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Ci. e Nat., Santa Maria v.42, e29, 2020

DOI:10.5902/2179460X41375

ISSN 2179-460X

Received 28/11/20  Accepted: 16/01/20  Published:24/06/20

 

 

 

Environment

 

 

PMV as a thermal evaluation method for air-conditioned spaces in hot climates: a systematic review

 

Luiz Bueno da SilvaI

Alinny Dantas AvelinoII

 

I Pós-doutor (PPGEP/UFPE, 2009; PPGEB/UNB, 2018)); doutor em Engenharia de Produção (UFSC, 2001), mestre em Engenharia de Produção (PPGEP/UFPB, 1996). Professor Titular da UFPB. - bueno@ct.ufpb.br

II Mestre em Engenharia de Produção (PPGEP/UFPB, 2017) - avelino.alinny@gmail.com

 

 

ABSTRACT

The homogeneity of the subjects that were studied to produce the PMV model developed by Fanger and the evolution of the physical environment have an impact on the usability of the model. Many studies show that the PMV model does not represent the real thermal sensations of the occupants of the analyzed building. The purpose of this review is to identify and group the studies that show some discrepancy between the PMV and the opinions of the subjects concerning modern climatized field environments in hot climates around the world. The PRISMA statement was used to recover and analyze the articles, and 23 studies were selected for this review. The majority of the articles indicated that the PMV model is not suited to evaluate this kind of environment.

Keywords: Predicted Mean Vote. Climatized Environment. Hot Climate.

 

 

1 INTRODUCTION

The predicted mean vote (PMV) is a mathematical model developed by Fanger (FANGER; NGER, 1973) to evaluate moderate indoor environments regarding their thermal comfort. Since its development between 1967 and 1973, there have been many studies that indicate that the method does not agree with the reality in a variety of environments (ANDREASI; LAMBERTS; CâNDIDO, 2010; ATTIA; HENSEN, 2014; AULICIEMS; SZOKOLAY, [s.d.]; CHOI; LOFTNESS; AZIZ, 2012; CONCEIÇÃO et al., 2012; CORGNATI; ANSALDI; FILIPPI, 2009; DE DEAR et al., 2013; DHAKA et al., 2015; FANGER; TOFTUM, 2002; HUMPHREYS; NICOL, 2002; KIM; MIN; KIM, 2013; KIM et al., 2015; MAITI, 2014; MOREIRA et al., 2012; RAJA; NICOL, 1996; RICCIARDI; BURATTI, 2012, 2015; STRAUB; KUCHEN, 2017; TALEGHANI et al., 2013). The reason for this may be that the original research by Fanger was limited to American college-age students. To verify the influence of national geographic location he considered a further set of 128 college-age Danish subjects in a climate chamber. This results in a lack of heterogeneity in the subjects and environment because the summer in Denmark is colder than the winter in some tropical regions.

The low heterogeneity in the subjects’ characteristics and the winter climate in Denmark cause the model to deviate from the real thermal sensations observed in field environments. This deficiency was identified by Fanger and Toftum (2002) who tried to compensate for this deficiency with an expectancy factor and a correction index for hot climates.

Over the last few years, the discrepancy has increase due to the evolution that indoor environments have been going through in hot climate countries. These environments have been climatized and received smart equipment, such as computers, that influences the room temperature and has made it unlikely that the PMV reflects the necessary conditions for thermal comfort (CHOI; LOFTNESS; AZIZ, 2012).

Therefore, this study aims to identify and group the studies that show some discrepancy between the PMV and the opinion of its subjects considering smart climatized field environments in hot climate locations around the world in order to show a consistency in these differences and identify the reasons identified by the researchers as to why this index does not reflect the reality of the analyzed environment.

 

 

2 MATERIAL AND METHODS

This review was based on the PRISMA statement method for systematic reviews and meta-analyses. To identify the studies to be filtered, two online databases were used: Scopus and Web Of Science. They were chosen because they both have similar filter options and search mechanisms, and, consequently, it was possible to reproduce the same search in both databases. The search occurred on January 8th, 2018. The terms used in the research were identified through a narrative review to obtain the maximum coverage of articles as in this phase nothing was excluded. The terms of the search were: “Fanger” OR "thermal comfort" OR “iso7730” OR "predicted mean vote" OR “PMV” OR "thermal sensation" AND “NOT outdoor.”

The exclusion criteria in both databases were set to include only articles or reviews written in English in the engineering or environmental science area and to exclude studies from before 1963. Articles from journals are usually peer reviewed and more reliable; therefore, all other types of documents were excluded. The English language is universal and will allow the information in this article to be reviewed by peers, which is something other languages cannot provide. The combination of words used tends to return results from biology and HRVAC fields that fall under the scope of this research; therefore, these results had to be excluded by limiting the study area. The year of 1963 is before the discussion about PMV began since the 7-point scale of opinion was only developed in 1967.

The eligibility criteria were that the study was conducted in an air-conditioned space in an indoor environment in a hot climate location. For the studies that did not use the term hot climate, the minimal temperature to be included was considered 25°C. Studies found in the narrative review were also considered after removing duplicates.

A quality assessment of the remaining articles was conducted to keep the papers that contained a methodological comparison between subjects’ opinions on thermal comfort and a measured, acquired PMV. It was also considered if sufficient information on the locations and subjects of the study was given in the methodological section of the paper.

The extraction form was elaborated based on the Data Extraction Template for Included Studies from the Cochrane Consumers & Communication Review Group because it consolidates information about publication quality, the article, subjects of the research and the minimum information required to answer this review’s research question. The extraction form was tested and refined using 15 randomly chosen studies.

The information on the extraction sheet was divided into four groups: (1) Characteristics and general information (including year, journal, author, title and impact factor), (2) Paper basic information (including objectives, methodology, existence of a new model, comparison of the new model and outdoor climate), (3) participant characteristics (including number of subjects, occupation, geographical location, activity, age and gender) and (4) minimal information (i.e., if the PMV reflects the temperature sensation vote (TSV) and the reasons given by the authors as to why it did not when applicable).

An individual bias analysis was also made in each study to identify bias, either appointed by the author of the study or noticed by the reviewers. All information or lack thereof that could change or better explain the results of the study was considered bias. Additionally, the number of subjects and conditions of the experiment were considered as they radically change the research and make it difficult to generalize.

Data were analyzed qualitatively through reading and extraction of information from each article to identify key aspects of frequency in them. All the information was organized in the extraction form and later summarized in a sheet.

 

 

3 RESULTS

A total of 27,243 papers were identified through the database search, and 110 papers were added from the narrative search. Of these, 28,801 studies were excluded because they were in a language other than English and did not explore the research question from an engineering or environmental science point of view. Of the remaining articles, 453 studies did not meet the inclusion criteria as described. After carefully reading the remaining articles, only 23 of them were used in this review as they answered the proposed question and provided quality information about the research that led to its publication. The corresponding flow diagram is in Figure 1.

 

Figure 1: PRISMA 2009 Flow Diagram

 

For each study, the characteristics of the participants evaluated were the number of subjects, occupation, location of the research (place, country, and continent), activity at the time of the research, age and gender. Not all the studies showed all the characteristics, but they were all included when present in Table 1.

Table 1 – Characteristics of the subjects

Occupation

Number of subjects

Continent

Country

Place

Activity

Age (years)

Gender

Bankers

NI

South America

Brazil

Bank

Sedentary

NI

NI

Welders

9

South America

Brazil

metal-mechanic industry

Welding

NI

NI

Diverse

110

Asia

Malaysia

facility department

Sedentary/office/catering

20-30

24.5% male and 75.5% female

Non-patient diverse work

188

Asia

Malaysia

Malaysia’s teaching hospitals

Sedentary/office/catering

NI

28.2% male and 71.8% female

NI

836

Australia

Australia

12 office buildings

Sedentary/office

Average: 33.5

41.5% male and 58.5% female

NI

235

Asia

Singapore

13 office buildings

Sedentary/office

17 - 60

Mostly between 21-40

38.7% male and 61.3% female

Students

100

Asia

Malaysia

lecture halls, labs and at work stations

Sedentary

18 - 28

Average: 23

NI

Students

2

North America

USA

Office/home

Sedentary

22 and 35

100% male

Prayers

NI

Asia

Malaysia

Mosque

Near sedentary

NI

100% male

Office workers

60

Asia

North Korea

Office

Sedentary/office

NI

NI

NI

18

Asia

South Korea

NI

Sedentary

25.44 ± 2.91

NI

Office workers

238

Europe

Greece

Office

Sedentary

40% male and 60% female

Students and staff

75

Asia

India

Electronics Laboratory

NI

21.3% under 20;

76% 20-40;

2.7% over 40

96% male and 4% female

Office workers

33

South America

Brazil

Office laboratory

Sedentary

20 - 40

51.5% male and 48.5% female

Customers and staff

1100

Europe

Sweeden

Supermarket

Shopping

Average: 45

46% male and 53% female

Office workers

NI

NI

Iran

Commercial and office building

Sedentary/Office work

37% under 30;

52% 31-50;

11% over 50

63% male and 37% female

Office workers

NI

Europe

Italy

Office space

Sedentary/Office work

average: 35

NI

Patient, visitors and medical staff

928

Thailand

Hospital

NI

Patients: 47;

visitors: 42

Staff: 31

35% male and 65% female

Textile workers

823

Asia

China

Textile company

NI

NI

37.7% male and 62.3% female

Textile workers and students

192

Asia

China

Textile company

Spinning Workshop

workers average: 44

students average: 23.5

47.4% male and 52.6% female

Diverse hospital occupation

114

Asia

Malaysia

Hospital

Diverse hospital activities

NI

12.3% male and 87.7% female

NI

28

Asia

Malaysia

National Museum

NI

20-28

71.4% male and 28.6% female

 

NI – Not Identified in the study

All the environments depicted in the studies are mechanically ventilated through air conditioning. Also, they are all located in a hot climate considering the minimal external temperature to be included was considered 25°C.

The studies were also read to determine whether the PMV matched the TSV and for the cases when they did not match, why they did not match. This information is summarized in Figure 2.

 

Figure 2: Does the PMV represent the TSV and why not?

 

The other items in the extraction table were to organize and assess the quality of each research. The summary of these data is available in Table 2.

Biases were identified for each study individually through reading. The authors of the studies pointed some out, and some were identified as bias by the reviewers. They were: clothing (Andreasi, Lamberts and Cândido, 2010), date of data collection (Andreasi, Lamberts and Cândido, 2010), definition and differentiation of groups (GILANI; KHAN; ALI, 2016; SATTAYAKORN; ICHINOSE; SASAKI, 2017), number of subjects (HASAN; ALSALEEM; RAFAIE, 2016), lack of characterization of the subjects (HUMPHREYS; NICOL, 2002; HUSSIN et al., 2015), measure points (HUSSIN et al., 2015), unorganized display of results(KOSMOPOULOS et al., 2012) and existing noise on the equipment(KUMAR; SINGH; SUD, 2010).

These results were grouped into two kinds of bias: technical and communication. The technical biases were clothing, date of data collection, number of subjects, measure points and existing noise on the equipment. They represent research choices that may have influenced the results of the studies. The other biases were considered communication biases that happen when the authors of the article do not display some of the information necessary to characterize the article in this review.

 

Table 2 – Summary of the research data

Year

Journal

JCR

Number

Title

Is there a new model

Outdoor climate

Is the new model more adjusted than the PMV?

2010

Building and Environment

4.053

(Andreasi, Lamberts and Cândido, 2010)

Thermal acceptability assessment in buildings located in hot and humid regions in Brazil

No

Hot

Does not apply

2014

International Journal of Industrial Ergonomics

1.415

(BRODAY; XAVIER; DE OLIVEIRA, 2014)

Comparative analysis of methods for determining the metabolic rate in order to provide a balance between man and the environment

Yes

Hot

Yes

2013

Indoor and Built Environment

1.181

(AZIZPOUR et al., 2013a)

A Thermal Comfort Investigation of a Facility Department of a Hospital in Hot-Humid Climate: Correlation between Objective and Subjective Measurements

No

Hot

Does not apply

2013

Energy and Buildings

4.067

(AZIZPOUR et al., 2013b)

Thermal comfort assessment of large-scale hospitals in tropical climates: A case study of University Kebangsaan Malaysia Medical Center (UKMMC)

No

Hot

Does not apply

 

ASHRAE Transactions: Research

 

(DE DEAR, RICHARD J; FOUNTAIN, [s.d.])

Field experiments on occupant comfort and office thermal environments in a hot-humid climate

No

Hot

Yes

1991

International Journal Of Biometeorology

2.204

(DE DEAR, 2004)

Thermal comfort in the humid tropics: Field experiments in air conditioned and naturally ventilated buildings in Singapore

No

Hot

Does not apply

2016

Applied Thermal Engineering

3.356

(GILANI; KHAN; ALI, 2016)

Revisiting Fanger’s thermal comfort model using mean blood pressure as a bio-marker: An experimental investigation

Yes

Hot

Yes

2016

Building and Environment

4.053

(HASAN; ALSALEEM; RAFAIE, 2016)

Sensitivity study for the PMV thermal comfort model and the use of wearable devices biometric data for metabolic rate estimation

Measure through fitbit

Hot

Yes

2002

Energy and Buildings

4.067

(HUMPHREYS; NICOL, 2002)

The validity of ISO-PMV for predicting comfort votes in every-day thermal environments

No

Does not apply

2015

Architectural Science Review

-

(HUSSIN et al., 2015)

The reliability of Predicted Mean Vote model predictions in an air-conditioned mosque during daily prayer times in Malaysia

No

Hot

Does not apply

2015

Energy and Buildings

4.067

(KIM et al., 2015)

Development of the adaptive PMV model for improving prediction performances

Yes

Hot and cold

Yes

2013

International Journal of Smart Home

-

(KIM; MIN; KIM, 2013)

Is the PMV Index an Indicator of Human Thermal Comfort Sensation?

No

Does not apply

2012

International Journal of Ventilation

0.391

(KOSMOPOULOS et al., 2012)

An Assessment of the Overall Comfort Sensation in Workplaces

Yes (non substitutive model)

Hot

Does not apply

2010

International Journal On Smart Sensing And Intelligent Systems

-

(KUMAR; SINGH; SUD, 2010)

An approach towards development of PMV based thermal comfort smart sensor

Yes

Hot

No

2006

HVAC and R Research

0.928

(LEITE; TRIBESS, 2006)

Analysis of thermal comfort in an office environment with underfloor air supply in a tropical climate

No

Hot

Does not apply

2017

International Journal of Refrigeration

2.779

(LINDBERG et al., 2017)

Thermal comfort in the supermarket environment – multiple enquiry methods and simultaneous measurements of the thermal environment

No

Hot and cold

Does not apply

2008

Indoor and Built Environment

1.181

(NASROLLAHI; KNIGHT; JONES, 2008)

Workplace satisfaction and thermal comfort in air-conditioned office buildings: Findings from a summer survey and field experiments in Iran

No

Hot

Does not apply

2012

Building and Environment

4.053

(RICCIARDI; BURATTI, 2012)

Thermal comfort in open plan offices in northern Italy: An adaptive approach

No

Hot

Does not apply

2017

Energy and Buildings

4.067

(SATTAYAKORN; ICHINOSE; SASAKI, 2017)

Clarifying thermal comfort of healthcare occupants in tropical region: A case of indoor environment in Thai hospitals

No

Hot

Does not apply

2015

Mathematical Problems in Engineering

0.802

(YANG; LIU; ZHOU, 2015)

Predicted Thermal Sensation Index for the Hot Environment in the Spinning Workshop

Yes

Hot

Yes

2015

Energy and Buildings

4.067

(YANG; LIU; REN, 2015)

Thermal environment in the cotton textile workshop

yes

Hot

Yes

2009

indoor air

4.383

(YAU; CHEW, 2009)

Thermal comfort study of hospital workers in Malaysia

No

Hot

Does not apply

2013

Indoor and Built Environment

1.181

(YAU; CHEW; SAIFULLAH, 2013)

A Field Study on Thermal Comfort of Occupants and Acceptable Neutral Temperature at the National Museum in Malaysia

No

Hot

Does not apply

 

 

4 DISCUSSION

In 18.18% of the reviewed papers, the PMV was considered adequate to predict the TSV among the subjects of each research. Predominantly, the PMV was found to be inadequate. In these cases, it more frequently overestimates the TSV for hotter votes while underestimating colder votes in the 7-point scale (HUMPHREYS; NICOL, 2002; KIM et al., 2015; KOSMOPOULOS et al., 2012; YAU; CHEW; SAIFULLAH, 2013).

Although many reviews have been made to show the discrepancies between the PMV and the TSV, this one shows that the PMV is also inaccurate to air-conditioned spaces in a hot climate, going against what’s stated by Fanger and Toftum (FANGER; TOFTUM, 2002) that the PMV model agrees well in buildings with HVAC systems, situated warm climates and studied during the summer.

Studies indicate that individuals residing in hot humid regions have a higher tolerance to high temperatures than those residing in temperate climates. The comparison of the thermal sensation indicates that those subjects have significantly higher or wider ranges of thermal comfort for tropical climates than those obtained in Central and Western Europe. (HIRASHIMA; ASSIS; NIKOLOPOULOU, 2016)

Research in air-conditioned buildings started in 1991 (DEDEAR; LEOW; FOO, 1991), mostly in university environments and offices and focusing on sedentary work. In university and industrial environments, the subjects are mostly male, while in hospitals and offices the subjects are mostly female. The papers do not classify their results based on the subjects’ characteristics such as gender or age.

China has been given attention as an emergent country in thermal comfort research (DE DEAR et al., 2013), and though investigations conducted in China appear in the review, Malaysia is the country that has been developing the majority of the studies in the field.

The main purpose of the articles was not in most cases to simply evaluate the environment. Of the papers, 31.81% presented a new model to evaluate thermal comfort in the said environments, and some of them included a comparison between PMV and the adaptive model, which is also listed as one of the tendencies of research in this field in the last few years (DE DEAR et al., 2013; DJAMILA, 2017). Of the new models proposed, 85.71% showed better results than the PMV in the environments to which they were applied.

The reasons appointed to justify the discrepancies found in the papers from the PMV to the TSV varied between the metabolic rate, acclimation, air velocity, clothing insulation, chair insulation, lack of gender differentiation, psycho-physiological reasons, environment temperature, scale, inconsideration of personal variables and limitations from the questionnaire.

The majority of the articles presented either metabolic rate or some criteria associated with it (such as personal variables) as the reason the opinions disagree with the PMV. A 15% error in the assessment of metabolic rate can easily lead to errors in the PMV greater than 0.3 depending on other conditions (HAVENITH et al., 2002), which can explain why the metabolic rate and associated factors appear as the most frequent bias in the papers reviewed.

As identified by some of the authors (ANDREASI; LAMBERTS; CÂNDIDO, 2010; HUSSIN et al., 2015) the lack of agreement between TSV and PMV is often due to sensitivities of the input values (ANDREASI; LAMBERTS; C??NDIDO, 2010; AZIZPOUR et al., 2013b; BRODAY et al., 2014; GILANI; KHAN; ALI, 2016; HASAN; ALSALEEM; RAFAIE, 2016; HAVENITH et al., 2002; HUSSIN et al., 2015; KIM et al., 2015; KUMAR; SINGH; SUD, 2010; LINDBERG et al., 2017; YANG; LIU; ZHOU, 2015).  The reasons appointed to justify the discrepancies found in the papers from the PMV to the TSV varied between the metabolic rate, acclimation, air velocity, clothing insulation, chair insulation, lack of gender differentiation, psycho-physiological reasons, environment temperature, scale, inconsideration of personal variables and limitations from the questionnaire.

The main appointed reason for the discrepancy was the metabolic rate or activity levels (BRODAY; XAVIER; DE OLIVEIRA, 2014; GILANI; KHAN; ALI, 2016; HASAN; ALSALEEM; RAFAIE, 2016; KIM et al., 2015; KUMAR; SINGH; SUD, 2010; LINDBERG et al., 2017). Therefore, it is possible to infer that unrealistic values for the metabolic rate can be the reason of overestimation/underestimation of the experienced thermal sensation. This can result in systematic errors which have just recently begun to be highlighted in the literature.

The papers do not disclosures enough of the questionnaires used in each of the studies to evaluate their elaboration, but one(RICCIARDI; BURATTI, 2012) of the 23 identifies the questionnaire as a possible reason to the inadequacy of the PMV to the studied environment.

The main limitation of this review is that the diagnosis of the reason as to why the PMV was inadequate for the environments was given by the author of each study, and therefore, each author limits what is adequate and what is inadequate.

The models presented in the studies that show a new model cannot be generalized as they were produced in a specific environment and can only be applied to that environment.

 

 

5 CONCLUSION

The data collected and analyzed through this review show that the PMV is not generally adequate to express people’s sensation considering just smart climatized field environments in hot climate locations. The studies selected included a variety of environments of the selected type showing that this issue is not concentrated to a specific kind of environment or related to a specific activity. It is obvious that the evolution in this field is steady, and people are constantly looking for models that best suit the environment researched.

As expected, the number of articles that fitted the research eligibility and exclusion criteria was very limited due to the bottleneck imposed by the searched criteria. As already known at the beginning of Fanger research activity, PMV model cannot be generalized in all conditions and cannot be applied to this specific type of environment.

In practical terms, the use of this model may cause excessive use of energy due to miscalculations of comfortable temperatures. Theoretically, it shows a gap that needs to be filled with models that consider the reasons discussed in this article as to why the PMV is not adequate for said environments.  Findings from field surveys like the ones presented in this paper are a step forward to improve the applicability of the PMV model. 

 

 

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