Artificial Neural Network Modeling and Forecasting of
Hourly PM2.5 in urban air in Edmonton
Paper # 50
Mohamed Gamal-El Din
Department of Civil and Environmental Engineering, University of Alberta, Room # 3093, NREF Building, Edmonton, Alberta, Canada
Madhan Selvaraj
Department of Civil and Environmental Engineering, University of Alberta, Edmonton,
Alberta, Canada
Ahmed Gamal-El Din
HNTB Corporation, Indianapolis, Indiana, USA
Ahmed Idriss
Alberta Environment, Edmonton, Alberta, Canada
ABSTRACT
Particulate Matter 2.5 (PM2.5) is formed due to anthropogenic (man-made) or biogenic
sources (natural). Studies have shown strong correlation between PM2.5 and health
effects, such as respiratory and cardiovascular problems. Artificial Neural Networks
(ANNs), an artificial intelligence (AI) technique has the ability to generalize historical
data patterns and make inferences about future trends. In this research, ANN technique
was used to model PM2.5 for the city of Edmonton, Alberta, Canada. Two models were
built, one for virtual monitoring and the other for forecasting. Because of the seasonal
variation of PM2.5 in Edmonton, season-specific versions of the ANN models were
developed for both the virtual monitoring and forecasting purposes.
Virtual monitor models were able to make good predictions for all the seasons with the
exception of summer season. Addition of one-hour lagged input PM2.5 data improved the
prediction capability of the virtual-monitor ANN models. Meanwhile, the addition of
one-hour lagged input PM2.5 data did not improve the prediction capability of the forecast
ANN models except for the spring season. Overall the models developed using a
systematic approach had better performance then the other models in the literature.
INTRODUCTION
Particles with aerodynamic diameter of 2.5 or less are called Particulate Matter 2.5
(PM2.5). PM2.5 is formed due to anthropogenic (man-made) or biogenic (natural) sources.
Epidemiological studies have linked PM2.5 as a cause for respiratory (asthma)
and cardiovascular problems (heart-attack)1. Visibility, which is the public perception of
the air quality is also affected due to elevated PM2.5 concentration2,3. Currently the
Canada Wide Standard (CWS) for PM2.5 is set at 30g/m3 (24-hour average) by the
Canadian Government3.
Artificial neural networks (ANNs), an artificial intelligence (AI) technique has the
ability to generalize historical data patterns and make inferences about the future trends.
ANNs have been widely used in water treatment operations4, wastewater treatment5 and
even prediction of change in weather patterns6. ANNs have also been used in the field of
air pollution for the prediction of various pollutants such as ozone (O3)7, sulphur dioxide
(SO2)8, oxides of nitrogen (NOx)9, 10 and particulate matter 10 (PM10)11. Prediction of
PM2.5 using ANNs has not been successful12, 13. This may be due to the complex nonlinear relationship between PM2.5 and meteorology. Lack of systematic approach in
arriving at the proper architecture and seasonal variation observed in the concentration of
PM2.5 are some of the reasons for the poor generalization ability of the previously
developed models.
PM2.5 is a major threat to the people and the environment and prediction of it in
advance is what environmental managers are trying to achieve. The main objective of this
research is to develop models for prediction (virtual monitor) and forecasting of PM2.5 for
the city of Edmonton, Canada using a systematic approach5. Analyzing the input
variables that is affecting the concentration of PM2.5 is the other objective of this
research.
ARTIFICIAL NEURAL NETWORKS
Artificial Neural Networks (ANNs), an artificial intelligence (AI) technique have been
used to understand complex non-linear mechanism by repeated presentation of data
patterns, several inputs and its related output. One of the advantages of ANNs is that no
priori knowledge of the study area is required. ANNs are classified in to feed-forward
networks and feed-back networks14.Feed-forward networks are classified into singlelayer perceptron (SLP), multi-layer perceptron (MLP) and radial basis function (RBF)
networks. MLP have been widely used because of its success in various fields15.
MLP consists of three or more layers of processing elements called neurons.
Normally there are three layers: input, hidden and output. Each layer has its own number
of neurons. Input layer processes the inputs and passes on to the hidden layer. Hidden
layer which may be one or two performs most of the processing of the three layers16 and
passes on to the output layer. The output layer processes the information from the hidden
layer and gives out prediction to the end user.
Neurons, which are processing elements in an MLP is connected to each other
through a set of weights. These weights are adjusted based on an error-minimization
technique called back-propagation rule. Activation functions, which are present in the
hidden and output layer produces output based on the summed weighted value passed to
them. There are several activation functions: Logistic, tanh, Gaussian, Sine, tanh15,
Gaussian complement and symmetric logistic.
Training of the model is done using a supervised learning methodologypresenting of historical data patterns, consisting of set of inputs and its related output.
Training is continued until the error between the desired output and actual output reduces
to a minimum. The back-propagation rule adjusts the weights to minimize the error. Test
set data is used as a check to asses the models training, so that the model does not
memorize the interactions. The trained model is applied to the production data set, which
is independent of the training data set, to ascertain the generalization ability of the model.
The model should be trained until the generalization performances reaches
maximum. One way to avoid over training is by using the early stopping technique.
DATA AND METHODS
Study Area
Edmonton, capital city for the province of Alberta, Canada is located at 53.5° N latitude
and 113.5° W longitude. Edmonton, the northern city in the province of Alberta, Canada
is characterized by short summers and long winters. Ground-based inversion is a
common phenomenon in this city17. Edmonton has gas fired power plants, petroleum
refineries, cement kilns, coal-fired power plants and asphalt roofing manufacturing
plants18. There are cases of forest fire, which enhanced the production of air pollutants in
the atmosphere in Edmonton19.
Air pollutants are monitored on a continuous basis in the three monitoring
stations: East, Central and North. In this research, data from the East monitoring station is
used for model development. Mixing height (twice daily) and opacity data is obtained
from the Stony Plain Station near Edmonton.
Model inputs used
The most important criteria in developing the model are in choosing the input variables
which may affect the output. The following are the input variables that are included in
developing the models. Average hourly data of pollutants and atmospheric variables are
used in the model development based on previous studies done in the field of air
pollution.
Carbon monoxide (CO). In Edmonton, source contribution of transportation sector was
48% of the total mass of PM2.518. CO is one of the pollutants emitted from the vehicle
related emission. It is included as an input for the prediction of PM2.5, because of their
linear correlation with PM2.520, 21. It is also used as an indirect measure of atmospheric
stability and WSP7.
Nitric oxide (NOX). NOx is the sum of NO and Nitrogen dioxide (NO2). Most Primary
NOx is emitted in the form of NO9.Vehicle and combustion processes are the major
sources of NOx. In Alberta, wild fires emits significant amount of NOx19.
NO reacts with ozone and form the secondary NOx, NO2. There is a strong correlation
between PM2.5 and NOX22. Both NO and NO2 was included as a separate input variable in
developing the model.
Ozone (O3). O3 is a secondary pollutant, formed through a reaction between NOx and
volatile organic compounds (VOCs) in the presence of sunlight. O3 is included as an
input to simulate the complex atmospheric chemistry.
Sulphur dioxide (SO2). In Edmonton, sulphate mass contributed 11% of the total mass
fraction of PM2.518.
Total hydrocarbon (THC). Vehicles are the major sources of hydrocarbons.
Carbonaceous compounds form major composition in the mass of PM2.522.
Temperature (Temp). Temp acts as a major indicator of seasonal change. In previous
study by Mckendry12, temperature was found to be an important variable. There is a
strong relation between high ozone episodes and high temperature23.
Mixing height (MH). Inclusion of MH simplifies the complex meteorological processes,
which acts as a ceiling in trapping air pollutants.
Wind speed (WSP). There is a strong inverse correlation between WSP and PM2.5
concentration22, 24. WSP plays important role in the distribution of pollutants over an area.
Many of the pollution episodes occurred only during low WSP levels.
Wind direction (WDR). WDR indicates the direction from which wind blows, and is
measured in degrees from North. Few studies used WDR as an input parameter in
modeling25.
Wind direction deviation (DEV). DEV may be used as a measure of atmospheric
stability10. DEV has been used as a description of atmospheric turbulence in dispersion
models26.
Opacity (OPA). OPA may be used as a surrogate of solar radiation received. Solar
radiation plays an important role in photochemical enhanced reactions. Particulate
concentration is higher during sunny days due to enhanced photochemical activity24.
Relative humidity (RH). RH may serve as a surrogate parameter of precipitation as
surface wetness controls the concentration of PM2.5. Studies12, 13 done on PM2.5 used RH
as an input parameter.
Month of the year. Month of the year was included as a separate input to account for
seasonality in the concentration of PM2.527. Indexing of variables was used in this
research for developing the models. For example, if the data pattern is for the month of
January, then an index of 1 was assigned to it and 0 was assigned to all other months.
Day of the week. Historical study on the data trends in Edmonton and elsewhere in the
world has shown that, concentration of PM2.5 is more in weekdays compared to
weekends27, 28, 29, 30,31. To account for this variation, an indexed variable for day of the
week is used.
Hour of the day. Diurnal variation in concentration of PM2.5 was observed in
Edmonton27, 29. Indexing of hour of the day was used to account for this variation.
Model development
Model development was done according to a systematic approach followed in wastewater
treatment modelling5. An optimum network structure is obtained, by following a
systematic approach of model development32. Hourly pollutant and meteorological data
from the Edmonton East station were used in the model development. MH data was
calculated in two ways: Interpolation between twice-daily balloon sounding data
measured in Stony Plain station and using three-hour centered averaged WSP data33.
Lower values of MH obtained by two methods were used in developing the models. In
developing the models, historical data from the year September 2000 to August 2003 was
used. Ward systems Group, Inc.’s Neuroshell 2 (Release 4.0) software and its associated
batch processor feature were used exclusively to develop the models.
Systematic approach. The historical data patterns obtained from the monitoring stations
were analyzed for erroneous entries using statistic tool in Microsoft Excel. The analyzed
data was divided in to training, testing and production data set in a ratio of 3:1:1. The
most appropriate activation function for the hidden and output layer was determined by
varying the number of hidden layer neurons and number of epochs at three settings:
lower, middle and higher (Table 1). The optimum network structure was obtained by
varying the number of hidden layer neurons and number of epochs. For example, the
number of hidden layer neurons such as 6, 7, 8, 9, 10, 15, 20 and number of epochs such
as 500, 1000, 1500, 2000, 2500, 3000.The input variables affecting the concentration of
PM2.5 was found out by removing the input variables one at a time from the best
performing model. Any change in R2 value by more than 0.03 is due to the importance
of that variable in prediction. Previous hour’s PM2.5 concentration was added to the
original input variables to check for persistence. The models performance was
ascertained by applying the developed model to the independent data set (production
data) and looking at the coefficient of multiple determination value (R2). Closer the
value to 1 better is the models generalization capability. The performance was also
analyzed by plotting the predicted and actual PM2.5 concentration against date.
RESULTS AND DISCUSSION
Systematic approach outlined in the previous section was followed in developing the
models. The Virtual monitor was developed first, and the complete set of historical data
after error cleansing, was used to develop it. The results obtained were poor, with R2
value in the 0.30 Range. So, the historical data were divided according to seasons: Spring
(March, April, and May), summer (June, July, and August), fall (September, October, and
November) and winter (December, January, and February), and models were developed
for all the seasons. Table-2 shows the basic statistic for all the seasons.
Table 1. Number of hidden layer neurons and training epochs setting for evaluating
hidden and output layer activation function.
Setting
No. of neurons in hidden layer
No. of training epochs
Low
5
500
Middle
20
4000
High
40
7500
Virtual monitor
Season specific models were developed because of the poor prediction ability of the
models developed with full year data. Activation functions were determined for the
hidden and output layer for all the seasons based on the systematic approach. The
seasonal models were optimized for number of hidden layer neurons and number of
training epochs based on the activation functions determined in the previous step (Figure
-1). From Figure-1 the optimum network structure (minimum of number of hidden layer
neurons and training epochs with higher R2 value) for fall season was found to be 500
training epochs and 15 hidden layer neurons.
Table 2. Basic statistics for different season historical data
(a) Fall season
(b) Winter season
th
Parameters
CO (ppm)
NO (ppm)
NO2 (ppm)
O3 (ppm)
SO2 (ppm)
THC (ppm)
MIX (m)
OPA (tenths)
RH (%)
TEMP (ºC)
WDR (º)
DEV(º)
WSP(km/h)
PM2.5 (µg/m3)
Mean
0.37
0.02
0.02
0.02
0.00
2.42
129.72
4.49
69.98
5.50
208.58
18.61
9.51
8.04
SD
0.27
0.03
0.01
0.01
0.00
0.85
169.05
3.96
19.44
7.93
84.76
16.86
4.95
7.01
Max
3.10
0.39
0.07
0.06
0.04
24.20
953.70
10.00
100.00
32.70
360.00
166.00
30.30
72.40
99
percentile
1.40
0.16
0.05
0.04
0.01
5.47
659.40
10.00
100.00
24.50
354.00
98.00
23.57
33.64
(c) Spring season
Parameters
CO (ppm)
NO (ppm)
NO2 (ppm)
O3 (ppm)
SO2 (ppm)
THC (ppm)
MIX (m)
OPA (tenths)
RH (%)
TEMP (ºC)
WDR (º)
DEV(º)
WSP(km/h)
PM2.5 (µg/m3)
Mean
0.33
0.01
0.02
0.03
0.00
2.3
261.28
4.61
59.87
2.53
200.11
17.80
10.50
7.51
SD
0.18
0.02
0.01
0.02
0.00
0.67
203.46
3.88
22.64
11.04
96.42
14.16
5.78
11.12
Mean
0.48
0.03
0.03
0.01
0.002
2.55
74.94
4.56
76.54
-7.27
200.78
15.96
8.87
8.59
SD
0.32
0.04
0.01
0.01
0.00
0.80
126.45
4.10
13.91
8.15
83.58
15.15
4.69
8.39
Max
2.80
0.48
0.08
0.05
0.04
12.10
965.28
10.00
100.00
14.00
360.00
163.00
36.90
123.30
99th
percentile
1.70
0.21
0.06
0.04
0.01
6.30
538.19
10.00
99.00
7.20
357.00
93.00
21.90
38.00
(d) Summer season
Max
1.8
0.20
0.08
0.07
0.06
13.1
2170.58
10.00
100.00
30.6
360.00
168.00
35.80
295.0
99th
percentile
1.00
0.10
0.06
0.06
0.01
4.90
826.39
10.00
100.00
25
357
82.69
26.2
37.63
Mean
0.25
0.01
0.01
0.03
0.00
2.33
220.07
3.98
60.59
18.33
206.46
23.46
8.78
9.19
SD
0.13
0.01
0.01
0.02
0.00
1.06
182.18
3.63
22.96
6.15
97.67
19.69
5.24
7.81
Max
1
0.14
0.06
0.10
0.03
25
2261.5
10.00
100.00
38
360.00
166.00
30.9
80.8
99th
percentile
0.70
0.06
0.04
0.07
0.01
6.50
716.18
10.00
100.00
33.34
357.00
116.84
23.10
36.88
2
R value
Figure 1 Optimization of model architecture for fall
season. The best architecture was found to be 15 hidden
layer neurons and 500 epochs.
0.72
0.71
0.70
0.69
0.68
0.67
0.66
0.65
10
15
20
25
30
35
500
1000
2000
40
3000
Number of epochs
The winter and spring season had the best prediction ability with a R2- value of 0.72.
However, the spring season was able to follow the higher and lower trends in PM2.5
concentration much better than the winter season (Figure 2 and 3). The summer season
had the least generalization ability (Figure 4). This may be due to the episodic events
such as forest fire and summer storm prevalent in the Edmonton area. Table-3
summarizes the seasonal virtual monitor model architecture characteristics.
PM 2.5 (µg/m3 )
Figure 2 Actual Vs. Network concentration for spring
season virtual monitor model.
40
35
30
25
20
15
10
5
0
Actual
3/1
3/2
3/5
Network
3/8 3/10 3/14 3/17 3/19 3/22 3/26 3/28 3/31
Date
PM 2.5 (µg/m3 )
Figure 3 Actual Vs. Network concentration for winter
season virtual monitor model.
45
40
35
30
25
20
15
10
5
0
12/1
Actual
12/4
Network
12/8 12/11 12/17 12/19 12/21 12/24 12/28 12/31
Date
Figure 4 Actual Vs. Network concentration for summer
season virtual monitor model.
35
PM 2.5 (µg/m3 )
30
Actual
Network
25
20
15
10
5
0
6/1 6/3
6/8 6/12 6/18 6/21 6/26 6/29 7/2 7/5
7/8 7/11 7/14
Date
Table 3. Summary of architecture followed in developing seasonal virtual monitor
models.
Architecture
No. of hidden layer neurons
Hidden layer activation function
Spring
5
Sine
Summer
20
Tanh
Fall
30
Logistic
Output layer activation function
Sine
Logistic
No. of epochs
500
Gaussian
complement
500
Winter
15
Gaussian
complement
Logistic
500
500
PM2.5 is formed through a complex mechanism between pollutants and
meteorology. Variables that affect the formation of PM2.5 was found out by removing the
variables one at a time keeping the pollutant parameters CO, NO, NO2, SO2, O3, THC
and indexed variables as constant based on the theoretical background about PM2.5. Any
drastic change in R2 value is because of that variable’s importance in prediction. Table-4
shows the important input variables for all the seasons.
Table 4. Input variables important in the prediction of PM2.5 for all the seasons
Input variable (No. of indexed
input)
Season
Fall
Winter
Spring
Summer
CO




NO




NO2




SO2




THC




O3




Month (12)




Day (7)




Hour (24)




Temp




RH



Opacity

WSP
WDR

DEV







MH
RH was found to be important for winter and fall season. This is due the high
mean RH prevalent in winter (77%) and fall (70%) seasons (Table-2) respectively in
Edmonton, which acts as a sink in removing particles. MH emerged as an unimportant
variable for all the seasons. This may be due to the disadvantages associated with the
calculation of hourly MH data. Opacity which acts as surrogate of solar radiation
received emerged as an important variable in summer, fall and spring season. One reason
for this may be due to the photochemical associated particulate formation in these
seasons. Temp was found to be important for all seasons.
WDR was found to be important for the fall and winter season. Meanwhile, WSP
was found to be unimportant during the same period of time. Location of cattle farms on
the southern side of Edmonton and predominance of WDR from southerly direction may
be the reason for WDR’s importance in fall and winter season.
Importance of WSP in the summer season may be due to the turbulent condition
prevalent in that area, which is evident from the mean WDR deviation value (23º), which
comes under the category unstable atmospheric condition34, 35. Transboundary transport
of pollutants is associated with turbulent conditions. There was a case of transboundary
intrusion of dust particles in Lower Fraser valley in the neighboring Province of British
Columbia36. However, standard deviation of wind direction was found to be unimportant
for summer season.
From Table-2, the inverse correlation between WSP (10.5km/hr) and PM2.5
concentration (7.5µg/m3) was observed to be higher for spring season. According to
Chaloulakou (2003)24, emissions from local sources dominate due to the strong inverse
correlation. This may be the reason for WSP’s importance in spring season.
Forecast models
In forecast models, the PM2.5 concentrations were predicted 1-hr in advance. Compared
to the virtual monitor models, this model uses present hours PM2.5 concentration as an
input. The hidden layer and output layer activation function that was used in developing
the virtual monitor models was used in the forecast models also. The forecast models
performed better than the virtual monitor models for all the seasons except the spring
season. Winter season had the highest prediction capability compared to the other season
forecast models with a R2 value of 0.78. The fall season forecast model, with a R2 value
of 0.76, was able to predict the lows of PM2.5 concentration but had difficulty in
predicting the higher trends. The summer season model had a R2 value of 0.58, was able
to predict the low values of PM2.5 concentration better than the higher values. The
prediction ability of spring season forecast model decreased, from a R2 value of 0.72 to
0.65, with the addition of present hour PM2.5 concentration, which is contrary to the other
seasonal models. Overall the forecast models had improved prediction ability compared
to the virtual monitor models except the spring season. Table 5 shows the characteristics
of virtual monitor and forecast models.
Check for persistence
Addition of persistence (lagged data) of data as an input and retraining the model was
carried out, as a part of the systematic approach of model building. In one study done in
Athens24, pollutant levels during the previous three days have been used as an input. In
the present study, previous hours PM2.5 data was included as an input data in addition to
the inputs used in the virtual monitor and forecast models.
Table 5. Virtual monitor and forecast models characteristics
Model
Virtual monitor
Forecast
Model characteristics
No. of hidden layer neurons
No. of epochs
R2-value
No. of hidden layer neurons
No. of epochs
R2-value
Spring
15
500
0.80
10
500
0.65
Summer
10
500
0.65
15
500
0.58
Fall
15
500
0.80
25
500
0.76
Winter
15
500
0.84
25
500
0.78
Virtual monitor. Addition of persistence of data improved the prediction ability of the
spring virtual monitor model. Model performance improved from R2 of 0.72 to 0.80.
Compared to the virtual monitor without lagged data, this model was able to predict the
higher values and lower values with precision. Figure 5 shows the prediction ability of
spring virtual monitor model with the addition of persistence of data. Summer virtual
monitor model, which had poor prediction ability (0.41) before the addition of lagged
data, improved its prediction with the addition of lagged data (0.65). This model was able
to predict the low values (Figure 6) better, compared to the virtual monitor model without
lagged input data (Figure 4). Prediction ability of fall virtual monitor model improved
from an R2 value of 0.66 to 0.80, with the addition of 1-hr lagged data as an input. The
winter season virtual monitor model with 1-hr lagged data was able to make better
generalization than the model without lagged data. R2 values increased from 0.72 to 0.84
with the addition of lagged PM data.
Forecast model. The prediction ability of spring forecast model improved from R2 value
of 0.65 to 0.78 with the addition of 1-hr lagged data as an input. However, addition of
previous hour PM data did not improve the prediction ability of the summer season
model. R2 value remained same at 0.58. Fall forecast models, prediction ability did not
improve much by the addition of 1-hr lagged data as an input. R2-value was 0.79, an
increase of 0.03 from the original 0.76. Addition of persistence (lagged data) of data did
not improve the prediction ability of winter forecast model. Prediction ability decreased
from R2 of 0.78 to 0.74, when previous hour PM data was added. Further study is
warranted to improve the prediction ability of forecast models.
Figure 5 Actual Vs. Network concentration for spring
season virtual monitor model with lagged data as an
input.
350
Actual
PM
2.5
(µg/m3)
300
Network
250
200
150
100
50
0
5/19 5/20 5/22 5/24 5/25 5/27 5/28 5/30 3/1 3/2 3/3 3/5
Date
PM 2.5 (µg/m3 )
Figure 6 Actual Vs. Network concentration for winter
season virtual monitor model with lagged data as an
input.
70
60
50
40
30
20
10
0
Actual
6/1 6/4
Network
6/8 6/12 6/15 6/18 6/21 6/25 6/28 7/1
7/5 7/9 7/13
Date
CONCLUSIONS
ANNs are a promising tool for predicting and forecasting PM2.5 in Edmonton when a
separate model was developed for each season. Addition of previous hour’s data as an
input improved the prediction ability virtual monitor models. The systematic approach
followed in this research for developing the model gave better prediction compared to
other models in the literature. The models developed in this research will assist in better
monitoring and forecasting of the pollutant PM2.5 for the city of Edmonton.
In the present study the forecast models were able to make 1-hr beforehand
prediction. More studies are required to improve the forecasting window. Usage of
precipitation data as an input should be attempted in future to improve the prediction
ability of the models. Attempt should be made to use dispersion modeling techniques for
calculating the meteorological variables.
ACKNOWLEDGMENTS
A special thanks to Diane Su and Kevin McCullum for guiding in various stages of this
research. Also, thanks to Dr. Warren Kindzierski and Brian Wiens for their insights on
the theoretical aspects of this research.
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Key words: PM2.5, Artificial neural network, systematic approach, virtual monitor,
forecast, persistence.