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DOI: 10.1039/D3EA00047H (Paper) Environ. Sci.: Atmos., 2024, 4, 57-72

Investigating the applicability of COPERT 5.5 emission software in Bangladesh and developing countrywide vehicular emission inventories†

Sakie Kawsar *, Sourav Biswas , Muntasir Noor and Md. Shahid Mamun
Ahsanullah University of Science and Technology, 141 & 142, Love Road, Tejgaon Industrial Area, Dhaka-1208, Bangladesh. E-mail: sakiekawsar@gmail.com

Received 29th March 2023 , Accepted 17th November 2023

First published on 21st November 2023

Abstract

The primary step to minimizing air pollution effects owing to motorized vehicles in Bangladesh is to establish accurate emission modelling methods. The total yearly amount of the primary greenhouse gas, carbon dioxide (CO2), emitted in Bangladesh up to 2020 was obtained by the World Bank. The percentage of total CO2 emissions released from the transport sector in Bangladesh was reportedly 14.2% in 2014 and 15% in 2020; 90% of this was from on-road vehicles. So, approximately 13% of the total amount of CO2 emissions in Bangladesh during those years found in the World Bank data can be considered to have come from its road transportation. However, Bangladesh still does not have a vehicular emission model of its own, so there is no straightforward method to quantify the harmful gases released by automobiles alone in this country as of yet. The purpose of this research is to fill this gap. This research investigated the applicability of the European emission model Computer Program to Estimate Emissions from Road Traffic Version 5.5 (COPERT 5.5) for Bangladesh. The yearly production of CO2 from different vehicular classes in Bangladesh from 2016 to 2020 was computed using COPERT 5.5, and estimations from World Bank data were used as a benchmark. The results of this study suggest that COPERT 5.5 emission software may be applicable to Bangladesh. This research also suggested updated emission factors for CO2 for different vehicle categories yielded by this software and developed countrywide annual vehicular emission inventories of CO2 and 12 other major pollutants from 2016 to 2020.


Environmental significance

Emissions from vehicles cause severe health effects and function as a blanket over the surface of the earth by trapping infrared radiation from the earth's surface, contributing to climate change. As Bangladesh does not have a vehicular emission model of its own, this study established the applicability of the COPERT model for Bangladesh, which will inspire academics to carry out broad research regarding the national air pollution rate and also pave the way for substantial positive impacts on the air quality in Bangladesh. Governmental departments may find COPERT estimations useful for updating the vehicular emission standards of Bangladesh by imposing new rules related to Euro standards and the fuel quality of vehicles in order to decrease emission levels.

1 Introduction

With Bangladesh being ranked as the country with the worst air quality in the world in 2021,5 and its capital Dhaka being ranked as the second most polluted city globally,6 vehicular emissions in Bangladesh have risen to the point that they have become a severe environmental concern with several adverse effects on human health. These poor rankings are attributed to emissions from brick kilns, vehicles, and construction sites.

The Environment Conservation Rules of 1997 established the initial set of ambient air quality guidelines for Bangladesh. Based on a recommendation from the World Bank-funded Air Quality Management Project (AQMP), which assessed the previous guidelines, the Government of Bangladesh replaced the 1997 standards with new ones in July 2005.7 The updated limit values for the criteria air pollutants are shown in Table 1, where a means not to be exceeded more than once per year, b means that the objective is attained when the annual arithmetic mean is less than or equal to 50 μg m−3, c means that the objective is attained when the expected number of days per calendar year with a 24 hours average of 150 μg m−3 is equal to or less than 1, and d means that the objective is attained when the expected number of days per calendar year with the maximum hourly average of 0.12 ppm is equal to or less than 1.8

Table 1 National Ambient Air Quality Standards (NAAQS) for Bangladesh8
Pollutant Limit value Averaging time
Carbon monoxide (CO) 10 mg m−3 (9 ppm) 8 hoursa
40 mg m−3 (35 ppm) 1 houra
Lead (Pb) 0.5 μg m−3 Annual
Nitrogen oxides (NOX) 100 μg m−3 (0.053 ppm) Annual
Particulate matter 10 μm or less in diameter (PM10) 50 μg m−3 Annualb
150 μg m−3 24 hoursc
Particulate matter 2.5 μm or less in diameter (PM2.5) 15 μg m−3 Annual
65 μg m−3 24 hours
Ozone (O3) 235 μg m−3 (0.12 ppm) 1 hourd
157 μg m−3 (0.08 ppm) 8 hours
Sulfur dioxide (SO2) 80 μg m−3 (0.03 ppm) Annual
365 μg m−3 (0.14 ppm) 24 hoursa

The Ministry of Environment, Forests, and Climate Change released a paper titled “Nationally Determined Contributions (NDCs) 2021” that includes the updated greenhouse gas (GHG) emission targets of Bangladesh. 2012 has been taken into consideration as the base year during which 169.05 million tons of CO2 equivalent (MtCO2e) of GHG emissions were produced, out of which 16.77 million tons came from the transportation sector.9 Based on the global warming potential (GWP) of the gas, the unit “CO2e” denotes an amount of a greenhouse gas whose atmospheric influence has been standardized to that of one unit mass of carbon dioxide (CO2).10 In the unconditional scenario, the target is to cut GHG emissions in the relevant sectors (transportation being one of the largest contributing sectors) by 89.47 Mt CO2e (21.85%) below Business As Usual (BAU) in 2030 compared to the base year. This is based on present local-level capabilities and will be financed with internal resources. The conditional emission reduction, which would lower GHG emissions in the same sectors by 27.56 Mt CO2e (6.73%) below BAU in 2030 compared to the base year, will be executed depending upon foreign finance and technological support.9

Around 15% of total emissions are generated from the transportation sector.3 As the growing economy demands the expansion of motorized transportation, it is reasonable to expect that already high levels of air pollution will only worsen in the future. As Bangladesh is a developing country where new technologies are usually embraced slowly, it lacks the sophisticated machinery needed to carry out manual testing of vehicular fumes. Therefore, it is imperative for us to carry out a quantitative analysis of vehicular emissions as a stepping stone to determining effective strategies to mitigate their harmful effects.

Currently, there is no vehicular emission model tailored for Bangladesh, nor are there any comprehensive air pollutant emission inventories in this country.11 Moreover, the latest vehicular emission factors that were proposed for Bangladesh are from seven years ago; these factors are area-specific but not country-specific, and they are for vehicles with older technologies, some of which are no longer running in Bangladesh.12 The deficiency of necessary resources makes it challenging to carry out extensive research regarding vehicular pollution in this country. This study attempts to fill in these gaps. The objective of this study is to prove COPERT to be suitable for this country in order to aid in research regarding the national air pollution rate and pave the way for substantial positive impacts on the air quality in Bangladesh. This paper also aims to provide academic advantages compared to works in the public domain by proposing updated Bangladesh-specific emission factors for CO2, which are dedicated to vehicles with more up-to-date technologies that are currently running throughout the whole country. Finally, this paper seeks to offer a better understanding of the magnitude of CO2 emissions released by different types of vehicles running in this country by preparing annual emission inventories and evaluating the contribution of CO2 emissions by different vehicle types, along with the total magnitude of emissions of 12 other major pollutants throughout the years of analysis.

Various countries throughout the world have developed software-based emission models like the Motor Vehicle Emission Simulator (MOVES),13 Mobile Source Emission Factor Model Version 6 (MOBILE6),14 COPERT,15 Comprehensive Modal Emissions Model (CMEM),16 International Vehicle Emissions (IVE) Model,17etc., which are popular in Europe and America. Bangladesh has adopted European emission standards since 2005.18 Since N. Kholod suggested that COPERT should be used in countries that have adopted European emission standards,19 this research utilized the European software COPERT 5.5 to compute vehicular emissions. The algorithms of the aforementioned models either follow a top-down or bottom-up methodology. Using the top-down methodology, yearly emissions for the whole region can be estimated, whereas using the bottom-up methodology, hourly emissions can be estimated by considering individual roads to be separate line sources.20 Both methodologies are offered by COPERT, but for this study, the top-down methodology was used because the only vehicular activity information that was obtainable for this country was in the Road User Cost (RUC) report, which was the average data for its national or regional highways and Zilla roads, collected by the Roads and Highways Department (RHD) from all seven divisions of Bangladesh.21

Emission inventories have been created using previous versions of COPERT in countries outside of Europe, like China,22,23 South Africa,24 and Latin America, where the same difficulties have been detected regarding the data compilation presented in this work.25–27 A study was conducted in Italy using the top-down approach, which primarily focused on estimating local emissions from vehicle transportation.28 The results were compared to those produced from a geographical decentralization of national surveys using simple surrogate variables defined by vehicle type and driving mode. Another research study based on different emission inventories discussed the methodology of COPERT and MOVES software and detailed the history and current state of vehicular emission simulation in Europe.20 These studies resulted in the development of a set of computer-based models and methodologies that address all motor vehicle emission concerns of policymakers, organizations, the locomotive industry, and the oil industry. A study in China created the emission inventory of gasoline-fuelled vehicles using Zibo city's complete emission factor method and provided a theoretical background for gasoline vehicle emission control schemes.29 Another study compared the mechanical measurement of emissions via PEMS (Portable Emissions Measurement System) with software-based measurement using COPERT emission factors by measuring nitrogen oxides and nitrogen dioxide emissions from diesel-fuelled passenger cars equipped with Euro-6 technology.30 One study used COPERT 4 to calculate atmospheric pollutant emissions associated with road traffic in the North-East region of Romania. They used this tool to assess emissions, specifically in terms of energy consumption and CO2 equivalent emissions. The software allowed them to model different scenarios, such as the impact of road network conditions on emissions.31 Although one prior study utilized COPERT in Bangladesh to calculate the decrease in GHG emissions due to the increase in fuel taxes and determine the best possible corrective fuel tax for automobiles in this country,32 the accuracy of the emissions yielded by COPERT was not checked by comparison with data from another reliable source. Therefore, to the best of our knowledge, the applicability of COPERT software in Bangladesh has not been established yet. Our research is intended to resolve this inquiry. This paper will be one of the first to introduce COPERT to Bangladesh as the remarkable open-source software that estimates nearly accurate vehicular emissions using country-specific data. As our results showed little to no difference from a reliable source of information, government agencies may find COPERT useful for updating Bangladesh's vehicular emission standards. Estimates from COPERT may assist policymakers in imposing new rules related to Euro standards and vehicle fuel quality in order to reduce emissions levels.

2 Methods and materials

All of the data used for this research has been accumulated by the authors from the Department of Environment (DOE) office, the Bangladesh Road Transport Authority (BRTA) office, and several research papers and government reports created by the Roads and Highways Department (RHD).

2.1 Vehicle classification and stock number

The quantity of motorized vehicles that were registered in Bangladesh from 1997 to 2009 was obtained from the Dhaka School of Economics (DScE) data bank,33 as shown in Table 2:
Table 2 Number of motor vehicles registered in Bangladesh from 1997 to 2009 from DScE33
Year Car Jeep & micro Taxi Bus Truck Auto Bike Others
1997 8354 1759 14 970 1282 6546 12[thin space (1/6-em)]080 —
1998 5876 2173 103 883 2733 4403 14[thin space (1/6-em)]525 —
1999 4986 1223 216 746 2018 2140 16[thin space (1/6-em)]511 —
2000 4087 1819 580 741 2725 4135 14[thin space (1/6-em)]614 —
2001 6587 2465 771 1812 2575 603 24[thin space (1/6-em)]409 —
2002 6757 3038 2233 3054 2377 5469 29[thin space (1/6-em)]047 —
2003 7045 1804 5020 2015 2795 13[thin space (1/6-em)]866 21[thin space (1/6-em)]096 —
2004 5410 2514 540 1479 2583 8974 24[thin space (1/6-em)]941 2761
2005 6431 3963 515 1144 2791 4877 43[thin space (1/6-em)]226 2931
2006 8447 5540 275 1261 3065 6898 51[thin space (1/6-em)]106 3713
2007 11[thin space (1/6-em)]941 5650 15 1750 2521 10[thin space (1/6-em)]530 85[thin space (1/6-em)]131 3734
2008 16[thin space (1/6-em)]927 6537 9 1649 2609 19[thin space (1/6-em)]071 93[thin space (1/6-em)]541 4076
2009 21[thin space (1/6-em)]461 9027 12 1504 6561 14[thin space (1/6-em)]902 45[thin space (1/6-em)]142 6634

The quantity of motorized vehicles that were registered in Bangladesh from up to 2010 to 2021 was obtained from BRTA34 as shown in Table 3:

Table 3 Number of motor vehicles registered in Bangladesh from up to 2010 to 2021 from BRTA34
Vehicle category Up to 2010 2011 2012 2013 2014 2015
Ambulance 2486 218 181 240 337 472
Auto rickshaw 110[thin space (1/6-em)]623 20[thin space (1/6-em)]406 23[thin space (1/6-em)]528 15[thin space (1/6-em)]633 19[thin space (1/6-em)]828 18[thin space (1/6-em)]700
Auto tempo 9446 175 626 393 472 1081
Bus 23[thin space (1/6-em)]385 1753 1438 1104 1486 2378
Cargo van 3363 489 282 686 605 398
Covered van 6022 2480 1511 2347 2950 2442
Delivery van 15[thin space (1/6-em)]391 1037 802 941 1235 1779
Human hauler 4827 1151 714 385 225 1129
Jeep 28[thin space (1/6-em)]131 2141 1575 1303 1849 3564
Microbus 62[thin space (1/6-em)]399 4037 3031 2530 4302 5177
Minibus 23[thin space (1/6-em)]070 271 246 148 257 320
Motor cycle 755[thin space (1/6-em)]514 116[thin space (1/6-em)]534 101[thin space (1/6-em)]895 85[thin space (1/6-em)]321 90[thin space (1/6-em)]401 229[thin space (1/6-em)]010
Pick up 29[thin space (1/6-em)]103 10[thin space (1/6-em)]314 7530 6443 9424 9992
Private passenger car 207[thin space (1/6-em)]989 12[thin space (1/6-em)]942 9220 10[thin space (1/6-em)]456 14[thin space (1/6-em)]681 21[thin space (1/6-em)]029
Special purpose vehicle 5022 391 225 228 174 298
Tanker 2606 309 188 218 350 319
Taxicab 35[thin space (1/6-em)]122 75 170 50 372 83
Tractor 14[thin space (1/6-em)]648 5195 3494 1885 1521 1689
Truck 65[thin space (1/6-em)]889 6853 4043 4838 7939 6022
Others 22[thin space (1/6-em)]332 1265 1062 1064 1580 2059
Vehicle category 2016 2017 2018 2019 2020 2021
Ambulance 374 493 563 665 788 755
Auto rickshaw 10[thin space (1/6-em)]656 8852 21[thin space (1/6-em)]593 29[thin space (1/6-em)]807 16[thin space (1/6-em)]724 9158
Auto tempo 1313 1592 609 224 77 25
Bus 3832 3757 2755 3558 2395 1517
Cargo van 1015 1413 1280 4 2 3
Covered van 3399 5201 5728 3070 2023 3800
Delivery van 2220 2420 2105 1523 1170 1436
Human hauler 3443 3393 1418 509 122 52
Jeep 4869 5419 5547 5627 4911 7602
Microbus 5789 5571 4131 3682 2779 4941
Minibus 459 491 436 835 620 392
Motor cycle 315[thin space (1/6-em)]089 325[thin space (1/6-em)]876 393[thin space (1/6-em)]545 401[thin space (1/6-em)]452 311[thin space (1/6-em)]016 375[thin space (1/6-em)]252
Pick up 11[thin space (1/6-em)]220 13[thin space (1/6-em)]454 13[thin space (1/6-em)]060 11[thin space (1/6-em)]918 10[thin space (1/6-em)]498 10[thin space (1/6-em)]897
Private passenger car 20[thin space (1/6-em)]268 21[thin space (1/6-em)]952 18[thin space (1/6-em)]222 16[thin space (1/6-em)]779 12[thin space (1/6-em)]403 16[thin space (1/6-em)]049
Special purpose vehicle 613 994 1334 1179 703 518
Tanker 380 317 527 417 304 248
Taxicab 43 14 159 11 8 0
Tractor 2535 2777 3553 2561 2498 2567
Truck 6605 10[thin space (1/6-em)]329 12[thin space (1/6-em)]644 8318 4719 5789
Others 3842 5018 5973 5293 3900 4029

However, the data for the year 2010 was not found from either of these sources, and there is a difference of opinion about the number of vehicles registered up to 2010 between DScE and BRTA, which gives a negative value for the year 2010 by back calculation. To fix this problem, data for the year 2010 was obtained from a report made by DOE18 as shown in Table 4:

Table 4 Number of vehicles registered in 2010 found from the revision of vehicular emission standards of Bangladesh report by DOE18
DOE report category Motor car Jeep/station wagon Taxi Bus Minibus Truck Auto rickshaw Motor cycle Others
2010 19[thin space (1/6-em)]557 6667 0 1101 142 4543 1362 30[thin space (1/6-em)]264 12[thin space (1/6-em)]225

DScE classified vehicles into 8 categories,33 BRTA classified them into 20 categories,34 and the DOE classified them into 9 categories.18 For greater accuracy, the 20 BRTA classifications were used for this study. So in order to convert the vehicle categories found in DScE and DOE into BRTA categories, we first found which categories were equivalent to which, as found in DScE,33 which is shown in Table 5:

Table 5 BRTA categories that are equivalent to DScE and DOE report categories33
DCsE category BRTA category DOE report category
Car Private car Motor car
Taxi Taxicabs Taxi
Jeep and micro Jeep Jeep/station wagon
Station wagons
Microbus Bus
Bus Bus
Minibus Minibus
Human hauler Others
Others Special vehicle
Delivery van
Ambulance
Tractor
Pick up
Truck Truck Truck
Tanker
Covered van
Cargo van
Auto Auto rickshaw Auto rickshaw
Auto tempo
Motorcycle Motorcycle Motor cycle

The combined data from the other two sources was broken down into its subcategories according to the average ratio of the subcategories observed in the years in the BRTA data. For example, jeep and micro (total) found by DScE were broken down into corresponding jeep and microbus by calculating the average ratio of jeeps and microbuses observed in the years in the BRTA data and then breaking down the given number of jeep and micro (total) into the 2 subcategories using the respective ratios.

The average lifespan of a car in Bangladesh is said to be 20 years.35 Thus, 20 years' data were taken into account for each year of analysis. Since vehicle activity data was collected from the RUC report by RHD, where vehicles were classified into 11 categories,21 stock numbers were in 20 BRTA categories,34 and COPERT itself had its own vehicular classifications,15 the vehicle classifications in COPERT 5.5, which are equivalent to both BRTA and RHD classifications, were determined. The BRTA and RHD equivalent vehicle classifications were obtained from the DScE.33 Then the characteristics of representative models of the vehicles (the most commonly purchased models in Bangladesh) like axle number, weight, and dimensions from the RUC report21 were compared with those of the COPERT classifications as described in the guidebook,15 in order to find the equivalent classifications, as summarized in Table 6:

Table 6 The COPERT 5.5 classifications which are equivalent to the BRTA and RHD classifications
BRTA classification(s) RHD classification Equivalent COPERT 5.5 classification
Cars and taxicabs Car Passenger car (medium)
Ambulance, jeep and pickup Jeep/pickup Passenger car (large-SUV-executive)
Auto rickshaw Auto rickshaw Passenger car (mini)
Tempo and human hauler Tempo Passenger car (small)
Large bus Large bus Buses (standard coaches ≤18 tonnes)
Minibus (diesel fueled) Minibus Buses (standard urban buses 15–18 tonnes)
Microbus (diesel fueled) Microbus Buses (urban buses midi ≤15 tonnes)
Minibus and microbus (CNG fueled) Mini bus Buses (urban CNG buses)
Cargo van, delivery van and covered van Small truck Light commercial vehicles (N1-II, <3.5 tonnes)
Tractors and trucks Small truck Heavy duty trucks (rigid ≤7.5 tonnes)
Trucks Medium truck Heavy duty trucks (rigid 14–20 tonnes)
Tankers and trucks Heavy truck Heavy duty trucks (rigid 20–26 tonnes)
Motor cycle Motorcycle 4 stroke <250 cm3 motorcycle

2.2 Breakdown of stock into fuel type

Since there is no comprehensive study on the percentage of vehicles using different types of fuel for all vehicular categories in Bangladesh, we used the results of a fuel split study carried out by the DOE found in the “Revisions of Vehicular Emission Standards in Bangladesh” to divide the number of each type of vehicle in terms of the type of fuel used,18 as shown in Table 7:
Table 7 Percentage of types of fuels used by different vehicular categories18
Vehicle category CNG (%) Petrol (%) Diesel (%)
Cars and taxis 96 4 0
Auto rickshaws 97 3 0
Jeeps, microbuses and station wagons 81 3 16
Delivery vans and mini trucks 44 1 55
Buses and minibuses 61 0 39
Motorcycles 0 100 0

However, the specific percentage breakdown of vehicles using these different fuels varies over time and is subject to government policies, fuel prices, and environmental initiatives; thus, adjustments were made considering the improvement in the automobile and fuel industries in Bangladesh from the year of the study to the year of this analysis and considering the state of the fuel industry during the years of analysis. For example, cars and taxis running on diesel were not considered to be 0%; instead, the yearly different fuel split for cars and taxis was taken from a comprehensive fuel split study for private cars carried out in Dhaka city.36

2.3 Breakdown of stock into Euro standards

Bangladesh has adopted European emission standards since 2005.18 However, there is no clear documentation about which emission standard was implemented in which year nationwide because some divisions of Bangladesh are less developed than others, and in reality, different parts of this country have maintained different emission standards simultaneously, so it is difficult to generalize for the entirety of Bangladesh given that the scarce information that can be found on the internet is division-centric. The “Revisions of Vehicular Emission Standards for Bangladesh” report written by the Department of Environment (DOE)18 mentioned that, compared to Europe, there is a lag time of 5 to 15 years for a European emission standard to be implemented in most Asian countries. After comprehensive research in the public domain, database queries, and consultation with experts in the field, this study made the assumption that each Euro standard was implemented in Bangladesh 5 years after being launched in Europe, as mentioned in the DOE report18 considering the unavailability of the required quality of fuel, the lack of necessary infrastructure, slow technological growth, and poor economic conditions in Bangladesh. Thus, the launching and ending years of production of different Euro-standard engines in Europe, which were collected from the COPERT guidebook,15 were used to assume those for Bangladesh. Future researchers could benefit from obtaining more in-depth information regarding the Euro standards of vehicles running in Bangladesh in order to enhance the accuracy of the findings of this study.

The launching and ending years of production of different Euro-standard engines in Europe15 and the corresponding assumed launching and ending years of production of the same Euro standards in Bangladesh are shown in Table 8:

Table 8 Timeline of implementation of different Euro standards in Europe15 and corresponding assumed timeline of implementation of the same Euro standards in Bangladesh
Vehicle category Type of fuel Euro standard Launching year in Europe Ending year of production in Europe Assumed year of launch in Bangladesh Assumed ending year of production in Bangladesh
Passenger cars Petrol 1 1992 1996 1997 2001
2 1996 1999 2001 2004
3 2000 2004 2005 2009
4 2005 2009 2010 2014
5 2011 2014 2016 2019
6 a/b/c 2014 2016 2019 2021
Diesel 1 1992 1996 1997 2001
2 1996 2000 2001 2005
3 2000 2005 2005 2010
4 2005 2010 2010 2015
5 2010 2014 2015 2019
6 a/b/c 2014 2019 2019 —
CNG 4 2005 2009 2010 2014
5 2010 2014 2015 2019
6 a/b/c 2015 2016 2020 2021
6 d-temp 2017 2019 2022 2024
Light commercial vehicles Petrol 1 1993 1997 1998 2002
2 1997 2001 2002 2006
3 2001 2006 2006 2011
4 2006 2010 2011 2015
5 2011 2015 2016 2020
6 a/b/c 2016 2017 2021 2022
Light commercial vehicles Diesel 1 1993 1997 1998 2002
2 1997 2001 2002 2006
3 2001 2006 2006 2011
4 2006 2011 2011 2016
5 2011 2015 2016 2020
6 a/b/c 2015 2017 2020 2022
Heavy duty trucks Diesel 1 1992 1995 1997 2000
2 1996 2000 2001 2005
3 2000 2005 2005 2010
4 2005 2008 2010 2013
5 2008 2013 2013 2018
6 a/b/c 2013 2019 2018 —
Motorcycles Petrol 1 1999 2003 2004 2008
2 2003 2006 2008 2011
3 2006 2013 2011 2018
4 2016 2020 2021 —

To divide the vehicular stock according to the types of technology available in COPERT 5.5, the number of registered vehicles registered per year in Bangladesh, as shown in Tables 2, 3, and 4, was assumed to have the Euro standard engine that was assumed to be launched in the particular year, as shown in Table 8. The number of vehicles running per year distributed into COPERT 5.5 classifications (category-fuel-segment-Euro standard) and equivalent BRTA and RHD classifications is shown in Table 9:

Table 9 Number of vehicles per year distributed into BRTA, RHD and COPERT 5.5 classifications (category-fuel-segment-Euro standard)
BRTA classification(s) RHD classification COPERT 5.5 classification (category-fuel-segment-Euro standard) 2016 stock 2017 stock
Auto rickshaw Auto rickshaw Passenger cars – petrol – mini – Euro 4 1175 1272
Passenger cars – petrol – mini – Euro 5 2786 3200
Passenger cars – petrol – mini – Euro 6 a/b/c 3934 3816
Tempo and human hauler Tempo Passenger cars – petrol – small – Euro 3 60 74
Passenger cars – petrol – small – Euro 4 202 235
Passenger cars – petrol – small – Euro 5 516 562
Passenger cars – petrol – small – Euro 6 a/b/c 878 1236
Cars and taxicabs Car Passenger cars – petrol – medium – Euro 3 1261 1286
Passenger cars – petrol – medium – Euro 4 1796 1951
Passenger cars – petrol – medium – Euro 5 3718 3647
Passenger cars – petrol – medium – Euro 6 a/b/c 3639 4391
Ambulance, jeep and pickup Jeep/pickup Passenger cars – petrol – large-SUV-executive – Euro 3 130 148
Passenger cars – petrol – large-SUV-executive – Euro 4 490 628
Passenger cars – petrol – large-SUV-executive – Euro 5 1626 1792
Passenger cars – petrol – large-SUV-executive – Euro 6 a/b/c 2365 2916
Tempo and human hauler Tempo Passenger cars – diesel – small – Euro 3 336 410
Passenger cars – diesel – small – Euro 4 695 714
Passenger cars – diesel – small – Euro 5 1219 1329
Passenger cars – diesel – small – Euro 6 a/b/c 2149 2981
Ambulance, jeep and pickup Jeep/pickup Passenger cars – diesel – large-SUV-executive – Euro 3 699 794
Passenger cars – diesel – large-SUV-executive – Euro 4 2219 2798
Passenger cars – diesel – large-SUV-executive – Euro 5 7126 7875
Passenger cars – diesel – large-SUV-executive – Euro 6 a/b/c 10[thin space (1/6-em)]083 11[thin space (1/6-em)]944
Auto rickshaw Auto rickshaw Passenger cars – CNG bifuel – mini – Euro 4 38[thin space (1/6-em)]102 40[thin space (1/6-em)]390
Passenger cars – CNG bifuel – mini – Euro 5 78[thin space (1/6-em)]822 90[thin space (1/6-em)]320
Passenger cars – CNG bifuel – mini – Euro 6 a/b/c 106[thin space (1/6-em)]159 95[thin space (1/6-em)]181
Tempo and human hauler Tempo Passenger cars – CNG bifuel – small – Euro 4 3333 3539
Passenger cars – CNG bifuel – small – Euro 5 5089 5641
Passenger cars – CNG bifuel – small – Euro 6 a/b/c 9668 12[thin space (1/6-em)]861
Cars and taxicabs Car Passenger cars – CNG bifuel – medium – Euro 4 50[thin space (1/6-em)]520 48[thin space (1/6-em)]225
Passenger cars – CNG bifuel – medium – Euro 5 89[thin space (1/6-em)]325 96[thin space (1/6-em)]170
Passenger cars – CNG bifuel – medium – Euro 6 a/b/c 86[thin space (1/6-em)]485 95[thin space (1/6-em)]305
Ambulance, jeep and pickup Jeep/pickup Passenger cars – CNG bifuel – large-SUV-executive – Euro 4 5365 6982
Passenger cars – CNG bifuel – large-SUV-executive – Euro 5 33[thin space (1/6-em)]334 41[thin space (1/6-em)]409
Passenger cars – CNG bifuel – large-SUV-executive – Euro 6 a/b/c 59[thin space (1/6-em)]028 64[thin space (1/6-em)]678
Cargo van, delivery van and covered van Small truck Light commercial vehicles – diesel – N1-II – Euro 3 3735 4096
Light commercial vehicles – diesel – N1-II – Euro 4 5327 5709
Light commercial vehicles – diesel – N1-II – Euro 5 13[thin space (1/6-em)]749 15[thin space (1/6-em)]246
Light commercial vehicles – diesel – N1-II – Euro 6 a/b/c 23[thin space (1/6-em)]197 29[thin space (1/6-em)]885
Tractors and trucks Small truck Heavy duty trucks – diesel – rigid ≤7.5 t – Euro III 2415 2648
Heavy duty trucks – diesel – rigid ≤7.5 t – Euro IV 4410 5041
Heavy duty trucks – diesel – rigid ≤7.5 t – Euro V 16[thin space (1/6-em)]665 20[thin space (1/6-em)]436
Heavy duty trucks – diesel – rigid ≤7.5 t – Euro VI A/B/C 21[thin space (1/6-em)]522 23[thin space (1/6-em)]150
Trucks Medium truck Heavy duty trucks – diesel – rigid 14–20 t – Euro III 2415 2648
Heavy duty trucks – diesel – rigid 14–20 t – Euro IV 2990 3057
Heavy duty trucks – diesel – rigid 14–20 t – Euro V 7276 8117
Heavy duty trucks – diesel – rigid 14–20 t – Euro VI A/B/C 10[thin space (1/6-em)]398 12[thin space (1/6-em)]743
Tankers and trucks Heavy truck Heavy duty trucks – diesel – rigid 20–26 t – Euro II 2760 3027
Heavy duty trucks – diesel – rigid 20–26 t – Euro III 3405 3476
Heavy duty trucks – diesel – rigid 20–26 t – Euro IV 8246 9198
Heavy duty trucks – diesel – rigid 20–26 t – Euro V 11[thin space (1/6-em)]853 14[thin space (1/6-em)]327
Microbus (diesel fueled) Microbus Buses – diesel – urban buses midi ≤15 t – Euro III 958 1089
Buses – diesel – urban buses midi ≤15 t – Euro IV 1804 2106
Buses – diesel – urban buses midi ≤15 t – Euro V 4100 4132
Buses – diesel – urban buses midi ≤15 t – Euro VI A/B/C 4541 5272
Minibus (diesel fueled) Minibus Buses – diesel – urban buses standard 15–18 t – Euro II 229 324
Buses – diesel – urban buses standard 15–18 t – Euro III 495 472
Buses – diesel – urban buses standard 15–18 t – Euro IV 804 852
Buses – diesel – urban buses standard 15–18 t – Euro V 1141 1485
Large bus Large bus Buses – diesel – coaches standard ≤18 t – Euro III 3299 4635
Buses – diesel – coaches standard ≤18 t – Euro IV 5826 5026
Buses – diesel – coaches standard ≤18 t – Euro V 6153 6501
Buses – diesel – coaches standard ≤18 t – Euro VI A/B/C 10[thin space (1/6-em)]823 13[thin space (1/6-em)]391
Minibus and microbus (CNG fueled) Mini bus Buses – CNG – urban CNG buses – Euro I 4455 5156
Buses – CNG – urban CNG buses – Euro II 8031 9108
Buses – CNG – urban CNG buses – Euro III 17[thin space (1/6-em)]011 17[thin space (1/6-em)]074
Buses – CNG – urban CNG buses – EEV 18[thin space (1/6-em)]325 20[thin space (1/6-em)]780
Motor cycle Motorcycle L-Category – petrol – motorcycles 4-stroke <250 cm3 – Euro 1 82[thin space (1/6-em)]139 99[thin space (1/6-em)]106
L-Category – petrol – motorcycles 4-stroke <250 cm3 – Euro 2 169[thin space (1/6-em)]506 225[thin space (1/6-em)]626
L-Category – petrol – motorcycles 4-stroke <250 cm3 – Euro 3 429[thin space (1/6-em)]203 445[thin space (1/6-em)]998
L-Category – petrol – motorcycles 4-stroke <250 cm3 – Euro 4 822[thin space (1/6-em)]301 1[thin space (1/6-em)]046[thin space (1/6-em)]531
BRTA classification(s) RHD classification COPERT 5.5 classification (category-fuel-segment-Euro standard) 2018 stock 2019 stock 2020 stock
Auto rickshaw Auto rickshaw Passenger cars – petrol – mini – Euro 4 1315 1488 1708
Passenger cars – petrol – mini – Euro 5 3804 4106 4454
Passenger cars – petrol – mini – Euro 6 a/b/c 4075 4773 4829
Tempo and human hauler Tempo Passenger cars – petrol – small – Euro 3 94 135 167
Passenger cars – petrol – small – Euro 4 278 323 443
Passenger cars – petrol – small – Euro 5 579 599 627
Passenger cars – petrol – small – Euro 6 a/b/c 1590 1839 1897
Cars and taxicabs Car Passenger cars – petrol – medium – Euro 3 1529 1585 1705
Passenger cars – petrol – medium – Euro 4 2186 2845 3496
Passenger cars – petrol – medium – Euro 5 3418 3189 3200
Passenger cars – petrol – medium – Euro 6 a/b/c 5022 5340 5110
Ambulance, jeep and pickup Jeep/pickup Passenger cars – petrol – large-SUV-executive – Euro 3 143 236 346
Passenger cars – petrol – large-SUV-executive – Euro 4 805 997 1337
Passenger cars – petrol – large-SUV-executive – Euro 5 1897 2035 2104
Passenger cars – petrol – large-SUV-executive – Euro 6 a/b/c 3568 4014 4198
Tempo and human hauler Tempo Passenger cars – diesel – small – Euro 3 515 615 664
Passenger cars – diesel – small – Euro 4 773 849 1049
Passenger cars – diesel – small – Euro 5 1295 1280 1453
Passenger cars – diesel – small – Euro 6 a/b/c 3497 3751 3548
Ambulance, jeep and pickup Jeep/pickup Passenger cars – diesel – large-SUV-executive – Euro 3 766 1147 1608
Passenger cars – diesel – large-SUV-executive – Euro 4 3571 4430 5806
Passenger cars – diesel – large-SUV-executive – Euro 5 8314 8933 9329
Passenger cars – diesel – large-SUV-executive – Euro 6 a/b/c 14[thin space (1/6-em)]050 15[thin space (1/6-em)]355 15[thin space (1/6-em)]820
Auto rickshaw Auto rickshaw Passenger cars – CNG bifuel – mini – Euro 4 27[thin space (1/6-em)]909 34[thin space (1/6-em)]299 35[thin space (1/6-em)]018
Passenger cars – CNG bifuel – mini – Euro 5 47[thin space (1/6-em)]045 52[thin space (1/6-em)]603 65[thin space (1/6-em)]871
Passenger cars – CNG bifuel – mini – Euro 6 a/b/c 89[thin space (1/6-em)]778 95[thin space (1/6-em)]163 95[thin space (1/6-em)]589
Passenger cars – CNG bifuel – mini – Euro 6 d-temp 78[thin space (1/6-em)]389 88[thin space (1/6-em)]317 86[thin space (1/6-em)]518
Tempo and human hauler Tempo Passenger cars – CNG bifuel – small – Euro 4 2615 3009 3141
Passenger cars – CNG bifuel – small – Euro 5 3232 3467 4069
Passenger cars – CNG bifuel – small – Euro 6 a/b/c 4869 4681 5582
Passenger cars – CNG bifuel – small – Euro 6 d-temp 13[thin space (1/6-em)]036 13[thin space (1/6-em)]314 11[thin space (1/6-em)]803
Cars and taxicabs Car Passenger cars – CNG bifuel – medium – Euro 4 36[thin space (1/6-em)]748 37[thin space (1/6-em)]493 39[thin space (1/6-em)]709
Passenger cars – CNG bifuel – medium – Euro 5 48[thin space (1/6-em)]655 63[thin space (1/6-em)]595 76[thin space (1/6-em)]891
Passenger cars – CNG bifuel – medium – Euro 6 a/b/c 72[thin space (1/6-em)]488 66[thin space (1/6-em)]353 66[thin space (1/6-em)]752
Passenger cars – CNG bifuel – medium – Euro 6 d-temp 94[thin space (1/6-em)]100 96[thin space (1/6-em)]016 87[thin space (1/6-em)]781
Ambulance, jeep and pickup Jeep/pickup Passenger cars – CNG bifuel – large-SUV-executive – Euro 4 3888 5705 7926
Passenger cars – CNG bifuel – large-SUV-executive – Euro 5 17[thin space (1/6-em)]406 21[thin space (1/6-em)]602 28[thin space (1/6-em)]153
Passenger cars – CNG bifuel – large-SUV-executive – Euro 6 a/b/c 40[thin space (1/6-em)]405 43[thin space (1/6-em)]428 45[thin space (1/6-em)]459
Passenger cars – CNG bifuel – large-SUV-executive – Euro 6 d-temp 66[thin space (1/6-em)]465 72[thin space (1/6-em)]059 73[thin space (1/6-em)]934
Cargo van, delivery van and covered van Small truck Light commercial vehicles – diesel – N1-II – Euro 3 4116 4549 4833
Light commercial vehicles – diesel – N1-II – Euro 4 6012 7671 10[thin space (1/6-em)]999
Light commercial vehicles – diesel – N1-II – Euro 5 18[thin space (1/6-em)]064 20[thin space (1/6-em)]189 20[thin space (1/6-em)]422
Light commercial vehicles – diesel – N1-II – Euro 6 a/b/c 35[thin space (1/6-em)]340 35[thin space (1/6-em)]395 34[thin space (1/6-em)]089
Tractors and trucks Small truck Heavy duty trucks – diesel – rigid ≤7.5 t – Euro III 2662 3225 3709
Heavy duty trucks – diesel – rigid ≤7.5 t – Euro IV 5655 7127 10[thin space (1/6-em)]349
Heavy duty trucks – diesel – rigid ≤7.5 t – Euro V 22[thin space (1/6-em)]791 24[thin space (1/6-em)]585 24[thin space (1/6-em)]118
Heavy duty trucks – diesel – rigid ≤7.5 t – Euro VI A/B/C 27[thin space (1/6-em)]736 29[thin space (1/6-em)]150 29[thin space (1/6-em)]643
Trucks Medium truck Heavy duty trucks – diesel – rigid 14–20 t – Euro III 2662 2808 2850
Heavy duty trucks – diesel – rigid 14–20 t – Euro IV 3056 3943 5594
Heavy duty trucks – diesel – rigid 14–20 t – Euro V 9202 10[thin space (1/6-em)]477 10[thin space (1/6-em)]334
Heavy duty trucks – diesel – rigid 14–20 t – Euro VI A/B/C 15[thin space (1/6-em)]661 16[thin space (1/6-em)]035 15[thin space (1/6-em)]719
Tankers and trucks Heavy truck Heavy duty trucks – diesel – rigid 20–26 t – Euro II 3042 3206 3250
Heavy duty trucks – diesel – rigid 20–26 t – Euro III 3469 4476 6348
Heavy duty trucks – diesel – rigid 20–26 t – Euro IV 10[thin space (1/6-em)]422 11[thin space (1/6-em)]848 11[thin space (1/6-em)]718
Heavy duty trucks – diesel – rigid 20–26 t – Euro V 17[thin space (1/6-em)]554 17[thin space (1/6-em)]995 17[thin space (1/6-em)]664
Microbus (diesel fueled) Microbus Buses – diesel – urban buses midi ≤15 t – Euro III 1051 1209 1455
Buses – diesel – urban buses midi ≤15 t – Euro IV 2627 3328 3845
Buses – diesel – urban buses midi ≤15 t – Euro V 3976 3902 4061
Buses – diesel – urban buses midi ≤15 t – Euro VI A/B/C 5893 6024 5687
Minibus (diesel fueled) Minibus Buses – diesel – urban buses standard 15–18 t – Euro II 376 436 483
Buses – diesel – urban buses standard 15–18 t – Euro III 495 534 705
Buses – diesel – urban buses standard 15–18 t – Euro IV 863 923 920
Buses – diesel – urban buses standard 15–18 t – Euro V 1914 2388 2624
Large bus Large bus Buses – diesel – coaches standard ≤18 t – Euro III 5361 5856 6143
Buses – diesel – coaches standard ≤18 t – Euro IV 4830 4884 5157
Buses – diesel – coaches standard ≤18 t – Euro V 6578 7129 8597
Buses – diesel – coaches standard ≤18 t – Euro VI A/B/C 15[thin space (1/6-em)]358 17[thin space (1/6-em)]678 17[thin space (1/6-em)]813
Minibus and microbus (CNG fueled) Mini bus Buses – CNG – urban CNG buses – Euro I 5076 5714 6700
Buses – CNG – urban CNG buses – Euro II 11[thin space (1/6-em)]174 14[thin space (1/6-em)]039 16[thin space (1/6-em)]018
Buses – CNG – urban CNG buses – Euro III 16[thin space (1/6-em)]291 15[thin space (1/6-em)]938 16[thin space (1/6-em)]644
Buses – CNG – urban CNG buses – EEV 22[thin space (1/6-em)]568 22[thin space (1/6-em)]667 21[thin space (1/6-em)]025
Motor cycle Motorcycle L-Category – petrol – motorcycles 4-stroke <250 cm3 – Euro 1 105[thin space (1/6-em)]677 114[thin space (1/6-em)]133 142[thin space (1/6-em)]773
L-Category – petrol – motorcycles 4-stroke <250 cm3 – Euro 2 298[thin space (1/6-em)]110 318[thin space (1/6-em)]349 363[thin space (1/6-em)]724
L-Category – petrol – motorcycles 4-stroke <250 cm3 – Euro 3 437[thin space (1/6-em)]807 483[thin space (1/6-em)]094 623[thin space (1/6-em)]599
L-Category – petrol – motorcycles 4-stroke <250 cm3 – Euro 4 1[thin space (1/6-em)]355[thin space (1/6-em)]071 1[thin space (1/6-em)]666[thin space (1/6-em)]370 1[thin space (1/6-em)]748[thin space (1/6-em)]494

2.4 Mileage and activity information

The annual kilometres travelled and average speeds of different vehicle categories throughout the years were collected from the RUC report,21 as shown in Table 10:
Table 10 Annual mileage and average speed of different vehicles21
Vehicle category 2004–2005 2016–2017
Annual km driven in 2004–2005 (km) Average speed in 2004–2005 (km h−1) Annual km driven in 2016–2017 (km) Average speed in 2016–2017 (km h−1)
Heavy truck — — 72[thin space (1/6-em)]200 31
Medium truck 80[thin space (1/6-em)]700 40 67[thin space (1/6-em)]200 31
Small truck 74[thin space (1/6-em)]000 42 59[thin space (1/6-em)]000 29
Large bus 129[thin space (1/6-em)]800 45 102[thin space (1/6-em)]700 37
Mini bus 66[thin space (1/6-em)]700 31 56[thin space (1/6-em)]300 26
Micro bus 56[thin space (1/6-em)]800 49 50[thin space (1/6-em)]600 36
Utility (jeep/pickup) 22[thin space (1/6-em)]000 25 31[thin space (1/6-em)]800 26
Car 50[thin space (1/6-em)]000 39 36[thin space (1/6-em)]094 33
Tempo 44[thin space (1/6-em)]000 21 40[thin space (1/6-em)]900 21
Auto rickshaw 46[thin space (1/6-em)]000 27 28[thin space (1/6-em)]700 17
Motor cycle 13[thin space (1/6-em)]000 22 24[thin space (1/6-em)]000 27

Here, the values of annual mileage and speed were found to be reduced in the 2016–2017 study compared to the 2004–2005 study, so the values were assumed to decrease at a uniform rate, and this rate was also used to predict the annual mileage and speed for different vehicles for the following years after 2017. For 2020, the vehicular mileage did decrease37 but it was assumed that the vehicular speed increased38 during that year because of the COVID-19 lockdown. Emissions calculated in COPERT 5.5 were found to be most impacted by the annual mileage and average speed data. Since this data is the most accurate for the years 2016 and 2017, as it was directly collected from the RUC report 2016–2017,21 and since the World Bank data is available up to 2020, results for the years 2016, 2017, 2018, 2019, and 2020 were checked against the World Bank data for examining the applicability of this software for Bangladesh.

For each year of analysis, the number of vehicles registered up to 20 years prior to the year was taken into account.35 This means that different vehicles running in a particular year with the same COPERT classification had different ages and thus different lifetime mileage values. However, COPERT only allows one lifetime mileage to be assigned to one vehicle class, so it was necessary to calculate the mean age of each vehicle class during a particular year using a frequency table. The lifetime mileage of each vehicle class for the particular year of analysis was then calculated by multiplying its mean age with its annual km travelled, as shown in eqn (1):

 
Lifetime mileage (km) = mean age using a frequency table × annual km travelled(1)

2.5 Fuel characteristics and additional parameters

Fuel characteristics like density and chemical content of petrol and diesel fuels for Dhaka, Chittagong, and the rest of the country were taken from the report on this country's updated vehicular emission standards18 for 2012 as well as for future years. The minimum value of fuel density and the maximum value of chemical content were considered for the worst-case scenario. The maximum monthly Reid vapour pressure at 38 °C was considered to be 10 psi.18 The most recent average trip length and average trip duration were recorded to be 5.37 km and 15 minutes, respectively.39 The highest and lowest monthly temperatures and the average humidity recorded per month in Bangladesh for each year of analysis were collected from the national meteorological department40–42 and from the Time and Date official website.43

2.6 COPERT 5.5 output used for this study

COPERT 5.5 calculates the emissions of GHGs and pollutants in kilograms or tonnes for each vehicle category mentioned earlier in this paper, as well as the total emissions for all vehicle categories per year. The software provides estimates for a wide range of emissions, including arsenic (As), black carbon (BC), cadmium (Cd), methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), chromium (Cr), copper (Cu), elemental carbon (EC), mercury (Hg), nitrous oxide (N2O), ammonia (NH3), nickel (Ni), non-methane volatile organic compounds (NMVOC), nitrogen monoxide (NO), nitrogen dioxide (NO2), nitrogen oxides (NOX), lead (Pb), particulate matter with a diameter of 10 μm or less (PM10), particulate matter with a diameter of 2.5 μm or less (PM2.5), total suspended particulate matter (PM TSP), selenium (Se), sulphur dioxide (SO2), volatile organic compounds (VOC), zinc (Zn), and more. COPERT generates yearly estimates for all of these greenhouse gases and pollutants. So when we carried out the analysis for 2016–2020, we did get the estimates for all of these pollutants. However, the primary focus of this paper is on carbon dioxide (CO2) emissions because it is the only emission that can be compared with a reliable data source. Unfortunately, sufficient data is not available in the public domain to assess the accuracy of other greenhouse gases and pollutants estimated by COPERT. Additionally, the National Ambient Air Quality Standards (NAAQS) for Bangladesh, as provided in Table 1, use different units for emission limits, making it difficult to directly compare them with COPERT estimations. Therefore, this paper summarizes the total results for 13 major pollutants: CO2, CO, CH4, NOX, SO2, PM10, PM2.5, NO2, N2O, NH3, NO, VOC, and NMVOC, and provides CO2 estimations by vehicular categories, comparisons with World Bank data, and the latest emission factors for CO2 for future research.

2.7 Methodological framework of COPERT 5.5

All information regarding the choice of method and algorithm of the software was found in the COPERT 5.5 guidebook published by the EEA (European Environment Agency).15 COPERT consists of three methodologies: Tier 1, 2, and 3. Tier 1 is adopted if the mileage of vehicles is unavailable, and Tier 2 is adopted if it is available. Tier 3 is used when the average vehicular speed is available in addition to vehicular mileage.15 The top-down approach (Tier 3 method) was used since Bangladesh had the necessary data from prior years. Using the Tier 3 method, it is possible to calculate emissions for urban roads, rural roads, and highways separately. But for Bangladesh, vehicular activity data for three different types of roads was not available; rather, the average data was collected by conducting field surveys on the National and Regional Highways and Zilla roads throughout all seven divisions of Bangladesh by the RHD,21 so the total emissions for all types of roads were calculated. The application of the fundamental methodology of COPERT is briefly described in Fig. 1:
image file: d3ea00047h-f1.tif
Fig. 1 Application of the fundamental methodology of COPERT.15

As the Tier 3 approach was employed, the total vehicular emissions were computed using eqn (2):

 
Total vehicular emissions = hot emissions + cold-start emissions(2)

Here, “hot emissions” refers to the emissions released when the engine runs at its operating temperature. This is calculated using eqn (3):

 
Hot emissions (gm) = emission factor (gm km−1) × quantity of vehicles running × km travelled by vehicle (km)(3)

“Cold-start emissions” refer to the toxic gases released when the fuel is just ignited during the starting of the engine. Its calculation is briefly expressed in eqn (4):

 
Cold emissions (gm) = (β × number of vehicles running × cumulative mileage of vehicle (km) × emission factor (gm km−1) × (emission quotient-1))(4)

Here, the β parameter is the portion of the distance travelled by the vehicle with the engine still cold, which is influenced by factors like ambient temperatures and the average trip length. The emission quotient is the ratio between the emissions per kilometre when the engine is cold and when it is hot. This is governed by the annual vehicular mileage and the type of pollutant under consideration. The calculation of the β parameter and emission quotient is extensively described in the algorithm given in the guidebook.15

For each year's COPERT analysis, different sets of emission factors were generated by using the particular average speeds, average trip lengths, temperatures, and humidity for the particular year of analysis. These coefficients were used by the software to calculate emissions. The emission factors of CO2 using the data for present times are shown in Table 11. Although Euro 6 emission standards have not been widely adopted in Bangladesh, they have been included because regulations and standards can change over time, and Bangladesh may update their standards to reduce vehicle emissions to improve air quality. Using eqn (3), these emission factors can be utilized to calculate emissions manually. However, for calculation in the present time, a greater percentage of vehicles should be considered to be using older technology compared to newer technology because new emission standards are not fully implemented 100% across the entire country, i.e., it may take more time for the less developed part of the country to implement new emission standards.

Table 11 Emission factors for CO2 generated by COPERT 5.5 (unit: gm km−1)
RHD category Fuel Euro standard CO2 RHD category Fuel Euro standard CO2
Auto rickshaw Petrol Euro 4 238.558 Utility (jeep/pickup) CNG bifuel Euro 6 (a/b/c) 203.576
Euro 5 238.558 Euro 6d-TEMP 203.565
Euro 6 (a/b/c) 238.537 Small truck Diesel Euro 3 298.734
Tempo Petrol Euro 3 223.084 Euro 4 298.734
Euro 4 237.083 Euro 5 275.197
Euro 5 237.083 Euro 6 (a/b/c) 275.171
Euro 6 (a/b/c) 237.062 Euro III 395.098
Car Petrol Euro 3 225.722 Euro IV 365.604
Euro 4 230.053 Euro V 348.045
Euro 5 230.053 Euro VI (A/B/C) 354.998
Euro 6 (a/b/c) 230.032 Medium truck Diesel Euro III 788.967
Utility (jeep/pickup) Petrol Euro 3 315.791 Euro IV 722.907
Euro 4 381.902 Euro V 719.765
Euro 5 381.902 Euro VI (A/B/C) 718.705
Euro 6 (a/b/c) 381.881 Heavy truck Diesel Euro II 954.601
Tempo Diesel Euro 3 206.959 Euro III 1001.591
Euro 4 206.959 Euro IV 932.474
Euro 5 206.959 Euro V 927.315
Euro 6 (a/b/c) 206.933 Micro bus Diesel Euro III 699.299
Utility (jeep/pickup) Diesel Euro 3 270.71 Euro IV 646.558
Euro 4 270.71 Euro V 630.773
Euro 5 270.71 Euro VI (A/B/C) 643.012
Euro 6 (a/b/c) 270.684 Mini bus Diesel Euro II 999.508
Auto rickshaw CNG bifuel Euro 4 267.113 Euro III 1048.697
Euro 5 267.113 Euro IV 974.015
Euro 6 (a/b/c) 267.091 Euro V 945.046
Euro 6d-TEMP 267.081 Large bus Diesel Euro III 994.315
Tempo CNG bifuel Euro 4 214.239 Euro IV 948.473
Euro 5 214.239 Euro V 933.114
Euro 6 (a/b/c) 214.218 Euro VI (A/B/C) 950.801
Euro 6d-TEMP 214.207 Mini bus CNG Euro I 1525.191
Car CNG bifuel Euro 4 182.225 Euro II 1415.459
Euro 5 182.225 Euro III 1250.86
Euro 6 (a/b/c) 182.204 Enhanced environmentally friendly vehicle (EEV) 1041.496
Euro 6d-TEMP 182.193 Motorcycle Petrol Euro 1 84.124
Utility (jeep/pickup) CNG bifuel Euro 4 203.597 Euro 2 76.828
Euro 5 203.597 Euro 3 63.214
Euro 4 60.365

3 Results and discussion

The quantity of total vehicular emissions of 13 major pollutants in tonnes from 2016 to 2019 found by analysis using COPERT 5.5 is given in Table 12:
Table 12 COPERT 5.5 estimation of total vehicular emissions of 13 major pollutants in tonnes from the year 2016 to 2020
Pollutant name 2016 2017 2018 2019 2020
Carbon dioxide, CO2 (t) 9578085.572 9794368.498 11057425.252 12137029.420 10645714.566
Carbon monoxide, CO (t) 60725.733 46283.221 53516.145 58650.085 63173.052
Methane, CH4 (t) 2467.320 1579.135 1871.868 2135.419 2360.620
Nitrogen oxides, NOX (t) 40476.589 44303.729 49189.194 54027.801 47914.282
Sulphur dioxide, SO2 (t) 6545.365 5615.712 4592.637 3147.054 2685.699
Particulate matter 10 μm or less in diameter, PM10 (t) 2128.136 2060.452 2291.114 2506.291 2356.354
Particulate matter 2.5 μm or less in diameter, PM2.5 (t) 1297.125 1255.524 1388.081 1526.293 1440.633
Nitrogen dioxide, NO2 (t) 5382.343 5963.306 6607.738 7260.564 6558.400
Nitrous oxide, N2O (t) 293.098 290.245 329.013 354.185 314.625
Ammonia, NH3 (t) 171.197 152.833 173.846 188.829 175.543
Nitrogen monoxide, NO (t) 35094.246 38340.423 42581.456 46767.237 41355.882
Volatile organic compound, VOC (t) 10148.197 7766.205 9101.781 10234.170 10798.480
Non-methane volatile organic compound, NMVOC (t) 7915.279 6277.716 7329.793 8214.324 8589.907

The total quantity of CO2 emissions yielded by COPERT 5.5 was 9578085.572 tonnes in 2016, 9794368.498 tonnes in 2017, 11057425.252 tonnes in 2018, 12137029.420 tonnes in 2019, and 10645714.566 tonnes in 2020. The quantity of CO2 in tonnes released by auto rickshaws, tempos, cars, jeeps/pickups, small trucks, medium trucks, heavy trucks, microbuses, minibuses, large buses, and motorcycles from the years 2016 to 2020 computed by the COPERT 5.5 emission software is illustrated in Fig. 2.


image file: d3ea00047h-f2.tif
Fig. 2 Vehicular emission inventories of CO2 from the year 2016 to 2020 in Bangladesh.

CO2 emissions were found to increase by 2.26% from 2016 to 2017, 12.90% from 2017 to 2018, 9.76% from 2018 to 2019, and from 2019 to 2020 it was found to decrease by 12.28%. The decrease from 2019 to 2020 was caused by the overall decrease in the number of vehicles registered34 and the assumed increased vehicular speed38 during that year because of the COVID-19 lockdown. Large buses, heavy trucks, small trucks, and motorcycles were found to contribute to the most significant quantity of CO2 emissions from 2016 to 2020, with large buses being the biggest contributors, as shown in Fig. 2. From the World Bank official website, the total annual quantity of CO2 emissions in Bangladesh was found for the years 1990–2020 (ref. 1) and the percentage of CO2 emissions by the transport sector with respect to total fuel combustion in Bangladesh was found for the years 1971 to 2014.2 The World Bank collected data regarding CO2 emissions in Bangladesh from Climate Watch: Historical GHG Emissions in Washington, DC: World Resources Institute. Climate Watch collected GHG emissions from the transportation sector from the International Energy Agency (IEA).44 The data sources and methodologies used are described on the Climate Watch website.45 The percentage of CO2 emissions by the transport sector in Bangladesh was reportedly 14.2% in 2014 (ref. 2) and 15% in 2020.3 Therefore, the percentage of CO2 emissions by the transport sector in Bangladesh was assumed to be between 14.2% and 15% of the total for the years 2016, 2017, 2018, 2019, and 2020. However, in order to determine the amount of CO2 emitted by on-road vehicles only, 10% of the amount of emissions from the transport sector was further deducted (7% from shipping and 3% from rail and aviation).4 So the total amount of CO2 emissions for a particular year calculated by COPERT 5.5 was checked against 13% (approximately 90% of the average between 14.2% and 15%) of the total CO2 emission data from the World Bank for that particular year. The comparison between the results obtained from COPERT 5.5 and the values of CO2 emissions from on-road vehicles in Bangladesh estimated from the World Bank data is summarized in Table 13:

Table 13 Comparison between COPERT 5.5 results and estimations using World Bank data
Year Total CO2 emissions in Bangladesh from the World Bank1 (tonnes) Range of CO2 emissions from on-road vehicles in Bangladesh (13% of total) (tonnes) COPERT 5.5 output (tonnes) Percentage deviation (%)
2016 81[thin space (1/6-em)]129[thin space (1/6-em)]000 10[thin space (1/6-em)]546[thin space (1/6-em)]770 9578085.572 9
2017 87[thin space (1/6-em)]658[thin space (1/6-em)]000 11[thin space (1/6-em)]395[thin space (1/6-em)]540 9794368.498 14
2018 95[thin space (1/6-em)]945[thin space (1/6-em)]000 12[thin space (1/6-em)]472[thin space (1/6-em)]850 11057425.25 11
2019 92[thin space (1/6-em)]645[thin space (1/6-em)]000 12[thin space (1/6-em)]043[thin space (1/6-em)]850 12137029.42 −1
2020 85[thin space (1/6-em)]493[thin space (1/6-em)]000 11[thin space (1/6-em)]114[thin space (1/6-em)]090 10645714.57 4

4 Conclusions

The percentage deviation of CO2 emissions found using COPERT 5.5 from estimations using World Bank data was found to be approximately 9% for the year 2016, about 14% for the year 2017, about 11% for the year 2018, about −1% for the year 2019, and about 4% for the year 2020, all of which are below 15%. Therefore, the results of this study suggest that COPERT 5.5 may be a suitable emission model for Bangladesh.

This study established the applicability of the COPERT model for Bangladesh, which will inspire academics to carry out broad research work by making the most of this software. This is vital for a country like Bangladesh, where it is essential to impose new rules related to vehicular emission standards and also to raise public awareness regarding the emission crisis. This study also appeals to the automotive industries of a developing country like Bangladesh to focus on quality over quantity, i.e., to shift towards cleaner technology.

From the comparative analysis of the emission characteristics of different types of vehicles, it was found that large buses, heavy trucks, small trucks, and motorcycles contributed to the most significant quantity of CO2 emissions. Thus, this paper attempts to discourage the use of the aforementioned vehicles and possibly aid the public in selecting a motorized vehicle that is less detrimental to the environment. Apart from the obvious benefits of the decrease in traffic congestion, like the decrease in travel time, it acts as an emission control measure in and of itself. If it is not feasible to change the type of vehicle being driven, for example, trucks being an irreplaceable mode of transport for goods and buses being an irreplaceable mode of public transport, the only possible mitigation measure is for policymakers to mandate the replacement of heavy trucks, large buses, medium trucks, and small trucks with those of improved technology (a higher Euro standard) in order to produce fewer emissions and thus bring about a deceleration in the inevitable worsening of the air quality in Bangladesh.

One major challenge of our study was the lack of updated information regarding the current average speed and annual mileage of each type of vehicle, fuel type split, and vehicle technology type split in Bangladesh. In order to obtain more accurate results from COPERT 5.5 for present times, it is important that the government carry out a study of current vehicle activity, fuel type, and technology type. Moreover, it is vital to simultaneously check the results of the models by carrying out manual testing of emissions from vehicles in Bangladesh using suitable machines (ex: PEMS), so this study strongly appeals to the authority to invest in this machinery. To successfully establish a suitable vehicular emission model for Bangladesh, researchers are recommended to test out further available emission software developed by other countries to determine if the algorithm of any other software suits Bangladesh better than that of COPERT 5.5 and ultimately develop an emission model specifically for Bangladesh.

Author contributions

All authors confirm equal contributions to the paper in the study conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, supervision, validation, visualization, roles/writing – original draft, writing – review & editing of the study.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

We would like to begin by expressing our gratitude to the Almighty for providing us with the strength, wisdom, skills, and opportunity to carry out this study and complete it successfully. We would like to pay our deepest homage to our parents and family members for their undying support and encouragement. We would also like to thank the anonymous referees of the Environmental Science: Atmospheres Journal of the Royal Society of Chemistry for their valuable comments and suggestions, which significantly improved the quality of this paper. We are grateful to Wim Verhoeve from EMISIA, the founding company of COPERT software, for providing us with valuable material to evaluate and learn the structure and methodology of COPERT 5.5. We are also grateful to Ms. Shahanaj Rahman, Deputy Director of the Department of Environment, for providing us with data for Bangladesh, which was vital for this work.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3ea00047h
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