![1
Advances in Airborne Pollution Forecasting
Using Soft Computing Techniques
Aceves-Fernandez Marco Antonio, Sotomayor-Olmedo Artemio,
Gorrostieta-Hurtado Efren, Pedraza-Ortega Jesus Carlos, Ramos-Arreguín
Juan Manuel, Canchola-Magdaleno Sandra and Vargas-Soto Emilio
Facultad de Informática, Universidad Autónoma de Querétaro,
México
1. Introduction
There are many investigations reported in the scientific literature about Particulate Matter
(PM) 2.5 and PM10 in urban and suburban environments [Vega et al 2002, Querol et al 2004,
Fuller et al 2004].
In this contribution, the information acquired from PMx monitoring systems is used to
accurately forecast particle concentration using diverse soft computing techniques.
A number of works have been published in the area of airborne particulates forecasting. For
example, Chelani[et al 2001] trained hidden layer neural networks for CO forecasting at
India. Caselli [et al 2009] used a feedforward neural network to predict PM10 concentration.
Other works such as Kurt’s [et al 2010] have constructed a neural networks model using
many input variables (e.g. wind, temperature, pressure, day of the week, Date,
concentration, etc) making the model too complex and inaccurate.
However, not many scientific literature discuss a number of robust forecasting methods
using soft computing techniques. These techniques include neuro-fuzzy inference methods,
fuzzy clustering techniques and support vector machines. Each one of these algorithms is
discussed separately and the results discussed. Furthermore, a comparison of all methods is
made to emphasize their advantages as well as their disadvantages.
2. Fuzzy inference methods
Fuzzy inference systems (FIS) are also known as fuzzy rule-based systems. This is a major
unit of a fuzzy logic system. The decision-making is an important part in the entire system.
The FIS formulates suitable rules and based upon the rules the decision is made. This is
mainly based on the concepts of the fuzzy set theory, fuzzy IF–THEN rules, and fuzzy
reasoning. FIS uses “IF - THEN” statements, and the connectors present in the rule
statement are “OR” or “AND” to make the necessary decision rules.
Fuzzy inference system consists of a fuzzification interface, a rule base, a database, a
decision-making unit, and finally a defuzzification interface as described in Chang(et al
2006). A FIS with five functional block described in Fig.1.](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-19-320.jpg)
![Advances in Airborne Pollution Forecasting Using Soft Computing Techniques 5
Compare the input variables with the membership functions on the antecedent part
to obtain the membership values of each linguistic label. (this step is often called
fuzzification.)
Combine (through a specific t-norm operator, usually multiplication or min) the
membership values on the premise part to get firing strength (weight) of each rule.
Generate the qualified consequents (either fuzzy or crisp) or each rule depending
on the firing strength.
Aggregate the qualified consequents to produce a crisp output. (This step is called
defuzzification.)
A typical fuzzy rule in a fuzzy model has the format shown in equation 1
IF x is A and y is B THEN z = f(x, y) (1)
where AB are fuzzy sets in the antecedent; Z = f(x, y) is a function in the consequent.
Usually f(x, y) is a polynomial in the input variables x and y, of the output of the system
within the fuzzy region specified by the antecedent of the rule.
A typical rule in a FIS model has the form (Sugeno et al1988): IF Input 1 = x AND Input 2 =
y, THEN Output is z = ax + by + c.
Furthermore, the final output of the system is the weighted average of all rule outputs,
computed as
1
1
N
i i
i
N
i
i
w z
FinalOutput
w
(2)
3. Fuzzy clustering techniques
There are a number of fuzzy clustering techniques available. In this work, two fuzzy
clustering methods have been chosen: fuzzy c-means clustering and fuzzy clustering
subtractive algorithms. These methods are proven to be the most reliable fuzzy clustering
methods as well as better forecasters in terms of absolute error according to some
authors[Sin, Gomez, Chiu].
Since 1985 when the fuzzy model methodology suggested by Takagi-Sugeno [Takagi et al
1985, Sugeno et al 1988], as well known as the TSK model, has been widely applied on
theoretical analysis, control applications and fuzzy modelling.
Fuzzy system needs the precedent and consequence to express the logical connection
between the input output datasets that are used as a basis to produce the desired system
behavior [Sin et al 1993].
3.1 Fuzzy clustering means (FCM)
Fuzzy C-Means clustering (FCM) is an iterative optimization algorithm that minimizes the
cost function given by:
= ∑ ∑ ‖ − ‖ (3)
Where n is the number of data points, c is the number of clusters, xk is the kth data point, vi
is the ith cluster center ik is the degree of membership of the kth data in the ith cluster, and
m is a constant greater than 1 (typically m=2)[Aceves et al 2011]. The degree of membership
ik is defined by:](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-21-320.jpg)
![Air Quality - Models and Applications
6
=
∑
( )
(4)
Starting with a desired number of clusters c and an initial guess for each cluster center vi, i =
1,2,3… c, FCM will converge to a solution for vi that represents either a local minimum or a
saddle point cost function [Bezdek et al 1985]. The FCM method utilizes fuzzy partitioning
such that each point can belong to several clusters with membership values between 0 and
1. FCM include predefined parameters such as the weighting exponent m and the number of
clusters c.
3.2 Fuzzy clustering subtractive
The subtractive clustering method assumes each data point is a potential cluster center and
calculates a measure of the likelihood that each data point would define the cluster center,
based on the density of surrounding data points. Consider m dimensions of n data point
(x1,x2, …, xn) and each data point is potential cluster center, the density function Di of data
point at xi is given by:
= ∑ (5)
where ra is a positive number. The data point with the highest potential is surrounded by
more data points. A radius defines a neighbour area, then the data points, which exceed ra,
have no influence on the density of data point.
After calculating the density function of each data point is possible to select the data point
with the highest potential and find the first cluster center. Assuming that Xc1 is selected and
Dc1 is its density, the density of each data point can be amended by:
= − −
‖ ‖
(6)
The density function of data point which is close to the first cluster center is reduced.
Therefore, these data points cannot become the next cluster center. rb defines an neighbour
area where the density function of data point is reduced. Usually constant rb > ra. In order to
avoid the overlapping of cluster centers near to other(s) is given by [Yager et al 1994]:
= ∙ (7)
4. Support vector machines
The support vector machines (SVM) theory, was developed by Vapnik in 1995, and is
applied in many machine-learning applications such as object classification, time series
prediction, regression analysis and pattern recognition. Support vector machines (SVM) are
based on the principle of structured risk minimization (SRM) [Vapnik et al 1995, 1997].
In the analysis using SVM, the main idea is to map the original data x into a feature space F
with higher dimensionality via non-linear mapping function , which is generally unknown,
and then carry on linear regression in the feature space [Vapnik 1995]. Thus, the regression](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-22-320.jpg)
![Advances in Airborne Pollution Forecasting Using Soft Computing Techniques 7
approximation addresses a problem of estimating function based on a given data set, which
is produced from the function. SVM method approximates the function by:
1
( ) ( )
m
i i
i
y w x b w x b
(8)
where w = [w1,…,wm] represent the weights vector, b is defined as the bias coefficients and
(x)=[1(x),…, m(x)] the basis function vector.
The learning task is transformed to the weights of the network at minimum. The error
function is defined through the -insensitive loss function, L(d,y(x)) and is given by:
( ) ( )
( , ( ))
0
d y x d y x
L d y x
others
(9)
The solution of the so defined optimization problem is solved by the introduction of the
Lagrange multipliers i, *
i
(where i=1,2,…,k) responsible for the functional constraints
defined in Eq. 9. The minimization of the Lagrange function has been changed to the dual
problem [Vapnik et al 1997]:
* *
1 1
1
( , )( , ) ( , )
2
k k
i i j j i j
i j
K x x
(10)
With constraints:
*
1
*
( , ) 0,
0 ,0
i
k
i
i
i i
C C
(11)
Where C is a regularized constant that determines the trade-off between the training risk
and the model uniformity.
According to the nature of quadratic programming, only those data corresponding to non-
zero *
i i
pairs can be referred to support vectors (nsv). In Eq. 10 K(xi , xj)=(xi)*(xj) is
the inner product kernel which satisfy Mercer’s condition [Osuna et al 1997] that is required
for the generation of kernel functions given by:
(12)
Thus, the support vectors associates with the desired outputs y(x) and with the input
training data x can be defined by:
(13)
Where xi are learning vectors. This leads to a SVM architecture (Fig. 2) [Vapnik 1997,
Cristianini et al 2000].
(,*
) di (i i
*
)
i1
k
(i i
*
)
i1
k
K(xi , xj ) (xi ),(xj )
y(x) (i ,i
*
)K(x, xi )
i1
Nsv
b](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-23-320.jpg)
![Advances in Airborne Pollution Forecasting Using Soft Computing Techniques 9
The methodology used for the design, training and testing of SVM is proposed as follows
based in a review of Vapnik, Osowski [et al 2007] and Sapankevych[et al 2009]
a. Preprocess the input data and select the most relevant features, scale the data in the
range [−1, 1], and check for possible outliers.
b. Select an appropriate kernel function that determines the hypothesis space of the
decision and regression function.
c. Select the parameters of the kernel function the variances of the Gaussian kernels.
d. Choose the penalty factor C and the desired accuracy by defining the ε-insensitive loss
function.
e. Validate the model obtained on some previously, during the training, unseen test data,
and if not pleased iterate between steps (c) (or, eventually b) and (e).
5. Discussion of results
Simulations were performed using fuzzy clustering algorithms using the equations [3-7], in
this case study, the datasets at Mexico City in 2007 were chosen to construct the fuzzy
model. Likewise, the data of 2008 and 2009 from the same geographic zone in each case
were used to training and validating the data, respectively. The result of the fuzzy clustering
model was compared then to the real data of Northwest Mexico in 2010.
The results obtained show an average least mean square error of 11.636 using Fuzzy
Clustering Means, whilst FCS shows an average least mean square error of 10.59. Table 1
shows a list of the experiments carried out. An example of these results is shown in figure 4
for FCM and figure 5 shows the estimation made using FCS at Northwest Mexico City.
Fig. 4. Fuzzy Clustering Means (FCM) Results at Northwest Mexico City. Raw Data VS. Fuzzy
Model](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-25-320.jpg)
![Air Quality - Models and Applications
24
3.4 Computational fluid dynamic models
Resolving the Navier-Stokes equation using finite difference and finite volume methods in
three dimensions provides a solution to conservation of mass and momentum.
Computational fluid dynamic (CFD) models use this approach to analyse flows in urban
areas. In numerous situation of planning and assessment and for the near-sources region,
obstacle-resolved modeling approaches are required. Large Eddy Simulations (LES) models
explicitly resolve the largest eddies, and parameterize the effect of the sub grid features.
Reynolds Averaged Navier Stokes (RANS) models parameterize all the turbulence, and
resolve only the mean motions. CFD (large eddy simulation [LES] or Reynolds-averaged
Navier-Stokes [RANS]) model can be used to explicitly resolve the urban infrastructure.
Galmarini et al., 2008 and Martilli and Santiago,2008, used CFD models to estimate spatial
averages required for Urban Canopy Parameters. Using CFD models good agreement in
overall wind flow was reported by field Gidhagen et al. (2004) .They also reported large
differences in velocities and turbulence levels for identical inputs.
3.5 The Gaussian steady-state dispersion model
The Gaussian Plume Model is one of the earliest models still widely used to calculate the
maximum ground level impact of plumes and the distance of maximum impact from the
source. These models are extensively used to assess the impacts of existing and proposed
sources of air pollution on local and urban air quality. An advantage of Gaussian modeling
systems is that they can treat a large number of emission sources, dispersion situations, and
a receptor grid network, which is sufficiently dense spatially (of the order of tens of meters).
Figure 5 shows a buoyant Gaussian air pollutant dispersion plume. The width of the plume
is determined by σy and σz, which are defined by stability classes(Pasquill 1961; Gifford Jr.
1976)
Fig. 5. A buoyant Gaussian air pollutant dispersion plume
The assumptions of basic Gaussian diffusion equations are:](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-40-320.jpg)
![Urban Air Pollution Modeling 27
3.6 Urban pollution and climate integrated modeling
Integrated air quality modelling systems are tools that help in understanding impacts from
aerosols and gas-phase compounds emitted from urban sources on the urban, regional, and
global climate. Piringer et al., 2007 have demonstrated that urban features essentially
influence atmospheric flow and microclimate, strongly enhance atmospheric turbulence,
and modify turbulent transport, dispersion, and deposition of atmospheric pollutants.
Numerical weather prediction (NWP) models with increased resolution helps to visualize a
more realistic reproduction of urban air flows and air pollution processes.
Integrated models thus link urban air pollution, tropospheric chemistry, and climate.
Integration time required is ≥ 10 years for tropospheric chemistry studies in order to
consider CH4 and O3 simulation and aerosol forcing assessment. Tropospheric chemistry
and climate interaction studies extend the integration time to ≥ 100 years.
Urban air quality and population exposure in the context of global to regional to urban
transport and climate change is proposed to be assessed by integrating urbanized NWP and
Atmospheric Chemistry (ACT) models (Baklanov et al., 2008; Korsholm et al., 2008). A. A.
Baklanov and R. B. Nuterman (2009) sugested a multi-scale modelling system which
comprised of downscaling from regional to city-scale with the Environment –HIgh
Resolution Limited Area Model (Enviro-HIRLAM) and to micro-scale with the obstacle-
resolved Microscale Model for Urban Environment (M2UE). Meteorology governs the
transport and transformations of anthropogenic and biogenic pollutants, drives urban air
quality and emergency preparedness models; meteorological and pollution components
have complex and combined effects on human health (e.g., hot spots, heat stresses); and
pollutants, especially urban aerosols, influence climate forcing and meteorological events
(precipitation, thunderstorms, etc.), thus this approach is closer to real life scenario. Examples
of integrated models are Enviro-HIRLAM: Baklanov and Korsholm, 2007, WRF-Chem: Grell et
al., 2005; EMS-FUMAPEX: Forecasting Urban Meteorology, Air Pollution and Population
Exposure; CFD (large eddy simulation [LES] or Reynolds-averaged Navier-Stokes [RANS])
models: Galmarini et al., 2008 and Martilli and Santiago., 2008; MIT Integrated Global System
Model Version 2 (IGSM2): A.P. Sokolov, C.A. Schlosser, S. Dutkiewicz, S. Paltsev, D.W.
Kicklighter,H.D. Jacoby, R.G. Prinn, C.E. Forest, J. Reilly, C. Wang, B. Felzer,M.C. Sarofim, J.
Scott, P.H. Stone, J.M. Melillo and J. Cohen., 2005; US EPA and NCAR communities for MM5
(Dupont et al., 2004; Bornstein et al., 2006; Taha et al., 2008), WRF models (Chen et al., 2006);
THOR - an Integrated Air Pollution Forecasting and Scenario Management System: National
Environmental Research Institute (NERI), Denmark.
The outline of overall methodology of FUMAPEX and MIT interactive chemistry model is
shown in Figure 6 and 7. Schematic of couplings between atmospheric model and the land
model components of the MIT IGSM2 is given in Figure 8.
Need of integrated models
All of these models have uncertainties associated with them. Chemical transport models,
such as Gaussian plume models and gridded photochemical models, begin with pollutant
emissions estimates and meteorological observations and use chemical and physical
principles to predict ambient pollutant concentrations. Since these models require
temporally and spatially resolved data and can be computationally intensive, they can only
be used for well-characterized regions and over select time periods. Eulerian grid models
are not suitable to assess individual source impacts, unless the emissions from the
individual source are a significant fraction of the domain total emissions. This limitation](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-43-320.jpg)
![Urban Air Pollution Modeling 33
Oke, T. R., 1988: "The urban energy balance," Progress in Physical Geography, vol.12, pp. 471-
508.
Paatero, P., 2004. User’s Guide for Positive Matrix Factorization Programs PMF2 and PMF3,
Part 1: Tutorial. University of Helsinki, Finland.
Pasquill, F. (1961). "The Estimation of the Dispersion of Windborne Material." Meteorology
Magazine 90(1063): pp. 33-40.
Pavageau, M., Rafailidis, S. and Schatzmann, M. (2001) 'A comprehensive experimental
databank for the verification of urban car emission dispersion models',
International Journal of Environment and Pollution, 15, 417-425
Physick. W.L., Noonan. J.A., MacGregor, J.L. (1994). LADM: a Lagrangian Atmospheric
Dispersion Model [Monograph], CSIRO Division of Atmospheric Research
technical paper.
Piringer M., Petz E., Groehn I., Schauberger G. (2007) A sensitivity study of separation
distances calculated with the Austrian Odour Dispersion Model (AODM),
Atmospheric Environment, pp.41, 725-1735.
Raupach, M.R., Thom, A.S., and Edwards, I.: 1980, “A Wind Tunnel Study of Turbulent Flow
Close to Regularly Arrayed Rough Surfaces”, Boundary-Layer Meteorol. 18, 373-397.
Reynolds, S., Roth, P., and Seinfeld, J., 1973, Mathematical modeling of photochemical air
pollution Atm.Env 7.
Rotach, M. W.: 1993.,Turbulence close to a rough urban surface, Part II: Variances and
gradients, Boundary Layer Meteorol., pp. 66, 75–92,
Rotach M W (1995) ‘Profiles of turbulence statistics in and above an urban street canyon’,
Atmospheric Environment, Vol 29, pp. 1473-1486
Shir, C.C. and L.J. Shieh, 1974, A generalized urban air pollution model and its application
to the study of SO2-distribution in the St. Louis Metropolitan area, J. Appl. Met. 19,
185-204.
2002: South Asia Urban Air Quality Management Briefing. Note No. 4., What Do We Know
About Air Pollution?—India Case Study, http://www.worldbank.org/sarurbanair
2002: South Asia Urban Air Quality Management Briefing Note No. 5., Impact of Traffic
Management, http://www.worldbank.org/sarurbanair.
Srivastava, Anjali ; Sengupta B., Dutta S.A., ‘Source apportionment of ambient VOCs in
Delhi City Science of The Total Environment’, Volume 343, Issues 1-3, 1
May2005,pp.207-220
Srivastava, Anjali ; ‘Source apportionment of ambient VOCS in Mumbai city Atmospheric
Environment’ , Volume 38, Issuey39, December2004, pp. 6829-6843
Stanners, D., Bourdeau, P. (Eds.), 1995. Europe's Environment: The Dobris Assessment,
European Environment Agency.Office for Publications of the European
Communities, Luxemburg.
Sokolov, A.P.,C.A. Schlosser, S. Dutkiewicz, S. Paltsev, D.W. Kicklighter, 2005 :The MIT
Integrated Global System Model (IGSM),Version 2: Model Description and Baseline
Evaluation,
Taha, H., 2008: Sensitivity of the urbanized MM5 (uMM5) to perturbations in surface
properties in Houston Texas. Boundary-Layer Meteorology, pp. 127: 193-218
Tennekes, H., 1973. A model for the dynamics of the inversion above a convective boundary layer.
Journal of Atmospheric Science 30, pp.550–567](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-49-320.jpg)
![[189]
after—and I just want to say, go slow. That’s all—go
slow.”
“All right, Salt. Will you send Miss Austin down here—
also, I must interview her alone.”
“Yes—I understand. But don’t be led away now, by
circumstantial evidence. You know yourself, it isn’t
always dependable.”
“Go along, Salt, don’t try to teach me my business.
Have you talked to the girl?”
“Not a word. My wife has, but she didn’t learn much.”
Adams went away, and in a few moments Anita Austin
came into the room.
A first glance showed Cray’s experienced eye that the
girl was what he called a siren.
Her oval, olive face was sad and sweet. The pale cheeks
were not touched up with artificial color, and the scarlet
lips were, even to his close scrutiny, also devoid of
applied art. She wore a smart little gown of black
taffeta, with crisp, chic frills of finely plaited white
organdie.
Whether this was meant as mourning wear or not, Cray
could not determine.
The frock was fashionably short, showing thin silk
stockings and black suede ties.
But Miss Mystery seemed wholly unconscious of her
clothes, and her great dark eyes were full of wondering](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-56-320.jpg)
![[190]
inquiry as she looked at the attorney, and then a little
diffidently offered a greeting hand.
The little brown paw touched Cray’s with a pathetic,
hopeful clasp, and he looked up quickly to find himself
looking into a pair of hopeful eyes, that, without a word,
expressed confidence and trust.
He shrugged his shoulders a trifle and secretly
admonished himself to keep a tight rein on his
sympathy.
Then relinquishing the lingering hand, he sat down
opposite the chair she had chosen to occupy.
“Miss Austin,” he began, and paused, for the first time in
his life uncertain what tack to take.
“Yes,” she said, as the pause grew longer, and her soft,
cultured voice helped him not at all.
How could he say to this lovely small person that he
suspected her of wrong doing?
“Go on, Mr. Cray,” she directed him, meantime looking at
him with eyes full of a haunting fear, “what is it?”
Cray had a sudden, insane feeling that he would give all
he was worth for the pleasure of removing that look of
fear, then commanding himself to behave, he said,
“I am sorry, Miss Austin, but I must ask you some
unpleasant questions.”
“That’s what I’m here for,” she said, with the ghost of a
smile on her curved red lips, and, smoothing down her](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-57-320.jpg)
![[191]
taffeta lap, she demurely clasped her sensitive little
hands and waited.
Those hands bothered Cray. Though they lay quietly, he
felt that at his speech they would flutter in anxiety—
even in fear, and he was loath to disturb them.
Because of this hesitancy, he plunged in more abruptly
than he meant to do.
“Where do you come from, Miss Austin?”
“New York City,” she said, a brighter look coming to her
face, as if she thought the ordeal would not be so
terrible after all.
“What address there?”
“One West Sixty-seventh Street.”
“You told some one else the Hotel Plaza.”
“Yes; I have lived at both addresses. Why?”
The “why” was disconcerting. After all, Cray thought, he
was not a census taker.
He gave up getting past history, and said, briefly,
“Were you at Doctor Waring’s house Sunday evening?”
“Not evening,” she returned, looking thoughtful. “I was
there Sunday afternoon.”
“And went back again, late in the evening—to see
Doctor Waring, in his study.”](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-58-320.jpg)
![[192]
[193]
“Why do you say that?” she asked quietly, but a small
red spot showed on either olive cheek.
“Because I must. How well do you—did you know the
Doctor?”
“Know Doctor Waring? Not at all. I never saw him in my
life until I came here to Corinth.”
“You are sure of that?”
“Almost sure—oh, why, yes—that is, I am quite sure.”
“Yet you went over there Sunday evening, and came
back to this house in possession of Doctor Waring’s
valuable pin, and a large sum of money.”
“Oh, no, Mr. Cray, I didn’t do any such thing!”
“Then can you explain your possession of those
articles?”
“You mean, I suppose the roll of bills that Miss Bascom
put into my top bureau drawer?”
“Miss Bascom put in the drawer!”
“Yes—that is, she must have done so, or—how else
could they have been found there? You know yourself,
now, don’t you, Mr. Cray, that I’m not a burglar—or a
bandit or a sneak thief? You know I never went in to
Doctor Waring’s study and took those things! So, as I
say, isn’t it the only plausible theory, that Miss Bascom,
who found the valuables so readily, first put them there
herself?”](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-59-320.jpg)
![[194]
CHAPTER XI
THE SPINSTER’S EVIDENCE
“That matter can easily be settled,” Cray said, and going
to the door he asked Mrs. Adams to send Miss Bascom
to them.
With an important air the spinster entered the room.
Holding herself very erect and even drawing aside her
skirts as she passed Miss Austin, she took a seat on the
other side of the room.
“Now, Miss Bascom,” Cray began at once, “what made
you think of looking in this lady’s bureau drawer for that
money?”
“I didn’t look for it, Mr. Cray. I merely felt that she had
done wrong and I thought perhaps some evidence
would be hidden away in her room. And a top drawer is
the place a woman oftenest hides things.”
Cray gave a short laugh. “Rather clever of you, I admit.
But Miss Austin says she did not put that money there,
herself—that it was a plant.”
“A plant?” Miss Bascom looked puzzled at the word.](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-60-320.jpg)
![[195]
“Yes; she thinks some in-disposed person put it there to
implicate her, falsely.”
“Oh, I see. Well, Mr. Cray, let her say who did it, and
who could have got that money to do it with.”
The hard old face took on a look that was almost
malignant in its accusation, and little Anita Austin gave a
low cry as she saw it, and hid her face in her hands.
“Take her away,” she moaned, “oh, take that woman
away.”
“You hear her,” Miss Bascom went on, unrelentingly.
“Now, Mr. Cray, I’m a bit of a detective myself, and while
you’ve been down here talking to Miss Mystery, I’ve
been searching her room more carefully, and I’ve found
a few more things, of which I should like to tell you.”
Cray was nonplused. His sympathies were all with the
poor little girl, who, clinging to the arms of her chair,
seemed about to go to pieces, nervously, but was
bravely holding on to herself. Yet, if the Bascom woman
was telling the truth, he must beware of the “poor little
girl.”
“I’m not sure you’re within your rights, Miss Bascom,”
he began, but he was interrupted with:
“Rights! Indeed, the rights of this matter are above your
jurisdiction! The blood of John Waring calls from the
ground! I am the instrument of justice that has been
chosen by an over-ruling Providence to discover the
criminal. She sits before you! That girl—that mysterious
wicked girl is both thief and murderess!”](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-61-320.jpg)
![[196]
“Oh, no!” Anita cried, putting up her arm as if to ward
off a physical blow.
Then she suddenly became quiet—almost rigid in her
composure.
“That is a grave accusation, Miss Bascom,” she said,
“you must prove it or retract it.”
Cray stared at the girl in astonishment. Her agonized cry
had been human, feminine, natural—but this sudden
change to stony calm, to icy hauteur was amazing—and,
to his mind, incriminating.
Miss Bascom, however, was in no way daunted.
“Prove it I will!” she said, sternly. “In another drawer,
Mr. Cray, I found the rolls of silver coin—exactly one
hundred dollars worth—that we have been told were in
the desk with the roll of bills. The ruby pin, you know
about. And so, these thefts are proved. Now, as to the
murder—I admit, it seems impossible that a girl should
commit the awful crime—but I do say that I have found
the weapon, with which it was done, hidden in Miss
Austin’s room.”
Again that short, low cry—more like a hurt animal than
a human being. And then, Anita Austin, the girl of
mystery fell back into the depths of her chair, and
closed her eyes.
“You needn’t faint—or pretend to,” admonished Miss
Bascom, brutally; “you’re caught red-handed, and you
know it, and you may as well give up.”
“I didn’t—I didn’t—” came in low moans, but the girl’s
bravery had deserted her. Limp and despairing, she](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-62-320.jpg)
![[197]
turned her great eyes toward Cray for help.
With an effort, he looked away from her pleading face,
and said:
“What is the weapon? Where did you find it?”
“It is a stiletto—an embroidery stiletto—and I found it
tucked down in the crevice between the back and seat
of a stuffed chair in Miss Austin’s room. Did you put it
there?”
She turned on the girl and fired the question at her with
intentional suddenness, and though Anita uttered a
scared, “No,” it was a palpable untruth.
“She did,” Miss Bascom went on. “You can see for
yourself, Mr. Cray, she is lying.”
“But even if she is, Miss Bascom, I must ask you to
cease torturing her! I can’t stand for such cruelty!”
Cray’s manhood revolted at the methods of the older
woman who was causing such anguish to the poor child
she accused.
“You are not a legal inquisitor, Miss Bascom,” he went
on; “it is for me to establish the truth or falsity of your
suspicions.”
“Yes, you! You’re like all the other men! If a girl is pretty
and alluring, you would believe her statement that white
is black!”
“I believe no statements that cannot be proved to my
satisfaction. Miss Austin, do you own an embroidery
stiletto?”](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-63-320.jpg)
![[198]
[199]
“Yes,” was the hesitating answer, and the dark eyes
swept him a beseeching glance that made Miss Bascom
fairly snort with scorn.
“Where is it?”
“I—I fear I must admit that it is just where Miss Bascom
says it is—unless she has removed it. Tell me, Mr. Cray,”
and Miss Mystery suddenly resumed her most
independent air, “must I submit to this? I thought
accused people were entitled to a—oh, you know,
counsel—a lawyer, or somebody to take care of them.”
“Wait, Miss Austin. You’re not accused yet—that is, not
by legal authority.”
“Oh, am I not? Then—” and she gave Miss Bascom a
glance of unutterable scorn, “I have nothing to say.”
“Nothing to say!” the spinster almost shrieked. “Nothing
to say! Of course she hasn’t! She kills a man, takes his
valuables, and then declares she has nothing to say.”
“Now, now, Miss Bascom, be careful! Why did you put
your stiletto in such a place, Miss Austin?”
“I don’t know.”
The dark eyes gave him a gaze of childlike innocence,
and Cray couldn’t decide whether he was looking at a
deep-dyed criminal or a helpless victim of unjust
suspicion.
“And where did you get the money and the ruby pin?”
“I don’t know—I mean I don’t know how they got in my
room. This lady says she found them there—that’s all I](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-64-320.jpg)
![[200]
know about them.”
An indifferent shrug of the slim shoulders seemed to
imply that was all Miss Mystery cared, either, and Cray
asked:
“Then, if the valuables—the pin and the money are not
yours, you are, of course, ready to relinquish possession
of them.”
“Of course I am not! Since I am accused of stealing
them, I propose to retain possession until that
accusation is proved or disproved! Perhaps Miss Bascom
wishes to take them herself.”
“You know, Miss Austin,” Mr. Cray spoke very gravely,
“you are making a mistake in treating this matter
flippantly. You are in danger—real danger, and you must
be careful what you say. Do you want a lawyer?”
“I don’t know,” the girl suddenly looked helpless. “Do
you think I ought to have one?”
“Have you funds?”
“Yes. I am not a rich girl—but, neither am I poor.
However, I think I shall ask advice of some one before I
decide upon any course.”
“Of whom? Perhaps no one can advise you better than I
can.”
“What is your advice, Mr. Cray?”
The sweet face looked at him hopefully, the curved red
lips quivered a little as the speaker added, “I am very
alone.”](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-65-320.jpg)
![[201]
Again Miss Bascom sniffed. Unattractive, herself, she
resented with a sort of angry jealousy the appealing
effect this girl had on men. She knew intuitively that
Cray would sympathize with and pity the lonely girl.
“My advice is, Miss Austin, first, that you dispel this
mystery that seems to surround you. Tell frankly who
you are, what is your errand in Corinth, how you came
into possession of Doctor Waring’s ruby, and why you
hid your stiletto, if it is merely one of your sewing
implements.”
Miss Mystery hesitated a moment, and then said,
quietly:
“Your advice is good, Mr. Cray. But, unfortunately, I
cannot follow it. However, I am willing to state, upon
oath, that I did not kill Doctor Waring with that stiletto.”
“I’m afraid your oath will be doubted,” Miss Bascom
intervened sharply. “And, too, Mr. Cray, even if this girl
did not strike the fatal blow, she well knows who did!
She is in league with the Japanese, Nogi. That I am
sure of!”
“Nogi!” exclaimed Anita.
“Yes, Nogi,” Miss Bascom went on, positively. “You came
here only a day or two after he did. You have a
Japanese kimono, and several Japanese ornaments
adorn your room. You went to the Waring house that
night, Nogi let you in and out, and though the Japanese
doubtless committed the murder, you stole the money
and the ruby, and then, your partner in crime departed
for parts unknown.”](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-66-320.jpg)
![[202]
Miss Bascom sat back in her chair with a look of triumph
on her plain, gaunt face.
Clearly, she was rejoiced at her denunciation of the girl
before her, and pleased at the irrefutable theory she had
promulgated.
“And how did Miss Austin or the Jap, either, leave the
room locked on the inside?” propounded Cray, his own
opinions already swayed by the arraignment.
“That,” said Miss Bascom, with an air of finality, “I can’t
explain definitely, but I am sure it was an example of
Japanese jugglery. When you remember the tales of
how the Japanese can do seemingly impossible tricks,
can swallow swords and get out of locked handcuffs, it
is quite within the realm of possibility that one could
lock a door behind him, and give it the appearance of
having been locked from the inside.”
Now, Cray had already concluded that the door had
been cleverly locked by some one, but he hadn’t before
thought of the cleverness of the Japanese.
He rose almost abruptly, and said, “I must look into
some of these matters. Miss Austin, you need not
attempt to leave town, for you will not be able to do so.”
“I most certainly shall not attempt to leave—as you
express it—if I am asked not to. But, I may say, that
when I am entirely at liberty to do so, I propose to go
away from Corinth.”
Her dignity gave no effect of a person afraid or alarmed
for her own safety, merely a courteous recognition of
Cray’s attitude and a frank statement of her own
intentions.](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-67-320.jpg)
![[203]
[204]
Miss Bascom sniffed and said:
“Don’t worry, Mr. Cray. I’ll see to it, that this young
woman does not succeed in evading justice, if she tries
to do so.”
At which Miss Mystery gave her a smile that was so
patronizing, even amused, that the spinster was more
irate than ever.
“And, now, Miss Austin,” the attorney said, “I’ll take your
finger prints, please, as they may be useful in proving
what you did not do.”
He smiled a little as the girl readily enough gave her
consent to the procedure.
“And,” he went on, more gravely, “I will ask you for one
of your shoes—one that you wore on Sunday.”
Surprised into a glance of dismay, Miss Mystery rose
without a word and went upstairs for the shoe.
She returned with the dainty, pretty thing, and merely
observed, “I’d like to have it back, when you are
through with it.”
Putting the shoe in his overcoat pocket, Cray went
away.
“Miss Bascom,” Anita said, turning to her enemy, “may
you never want a friend as much as I do now.”
“The nerve of her!” Liza Bascom muttered to herself, as
Miss Mystery went upstairs to her own room.](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-68-320.jpg)
![[205]
“There’s a very deep mystery here!” Cray soliloquized,
as he returned to the Waring house. “But I’m getting
light on it.”
Cray was far from lacking in ingenuity, and he
proceeded at once to compare the finger prints he had
of Anita Austin with the prints on the small black-framed
chair that had been found drawn up to the desk chair of
John Waring.
They were identical and Cray mused over the fact.
“That girl was here that night,” he decided; “there’s no
gainsaying that.” He called the butler to him.
“Ito,” he began, “did you let in any one late Sunday
night—after you came home?”
“No, sir,” the imperturbable Jap declared, thinking the
question foolish, as all the inquirers knew the details of
his Sunday evening movements.
“Do you remember seeing this chair, Monday morning?”
“Distinctly. I saw Mr. Lockwood smoothing its back.”
“Smoothing its back! What do you mean?”
“I looked through from the dining-room window, to see
if Mr. Lockwood was coming to breakfast, and I
perceived him carefully smoothing the plush of the little
chair, sir.”
Cray meditated. Here was a point of evidence.
Lockwood was not the sort to absent-mindedly paw over
a chair back. He was doing it on purpose. For what](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-69-320.jpg)
![[206]
reason? What reason could be, save to erase some
evidence?
Cray examined the chair. It had a frame of shiny black
wood, while seat and back were covered with a dark
plush of a fine soft quality.
Cray drew his fingers across the back. They left a
distinct trail of furrows in the fabric.
Ito, watching, nodded his head, gravely.
“Not finger-prints,” Cray said to himself—“but, maybe
finger-marks. Whose?”
“You surely saw this, Ito?”
“Yes, sir; and Miss Peyton also saw. She was then in the
doorway, asking Mr. Lockwood to come to breakfast.”
Cray went in search of Helen and put the question to
her suddenly.
“What was Gordon Lockwood doing, when you went to
call him to breakfast, Monday morning?”
“He was—I don’t remember.”
“Speak the truth—or it may be mean trouble for you
and him, too.”
“He was—he seemed to be dusting off a chair.”
“With a duster?”
“No; just passing over it with his hand.”
“That isn’t dusting it.”](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-70-320.jpg)
![[207]
“Well, I don’t know what you call it! Perhaps he was
merely pushing the chair into place.”
“It isn’t his custom to push the study furniture into
place. He was erasing indicative marks on that plush
chair back—that’s what he was doing.”
“Absurd!” Helen cried; “what marks could there be?”
“I don’t know. Come and let us see.”
Cray took Helen to the study, and asked her to sit in the
chair.
“Lean back,” he directed. “Now, get up.”
The girl obeyed, and there was plainly seen on the
plush the faint but unmistakable imprint of the beaded
design that adorned the back of the frock she wore.
“I told you so!” Cray said, in triumph. “That plush
registers every impress, and when Lockwood rubbed it
smooth it was to erase a damaging bit of testimony.”
“Rather far-fetched, Mr. Cray,” said Gordon Lockwood
himself, who had come in and had heard and seen the
latter part of the detective’s investigation.
“Not so very, Mr. Lockwood, when you learn that the
finger prints on the chair frame are your own and those
of a certain young person who is already under
suspicion.”
Gordon Lockwood, as always under a sudden stress,
became even more impassive, and his eyes glittered as
he faced the attorney.](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-71-320.jpg)
![[208]
“Don’t be too absurd, Mr. Cray,” he advised, coldly. “I
suppose you mean Miss Austin—I prefer to have no
veiled allusions. But the finding of her finger prints on a
chair in this room, and mine also, does not seem to me
to be in any way evidence of crime.”
“No?” Cray gave him scorn for scorn. “Perhaps then, you
can explain Miss Austin’s presence here that night.”
“I don’t know that she was here—and I most certainly
could not explain any of her movements. But I do deny
your right to assume her guilty from her presence.”
“Ah, you tacitly admit her presence, then. Indeed, one
can scarcely doubt it, when it is shown that this little
shoe of hers,” he took it from his pocket, “exactly fits
the prints that cross the field of snow between here and
the Adams house.”
“To measure footprints—after all this time!” and
Lockwood’s lip curled.
“The prints are exactly as they were made, Mr.
Lockwood. The unchanging cold weather has kept them
intact. I tried this shoe, and the prints are unmistakable.
Moreover, the short stride is just the measure of the
natural steps of Miss Austin. The footprints lead from
the Adams house over here and back again. The
returning prints occasionally overlap the ones that came
this way, showing that the trip away from this house
was made latest. Miss Austin was seen to come over in
this direction—well, none but a half-wit would be blind
to the inevitable conclusions!”
“None but a half-wit would read into this evidence what
you pretend to see,” retorted Lockwood, almost losing
his calm.](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-72-320.jpg)
![[209]
[210]
“That’s my business,” Cray said, sharply: “now, Mr.
Lockwood, why did you smooth off that chair back?
Careful, now, two witnesses saw you do it.”
“I’m not denying it”—Lockwood smiled in a bored,
superior way, “but if I did it, I was—and am unconscious
of it. One often touches a piece of furniture in passing
with no thought of doing so.”
“That won’t go down. Both the butler and Miss Peyton
saw you definitely and deliberately rub over the back of
that chair. Why did you do it?”
Cray was inexorable.
But the impassive secretary merely shrugged his
shoulders.
“I can’t answer you, Mr. Cray. I can only repeat it must
have been an unconscious act on my part, and it has no
sinister significance. I may have been merely pushing
the chair out of my way, you know.”
“Look here, Mr. Lockwood, you are a man of honor. Do
you, upon oath, declare that you did not purposely
smooth that chairback, for the reason that it showed
some incriminating impress?”
“I am not under oath. I have stated that I did not do
what you accuse me of, and I have nothing further to
say on the subject.”
Lockwood drew himself up and leaned with folded arms
against the mantelpiece.
Cray dropped the subject, but his snapping eyes and
compressed lips seemed to show he had not finally](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-73-320.jpg)
![[211]
dismissed it.
“At what time,” he said, abruptly, “did Doctor Waring
lock his study door?”
“About ten o’clock,” the secretary replied.
“And you heard nothing from the room after that? No
sound of voices? Nobody coming in at the French
window?”
“No,” replied Lockwood.
“Then we are forced to the conclusion that whoever
entered did so very quietly, that it was with the
knowledge and permission of Doctor Waring himself,
that the visitor was the person whose footprints lead
straight to the door, and whose finger prints are on the
chair that stood near the Doctor’s own chair. We are
borne out in this view by the fact that the same person
now possesses the money and the ruby pin which we
know Doctor Waring had in his room with him, and we
know that the person is here in Corinth for unexplained
reasons, and is, in fact, so peculiar that she is known as
—Miss Mystery. Just why, Mr. Lockwood, are you
arguing against these obvious inferences, and why do
you undertake to free from suspicion one against whom
everything is so definitely black?”
“Because,” Lockwood spoke very quietly, but his jaw
was set in a stubborn way, “the lady you call Miss
Mystery, is a young and defenseless girl, without, so far
as I know, a friend in this town. It is unfair to accuse
her on the strength of this fantastic story and it is unfair
to condemn her unheard.”](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-74-320.jpg)
![[212]
“Not unheard,” said the attorney, “but what she says
only incriminates her more deeply.”](https://image.slidesharecdn.com/2056726-250603051249-bed88ce3/85/Air-Quality-Models-And-Applications-D-Popovic-75-320.jpg)
Air Quality Models And Applications D Popovic
- 1. Air Quality Models And Applications D Popovic download https://ebookbell.com/product/air-quality-models-and- applications-d-popovic-4113452 Explore and download more ebooks at ebookbell.com
- 2. Here are some recommended products that we believe you will be interested in. You can click the link to download. Airflow And Indoor Air Quality Models Of Doe Reference Commercial Buildings Lisa C Ng https://ebookbell.com/product/airflow-and-indoor-air-quality-models- of-doe-reference-commercial-buildings-lisa-c-ng-4962228 Meteorological And Air Quality Models For Urban Areas 1st Edition Jason Ching https://ebookbell.com/product/meteorological-and-air-quality-models- for-urban-areas-1st-edition-jason-ching-2349588 Air Quality Networks Data Analysis Calibration Data Fusion Saverio De Vito https://ebookbell.com/product/air-quality-networks-data-analysis- calibration-data-fusion-saverio-de-vito-48813670 Air Quality Monitoring And Advanced Bayesian Modeling Yongjie Li https://ebookbell.com/product/air-quality-monitoring-and-advanced- bayesian-modeling-yongjie-li-49169088
- 3. Air Quality And Human Health Pratap Kumar Padhy Soumya Niyogi Pulak Kumar Patra Markus Hecker https://ebookbell.com/product/air-quality-and-human-health-pratap- kumar-padhy-soumya-niyogi-pulak-kumar-patra-markus-hecker-58440422 Air Quality Management In The United States Committee On Air Quality Management In U S https://ebookbell.com/product/air-quality-management-in-the-united- states-committee-on-air-quality-management-in-u-s-2161926 Air Quality Monitoring Assessment And Management Nicols A Mazzeo Ed https://ebookbell.com/product/air-quality-monitoring-assessment-and- management-nicols-a-mazzeo-ed-2353596 Air Quality In The 21st Century Gaia C Romano https://ebookbell.com/product/air-quality-in-the-21st-century-gaia-c- romano-33349128 Air Quality Assessment Standards And Sustainable Development In Developing Countries Illustrated Weixin Yang Editor https://ebookbell.com/product/air-quality-assessment-standards-and- sustainable-development-in-developing-countries-illustrated-weixin- yang-editor-36371940
- 5. AIR QUALITY ‐ MODELS AND APPLICATIONS Edited by Dragana Popović
- 6. Air Quality - Models and Applications Edited by Dragana Popović Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Natalia Reinic Technical Editor Teodora Smiljanic Cover Designer Jan Hyrat Image Copyright MADDRAT, 2010. Used under license from Shutterstock.com First published June, 2011 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechweb.org Air Quality - Models and Applications, Edited by Dragana Popović p. cm. ISBN 978-953-307-307-1
- 7. free online editions of InTech Books and Journals can be found at www.intechopen.com
- 9. Contents Preface IX Part 1 Mathematical Models and Computing Techniques 1 Chapter 1 Advances in Airborne Pollution Forecasting Using Soft Computing Techniques 3 Aceves-Fernandez Marco Antonio, Sotomayor-Olmedo Artemio, Gorrostieta-Hurtado Efren, Pedraza-Ortega Jesus Carlos, Ramos-Arreguín Juan Manuel, Canchola-Magdaleno Sandra and Vargas-Soto Emilio Chapter 2 Urban Air Pollution Modeling 15 Anjali Srivastava and B. Padma S. Rao Chapter 3 Artificial Neural Network Models for Prediction of Ozone Concentrations in Guadalajara, Mexico 35 Ignacio García, José G. Rodríguez and Yenisse M. Tenorio Chapter 4 Meandering Dispersion Model Applied to Air Pollution 53 Gervásio A. Degrazia, Andréa U. Timm, Virnei S. Moreira and Débora R. Roberti Chapter 5 Bioaerosol Emissions: A Stochastic Approach 67 Sandra M. Godoy, Alejandro S. M. Santa Cruz and Nicolás J. Scenna Chapter 6 Particle Dispersion Within a Deep Open Cast Coal Mine 81 Sumanth Chinthala and Mukesh Khare Part 2 Air Pollution Models and Application 99 Chapter 7 Mathematical Modeling of Air Pollutants: An Application to Indian Urban City 101 P. Goyal and Anikender Kumar
- 10. VI Contents Chapter 8 A Gibbs Sampling Algorithm to Estimate the Occurrence of Ozone Exceedances in Mexico City 131 Eliane R. Rodrigues, Jorge A. Achcar and Julián Jara-Ettinger Part 3 Measuring Methodologies in Air Pollution Monitoring and Control 151 Chapter 9 Optical Measurements of Atmospheric Aerosols in Air Quality Monitoring 153 Jolanta Kuśmierczyk-Michulec Chapter 10 A Mobile Measuring Methodology to Determine Near Surface Carbon Dioxide within Urban Areas 173 Sascha Henninger Part 4 Urban Air Pollution: Case Studies 195 Chapter 11 Impacts of Photoexcited NO2 Chemistry and Heterogeneous Reactions on Concentrations of O3 and NOy in Beijing,Tianjin and Hebei Province of China 197 Junling An, Ying Li, Feng Wang and Pinhua Xie Chapter 12 Analyzing Black Cloud Dynamics over Cairo, Nile Delta Region and Alexandria using Aerosols and Water Vapor Data 211 Hesham M. El-Askary, Anup K. Prasad, George Kallos, Mohamed El-Raey and Menas Kafatos Chapter 13 Spatial Variation, Sources and Emission Rates of Volatile Organic Compounds Over the Northeastern U.S. 233 Rachel S. Russo,Marguerite L. White, Yong Zhou, Karl B. Haase, Jesse L. Ambrose, Leanna Conway, Elizabeth Mentis, Robert Talbot, and Barkley C. Sive Chapter 14 Evaluation of an Emission Inventory and Air Pollution in the Metropolitan Area of Buenos Aires 261 Laura E. Venegas, Nicolás A. Mazzeo and Andrea L. Pineda Rojas Chapter 15 Variation of Greenhouse Gases in Urban Areas-Case Study: CO2, CO and CH4 in Three Romanian Cities 289 Iovanca Haiduc and Mihail Simion Beldean-Galea
- 11. Contents VII Part 5 Urban Air Pollution: Health Effects 319 Chapter 16 Assessment of Environmental Exposure to Benzene: Traditional and New Biomarkers of Internal Dose 321 Piero Lovreglio,Maria Nicolà D’Errico, Silvia Fustinoni, Ignazio Drago, Anna Barbieri, Laura Sabatini, Mariella Carrieri, Pietro Apostoli, Leonardo Soleo Chapter 17 The Influence of Air Pollutants on the Acute Respiratory Diseases in Children in the Urban Area of Guadalajara 341 Ramírez-Sánchez HU, Meulenert-Peña AR, García-Guadalupe ME, García-Concepción FO, Alcalá-Gutiérrez J and Ulloa-Godínez HH
- 13. Preface Air pollution has been a major transboundary problem and a matter of global concern for decades. High concentrations of different air pollutants may be particularly harm‐ ful to residents of major city areas, where numerous anthropogenic activities (primari‐ ly heavy traffic, domestic and public heating, and various industrial activities), strong‐ ly influence the quality of air. Consequently, air quality monitoring programs become a part of urban areas monitoring network and strict air quality standards in urban are‐ as were in the focus of interest of environmental pollution studies in the last decade of the 20th century. Although there are many books on the subject, the one in front of you will hopefully fulfill some of the gaps in the area of air quality monitoring and model‐ ing, and be of help to graduate students, professionals and researchers. The authors, all of them experts in their field, have been invited by the publisher, and also some recommendations have been given to them mainly concerning technical details of the text, the views and statements they express in the book is their own responsibility. The book is divided in five different sections. The first section discusses mathematical models and computing techniques used in air pollution monitoring and forecasting. The chapter by Aceves‐Fernandez Marco Anto‐ nio et al., presents and compares the advantages and disadvantages of some airborne pollution forecasting methods using soft computing techniques, that include neuro‐ fuzzy inference methods, fuzzy clustering techniques and support vector machines, while the chapter on urban air pollution modeling, by Anjali Srivastava and B. Padma S. Rao, is a general overview of the air quality modeling that provides a useful support to decision making processes incorporating environmental policies and management process. The chapter focuses on urban air models, physical, mathematical and statisti‐ cal, on local to regional scale. An interesting approach is presented in the next chapter on artificial neural network (ANN) models for prediction of ozone concentrations, by Ignacio García et al.. The authors consider to the great flexibility, efficiency and accu‐ racy of the models that, since having a large number of features similar to those of the brain, are capable to learn and thus perform tasks based on training or initial experi‐ ence. The model is applied to the study of tropospheric ozone, as the main component of photochemical smog, in the Metropolitan Zone of Guadalajara, Mexico.
- 14. X Preface In the chapter presenting a meandering dispersion model applied to air pollution by Gervásio A. Degrazia et al., the authors discuss the turbulence parameterization tech‐ nique that can be employed in Lagrangian stochastic dispersion models to describe the air pollution dispersion in the low wind velocity stable conditions, using two classical approaches to obtain the turbulent velocity variances and the decorrelation time scales: Taylor statistical diffusion theory based on the observed turbulent velocity spectra, and the Hanna (1982) approach based on analyses of field experiments, theo‐ retical considerations and second‐order closure model. Also, in this section Sandra Godoy and the co‐workers in their chapter deal with the stochastic approach to the mechanisms of bio aerosols dispersion is atmospheric transport, as a phenomenon that cause serious social, health and economic conse‐ quences. Finally, the chapter on particle dispersion within a deep open cast coal mine, by Sumanth Chinthala & Mukesh Khare, presents a comprehensive overview of the dispersion mechanisms in the deep open pit coal mines considering the topographic, thermal and meteorological factors. The second section presents two chapters on air pollution models and application. First chapter on Mathematical modeling of air pollutants: An application to Indian ur‐ ban city, by P. Goyal and Anikender Kumar, formulates and uses the statistical and Eulerian analytical models for prediction of concentrations of air pollutants released from different sources and different boundary conditions. The model is applied to the city of Delhi, the capital of India, and is validated by the observed data of concentra‐ tion of respirable suspended particulate matter in air. In the second chapter in this sec‐ tion, the authors Eliane R. Rodrigues et al., apply Gibbs sampling algorithm to esti‐ mate the occurrence of ozone exceeding events in Mexico City. The third section of the book contains two chapters on measuring methodologies in air pollution monitoring and control. The first one, by Jolanta Kuśmierczyk‐Michulec, presents an optical method for measuring atmospheric aerosols. The chapter is an overview of various efforts tending toward finding a relationship between atmospher‐ ic optical thickness and particulate matter, and discussing possibilities of using the Angstrom coefficient in air quality estimation. The second chapter, by Sasha Hen‐ ninger, presents the advantages of a mobile measuring methodology to determine near surface carbon dioxide in urban areas. Five chapters in the section four are dealing with experimental data on urban air pol‐ lution. The first one, by Junling An et al., discusses the impacts of photoexcited NO2 chemistry and heterogeneous reactions on concentrations of O3 and NO2 in Beijing, Tianjin and Hebei Province of China, using WRF‐CHEM model. The second one, by Hesham El‐Askary et al., analyses the phenomena of the Black Cloud pollution event over Cairo, Nile Delta Region and Alexandria, Egypt, using aerosols and water vapor data, and the. main sources of air pollution in the region, including heavy traffic, in‐ dustrial, residential, commercial and mixed emissions or biomass burning. In the chapter on Spatial Variation, Sources, and Emission Rates of Volatile Organic Com‐
- 15. Preface XI pounds over the Northeastern U.S., the authors Rachel S. Russo et al., study the chem‐ ical and physical mechanisms influencing the atmospheric composition over New England, applying the University of New Hampshire’s AIRMAP program, that was developed to conduct continuous measurements of important trace gases, meteorolog‐ ical parameters and volatile organic compounds. The chapter four in this section is an evaluation of emission inventory and air pollution in the central area of Buenos Aires, presented by Laura E. Venegas et al. The chapter is a summary of the development and results of a high spatial and temporal resolution version of the emission inventory of carbon monoxide and nitrogen oxides in this area, including area source emissions (motor vehicles, aircrafts, residential heating systems, commercial combustion and small industries), estimated by an urban atmospheric dispersion model (DAUMOD). Finally, Iovanca Haiduc and Mihail S. Beldean‐Galea, in the chapter on Variation of Greenhouse Gases in Urban Areas, present the results of a case study of CO2, CH4 and CO variations during one year, as well as the 13CO2 and 13CH4 isotopic composition in three selected cities from Romania, in order to identify the influence of biogenic and anthropogenic sources to the budget of the greenhouse gases. The final section of the book deals of the health effects and contains only two chapters. The first one, titled Assessment of Environmental Exposure to Benzene: Traditional and New Biomarkers of Internal Dose, by Piero Lovreglio et al., is aimed to assess the significance and limits of t,t‐MA, SPMA and urinary benzene for biological monitoring of subjects with non occupational exposure to very low concentrations of benzene, as well as to study the influence of the different sources of environmental exposure on these biomarkers. The second one, on the influence of air pollutants on the acute res‐ piratory diseases in children living in the urban area of Guadalajara, by Ramirez Sanchez et al., presents the epidemiological evidence that the exposure to atmospheric contaminants, even at low levels, is associated with an increase in respiratory diseases in small children. However, besides the efforts of the authors of the individual chapters, the book is pri‐ marily the result of the hard work of the editing and technical team of the publisher, as the accomplishment of its goal to present a highly professional and informative text in air pollution and quality research. Prof Dragana Popovic Department of Physics and Biophysics, Faculty of Veterinary Medicine, University of Belgrade, Serbia
- 17. Part 1 Mathematical Models and Computing Techniques
- 19. 1 Advances in Airborne Pollution Forecasting Using Soft Computing Techniques Aceves-Fernandez Marco Antonio, Sotomayor-Olmedo Artemio, Gorrostieta-Hurtado Efren, Pedraza-Ortega Jesus Carlos, Ramos-Arreguín Juan Manuel, Canchola-Magdaleno Sandra and Vargas-Soto Emilio Facultad de Informática, Universidad Autónoma de Querétaro, México 1. Introduction There are many investigations reported in the scientific literature about Particulate Matter (PM) 2.5 and PM10 in urban and suburban environments [Vega et al 2002, Querol et al 2004, Fuller et al 2004]. In this contribution, the information acquired from PMx monitoring systems is used to accurately forecast particle concentration using diverse soft computing techniques. A number of works have been published in the area of airborne particulates forecasting. For example, Chelani[et al 2001] trained hidden layer neural networks for CO forecasting at India. Caselli [et al 2009] used a feedforward neural network to predict PM10 concentration. Other works such as Kurt’s [et al 2010] have constructed a neural networks model using many input variables (e.g. wind, temperature, pressure, day of the week, Date, concentration, etc) making the model too complex and inaccurate. However, not many scientific literature discuss a number of robust forecasting methods using soft computing techniques. These techniques include neuro-fuzzy inference methods, fuzzy clustering techniques and support vector machines. Each one of these algorithms is discussed separately and the results discussed. Furthermore, a comparison of all methods is made to emphasize their advantages as well as their disadvantages. 2. Fuzzy inference methods Fuzzy inference systems (FIS) are also known as fuzzy rule-based systems. This is a major unit of a fuzzy logic system. The decision-making is an important part in the entire system. The FIS formulates suitable rules and based upon the rules the decision is made. This is mainly based on the concepts of the fuzzy set theory, fuzzy IF–THEN rules, and fuzzy reasoning. FIS uses “IF - THEN” statements, and the connectors present in the rule statement are “OR” or “AND” to make the necessary decision rules. Fuzzy inference system consists of a fuzzification interface, a rule base, a database, a decision-making unit, and finally a defuzzification interface as described in Chang(et al 2006). A FIS with five functional block described in Fig.1.
- 20. Air Quality - Models and Applications 4 Fig. 1. Fuzzy Inference System The function of each block is as follows: - A rule base containing a number of fuzzy IF–THEN rules; - A database which defines the membership functions of the fuzzy sets used in the fuzzy rules; - A decision-making unit which performs the inference operations on the rules; - A fuzzification interface which transforms the crisp inputs into degrees of match with linguistic values; and - A defuzzification interface which transforms the fuzzy results of the inference into a crisp output. The working of FIS is as follows. The inputs are converted in to fuzzy by using fuzzification method. After fuzzification the rule base is formed. The rule base and the database are jointly referred to as the knowledge base. Defuzzification is used to convert fuzzy value to the real world value which is the output. The steps of fuzzy reasoning (inference operations upon fuzzy IF–THEN rules) performed by FISs are:
- 21. Advances in Airborne Pollution Forecasting Using Soft Computing Techniques 5 Compare the input variables with the membership functions on the antecedent part to obtain the membership values of each linguistic label. (this step is often called fuzzification.) Combine (through a specific t-norm operator, usually multiplication or min) the membership values on the premise part to get firing strength (weight) of each rule. Generate the qualified consequents (either fuzzy or crisp) or each rule depending on the firing strength. Aggregate the qualified consequents to produce a crisp output. (This step is called defuzzification.) A typical fuzzy rule in a fuzzy model has the format shown in equation 1 IF x is A and y is B THEN z = f(x, y) (1) where AB are fuzzy sets in the antecedent; Z = f(x, y) is a function in the consequent. Usually f(x, y) is a polynomial in the input variables x and y, of the output of the system within the fuzzy region specified by the antecedent of the rule. A typical rule in a FIS model has the form (Sugeno et al1988): IF Input 1 = x AND Input 2 = y, THEN Output is z = ax + by + c. Furthermore, the final output of the system is the weighted average of all rule outputs, computed as 1 1 N i i i N i i w z FinalOutput w (2) 3. Fuzzy clustering techniques There are a number of fuzzy clustering techniques available. In this work, two fuzzy clustering methods have been chosen: fuzzy c-means clustering and fuzzy clustering subtractive algorithms. These methods are proven to be the most reliable fuzzy clustering methods as well as better forecasters in terms of absolute error according to some authors[Sin, Gomez, Chiu]. Since 1985 when the fuzzy model methodology suggested by Takagi-Sugeno [Takagi et al 1985, Sugeno et al 1988], as well known as the TSK model, has been widely applied on theoretical analysis, control applications and fuzzy modelling. Fuzzy system needs the precedent and consequence to express the logical connection between the input output datasets that are used as a basis to produce the desired system behavior [Sin et al 1993]. 3.1 Fuzzy clustering means (FCM) Fuzzy C-Means clustering (FCM) is an iterative optimization algorithm that minimizes the cost function given by: = ∑ ∑ ‖ − ‖ (3) Where n is the number of data points, c is the number of clusters, xk is the kth data point, vi is the ith cluster center ik is the degree of membership of the kth data in the ith cluster, and m is a constant greater than 1 (typically m=2)[Aceves et al 2011]. The degree of membership ik is defined by:
- 22. Air Quality - Models and Applications 6 = ∑ ( ) (4) Starting with a desired number of clusters c and an initial guess for each cluster center vi, i = 1,2,3… c, FCM will converge to a solution for vi that represents either a local minimum or a saddle point cost function [Bezdek et al 1985]. The FCM method utilizes fuzzy partitioning such that each point can belong to several clusters with membership values between 0 and 1. FCM include predefined parameters such as the weighting exponent m and the number of clusters c. 3.2 Fuzzy clustering subtractive The subtractive clustering method assumes each data point is a potential cluster center and calculates a measure of the likelihood that each data point would define the cluster center, based on the density of surrounding data points. Consider m dimensions of n data point (x1,x2, …, xn) and each data point is potential cluster center, the density function Di of data point at xi is given by: = ∑ (5) where ra is a positive number. The data point with the highest potential is surrounded by more data points. A radius defines a neighbour area, then the data points, which exceed ra, have no influence on the density of data point. After calculating the density function of each data point is possible to select the data point with the highest potential and find the first cluster center. Assuming that Xc1 is selected and Dc1 is its density, the density of each data point can be amended by: = − − ‖ ‖ (6) The density function of data point which is close to the first cluster center is reduced. Therefore, these data points cannot become the next cluster center. rb defines an neighbour area where the density function of data point is reduced. Usually constant rb > ra. In order to avoid the overlapping of cluster centers near to other(s) is given by [Yager et al 1994]: = ∙ (7) 4. Support vector machines The support vector machines (SVM) theory, was developed by Vapnik in 1995, and is applied in many machine-learning applications such as object classification, time series prediction, regression analysis and pattern recognition. Support vector machines (SVM) are based on the principle of structured risk minimization (SRM) [Vapnik et al 1995, 1997]. In the analysis using SVM, the main idea is to map the original data x into a feature space F with higher dimensionality via non-linear mapping function , which is generally unknown, and then carry on linear regression in the feature space [Vapnik 1995]. Thus, the regression
- 23. Advances in Airborne Pollution Forecasting Using Soft Computing Techniques 7 approximation addresses a problem of estimating function based on a given data set, which is produced from the function. SVM method approximates the function by: 1 ( ) ( ) m i i i y w x b w x b (8) where w = [w1,…,wm] represent the weights vector, b is defined as the bias coefficients and (x)=[1(x),…, m(x)] the basis function vector. The learning task is transformed to the weights of the network at minimum. The error function is defined through the -insensitive loss function, L(d,y(x)) and is given by: ( ) ( ) ( , ( )) 0 d y x d y x L d y x others (9) The solution of the so defined optimization problem is solved by the introduction of the Lagrange multipliers i, * i (where i=1,2,…,k) responsible for the functional constraints defined in Eq. 9. The minimization of the Lagrange function has been changed to the dual problem [Vapnik et al 1997]: * * 1 1 1 ( , )( , ) ( , ) 2 k k i i j j i j i j K x x (10) With constraints: * 1 * ( , ) 0, 0 ,0 i k i i i i C C (11) Where C is a regularized constant that determines the trade-off between the training risk and the model uniformity. According to the nature of quadratic programming, only those data corresponding to non- zero * i i pairs can be referred to support vectors (nsv). In Eq. 10 K(xi , xj)=(xi)*(xj) is the inner product kernel which satisfy Mercer’s condition [Osuna et al 1997] that is required for the generation of kernel functions given by: (12) Thus, the support vectors associates with the desired outputs y(x) and with the input training data x can be defined by: (13) Where xi are learning vectors. This leads to a SVM architecture (Fig. 2) [Vapnik 1997, Cristianini et al 2000]. (,* ) di (i i * ) i1 k (i i * ) i1 k K(xi , xj ) (xi ),(xj ) y(x) (i ,i * )K(x, xi ) i1 Nsv b
- 24. Air Quality - Models and Applications 8 Fig. 2. Support Vector Machine Architecture. Fig. 3. Support Vector Machine Methodology.
- 25. Advances in Airborne Pollution Forecasting Using Soft Computing Techniques 9 The methodology used for the design, training and testing of SVM is proposed as follows based in a review of Vapnik, Osowski [et al 2007] and Sapankevych[et al 2009] a. Preprocess the input data and select the most relevant features, scale the data in the range [−1, 1], and check for possible outliers. b. Select an appropriate kernel function that determines the hypothesis space of the decision and regression function. c. Select the parameters of the kernel function the variances of the Gaussian kernels. d. Choose the penalty factor C and the desired accuracy by defining the ε-insensitive loss function. e. Validate the model obtained on some previously, during the training, unseen test data, and if not pleased iterate between steps (c) (or, eventually b) and (e). 5. Discussion of results Simulations were performed using fuzzy clustering algorithms using the equations [3-7], in this case study, the datasets at Mexico City in 2007 were chosen to construct the fuzzy model. Likewise, the data of 2008 and 2009 from the same geographic zone in each case were used to training and validating the data, respectively. The result of the fuzzy clustering model was compared then to the real data of Northwest Mexico in 2010. The results obtained show an average least mean square error of 11.636 using Fuzzy Clustering Means, whilst FCS shows an average least mean square error of 10.59. Table 1 shows a list of the experiments carried out. An example of these results is shown in figure 4 for FCM and figure 5 shows the estimation made using FCS at Northwest Mexico City. Fig. 4. Fuzzy Clustering Means (FCM) Results at Northwest Mexico City. Raw Data VS. Fuzzy Model
- 26. Air Quality - Models and Applications 10 Fig. 5. Fuzzy Clustering Subtractive (FCS) Results at Northwest Mexico City. Raw Data VS. Fuzzy Model In figures 4 and 5, the raw data (shown in blue solid line) and the constructed fuzzy model (in dashed-starred green line) shown that the trained model is approximated to the raw data with an average least mean square error of 8.7%, implying that a fuzzy model can be accurately constructed using this technique. Site LMSE using FCM LMSE using FCS Northwest 10.1917 7.4807 Northeast 13.6282 13.7374 Center 18.5757 15.1409 Southwest 5.0411 7.4953 Southeast 10.7428 9.1188 Table 1. List of the experiments carried out using FCM and FCS. In table 1 is shown that the best prediction in terms of error percentage is given at southwest for both fuzzy clustering means and fuzzy clustering subtractive, whilst the lessen estimation is given at the city center. This may be due to the high variations in terms of PM10 particles making it more difficult to predict. However, more research is needed to confirm this. Furthermore, detailed simulations were carried out using Support Vector Machines following the proposed methodology shown in figure 3. These simulations were carried out
- 27. Advances in Airborne Pollution Forecasting Using Soft Computing Techniques 11 using the same dataset as the fuzzy clustering technique. In this case, values 2 σ was chosen, and an ε of 11 and 13 were chosen since it was demonstrated to give better results in previous contributions (Sotomayor et al 2010, Sotomayor et al 2011). Figure 6 shows the results of the model using support vector machines with a Gaussian kernel, whilst figure 7 shows the results using the same datasets, with polynomial kernel a) SVM Estimated with free parameters of ε = 13 and σ = 2 b) SVM Estimated with free parameters of ε = 11 and σ = 2 Fig. 6. SVM Results at Northwest Mexico City using Gaussian Kernel. Figure 6 indicates a summary of the results with the Support vector machine (in red circles), the raw data (black cross) and the behavior of the data (solid black line). These results show that for Gaussian Kernel (fig 6) gives 11.8 error using the same LMSE Algorithm than the
- 28. Air Quality - Models and Applications 12 fuzzy model with an epsilon of 13 giving a total number of support vector machines of 157. In the case of figure 5b, using the Gaussian kernel, it was also used the same σ and an epsilon of 11. For this figure, the support vector shows an improvement by having an LMSE of 8.7. a) SVM Estimated with free parameters of ε = 13 and σ = 2 b) SVM Estimated with free parameters of ε = 11 and σ = 2 Fig. 7. SVM Results at Northwest Mexico City using Polynomial Kernel. For figure 7a, the estimation gives an error of 9.8 using an σ of 2 and an epsilon of 11 using 177 support vector machines. Likewise, figure 7b also shows the estimation using a third degree polynomial kernel with an ε of 13. In this case, a 10.1 LMSE is shown by having 183 support vector machines.
- 29. Advances in Airborne Pollution Forecasting Using Soft Computing Techniques 13 6. Conclusions and further work An assessment in the performance of both fuzzy systems generated using Fuzzy Clustering Subtractive and Fuzzy C-Means was made taking in account the number or membership functions, rules, and Least Mean Square Error for PM10 particles. As a case study, Estimations were made at Northwest Mexico City in 2010, giving consistent results. In case of SVMs, it can be concluded that for this case study an ε of 11 gives a better estimation than an ε of 13 for the Gaussian kernel. In general, the Gaussian kernel gives better results in terms of estimation than its corresponding polynomial kernel. In general terms, fuzzy clustering gives a better estimation than Gaussian and polynomial kernels, although in-depth studies are needed to corroborate these results for other scenarios. For future work, more SVM kernels can be implemented and comparison can be made to find out which kernels give better estimation. Also, SVMs can be implemented along with other techniques such as wavelet transform to improve the performance of these algorithms 7. References Aceves-Fernández M.A., Sotomayor-Olmedo A., Gorrostieta-Hurtado E., Pedraza-Ortega J.C., Tovar-Arriaga S., Ramos-Arreguin J.M., Performance Assessment of Fuzzy Clustering Models Applied to Urban Airborne Pollution, CONIELECOMP 2011, 21th International Conference on Electrical Communications, pp. 212-216 (2011). Bezdek, J. C., “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press, NY, 1981. Caselli M. & Trizio L. & de Gennaro G. & Ielpo P., “A Simple Feedforward Neural Network for the PM10 Forecasting: Comparison with a Radial Basis Function Network and a Multivariate Linear Regression Model”, Water Air Soil Pollut (2009) 201:365–377 Chang Wook A., “Advances in Evolutionary Algorithms: Theory, Design and Practice”, Springer, ISSN: 1860-949X, 2006. Chelani A.B.; Hasan M. Z., “Forecasting nitrogen dioxide concentration in ambient air using artificial Neural networks”, International Journal of Environmental Studies, 2001, Vol. 58, pp. 487-499 Chiu S, “Fuzzy model identification based on cluster estimation”, Journal of Intelligent and Fuzzy Systems; September 1994, 2, pp. 267–78. Cristianini, N., Shawe-Taylor, J., An introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press, Cambridge, UK (2000) Fuller G W and Green D., “The impact of local fugitive PM10 from building works land road works on the assessment of the European Union Limit Value”, Atmospheric Environment 2004, 38, pp. 4493–5002. Gomez, A. F., M. Delgado, and M. A. Vila, “About the Use of Fuzzy Clustering Techniques for Fuzzy Model Identification”, Fuzzy Set and System,. 1999, pp. 179-188. Kurt Atakan, Oktay Ayse Betül, “Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks”, Expert Systems with Applications, 37 (2010) 7986–7992. Osowski S. and Garanty K., "Forecasting of the daily meteorological pollution using wavelets and support vector machine," Engineering Applications of Artificial Intelligence, vol. 20, no. 6, pp. 745-755, September 2007.
- 30. Air Quality - Models and Applications 14 Osuna, E., R. Freund, F. Girosi.:Support vector machines: Training and applications. AI Memo 1602, Massachusetts Institute of Technology, Cambridge, MA 44. (1997). Querol X, Alastvey A, Ruiz C.R., Avtinano B, Hausson H.C., Harrison R.M, Buringh E, Ten Brink H.M, Lutz M, Bruckmann P, Straehl P and Schnerflev J., “Speciation and origin of PM10 and PM 2.5 in selected European cities”, Atmospheric Environment. 2004, 38, pp. 6547 – 6555. Sapankevych I. and Sankar R., "Time series prediction using support vector machines: A survey," Computational Intelligence Magazine, IEEE, vol. 4, no. 2, pp. 24-38, 2009. Schölkfopf B.: Smola A. J.: and Burges C.: Advances in Kernel Methods –Support Vector Learning. Cambridge, M.A.: MIT Press. 1999. Sin, S. K., and De Figueiredo, “Fuzzy System Designing Through Fuzzy Clustering and Optimal preDefuzzification”, Proc. IEEE International Conference on Fuzzy Systems. 1993 2, 190-195. Sotomayor-Olmedo A., Aceves-Fernandez M.A., Gorrostieta-Hurtado E., Pedraza-Ortega J.C., Ramos-Arreguin J.M., Vargas-Soto J.E., Tovar-Arriaga S., “Modeling Trends of Airborne Particulate Matter by using Support Vector Machines”, 7th International Conference on Electrical and Electronics Engineering Research (CIIIEE 2010), November 10-12 2010, Aguascalientes, Ags. Mexico, ISBN: 978-607-95060-3-2 Sotomayor-Olmedo A., Aceves-Fernandez M.A., Gorrostieta-Hurtado E., Pedraza-Ortega J.C., Vargas-Soto J.E., Ramos-Arreguin J.M., Villaseñor-Carillo U., “Evaluating Trends of Airborne Contaminants by using Support Vector Regression Techniques”, CONIELECOMP 2011, 21th International Conference on Electrical Communications, pp. 137-141 (2011). Sugeno, M., and G. T. Kang. “Structure Identification of Fuzzy Model”, Fuzzy Sets and Systems. 1988, 28, pp. 15-33. Takagi, T., and M. Sugeno, “Fuzzy Identification of Systems and its Application to Modeling and Control”, IEEE Trans. Systems Man and Cybernetics. 1985 -15, pp. 116-132. Vapnik, V.: The Nature of Statical Learning Theory. Springer-Verlang, New York. 1995. Vapnik, V., Golowich, S., Smola A.: Support method for function approximation regression estimation, and signal processing. Advance in Neural Information Processing System 9. MIT Press, Cambridge, MA. 1997. Vega E, Reyes E Sanchez G, Ortiz E, Ruiz M, Chow J, Watson J and Edgerton S, “Basic Statistics of PM2.5 and PM10 in the atmosphere of Mexico City”, The science of the total environment 2002, 287,pp. 167-176. Yager, R. and D. Filev, “Generation of Fuzzy Rules by Mountain Clustering”, Journal of Intelligent & Fuzzy Systems, 1994, 2, pp. 209- 219.
- 31. 2 Urban Air Pollution Modeling Anjali Srivastava and B. Padma S. Rao National Environmental Engineering Research Institute, Kolkata Zonal Centre India 1. Introduction All life form on this planet depends on clean air. Air quality not only affects human health but also components of environment such as water, soil, and forests, which are the vital resources for human development. Urbanization is a process of relative growth in a country’s urban population accompanied by an even faster increase in the economic, political, and cultural importance of cities relative to rural areas. Urbanization is the integral part of economic development. It brings in its wake number of challenges like increase in population of urban settlement, high population density, increase in industrial activities (medium and small scale within the urban limits and large scale in the vicinity), high rise buildings and increased vehicular movement. All these activities contribute to air pollution. The shape of a city and the land use distribution determine the location of emission sources and the pattern of urban traffic, affecting urban air quality (World Bank Reports 2002). The dispersion and distribution of air pollutants and thus the major factor affecting urban air quality are geographical setting, climatological and meteorological factors, city planning and design and human activities. Cities in the developing countries are characterized by old city and new development. The old cities have higher population density, narrow lanes and fortified structures. In order to ensure clean air in urban settlements urban planning and urban air quality management play an important role. New legislations, public awareness, growth of urban areas, increases in power consumption and traffic pose continuous challenges to urban air quality management. UNEP (2005) has identified niche areas Urban planning need to primarily focus on as: Promotion of efficient provision of urban infrastructure and allocation of land use, thereby contributing to economic growth, managing spatial extension while minimizing infrastructure costs, improving and maintaining the quality of the urban environment and Prerecording the natural environment immediately outside the urban area. Air quality modelling provides a useful support to decision making processes incorporating environmental policies and management process. They generate information that can be used in the decision making process. The main objectives of models are: to integrate observations, to predict the response of the system to the future changes, to make provision for future development without compromising with quality
- 32. Air Quality - Models and Applications 16 2. Urban air quality The urban air is a complex mixture of toxic gases and particulates, the major source is combustion of fossil fuels.Emissions from fossil fuel combustion are reactive and govern local atmospheric chemistry.Urban air pollution thus in turn affect global troposphere chemistry and climate (e.g. tropospheric O3 and NOX budgets, radiative forcing by O3 and aerosols). Sources of air pollutants in urban area, their effect and area of concern are summarized in Table 1. Source Pollutants Effects Area of concern Large number of vehicles Particulate matters (PM10, PM2.5), Lead (Pb), Sulphur dioxide (SO2), Oxides of nitrogen (NOx), Ozone (O3), Hydro carbons (HCs), Carbon monoxide (CO), Hydrogen fluoride (HF), Heavy metals (e.g. Pb, Hg, Cd etc.) Human Health (acute and chronic) Local, Regional and Global Use of diesel powered vehicle in large number Use of obsolete vehicles in large number Large number of motor cycles/three wheelers (2 stroke and three stroke) Ecosystem (acute and chronic) Local, Regional and Global Unpaved and/or poorly maintained street Open burning Inadequate infrastructure Greenhouse gas emission Global Low quality of fuel/fuel adulteration Little emission control & technology in industry Presence of industries (e.g. ceramic, brick works, agrochemical factory) Acid rain Global Waste incineration Stratospheric ozone depletion Global Limited dry deposition of pollutants Long-range transport Global Table 1. Urban sources of air pollutants, their effect and area of concern. Urban air pollution involves physical and chemical process ranging over a wide scale of time and space. The urban scale modeling systems should consider variations of local scale
- 33. Urban Air Pollution Modeling 17 effects, for example, the influence of buildings and obstacles, downwash phenomena and plume rise, together with chemical transformation and deposition. Atmospheric boundary layer, over 10 to 30 km distances, governs the dispersion of pollutants from near ground level sources. Vehicular emissions are one the major pollution source in urban areas. Ultrafine particles are formed at the tailpipe due to mixing process between exhaust gas and the atmosphere. Processes at urban scale provide momentum sink, heat and pollutant source thereby influencing the larger regional scale (up to 200 km). Typical domain lengths for different scale models is given in table 2. Model Typical Domain Scale Typical resolution Motion Example Micro scale 200x200x100m 5m Molecular diffusion, Molecular viscosity Mesoscale (urban) 100x100x5km 2km Eddies, small plumes, car exhaust, cumulus clouds Regional 1000x1000x10km 36km Gravity waves, thunderstorms, tornados, cloud clusters, local winds, urban air pollution Synoptic (continental) 3000x3000x20km 80 km High and low pressure system, weather fronts, tropical storms, Hurricanes Antarctic ozone hole, Global 65000x65000x20km 4° x 5° Global wind speed, rossby (planetary) waves stratospheric ozone reduction Global worming Table 2. Typical domain length for different scale model Piringer et al., 2007, have demonstrated that atmospheric flow and microclimate are influenced by urban features, and they enhance atmospheric turbulence, and modify turbulent transport, dispersion, and deposition of atmospheric pollutants. Any urban scale modeling systems should consider effects of the various local scales, for example, the influence of buildings and obstacles, downwash phenomena and plume rise, chemical transformation and deposition. The modelling systems also require information on emissions from various sources including urban mobile pollution sources. Simple dispersion air quality pollution transport models and complex numerical simulation model require wind, turbulence profiles, surface heat flux and mixing height as inputs. In urban areas mixing height is mainly influenced by the structure heights and construction materials, in terms of heat flux. Oke (1987, 1988, 1994), Tennekes (1973), Garrat (1978, 1980), Raupach et al (1980) and Rotach (1993, 1995) divided the Atmospheric Boundary Layer within the urban structures into four sub layers (Figure 1).
- 34. Air Quality - Models and Applications 18 Fig. 1. Boundary- layer structure over a rough urban built- up area A daytime situation is displayed where Z I denotes the mixed layer height. Modefied after Oke ( 1988) and Rotach (1993). In urban establishments anthropogenic activities take place between the top of highest building and the ground. People also live in this area. The layer of atmosphere in this volume is termed as Urban Canopy. The thermal exchanges and presence of structures in urban canopy modify the air flows significantly and this makes the atmospheric circulations in urban canopy highly complex. The heterogeneity of urban canopies poses a challenge for air quality modeling in urban areas. The importance of various parameters in different models for urban atmosphere study is given in Table 3. Figure 2 shows the flow and scale lengths within an urban boundary layer, UBL. Parameter Air Quality Urban Climatology Urban Planning Wind speed Very important Important Very Important Wind Direction Very important Important Very Important Temperature Humidity Important Extremely important Very Important Pollutant Concentration Extremely important Very important Very important Turbulent Fluxes Very important Very important Very important Table 3. Ranking of parameters in different applications for urban air environment
- 35. Urban Air Pollution Modeling 19 Fig. 2. Schematic diagram showing processes, flow and scale lengths within an urban boundary layer, UBL. This is set in the context of the planetary boundary layer, PBL, the urban canopy layer, UCL, and the sky view factor, SVF, a measure of the degree to which the sky is obscured by surrounding buildings at a given point which characterises the geometry of the urban canopy. Ref:. Meteorology applied to urban pollution problems-Final report COST Action 715. Dementra Ltd Publishers Vehicles are one of the important pollution sources in urban areas. Maximum exposure to local public is from this source and thus they form important receptor group. Pollutant dispersion of vehicular pollution is at street scale and is the smallest scale in urban environment. Hosker (1985) showed that flows in street canyon are like recirculating eddy driven by the wind flow at the top with a shear layer which separates the above canyon flows from those within it. In deep street canyons the primary vortex does not extend to the ground but a weak contra rotating vortex is formed near the ground and is relatively shallow (Figure 3). Pavageau et al (2001) demonstrated that wind directions which are not normal to the street axis cause variations in the flow. The real geometry of the street canyon and the mean flow and turbulence generated by vehicles within the canyon also affect the recirculating flow. Concentrations of pollutants at a receptor are governed by advection, dispersion and deposition. Air pollutants can be divided into two main categories namely conventional air pollutants and Hazardous Air Pollutants (HAPs). Conventional air pollutants include particulate matters, sulphur dioxide, nitrogen dioxide, carbon monoxide, particles, lead and the secondary pollutant ozone. HAPs include Volatile Organic Compounds, toxic metals
- 36. Air Quality - Models and Applications 20 Fig. 3. Air flow pattern in a Street Canyon and biological agents of many types. All pollutants are not emitted in significant quantities. Secondary pollutants like some VOCs, carbonyls and ozone are formed due to chemical transformation in air. These reactions are often photochemical. The important components of air quality modelling are thus, Knowledge of sources and emissions Transport, diffusion and parametrisation Chemical transformations Removal process Meteorology Understanding contribution from various sources to air quality is the key for effective management of the air quality. Air quality models offer a useful tool in comprehending these issues. These models evaluate the relationship between air pollutant emissions and their resulting concentration in the ambient air. Commonly used air quality models are: 1) Conceptual Models 2) Emission Models 3) Meteorological Models, 4) Chemical Models, 5) Source Oriented Models and 6) Receptor Models. 3. Air quality model classification Air quality models cover either separately or together atmospheric phenomena at various temporal and spatial scales. Urban air models generally focus from local (micro- tens of meters to tens of kilometers) to regional (meso) scale. Models can be broadly divided into two types namely physical and mathematical. Physical models involve reproducing urban area in the wind tunnel. Scale reduction in the replica and producing scaling down actual flows of atmospheric motion result in limited utility of such models. Moreover these are economically undesirable. Mathematical models use either use statistics to analyse the available data or mathematical representation of all the process of concern. The second type of mathematical models is constrained by the ability to represent physical and chemical processes in equations without assumptions.
- 37. Urban Air Pollution Modeling 21 Statistical model are simple but they do not explicitly describe causal relationships and they cannot be extrapolated beyond limits of data used in their derivation. Thus dependence on past data becomes their major weakness. These cannot be used for planning as they cannot predict effect of changes in emissions. 3.1 Eulerian and lagrangian models Eulerian approach has been used to predict air pollutant concentrations in urban areas. The space domain (geographical area or air volume), are divided into "small" squares (two- dimensional) or volumes (three-dimensional), i.e. grid cells. Thus Eulerian models are sometimes called "grid models". Equidistant grids are normally used in air pollution modeling. Then the spatial derivatives involved in the system of Partial Differential Equations are discretized on the grid chosen. The transport, diffusion, transformation, and deposition of pollutant emissions in each cell are described by a set of mathematical expressions in a fixed coordinate system. Chemical transformations can also be included. Long range transport, air quality over entire air shed, that is, large scale simulations are mostly done using Eulerian models. Reynolds (1973), Shir and Shieh (1974) applied Eulerian model for ozone and for SO2 concentration simulation in urban areas, and Egan (1976) and Carmichael (1979) for regional scale sulfur. Holmes and Morawska (2006) used Eulerian model to calculate the transport and dispersion over long distances. The modeling studies by Reynolds (1973) on the Los Angeles basin formed the basis of the, the well-known Urban Air shed Model-UAM. Examples of Eulerian models are CALGRID model and ARIA Regional model or the Danish Eulerian Hemispheric Model (DEHM). Lagrangian Model approach is based on calculation of wind trajectories and on the transportation of air parcels along these trajectories. In the source oriented models the trajectories are calculated forward in time from the release of a pollutant-containing air parcel by a source (forward trajectories from a fixed source) until it reaches a receptor site. And in receptor oriented models the trajectories are calculated backward in time from the arrival of an air parcel at a receptor of interest (backward trajectories from a fixed receptor). Numerical treatment of both backward and forward trajectories is the same. The choice of use of either method depends on specific case. As the air parcel moves it receives the emissions from ground sources, chemical transformations, dry and wet depositions take place. If the models provide average time-varying concentration estimates along the box trajectory then Lagrangian box models have been used for photochemical modeling. The major shortcoming of the approach is the assumption that wind speed and direction are constant throughout the Physical Boundary Layer. As compared to the Eulerian box models the Lagrangian box models can save computational cost as they perform computations of chemical and photochemical reactions on a smaller number of moving cells instead of at each fixed grid cell of Eulerian models. Versions of EMEP (European Monitoring and Evaluation Programme) are examples of Lagrangian models. These models assume pollutants to be evenly distributed within the boundary layer and simplified exchange within the troposphere is considered. 3.2 Box models Box models are based on the conservation of mass. The receptor is considered as a box into which pollutants are emitted and undergo chemical and physical processes. Input to the model is simple meteorology. Emissions and the movement of pollutants in and out of the
- 38. Air Quality - Models and Applications 22 box is allowed. The air mass is considered as well mixed and concentrations to be uniform throughout. Advantage of the box model is simple meteorology input and detailed chemical reaction schemes, detailed aerosol dynamics treatment. However, following inputs of the initial conditions a box model simulates the formation of pollutants within the box without providing any information on the local concentrations of the pollutants. Box models are not suitable to model the particle concentrations within a local environment, as it does not provide any information on the local concentrations, where concentrations and particle dynamics are highly influenced by local changes to the wind field and emissions. 3.3 Receptor models Receptor modeling approach is the apportionment of the contribution of each source, or group of sources, to the measured concentrations without considering the dispersion pattern of the pollutants. The starting point of Receptor models is the observed ambient concentrations at receptors and it aims to apportion the observed concentrations among various source types based on the known source profile (i.e. chemical fractions) of source emissions. Mathematically, the receptor model can be generally expressed in terms of the contribution from ‘n’ independent sources to ‘p’ chemical species in ‘m’ samples as follows: n ik ij jk j 1 C a f (1) Where Cik is the measured concentration of the kth species in the ith sample, aik is the concentration from the jth source contributing to the ith sample, and fjk is the kth species fraction from the jth source. Receptor models can be grouped into Chemical mass balance (CMB), Principal Component Analysis (PCA) or Factor analysis, and Multiple Linear Regression Analysis (MLR) and multivariate receptor models. The Chemical Mass Balance (CMB) Receptor Model used by Friedlander, 1973 uses the chemical and physical characteristics of gases and particulate at source receptor to both identify the presence of and to quantify source contributions of pollutants measured at the receptor. Hopke (1973, 1985) christened this approach as receptor modelling. The CMB model obtains a least square solution to a set of linear equation, expressing each receptor concentration of a chemical species as a linear sum product of source profile species and source contributions. The output to the model consists of the amount contributed by each source type to each chemical species. The model calculates the contribution from each source and uncertainties of those values. CMB model applied to the VOC emissions in the city of Delhi and Mumbai (Figure 4 ) shows that emissions from petrol pumps and vehicles at traffic intersection dominate. PCA and MLR are statistical models and both PMF and UNMIX are advanced multivariate receptor models that determine the number of sources and their chemical compositions and contributions without source profiles. The data in PMF are weighted by the inverse of the measurement errors for each observation. Factors in PMF are constrained to be nonnegative. PMF incorporates error estimates of the data to solve matrix factorization as a constrained, weighted least-squares problem (Miller et al., 2002; Paatero, 2004). Geometrical approach is used in UNMIX to identify contributing sources. If the data consist of ‘m’ observations of ‘p’ species, then the data can be plotted in a p-dimensional data space, where the coordinates of a data point are the observed concentrations of the species during a
- 39. Urban Air Pollution Modeling 23 sampling period. If n sources exist, the data space can be reduced to a (n-1) dimensional space. An assumption that for each source, some data points termed as edge points exist for which the contribution of the source is not present or small compared to the other sources. Fig. 4. Category wise Contribution to Total VOCs at Mumbai and Delhi based on CMB results(Ref: Anjali Srivastava 2004, 2005) UNMIX algorithm identifies these points and fits a hyperplane through them; this hyperplane is called an edge. If n sources exist, then the intersection of n-1 of these edges defines a point that has only one contributing source. Thus, this point gives the source composition. In this way, compositions of the n sources are determined which are used to calculate the source contributions (Henry, 2003).
- 40. Air Quality - Models and Applications 24 3.4 Computational fluid dynamic models Resolving the Navier-Stokes equation using finite difference and finite volume methods in three dimensions provides a solution to conservation of mass and momentum. Computational fluid dynamic (CFD) models use this approach to analyse flows in urban areas. In numerous situation of planning and assessment and for the near-sources region, obstacle-resolved modeling approaches are required. Large Eddy Simulations (LES) models explicitly resolve the largest eddies, and parameterize the effect of the sub grid features. Reynolds Averaged Navier Stokes (RANS) models parameterize all the turbulence, and resolve only the mean motions. CFD (large eddy simulation [LES] or Reynolds-averaged Navier-Stokes [RANS]) model can be used to explicitly resolve the urban infrastructure. Galmarini et al., 2008 and Martilli and Santiago,2008, used CFD models to estimate spatial averages required for Urban Canopy Parameters. Using CFD models good agreement in overall wind flow was reported by field Gidhagen et al. (2004) .They also reported large differences in velocities and turbulence levels for identical inputs. 3.5 The Gaussian steady-state dispersion model The Gaussian Plume Model is one of the earliest models still widely used to calculate the maximum ground level impact of plumes and the distance of maximum impact from the source. These models are extensively used to assess the impacts of existing and proposed sources of air pollution on local and urban air quality. An advantage of Gaussian modeling systems is that they can treat a large number of emission sources, dispersion situations, and a receptor grid network, which is sufficiently dense spatially (of the order of tens of meters). Figure 5 shows a buoyant Gaussian air pollutant dispersion plume. The width of the plume is determined by σy and σz, which are defined by stability classes(Pasquill 1961; Gifford Jr. 1976) Fig. 5. A buoyant Gaussian air pollutant dispersion plume The assumptions of basic Gaussian diffusion equations are:
- 41. Urban Air Pollution Modeling 25 that atmospheric stability and all other meteorological parameters are uniform and constant throughout the layer into which the pollutants are discharged, and in particular that wind speed and direction are uniform and constant in the domain; that turbulent diffusion is a random activity and therefore the dilution of the pollutant can be described in both horizontal and vertical directions by the Gaussian or normal distribution; that the pollutant is released at a height above the ground that is given by the physical stack height and the rise of the plume due to its momentum and buoyancy (together forming the effective stack height); that the degree of dilution is inversely proportional to the wind speed; that pollutant material reaching the ground level is reflected back into the atmosphere; that the pollutant is conservative, i.e., not undergoing any chemical reactions, transformation or decay. The spatial dynamics of pollution dispersion is described by the following type of equation in a Gaussian model: 2 2 2 2 2 2 ( , , ; ) 2 exp exp exp 2 2 2 y z y z z Q C x y z He u z He z He y (2) Where C(x, y, z) : pollutant concentration at. point ( x, y, z ); U: wind speed (in the x "downwind" direction, m/s) Σ: represents the standard deviation of the concentration in the x and y direction, i.e., in the wind direction and cross-wind, in meters; Q: is the emission strength (g/s) He: is the effective stack height, see below. From the above equation, the concentration in any point ( x, y, z ) in the model domain, from a constant emission rate source, in steady state can be calculated. Plume rise equations have been developed by Briggs (1975). The effective stack height (physical stack height plus plume rise) depends on exit velocity of gas, stack diameter, average ambient velocity, stack gas temperature and stability of atmosphere 3 8 1 4 15 , 1.4 , 3600 P G e H H d QC T H H H H Q Q dz (3) Where H: height of stack TG : Temperature of exit gas Q: Volume of exit gas dθ/dz : Temperature Gradient ρ: Density of exit gas CP: Specific heat at constant pressure Some major air pollution dispersion models in current use
- 42. Air Quality - Models and Applications 26 ADMS 3: Developed in the United Kingdom (www.cerc.co.uk) AERMOD: Developed in the United States , (www.epa.gov/scram001/dispersion_prefrec.htm) AUSPLUME: Developed in Australia, (http://www.epa.vic.gov.au/air/epa) CALPUFF: Developed in the United States , (www.src.com/calpuff/calpuff1.htm) DISPERSION2:Developed in Sweden ,( www.smhi.se/foretag/m/dispersion_eng.htm) ISC3: Developed in the United States, (www.epa.gov/ttn/scram/dispersion_alt.htm) LADM: Developed in Australia, (Physick, W.L,et al, 1994 ) NAME: Developed in the United Kingdom,(www.metoffice.gov.uk/research/modelling-systems/dispersion-model) MERCURE: Developed in France, (www.edf.com) RIMPUFF: Developed in Denmark, (http://www.risoe.dtu.dk) AQI of ambient air Description of air quality Below 20 Excellent Between 20 and 39 Good Between 40 and 59 Fair Between 60 and 79 Poor Between 80 and 99 Bad Beyond 100 Dangerous Fig. 6. Air Quality Index of an Industrial Area: Orissa, India 8 regional air quality modeling leading to setting up of air quality index for an industrial area in India is given in Fig 2. This study has resulted in estimating the air assimilative capacity of the region and delineating developmental plans accordingly
- 43. Urban Air Pollution Modeling 27 3.6 Urban pollution and climate integrated modeling Integrated air quality modelling systems are tools that help in understanding impacts from aerosols and gas-phase compounds emitted from urban sources on the urban, regional, and global climate. Piringer et al., 2007 have demonstrated that urban features essentially influence atmospheric flow and microclimate, strongly enhance atmospheric turbulence, and modify turbulent transport, dispersion, and deposition of atmospheric pollutants. Numerical weather prediction (NWP) models with increased resolution helps to visualize a more realistic reproduction of urban air flows and air pollution processes. Integrated models thus link urban air pollution, tropospheric chemistry, and climate. Integration time required is ≥ 10 years for tropospheric chemistry studies in order to consider CH4 and O3 simulation and aerosol forcing assessment. Tropospheric chemistry and climate interaction studies extend the integration time to ≥ 100 years. Urban air quality and population exposure in the context of global to regional to urban transport and climate change is proposed to be assessed by integrating urbanized NWP and Atmospheric Chemistry (ACT) models (Baklanov et al., 2008; Korsholm et al., 2008). A. A. Baklanov and R. B. Nuterman (2009) sugested a multi-scale modelling system which comprised of downscaling from regional to city-scale with the Environment –HIgh Resolution Limited Area Model (Enviro-HIRLAM) and to micro-scale with the obstacle- resolved Microscale Model for Urban Environment (M2UE). Meteorology governs the transport and transformations of anthropogenic and biogenic pollutants, drives urban air quality and emergency preparedness models; meteorological and pollution components have complex and combined effects on human health (e.g., hot spots, heat stresses); and pollutants, especially urban aerosols, influence climate forcing and meteorological events (precipitation, thunderstorms, etc.), thus this approach is closer to real life scenario. Examples of integrated models are Enviro-HIRLAM: Baklanov and Korsholm, 2007, WRF-Chem: Grell et al., 2005; EMS-FUMAPEX: Forecasting Urban Meteorology, Air Pollution and Population Exposure; CFD (large eddy simulation [LES] or Reynolds-averaged Navier-Stokes [RANS]) models: Galmarini et al., 2008 and Martilli and Santiago., 2008; MIT Integrated Global System Model Version 2 (IGSM2): A.P. Sokolov, C.A. Schlosser, S. Dutkiewicz, S. Paltsev, D.W. Kicklighter,H.D. Jacoby, R.G. Prinn, C.E. Forest, J. Reilly, C. Wang, B. Felzer,M.C. Sarofim, J. Scott, P.H. Stone, J.M. Melillo and J. Cohen., 2005; US EPA and NCAR communities for MM5 (Dupont et al., 2004; Bornstein et al., 2006; Taha et al., 2008), WRF models (Chen et al., 2006); THOR - an Integrated Air Pollution Forecasting and Scenario Management System: National Environmental Research Institute (NERI), Denmark. The outline of overall methodology of FUMAPEX and MIT interactive chemistry model is shown in Figure 6 and 7. Schematic of couplings between atmospheric model and the land model components of the MIT IGSM2 is given in Figure 8. Need of integrated models All of these models have uncertainties associated with them. Chemical transport models, such as Gaussian plume models and gridded photochemical models, begin with pollutant emissions estimates and meteorological observations and use chemical and physical principles to predict ambient pollutant concentrations. Since these models require temporally and spatially resolved data and can be computationally intensive, they can only be used for well-characterized regions and over select time periods. Eulerian grid models are not suitable to assess individual source impacts, unless the emissions from the individual source are a significant fraction of the domain total emissions. This limitation
- 44. Air Quality - Models and Applications 28 Fig. 7. General scheme of the FUMAPEX urban module for NWP models. Atmospheric Chemistry model 25 Chemicals 4 Aerosol groups Urban Air Pollution Model Natural Emmision Model Terristrial Ecosystem Model Concentrations Winds, T, H2O Precipitation Climate Model MIT 2DLO NCAR CCM/CSM MIT AIM/O GCM Fig. 8. Overall Scheme MIT Interactive Chemistry-Climate Model
- 45. Urban Air Pollution Modeling 29 Fig. 9. Schematic of coupling between the atmospheric model (which also includes linkages to the air chemistry and ocean models) and the land model components of the IGSM2, also shown are the linkages between the biogeophysical (CLM) and biogeochemical (TEM) subcomponents. All green shaded boxes indicate fluxes/storage that are explicitly calculated/tracked by this Global Land System (GLS). The blue shaded boxes indicate those quantities that are calculated by the atmospheric model of the IGSM2. arises from the assumption that emissions are uniformly mixed within the grid cell, and thus do not properly address the initial growth and dispersion of the pollutants. Lagrangian plume and puff models account for chemical processes by simple linear transformations in time. These models can track individual source impacts, thus enabling user to outline source specific air pollution control strategies. Considerable differences are observed when concentrations are compared in time and space because of uncertainties in the characterization of the direction of transport that are of the order of the actual plume width. The observed and simulated concentrations for fixed receptors, give estimates of maximum concentration values within a factor of two or three of those observed. These differences are an order of magnitude larger than those observed for estimates of secondary pollutants. Both Eulerian and Lagrangian, models are not suitable to handle inert pollutants and secondary pollutants whose concentrations depend on reaction rates and are photochemical in nature. Receptor models, such as Positive Matrix Factorization and Chemical Mass Balance (CMB), source apportionment addresses the problem by statistical inference of source contributions to total pollution from observations of ambient air chemical composition. Mass balance methods of source apportionment use linear models with chemical composition vectors of sources as covariates. Knowledge of meteorological variables is not required but may be
- 46. Air Quality - Models and Applications 30 used to refine the analysis. Knowledge of emission sources is useful for the interpretation of results from statistical-based receptor models and is required by receptor models that use a mass balance approach. Less data and computational resource requirement by Receptor models as compared to chemical transport models, make them more convenient tool for evaluation of ambient pollutant concentrations and pollutant emission inventory. However, their utility for reactive air pollutants is uncertain and questionable. The disadvantage of CMB model arises from its assumptions. such as constant compositions of source emissions over the period of ambient and source sampling; linear additive and unreactive chemical species; identification of all sources contributing to the receptor and knowledge of their emission profile, linearly independent emission profiles. The urban air quality models requires - Good net work ambient air concentrations of pollutants of concern: Geography of the urbanarea, constructed clusters, road network, location of bluidings etc play a major role in dispersion of pollutants. Thus to understand the ambient status of pollutants it is necessary to have sufficient number of monitoring locations to cover the urban sprawl of concern. - Micro metereology data: The wind patterns, temperature, humidity alter in urban areas according to anthropogenic activity and architecture - Bluilding details: To account for the effect of anthropogenic architecture falling in path of plume, its geometry is required to be known. - Knowledge of all sources: All sources and their emission profiles are required to be known to plan for further development in urban area and control of pollutant emission - Atmospheric Chemistry: All transformations of emitted chemical species, their reaction rates pathways must be known to account for observed concentration of pollutants. - Healthy Impacts: Models need to incorporate health effect of pollutants None of the models available can handle all the requirements of urban air quality management. Each one focuses of one aspect and thus coupling of different models are required. 4. Further issues to be addressed COST an intergovernmental framework for European Cooperation in Science and Technology, Europe, addressed issues related to urban air quality models in its action programmes. Cost 728 focussed on enhancing mesoscale meteorological Modeling capabilities for air pollution and Dispersion applications under larger programme of urbanization of meteorological and air quality models. The issues identified for improvements to the state of urbanization of models can be summarized as Systematic evaluation of urban land surface schemes Increasing the range of variables observed to ensure as complete a range of evaluation as possible evaluation over a broad spectrum of conditions (meteorological, morphological, geographical setting, etc. Testbeds and observatories with different objectives and dataset richness. A deeper understanding of urban PBL dynamics i.e development of long-term urban test beds in a variety of geographic regions (e.g., inland, coastal, complex terrain) and in
- 47. Urban Air Pollution Modeling 31 many climate regimes, with a variety of urban core types (e.g., deep versus shallow, homogeneous versus heterogeneous). A framework to address conceptual issue of evaluation of model prediction of the flow within the canopy User friendly and multifaceted urban databases and enabling technology Developing core capabilities for advancing urban modeling and boundary layer research An open database to address issues of availability and sources of high-resolution data sets easily to all with mechanism for its maintenance, upgrading, updating, and archiving. www.unep.org/urban_environment/pdfs/handbook.pdf 5. References Baklanov, A., and U. Korsholm, 2007: On-line integrated meteorological and chemical transport modelling: advantages and prospective. In: Preprints ITM 2007: 29th NATO/SPS International Technical Meeting on Air Pollution. Modelling and its Application, 24-28.09.2007, University of Aveiro, Portugal, pp. 21-34. Baklanov, A., Korsholm, U., Mahura, A., Petersen, C., and Gross, A.: ENVIRO-HIRLAM: on- line coupled modelling of urban meteorology and air pollution, Adv. Sci. Res., pp. 2, 41–46, 2008. Baklanov A. A. and Nuterman. R. B., Multi-scale atmospheric environment modeling for urban areas, Advances in Science and Research, 3, 53–57, 2009 Bornstein, R., R. Balmori, H. Taha, D. Byun, B. Cheng, J. Nielsen-Gammon, S. Burian, S. Stetson, M. Estes, D.Nowak, and P. Smith, 2006: Modeling the effects of land-use land cover modifications on the urban heat island phenomena in Houston, Texas. SJSU Final Report to Houston Advanced Research Center for Project No. R-04-0055, pp. 127. Briggs, G. A. (1975). Plume Rise Predictions. Lectures on Air Pollution and Environmental Impact Analysis. D. A. Haugen. Boston, MA, American Meteorology Society: pp.59- 111. Carmichael, G.R., and Peters, L.K., 1979, Numerical simulation of the regional transport of SO2 and sulfate in the eastern United States, Proc. 4 th Symp. on turbulence, diffusion and air pollution, AMS 337. Chen, F., M. Tewari, H. Kusaka and T. L. Warner, 2006: Current status of urban modeling in the community Weather Research and Forecast (WRF) model. Sixth AMS Symposium on the Urban Environment,Atlanta GA, January 2006. Dupont, S., T.L. Otte, and J.K.S. Ching, 2004: Simulation of meteorological fields within and above urban and rural canopies with a mesoscale model (MM5) Boundary-Layer Meteor., 2004, 113:111-158. Egan, B.A., Rao, K.S., and Bass, A., 1976, A three dimensional advective-diffusive model for long-range sulfate transport and transformation 7 th ITM, 697, Airlie House. Friedlander, S.K. (1973). Chemical element balances and identification of air pollution sources, Environmental Science and Technology 7, 235–240 Galmarini, S., J.-F. Vinuesa, and A. Martilli, 2008: Relating small-scale emission and concentration variability in air quality models, Chapter 1.2, URBANIZATION OF
- 48. Air Quality - Models and Applications 32 METEOROLOGICAL AND AIR QUALITY MODELS, COST Action 728, 15 May 2008, http://www.cost728.org Garratt, J. R. (1978): Transfer Characteristics for a Heterogeneous Surface of Large Aerodynamic Roughness. Quart. J. R. Meteorol. Soc., 104: pp.491-502. Garratt J.R. (1980) Surface influence upon vertical profiles in the nocturnal boundary layer Boundary-Layer Meteorology Volume 26, Number 1, pp. 69-80, DOI: 10.1007/BF00164331 Garratt J.R. 1994. Review: the atmospheric boundary layer, Earth-Science Reviews, 37, pp.89- 134. Gidhagen, L., C. Johansson, J. Langner and G. Olivares. (2004). "Simulation of NOx and ultrafine particles in a street canyon in Stockholm, Sweden." Atmospheric Environment 38(14): 2029-2044. Gifford Jr., F. A. (1976). "Consequences of Effluent Releases." Nuclear Safety 17(1): 68-86. Grell, G. A., S. E. Peckham, R. Schmitz, S. A. McKeen, G. Frost, W. C. Skamarock, and B. Eder, 2005: Fully coupled “online“ chemistry within the WRF model, Atmos. Environ., 39(37), 6957–6975. Henry, R.C., 2003. Multivariate receptor modeling by N-dimensional edge detection. Chemometrics and Intelligent Laboratory Systems pp.65, 179-189. Holmes, N. S., L. Morawska, et al. (2005). "Spatial distribution of submicrometre particles and CO in an urban microscale environment." Atmospheric Environment 39(22): 3977-3988. Holmes, N. S., Morawska, L. (2006) A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available. Atmospheric Environment, pp. 40, 5902–5928. Hopke, P.K. (1985). Receptor Modeling in Environmental Chemistry, Wiley, New York. Hopke, P.K., ed. (1991). Receptor Modeling for Air Quality Management, Elsevier, Amsterdam. Hosker, G. L. 1985 Clin. Phys. Physiol. Meas. 6 131 A uni-directional urethral force gauge Pavageau, M., Rafailidis, S. and Schatzmann, M. (2001) 'A comprehensive experimental databank for the verification of urban car emission dispersion models', International Journal of Environment and Pollution, 15, pp. 417-425 Jacoby, H.D., Prinn, R.G., Forest, C.E. , Reilly, J., Wang, C., Felzer, B., Sarofim, M.C. , Scott, J., Stone, P.H., Melillo J.M. and Cohen, J., Report No. 124, July 2005 Korsholm, U. S., Baklanov, A., Gross, A., and Sørensen, J. H.: On the importance of the meteorological coupling interval in dispersion modeling during ETEX-1, Atmos. Environ., doi:10.1016/j.atmosenv.2008.11.017, 2008. Martilli, A., and J.L. Santiago, 2008: How to use computational fluid dynamics models for urban canopy parameterizations. Chapter 2.1, URBANIZATION OF METEOROLOGICAL AND AIR QUALITY MODELS, COST Action 728, 15 May 2008, http://www.cost728.org Miller, S.L., Anderson, M.J., Daly, E.P., Milford, J.B., 2002. Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data. Atmospheric Environment pp. 36, 3629-3641. Mumovic, D., Crowther, J., Stevanovic, Z. (2006). Integrated Air Quality Modelling for a Designated Air Quality Management Area in Glasgow, Building and Environment, 41(12): 1703-1712. doi:10.1016/j.buildenv.2005.07.006 Mumovic, D., Crowther, J. (2006). Assessing Urban Air Quality using Microscale CFD Modeling, PHOENICS CFD Newsletter Spring 2006, 10-10
- 49. Urban Air Pollution Modeling 33 Oke, T. R., 1988: "The urban energy balance," Progress in Physical Geography, vol.12, pp. 471- 508. Paatero, P., 2004. User’s Guide for Positive Matrix Factorization Programs PMF2 and PMF3, Part 1: Tutorial. University of Helsinki, Finland. Pasquill, F. (1961). "The Estimation of the Dispersion of Windborne Material." Meteorology Magazine 90(1063): pp. 33-40. Pavageau, M., Rafailidis, S. and Schatzmann, M. (2001) 'A comprehensive experimental databank for the verification of urban car emission dispersion models', International Journal of Environment and Pollution, 15, 417-425 Physick. W.L., Noonan. J.A., MacGregor, J.L. (1994). LADM: a Lagrangian Atmospheric Dispersion Model [Monograph], CSIRO Division of Atmospheric Research technical paper. Piringer M., Petz E., Groehn I., Schauberger G. (2007) A sensitivity study of separation distances calculated with the Austrian Odour Dispersion Model (AODM), Atmospheric Environment, pp.41, 725-1735. Raupach, M.R., Thom, A.S., and Edwards, I.: 1980, “A Wind Tunnel Study of Turbulent Flow Close to Regularly Arrayed Rough Surfaces”, Boundary-Layer Meteorol. 18, 373-397. Reynolds, S., Roth, P., and Seinfeld, J., 1973, Mathematical modeling of photochemical air pollution Atm.Env 7. Rotach, M. W.: 1993.,Turbulence close to a rough urban surface, Part II: Variances and gradients, Boundary Layer Meteorol., pp. 66, 75–92, Rotach M W (1995) ‘Profiles of turbulence statistics in and above an urban street canyon’, Atmospheric Environment, Vol 29, pp. 1473-1486 Shir, C.C. and L.J. Shieh, 1974, A generalized urban air pollution model and its application to the study of SO2-distribution in the St. Louis Metropolitan area, J. Appl. Met. 19, 185-204. 2002: South Asia Urban Air Quality Management Briefing. Note No. 4., What Do We Know About Air Pollution?—India Case Study, http://www.worldbank.org/sarurbanair 2002: South Asia Urban Air Quality Management Briefing Note No. 5., Impact of Traffic Management, http://www.worldbank.org/sarurbanair. Srivastava, Anjali ; Sengupta B., Dutta S.A., ‘Source apportionment of ambient VOCs in Delhi City Science of The Total Environment’, Volume 343, Issues 1-3, 1 May2005,pp.207-220 Srivastava, Anjali ; ‘Source apportionment of ambient VOCS in Mumbai city Atmospheric Environment’ , Volume 38, Issuey39, December2004, pp. 6829-6843 Stanners, D., Bourdeau, P. (Eds.), 1995. Europe's Environment: The Dobris Assessment, European Environment Agency.Office for Publications of the European Communities, Luxemburg. Sokolov, A.P.,C.A. Schlosser, S. Dutkiewicz, S. Paltsev, D.W. Kicklighter, 2005 :The MIT Integrated Global System Model (IGSM),Version 2: Model Description and Baseline Evaluation, Taha, H., 2008: Sensitivity of the urbanized MM5 (uMM5) to perturbations in surface properties in Houston Texas. Boundary-Layer Meteorology, pp. 127: 193-218 Tennekes, H., 1973. A model for the dynamics of the inversion above a convective boundary layer. Journal of Atmospheric Science 30, pp.550–567
- 50. Air Quality - Models and Applications 34 THOR - an Integrated Air Pollution Forecasting and Scenario Management System. Available at thor.dmu.dk National Environmental Research Institute (NERI), Denmark Urban Environnent Management, Tool book United Nations Environment Programme, United Nations Human Settlements Programme. Available at www.unep.org/urban_environment/pdfs/handbook.pdf
- 51. 3 Artificial Neural Network Models for Prediction of Ozone Concentrations in Guadalajara, Mexico Ignacio García1, José G. Rodríguez2 and Yenisse M. Tenorio2 1Centro Mexicano para la Producción Más Limpia (IPN), Departamento de Posgrado, Barrio La Laguna, Col. Ticomán, Delegación Gustavo A. Madero, 2Escuela Superior de Ingeniería y Arquitectura (IPN-ESIA Zacatenco), Sección de Investigación y Posgrado, U. Prof. “Adolfo López Mateos”, Zacatenco, Del. Gustavo A. Madero, México, 1. Introduction Advances in mathematical models to describe the formation, emission, transport and disappearance of air pollutants have led to a greater understanding of the dynamics of these pollutants. However, the more complex the model, the more information is required for their application to have sufficient certainty that the results will have technical or scientific value (Russell & Dennis, 2000). These deterministic models require much information that is not always possible to obtain; the data available have not always resulted in successful outcomes upon application of the model (Roth, 1999), or the cost of obtaining reliable data can be prohibitive (Pun & Louis, 2000). There are other methods requiring less information that can be used to study air pollution in some areas. These methods generally make use of statistical techniques such as regression or other data-fitting methods using numerical techniques to establish the respective relationships between the various physicochemical parameters and variable of interest based on routinely-measured historical data. The main objectives of these methods include investigating and assessing trends in air quality, making environmental forecasts and increasing scientific understanding of the mechanisms that govern air quality (Thompson et al., 2001). Among the techniques being examined to relate air quality in a given area to measured physical and chemical parameters, the three that have been used most often are i) multivariate regression (Hubbard & Cobourne, 1998, Comrie & Diem, 1999 , Davis & Speakman, 1999; Draxler, 2000, Gardner & Dorling, 2000), ii) artificial neural networks (ANN) (Perez & Reyes, 2006; Brunelli et al., 2006; Thomas & Jacko, 2007; Grivas & Chaloulakou, 2005; Gardner & Dorling, 1999), and iii) time series and spectral analysis (Raga & Moyne, 1996, Chen et al., 1998; Milanchus et al., 1998, Salcedo et al., 1999, Sebald et al., 2000). Artificial neural networks have greater flexibility, efficiency and accuracy, since they have a large number of features similar to those of the brain; i.e., they are capable of learning from experience, of generalizing from previous cases to new cases, and of abstracting essential features from inputs containing irrelevant information; they use adaptive learning, one of
- 52. Air Quality - Models and Applications 36 the most attractive features of ANN, as well as the ability to learn to perform tasks based on training or initial experience. ANN do not need an algorithm to solve a problem because they can generate their own distribution of the weights of the links through learning and are easily inserted into the existing technology. Because of these characteristics, ANN generally has low computational requirements and their construction is less complex. The pollutant of interest in this study is tropospheric ozone, as it is the main component of a type of air pollution known as smog or photochemical smog. According to the National Ecology Institute (NEI), the Metropolitan Zone of Guadalajara, Mexico (GMA) is in second place in Mexico in exceeding the NOM-020-SSA1-1993 Mexican air pollution standard. Tropospheric ozone is one of the five major pollutants with harmful effects on human health, causing respiratory problems and ailments such as headaches, and eye irritation as well as affecting vegetation, metals and construction materials, dyes and pigments. 1.1 Tropospheric ozone formation Photochemical smog is formed through a photochemical process from a combination of gases in the troposphere, such as nitrogen oxides (NOX, i.e., NO and NO2), volatile organic compounds (VOCs) and carbon monoxide (CO), as has been documented (Seinfeld, 1978; Boubel, 1994 & Godish, 1991, as cited scientist in Comrie, 1997). The sequence of events begins in the early hours of the morning when a heavy emission of hydrocarbons (HC) and nitrogen monoxide (NO) is produced at the start of human activity in large cities (heaters are turned on, and traffic density increases). Nitric oxide (NO) is oxidized to nitrogen dioxide (NO2), increasing the concentration of the latter in the atmosphere. Higher concentrations of NO2 together with increasing solar radiation as the morning wears on starts the photolytic NO2 cycle, generating atomic oxygen which, as it is transformed into ozone, leads to an increase in the concentration of oxygen and hydrocarbon free radicals. These, when combined with significant amounts of NO, cause NO in the atmosphere to decrease. This impedes completion of the photolytic cycle, rapidly increasing the ozone (O3) concentration (Comrie, 1997). These relationships can be expressed conceptually; the polluted urban atmosphere contains approximately one hundred different hydrocarbons, olefins being the most reactive. The result of the atomic oxygen attack on the olefin produces two free radicals. In the case of propylene, the first stage of the reaction is the addition of oxygen to the double bond to give a reactive complex (1) 3 2 3 2 H C HC CH O H C HC CH O (1) which can break up in two different ways (reactions 2 and 3) 3 2 3 H C HC CH O H C HC CH O (2) 3 2 3 3 H C HC CH O H C H C C O (3) The more likely reaction is (2), since it implies less regrouping of the activated complex than (H). CHO and 3 CH CO radicals quickly form formaldehyde and acetaldehyde, respectively. Reactions (2) and (3) are the initial stages of a chain process 3 2 3 2 CH O CH O (4)
- 53. Artificial Neural Network Models for Prediction of Ozone Concentrations in Guadalajara, Mexico 37 3 2 3 2 CH O NO CH O NO (5) 3 2 2 CH O O HCHO HO (6) 2 2 HO NO OH NO (7) 3 6 3 2 2 C H OH CH CH H O (8) The chain reaction enables rapid oxidation of NO to NO2 by alkoxyl radicals ( RO ) and peroxyacyl ( 2 RO ) without the intervention of atomic oxygen and O3, which provides some explanation for the changes observed in the concentration of gaseous pollutants during the day. When atmospheric concentrations of hydrocarbons increase because of motor vehicle activity, the photolytic cycle of NO2 is disturbed and NO is oxidized to NO2 by the chain reaction involving the hydrocarbon radical (equations 2–8). As a result, the constant low O3 concentration found in the photolytic cycle of NO2 grows, and ozone is not consumed in the oxidation of NO to NO2 (Seinfeld, 1978). As the morning advances, solar radiation promotes the formation of photochemical oxidants, increasing their concentration in the atmosphere. When concentrations of precursors (NOX and HC) in the atmosphere are lowered, the formation of oxidants stops and their concentrations decrease as the day progresses. Hence, photochemical pollution in cities builds up mainly in the mornings. Due to industrial development in the GMA in recent years, there has been an urban–green– industrial zone imbalance, leading to the generation of various kinds of pollutants that alter the quality of the environment and exceed the assimilative capacity of the ecosystem. Given this situation, it is vital to have a mathematical model that correctly predicts ozone concentrations at any given time, as this will help determine preventive measures and/or corrective actions to prevent exposure to high ozone concentrations. These models are able to relate air quality to certain other specific parameters of the air shed, such as emission levels and weather conditions. 2. Data sources From an analysis of reports from 2002–2005, it was determined that the highest ozone concentrations were in the southern area of the GMA, so specific data for meteorological and chemical variables were obtained from the Miravalle weather station, located in the south. These are shown in Table 1. Year Station 2002 2003 2004 2005 Las Águilas 0.169 0.165 0.164 0.131 Atemajac 0.152 0.185 0.165 0.144 Centro 0.166 0.171 0.157 0.137 L. Dorada 0.225 0.195 0.197 0.215 Miravalle 0.232 0.225 0.226 0.154 Tlaquepaque 0.142 0.149 0.138 0.109 Vallarta 0.171 0.217 0.175 0.096 Table 1. Peak ozone concentrations (ppm) for the years 2002, 2003, 2004 and 2005 (Semades, 2005)
- 54. Air Quality - Models and Applications 38 2.1 Meteorological and chemical variables Meteorological data for the period April 1999 to June 2005 were obtained from the Mexican National Weather Service (MNWS). These data consist of averages over time intervals ranging from 0 to 23 hrs. The meteorological variables are Wind Direction (average and maximum average) (degrees), wind speed (average and maximum average) (km/h) Average Temperature (°C), Relative Humidity (%). Barometric Pressure (mbar), Precipitation (mm) and Solar Radiation (W/m2). The data were obtained from the Chapala station, which belongs to the Automatic Monitoring Stations (AMS) system. Data on the following chemical variables were provided by the National Ecology Institute (NEI) for the Miravalle station; Ozone, Nitrogen Oxides— NOX and NO2, as shown in Figure 1. Fig. 1. Distribution of GMA Atmospheric Monitoring Automatic Network (Semarnat & INE, 2009). 3. Selection of meteorological and chemical variables Meteorological and chemical variables used to carry out ground-level ozone forecasts were selected based on existing knowledge from the scientific literature and an analysis of correlations between different variables, and on availability of data from monitoring stations. 3.1 Analysis of meteorological variables 3.1.1 Wind speed Atmospheric movements of the air (i.e. winds) are responsible for the spread of high concentrations of pollutants (in this case the O3 and its precursors) through the atmosphere, but this may or not occur quickly, because if the winds are calm, i.e., the wind speed is low and the topography traps the air mass, pollutants can not disperse. More pollutants continue to accumulate and their concentration can reach very high levels. In contrast, if wind speeds are high, the pollutants tend to disperse quickly (Melas et al., 2000).
- 55. Another Random Scribd Document with Unrelated Content
- 56. [189] after—and I just want to say, go slow. That’s all—go slow.” “All right, Salt. Will you send Miss Austin down here— also, I must interview her alone.” “Yes—I understand. But don’t be led away now, by circumstantial evidence. You know yourself, it isn’t always dependable.” “Go along, Salt, don’t try to teach me my business. Have you talked to the girl?” “Not a word. My wife has, but she didn’t learn much.” Adams went away, and in a few moments Anita Austin came into the room. A first glance showed Cray’s experienced eye that the girl was what he called a siren. Her oval, olive face was sad and sweet. The pale cheeks were not touched up with artificial color, and the scarlet lips were, even to his close scrutiny, also devoid of applied art. She wore a smart little gown of black taffeta, with crisp, chic frills of finely plaited white organdie. Whether this was meant as mourning wear or not, Cray could not determine. The frock was fashionably short, showing thin silk stockings and black suede ties. But Miss Mystery seemed wholly unconscious of her clothes, and her great dark eyes were full of wondering
- 57. [190] inquiry as she looked at the attorney, and then a little diffidently offered a greeting hand. The little brown paw touched Cray’s with a pathetic, hopeful clasp, and he looked up quickly to find himself looking into a pair of hopeful eyes, that, without a word, expressed confidence and trust. He shrugged his shoulders a trifle and secretly admonished himself to keep a tight rein on his sympathy. Then relinquishing the lingering hand, he sat down opposite the chair she had chosen to occupy. “Miss Austin,” he began, and paused, for the first time in his life uncertain what tack to take. “Yes,” she said, as the pause grew longer, and her soft, cultured voice helped him not at all. How could he say to this lovely small person that he suspected her of wrong doing? “Go on, Mr. Cray,” she directed him, meantime looking at him with eyes full of a haunting fear, “what is it?” Cray had a sudden, insane feeling that he would give all he was worth for the pleasure of removing that look of fear, then commanding himself to behave, he said, “I am sorry, Miss Austin, but I must ask you some unpleasant questions.” “That’s what I’m here for,” she said, with the ghost of a smile on her curved red lips, and, smoothing down her
- 58. [191] taffeta lap, she demurely clasped her sensitive little hands and waited. Those hands bothered Cray. Though they lay quietly, he felt that at his speech they would flutter in anxiety— even in fear, and he was loath to disturb them. Because of this hesitancy, he plunged in more abruptly than he meant to do. “Where do you come from, Miss Austin?” “New York City,” she said, a brighter look coming to her face, as if she thought the ordeal would not be so terrible after all. “What address there?” “One West Sixty-seventh Street.” “You told some one else the Hotel Plaza.” “Yes; I have lived at both addresses. Why?” The “why” was disconcerting. After all, Cray thought, he was not a census taker. He gave up getting past history, and said, briefly, “Were you at Doctor Waring’s house Sunday evening?” “Not evening,” she returned, looking thoughtful. “I was there Sunday afternoon.” “And went back again, late in the evening—to see Doctor Waring, in his study.”
- 59. [192] [193] “Why do you say that?” she asked quietly, but a small red spot showed on either olive cheek. “Because I must. How well do you—did you know the Doctor?” “Know Doctor Waring? Not at all. I never saw him in my life until I came here to Corinth.” “You are sure of that?” “Almost sure—oh, why, yes—that is, I am quite sure.” “Yet you went over there Sunday evening, and came back to this house in possession of Doctor Waring’s valuable pin, and a large sum of money.” “Oh, no, Mr. Cray, I didn’t do any such thing!” “Then can you explain your possession of those articles?” “You mean, I suppose the roll of bills that Miss Bascom put into my top bureau drawer?” “Miss Bascom put in the drawer!” “Yes—that is, she must have done so, or—how else could they have been found there? You know yourself, now, don’t you, Mr. Cray, that I’m not a burglar—or a bandit or a sneak thief? You know I never went in to Doctor Waring’s study and took those things! So, as I say, isn’t it the only plausible theory, that Miss Bascom, who found the valuables so readily, first put them there herself?”
- 60. [194] CHAPTER XI THE SPINSTER’S EVIDENCE “That matter can easily be settled,” Cray said, and going to the door he asked Mrs. Adams to send Miss Bascom to them. With an important air the spinster entered the room. Holding herself very erect and even drawing aside her skirts as she passed Miss Austin, she took a seat on the other side of the room. “Now, Miss Bascom,” Cray began at once, “what made you think of looking in this lady’s bureau drawer for that money?” “I didn’t look for it, Mr. Cray. I merely felt that she had done wrong and I thought perhaps some evidence would be hidden away in her room. And a top drawer is the place a woman oftenest hides things.” Cray gave a short laugh. “Rather clever of you, I admit. But Miss Austin says she did not put that money there, herself—that it was a plant.” “A plant?” Miss Bascom looked puzzled at the word.
- 61. [195] “Yes; she thinks some in-disposed person put it there to implicate her, falsely.” “Oh, I see. Well, Mr. Cray, let her say who did it, and who could have got that money to do it with.” The hard old face took on a look that was almost malignant in its accusation, and little Anita Austin gave a low cry as she saw it, and hid her face in her hands. “Take her away,” she moaned, “oh, take that woman away.” “You hear her,” Miss Bascom went on, unrelentingly. “Now, Mr. Cray, I’m a bit of a detective myself, and while you’ve been down here talking to Miss Mystery, I’ve been searching her room more carefully, and I’ve found a few more things, of which I should like to tell you.” Cray was nonplused. His sympathies were all with the poor little girl, who, clinging to the arms of her chair, seemed about to go to pieces, nervously, but was bravely holding on to herself. Yet, if the Bascom woman was telling the truth, he must beware of the “poor little girl.” “I’m not sure you’re within your rights, Miss Bascom,” he began, but he was interrupted with: “Rights! Indeed, the rights of this matter are above your jurisdiction! The blood of John Waring calls from the ground! I am the instrument of justice that has been chosen by an over-ruling Providence to discover the criminal. She sits before you! That girl—that mysterious wicked girl is both thief and murderess!”
- 62. [196] “Oh, no!” Anita cried, putting up her arm as if to ward off a physical blow. Then she suddenly became quiet—almost rigid in her composure. “That is a grave accusation, Miss Bascom,” she said, “you must prove it or retract it.” Cray stared at the girl in astonishment. Her agonized cry had been human, feminine, natural—but this sudden change to stony calm, to icy hauteur was amazing—and, to his mind, incriminating. Miss Bascom, however, was in no way daunted. “Prove it I will!” she said, sternly. “In another drawer, Mr. Cray, I found the rolls of silver coin—exactly one hundred dollars worth—that we have been told were in the desk with the roll of bills. The ruby pin, you know about. And so, these thefts are proved. Now, as to the murder—I admit, it seems impossible that a girl should commit the awful crime—but I do say that I have found the weapon, with which it was done, hidden in Miss Austin’s room.” Again that short, low cry—more like a hurt animal than a human being. And then, Anita Austin, the girl of mystery fell back into the depths of her chair, and closed her eyes. “You needn’t faint—or pretend to,” admonished Miss Bascom, brutally; “you’re caught red-handed, and you know it, and you may as well give up.” “I didn’t—I didn’t—” came in low moans, but the girl’s bravery had deserted her. Limp and despairing, she
- 63. [197] turned her great eyes toward Cray for help. With an effort, he looked away from her pleading face, and said: “What is the weapon? Where did you find it?” “It is a stiletto—an embroidery stiletto—and I found it tucked down in the crevice between the back and seat of a stuffed chair in Miss Austin’s room. Did you put it there?” She turned on the girl and fired the question at her with intentional suddenness, and though Anita uttered a scared, “No,” it was a palpable untruth. “She did,” Miss Bascom went on. “You can see for yourself, Mr. Cray, she is lying.” “But even if she is, Miss Bascom, I must ask you to cease torturing her! I can’t stand for such cruelty!” Cray’s manhood revolted at the methods of the older woman who was causing such anguish to the poor child she accused. “You are not a legal inquisitor, Miss Bascom,” he went on; “it is for me to establish the truth or falsity of your suspicions.” “Yes, you! You’re like all the other men! If a girl is pretty and alluring, you would believe her statement that white is black!” “I believe no statements that cannot be proved to my satisfaction. Miss Austin, do you own an embroidery stiletto?”
- 64. [198] [199] “Yes,” was the hesitating answer, and the dark eyes swept him a beseeching glance that made Miss Bascom fairly snort with scorn. “Where is it?” “I—I fear I must admit that it is just where Miss Bascom says it is—unless she has removed it. Tell me, Mr. Cray,” and Miss Mystery suddenly resumed her most independent air, “must I submit to this? I thought accused people were entitled to a—oh, you know, counsel—a lawyer, or somebody to take care of them.” “Wait, Miss Austin. You’re not accused yet—that is, not by legal authority.” “Oh, am I not? Then—” and she gave Miss Bascom a glance of unutterable scorn, “I have nothing to say.” “Nothing to say!” the spinster almost shrieked. “Nothing to say! Of course she hasn’t! She kills a man, takes his valuables, and then declares she has nothing to say.” “Now, now, Miss Bascom, be careful! Why did you put your stiletto in such a place, Miss Austin?” “I don’t know.” The dark eyes gave him a gaze of childlike innocence, and Cray couldn’t decide whether he was looking at a deep-dyed criminal or a helpless victim of unjust suspicion. “And where did you get the money and the ruby pin?” “I don’t know—I mean I don’t know how they got in my room. This lady says she found them there—that’s all I
- 65. [200] know about them.” An indifferent shrug of the slim shoulders seemed to imply that was all Miss Mystery cared, either, and Cray asked: “Then, if the valuables—the pin and the money are not yours, you are, of course, ready to relinquish possession of them.” “Of course I am not! Since I am accused of stealing them, I propose to retain possession until that accusation is proved or disproved! Perhaps Miss Bascom wishes to take them herself.” “You know, Miss Austin,” Mr. Cray spoke very gravely, “you are making a mistake in treating this matter flippantly. You are in danger—real danger, and you must be careful what you say. Do you want a lawyer?” “I don’t know,” the girl suddenly looked helpless. “Do you think I ought to have one?” “Have you funds?” “Yes. I am not a rich girl—but, neither am I poor. However, I think I shall ask advice of some one before I decide upon any course.” “Of whom? Perhaps no one can advise you better than I can.” “What is your advice, Mr. Cray?” The sweet face looked at him hopefully, the curved red lips quivered a little as the speaker added, “I am very alone.”
- 66. [201] Again Miss Bascom sniffed. Unattractive, herself, she resented with a sort of angry jealousy the appealing effect this girl had on men. She knew intuitively that Cray would sympathize with and pity the lonely girl. “My advice is, Miss Austin, first, that you dispel this mystery that seems to surround you. Tell frankly who you are, what is your errand in Corinth, how you came into possession of Doctor Waring’s ruby, and why you hid your stiletto, if it is merely one of your sewing implements.” Miss Mystery hesitated a moment, and then said, quietly: “Your advice is good, Mr. Cray. But, unfortunately, I cannot follow it. However, I am willing to state, upon oath, that I did not kill Doctor Waring with that stiletto.” “I’m afraid your oath will be doubted,” Miss Bascom intervened sharply. “And, too, Mr. Cray, even if this girl did not strike the fatal blow, she well knows who did! She is in league with the Japanese, Nogi. That I am sure of!” “Nogi!” exclaimed Anita. “Yes, Nogi,” Miss Bascom went on, positively. “You came here only a day or two after he did. You have a Japanese kimono, and several Japanese ornaments adorn your room. You went to the Waring house that night, Nogi let you in and out, and though the Japanese doubtless committed the murder, you stole the money and the ruby, and then, your partner in crime departed for parts unknown.”
- 67. [202] Miss Bascom sat back in her chair with a look of triumph on her plain, gaunt face. Clearly, she was rejoiced at her denunciation of the girl before her, and pleased at the irrefutable theory she had promulgated. “And how did Miss Austin or the Jap, either, leave the room locked on the inside?” propounded Cray, his own opinions already swayed by the arraignment. “That,” said Miss Bascom, with an air of finality, “I can’t explain definitely, but I am sure it was an example of Japanese jugglery. When you remember the tales of how the Japanese can do seemingly impossible tricks, can swallow swords and get out of locked handcuffs, it is quite within the realm of possibility that one could lock a door behind him, and give it the appearance of having been locked from the inside.” Now, Cray had already concluded that the door had been cleverly locked by some one, but he hadn’t before thought of the cleverness of the Japanese. He rose almost abruptly, and said, “I must look into some of these matters. Miss Austin, you need not attempt to leave town, for you will not be able to do so.” “I most certainly shall not attempt to leave—as you express it—if I am asked not to. But, I may say, that when I am entirely at liberty to do so, I propose to go away from Corinth.” Her dignity gave no effect of a person afraid or alarmed for her own safety, merely a courteous recognition of Cray’s attitude and a frank statement of her own intentions.
- 68. [203] [204] Miss Bascom sniffed and said: “Don’t worry, Mr. Cray. I’ll see to it, that this young woman does not succeed in evading justice, if she tries to do so.” At which Miss Mystery gave her a smile that was so patronizing, even amused, that the spinster was more irate than ever. “And, now, Miss Austin,” the attorney said, “I’ll take your finger prints, please, as they may be useful in proving what you did not do.” He smiled a little as the girl readily enough gave her consent to the procedure. “And,” he went on, more gravely, “I will ask you for one of your shoes—one that you wore on Sunday.” Surprised into a glance of dismay, Miss Mystery rose without a word and went upstairs for the shoe. She returned with the dainty, pretty thing, and merely observed, “I’d like to have it back, when you are through with it.” Putting the shoe in his overcoat pocket, Cray went away. “Miss Bascom,” Anita said, turning to her enemy, “may you never want a friend as much as I do now.” “The nerve of her!” Liza Bascom muttered to herself, as Miss Mystery went upstairs to her own room.
- 69. [205] “There’s a very deep mystery here!” Cray soliloquized, as he returned to the Waring house. “But I’m getting light on it.” Cray was far from lacking in ingenuity, and he proceeded at once to compare the finger prints he had of Anita Austin with the prints on the small black-framed chair that had been found drawn up to the desk chair of John Waring. They were identical and Cray mused over the fact. “That girl was here that night,” he decided; “there’s no gainsaying that.” He called the butler to him. “Ito,” he began, “did you let in any one late Sunday night—after you came home?” “No, sir,” the imperturbable Jap declared, thinking the question foolish, as all the inquirers knew the details of his Sunday evening movements. “Do you remember seeing this chair, Monday morning?” “Distinctly. I saw Mr. Lockwood smoothing its back.” “Smoothing its back! What do you mean?” “I looked through from the dining-room window, to see if Mr. Lockwood was coming to breakfast, and I perceived him carefully smoothing the plush of the little chair, sir.” Cray meditated. Here was a point of evidence. Lockwood was not the sort to absent-mindedly paw over a chair back. He was doing it on purpose. For what
- 70. [206] reason? What reason could be, save to erase some evidence? Cray examined the chair. It had a frame of shiny black wood, while seat and back were covered with a dark plush of a fine soft quality. Cray drew his fingers across the back. They left a distinct trail of furrows in the fabric. Ito, watching, nodded his head, gravely. “Not finger-prints,” Cray said to himself—“but, maybe finger-marks. Whose?” “You surely saw this, Ito?” “Yes, sir; and Miss Peyton also saw. She was then in the doorway, asking Mr. Lockwood to come to breakfast.” Cray went in search of Helen and put the question to her suddenly. “What was Gordon Lockwood doing, when you went to call him to breakfast, Monday morning?” “He was—I don’t remember.” “Speak the truth—or it may be mean trouble for you and him, too.” “He was—he seemed to be dusting off a chair.” “With a duster?” “No; just passing over it with his hand.” “That isn’t dusting it.”
- 71. [207] “Well, I don’t know what you call it! Perhaps he was merely pushing the chair into place.” “It isn’t his custom to push the study furniture into place. He was erasing indicative marks on that plush chair back—that’s what he was doing.” “Absurd!” Helen cried; “what marks could there be?” “I don’t know. Come and let us see.” Cray took Helen to the study, and asked her to sit in the chair. “Lean back,” he directed. “Now, get up.” The girl obeyed, and there was plainly seen on the plush the faint but unmistakable imprint of the beaded design that adorned the back of the frock she wore. “I told you so!” Cray said, in triumph. “That plush registers every impress, and when Lockwood rubbed it smooth it was to erase a damaging bit of testimony.” “Rather far-fetched, Mr. Cray,” said Gordon Lockwood himself, who had come in and had heard and seen the latter part of the detective’s investigation. “Not so very, Mr. Lockwood, when you learn that the finger prints on the chair frame are your own and those of a certain young person who is already under suspicion.” Gordon Lockwood, as always under a sudden stress, became even more impassive, and his eyes glittered as he faced the attorney.
- 72. [208] “Don’t be too absurd, Mr. Cray,” he advised, coldly. “I suppose you mean Miss Austin—I prefer to have no veiled allusions. But the finding of her finger prints on a chair in this room, and mine also, does not seem to me to be in any way evidence of crime.” “No?” Cray gave him scorn for scorn. “Perhaps then, you can explain Miss Austin’s presence here that night.” “I don’t know that she was here—and I most certainly could not explain any of her movements. But I do deny your right to assume her guilty from her presence.” “Ah, you tacitly admit her presence, then. Indeed, one can scarcely doubt it, when it is shown that this little shoe of hers,” he took it from his pocket, “exactly fits the prints that cross the field of snow between here and the Adams house.” “To measure footprints—after all this time!” and Lockwood’s lip curled. “The prints are exactly as they were made, Mr. Lockwood. The unchanging cold weather has kept them intact. I tried this shoe, and the prints are unmistakable. Moreover, the short stride is just the measure of the natural steps of Miss Austin. The footprints lead from the Adams house over here and back again. The returning prints occasionally overlap the ones that came this way, showing that the trip away from this house was made latest. Miss Austin was seen to come over in this direction—well, none but a half-wit would be blind to the inevitable conclusions!” “None but a half-wit would read into this evidence what you pretend to see,” retorted Lockwood, almost losing his calm.
- 73. [209] [210] “That’s my business,” Cray said, sharply: “now, Mr. Lockwood, why did you smooth off that chair back? Careful, now, two witnesses saw you do it.” “I’m not denying it”—Lockwood smiled in a bored, superior way, “but if I did it, I was—and am unconscious of it. One often touches a piece of furniture in passing with no thought of doing so.” “That won’t go down. Both the butler and Miss Peyton saw you definitely and deliberately rub over the back of that chair. Why did you do it?” Cray was inexorable. But the impassive secretary merely shrugged his shoulders. “I can’t answer you, Mr. Cray. I can only repeat it must have been an unconscious act on my part, and it has no sinister significance. I may have been merely pushing the chair out of my way, you know.” “Look here, Mr. Lockwood, you are a man of honor. Do you, upon oath, declare that you did not purposely smooth that chairback, for the reason that it showed some incriminating impress?” “I am not under oath. I have stated that I did not do what you accuse me of, and I have nothing further to say on the subject.” Lockwood drew himself up and leaned with folded arms against the mantelpiece. Cray dropped the subject, but his snapping eyes and compressed lips seemed to show he had not finally
- 74. [211] dismissed it. “At what time,” he said, abruptly, “did Doctor Waring lock his study door?” “About ten o’clock,” the secretary replied. “And you heard nothing from the room after that? No sound of voices? Nobody coming in at the French window?” “No,” replied Lockwood. “Then we are forced to the conclusion that whoever entered did so very quietly, that it was with the knowledge and permission of Doctor Waring himself, that the visitor was the person whose footprints lead straight to the door, and whose finger prints are on the chair that stood near the Doctor’s own chair. We are borne out in this view by the fact that the same person now possesses the money and the ruby pin which we know Doctor Waring had in his room with him, and we know that the person is here in Corinth for unexplained reasons, and is, in fact, so peculiar that she is known as —Miss Mystery. Just why, Mr. Lockwood, are you arguing against these obvious inferences, and why do you undertake to free from suspicion one against whom everything is so definitely black?” “Because,” Lockwood spoke very quietly, but his jaw was set in a stubborn way, “the lady you call Miss Mystery, is a young and defenseless girl, without, so far as I know, a friend in this town. It is unfair to accuse her on the strength of this fantastic story and it is unfair to condemn her unheard.”
- 75. [212] “Not unheard,” said the attorney, “but what she says only incriminates her more deeply.”
- 76. CHAPTER XII MAURICE TRASK, HEIR The funeral services of John Waring were solemn and impressive. No reference was made to the manner of his taking-off, save to call it mysterious, and the encomiums heaped upon him by the clergy and the college faculty were as sincere as they were well- deserved. There were two members of the great audience who were looked at with curiosity by many. One of these was Miss Mystery, the girl who, it was vaguely rumored was in some way connected with the tragedy. To look at her, this seemed impossible, for a sweeter face or a gentler manner could scarce be imagined. Anita Austin sat near the front, on one of the side aisles. She wore a gown of taupe-colored duvetyn, and a velvet toque of the same color. Her olive face was pale, and now and then her small white teeth bit into her scarlet lower lip, as if she were keeping her self-control only by determined effort.
- 77. Welcome to our website – the perfect destination for book lovers and knowledge seekers. We believe that every book holds a new world, offering opportunities for learning, discovery, and personal growth. That’s why we are dedicated to bringing you a diverse collection of books, ranging from classic literature and specialized publications to self-development guides and children's books. More than just a book-buying platform, we strive to be a bridge connecting you with timeless cultural and intellectual values. With an elegant, user-friendly interface and a smart search system, you can quickly find the books that best suit your interests. Additionally, our special promotions and home delivery services help you save time and fully enjoy the joy of reading. Join us on a journey of knowledge exploration, passion nurturing, and personal growth every day! ebookbell.com