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Internal combustion engines that power most transportation modes emit a variety of products that can affect air quality. Estimating the amount of these emissions from “mobile sources” from the top down can be done by measuring the amount of fuel that is sold. Getting more precise estimates, and forecasting the future emissions under different scenarios, requires the use of travel models. Usually, the travel model forecasts the amount of vehicle travel, the mode and other characteristics of the trips such as speed. These results are fed into an emissions model that contains information about fuel use and emissions characteristics of the appropriate vehicle fleet.

In the United States, MPO’s are required to forecast the future emissions of motorized transportation in regions that are designated as non-attainment of air pollution standards for certain pollutants that are hazardous to human health. These techniques are fairly well developed and extensive guidance is available. Climate change concerns have led to a need for developing methods to model the emissions of Greenhouse Gases (GHG) and the various strategies that may be used to reduce them.


Criteria pollutants vs GHG

Under the Clean Air Act (CAA) the EPA sets concentration limits, called National Ambient Air Quality Standards (NAAQS), for six common “criteria” air pollutants that are known to damage human health at certain concentrations. The pollutants are particle pollution (often referred to as particulate matter), ground-level ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. Areas of the country where the air has been measured to exceed these concentrations over a set period of time are designated as “non-attainment” areas; these areas are generally within the metropolitan boundaries of an MPO. States must develop a general plan keep non-attainment areas from exceeding the pollution concentration limits. These plans, known as State Implementation Plans or SIPs, are developed by state and local air quality management agencies and submitted to EPA for approval.

A SIP contains, among other things, a pollutant budget for each non-attainment area which specifies the maximum number of tons of each pollutant that can be safely emitted over a period of time from sources within the area before the concentration standard is expected to be exceeded. The SIP allocates a certain amount of the budget to mobile sources, based on historical inventories and considering economic, political or other factors. The CAA requires that MPO transportation plans are “consistent” with air quality goals. In practice this means that MPOs must demonstrate that the projected emissions of criteria pollutants of future transportation plans cannot exceed the budgeted amounts in the SIP.

In 2007 the Supreme Court ruled in Massachusetts v. EPA that greenhouse gases are covered by the CAA's definition of air pollutant and that EPA must determine whether or not emissions of greenhouse gases (GHG) from new motor vehicles cause or contribute to air pollution which may reasonably be anticipated to endanger public health or welfare. In 2009, the EPA issued two findings regarding greenhouse gases under the Clean Air Act, which were later upheld in Appeals Court:

• The Endangerment Finding: that the current and projected concentrations of greenhouse gases in the atmosphere threaten the public health and welfare of current and future generations.

• The Cause or Contribute Finding: that the combined emissions of greenhouse gases from motor vehicles contribute to the greenhouse gas pollution which threatens public health and welfare.

So far transportation planning is regulated under the CAA with regard to GHG emissions, though emissions standards have been imposed on motor vehicles themselves.

Although concentrated criteria pollutants can have immediate danger to human health and the environment, they also tend to breakdown or disperse after a period of time. In contrast, GHGs at atmospheric concentrations generally do not affect humans directly and the environment only gradually. They are persistent for decades or centuries, however, and their cumulative concentration in the atmosphere ultimately affects humans by changing the global climate. This difference is important in terms of regulation and modeling in at least two ways. First, the geographical location, time and weather conditions of criteria emissions are factors affecting the concentration levels, whereas GHG emissions in any location and time are equally harmful to the climate. Second, different control strategies can affect different types of transportation system criteria pollutants; paving high traffic roadways reduces certain particulate emissions, for example, and various technologies on vehicles can address VOC, CO or NOX specifically. Although short lived GHGs like HFCs, methane, etc., require special control strategies, the most important GHG, carbon dioxide, is directly related to the amount of fossil fuel that is consumed.


Sketch models vs full models

The analysis of the effect that various scenarios for land use and transportation will have on air quality can be analyzed with full models or with sketch planning models. In this context a sketch model is defined as using default values and assumptions to obtain results quickly and with low effort. In many cases, sketch models are not Spatial Interaction Models. For example, in an effort to quickly determine the emissions impact that might result from constructing a new development, a sketch model could be created by estimating the expected new trips generated by the development and multiplying them by the average trip length in the region. The resulting quantity of vehicle miles could then be multiplied by national average light vehicle emissions per mile to get an estimate of the emissions impact. A different type of sketch model might estimate the GHG reductions from doubling the percentage of mixed use housing in new residential development by using survey data taken from existing single family and mixed use developments in the region.

Sketch models are often used in the early stages of planning and project preparation to screen a list of alternatives that will then undergo more rigorous analysis. They can be useful for visualizing the expected results of various general concepts, for example substituting investment in transit for investment in roadways, or adding carpool lanes instead of regular lanes.

The weakness of sketch models is a result of their dependence on default parameters. The effect of real transport and land use approaches is highly dependent on the particulars of the place in which they are deployed. Not only does the spatial interaction component have a strong role. Local cultural habits, weather and geography, and demographic details can change the performance and penetration of strategies. Much more sophisticated and expensive models are needed to capture all these effects. These models must also be calibrated to the idiosyncrasies of the area, which requires additional data collection.

The need for powerful models is often driven by regulatory needs. Certainly the Clean Air Act has put pressure on MPOs to develop models that can demonstrate that a certain set of strategies can meet conformity. However, sophisticated models that can predict behavior changes due to non-motorized investment, street design and even technological innovation in vehicle and transit systems can help planners and decision makers evaluate other benefits of those types of strategies at the same time.

Co-benefit such as new jobs, reduction in traffic congestion, lower cost of travel or preservation of open space can make or break the political support for a project. Once models are developed that can link land use to transport investment, and even to estimate the economic impact of strategies, sketch models are often replaced with more detailed modeling.

The development of the MOVES model has increased the potential input requirements that a travel forecast must produce. With its ability to accurately model cold starts, acceleration, grades and other aspects of the vehicle trip, MOVES has put the onus on the forecasting model to produce data that is very specific. At the same time changes in driving patterns, increased use of non –motorized modes and the spread of electric and hybrid vehicles have added additional variable to the mix. Sketch models are not usually capable of providing the level of data required for highly accurate air quality modeling.


Clean Air Act and Conformity analysis

The Clean Air Act was passed to ensure that pollutant concentrations in the United States do not exceed the National Ambient Air Quality Standards (NAAQS). It specifies procedures for inventorying existing pollution and making plans to reduce it, or make sure it does not become worse. Pollution from motor vehicles is called mobile source pollution. Mobile sources are only a part of the total number of pollution sources, and the first goal of an inventory is to determine the amounts of pollution that come from mobile, point and areas sources by counting the potential emitters and estimating how much they emit. For mobile sources the inventory is done by modeling the amount of travel the motor vehicle fleet performs during a base year and multiplying that by the emissions per distance traveled.

Two separate models are required, a travel model and a fleet emissions model. The US has been a leader in developing emissions models that are based on extensive research and vehicle measurements. Recently the EPA has developed MOVES, replacing the MOBILE series models that had previously been the official emissions model for cars, trucks and motorcycles. EPA allows California to use EMFAC because it is developed specifically for that state. The EPA, in conjunction with the FHWA, sets a regulatory framework for the quality of the travel models, but does not develop a national standard model as for emissions.

Since emissions per distance from a vehicle vary depending upon speed and other factors, the vehicle fleet emissions model uses these inputs and therefore the travel model should be able to estimate these parameters.

State Implementation Plans

States prepare their plan of control measures to attain and maintain the NAAQS. These plans are called State Implementations plans (SIPs). The Emissions reductions needed to achieve the NAAQS are determined based on the emissions inventory. After considering the cost-effectiveness and feasibility of different strategies, the state makes a list of chosen control measures and sets an emissions budget based on implementing them.

If substantial reductions will be needed from the transportation sector in order to reduce CO, VOC, NOx, PM2.5, and PM10 emissions it is important that transportation and air quality officials participate in decision-making on the SIP. The transportation planning entities, such as an MPO, will then be required to plan for and implement SIP measures and to demonstrate, via additional modeling, how their plans to do so will result in total emissions within the budget. Adherence to the motor vehicle emissions budget becomes the key measure of conformity between transportation plans, programs and projects and the SIP.

An MPO can include multiple measures and strategies in its plans to reduce emissions. Some of these may be part of the SIP and others could be in addition to it. Measures might include:

The use of reformulated gasoline, implementation of Inspection and Maintenance (I & M) Programs The use of alternative modes of transportation such as transit, walking, bicycling; Land use patterns and urban forms that reduce trip length and/or encourage mode shifts Transportation investments that are designed to reduce congestion (e.g. signal synchronization programs, congestion pricing)

Obviously, an MPO must have the capacity to effectively model these measures if it is to adequately incorporate them into the SIP in the first place. Just as importantly, it needs the ability to demonstrate that actual projects that are funded will result in emissions reductions and that the planning process is always in conformity with the CAA and the SIP.

Conformity Demonstration

The conformity demonstration is required whenever a Metropolitan Transportation Plan or a Transportation Improvement Program is updated or amended or when non-exempt transit or highway projects are approved or funded by federal agencies. There is a required formal interagency process among the MPO, State DOT, state and local air quality agencies, EPA, FHWA and FTA. This involves review of the determination documents and methodology by each agency. There is also a public participation process. This review means that the travel model and the emissions calculations must be done to consistent standards and follow a strict schedule.

The rules and guidance surrounding conformity determinations are highly complicated. Over the past two decades documents have been issued on numerous aspects of transportation emissions conformity ranging from methods for incorporating re-entrained dust into particulate analysis to how to integrate land use strategies into models. Much of this guidance applies to project level impact analysis under NEPA, or rules concerning when new models are required during the planning cycle.

At the macro level of regional transportation planning and programming, perhaps the most important rule is the requirement to use the “latest planning assumptions”. What this means is that the travel demand models must reflect the best knowledge of current and future trends in socio-economic data, e.g. population, employment, etc., as well as data on travel, congestion, and the effectiveness of the transportation control measures (TCMs) and other implementation plan measures. Similarly, the emissions model must use the best available data about the makeup of the vehicle fleet. While any future projection is uncertain, this rule is an attempt to address the issue of “garbage in, garbage out” that can cause the most sophisticated model to give poor results due to inputs that are erroneous or reflect conscious or unconscious bias of the modelers and decision makers.


GHGs and energy use

Transportation is responsible for about one third of the US emissions of manmade Greenhouse Gasses. The amount of GHG emissions from motor vehicles is the product of three factors: fuel carbon content, vehicle fuel economy and vehicle travel activity. Since travel models exist to provide estimates of current and future travel activity it follows that they can be helpful in estimating GHG emissions. Models can also provide estimates of factors that affect vehicle fuel economy such as the mode split between transit and private vehicles, and the speed and routing of vehicle trips.

For MPOs and State DOTs, it is worth noting that estimating GHG emissions in a transportation planning context has much in common with the air quality conformity process, discussed elsewhere in this resource, and practitioners will share many of the same concerns. The conformity process requires that the inputs of a calibrated and validated travel model be input into an approved emissions calculator to produce estimates of pollutants. Conformity has been a legally required part of transportation planning since 1990 and there is a large body of information and guidance about how to use travel models to project the amount of criteria pollutant emissions expected from the implementation of a regional transportation plan. Environmental assessments can also require analysis of transportation related emissions from many types of projects or policies, and the state of the practice has evolved over the years.

Measures to reduce GHG emissions are often the same as those proposed to reduce criteria pollutants and cover a wide range of transportation alternatives. Models may need the ability to represent congestion relief, travel demand management projects, pricing policies, land use details, public transit options, non-motorized modes and freight movement. This could require improved mode choice models, advanced trip assignment techniques, activity or tour based models and so on. Patterning GHG modeling after the air quality conformity process can be helpful, but there are important differences that need to be considered.

Much of the difference between the conformity experience and GHG analysis is institutional. Importantly, there is no federal requirement to analyze the GHG emissions from transportation planning activities, although some states are moving towards such a mandate. (California comes closest, with its SB 375 legislation that requires MPOs to have either a true transportation and land use plan for meeting GHG targets or an alternative plan showing how targets could be met given alternative circumstances.)

Another difference between GHG modeling and conformity is that without a government mandate, no common set of modeling standards has evolved for GHG. Just as with other pollutants, the level of modeling accuracy can be tailored to the needs of the user, but there is not a particular level that is more important than others. Finally, conformity analysis is only required for non-attainment areas, whose air basins exceed threshold concentrations of pollutants. The pollutants emitted in other areas are not considered to be dangerous because they do not concentrate. GHG emissions persist and disperse in the global atmosphere, so a ton of CO2 from one area is theoretically of equal importance as any other ton of CO2 and should probably be modeled and measured everywhere.

The lack of a fully developed institutional structure means that inventories, baselines and targets for transportation GHG emissions are not always available and may need to be developed. It is vital to incorporate travel modeling into these efforts from the beginning so that the models are in tune with the initial results and can be effectively used for future planning.

The worldwide, persistent nature of GHG impacts can also lead to some technical differences in how travel modeling is used. The interrelation between near term and long term emissions may need to be monitored. The way origins and destinations in one geographic area are linked to another area and the geographic boundaries of a region’s responsibility are also issues that rise in importance. The tight integration between the economy, land use patterns and fossil fuel powered vehicles increases the need for advanced models that can provide estimates of the economic and urban form impacts of proposed GHG reduction scenarios.

Baselines and Inventories

Often, the first step in transportation planning for GHG reduction is to estimate the amount of GHG currently being emitted from the vehicles in a given study area over a given time. The transportation contribution is often shown as a percentage of all manmade GHG emissions. This information serves as the foundation for comparing various planning scenarios. An inventory is the term commonly used for an accounting of all the factors add up to the total amount of GHG for a region. The term baseline usually refers to a series of data that show the changes in emissions over time and may also predict change into the future.

Carbon dioxide is by far the largest GHG contributor to climate change, accounting for 95 percent of mobile source GHG emissions. It is known that one gallon of gasoline, when combusted in a typical car engine, results in about 2,421 grams of carbon dioxide (19.4 lbs). Burning one gallon of diesel fuel results in about 2,778 grams (22.2 lbs.) In simplest terms then, the problem is to determine the amount of fuel that is being burned. The volume of motor fuel sales can be found from tax records or other sources and used to get a direct estimate of the amount of CO2 emitted. Fuel sales are a very simple calculation. Unfortunately, it is extremely likely that not all the fuel sold in an area is burned there, while fuel sold elsewhere may result in CO2 within the study area. Fuel may not be combusted during the same time period that is being analyzed. These situations can result in an inaccurate baseline estimate. For this reason, although fuel sales are an important check on baseline estimates, it is usually necessary to determine the total vehicle miles traveled (VMT). VMT can be estimated from traffic counts, such as the HPMS, or other sources such as surveys or odometer readings. All these sources are much more direct than an estimate from a travel model.

Using a travel model for estimating baselines can offer certain advantages, however. A calibrated and validated model can be used in assessing future scenarios. A model can provide vehicle mile estimates of speeds and congestion, which have an effect on fuel economy and thus the amount of fuel used. Information about origins and destinations of trips are available from travel models. A model can represent a typical travel day not subject to random fluctuations in traffic and can aggregate results by geographical boundaries.

Target setting

Setting GHG targets is usually done as a cooperative exercise, similar to the way criteria pollutant budgets are set. A bottom up approach that considers the possible strategies and measures that might be undertaken, and estimates the emissions that would result if they are implemented successfully, can lead to a target with strong political support.

Cost/benefit estimation ($/ton)

Modeling the effect of different strategies

Vehicle technology and fuels – induced travel, electricity generation emissions

Scenario planning:

Mode shift/vehicle occupancy/non-motorized modes – who is shifting?

Reduction in VMT/travel activity - performance and penetration - land use

Strategies affecting speed and acceleration


Interface between travel model and emissions model

Transferring data between a travel forecasting model and an emissions model can be a complicated process.

MOVES/Mobile series/EMFAC

The EPA has developed sophisticated motor vehicle emissions software for use by MPO's and air quality agencies during the inventory and conformity processes.

Speed distribution

VMT by vehicle type

VMT fraction by vehicle type

Road type distribution

VMT fraction by facility

Ramp fraction

VMT by hour

VMT by day and month

Treatment of centroid connectors

Depending upon the level of detail of the travel model network and the type of model, local streets may not be fully represented. Typically the local streets within a traffic analysis zone are represented by one or more centroid connector links that load onto the nearest arterial street. These links often do not have the speed and capacity characteristics of real local streets. If the emissions model is to have correct speed data these links must be accounted for.

Fleet assumptions

Dispersion Models

Resources

FHWA Air Quality

EPA MOVES

EMFAC

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