Suggested remote sensing of on-road vehicle emissions in Stavanger

Measurement of exhaust emissions from vehicles on road is necessary for an effective system of controlling air pollution in the transportation sector (Dallmann, 2018). Two widely known techniques are portable emissions measurement system (PEMS) and remote sensing (RS), which differ mainly on which/how many vehicles are selected and how their emissions are measured (Sjödin, et al., 2018). PEMS uses sensors mounted on an individual vehicle to analyze tailpipe exhaust and produce a detailed, second-by-second record of emissions on a single basis (Dallmann, 2018). On the other hand, remote sensing can measure emissions from thousands of vehicles per day as they pass by sensors on the road by absorption spectroscopy without interference with the vehicle, its driver, or the driving (Borken-Kleefeld & Dallmann, 2018). In this way, CO2 ratios (expressed in g per kg or litter fuel burned) can be measured directly through the raw vehicle exhaust and the fuel combustion equation (Sjödin, et al., 2018). Compared to PEMS testing, remote sensing is argued as less time consuming and less expensive (Dallmann, 2018).

Additionally, the “remote” nature of sensors makes remote-sensing technique well-suited to fleet monitoring and surveillance since it can scan a potentially very large number of vehicles. For these reasons on-road remote sensing is inherently an effective, economical and socially acceptable tool for automobile emission control.

The remote sensing instrument was first developed in the late 1980s (Bishop, Schuchmann, Stedman, & Lawson, 2012). The recent remote sensing system is Emission Detection And

20 Reporting (EDAR), which has been developed since 2009 with laser light usage (Borken-Kleefeld

& Dallmann, 2018). This allows to determine gas pollutants with much higher accuracy. Besides CO2, a variety of other environmental critical gases such as CO, NO, NO2, HC and PM coming out of moving vehicles can be measured by EDAR with infrared (IR) and ultraviolet (UV) beam sources and detector (Hager, 2017). Each gas has a specific wavelength attenuation to be detected in the IR and UV regions when the beam passes through the exhaust plume (Huang, et al., 2018).

Table 3.1 lists the beam wavelengths covered by EDAR.

Table 3.2 Wavelengths of the IR and UV beams used in remote sensing – CO2 is covered in IR beam

Pollutant IR beam wavelength UV beam wavelength

CO2 4.3 µm N/A

CO 4.6 µm N/A

HC 3.4 µm N/A

NO N/A 227 nm

NO2 N/A 438 nm

PM 3.9 µm and 240 nm 3.9 µm and 240 nm

Source: (Huang, et al., 2018)

As shown in Table 3.2, CO2, CO and HC emissions are measured in the IR spectrum whereas NO and NO2 emissions are measured in the UV region. PM belongs to both IR and UV region.

Although remote sensing can measure a wide range of emissions in the vehicle exhaust, in this thesis, we focus only on CO2 emissions as the main source of pollution from road transport in Stavanger Municipality. Therefore, only IR beam source is included in the EDAR system as illustrated in Figure 3.1

21 Figure 3.1 Sensor above the Roadway - Three units

Source: Own illustration combined with (Borken-Kleefeld & Dallmann, 2018)

EDAR is an unmanned automatic system that consists of a laser-based infrared gas sensor, a vehicular speed sensor, and a license plate reader.

• The first unit is an above-road gas sensor that measures passing vehicle emissions by absorption spectroscopy (Borken-Kleefeld & Dallmann, 2018). Laser plays the role of a light source, making CO2 measurement more selective and precise. Laser is triggered when a forward-facing camera detects an on-coming vehicle. Infrared laser light is then scattered off a reflector strip on the road surface. Because the gas sensor looks down from above, it can sweep a whole lane of the road and detect entire exhaust plume as it exits the vehicle (Hager, 2017). After that, the scattered light is reflected back by the reflector strip to EDAR sensor with required data (Ropkins K., 2017). The CO2 concentration is proportional to the measured attenuation of the laser light. The background pollution such as pollutants beside the vehicle or just before the vehicle crosses the beam is subtracted to leave the remaining difference as the vehicle exhaust (Borken-Kleefeld & Dallmann, 2018). Furthermore, infrared images of the vehicles passing below the sensor can also be

22 taken by EDAR, allowing their shape to be determined whether it is a passenger car, heavy truck, or a vehicle pulling a trailer (Hager, 2017) . In the case of Stavanger where toll booths are well set in place, laser and detector can be mounted together with other sensors of toll booths above the road, with the beam looking down the street. In addition, this overhead configuration makes it easier to conduct measurements at roads with multiple lanes and/or denser traffic.

• The second unit captures the speed and acceleration of vehicles, which provides a measure for the vehicle’s engine load. This load is correlated with the instantaneous emission rate.

Besides the function of supporting to measure CO2 emissions, this unit can act as a tool for Stavanger Municipality and traffic police to monitor proper driving of citizens by comparing actual speed of vehicles and speed limit on particular roads.

• The third unit is a camera to record the license plate of the vehicle, which is already well-established in Stavanger on toll booths. Recall the road toll system in Norway with AutoPASS tag in each vehicle linked to the registration number and interacted with the camera of toll booths (part 3.1.2). As a result, it enables the retrieval of essential vehicle information such as make, model, manufacturing year, certified emission standard, fuel type etc. from the vehicle registration database for further decision-making process.

The combined information generated from these three units indicates the emission rate expressed in grams of pollutant per kilogram (or litter) of fuel burned at a certain engine load. The US Environmental Protection Agency has officially approved this technology for use in vehicle exhaust emission measurements and air quality management (Borken-Kleefeld & Dallmann, 2018). This all-in-one EDAR system has also been used in Europe in various applications (Sjödin, et al., 2018).

By and large, vehicle emission remote sensing can join with the current facilities of the road toll system in Stavanger to determine CO2 emission rates of the whole fleet in a relatively quick and cost-effective manner. There are several clear areas in which remote sensing could supplement the governance of CO2 reduction target of Stavanger Municipality. First of all, the Background chapter of this thesis reveals that vehicle emissions are a major contributor to air pollution in Stavanger.

There is abundant room to shrink this huge contribution by better monitoring CO2 emissions locally with EDAR system. Second, the municipality cannot achieve the ambitious CO2 reduction target in both short and long term without citizen engagement. EDAR system can act as an enabler

23 for an interactive urban data platform in the conceptual model below (part 3.3). Third, it is advantageous for Stavanger Municipality to apply this technology since the technical characteristics are known from the vehicle’s registration data through the road toll system. In addition, EDAR system occupies a humble space in either a temporary or permanent application and is fully weatherproofed against environmental elements such as temperature, humidity, fog, rain, snow, wind, etc. while other technologies in general cannot operate in severe weather conditions (Hager, 2017). Fourth, the social decision-making is increasingly data-driven. Selected data from EDAR system can be combined with the open data portal of Stavanger Municipality to be made available to public. There are 23 categories of data processed by the toll collection systems (AutoPASS, 2019) while another 100 parameters, two thirds of which related to remote sensing parameters and one third related to vehicle information, can be produced by EDAR system (Sjödin, et al., 2018). In total, 123 datasets can potentially enrich the open data platform of Stavanger Municipality on top of the current 234 datasets to reach up to 357 datasets, which is the highest ever in Norway for a municipal level. The categories of toll data and database parameters of EDAR system are listed in the Appendices.