development of a multiple objective planning theory and system for sustainable air quality...

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Science of the Total Environ

Development of a multiple objective planning theory and system for

sustainable air quality monitoring networks

Ching-Ho Chen a,*, Wei-Lin Liu b, Chia-Hsing Chen c

a Department of Civil Engineering, Nanya Institute of Technology, 414, Sec. 3, Chung-Shang E. Rd., Jungli, Taoyuan, Taiwan 320, R.O.C.b Graduate Institute of Environmental Engineering, National Central University, Jungli, Taiwan 320, R.O.C.

c Environmental Protection Bureau, Taoyuan County Government, Taoyuan, Taiwan 330, R.O.C.

Received 14 April 2005; received in revised form 9 August 2005; accepted 12 August 2005

Available online 20 October 2005

Abstract

Air quality monitoring data are important bases for air quality management strategies planning and performance assessment.

Therefore, the environmental protection authorities need to plan the air quality monitoring network effectively. However, in

Taiwan, the national Environmental Protection Administration (EPA) and some county environmental protection bureaus (EPB)

separately installed their own monitoring stations. This study developed an integrated methodology and computer system for

planning air quality monitoring networks. The environmental, social, and economic objectives and sub-objectives, and their

weights were identified using system analysis and multiple objective planning, based on the principles of sustainable development.

A multiple objective optimization model and procedure for sustainable air quality monitoring networks planning are developed in

this study. According to the procedure, a multiple objective planning system for sustainable air quality monitoring networks

(MOPSSAQMN) is developed using computer software based on the modified bounded implicit enumeration algorithm with the

constraint arrangement method. The air quality monitoring network of Taoyuan County, in northern Taiwan, was used as a case

study to demonstrate the proposed method. Two satisfactory alternatives based on different conditions were generated using

MOPSSAQMN. The compared results show that this study generated better alternatives than the current monitoring network. An

installation schedule for the alternative was proposed, and its first step is now being implemented by the EPB of Taoyuan County

Government. The procedure and computer system developed in this study can be used to assist the competent authorities to devise

good and different alternatives for air quality monitoring networks planning.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Air quality monitoring networks; Principles of sustainable development; Multiple objective planning; Optimization model

1. Introduction

Planning and installing air quality monitoring net-

works is an important task for environmental protec-

tion authorities, involving: (1) ensuring that the air

0048-9697/$ - see front matter D 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.scitotenv.2005.08.018

* Corresponding author. Tel.: +886 3 4361070x517; fax: +886 3

4563674.

E-mail addresses: [email protected],

[email protected] (C.-H. Chen).

quality standard is achieved; (2) planning and imple-

menting air quality protection and air pollution control

strategies; and (3) preventing or responding quickly to

air quality deterioration. Therefore, the environmental

protection authorities need to plan and install air

quality monitoring networks effectively and systemat-

ically. The first air quality monitoring network in

Taiwan was installed by the Environmental Protection

Administration (EPA) in 1980. In 2004, the current

network comprised 72 stations. Additionally, the en-

ment 354 (2006) 1–19

C.-H. Chen et al. / Science of the Total Environment 354 (2006) 1–192

vironmental protection bureaus (EPB) of some county

governments have also installed their own air quality

monitoring stations. According to the relevant regula-

tions of air quality monitoring in Taiwan, the EPA and

EPB stations share the same air quality monitoring

procedures, similar equipment, standard operating pro-

cedures, and QA/QC procedures and system. Mean-

while, the monitoring objectives of these stations are

the same. However, these stations were not systemat-

ically planned or integrated with the national network

leading to defects in the monitoring network. For

example, Taoyuan County, in northern Taiwan, has

six EPA stations and five EPB stations, as shown in

Fig. 1. Based on theoretical and practical considera-

tions, the EPA stations were used to conceptually

divide Taoyuan County into several involving regions.

The monitoring data of each EPA station are used to

represent the air quality of an involving region. For

gathering more air quality data, the EPB additionally

installed their own monitoring stations but the EPB

stations were not used with the EPA stations to re-

divide the involving regions. Because the two classes

of monitoring stations were not systematically

planned, the area, populations, and emission quantity

of each involving region cannot be uniformly distrib-

Fig. 1. The current monitoring ne

uted. Moreover, based on the analytic results of the air

quality monitoring data and the air pollution emission

data, the EPA stations are not enough to represent the

air quality of the original involving regions. Therefore,

the current air quality monitoring networks need to be

reviewed and possibly restructured.

In Taiwan, the current air quality monitoring net-

work was installed to measure the exposed concentra-

tions of air pollutants for the sensitive receptors and to

assess the influence of the pollution sources on the

receptors. Therefore, the distributions of the air pol-

lutant concentrations and populations were used as the

major criteria to determine the number and locations

of the monitoring stations (Liu, 1991). Shei and Kao

(1997) proposed the following criteria for an industrial

park air quality monitoring network: (1) high detect-

able frequency of pollutants; (2) sharply accumulating

concentrations of pollutants; (3) heavy influence from

the industrial park; (4) large monitored area and (5)

large protected population. The relocation of the mon-

itoring network of Kaohsiung City considered differ-

ent criteria that the monitoring stations should be

located: (1) in areas of highly populated region; (2)

in areas where pollution concentrations are expected to

be the highest; (3) in areas where the highest frequen-

twork of Taoyuan County.

C.-H. Chen et al. / Science of the Total Environment 354 (2006) 1–19 3

cy of violation can be detected; (4) in areas where

significant economic growth is expected to occur and

(5) near major industrial sources (Tseng and Chang,

2001).

Some cases in other countries are discussed as

follows. Over 4300 monitoring sites operate in

North America as part of three-nation (United States,

Canada, and Mexico) air quality monitoring net-

works. The siting of monitors and their number

density is highly dependent on the operational objec-

tives of the monitoring networks. The design criteria

are the population, traffic volume, emission density,

pollutant concentration and population exposure

(Demerjian, 2000). In Sweden, the sampling sites of

the Swedish Urban Air Quality Network, which was

established in 1986, were based on another set of

criteria: (1) within a city center; (2) high population

density; (3) no nearby point source or traffic and (4)

open space (Svanberg et al., 1998). In Denmark, the

main objectives for urban air quality monitoring have

been to provide a comprehensive description of the

levels of inorganic pollutants and to assess the

sources contributions. The network was established

on the representative sites, which were selected by

using the dispersion models (Kemp and Palmgren,

1996).

Arbeloa et al. (1993) developed a technique for

designing an optimal air quality monitoring networks.

The concept of potential of violation and the spatial

correlation analysis technique are used to compare the

information given by the potential sites. Chu (1995)

developed a method of constructing a wind rose to site

photochemical pollutant monitors based on the ozone

conducive meteorological conditions. Ozone condu-

cive meteorological conditions were identified based

on statistics derived from local meteorological data of

31 eastern U.S. cities in a period of ten summers

(1981–1990). Croxford and Penn (1998) proposed a

methodology for monitoring urban air-borne pollution

at the fine scale. The methodology concentrated on

showing the effect of local prevailing wind direction

and the form of the local urban area on pollutant

concentrations. The location-specific concentration

profiles were used to compare pollution exposure at

different sites throughout the full range of readings.

Silva and Quiroz (2003) attempted to optimize Santia-

go’s atmospheric monitoring network by excluding the

least informative stations with respect to different air

pollutants. An index of multivariate effectiveness,

based on Shannon information index, is applied to

that network to represent the information for each

air pollutant.

The above studies proposed various planning prin-

ciples and methods but all consider the environmental

factor to be most important in monitoring network

planning. However, the concept of sustainable devel-

opment has become the central principle for any

government to implement the tasks of strategy plan-

ning for any policy. Therefore, the air quality moni-

toring networks should be planned based on the

principles of sustainable development, which requires

the balancing of environmental, economic and social

objectives in decision-making (Chen et al., 2005;

Piper, 2002).

In terms of social objective, most people would

request that stations should be installed near all densely

populated areas, schools, and hospitals. Nevertheless,

installing so many stations would be almost impossible

because budget controls based on the economic con-

sideration. Therefore, the social objective conflicts the

economic objective. Additionally, regarding the envi-

ronmental objective, since the representative air quality

monitoring data is important for air quality manage-

ment, the stations should be installed in areas with high

concentrations of air pollutants and near major air

pollution sources. Conversely, the background stations

should be installed in areas with low concentrations of

air pollutants and no major air pollution source. In-

stalling many stations helps the environmental objec-

tive. However, the economic objective is to pursue

minimization of the installation cost. In other words,

the environmental objective conflicts the economic

objective. Therefore, in planning sustainable air quality

monitoring networks, considering the environmental,

social and economic objectives comprehensively is an

important but difficult task for environmental protec-

tion authorities.

The main purpose of this study is to develop a

methodology and a computer system for planning air

quality monitoring networks. The environmental, social

and economic objectives would be simultaneously con-

sidered for assisting the competent authorities to gen-

erate the plans for sustainable air quality monitoring

networks. The development and application of the the-

ory and the computer system are described and dis-

cussed below.

2. Development of a multiple objective planning

theory for sustainable air quality monitoring

networks

System analysis and multiple objective planning

(MOP) were employed to develop the multiple objec-

tive planning procedure for sustainable air quality


monitoring networks with the comprehensive consid-

erations of environmental, social, and economic objec-

tives. The procedure, shown in Fig. 2, is described as

follows.

Fig. 2. The multiple objective planning procedure fo

2.1. Identification of a system and its components

This investigation used the administrative region of

a county as the system range and boundary. Fig. 3

r sustainable air quality monitoring networks.

Air quality monitoring stations

Air pollutants

Pollution sources (industry, mobile, and area sources)

Population

Traffic volume

Sensitive receptors

System boundary

Sub-system

Fig. 3. The conceptual system diagram.


shows the conceptual system diagram. The region can

be conceptually divided into grid squares as subsys-

tems. Each grid square was set as 4�4 km. The com-

ponents of each subsystem include the air quality

monitoring stations, air pollutants, air pollution sources

(including the industry, mobile, and area sources) and

human society (including the quantity of population,

traffic volume, and sensitive receptors). Four classes of

monitoring stations, general, traffic, background, and

Goals of sustainabmonitoring n

Environmental objective Economic ob

Installin

Concentration exceeding the

regulation standard

Averageconcentration

Highest concentration

Total emissionquantity

Emission quantity ofindustry sources

Vatiation range of concentration

Emission quantitymobile sources

Fig. 4. The framework of objectives of susta

industrial stations, could be installed in each grid

square.

2.2. Identification of the goals, objectives and sub-

objectives

This study developed an integrated framework,

shown in Fig. 4, to identify the goals and objectives

of air quality monitoring network planning. The iden-

le air qualityetworks

jective Social objective

g cost Populations

Sensitive receptors

Traffic

Air pollution petitions

inable air quality monitoring networks.


tified goals are to maximize the sum of the sustainable

effectiveness of air quality monitoring across all grid

squares. The objectives are derived from the principles

of sustainable development, which consisted of envi-

ronmental, social, and economic phases. Therefore, the

sustainable effectiveness value of each grid square was

identified as the sum of the environmental, social, and

economic objective values. Each objective consisted of

several sub-objectives. The objective value was identi-

fied as the sum of each sub-objective value multiplied

by the weight. The weight value was determined by the

class of the selected monitoring station in each grid

square. That is, each class of monitoring station had

different weights. Although each sub-objective value in

each grid square was calculated using the same method

as the following sections, different weights were used

for each selected class of monitoring station in each

grid square. Therefore, the environmental, social and

economic objective values of each grid square are

different for each class of monitoring station.

The environmental objective for choice of air quality

monitoring station site included seven sub-objectives,

as follows: (1) highest pollutant concentration, (2) high-

est average concentration, (3) largest range of the con-

centration exceeding the regulation standard, (4) largest

variation range of pollutant concentration, (5) largest

total emission quantity, (6) largest emission quantity of

the industry sources, and (7) largest emission quantity

of the mobile sources. The social objective included

four sub-objectives, as follows: (1) largest population,

(2) largest number of sensitive receptors, (3) largest

traffic volume, and (4) largest number of air pollution

petitions. Sensitive receptors include schools and hos-

pitals. Air pollution petitions mean the cases which

people ask the EPB to investigate the air pollution

sources and punishing the ones who violate the regula-

tions. Furthermore, the economic objective included

one sub-objective, the lowest cost of installation. Con-

versely, the sub-objectives for the background station

were to pursue the smallest value, such as the lowest

pollutant concentration.

2.3. Estimation of the emission quantities and concen-

trations of air pollutants

Taoyuan County, located in northern Taiwan, is used

as a case to demonstrate the proposed approach. The

spatial and attribute data of the industry pollution

sources, roads, traffic volume, populations, land use

area, sensitive receptors, and air pollution petitions

were established using ArcView (ESRI, 2003), a geo-

graphic information system (GIS) software package.

The total road length, traffic volume, populations,

land use area, sensitive receptors, and air pollution

petitions in each grid square and the whole county

were calculated. The ratios of each amount in each

grid square over the amount in the whole county were

also calculated.

The EPB of Taoyuan County Government had

accomplished an emission inventory of industry pol-

lution sources. The air pollution emission quantity of

each industry pollution source was estimated based

on the inventory, and established as the attribute data.

The air pollution emission quantities of all industry

pollution sources in the same grid were summed as

the industrial air pollution emission quantity in the

grid square.

The total air pollution emission quantity of each class

of mobile source in the county was estimated from the

emission factors and the investigated data from the EPA

(2002). Each class of vehicle had its own emission

factor. The information of fuel consumption, traveled

vehicle-kilometers and average speed is individually

investigated with each different class of vehicle. The

air pollution emission quantity of each class of mobile

source in each grid square was calculated as the total

emission quantity of each class of mobile source of the

county multiplied by the ratio of road lengths in each

grid square. The air pollution emission quantities of all

classes of mobile source in the same grid square were

calculated as the sum of air pollution emission quantity

of the mobile sources in the grid square.

The total air pollution emission quantity of each

class of area pollution source in the county was esti-

mated using Taiwan Emission Data System (TEDS)

based on the investigated data from EPA (2002), in-

cluding the populations, land use area and road length

in the county. The air pollution emission quantity of

each class of area pollution sources in each grid square

was computed as the total air pollution emission quan-

tity of the area pollution sources in the county multi-

plied by the ratio of each class of area pollution source

in each grid square. The air pollution emission quanti-

ties of each class of area pollution source in the same

grid square were calculated as the sum of air pollution

emission quantity of the area pollution sources in the

grid square.

The total air pollution emission quantity in each grid

square was calculated as the sum of the emission quan-

tity of the industrial, mobile, and area pollution sources

in each grid square. The air pollutants considered in this

study included particulate matters (PM10), sulfur oxides

(SOX), oxides of nitrogen (NOX) and non-methane

hydrocarbon (NMHC). The four pollutants were all


the necessarily monitored pollutants according to the

regulations. Furthermore, ozone, which is a secondary

pollutant is mainly contributed by NMHC and NOX in

Taiwan. Therefore, NMHC and NOX were necessarily

monitored because of ozone problems.

The concentrations were simulated using the Indus-

trial Source Complex (ISC3) dispersion model (USEPA,

1995) based on the above total emission quantities in the

grid squares. For each grid square, the emission quantity

was assumed to occur at the central point. The meteo-

rology data in each hour of a year were simultaneously

collected and used for the simulation. This work then

simulated the concentrations at the central point of each

grid for each hour of a year. The simulated results were

screened to find the highest concentration in each grid

square. Additionally, the simulated results were used to

calculate the average concentration in each grid square.

The range of the concentration exceeding the regulation

standard was computed as the difference between the

highest concentration and the regulation standard. Final-

ly, the variation range of the concentrations is calculated

as the difference between the highest concentration and

the average concentration.

2.4. Identification of the weights of each sub-objective

for each class of station

The importance of each sub-objective for each class

of monitoring station is different. The general station

should be able to involve all classes of pollution sources

and monitor the general air quality. The traffic and the

industrial stations should be able to involve traffic and

Table 1

The weights of each sub-objective for each class of monitoring station

Sub-objective General

Environmental objective

Highest concentration 0.15

Average concentration 0.05

Concentration exceeding the standard 0.1

Variation range of concentration 0.05

Total emission quantity 0.1

Emission quantity of industrial sources 0.05

Emission quantity of mobile sources –

Social objective

Population 0.2

Sensitive receptor 0.1

Traffic –

Air pollution petition –

Economic objective

Installing cost 0.2

industrial pollution sources, respectively. The back-

ground station should be able to monitor the background

air quality, without interference from air pollution

sources.

Therefore, the sub-objectives concerning the air

pollutant concentrations, population, and installation

cost should be considered for all stations. The general

station should consider the total emission quantity

sub-objective. Moreover, the traffic station should

consider the sub-objective about the emission quantity

of the mobile sources, and the industrial station

should consider the sub-objective about the emission

quantity of the industry pollution sources.

The weights of each sub-objectives identified in this

study were shown in Table 1. This study identified the

environmental factors as the most important, with the

environmental objective weighted at 0.5. Furthermore,

because the social factors were considered as more im-

portant than the economic factors, the social objective

was weighted at 0.3 and the economic objective was

weighted at 0.2. Then, weights were assigned for the

sub-objective depending on the class of monitoring sta-

tion. The weight of each pollutant is considered to be

equal because the four pollutants are necessary to be

monitored based on the regulations. The sensitivity of

the weights had been analyzed according to the method

of system analysis in this study. A great deal of combina-

tions of different weights were analyzed and compared.

The analytical results were evaluated by several specia-

lists and superior officers of the EPB. The adequate

weights which were decided by all the above persons

are listed in Table 1.

Traffic Background Industrial

0.1 0.1 0.1

0.05 0.05 0.05

0.05 0.1 0.05

0.05 0.05 0.05

– 0.2 –

– – 0.25

0.25 – –

0.1 0.3 0.15

– – –

0.2 – –

– – 0.15

0.2 0.2 0.2


2.5. Identification of the constraints

Some constraints were considered and identified as

follows:

(1) The lowest number of air quality monitoring sta-

tions is based on the population size and density.

(2) The installing cost is restricted by the budget.

(3) The distance between two of the same class of

air quality monitoring station should be long

enough.

(4) Nomore than one station should be installed in each

grid square except that a traffic air quality monitor-

ing station and a general air quality monitoring

station can both be installed in a grid square.

(5) The general air quality monitoring station should

be installed in the area with high population

density.

(6) The traffic air quality monitoring station should

be installed in the area with large traffic volume.

(7) The background air quality monitoring station

should be installed in the area with low popula-

tion density.

(8) The industrial air quality monitoring station

should be installed in the area mainly influenced

by the industrial park, that is, a grid square near

to, and in the downwind direction of, an indus-

trial park.

2.6. Development of the multiple-objective planning

optimization model for sustainable air quality monitor-

ing network

Based on the objectives, weights, and constraints

identified above, the multiple objective planning op-

timization model for the sustainable air quality mon-

itoring network was developed in this study.

Sustainable air quality monitoring network planning

has the characteristics for multiple stages and multiple

options. Each grid square can be considered as one

stage and can be figured in six options: no station,

general station only, traffic station only, background

station only, industry station only, and general station

plus traffic station. The values of environmental,

social, and economic objectives of each grid square

for installing different classes of monitoring stations

were calculated as described above. This study pur-

sued the maximal sustainable effectiveness value of

all possible options of all stages. The proposed mul-

tiple-objective planning optimization model for the

sustainable air quality monitoring network planning

is an optimization model for multiple stages, multiple

options and mixed integer programming. The concep-

tual model is shown below:

Max: Z ¼XL

i¼1

XM

j¼1

XN

k¼1

Wj � OBijk

� �

s:t:NGzPG

NTzPT

NBzPB

NIzPI

NG� CGþ NT � CT þ NB� CBþ NI � CIð ÞVQRabNRc

Wj weights of options for sub-objective j, includ-

ing: 0, for Xi=1; WGj, for Xi =2; WTj, for

Xi =3; WBj, for Xi =4; WIj, for Xi =5;

WGj+WTj, for Xi=6

WGj weight of sub-objective j for general station

WTj weight of sub-objective j for traffic station

WBj weight of sub-objective j for background

station

WIj weight of sub-objective j for industrial station

Xi class of station installed in grid square i,

including: 1, no station; 2, general station;

3, traffic station; 4, background station; 5,

industrial station; 6, general station and traffic

station

OBijk value of sub-objective j for pollutant k in grid

square i, including: j=1: highest concentra-

tion; j =2: average concentration; j=3: range

of concentration exceeding the regulation stan-

dard; j=4: variation range of the concentra-

tion; j =5: total emission quantity; j=6:

emission quantity of industrial pollution

sources; j =7: emission quantity of mobile

sources; j =8: populations; j =9: number of

sensitive receptors; j=10: traffic volume;

j =11: number of air pollution petitions;

j =12: installation costs; k =1: PM10; k =2:

SOX; k =3: NOX; k =4: NMHC

NG number of planned general station

NT number of planned traffic station

NB number of planned background station

NI number of planned industrial station

PG lowest number of general station

PT lowest number of traffic station

PB lowest number of background station

PI lowest number of industrial station

CG installation cost of a general station

CT installation cost of a traffic station

CB installation cost of a background station


CI installation cost of an industry station

Q budget for installing the stations

Rab distance between two stations

Rc required distance between two stations

STA

i=1, X

Z+SUB(i)>L

i=5i=i+1, X(i)=1

LowerBo

Ye

No

Ye

X(i)=

i=1

EN

Ye

Ye

X(i)=X(i)+1

No

Z=0, Lower

Z=Z+SDV

Constraints for thedistan

OK

Ye

Outp

Z=Z-SDV(i,X(i))

SDV(i,X(i))=Σ[W(for j=1 to 12, k=

UB(t)=max(SDV(t,1),…

Upper_Bound: SUB(i) =

Constraints for thstatio

OK

Ye

Screening the suitable grids:(1) general station: population > 20,00(2) traffic station: traffic > 200,000(3) background station: population < 2(4) industry station: nearby and in dow

Fig. 5. The conceptual calculation procedure of the mod

Because the data used to calculate the objective

values in each environment, social and economic sub-

objective, such as concentrations of pollutants, emission

quantities of pollutants, and populations, had different

RT

(i)=1

owerBound

9

und=Z

s

s

n(i)

D

s

s

No

i=i-1

No

Bound=-∞

(i,X(i))

budget and the ce

?

s

No

ut

Z=Z-SDV(i,X(i))

j,X(i))×OB(i,j,k)]1 to 4, at i,X(i)

,SDV(t,6)), t=2,… ,59

1,...,58i ,UB(t)59

t=l+1=Σ

e amount of the ns

?

s

No

0

,000nwind direction of industrial park

ified BIE with the constraint arrangement method.

Fig. 6. The forbidden grids for installing the same class of monitoring

station.


units, the values could not be compared directly. There-

fore, the value of each sub-objective was normalized.

The original data were sorted to find the highest and

lowest values. The highest value was converted to 100,

and the lowest value was converted to 1. The data

between the highest and lowest values were converted

linearly. For example, the highest population value in

the entire grid is 131,366, and the lowest is 211. There-

fore, 131,366 was converted to 100 and 211 was con-

verted to 1. Using this scale, a population value of

45,549, it would be converted to 35.2.

2.7. Generation of sustainable air quality monitoring

network alternatives

The implicit enumeration (IE), the cutting-plane,

and dbranch and boundT methods can be used to

solve an optimization model for multiple stages, mul-

tiple options and mixed integer programming. IE is an

improvement on the total enumeration (TE) method.

To find the maximum solution to a problem, the meth-

od sets the lower bound as the temporary maximum

goal value among the searched combinations, and

compares it with the goal value of the combinations

which have not been searched. If the goal value of a

combination is less than the lower bound, then the

combination cannot be the maximum solution and

should be eliminated.

However, if the lower bound is increased slowly in

the searching process, the number of un-eliminated

combinations would be still very large, and resulting

in a long searching time. Chang and Law (1987)

developed the bounded implicit enumeration (BIE),

which includes upper bounds to improve the solving

efficiency. The upper bounds are identified as the

largest goal values of the rest stages of the searching

stage. If the sum of the goal values of a combination

adding the upper bound is less than the lower bound,

the remaining combinations cannot be the maximum

solution and can be eliminated. However, because the

BIE needs to be calculated from the first stage when

searching in each time, it would still waste time. Chen

et al. (1997, 2000) presented the variable memorization

to improve BIE to solve a water–land resources man-

agement problem with 1.634�1021 possible combina-

tions. The calculated values of all the stages are stored

as memorized variables, eliminating many impossible

combinations and thus improving the solving efficien-

cy of the algorithm.

However, this study divided Taoyuan County into 59

grid squares, as shown in Fig. 1, leading to nearly

8.15�1045 possible combinations. This study devel-

oped the constraint arrangement method to decrease

the number of combinations which need to be calculat-

ed. The conceptual calculating procedure, shown in Fig.

5, is based on the modified BIE algorithm with the

constraint arrangement method. The constraints for

screening the suitable grid squares for installing the

stations were used before calculating the upper bounds.

The constraints for checking the distance between two

stations and the total installing cost were placed after

checking if the sum with the lower bound. The con-

straints for checking the amount of the stations are used

before replacing the lower bound.

In the calculation procedure, the objective value of

each grid square was computed from the options for

the class of monitoring station, the inputted environ-

mental, social, and economic data, and the weights.

Under the relevant constraints, the maximum objec-

tive value totals in grid squares can be obtained as the

optimal solution using the modified BIE with the

constraint arrangement method. The method of elim-

inating grid squares for the distance constraint be-

tween stations is shown in Fig. 6. When a station is

installed in a grid square, the nearby grid squares are

marked as not to be installed with the same class of

station. When searching for solutions, the marked grid

squares are skipped enhancing the solution searching

efficiency.

2.8. Displaying a set of satisfactory alternatives

The alternatives obtained using the model are dis-

played in this step, including the objective value, the

number of monitoring stations of each class, and the


installation locations. If the decision makers cannot

accept the optimal solution, then they can modify the

weights or constraints. The phase feeds back to Step 3

and proceeds through Steps 4 to 7 in Fig. 2. The input

data in Step 3 for the contents of the existing alternatives

or the generation criteria for alternatives are modified.

The five steps are not finished until decision makers find

an acceptable alternative of monitoring network.

Input for the objective:(1) highest concentration.(2) average concentration.(3) concentration exceeding the regulation standard.(4) variation range of concentration.(5) total emission quantity.(6) emission quantity ofindustry sources.(7) emission quantity of mobile sources.

Input for the objective: (1) population.(2) traffic.(3) sensitive receptor.(4) air pollution petition.

Enviro

Input the w(1) weightsgeneral sta(2) weighttraffic stati(3) weightbackgroun(4) weightsindustry st

Society

Input the constraints:(1) general station: population > 20,000.(2) traffic station: traffic > 200,000(3) background station: population < 2,000.

Input the weights:(1) weights for the general station.(2) weights for the traffic station.(3) weights for the background station.(4) weights for the industry station.

Calculation of environmental,

social, and economic

objective values of each grid.

Modified BIE algorithm

combined with the constraint arrangement

method.

Fig. 7. The conceptual system fra

3. Development of a multiple objective planning

system for sustainable air quality monitoring

networks

Based on the above methodology and procedure, a

multiple objective planning system for sustainable air

quality monitoring networks (MOPSSAQMN) was de-

veloped in this study. MOPSSAQMN was developed

Input for the objective: (1) installing cost

nment

eights: for the tion.s for the on.s for the d station. for the

ation.

Input the constraints:(1) amount of stations stipulated in the regulations.(2) distancebetween two same kind of stations.(3) industry station: nearby and in downwind direction of industrial park.

Economy

Input the weights:(1) weights for the general station.(2) weights for the traffic station.(3) weights for the background station.(4) weights for the industry station.

Input the constraints:(1) budget.

Generation of sustainable air

quality monitoring

network alternatives .

Display a set of satisfactory alternatives:(1) amount of each sort of stations.(2) sites of each sort of stations.(3) objective value of alternative.

mework of MOPSSAQMN.


using Visual Basic, MS Excel andMSAccess (Microsoft

Corporation, 2000).

The conceptual system framework of MOPS-

SAQMN, as shown in Fig. 7, includes: (1) input data

for the environmental, social and economic objectives,

and the weights and constraints; (2) calculations of the

environmental, social, and economic objective values of

each grid square; (3) the modified BIE algorithmwith the

constraint arrangement method; (4) generation of sus-

tainable air quality monitoring network alternatives; and

(5) a list of satisfactory alternatives.

The function for inputting data for the environmental,

social and economic objectives, weights, and constraints

corresponds to Steps 1 and 5 of the above procedure.

MS Excel is used as the interface for editing the input

file. The required data for environmental, social and

economic objectives were input in systemic tables en-

abling users to input or edit the data conveniently. The

first worksheet of the input interface, as listed in Table 2,

was used for PM10 data of the environmental objective.

The first column represents the grid square number. The

other columns denote the highest concentrations, aver-

age concentrations, etc. The 2nd to 4th worksheets were

used to store the SOX, NOX, and NMHC data, which

have the same formats as the first worksheet. The 5th

worksheet was used to store the social objective data.

The columns represent the grid square number, popula-

tions, etc. The 6th worksheet was used to store econom-

ic objective data. The columns denote the installation

costs of the different classes of stations.

The second MOPSSAQMN function computes the

environmental, social, and economic objective values for

different classes of monitoring stations in each grid

square. The database holding the data sets was developed

in MS Access. The modified BIE algorithm with the

constraint arrangement method was used in the third

function. The maximum sum of all the objective values

of grid squares can be obtained by fitting all the con-

straints. Since the efficient algorithm has been developed

and applied in MOPSSAQMN, the case in this study

with the possible combinations up to 8.15�1045 can be

successfully solved. Based on different constraints, the

case can be solved from oneminute to twentyminutes for

some different constraints by using a personal computer

which the central processor unit (CPU) speed is 1.5 GHz.

The sustainable air quality monitoring network alter-

natives can be obtained in the fourth function. If the

decision makers cannot accept the proposed alterna-

tives, then the first function is run again to modify the

weights or the constraints until satisfactory alternatives

are generated. Excel is also used as the output interface

to display the number of monitoring station of each

class, the site of each station, and the objective value

of the alternative. The graphic layout of the alternative

can be also obtained in the function. MOPSSAQMN can

be used to assist the users to obtain the appropriate

alternatives of the air quality monitoring networks con-

veniently and rapidly under different considerations.

4. Case study

Taoyuan County, which is located in northern Taiwan,

was used as the case of this study. The current monitoring

network of Taoyuan County is shown in Fig. 1. Six

monitoring stations are installed by the EPA, comprising

four general stations (AG1, AG2, AG3, and AG4), one

traffic station (AT1) and one background station (AB1).

Moreover, five stations are installed by the EPB of the

Taoyuan County Government, comprising two general

stations (BG1 and BG2), one traffic station (BT1), one

incinerator station (BW1) and one industry station (BI1).

Because the stations were installed in two different per-

iods, the suitability of the classes and sites of the stations

has been challenged. Many people in Taoyuan County

have requested that the air quality monitoring data be-

come more representative and that a new monitoring

station be installed. Therefore, the EPB of the Taoyuan

County Government considers that the air quality mon-

itoring network should be comprehensively re-planned

to improve the network’s effectiveness. MOPSSAQMN

was used to help plan a new monitoring network. Two

alternatives were generated, one the same number of

stations as in the current monitoring network, and one

with one more than the current monitoring network.

4.1. Identification of a system and the components

The administrative region of Taoyuan County is set

as the system boundary and was conceptually divided

into 59 grid squares (4�4 km) as the subsystem as

shown in Fig. 1.

4.2. Identification of the goals, objectives and

sub-objectives

The above framework, shown in Fig. 4, was used to

identify the goals, objectives and sub-objectives of the

air quality monitoring network planning.

4.3. Estimation of the emission quantities and concen-

trations of air pollutants

The emission quantities and concentrations of air

pollutants were estimated using the method described

Table 2

The example of the worksheet for inputting PM10

Grid no. Highest

concentration

Average

concentration

Concentration exceeding

the regulation standard

Variation range

of concentration

Total emission

quantity

Emission quantity

of industry sources

Emission quantity

of mobile sources

02 30.1 29.9 60.0 1.0 1.0 1.0 1.2

03 33.4 31.5 63.0 1.0 32.9 32.1 13.9

06 24.1 25.8 55.6 1.0 2.4 2.4 1.5

07 28.2 31.3 53.7 1.0 28.2 29.7 2.1

08 34.8 30.4 68.2 1.0 1.0 1.0 1.0

09 41.4 37.4 65.9 1.0 36.0 38.6 14.7

– – – – – – – –

72 24.9 21.6 68.8 1.0 1.0 1.0 1.0

73 23.0 19.2 71.1 1.0 1.0 1.0 1.0

Annotation: The data have been normalized.


above. The spatial and attribute data of the industry

pollution sources, and the air pollution petitions, were

collected from the EPB of Taoyuan County Government

(2003). The investigated data for estimating the emis-

sion quantity of the area pollution sources were gathered

from the EPA (2002). The data of roads, populations and

sensitive receptors were obtained from the Ministry of

the Interior (2002). The data of traffic volume were

collected from the Highway General Bureau of Ministry

of Transportation and Communications (2002). The

installing costs for each general, traffic, background,

Table 3

The example for inputting the data of grid 9

Environmental objective

Highest

concentration of

PM10

Average

concentration of

PM10

PM10 concentration

exceeding the

regulation standard

Variation ra

of concentra

of PM10

41.4 37.4 65.9 1.0

Highest

concentration of

SOX

Average

concentration of

SOX

SOX concentration

exceeding the

regulation standard

Variation ra

of concentra

of SOX

10.7 43.6 24.4 1.0

Highest

concentration of

NOX

Average

concentration of

NOX

NOX concentration

exceeding the

regulation standard

Variation ra

of concentra

of NOX

17.8 38.7 35.0 1.0

Highest

concentration

of NMHC

Average

concentration

of NMHC

NMHC concentration

exceeding the

regulation

standard

Variation ra

of concentra

of NMHC

75.0 51.4 65.8 75.0

Social objective

Population Sensitive receptor Traffic

21.2 7.7 15.7

Economic objective

General station Traffic station Background stati

94.4 86.9 94.4

Annotation: The data have been normalized.

industry, and incinerator station are assumed as NT$

7.3, 6.7, 7.3, 7.7, 7.3 million based on the practical data

of the EPA and EPB. As an example, Table 3 shows the

input data abstracted from obtained the MOPSSAQMN

worksheets for grid square 9 in Fig. 1.

4.4. Identification of the weights of each sub-objective

for each class of station

The weights of each sub-objective for each class of

station were obtained from Table 1.

nge

tion

Total emission

quantity of PM10

PM10 emission

quantity of

industry sources

PM10 emissions

from mobile

sources

36.0 38.6 14.7

nge

tion

Total emission

quantity of

SOX

SOX emission

quantity of industry

sources

SOX emission

quantity of

mobile sources

1.0 1.0 13.6

nge

tion

Total emission

quantity of

NOX

NOX emission

quantity of

industry sources

NOX emission

quantity of

mobile sources

1.0 1.0 17.0

nge

tion

Total emission

quantity of

NMHC

NMHC emission

quantity of industry

sources

NMHC emission

quantity of mobile

sources

1.0 1.0 12.1

Air pollution petition

1.0

on Industry station

100.0


4.5. Identification of the constraints

This study considered and identified the constraints

as follows:

(1) According to the regulations, the minimum num-

ber of monitoring stations in Taoyuan County was

set to 5 general stations, 1 traffic station, 1 back-

ground station, 1 industry station, and 1 incinerator

station.

(2) The maximum total installing cost was set to NT$

80,000,000.

(3) Based on the total area of Taoyuan County, the

minimum distance between two monitoring sta-

tions of the same class was set to 8 km.

(4) A general station could be installed in a grid square

if its population was more than 20,000 people.

(5) A traffic station could be installed in a grid square

if its traffic volume was more than 200,000 PCU/

year.

(6) A background station could be installed in a grid

square if its population was less than 2000

people.

(7) An industrial station could be installed in a grid

square if it was near to, and in the downwind

direction of, an industrial park.

Fig. 8. The alternative with the same number of st

4.6. Development of the multiple-objective planning

optimization model for sustainable air quality monitor-

ing networks

The multiple-objective planning optimization model

for sustainable air quality monitoring networks was

established in MOPSSAQMN using Visual Basic, MS

Excel, and MS Access.

4.7. Generation of sustainable air quality monitoring

network alternatives

Based on both the theoretical and practical consid-

erations, different conditions are discussed in this

study to generate different alternatives of air quality

monitoring networks using MOPSSAQMN. The

obtained alternatives are discussed as follows. The

possible maximum number of combinations for this

case is nearly to 8.15�1045.

4.8. Displaying a set of satisfactory alternatives

Two satisfactory alternatives were generated, with

the same number of stations as in the current monitor-

ing network, and with one more station than in the

current monitoring network.

ations as in the current monitoring network.

Fig. 9. The involving regions based on the current monitoring stations.


4.8.1. Alternative with the same number of stations as

in the current monitoring network

The number of existing monitoring stations in

Taoyuan County is more than the number required by

law. Furthermore, the density of monitoring station in

Taoyuan County is higher than that in most other

counties in Taiwan. Therefore, the number of monitoring

stations is assumed the same as the current condition.

The optimal alternative of air quality monitoring net-

work is described below, and is shown in Fig. 8.

(1) Six general monitoring stations should be in-

stalled in grid squares 9, 30, 39, 55, 59 and 63.

The current network already has general stations

in grid squares 30, 39 and 63.

(2) Two traffic monitoring stations should be in-

stalled in grid squares 30 and 39, as in the current

network.

(3) One background monitoring station should be

installed in grid square 24.

(4) One incinerator monitoring station should be in-

stalled in grid square 40, as in the current network.

Table 4

The characteristics of the involving regions based on the current network

Involving region Region 1 Region 2

Populations 646,400 78,967

Area (km2) 144 87

Emission quantity (ton/year) 33,759 4947

(5) One industrial monitoring station should be in-


The distribution of the monitoring stations in the

proposed network is better than that of the current

network. In the current network, the distances between

AG1, AG2, BG1 and BG2 stations are all less than 5

km. Therefore, the current data monitoring patterns of

these stations are similar. In this alternative, the dis-

tances between AG1, AG2, BG1 and BG2 stations are

all more than 8 km. Therefore, data monitoring pat-

terns could be distinguished more clearly than in the

current network.

The divided result of the involving regions based

on this proposal is also better than that based on the

current network. The six involving regions based on

the current monitoring stations are shown in Fig. 9

and the characteristics of the regions are listed in

Table 4. The largest area of any region is 213 km2.

Furthermore, the largest population of any region is

673,000 people, and the largest emission quantity is

33,759 ton/year.

Region 3 Region 4 Region 5 Region 6

672,796 105,051 120,829 113,535

213 173 180 75

24,792 5296 5682 8373

Fig. 10. The involving regions based on the proposed monitoring stations.


Fig. 10 illustrates the seven involving regions based

on this alternative, and Table 5 lists the characteristics of

the regions. One additional involving region is available

with the same number of stations as in the current

monitoring network. Additionally, the largest area of

any region in the proposed network is 173 km2, which

is 81% of the largest area in the current network. There-

fore, the concentrations of air pollutants in the involving

regions are more representative in the proposed network

than in the current network. The largest population of

any region in the proposed network is 537,000 people,

which is 80% of the largest area in the current network.

Therefore, the degree of human exposure to air pollution

can be more accurately estimated in the proposed net-

work than in the current network. Furthermore, air pol-

lution control strategies can be effectively assessed in the

proposed network because the largest emission quantity

of any region is 25,480 ton/year, which is 75% of that in

the current network. If the air quality is deteriorated, the

authorities can use the more representative and precise

monitoring data based on the proposed involving regions

to carry out emergency responses more effectively.

Table 5

The characteristics of involving regions based on the alternatives generated

Involving region Region 1 Region 2 Region

Populations 477,490 192,502 537,85

Area (km2) 107 163 12

Emission quantity (ton/year) 25,480 13,320 14,63

4.8.2. Alternative with the one more station than the

current monitoring network

As discussed above, Taoyuan County is considering

installing a new monitoring station. Since the new

network plan is better than the current network, the

class and site of the new station is also determined

using MOPSSAQMN based on the same objectives,

weights, and constraints as in the above alternative.

However, if the new station is installed, the budget

needs to be raised to NT$ 88 million. This proposed

air quality monitoring network is described below and

shown in Fig. 11.

(1) Six general monitoring stations should be in-

stalled in grid squares 9, 30, 39, 55, 59, and

63. The current network already has general

stations in grid squares 30, 39 and 63.

(2) Two traffic monitoring stations should be in-

stalled in grid squares 30 and 39, as in the current

network.

(3) One background monitoring station should be

installed in grid square 24.

by MOPSSAQMN

3 Region 4 Region 5 Region 6 Region 7

9 109,554 105,051 253,407 134,937

4 75 173 139 89

8 2646 5296 11,315 10,514

Fig. 11. The alternative with the one more station than the current monitoring network.


(4) One incinerator monitoring station should be in-


(5) Two industrial monitoring stations should be in-

stalled in grid squares 15 and 41. The current

network already has industrial station in grid

square 41.

In the proposed network, the new station should be

an industrial station and installed in grid square 15. The

effectiveness for monitoring industrial pollution emis-

sions of this proposed network is better than that of the

network in the above alternative. Taoyuan County has

seven important industrial parks which have been

pleaded for their air pollution many times. Therefore,

the alternative with a new industrial station is appro-

priate for Taoyuan County.

An installation schedule for the alternative has been

presented to the EPB of Taoyuan County Government.

The schedule considers the practical condition that the

stations are separately installed and managed by the

EPA and the EPB. In the short term, the new indus-

trial station BI2-N is planned to be installed in grid

square 15. In the medium term, the EPB BG1 and

BG2 general stations would be moved to grid squares

55 and 59, respectively. In the long term, the EPA

AG2 general station would be moved to grid square 9

and the EPA AB1 background station would be

moved to grid square 24.

Since the EPA and the EPB planned and installed

their monitoring stations separately, the current mon-

itoring network is not always effective. The analytical

results of this study indicate that distribution of the

monitoring stations, accuracy for exposure estimation

and effectiveness for strategy assessment of the pro-

posed network generated by MOPSSAQMN are better

than those of the current network. Therefore, the

competent authorities should progressively implement

the alternative to improve monitoring effectiveness.

Since the installation schedule considers both theoret-

ical and practical factors, the EPB of Taoyuan County

Government has recently started to implement the first

step of the schedule.

5. Conclusions

A multiple objective planning procedure for sustain-

able air quality monitoring networks was developed in

this study. Based on the principles of sustainable de-

velopment, the procedure simultaneously considered

the environmental, social and economic objectives, in-

cluding their sub-objectives and weights. The proce-

dure was developed by combining system analysis and


multiple objective planning. Hence, the alternative

monitoring network for which the environmental, so-

cial, and economic objectives have been simultaneously

considered can be generated using the procedure. This

study helps eliminate the defects of the current network

by considering environmental factors to be most im-

portant in planning monitoring networks.

Based on the above procedure, this study devel-

oped a multiple objectives planning system named

MOPSSAQMN using Visual Basic, MS Excel, and

MS Access. The environmental, social, and economic

objectives, weights, and constraints can be simulta-

neously input and calculated in MOPSSAQMN. The

modified BIE algorithm with the constraint arrange-

ment method was developed and applied to find the

maximum objective values in large possible combina-

tions. MOPSSAQMN can be used to assist the deci-

sion-makers to obtain the satisfactory alternatives of

the air quality monitoring networks conveniently and

rapidly based on different considerations.

MOPSSAQMN was employed to generate satisfac-

tory alternatives of monitoring network for Taoyuan

County. Two different network plans were considered.

In the optimal alternative of monitoring network with the

same number of stations as in the current network, three

general stations and one background station would be

moved. In terms of distribution of the monitoring sta-

tions, accuracy for exposure estimation and effectiveness

for strategy assessment, the proposed network was found

to be better than the current network.

In the network plan with an additional station, an

additional industrial station was proposed. This net-

work plan is better for monitoring industrial pollution

emissions than the other network plan.

An installation schedule for this network has been

proposed to the EPB of Taoyuan County Government.

For the practical consideration, the stations installed by

the EPB would be moved in the earlier steps, while the

stations installed by the EPA would be moved in the

later steps. Since this proposed network is more effec-

tive than the current network, the EPB of Taoyuan

County Government has recently started to implement

the first step of the schedule. In summary, the theory

and computer system developed in this study can be

used to assist the competent authorities to generate

appropriate alternatives for planning air quality moni-

toring networks.

Acknowledgment

The authors would like to thank the Environmen-

tal Protection Bureau of Taoyuan County Govern-

ment, Taiwan, R.O.C., for financially supporting

this research.

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