J. Ita/. Statist. Soc. (/999)1, pp. 61-73

OPTIMAL DESIGN OFAIR QUALITY NETWORKS DETECTING

WARNING AND ALERT CONDITIONS

Daniela Romano*,Mario C. CirilloNational Environmental Protection Agency, Italy

Renato Coppi, Pierpaolo D'UrsoUniversity ofRome «La Sapienza», Roma, Italy

Summary

A statistical method is presented to determine the optima design of air quality networksdetecting warning and alert levels. A simulation model is used to describe temporal andspatial variations of atmospheric pollutants; air quality patterns serve as the database ofthe procedure to design the network. Only the sites exceeding warning and alert levels, atdifferent meteorological scenarios, are considered as potential monitoring stations. Forthe selection of the optima set, spatial and temporal representativity criteria are intro­duced; accordingly, the optima set provides a complete representativity of the space andtime considered. The method is applied to the Mestre urban area, in Venice district, for thecarbon monoxide pollutant.

Keywords: Air pollution, monitoring network, warning and alert levels.

1. Introduction

Air quality monitoring networks are designed and iiperated for a lot of purposeswhich include assessing compliance with air quality standards, validating disper­sion models, assessing control strategy effectiveness and determining risk of dam­age to especially sensitive receptors, such as population, crops etc. Therefore,there are several functions that a monitoring system can serve and, consequently,there are also different criteria to design a network. The primary objectives ofmost air quality programs are mainly monitoring for trend, where spatial andtemporal dependence is of importance, and monitoring for «hot spots», or re­gions of local high intensity, which is often used for monitoring compliance withpollution regulations.

* Address for correspondence: ANPA-Environment Department, Via V. Brancati, 48 ­1-00144, Rome, Italy. E-mail: daniela.romano@anpa.it

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D. ROMANO' M. C. CIRILLO' R. COPPI' P. D'URSO

The basic theory of optimal design for spatial random fields was developedby Ripley (1981). Systematic random sampling designs, in which a point is cho­sen uniformly over the study area, and a regular design (consisting of squares,triangles, or hexagons) is put down starting at the chosen point are the mostcommonly used. These schemes are the most efficient when dealing with iso­tropic fields (Matern, 1986). As far as heterogeneous random fields are con­cerned different theories have been developed. Caselton et al. (1992) proposedan approach which maximizes the amount of information (entropy in the senseof Shannon (1948)) about the ungauged sites that can be obtained from the gaugedsites. The use of entropy is dictated by certain basic requirements as the selec­tion of those points that maximize the increase of information. This reasoningthen leads inevitably to network designs which minimize certain entropies asthe optimal solution to the designer's problem. A different approach to design­ing monitoring networks is taken by Federov and Mueller (1989) who focus onthe classical theory of optimum design by a regression model and assume, as anobjective function, anyone of a number of functionals of the covariance matrixof the estimates of the regression coefficients. All these designs may fail to beoptimal with respect to any single given objective, but they seek to best meet thecommon features of the multiplicity of uses to which the resulting data may beput.

The methodology hereby proposed designs an optimal network by selectingthe sites which are of interest for critical condition in atmospheric pollution, thatis those which detect warning and alert concentration.

As provided by the Italian legislation (DM 12/11/92), air quality networks areimportant for monitoring pollutant concentration values which, if exceeded, canbe potentially dangerous for the population (warning and alert limits). In thiscase immediate solutions are necessary to re-establish concentration values intothe fixed limits (traffic circulation limitation, hours of central heating reductionetc.).

The method is based on an air quality simulation model to represent the spatialand temporal distribution of the pollutant considered. Spatial and temporal rep­resentativity is taken into account to choose the potential monitoring sites. Ac­cording to the specific representativity, a solution is found. The solution explicit­ly ranks the selected stations in terms of their representativity, showing the flex­ibility of the method, to be linked with given budget constraints in practical ap­plications.

The study is applied to the Mestre urban area, in Venice district, for the carbonmonoxide (CO) pollutant.

The paper is organized as follows. First the issues of the proposed methodolo­gy are introduced. Then the study area, the sources, the simulation model arepresented and the optimization method is applied. The final section summarizesthe results.

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OPTIMAL DESIGN OF AIR QUALITY NETWORKS

2. Methodological issues

In this section the details of the methodology to design the optimal monitoringnetwork are illustrated. Basic definitions are firstly introduced. The spatial andtemporal dominance criteria are defined in order to select the potential monitor­ing sites. Spatial and temporal representativity and overlapping, are described inorder to choose the optimal number and location of the monitoring sites. Finally,the steps of the procedure are summarized.

2.1. Basic definitions

Let us consider the following definitions:

• L: concentration level, warning or alert value, for a specific pollutant;• ei: k-th scenario, characterized by meteorological conditions, pollutant emis­

sions and pollutant concentrations;· t: occurrence frequency, of the k-th event «e.» in the temporal period consid­

K

ered (i.e. a year); if k are all the «possible» events are considered: IJk =1;k=l

• Rt: i-th receptor (located, for instance, on a regular grid);

• N: total number of the receptors;• Cik : pollutant concentration at the i-th receptor, regarding the k-th event;• L1ik : receptor/event characteristic function of the i-th receptor and the k-th sce­

nario defined as:

{I if c, ? L

L1'k =I 0 if C

ik< L

• L1ik : characteristic function of the k-th scenario defined as:

{I if3 Rj:Cjk ? L

L1k = o otherwise

(2.1);

(2.2);

• Sjk: set of receptors R, associated with the R, receptor for which the pollutantconcentration exceeds the level L in ei:

(2.3);

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D. ROMANO' M. C. CIRILLO' R. COPPI • P. D'URSO

• N{S;d: cardinality ofSjk;• N(uj Sjk): number of receptors exceeding the concentration level L referring to

ek• Obviously:

{o if c. < L

N{S;d= N(u.S.k) 'fC >LJ J 1 ik -

2.2. Spatial and temporal dominance

(2.4);

A subset of potential monitoring sites is selected considering the following crite­ria of dominance:

• R, dominates R, in space (R;>-s R) if Sik ;;2 Sjk; 'ifk =1, ..., K.For each k:

i) S;k = Sjk = 0 if L is not exceeded either at R;or Rj;ii) S;k:::> Sjk if L is exceeded only at R;;iii) S;k == Sjk ifL is exceeded at both receptors.

• R, dominates R, in time (R,>-T R) if L1ik~ L1jk; 'ifk =1, ... , K.Obviously, the spatial dominance implies the temporal dominance and vice

versa. In fact, if R;>-s Rj either:i) Sjk = 0 and S;k = 0 so that L1;k > L1jk;or:ii) S;k == Sjk so that L1ik= L1jk;then L1;k ~ L1jk. Analogously for the other implication.

Then Ri dominates RiR; >-T Rj ) .

Applying the dominance criteria, all the «dominated» receptors are eliminatedand the remaining receptors are considered as potential monitoring sites.

2.3. Spatial and temporal representativity

After the potential monitoring sites have been selected, spatial and temporal cov­erage is considered. The following indices are introduced.• Spatial representativity ofthe receptor R:

average number of the receptors exceeding L represented by R, out of the totalaverage number of receptors exceeding L:

(2.5).

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OPTIMAL DESIGN OF AIR QUALITY NETWORKS

The maximum representativity of the site R; (S; = 1) occurs when the concentra­tion value at R; is always above the level L, the minimum (S; = 0) when R, isalways below the level.• Temporal representativity of the receptor R;:

average number of times in which L is exceeded at the receptor R; out of thetotal average number of events in which at least an exceedance occurs:

(2.6);

• Spatial representativity ofR;U Ri

(2.7);

• Temporal representativity ofR; U Rj :

where

(2.8)

A k ={lI) 0

if Ll;k + Ll jk ~ 1

if Llik + Ll jk =O·

2.4. Spatial and temporal overlapping

The selection of the potential monitoring sites can yield redundant informationdue to spatial and temporal overlapping. It is important to consider the followingindices.

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D. ROMANO' M. C. CIRILLO' R. COPPI . P. D'URSO

• Spatial overlapping ofR; and ~:

average number of receptors exceeding L represented by both R;and R, out ofthe total average number of receptors exceeding L:

K

LN(S;k nSjk)fkS;nj = -",k-=,;~.,- _

LN(ujSjk)hk=l

(2.9)

• Temporal overlapping ofR; and R;average number of times in which L is exceeded at both the receptors R;and R,outof the total average number of events in which at least an exceedance occurs:

(2.10)

2.5. Monitoring sites selection

The optimal monitoring sites are selected by the following steps:

1. identification of the receptors at which Cik > L, for some k:2. elimination of the «dominated» receptors; if R;>- R, and Rj >- R;the receptor is

selected considering: m:x{c..Cjk} ;

3. ranking of the remaining receptors according to spatial and temporal repre-sentativity;

4. selection of the minimum set such that: Sv, =1 and Tv,=1.

The optimal set of receptors obtained by the previous steps provides a total spa­tial and temporal coverage. In addition, the monitoring sites can be selected tak­ing into account practical constraints, thus the optimal set cannot represent com­pletely the spatial and temporal domain considered.

3. Study of warning and alert conditions in an urban area

The methodology has been applied to the Mestre urban area, in Venice district,for carbon monoxide. The emissions have been calculated by modelling vehicu-

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OPTIMAL DESIGN OF AIR QUALITY NETWORKS

lar fluxes. The air quality model employed to simulate spatial and temporal vari­ations of CO patterns at different meteorological scenarios has been DIMULA(Cirillo and Manzi, 1991). The results of the model has been validated by empir­ical data. The estimated concentration fields are the database to design the opti­mal monitoring network.

3.1. The modelling region

The modelling region is a 2.5 k:mby 3 k:marea which includes the Mestre urbanarea, in Venice district. The region has been divided into 0.12 k:m by 0.12 k:msquares conforming to the resolution of CO emissions and the airshed modelrequirements.

The emission sources have been divided into linear and areal sources. Linearsources represent the main urban streets, the extraurban roads and the highway.Areal sources represent the urban streets which are characterized by low trafficflux. Especially, 81 linear and 19 areal sources have been individuated.

The CO emissions are available by CORINAIR (1991), the Italian EmissionInventory. The emission values have been assigned to each source considering theinformation about traffic trends in the study area (Gualdi, 1990). In order to evalu­ate warning and alert conditions, only maximum emission values have been exam­ined. In this study maximum values occur between 6.00 and 7.00 p.m. in a stand­ard working day; these values are the input emission data for the simulation model.

3.2. Simulation ofair quality patterns

The hourly CO concentrations have been estimated by a multisource gaussiangrid model: DIMULA (Cirillo and Manzi, 1991). The model utilizes a PlumeGaussian Model (Seinfeld, 1986) to simulate the dispersion of continuos emis­sions in stationary conditions and a Puff Gaussian Model (Seinfeld, 1986) forinstantaneous emissions.

In order to evaluate the CO dispersion at different atmospheric conditions (windspeed, wind direction and stability class), 19 meteorological scenarios, whichconstitute 90% of the real annual data, have been considered. For each scenario,CO concentration patterns, between 6.00 and 7.00 p.m., have been simulated.

The performance of the dispersion model has been validated. Estimated valueshave been compared with monitored data measured at four sites in the Mestre area.

Table 1 shows the results.Estimated and measured values show a good agreement. The correlation coeffi­cient between the monitored and the estimated, in terms of the mean of the range,values is 0.93.

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D. ROMANO' M. C. CIRILLO' R. COPPI . P. D'URSO

Table 1CO monitored and estimated concentration values

Monitoring Monitored values Range of estimatedStations (mg/m J

) values (mg/mJ)*

Mestre Ospedale 8 3-15San Marco 6 5-28Fradeletto 50 15-54

Piave 11 6-14* The range of the estimated values refers to the vertices of the grid points including the monitoringstation.

3.4. Optimal network configuration

Estimated CO concentration patterns are the data base for the optimal networkdesign. Warning and alert values, fixed by the Italian legislation at 15 mg/m' and30 mg/m' (DM 12/11/92) respectively, have been considered. The algorithm hasbeen applied considering the thresholds separately.

First the points exceeding warning and alert thresholds, respectively, have beenindividuated for each meteorological scenario. The most representative sites, infact, are those exceeding the threshold in the maximum number of scenario; thesepoints are potentially to be selected as monitoring sites. Then every point of thegrid has been characterized by the number of times the threshold is exceeded.

Table 2 and Table 3 summarize the number of the exceedances for warningand alert limits in the spatial domain.

Finally, the optimization method has been applied.Concerning the warning limit, the site with the maximum number of exceed­

ances, specifically in 12 scenarios, has been selected, Rj • Then the dominance ofR, on all the receptors exceeding the warning limit has been studied. Applyingthe dominance criteria (§2.2) it appears that only one receptor, R2 is not dominat­ed by R j ; at the site Rz the warning value is exceeded in 11 scenarios. So both thereceptors R, and R2 are considered as potential monitoring sites. In order to de­sign the optimal network, spatial and temporal representativity of R j , R2 andR, u R2 has been calculated as well as spatial and temporal overlapping of R, andR2• In table 4 the values of spatial and temporal representativity are reported.

Spatial and temporal overlapping has been calculated by the (2.9) and (2.10).The results are 0.95 and 1, respectively.

For the alert limit, two potential monitoring sites have been selected, R3 andR4, which exceed the threshold in 7 and 6 scenarios, respectively. Spatial andtemporal representativity of R3, R4 and R3 u R4 is summarized in Table 5.

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OPTIMAL DESIGN OF AIR QUALITY NETWORKS

Table 2Number ofexceedances: warning limit (15 mglm3

)

3 I 0 I I 0 0 0 I I I I I 0 0 0 0 0 0 0 0I I 0 I I 0 0 I I I I I I 2 0 0 0 0 0 0 00 0 0 I I I I 2 3 2 2 3 0 I 0 0 0 0 0 0 02 0 0 I 1 0 0 1 3 1 I 3 0 I 0 0 0 0 0 0 08 0 0 1 I 0 0 I 3 I I I 0 0 0 0 0 0 0 0 0I 0 0 I I 0 0 I 3 I I I I 0 0 0 0 0 0 0 00 7 I I 1 I 0 I 4 I I 2 I 0 0 0 0 0 0 0 00 2 2 I I I 1 I 4 I 2 2 I 0 0 0 0 0 0 0 0I I 3 I 2 4 2 2 4 I 5 2 I 0 0 0 0 0 0 0 00 0 7 I I 0 I 2 4 2 6 3 1 0 0 0 0 4 0 0 00 0 I 3 I 1 I I 4 2 7 3 2 I 0 0 I 0 0 0 00 0 0 5 2 2 3 3 4 I 5 3 2 2 0 0 0 0 0 0 00 I 4 II 2 2 3 4 3 2 4 2 I 0 0 0 0 0 0 0 0I 0 2 12 2 I 2 I 3 2 4 2 2 0 0 0 I I 0 0 01 I 2 5 2 3 I 0 5 3 5 2 I 0 1 0 0 0 I 1 0I 2 1 4 8 2 2 2 4 3 4 I I 3 I 0 I 0 0 0 II 3 4 4 4 I 2 2 5 3 5 I I I 2 I 0 0 I I 02 3 2 5 4 4 3 4 4 3 7 I I I I 0 I I 0 0 02 3 5 4 4 6 3 3 4 2 7 I I I 0 0 0 0 3 0 02 3 4 3 4 9 3 I 3 2 7 I I I 0 0 0 0 0 0 I3 2 3 3 5 5 3 I 4 2 5 I I I 0 0 0 0 0 0 0I 2 3 4 3 I 3 3 4 2 4 I I 0 0 0 0 0 0 0 0I 3 2 I 3 2 3 2 5 2 4 I I 0 0 0 0 0 0 0 0I 2 2 I 3 2 4 3 6 7 8 I 0 0 0 0 0 0 0 0 0I 2 I 2 I 2 6 3 7 6 7 I 0 0 0 0 0 0 0 0 0

Considering (2.9) and (2.10) the spatial and temporal overlapping results are 0.60and 0.92, respectively.

The optimal monitoring network has thus been individuated considering theabove results. The receptor R, has been selected to detect the warning limit; ityields a complete spatial representativity and a temporal representativity of98%.For the alert limit, the receptors R] and R4 achieve a complete spatial and tempo­ral representativity.

The location of the monitoring stations in the study area is shown in Figure 1.

4. Conclusions

A methodology to determine the optimal number and location of the stationsmonitoring air pollution warning and alert levels is presented. The procedure hasbeen applied to the Mestre urban area. An air quality model is used to simulate

69

D. ROMANO' M. C. CIRILLO' R. COPPI' P. D'URSO

Table 3Number ofexceedances: alert limit (30 mg/m')

I 0 0 0 0 0 0 0 I 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 I 0 0 2 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 03 a 0 0 0 a 0 a 1 0 a a 0 a 0 a a a 0 0 a0 0 0 a 0 0 a 0 1 0 a 0 0 0 a 0 0 0 0 a aa 2 0 0 0 0 0 a I 0 1 0 0 a 0 a a a 0 0 a0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 a 0 0 0 a0 0 0 1 0 1 0 0 a 0 I 0 0 0 0 0 a 0' 0 0 00 0 3 1 0 0 0 a 0 0 4 0 0 0 0 0 0 I 0 0 00 0 0 I 0 0 0 a 0 0 5 0 0 0 0 0 0 0 0 0 00 0 0 3 1 0 1 1 0 0 3 0 a 0 0 0 0 0 0 0 00 0 2 6 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 00 0 0 6 0 0 0 0 I 0 1 0 0 0 0 0 0 0 0 0 00 0 0 I 1 0 0 0 1 0 2 0 0 0 0 0 0 0 0 I 00 0 0 1 7 0 0 0 I 0 1 0 0 I 0 0 0 0 0 0 00 0 0 1 0 0 0 1 2 1 4 0 0 0 0 0 0 0 0 0 00 0 0 1 1 a 0 0 1 0 5 0 0 0 0 0 1 0 0 0 00 0 I 0 0 2 0 0 I 0 5 0 0 0 0 0 0 0 1 0 00 0 0 0 0 6 0 0 1 a 5 0 0 0 0 0 0 0 0 0 00 0 0 0 1 1 0 0 0 0 4 0 0 a 0 0 0 0 0 0 00 0 1 I 1 0 0 0 0 0 2 0 0 a 0 0 0 0 0 0 00 1 0 1 0 0 0 0 2 1 3 0 0 a 0 a a 0 0 0 aa a 0 0 0 a 2 a 3 2 3 0 0 a 0 0 0 0 0 0 00 0 0 0 0 0 4 0 3 2 3 0 0 0 0 0 0 0 0 0 0

Table 4Spatial and temporal representativity

WARNING LIMIT

receptor representativityspatial temporal

R, 1 0.98Rz 1 0.96

R,uR2 1 1

the spatial and temporal distribution of the carbon monoxide. The optimal net­work is determined considering the concepts of spatial and temporal dominance,spatial and temporal representativity and overlapping.

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OPTIMAL DESIGN OF AIR QUALITY NETWORKS

Table 5Spatial and temporal representativity

ALERT LIMIT

receptor representativityspatial temporal

R3 0.94 0.90R4 0.99 0.71

R3uR4 1 1

\ I .~'\..Q" It l\l.: ~, ,1 \ 1st ; - '""'-lo!;;f=.r:::- ,......D...\r.\i':'~.iq, . ,',:';' '. ~'J' ~·t-'-~!j;-I" ',-';:'-. ~ .-. ""~ .:1 I ' ,i!':r~

~l \~~. ,~':.:.;o j!1 £=~::::>o. '" __ -.:... -:--<--..4a warning level • alarm level

Fig. 1 - Mestre. Simulation area (2.5 km by 3 km). Location of the 'optimal monitoringstations.

71

D. ROMANO·' M. C. CIRILLO' R. COPPI . P. D'URSO

Considering the warning level, only one station has been selected; it yields acomplete spatial representativity and a temporal representativity of98%. For thealert limit, with two receptors a complete spatial and temporal representativity isachieved.

It is worthwhile to observe that it is possible to rank the various stations interms of spatial and temporal representativity. This enhances the flexibility of themethod, to be linked with given budget constraints in practical application.

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