ESTIMATION OF EMISSIONS NEAR ROAD CONSTRUCTION
PROJECTS AND THEIR IMPACTS ON WORKERS HEALTH

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 (NO_X) 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.

INTODUCTION

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).

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).

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).

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.

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 NO_X;
replacing Tier 0 equipment with Tier 4 equipment in 2015 would decrease
emissions by 99% for PM and 92% for NO_X (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)

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)

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)

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.

• RatesCalc – Interim Rates
  Databases: Using RatesCalc, the mass/SHP off-network evaporative process rates
  are calculated using data from the county input database (CDB), the MOVES
  default database, and the MOVES rateperprofile and ratepervehicle emissions
  rate output. The utility will also copy the mass/mile, mass/start, mass/hour
  rates along with the units into emissions rate tables. It will create an output
  database containing the rates tables input to the RatesAdj utility
• RatesAdj – Final Rate
  Databases: Using RatesAdj, the final emissions rates are produced for input to
  the EmsCalc emissions calculator. RatesAdj will extract emissions rates from
  the RatesCalc rate tables for only those pollutants needed in the emissions
  calculations. The ratesadj output database, is created one for each county and
  year created for input to the emissions calculations.

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).

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.

3.3 Traffic Data and On-road Emissions

Real-time
traffic activity data on IH 10 is required to separate the contributions of on-road
vehicles, especially heavy-duty vehicles, from the contribution of construction
activities to ambient pollutant concentrations measured at the construction
zone. To address this issue, the Texas Department of Transportation (TxDOT) provided
with access to its online Traffic Count Database System (TCDS), which contains
traffic count data for Highway Performance Monitoring System (HPMS) sites
across the state.

The Traffic Count Database System (TCDS)
provide traffic count data on IH 10 near the construction project site which
can be used in the MOVES model to estimate the emissions from on-road vehicles
near the construction site. The MOVES model is equipped with default modeling values
for the range of conditions that affect emissions. MOVES defaults are 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
input file (RunSpec or MRS) and MOVES CDB (county input database). Inputs were
developed and used to produce annual and daily emissions factors reflecting
local conditions including area from January 2015 period average weather
conditions, summer fuel properties, vehicle fleet characteristics and emissions
control programs.

To estimate emissions from on-road vehicle
traffic on IH 10, emission factors from EPA’s MOVES model were applied to traffic
count data obtained from TxDOT. MOVES was run using the following input data
sources:

• Age distributions
  calculated from vehicle registration data for Bexar County provided by TxDOT.
• Default fuels
  characteristics (e.g., sulfur content) for Bexar County provided by the
  Environmental Protection Agency (EPA).
• Average VMT mix and
  hourly VMT fractions calculated from traffic counts collected at the Highway
  Performance Monitoring System (HPMS) site on IH 10.
• Hourly temperature and
  relative humidity for an average day during each month of 2015 calculated from
  meteorological data collected by TCEQ (Texas Commission on Environmental
  Quality) at the IH 10 construction site in Bexar county.

MOVES emission factors by source type,
vehicle population and speed bin were generated for each month in 2015 and
applied to VMT derived from traffic count data to estimate hourly emissions.

On an average day in 2015, 8,789 vehicles passed through the IH construction zone. Average weekday traffic volumes were higher than average weekend day traffic volumes. Weekday traffic volumes on IH 10 peaked at 7:00 a.m. and 5:00 p.m traffic volumes were consistent on a weekly basis.

3.4 Protective Gear for the Highway Construction Workers

The
workers employed on the IH 10 road widening project were provided with Personal
Protective Equipment (PPE) to protect the workers from harmful pollutants and
emissions from both on-road and off-road sources. The protective equipment
protects the employees from physical, electrical, heat, chemicals, biohazards,
and airborne particulate matter. Protective equipment was compulsory for all
the workers for the construction occupational safety and health purposes.

Various protective equipment, including
personal protective equipment for eyes, face, head, and extremities, protective
clothing, respiratory devices, and protective shields and barriers, were
provided and used. The various protective equipment used in this project by the
workers were:

• Head protection:
  Protective helmets were provided to the employees working in this project as there
  is a possible danger of head injury from impact, or from falling or flying
  objects, or from electrical shock and burns.

• Hearing protection: Earplugs or earmuffs were provided to help prevent damage to hearing. Exposure to high noise levels can cause irreversible hearing loss or impairment as well as physical and psychological stress.

• Eye and face protection: Besides spectacles and goggles, personal protective equipment such as special helmets or shields, spectacles with side shields, and face shields were provided to protect workers from the hazards of flying fragments, large chips, hot sparks, optical radiation, splashes from molten metals, as well as objects, particles, sand, dirt, mists, dusts, and glare which occur due to various activities involved in the construction.
• Respiratory protection: The workers were provided with appropriate respirators to protect against adverse health effects caused by breathing air contaminated with harmful dusts, fogs, fumes, mists, gases, smokes, sprays, or vapors. Respirators generally cover the nose and mouth or the entire face or head and help prevent illness and injury.

• Foot and leg protection: In
  addition to foot guards and safety shoes, leggings made with leather,
  aluminized rayon, or other appropriate material were provided to help prevent
  injuries by protecting workers from hazards such as falling or rolling objects,
  sharp objects, wet and slippery bitumen and asphalt surfaces, hot surfaces, and
  other hazards.

3.5 Summary of Emissions

The
summary of calculated emissions from the two core components of the IH 10 field
study which involved tracking of construction equipment activity and subsequent
estimate of emissions produced by those activities; and collection of on-road
vehicle data and estimate of emissions produced by these sources is described
in this section.

3.5.1 Emissions from Construction Equipment

For
the assessment of PM2.5, PM10, and NOx emissions from the various phases of a
road-widening project, we have used the activity data described above to
develop equipment exhaust and fugitive dust emissions estimates for
construction activity.

Table 3.3 Estimated Off-Road Emissions Near IH 10 construction project (RCEM, 2015)

The total exhaust and fugitive dust emissions, for the year 2015 construction activity at the IH 10 project produced 7,762 lb. of PM10, 1,610 lb. of PM2.5, and 7,828 lb. of NOx. Fugitive dust accounted for 92% of the total PM10 emissions associated with construction activities and 63% of the total PM2.5 emissions associated with construction activities On an average day in 2015, construction activity at the IH 10 project produced 32 lb. of PM10, 7 lb. of PM2.5, and 33 lb. of NOx.

3.5.2 Emissions from On-Road Sources

Table
3.5.2 shows annual and daily estimates of NOx, PM10, and PM2.5 exhaust emissions
from on-road motor vehicles traveling on IH 10 through the project area (for
emission estimation purposes, we assumed each vehicle traveled 2.3 miles
through the project zone). On-road NOx emissions for 2015 were about 2.5 times
higher than total NOx emissions from construction activities. However, on-road
PM10 and PM2.5 emissions for 2015 were only 6% and 19%, respectively, of the
emissions produced by construction activities.

Table 3.4 Estimated On-road vehicle Emissions near IH 10 construction project (MOVES, 2016)

4. DISCUSSIONS

4.1 Effect of Emissions on Workers Health

It
is well known that motor vehicle exhaust is a significant source of air
pollution. The above reported pollutants in vehicular exhaust include carbon
monoxide, nitrogen and sulfur oxides, unburned hydrocarbons, particulate
matter, polycyclic aromatic hydrocarbons, and other organic compounds that
derive from combustion. Several lines of evidence now suggest that steep gradients
of certain pollutants exist next to heavily traveled highways and that living
within these elevated pollution zones can have detrimental effects on human
health (Rioux, 2007).

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. Even when
daily doses of some of the emissions produced during construction are under the
dangerous limit, workers and equipment operators are exposed to these emissions
daily for extended periods of time. Reducing the possible negative effects of pollutants
in the work environment can further improve the working conditions for
construction and maintenance workers.

The heath impact of the pollutants is
dependent on the physicochemical characteristics of the pollutants, their concentration
inhaled, and the rates and routes by which the deposited pollutants are cleared
from the respiratory tract or transported to other organs. The health impacts
on the workers due to various emissions may be as follows.

• Exposure to Carbon Monoxide
  (CO): Carbon monoxide is a colorless, odorless, poisonous gas emitted from the
  vehicle’s exhaust because of incomplete combustion. It interferes with the
  blood’s ability to carry oxygen to the brain, heart, and other tissues. Unborn
  or newborn children and people with heart disease are in greatest danger from
  this pollutant, but even healthy people can experience headaches, fatigue and
  reduced reflexes due to CO exposure (Wickman, 2008).
• Exposure to Sulphur
  Dioxide (SO₂): Sulfur dioxide is emitted when fuel containing sulfur
  is burned in diesel engines. Sulfur dioxide exposure constricts air passages,
  creating problems for people with asthma (WHO, 2005).
• Exposure to Nitrogen
  Oxides (NO_X): Nitrogen dioxide and related nitrogen oxides (NOx) are
  produced when fuel is burned. These compounds contribute to ozone formation and
  are a health problem themselves. The effect of NOx exposure Sulfur dioxide
  exposure constricts air passages in the respiratory system (Hoek, 2002).
• Exposure to Particulate
  Matter (PM 2.5, PM 10): Particulate
  matter includes microscopic particles and tiny droplets of liquid. Because of
  their small size, these particles are not stopped in the nose and upper lungs
  by the body’s natural defenses but go deep into the lungs, where they may
  become trapped and cause irritation. Exposure to particulate matter can cause
  wheezing and similar symptoms in people with asthma or sensitive airways.
  Particulate matter can serve as a vector for toxic air pollutants (WHO, 2005).

4.2 Mitigation Measures to Reduce Emissions

To
determine the potential health impacts on the workers, exhaust and dust emissions
have been evaluated for both on-road and off-road source of emissions. There
are various measures that can help to control the exhaust emissions from
different sources that were discussed earlier. Few of the mitigation measures
suggested are as follows:

• Operational measures, such as limiting time spent with the engine idling by shutting down equipment when not in use.
• Regular preventive maintenance for the construction equipment to prevent emission increases due to engine problems.
• Use of low sulfur and low aromatic fuel meeting California standards for motor vehicle Diesel fuel.
• Encouraging Use of Newer, Lower Emitting Equipment: Various programs are in place to require or encourage the replacement of older equipment with newer, lower-emitting equipment.
• “After-treatment” technologies must be placed in a vehicle’s exhaust system to control tailpipe emissions. Diesel Oxidation Catalysts (DOC) and Diesel Particulate Filters (DPF) can be used to reduce PM 2.5 and PM 10 emissions from the equipment.
• Use of low-emitting gas and diesel engines meeting state and federal emissions standards (Tier I and II) for construction equipment, including, but not limited to catalytic converter systems and particulate filter systems.
•  Use either water application or chemical dust suppressant application to control dust emissions from on-site unpaved road travel and unpaved parking areas.
• Use vacuum sweeping and water flushing of paved road surface to remove buildup of loose material to control dust emissions from travel on the paved access road
• and paved parking areas.
• Cover all trucks hauling
  soil, sand, and other loose materials or require all trucks to maintain at
  least two feet of freeboard.
• Limit traffic speeds on all
  unpaved site areas to 5 mph.
• Install sandbags or other
  erosion control measures to prevent silt runoff to roadways.
• Replant vegetation in
  disturbed areas as quickly as possible.
• Use wheel washers or wash
  off tires of all trucks exiting construction site.
• Mitigate fugitive dust
  emissions from wind erosion of areas disturbed from construction activities
  (including storage piles) by application of either water or chemical dust suppressant.

5. CONCLUSION

During
the year 2015, when Texas Department of Transportation (TxDOT) contractors were
widening IH 10 in a section of North West San Antonio, Texas, we have collected
local and onsite data to evaluate whether the construction activity resulted in
measurable air quality impacts. The air quality, meteorological, on-road
travel, and construction equipment spatial and temporal activity and fuel use
data for a one-year period. The study measurements covered all phases of
construction activity, although some of the construction work began in late
2012 prior to this study, and final paving work is still under progress.
Previous to this study, there had been few efforts to comprehensively examine
how roadway construction activities affect emissions and air quality. This
study provides important new data resources to facilitate a variety of
construction-related assessments.

Overall, the study results indicate that construction work did affect near-field PM10 concentrations,
but did not substantively affect near-field PM2.5 concentrations. Over the one
year period analyzed in this study, monitored pollutant concentrations did not exceed
National Ambient Air Quality Standards (NAAQS) for PM10, PM2.5, CO, or NO2.

As a part of this study we have identified the impacts of the emissions on the worker’s health and construction-related emission control options currently in use that help in improving the air quality. Many of these controls focus on retiring or retrofitting older, higher-emitting equipment as a method of reducing exhaust emissions.

REFERENCES

• Air Quality Impacts of
  Construction and Scheduling, Dennis Perkinson
• Construction Activity,
  Emissions, and Air Quality Impacts: Real-World Observations from Road-Widening,
  Paul Roberts and Stephen Reid
• Degradation in urban air
  quality from construction activity and increased traffic arising from a road
  widening scheme, Timothy Baker and Ian Mudway
•  U.S Environmental Protection Agency (2014) MOVES2014a User Guide
• Jones & Stokes and
  Rimpo and Associates (2009) Road construction emissions model, version 6.3.1.
  Spreadsheet system prepared for the Sacramento Metropolitan Air Quality
  Management District.
• California Air Resources
  Board Mobile Sources Emissions Inventory Program, 2007
• U.S Environmental
  Protection Agency (2016) Using MOVES for Estimating State and Local Inventories
  of On road Greenhouse Gas Emissions and Energy Consumption
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