Ehsan Mousavi

Urban residents spend an estimated 80–90% of their time indoors, yet urban indoor air quality (IAQ) science remains fragmented across pollutant types, settings, mitigation strategies, and governance contexts, contributing to an estimated 6.7 million deaths annually from indoor air pollution worldwide. This review asks the following question: what are the critical, multi-dimensional research and governance gaps in urban IAQ, and how can they be systematically derived and organized into a reference framework for future research and policy? A PRISMA 2020-aligned hybrid systematic evidence synthesis, combining bibliometric science mapping and structured thematic synthesis, was conducted across 105 records, primarily published between 2011 and 2026, with three pre-2011 foundational records retained, spanning 15 national contexts. A seven-stage hybrid deductive–inductive derivation procedure was applied to the coded corpus to produce the Multi-Dimensional Gap Identification Framework (MGIF), organizing research gaps across five dimensions: knowledge, methodological, technological, policy and implementation, and equity and urban context. Recurrent gaps include the absence of multi-pollutant mixture assessment in monitoring frameworks, the lack of standardized measurement protocols limiting cross-study comparability, a systematic gap between laboratory-validated and field-measured intervention performance, the near-total absence of enforceable indoor air quality standards across most jurisdictions, and the severe underrepresentation of Global South populations in both primary evidence and regulatory design. Building on the MGIF output, the Urban Indoor Air Quality Nexus (UIAQN) is proposed as a four-level conceptual organizing architecture linking pollutant dynamics, building systems, personal exposure, and governance mechanisms. Both frameworks are grounded in the coded corpus, have not been subjected to external validation, and are designed as structured reference architectures for future research investment, standard harmonization, and equity-centered policy design rather than as empirically validated predictive models.

Occupant interactions with built environments significantly influence indoor airflow patterns. Among these interactions, door openings are common, which affect airflow fields and contribute to the dispersion of airborne contaminants. The wakes originating from alterations in airflow patterns contribute to the transport of pollutants and must be carefully considered for system design in critical environments to avoid cross-contamination of susceptible bodies (e.g., patients, precision manufacturing, etc.). Therefore, knowledge about the movement patterns of these wakes is crucial in the context of indoor air quality. In this study, a series of experiments were conducted in a controlled chamber under two different schemes of a swing door opening and three different flow regimes to study the turbulent vortices produced from door openings and their spatiotemporal propagation. Additionally, an enhanced event-based modeling (EBM) approach was employed to develop a data-driven prediction of the transient indoor air patterns resulting from door-opening activities. The results suggest a significant effect of a door opening on indoor airflow fields. The velocity fields demonstrate that consecutive openings under different ventilation conditions have a prolonged impact on the propagation of door-opening-induced wakes farther into the test chamber. The quantification of the change in kinetic energy from the door opening also shows that the level of ventilation governs the flow patterns resulting from human-induced perturbation of the steady-state flow fields. The EBM approach effectively approximated the airflow patterns and demonstrated its potential to predict transient airflow disturbances caused by door operations.

The COVID-19 pandemic has reshaped the global understanding of airborne disease transmission, particularly in healthcare environments. This literature review examines how building ventilation and indoor air quality strategies have evolved in response to SARS-CoV-2, with a specific focus on healthcare settings. A systematic review of 163 post-pandemic studies, alongside a selective review of pre-COVID-19 literature, was conducted to assess how scientific knowledge, practical recommendations, and HVAC-related interventions have changed. The review categorizes studies across detection methods, simulation models, observational analyses, and policy recommendations, drawing attention to novel findings and evidence-supported practices. While the body of research reaffirms the critical role of ventilation, many recommendations remain unevaluated through empirical methods. This study identifies the gaps in evidence and highlights the most impactful advances that can inform future design, maintenance, and operational protocols in healthcare facilities to mitigate airborne infection risks.

Ventilation performance has historically been assessed using diverse metrics, ranging from air change rates and contaminant concentrations to occupant perception. This paper traces the evolution of these performance measures across research and practice, highlighting the progression from simple ventilation rate benchmarks to more sophisticated indicators like contaminant removal effectiveness (CRE), air exchange effectiveness (AEE), and age of air. The limitations of conventional metrics—particularly their inability to capture spatial variability, energy implications, and real-time contaminant removal—are critically examined. In addition, the historical evolution of these metrics and the rationale for their adoption is studied, specifically in the context of building codes and standards in the United States. A framework is proposed to categorize performance measures into ventilation rate-based, contaminant-based, air distribution-based, and perception-based groups, facilitating their comparison and selection. This critical review aims to support the development of more effective and context-sensitive ventilation assessment strategies, with implications for future research and building standards.

Project scope development is a crucial step for transport infrastructure projects. This paper addresses the need for a streamlined and automated project scope development process. A user-driven decision framework for scope development in transport infrastructure design services is created to automate the process and enhance the quality and consistency of the project scopes. The study involved a systematic literature search, state Department of Transportation (DOT) historical data, input from subject matter experts (SMEs), and analysis of state DOTs practices to inform the development of the comprehensive scope template for transportation engineering design services. The scope template was developed and then validated for comprehensiveness, text, language, format, and organization by the SMEs. Fifty dichotomous decision variables were identified that influence project scope. To enhance user experience and facilitate the process, a user interface was developed, enabling users to input project-specific decisions. Programming language and data analysis techniques were used to automate the generation of a project-specific, customized scope document based on user decisions. The generated scope document was designed to be editable, allowing users to fine-tune and customize the document further as needed. This method expedites the scope development process and minimizes the likelihood of misinterpretations of scope text and language. Consequently, it will lead to enhanced project delivery and increased client satisfaction.

Data is needed for making informed decisions regarding managing waste in the time of construction and demolition phases of buildings. However, data availability is very limited in most developing countries in the area of waste generation. The objective of this study is to employ an artificial intelligence (AI)-based approach to develop a reliable model for forecasting monthly construction and demolition waste (C&DW) generation in the case study of Tehran, Iran. We have trained different prediction models using various AI algorithms, including multilayer perceptron neural network, radial basis function neural network, support vector machines, and adaptive neuro-fuzzy inference system (ANFIS). According to the findings, all employed AI algorithms demonstrated high prediction performance for C&DW forecasting models. The ANFIS model, with R² = 0.96 and RMSE = 0.04209, was identified as the model that better represented the observed values of C&DW generation. The better efficiency of the ANFIS model could be due to its effective enhancement of neural networks to model subjective variables based on fuzzy logic capabilities. The developed prediction model can be employed as an efficient tool for policy and decision-making for C&DW management by predicting waste quantities in the future.

Cleanroom ventilation systems are well-established; however, the advantages and limitations of current practices need to be examined and explained further. This study begins by looking over the history of cleanroom ventilation systems that creates the basis for understanding ventilation rate specifications, terminologies employed in ventilation effectiveness, and recognizing scientific studies that correlate ventilation effectiveness with air change rates. This systematic review includes a comprehensive summary that contains a set of historical data and evidence that may be used to specify ventilation requirements in cleanrooms. Scientific articles are classified in terms of laboratory experiments, simulations/numerical analysis, or hybrid. HVAC designers and operators can use published codes and guidelines more efficiently if the terminology is properly understood and the design solutions are easy to implement. The present study aims to provide a deep insight into understanding the role of ventilation on the transport mechanisms of unwanted particles in cleanrooms. Historically, the ventilation rate is typically over-estimated, based on the experience of the designer, to ensure indoor air quality and thermal performance. However, the excess rate significantly impacts the system’s energy consumption. Hence, it is crucial to investigate existing recommendations to ensure if they have a scientific basis or could be proven theoretically before further implementations. Besides, any possible risks and influences associated with the traditional methods must be assessed to guarantee the facility’s performance, sustainability, and energy efficiency.