Definitions for criteria scores
1. Introduction
The air quality monitoring network (AQMN) is the essential part for air quality management, strategies planning, and performance assessment (Mofarrah and Husain, 2010). Existing methods of establishing ambient air quality monitoring networks typically evaluate the parameters related to air pollutant concentrations, emission source characteristics, atmospheric transport and dispersion, secondary reactions, deposition characteristics, and local topography (Harrison and Deacon, 1998; Bladauf et al., 2002). In most of the cases, AQMN is designed to measure the pollutants of concern such as particulate matter (PM10), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), nitrogen oxides (NOx), and total hydrocarbons (Chang and Tseng, 1999). Most of the reported AQMN design methods applied to a specific situation wherein one or two specific objectives are considered (Harrison and Deacon, 1998; Mofarrah and Husain, 2010). However, design of AQMN considering the multiple-criteria including multiple pollutants is complicated because air pollution phenomena are complex and dynamic in nature, depends on the meteorological and topographical conditions and involves not only irregularity of atmospheric movement but also uncertainty of human activities. The objective of this study is to develop a systematic approach for designing urban AQMN considering multi-criteria including multiple air pollutants in the system. The optimization is approached based on the utility scores gained from the fuzzy analytical hierarchy process associated with a candidate station, which is estimated over the representative zone (
2. Fuzzy Analytical Hierarchy Process
Fuzzy Analytical Hierarchy Process (AHP) is the extension of analytical hierarchy process (Saaty, 1980) is used for structuring the problem. AHP is an efficient method in which hierarchical structure is developed by a pair-wise comparison between any two criteria. The levels of the pair-wise comparisons range from 1 to 9, where ‘1’ represents that two criteria are equally important, while the other extreme ‘9’ represents that one criterion is absolutely more important than the other (Saaty, 1980). The AHP uses objective mathematics to process the subjective and personal preferences of an individual or a group of decision maker (Saaty, 1980). Generally, decision-making processes are subject to insufficiency of data and lack of knowledge (Tesfamariam and Sadiq, 2006). In fact, even if the data are available, criteria often contain linguistic definitions involving human judgment and subjectivity, which introduce uncertainties in the decision making process. In application to actual system traditional AHP is not so effective in capturing uncertainty and subjective judgments of different experts. The fuzzy AHP developed Zadeh (1965) is modified version of AHP, and can be used to handle the fuzziness of the data. It is easier to understand and can effectively handle both qualitative and quantitative data in the multiple-criteria problems. In this paper triangular fuzzy numbers (TFNs) are used to judge the qualitative information related to AQMN design. The TFN is defined by three real numbers, expressed as (
The TFNs for this study are developed in such a way that the most likely value has a membership grade of unity, considering the fact that the lower and upper bonds have a membership value of zero in that fuzzy set. The arithmetic of fuzzy set is little different than regular arithmetic. For example the fuzzy algebraic operations of two TFNs, namely
3. Methodology used in this work
A systematic methodology for designing urban AQMN is developed by using multiple criteria, which covered environmental (e.g., air quality), social (i.e., location sensitivity (
At the beginning, the study area is divided into a continuous grid system in which each grid represents a potential candidate location for monitoring station. Fuzzy synthetic optimization technique is used to identify the potential monitoring sites. The key functions of the methodology are described in the following sections.
3.1. Air quality exceedance index
An air quality index (
Criteria | Assigned scores |
Location sensitivity (LS)/ available amenities /grid | LSi |
No-basic facility | (1,2,4) |
Facilities of low value (e.g., storage facilities) | (1,3,5) |
Factories and industry | (2,4,6) |
Residential, parks | (3,5,7) |
Schools, churches, heritage places | (4,6,8) |
Hospitals, sensitive locations | (5,7,9) |
Population density (PD)/ number of people /grid | PDi |
<50 | (1,2,4) |
51- 250 | (1,3,5) |
251-450 | (2,4,6) |
451-650 | (3,5,7) |
650-850 | (4,6,8) |
"/>850 | (5,7,9) |
Population sensitivity (PS)/ sensitive population /grid | PSi |
<10 | (1,2,4) |
11-20 | (1,3,5) |
21-30 | (2,4,6) |
31-40 | (3,5,7) |
41-50 | (4,6,8) |
"/>50 | (5,7,9) |
Cost criteria | Ci |
Installation cost "/>$7000 | (1,2,4) |
$7000-$6000 | (1,3,5) |
$5900-$5000 | (2,4,6) |
$4900-$4000 | (3,5,7) |
$3900-$3000 | (4,6,8) |
Installation cost <$3000 | (5,7,9) |
3.2. Cost objective
An important objective of any AQMN is to minimization of its cost. This objective can also be interpreted as a budgetary constraint. Cost criteria consideration in this evaluation is installation cost. Generally, the installation cost is varied depending on the site location, local labour force and communication facilities. To compare the installation cost within the area of interest a cost index (
where,
3.3. Definitions of social criteria
In each grid, the social criteria such as
3.4. Determining of screening scores
The screening score is the composition components of an environmental parameters (eq. 2), cost objective (eq. 3), and social criteria (eq. 4). It is defined by a dimension less function, called screening score (
where,
The
where,
3.5. Determination of representative zone (RZ)
After the identification and quantification of the objectives of the monitoring network, the second step is to determine the degree of representativeness (
The
where,
where,
where,
4. Application of the methodology
Riyadh, the capital city of Saudi Arabia was considered to demonstrate the proposed methodology. Riyadh is one of the major industrial cities in the Kingdom of Saudi Arabia; it has multiple types of heavy and light industries such as oil refinery, power plant, cement industry etc. The population of the city is above four million with very high growth rates. Therefore, to maintain the air quality standard the city authorities have planned to re-assess the current air quality monitoring network. There are six existing air quality monitoring stations in Riyadh city owned and operated by different organizations. Most of these existing air AQMNs are not working properly or not serving at satisfactory levels. The main objective of this study is to design the air quality monitoring network for Riyadh city and to identify the optimal station locations to satisfy the future air quality monitoring demands. To design the AQMN three major emission sources such as point sources, area sources and line sources were considered. The detailed emission inventory can be found elsewhere (Mofarrah and Husain, 2010). The major point sources in Riyadh city are power plants, refinery and cement industries. The old and new industrial cities under development are considered as the area source. The automobile sources for the selected major roads based on traffic counts, composition of traffic, and model years were considered as the line sources in this study. The database for emission inventory was developed based on production rate, fuel consumption and the emission factors as suggested by USEPA.
At the beginning, the study area (40km x 60km) was conceptually divided into 441 square grids as subsystems. Each grid component includes environmental parameters (i.e., sulfur dioxide (SO2), nitrogen dioxides (NO2), carbon monoxide (CO) and fine particulate matters (PM10)), social objectives (i.e
Pollutant | Measurement period | Limit |
SO2 | 30 day period, one hour average | 730 µg/m3 |
12 month period, 24 hour average | 365 µg/m3 | |
12month period, annual average | 80(µg/m3) | |
Inhalable Particulates (fpm) | 12-month period, the 24-hour maximum | 340(µg/m3) |
12-month period, the annual average | 80(µg/m3) | |
Nitrogen Oxides Defined as Nitrogen Dioxide (NO2) | 30 day period, the one-hour average | 660(µg/m3) |
12-month period, the annual average | 100(µg/m3) | |
Carbon Monoxide (CO) | 30-day period, the one-hour average | 40 (mg/m3) |
30-day period, the 8-hour average | 10(mg/m3) |
4.1. Criteria weight computation
Based on the importance of each criterion on AQMN design, a fuzzy pair-wise comparisons matrix (PCM)
How important is A relative to B? | Preference index (Saaty 1988) | Fuzzy value ( l, m, u; Jie et al. 2006) |
Equally important | 1 | (1, 1, 1) |
Moderately more important | 3 | (1,3,5) |
Strongly more important | 5 | (3,5,7) |
Very strongly more important | 7 | (5,7,9) |
Overwhelmingly more important | 9 | (7,9,11) |
Intermediate values (Need to judge two) | 2 | (1,2,4) |
4 | (2,4,6) | |
6 | (4,6,8) | |
8 | (6,8,10) |
ÃF= | C1 | C2 | C3 | C4 | C5 | |
C1 | 1, 1, 1 | 0.33,0.5,1.0 | 0.25,0.33,1 | 0.33,0.5,1 | 2,3,4 | |
C2 | 1, 2,3 | 1, 1, 1 | 0.33,0.5,1 | 0.17,0.2,0.25 | 0.5,1,1 | |
C3 | 1,3,4 | 1,2,3 | 1, 1, 1 | 0.2,0.25,0.33 | 0.33,0.5,1 | |
C4 | 1,2,3 | 4,5,6 | 3,4,5 | 1, 1, 1 | 0.17,0.2,0.25 | |
C5 | 0.25,0.33,0.50 | 1,1,2 | 1,2,3 | 4,5,6 | 1, 1, 1 |
After constructing, relative weights of each criterion is calculated by using fuzzy extent analysis (Lee et al., 2006) as follows:
Row | Left | Middle | right |
The first row sum | 3.92 | 5.34 | 8.00 |
The 2nd row sum | 3.00 | 4.70 | 6.25 |
The 3rd row sum | 3.53 | 6.75 | 9.33 |
The 4th row sum | 9.17 | 12.20 | 15.25 |
The 5th row sum | 7.25 | 9.33 | 12.50 |
Total | 26.87 | 38.32 | 51.34 |
Criteria | Left | Middle | right |
LS (C1) | 3.92/51.34= 0.0763 | 5.34/38.32 = 0.1393 | 8.0/26.87 = 0.2978 |
PS (C2) | 3.00/51.34= 0.0584 | 7.40/38.32 = 0.1227 | 6.25/26.87 = 0.2326 |
Cost (C3) | 3.53/51.34= 0.0688 | 6.75/38.32 = 0.1762 | 9.33/26.87 = 0.3474 |
AQ (C4) | 9.17/51.34= 0.1786 | 12.20/38.32 = 0.3184 | 15.25/26.87 = 0.5676 |
PD (C5) | 7.25/51.34= 0.1412 | 9.33/38.32 = 0.2436 | 12.5/26.87 = 0.4653 |
The weights of
Criteria | Weights | Sub-criteria | |||
CO | SO2 | PM10 | NO2 | ||
LS (C1) | w1=(0.0763,0.1393,0.2978) | - | - | - | - |
PS (C2) | w2=(0.0584,0.1227,0.2326) | - | - | - | - |
Cost (C3) | w3=(0.0688,0.1762,0.3474) | - | - | - | - |
AQ (C4) | w4=(0.1786,0.3184, .5676) | C41 = 20% of C4 | C42 = 25% of C4 | C43 = 35% of C4 | C44 = 20% of C4 |
PD (C5) | w5=(0.1412,0.2436,0.4653) | - | - | - |
5. Results and discussions
The concentration level of each pollutant was compared with the Saudi National Air quality standards (Table 2) to calculate the air quality index (
Grid no | AQI (SO2) | AQI (NO2) | AQI (CO) | AQI (PM10) | Assigned scores (LS) | Assigned scores (PS) | Assigned scores (CI) | Assigned scores (PD) |
A1 | 22.7798 | 38.8779 | 58.3944 | 27.4490 | (1,2,4) | (1,2,4) | (3,5,7) | (5,7,9) |
A2 | 22.4193 | 38.9320 | 65.7578 | 24.7020 | (1,2,4) | (1,2,4) | (3,5,7) | (5,7,9) |
A3 | 31.5411 | 51.0081 | 81.7773 | 32.3196 | (1,3,5) | (1,3,5) | (3,5,7) | (4.6.8) |
A4 | 45.5648 | 69.3000 | 97.5538 | 45.8194 | (2,4,6) | (2,4,6) | (3,5,7) | (5,7,9) |
A5 | 66.8583 | 96.5431 | 107.2574 | 58.2306 | (2,4,6) | (1,2,4) | (3,5,7) | (4,6,8) |
A6 | 46.5463 | 78.2129 | 109.7475 | 45.0471 | (1,2,4) | (1,2,4) | (3,5,7) | (5,7,9) |
. . | . . | . . | . . | . . | . . | . . | . . | |
A436 | 2.486 | 14.478 | 19.775 | 24.351 | (1,2,4) | (1,2,4) | (3,5,7) | (5,7,9) |
A437 | 3.204 | 17.229 | 21.650 | 21.579 | (1,2,4) | (1,2,4) | (3,5,7) | (5,7,9) |
A438 | 5.808 | 21.943 | 25.775 | 17.283 | (1,3,5) | (1,3,5) | (3,5,7) | (4.6.8) |
A439 | 13.402 | 69.518 | 73.369 | 17.328 | (2,4,6) | (2,4,6) | (3,5,7) | (5,7,9) |
A440 | 31.328 | 346.130 | 343.296 | 21.558 | (1,2,4) | (1,2,4) | (3,5,7) | (5,7,9) |
A441 | 5.118 | 21.599 | 23.418 | 16.571 | (1,2,4) | (1,2,4) | (3,5,7) | (5,7,9) |
Grid no | AQI (SO2) | AQI (NO2) | AQI (CO) | AQI (PM10) | (LS) | (PS) | (CI) | (PD) |
A1 | 1.017,1.813,3.232 | 1.389,2.476,4.413 | 2.086,3.719,6.629 | 1.716,3.059,5.453 | 0.076,0.279,1.191 | 0.058,0.245,0.93 | 0.206,0.881,2.432 | 0.706,1.705,4.188 |
A2 | 1.001,1.785,3.181 | 1.391,2.479,4.42 | 2.349,4.187,7.465 | 1.544,2.753,4.907 | 0.076,0.279,1.191 | 0.058,0.245,0.93 | 0.206,0.881,2.432 | 0.706,1.705,4.188 |
A3 | 1.408,2.511,4.476 | 1.822,3.248,5.79 | 2.921,5.208,9.283 | 2.02,3.602,6.421 | 0.076,0.418,1.489 | 0.058,0.368,1.163 | 0.206,0.881,2.432 | 0.565,1.462,3.722 |
A4 | 2.034,3.627,6.466 | 2.475,4.413,7.867 | 3.485,6.212,11.074 | 2.864,5.106,9.102 | 0.153,0.557,1.787 | 0.117,0.491,1.396 | 0.206,0.881,2.432 | 0.706,1.705,4.188 |
A5 | 2.985,5.322,9.487 | 3.449,6.148,10.96 | 3.831,6.83,12.176 | 3.64,6.489,11.568 | 0.153,0.557,1.787 | 0.117,0.491,1.396 | 0.206,0.881,2.432 | 0.565,1.462,3.722 |
A6 | 2.078,3.705,6.605 | 2.794,4.981,8.879 | 3.92,6.989,12.459 | 2.816,5.02,8.949 | 0.076,0.279,1.191 | 0.058,0.245,0.93 | 0.206,0.881,2.432 | 0.706,1.705,4.188 |
. . | . . | . . | . . | . . | . . | . . | . . | . . |
A436 | 0.646,1.152,2.054 | 0.706,1.259,2.245 | 0.87,1.551,2.764 | 1.056,1.883,3.357 | 0.076,0.279,1.191 | 0.058,0.245,0.93 | 0.206,0.881,2.432 | 0.706,1.705,4.188 |
A437 | 0.769,1.371,2.445 | 0.773,1.379,2.458 | 0.771,1.374,2.45 | 1.362,2.427,4.327 | 0.076,0.418,1.489 | 0.058,0.368,1.163 | 0.206,0.881,2.432 | 0.565,1.462,3.722 |
A438 | 0.98,1.747,3.114 | 0.921,1.641,2.926 | 0.617,1.101,1.962 | 2.468,4.4,7.843 | 0.153,0.557,1.787 | 0.117,0.491,1.396 | 0.206,0.881,2.432 | 0.706,1.705,4.188 |
A439 | 3.104,5.534,9.865 | 2.621,4.672,8.329 | 0.619,1.103,1.967 | 5.695,10.153,18.098 | 0.153,0.557,1.787 | 0.117,0.491,1.396 | 0.206,0.881,2.432 | 0.565,1.462,3.722 |
A440 | 15.455,27.552,49.116 | 12.263,21.861,38.971 | 0.77,1.373,2.447 | 13.312,23.732,42.307 | 0.076,0.279,1.191 | 0.058,0.245,0.93 | 0.206,0.881,2.432 | 0.706,1.705,4.188 |
A441 | 0.964,1.719,3.065 | 0.836,1.491,2.658 | 0.592,1.055,1.881 | 2.175,3.877,6.912 | 0.382,0.975,2.68 | 0.117,0.491,1.396 | 0.138,0.705,2.084 | 0.706,1.705,4.188 |
The crisp values of weighted fuzzy screening scores (
Grid no | Grid screening scores (SC) | Crisp values of (SCx) |
A1 | 0.065,0.133,0.278 | 0.1586 |
A2 | 0.067,0.137,0.285 | 0.1628 |
A3 | 0.076,0.16,0.326 | 0.1871 |
A4 | 0.099,0.198,0.392 | 0.2298 |
A5 | 0.112,0.223,0.436 | 0.2569 |
A6 | 0.099,0.194,0.388 | 0.2273 |
. . | . . | . . |
A436 | 0.045,0.098,0.217 | 0.1202 |
A437 | 0.043,0.103,0.223 | 0.1231 |
A438 | 0.055,0.12,0.253 | 0.1428 |
A439 | 0.083,0.171,0.344 | 0.1990 |
A440 | 0.231,0.429,0.806 | 0.4887 |
A441 | 0.06,0.125,0.261 | 0.1483 |
To determine the degree of representativeness (Dr) and the representative zone (RZ) associated with each candidate monitoring location, three different cutoff values (Rc), 0.45, 0.60 and 0.75 were used separately and compared with the coefficient in the spatial correlation (
For a specific situation the agency can choose either a high or a low value of Rc. A high Rc based network may not necessarily cover more area, but the covered region is well represented. On the other hand a low Rc based network, would offer more coverage of the region, but the covered region may not be satisfactorily represented (Mofarrah and Husain, 2010; Elkamel et al., 2008). The final decision in such a case is of course dependent on the respective agency. It should be noted that the design of an air quality network with higher cutoff values (
6. Conclusions
The AQMN represents an essential tool to monitor and control atmospheric pollution. The use of some specific criteria in conjunction with the mathematical models provides a general approach to determine the optimal number of monitoring stations. In this study, fuzzy multiple-criteria approach in conjunction with the degree of representativeness technique was used to develop optimal AQMN design. The triangular fuzzy numbers (TFNs) were used to capture the uncertainty associated from human judgement (e.g., assigning weights, scoring). The coverage area of the monitoring station is an essential part of an AQMN which was determined on the basis of representative zone. The effect of the correlation coefficient as well as the cutoff values on coverage of the network was also studied by changing the cutoff values. This methodology provides a systematic approach, which allows multiple-criteria and multiple pollutants in AQMN design. However, the design of an AQMN depends on many site-specific issues and good upfront planning is therefore crucial in properly assessing the problem and designing an optimal AQMN.
Acknowledgments
Financial support provided by the Natural Science and Engineering Research Council of Canada (NSERC) is highly appreciated.
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