Assessment of Occupational Risk Using Multi-Criteria Fuzzy AHP Methodology in a University Laboratory

Abstract

Academic laboratories operate in diverse fields. However, they expose individuals to occupational risks. To ensure social and economic sustainability, organizations must assess these risks. Accident prevention reduces injuries and financial losses. This study aims to structure an occupational risk assessment process in a university chemical laboratory in Brazil using the fuzzy analytic hierarchy process (FAHP). The methodology consists of identifying risks and applying the FAHP with linguistic variables to evaluate and prioritize them. Fifteen hazard sources were identified. In the risk assessment phase, the “Chemical Risk” criterion was given the highest priority, accounting for 54%, followed by “Accident Risk” at 26%, “Physical Risk” at 13%, and “Ergonomic risk” at 7%. This study contributes to applying a novel scientific method that enables low-cost risk assessment, reducing the need for significant investments in technology or specialized consultancy services. Furthermore, the research suggests applying this technique to different economic sectors, broadening the applicability of FAHP in occupational risk assessment.

1. Introduction

Laboratories and research facilities play a fundamental role in the training of students and researchers at universities [1]. However, the chemical substances used in these environments pose various risks and can lead to accidents.

University laboratories expose individuals to various occupational hazards, such as chemical, physical, mechanical, biological, and ergonomic risks [2]. These dangers emerge during research activities. Without proper training, the likelihood of accidents increases, particularly when handling chemicals and operating equipment [3]. Thus, laboratory staff must receive comprehensive safety training.

Studies on health and safety in university laboratories have documented numerous incidents that caused fatalities and injuries. The causes include equipment fires and explosions, which have led to severe injuries and deaths [4]. In the United States, approximately 18% of occupational accidents in universities are related to laboratory environments, with students being the primary victims in nearly one-third of these incidents [5,6].

Assessing occupational risks helps identify hazards, evaluate risks, and prevent accidents [7]. Despite this, many universities fail to conduct risk assessments before academic activities [8]; failing to conduct risk assessments undermines social and economic sustainability. Laboratory accidents injure individuals, increase operational costs, and impose financial burdens on institutions. Additionally, they affect the well-being of researchers and students [9].

According to [1], risk assessment effectively identifies hazards and guides risk mitigation. However, workplace risk levels vary based on task safety, even within the same environment. Therefore, researchers must tailor laboratory risk assessments to each task’s specific conditions and requirements to ensure effective control measures [10].

Researchers have developed numerous occupational risk assessment methods to identify accident causes and characteristics across various economic sectors. These methods have been developed, improved, and adapted over the years [11,12].

In recent decades, researchers have increasingly utilized multi-criteria decision-making (MCDM) methods for risk assessment [13,14]. The MCDM-based approach is one of the most significant tools in this field. It is particularly useful when optimal decision values are unclear due to conflicting criteria and subjective judgments [15,16]. Several methods have been developed to address MCDM problems, including the analytic hierarchy process (AHP) and the FAHP. However, no single technique is universally superior; rather, some are better suited to specific decision-making contexts [17].

Researchers favor the FAHP in risk assessment because it is simple and effectively manages ambiguous data [18,19]. It efficiently handles uncertainty in subjective judgments [20]. Decision-makers favor linguistic variables over precise numerical values for evaluations [21].

In the field of environmental risk assessment, [22] highlights that fuzzy theory-based methods enhance risk evaluations and yield promising results in improving assessment quality. Additionally, [23] argues that fuzzy logic is a suitable approach for the comprehensive study of accident prevention and occupational health protection, as workplace safety is influenced not only by workers’ perceptions but also by numerous uncertain factors [24]. Due to these advantages, the FAHP is a highly suitable multi-criteria method for evaluating occupational safety.

The literature presents several applications of the fuzzy approach in occupational safety. One study developed a fuzzy system to assess health, safety, environmental, and ergonomic factors in a gas refinery [25]. Researchers combined the FAHP with Failure and Effects Analysis (FMEA) to predict operational failures. Triangular fuzzy numbers (TFNs) classified the risks of explosions, chemical leaks, and mechanical failures. The FAHP improved decision reliability by integrating multiple criteria. This approach reduced the likelihood of explosions and leaks [25].

Another study proposed a hybrid FAHP–neural network model to assess risks in oilfield gathering stations [26]. The methodology used triangular fuzzy numbers to calculate failure probabilities in critical equipment. This method improved emergency planning and prevented operational failures [26].

In the construction industry, researchers developed a fuzzy methodology to assess risk exposure [27]. The study evaluated consequences related to time, cost, quality, and safety performance. Trapezoidal fuzzy numbers modeled risk severity and frequency. The analysis considered structural instability, heavy equipment, and human failures. This approach enhanced decision making in project planning. It optimized resources and minimized risks [27].

Researchers applied the FAHP to assess environmental risks in the aluminum extrusion industry [15]. The study used triangular fuzzy numbers to model uncertainty. A three-level hierarchical model included environmental criteria, operational safety, and economic impacts. The methodology reduced subjectivity in environmental risk assessment. It also incorporated expert opinions. The FAHP improved environmental management, reduced impacts, and optimized costs. This approach strengthened risk assessment [15].

Researchers at the University of Coimbra proposed an FAHP methodology to select the best risk assessment methods for small and medium-sized companies [28]. The study used triangular fuzzy numbers and integrated the FAHP with the TOPSIS fuzzy technique. The methodology considered factors such as cost, applicability, reliability, and ease of use. This approach made occupational safety more accessible to companies with financial and technical limitations [28].

In the mining sector, a study applied the FAHP to assess environmental risks in open-cast mines [29]. Researchers integrated the FAHP and GIS (Geographic Information System) to analyze geotechnical and environmental risks. They used triangular fuzzy numbers to calculate slope stability and landslide risks. The FAHP enabled preventive measures to avoid structural collapse [29].

Ref. [30] developed a three-stage hierarchical structure based on fuzzy numbers to assess environmental risks using the AHP. The study used trapezoidal fuzzy numbers to weigh risk severity. It considered operational, environmental, and ergonomic safety variables. The methodology resulted in a risk prioritization model for the industry. This approach enabled more efficient safety planning [30].

Researchers applied the FAHP to underground coal mine safety [31]. They used a non-linear FAHP to estimate and classify risks. Triangular fuzzy numbers assessed workers’ exposure to toxic gasses and structural hazards. The methodology integrated the FAHP with probabilistic modeling. This combination improved environmental risk prediction. The approach helped minimize collapses and explosions. It also ensured a safer work environment [31].

Finally, a study applied the trapezoidal FAHP approach to assess work safety in hot and humid environments [21]. The methodology captured subjective variations, such as workers’ perception of thermal discomfort. Trapezoidal fuzzy numbers ensured greater flexibility in modeling uncertainty. The research led to the creation of safety protocols for extreme climates. These protocols reduced the risks of heat exhaustion and heatstroke. The study also established a monitoring system to help managers prevent occupational illnesses [21].

According to [2], few studies in occupational health and safety risk management focus on laboratory environments. The same authors highlighted that the number of risk assessment studies in the educational sector is even more limited.

This research proposes a structured risk assessment process based on the FAHP method with triangular fuzzy numbers. The study encompasses the identification, classification, and evaluation of risks and hazards in a chemical laboratory at a federal university in Brazil.

This article is structured as follows: Section 1 provides an introduction and literature review on the proposed topic. Section 2 outlines the research methods applied in the study. Section 3 presents the results obtained. Section 4 discusses the findings related to the laboratory’s risk assessment. Finally, Section 5 presents the conclusions, research limitations, contributions of the study, and recommendations for future research.

2. Materials and Method

2.1. The Fuzzy Theory

Introduced by [32], fuzzy numbers are used to represent patterns of uncertain reasoning, which play a significant role in human decision making in imprecise and uncertain scenarios.

Uncertainty in risk assessment arises from the difficulty of quantifying factors such as fatigue, stress, and adverse ergonomic conditions [33]. Fuzzy numbers effectively model these elements [34].

Among the different types of fuzzy numbers, triangular fuzzy numbers offer mathematical simplicity and easy interpretation [35]. Triangular pertinence functions efficiently represent uncertainties in subjective evaluations. They approximate reality well without requiring excessively complex calculations [36]. Due to these advantages, this research adopts triangular fuzzy numbers for risk assessment.

A triangular fuzzy number (TFN) has a continuous linear membership function, as shown in Equation (1). In this Function (1), (1) l for m is an increasing function, (2) m for u is a decreasing function, and (3l ≤ m ≤ u and can be represented by three points, $\tilde{\mathit{X}}$= (l, m, u), whose graphical representation is shown in Figure 1 [37].

$${\mu }_{Ã}(x)=\left\{\begin{array}{c}0 for x<l, x>u\\ x-1/m-l for l\le x \le m\\ u-x/u-m for m\le x \le u\end{array}\right.$$

(1)

The operations with two TFNs${\tilde{X}}_1$ = (l₁,m₁,u₁) and ${\tilde{X}}_2$ = (l₂,m₂,u₂) are defined as follows [37]:

$$\mathrm{Addition}:{\tilde{X}}_1\oplus {\tilde{X}}_2=\left(l_1,m_1,u_1\right)\oplus \left(l_2,m_2,u_2\right)=(l_1+l_2,m_1+m_2,u_1+u_2)$$

(2)

$$\mathrm{Multiplication}: {\tilde{X}}_1⨂{\tilde{X}}_2=\left(l_1,m_1,u_1\right)⨂\left(l_2,m_2,u_2\right)=(l_1{l}_2,m_1{m}_2,u_1{u}_2)$$

(3)

$$\mathrm{Inverse}: {{\tilde{X}}_1}^{-1}={\left(l_1,m_1,u_1\right)}^{-1}=\left(1/u_1, 1/m_1, 1/l_1\right)$$

(4)

In fuzzy logic, decision-makers evaluate variables using words, known as linguistic terms. These terms function as extensions of numerical variables [38]. The linguistic terms utilized in this study are presented in

Table 1. Linguistic terms for the FAHP method [39].

Linguistic Terms	Code	Satay’s Scale	TFN Equivalents
Ai is vastly less important than Aj	EL	1/9	(1/9,1/9,1/7)
Ai is much less important than Aj	VL	1/7	(1/9,1/7,1/5)
Ai is less important than Aj.	L	1/5	(1/7,1/5,1/3)
Ai is moderately less important than Aj	ML	1/3	(1/5,1/3,1)
Ai is equally important as Aj.	E	1	(1,1,3)
Ai is moderately more important than Aj	MM	3	(1,3,5)
Ai is more important than Aj.	M	5	(3,5,7)
Ai is much more important than Aj	VM	7	(5,7,9)
Ai is vastly more important than Aj	EM	9	(7,9,9)

[39].

Each element in the range of fuzzy sets has a degree of membership, denoted as μ(x). Unlike traditional logic, where an element can only be either inside or outside a set, fuzzy logic allows an element to be partially or fully inside or outside the set.

If an element completely belongs to the set, its membership degree is equal to one (μ = 1); if it does not belong, then μ = 0. When the element partially belongs to the fuzzy set, μ takes any value between zero and one (0 < μ < 1). Figure 2 illustrates the corresponding membership functions for visualization purposes.

The values of the μ(x) function can be determined based on historical data and expert opinions from decision-makers [37].

2.2. Fuzzy Analytic Hierarchy Process (FAHP)

The AHP (analytic hierarchy process) was proposed by Thomas L. Saaty as a theory for measuring intangible criteria [40].

In the analytic hierarchy process (AHP), at each hierarchical level, decision-makers (DMs) evaluate the priorities of alternatives by leveraging their experience and knowledge through pairwise comparisons organized in a matrix [41]. Additionally, the AHP enables DMs to assess the consistency of evaluations, as comparisons are based on personal or subjective information, which can lead to inconsistencies [42].

For example, the consistency of a judgment can be interpreted as follows: if element E1 is preferred over E2, and E2 is preferred over E3, then it is expected that E1 is also preferred over E3.

Saaty’s AHP was extended by [43], allowing DMs to use fuzzy ratios instead of exact numerical ratios. This approach is more intuitive and human-centric, as it is easier to express that “criterion A is much more important than criterion B” rather than to quantify that “the importance of principle A relative to principle B is seven to one”.

The FAHP is an effective tool for handling qualitative assessments. Moreover, some factors influencing workplace safety in laboratory environments are qualitative rather than measurable. Therefore, evaluating these qualitative factors using fuzzy numbers instead of real numbers facilitates decision making and yields more realistic results [24]. This study primarily bases the FAHP method on the works of [43,44].

2.3. Steps of the Proposed Approach

Step 1. The decision problem is decomposed into a hierarchical structure comprising objectives, criteria, sub-criteria, and alternatives.

Step 2. Pairwise comparison matrices are built using linguistic terms.

The element (${\tilde{x}}_{ij}$) of the pairwise comparison matrix ${\tilde{X}}_k$ for each decision-maker “k” represents a fuzzy number corresponding to its associated linguistic term, as shown in Table 1. The pairwise comparison matrix is given by [21]

$${\tilde{X}}_k=\left[\begin{array}{cccc}1& {\tilde{x}}_{12}& \dots & {\tilde{x}}_{1j}\\ {\tilde{x}}_{21}& 1& \cdots & {\tilde{x}}_{2j}\\ ⋮& ⋮& ⋮⋮⋮& ⋮\\ {\tilde{x}}_{i1}& {\tilde{x}}_{i2}& \cdots & 1\end{array}\right]$$

(5)

assuming that the fuzzy numbers are triangular and taking into account that ${\tilde{x}}_{ij}=\left(l_{ij},m_{ij},u_{ij}\right)$.

Step 3. The consistency of each fuzzy pairwise comparison matrix is evaluated using the consistency ratio (CR). The CR must be verified for each decision-maker’s matrix. First, the pairwise comparison values are defuzzified using the Center of Area (COA) method, as defined in Equation (6) [44].

$$d_{ij}={\displaystyle \frac{\left(u_i-l_i\right)+(m_i-l_i)}{3}}+l_i,\forall i$$

(6)

For n criteria, the comparison matrix X is defined according to Equation (7) [21].

$$X=\left[\begin{array}{cccc}1& x_{12}& \cdots & x_{1j}\\ x_{21}& 1& \cdots & x_{2j}\\ ⋮& ⋮& \ddots & ⋮\\ x_{i1}& x_{i2}& \cdots & 1\end{array}\right]$$

(7)

The relative weights of criteria and alternatives are calculated using the eigenvalue method, resulting in a priority vector. In Equation (8), λmax represents the largest eigenvalue of X, and ω denotes the principal eigenvector of the matrix [45].

$$X·\omega =\lambda \mathit{max}·\omega$$

(8)

The consistency of the judgments is checked using Equation (9)

$$CR={\displaystyle \frac{CI}{RI}}$$

(9)

where

$$CI={\displaystyle \frac{(\lambda m\mathsf{á}x-n)}{(n-1)}}$$

(10)

and “n” is the order of the matrix. The random index (RI) shown in

Table 2. Random index [46].

n	1	2	3	4	5	6	7	8	9	10
IR	0.00	0.00	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49

is obtained according to “n” [46].

The matrix X is considered consistent only if the consistency ratio (CR) is above 0.1; otherwise, decision-makers must review the pairwise comparisons [46]. If the result of the X-matrix comparisons is consistent, then the corresponding matrix with fuzzy numbers will also be consistent [43].

Step 4. The fuzzy comparison matrices of the decision-makers are aggregated using the geometric mean, as defined in Equation (11) [43]. This assumes there are k decision-makers.

$${\tilde{U}}_{ij}={[{\tilde{x}}_{ij}^{k1}⨂{\tilde{x}}_{ij}^{k2}⨂ \dots ⨂ {\tilde{x}}_{ij}^{kn}]}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$k$}\right.}}$$

(11)

Step 5. To weigh the criteria and sub-criteria, the fuzzy geometric mean is calculated for each row of the matrices. The geometric means of the first parameters (l) of the triangular fuzzy numbers in each row are computed according to Equation (12) [43].

$${\tilde{r}}_l={({\tilde{x}}_{11l}⨂{\tilde{x}}_{12l}⨂ \dots ⨂ {\tilde{x}}_{nl})}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}$$

(12)

where n represents the number of elements in the matrix.

Next, the geometric means of the second and third parameters (m,u) of the triangular fuzzy numbers in each row are calculated according to Equations (13) and (14), respectively.

$${\tilde{r}}_m={({\tilde{x}}_{11m}⨂{\tilde{x}}_{12m}⨂ \dots ⨂ {\tilde{x}}_{nl})}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}$$

(13)

$${\tilde{r}}_u={({\tilde{x}}_{11u}⨂{\tilde{x}}_{12u}⨂ \dots ⨂ {\tilde{x}}_{nu})}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}$$

(14)

Step 6. Suppose that the sums of the geometric mean values ${\tilde{r}}_i$ of the lower parameter “l” are denoted as “a₁”; the sum of the mean parameters “m” is denoted as “a₂”; and the sum of the upper parameters “u” is denoted as “a₃”. Finally, the matrices ${\tilde{w}}_j$ and ${\tilde{r}}_{ij}$ are obtained using the values of ${\tilde{r}}_l,{\tilde{r}}_m$, and ${\tilde{r}}_u$, as defined in Equation (15) [47].

$${\tilde{w}}_j=\left|\begin{array}{cccc}Criterion 1& Criterion 2& & Criterion j\\ \left({\displaystyle \frac{l_1}{a_3}},{\displaystyle \frac{m_1}{a_2}},{\displaystyle \frac{u_1}{a_1}}\right)& \left({\displaystyle \frac{l_1}{a_3}},{\displaystyle \frac{m_1}{a_2}},{\displaystyle \frac{u_1}{a_1}}\right)& \dots & \left({\displaystyle \frac{l_1}{a_3}},{\displaystyle \frac{m_1}{a_2}},{\displaystyle \frac{u_1}{a_1}}\right)\\ \left({\displaystyle \frac{l_2}{a_3}},{\displaystyle \frac{m_2}{a_2}},{\displaystyle \frac{u_2}{a_1}}\right)& \left({\displaystyle \frac{l_2}{a_3}},{\displaystyle \frac{m_2}{a_2}},{\displaystyle \frac{u_2}{a_1}}\right)& \dots & \left({\displaystyle \frac{l_2}{a_3}},{\displaystyle \frac{m_2}{a_2}},{\displaystyle \frac{u_2}{a_1}}\right)\\ \dots & \dots & ⋮⋮⋮& \dots \\ \left({\displaystyle \frac{l_n}{a_3}},{\displaystyle \frac{m_n}{a_2}},{\displaystyle \frac{u_n}{a_1}}\right)& \left({\displaystyle \frac{l_n}{a_3}},{\displaystyle \frac{m_n}{a_2}},{\displaystyle \frac{u_n}{a_1}}\right)& \dots & \left({\displaystyle \frac{l_n}{a_3}},{\displaystyle \frac{m_n}{a_2}},{\displaystyle \frac{u_n}{a_1}}\right)\end{array}\right|$$

(15)

assuming that ${\tilde{w}}_j$ is the weight of criterion j, and ${\tilde{r}}_{ij}$ is the performance score of the respective sub-criterion i in relation to j.

Step 7. The fuzzy weights and performance scores are aggregated as defined in Equation (16) [43].

$${\tilde{v}}_i={\tilde{w}}_j⨂{\tilde{r}}_{ij}$$

(16)

Step 8. The fuzzy weights (${\tilde{v}}_i$) are defuzzified using Equation (17) and then normalized using the linear sum method, as defined in Equation (18) [44,48].

$${BNP}_{ij}={\displaystyle \frac{\left(u_i-l_i\right)+(m_i-l_i)}{3}}+l_i,\forall i$$

(17)

$$n_i={BNP}_i/{\sum }_{i=1}^n{BNP}_i$$

(18)

where BNP stands for best non-fuzzy performance and n_i is in the range 0–1.

Step 9. Sensitivity analysis is carried out at intervals of 10%, 30%, and 50%. For the most significant criteria, the normalized weights are reduced, and for the less significant criteria, the normalized weights are increased.

3. Results

This section presents the environmental risk assessment conducted in a federal university’s chemical laboratory in southern Brazil, as shown in Figure 3. This laboratory’s main activity is to study biochar’s adsorption to decontaminate water and effluents and identify and quantify mineral contaminants (arsenic, cadmium, lead, and mercury) in sediment, breast milk, and hospital food.

Step 1.

Problem definition and hierarchy structuring

The decision problem was defined as identifying and assessing occupational risks present in the chemical laboratory. Work activities and occupational risks were identified using the Preliminary Hazard Analysis (PHA) method, as outlined in the IEC 31010:2019 standard [49].

This study identified 15 sources of hazards classified into accident, ergonomic, physical, and chemical risks. These categories were used as criteria, with specific risk factors as sub-criteria.

It is important to note that risk sources are elements with an intrinsic potential to generate risk. Another essential concept is that of risk itself, which can be defined in various ways, such as “an action that endangers or threatens something of value” or “the combination of the probability of an undesired event occurring and its consequences”, according to the British Standard BS 8800 [50,51].

The identified risks and risk factors are detailed in

Table 3. Description of criteria and sub-criteria.

Criteria	Sub-Criteria
Accident (AC)	Electric shock (AC1)
	Skin burning (AC2)
	Skin cutting (AC3)
Ergonomic (ER)	Prolonged standing up (ER1)
	Inadequate table and chairs (ER2)
	Acoustic discomfort (ER3)
Physical (PH)	Intermittent noise (PH1)
Chemical (CH)	Inhalation of toxic gasses (CH1)
	Sulphuric acid and nitric acid (CH2)
	Potassium antimony tartrate and ammonium molybdate (CH3)
	Acetone (CH4)
	Sodium hypochlorite and potassium permanganate (CH5)
	Isopropyl alcohol and ethyl alcohol (CH6)
	Nitric acid and hydrogen peroxide (CH7)
	Cadmium and lead with nitric acid (CH8)

. The hierarchical structure is illustrated in Figure 4.

Occupational risks can be identified through personal observation, structured or unstructured interviews, informal conversations, participation in meetings and events, organizational surveys, collection of objective data, and review of archival sources [52].

For the FAHP method to be effectively applied in risk assessment, it is essential to carefully collect and review information from the risk identification stage. This process should be based on historical records of workplace accidents and the organization’s official risk prevention documents. Ensuring a thorough understanding of workers’ activities and occupational risks enhances the clarity of risk identification and improves the accuracy of expert judgments.

Step 2.

Pairwise comparison matrices

The decision-makers (DMs) evaluated linguistic variables to determine the importance of the criteria and sub-criteria. The evaluations are presented in

Table 4. Linguistic evaluations for environmental risk.

Criteria	AC (DM1, DM2, DM3, DM4, DM5)	ER	PH	CH
AC	(E, E, E, E, E)	(M, VM, VM, M, E)	(ML, M, MM, MM, M)	(VL, ML, E, E, VL)
ER	(L, VL, VL, L, E)	(E, E, E, E, E)	(L, ML, E, L, MM)	(EL, EL, VL, L, VL)
PH	(MM, L, ML, ML, L)	(M, MM, E, M, ML)	(E, E, E, E, E)	(ML, L, L, ML, EL)
CH	(VM, MM, E, E, VM)	(EM, EM, EM, M, VM)	(MM, M, M, MM, EM)	(E, E, E, E, E)

,

Table 5. Linguistic evaluations for accident risk (AC).

Sub-Criteria	AC1	AC2	AC3
AC1	(E, E, E, E, E)	(M, EL, EM, M, M)	(EM, L, VM, VM, VM)
AC2	(L, EM, EL, L, L)	(E, E, E, E, E)	(MM, MM, E, MM, MM)
AC3	(EL, M, VL, VL, VL)	(ML, ML, E, ML, ML)	(E, E, E, E, E)

,

Table 6. Linguistic evaluations for ergonomic risk (ER).

Sub-Criteria	ER1	ER2	ER3
ER1	(E, E, E, E, E)	(VM, VL, E, ML, ML)	(M, ML, M, MM, VL)
ER2	(VL, VM, E, MM, MM)	(E, E, E, E, E)	(E, MM, M, M, L)
ER3	(L, MM, ML, ML, VM)	(E, ML, L, L, M)	(E, E, E, E, E)

and

Table 7. Linguistic evaluations for chemical risk (CH).

Sub- Criteria	CH 1	CH 2	CH 3	CH 4	CH 5	CH 6	CH 7	CH 8
CH1	(E, E, E, E, E)	(VL, ML, E, L, L)	(M, MM, MM, M, ML)	(VL, M, E, MM, E)	(MM, MM, ML, M, E)	(E, ML, E, M, MM)	(L, L, ML, ML, L)	(EL, VL, MM, ML, EL)
CH2	(VM, MM, E, M, M)	(E, E, E, E, E)	(EM, MM, MM, VM, MM)	(MM, EM, MM, M, MM)	(EM, M, ML, EM, VM)	(VM, ML, ML, M, EM)	(E, L, E, M, E)	(ML, VL, E, MM, ML)
CH3	(L, ML, ML, L, MM)	EL, ML, ML, VL, ML)	(E, E, E, E, E)	(L, M, ML, ML, MM)	(ML, MM, ML, M, M)	(ML, L, ML, MLVM)	(VL, VL, L, L, ML)	(EL, EL, L, VL, L)
CH4	(VM, L, E, ML, E)	(ML, EL, ML, L, ML)	(M, L, MM, MM, ML)	(E, E, E, E, E)	(MM, ML, E, M, MM)	(E, L, E, MM, M)	(L, VL, MM, L, ML)	(VL, EL, L, L, VL)
CH5	(ML, ML, MM, L, E)	(EL, L, MM, EL, VL)	(MM, ML, MM, L, L)	(ML, MM, E, L, ML)	(E, E, E, E, E)	(E, L, MM, ML, E)	(L, VL, MM, VL, VL)	(VL, EL, ML, VL, EL)
CH6	(E, MM, E, L, ML)	(VL, MM, MM, L, EL)	(MM, M, MM, MM, VL)	(E, M, E, ML, L)	(E, M, ML, MM, E)	(E, E, E, E, E)	(L, ML, ML, L, EL)	(VL, VL, ML, L, EL)
CH7	(M, M, MM, MM, M)	(E, M, E, L, E)	(VM, VM, M, M, MM)	(M, VM, MM, M, MM)	(M, VM, MM, VMVM)	(M, MM, E, M, EM)	(E, E, E, E, E)	(ML, ML, E, E, E)
CH8	(EM, VM, MM, MM, EM)	(MM, VM, E, ML, MM)	(EM, EM, M, VM, M)	(VM, EM, M, M, VM)	(VM, EM, MM, VM, EM)	(VM, VM, MM, M, EM)	(MM, MM, E, E, E)	(E, E, E, E, E)

.

The researchers established the following criteria for selecting the DMs: (i) an academic degree in occupational safety engineering, (ii) at least three years of professional experience in occupational health and safety, (iii) working as an occupational safety engineer at a federal university in Brazil with a chemical laboratory, and (iv) certification in occupational hygiene or ergonomics.

Previous studies that applied the FAHP method and reported the number of DMs found a range of three to seven members [53,54,55,56,57]. Based on this, five occupational safety engineers responsible for managing occupational risks in laboratories at federal universities were selected.

These professionals met the established criteria and were designated as DM1, DM2, DM3, DM4, and DM5. Each engineer independently conducted the assessments based on their experience, intuition, and expertise, allowing for the construction of pairwise comparison matrices to determine the importance of each environmental risk.

Step 3.

Consistency of the fuzzy pairwise comparison matrix

The results of the consistency check are presented in

Table 8. Consistency analysis.

DMs	Environmental Risks	Accident Risk (AC)	Ergonomic Risk (ER)	Chemical Risk (CH)
1	0.07	0.04	0.08	0.08
2	0.02	0.03	0.00	0.08
3	0.09	0.05	0.03	0.1
4	0.04	0.06	0.00	0.08
5	0.07	0.06	0.01	0.05

, confirming that all comparison matrices were consistent.

Step 4.

Aggregation of decision-makers’ fuzzy comparison matrices

The pairwise comparison matrices, aggregated according to Equation (11), are presented in

Table 9. Aggregated fuzzy comparison matrix of the environmental risk.

Criteria	AC			ER			PH			CH
	l	m	u	l	m	u	l	m	u	l	m	u
AC	1.00	1.00	1.00	2.95	4.15	6.53	1.12	2.37	4.15	0.30	0.37	0.82
ER	0.19	0.24	0.42	1.00	1.00	1.00	0.33	0.44	1.11	0.12	0.14	0.19
PH	0.24	0.42	0.89	1.12	1.90	3.74	1.00	1.00	1.00	0.16	0.22	0.44
CH	1.90	2.71	5.16	5.52	7.61	8.56	2.29	4.58	6.43	1.00	1.00	1.00

,

Table 10. Aggregated fuzzy comparison matrix of the accident risk (AC).

Sub-Criteria	AC1			AC2			AC3
	l	m	u	l	m	u	l	m	u
AC1	1.00	1.00	1.00	1.84	2.63	3.38	2.63	3.62	4.66
AC2	0.30	0.38	0.54	1.00	1.00	1.00	1.15	2.41	4.32
AC3	0.21	0.28	0.38	0.28	0.42	1.25	1.00	1.00	1.00

,

Table 11. Aggregated fuzzy comparison matrix of the ergonomic risk (ER).

Sub-Criteria	ER1			ER2			ER3
	l	m	u	l	m	u	l	m	u
ER1	1.00	1.00	1.00	0.47	0.64	1.40	0.72	1.29	2.18
ER2	0.89	1.55	2.67	1.00	1.00	1.00	1.05	1.72	3.00
ER3	0.49	0.86	1.72	0.41	0.58	1.18	1.00	1.00	1.00

and

Table 12. Aggregated fuzzy comparison matrix of the chemical risk (CH).

Sub-Criteria		CH 1	CH 2	CH 3	CH 4	CH 5	CH 6	CH 7	CH 8
CH1	l	1.00	0.21	1.12	0.80	0.90	090	0.16	0.14
	m	1.00	0.29	2.37	1.16	1.72	1.38	0.25	0.18
	u	1.00	0.58	4.15	2.29	3.50	3.16	0.52	0.33
CH2	l	2.14	1.00	2.04	1.84	2.71	1.33	0.84	0.34
	m	3.50	1.00	4.43	4.14	3.94	2.04	1.00	0.54
	u	5.81	1.00	6.33	6.02	5.52	3.55	2.29	1.25
CH3	l	0.24	0.16	1.00	0.44	0.82	0.36	0.14	0.12
	m	0.42	0.23	1.00	0.80	1.53	0.55	0.19	0.15
	u	0.89	0.49	1.00	1.63	3.00	1.25	0.34	0.21
CH4	l	0.68	0.17	0.61	1.00	0.90	0.84	0.16	0.12
	m	0.86	0.24	1.25	1.00	1.72	1.25	0.23	0.16
	u	1.93	0.54	2.26	1.00	3.50	2.54	0.47	0.23
CH5	l	0.36	0.18	0.33	0.36	1.00	0.49	0.13	0.12
	m	0.58	0.25	0.65	0.58	1.00	0.72	0.18	0.15
	u	1.38	0.37	1.23	1.38	1.00	1.72	0.31	0.24
CH6	l	0.49	0.28	0.80	0.61	0.90	1.00	0.16	0.13
	m	0.72	0.49	1.81	0.80	1.38	1.00	0.22	0.17
	u	1.72	0.75	2.81	1.84	3.16	1.00	0.44	0.29
CH7	l	1.93	0.84	2.95	2.14	3.27	2.29	1.00	0.53
	m	4.08	1.00	5.16	4.36	5.52	3.68	1.00	0.64
	u	6.12	2.29	7.24	6.43	7.61	5.81	1.00	1.93
CH8	l	3.00	1.00	4.66	4.36	4.15	3.50	1.00	1.00
	m	5.52	1.84	6.77	6.43	6.53	5.81	1.55	1.00
	u	7.11	3.68	8.14	8.14	8.00	7.61	3.68	1.00

.

Step 5.

Calculation of the weights for criteria and sub-criteria

The fuzzy geometric mean for each row of the matrices ${\tilde{\mathrm{X}}}_{\mathrm{i}\mathrm{j}}$ is calculated to assign weights to the criteria and sub-criteria, as shown in Equations (12)–(14). As an example, the geometric mean for the accident risk (AC) criterion is calculated as follows:

$$\begin{gathered} {\tilde{r}}_{iAC}=\left[\right(1.00 ⨂2.95 ⨂1.12 ⨂0.30{)}^{1/4}], [(1.00 ⨂4.15 ⨂2.37 ⨂0.37{)}^{1/4}], [(1.00 ⨂6.53 ⨂4.15 ⨂ \\ 0.82{)}^{1/4}=[1.00,1.38,2.17] \end{gathered}$$

Step 6.

Calculate matrices ${\tilde{w}}_j$ e ${\tilde{r}}_{ij}$

The matrices ${\tilde{w}}_j$, e, and ${\tilde{r}}_{ij}$ are calculated according to Equation (15). As an example, the sum of the geometric mean values ${\tilde{r}}_i$ for the criteria is as follows: a1 = 3.96, a2 = 5.49, and a3 = 7.91. The matrix ${\tilde{w}}_j$ for the accident risk (AC) criterion is illustrated below.

$${\tilde{w}}_{jAC}=\left|\begin{array}{c}{\displaystyle \frac{1}{7.92}},{\displaystyle \frac{1.38}{5.49}},{\displaystyle \frac{2.17}{3.96}}\end{array}\right|=[0.13,0.25,0.55]$$

Step 7.

Calculate fuzzy weights and performance scores

The fuzzy weights of the risk factors are determined using Equation (16). The results of the calculations are presented in

Table 13. Results of the proposed approach criteria.

Criteria	j	$\mathbf{Fuzzy} \mathbf{Weight} \mathbf{\left(}{\tilde{\mathit{w}}}_{\mathit{i}}\mathbf{\right)}$	$\mathbf{Defuzzified} \mathbf{Weight} \mathbf{\left(}{\mathit{x}}_{\mathbf{i}}\mathbf{\right)}$	$\mathbf{Normalized} \mathbf{Weight} \mathbf{\left(}{\mathit{n}}_{\mathbf{i}}\mathbf{\right)}$
AC	1	(0.13, 0.25, 0.55)	0.31	0.26
ER	2	(0.04, 0.06, 0.14)	0.08	0.07
PH	3	(0.06, 0.12, 0.28)	0.15	0.13
CH	4	(0.28, 0.57, 1.04)	0.63	0.54

and

Table 14. Results of the proposed approach sub-criteria.

Sub-Criteria	$\mathbf{Fuzzy} \mathbf{Weight} \mathbf{\left(}{\tilde{\mathit{v}}}_{\mathit{i}\mathit{j}}\mathbf{\right)}$	$\mathbf{Defuzzified} \mathbf{Weight} \mathbf{\left(}{\mathit{B}\mathit{N}\mathit{P}}_{\mathbf{i}\mathbf{j}}\mathbf{\right)}$	$\mathbf{Normalized} \mathbf{Weight} \mathbf{\left(}{\mathit{n}}_{\mathbf{i}\mathbf{j}}\mathbf{\right)}$	Ranking
CH8	(11.31, 22.00, 37.20)	23.50	0.2871	1º
CH7	(7.70, 14.98, 29.18)	17.29	0.2111	2º
CH2	(6.31, 12.49, 24.17)	14.32	0.1749	3º
CH1	(2.40, 4.53, 9.99)	5.64	0.0689	4º
CH6	(2.10, 3.93, 8.40)	4.81	0.0587	5º
CH4	(2.04, 3.78, 8.24)	4.69	0.0573	6º
CH3	(1.50, 2.81, 6.06)	3.46	0.0422	7º
CH5	(1.44, 2.64, 5.64)	3.24	0.0396	8º
AC1	(0.98, 1.90, 3,81)	2.23	0.0273	9º
AC2	(0.41, 0.87, 2.02)	1.10	0.0134	10º
AC3	(0.23, 0.44, 1.19)	0.62	0.0075	11º
ER2	(0.17, 0.27, 0.63)	0.36	0.0044	12º
ER1	(0.12, 0.19, 0.45)	0.25	0.0031	13º
ER3	(0.10, 0.16, 0.40)	0.22	0.0027	14º
PH1	(0.06, 0.12, 0.28)	0.15	0.0018	15º

. As an example, the priority weight of the sub-criterion (AC1) is calculated as follows:

$${\tilde{v}}_{iAC1}=[0.13 ⨂7.80], [0.25⨂7.57], [0.55 ⨂ 6.96]=[0.99,1.90,3.81]$$

Step 8.

Defuzzification and normalization calculations

The fuzzy numbers are defuzzified and normalized. Following the example of risk factor (AC1), the calculations are performed as follows:

$${BNP}_{AC1}={\displaystyle \frac{\left(3.81-0.99\right)+(1.90-0.99)}{3}}+0.99=2.23$$

$$n_{AC1}={\displaystyle \frac{2.23}{82.03}}=0.0272$$

The results of the calculations are presented in Table 13 and Table 14.

A statistical analysis was performed on the results in Table 13 and Table 14 by calculating 95% confidence intervals to assess potential differences in the normalized weights.

For the criteria in Table 13, the average normalized weight is 0.25, with a standard deviation of 0.1809. The 95% confidence interval for the population mean ranges from 0.072 to 0.427. This means that, with 95% confidence, the true population mean of the normalized weights lies within this interval.

For the sub-criteria in Table 14, the average normalized weight is 0.0667, with a standard deviation of 0.0844. The 95% confidence interval for the population mean of the sub-criteria ranges from 0.0661 to 0.0677. This interval is narrower, indicating greater consistency in the sub-criteria compared to the criteria.

Overall, the 95% confidence intervals show that, despite some variation, the data are fairly consistent.

Step 9.

Sensitivity analysis

The sensitivity analysis assessed the stability of the sub-criteria under variations in normalized weights (ni) across three scenarios: 10%, 30%, and 50%, as shown in

Table 15. The results of sensitivity analysis of sub-criteria.

Sub-Criteria	Initial Value		10%		30%		50%
	ni	Raking	ni	Raking	ni	Raking	ni	Raking
CH8	0.2871	1º	0.2863	1º	0.2819	1º	0.2803	1º
CH7	0.2111	2º	0.2106	2º	0.2073	2º	0.2062	2º
CH2	0.1749	3º	0.1745	3º	0.1718	3º	0.1708	3º
CH1	0.0689	4º	0.0687	4º	0.0676	4º	0.0672	4º
CH6	0.0587	5º	0.0586	5º	0.0577	5º	0.0574	5º
CH4	0.0573	6º	0.0571	6º	0.0562	6º	0.0559	6º
CH3	0.0422	7º	0.0421	7º	0.0415	7º	0.0412	7º
CH5	0.0396	8º	0.0395	8º	0.0389	8º	0.0387	8º
AC1	0.0273	9º	0.0272	9º	0.0311	9º	0.0266	9º
AC2	0.0134	10º	0.0134	10º	0.0155	10º	0.0131	10º
AC3	0.0075	11º	0.0075	11º	0.0087	11º	0.0074	14º
ER2	0.0044	12º	0.0053	12º	0.0080	12º	0.0128	11º
ER1	0.0031	13º	0.0038	13º	0.0057	13º	0.0091	12º
ER3	0.0027	14º	0.0033	14º	0.0049	14º	0.0078	13º
PH1	0.0018	15º	0.0022	15º	0.0034	15º	0.0054	15º

.

The top three sub-criteria (CH8, CH7, and CH2) retained their positions in all cases, demonstrating strong robustness. Higher-weighted sub-criteria showed minimal variation, confirming their stability. Intermediate-weight sub-criteria remained unchanged at 10% and 30% variations, with only slight fluctuations at 50%, keeping their relative rankings intact.

Lower-weighted sub-criteria exhibited greater sensitivity to changes. A 10% variation caused minor fluctuations without affecting rankings. At 30%, AC3 dropped from 11th to 14th place. A 50% variation led to more significant shifts, with ER2 rising to the 11th position and ER1 to the 12th.

Overall, the model remained stable, particularly for the highest-weighted sub-criteria. Small variations did not significantly alter rankings, and intermediate sub-criteria showed strong consistency even in extreme cases. Lower-weighted sub-criteria, however, were more susceptible to high variations, making their prioritization more sensitive to weight adjustments.

4. Discussion

Table 12 indicates that decision-makers assigned the highest priority to ’Chemical Risk’ (CH), with a normalized weight of 0.52.

Since this laboratory specializes in environmental and inorganic contaminant analysis using chemical agents, exposure to volatile substances and corrosive reagents poses significant health risks. Without proper safety measures, users may suffer severe adverse effects.

Prioritizing chemical risks highlights the need for strict safety policies, proper ventilation, ongoing employee training, and protective equipment.

According to the decision-makers’ assessment, ’Accident Risk’ (AC) ranked second, with a weight of 0.27. Laboratory accidents can cause immediate injuries, affecting not only students and researchers but also the broader university community, especially in incidents such as fires. Therefore, preventive measures should be implemented to minimize such risks.

The ’Physical Risk’ (PH) category ranked third, with a weight of 0.14, primarily due to the sub-criterion ’intermittent noise’. ’Ergonomic Risk’ (ER) received the lowest priority, with a weight of 0.07.

The results shown in Table 13 indicate that the three highest-priority sub-criteria were CH8 (cadmium and lead with nitric acid), used as a standard chemical solution, with a normalized weight of 0.2866; CH7 (nitric acid with hydrogen peroxide), used for sediment dissolution and reactor cleaning, with a weight of 0.2108; and CH2 (sulfuric acid with nitric acid), used to distill and purify samples, with a weight of 0.1746.

This result confirms decision-maker consistency, as sulfuric acid (H₂SO₄) and nitric acid (HNO₃) share similar characteristics due to their inorganic nature and high oxygen content. Consequently, these chemical agents require comparable control measures.

A comparison with ref. [2] reveals both differences and similarities in methodology. Their study uses a more detailed approach, combining 5S, FMEA, IT2FSs, AHP, and VIKOR to prioritize risks. This level of detail enhances risk assessment but demands more technical resources and specialized expertise, which can limit its use in some university settings.

Ref. [2] identifies 38 failure modes and classifies them based on severity, occurrence, and detectability. The IT2FVIKOR method prioritizes these risks and defines control measures. Both studies recognize chemical risks as the most critical, though [2] places greater emphasis on fire and explosion hazards.

By integrating multiple methods, [2] built a complex and structured risk hierarchy. In contrast, this study offers a more straightforward and accessible approach, making implementation easier for laboratories with fewer technical resources/

Additionally, their study highlighted the importance of categorizing risks to facilitate decision making and mitigate safety impacts in laboratories [2]. They also affirmed the need for systematic methodologies to assess risks in university laboratory environments.

In summary, this study offers a practical and accurate risk assessment method for university laboratories. The approach that ref. [2] proposed suits institutions with advanced resources that require a highly detailed model. Therefore, institutions should choose a method based on their specific needs and capabilities.

The evaluation data indicated that 80% of the decision-makers considered the sub-criterion CH8 the highest priority. Once absorbed by the body, cadmium is released into the bloodstream, potentially causing adverse effects such as genetic diseases, cell death, inflammation, and fibrosis [58].

Lead, on the other hand, is one of the ten most hazardous chemicals for public health [59]. Lead exposure ranks fourth among the major health risk factors for environmental health, following air pollution by particles, indoor air pollution from solid fuels, and unsafe drinking water, sanitation, and handwashing at home [60].

Health effects caused by lead exposure include heart disease, chronic kidney disease, and intellectual disability [61].

The sub-criteria ’Inhalation of Toxic Gases’ (CH1); ’Isopropyl Alcohol with Ethyl Alcohol’ (CH6) for glassware cleaning; ’Acetone’ (CH4) for reactor cleaning; ’Antimony Tartrate with Potassium and Ammonium Molybdate’ (CH3) for reagent storage; and ’Sodium Hypochlorite with Potassium Permanganate’ (CH5) for reactor cleaning presented small numerical differences between their normalized weights.

In the evaluation of accident risk factors, the results indicate that ’electric shock’ (AC1) from an unsealed electrical outlet, with a normalized weight of 0.0272, is the highest-ranked factor.

It is important to highlight that 80% of the DMs evaluated that electric shock is a risk factor that could cause more harm to workers’ health compared to other accident hazards, as this danger can lead to muscle contractions, cardiac arrest, burns, and damage to other organs. Therefore, measures should be taken to prevent laboratory workers and students from exposure to electrical energy.

A notable point in Table 13 was the low ranking of ergonomic and physical risk factors, with a ’Normalized Weight’ below 0.01. This indicates that although these factors are relevant to workers’ well-being, the decision-makers considered chemical and accident risks to be more pressing threats that require prioritized attention.

Among the ergonomic factors, ’handling the computer with inadequate furniture’ (ER2) had a normalized weight of 0.044 in the 12th position, ’prolonged standing posture’ (ER1) ranked 14th, and ’acoustic discomfort’ (ER3) ranked 15th.

According to [62], for both seated or standing manual work, adjustable furniture is essential for providing comfort and preventing negative effects such as chronic fatigue and reduced concentration. Furthermore, indoor environments requiring high attention should ensure acoustic comfort to minimize stress and irritability.

Based on the results, decision-makers recommend adopting specific measures to mitigate the main identified risks.

Ref. [63] defines intermittent noise (PH1) as a sound or group of sounds with an intensity that may lead to illness or negative interference in the communication process.

The damage caused by excessive noise can lead to hearing loss, sleep disruption, recurrent headaches, communication difficulties, and increased absenteeism [64].

After assessing risks, organizations should implement control measures to keep them at acceptable levels. Studies highlight cost-effective strategies like labeling storage units, establishing collaboration systems, and using portable shelves [65,66].

However, despite these measures, recent research shows a rise in workplace accidents and injuries, largely due to human error and inadequate training. This suggests that safety protocols alone are insufficient without proper worker training [67,68].

Laboratory supervisors should conduct periodic safety training to reduce chemical risks and prevent accidents. Improving student safety training plays a key role in an effective safety program [8].

In laboratories where chemicals are stored and handled, reactive substances need to be kept away from heat sources and remain in appropriate containers. An inventory system helps track the movement of hazardous substances, ensuring better control [2].

According to [69], employers must take all necessary steps to control risks.

Table 16. Preventive measures for occupational safety and health [69].

Typology	Measures	Actions
Primary measures	Risk elimination	The measures must directly impact the source of risk factors (intrinsic prevention).
Secondary measures	Risk isolation	While risk factors may persist, taking collective protection measures can prevent or reduce their impact on workers.
Tertiary measures	Risk avoidance	Organizational measures and regulations on behavior act as barriers to the interaction between risk factors and workers.
Quaternary measures	Isolation of the worker	Individual protection can be achieved by wearing personal protective equipment to limit the impact of risk factors.

outlines the hierarchy of preventive measures, starting with risk elimination, followed by substitution, engineering controls, administrative controls, and PPE. Key measures include using PPE, following safety procedures, and ensuring proper worker training [69].

In occupational safety, professionals use the risk matrix to prioritize and order control measures that reduce risks during the decision-making process. This tool is easy to apply and interpret and is often used by individuals without experience in risk management [70,71,72,73,74].

Risk matrices are popular in terrorism risk analysis, highway construction project management, and office building risk analysis. However, few studies rigorously validate the performance of these matrices in improving actual risk management decisions [75].

The IEC 31010:2019 standard states that the use of the matrix can be highly subjective, as different assessors may assign different ratings to the same risk. This often leads to significant variation between classifiers and leaves it open to manipulation.

In the FAHP method, decision-makers may assign different priorities to the same risk, as weights and priorities derive from subjective judgments of the assessors or participants involved in the process. However, this method allows for the analysis and evaluation of the logical consistency of judgments through the consistency ratio (CR), which can result in significant changes in the final outcome.

The FAHP also enables the ranking of alternatives using statistical tools such as the geometric mean, arithmetic mean, and normalization of eigenvectors. This makes the evaluation more rational and consistent.

5. Conclusions

Environmental risk assessment is essential for safeguarding workers’ health and ensuring organizational sustainability. However, the process involves numerous parameters that are often difficult to quantify. To address this challenge, this study applied fuzzy logic.

To minimize biases in risk assessment using the FAHP method, organizations should rely on experienced professionals in occupational health and safety. Expertise is crucial to ensuring accurate and reliable evaluations.

This study applied the FAHP with triangular numbers to assess and prioritize risks in a university chemical laboratory.

The methodology included decision-makers with expertise in occupational health and safety in universities, as well as extensive knowledge in environmental risk management.

With a priority of 52%, ’Chemical Risk’ (CH) emerged as the most significant risk, emphasizing the dangers of cadmium, lead, and corrosive acids (CH8, CH7, and CH2) in laboratory procedures. Additionally, we identified ’Accident Risk’ as the second most relevant (27%), with electrical shock from unsealed outlets being the primary risk factor in this category.

Although ergonomic risks ranked lower in priority, addressing issues such as inadequate furniture and prolonged postures remains essential to preventing fatigue and long-term musculoskeletal disorders.

A key limitation of this study is reliance on direct laboratory observations for risk identification, which may introduce observational bias. Future research should incorporate official organizational records and validate data collection methods to enhance accuracy.

Considering that the goal of this study was to use the FAHP in the risk assessment process, the IEC 31010:2019 standard provided valuable information regarding the risks and their factors in the laboratory.

This study contributes academically by presenting a structured scientific method for occupational safety assessment in university laboratories. Technically, it demonstrates that the FAHP provides a cost-effective approach to risk assessment, reducing the need for expensive technologies or specialized consultancy, thus making it more accessible to budget-constrained institutions.

Future studies should compare occupational risk assessments using different fuzzy sets. We also propose applying the FAHP method to assess occupational risks in laboratories at other universities and compare findings with the results of this research.

This method supports adaptable and efficient risk management across sectors.