GIS-Based On-Road Vehicular Emission Inventory for Lucknow, India

GIS-Based On-Road Vehicular Emission Inventory for Lucknow, India Downloaded from ascelibrary.org by University of California, San Diego on 02/28/16. Copyright ASCE. For personal use only; all rights reserved. Dhirendra Singh 1; Sheo Prasad Shukla 2; Mukesh Sharma 3; Sailesh N. Behera 4; Devendra Mohan 5; Narendra Bahadur Singh 6; and Govind Pandey 7 Abstract: Megacities in India (population more than 10 million) have taken actions to control air-pollution emissions. However, the secondlevel cities (population between one and 10 million) have not drawn an action plan, and people face serious air pollution in these cities. For one such city, Lucknow, a geographic information system (GIS)-based methodology for emission inventory of on-road vehicles has been developed. The pollutants include: sulfur dioxide (SO2 ); oxides of nitrogen (NOx ); carbon monoxide (CO); particulate matter (PM); 1,3 butadiene; formaldehyde; acetaldehyde; total aldehydes; and total polycyclic aromatic hydrocarbons (PAHs). Video recording was done at nine road intersections of varying land-use patterns to assess traffic count and vehicle kilometer travel. Parking lot surveys were carried out for assessing engine type, vehicle age, etc. and to arrive at a suitable net emission factor for each vehicle category. The 2-wheelers (2-Ws) and 4-wheelers (4-Ws) dominate the total traffic with an 83% share and are main sources of NOx (46%) and CO (77%). The heavy duty vehicles (HDVs: buses and trucks), although they account for only 2% of the vehicle fleet, emit disproportionately high emissions (23% of SO2 , 36% of NOx , and 28% of PM). Spatial cell (2 × 2 km)-wise emission inventory of pollutants indicates that the city center has the highest pollutant emissions resulting from a large number of vehicles, mostly 2-Ws, 3-Ws, and passenger cars. The inventory information can be used for short-term and long-term planning to reduce emissions. DOI: 10.1061/(ASCE)HZ.2153-5515.0000244. © 2014 American Society of Civil Engineers. Author keywords: Emissions estimation; Lucknow; India; Vehicular emission inventory; Traffic; Air pollution. Introduction Air pollution has emerged as a major challenge, particularly in urban areas. The problem becomes more complex due to the multiplicity and complexity of the air polluting source mix (e.g., industries, automobiles, generators, domestic fuel burning, road side dusts, construction activities, etc.). Indian cities have experienced a phenomenal growth in terms of population, industry, and vehicles. The burgeoning population coupled with rapid growth in terms of vehicles, construction, and energy consumption has resulted in serious environmental concerns in Indian cities. At the urban level, air quality is severely affected by vehicular emissions (Sharma and Khare 2001; Shukla and Sharma 2008). Traffic congestion increases emissions from on-road vehicles (Litman 2013), 1 Research Scholar, Civil Engineering Dept., Institute of Engineering and Technology Lucknow, Lucknow 226021, India. 2 Professor, Civil Engineering Dept., Institute of Engineering and Technology Lucknow, Lucknow 226021, India (corresponding author). E-mail: [email protected] 3 Professor, Civil Engineering Dept., Indian Institute of Technology Kanpur, Kanpur 208016, India. 4 Post-Doctoral Research Fellow, Dept. of Civil and Environmental Engineering, National Univ. of Singapore, Singapore 117411, Singapore. 5 Professor and Head, Civil Engineering Dept., Indian Institute of Technology (B.H.U.), Varanasi 221005, India. 6 Professor, Civil Engineering Dept., Institute of Engineering and Technology Lucknow, Lucknow 226021, India. 7 Associate Professor, Civil Engineering Dept., Madan Mohan Malaviya Univ. of Technology Gorakhpur, Gorakhpur 273010, India. Note. This manuscript was submitted on December 12, 2013; approved on July 25, 2014; published online on September 4, 2014. Discussion period open until February 4, 2015; separate discussions must be submitted for individual papers. This paper is part of the Journal of Hazardous, Toxic, and Radioactive Waste, © ASCE, ISSN 2153-5493/A4014006(10)/$25.00. © ASCE as vehicles spend more time on-road. Vehicular emission in terms of mass is the major contributor (over 50%) among the various sources in urban areas: Delhi (64%) and Mumbai (52%) (Gupta 2006). A study by Sood (2012) and a national report on source apportionment (CPCB 2011), clearly indicate a large vehicular emission load: 60–70% of carbon monoxide (CO), 30–50% of oxides of nitrogen (NOx ), and 20–30% of suspended particulate matter in many cities (Delhi, Kanpur, Chennai, etc.). In addition, all vehicular emissions occur at ground level and therefore will have a much greater impact on air quality than elevated sources. Megacities (population more than 10 million) like Delhi have taken actions in nearly all sectors to control air pollution over the past two decades—relocation of polluting industries, introduction of improved emission norms for vehicles, phasing out lead from gasoline, reduction of sulfur in diesel and benzene in gasoline, a city public transport fleet running on compressed natural gas (CNG), and banning of 15-year-old commercial vehicles (CPCB 2011). However, second-level cities, having population between one and 10 million, are yet to initiate air pollution control activities. Lucknow, the capital city of the state of Uttar Pradesh (Fig. 1; 26°52′ N latitude and 80°56′ E longitude), is one such city that has been selected as a study area. In Lucknow, air pollution levels for PM10 (particles of size less than 10 μm) have exceed both 24-h and annual national air quality standards by a factor of 2–3 over the past five years (UPENVIS 2014). The city of Lucknow (and other such cities) lacks in basic information on emissions sources, their locations and strengths. Although the city has no major industries, the number of vehicles is increasing at a rapid rate; for example, nearly 100,000 vehicles are added annually in Lucknow (Pandey et al. 2012). As per the road transport office, the city of Lucknow has over 1.2 million registered vehicles in 2011 (CSIR-IITR 2012). The major vehicle types are: 2-wheelers (2-Ws), 3-wheelers (3-Ws), 4-wheelers A4014006-1 J. Hazard. Toxic Radioact. Waste, A4014006 J. Hazard. Toxic Radioact. Waste Downloaded from ascelibrary.org by University of California, San Diego on 02/28/16. Copyright ASCE. For personal use only; all rights reserved. Fig. 1. The study area: (a) India (adapted from Delhi Tourism 2013); (b) state of Uttar Pradesh (adapted from Irrigation Department, Uttar Pradesh 2008); (c) city of Lucknow (adapted from Lucknow Nagar Nigam 2013) (4-Ws), light commercial vehicles (LCVs), and heavy-duty vehicles (HDVs). Emission inventory (EI) is a basic necessity for planning air pollution control activities. EI provides a reliable estimate of total emissions of different pollutants, their spatial and temporal distribution, and identification and characterization of main sources. This information on EI is an essential input to air quality models for developing strategies and policies. With the above background in view, as the first step to drawing up an air pollution action plan, this paper focuses on developing EI of on-road vehicles on a geographic information system (GIS) platform for the city of Lucknow in terms of emissions of particulate matter (PM); sulfur dioxide (SO2 ); NOx ; CO; 1,3 butadiene; formaldehyde; acetaldehyde; total aldehydes; and total polycyclic aromatic hydrocarbons (PAHs; compounds having two or more benzene rings). The approach is unique, as the inventory will be available on GIS and all ArcGIS tools like ArcMap, ArcCatalogue, etc., which can be effectively used by various stakeholders for planning air pollution control and other activities. It is expected that the methodology presented in this paper can be adopted by other cities in India for initiating air pollution control activities. Methodology Fig. 2 summarizes the stepwise methodology used in this study. Various maps (of wards, road networks, bodies of water, etc.) of © ASCE Lucknow were collected from different agencies (e.g., Lucknow Nagar Nigam, Lucknow Development Authority, etc.) and digitized using ArcGIS 9.2. The topographical map, issued by the Survey of India (SOI) (prepared in 1977), having a scale of 1∶50,000, was geo-coded as the base map for geo-referencing other maps. The map projection chosen was world geodetic system (WGS) 1984 (UTM Zone 44 N). The city was divided into 89 cells of 2 × 2 km size. Geo-referenced maps were digitized to extract desired information like city boundaries, road networks, landmark locations, and bodies of water. All the digitized features were superimposed upon a layer of cells. Road lengths in each cell for major (number of vehicles more than 10,000 per day) and minor (number of vehicles less than 10,000 per day) roads were calculated from the digitizing maps using the ArcGIS tool, ArcMap. Nine traffic intersections (Fig. 1) were video recorded from 9:00 a.m. to 9:00 p.m. for obtaining traffic composition, i.e., 2-Ws; 3-Ws; 4-Ws; LCVs; HDVs; and also the traffic volume (number of vehicles plying on-road per hour). Various agencies [e.g., U.S. Environmental Protection Agency (USEPA), U.K. National Atmospheric Emission Inventory (NAEI), etc.] have developed emission factors for vehicles depending on their speed, acceleration, braking, road type, number of axles, and driving cycle. The Central Pollution Control Board (CPCB 2011) has prescribed emission factors for Indian conditions. The Indian emission factors are based on engine cubic capacity, vehicle age, test vehicle model, and engine technology (ARAI 2008). A4014006-2 J. Hazard. Toxic Radioact. Waste, A4014006 J. Hazard. Toxic Radioact. Waste NOx , CO, and PM, respectively. In the United Kingdom, the emission factors for all passenger cars for urban environments are reported as: 0.423, 2.867, and 0.015 g=km for NOx, CO, and PM10 , respectively (NAEI 2011). Because internationally available emission factors may not be applicable in India due to varying driving cycles, road conditions, vehicle technology, fuel composition, etc., the CPCB (2011) emission factors have been used in this study. Downloaded from ascelibrary.org by University of California, San Diego on 02/28/16. Copyright ASCE. For personal use only; all rights reserved. Parking Lot Surveys Fig. 2. Stepwise methodology Wang et al. (2008) used international vehicle emission (IVE) model (http://www.issrc.org/ive/) in the Shanghai area with emission factors for passenger cars such as: 1.58, 13.34, and 0.01 g=km for NOx, CO, and PM, respectively. The Indian emission factors for passenger cars range as: 0.09–0.95, 0.84–8.09, and 0.002–0.180 g=km for To obtain the prevalence of vehicle technology types operating on the city roads and fuel used, parking lot questionnaire surveys (engine technology and capacity, vehicle age, fuel use, etc.) were done at seven locations (Saharaganj, Bhoothnath, IET, Charbagh, Aminabad, Chinhut, and Transportnagar) in the city of Lucknow. Out of total 2,340 vehicles surveyed, the breakdown was: 808 2-Ws; 354 3-Ws; 553 4-Ws; 272 LCVs; and 353 HDVs (Singh 2011). All HDVs and LCVs use diesel. Twenty-one percent of 4-Ws use diesel and the remaining 79% use gasoline. 3-Ws use compressed natural gas (CNG) and all 2-Ws use gasoline. As an example, the parking lot survey results of 2-Ws are shown in Fig. 3. A net emission factor (for each pollutant) was estimated for each vehicle type (e.g., 2-Ws, 3-Ws etc.). For example, the net emission factor for 2-Ws in terms mass=km=2-Ws was derived by multiplying the fraction of each 2-W category (obtained from parking lot survey) by its corresponding emission factor (of that vehicle category) and then adding these products for all vehicle categories. Fig. 3. Distribution of 2-Ws in study area (parking lot survey) Table 1. Derived Net Emission Factors Net emission factors (mg=km=vehicle) Vehicle type 2-Ws 3-Ws 4-Ws LCVs HDVs © ASCE Net emission factors (μg=km=vehicle) CO NOx PM 1,3 butadiene Formaldehyde Acetaldehyde Total aldehyde Total PAHs 2,000 1,890 1,990 3,320 8,720 310 600 400 2,410 9,650 20 200 50 630 830 5.33 5.52 7.83 270.69 6.93 3.97 12.16 10.85 78.81 41.17 0.88 6.49 1.73 8.06 8.29 8.77 29.35 18.13 140.90 79.81 655.09 938.36 283.46 6,640.24 1,765.13 A4014006-3 J. Hazard. Toxic Radioact. Waste, A4014006 J. Hazard. Toxic Radioact. Waste Table 2. Vehicle Fleet Composition Vehicle type Downloaded from ascelibrary.org by University of California, San Diego on 02/28/16. Copyright ASCE. For personal use only; all rights reserved. 2-Ws 3-Ws 4-Ws LCVs HDVs Total Total number of vehicles VKT=day 563,710 136,471 266,873 14,717 23,634 1,005,405 16,097,830 2,587,670 7,519,602 278,188 499,379 26,982,671 Similarly, net emissions factors for other vehicles types plying on the roads of the city of Lucknow were derived (Table 1). The emission for each pollutant in all cells was calculated using data such as: distance traveled by each vehicle per day, number of vehicles, net emission factors, and road lengths in each cell. The following expression was used for estimating the emissions: Ej ¼  X nVehi × Di × EFij ð1Þ where Ej = mass emission per day for pollutant j; i = vehicle type (e.g., 2-Ws, 3-Ws, etc.); nVeh = number of vehicles per day; D = distance traveled in km per day; and EF = mass emission factor per vehicle per km. For estimating SO2 emissions, only diesel vehicles are considered, as sulfur content in gasoline is much less (0.005%) than in diesel (0.05%) (CPCB 2010; IITK 2010). Average kilometer run per liter of diesel is taken as: 10 km for 3-Ws; 15 km for 4-Ws; 7 km for LCVs; and 5 km for HDVs. It is assumed that all the sulfur is burnt to SO2 (IITK 2010). Vehicular data in each cell were calculated by the traffic count survey for each road category (e.g., main highway, minor road, service road, etc.). The daily vehicular kilometer traveled (VKT) for all categories of vehicles in each cell were estimated on the basis of the parking lot surveys and road lengths in each cell. The emission depends upon emission factor and VKT. For 2-Ws, 3-Ws, 4-Ws, and LCVs, the emission factors in descending order are: CO > NOx > PM. The VKT is highest for 2-Ws. In case of HDVs, the emission factors in descending order are: NOx > CO > PM. However, it is expected that CO emission will be the largest, as for most vehicles, CO emission factor is the highest. The emissions for each cell were extracted and mapped to the database prepared using ArcGIS. Finally, spatially resolved (a) (b) (c) (d) Fig. 4. Pollutant specific emission contribution of each vehicle type in city of Lucknow: (a) SO2 , PM, and NOx ; (b) CO; (c) total aldehydes, acetaldehyde, formaldehyde, and 1, 3 butadiene; (d) total PAHs © ASCE A4014006-4 J. Hazard. Toxic Radioact. Waste, A4014006 J. Hazard. Toxic Radioact. Waste Table 3. Traffic and Emission Studies in Different Cities Serial number 1 2 3 4 5 6 7 Study area Population PM (tons=day) NOx (tons=day) Lucknow, Indiaa (present study) Kanpur, Indiab,c Bangalore, Indiab,d Pune, Indiaa Delhi, Indiaa,e Mexico City, Mexicoa Sao Paulo, Brazila 2,245,509 3.5 24.6 95.9 2,715,555 5,701,446 3,760,636 12,877,470 15,175,862 11,253,503 1.9 22.4 3.3 15.0 15.0 44.7 10.5 146.3 5.0 194.0 411.0 116.0 32.1 — 419.0 509.0 3,900.0 8,214.9 CO (tons=day) Downloaded from ascelibrary.org by University of California, San Diego on 02/28/16. Copyright ASCE. For personal use only; all rights reserved. a Research done using international vehicle emission (IVE) model (http://www.issrc.org/ive/). Research done using GIS platform without computational model. c IITK (2010). d TERI (2010). e Goyal et al. (2013). b maps for various pollutants were generated for the city of Lucknow. Results and Discussion The 2-Ws, 3-Ws, and 4-Ws show morning and evening peaks between 9:00 and 11:00 a.m. and between 5:00 and 7:00 p.m. The highest traffic count (214,244) was observed between 5:00 to 7:00 p.m. Large traffic flow of LCVs and HDVs were seen on highways (Lucknow-Sitapur, Lucknow-Hardoi, and Lucknow-Kanpur highways). In the inner part of the city, the traffic largely consisted of 2-Ws, 3-Ws, and 4-Ws. Table 2 presents fleet composition and VKT. The percentage share (in terms of vehicle count and VKT) of 2-Ws is the maximum and of LCVs is the minimum. The emission contributions of each type of vehicle are shown in Fig. 4. There were 563,710 2-Ws in the city of Lucknow, which accounted for 56% of the vehicle population. These were responsible for 31% of NOx and 54% of CO emissions. This is because 2-Ws are responsible for 59% of total VKT and the number of 2-Ws was high. 3-Ws, HDVs, 4-Ws, 2-Ws, and LCVs contributed 33, 28, 16, 13, and 11%, respectively, to the total vehicular emissions of PM. The 3-Ws are one of the frequent modes of public transport within the city. The parking lot survey reveals that the majority of 3-Ws were 10–15 years old and were diesel powered. The HDVs account for only 2% of the vehicle population, but were responsible for 23% of SO2 , 36% of NOx , and 28% of PM emissions. HDVs are diesel powered and are 10–15 years old and poorly maintained, which may be responsible for high SO2 , NOx , and PM emissions. 3-Ws and 2-Ws are major contributors to formaldehyde (42 and 25%), acetaldehyde (68 and 17%), and total aldehydes (47 and 26%) emissions. 2-Ws, LCVs, and 3-Ws contribute 36, 27, and 21%, respectively, to 1,3 butadiene emissions. 2-Ws and 3-Ws contribute 47 and 37%, respectively, to total PAHs emissions. This reveals that limiting the usage of 2-Ws and passenger cars can help to bring down CO emissions, and improving the emission control technology for heavy-duty vehicles is the key to decreasing the vehicular emissions of SO2 , NOx , and PM emissions in the city. Table 3 presents the comparison of previous studies with the current study on vehicular emissions carried in various cities. It can be seen that there is a large variation in the vehicular emissions Fig. 5. Spatial cell-wise emission inventory of PM (kg=day) (2 × 2 km cell size) © ASCE A4014006-5 J. Hazard. Toxic Radioact. Waste, A4014006 J. Hazard. Toxic Radioact. Waste Downloaded from ascelibrary.org by University of California, San Diego on 02/28/16. Copyright ASCE. For personal use only; all rights reserved. Fig. 6. Spatial cell-wise emission inventory of SO2 (kg=day) (2 × 2 km cell size) in different cities even though they have similar human populations. The emission depends on the number of vehicles, availability of good public transport (e.g., metro or local rail network, etc.), congestion, etc. Figs. 5–11 show the spatial distribution of PM, SO2 , CO, NOx , 1,3 butadiene, total aldehydes, and total PAHs emission loads in 2009. The high emission cells, LK-72, 73, 74, 75, 87 in the inner city and LK-44, 89, and 102 in the outer city have been identified as critical cells. In inner critical cells, the emission is due to significant movement of all types of vehicles except HDVs. Table 4 presents the population, emissions, and major vehicle type responsible for high emissions in each critical cell of the city. It can be seen from Table 4 that in the outer city critical cell, the HDVs and 4-Ws provide a major contribution to emission. In the critical cells of the inner city, the 2-Ws contribution to PM is high. The 2-Ws and 3-Ws share high emissions of formaldehyde, acetaldehyde, and total aldehydes due to their high VKT. The variations in the emission levels in various cells can be explained by comparing land-use pattern, socioeconomic conditions of the populations, and its proximity with highway/industrial Fig. 7. Spatial cell-wise emission inventory of NOx (kg=day) (2 × 2 km cell size) © ASCE A4014006-6 J. Hazard. Toxic Radioact. Waste, A4014006 J. Hazard. Toxic Radioact. Waste Downloaded from ascelibrary.org by University of California, San Diego on 02/28/16. Copyright ASCE. For personal use only; all rights reserved. Fig. 8. Spatial cell-wise emission inventory of CO (kg=day) (2 × 2 km cell size) activity. The cell numbers LK-72, 73, 74, and 75 fall in the center of the city, where the land-use pattern is mainly residential and commercial. There are a number of offices (government as well as private) in this area and commercial activities (shopping centers, restaurants, etc.) are also prevalent. One of the prominent reasons of high vehicular emission load in these cells is that the vehicle movement (dominated by 2-Ws, 4-Ws and public transport) is high in comparison to other cells in the city. Cell numbers LK-76, 89, 100, 102, 113, and 114 are at the outskirts of the city where land use is mainly residential, but these cells are in proximity to national and state highways. The movement of 4-Ws and HDVs is high in these cells, and these are predominant vehicle types responsible for emission. The areas that have the minimum vehicular emission (Figs. 5–11) have low population density and the land-use pattern is purely residential. The majority of areas are newly developed or under development and have low occupancy. One of the major Fig. 9. Spatial cell-wise emission inventory of 1,3 butadiene (kg=day) (2 × 2 km cell size) © ASCE A4014006-7 J. Hazard. Toxic Radioact. Waste, A4014006 J. Hazard. Toxic Radioact. Waste Downloaded from ascelibrary.org by University of California, San Diego on 02/28/16. Copyright ASCE. For personal use only; all rights reserved. Fig. 10. Spatial cell-wise emission inventory of total aldehyde (kg=day) (2 × 2 km cell size) reasons of low emission in these areas is the fact that the minor roads account for about 80% of road length, and movement of HDVs and LCVs is restricted to a certain extent. The vehicular emission inventory developed in this study can be used both for making broad decisions at the city level and at the cell level to reduce emission. It is important to note that 3-Ws contribute significantly in inner cells of the city, and technological improvements in 3-Ws including changes in fuel from diesel to CNG may greatly help in reducing the emissions. For long-term planning, a public transport system should be revamped to include introduction of a nonpolluting metro system so that people would feel discouraged to use individual 2-Ws, which also contribute heavily to emissions. The work presented in this paper has further scope for improvement. It is important to validate the data, apply more accurate and locally relevant emission factors, and account for nonexhaust emissions caused by road-tire interaction. The emission factors used in this study do not account for driving cycle, including speed of vehicles. Braking, acceleration, idling, and speed can all influence emissions. In addition, cold and hot starts of engines also influence emissions. In future work, the authors propose to establish a driving cycle in Indian cities using a global positioning system (GPS) and account for start and stop emissions using vehicle occupancy characteristics enumerator (VOCE) units. Fig. 11. Spatial cell-wise emission inventory of total PAHs (kg=day) (2 × 2 km cell size) © ASCE A4014006-8 J. Hazard. Toxic Radioact. Waste, A4014006 J. Hazard. Toxic Radioact. Waste 2-Ws and 3-Ws 2-Ws and 4-Ws HDVs and LCVs 3-Ws and 4-Ws 2-Ws and 3-Ws 3-Ws and LCVs HDVs and 3-Ws 2-Ws and 3-Ws References 710.63 585.41 842.15 995.48 941.55 854.72 666.11 833.31 696.97 772.67 711.73 864.16 1,573.78 2,125.13 4,067.44 4,171.20 4,140.81 3,696.81 3,045.54 3,954.47 3,324.77 3,567.65 3,244.75 3,660.93 Cell number LK-44 LK-58 LK-73 LK-72 LK-74 LK-75 LK-84 LK-85 LK-89 LK-87 LK-88 LK-102 1 2 4 3 5 6 7 8 11 9 10 12 19,480 17,011 14,759 34,919 11,646 18,713 10,452 13,312 10,733 11,783 19,630 14,749 kg=day 179.44 89.29 272.38 256.50 221.38 213.24 184.18 222.96 177.08 201.58 187.53 187.50 25.54 30.32 39.24 43.59 35.98 35.41 29.99 36.15 26.23 29.97 27.92 28.32 22.93 33.51 34.80 48.20 38.00 16.22 8.46 14.89 7.11 16.44 15.65 25.39 g=day 774.90 1190.09 1386.99 1807.49 1526.18 777.18 382.02 777.44 296.83 777.84 728.30 945.21 HDVs and 4-Ws HDVs and 2-Ws HDVs and 4-Ws HDVs and 2-Ws 1,3 butadiene, and total PAHs Formaldehyde, acetaldehyde, and total aldehydes NOx and CO A GIS-based methodology has been demonstrated and established for developing a vehicular emission inventory for the city of Lucknow in terms of PM; SO2 ; NOx ; CO; 1,3 butadiene; formaldehyde; acetaldehyde; total aldehydes; and total PAHs. The 2-Ws dominate the total traffic with a 56% share and are the main sources of NOx and CO emissions. 2-Ws are responsible for 59% of total VKT, and 3-Ws contribute over 30% to the total the emissions of PM. The HDVs make up 2% of the vehicle population but were responsible for 23% of SO2 , 36% of NOx , and 28% of PM emissions. The 2-Ws are major contributors to 1,3 butadiene (36%) and total PAHs (47%) emissions in the city. 3-Ws is the major contributor to formaldehyde (42%), acetaldehyde (68%), and total aldehydes (47%). A spatial cell-wise emission inventory of pollutants indicates that the city center has the highest pollutant emissions resulting from a large number of vehicles, which is a mix of mostly 2-Ws, 3-Ws, and passenger cars. Serial number Human population CO NOx PM SO2 Total aldehydes Total PAHs PM and SO2 Major vehicle type contributor in emissions Emissions Downloaded from ascelibrary.org by University of California, San Diego on 02/28/16. Copyright ASCE. For personal use only; all rights reserved. Table 4. Emissions in Critical Cells with Human Population © ASCE Conclusions ArcGIS version 9.2 [Computer software]. Redlands, CA, ESRI. Automotive Research Association of India (ARAI). (2008). “Air quality monitoring project-Indian clean air programme (ICAP).” Rep. on Emission Factor Development for Indian Vehicles, Pune, India, 7–15, 〈http:// cpcb.nic.in/Emission_Factors_Vehicles.pdf〉 (Mar. 29, 2014). Central Pollution Control Board (CPCB). (2010). “Status of the vehicular pollution control programme in India.” Program objective series, PROBES/136/2010, Delhi, India. Central Pollution Control Board (CPCB). (2011). “Air quality monitoring, emission inventory and source apportionment study for Indian cities.” New Delhi, India, 46–67, 〈http://cpcb.nic.in/FinalNationalSummary .pdf〉 (Mar. 29, 2014). Council of Scientific, and Industrial Research-Indian Institute of Toxicology Research (CSIR-IITR). 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