High resolution vehicular exhaust and non-exhaust emission analysis of urban-rural district of India

https://doi.org/10.1016/j.scitotenv.2021.150255Get rights and content

Highlights

  • Construction of high resolution emission inventory for an urban-rural landscape.

  • Novel trip simulation methodology for high resolution VKT and emissions allocation.

  • Estimation of PM2.5 concentration in an urban-rural landscape using AERMOD.

  • Particle resuspension is a dominant source of PM2.5 on all road types.

  • 2W and LCVs contribute major PM2.5 fraction on urban and rural roads, respectively.

Abstract

Air quality deterioration due to vehicular emissions in smaller Indian cities and rural areas remains unacknowledged, even though the situation is alarmingly similar to megacities. The resulting lack of knowledge on travel behavior and vehicle characteristics impacts accuracy of emission studies in these regions. This study uses a novel approach and appropriate primary and secondary data sets to allocate vehicular activities (vehicle population and vehicle kilometer travelled) and associated emissions at a high spatial resolution for estimation and dispersion analysis of vehicular exhaust and non-exhaust PM2.5 emission in an Indian urban-rural landscape. The study indicates that using approaches that do not allocate vehicles kilometers travelled to areas of their expected travel results in underestimating the percent share of PM2.5 emissions from rural roads and motorways while overestimating overall PM2.5 emissions. Particulate matter resuspension is the dominant form of PM2.5 emissions from the vehicular sector on all road types, constituting an even higher fraction on rural roads. Two-wheelers contribute a high fraction of PM2.5 emissions (exhaust and non-exhaust combined), followed by heavy commercial vehicles and four-wheelers on urban roads. Light commercial vehicles, especially agricultural tractors dominate these emissions on rural roads. PM2.5 hotspots are prevalent in urban areas, but several rural areas also experience heavy particulate matter concentrations. Thus, vehicle movement incorporation results in more accurate emission estimation, especially in an urban-rural landscape.

Introduction

Increasing air pollution levels in the Indian subcontinent has become one of the major risk factors, responsible for 17.8% of the total country's premature deaths and 1.36% economic loss of gross domestic product (GDP) in 2019 (Dandona et al., 2019; Li and Ling Jin, 2019; Rajak and Chattopadhyay, 2020). The severity of the problem can be assessed from the fact that India houses 21 of the world's 30 most polluted cities (IQAir, 2019). Though air pollution is considered a metro and megacities’ problem, new studies suggests that ambient air is as toxic in smaller cities and rural parts of India as in metro and mega cities (Guttikunda et al., 2019a; Ravishankara et al., 2020). Several studies have reported high pollution levels in smaller towns and rural areas, sometimes even higher than urban areas (Chatterjee, 2019; Grover and Chaudhry, 2019; Karambelas et al., 2018; Ravishankara et al., 2020). However, metro and megacities have remained the focal point of most air pollution research in India (Garaga et al., 2018; Gordon et al., 2018; Karambelas et al., 2018).

Road transport is one of the primary sources of ambient air pollution and has been extensively studied over the years in Indian cities (Guttikunda et al., 2019b; Kandlikar and Ramachandran, 2000; Karagulian et al., 2015; Ramachandra et al., 2015). Researchers have used different methods for estimating vehicle exhaust and non-exhaust emission and kept the study domain confined to major Indian cities, especially Delhi, Chennai, Bangalore, Mumbai, and Kolkata. However, several source analysis, monitoring, and characterization studies covering smaller sections of rural areas have indicated high prevalence of air pollution from the transport sector, mainly from agriculture tractors, buses, and heavy-duty vehicles, as they are expected to operate predominantly in rural areas (Rehman et al., 2011; Shao, 2016; Arif et al., 2018; Karambelas et al., 2018). However, no detailed study analysing rural transportation systems' contribution for local and regional pollution, especially for any smaller city and surrounding rural areas of India's highly polluted IGP (Indo- Gangetic plains) region is currently available.

On-road vehicles are responsible for both exhaust and non-exhaust emissions (i.e., brake wear, tire wear, road surface wear, and resuspension of road dust). However, most of the available studies have only focused on exhaust emissions (Baidya and Borken-Kleefeld, 2009; Goel and Guttikunda, 2015; Gurjar et al., 2004; Jain et al., 2016; Mohan et al., 2012; Nesamani, 2010; Sadavarte and Venkataraman, 2014) and in some cases specific non-exhaust source, i.e. particulate matter resuspension, brake wear, tire wear, and road wear (Gargava et al., 2014; Guttikunda and Calori, 2013; Guttikunda et al., 2019a; Kumari et al., 2013; Majumdar et al., 2020; Nagpure et al., 2016; Sahu et al., 2011). Currently, studies on contribution analysis of both exhaust and non-exhaust vehicular emissions at high resolution (ward and village scale) for both urban and rural areas of any region of India are unavailable. Although, 412 out of 468 urban agglomerations in India are smaller class I cities (10 million > population > 1,00,000) and majority of Indian population lives in these smaller cities and nearby rural areas, still no study has analysed how various transport-related emissions are responsible for air pollution in these smaller Indian cities and surrounding rural areas at high resolution. Majority of the available studies for rural areas have only considered crop residue burning and household cooking (Rastogi et al., 2016; Ravindra et al., 2019; Saud et al., 2011, Saud et al., 2012; D. P. Singh et al., 2013; Sinha et al., 2014).

The popular method to formulate a high-resolution emission inventory involves estimating emissions using total vehicles registered in an area, vehicle kilometer travel (VKTs), and corresponding emissions factors. The method further distributes estimated emissions into different grids by using distribution indices such as population, road network, and vehicle ownership data (Guttikunda and Calori, 2013; Sahu et al., 2011; Sharma and Dixit, 2015; Sindhwani et al., 2015; V. Singh et al., 2020; Jiang et al., 2020). However, this method does not accurately capture the ground realities as vehicle movements vary across the different grids. It does not account for particular grid vehicle movement to other grids and vehicle movement overlapping in grids to estimate emissions. Traffic flow data inclusion in emission inventory construction process is explored extensively by researchers to increase accuracy at high resolution. Better estimates of level and spatial distribution of emission is achieved by investigating traffic volume data on different road types using floating cars, videography, and road side surveys (Jing et al., 2016; Sun et al., 2021). Other studies constructed link-level emission inventory using traffic flow data gathered from road traffic monitoring networks (Yang et al., 2019; Zhang et al., 2016). High resolution of emission estimation is also achieved using GPS (global positioning system) trajectory data, but traffic simulation is deemed more straight forward and reliable compared to GPS data (Ibarra-Espinosa et al., 2020; Nyhan et al., 2016). Modelled traffic flow data is also used in various high resolution studies by extrapolating traffic flow results of a surveyed road type (Maes et al., 2019; Ibarra-Espinosa et al., 2018). However, these methodologies are either limited to small study domain or use extrapolation of traffic flow data, which can lead to ambiguity in activity data depending on several socioeconomic factors.

The current study develops a novel methodology to estimate high-resolution vehicular movement-related exhaust and non-exhaust emissions for smaller cities and rural areas to overcome these limitations. The emissions are estimated in individual vehicle categories, namely: two-wheelers (2W), three wheelers (3W), four wheelers (4W), buses, light commercial vehicles (LCV), and heavy commercial vehicles (HCV). To assess the impact of the methodology on overall PM2.5 emissions and urban-rural percent share, comparisons are also drawn with high resolution emission inventories constructed using two other methodologies, which makes use of high resolution population and vehicle ownership data. The method developed in the current study not only helps to estimate the air pollution emissions at high resolution but will also be useful for other transport planning and policy studies. The method is of even higher importance in urban-rural road clusters, as road conditions differ vastly from each other in these clusters. The current approach finds application in areas providing travel to work data and lacking high resolution information on traffic activities, mostly in developing parts of the world.

Section snippets

Methodologies

North Indian district Saharanpur, an urban-rural landscape of IGP region, has been selected for the current study. The district is spread across a 3689 km2 area and has a population density of 940/km2 (Census of India, 2011). The district is economically dependent on agriculture and agro-based industries. The local air pollution levels and source information of the domain is largely unknown, despite falling in the extensively studied IGP region.

The on-road vehicle population, vehicle kilometers

Exhaust and non-exhaust emission

Annual exhaust and non-exhaust PM2.5 emission from all vehicle categories combined are found to be 97 t/year and 236 t/year for urban and rural areas of Saharanpur district, respectively. On urban roads the exhaust emissions, tire wear, brake wear, road abrasion; and particulate matter resuspension contributes 15% (14 t), 4% (4 t), 3% (3 t), 3% (3 t), and 75% (73 t) to annual PM2.5 emissions, respectively. On rural roads the exhaust emissions, tire wear, brake wear, road abrasion, and

Conclusion

The study suggests that in a rural-urban landscape non-exhaust emission are major source of PM2.5 from vehicular activities. Particulate matter suspension forms 89% and 75% of the total PM2.5 on rural and urban roads, respectively. The share is higher in rural areas because of poor condition of roads. Additionally, vehicles plying on rural roads contribute to emissions in a similar proportion to that of rural road length. PM2.5 emission load on rural roads contributes 42% to the total emission

CRediT authorship contribution statement

Gaurav Tomar: Conceptualization, Methodology, Writing – original draft. Ajay Singh Nagpure: Writing – review & editing, Methodology, Data curation. Vivek Kumar: Resources, Writing – review & editing, Supervision. Yash Jain: Formal analysis, Data curation.

Declaration of competing interest

We declare that this manuscript is original, has not been published earlier, and is not currently being considered for publication elsewhere. We have no conflicts of interest to disclose.

Acknowledgement

The author (Gaurav Tomar) is grateful to Indian Institute of Technology (IIT) Delhi and Ministry of Human Resource and Development (MHRD), Government of India, New Delhi for providing research fellowship.

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