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# Re

- analy sis of ARTC D ata on

Particulate Emissions from Coal Trains

Author : Prof Louise Ryan

Contributing Author: Prof Matthew Wand

on behalf of accessUTS Pty Ltd



# for NSW Environment Protection Authority

25 February 2014

Project No : 2013001693ii



# TABLE OF CONTENTS

1. SUMMARY

................................ ................................ ................................ ......... 3

2. INTRODUCTION AND BAC KGROUND

................................ ................................ .......... 4

3. THE DATA

................................ ................................ ................................ ......... 5

4. STATISTICAL MOD ELLING STRATEGY

................................ ................................ ......... 7

5. RESULTS

................................ ................................ ................................ ........... 9

6. DISCUSSION AND CONCL USION

................................ ................................ .............. 12

7. GRAPHS AND TABLES

................................ ................................ .......................... 15Re

- analysis of ARTC Data on Particulate Emissions from Coal Trains 3



## 1. SUMMARY

This report describes a re

- analy sis of ARTC ’s data on particle emissions from coal and other

trains in the Hunter rail corridor. Our analysis was based on reg ression modelling, as

recommended in a previous critique by the first author of an analysis of those same data by

Katestone Environmental Pty Ltd

.

The data analysis was challenging, complicated by the large volume of data available, as well

as the strong levels of serial correlation. We found clear evidence that particulate levels were

elevated when trains passed by the monitoring station. Effects were strongest and of a similar

magnitude (approximately 10% increase above background levels) and highly statistically

significant for freight and coal trains, both loaded and empty. Effects for passenger trains

were mostly statistically significant, but of a smaller magnitude. The effect for passenger

trains became non

- significant when we excluded times when multiple trains were passing

simultaneously. There was no evidence that loaded coal trains produce more dust than empty

coal trains. In fact, whi le differences were not statistically significant, particulate levels

associated with the passing of unloaded coal trains were higher than those associated with

loaded c oal trains and freight trains.

The effects were apparent for all available particulate measures, including TSP, PM10, PM2.5

and PM1, especially for freight and coal trains (loaded and empty). Passenger train effects

were non

- significant for PM1 and only marg inally significant for PM2.5. Since coal dust is

likely to be reflected in the larg er particle counts (TSP and PM10), this finding suggests that

other contaminants such as diesel may be of more concern than coal dust. This conclusion is

further supported by the fact that effect sizes were similar for freight, loaded and unloaded

coal tr ains, all of which are pulled by diesel locomotives.

The report concludes with suggestions about further analysis that would help understand the

nature of particulate exposure in the Hunter Valley.Re

- analysis of ARTC Data on Particulate Emissions from Coal Trains 4



## 2. INTRODUCTION AND BAC KGROUND

Katestone Environmental Pty Ltd (referred to hereafter as Katestone) was commissioned by

Australian Rail Track Corporation to conduct a monitoring study of particulate matter, with a

view to determining

1. Whether trains operating on the Hunter Valley rail network ar e associated with elevated

particulate matter concentrations; and

2. Whether trains loaded with coal have a stronger association compared with unloaded coal

trains or other trains on the network.

To address these questions, a continuous particulate monitoring station was installed to

measure particulate levels in the rail corridor adjacent to tracks carrying different types of

trains. Specific details of the monitoring design were informed by a previously conducted

pilot study. In addition to continuous mon itoring of parti culate levels, the time of each train

pass

- by was recorded, along with other details abo ut each corresponding train. Additional

data were available regarding wind speed and direction, as well as rainfall.

After the final report was submitt ed, a number of independent reviews were undertaken to

evaluate the reliability of the results. Several of the reviews raised concerns regarding the

statistical methods used to analyse the data. Professor Louise Ryan was engaged as an

independent, expe rt statistica l reviewer, to assess the suitability and reliability of the statistical

methods used to analyse the study data. Her report discussed several concerns:

 Use of only a single monitoring site made it difficult to generalize the results

 Kateston e’s approach of treating the outcome of interest as the average particulate

levels associated with various kinds of train passings.

Professor Ryan argued that while multiple monitoring stations would have been helpful, a

continuous time series at this sin gle site should still provide useful insight into the questions

of interest. She recommended that the study be reanalysed using regression analysis

methodologies, taking into consideration the likelihood of serial correlation due to the time

series natu re of the data. She suggested that the outcome variable for the analysis should be

the individual particulate measurements (that is, without any averaging). Such an analysis

would allow for incorporation of covariates indicating train type, wind speed, precipitation, as

well as additional variables reflecting time of day, day of week and other temporal effects.

She also recommended that preliminary exploratory analysis be conducted to see if a log

transformation of the data would improve the modelling process. Such an approach would

more appropriate ly address the questions of interest, whilst accounting for possible

confounding effects. The purpose of this present report is to describe such an analysis.Re

- analysis of ARTC Data on Particulate Emissions from Coal Trains 5



## 3. THE DATA

We obtained all 61 days of monitori ng, weather and train passing data from Katestone.

Particulate data: The monitoring system was set up to measure particulate levels every 6

seconds throughout each day over a 61 day period, yielding a maximum total number of

24*60*60/6 = 14,400 sets of measurements each day. As described in Katestone’s final

report, many of the days collected substantially less than this amount of data. Two days

(December 2 nd 

### and 16 th 

### ) had no measurements while an additional three days (December 5 th



### ,

December 15 th 

### and January 1st) had less than 1000 data points (100 minutes of data). Our

analysis reports on the subset of 56 days that had at least 1000 monitoring measurements.

On these days, the mean number of observations was 10,990 (approximately 18 hours). 48%

of the days had at least 22 hours of monitoring data.

Wind data: Data on concurrent wind speed and direction were available for approximately

75% of the particulate observations. These observations were spread over 45 days, with an

average of 17.5 hours of wind speed and direction data concurrent with particulate data

monitoring, per day. The first day of observation (November 30 th 

### ) had only 867 concurrent

wind observations. Thus, we restricted our analyses involving wind speed and direction to t he

subset of 44 days with at least 1000 data points.

Train data: Over the course of the study period, a total of 7,315 trains passed by the

monitoring station, with a median of 137 trains per day. Table 1 shows the breakdown by

different train types, a long with the duration (in seconds) of each passing and its speed. Table

entries show medians, along with lower and upper quartiles.

As expected, there is a tendency towards a lower frequency of trains on weekends and in the

middle of the night. This is especially so for passenger trains. Table 1 shows that passenger

trains are the most frequent type of train, that they pass by very quickly (around 2 seconds)

and at a relatively fast speed. Empty coal trains are the next most common. These pass by at

a medium speed (median 19.8 m/sec) fo r a longish duration (median 73 seconds) . Freight

trains are less common (a median of 6 per day), but they show a similar pattern to unloaded

coal trains in terms of their speed and duration of passing. There are generally just a couple

of unknown train types each day.

Figure 1 shows some of the data on TSP from a typical day (January 8 th 

### ). Both panels of the

Figure show time (24 hour clock) on the horizontal axis. The top panel shows TSP levels

measured on the original scale (ug/m 3 

### ) while the bottom panel uses a log transformation. A

value of 1 is added to each TSP value to avoid problems associated with taking logs of zero.

Data are shown for the time period between 10am and 4pm. The presence of various train

types is represented using different colours, as indicated in the legend.

Figure 1 reveals a number of interesting and important features. First of all, we see that the

data are quite skewed on the original scale, with a number of extreme values. In contrast, theRe

- analysis of ARTC Data on Particulate Emissions from Coal Trains 6



### log

- transformed data are much more symmetrically distributed around the overall trend.

Consequently, statistical analyses based on log

- transformed data are likely to be more reliable

and stable. All subsequent analyses were thus done on log

- transformed data. The Figure

also shows that the data tend to reflect a changing trend as time progresses. This may be

explainable by changes in wind patterns or perhaps other unmeasured factors. Thus it will be

important for our analyses to ei ther directly account for wind and time of day, or build in the

likelihood that there might be significant serial in the data. The plots suggest that some of

the observed train passings are associated with a strong spike in TSP levels, while others are

not. This observed variability underscores the importance of using reliable statistical methods

that can help to sort out the patterns. While an eye

- ball assessment of the plot might suggest

that TSP levels might be sl ightly higher when a coal train (loa ded or unloaded) or freight

trains pass, it is also cle ar that the effects are moderate in magnitude relative to background

levels . The plots also suggest that TSP levels associated with empty coal trains are as high as,

if not higher than TSP levels asso ciated with loaded coal trains. This observation will be born

out with our subsequent statistical modelling.

Figure 2 shows plots of TSP, PM10, PM2.5 and PM1.0, all on the log scale, for a four hour

period (4am to 8am) on another typical day, January 5 th 

### 2013. Note that constants of 1, 2 and

5, respectively, were added to the PM10, PM2.5 and PM1 levels in order to avoid the problem

of taking logs of negative numbers. The Figure s hows t hat TSP and PM10 levels have f airly

similar patterns, though PM10 levels are less noisy than TSP . In contrast, the plots for

PM2.5 and PM1.0 suggest that levels are generally relatively low, except during train

passings. It is likely that these small particles correspond to diesel particles, not coal dust

particles, which are more likely to be reflected in PM10 and TSP . In general, it appears that

the spikes observed in the PM2.5 and PM1 plots are also reflected in the TSP and PM10 plots,

though with more noise generally added in. This reflects the fact that there a re likely to be a

number of different sources reflected in the measurements of the larger particles, including

coal dust and general dust.Re

- analysis of ARTC Data on Particulate Emissions from Coal Trains 7



## 4. STATISTICAL MODELLIN G STRATEGY

We analysed the data using a variant of linear regression, with outcome variable

corresponding to one of the four particulate measures (PM1, PM2.5, PM10 or TSP)

transformed to the log scale, as described above. The advantage of regression analysis is that

it allows for simultaneous adjustment with respect to various confounding factors that may

otherwise bias or distort the analysis. For example, loaded and unloaded coal trains were

more commonly seen during the early morning and evening hours, compared with passenger

trains which tended to be more frequent in daytime hours

. S ince particulate levels are likely

to be generally higher in the daytime hours, a naïve comparison of particle levels associated

with the different train ty pes would be biased. A regression model that includes appropriate

terms corresponding to time of day and day of study provides an adjustment that puts train

type comparisons on an equal footing. We used an advanced version of linear regression

analysis, the so

- called generalized additive model , which allows for the flexible modelling of

continuous functions using splines and other kinds of functions [1] 

### . All statistical analysis was

conducted in the statistical package R [2] 

### , using a function called gam() , which is part of the

package mgcv deve loped by Wood [1]



### . O ne of the variables included in the model was a

smooth spline function of time of day . Inclusion of this term in the model allowed for the

likelihood that there might be a diurnal pattern in the data. Similarly, we included another

smooth spline function of day of observation . Inclusion of this term allowed for the

possibility that there might be a longer term pattern, for example associated with rainfall or

temperature changes over time. Our model also included day of week indica tors. We first

conducted analyses excluding the wind speed and wind direction variables, since these data

were not available on all study days.

Exploratory analysis of the residuals from our regression models suggested the presence of

strong autocorre lation. This is to be expected in data such as these which represent a long

time series of observations measured closely together in time . While there is a variation of

the gam() function available that allows for autocorrelation, we found that because o f the

magnitude of our analysis datasets, the models were extremely slow to run or did not run at

all. Consequently we used a bootstrap [3] 

### to adjust the standard errors computed in our models

for autocorrelation. In particular, we used a specific varia nt, the so called blocked

bootstrap [4] 

### , which has been developed for use with serially correlated data. In our setting, a

particularly simple implementation of the blocked bootstrap was achieved by resampling

days. Standard errors were computed based a t otal of 50 bootstrap samples.

The large size of the analysis dataset meant that each model took several minutes to run and

the bootstrap several hours. In order to explore the data and to identify suitable models, we

first ran analyses using ordinary li near regression with polynomial terms in time of day and

day of study. Our final analyses were then repeated using the more computationally

intensive but accurate bootstrapped gam () function

. In general, we found that linearRe

- analysis of ARTC Data on Particulate Emissions from Coal Trains 8



### regression models gave qualitatively similar results to the gam () analyses and hence they

provided a useful practical approach to exploratory analysis.

The main variables of interest in all our analysis related to the various occurrences of train

passings . For each of the 5 different kinds of train types, we created three different variables

for inclusion in our model:

1. An indicator that took the value 1 while a particular train type was passing, 0

otherwise;

2. An indicator that took the value 1 for a peri od of T before 

### seconds before a particular

train type arrived;

3. An indicator that took the value 1 for a period of T after 

### seconds after a par ticular train

type had passed.

For these second and third variables, we considered a range of different values for T before 

### and

T

a fter 

### , ranging from 0 up to 70 seconds. The concept here was to allow the models to capture

the likely effect whereby air disturbances just prior to and just after a train passing would be

likely to stir up dust and other particles. As dis cussed presently, these variables turned out to

be quite important. To select appropriate values of T before 

### and T af ter 

### , we reran our models over

a range of feasible values and picked the values that gave us the best fit to the data according

to the Akaik e Information Criterion (AIC) [5]



### . We found that the optimal value of T before 

### was

3 minutes, while the optimal value of T after 

### was 5 minutes. A s discussed in the conclusions

section, there are some more sophisticated approaches that would be useful to ex plore , given

more time. However, it is unlikely that these would lead to any substantial changes to our

conclusions from a qualitative perspective.Re

- analysis of ARTC Data on Particulate Emissions from Coal Trains 9



## 5. RESULTS

Table 2 shows geometric means of all four particulate types corresponding to the periods

when different types of trains are passing. Geometric means simply correspond to the

exponent of the mean of the logged variables. Geometric means are commonly used when

analysing environmental exposure data which often tend to highly skewed, just as we have

found in the current context. In addition to showing particle levels while trains are passing,

the Table also shows the geometric means corresponding to different periods of time prior to

e ach train arrival, as well as after it has passed. For comparison, the last column ( “No

T rain ” ) shows particulate levels during periods where there is at least 3 minutes before the

next train arrival and 5 minutes after the most recent train passing. A comparison of the

columns labelled “During Train Passing” and “No Train” suggests that all particle types are

elevated when the various trains pass by. The pattern persists if we consider “Any Train

Type” (the first block of numbers in the table), or eac h of the 5 specific train types (Passenger,

Freight, Loaded Coal, Empty Coal and Unknown). The “Any Train Type” variable was

computed by ignoring the specific train types. Differences between the “During Train

Passing” and “No Train” columns are quite small for passenger trains, but of a similar

magnitude (approximately 10% to 15%) for freight, loaded coal, empty coal and unknown

train types.

Table 2 reveals some interesting patterns corresponding to particle concentrations in the

periods before train s arrive and after they have passed. For all train types and all particle

types, we see that concentrations are elevated in the few minutes before and the few minutes

after a train has passed. These elevations are apparent whether we look at 1, 2 or 3 mi nute

periods prior to train arrival or 1, 2, 3, 4 or 5 minute periods after train passings. Our

approach was a little different than the Katestone approach: they considered an entrainment

period corresponding to the period after the train passed and last ing for a period

corresponding to 3 times each train’s passing duration. We come back to this point in the

discussion and conclusion section of the report.

We have purposely not included standard errors in Table 2 since a naïve comparison between

these columns would be inappropriate due to presence of serial correlation (see discussion in

the statistical modelling section above). Also, computation of standard errors is complicated

by the presence of serial correlation in the data. Instead, we assign statistical significance to

the observed differences on the basis of generalized additive regression modelling as

described previously. Table 3 shows the estimated regression coefficients, standard errors

and p

- values associated with the various type trai n indicators, based on using a blocked

bootstrap applied to the gam() models described earlier. The first column of the Table

indicates train type. Information for the four available particulate measures (TSP, PM10,

PM2.5 and PM1) are shown in the four v ertical blocks. Within each block, the first column is

the estimated regression coefficient from an analysis of the log transformed particulate

measurement. The second column within each block shows the estimated standard error andRe

- analysis of ARTC Data on Particulate Emissions from Coal Trains 10



### the third block shows the estimated p

- value. These p

- values can be used to attach

significance to the observed differences seen in Table 2. Small p

- values (say <.001) can be

considered highly significant. Values between .001 and .01 can be considered significant,

while valu es between .05 and .01 could be considered marginally significant. Some readers

may be used to considering any p

- value <.05 as significant. In the current context, however,

we recommend being slightly more conservative due to the large number of p

- values that

have been calculated. Larger p

- values (>.05) should be considered as non

- significant.

To illustrate how to interpret Table 2 and 3 together, consider TSP levels associated with the

passing of a loaded coal train. From Table 3, the coefficient of “L oadedCoal” from the

regression analysis on TSP is 0.078. The associated standard error is 0.0146, resulting in a

standardized regression coefficient of .078/.0146=5.34. Comparing this value to the standard

normal tables tells us that this is a highly sig nificant value (p<.0001). In other words, the

analysis tells us that TSP levels are significantly higher than background levels when a loaded

coal train is passing. To understand the magnitude of the effect, we need to convert this

coefficient computed f

rom a regression analysis on the log scale, back to the original scale,

relative to background. More precisely, suppose we take as background level the “No Train”

average of 28.93ug/m 3 

### seen in Table 2. The regression results imply that the TSP levels wil l

rise to 28.93*e xp(.07

8 ) =31.33 ug/m 3 

### when a loaded coal train is passing. This in turn means

that the passing of a loaded coal train is associated with an average increase of 31.33

-

28.93=2.4ug/m 3 

### in TSP levels. This number is smaller than the difference we observe in

Table 2 (33.02

- 28.93=4.09) because of the concurrent adjustments for time of day, day of

study etc. Going through the same calculations for passenger trains, we find an estimated

coefficient from Table 3 of .32, a corresponding standard e rror of .012 and p

- value of .007 (a

significant effect). Again taking background levels as 28.93, the regression analysis implies

that TSP levels rise to 28.93*exp(.032 ) =29.87 ug/m 3 

### when a passenger train passes. Thus,

the passing of a passenger train is associated with an average increase of 29.87

- 28.93=0.94.

This number is again slight ly smaller than the difference in geometric means (1.06) seen in

Table 2 because of adjustment for other factors.

From Table 3, we see that with the exception of u nknown train types (which are relatively

infrequent anyway), the train coming and train passed variables are all either highly

significant or marginally significant. As discussed in our conclusions section, we believe this

is an area worthy of further exp loration.

We ran a variety of additional models to explore other possible important features. For

example, we re

- ran our analyses excluding the 2189 seconds where multiple trains were

passing at the same time. In this scenario, we found that there was no significant difference

in the level of particulate levels associated with the passing of a passenger train. We also re

ran models that included information on the speed of each train at the time of its passing.

This did not have any significant effec t in terms of predicting particle levels. This finding is

consistent with the Katestone report.Re

- analysis of ARTC Data on Particulate Emissions from Coal Trains 11



### We explored the impact of wind speed by including as a smooth, non

- linear term in the

model, as a linear term and finally as a binary indicator of high vers us low wind speed. None

of these variables had a significant impact in terms of predicting particulate levels. This

finding is consistent with the Katestone report.

We explored the impact of wind direction by creating an i ndicator function that took the

value 1 when wind was blowing from the rail tracks towards the monitoring station (wind

directions between 150 and 300 degrees) and then including these terms in our models. As

reported by Katestone, wind direction does have significant impact. For example, in the “No

Train” periods discussed above in the context of Table 2, we found that a geometric mean of

33.41ug/m 3 

### in TSP levels when wind was blowing towards the monitor, compared with

26.28ug/m 3 

### when wind was blowing away from the monitor. To ex plore the eff ect of wind

direction further, we re ran our models for the subset of data where the wind was blowing

towards the monitor and then again in the subset where the wind was blowing away from the

monitor. We found that the magnitude of the effect s associated with each different train type

increased when the wind was blowing towards the monitor. However, qualitative conclusions

were unchanged. That is the magnitudes of the effect of a train passing were similar for

freight and coal trains both e mpty and loaded. Passenger train effects were statistically

significant, but of a smaller magnitude than for the freight and coal trains. With the

exception of some of the “coming” variables, all effects were statistically significant. When

wind was b lowing away from the monitor, all effects were dampened and some effects lost

significance. However, even when wind was blowing away from the monitor, we found a

statistically significant increase in particulate concentrations associated with the passing of

loaded and unloaded coal trains. The magnitude of these two effects were very close, with the

effect of unloaded coal slightly higher.Re

- analysis of ARTC Data on Particulate Emissions from Coal Trains 1 2



## 6. DISCUSSION AND CONCL USION

Despite the limitation of having data at only a single monitoring site, this two month time

series of data provides useful information that can be used to address the two questions of

interest, namely

1) Are trains operating on th e Hunter Valley rail netw ork associated with elevated particulate

matter concentrations ? and

2) Do trains loaded with coal have a stronger association compared with unloaded coal trains

or other trains on the network ?

With regard to Question 1, our analysis shows that there are clear and statistically significant

elevations in particulate concentrations when a train passes by the monitoring station. These

concentration elevations are apparent for all four particle type s, including TSP, PM10, PM2.5

and PM1. In answer to Question 2, there is no evidence that loaded coal trains have a

stronger association compared with unloaded coal trains or freight trains. Our analysis

suggests that the freight trains and coal train s (loaded and empty) all have similar profiles.

The elevations are of modest magnitude, with an increase of approximately 2.4 to 2.8 ug/m 3



### relative to background levels for freight and coal trains (loaded and empty) for TSP.

Corresponding increases for PM10, PM2.5 and PM1 are approximately 2, 0.7 and 0.12,

respectively. In other words, there is approximately a 10% increase in the various kinds of

particulate measurements associated with freight and coal trains. The impact of passenger

train passings is much smaller, though still mostly statistically significant. When we reran our

analyses excluding cases where there were multiple trains passing at the same time, the effect

of a passenger train passing became non

- significant. Our interpretation is th at passenger

trains do not contribute by themselves to elevations in particulate levels, but that the air

turbulence associated with their passing causes an observed increase with other kinds of trains

are passing at the same time.

The effects were appa rent and remained significant for all available particulate measures,

including TSP, PM10, PM2.5 and PM1, especially for freight and coal trains (loaded or

empty). Effects were non

- significant or only marginally significant for the smaller particles

when passenger trains were passing. Since coal dust is likely to be reflected in the large

particle counts (TSP and PM10), the fact that the signal remains strong for the smaller particle

counts suggests that contaminants such as diesel may be of more concern t han coal dust. This

conclusion is further supported by the fact that effects were similar for freight, loaded and

unloaded coal trains, all of which are pulled by diesel locomotives.

There are a number of areas where further analysis would be useful. For example, it would

be useful to obtain information from the ARTC on the number of locomotives on each train,

as well as indications of whether the locomotives were diesel or electric. If particles levels,

especially smaller ones, increase with the num ber of diesel locomotives, this would lendRe

- analysis of ARTC Data on Particulate Emissions from Coal Trains 13



### credence to the possibility that diesel exhaust is contributing to particulate exposure in the

Hunter region.

Much more work could usefully be done on the exposure profile associated with the various

kinds of t rain passings. Our analysis has achieved a fairly simple, first pass assessment,

distinguishing three time segment associated with the passing of a train: train approaching,

train actually passing and train recently passed. We found some intriguing patt erns that

suggest that particulate levels begin to rise prior to a train’s passing and stay elevated for

several minutes. To accurately quantify the overall exposure level associated with a train’s

passing would require a more precise characterization of the exposure profile corresponding

to the various stages of a train’s passing. This could be achieved with a combination of

considering physical concepts such as aerodynamics as well as applying more sophisticated

statistical modelling techniques to empir ically capture and model the profiles. A more

accurate characterization of the train passing profile would also allow computation of the total

exposure increase associated with a passing, rather than simply reporting on the average

increase in concentrati on. For example while our analysis has shown that the average air

concentrations in particulate levels are fairly similar for both loaded and unloaded coal trains,

the total aggregate exposure associated with a loaded coal train may be different than that for

an unloaded coal train because the passing durations vary so much.

There are also some technical aspects of our statistical modelling approach that would be

worth exploring in more detail. For example, we found that the data exhibited significan t

serial correlation, even after adjusting for diurnal effects and longer term time effects. We

adjusted for serial correlation through the use of a blocked bootstrap. It would be worthwhile

exploring the nature of the serial correlation in the data to d etermine if other approaches might

be applicable.

Finally, it would be worthwhile assessing the accuracy and reliability of the ARTC’s train

passing data. In particularly, there appeared to be occasional spikes in the observed

particulate counts where no train passing had been reported. It is likely that such spikes

corresponded to the passing of vehicles other than trains, for example maintenance vehicles.

Obtaining such information would be useful and would improve the overall accuracy of the

modell ing. It would also be useful to consider the incorporation of other sources of

information about ambient particulate exposure levels in the Hunter region. It is clear from

our analysis that the data are quite noisy and that they display fairly strong diu rnal and daily

patterns. Incorporating additional information pertaining to regional background levels

would help to isolate contributions due to train traffic in the Hunter corridor.Re

- analysis of ARTC Data on Particulate Emissions from Coal Trains 14



### References :

1. Wood, S (2006). Generalized Additive Models: An In troduction with R . Chapman &

Hall/CRC Texts in Statistical Science.

2. R Development Core Team (2008). R: A language and environment for statistical

c omputing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3

- 900051

- 07

-

0, URL http://www.R

- project.org.

3. Efron, B. and Tibshirani, R. J. (1994). An introduction to the bootstrap (Vol. 57). CRC

press.

4. Bühlmann, P. (2002). Bootstraps for time series. Statistical Science , 52

- 72

5. Akaike, H. (1970). Statistical predictor identification. Annals of the Institute of Statistical

Mathematics

, 22 (1), 203

- 217.

Pro fessor Louise Ryan

School of Mathematical Sciences

University of Technology, SydneyRe

- analysis of ARTC Data on Particulate Emissions from Coal Trains 15



## 7. GRAPHS AND TABLES16Figure1:TSPandlog(TSP+1)from 10amuntil 4pmonJanuary8th,201317Figure 2:TSP, PM10, PM2.5 and PM1 from 4am until 8am on January 5th, 201318Figure 3: Estimated profiles of increases in TSP associated with anunloaded (left panel) and loaded (right Panel) coal train

### 19 

### Train type Number per day

Median (LQ,UQ) Duration (secs)

Mean (LQ,UQ) Speed (m/sec)

Mean (LQ,UQ)

Empty Coal 40 (27, 47) 72 (67, 78) 19.8 (18.5,20.7)

Freight 6 (2,8) 69 (40, 85) 18.7 (15.1, 20.5)

Loaded Coal 36 (24, 41) 103 (93, 117) 13.9 (11.8, 15.1)

Passenger 60 (55, 92) 2 (2,2) 24.9 (23.9, 28.8)

Unknown 3 (2, 3) 2 (2, 2) 24.6 (22.2, 27.6)

Table 1: Summary of daily train activity20Table 2:Geometric means of particulate levels (TSP, PM10, PM2.5 and PM1) in ug/m3for different kinds of train passings and atduration durations before and after trainarrival. The category “Any train type” refers to the presenceof a passenger, freight, loaded coal, empty coal or unknown train type.TrainTypeParticleTypePeriodbeforetrainarrivalDuringtrainpassingPeriodaftertrainhaspassedNotrain3min2min1min1min2min3min4min5minAnyTrainTypeTSP30.8130.9931.1633.0132.2332.4832.3632.0131.7528.93PM1024.2124.3624.4725.8825.2925.4625.3625.124.922.78PM2.510.0410.110.1410.5410.4310.4710.4310.3410.269.35PM16.476.486.486.596.566.576.566.546.526.34PassengerTrainTSP29.9230.023029.9930.1430.3330.4230.430.3328.93PM1023.4923.5823.5623.4723.6823.8323.8923.8623.8122.78PM2.59.869.99.899.889.9810.0410.0510.03109.35PM16.416.426.416.416.446.466.466.466.466.34FreightTrainTSP33.3933.6433.9836.235.8936.5736.2235.5635.0628.93PM1025.8926.0426.228.1327.8528.2428.0227.5127.1622.78PM2.510.2110.2710.3110.9910.9911.0510.9610.810.699.35PM16.456.466.56.656.646.656.636.66.596.34LoadedCoalTrainTSP3131.3331.6233.0233.734.0833.7733.2332.7128.93PM1024.4424.724.8825.9326.4526.6626.4126.0125.6522.78PM2.510.1110.1910.2210.5510.7410.7910.710.5810.469.35PM16.56.526.526.666.646.656.636.596.566.34EmptyCoalTrainTSP31.6132.0232.5133.1934.6435.0334.7434.1533.728.93PM1024.8525.1625.5225.9827.1627.427.1926.7626.4222.78PM2.510.2310.3610.4810.5810.9911.0710.9910.8510.739.35PM16.526.556.576.596.736.746.726.686.646.34UnknownTrainTSP32.7732.9333.9137.2535.9435.4135.134.2933.8528.93PM1025.82626.4529.4528.0727.6527.5427.0326.6422.78PM2.510.5210.6310.7311.7311.3211.1511.110.9210.789.35PM16.746.756.736.886.866.786.786.726.696.3421Log(TSP+1)Log(PM10+1)Log(PM2.5+2)Log(PM1+5)Estimate StdErrorp-Value Estimate StdErrorp-Value Estimate StdErrorp-Value Estimate StdErrorp-ValueEmpty CoalComing0.0530.0131<0.0010.0500.0133<0.0010.0450.0126<0.0010.0170.0047<0.001Empty Coal0.0950.0158<0.0010.0890.0155<0.0010.0730.0138<0.0010.0260.0047<0.001Empty CoalPassed0.1210.0151<0.0010.1150.0155<0.0010.0960.0150<0.0010.0370.0056<0.001FreightComing0.0430.02490.0880.0320.02380.1670.0080.02200.703-0.0010.01140.938Freight0.0910.0258<0.0010.0870.0260<0.0010.0630.02310.0060.0230.00960.016FreightPassed0.0990.0233<0.0000.0890.0221<0.0010.0610.02050.0020.0220.01020.029Loaded CoalComing0.0260.01320.0530.0270.01280.0350.0280.01180.0140.0090.00460.030Loaded Coal0.0780.0146<0.0010.0750.0141<0.0010.0630.0111<0.0010.0210.0040<0.001Loaded CoalPassed0.0860.0128<0.0010.0820.0125<0.0010.0670.0116<0.0010.0200.0041<0.001PassengerComing0.0240.00940.0120.0210.00910.0160.0180.00810.0210.0030.00270.229Passenger0.0320.01200.0070.0250.01070.0190.0220.01120.0470.0040.00400.276PassengerPassed0.0410.0109<0.0010.0390.0102<0.0010.0360.0096<0.0010.0110.00340.001UnknownComing0.0390.03880.3160.0420.03810.2650.0490.04060.2180.0400.02340.085Unknown0.1180.05490.0310.1260.05410.0190.1250.05740.0280.0430.03370.201UnknownPassed0.0580.03760.1190.0500.0133<0.0010.0620.03800.1000.0240.01790.174Table 3:Coefficients of the train variables, their associated standarderrors and p-values from gam() models that include smooth function of time sincemidnight, day of study and day of week.