e110.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e280.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e266.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e970.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e240.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOn 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\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e 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\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e exceed those of both transportation modes.\u003c/p\u003e \u003cp\u003eInterestingly, 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.\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Exposure risk analysis\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 \u003cem\u003eHourly average exposure for different mode of travelled and at fixed-site\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThis section presents the results of an exposure analysis to PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e 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\u003csub\u003e2.5\u003c/sub\u003e (15 \u0026micro;g/m\u0026sup3;) and PM\u003csub\u003e2.5\u003c/sub\u003e (45 \u0026micro;g/m\u0026sup3;). The results of the exposure analysis are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, showing the hourly average levels of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e for the two modes of transport and concentrations measured in fixed-site.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eHourly average exposure to PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eand\u003c/em\u003e PM\u003csub\u003e10\u003c/sub\u003e \u003cem\u003eduring peak-traffic hours\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e (\u0026micro;g/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e (\u0026micro;g/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo-wheeler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed-site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data reveals that individuals walking during peak hours were exposed to an average of 61.57 \u0026micro;g/m\u0026sup3; of PM\u003csub\u003e2.5\u003c/sub\u003e and 102.34 \u0026micro;g/m\u0026sup3; of PM\u003csub\u003e10\u003c/sub\u003e. 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\u003csub\u003e2.5\u003c/sub\u003e concentrations averaging 94.89 \u0026micro;g/m\u0026sup3; and PM\u003csub\u003e10\u003c/sub\u003e reaching 157.28 \u0026micro;g/m\u0026sup3;. In contrast, the fixed monitoring site, intended as a stationary reference point, showed PM\u003csub\u003e2.5\u003c/sub\u003e levels of 61.57 \u0026micro;g/m\u0026sup3; and PM\u003csub\u003e10\u003c/sub\u003e levels of 104.28 \u0026micro;g/m\u0026sup3;.\u003c/p\u003e \u003cp\u003eThese 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 [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] 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 [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] 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.\u003c/p\u003e \u003cp\u003eThis 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 \u003cem\u003eComparison with WHO Air Quality Guidelines\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe hourly exceedance of WHO PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e guidelines for assessing air pollution exposure and its health implications is calculated for each travelled mode and concentrations measured at the fixed-site. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the exceedance factor of these values by different travelled modes and fixed-site.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Exposure Levels with WHO Guidelines\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExceedance factor (PM\u003csub\u003e2.5\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExceedance factor (PM\u003csub\u003e10\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo-wheeler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed-site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e 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\u003csub\u003e2.5\u003c/sub\u003e concentrations were 4.67 times higher than the recommended limit of 15 \u0026micro;g/m\u0026sup3;, while PM\u003csub\u003e10\u003c/sub\u003e levels were 2.27 times above the recommended 45 \u0026micro;g/m\u0026sup3;. This highlights that pedestrians in urban environments are exposed to harmful levels of particulate matter, which can lead to serious health implications over time.\u003c/p\u003e \u003cp\u003eTwo-wheeler users experienced the highest exceedance, with PM\u003csub\u003e2.5\u003c/sub\u003e levels being 6.33 times greater than the WHO guidelines and PM\u003csub\u003e10\u003c/sub\u003e 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 [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the fixed-site monitoring station, PM\u003csub\u003e2.5\u003c/sub\u003e levels were found to be 4.10 times higher than the WHO guideline, and PM\u003csub\u003e10\u003c/sub\u003e 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\u0026ndash;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.\u003c/p\u003e \u003cp\u003eThis 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.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study clearly demonstrates that urban commuters, especially two-wheeler users, are exposed to extremely high concentrations of particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e) 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\u003csub\u003e2.5\u003c/sub\u003e and 3.50 times for PM\u003csub\u003e10\u003c/sub\u003e, these commuters are particularly vulnerable to respiratory and cardiovascular health problems. Numerous studies, such as those by [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], 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.\u003c/p\u003e \u003cp\u003ePedestrians are also not immune to these risks. The findings show that their exposure levels to PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e 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 [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] 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.\u003c/p\u003e \u003cp\u003e 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\u0026ndash;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.\u003c/p\u003e \u003cp\u003eThese 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.\u003c/p\u003e \u003cp\u003ePromoting 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.\u003c/p\u003e \u003cp\u003eIn conclusion, long-term exposure to elevated levels of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eChereefree 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.\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eData will be available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFaour, A., Abboud, M., Germanos, G., \u0026amp; Farah, W. (2023). Assessment of the exposure to PM2. 5 in different Lebanese microenvironments at different temporal scales. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, \u003cem\u003e195\u003c/em\u003e(1), 21. https://doi.org/10.1007/s10661-022-10607-6\u003c/li\u003e\n\u003cli\u003eWesseling J, Hendricx W, de Ruiter H, van Ratingen S, Drukker D, Huitema M, Schouwenaar C, Janssen G, van Aken S, Smeenk JW, et al. 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Lioi, A. Cattaneo, A. Spinazz\u0026egrave;, D.M. Cavallo, S. Fustinoni (2021). Commuting by car, public transport, and bike: exposure assessment and estimation of the inhaled dose of multiple airborne pollutants\u003cem\u003e. \u003c/em\u003e\u003cem\u003eAtmospheric Environment\u003c/em\u003e\u003cem\u003e,\u003c/em\u003eVolume 262. https://doi.org/10.1016/j.atmosenv.2021.118613\u003c/li\u003e\n\u003cli\u003eA. McCreddin, M.S. Alam, A. McNabola (2015). Modelling personal exposure to particulate air pollution: An assessment of time-integrated activity modelling, Monte Carlo simulation \u0026amp; artificial neural network approaches. \u003cem\u003eInternational Journal of Hygiene and Environmental Health\u003c/em\u003e, Volume 218, Issue 1. https://doi.org/10.1016/j.ijheh.2014.08.004\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"particulate matter, Air quality monitoring, traffic corridor, Imphal city, transportation modes, commuter, personal exposure.","lastPublishedDoi":"10.21203/rs.3.rs-5217315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5217315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study looks at the levels of PM\u003csub\u003e2.5 \u003c/sub\u003eand PM\u003csub\u003e10 \u003c/sub\u003epeople 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\u003csub\u003e2.5 \u003c/sub\u003eand 45 µg/m³ for PM\u003csub\u003e10\u003c/sub\u003e, 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\u003csub\u003e2.5 \u003c/sub\u003eand 131.48±69.32 µg/m³ for PM\u003csub\u003e10\u003c/sub\u003e in the morning, and 109.15±38.63 µg/m³ for PM\u003csub\u003e2.5 \u003c/sub\u003eand 181.25±64.22 µg/m³ for PM\u003csub\u003e10\u003c/sub\u003e 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\u003csub\u003e2.5 \u003c/sub\u003eand PM\u003csub\u003e10\u003c/sub\u003e, respectively. Whereas, exceedance factors of walking were 4.10 and 2.27 and for fixed-site were 4.10 and 2.32 for PM\u003csub\u003e2.5 \u003c/sub\u003eand PM\u003csub\u003e10\u003c/sub\u003e, 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.\u003c/p\u003e","manuscriptTitle":"Assessment of PM2.5 and PM10 Exposure and Health Risks: A Study of Pedestrian and Two-Wheeler Transport During Peak-Traffic in Imphal, Manipur","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 15:23:20","doi":"10.21203/rs.3.rs-5217315/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf4299a6-ab1c-4d07-8856-f08bb7e87121","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-15T04:53:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-23 15:23:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5217315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5217315","identity":"rs-5217315","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}