Table 2.1: Type of vehicle fleet in Bangladesh
RHD Vehicle Category & Description
Motorized- Vehicle
1. Heavy Truck: There are three or more axles. Examples are tandem trucks with many axles, container carriers, and other articulated trucks.
2. Medium Truck: Two axle rigid trucks with a payload of three tons. Tractors and trailers for agriculture are also covered.
3. Small Truck: Two axles are rigid with a payload range of three tonnes.
4. Large Bus: Greater than 40 seats and 36 chassis
5. Small Bus: Less than 40 seats and 36 chassis.
6. Mini Bus: 16 to 39 seats and less than 36 chassis.
7. Micro Bus: Up to 16 seats
8. Utility: Jeeps, pick-ups, and four-wheel-drive vehicles
9. Car: Cars and taxis of all sorts
10. Motor Cycle: All motorized two-wheeled vehicles
11. Auto Rickshaw: Vehicles with three wheels which include Baby taxis
12. Tempo: Auto Tempo/Van with a large passenger and load capacity
Non-motorized- Vehicle
13. Cycle Rickshaw: Non-motorized three-wheel passenger vehicle
14. Rickshaw Van: Non-motorized three-wheel cargo vehicle
15. Cart: Different types of animal carts and human drawn/pushed carts
16. Bicycle: All two-wheel pedal cycles
(According to MCC Traffic Guide (RHD), October 2001)
2.3 About COPERT:
COPERT is the European Union's standard vehicle emissions calculator. It calculates emissions and fuel consumption for a country or region based on vehicle population, mileage, speed, and other factors such as temperature and humidity. COPERT is a Microsoft Windows application that calculates automobile traffic pollution emissions. As part of the European Topic Centre on Air and Climate Change's operations, the European Environment Agency (EEA) funds COPERT's technical development. Since 2007, the Joint Research Centre of the European Commission has been in charge of the model's scientific development. COPERT was created in concept for use by National Experts in calculating greenhouse gas emissions from road transport for inclusion in official annual national inventories. The use of a software application to calculate road transport emissions allows for a transparent and consistent data gathering and reporting strategy, resulting in consistent and comparable results that are compliant with international agreements and protocols and EU legislation.
Also, COPERT is:
It is widely acknowledged, and several European countries use it to publish official emissions data.
A research tool for calculating emissions on a national, regional, or local basis, with estimates ranging from annual to daily, on a national, regional, or local size.
The technique used by COPERT is published and peer-reviewed by experts from the UNECE LRTAP Convention, making it technologically complex and transparent.
All significant pollutants are mentioned, such as greenhouse gases, air pollutants, and dangerous species.
2.4 Factors Influencing Emission:
2.4.1 Changes in the Climate
The outside weather condition of the car has a significant impact on vehicle emissions. As a result, country-specific variables such as monthly average temperature and relative humidity are critical for predicting vehicle emissions. Temperature and relative humidity have an impact on emissions. Global warming is no longer a hypothetical situation; it is now a reality. The Inter-Governmental Panel on Climate Change (IPCC) defines climate change as "a change in the state of the climate that may be quantified (e.g., using statistical tests) by modifications in the mean and variability of its attributes over time, generally decades or more." Bangladesh is one of the countries most vulnerable to climate change (Harmeling, 2008). The IPCC likewise considers Bangladesh to be one of the world's most vulnerable countries to the negative consequences of climate change.
2.4.1. a) Temperature:
The ambient temperature has a direct impact on the emission factor. Fuel losses may occur due to temperature fluctuations during the day. HOT emissions from automobiles grow as the ambient temperature rises, according to COPERT. In this phase, emissions are higher and largely reliant on environmental conditions. Cold-start pushes emission zeroes as the car warms up, leaving just hot emissions. COLD emissions from vehicles, on the other hand, increase when the temperature drops.
Basak J. K. et al. (2013) did a previous study on climate change. The article examines climate change and variability based on a survey of historical temperature and rainfall data collected at 34 meteorological stations throughout seven Bangladeshi locations from 1976 to 2008. The annual average maximum temperature trend was found to be increasing at a rate of 0.01860C per year. In contrast, the average yearly minimum temperature trend was seen to be rising at a rate of 0.01520C per year. The monthly average maximum temperature increased in all months except January and April. The rising trend was most evident between May and September and January and February. Every average monthly minimum temperature record showed growing trends except January and November.
These findings are critical for assessing Bangladesh's emissions because the temperature substantially impacts the annual emission rate. Even though this study was conducted in 2013 for the years 1976 to 2008, the increasing rate of temperature in recent years may not be entirely justified because temperature fluctuation is influenced by various elements that alter year to year. It's also true that this type of research isn't carried out very often in Bangladesh. And, because the rate of temperature change isn't speedy, this observation could be used to forecast future temperatures.
2.4.2. b) Humidity:
Choi, David, and colleagues (2010) investigated the effect of temperature and humidity on emission. According to the findings, HC and CO were affected by humidity sensitivity at temperatures over 75 degrees Fahrenheit. Still, NOx was affected at temperatures above 25 degrees Fahrenheit, with higher sensitivity as relative humidity rose. The sensitivity of gasoline cars to moisture was higher than that of diesel cars. The studies also revealed that humidity fluctuations had little effect on overall PM2.5 levels in gasoline and diesel vehicles.
Due to many input parameters and their interdependencies, this paper focuses primarily on temperature and humidity, analyzing changes in emissions from these parameters separately and comparing the impact of each parameter on emission findings by measuring percent change in emissions
A study on relative humidity and dew point temperature was undertaken by Mortuza, Md et al. (2014). This study looked at trends in annual and seasonal relative humidity and dew point temperature data series for 15 sites in Bangladesh from 1961 to 2010. The magnitude of climate trends was calculated using the slopes of Sen's estimator. The statistical significance of the patterns was determined using the Mann–Kendall test.
Figure 2.1 depicts the spatial distribution of the mean annual (1) relative humidity and (2) dew point temperature from 1961 to 2010.
The analysis found that the proportion of stations suggesting a discernible trend in relative humidity is modest at the 95 percent confidence level but substantially higher in the dew point temperature series. In the western United States, relative humidity increased by 0.53, 0.86, and 1.18 percent each decade on an annual, winter, and pre-monsoon scale, respectively. Significant negative patterns in the relative humidity series could be noticed during the monsoon season. The time-series data for seasonal and yearly dew point temperatures at any station, on the other hand, show no discernible downward trend. The size of the relevant changes was more substantial in the country's western regions. The majority of the slopes are substantially above zero, indicating that dew point temperatures in Bangladesh were increasing year after year and season after season.
