Transportation Research Part D 14 (2009) 32–41
Contents lists available at ScienceDirect
Transportation Research Part D
journal homepage: www.elsevier.com/locate/trd
Prediction of hourly air pollutant concentrations near urban arterials
using artificial neural network approach
Ming Cai a,b,*, Yafeng Yin a, Min Xie b
a
b
Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611-6580, USA
School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong 510275, China
a r t i c l e
i n f o
Keywords:
Hourly pollutant concentration
Artificial neural network
Prediction
Influential factors
a b s t r a c t
This paper applies artificial neural network to predict hourly air pollutant concentrations
near an arterial in Guangzhou, China. Factors that influence pollutant concentrations are
classified into four categories: traffic-related, background concentration, meteorological
and geographical. The hourly averages of these influential factors and concentrations of
carbon monoxide, nitrogen dioxide, particular matter and ozone were measured at three
selected sites near the arterial using vehicular automatic monitoring equipments. Models
based on back-propagation neural network were trained, validated and tested using the
collected data. It is demonstrated that the models are able to produce accurate prediction
of hourly concentrations of the pollutants respectively more than 10 h in advance. A comparison study shows that the neural network models outperform multiple linear regression
models and the California line source dispersion model.
Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction
Vehicular exhaust emission is one of the main sources of air pollution in megacities with a population more than 10 million people (Mage et al., 1996). Guangzhou is the most developed city in southern China and had 10.05 million permanent
residents and more than 1.8 million vehicles by the end of 2007 (Guangzhou Statistics Bureau, 2008). The air pollution in
Guangzhou, especially at the locations close to arterials, is becoming more severe and a threat to residents’ health. Usually,
air quality forecasts for the city are based on the daily average pollutant concentrations measured from air monitoring stations and do not represent the time-varying characteristics of pollutant concentrations. Predicting hourly pollutant concentrations near arterials may be of more use in guiding residents’ trip-making decisions and helping public authorities to derive
traffic management policies (Yin and Lawphongpanich, 2006) to reduce traffic emissions.
Air pollutant concentrations near urban arterials are related to traffic emissions, background concentration, meteorological and geographical conditions, and some other local characteristics. The relationship is complex and strongly nonlinear
making conventional deterministic models inappropriate (Esplin, 1995). On the other hand, artificial neural network
(ANN) is particularly suitable for modeling multifactor, uncertainty and nonlinearity (Kukkonen et al., 2003). Unlike the stochastic approach, it makes no prior assumptions about the data distribution (Milionis and Davis, 1994). Therefore, ANN has
been widely applied to predict some well-known air pollutant concentrations such as NO2 (Gardner and Dorling, 1999), PM10
(Hooyberghs et al., 2005), O3 (Elkamel et al., 2001), and SO2 (Boznar et al., 1993).
Few studies have been conducted to predict the concentrations near urban arterials. Moseholm et al. (1996) studied the
relationship between traffic parameters and CO concentration measured near a sheltered intersection using video traffic
* Corresponding author. Address: Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611-6580, USA.
E-mail address: [email protected] (M. Cai).
1361-9209/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.trd.2008.10.004
M. Cai et al. / Transportation Research Part D 14 (2009) 32–41
33
Fig. 1. The monitoring sites.
surveillance and neural networks. Perez and Trier (2001) predicted the NO and NO2 concentrations at noon near a street with
heavy traffic in Santiago, Chile. Recently, Viotti et al. (2002) used ANN to forecast short and middle long-term concentration
levels for some pollutants in the urban area of Perugia. More recently, Nagendra and Khare (2006) described a step-by-step
procedure to model the NO2 dispersion phenomena adopting the ANN technique. Unfortunately, most previous studies neglect the background concentrations that represent the accumulation of pollutants and influence of other sources of pollutants. Moreover, the proposed models are all based on a sole monitoring site, implying that the geographic characteristics of
the monitoring sites were not considered as influential factors. Consequently, the models may not be transferable to another
site.
2. Data collection
Three monitoring sites, we call A, B and C, are all located along Xingangxi Road in Guangzhou. Xingangxi Road is an arterial in Haizhu District with eight standard lanes two-way and traffic volume of approximately 4500 vehicles per hour daytime. The locations of three sites are shown in Fig. 1 and sites A, B and C are, respectively 18, 17 and 17.5 m off the centerline
of the road with the same height of 6 m. These monitoring sites were selected to ensure accurate measurements near the
arterial. Note that the measurements from regular fixed monitoring stations may no be able to measure the air quality that
people are exposed to on the ground level near arterials, due to the limitation of monitoring location and height.
Eliminating the time for calibrating the instruments, the valid monitoring periods of sites A, B and C are, respectively1
2007-1-16T00:00 to 2007-1-22T21:00, 2007-1-28T00:00 to 2007-2-3T21:00 and 2007-9-27T00:00 to 2007-10-7T00:00. The
automatic monitoring items are the real-time concentrations of CO, NOx (NO2 and NO), PM10 and O3, and seven meteorological
parameters including atmospheric temperature, atmospheric pressure, wind speed, wind direction, solar radiation, rainfall and
relative humidity. Details of the instruments for each parameter are shown in Table 1. The traffic condition of Xingangxi Road
was recorded by video, and a vehicle detection software was used for hourly traffic counting.
3. ANN-based prediction models
3.1. Basis consideration
Artificial neural network is a system consisting of many parallel processing elements that can store experiential knowledge and make it available for use. It resembles the brain in two ways: knowledge is acquired by the network through a
learning process and inter-neuron connection strengths known as synaptic weights are used to store the knowledge (Haykin,
1994). There are many kinds of ANNs among which back-propagation (BP) neural network is one of the simplest and most
widely-used (Rumelhart et al., 1986). BP algorithm refers to the method for computing the error gradient for a feed-forward
network, an implementation of the delta rule (Battiti,1992). This algorithm is a supervised learning method that requires a
teacher who knows the desired output for any given input. Fig. 2 shows the structure of BP network used in this paper, from
left to right are input, hidden and output layers and they are connected with synaptic weights.
BP network’s learning process consists of two iterative steps: forward computing of data stream and backward propagation of error signals. During the forward computing, original data are transmitted from the input layer to the output layer
through the hidden processing layer and the neurons of each layer can only affect the next layer’s neurons. If the desired
output cannot be obtained from the output layer, it turns to the process of backward propagation in which error is propagated backward through the network against the direction of forward computing. During the process, the synaptic weights
are all adjusted in accordance with the error signals. With these two steps performing iteratively, the error between network
output and desired output can be minimized using the delta rule.
1
The US method of dating is used (namely month-day) rather than the more common day-month.
34
M. Cai et al. / Transportation Research Part D 14 (2009) 32–41
Table 1
Details of the monitoring instruments.
Parameter
Reaction time
Accuracy
Range
Technique
NO, NO2, NOx
O3
CO
PM10
Wind speed
Wind direction
Relative humidity
Temperature
Air pressure
Rainfall
Solar radiation
20 s
20 s
20 s
4s
1s
1s
15 s
10 s
1s
3s
10 ms
1.0 ppb
1.0 ppb
0.1 ppm
±2.0 lg/m3
±1.5%
±5°
±2%
±0.1 °C
±0.125%
±1%
±5%
0–0.5 100 ppm
0–0.5 200 ppm
0–1 10,000 ppm
0–10,000 lg/m3
0–60 m/s
360°
0–100%
50 to 50 °C
880–1080 hpa
–
400–1100 nm
Chemiluminescence analyzer
UV photometric O3 analyzer
CO gas filter correlation analyzer
Beta absorption particulate monitor
3-cup anemometer
Air-foil vane, potentiometer
Sensing element: Thin-film capacitor
Temperature sensor
Barometric pressure sensor
Tipping bucket rain gauge
Naturally aspirated solar radiation shield
Fig. 2. Architecture of the proposed BP neural network with one hidden layer.
In a BP neural network, hidden layers act as feature detectors, and according to the universal approximation theory
(Tampe et al., 1996), a network with a single hidden layer with a sufficiently large number of neurons can approximate
any smooth, measurable function between input and output vectors by selecting a suitable set of connecting weights and
transfer functions (Hornik et al., 1989). Therefore, in this paper we design all the BP networks with a single hidden layer.
The transfer functions, also called activation functions, are needed to introduce nonlinearity into the network. Without nonlinearity, hidden units would not make neural networks so powerful (McCullagh and Nelder, 1989). Almost any nonlinear
function works, but for the BP learning it must be differentiable and better to be bounded. This paper adopts a simple
and widely-used sigmoid transfer function:
f ðSkhn Þ ¼ 1=ð1 þ expðSkhn ÞÞ
ð1Þ
M. Cai et al. / Transportation Research Part D 14 (2009) 32–41
35
where Skhn is the state of the nth neuron of the hidden layer with the kth sample, and
Skhn ¼
X
xmn okim þ bhn
ð2Þ
m
where xmn is the weigh of the mth neuron of the input layer to the nth neuron of the hidden layer; okim is the output of the
mth neuron of the input layer in the kth sample while bhn is the bias invariant of the nth neuron of the hidden layer. Biases
are added to the hidden and output layers to preserve the universal approximation of the network (Hornik, 1993). Consequently the output of the nth neuron of the hidden layer in the kth sample can be expressed as
,
okhn ¼ f ðSkhn Þ ¼ 1
1 þ exp X
!!
xmn okim bhn
ð3Þ
m
The total errors of the neural network is
E¼
X
Ek ¼ 1=2ðtk oko Þ2
Ek ;
ð4Þ
k
where Ek is half a square error for the kth sample; t k is the desired (measured) value of the kth sample, and oko is the output of
the output layer in the kth sample. The delta rule that is given by gradient descent on the square error is used in the BP training method to minimize the total errors (Battiti,1992). Similarly, Sko and xn are defined as the state of the output neuron in
the kth sample and the weight between the nth hidden neuron and output neuron, respectively. Then the modified increment of xn can be calculated as
Dxn ¼ g
@Ek
@Ek @ok @Sko
¼ g k ko
¼ gðt k oko Þ oko ð1 oko Þ okhn
@ xn
@oo @So @ xn
ð5Þ
the modified increment of xmn is
Dxmn ¼ g
@Ek
@Ek @ok @Sk @okhn @Skhn
¼ g k ko ko
¼ gðt k oko Þ oko ð1 oko Þ xn okhn ð1 okhn Þ okim
@ xmn
@oo @So @ohn @Skhn xmn
ð6Þ
In Eqs. (5) and (6), learning rate g controls the speed of convergence to the minimum of errors. And to avoid the network
is often added to Eqs. (5) and (6), in which l is
oscillation in the training process, a trend term with the shape of lDx
is the modified increment of weight in prior iteration. In this paper, g ¼ 0:3 and l ¼ 0:3.
momentum rate and Dx
3.2. Selection of influential factors
Two aspects need to be considered when predicting the pollutants concentrations near urban arterials: sources of pollution and conditions of pollutant dispersion. Vehicular exhaust is one of the major sources of roadside pollution, and vehicular
exhaust emissions are related to traffic volume, vehicle type and speed etc. Traffic volume and vehicle type were obtained
during the data collection. Vehicles are divided into two types: light- and heavy-duty and according to the emission genes of
both types of vehicles in Guangzhou (Zhu, 1997), the equivalent hourly traffic volumes are computed and used. Our field data
collection did not measure vehicle speed. Because the vehicle speed profile across times of day and days of week is pretty
stable, we use these two variables as a proxy for vehicle speed. In fact, the time of day and day of week also affect many
other factors. It is worth noting that turbulent air caused by traffic, although not a source of emissions, can impact pollutant
concentrations (Nagendra and Khare, 2004). The effect is relate to vehicle speed, and can be captured by wind speed and
wind direction of the monitoring site, and we thus do not consider it separately. Therefore, traffic factors considered in this
paper contain three variables: traffic volume, time of day and day of week.
In addition to vehicle emissions, the impacts of industrial, restaurant and resident emissions should not be completely
ignored. However, since there are many such sources whose distributions are not regular and emission times are difficult
to measure, we could not find direct variables to represent their effects. Considering that the pollutant concentrations a
few hours prior to prediction implicate the impacts of these sources, we adopt the pollutant concentrations at 1, 2 and
3 h, respectively prior to the prediction to approximate their impacts and name them as background concentrations factors.
Meteorology is another dominant factor that influences dispersion and concentration distribution of air pollution. Atmospheric temperature and pressure, relative humidity, wind speed and direction, rainfall and solar radiation are associated
with dispersion and transform of pollutants. Moreover, they are routine meteorological parameters that can be measured
by almost every meteorological station. Therefore, these parameters are selected as input factors.
The geographical location of a monitoring site is another influential factor of pollutant concentrations, which, however, is
seldom incorporated in previous studies. It is obvious that the pollutant concentrations change with the distance from road.
We use D to denote the distance from the monitoring site to the road centerline, in unit of meter. Considering that the direction of the street also affects the results, we define another variable h to represent it, in unit of degree. Furthermore, buildings
on both sides of the street will block airflow that helps the spread of pollutants. Previous studies show that the air flow pattern in street canyon depends on the geometric shapes of street and surrounding constructions, especially the street aspect
36
M. Cai et al. / Transportation Research Part D 14 (2009) 32–41
ratio (Oke, 1988), which is the ratio of building height and street width. In summary, we choose distance to road centerline,
street direction and street aspect ratio as geographical factors.
Overall, the factors that affect the pollutant concentrations of a monitoring site are classified into four categories: traffic,
background concentration, meteorological and geographical, and 16 variables are selected as influential factors of the prediction models.
3.3. Classification and normalization of the samples
The valid dataset contains 570 hourly average samples collected from three monitoring sites, among which, only 561 are
usable because each usable sample needs to have pollutant concentrations of three preceding hours. The samples are further
divided into three subsets, namely training (495 groups), evaluation (24 groups) and a test set (42 groups). As its name suggests, the training set consists of examples used for learning, i.e., fitting the weights for the desired output; the validation set
is set of samples used to tune the parameters of the neural network, e.g., choosing the number of hidden units in a neural
network, and the test set is used only to assess the performance of the network after learning (Ripley, 1996). To validate and
test the models for all three monitoring sites, the samples of the validation and test set are selected from three sites
uniformly.
To avoid overflows of network due to very large or small weights and eliminate the influence of different dimensions of
data, the inputs of neural network have to be normalized (Gardner and Dorling, 1999). In this paper, the data are normalized
into the range [0, 1] with:
X norm ¼ ðX i X min Þ=ðX max X min Þ
ð7Þ
where Xnorm is the normalized value, Xi is the original value, and Xmin and Xmax are the minimum and maximum values of Xi.
The output data are transformed back to the real values after prediction.
3.4. Building neural networks
The numbers of input and output neurons should be determined by the nature of the problem. Here, 16 influential factors
are used as input neurons of the neural network, and the corresponding pollutant concentration is the output neuron. On the
other hand, the number of hidden neurons should be decided empirically and may differ in particular instances (Swingler,
1996). Whereas, with few hidden neurons, the anticipated accuracy may not be reached while too many hidden neurons
would cause the ‘overfitting’ or ‘overtraining’ phenomenon, thereby reducing the generalization capability of a network.
In this work, ten ANN models based on CO with different numbers of hidden neurons were established and trained using
the same set of training data, and their performances were then compared using the validation set. The mean relative error
(MRE), mean absolute error (MAE) and root mean square error (RMSE) are used to evaluate the prediction results, which are
shown in Table 2. It can be found that with eight hidden neurons, the neural network produces the best prediction. Fig. 2
presents the architecture of ANN used.
3.5. Different combination of influential factors
Because the input neurons of ANN contain three previous hourly concentrations of the output pollutant, there are two
ways to accomplish the prediction. One is to use actual measured values as inputs. We call this approach as known-background-concentrations prediction (KBCP), which can only forecast the pollutant concentration an hour in advance. The other
uses predicted concentration values as background factors for the following hour prediction, which we call unknown-background-concentrations prediction (UKBCP). This approach may predict the pollutant concentration an arbitrary number of
hours in advance, although the prediction accuracy is likely be lower than the former. Both approaches were implemented
in the paper.
Using different combinations of four categories of influential factors as the inputs of the network, the prediction results
are shown in Table 3. It is clear that taking into account all four categories leads to the best results. In addition, the MRE, MAE
and RMSE of the prediction models without background concentration factors increase 3.51%, 103.8 lg/m3 and 119.2 lg/m3
than those of UKBCP models with all four factors, respectively, suggesting that the prediction capability of the latter is better
than the former although both can predict the pollutant concentrations arbitrarily many hours in advance.
Table 2
Prediction results of different number of hidden neurons in CO-based ANN.
Number of hidden neurons
4
6
7
8
9
10
12
16
24
32
MRE (%)
MAE (lg/m3)
RMSE (lg/m3)
13.84
248.0
300.6
13.51
237.9
286.2
10.32
188.0
234.3
8.56
152.1
208.1
9.95
178.1
232.3
11.13
196.8
249.6
11.84
216.1
289.1
13.28
237.6
302.6
11.15
194.3
236.4
13.16
240.9
293.3
37
M. Cai et al. / Transportation Research Part D 14 (2009) 32–41
Table 3
Prediction results of CO-based model with different combination of influential factors.
Input neurons
Best structure
of ANN
All four influential factors
Traffic, background concentration, meteorology
Traffic, background concentration, geographical
Traffic, meteorology, geographical
Background concentration, meteorology, geographical
a
16-8-1a
13-7-1
9-8-1
13-7-1
13-7-1
KBCP
UKBCP
MRE (%)
MAE
(lg/m3)
RMSE
(lg/m3)
MRE
(%)
MAE
(lg/m3)
RMSE
(lg/m3)
10.21
12.72
10.26
–
13.05
233.5
294.0
253.8
–
303.2
276.2
349.1
332.8
–
362.8
14.79
36.83
39.24
18.30
19.17
333.8
872.4
980.1
437.6
434.1
388.8
993.0
1140.6
508.0
529.8
Structure x–y–z means a network with x number of inputs, y number of hidden neurons and z number of outputs.
4. Results
4.1. Prediction of CO, NO2, PM10 and O3 concentrations
Four BP neural networks for CO, NO2, PM10 and O3 were trained and validated, and their predictive performances regarding concentrations 14 hours in advance are compared using the test set, as shown in Table 4. The correlation analysis between predicted and measured values is conducted and all the correlation coefficients shown in Table 4 are significant at
the 0.01 confidence level, suggesting a good correlation. The time-series chart between predicted and measured values of
all 561 samples included the training, validation and test sets is shown in Fig. 3.
It can be seen that the models for CO, NO2 and PM10 have smaller MREs than the model for O3. The reason is that CO, NO2
and PM10 are direct emissions from vehicles, whereas O3 is a secondary pollutant that is produced from photochemical reaction between hydrocarbon (HC) and NOx under ultraviolet sunlight. To improve the prediction accuracy for O3, the NOx concentration an hour before is added to the model as the 17th input while HC is not involved due to the lack of data. The results
are presented in the last row of Table 4, which shows slight improvements. It may be difficult to improve further given that
the absolute value of O3 is small and the photochemical reaction is recurrent.
The models are created for all three sites. We also can build separate models for each site in which the geographical factors are not incorporated. Table 5 shows that the prediction accuracies of separate models for their own sites are better than
the uniform models using CO prediction as an example. However, the former have bad transferability for new sites.
4.2. Sensitivity analysis
In view of that influential factors used for prediction are likely subject to measurement errors, we conducted a sensitivity
analysis. The results are shown in Table 6. With a random error between 2% and 2% for traffic volume (scenario 1),
Table 4
Results of prediction with ANN.
CO
NO2
PM10
O3
O3
Structure of
ANN
KBCP
MRE (%)
MAE
(lg/m3)
RMSE
(lg/m3)
Correlation
coefficient
MRE (%)
MAE
(lg/m3)
RMSE
(lg/m3)
Correlation
coefficient
16-8-1
16-8-1
16-8-1
16-8-1
17-8-1
10.21
11.57
12.86
45.15
32.93
233.5
14.9
15.5
7.5
6.6
276.2
21.1
20.7
10.3
9.5
0.874
0.935
0.961
0.941
0.951
14.79
19.09
22.38
67.12
41.57
333.8
27.7
35.0
11.5
7.5
388.8
35.6
57.5
16.1
10.2
0.808
0.927
0.912
0.855
0.948
CO (mg/m3)
Pollutant
UKBCP
12
Measured value
10
Predicted value
8
6
4
2
0
0
100
200
300
400
Hours
Fig. 3. The different between predicted and measured values of time-series.
500
38
M. Cai et al. / Transportation Research Part D 14 (2009) 32–41
Table 5
Prediction results of CO concentrations using separate models and uniform model.
MRE (%)
Site A
Site A model
Site B model
Site C model
Uniform model
Site B
Site C
KBCP
UKBCP
KBCP
UKBCP
KBCP
UKBCP
5.34
85.49
51.31
9.62
12.14
248.37
65.24
17.69
12.73
8.53
67.28
11.14
21.97
13.45
81.36
13.78
16.63
168.67
9.18
9.87
25.14
300.50
12.96
12.89
Table 6
Prediction results of the ANN for CO with noises in input factors.
Input noise
KBCP
No noise
Scenario 1
Scenario 2
UKBCP
MRE (%)
MAE (lg/m3)
RMSE (lg/m3)
MRE (%)
MAE (lg/m3)
RMSE (lg/m3)
10.21
10.22
11.68
233.5
238.8
268.7
276.2
287.2
310.7
14.79
16.43
19.50
333.8
382.2
464.0
388.8
457.1
535.2
the CO-based model still performs well. With more perturbation (scenario 2) including 5% to 5% random error for traffic
volume, wind speed, wind direction and background concentrations, the performance of the model deteriorates slightly.
We thus draw the conclusion that the ANN model is pretty robust to the measurement errors.
5. Comparison with other models
5.1. Comparison with multiple linear regression models
For comparison, four multiple linear regression (MLR) models were established to predict CO, NO2, PM10 and O3 concentrations with independent variables the same as the input factors of the ANN models. The data of the training and validation
sets for the ANN models were used for the model calibration. An independent variable selection method, namely backward
elimination, was applied and the resulting models are as follows:
CO ¼ 2:34504 þ 0:012332 h þ 4:026562 R þ 0:01119 TIME þ 0:000432 WD 0:11756 WS þ 0:007012 RH
0:00027 SR 0:34672 H2 þ 1:192552 H1
ð8Þ
NO2 ¼ 0:18586 þ 0:00079 h þ 0:322634 R þ 0:0000155 WD 0:00918 WS þ 0:001246 T þ 0:00025 RH
þ 0:00000265 V 0:10896 H2 þ 0:939575 H1
ð9Þ
Table 7
F and t values of MLR models.
t-Value
H3
H2
SR
WS
h
RH
WD
R
TIME
CO
NO2
PM10
O3
29.11
21.24
23.54
23.63
8.58
2.54
3.10
5.48
2.76
–
3.87
8.23
2.95
5.43
3.17
–
3.86
3.71
–
–
2.92
2.39
1.68
–
2.28
2.01
–
–
3.94
3.13
3.13
–
4.11
–
–
–
t-Value
CO
NO2
PM10
O3
DAY
–
–
–
–
V
–
3.82
4.08
–2.63
P
–
–
–
–2.75
Constant
3.37
3.07
2.57
2.84
RAIN
–
–
–
–
T
–
2.50
–
–
H1
–
–
1.66
–
D
–
–
3.22
–
F-value
547.21
704.56
1039.97
576.97
Table 8
Results of prediction with MLR.
Pollutant
CO
NO2
PM10
O3
KBCP
UKBCP
MRE (%)
MAE (lg/m3)
RMSE (lg/m3)
Correlation coefficient
MRE (%)
MAE (lg/m3)
RMSE (lg/m3)
Correlation coefficient
8.12
13.11
10.54
52.59
189.2
14.9
13.0
8.1
266.1
19.9
17.9
11.4
0.879
0.940
0.971
0.936
18.3
26.90
37.51
138.52
425.0
25.5
36.9
15.8
497.9
29.1
40.5
18.5
0.675
0.892
0.894
0.861
39
M. Cai et al. / Transportation Research Part D 14 (2009) 32–41
5
Measured(mg/m3)
Measured(mg/m3)
5
4
3
2
1
0
1
0
2
4
3
4
3
2
1
0
5
0
1
3
Predicted value by ANN(mg/m )
2
3
4
5
3
Predicted value by MLR(mg/m )
Fig. 4. CO-based scatter plots by UKBCP of ANN and MLR.
Measured(mg/m3)
5
4
3
2
1
0
1
0
3
2
5
4
Predicted value by CL4(mg/m3)
Fig. 5. CO-based scatter plot by CL4.
CO (mg/m3)
4
3
Measured value
2
ANN
1
CL4
MLR
0
7
8
9
10
11
12
13
14
15
16
17
18
19
20
CO (mg/m3)
Hours (2007-1-22)
Site A
7
6
5
4
3
2
1
0
Measured value
ANN
CL4
MLR
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Hours (2007-2-3)
Site B
CO (mg/m3)
5
4
Measured value
3
ANN
2
CL4
1
MLR
0
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Hours (2007-10-7)
Fig. 6. Comparison with measured and predicted values.
Site C
40
M. Cai et al. / Transportation Research Part D 14 (2009) 32–41
PM10 ¼ 0:07426 0:0088 D þ 0:237732 R 0:00706 WS þ 0:000208 RH 0:000024 SR þ 0:00000348 V
þ 0:071054 H3 0:19441 H2 þ 1:034259 H1
O3 ¼ 0:13385 0:00013 P þ 0:0000352 SR 0:0000015 V 0:22809 H2 þ 1:014281 H1
ð10Þ
ð11Þ
In the functions, R is street aspect ratio, TIME is time of day, P is air pressure, WD is wind direction, WS is wind speed, T is
temperature, RH is relative humidity, SR is solar radiation, V is traffic volume, and H1, H2, H3 are corresponding pollutant
concentrations the first, second and third hours before prediction. The F and t values of MLR models are shown in Table
7, where DAY is day of week, RAIN is rainfall.
The same test set as ANN model is used to test the prediction performance of MLR models, the results are shown in Table
8. It is obvious the results of UKBCP by ANN are better than those by MLR, the CO-based scatter plots (Fig. 4) can show this
difference directly.
5.2. Comparison with California line source dispersion model
The California line source dispersion model, version 4 (CL4), is a plume dispersion model that predicts CO impacts near
roadways (Benson, 1989). It is based on the Gaussian diffusion equation and employs a mixing zone concept to characterize
pollutant dispersion over the roadway. The user defines the proposed roadway geometry, worst-case meteorological parameters, anticipated traffic volumes, and receptor positions, and the prediction of CO concentrations can be obtained with a
Windows user interface. The MRE, MAE, RMSE and correlation coefficient of CL4 are 43.55%, 1038.1 lg/m3, 1217.9 lg/m3
and 0.207, which are worse than those of the corresponding ANN model. This may due to the fact that the Gaussian diffusion
assumed in CL4 is not very realistic. The scatter plot and comparison with ANN are shown in Figs. 5 and 6.
6. Conclusions
Air pollutant concentrations near urban arterials have a complex relationship with many factors. This paper has analyzed
those factors and divided them into four categories: traffic, background concentrations, meteorological and geographical. All
influential factors have been used as inputs to the neural networks to predict hourly CO, NO2, PM10 and O3 concentrations on
roadside. Comparing with the MLR and CL4 models, the ANN models are more accurate. Moreover, the ANN models presented in this paper are robust and have a good transferability, offering an effective approach for predicting air pollutant
concentrations near urban arterials.
Acknowledgements
This work was partially supported by National Natural Science Foundation of China (No. 50808181), Science and Technology Program of Guangdong Province, China (No. 2007B030102003) and State Scholarship Fund from China Scholarship
Council.
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