A Review of Chronological Evolution of Air Quality Indexing Systems (1966 To 2021)

Air quality index (AQI) also known as air pollution index (API) is the way of describing ambient air quality to assess the health risk associated with pollution. With the advent of time, there have been several air quality indexing systems starting from the first air Quality Index developed in 1966 by Marvin H. Green and various modifications have been made ever since to improve the accuracy of measurement. Such systems can assess the air quality by several factors like the concentration of different pollutants or by various empirically established formulas based on past experiences. In this review article, an effort has been made to chronologically evaluate the AQI system developed across the world from 1966 to 2021. Every indexing system has its own unique method for air quality determination and each method has its own merits and demerits. This pape rcovers various parameters, empirical relationships, standards, merits, and demerits, which in hind sight will help to develop an amalgamation of various indexing systems that can be used as a standard method for monitoring the quality of air. This paper also covers the AQI systems that prevail in India. A fuzzy logic system is very helpful in handling the uncertainty in air quality assessment. So, fuzzy-based air quality indexing systems developed from 2010 to 2017 have also been reviewed. The review of articles established that the results obtained through fuzzybased AQI aremore reliable than the other methods. Out of all the above describing methods, fuzzy synthetic evaluation-based AQI system and fuzzy air quality health index (FAQHI) are more powerful tools to describe the air quality. But till 2017, thereis no development of AQI systems based on fuzzy logic, considering PM2.5 as one of the pollutants. So, there is a need to develop the fuzzy-based AQI system considering PM2.5 as a pollutant with other air pollutants.

Integrating building information modelling for improving facility management operations: a fuzzy synthetic evaluation of the critical success factors

Purpose Building information modelling (BIM) is a novel technological advancement in the built environment. Despite the potentials of BIM, its adoption and implementation are undermined in facility management (FM) operations. This might be because of limited information on the critical success factors (CSFs) that can enhance its adoption. The study aims to integrate building information modelling to improve facility management operation by adopting fuzzy synthetic approach for evaluating the critical success factors. Design/methodology/approach Data for the study were sourced from practising and registered facility managers within Lagos metropolis, Nigeria. The data collected were analysed using a combination of methods which include mean item score, factor analysis and fuzzy synthetic evaluation (FSE). Findings The factor analysis results showed that six underlying groups of CSFs would enhance the effective adoption of BIM in facility operations. The FSE results showed that out of the six groups, the three topmost important CSF grouping (CSFG) in the decision rule would enhance the effectiveness of BIM adoption for FM operations. Practical implications The result of this study provides a credible road map for facility managers, policymakers and other stakeholders in FM operations on the CSFs and CSFG required for the adoption of BIM. Originality/value Previous studies that aimed at integrating BIM into FM are limited. Hence, this study provides a broad perspective on the CSF required for BIM adoption and implementation in FM operations using the FSE approach.

Unravelling the Factors Influencing Construction Organisations’ Intention to Adopt Big Data Analytics in South Africa

The construction industry has been producing massive data that can be transformed for improved decision-making and better construction project delivery. However, the industry has been adjudged as a slow adopter of digital technologies such as big data analytics (BDA) to improve its service delivery. The implication of this slow adoption is the lack of innovativeness and unsustainable project delivery that has characterised the industry in most countries, particularly in developing ones like South Africa. Therefore, this study assessed the intention to adopt BDA by construction organisations using the unified theory of technology adoption and use of technology (UTAUT) model. A post-positivism philosophical stance was employed, which informed the use of quantitative research with a questionnaire designed to solicit information from construction organisations in South Africa. Data analysis was done using Cronbach alpha to test for reliability and Fuzzy Synthetic Evaluation to evaluate the impact of the different constructs of the UTAUT on the adoption of BDA by construction organisations in South Africa. The study found that variables relating to facilitating conditions, performance expectancy, and social influence will significantly impact an organisation’s intention to adopt BDA. However, issues surrounding effort expectancy, resistance to use, and perceived risk cannot be overlooked as they also have high impact levels. The study provides an excellent theoretical and practical contribution to the existing discourse on construction digitalisation.

Investigating Spatiotemporal Variability of Water, Energy, and Carbon Flows: A Probabilistic Fuzzy Synthetic Evaluation Framework for Higher Education Institutions

Higher education institutions (HEIs) consume significant energy and water and contribute to greenhouse gas (GHG) emissions. HEIs are under pressure internally and externally to improve their overall performance on reducing GHG emissions within their boundaries. It is necessary to identify critical areas of high GHG emissions within a campus to help find solutions to improve the overall sustainability performance of the campus. An integrated probabilistic-fuzzy framework is developed to help universities address the uncertainty associated with the reporting of water, energy, and carbon (WEC) flows within a campus. The probabilistic assessment using Monte Carlo Simulations effectively addressed the aleatory uncertainties, due to the randomness in the variations of the recorded WEC usages, while the fuzzy synthetic evaluation addressed the epistemic uncertainties, due to vagueness in the linguistic variables associated with WEC benchmarks. The developed framework is applied to operational, academic, and residential buildings at the University of British Columbia (Okanagan Campus). Three scenarios are analyzed, allocating the partial preference to water, or energy, or carbon. Furthermore, nine temporal seasons are generated to assess the variability, due to occupancy and climate changes. Finally, the aggregation is completed for the assessed buildings. The study reveals that climatic and type of buildings significantly affect the overall performance of a university. This study will help the sustainability centers and divisions in HEIs assess the spatiotemporal variability of WEC flows and effectively address the uncertainties to cover a wide range of human judgment.

A fuzzy synthetic evaluation of vulnerabilities affecting supply chain resilience of industrialized construction in Hong Kong

PurposeDemands for Industrialized Construction (IC) have intensified with growing construction industry imperatives to (A) boost performance; (B) reduce reliance on “in-situ and on-site” operations; and (C) strengthen supply chain resilience (SCR) not just for survival but also to fulfill obligations to clients in the coronavirus disease 2019–induced (COVID-19–induced) “new normal”. In addressing these imperatives, this paper targets more effective leveraging of latent efficiencies of off-site-manufacture, based on findings from a Hong Kong (HK)–based study on assessing and improving SCR in IC in a high-density city.Design/methodology/approachStarting with the identification of critical supply chain vulnerabilities (CSCVs), this study developed a multilevel–multicriteria mathematical model to evaluate the vulnerability levels of IC supply chains (SCs) in HK based on an in-depth questionnaire survey followed by experts' inputs and analyzing them using fuzzy synthetic evaluation (FSE).FindingsThe overall vulnerability index indicates that IC in HK is substantially vulnerable to disruptions, while production-based vulnerabilities have the highest impact. Top management attention is needed to address these CSCVs in IC in HK.Originality/valueTo the authors' knowledge, this is the first structured evaluation model that measures the vulnerability level of IC, providing useful insights to industry stakeholders for well-informed decision-making in achieving resilient, sustainable and performance-enhanced SCs.