Statement of Problem: Environmental engineering confronts complex challenges characterized by significant uncertainties. Traditional modeling methods often fail to effectively address these uncertainties. As a promising direction, this study explores fuzzy machine learning (ML) as an underutilized alternative. Research Question: Although the potential of fuzzy logic is widely acknowledged, can its capabilities truly enhance environmental engineering applications? Purpose: This research aims to deepen the understanding of the role and significance of fuzzy logic in managing uncertainty within environmental engineering applications. The objective is to contribute to both theoretical insights and practical implementations in this domain. Method: This research performs a systematic review carried out in alignment with PRISMA guidelines, encompassing 27 earlier studies that compare fuzzy ML with other methods across a variety of applications within the field of environmental engineering. Results: The findings demonstrate how fuzzy-based models consistently outperform traditional methods in scenarios marked by uncertainty. The originality of this research lies in its systematic comparison and the identification of fuzzy logic’s transparent, interpretable nature as particularly suited for environmental engineering challenges. This approach provides a new perspective on integrating fuzzy logic into environmental engineering, emphasizing its capability to offer more adaptable and resilient solutions. Conclusions: The analysis reveals that fuzzy-based models significantly excel in managing uncertainty compared to other methods. However, the study advocates for a case-by-case evaluation rather than a blanket replacement of traditional methods with fuzzy models. This approach encourages optimal selection based on specific project needs. Practical Implications: Our findings offer actionable insights for researchers and engineers, highlighting the transparent and interpretable nature of fuzzy models, along with their superior ability to handle uncertainties. Such attributes position fuzzy logic as a promising alternative in environmental engineering applications. Moreover, policymakers can leverage the reliability of fuzzy logic in developing ML-aided sustainable policies, thereby enhancing decision-making processes in environmental management.

Mental health benefits have been linked to human interaction with nature. However, most studies have been carried out in developed countries, limiting the generalizability of empirical findings to other parts of the world. To address this gap, this study was conducted in Brazil. The objective of the study was to assess whether the frequency of contact with nature affects the occurrence of anxiety, stress, and depression. Data were collected between June and July 2022 through an online survey (n = 1186, 1 − α = 0.95, p = 0.05, 1 − β = 0.85, rho = 0.1). Thus, the public online survey made it possible to carry out voluntary response sampling suitable for an exploratory study, with the purpose of an initial understanding of an under-researched population. First, a logistic regression was performed for quantifying the association between contact with nature and mental symptoms. In addition, three groups of people having different frequencies (low, medium, and high) of contact with nature and a reference group, comprised of those who reported no contact, were compared using Kruskal–Wallis and Dwass–Steel–Chritchlow–Fligner tests. This study employs a cross-sectional design and relies on retrospective recall. As a result, the research hypothesis was confirmed. People who very rarely have contact with nature had a 97.95% probability of moderate occurrence of stress, which decreases to 20.98% for people who have contact with nature frequently. Furthermore, in the same comparison, the probability of occurrence was 3.6 times lower for anxiety and 4.8 times lower for depression. In conclusion, the evidence indicates that the greater the frequency of contact with nature, the lower the occurrence of stress, anxiety, and depression symptoms. Nevertheless, the benefits of this contact were significant only when its frequency was moderate (about once or twice a week) or higher.

This study evaluated the effect of the combination of three different onlay preparation designs and two restorative materials on the stress distribution, using 3D-finite element analysis. Six models of first lower molars were created according to three preparation designs: non-retentive (nRET), traditional with occlusal isthmus reduction (IST), and traditional without occlusal isthmus reduction (wIST); and according to two restorative materials: lithium-disilicate (LD) and nanoceramic resin (NR). A 600 N axial load was applied at the central fossa. All solids were considered isotropic, homogeneous, and linearly elastic. A static linear analysis was performed, and the Maximum Principal Stress (MPS) criteria were used to evaluate the results and compare the stress in MPa on the restoration, cement layer, and tooth structure (enamel and dentin). A novel statistical approach was used for quantitative analysis of the finite element analysis results. On restoration and cement layer, nRET showed a more homogeneous stress distribution, while the highest stress peaks were calculated for LD onlays (restoration: 69–110; cement layer: 10.2–13.3). On the tooth structure, the material had more influence, with better results for LD (27–38). It can be concluded that nRET design showed the best mechanical behavior compared to IST and wIST, with LD being more advantageous for tooth structure and NR for the restoration and cement layer.

Coagulation is the most sensitive step in drinking water treatment. Underdosing may not yield the required water quality, whereas overdosing may result in higher costs and excess sludge. Traditionally, the coagulant dosage is set based on bath experiments performed manually, known as jar tests. Therefore, this test does not allow real-time dosing control, and its accuracy is subject to operator experience. Alternatively, solutions based on machine learning (ML) have been evaluated as computer-aided alternatives. Despite these advances, there is open debate on the most suitable ML method applied to the coagulation process, capable of the most highly accurate prediction. This study addresses this gap, where a comparative analysis between ML methods was performed. As a research hypothesis, a data-driven (D²) fuzzy inference system (FIS) should provide the best performance due to its ability to deal with uncertainties inherent to complex processes. Although ML methods have been widely investigated, only a few studies report hybrid neuro-fuzzy systems applied to coagulation. Thus, to the best of our knowledge, this is the first study thus far to address the accuracy of this non-hybrid data-driven FIS (D²FIS) for such an application. The D²FIS provided the smallest error (0.69 mg/L), overcoming the adaptive neuro-fuzzy inference system (1.09), cascade-correlation network (1.18), gene expression programming (1.15), polynomial neural network (1.20), probabilistic network (1.17), random forest (1.26), radial basis function network (1.28), stochastic gradient tree boost (1.25), and support vector machine (1.17). This finding points to the D²FIS as a promising alternative tool for accurate real-time coagulant dosage in drinking water treatment. In conclusion, the D²FIS can help WTPs to reduce operating costs, prevent errors associated with manual processes and operator experience, and standardize the efficacy with real-time and highly accurate predictions, and enhance safety for the water industry. Moreover, the evidence from this study can assist in filling the gap with the most suitable ML method and identifying a promising alternative for computer-aided coagulant dosing. For further advances, future studies should address the potential of the D²FIS for the control and optimization of other unit operations in drinking water treatment.

Statement of problem: Urbanization has brought significant advancements in human well-being; however, it poses challenges to urban green spaces (UGSs), affecting environmental quality and public health. Research gap: Previous studies have established the importance of UGSs for urban well-being but have not sufficiently explored how the naturalness of these spaces—ranging from untouched natural areas to human-designed landscapes—affects mental health outcomes in the context of developing countries, particularly Brazil. Purpose: This study aimed to bridge the research gap by investigating the relationship between the degree of naturalness in UGSs and mental health among residents of Brazilian metropolitan areas. Method: Data were collected through an online survey involving 2136 respondents from various Brazilian urban regions. The study used Welch’s ANOVA and Games–Howell post hoc tests to analyze the impact of UGS naturalness on mental health, considering depression, anxiety, and stress levels. Results and conclusions: The findings revealed that higher degrees of naturalness in UGSs significantly correlate with lower levels of mental distress. These results underscore the necessity of integrating natural elements into urban planning to enhance public health. Practical implications: Urban planners and policymakers are encouraged to prioritize the preservation and creation of naturalistic UGSs in urban environments to improve mental health outcomes. Future directions: Further research should explore the specific attributes of naturalness that most contribute to well-being and examine the scalability of these findings across different cultural and environmental contexts.