ANN Modeling of O3 at Rural Monitoring Sites in the West
Central Airshed Zone of Alberta
Paper # 1120
Warren B. Kindzierski, Mohamed Gamal El-Din, Madhan Selvaraj, and Yaming He
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta
T6G 2M8
ABSTRACT
The West Central Airshed Society zone in west central Alberta has experienced high ground
level ozone concentrations at a rural background station (Hightower Ridge) and at a rural station
closer to anthropogenic activity (Tomahawk). The objective of this study was to assess the
feasibility of using artificial neural network (ANN) modeling to evaluate historical ambient air
quality and meteorological data collected at these two stations. The purpose of the evaluation
was to attempt to explain the role that meteorological versus anthropogenic factors had in
contributing to observed hourly average ozone concentrations.
Hourly average ozone concentrations were analyzed using data for the months of March through
September over the years 2001 and 2002. It was found that peak averaged ozone concentrations
occurred between the hours of 3:00 pm to 5:00 pm in the range 79 to 88 µg/m3 (40 to 45 ppb).
The highest averaged ozone concentrations occurred at the higher elevation station (Hightower
Ridge, elevation 1,500 m above sea level). The lowest averaged ozone concentrations occurred
during morning hours at each station. Much lower concentrations occur at the lower elevation
station (Tomahawk, elevation 800 m above sea level).
This diurnal pattern – i.e. peak levels in mid-afternoon and lower levels in early morning – are
due to vertical convective mixing during day time hours and absence of this mixing during night
time and early morning hours. Meteorological conditions are the most important factors related
to the behavior of ozone concentrations observed at these stations. This behavior was confirmed
using ANN modeling. Temperature, relative humidity, and pressure were all related to ozone
concentrations. This strongly points to the importance of natural phenomena in mostly
contributing to the presence of ground level ozone at these stations. Anthropogenic factors
(precursor air pollutants originating from the activity of humans) are much less important.
INTRODUCTION
Ozone (O3) is a reactive gas that can form from the action of sunlight on man-made precursors
(i.e. hydrocarbons and nitrogen oxides emitted in fuel combustion) in urban areas. Exposure to
elevated levels of O3 in urban areas is reported to cause a wide variety of toxicological effects in
sensitive receptors.1-3 Exposure-related effects are reported to include lung inflammation, effects
on host-defense mechanisms, reduced pulmonary function, and adverse changes in lung
biochemistry.
O3 is also a naturally occurring trace constituent of the atmosphere. There is controversy
regarding how much of ambient O3 monitored at ground level is natural and how much is
1
produced from man-made precursors.2 Estimates of the natural component of O3 vary in
literature. Background O3 concentrations vary by geographic location, altitude and season. The
natural component of the background originates from three sources:2



Stratospheric O3 that is transported down to the troposphere (lower part of the
atmosphere extending from the surface up to a height about 7 km).
O3 formed from photochemically-initiated oxidation of methane and carbon monoxide
produced by biogenic sources (living organisms or their remains) and released by
geological material.
Photochemically-initiated oxidation of biogenic volatile organic compounds (VOCs).
At certain times of the year there may be stratospheric intrusion of ozone down to ground level.4
These occurrences are reported to be rare, of short duration, and typically associated with strong
frontal passages or severe thunderstorms.4 Rural long-term average O3 concentrations are
reported to be relatively high in the Canadian prairies when compared with concentrations in
cities or more southerly locations.1 Further, background O3 transported from rural locations
plays a role in the occurrence high O3 concentrations at urban air monitoring stations. These
occurrences generally arise under hot stagnant weather conditions when photochemical reactions
involving man-made precursor (anthropogenic) emissions can add to the background (natural)
ozone.
West Central Airshed Society (WCAS) zone encompasses an area of 35,000 square kilometers
and is located on the west central region in the province of Alberta (Figure 1). The western side
of the air shed is heavily forested and characterized by foothills and mountainous area. The
eastern side of the air shed encroaches upon the Capital Region of Alberta (Edmonton and
surrounding area) and is characterized by more gently rolling terrain with greater anthropogenic
development (e.g. gas plants and coal-fired power plants) and residential acreage developments.
Air-quality is monitored on a continuous basis at five stations in the WCAS airshed. In the past
these stations have experienced high hourly-average ground level ozone at rural air monitoring
stations (e.g. Hightower Ridge, elevation 1,500 m above sea level) and at a station situated in
closer proximity to anthropogenic activity (e.g. Tomahawk, elevation 800 m above sea level).
The objective of this study was to assess the feasibility of using artificial neural network (ANN)
modeling to evaluate historical ambient air quality and meteorological data collected at West
Central Airshed Society air monitoring stations. The purpose of the evaluation was to attempt to
explain the role that meteorological versus anthropogenic factors had in contributing to observed
hourly-average ground level O3 levels.
METHODS
A systematic approach was followed in developing the ANN models. Two years of historical
data – 2001 to 2002 – were initially considered for this study. Only data for the months of
March to September of each year were used in developing the models. This is because peak O3
concentrations are mostly experienced during this time period over the course of the year.
Focusing only on this time period would allow a better understanding of what input variables
may be important in contributing to peak O3 concentration events.
2
Figure 1. Province of Alberta showing West Central Airshed Society Zone and location of
Hightower Ridge and Tomahawk air monitoring stations.
West Central
Airshed Society
Zone
Edmonton
Hightower Ridge
Tomahawk
Calgary
Hourly average concentrations of NO, NO2, SO2, and PM2.5 were used as precursor and surrogate
air pollutants originating from the activity of humans in developing the models for the stations.
Summary statistics for these pollutants and for ozone are presented in Table 1 for the Hightower
Ridge and Tomahawk station. Hourly average values for meteorological input variables for each
of the stations included: wind speed, wind direction, global solar radiation, relative humidity,
temperature, deviation of wind direction, and deviation of wind speed. Hourly average
atmospheric pressure data for the same time period were obtained in an electronic format from
Environment Canada, Edmonton, AB and used in the following manner:

Hourly atmospheric pressure data from an Environment Canada weather station location in
Jasper, Alberta were matched to the Hightower Ridge station dataset.
3

Hourly atmospheric pressure data from the Environment Canada weather station location in
Violet Grove, Alberta were matched to the Tomahawk station dataset.
Table 1.
Summary hourly average concentrations for the Hightower Ridge and Tomahawk
monitoring stations using data for the months of March through September over the
years 2001 and 2002.
NO
NO2
SO2
PM2.5
3
O3
(ppb)
(ppb)
(ppb)
(µg/m )
(ppb)
Mean
0.1
0.7
0.2
2.4
44
S.D.
0.3
0.9
0.6
3.0
12
Min
0
0
0
0.
5.3
Max
14.8
15
11
36
81
90%ile
0.2
1.4
0.6
5.5
59
Mean
0.47
3.4
1.1
3.7
34
S.D.
1.3
3.4
2.4
4.8
13
Min
0
0
0
0
0.8
Max
28.3
36
52
121
91
90%ile
1.3
6.9
2.4
S.D. = standard deviation; Min = minimum; Max = maximum
8.3
50
Hightower Ridge
Tomahawk
Indexed temporal input variables – year, month, day and hour – were included as separate inputs.
Model performance was evaluated after training each network. Generally there are a number of
criteria to do this. However the coefficient of determination (R2) was used in this study. R2
indicates the proportion of variance in the model (dependent) variable – or output variable – that
is explained by the input (independent) variables. Results from ANN models with different
structures can be directly assessed without confusion using this criterion. ANN modeling was
performed using the commercial neural net software product NeuroShell®2 (Ward Systems
Group Inc., Frederick, MD) operated on an IBM® compatible computer in a Microsoft®
Windows environment. The following systematic approach was used:
1. Historical data were initially screened for erroneous data using basic statistics. Missing
values or extremely high values were removed from the data patterns.
2. The remaining data patterns for each station were divided into two subsets – training and
production subsets – in a ratio of 3 to 2.
3. The next step involved determining initial best performing (baseline) ANN models by
running a series of simulations with the data patterns and varying the number hidden layer
neurons and number of epochs at different settings. A logistic activation function was used
for the hidden and output layer.
4. Using the best performing ANN models based on Step 3, another series of simulations were
run to identify input variables that were important contributors to prediction of the output
4
variable (hourly-average ground level O3 concentration). This was accomplished by
removing one input variable at a time and re-running the ANN model with the remaining
input variables. Any change in R2 by an amount 0.03 or more was deemed to indicate that
the removed input variable was an important contributor in the prediction of the output
(hourly average O3 concentration). For the purposes of this evaluation, input variables that
were observed to have this feature were considered “core” or “important” variables.
5. Another series of simulations were run to identify input variables of secondary importance.
This was accomplished by adding one input variable at a time to the models developed with
the core input variables and then re-running the models. Any increase in R2 would be due to
the importance of the added input variable.
6. A final series of simulations were run to optimize the network architecture of the ANN
models by using the “core” and secondary important input variables and varying the number
of epochs and number of hidden layer neurons. Model results were then evaluated for the
purpose of identifying the best network architecture for ANN modeling.
RESULTS AND DISCUSSION
Hourly Average O3 Concentrations
Hourly average O3 concentrations were computed for each station using data for the months of
March through September over the years 2001 and 2002. These concentrations are plotted in
Figures 2 and 3 for the Hightower Ridge and Tomahawk stations. In these figures, “0”
represents midnight and “12” represents noon (12:00 pm). Figures 2 and 3 indicate that peak
hourly-averaged concentrations between 15 and 17 (3:00 pm to 5:00 pm) in the range 79 to 88
µg/m3 (40 to 45 ppb). Slightly higher hourly-averaged concentrations occur at the higher
elevation station (Hightower Ridge). The lowest hourly-averaged concentrations occur during
the morning hours. In addition, much lower concentrations occur at the lower elevation station
(Tomahawk) compared to Hightower Ridge. These trends are notable and their significance is
explained below.
The distinctly different diurnal hourly average O3 concentrations for Hightower Ridge (high
elevation rural site) and Tomahawk (lower elevation rural site) have been observed by others at
high versus low elevation sites in rural eastern United States.5 It was reported that these patterns
reflect the fact that the O3 concentration is strongly influenced by meteorological factors.
Similar afternoon O3 concentrations were observed at both high and low rural elevation sites,
however much lower concentrations were observed at low elevation sites during the stable
nighttime hours. This similar trend was also observed here – much lower O3 concentrations
occurred during morning hours at the lower elevation sites (Tomahawk and Violet Grove)
compared to the higher elevation site (Hightower Ridge).
This trend is due to the fact that at night, O3 is destroyed near the surface due to physical contact
with the surface vegetation.5 Since O3 formation and vertical convective mixing are inhibited at
night, the O3 destroyed near the earth's surface is not replenished during the night hours. In the
morning hours, with the onset of vertical convective mixing, O3 from the upper layers is
delivered to the surface resulting in an increase in the surface concentration. By 3:00 pm to 5:00
pm (in the case of WCAS monitoring stations), the intensity of the vertical mixing is such that
5
there is no vertical gradient of ozone. During this time, monitors located at low and high
elevation sites show similar hourly average values (i.e. 40 to 45 ppb). With the diminishing of
convective mixing in the late afternoon, O3 destruction near the surface causes concentrations to
decline once again. At high elevation sites, O3 monitors are exposed to air masses that are not in
contact with the surface, which explains near-constant O3 values throughout a day.5
Figure 2. Hourly average O3 concentrations for Hightower Ridge station using data for the
months of March through September over the years 2001 and 2002.
50
O3 concentration (ppb)
45
40
35
30
25
20
15
10
5
0
0
2
5
7
9
11
13
15
17
19
21
23
Hour of the Day
Figure 3. Hourly average O3 concentrations for Tomahawk station using data for the months of
March through September over the years 2001 and 2002.
50
O3 concentration (ppb)
45
40
35
30
25
20
15
10
5
0
0
2
5
7
9
11
13
15
17
19
21
23
Hour of the day
6
Hightower Ridge Station ANN Model
As a result of Step 3, the initial (baseline) ANN model developed for the Hightower Ridge
station using all variables had a coefficient of determination (R2) value of 0.79 (Table 2). In
identifying input variables that were important contributors to prediction of the output variable
(Step 4), month of the year and relative humidity emerged as core variables. A number of
variables were identified as being of secondary importance (Step 5): year; hour, SO2, NO2, NO,
PM2.5, temperature, global solar radiation, pressure, wind direction, and wind direction deviation.
Table 2.
Results of ANN modeling for Hightower Ridge and Tomahawk station using data for
the months of March through September over the years 2001 and 2002.
Initial (baseline) ANN model coefficient of
determination (R2) value using all variables
Core variables
Variables of secondary importance
Retrained ANN model coefficient of
determination (R2) value using only core variables
and variables of secondary importance
Hightower Ridge Station
0.79
Tomahawk Station
0.81
month
relative humidity
year
month
NO2 concentration
temperature
relative humidity
pressure
none
year
hour
SO2 concentration
NO2 concentration
NO concentration
PM2.5 concentration
temperature
global solar radiation
pressure
wind direction
wind direction deviation
0.81
0.78
Relative humidity and temperature were observed to be important and have been shown by
others6 to be important variables related to ground level O3 concentrations. The importance of
hour of day is assumed an artifact of diurnal variation that is observed in the hourly-average O3
concentration (Figure 2) and for meteorological parameters (e.g. temperature). Atmospheric
pressure values were related to O3 concentrations, although the model results did not indicate an
obvious and strong relationship. As indicated previously, at certain times of the year and in
special geographical settings there may be stratospheric intrusion of ozone down to ground
level.4 These occurrences are reported to be of short duration and typically with strong frontal
passages or severe thunderstorms. These types of weather phenomena are associated with
changes in atmospheric pressure.
7
Wind direction deviation was found to be an important input variable for the Hightower Ridge
station. The annual average value of this parameter was found to be 27º for the Hightower Ridge
station, which comes under the category of unstable atmospheric conditions.7,8 These turbulent
conditions have been reported by others9,10 to be a major cause for natural phenomenon
associated with O3 concentrations. Global solar radiation emerged as an important input
variable. Global solar exposure would tend to be highest in the spring and early summer months
coincident with the position of the sun over the northern hemisphere. Springtime high O3
concentrations are a common occurrence in the northern hemisphere.11
It was unexpected that precursor and/or surrogate indicators of anthropogenic factors (i.e. air
pollutant concentrations for NO, NO2, SO2, and PM2.5) were shown to be important input
parameters. However, air pollutant concentrations at the Hightower Ridge station are much less
compared to that observed at Tomahawk (Table 1). As indicated previously, the Hightower
Ridge station is located at an elevation of 1,500 m above sea level and is used an indicator of
regional background air quality of that area. Air pollutant concentrations for these parameters at
the Tomahawk station are higher.
Upon closer examination of wind direction statistics (data not shown), it was observed that wind
direction occurs from westerly and south-westerly directions a majority of time. The origins of
these winds are from wilderness/mountainous areas that lack major anthropogenic sources within
the airshed. Thus it is suggested that the origins of some the air pollutants related to O3
concentrations (e.g. at least SO2) are from air masses originating from the south and southwest
outside of the airshed (e.g. transboundary origins). As hourly-average O3 concentrations were
related to SO2, this indicates that at least some of the O3 has a transboundary origin (i.e. related
to westerly and south-westerly directions).
The final step (Step 6) consisted of using the core and secondary important input variables
identified from Steps 4 and 5 and retraining the model to identify the optimum network
architecture. The best (most efficient) performing model had a R2 value of 0.81 when trained
with 1,300 epochs and 15 hidden layer neurons. The final model optimized for the best
architecture was compared against actual output data to examine how well it could predict
hourly-average O3 concentrations (Figure 4). This model was able to follow the highs and lows
with a reasonable degree of accuracy.
Tomahawk Station ANN Model
As a result of Step 3, the initial (baseline) ANN model developed for the Tomahawk station had
a coefficient of determination (R2) value of 0.81 (Table 2). In identifying input variables that
were important contributors to prediction of the output variable (Step 4), year, month, NO2
concentration, temperature, relative humidity, and pressure emerged as core variables for the
Tomahawk station (Table 2). Based upon the results of Step 5, none of the input variables were
identified as being of secondary importance. Year emerged as an important variable. The
reasons for this are unknown as only two separate years worth of data were evaluated. Inclusion
of data from the months of March to September, and monthly variation observed in the
concentration of O3 and other meteorological parameters (data not shown) may be the reason for
the importance of month as an input variable.
8
Figure 4. Actual versus predicted ground level O3 concentrations for the Hightower Ridge
station ANN model optimized for the best network architecture (R2 = 0.81).
Actual
Network
70
O3 concentration (ppb)
60
50
40
30
20
10
0
1
10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199
Pattern number
As indicated previously, relative humidity and temperature were observed to be important
variables related to ground level O3 concentrations.6 The annual average relative humidity value
for the Tomahawk station was 64%. Relative humidity values >60% were related to elevated O3
concentrations in urban areas of Edmonton and Calgary, Alberta.6 Atmospheric pressure values
were related to O3 concentrations. These findings again indicate a possible relationship with
weather phenomena (i.e. associated changes in atmospheric pressure) and ground level O3
concentrations at the Tomahawk station.
It was unknown whether indicators of anthropogenic factors (e.g. air pollutant concentrations for
NO, NO2, SO2, and/or PM2.5) would be shown to be important input parameters. Air pollutant
concentrations for these parameters at the Tomahawk station are higher that that observed at
Hightower Ridge (refer to Table 1). Four coal-fired power plants and three natural gas
processing plants exist within a 35-km radium of the Tomahawk station within the West Central
Airshed Society zone. Only NO2 concentrations were related to O3 concentrations, which may
be indicating a more important role of anthropogenic activities at this station.
The final step (Step 6) consisted of using the core and secondary important input variables
identified from Steps 4 and 5 and retraining the model to identify the optimum network
architecture. The best (most efficient) performing model had a R2 value of 0.78 when trained
with 500 epochs and 14 hidden layer neurons. The final model optimized for the best
architecture was compared against actual output data to examine how well it could predict
hourly-average O3 concentrations (Figure 5). This model was able to follow the highs and lows
with a reasonable degree of accuracy.
9
Figure 5. Actual versus predicted ground level O3 concentrations for the Tomahawk station
ANN model optimized for the best network architecture (R2 = 0.78).
Actual
Network
O3 (ppb) concentration
60
50
40
30
20
10
0
1
10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199
Pattern number
FINDINGS
Evaluation of hourly average O3 concentrations at two rural air monitoring stations in west
central Alberta for the months of March through September found that peak averaged levels
occurred between the hours of 3:00 pm to 5:00 pm in the range 79 to 88 µg/m3 (40 to 45 ppb).
The highest averaged O3 concentrations occurred at the higher elevation background station
(Hightower Ridge). The lowest averaged ozone concentrations occurred during morning hours
at each station. Much lower concentrations occur at the lower elevation station (Tomahawk).
This diurnal pattern is due to vertical convective mixing during day time hours and absence of
this mixing during night time and early morning hours.
Meteorological conditions are the most important factors related to the behavior of O3
concentrations observed at these stations. This behavior was confirmed using ANN modeling.
Temperature, relative humidity, and pressure were all related to ozone concentrations. This
strongly points to the importance of natural phenomena in mostly contributing to the presence of
ground level ozone at these stations. Anthropogenic factors (precursor air pollutants originating
from the activity of humans) was unimportant at the rural background station (Hightower Ridge)
and much less important at Tomahawk.
REFERENCES
1. National Ambient Air Quality Objectives for Ground-level Ozone: Science Objectives and
Guidelines, Health Canada: Ottawa, ON, 1999.
10
2. Review of National Ambient Air Quality Standards for Ozone: Assessment of Scientific and
Technical Information, U.S. Environmental Protection Agency, U.S. Government Printing
Office: Washington, D.C., 1996.
3.
Burnett, R.T.; Brook, J.R.; Yung, W.T.; Dales, R.E. Environ. Res. 1997, 72, 24-31.
4.
Guidance Document for the Management of Fine Particulate Matter and Ozone in Alberta,
Clean Air Strategic Alliance: Edmonton, AB, 2003.
5.
1992 Regional Ozone Concentrations in the Northeastern United States, Northeast States
for Coordinated Air Use Management: Boston, MA, 1993.
6.
Application of Artificial Neural Network for Modeling Ground-level Ozone Concentrations
in Calgary and Edmonton, Alberta, Su, H., Department of Civil and Environmental
Engineering, University of Alberta: Edmonton, AB, 2004.
7.
Guideline on Air Quality Models (revised), U.S. Environmental Protection Agency, U.S.
Government Printing Office: Washington, D.C., 1986.
8.
Mitchell, A.E.; Timbre, K.O. Atmospheric stability class from horizontal wind fluctuation.
72nd Ann. Meet. Air Pollut. Control Assoc., Cincinnati, OH, 1979.
9.
Davies, T.D.; Schuepback, E. Atmos. Environ. 1994, 28, 53-68.
10. Chung, Y.S., and Dann, T. Atmos. Environ. 1985, 19, 157-162.
11. Monks, P.S. Atmos. Environ. 2000, 34, 3545-3561
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