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Re-analysis of ARTC Data 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: 2013001693
ii

TABLE OF CONTENTS

1. SUMMARY .........................................................................................................3

2. INTRODUCTION AND BACKGROUND ..........................................................................4

3. THE DATA.........................................................................................................5

4. STATISTICAL MODELLING STRATEGY .........................................................................7

5. RESULTS...........................................................................................................9

6. DISCUSSION AND CONCLUSION .............................................................................. 12

7. GRAPHS AND TABLES.......................................................................................... 15
Re-analysis of ARTC Data on Particulate Emissions from Coal Trains 3

1. SUMMARY

This report describes a re-analysis of ARTC’s data on particle emissions from coal and other 

trains in the Hunter rail corridor. Our analysis was based on regression 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, while differences were not statistically significant, particulate levels 

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

loaded coal 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 marginally significant for PM2.5. Since coal dust is 

likely to be reflected in the larger 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 trains, 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 BACKGROUND

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 are 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 monitoring of particulate levels, the time of each train 

pass-by was recorded, along with other details about each corresponding train. Additional 

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

After the final report was submitted, 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, expert statistical 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

 Katestone’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 single 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 timeseries nature 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 appropriately 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 monitoring, 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 2nd and 16th) had no measurements while an additional three days (December 5th, 

December 15th 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 30th) had only 867 concurrent 

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

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, along 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) for 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 8th). 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/m3) 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, the 
Re-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 either 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 slightly higher when a coal train (loaded or unloaded) or freight 

trains pass, it is also clear 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 associated 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 shows that TSP and PM10 levels have fairly 

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 are 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 MODELLING 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. Since particulate levels are likely 

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

with the different train types 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 developed by Wood[1]. One 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 indicators. 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 autocorrelation. 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 of 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 variant, 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 total 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 linear 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 linear 
Re-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 period of Tbefore seconds before a particular 

train type arrived;

3. An indicator that took the value 1 for a period of Tafter seconds after a particular train 

type had passed. 

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

Tafter, 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 discussed presently, these variables turned out to 

be quite important. To select appropriate values of Tbefore and Tafter, 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 Akaike Information Criterion (AIC)[5]. We found that the optimal value of Tbefore was 

3 minutes, while the optimal value of Tafter was 5 minutes. As discussed in the conclusions 

section, there are some more sophisticated approaches that would be useful to explore, 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 

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

Train”) 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 each 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 trains 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 minute 

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 lasting 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 train 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 vertical 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 and 
Re-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 values 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 “LoadedCoal” 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 significant 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 from 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/m3 seen in Table 2. The regression results imply that the TSP levels will 

rise to 28.93*exp(.078)=31.33ug/m3 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/m3 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 error 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/m3 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 slightly 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 unknown 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 exploration. 

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

This did not have any significant effect 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 versus 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 indicator 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/m3in TSP levels when wind was blowing towards the monitor, compared with 

26.28ug/m3 when wind was blowing away from the monitor. To explore the effect of wind 

direction further, we reran 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 effects 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 empty 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 blowing 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 12

6. DISCUSSION AND CONCLUSION

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 the Hunter Valley rail network 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 types, 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 trains (loaded and empty) all have similar profiles. 

The elevations are of modest magnitude, with an increase of approximately 2.4 to 2.8 ug/m3 

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 that 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 apparent 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 than 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 number of diesel locomotives, this would lend 
Re-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 train 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 patterns 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 empirically 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 concentration. 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 significant 

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 determine 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 

modelling. 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 diurnal 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 Introduction with R. Chapman & 

Hall/CRC Texts in Statistical Science. 

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

computing. 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.

Professor Louise Ryan

School of Mathematical Sciences

University of Technology, Sydney
Re-analysis of ARTC Data on Particulate Emissions from Coal Trains 15

7. GRAPHS AND TABLES
Figure 1: TSP and log(TSP+1) from 10am until 4pm on January 8th, 20
Figure 2: TSP, PM10, PM2.5 and PM1 from 4am until 8am on Janua
Figure 3: Estimated profiles of increases in TSP associated with an unloaded (left panel) and load
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 activity 
Table 2: Geometric means of particulate levels (TSP, PM10, PM2.5 and PM1) in ug/m3 for different kinds of train passings 

arrival. The category “Any train type” refers to the presence of a passenger, freight, loaded coal, empty c

Train Type Particle

Type

Period before train arrival During train

passing

Period a

train has p

3 min 2 min 1 min 1 min 2 min 3 min 

Any Train Type TSP 30.81 30.99 31.16 33.01 32.23 32.48 32.36 

PM10 24.21 24.36 24.47 25.88 25.29 25.46 25.36 

PM2.5 10.04 10.1 10.14 10.54 10.43 10.47 10.43 

PM1 6.47 6.48 6.48 6.59 6.56 6.57 6.56 

Passenger Train TSP 29.92 30.02 30 29.99 30.14 30.33 30.42 

PM10 23.49 23.58 23.56 23.47 23.68 23.83 23.89 

PM2.5 9.86 9.9 9.89 9.88 9.98 10.04 10.05 

PM1 6.41 6.42 6.41 6.41 6.44 6.46 6.46 

Freight Train TSP 33.39 33.64 33.98 36.2 35.89 36.57 36.22 

PM10 25.89 26.04 26.2 28.13 27.85 28.24 28.02 

PM2.5 10.21 10.27 10.31 10.99 10.99 11.05 10.96 

PM1 6.45 6.46 6.5 6.65 6.64 6.65 6.63 

Loaded Coal Train TSP 31 31.33 31.62 33.02 33.7 34.08 33.77 

PM10 24.44 24.7 24.88 25.93 26.45 26.66 26.41 

PM2.5 10.11 10.19 10.22 10.55 10.74 10.79 10.7 

PM1 6.5 6.52 6.52 6.66 6.64 6.65 6.63 

Empty Coal Train TSP 31.61 32.02 32.51 33.19 34.64 35.03 34.74 

PM10 24.85 25.16 25.52 25.98 27.16 27.4 27.19 

PM2.5 10.23 10.36 10.48 10.58 10.99 11.07 10.99 

PM1 6.52 6.55 6.57 6.59 6.73 6.74 6.72 

Unknown Train TSP 32.77 32.93 33.91 37.25 35.94 35.41 35.1 

PM10 25.8 26 26.45 29.45 28.07 27.65 27.54 

PM2.5 10.52 10.63 10.73 11.73 11.32 11.15 11.1 

PM1 6.74 6.75 6.73 6.88 6.86 6.78 6.78 
Log(TSP+1) Log(PM10+1) Log(PM2.5+2) 

Estimate Std 

Error 

p-Value Estimate Std 

Error 

p-Value Estimate Std 

Error 

p-Value 

Empty Coal Coming 

0.053 0.0131 <0.001 0.050 0.0133 <0.001 0.045 0.0126 <0.001 

Empty Coal 0.095 0.0158 <0.001 0.089 0.0155 <0.001 0.073 0.0138 <0.001 Empty Coal Passed 

0.121 0.0151 <0.001 0.115 0.0155 <0.001 0.096 0.0150 <0.001 

Freight Coming 

0.043 0.0249 0.088 0.032 0.0238 0.167 0.008 0.0220 0.703 

Freight 0.091 0.0258 <0.001 0.087 0.0260 <0.001 0.063 0.0231 0.006 Freight Passed 

0.099 0.0233 <0.000 0.089 0.0221 <0.001 0.061 0.0205 0.002 

Loaded Coal Coming 

0.026 0.0132 0.053 0.027 0.0128 0.035 0.028 0.0118 0.014 

Loaded Coal 0.078 0.0146 <0.001 0.075 0.0141 <0.001 0.063 0.0111 <0.001 Loaded Coal Passed 

0.086 0.0128 <0.001 0.082 0.0125 <0.001 0.067 0.0116 <0.001 

Passenger Coming 

0.024 0.0094 0.012 0.021 0.0091 0.016 0.018 0.0081 0.021 

Passenger 0.032 0.0120 0.007 0.025 0.0107 0.019 0.022 0.0112 0.047 Passenger Passed 

0.041 0.0109 <0.001 0.039 0.0102 <0.001 0.036 0.0096 <0.001 

Unknown Coming 

0.039 0.0388 0.316 0.042 0.0381 0.265 0.049 0.0406 0.218 

Unknown 0.118 0.0549 0.031 0.126 0.0541 0.019 0.125 0.0574 0.028 Unknown Passed 

0.058 0.0376 0.119 0.050 0.0133 <0.001 0.062 0.0380 0.100 

Table 3: Coefficients of the train variables, their associated standard errors and p-values from gam() models tha

midnight, day of study and day of week.