ABSTRACT
Highway maintenance workers spend most of their work time in traffic and are constantly exposed to traffic-related emissions produced from various sources that have severe impacts on their health. The prime objective of this study is to determine the air quality impacts due to pollutants and estimate the emissions produced from these sources near road construction projects and describe methods to mitigate these emissions.
In this study, we aim to analyze the emissions produced from various on road and off road construction equipment used in the road construction projects. The study mainly focuses on assessing emissions like Particulate Matter PM 2.5, PM 10, Carbon Monoxide(CO), Oxides of Nitrogen (NOX) and Carbon Dioxide (CO2). The emissions are estimated using construction equipment data, traffic data obtained from TxDOT combined with emission factors obtained from the U.S Environmental Protection Agency (EPA).We conclude that highway maintenance workers are frequently exposed to elevated airborne particle and noise levels compared with the average population. This elevated exposure is a consequence of the permanent proximity to highway traffic with additional peak exposures caused by emissions of the work-related equipment.
CONTENTS
Acknowledgement ………………………………………………………………………i
Abstract …………………………………………………………………………………….ii
Contents …………………………………………………………………………..……..iii
List of Figures ……………………………………………………………………….v
List of Tables …………………………………………………………………………v
2.1 Overview …………………………………………………………………………..6
2.2 Equipment Activity…………………………………………………………………7
2.3 Emissions………………………………………………………………………….10
2.4 Emissions Modelling……………………………………………………………..12
2.5 Methodology for Estimating On Road Vehicle Emissions using MOVES………12
2.6 Off Road Emissions Calculation…………………………………………………26
4.1 Effect of Emissions on Workers Health…………………………………………..37
4.2 Mitigation measures to Reduce Emissions………………………………………..38
LIST OF FIGURES
Figure 2.2 Average Equipment Use….. ……………………………………………………8
Figure 3.1 Geographic Location of Construction Site…………………………………28
Figure 3.2 Weekday Traffic on IH 10…………….…………………………….……..32
Figure 3.3 Head Protection for Workers ……….………………………………………………33
Figure 3.4 Hearing Protection for Workers ………………………………………………..33
Figure 3.5 Respiratory Protection for Workers…………………………………………34
Figure 3.6 Off Road Emissions by Equipment Type………………..…..…………. ..35
LIST OF TABLES
Table 2.1 Phases of Road Construction Projects and Equipment Used…………………9
Table 2.2 ARB Guidance for Acres Distributed ……………………………………………………10
Table 2.3 Classification of Non-Road Equipment……………………………….…….10
Table 2.4 Road Type Classification for MOVES……………………………….….…..15
Table 2.5 MOVES Source Types and HPMS Vehicle Types………………….………19
Table 2.6 MOVES Speed-bin Classification ……………………………..…………….22
Table 3.1 List of Off-Road Equipment used…………………..……….…………………29
Table 3.2 Input Parameters to run RCEM Spreadsheet…………..…………..………………30
Table 3.3 Estimated off Road Emissions………………………….…….….. ………….35
Table 3.4 Estimated On Road Emissions………………………..……..……………….…36
1.1 Study Purpose
A large number of studies have shown excess health risks from living in close proximity to roads; however, their attribution to single air pollutant is less clear. The adverse health effects from road proximity might be due to tailpipe emissions of airborne particles, particles from non-exhaust sources such as tire and brake wear or gaseous pollutants. New or widened roads are often proposed to relieve congestion or to support economic growth, however, there is a little appraisal of road schemes once they are built. It has been shown through various studies that the benefits from reduced congestion and shorter journey times are often short lived as new road network capacity is taken up by induced traffic growth (Perkinson, 1998).
Highway maintenance workers spend most of their work time in traffic and are constantly exposed to traffic-related emissions that have been linked to myocardial infarction as well as increased cardiovascular morbidity and mortality. Traffic emissions are composed of a complex mixture of particulate and volatile air pollutants on one hand and noise on the other. Levels of particulate matter (PM), carbon monoxide (CO), nitrogen oxides as well as volatile compounds including aldehydes and hydrocarbons are significantly elevated in traffic environments (Cascio, 2012).
An important air pollution compound in regard to health effects is the particulate fraction originating from engine exhaust, brake wear, tire wear, and road surface abrasion. The PM fraction includes coarse particles with aerodynamic diameters between 2.5 and 10 μm, fine particles (PM2.5) with diameters <2.5 μm, and ultrafine particles (UFP) with diameters <0.1 μm. PM is a complex mixture of small airborne particles and liquid droplets (Dennis, 2005).
Exposure to particle pollution is linked to a variety of health problems including reduced lung function, chronic bronchitis, and asthma, and has been associated with heart attacks in people with pre-existing heart disease. In addition, PM pollution is the main cause of visibility impairment and contributes to acid rain. In the context of construction equipment use, two PM-related concerns are of key interest: PM2.5 and PM10. The exhaust from diesel-powered construction equipment includes fine particles, virtually all of which are PM2.5 or smaller in diameter; exhaust particulate is sometimes called primary PM2.5. PM2.5 is also chemically formed in the atmosphere from various pollutants, some of which are emitted by diesel-powered equipment, and these particles are referred to as secondary PM2.5. In addition, the use of construction equipment loosens and disturbs the soil. The disturbed soil contributes to windblown dust problems—sometimes called fugitive dust—and the movement of dirt from the construction site onto nearby roadways. Once dirt from a construction site has been tracked onto a road, passing vehicles can cause the dirt to become suspended in the air, a problem called re-entrained road dust. Bulk material operations on construction sites, such as rock crushing activities, can also contribute to windblown dust. Most fugitive and re-entrained dust particles are larger in size than exhaust particles and construction-related dust can contribute to PM10 problems. Direct effects of particulate matter on the cardiovascular system are well established and recent studies with a focus on ultrafine particles suggest an important role of this fraction due to its small size and large surface area (Baker, 2014).
Although many studies have investigated health effects of traffic exposure in relation to air pollution, fewer have addressed health effects of traffic noise. There is evidence that traffic noise interacts with the cardiovascular system and it has been directly linked to myocardial infarction and hypertension. Although elevated noise levels during resting periods and at night may be most critical, cumulative exposure to high noise levels in occupational settings has also been related to hypertension (Meier, 2012).
Workers in traffic environments are exposed continuously to particles and noise and may, therefore, be at higher risk for cardiovascular diseases compared with the average population. Elevated exposure to air pollutants has been reported for policemen and workers exposed to motor exhaust. The noise was not measured in these studies. Only a few studies describe combined particle and noise measurements at traffic locations and the same is true for combined health effects that were assessed in cohort studies only recently and only for long-term effects. Highway maintenance workers are frequently exposed to air pollutants and noise originating from road traffic or working equipment as generators or brush cutters. This mixed exposure may contribute to an increased risk for cardiovascular diseases. Our exposure assessment for this worker population serves as the basis to evaluate probable health effects and to develop strategies to better protect the workers’ health (Niemeir, 2007).
1.2 Background
In September 1996, a Research was performed in cooperation with the U.S. Department of Transportation, Federal Highway Administration, and the Texas Department of Transportation on the Air Quality Impacts of Highway Construction and Scheduling. The passage of the 1990 Clean Air Act Amendments (CAAA) has resulted in several urban areas in Texas being designated non-attainment areas. While the non-attainment areas are acting to improve air quality, several near non- attainment areas need to act to maintain their air quality at current levels to avoid being designated a non-attainment area. These areas are reviewing several transportation-related strategies to reduce emissions and prevent further degradation of air quality. One option already being implemented is the observation of ozone action days that encourage citizens to seek alternative modes of travel such as transit and car pool/van pool on days conducive to the formation of high ozone levels. This research aimed at Alternative highway construction practices which may also offer air quality benefits, especially on ozone action days. Reconstruction and rehabilitation activities requiring lane closures on high-volume roadways result in traffic congestion and delays. The traffic congestion caused by construction activities and the materials and equipment used in construction may aggravate the air quality problem in non-attainment areas, especially during hot summer months when atmospheric conditions lead to the formation of high ozone levels. The impact of highway construction on air quality and alternative construction practices designed to minimize the detrimental effects on ambient air quality have been determined (Perkinson, 1998).
Texas Natural Resource Conservation Commission (TNRCC) rules, now withdrawn, were to affect the Texas Department of Transportation (TxDOT) by placing restrictions on construction equipment activity. One rule, which was to become effective in 2005, sought to reduce ozone formation by limiting the amount of oxides of nitrogen (NOx) produced by heavy-duty diesel equipment in the early morning hours. The rule restricted the use of equipment greater than or equal to 50 horsepower during the morning hours of the ozone season at construction sites in the Dallas/FortWorth (DFW) and Houston/Galveston (HG) nonattainment areas. Exemptions to this rule were to be filed with TNRCC by May 31, 2002.This project reviewed TNRCC’s analysis, evaluated potential project cost, and schedule impacts, assessed alternative emission control technologies, and evaluated the impacts of work zone lane closures for on-road emissions (Jason, 2006).
As of 2010, there was no consistent and widely accepted guidelines for estimating emissions from road construction projects. In addition, there was a comparative lack of data regarding construction equipment activity and emissions. Thus, ADOT (Arizona Department of Transportation) sought technical assistance to better understand construction-related activities and resulting air impacts. To accomplish these goals, ADOT sponsored a field study to quantitatively assess the air quality impacts from an example road-widening construction project. The study assessed activity, emissions, and air quality impacts associated with construction to widen SR 92, a two-lane highway. The construction project, called the “Sierra Vista—Bisbee Highway (SR 92) Carr Canyon Road—Hunter Canyon Project” covered an approximate four-mile segment of SR 92 in Cochise County. The study site was in a relatively remote area of Arizona and was selected to minimize background pollution and easily identify observed impacts related to construction equipment use. The construction project involved a number of activities, including widening SR 92 from a two-lane to a five-lane road, improving the roadside with curbs and gutters, and improving an area where SR 92 intersected with a local road (David, 2010).
1.3 Project Objectives
The aims of our study are to better define the workers’ exposure to traffic stressors, particularly inhalable particles and noise, for the purpose of evaluating short-term effects on various health endpoints. We hypothesized that the workers’ exposure significantly exceeds the exposure of the average population what could lead to an increased risk for cardiovascular diseases. In this study, we present the mixed exposure of highway maintenance workers to PM2.5, particle number concentration (PNC), and noise as well as to the co-pollutants CO, nitrogen dioxide (NO2), and ozone (O3). First, construction equipment usage was monitored to quantify equipment activity. Second, air quality and meteorological data were monitored at road construction locations in Texas. In addition, traffic data were obtained from TxDOT to facilitate comparisons among monitored air quality, construction equipment use, and on-road traffic.
In contrast to emissions from on-road motor vehicles, which have been regulated since the 1960s, emissions from off-road equipment remained unregulated until 1996. Given the relatively recent regulatory focus on off-road equipment, the diversity of off-road equipment types, and the resulting challenges associated with measuring real-world in-use equipment emissions, there is substantial uncertainty concerning construction equipment emissions. A key element of this uncertainty revolves around equipment use, or activity. Emissions are a function of two variables: an activity, such as the hours of operation of a piece of equipment, and the emission rate associated with that activity, such as the grams of PM emitted per hour of operation. Although information about construction equipment emission rates is generally available, very little data about equipment activity is available. Therefore, this study aims at improving understanding of the real-world equipment used to construct road projects, including information concerning day-to-day activity, equipment age distributions, fuel use, and resulting emissions.
Also of importance is the study’s investigation of near-road air quality impacts. There has been a growing body of peer-reviewed literature documenting the fact that roadway-related pollutant concentrations can be higher in near-road environments than in areas further from roads, and that there is a correlation between some observed health impacts and proximity to heavily-traveled roads. Therefore, one of the goals of this study is to monitor near-road pollutant concentrations and to identify whether construction work affected near-road air quality.
2. MECHANISM AND METHODOLOGY
2.1 Overview
This study will help us reduce the negative effects of air quality impacts on highway construction workers as a result of various activities involved in constructing transportation projects. Effective mitigation requires an overall knowledge of two things: the most important construction-related emissions sources, and the control options available to decrease emissions from those sources.
Various equipment like air compressors, bore/drill rigs, cranes, excavators, forklifts, generator sets, pavers, rollers, scrapers, tractors, loaders, backhoes, and welders are used often across various types of construction projects. In highway construction projects, signal boards also operate for many hours; however, their contribution to emissions may be negligible if they are solar powered (Eisinger, 2007).
In addition to non-road equipment, there are a number of on-road vehicles that contribute to the construction of transportation projects, including trucks hauling materials to and from the job site. However, when completing construction-specific emission assessments, emissions from the on-road fleet are sometimes ignored for the purpose of near-field or hotspot assessment work, since the bulk of the emissions from these vehicles occurs while they are in transit between the job site and other destinations. An example—one that proved to be especially important in the highway construction activity observed here—involves the use of watering trucks for dust control at the construction site.
Watering trucks, though categorized as on-road vehicles, can operate for substantial periods of time at construction sites (Roberts, 2013).
The age of non-road and on-road diesel-powered equipment plays an important role in their contribution to emissions. Non-road equipment exhaust emissions were unregulated prior to the 1996 model year, and many types of equipment remained unregulated through the 1999 model year; this equipment is referred to as Tier 0 equipment. More stringent emissions standards have been phased in over time. Tier 1 regulations were phased in from 1996-2000 but have since been succeeded by Tiers 2, 3, and 4. Once the most stringent standards (Tier 4) are largely phased in by 2014, they will reduce particulate matter (PM) and NOx emissions by 90% or more compared to earlier equipment. Diesel-powered construction equipment can remain in use for several decades. Therefore, although the new equipment is lower-emitting, older equipment continues to operate. Similarly, on-road diesel-powered trucks have had to meet increasingly stringent emissions standards over time. On-road, heavy-duty diesel-powered trucks were unregulated for PM emissions until the 1980s, after which increasingly stringent new-vehicle emissions standards took effect through the 1990s and 2000s. As documented by the National Research Council, pre-1980 trucks emit 10 times the PM of post-1996 trucks. As of 2010, the required use of ultra-low sulfur diesel (ULSD) fuel for both on-road and non-road vehicles has contributed to achieving the most recent federally mandated emissions standards (Lewis, 2009).
Regardless of equipment age, construction equipment use disturbs soil and contributes to fugitive dust; if left uncontrolled, the dust can be tracked onto roadways, where on-road vehicles suspend the material and contribute to airborne PM (Holden, 2009).
2.2 Equipment Activity
Emission estimates are prepared by pairing emission factors per unit of activity (such as grams of particulate matter emitted per hour of equipment operation) with the total units of activity associated with a particular project or work effort (such as the total hours of equipment operation). Unfortunately, in the construction arena, few published resources document equipment activity. Notably, there is a wide array of construction equipment and it is used across numerous applications that are difficult to generalize. In addition, the first non-road equipment emissions standards were promulgated by the U.S. Environmental Protection Agency (EPA) in 1994, relatively recently compared to other control programs. Therefore, less time has been devoted to assessing and documenting real-world, non-road equipment use than to on-road mobile source activities; as a result, there is a substantial amount of uncertainty associated with construction equipment activity (Abolhasani, 2006).
Generally, two information resources are available to characterize equipment populations and activity. “Top-down” information covers regional, statewide, or nationwide estimates of equipment populations and their average use over time, typically distributed by age (model year). Top-down data, embedded in California and other federal modeling tools, is usually based on surveys of agencies and private sector construction firms, and is used to prepare regional emissions inventories. “Bottom-up” equipment population and use data are sometimes collected for individual construction projects. Owing to the paucity of published bottom-up studies and the wide array of equipment applications, an ongoing challenge is synthesizing the bottom-up data into information that can be used across multiple project types and at regional or larger scales (Niemier, 2007).
A study of construction equipment used to complete Texas transportation projects found that the most-used equipment types included signal boards, rollers, tractors/loaders/backhoes, rubber tire loaders, pavers, and generator sets (Kable, 2006).
A related study found that, based on their contribution to NOx emissions during transportation project construction work, the most important equipment types included rollers, rubber tire loaders, graders, generator sets, scrapers, and tractors/loaders/backhoes (Eisinger, 2007).
Figure 2.2 Average per-project equipment use by number of hours in road construction projects (Lee, 2009).
Among the equipment deployed to construct a transportation project, actual use can vary in terms of time spent under load vs. idling. For example, using global positioning system (GPS) data, interviews, video recordings, and operation logs, researchers found that grader activity in the Texas Department of Transportation’s fleet was distributed among three modes: operations (70%), idling (20%), and driving (10%). (Lee, 2009).
In another study, researchers in southern California collected onboard activity data for graders, dozers, loaders, backhoes, a compactor, and a scrapper used for street and flood control area maintenance operations and landfill work; they found that equipment idled 25% of the time, on average (Huai, 2005).
Generally, the time under load is assumed to vary depending upon the equipment used. For example, one study of six important equipment types used in transportation projects found that ARB’s OFFROAD model included embedded assumptions that equipment was under load 54% (for rubber tire loaders) to over 70% of the time (e.g., scrapers) (Wang, 2008).
Equipment use varies depending upon the construction work phase. The table below summarizes typical transportation project construction phases and the main non-road equipment types used during each phase.
Table 2.1 Various phases of Roadway Construction projects and General Equipment used in each phase (Wang, 2008).
| Construction Phase | Description | Key Equipment Types |
| Land Clearing and Grubbing | Removal of trees, vegetation from the construction site | Excavators, Dozers, Tractors |
| Roadway Excavation | Excavating, grading and disposing soil for construction of lanes and shoulders | Rollers, scrapers, tractors, dozer, excavator, rubber tire loader |
| Structural Excavation | Excavating, grading and disposing soil for construction of structural elements | Tractors, backhoes, excavator |
| Base and Sub-Base | Construction of road bed foundation | Graders, rollers, scrapers, dozers |
| Structural Concrete | Construction of retaining walls, curbs and gutters | Rough terrain forklifts, generator sets, backhoes, air compressors |
| Paving | Application of Asphalt | Rollers, pavers, paving equipment, tractors, loaders |
| Drainage and Landscaping | Erosion control, planting and irrigation | Generator sets, pumps, tractors, loaders, backhoes |
There are simple ways to estimate the construction-related equipment and vehicles required to complete a particular project. We can often relate activity and emissions to acreage disturbed and materials (such as soils) that need to be moved. For example, ARB relates construction-related road dust emissions to acres disturbed per mile of road construction; ARB’s method of estimating acres disturbed is shown in the table below. Once estimates of acres disturbed are obtained, they can be used to estimate materials movements needed to complete a project. Once acres disturbed and materials movement activities are known, we can use these assumptions to estimate emissions. Most of the model applications used to estimate construction emissions relies on estimated acres disturbed and materials movement to generate emission estimates.
Table 2.2 ARB guidance for number of acres distributed per mile of construction of different types of road construction projects (ARB, 1997).
| Road Type | Freeway | Highway | City and County |
| Acres distributed per mile of construction | 12.1 | 9.2 | 7.8 |
2.3. Emissions
In contrast to on-road motor vehicle emissions, which have tended to decrease over time due to fleet turnover to lower-emitting vehicles, non-road mobile source emissions have increased over time due to increased activity and the relatively long life of in-use equipment. In response to the growing importance of non-road equipment emissions, regulators have promulgated increasingly more stringent emissions standards for new equipment.
| Standard (Tier) | Phase in period | Model Year |
| 1 | 1996-2000 | 1996-2005 |
| 2 | 2001-2006 | 2001-2010 |
| 3 | 2006-2008 | 2006-2012 |
| 4(transitional) | 2011 | 2008-2013 |
| 4(final) | 2013 | 2013 and later |
Table 2.3 Federal classification of non-road equipment emission standards (Schatanek, 2005).
Therefore, a key factor governing transportation project construction emissions is age of the equipment used at the time of the project. Equipment manufactured before 1996 was essentially uncontrolled; equipment manufactured since 1996 has had to meet progressively more stringent emissions standards the later the model year.
Unregulated (Tier 0) and older regulated equipment (Tiers 1 or 2) can be much higher-emitting per hour of operation than more modern (Tier 3 or Tier 4) equipment. One study of key transportation project construction equipment types found, for example, that if Tier 0 equipment was replaced by Tier 3 equipment in 2010, exhaust emissions would decrease by 83% for PM and 77% for NOX; replacing Tier 0 equipment with Tier 4 equipment in 2015 would decrease emissions by 99% for PM and 92% for NOX (Wang, 2008).
In addition to equipment age or model year, other key factors that govern emissions from a single piece of construction equipment include the degree to which the equipment and its emission controls have deteriorated over time, the percentage of time the equipment is under load (emission rates are higher when equipment is under load than when it is idling), fuel type, horsepower rating (emission rates increase with horsepower), and hours of operation (Gautam, 2002).
Historically, non-road emissions have been estimated based on tests of individual engines. The engine, after being removed from a vehicle or piece of equipment, is tested using a dynamometer test bed configured to simulate operations. Each engine is operated on the test bed either at constant speed and load (i.e., steady state) for a specified time interval or following a predefined chassis dynamometer test (Frey, 2008).
Some studies have used onboard Portable Emissions Measurement Systems (PEMS) to assess typical operation and develop cycles for dynamometer testing. PEMS can collect emission rates for a range of pollutants, including hydrocarbons (HC), NO, PM, carbon monoxide (CO), and carbon dioxide (CO2), and engine parameters such as manifold absolute pressure (MAP) (Harley, 2000).
Other studies have estimated PM emissions with a light scattering technique. In addition, some studies have used fuel consumption information to estimate non-road diesel engine emissions, as well as heavy-duty diesel truck emissions (California Air Resources Board Mobile Source Emissions Inventory Program, 2007).
2.4. Emissions Modelling
The Environmental Protection Agency’s (EPA) on road Motor Vehicle Emissions Simulator (MOVES2014) release in October 2014 is used to compute vehicle emissions for this study. MOVES is a state-of-the-science model for estimating air pollution emissions from mobile sources under a wide range of user-defined conditions. MOVES incorporates analysis of millions of emission test results and considerable advances in the EPA’s understanding of vehicle emissions. MOVES can estimate emissions from running and evaporative processes as well as brake and tire wear emissions for all types of on-road vehicles across multiple geographic scales for any part of the country, except California. MOVES is EPA’s best tool for estimating emissions from on-road mobile sources.
For estimating the off road vehicle emissions, the Sacramento Metropolitan Air Quality Management District (SMAQMD) sponsored development of a spreadsheet tool to estimate emissions from transportation construction projects. Emissions are calculated by project phase and for the overall project lifetime. A data entry sheet requires user input of project specifications including name and start year, project type (new road construction, road widening, or bridge/overpass construction), time length and acreage of the project, truck capacity involved, and expected soil volume. The model estimates emissions of reactive organic gases (ROG), CO, NOx, PM10, PM2.5, and CO2 for different project phases (land clearing, grading/excavation, drainage/utilities/sub-grade, paving) (ALAPCO, 2006).
2.5 Methodology for Estimating On-road Vehicle Emissions using MOVES
MOVES is a state-of-the-science model for estimating air pollution emissions from mobile sources under a wide range of user-defined conditions. MOVES incorporates analysis of millions of emission test results and considerable advances in the EPA’s understanding of vehicle emissions. MOVES can estimate emissions from running and evaporative processes as well as brake and tire wear emissions for all types of on-road vehicles across multiple geographic scales for any part of the country. MOVES is EPA’s best tool for estimating emissions from on-road mobile sources.
MOVES can estimate emissions and energy consumption at a variety of geographic scales – national, county, and project – for various time spans. The national and county scales can be used to produce inventories for the nation an individual county or state, and multi-state, multi-county, and metropolitan regions. MOVES can create an annual emissions inventory for the year 1990 and any calendar year from 1999 through 2050. MOVES can also be used for scenario planning and policy efficacy analysis in the area being modeled, be it a state, a region, or a county or a portion of a county. For example, with user-supplied information on travel activity, such as vehicle miles traveled (VMT) and speeds, MOVES can perform scenario analyses to assess the emissions and energy use impacts of various travel efficiency strategies (EPA, 2014).
In addition, MOVES can be used to evaluate emissions or energy impacts of other types of strategies, such as those that affect vehicle and fuel technologies or that are designed to change the composition of the vehicle fleet. MOVES captures the effects of fleet turnover and the change in vehicle emissions and fuels over time. MOVES includes vehicle and fuel technologies that are currently in widespread use. Since MOVES emissions estimates depend on vehicle types, vehicle ages, vehicle activity (including speeds and operating modes), road types, and fuel types, MOVES can be used to summarize how emissions would change in the future under various scenarios that affect any of these inputs. MOVES can estimate the effects of individual control measures and emission reduction strategies, or combinations of them, in any future year up to 2050 (EPA, 2014).
2.5.1 MOVES Inputs
The MOVES model is equipped with default modeling values for the range of conditions that affect emissions. MOVES defaults may be replaced by alternate input data sets that better reflect local scenario conditions. Where local data were available and consistent with the methodology, MOVES defaults were replaced by local input values via the MOVES Run Specification (MRS) input file (RunSpec) and MOVES CDB (county input database). (The MRS, CDB, and MOVES default database provide the data for each local scenario model run.) Inputs were developed and used to produce emissions factors reflecting local conditions including area, timeperiod, average local weather conditions, fuel properties, vehicle fleet characteristics (e.g., age), and emissions control programs (EPA, 2014).
In the case of the activity input data to MOVES, the MOVES defaults were in general used, which is basic to the emissions rates method (e.g., default activity is normalized in the emissions rates, and the emissions rates are later multiplied by the local activity estimates to calculate emissions external to MOVES).
There is one RunSpec required per county for emissions analysis, and a corresponding County Input Database (CDB) and output database. Therefore, for each county, there will be one of each RunSpec input files, CDB inputs, MOVES output databases, RatesCalc output databases, and RatesAdj final rate output databases. RatesAdj produces the final rates by extracting and storing the rates for the inventoried pollutants in a separate, smaller database for input to the emissions runs (EPA, 2014).
MOVES Run Specifications
The Moves Run Specification (MRS) defines the place, time, road categories, vehicle and fuel types, pollutants and emissions processes, and the overall scale and level of output detail for the modeling scenario (EPA, 2014).
2.5.1.1 Scale, Time Spans, and Geographic Bounds
The MOVES Domain/Scale “County” was selected as it is required for inventory estimates. The MOVES Calculation Type “Emissions Rates” was selected for MOVES to produce the emissions rates with speed bin indexing, as needed for the detailed emissions estimation process (EPA, 2016).
The Time Spans parameters were specified to provide the most detail available, which is the hourly aggregation level, for all hours of the day, for the selected year, month, and day type. The analysis year is selected (EPA, 2016).
Under Geographic Bounds for the County Domain Scale, only one county may be selected. The local County Input Database (CDB) containing the calendar year scenario-specific input data for the county was specified as the County Domain Input Database, and under Region, “Zone & Link” was selected as required for the emissions rates calculation type (EPA, 2016).
2.5.1.2 On-Road Vehicle Equipment and Road Type
MOVES describes vehicles by a combination of vehicle characteristics (e.g. passenger car, passenger truck, light commercial truck, etc.) and the fuel that the vehicle can use (gasoline, diesel, etc.). The Vehicles/Equipment Panel is used to specify the vehicle types included in the MOVES run. MOVES allows the user to select from 13 “source use types”, and six different fuel types (gasoline, diesel, ethanol E-85, compressed natural gas (CNG), electricity, and liquefied petroleum gas (LPG) 25). Some source/fuel type combinations are not valid and therefore not included in the MOVES database (e.g., diesel motorcycles) (EPA, 2014).
For estimating on-road emissions, we have to select the appropriate fuel and vehicle type combinations in the On Road Vehicle Equipment Panel to reflect the full range of vehicles that will operate in the county. In general, users should select all valid Compressed Natural Gas, Diesel Fuel, Ethanol (E-85), and Gasoline vehicle and fuel combinations. Ethanol should be selected even if there is no E-85 fuel sold in the area (EPA, 2016).
The Road Type Panel is used to define the types of roads that are included in the run. MOVES defines five different road types as shown in the Table below.
Table 2.4 Road Type Classification for MOVES input (EPA, 2016)
| Road Type ID | Road Type | Description |
| 1 | Off-Road | Locations where the predominant activity is vehicle starts, parking and idling (parking lots, truck stops, rest areas, freight or bus terminals) |
| 2 | Rural Restricted access | Rural highways that can be accessed only by an on-ramp |
| 3 | Rural Un-restricted access | All other rural roads (arterials, connectors, and local streets) |
| 4 | Urban Restricted access | Urban highways that can be accessed only by an on-ramp |
| 5 | Urban Un-restricted access | All other urban roads (arterials, connectors, and local streets) |
All five MOVES road type categories have to be selected to compute the emissions. Selection of road types in the Road Type Panel also determines the road types that will be included in the MOVES run results. Different characteristics of local activity by road type are entered in the County Input Database (CDB) using the Average Speed Distribution and Road Type Distribution importers as described in the next sections.
2.5.1.3 Pollutants and Processes
The Pollutants and Processes Panel allows users to select from various pollutants, types of energy consumption, and associated processes of interest. In MOVES, a pollutant refers to types of pollutants or precursors of a pollutant but also includes energy consumption choices. Processes refer to the mechanism by which emissions are created, such as running exhaust or start exhaust. Users should select all processes associated with a pollutant in order to account for all emissions of that pollutant. This can be done by checking the box to the left of the pollutant, which selects all the relevant processes for that pollutant. Note that checking the box next to any of the greenhouse gas pollutants selects only running, start, and hoteling processes. Evaporative processes do not produce greenhouse emissions. For many pollutants, the emissions calculation is based on calculations of another pollutant. In such cases, users must select all the associated pollutants and processes. MOVES will display warning messages in the box on the Pollutants and Processes screen until all necessary base pollutants are selected.
The total emissions for a pollutant are the sum of the product of emission rates and the appropriate activity measure (vehicle miles travelled or vehicle population) for each vehicle type for all pollutant processes that apply to that pollutant and vehicle type (EPA,2014).
2.5.1.4 Manage Input Data Set
We will not use the Manage Input Data Sets panel for our study as we compute emissions. However, The Manage Input Data Sets feature allows alternate inputs other than those included in the County Input Database (CDB).
2.5.1.5 Strategies
The Strategies option in the Navigation Panel provides access to the Rate-of-Progress check box. The Rate of Progress Panel applies only to SIP analyses in certain ozone nonattainment areas. It is not applicable for the computation of county level emission inventories, therefore is not required for our study.
2.5.1.6 Output
The Output option in the Navigation Panel provides access to two panels – General Output and Output Emissions Detail. In general, users can generate output in whatever form works best for their specific needs. The General Output Panel includes three sections: Output Database, Units, and Activity (EPA, 2014).
The output units are selected to be pounds, kilojoules, and miles. The activity categories are pre-set by MOVES for inclusion in the output database. The output detail level was by hour, link, pollutant, process, source use type, and fuel type.
2.5.2 MOVES County Input Database (CDB)
After completing the RunSpec, the next step is to supply MOVES with data to create an input database. We will be using the County scale, so the County Data Manager (CDM) is used to create an input database and populate it with local data.
As with any model, the quality of the data inputs greatly affect the accuracy of the outputs. MOVES requires input data to describe the location, time, and characteristics of the vehicle fleet being modeled to calculate emissions.
The County Data Manager, which is available with the County scale, serves the function of simplifying importing specific local data without requiring direct interaction with the underlying input database. The Data Importer and County Data Manager (CDM) include multiple tabs, each one of which opens importers that are used to enter specific local data. These tabs and importers include the following:
• Meteorology Data
• Source Type Population
• Age Distribution
• Vehicle Type VMT
• Average Speed Distribution
• Road Type Distribution
• Ramp Fraction
• Fuel
• I/M Programs
• Zone
• Starts
• Hoteling
• Retrofit Data
• Generic
Each of the importers allows the user to create an import template file with required data field names and some key fields populated. We will then edit these templates to add specific local data with a spreadsheet application or other tool, and import each data file into an input database for the run (EPA, 2014).
In order to complete a RunSpec at the County scale, we must review and import default data for each tab in the County Data Manager (CDM) except for Ramp Fraction.
The CDM can be accessed either from the “Pre-Processing” pull-down menu at the top of the MOVES User Interface, or by selecting “Enter/Edit Data” on the Geographic Bounds Panel. Before entering any locality specific data, an input database must be created on the Database Tab on the Geographic Bounds Panel. When the database is created, MOVES keeps track of the selections made in the RunSpec at that moment (EPA,2014).
2.5.2.1 Meteorology Data
Ambient temperature and relative humidity data are important inputs for estimating on-road emissions with MOVES. Ambient temperature and relative humidity are important for estimating emissions from motor vehicles as these affect air conditioner use. MOVES requires a temperature (in degrees Fahrenheit) and relative humidity (in terms of a percentage, on a scale from 0 to 100) for each hour selected in the RunSpec. For example, MOVES requires a 24-hour temperature and humidity profile to model a full day of emissions on an hourly basis. EPA has created a tool that takes minimum and maximum daily temperatures and creates an hourly temperature profile that could be used as input to MOVES (EPA, 2016).
Temperature assumptions used for estimating on-road emissions have to be based on the latest available information. The MOVES database includes default monthly temperature and humidity data for every county in the country. These default data are based on average monthly temperatures for each county from the National Climatic Data Center (EPA, 2016).
As we choose the Emission Rate calculation type is chosen in the RunSpec, we can enter a different temperature and humidity for each hour of the day to create an emission rate table that varies by temperature for running emissions processes. Emission rates for all running processes that vary by temperature can be post-processed outside of MOVES to calculate emissions for any combination of temperatures that can occur during a day.
However, for emissions from any non-running processes that occur on the “off-network” road type, i.e., start and hoteling emissions, it is still necessary to define a temperature profile for each hour of the day. Unlike running emissions that depend entirely. It is possible to model both running and off-network emission rates in one run to create a lookup table that can be post-processed into an inventory (EPA, 2016).
2.5.2.2 Source Type Population
Source type (vehicle type) population is used by MOVES to calculate start and hoteling emissions. It is also used to calculated evaporative emissions, but as stated earlier, evaporative emissions are not necessary when estimating greenhouse emissions. Start and hoteling emissions depend more on how many vehicles are parked and started, rather than how many miles they are driven (EPA, 2014).
Table 2.5 MOVES Source Types and HPMS Vehicle Types (EPA, 2014)
| MOVES | HPMS | ||
| Source Type ID | Source Type | Vehicle Type ID | Vehicle Type |
| 11 | Motorcycle | 10 | Motorcycle |
| 21 | Passenger Car | 25 | Light Duty Vehicles |
| 31 | Passenger Truck | ||
| 32 | Light Commercial Truck | ||
| 41 | Intercity Bus | 40 | Buses |
| 42 | Transit Bus | ||
| 43 | School Bus | ||
| 51 | Refuse Truck | 50 | Single Unit Trucks |
| 52 | Single Unit Short-haul Truck | ||
| 53 | Single Unit Long-haul Truck | ||
| 54 | Motor Home | ||
| 61 | Combination Short-haul Truck | 60 | Combination Trucks |
| 62 | Combination Long-haul Truck | ||
Therefore, in MOVES, start emissions are a function of the population of vehicles in an area and therefore we need to develop local data for vehicle population. MOVES categorizes vehicles into 13 source types, which are subsets of five HPMS vehicle types in MOVES, as shown in the table above.
MOVES will produce emission rates for start emissions and hoteling emissions by source type in terms of unit of mass (e.g., grams) per vehicle. Total start and hoteling emissions would then be calculated outside of MOVES by multiplying the emission rates by the vehicle populations for each source type. However, for this study will still need to enter data using the Source Type Population Importer that represents the population of vehicles in the total area where the lookup table results will be applied. This is necessary because MOVES uses the relationship between source type population and VMT to determine the relative amount of time vehicles spend parking vs. running (EPA, 2014).
2.5.2.3 Age Distribution
A typical on-road vehicle fleet includes a mix of vehicles of different ages, referred to as Age Distribution in MOVES. The age distribution of vehicle fleets can vary significantly from area to area and affects emissions. Generally, fleets with a higher percentage of older vehicles have higher emissions. Older vehicles have typically been driven more miles and experience more deterioration in emission control systems. Likewise, a higher percentage of older vehicles means that there are more vehicles in the fleet that do not meet newer, more stringent emission standards (EPA, 2014).
For estimating on-road emissions, we must collect the age distributions of vehicle fleet that are applicable to the area being analyzed. Local age distributions can be estimated from local vehicle registration data.
2.5.2.4 Vehicle Type VMT
Vehicle Miles Travelled (VMT) inputs have the greatest impact on the results of an energy consumption analysis. MOVES estimates emissions based on travel activity multiplied by emission factors. MOVES will multiply the VMT from each vehicle source type, on each road type, by the corresponding emission factors to generate an emissions inventory (EPA,2014).
MOVES will produce emission rates for running emissions by source type and road type in terms of grams per mile. Total running emissions would then be calculated outside of MOVES by multiplying the emission rates by the VMT for each source type and road type. However, we will still need to enter data using the Vehicle Type VMT Importer that reflects the vehicle miles travelled (VMT) in the total area where the lookup table results will be applied. This is necessary because MOVES uses the relationship between source type population and vehicle miles travelled (VMT) to determine the relative amount of time vehicles spend parked vs. running (EPA,2014).
2.5.2.5 Average Speed Distribution
Vehicle power, speed, and acceleration have a significant effect on vehicle emissions, including greenhouse emissions. The model also uses the distribution of vehicle hours traveled (VHT) by average speed to determine an appropriate operating mode distribution. For estimating on-road emissions where activity is averaged over a wide variety of driving patterns, a local speed distribution by road type and source type is a reasonable surrogate for more detailed local drive cycles or operating mode distributions The Average Speed Distribution Importer in MOVES calls for a speed distribution in vehicle hours travelled (VHT) in 16 speed bins, by each road type, source type, and hour of the day included in the analysis (EPA, 2014).
Average speeds, as used in MOVES, will tend to be less than nominal speed limits for individual roadway links. Speed is entered in MOVES as a distribution rather than a single value. Use of a distribution will give a more accurate estimate of emissions than use of a single average speed.
Table below shows the speed bin structure that MOVES uses for speed distribution input.
Table 2.6 MOVES speed bins classification (EPA, 2014)
| Speed Bin ID | Average Bin Speed | Speed Bin Range |
| 1 | 2.5 | speed < 2.5mph |
| 2 | 5 | 2.5mph <= speed < 7.5mph |
| 3 | 10 | 7.5mph<= speed <12.5mph |
| 4 | 15 | 12.5mph<= speed <17.5mph |
| 5 | 20 | 17.5mph<= speed <22.5mph |
| 6 | 25 | 22.5mph<= speed <27.5mph |
| 7 | 30 | 27.5mph<= speed <32.5mph |
| 8 | 35 | 32.5mph<= speed <37.5mph |
| 9 | 40 | 37.5mph<= speed <42.5mph |
| 10 | 45 | 42.5mph<= speed <47.5mph |
| 11 | 50 | 47.5mph<= speed <52.5mph |
| 12 | 55 | 52.5mph<= speed <57.5mph |
| 13 | 60 | 57.5mph<= speed <62.5mph |
| 14 | 65 | 62.5mph<= speed <67.5mph |
| 15 | 70 | 67.5mph<= speed <72.5mph |
| 16 | 75 | 72.5mph <= speed |
MOVES will produce a table of emission rates by source type and road type for each speed bin. Total running emissions would then be calculated by multiplying the emission rates by the VMT for each source type in each speed bin.
2.5.2.6 Road Type Distribution
The fraction of vehicle miles travelled (VMT) by road type varies from area to area and can have a significant effect on emissions from on-road mobile sources.
As we use the Emission Rates option, MOVES will automatically produce a table of running emission rates by road type. Running emissions would then be calculated by multiplying the emission rates by the VMT on each road type for each source type in each speed bin. In that case, data entered using the Road Type Distribution Importer is still required, but is not used by MOVES to calculate the rate. The road type distribution inputs are important for this Emission Rates runs involving non-running processes, because they are used by MOVES to calculate the relative amounts of running and non-running activity, which in turn affects the rates for the non-running processes (EPA, 2014).
2.5.2.7 Ramp Fraction
The default ramp fraction on both rural restricted roads (Road Type 2) and urban restricted roads (road type 4) is 8% of VHT and this default value of 8% will be automatically applied and we do not need to import local data (EPA,2016).
2.5.2.8 Fuel
The fuel tab generates four tables called FuelFormulation, FuelSupply, FuelUsageFraction, and AVFT (alternative vehicle fuels and technology) that require inputs to define the fuels used in the area being modeled. We use the MOVES default data as inputs for all the four tables that are accessible using the Export Default Data button in the County Data Manager (CDM).
• The FuelSupply Table defines the fuel formulations used in a region (the regionCounty Table defines which specific counties that are included in these regions) and each formulation’s respective market share in the area. The marketshare is each fuel formulation’s fraction of the volume consumed in the area (EPA, 2016).
• The FuelFormulation Table defines the attributes (such as RVP, sulfur level, ethanol volume, etc.) of each fuel (EPA, 2016).
• The FuelUsageFraction Table defines the frequency at which E-85 capable vehicles use E-85 fuel vs. conventional fuel, when appropriate. MOVES2014 contains default estimates of E-85 fuel usage for each county in the United States (EPA, 2016).
• The AVFT (Alternative Vehicle Fuels and Technology) Table is used to specify the the fraction of vehicles using different fuels and technologies in each model year. In other words, the Fuel Tab allows users to define the split between diesel, gasoline, ethanol, CNG, and electricity, for each vehicle type and model year (EPA, 2016).
2.5.2.9 Inspection and Maintenance (I/M) Programs
MOVES calculates emissions rates that reflect the emissions-reducing benefits of the I/M program design reflected in parameters specified in the MOVES IMcoverage table generated by the county data manager (CDM). The MOVES default values in the IMcoverage table are replaced by the set of MOVES IMcoverage records for Texas produced by the Texas Training Institute (TTI) (EPA, 2016).
2.5.2.10 Zone
The Zone Tab will appear only when a custom domain is selected in the Geographic Bounds Panel. For the emissions calculation, we do not use the Zone tab as we use the county domain but not the custom domain.
2.5.2.11 Starts
The Starts Tab is used to import local information on vehicle start activity. For this study, we do not use his input. MOVES will calculate start activity based on the vehicle populations (sourcetypeyear input) and default assumptions of vehicle activity.
2.5.2.12 Hoteling
The Hoteling Tab is used to import information on combination truck hoteling activity. All hoteling processes only apply to long-haul combination trucks. However, for this study, leave the hoteling tab in the county input database with default values.
2.5.2.13 Retrofit Data
The Retrofit Data Tab in the county data manager (CDM) allows to enter retrofit program data that apply adjustments to vehicle emission rates. There are no default retrofit data in MOVES. However, we do not input retrofit data into MOVES as we do not have a retrofit program that we wish to model.
2.5.2.14 Generic
The Generic Tab can be used to export, modify, and re-import any of the default MOVES tables not covered by the County Data Manager. The generic tab is not used in this study as we do not use any default MOVES tables.
2.5.3 Checks and Runs
After completing the input data preparation, the county input database (CDB) have to be checked to verify that all the tables are in the county input database (CDB) and the tables are populated with data as intended. The MOVES RunSpecs are run in batches using the MOVES command line tool. The batches are designed to write each MOVES run log to a text file for subsequent error/warning checks, of which none were found.
2.5.4 Post-Processing Runs
Each MOVES output database is post-processed using a two-step process – for each county and year, an interim RatesCalc rate database is produced, followed by the final rates RatesAdj database containing the emissions rate tables for input to the emissions inventory calculations. The following post-processing steps are performed on the MOVES output databases.
2.5.5 On-road Emissions Calculations
The emissions inventories are calculated using the EmsCalc utility. The VMT-based emissions calculation is used in order to calculate emissions. The VMT-based emissions calculations use the roadway-based rates and the VMT and speeds to estimate emissions.
The EmsCalc output includes three files: a listing file, a standard tab-delimited inventory summary, and a tab-delimited 24-hour inventory summary by source classification codes. The county source classification inventory summaries for each year were input to the MOVESsccXMLformat utility, which converts them into an XML inventory format.
2.6 Off-Road Emissions Calculations
In order to estimate the emissions produced by the off-road construction equipment, we use the Roadway Construction Emissions Model (RCEM) spreadsheet tool developed by the Sacramento Metropolitan Air Quality Management District (SMAQMD). Emissions are calculated by project phase and for the overall project lifetime.
The data entry sheet requires inputs of project specifications including name and start year, project type (new road construction, road widening, or bridge/overpass construction), time length and acreage of the project, truck capacity involved, and expected soil volume. The model estimates emissions of reactive organic gases, CO, NOx, PM10, PM2.5, and CO2 for different project phases like land clearing, grading/excavation, drainage/utilities/sub-grade and paving.
All the data required for the emissions calculation is entered in the Data Entry worksheet. Required data fields are highlighted in yellow. The data has to be entered in these yellow cells.
The emissions are then calculated by the RCEM spreadsheet and displayed in the Emissions Estimates worksheet.
3. CASE STUDY
The collection of site activity data is a part of this study. The activity data along with the on-road vehicle data are used to estimate the vehicle emissions associated with major road construction activity and their impacts on the health of road construction workers. An overview of the road construction site case study is provided here.
3.1 Site Selection
The case study involves construction to widen 6 lane expressways to 8 lane expressways on Interstate Highway 10 (IH-10). The road construction project covers approximately a two-mile segment of IH 10 in Bexar County (South Texas). The project boundaries were Loop 1604 to one mile north of Huebner Road. Based on U.S. Federal Highway Administration (FHWA) functional classifications, IH 10 is an Interstate Highway. The construction project cost approximately $25 million (TxDOT, 2016).
Construction began in January 2012 and was scheduled to finish by December 2015.
Overall, this field study could monitor equipment activity and air quality across virtually all construction activities involved in this project. Construction took place during the day, typically Mondays through Fridays from approximately 7:00 a.m. to 6:00 p.m. During the construction period, one lane of traffic remained open in each direction, unless special construction work was warranted. Figure 3.1 illustrates the general geographic location of the IH 10 study site (TxDOT, 2016).
Figure 3.1 Geographic location of the IH 10 construction site (TxDOT, 2016)
3.2 Construction and Equipment Activity
Work officially began on the IH 10 improvement project on January 2, 2012. The first few months primarily involved planning activities; field work involving substantial use of diesel-powered equipment did not begin until April 2012. Work in the two-mile construction zone consisted of both pavement widening and pavement reconstruction. Reconstruction work involved the removal of existing pavement and reconstruction of the pavement at a new grade. Starting at the north end of the project, the Texas Department of Transportation (TxDOT) widened the expressway which initially had 3 lanes on each direction to 4 lanes on each direction.
To support quantitative assessment of the air quality impacts of the road construction
project, we have collected information on the fleet of construction equipment operating at the IH 10 road widening project and on the timing and location of construction activities. These data were collected from TxDot, and analyses of the resulting data sets are performed to quantify emissions associated with the related construction activities.
Fugitive dust emission-producing activities include land clearing, demolition, ground excavation, and earth moving; levels of dust emissions are influenced by variables such as the size of the area under construction, meteorological conditions, the composition of the soil, and the use of control measures such as wet suppression and wind barriers. Emission factors for estimating PM10 emissions associated with construction dust are typically based on the number of acres disturbed during construction, though more detailed emission factors are available for specific processes (e.g., general land clearing, topsoil removal) (Alison, 2009).
For the IH 10 project, TxDOT provided us with a list of equipment that was dedicated to the project. This data is inserted in the RCEM spreadsheet to estimate the emissions from the off-road equipment. The table below lists the equipment used by TxDot for the IH 10 project.
Table 3.1 List of off-road equipment for IH 10 project (TxDOT, 2015)
RCEM estimates road construction project emissions from simple user inputs (e.g., project type, project length, total soil imported and exported) and default assumptions for equipment activities. We used this tool using input data representative of the IH 10 project. The table below shows the input parameters used to run this tool.
Table 3.2 Input parameters used to run RCEM for IH 10 project.
| Parameter | Input Value | Input Alternatives |
| Construction Start Year | 2012 | Year |
| Project Type | 2 | 1. New road construction |
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