Atmospheric Pollution Research 5 (2014) 344‐351   

© Author(s) 2014. This work is distributed under the Creative Commons Attribution 3.0 License.

Atm spheric Pollution Research

www.atmospolres.com

Diesel particulate matter emission factors and air quality implications 

from in–service rail in Washington State, USA 

Daniel A. Jaffe 1,2, Greg Hof 1, Sofya Malashanka 1,2, Justin Putz 1, Jeffrey Thayer 2, Juliane L. Fry 3,  

Benjamin Ayres 3

, Jeffrey R. Pierce 4

1 University of Washington–Bothell, School of STEM, Bothell, WA USA 2 University of Washington–Seattle, Department of Atmospheric Sciences, Seattle, WA USA 3 Reed College, Department of Chemistry, Reed College, Portland, OR USA 4 Colorado State University, Department of Atmospheric Science, Fort Collins, CO USA

ABSTRACT

We sought to evaluate the air quality implications of rail traffic at two sites in Washington State. Our goals were to

quantify the exposure to diesel particulate matter (DPM) and airborne coal dust from current trains for residents living

near the rail lines and to measure the DPM and black carbon emission factors (EFs). We chose two sites in Washington

State, one at a residence along the rail lines in the city of Seattle and one near the town of Lyle in the Columbia River

Gorge (CRG). At each site, we made measurements of size–segregated particulate matter (PM1, PM2.5 and PM10), CO2 and

meteorology, and used a motion–activated camera to capture video of each train for identification. We measured an

average DPM EF of 0.94 g/kg diesel fuel, with an uncertainty of 20%, based on PM1 and CO2 measurements from more

than 450 diesel trains. We found no significant difference in the average DPM EFs measured at the two sites. Open coal

trains have a significantly higher concentration of particles greater than 1 μm diameter, likely coal dust. Measurements

of black carbon (BC) at the CRG site show a strong correlation with PM1 and give an average BC/DPM ratio of 52% from

diesel rail emissions. Our measurements of PM2.5 show that living close to the rail lines significantly increases PM2.5

exposure. For the one month of measurements at the Seattle site, the average PM2.5 concentration was 6.8 μg/m3

higher

near the rail lines compared to the average from several background locations. Because the excess PM2.5 exposure for

residents living near the rail lines is likely to be linearly related to the diesel rail traffic density, a 50% increase in rail

traffic may put these residents over the new U.S. National Ambient Air Quality Standards, an annual average of 12 μg/m3

.

Keywords: Diesel particulate matter, diesel emissions, train emissions, coal dust, DPM

Corresponding Author:

Daniel A. Jaffe 

 : +1‐425‐352‐5357

 : +1‐425‐352‐5335

 : djaffe@u.washington.edu

Article History:

Received: 03 November 2013

Revised: 27 January 2014

Accepted: 28 January 2014

doi: 10.5094/APR.2014.040

1. Introduction

Rail is an efficient way to move people and freight. However,

diesel–powered trains may have a significant impact on air quality.

In Washington State, nearly all rail locomotives are powered with

diesel fuel and many rail lines are located in busy urban corridors,

including Seattle, Tacoma and Spokane, and also pass through the

Columbia River Gorge National Scenic Area. At present, there is

limited information to evaluate the air quality impacts associated

with rail transport for residents living close to the train lines.

Recently, there have been proposals to increase rail shipments

through Washington and Oregon for transporting coal to west

coast ports for export to Asian markets. One proposed facility, the

Gateway Pacific Terminal near Bellingham, Washington, could

export up to 54 million metric tons of coal per year (WA DOE,

2013). Similar facilities have also been proposed at two other sites

in Washington and Oregon.

According to the U.S. Department of Health and Human

Services, diesel particulate matter (DPM) is “reasonably anticipated

to be a human carcinogen” (U.S.DHHS, 2011); in addition, the

World Health Organization classifies it as “carcinogenic to humans”

(WHO, 2012). In Seattle and other urban areas, DPM is the most

important “air toxic” in the metropolitan area and contributes

more than 80% of the risk for cancer from airborne air toxics (Keill

and Maykut, 2003; PSCAA, 2005). Monitoring and a chemical mass

balance model have found average DPM concentrations to range

from 1.4–1.9 μg/m3 for the Seattle area (Keill and Maykut, 2003;

Maykut et al., 2003). These concentrations are about 15–20% by

mass of the total PM2.5, particulate matter with diameter less than

2.5 μm. Sources of DPM include on–road and off–road diesel

trucks, ships and rail locomotives.  

The U.S. Environmental Protection Agency (EPA) has

developed emissions standards for new and remanufactured

locomotives (40 CFR part 1033). The emission standards, in g/bhp–

hr, decrease steadily for locomotives manufactured between

1973–2001 (Tier 0), 2002–2004 (Tier 1), 2005–2010 (Tier 2), 2011–

2014 (Tier 3) and after 2015 (Tier 4) (U.S. EPA, 2013). For Tier 4,

locomotives must meet a PM10 emission standard of 0.03 g/bhp–

hr, or approximately 0.19 g per kg of fuel burned (U.S. EPA, 2009).  

A few studies have examined rail yards as sources of air

pollutants and have found that diesel fuel combustion is the

primary source of PM2.5 at these facilities. Galvis et al. (2013)

looked at the influence of DPM emissions on PM2.5 concentrations

near a rail yard in Atlanta. Based on measurements up–

wind/down–wind of the facility, they concluded that the average

“neighborhood” contribution to PM2.5 was 1.7 μg/m3

. They also

derived fuel–based emission factors (EFs) of 0.4–2.3 grams DPM

per kg of diesel fuel consumed. These EFs are not based on

measurements from individual trains but were calculated using

three different methods, each of which requires a different set of

assumptions. Two studies on a rail yard in Roseville, CA, also found
Jaffe et al. – Atmospheric Pollution Research (APR) 345

significant enhancements in PM2.5 from the facility. Based on

upwind/downwind measurements, Cahill et al. (2011) reported an

average enhancement of 4.6 μg/m3

. In another study (Campbell

and Fujita, 2006), larger contributions for the same facility were

reported (7.2–12.2 μg/m3

). Cahill et al. (2011) also showed that the

major component of aerosol mass from diesel rail facilities is from

very fine PM, with diameters less than 0.26 μm. Abbasi et al.

(2013) provide a review of PM concentrations inside trains and

near rail lines. They report substantially elevated PM2.5 and PM10

concentrations, especially in underground rail stations. Gehrig et

al. (2007) examined the influence on PM10 concentrations from

dust associated with electric trains in Switzerland. A number of

previous studies have reported EFs for on–road diesel trucks and

buses (Jamriska et al., 2004; Zhu et al., 2005; Cheng et al., 2006;

Park et al., 2011; Dallmann et al., 2012), but to our knowledge,

similar studies have not been reported for diesel rail.

In addition to DPM emissions, trains carrying coal in

uncovered loads may emit coal dust into the atmosphere. This has

been a topic of some controversy. Rail transport companies are

attempting to mitigate this problem (see BNSF Railway, 2013), but

few studies have been reported in the scientific literature. We

expect that combustion–related DPM and mechanically generated

coal dust are associated with very different particle sizes, so size–

segregated PM should be able to distinguish these source types

(Seinfeld, 1986).

Black carbon (BC) accounts for a significant fraction (44–60%)

of PM2.5 mass from diesel engines (Bond et al., 2004; Kirchstetter

and Novakov, 2007; Ramanathan and Carmichael, 2008). As the

major light–absorbing species in atmospheric aerosol, radiative

forcing due to BC is important on a global and regional scale

(Jacobson, 2001; Ramanathan and Carmichael, 2008).

Furthermore, the surface properties of black carbon allow for

adsorption and transport of semi–volatile compounds like

polyaromatic hydrocarbons (PAHs) (Dachs and Eisenreich, 2000).

BC is under scrutiny by health organizations due to its role in

adverse effects caused by PM2.5, including cardiopulmonary and

respiratory disease (Jansen et al., 2005; Janssen et al., 2011; U.S.

EPA, 2012).

The City of Seattle has conducted an analysis of potential

impacts associated with increasing train traffic. This analysis

indicates that the proposed coal export terminal would increase

rail traffic by up to 18 additional trains per day if approved (City of

Seattle, 2012). Given the lack of information on PM2.5 concentra‐

tions and human exposure from diesel trains, the controversy over

coal dust and the limited information on EFs from diesel trains, we

sought to quantify these air quality impacts by addressing the

following questions:

(i) What is the exposure to size–segregated PM (e.g., PM1, PM2.5

and PM10) for residents living near the rail lines?

(ii) Can we estimate the potential exposure to size–segregated PM

(e.g., PM1, PM2.5 and PM10) for people living near the rail lines if

rail traffic increases?

(iii) Do coal trains emit coal dust into the air?

(iv) How do the observed DPM and BC emission factors for

locomotives compare with other published EFs?

To address these questions we measured size–segregated

PM1, PM2.5, PM10, total suspended particulate (TSP), CO2 and

meteorology at two locations adjacent to rail lines. Because our

goal is to quantify the exposure to DPM and coal dust, if present,

and the EFs from individual trains, we made 10–second measure‐

ments so as to capture the air quality impacts from individual

passing trains.  

2. Experimental

Measurements on train emissions were made at two sites in

Washington State (Figure 1). The first site was located in the

residential Blue Ridge (BR) neighborhood (47.70°N, 122.40°W), in

the City of Seattle, approximately 10 km north of downtown. The

instruments and camera were housed on the patio of a residence,

which is approximately 25 meters from two active rail lines. The

rail lines are immediately adjacent to the shores of Puget Sound

and there are no roads in this direction before the shoreline. A

video camera was co–located with the instrumentation and could

identify train types both day and night at this site. The second site

was located in the Columbia River Gorge (CRG), between the towns

of Lyle and Dallesport, Washington (45.67°N, 121.20°W). Here the

instruments were housed in a small tent, which was located on a

small rock outcropping, approximately 10 meters above and

30 meters north of the rail line. The camera was separate from the

other instrumentation and located about 25 meters from the

tracks but at a 40–degree angle to it. The lower ambient light

levels, the camera angle and distance made it impossible to

identify train types at night at this site. The rail lines are along the

Columbia River and there were no other roads in that direction

before the river. This site was about 100 meters south of

Washington Route 14, which is a lightly traveled state highway.

The instruments were identical at both the Seattle and CRG sites,

except that BC was measured only at the CRG site. The data

capture at the Seattle site was greater than 95%, whereas

instrument and computer failures at the CRG site gave us lower

data capture. At both sites the rail line was essentially flat, with a

maximum grade of 1 meter per km in the adjacent few km in either

direction.

A third site was used only for comparisons of two different

DustTrak instruments with a tapered element oscillating micro‐

balance (TEOM). This site is one of the regular Seattle air quality

monitoring stations operated by the Puget Sound Clean Air Agency

(pscleanair.org). The site is located along the Duwamish Waterway

in the industrial Duwamish Valley, which has a heavy concentration

of diesel trucks, trains and ships, due to its proximity to a major

port facility. At this site, a Rupprecht and Patashnick TEOM model

1400AB with Filter Dynamics Measurement Systems (FDMS) 8500

is operated as Federal Equivalent Method (Ray and Vaughn, 2009).

An Ecotech M9003 nephelometer was also operated to measure

scattering coefficients. The scattering coefficients are converted

into PM2.5 (μg/m3

) based on a 3–year comparison with a Federal

Reference Method. The two DustTrak instruments (described

below) were operated at this site in the same way as was done at

the train sampling sites. This site was chosen for the DustTrak

comparison, as it regularly reports the highest concentrations of

PM2.5 in the Seattle area, due to the high number of diesel vehicles

in the area (Keill and Maykut, 2003).

We measured size–segregated PM measurements using a

DustTrak DRX Aerosol Monitor (Model #8533, TSI, Inc., Shoreview,

MN). This instrument reports PM mass concentrations in 4 size

fractions: PM1, PM2.5, PM10 and TSP. Because the DustTrak uses

aerosol scattering as the basis for its measurements, the

measurements are not identical to a mass–based measurement

(Wang et al., 2009). The instrument comes calibrated against

standard Arizona road dust (ISO 12103–1), but this will not

accurately reflect the scattering efficiency for many aerosol types.

This may be especially true for diesel given the small size of

particles (Park et al., 2011). Instead, accurate measurements using

the DustTrak require a comparison against a mass–based

measurement for the aerosol of interest (Moosmuller et al., 2001).

A number of previous EF studies have also used the DustTrak to

rapidly measure several size fractions of PM and calculate EFs from

individual vehicles (e.g., Park et al., 2011; Dallmann et al., 2012),

but usually after calibration of the response factor against a mass–

based method (Jamriska et al., 2004; Zhu et al., 2005; Cheng et al.,

2006). For our study, we calibrated the DustTrak against a mass–
Jaffe et al. – Atmospheric Pollution Research (APR) 346

based TEOM measurement (described above). The inlet for the

DustTrak was downward–facing stainless steel tubing (5.0 mm i.d.)

at a height of approximately 2 meters above ground level. The flow

through this inlet was 3.0 liters per minute. Under these

conditions, the flow is laminar and we would expect greater than

90% particle transmissions for particles up to 2.5 μm in diameter at

wind speeds below 10 m/s (von der Weiden et al., 2009). At higher

wind speeds and for larger particle sizes, the sampling efficiency

will be reduced. Data were stored as 10–second averages.

Figure 1. Map showing air sampling locations in the Seattle area. Major

roads and highways are shown by yellow lines and the rail lines are

shown in blue. The Duwamish Valley, Lynnwood and Beacon Hill sites

are operated by the Puget Sound Clean Air Agency (PSCAA, 2013). At the

BR site, PM and CO2 instrumentation were set up at a residence,

approximately 25 meters from the rail lines. The site in the Columbia

River Gorge (not shown) is 227 km to the south‐southeast of Seattle.

CO2 was measured using a Licor–820 (Licor, Inc., Lincoln, NE).

Air was pulled through the Licor instrument using a small vacuum

pump. The inlet consisted of a 5.0 mm i.d. stainless steel inlet that

connected to PFA tubing. The instrument was zeroed by pumping

CO2–free air through it and calibrated with a 395 ppmv standard

(Airgas, Inc.). The instrument was calibrated before the Seattle

deployment and after the CRG deployment, and the instrument

response had drifted by less than 1 ppmv between these times.

Data from the DustTrak and the Licor–820 (CO2, cell temperature

and pressure) and the meteorological data were recorded using

DAQFactory on a PC. Data were recorded as 10–second averages.  

Train types were identified using video taken by a Night Owl

camera equipped with infrared night vision (Model CAM–MZ420–

425M). The camera was motion activated and controlled using iSpy

open source security camera software. At the Seattle site, we were

able to classify train types (freight, passenger, etc.) both day and

night due to greater ambient light. At the CRG site the camera was

able to identify a passing train day or night, but the train type

could be identified only in the daytime due to the camera angle,

distance and lower ambient light levels.  

At the CRG site only, BC measurements were taken using a

two–wavelength aethalometer (AE–22, Magee Scientific). BC

sampling was performed at 1–minute resolution at 370 nm and

880 nm. Data from the 880 nm infrared absorption signal were

used to determine BC loading, as 370 nm is susceptible to

absorbance of other organic aerosol from diesel plumes (Wang et

al., 2011). The aethalometer measures attenuation (ATN) values

(1/m) and determines BC concentration (g/m3

) via:  

BC0=ATN/σ (1)

where, σ is Magee Scientific’s calibrated cross–section of

1.4625x104

/λ (at 880 nm, σ=16.6 m2/g). However, since

attenuation diminishes as the BC loading on the filter increases, we

apply a correction to the BC concentrations following Kirchstetter

and Novakov (2007). Transmission (Tr) can be calculated from the

attenuation values as:

Tr=e–ATN/100 (2)

The corrected BC loading (ng/m3

) can then be calculated

following Kirchstetter and Novakov (2007) as:

BCcorr=BC0/(0.88xTr+0.12) (3)

Both PM1 and BC EFs are quantified as emissions per kg of

diesel fuel burned. These are calculated for each passing train. The

EFs for PM1 and BC are calculated from:

EF (PM1)=ΔPM1/ΔCO2xWc (4)

EF (BC)=ΔBC/ΔCO2xWc (5)

where, ΔPM1/ΔCO2 is calculated from the slopes of the regression

lines using the 10–second CO2 and PM1 data for each passing train.

For BC, the ratio ΔBC/ΔCO2 is obtained from the one–minute data

by subtracting the background concentrations before and after the

train passes:

, ,

2

2 2

BC/ CO 

[] []

Corr train Corr baseline

train baseline

BC BC

CO CO

    (6)

CO2 concentrations are converted to g C/m3 units using the

ideal gas law at 1 atm and 25 °C. WC is the mass fraction of carbon

in diesel fuel (0.87 kg C/kg fuel, Lloyd’s Register, 1995; Cooper,

2003), giving overall units on the EF of g PM1/kg fuel consumed or

g BC/kg fuel consumed. Yanowitz et al. (2000) show that more than

95% of the diesel fuel carbon is emitted as CO2. We chose to use

PM1 in these calculations because this is least likely to be

influenced from coal dust or dust from other sources. Using the

information presented later in our analysis, one could adjust our

EFs for other size fractions.  

At the BR and CRG sites, measurements of train emissions and

PM were conducted from July 23–August 19, 2013, and August 27–

September 2, 2013, respectively. At the Duwamish site, the

DustTrak–TEOM comparison was carried out from September 23–

October 13, 2013.  

3. Results

3.1. Calibration of the DustTrak

Figure 2 shows a scatter plot of the hourly PM2.5 concentra‐

tions measured by the DustTrak and the TEOM at the Duwamish
Jaffe et al. – Atmospheric Pollution Research (APR) 347

site. There is an excellent correlation between the TEOM and the

DustTrak measurements (R2

=0.83), but the slope is far from 1.0.

There was also an excellent agreement between the two DustTrak

instruments (R2=0.99), with a slope of 1.00 and essentially no

offset between instruments.  

Figure 2. Comparison of hourly PM2.5 concentrations measured by the

DustTrak and TEOM‐FDMS at the Duwamish site for 9/23/2013 through

10/13/2013. The line shows the RMA regression fit. The regression

parameters are m=0.4913, b=4.414 (y=mx+b) and R2=0.83.

We determined the regression relationship between the

TEOM and DustTrak (serial number 8533131306) using Reduced

Major Axis (RMA) regression (Ayers, 2001; Cantrell, 2008):

TEOM PM2.5 (μg/m3

)=DustTrak PM2.5 (μg/m3)x0.4913+4.414 (7)

The 95% confidence interval (CI) for the slope and intercept

from the RMA regression are 0.47–0.51 and 4.1–4.7, respectively.

Our result agrees remarkably well with a similar comparison on

DPM by Jamriska et al. (2004), using both a DustTrak and a TEOM,

who reported this relationship:  

TEOM PM2.5 (μg/m3

)=DustTrak PM2.5x0.458+4.882 (8)

We also compared the DustTrak with the Ecotech nephelo‐

meter at the Duwamish site, to obtain the following relationship:

Ecotech Nephelometer PM2.5 (μg/m3

)= DustTrak PM2.5

x0.4176+2.926 (9)

The R2 for the DustTrak–nephelometer regression is 0.98,

likely due to the fact that both methods are scattering based.

However, the intercept using the nephelometer data is smaller

(2.9 μg/m3 vs. 4.4 μg/m3

) compared to the TEOM, suggesting an

uncertainty in the intercept of ±2 μg/m3

. The overall uncertainty in

our PM measurements made with the DustTrak is due to

uncertainty in the slope (10%) and the intercept (±2 μg/m3

). For

the remainder of this paper, we will use the corrected DustTrak

data, based on our measured relationship to the TEOM data from

the Duwamish site. To maintain consistency with different size

bins, we correct all PM concentrations (e.g., PM1, PM2.5, etc.) to

the TEOM values using Equation (7).  

3.2. Observations of PM and CO2  

Figure 3 shows a time series of PM1 (μg/m3

) and CO2 (ppmv)

concentrations for a 6–hour period at the Seattle site. We define a

“train event” as a single spike or enhancement in PM and CO2 that

is confirmed by the video images. During the period shown in

Figure 3 we identified 8 train events. Each train event was

confirmed and classified (freight, coal, passenger or other) using

the videos. Typical train events last from 1 to 5 minutes, depending

on the length of the train, the number of locomotives and

meteorology. For each train event, we calculated the regression fit

for the following relationships: PM1–CO2 ̧ PM1–TSP, PM2.5–TSP and

PM10–TSP. Figure 4 shows an example of the PM1/CO2 relationship

for one train event. The slope from the linear correlation

(∆PM1/∆CO2) is used to derive the DPM EF using Equation (4).  

Note that not all trains will be detected by the atmospheric

data. For example, if the winds are blowing strongly or are from

the wrong direction, our sensors will record only small peaks, or no

peaks, in PM1 and CO2. These smaller events will generally have a

lower PM1–CO2 correlation coefficient, so we screened out smaller

events with an R2

<0.5 or with ∆CO2<3 ppmv. This results in

456 train events that passed this QC screen, out of a total of 584

for both sites.

Table 1 shows statistics on the ∆PM1/∆CO2 slope for the

456 trains we identified at both sites. For the Seattle site, these are

separated by train type (freight, coal, passenger and other or

unidentified). The distributions are slightly skewed, as shown by

the higher means compared to median values. The average slopes

range from 0.45 μg/m3

/ppmv for coal trains to 0.59 μg/m3/ppmv

for passenger trains. The difference between passenger and freight

trains is statistically significant with greater than 95% confidence.

The other differences are not statistically significant. For the CRG

site, given the very small number of identifiable coal (3) and

passenger (8) trains, we do not report statistics separately for

different train types.  

Figure 3. PM1 (μg/m3) and CO2 (ppmv) data (10‐second averages) from the Blue Ridge site for July 25, 2013, between 06:00

and 12:00 local time. During this period, we identified 8 trains from the atmospheric data and confirmed by video images.
Jaffe et al. – Atmospheric Pollution Research (APR) 348

Table 1. Data on ΔPM1/ΔCO2 slopes (μg/m3/ppmv) for different train types at the Blue Ridge location and for all trains at the Columbia River Gorge location.

To convert to EFs in g/kg, multiply by 1.81

Blue Ridge CRG

Freight Coal Passenger Other All trains All trains

Count 236 36 93 7 372 84

Average 0.52 0.45 0.59 0.77 0.53 0.51

SD   0.28 0.47 0.35 0.73 0.33 0.36

Median 0.47 0.33 0.53 0.44 0.47 0.45

Figure 4. Linear correlation between PM1 (μg/m3) and CO2 (ppmv) for the

train event starting at 06:03 local time on 7/25/2013 (first event shown

in Figure 3). The linear regression parameters are m=0.4888, b=‐184.11

(y=mx+b) and R2=0.989.

3.3. Emission factors  

The average ∆PM1/∆CO2 slope for all 456 train events was

0.53 μg/m3

/ppmv, with a 95% confidence interval of

±0.03 μg/m3

/ppmv. This converts to a PM1 EF of 0.94 g/kg diesel

fuel consumed, with a 95% confidence interval of 0.06 g/kg. Given

the uncertainty in the DustTrak calibration factor (Section 3.1), we

assign an overall uncertainty of 20% to our mean EF based on the

mean PM1 enhancement. For comparison, an older study by Kean

et al. (2000) reports locomotive emission factors using the EPA

“NONROAD” model of between 1.8–2.1 g/kg. A 2009 report (U.S.

EPA, 2009) projected future emission factors for the fleet

averaged, in–use diesel locomotives. The EPA–estimated average

EF for 2013 is 1.2 g/kg. A study by Sierra Research (2004) projected

a slower reduction in the diesel locomotive EFs, compared to U.S.

EPA (2009), and projected a value of 1.5 g/kg for 2013. A study by

Galvis et al. (2013) derived EFs for diesel locomotives of 0.4–

2.3 g/kg, depending on the assumptions made. Given the

uncertainty in our EF, our average value is consistent with the

values given for the 2013 time frame.  

At the CRG site, the observed BC and PM1 measurements on

84 trains reveal that on average, 52% of the PM1 is BC (Figure 5).

This is broadly consistent with previous measurements of black

carbon in diesel engine particulate emissions in trucks; Hildemann

et al. (1991) report 55% black carbon, and Watson et al. (1994)

report 45% in diesel engine particulate emissions, in both cases

larger than the fraction observed in gasoline emissions. BC to PM2.5

ratios at 47–52% have also been reported for diesel train emissions

in Atlanta (Galvis et al., 2013). The average BC EF of 0.66 gBC/kgfuel

(see the Supporting Material, SM, Figure S1) suggests that rail BC

emissions are similar to those reported from heavy–duty diesel

trucks, 0.54 gBC/kgfuel (Dallmann et al., 2012). Because only very

few coal trains were identified at the CRG site, there was

insufficient data to clearly identify coal dust in the BC time–series

data.

Figure 5. Correlation of black carbon mass with PM1 from individual train

events. The regression line is y=0.518x‐2.009 (R2=0.85).

3.4. Size distributions

The DustTrak measures PM concentrations in four size ranges.

For the majority of trains measured, the mass fraction of the TSP

concentration was dominated by particles smaller than 1 μm

diameter. To compare the size distributions from different train

types, we required that R2

>0.5 for the PM1–TSP correlation for an

event to be included in this analysis. This yielded 449 train events

from the Seattle site for this analysis, out of 487 possible events.

Data on the PM size distributions are given in Table S1 (see the

SM). Note that because we use different QC criteria than for the

PM1/CO2 slope, the number of train events included in this analysis

is different from the number shown in Table 1. For these 449

events, the average PM/TSP mass fraction for all trains was found

to be 0.86, 0.89 and 0.97 for PM1, PM2.5 and PM10, respectively.  

For some trains, there was evidence for larger particles

present. Figure 6 shows the measured PM1, TSP and CO2 concen‐

trations for a coal train that passed the Seattle site starting at 9:56

local time on August 13, 2013. For this train event, the first peak,

between 9:56 and 9:57, shows an excellent correlation between

PM1 and CO2 (R2

=0.98) and nearly all aerosol mass is due to

particles less than 1 μm diameter (PM1≈TSP). However, there is a

second peak in PM1 at 9:59 without a corresponding CO2 peak. For

this peak, TSP is now significantly larger than PM1, indicating the

presence of larger particles. It is important to note that our inlet

likely excludes a significant fraction of larger particles, so our

measured TSP concentrations are likely an underestimate. We

examined the data to see if there was a statistically significant

difference between the PM fractions by train type. Figure 7 shows

the average PM size fraction (PM1/TSP, PM2.5/TSP and PM10/TSP)

separated by train types for the Seattle site. On average, the PM

fractions show that coal trains emit larger particles into the air.

These PM1/TSP fractions, 0.87, 0.77 and 0.88 for freight, coal and

passenger trains, respectively, are significantly different at a P

value of <0.02. Though we did not collect PM samples for chemical

analysis, it seems highly likely that the relative contribution of

larger particles due to the total PM mass consist of aerosolized coal

dust from the uncovered coal trains.
Jaffe et al. – Atmospheric Pollution Research (APR) 349

Figure 6. PM1 and total suspended particulate (TSP) in μg/m3 and CO2 (ppmv) concentrations during

passage of a coal train at the Blue Ridge site at 9:56 (PDT) on August 13, 2013.

Figure 7. Fraction of total suspended particulate mass in each size range as measured by the DustTrak at

the Blue Ridge site. For this comparison there were 296, 49 and 104 freight, coal and passenger trains,

respectively, which had an R2 for the PM1‐TSP relationship of >0.5. For the PM1/TSP fraction, the coal

trains are significantly lower than freight and passenger trains (p<0.01), indicating that a larger fraction

of the total PM mass is greater than 1 μm, compared to the other train types.

3.5. PM2.5 exposure due to trains

We measured average PM2.5 concentrations at the Seattle and

CRG sites of 11.0 and 7.4 μg/m3

, respectively. The lower concentra‐

tions at the CRG site reflect the fact that this region is charac‐

terized by higher wind speeds and the fact that our site was on a

bluff overlooking the river and railroad tracks. At the Seattle site,

the instruments were located only a few meters higher than the

tracks and the local topography likely creates a greater barrier to

dilution of the train emissions. An additional factor is the cold

temperatures of Puget Sound (10–12 °C), which cause a stable

layer near the surface of Puget Sound (Mass, 2013).

Figure S2 (see the SM) shows the daily average PM2.5 concen‐

trations for the BR site during our measurement period, along with

three other sites in the region operated by the Puget Sound Clean

Air Agency. The site locations are shown in Figure 1. The daily

variations at all sites are well correlated and reflect regional PM2.5

source/sink relationships. For example, all sites had lower PM2.5 on

August 1–2, 2013, when cooler, wetter and windier conditions

prevailed across the region. During the 4–week measurement

period, the average PM2.5 concentrations at the Blue Ridge,

Lynnwood, Beacon Hill and Duwamish sites were 11.0, 4.3, 6.6 and

11.1 μg/m3

, respectively. The BR and industrial Duwamish Valley

sites show similar concentrations, despite the fact that there are

no major roads or industries near the BR site. It is possible that

marine vessels could have contributed to the enhanced PM2.5

observed at the BR site. To evaluate this possibility, we examined

PM2.5 concentrations at four marine sites along Puget Sound for

the same time period using data from the Puget Sound Clean Air
Jaffe et al. – Atmospheric Pollution Research (APR) 350

Agency (PSCAA, 2013). For the sites at Bremerton, Oak Harbor,

Port Angeles and Port Townsend, the average PM2.5 concentrations

during this same time period were 5.0, 3.2, 3.5 and 5.0 μg/m3

,

respectively. Thus it appears that marine shipping cannot explain

the much higher concentrations observed at the BR site. We

attribute the additional PM2.5 to the presence of the nearby rail

line and trains. The difference between the measured PM at the BR

site and the average of the four marine sites (11.0–4.2 μg/m3

)

represents the additional PM2.5 at the BR site due to diesel trains

(6.8 μg/m3

).

The enhancement in PM2.5 is not only due to the “spikes” that

occur as a train passes, but also the residual that accumulates in

the local airshed. The topography in the Puget Sound region may

also exacerbate the accumulation of PM2.5 from trains. This is

because the rail line runs approximately north–south (see Figure 1)

in the same direction as the prevailing summer winds, and at the

foot of a 50–100 meter–high bluff that further limits mixing. These

factors contribute to the PM2.5 enhancement due to rail traffic.

We can estimate possible impacts of increasing rail traffic on

PM2.5 concentrations at the BR site. We assume that the PM2.5

enhancement due to trains, 6.8 μg/m3

, is linearly related to the

total train traffic. Using this assumption, a 50% increase in diesel

train traffic would increase the PM2.5 due to trains to 10.2 μg/m3

.

When added to the regional background (4.2 μg/m3

), this would

bring the PM2.5 concentrations at the BR site up to approximately

14 μg/m3

, which is higher than the new U.S. National Ambient Air

Quality Standard (NAAQS) of 12.0 μg/m3 (annual average). It is

important to note that compliance with the NAAQS is based on

three years of data, and thus our one month of observations

cannot indicate compliance. But nonetheless, this calculation

suggests that a 50% increase in rail traffic will put some residences,

such as our BR home, near or over the NAAQS. This assumes that

each train contributes uniformly to the PM exposure. While our

diesel emission factors for coal and freight train types were

statistically indistinguishable, the train length, number of locomo‐

tives and fuel consumption may vary for different train types. Thus

a more complete estimate of future impacts on air quality from rail

traffic should consider these factors.  

4. Summary

We measured the PM1 emission factors for over 450 trains at

two sites in Washington State and the resulting PM2.5 exposure

(μg/m3

). For 84 of these trains, we also measured the black carbon

emission factors. Our measurements demonstrate that rail traffic

emits substantial quantities of diesel exhaust and that the PM2.5

concentrations are significantly enhanced for residents living close

to the rail lines. Future growth in rail traffic will increase the PM2.5

exposure and, for some homes, may result in concentrations that

exceed the U.S. NAAQS. Our results also show that after passage of

coal trains there was a statistically significant enhancement in

larger particles, compared to other train types. These larger

particles most likely consist of aerosolized coal dust. Our study

addresses exposure to residents who live close to the rail lines.

Future studies should examine several questions that were not

addressed by our study:

(i) How does the concentration of PM2.5 vary with proximity to the

rail lines?

(ii) What are the total emissions from rail traffic and what is the

net contribution to PM2.5 across the broader Seattle

metropolitan area?

(iii) What are the health effects associated with PM2.5 and coal dust

from rail traffic?

Acknowledgements

The authors would like to acknowledge the financial

contributions of over 270 individuals via microryza.com and a

donation by the Sierra Club. These donations were made as gifts to

the University of Washington. The authors are also grateful to

several anonymous individuals who let us use their home and/or

property to conduct these measurements. BA and JLF would like to

thank Tony Hansen and Magee Scientific for the generous loan of

the AE–22 instrument used to conduct these experiments.

Supporting Material Available

BC emission factors calculated for each train event (Figure S1),

Daily average PM2.5 for four sites in the Seattle metropolitan region

(Figure S2), Fraction of total suspended particulate (TSP) mass in

the PM1 and PM2.5 size ranges, for each train type (Table S1). This

information is available free of charge via the Internet at

http://www.atmospolres.com.

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