10 © 2021 by the authors; licensee Asian Online Journal Publishing Group Asian Review of Environmental and Earth Sciences Vol. 8, No. 1, 10-17, 2021 ISSN(E) 2313-8173 / ISSN(P) 2518-0134 DOI: 10.20448/journal.506.2021.81.10.17 © 2021 by the authors; licensee Asian Online Journal Publishing Group Analytic Hierarchy Process–Based Environmental Criteria Weight Assessment and Prioritization for Suitable Petroleum Refining Plant siting in Edo State, Nigeria Aweh D.S.1 Igbokwe J. I.2 Ejikeme, J.O.3 ( Corresponding Author) 1Department of Surveying and Geoinformatics, Nnamdi Azikiwe University, Awka, Nigeria & Department of Surveying and Geoinformatics, Auchi Polytechnic, Auchi, Nigeria. 2,3Department of Surveying and Geoinformatics, Nnamdi Azikiwe University, Awka, Nigeria. Abstract In this paper, Saaty’s Analytical Hierarchy Process (AHP) of Multi-Criteria Evaluation (MCE) technique was used to estimate the weights of some selected environmental criteria in the suitable siting of a Petroleum refining Plant. In doing this, fifteen (15) key environmental criteria, based on reviewed literature, specifications, and Environmental Impact Assessment Act (EIA) on the siting of petroleum refining plants were selected for this study, which includes Ground Water level, Proximity to major settlement, Proximity to Water bodies, Proximity to the Electricity transmission line, Land use / Land cover, Topography, Sensitive and Protected Areas proximity, Critically Polluted Areas (refuse dumpsites, landfill sites, etc.), Proximity to Existing Industrial areas, Major Transportation network, Flood zones, Proximity to crude oil supply (oil well), Wind speed, Large-scale Mines and Population density. The result from the calculated environmental criteria weights show proximities of Petroleum refinery to existing industrial areas; source of crude oil and Landuse/Landcover to have the highest influence value, followed by proximity to Water Body; Population Density; Groundwater Level; Major Settlement; Wind Speed; Transportation Network, Topography, while Sensitive/Protected Areas, Critically Polluted areas and Flood zones with the least influence value. The result of this AHP – based weight assessment will be used generally by Planners, Investors, Environmental Managers, and Oil and Gas experts as a general guide in the prioritization of criteria weights based on the influence of one criterion over the other in the suitability siting of petroleum refining plant. Keywords: Criteria weight, Site selection, AHP-BASED MCE, Petroleum refinery, Edo State. Citation | Aweh D.S., Igbokwe J. I., Ejikeme, J.O. (2021). Analytic Hierarchy Process–Based Environmental Criteria Weight Assessment and Prioritization for Suitable Petroleum Refining Plant siting in Edo State, Nigeria. Asian Review of Environmental and Earth Sciences, 8(1): 10-17. History: Received: 22 January 2021 Revised: 26 February 2021 Accepted: 18 March 2021 Published: 20 April 2021 Licensed: This work is licensed under a Creative Commons Attribution 3.0 License Publisher: Asian Online Journal Publishing Group Acknowledgement: All authors contributed to the conception and design of the study. Funding: This study received no specific financial support. Competing Interests: The authors declare that they have no conflict of interests. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study was reported; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. Ethical: This study follows all ethical practices during writing. Contents 1. Introduction ...................................................................................................................................................................................... 11 2. Materials and Methods ................................................................................................................................................................... 11 3. Result and Analysis .......................................................................................................................................................................... 16 4. Conclusion ......................................................................................................................................................................................... 17 References .............................................................................................................................................................................................. 17 http://crossmark.crossref.org/dialog/?doi=10.20448/journal.506.2021.81.10.17&domain=pdf&date_stamp=2017-01-14 http://creativecommons.org/licenses/by/3.0/ http://creativecommons.org/licenses/by/3.0/ https://www.asianonlinejournals.com/index.php/AREES/article/view/2817 Asian Review of Environmental and Earth Sciences, 2021, 8(1): 10-17 11 © 2021 by the authors; licensee Asian Online Journal Publishing Group Contribution of this paper to the literature In this paper, Saaty’s Analytical Hierarchy Process (AHP) of Multi-Criteria Evaluation (MCE) technique was used to estimate the weights of some selected environmental criteria in the suitable siting of a Petroleum refining Plant. 1. Introduction The petroleum industry provides most of the needed fuel for everyday use for industrial, commercial, and domestic purposes. In fact, modern industrial civilization depends on petroleum and its products; the physical structure and way of life of the suburban communities that surround the great cities are the result of an ample and inexpensive supply of petroleum [1, 2]. In addition, the goals of developing countries - to exploit their natural resources and to supply foodstuffs for the burgeoning populations are based on the assumption of petroleum availability. Most of the world’s supply of petroleum comes from the Middle East, Nigeria, Angola, Libya, etc. [3- 6]. Despite this monumental projected importance of modular petroleum plants in remedying unemployment and economic issues, the adverse impact of these modular refineries on their immediate environment and beyond cannot be underemphasized. Therefore, stakeholders should be fully aware of these implications and should take necessary steps while setting up industries so as to minimize the possible adverse effects on environmental resources and quality of life, through careful selection and assessment of identified criteria [7]. The identification of a suitable site for Industrial Development is one of the critical issues in the process of planning, starting, expanding, or changing the location of industrial systems of all kinds [8]. One of the main objectives in industrial site selection is finding the most appropriate site with desired conditions defined by the selected criteria. In a site selection process, the analyst strives to determine the optimum location that would satisfy specified conditions which is a direct determinant of the influence level (weight) of the selected criteria. The most frequently raised problem in MCE is how to establish weights for a set of activities according to importance Mohamed, et al. [9]. Saaty [10] has shown that this weighting of activities in MCE can be dealt with using a theory of measurement in a hierarchical structure. The analytic hierarchy process (AHP) is a comprehensive, logical, and structural framework, which allows improving the understanding of complex decisions by decomposing the problem in a hierarchical structure. The incorporation of all relevant decision criteria and their pairwise comparison allows the decision-maker to determine the trade-offs among objectives. Such multi-criteria decision problems are typical for industrial site selection. The AHP allows decision-makers to model a complex problem in a hierarchical structure showing the relationship of the goal, objectives (criteria), sub-objectives, and alternatives [9]. Uncertainties and other influencing factors can also be included. It does not only supports decision makers by enabling them to structure complexity and exercise judgment, but also allows them to incorporate both objective and subjective considerations in the decision process [10]. 2. Materials and Methods 2.1. Study Area The area selected for this study, Edo state is in the South-South geopolitical zones of Nigeria. Edo is a state in southern Nigeria. It was created on 27th August 1991 when the former Bendel State (now Edo and Delta) was separated into these two states. Edo state is located between latitudes 7°18'8.61"N to 5°52'48.77"N of the equator and longitudes 6°36'59.29"E to 5°12'58.38"E of the Greenwich meridian, with a surface area of approximately 19,603 km2. Its capital is Benin City. With a population of 3,218,332 million people (2006 Census). It is bounded in the north and east by Kogi State, in the south by Delta State, and in the west by Ondo State. Economically, Edo state is more of a commercial and agricultural driven state. The majority of its population are either farmers or traders. There are various solid mineral deposits within the state such as; industrial clay, silica, lignite, kaolin, tar sand, decorative rocks, limestone, etc. still waiting to be properly used to their full potential. Edo state also has deposits of crude oil and this makes it one of the petroleum-producing states in Nigeria. Figure 1, is the Map of Nigeria showing the study area (Edo state) and the satellite image of Edo state. 2.2. Research Instrument A total of one hundred (100) experts which consist of Planners, Oil and Gas Engineers, and Geomaticians/Environmentalists within Edo state were proposed via a simple random sampling system. However, since the population is finite, it becomes imperative to apply a statistical model in determining the sample size. Thus, Yamane [11] formula was employed, at an acceptable level of probability of whose confidence level is set at 0.05 as displayed in Equation 1. Thus; n = 𝑁 1+𝑁(𝑒)2 (1) Where; n = Sample size e = level of significance (e = 0.05) N = Population size (N= 100) 1 = Constant Applying the above formula, population size was computed as follows: n = 100 1+100 (0.05)2 n = 100 1+100 (0.0025) = 100 1.25 = 80 Asian Review of Environmental and Earth Sciences, 2021, 8(1): 10-17 12 © 2021 by the authors; licensee Asian Online Journal Publishing Group 2.3. Selection of Criteria Based on reviewed pieces of literature and guidelines from relevant authorities, in this case, Department of Petroleum Resources, Nigerian National Petroleum Company, and Environmental Impact Assessment Act (EIA) on the siting of petroleum refining plants, the following fifteen (15) criteria (factors/constraints) were identified and considered in this research. Table 1 shows the criteria (factors/constraints) and the applied codes. Figure-1. Map of Nigeria showing the study area (Edo state) and the satellite image of Edo State. Asian Review of Environmental and Earth Sciences, 2021, 8(1): 10-17 13 © 2021 by the authors; licensee Asian Online Journal Publishing Group Table-1. Criteria (factors / constraints) and Codes Used. S/n Criterion ( Input) Factors/ Constraints Code 1 Water bodies proximity Factor MWB 2 Electricity transmission line proximity Factor ETL 3 Land use / Land cover Factor LULC 4 Topography Factor TOPO 5 Sensitive and protected areas proximity Constraint SPA 6 Critically Polluted areas (refuse dump sites, landfill sites etc.) Constraint CPA 7 Proximity to Existing Industrial areas Factor EIA 8 Transportation network Factor MTN 9 Flood zones Constraint FZ 10 Raw material supply (Crude oil) Factor SRM 11 Wind speed Factor WS 12 Large-scale Mines Constraint LSM 13 Population density Factor PD 14 Proximity to major settlement Constraint MS 15 Ground Water level Constraint GWL 2.4. Pairwise Comparisons The pairwise comparisons method was developed by Saaty [10] in the context of the Analytical Hierarchy Process (AHP). This method of criteria weight assessment involves pairwise comparisons to create a ratio matrix. As input, it takes the pairwise comparisons of the parameters in numerical form (i.e. on a scale of 1 to 9) and produces their relative weights as output. One (1) on the scale means that the two factors are equally important and 9 means that the one factor is absolutely more important than the other as shown in Table 2. If a factor is less important than another then this is indicated by reciprocals of the 1 to 9 values (i.e.1/2 to 1/9). Table-2. Preferences made on 1-9 Scale. Numeric Rating (Judgment value) AHP Scale of Importance for comparison pair 9 Extreme importance 8 Very strong to extremely 7 Very strong importance 6 Strongly to very strong 5 Strong importance 4 Moderately to strong 3 Moderate importance 2 Equally to moderately 1 Equal importance Source: Saaty [10]. 2.4.1. Pairwise Comparison Matrix Formation The pairwise matrix (Table 3) was formed using the judgment value of comparison as the matrix elements and following the basic formation rules as established by Saaty [10] and Mohamed, et al. [9]. For example, the judgment value of MWB (LHS) against ETL (RHS) is “6”, therefore, the matrix element in a cell, second roll, the third column (2, 3) is “6”; the judgment value of FZ (LHS) against TOPO (RHS) is “3” therefore, the matrix element in a cell, tenth roll, fifth column (10, 5) is “3”; (i.e. 1/3), etc. Table-3. Pair-wise comparison matrix of the study. MWB ETL LULC TOPO SPA CPA EIA MTN FZ SRM WS LSM PD MS GWL MWB 1.00 6.00 0.25 3.00 7.00 5.00 0.25 5.00 7.00 0.33 4.00 6.00 1.00 4.00 1.00 ETL 0.17 1.00 0.11 0.33 1.00 2.00 0.11 0.50 2.00 0.11 0.33 1.00 0.17 0.50 0.17 LULC 4.00 9.00 1.00 6.00 9.00 8.00 1.00 7.00 9.00 1.00 6.00 8.00 3.00 7.00 3.00 TOPO 0.33 3.00 0.17 1.00 3.00 2.00 0.17 1.00 3.00 0.17 1.00 3.00 0.33 2.00 0.33 SPA 0.14 1.00 0.11 0.33 1.00 1.00 0.11 0.25 1.00 0.11 0.25 1.00 0.17 0.50 0.17 CPA 0.20 0.50 0.13 0.50 1.00 1.00 0.11 0.33 1.00 0.11 0.33 1.00 0.20 0.33 0.20 EIA 4.00 9.00 1.00 6.00 9.00 9.00 1.00 6.00 9.00 1.00 6.00 9.00 4.00 6.00 4.00 MTN 0.20 2.00 0.14 1.00 4.00 3.00 0.17 1.00 3.00 0.17 1.00 3.00 0.50 1.00 0.50 FZ 0.14 0.50 0.11 0.33 1.00 1.00 0.11 0.33 1.00 0.11 0.25 1.00 0.20 0.33 0.20 SRM 3.00 9.00 1.00 6.00 9.00 9.00 1.00 6.00 9.00 1.00 5.00 9.00 4.00 6.00 4.00 WS 0.25 3.00 0.17 1.00 4.00 3.00 0.17 1.00 4.00 0.20 1.00 4.00 0.50 1.00 1.00 LSM 0.17 1.00 0.13 0.33 1.00 1.00 0.11 0.33 1.00 0.11 0.25 1.00 0.17 0.25 0.25 PD 1.00 6.00 0.33 3.00 6.00 5.00 0.25 2.00 5.00 0.25 2.00 6.00 1.00 2.00 2.00 MS 0.25 2.00 0.14 0.50 2.00 3.00 0.17 1.00 3.00 0.17 1.00 4.00 0.50 1.00 1.00 GWL 1.00 6.00 0.33 3.00 6.00 5.00 0.25 2.00 5.00 0.25 1.00 4.00 0.50 1.00 1.00 Total 15.85 59.00 5.12 32.33 64.00 58.00 4.97 33.75 63.00 5.09 29.42 61.00 16.23 32.92 18.82 Basic Rule: If the criterion in the column is preferred to the criteria in the row, then, the inverse of the judgment value is given, otherwise, the actual judgment value. Note: The numbers represent expert judgment values of the importance of one factors in relation to other factors in the suitable siting of a modular refinery. 2.5. Demonstration of Satty’s Ranking Scale for Judgment Values Asian Review of Environmental and Earth Sciences, 2021, 8(1): 10-17 14 © 2021 by the authors; licensee Asian Online Journal Publishing Group By this judgment value, it means that siting the refinery in close proximity to major water bodies (MWB) is strongly important than its proximity to electricity transmission line (ELT). By this judgment value, it means that topography (slope) is moderately important than proximity to flood zone (FZ). By this judgment value, Population density is slightly more important than Major Settlement. 2.6. Computation of the Criterion Weights This involves the following operations; (a) Formation of normalized pairwise comparison matrix. (b) Formation of the prioritization weight matrix. 2.6.1. Normalized Pairwise Comparison Matrix Formation The normalized pairwise comparison matrix was formed from the Pairwise Comparison Matrix in Table 4. Here, the elements of the normalized matrix were formed by dividing the elements of each column by their sum total. For instance, the first element of the first column of the normalized matrix is computed thus; 1/15.85 = 0.06. Where 1 is the first element of the first column of the pairwise comparison matrix and 15.85 is the sum total of its column. Table-4. Normalized pairwise comparison matrix. MWB ETL LULC TOPO SPA CPA EIA MTN FZ SRM WS LSM PD MS GWL MWB 0.06 0.10 0.05 0.09 0.11 0.09 0.05 0.15 0.11 0.07 0.14 0.10 0.06 0.12 0.05 ETL 0.01 0.02 0.02 0.01 0.02 0.03 0.02 0.01 0.03 0.02 0.01 0.02 0.01 0.02 0.01 LULC 0.25 0.15 0.20 0.19 0.14 0.14 0.20 0.21 0.14 0.20 0.20 0.13 0.18 0.21 0.16 TOPO 0.02 0.05 0.03 0.03 0.05 0.03 0.03 0.03 0.05 0.03 0.03 0.05 0.02 0.06 0.02 SPA 0.01 0.02 0.02 0.01 0.02 0.02 0.02 0.01 0.02 0.02 0.01 0.02 0.01 0.02 0.01 CPA 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.01 0.02 0.02 0.01 0.02 0.01 0.01 0.01 EIA 0.25 0.15 0.20 0.19 0.14 0.16 0.20 0.18 0.14 0.20 0.20 0.15 0.25 0.18 0.21 MTN 0.01 0.03 0.03 0.03 0.06 0.05 0.03 0.03 0.05 0.03 0.03 0.05 0.03 0.03 0.03 PFZ 0.01 0.01 0.02 0.01 0.02 0.02 0.02 0.01 0.02 0.02 0.01 0.02 0.01 0.01 0.01 SRM 0.19 0.15 0.20 0.19 0.14 0.16 0.20 0.18 0.14 0.20 0.17 0.15 0.25 0.18 0.21 WS 0.02 0.05 0.03 0.03 0.06 0.05 0.03 0.03 0.06 0.04 0.03 0.07 0.03 0.03 0.05 LSM 0.01 0.02 0.02 0.01 0.02 0.02 0.02 0.01 0.02 0.02 0.01 0.02 0.01 0.01 0.01 PD 0.06 0.10 0.07 0.09 0.09 0.09 0.05 0.06 0.08 0.05 0.07 0.10 0.06 0.06 0.11 PMS 0.02 0.03 0.03 0.02 0.03 0.05 0.03 0.03 0.05 0.03 0.03 0.07 0.03 0.03 0.05 GWL 0.06 0.10 0.07 0.09 0.09 0.09 0.05 0.06 0.08 0.05 0.03 0.07 0.03 0.03 0.05 Total 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2.6.2. Prioritization Weight Matrix Formation In computing the element of this matrix, the normalized sum of each row is divided by the total number of its criteria. The obtained values (average) provides the relative weights of the criteria being compared as described in the studies by Permadi [12] and Odell [4]. For instance, the criteria weight of Major Water Bodies (MWB) in this can be obtained thus; 0.06 + 0.10 + 0.05 + 0.09 + 0.11 + 0.09 + 0.05 + 0.15 + 0.11 + 0.07 + 0.14 + 0.10 + 0.06 + 0.12 + 0.05 (sum of the elements in first row) = 1.35 Total number of criteria in first row = 15 Therefore, the weight of Major Water bodies (MWB) = 1.35/15 = 0.09 Weight of the criteria in percentage = 0.09 x 100 = 9% 2.6.3. Consistency Ratio Check In checking the reliability of the respondent’s judgments, the consistency ratio (CR) for the judgment values provided by the consulted experts were calculated. According to Saaty [10] if this ratio is found to be less than 10% (CR< 0.1) then the respondent judgment is consistent if otherwise (i.e. CR> 0.1) then the expert or respondent judgment is not consistent. Calculating the consistency ratio of a judgment involves the following operations: Formation of the weight as factor matrix by introducing the weights of the criteria as the first row of the matrix of comparison. Calculation of the weighted column and weighted sum (consistency vector) by multiplying matrix of comparisons by the vector of priorities (corresponding weights) to get a new column vector. Calculation of the Average Value of the Consistency Vector by dividing the first component of a new column vector (first element of the weighted sum matrix) by the first component of priorities vector (first element of the weight matrix), the second component of new column vector by the second component of priorities vector, and so on. Calculation of the consistency index by adding up all the elements of the consistency vector (λ max) and applying the mathematical expression CI = (λ-n) / (n-1) where n is the total number of criteria under consideration (in this case 15) Finally, estimating the consistency ratio via the mathematical expression CR = CI/RI according to Saaty [10] where CI is the consistency index and RI the random index (as given by Saaty [10]). Table 5 below is a Asian Review of Environmental and Earth Sciences, 2021, 8(1): 10-17 15 © 2021 by the authors; licensee Asian Online Journal Publishing Group Matrix whose comparison factors are the considered factors on one side and their respective derived weights on the other. With this matrix, the consistency vector was estimated. Table-5. Prioritization weight matrix. Criteria Code Row total of the Normalized Matrix Average Weight (%) Criteria Name MWB 1.35 0.09 9.00 Major WaterBody ETL 0.26 0.02 2.00 Electricity Transmission Line LULC 2.70 0.18 18.0 Landuse/Landcover TOPO 0.54 0.04 4.00 Topography (Slope) SPA 0.22 0.01 1.00 Sensitive/Protected Areas CPA 0.22 0.01 1.00 Critically Polluted Areas EIA 2.79 0.19 19.0 Existing Industrial Areas MTN 0.53 0.04 4.00 Major Transportation Network PFZ 0.21 0.01 1.00 Proximity to Flood Zones SRM 2.70 0.18 18.0 Source of Raw Materials WS 0.62 0.04 4.00 Wind Speed LSM 0.22 0.01 1.00 Large Scale Mines PD 1.14 0.08 8.00 Population Density PMS 0.53 0.04 4.00 Proximity to Major Settlements GWL 0.95 0.06 6.00 Groundwater Level Total 15.00 1.00 100.00 Table-6. The weights as factor matrix. Code MWB ETL LULC TOPO SPA CPA EIA MTN PFZ SRM WS LSM PD PMS GWL weights 0.09 0.02 0.18 0.04 0.01 0.01 0.19 0.04 0.01 0.18 0.04 0.01 0.08 0.04 0.06 MWB 1.00 6.00 0.25 3.00 7.00 5.00 0.25 5.00 7.00 0.33 4.00 6.00 1.00 4.00 1.00 ETL 0.02 1.00 0.11 0.33 1.00 2.00 0.11 0.50 2.00 0.11 0.33 1.00 0.17 0.50 0.17 LULC 4.00 9.00 1.00 6.00 9.00 8.00 1.00 7.00 9.00 1.00 6.00 8.00 3.00 7.00 3.00 TOPO 0.33 3.00 0.17 1.00 3.00 2.00 0.17 1.00 3.00 0.17 1.00 3.00 0.33 2.00 0.33 SPA 0.14 1.00 0.11 0.33 1.00 1.00 0.11 0.25 1.00 0.11 0.25 1.00 0.17 0.50 0.17 CPA 0.20 0.50 0.13 0.50 1.00 1.00 0.11 0.33 1.00 0.11 0.33 1.00 0.20 0.33 0.20 EIA 4.00 9.00 1.00 6.00 9.00 9.00 1.00 6.00 9.00 1.00 6.00 9.00 4.00 6.00 4.00 MTN 0.20 2.00 0.14 1.00 4.00 3.00 0.17 1.00 3.00 0.17 1.00 3.00 0.50 1.00 0.50 PFZ 0.14 0.50 0.11 0.33 1.00 1.00 0.11 0.33 1.00 0.11 0.25 1.00 0.20 0.33 0.20 SRM 3.00 9.00 1.00 6.00 9.00 9.00 1.00 6.00 9.00 1.00 5.00 9.00 4.00 6.00 4.00 WS 0.25 3.00 0.17 1.00 4.00 3.00 0.17 1.00 4.00 0.20 1.00 4.00 0.50 1.00 1.00 LSM 0.17 1.00 0.13 0.33 1.00 1.00 0.11 0.33 1.00 0.11 0.25 1.00 0.17 0.25 0.25 PD 1.00 6.00 0.33 3.00 6.00 5.00 0.25 2.00 5.00 0.25 2.00 6.00 1.00 2.00 2.00 PMS 0.25 2.00 0.14 0.50 2.00 3.00 0.17 1.00 3.00 0.17 1.00 4.00 0.50 1.00 1.00 GWL 1.00 6.00 0.33 3.00 6.00 5.00 0.25 2.00 5.00 0.25 1.00 4.00 0.50 1.00 1.00 TOTAL 15.85 59.00 5.12 32.33 64.00 58.00 4.97 33.75 63.00 5.09 29.42 61.00 16.23 32.92 18.82 2.6.4. Calculation of Weighted Column and Weighted Sum (Consistency Vector) The following steps were observed in calculating the consistency vector: a. A matrix showing the judgment comparisons and derived weights was started. b. Using the priorities (weight) as factors for each column as shown in Table 6 above, the values in each column of the comparison matrix were multiplied by their corresponding criterion weight in that column. E.g. 0.09 x 1 = 0.09, 0.09 x 0.02 = 0.018; 0.09 x 4 = 0.36 …………….. 0.09 x 1 = 0.09 c. Adding up all the values in each row (weighted sum) e.g. for row 1: (0.09 + 0.11 + 0.05 + 0.11 + 0.10 + 0.08 + 0.05 + 0.18 + 0.10 + 0.06 + 0.17 + 0.09 + 0.08 + 0.14 + 0.06) = 1.44. Table 7 shows the obtained Weighted Column and Consistency Sum Matrix. Table 8 shows the result of the consistency vector. This consistency vector is calculated thus: Divide each of the weighted sums by their corresponding weighted value. Table-7. Obtained weighted column and consistency sum matrix. MWB ETL LULC TOPO SPA CPA EIA MTN PFZ SRM WS LSM PD PMS GWL Weightedsum 0.09 0.11 0.05 0.11 0.10 0.08 0.05 0.18 0.10 0.06 0.17 0.09 0.08 0.14 0.06 1.44 0.01 0.02 0.02 0.01 0.01 0.03 0.02 0.02 0.03 0.02 0.01 0.01 0.01 0.02 0.01 0.27 0.36 0.16 0.18 0.22 0.13 0.12 0.19 0.25 0.13 0.18 0.25 0.12 0.23 0.25 0.19 2.94 0.03 0.05 0.03 0.04 0.04 0.03 0.03 0.04 0.04 0.03 0.04 0.04 0.03 0.07 0.02 0.56 0.01 0.02 0.02 0.01 0.01 0.02 0.02 0.01 0.01 0.02 0.01 0.01 0.01 0.02 0.01 0.22 0.02 0.01 0.02 0.02 0.01 0.02 0.02 0.01 0.01 0.02 0.01 0.01 0.02 0.01 0.01 0.23 0.36 0.16 0.18 0.22 0.13 0.14 0.19 0.21 0.13 0.18 0.25 0.13 0.30 0.21 0.25 3.04 0.02 0.04 0.03 0.04 0.06 0.05 0.03 0.04 0.04 0.03 0.04 0.04 0.04 0.04 0.03 0.55 0.01 0.01 0.02 0.01 0.01 0.02 0.02 0.01 0.01 0.02 0.01 0.01 0.02 0.01 0.01 0.21 0.27 0.16 0.18 0.22 0.13 0.14 0.19 0.21 0.13 0.18 0.21 0.13 0.30 0.21 0.25 2.91 0.02 0.05 0.03 0.04 0.06 0.05 0.03 0.04 0.06 0.04 0.04 0.06 0.04 0.04 0.06 0.64 0.01 0.02 0.02 0.01 0.01 0.02 0.02 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.02 0.23 0.09 0.10 0.06 0.11 0.09 0.08 0.05 0.07 0.07 0.04 0.08 0.09 0.08 0.07 0.13 1.20 0.02 0.04 0.03 0.02 0.03 0.05 0.03 0.04 0.04 0.03 0.04 0.06 0.04 0.04 0.06 0.55 0.09 0.10 0.06 0.11 0.09 0.08 0.05 0.07 0.07 0.04 0.04 0.06 0.04 0.04 0.06 1.00 Asian Review of Environmental and Earth Sciences, 2021, 8(1): 10-17 16 © 2021 by the authors; licensee Asian Online Journal Publishing Group Table-8. The consistency vector. Weighted sum Consistency vector 1.44 / 0.089 16.18 0.27 / 0.018 15.00 2.94 / 0.180 16.33 0.56 / 0.036 15.55 0.22 / 0.015 14.62 0.23 / 0.015 15.33 3.04 / 0.186 16.34 0.55 / 0.036 15.28 0.21 / 0.014 15.00 2.91 / 0.179 16.26 0.64 / 0.042 15.24 0.23 / 0.015 15.33 1.20 / 0.076 15.97 0.55 / 0.036 15.28 1.00 / 0.064 15.63 2.6.5. Calculation of the Consistency Index Where n = total number of considered factors which in this case is 15, and (λ max) the summation of the elements of the consistency vector, CI (consistency index) can be calculated by the formula; CI = {(λ-n) / (n-1)} [10] Therefore, λ max = (16.18 + 15.00 + 16.33 + 15.55 + 14.67 + 15.33 + 16.34 + 15.28 + 15.00 + 16.26 + 15.24 + 15.33 + 15.79 + 15.28 + 15.63) / 15 = (233.205 / 15) = 15.547 CI = (λ-n) / (n-1) CI = (15.547 – 15) / (15 – 1) = 0.0366. In calculating the constituency value, which is a ratio of consistency index to the random index, the mathematical expression; CR = CI/RI according to Saaty [10] was used. Random index (RI) is the inconsistency index of a randomly generated pair-wise comparison matrix of order 1 to 15 obtained by approximating random indices. See Table 9. Table-9. Random Inconsistency Indices (CI) n 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 RI 0 0.00 0.52 0.89 1.11 1.25 1.35 1.40 1.45 1.49 1.52 1.54 1.56 1.58 1.59 1.73 Where n = order of matrix When the order of a comparison matrix is n > 15, the average values of the random index CI may be roughly calculated using Podvezko [13] formula as displayed in Equation 2: 𝐶𝑙 = 1.98 (𝑛−2) 𝑛 (2) Where n = number of criteria under consideration. Consequently, the consistency ratio, in this case is CR = CI / RI. CR in this case is; 0.0366 / 1.59 = 0.023 3. Result and Analysis Table 5 shows the Computed Average Criteria Weight for Consistent Respondents which revealed the influence level of the individual criteria considered for this study. The result from the calculated environmental criteria weights show that proximity of Petroleum refinery to existing industrial areas; source of crude oil and Landuse/Landcover to have the highest influence value, followed by proximity to Water Body; Population Density; Groundwater Level; Major Settlement; Wind Speed; Transportation Network, Topography, while Sensitive/Protected Areas, Critically Polluted areas, and Flood zones with the least influence value. The result of this AHP – based weight assessment would play a key role in any decision-making process involving the siting of a petroleum refining plant. It is anticipated that the results of this assessment can be used generally by Planners, Investors, Environmental Managers, and Oil and Gas experts as a general guide in the prioritization of criteria weights based on the influence of one criterion over the other in the suitability siting of petroleum refining plant. The result of this AHP – based weight assessment will play a key role in any decision-making process involving the siting of a petroleum refining plant. It is anticipated that the results of this assessment can be used generally by Planners, Investors, Environmental Managers and Oil and Gas experts as a general guide in the prioritization of criteria weights based on the influence of one criterion over the other in the suitability siting of petroleum refining plant. The findings agreed with the several studies [11, 13]. As a check on the reliability of respondents’ judgments, the Consistency Ratio (CR) for the judgment values was calculated. This Consistency Ratio value was found to be 0.023, which is less than 0.1. According to Saaty [10] if this ratio is found to be less than 10% (CR< 0.1) then the respondent judgment is consistent, if otherwise (i.e. CR> 0.1) then the judgment is not consistent. By the result of this study, it means that there is a reasonable level of consistency in the comparison of the criteria by the respondent of this very outcome and that the computed weights are within the acceptable limit. If the reverse was the case (CR > 0.1) it means that the weights obtained are inconsistent and the judgment values need to be checked for logical correctness. 75% (60) of the total number of respondents were consistent in their judgment and the remaining 25% (14) were inconsistent. These inconsistencies in judgment by the responders may be a lack of logical knowledge of the Pairwise Comparison. Asian Review of Environmental and Earth Sciences, 2021, 8(1): 10-17 17 © 2021 by the authors; licensee Asian Online Journal Publishing Group 4. Conclusion Investors are constantly faced with tough decisions on the location of facilities that will yield the highest returns on low environmental impact. To optimize the benefits and constraints of particular land uses in a certain area, a planner needs to have a good knowledge of the influence of one criterion over the other. The Analytic Hierarchy Process (AHP) has been proved by literature and existing projects to produce reliable and consistent results since it provides an alternative for a check and the fact that it can deal with inconsistent judgments and provides a measure of the inconsistency of the judgment makes the methodology a better solution that can help deal with the complex problems of a suitable site a selection like the one of this research (modular petroleum refining plant siting), which involves the consideration of multi-criteria/alternatives simultaneously. It is anticipated that the results of this assessment can be used generally by Planners, Investors, Environmental Managers, and Oil and Gas experts as a general guide during criteria weighting and prioritization based on the influence of one criterion over the other in the suitability siting of petroleum refining plant. References [1] J. F. K. 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Podvezko, "Complex evaluation of complex quantities," Business: Theory and Practice, vol. 9, pp. 160-168, 2008. Asian Online Journal Publishing Group is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use of the content. Any queries should be directed to the corresponding author of the article.