Msc Dissertation , Bartlett, UCL

1

KTKZ5

September 2021

MODULE CODE: BENV0008 “Indoor air quality and climate resilience in low carbon schools”

by

“KTKZ5” “01-09-2021” “10,000”

Dissertation submitted in part fulfilment of the Degree of Master of Health, Wellbeing and Sustainable Buildings Bartlett School of Energy, Environment and Resources University College London

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

2

KTKZ5

Declaration:

September 2021

I declare that this dissertation is my own work and that all sources have been

acknowledged.

Acknowledgements: I would like to firstly thank my Supervisor Dr. Anna Mavrogianni for her extensive support, her periodic meetings, and her continuous guidance and support. Secondly, would like to thank Duncan for his technical support and insights on the software as well as on the subject concepts. I am grateful for being a part of this ASPIRE project and thank all the lectures and UCL IEDE team members for their continuous support despite the pandemic and the covid 19 restrictions. I would like to thank my family and friends for believing in me and giving me all the care and motivation as and when the situation demanded.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

3

KTKZ5

September 2021

CONTENT

Content .................................................................................................................................................... 3 List of Figures: ..................................................................................................................................... 4 Figure 1.1 The conflicts and tradeoffs arising in terms of IAQ and thermal comfort in a classroom ... 4 List of Graphs: ..................................................................................................................................... 5 List of Tables: ...................................................................................................................................... 5 Table 2.5 Review of Current recommendations and standards .......................................................... 5 ABSTRACT ............................................................................................................................................. 6 List of abbreviations ................................................................................................................................ 7 1 Introduction .......................................................................................................................................... 8 1.1 Background ................................................................................................................................... 8 1.1 Research overview ........................................................................................................................ 9 1.2 Study Aims .................................................................................................................................. 10 1.3 Objectives .................................................................................................................................... 10 1.4 Potential Study implication .......................................................................................................... 10 2 Literature Study .................................................................................................................................. 11 2.1 Overview ...................................................................................................................................... 11 2.2 Indoor Air quality and on Health, Wellbeing and Comfort of students in schools ....................... 11 2.2.1 Classification / Definitions ..................................................................................................... 12 2.2.2 Sources Factors affecting NO2 and PM2.5............................................................................. 12 2.2.3 Effects on school children health and wellbeing ................................................................... 14 2.3 CO2 levels in schools ................................................................................................................... 14 2.4 Thermal comfort on Health, Wellbeing and Comfort of students in schools ............................... 15 2.4.1 Impacts of thermal comfort in schools, energy, on students’ wellbeing ............................... 15 2.4.2 Overheating in schools and Climate change ........................................................................ 16 2.5 Design conflicts/ Knowledge gaps and findings .......................................................................... 16 2.5.1 Retrofitting of buildings ......................................................................................................... 17 2.6 Review of Current recommendations and standards .................................................................. 17 2.6.1 Climate resilient / adaptive regulatory framework ................................................................. 18 3 Methods.............................................................................................................................................. 19 3.1 Overview ...................................................................................................................................... 19 3.2 Data collection – Archetype ......................................................................................................... 19 3.2.1 Archetype .............................................................................................................................. 19 3.2.2 The SEED model .................................................................................................................. 20 3.2.3 Airflow network model ........................................................................................................... 21 3.3 Methods Framework Overview .................................................................................................... 21 3.3.1 Zone outdoor pollutant Schedules: ....................................................................................... 23 3.3.2 Processing the energy outputs ............................................................................................. 23 3.3.3 Baseline vs Retrofitted/ refurbished model ........................................................................... 23

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

4

KTKZ5

September 2021

3.3.4 Scenarios .............................................................................................................................. 25 3.3.5 Overheating analysis ............................................................................................................ 27 3.3.6 Climate Resilience – Future climate files .............................................................................. 27 4 Results & Analysis ............................................................................................................................. 28 4.1 CO2, Thermal comfort & Risk of overheating in Baseline and Refurbished model ..................... 28 4.2 Indoor vs outdoor concentrations of PM2.5 and NO2 ................................................................... 30 4.3 Comparison of scenarios ............................................................................................................. 31 4.3.1 CO2 levels ............................................................................................................................. 33 4.4 Comparison of environmental parameters and Indoor contaminants ......................................... 34 4.5 Overheating ................................................................................................................................. 34 4.6 Climate resilience – Testing the final design with future weather files ........................................ 35 5 Discussions ........................................................................................................................................ 37 5.1 Summary of interpreted results ................................................................................................... 37 5.2 Impact of Thermal efficiencies: ................................................................................................ 38 5.3 Outlining Limitations .................................................................................................................... 39 5.4 Recommendations ....................................................................................................................... 40 5.4.1 To the policy makers architects and consultants .................................................................. 40 5.4.2 For teachers/ student and management ............................................................................... 40 5.4.3 Recommendations for future works ...................................................................................... 41 6 Conclusions ........................................................................................................................................ 42 References ............................................................................................................................................ 43 Appendix ............................................................................................................................................... 48 Appendix 01 ....................................................................................................................................... 48 Appendix 02 ....................................................................................................................................... 50 Appendix 03 ....................................................................................................................................... 52 ....................................................................................................................................................... 52 Appendix 04 ....................................................................................................................................... 53

List of Figures: Figure 1.1 The conflicts and tradeoffs arising in terms of IAQ and thermal comfort in a classroom Figure 2.1 Literature review methodology PRISMA Figure 2.2.2 (a) Inadequate Ventilation is the single most common cause of pollutant buildup (b) Inefficient filtration is the second most important factor in the cause of indoor pollution Figure 3.1 Archetypes and approach Figure 3.2 Archetypes and SEED Model Figure 3.2.3 The external fabric of seed classroom Figure 3.3 Project Framework Figure 4.1 Annual Mean concentrations of CO2 concentrations in ppm across Base model and Retrofitted model in 1919 and 1976 archetypes

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

5

KTKZ5

September 2021

Figure 4.2 Comparison of Optimal temperature in base model and retrofitted model in 1919 and 1976 archetypes Figure 4.3 Seasonal Mean concentrations of CO2 concentrations in ppm across Base model and Retrofitted model in 1919 and 1976 archetypes

List of Graphs: Graph 1.1 The conflicts and tradeoffs arising in terms of IAQ and thermal comfort in a classrooms Graph 4.2.1 Simulated annual PM2.5 and NO2 concentrations across Baseline and Retrofitted model across 1919 and 1976 archetypes compared against WHO guideline Graph 4.3.1 Comparison of Baseline PM2.5 and NO2 with % of outdoor air flow in Base and Final model of both archetypes 1919 and 1976 Chart 4.3.1 Comparison of I/O ratio and operative temperature in Archetypes 1919 and 1976 Chart 4.3.2 A typical week showing the CO2 levels in Jan and July and the % of outside air flow in Archetype

List of Tables: Table 2.3 Appropriate maximum sedentary CO2 concentrations associated with CEN indoor air quality standards Table 2.5 Review of Current recommendations and standards Table 2.5.1 Refurbishment standards for optimal performance Table 3.2 (a) Model inputs – Building fabric characteristics (b) Model inputs Table 3.3.3a Base model Inputs for simulation for Archetypes 1919 and 1976 Table 4.2.1 Comparison of No2 and Pm2.5 Indoor levels in Base and Retrofitted model of both archetypes across 4 geometrical directions Table 4.2.2 Simulated annual PM2.5 and NO2 I/O ratios and their absolute average values across Baseline and Retrofitted model across 1919 and 1976 archetypes Table 4.2.3 Comparison of Optimal temperature in base model and retrofitted model in 1919 and 1976 archetypes across 4 different orientations Table 4.3.1 Comparison of Scenarios IAQ and thermal comfort in Base and Retrofitted model of both archetypes 1919 and 1976 Table 4.5 Overheating assessment from May 1st to September 30th in final retrofitted archetypes 1919 and 1976 Table 4.5.1 Comparison of monthly average of CO2 and thermal comfort factors in final 1919 & 1976 archetype for present, 2030 and 2050 Table 4.5.1 Comparison of monthly average of NO2 and PM2.5 in final 1919 & 1976 archetype for present, 2030 and 2050 Table 5.2 Comparison of U values of Archetypes across Baseline, Retrofitted and Final design

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

6

KTKZ5

September 2021

ABSTRACT Indoor Air Quality (IAQ) and thermal comfort are primary factors in a school classroom environment as they enable in improving the cognitive performance of students and productivity of teachers. The ongoing COVID 19 pandemic and general school design in the temperate region demands windows to open for natural ventilation, but these have impacts on reducing the CO2 levels but allowing the outdoor pollutant concentrations inside and noise ingress. The paper aims at integrating the knowledge gap that is existing at present. A building performance modelling is carried out on existing school archetypes in London, UK, which are a representation of entire England school buildings in the UK. 5 archetypes are categorized based on their construction era and thermal efficiencies, but the oldest and latest archetype (pre 1919 and post 1976) is taken for the study and evaluation. These archetypes are further categorized into ’seed’ classroom models of 4 geometric orientations (North, East, South, West). The indoor CO2, PM2.5, and NO2 levels alongside their thermal comfort and risk of overheating as per BB101 and relevant European (EU) standards and guidelines are carried out. To enhance the model and avoid overheating, retrofitting (alterations of U values on the building envelope and window glazing) is done which showed significant improvement in the thermal comfort and CO2 levels. The worst performing orientation from the results are taken for further interventions. Four different scenarios are modelled further by altering the window orientation, window to wall ratio (WWR), glazing and external shading features. These scenarios are further tested with London weather files, and the most effective scenario is chosen and overheating, and climate resilience (future weather file) testing is done.

It is observed that upon refurbishment and addition of external shades and appropriate window placements, there is a synergy between thermal comfort and IAQ. The following are the key findings.

1.

North orientation has the lowest risk of overheating but the highest CO2 concentrations in both archetypes.

2.

Summer (June to August) has the highest PM2.5 and NO2 concentrations whereas the lowest CO2 levels and vice versa in winter (Dec to Feb).

3.

The refurbished model performs well in terms of airtightness, thermal comfort, and overheating, and overall IAQ, comply with the EU and WHO guidelines and best practices.

4.

Centrally located windows allow more inflow of air when compared to equally distributed and tall windows perform better in terms of IAQ and thermal comfort than wide windows.

5.

Strong positive correlation with I/O ratios of PM2.5 and NO2 and wind speed as well as operative temperature, whereas strong negative correlation with operative temperature and wind speed and CO2 levels.

The limitation of the study involved noise and vegetation integration and precipitation. Control strategy and recommendations are provided for architects, designers, and schoolteachers on improving the IAQ and thermal comfort. Further recommendations for the future included cost/value engineering integration to the model alongside healthy lighting strategies for achieving a holistic Indoor environmental quality (IEQ) as well as improving the energy demands and reducing the carbon footprints. Other recommendations were also given for further improvements to the existing study.

KEYWORDS – Indoor Air Quality, Thermal comfort, IEQ, Health impacts of NO2, PM2.5 and CO2, Building stock modelling.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

7

KTKZ5

September 2021

LIST OF ABBREVIATIONS ‘CIBSE - Chartered Institution of Building Services Engineers TM52 - The Limits of Thermal Comfort: Avoiding Overheating in European Buildings BB101- Building Bulletin 101 formulated by the UK government is a document that gives guidance on thermal comfort, ventilation, and indoor air quality in schools.

Dfe- Department for Education CO2 – Carbon dioxide

Trm – Running Mean Temperature

NO2 – Nitrogen Dioxide

ug/m3 – Micrograms per cubic meter

Top – Operative Temperature

PM2.5 – Particulate Matter

ppm - Parts per million

Low-E – A measure of emissivity, the characteristic of a material to radiate thermal energy. Glass is typically highly emissive, warming indoor spaces. Low-E glass typically has a coating or other additive to reduce the heat transfer to inside spaces.

Passivhaus Standard: One of the most stringent voluntary standards for energy efficient buildings in the world. The requirements are defined by final energy consumption and airtightness R-value: Like U-value, R-Value is a measurement of thermal performance. However, instead of measuring thermal conductivity (how easily heat passes through a material) it measures resistance to heat transfer. Some countries use R-value for their standards instead of U-value Solar reflectance index (SRI): This index is a method to calculate the albedo of a material. In warm climates, materials with a high SRI number are suggested Thermal mass: The property of a building that uses materials to absorb heat to buffer to changes in outside temperatures. Stone floors or wall have a high thermal mass. Wood walls have a low thermal mass.

Urban heat island (UHI): An urban area that is significantly warmer than its surrounding rural areas due to human activities. The temperature difference is usually larger at night than during the day and is most apparent when winds are weak U-value: This indicates the thermal transmittance of a property and indicates its thermal performance.

U-value is the property of heat transmission in unit time through unit area of a building material or assembly and the boundary air films, induced by unit temperature difference, between the environments on each side. The lower the U-value of a material, the better its heat-insulating capacity.

Window-to-wall ratio (WWR): The ratio of glazing (windows, skylights, etc. divided by the total exterior wall area of a building. This is an important guideline because windows have a large impact on the energy needs of a building’.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

8

KTKZ5

September 2021

1 INTRODUCTION 1.1 Background The classroom environment affects student learning performance. It further enables in developing clear learning goals, relevant content, and building social skills & strategies to aid the student to succeed (Weimer, 2009). Multiple factors affect student’s health, wellbeing, and comfort. Good Indoor Environmental Quality (IEQ) is necessary to maintain a good learning environment. IEQ consists of thermal comfort, visual Comfort (VC), indoor air quality (IAQ) and acoustical comfort (AC) (Lai et al., 2009).Of these, Indoor Air Quality (IAQ) is one of the primary parameters affecting classroom and student learning potential (Kukadia and Upton, 2019). IAQ refers to the air quality within and around buildings and structures, especially as it relates to the health and comfort of building occupants (Fanger, 2006). In the UK 10 million children spend 30% of their life at school, around 70% of that time inside a classroom, thus, maintaining an ambient level of indoor environmental quality is vital. However, (Fanger, 2006) and other research have shown that indoor air can be more polluted than outdoor air, and poor indoor air quality has been linked to negative health outcomes such as asthma, cardio vascular problems and even mortality . Besides, the developing bodies of children might be more susceptible to environmental exposures than those of adults (Turanjanin et al., 2014). Children breathe more air, eat more food, and drink more liquid in proportion to their body weight than adults. Therefore, air quality in schools is of particular concern. Ventilation rates affect Indoor air quality (Taylor, 2015, Chen, 2016). It has been shown that indoor air pollutants have the potential to damage children’s central nervous system (Sunyer, 2008). Asthma and allergic respiratory diseases are triggered due to the exposure and sensitization of indoor allergens, they further act as a trigger and cause illness in vulnerable/susceptible populations like children (Salo, Sever and Zeldin, 2009). A school design requires the windows to be open for ventilation as well as for solar penetrations & thermal gains as opposed to an office environment especially in the temperate region. Moreover, due to the pressing global climate change and ongoing COVID 19 pandemics, the need for better IAQ is increasing, but there exist considerable tradeoffs and conflicts concerning opening a window as they contribute to overheating in summer and allow the outdoor air pollutants within the space. Figure 1.1 summarizes the conflicts, synergies, and tradeoffs arising.

Fig 1.1 The conflicts and tradeoffs arising in terms of IAQ and thermal comfort in a classroom Source: Author

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

9

KTKZ5

September 2021

Graph 1.1 The conflicts and tradeoffs arising in terms of IAQ and thermal comfort in a classrooms Source Author

The major IEQ parameters affecting an indoor environment in a school are Ventilation rate, Indoor and Outdoor pollutant concentration arising due to the opening of windows. A minimum ventilation rate of 8 liters per second is mentioned in Building Bulletin 101( BB101) (Funding, 2016).Due to the ongoing COVID 19 pandemic World Health Organization (WHO) and The Federation of European Heating,

Ventilation and Air Conditioning associations (RHEVA) sets 15l/s/person as a minimum ventilation rate when the space is fully occupied (REHVA, Federation of European Heating, 2012). Graph 1.1 explains that when there is decreased ventilation the Carbon dioxide (CO2) level in the indoor environment increases (Mahyuddin and Awbi, 2012). Increased CO2 levels affect the cognitive performance of students (Authority et al., no date). It also causes fatigue and SBS (Sick building syndrome) and increased absenteeism (Shendell et al., 2004). On the contrary improved ventilation of about 20l/s/person, level enables students learning efficiency by improving their alertness and by maintaining health.CO2 level of less than 800ppm can repress Sick building syndrome (Seppänen, 1999). An adequate level of ‘fresh air’ supply helps in improving the indoor air quality by diluting the indoor pollutant concentration level (Turanjanin et al., 2014). The tradeoff of the naturally ventilated building is that as the ventilation increase it carries the outdoor air pollutant contaminants like PM 2.5, PM10, and NO2, O3 within the classroom environment. But, in the context of London, UK, the outdoor air pollutant concentrations like NO2 and PM2.5 are higher in the urban roadside area (Department for Environment Food and Rural Affairs, 2007). Both Long term and short-term exposures to these contaminants affect lung functioning and induce asthma (Quality and Group, no date) (Chan et al., no date). Apart from the pollutants, the major conflict arises when there is increased solar thermal gain during the summer. This causes summertime overheating and brings discomfort to the students and teachers, overheating affects cognitive performance and sense of wellbeing (Simion, Socaciu and Unguresan, 2016). Apart from these Occupants can play a big role in indoor air quality, particularly through the window opening.

1.1 Research overview Most of the existing research studies mainly focus on the ways how outdoor pollutants enter rooms.

There are not many studies on occupant behaviours and IAQ and thermal comfort in the UK or temperate climate/ cold regions. Due to climate change and urban heat islands (UHI), the global

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

10

KTKZ5

September 2021

temperature has increased by 2̊ C. Overheating is a pressing issue in the UK as buildings are heating dominant, i.e., designed for solar ingress and thermal gains. The recent UK government plans on net zero buildings shows greater emphasis on building envelope and energy (Friedlingstein et al., 2019), but questioned the inert relationship between IEQ and energy Pollution and IAQ also play a significant role in the school environment, there is not much research integrating IAQ and thermal comfort in the UK and temperate regions.

1.2 Study Aims Taking the above into account the aim of the paper is to evaluate the role of indoor air quality (IAQ) and thermal comfort in school archetypes across the London, UK Urban area and comparing them against European regulations and best practices and WHO guidelines in order to evaluate their potential health, comfort, and wellbeing impacts on primary school children.

1.3 Objectives •

To carry out a building performance evaluation 1 on the school archetypes and projecting various scenarios and comparing them against European regulations like BB101, CIBSE, and regulatory frameworks & WHO standards

•

To identify the impact of retrofitting on the building envelope and focus on the role of window openings in altering the indoor vs outdoor pollutant concentrations in classrooms and CO2 levels and their thermal comfort levels, establishing a control strategy based on findings to improve students learning performance and health.

•

To identify the role of window orientation and positioning of different primary school typologies/archetypes have on indoor air quality & thermal comfort and critically examine their health impacts (cognitive health impacts) on primary school children in London urban area.

•

Testing different scenarios for overheating and climate resilience.

1.4 Potential study implication Lastly, the paper aim at providing recommendations and facade retrofitting strategies through different refurbishment schedules to enhance the overall performance of the classroom as well the health, comfort, and wellbeing of the students and bring in scientific evidence to guide designers, occupants, and policymakers, and researchers to a better understanding of the role of a window opening and position.

1

For this research study the exposures studied and modelled will be limited to CO 2, PM2.5, NO2 and Operative temperature.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

11

KTKZ5

September 2021

2 LITERATURE STUDY 2.1 Overview Figure 2.1 Literature review methodology PRISMA

The Literature review aims at providing key insights on the role of IAQ and thermal comfort and overheating in school buildings across the UK and discusses their potential health impacts. It also reviews the existing standards and guidelines that are followed. As per figure 2.1, the PRISMA methodology was carried out to search and exclude documents necessary for this research. The primary documents are searched from Web of Science where peer-reviewed journals are considered using appropriate keywords like thermal comfort, IAQ, school buildings, students’ health, and wellbeing.

They were further screened based on their region and typology as well as database ranging from December 2000. Other primary documents considered are grey literature like conference papers, UK Government websites reports, and articles. WHO guidelines and reports, etc. Other secondary data sources include textbooks and other literature reviews.

Exclusion criteria used here are based on

climate, and building typology (restricted to schools), and IAQ pollutants like PM 2.5 and NO2 and CO2.

2.2 Indoor Air quality and on Health, Wellbeing and Comfort of students in schools According to Education and Skills Funding Agency (ESFA) Generic Design Brief Section 1.7.6 “Health and Wellbeing in schools ‘aims at providing a healthy indoor environment, which encompasses integrating the environment with daylight and electric lighting, ventilation, thermal comfort, and acoustics that are designed to support educational attainment”.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

12

KTKZ5

September 2021

2.2.1 Classification / Definitions Indoor Air Quality (IAQ) is defined as the rate of concentrations of pollutants and thermal conditions that may negatively affect the health, comfort, and performance of a building’s occupants (Petty, 2017).Whereas (Riggs, 2016) defines IAQ as the physical, chemical, and biological characteristics of air in the indoor environment and how it affects occupant’s physical and psychological health, comfort, and productivity. The outdoor air consists of harmful pollutants, and they are broadly classified into 3 categories by the United States Environmental Protection Agency (EPA) (张静, 2016) (EPA,2017).

“1. Biological pollutants consist of bacteria, moulds, pollens, viruses like COVID19 -SARS virus.

2. Chemical pollutants are emissions from the outdoor environment, off-gassing. The pollutants include Carbon dioxide (Co2), Nitrogen dioxide, Ozone, Ammonia, VOCs, Chlorine, Formaldehyde, Lead, NO, So2. NO and No2 are together referred as NOx. 3. Particulate pollutants include tiny dust and smoke suspended in the atmosphere that are invisible to the naked eye, small enough to pass through our lungs and into our organs. They are minuscule pollutants and are defined as particulate matter (PM) (EPA,2017). They are divided into 2 based on their size / aerodynamic diameter, where PM2.5 or fine particles is Particulate matter less than 2.5 micrograms and PM10 is Particulate with a diameter less than 10 micrograms (Boldo et al., 2011)”.

I/O ratio ‘The relationship between the indoor (I) and outdoor (O) air pollution level for a building at a given time is usually expressed in terms of the I/O ratio (Chatzidiakou, Mumovic and Dockrell, 2014). The I/O ratio gives an indication of the protective effect of a building for a given pollutant. However, I/O ratios are affected by many factors, such as ventilation rates and the local meteorology. In fact, I/O ratios have been shown to vary greatly, even for an individual building’.

2.2.2 Sources Factors affecting NO2 and PM2.5 Chemical pollutants like NOx are released into the atmosphere when the fuel burns, like petrol or diesel in a car engine or the natural gas in domestic central power station, heating boiler the major sources are combustion and transport. Whereas, particulate pollutants are produced due to incomplete combustion, and from car emissions, domestic heating, through power generation.

Indoor Air pollution is caused by insufficient ventilation, it is the single most important cause for pollutant build up. Whereas ineffective filtration is attributed as the second important cause as shown in figure 2.2. However, EPA says ventilation is a key measure to reduce IAQ alongside source control and in addition to filtration.

There are many sources of indoor air pollution. They can include Fuel-burning combustion appliances, Tobacco products. Building materials and furnishings as diverse as: ‘Deteriorated asbestos-containing insulation, newly installed flooring, upholstery, or carpet, cabinetry or furniture made of certain pressed

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

13

KTKZ5

September 2021

wood products. Products for household cleaning and maintenance, personal care, or hobbies. Central heating and cooling systems and humidification devices and Excess moisture. Outdoor sources such as: Radon, Pesticides, Outdoor air pollution can cause indoor air pollution’ (EPA,2017).

Figure 2.2 Inadequate Ventilation is the single most common cause of pollutant buildup. (b) Inefficient filtration is the second most important factor in the cause of indoor pollution. Source: HBI Database

.

Figure 2.3 Factors affecting Indoor Air Quality in a classroom (Salthammer et al., 2016)

From Fig 2.3 it is evident that thermal comfort, air pollution, school building characteristics, and mobility are the primary factors linked with IAQ that can plausibly affect the student’s health and wellbeing.

Key environmental and behavioural factors such as occupancy, ventilation rates, building characteristics, and seasonality may influence the school indoor PM 2.5 concentration and composition, including the amount of traffic PM2.5 that can come indoors. School buildings characteristics such as age, building materials, and ventilation; and factors such as occupant density per classroom volume, affect the transport and mixing of pollutants, which pose a risk to the health of the occupants, often affecting their performance in school (MacNaughton et al., 2017). Koo found that student learning decreased in natural ventilation as compared to air-conditioned classrooms (Lee, 1999). Building materials can act as reservoirs of irritants that can contribute to the gas-particle processes, affecting the indoor air quality of the classrooms (Smedje, Mattsson and Wålinder, 2011).

Occupant activities can increase the concentration of pollutants by resuspension of previously deposited particles and by introducing new particles through clothing and shoes (Stranger, Potgieter-

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

14

KTKZ5

September 2021

Vermaak and Van Grieken, 2008).Varying environmental factors such as temperature gradients between indoor and outdoor, relative humidity, wind direction, and speed influence the penetration rate of outdoor pollutants to indoor settings (Chatzidiakou, Mumovic and Summerfield, 2012). Alongside environmental

parameters,

behavioural

factors

like

occupancy,

ventilation

rates,

building

characteristics, and seasonality may influence the school indoor PM 2.5 concentration and composition, including the outdoor PM2.5 concentrations coming from the outdoors. (Chatzidiakou, Mumovic and Summerfield, 2012) Yearlong studies examining seasonal variability of PM levels in schools are scarce and limited investigations have shown differences in ‘PM concentrations between summer and winter where the temperature difference between indoor and outdoor might affect the infiltration of the pollutants’ (Fromme et al., 2007).

Students found it difficult to concentrate when the air changes per hour were low (Bakó-Biró et al., 2012). A long-term study on examining the seasonal variability of ‘PM levels in schools are scarce and that the PM levels in schools between summer and winter might be affected by the difference in indoor and outdoor temperature which eventually might affect the infiltration rate’ (Goyal and Khare, 2009) (Fromme et al., 2007).

2.2.3 Effects on school children health and wellbeing Both short- and long-term exposure to NO2 can cause inflammation of the airways, respiratory illnesses and possibly increases the risk of lung infections. Young children and people with asthma are the most sensitive to NO2. It plays a major role in the development of chronic obstructive pulmonary disease in adults which will affect more people than heart disease by 2020.Long-term exposure may also affect lung function and can enhance responses to allergens in sensitised individuals. High level of NO2 can inflame the airway in our lungs and prolonged exposure causes lung blockage and failure. It particularly affects people with asthma and is fatal (Quality and Group, no date). In the UK mortality occurs when exposed to ambient NO2 levels, PM2.5 and CO2. These particles can be suspended in the atmosphere for a longer period and have an impact on climate and precipitation and a negative impact on human health (Chen et al., 2017).

PM2.5 particles have been linked with several respiratory illnesses, including asthma and chronic bronchitis. Long-term exposure to fine particles can cause premature death from heart disease and lung disease including cancer. Short-term exposure to higher levels of fine particle concentrations have also been linked with cardio-vascular problems and increased death rates. Exposure to fine particles has also been linked to prevalent anxiety and hypertensive disorders.’ A study implicated indoor school PM from traffic, but not from other sources, as an influence on child neurocognition (Forns et al., 2016).

2.3 CO2 levels in schools (Chatzidiakou, Mumovic and Summerfield, 2012) in their work within the SINPHONIE project (Schools Indoor Pollution and Health: Observatory Network in Europe) concluded that simultaneous provision for limiting indoor CO2 levels and thermal conditions below current guidelines (i.e., below 1000 ppm and

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

15

KTKZ5

September 2021

26 ̊ C or 22 ̊ C depending on season) can limit indoor airborne particulate matter concentrations below recommended annual WHO 2010 guidelines and may improve perceived IAQ. CO2 levels generally vary from 350ppm to 2500 ppm (Constantin et al., 2016). Higher concentrations of CO2 like above 1000ppm have negative impacts on health, they affect the cognitive wellbeing and productivity of students (Chatzidiakou, Mumovic and Summerfield, 2012). Recent Literature also suggests that CO2 concentrations in the atmosphere can be taken as a proxy for evaluating the indoor air quality levels and VOCs. Table below shows the European standard for CO2 levels in ppm in the indoor area.

Table 2.3 Appropriate maximum sedentary CO2 concentrations associated with CEN indoor air quality standards (BS EN 13779)

However, the measure of CO2 to be used as a measure of building’s ventilation rate is limited as concentration levels depends on both the quality and rate of air supply. Nevertheless, Indoor CO2 levels are considered as one of the main drivers for ventilation requirements.CO2 concentration levels depend on the no of occupants within a building as well as the external outdoor CO2 pollutant concentration.

The outdoor CO2 levels have increased due to climate change (Greenhouse gases).

2.4 Thermal comfort on Health, Wellbeing and Comfort of students in schools ISO Standard 7730:2005 defines Thermal comfort as ‘that condition of mind that expresses satisfaction with the thermal environment’. There are 2 approaches to thermal comfort the Static and The Adaptive thermal comfort. The primary factors affecting thermal comfort are relative humidity(RH), atmospheric temperature, radiant temperature, air velocity, clothing insulation and metabolic heat (Simion, Socaciu and Unguresan, 2016).

2.4.1 Impacts of thermal comfort in schools, energy, on students’ wellbeing Children have higher metabolic rates than adults, and when they are unsatisfied with the thermal conditions, they do not necessarily behave like adults to adapt to the environment (e.g., take off/add clothes, open/close windows). Thermal comfort depends on six parameters, out of which four are environmental parameters such as relative humidity, airspeed, mean radiant temperature, and dry bulb temperature. Two personal parameters that majorly affect thermal comfort are metabolic rate and clothing insulation (Simion, Socaciu and Unguresan, 2016). In addition, four localized factors are also considered nowadays while assessing thermal comfort; these are vertical air temperature difference, radiant temperature asymmetry, floor temperature, and drafts age, gender, race, individual condition, geographic location, cultural impact, type of work, and climate are various other factors that affect the occupant's perception of thermal sensation. Pinto et al (Campano et al., 2017) explain the relationship between ventilation and thermal comfort in the naturally ventilated school building. However, according

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

16

KTKZ5

September 2021

to the authors, most of the teachers prefer a closed window to control outside noise and extra heat coming inside from the open window in naturally ventilated schools. (Wargocki and Wyon, 2007) discussed the impacts of the thermal environment on teaching and distraction in naturally ventilated classrooms. (Mendes et al., 2016) showed that the causes of sick building syndrome (SBS) are associated with increased temperature causes. Higher temperature causes breathlessness (Honjo, 2009).As per BB101 and CIBSE guide An Overheating occurs when the temperature range is above 28 ̊ C (CIBSE Guide, 1999). However, as per BB101 the thermal comfort is measured using operative temperature. Each spaces have different requirement of thermal comfort. For e.g., a teaching space with minimal activity sets an operative temperature of minimum 20 ̊ C in winter and maximum of 25̊ C. An ideal temperature ranges between 20 ̊ C to 22 ̊ C in Winter, whereas 22 ̊ C to 24 ̊ C in Summer.

(Salthammer et al., 2016). The BB101(2018) uses adaptive thermal comfort approach which is more realistic stating the adaptive nature of human body to varying seasons and temperatures.

2.4.2 Overheating in schools and Climate change The Urban heat island (UHI) in Urban areas increases the global temperature by 2 ̊ C and the climate changes that cause the climate extremities like summer getting hotter and winter to get colder have affected the environment and the occupants to a greater extent. The heatwaves in the summer UK have caused many casualties with overheating are predominant in the city centre (Kolokotroni et al., 2012).

Overheating in schools leads to poor performance, increased absenteeism, and fatigue. It affects the teachers productively as well (Chatzidiakou, Mumovic and Dockrell, 2014). The older guidelines were less stringent and allowed overheating to occur whereas the UK governments action on overheating by altering the BB101 overheating criteria from 2006 to 2018 offers more accurate results by reducing overheating but requires occupants’ reflection on perceived thermal comfort (Montazami and Nicol, 2013).

2.5 Design conflicts/ Knowledge gaps and findings The developing bodies of children might be more susceptible to environmental exposures than those of adults (Ferguson, Penney and Solo-Gabriele, 2017). Children breathe more air, eat more food, and drink more liquid in proportion to their body weight than adults. Therefore, air quality in schools is of particular concern. Ventilation rates affect Indoor air quality (Taylor, 2015, Chen, 2016). Thus, A minimum ventilation rate of 8 liters per second is mentioned in Building bulletin 101 (2006). However, occupants can play a big role in indoor air quality, particularly through the window opening. Classrooms are occupied with 4 times the average occupant requirement which has resulted in increased thermal gains

(Mumovic et al., 2009) .Increased Indoor temperatures and lesser ventilation has been a

recurrent issue in schools than in offices (Wargocki and Wyon, 2017).

The ongoing ASPIRE projects existing paper “Indoor Air Quality and Overheating in UK Classrooms – an Archetype Stock Modelling Approach” where the entire UK school building stocks are represented in terms of 5 archetypes based on their construction era and thermal efficiencies (Dong et al., 2020).

According to that paper the rooms with South facing windows have the lowest average CO2 concentrations and north facing windows has the highest. This is attributed to the improved thermal performance of the envelope in these schools. Schools built after WW2 (1945 – 1967) had the lowest

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

17

KTKZ5

September 2021

risk of overheating, while other archetypes did not show significant difference. Moreover, schools that were built after 1976 are at higher risk of overheating. North faced has lowest overheating, South and east had the highest. Moreover, Winter months CO2 was high (Jan to march) which is due to greater air tightness. There seems to be conflict in terms of CO2 levels and overheating.

Findings from other literatures in school building typologies located in UK showed that window design plays a crucial role in design, and it depends on the air velocity, direction etc (Hassan, Shaalan and ElShazly, 2004). (Abdullah and Alibaba, 2020) concluded that cross ventilation is the most effective in south and east direction rather than north and western side. the centrally located windows with more area had higher infiltration rate than the off-centre window location where the amount of ventilation air decreased (Peng, Deng and Tenorio, 2017).

2.5.1 Retrofitting of buildings The lower the U values, the better the level of insulation, thus less energy is required to heat the building, resulting in lower energy costs. It also reduces the carbon footprint and overall impact on the environment. Improving ventilation and air flow reduces the chance of condensation and harmful mould growth, thereby creating healthier atmosphere (Bhardwaj and Belali, 2015) .

(Taylor et al., 2015) improved the U-values of (wall, floor, roof), glazing, and airtightness in eight residential archetypes in UK wherein indoor-outdoor pollutants modelled under retrofit and future climate, helped in quantifying the contaminant and determining the overheating risk. Retrofitting (insulation, thermal mass, shading, and ventilation) proved to optimize the overall building energy performance as well as reduce the risk of overheating (Oikonomou et al., 2020).

2.6 Review of Current recommendations and standards BB101(2018) has been used predominantly. The summary of the current standards is summarized in the table 2.5 which are used in this report.

Table 2.5.1 Key Performance guideline Target

Key Performance Indicator

Source

‘Thermal

When spaces are passively cooled, the criteria for defining overheating in free

BS EN 15251

comfort/Overheating

running buildings, as set out in CIBSE TM52, should be met. This includes:

(BSI

2007), TM52

•

Criterion 1: Hours of exceedance

CIBSE

•

Criterion 2: Daily weighted exceedance

(CIBSE 2013)

•

Criterion 3: Upper limit temperature

(CIBSE,

The criteria are all defined in terms of ΔT, which is calculated as a function of actual

date)

operative temperature in the room at any time (Top) and the running mean of the

(CIBSE

outdoor temperature (Trm), as follows:

2013)

ΔT = Top – 0.33 Trm – 20.8 ( C) o

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

no

Tm52,

18

KTKZ5

September 2021

Indoor air quality

Where natural ventilation is used or when hybrid systems are operating in natural

BB101

mode, sufficient outdoor air should be provided to achieve:

(DfE 2018)

•

a daily average concentration of CO2 during the occupied period of less than 1500 ppm, and

•

a maximum concentration that does not exceed 2000 ppm for more than 20 consecutive minutes each day,

when the number of room occupants is equal to, or less than the design occupancy.

Indoor/

outdoor

Pollutants

PM2.5 – The annual mean should not exceed 10 ug/3.

WHO

Short term exposure (24h mean) should not exceed 25 ug/m3.

guidelines

No2 - The annual mean should not exceed 40 ug/3.

Short term exposure (1h mean) should not exceed 200 ug/m3.

Operative Temperature

Normal maintained operative temperature during heating season is 20 ̊ C and

BB101

Maximum operative temperature during the heating season at maximum occupancy

(DfE 2018)

is 25 ̊ C’

2.6.1 Climate resilient / adaptive regulatory framework For retrofitting / refurbishment of school buildings, the thermal values and glazing percentages are summarised in the table 2.6. These are for high performance of a school buildings, the DfE Standards and Passive Haus standards are given in table 2.5.1 as a guideline.

Table 2.5.1a Refurbishment standards for optimal performance Parameters

Dfe

Passive haus

U- Values (W/m2 K) Walls

0.18

0.17

Interior Wall

0.18

0.17

Floor

0.18

0.14

Roof

0.15

0.13

Windows

1.6

0.8

Doors

1.6

0.8

Ventilation strategy

Mixed

MVHR

mode Heat loss Form factor

4

3

Glazing percentage North

20

15

East

30

20

South

35

25

West

30

20

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

19

KTKZ5

September 2021

3 METHODS 3.1 Overview This chapter gives a brief discussion on the methods involved in carrying out the paper. A Building simulation/modeling approach is carried out to ascertain the impact of IAQ and thermal comfort on school children using dynamic simulation software ‘Energy plus’ (US Department of Energy, 2015).

3.2 Data collection – Archetype A building stock modeling approach is widely carried out to predict the energy performance of building stocks at both macro and micro (neighborhood, city, national and cross-national). However, An Archetype stock modeling approach is used for building stock modeling’s data and models used in this paper. The data are from existing Advancing School Performance: Indoor environmental quality, Resilience and Educational outcomes (ASPIRE) projects funded by EPSRC, UK government. The school building typologies across the entire UK are collectively represented in terms of an archetype.

These archetypes are classified based on the building age, construction type, and internal environments. ASPIRE model files uses a novel stock-modeling framework- Data dRiven Engine for Archetype Models of Schools (DREAMS) - which can represent the English primary school building stock in detail. The DREAMS collects its information from 2 large databases a) the Property Data Survey Programme (PDSP) dataset (‘Property data survey programme’, no date), which is a survey of school buildings in England, and b) Display Energy Certificates (DEC), which contains thermal properties of a wide range of public buildings (Schwartz et al., 2021). Figure 3.1 gives the overview of development of the archetypes.

Figure 3.1 Archetypes and approach (Schwartz et al., 2021)

3.2.1 Archetype It can be observed that the school building stock across the entire UK is categorized into 5 different archetypes based on their construction era as Pre 1919, Inter-War, 1945 to 1966, 1967 to 1976, and Post 1976. These archetypes have distinct thermal values and building forms/shapes, figure 3.2.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

20

KTKZ5

September 2021

Figure 3.2 Archetypes and SEED Model Source: Author

These models are in energy plus input files ‘*idf’ file format which consists of the thermal properties associated with each era and other set environmental parameters. They vary based on their thermal efficiencies and shapes. Average Window Wall Ratio (WWR) was calculated for each archetype. Each of the 5 archetypes consists of 2 files a) ‘Single block’ and b) ‘Multi’. The ‘Single block’ files represent the building stocks that have single school block within the entire premise, whereas the ‘multi-block’ files consist of more than a single building.

3.2.2 The SEED model They are further categorized into ‘seed’ models (fig 3.2) which is a typical school classroom model with 4 different geometric orientations, North to South, East to West. These ‘seed’ classrooms are 6.5 x 8.0 m in length and breadth and 3.5m in height. These models consisted of inputs on the schedules, thermostat, internal gains, and ventilation but the model did not have specific window locations and building site location. Table 3.2 a 3.2b gives the typical building fabric characteristics and thermal modelling inputs used.

The archetypes are modelled using a stand-alone energy simulation program- Energy plus, which is widely used by researchers and designers.

Table 3.2 a Model inputs – Building fabric characteristics Interior wall

Interior ceiling

Interior floor

Plasterboard XPS 70mm Plasterboard

Timber flooring XPS 70mm Plaster

Plaster XPS 70mm Timber flooring

UCL Institute of Energy and Environmental Engineering

Single glazed window Glass pan 6mm

BSEER

Curtains Glass pan 6mm Drapes 0.004

September 2021

21

KTKZ5

September 2021

Table 3.2 b Model inputs Ventilation

Value

Time on

Infiltration

Air flow mass

3.2.3 Airflow network model An airflow network model (AFN) is used rather than fixed

ventilation

schedule

of

8l/s/person

and

coefficient

3l/s/person forced ventilation. The AFN gives a

Natural

Air flow

realistic condition based on the window opening and

Ventilation

network Model

Internal

Loads

Schedule

Lighting

5.1 W/m2

09:00 – 16:00: 100%

are accounted when windows are opened for

Occupancy

0.56 ppl/m2 with 09:00 – 16:00: 100%

ventilation, moreover they show the heat gain from

110 w/p

illumination

closing. The advantage of using AFN is that it gives

loads

2

Electrical

3.3 W/m

equipment

1 W/person

09:00 – 16:00: 100%

the wind speed and different geographical locations

are

dependant

on

the

classroom

orientations. Fig 3.3 external fabric is used to generate an air flow network model. The modelling parameters

Metabolic rate Thermostat

Temperature

Schedule

of air flow network model input in energy plus is given

Heating

20°C

09:00 – 16:00

in the appendix.

12°C

All other period

setpoint

The figure 3.2.3 shows the external fabric of seed classroom with cracks at the top and bottom for infiltration

and trickle ventilation for ventilation.

3.3 Methods Framework Overview Chart 3.3 summarizes the research study framework in a detailed step. As explained in figure 2.2 From the total 5 archetypes, the oldest (pre-1919) and the latest (Post 1976) is chosen. These archetypes vary in their building form as well their thermal values due to their construction eras. The ‘seed’ model of these archetypes is simulated for 2 scenarios a) Baseline and b) Refurbished for both 1919 and 1976 archetypes. In the baseline scenario, the existing ASPIRE model parameters are simulated using energy plus software, whereas in the Refurbished models the thermal efficiency of the building envelope is enhanced, and the models are simulated. The detailed modeling parameters along with their construction schedules and U values are given in Table 3.3.1. Further detailed procedures are explained in section 3.3.1. The base model and refurbished SEED models are tested for simulation with 'London. TRY' CIBSE weather file for 4 different geometric orientations. Further on comparing the results, the most problematic orientation from both archetypes is identified and is further simulated upon design interventions, like altering the WWR, window area and size, glazing features, and altering the U and G values of windows, and adding external shading devices. Further, 4 different scenarios are generated for the most problematic orientation. Each of the parameters and scenarios is explained in detail in section 3.3.2.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

22

KTKZ5

September 2021

Figure 3.3. Project Framework Source Author

Upon comparison of each scenario, the most suitable scenario is chosen and is tested for ‘Overheating analysis’ as per BB101(2018) (ESFA, 2016) . These final retrofitted models are further tested for climate resilience by testing them for future climate weather conditions as per CIBSE Met office data for London 2030s and 2050 TRY weather data.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

23

KTKZ5

September 2021

Weather file The weather file is obtained from CIBSE (Met office) hourly weather data Test reference year (TRY) for simulations and Design Summer year (DSY) for summertime overheating risk assessment. The London weather file (DSY and TRY) is chosen from 14 different location.

3.3.1 Zone outdoor pollutant Schedules: CO2 – Outdoor CO2 values are set a constant of 415 ppm based on atmospheric measurements from the start of 2021 taken from Mauna Loa in Hawaii (NASA, 2021). The indoor CO2 value depends on the occupancy ratio. The classroom environment is modelled with an occupancy time from morning 9am to evening 4pm. The energy plus uses this value to calculate the CO2 values. The outdoor PM2.5 and No2 values in this paper are taken from Department for Environment, Food and Rural Affairs (DEFRA) website. The last 5 years averaged values are taken for NO 2 and PM2.5 values as 35.4ug/m3 and 10.5ug/m3 as previously used in ASPIRE project paper. The external concentrations for calculating NO 2 and PM 2.5 are different as they used indoor to outdoor ratio thus, they are set to 1.

Deposition velocities k: Deposition rates of 0.87 h-1 (Emmerich and Persily, 1998) and 0.19 h-1 (Long et al., 2001) for NO2 and PM2.5 respectively have been reported previously from experimental data. The surface area to volume ratio of the classroom based on the geometry presented in Section 2.1.1 was determined to be 1.129 m-1, which was then used to calculate deposition velocities of 4.7 e -5 m/s and 2.1 e-4 m/s Penetration factors, P: This number represents the fraction of particles that infiltrate through the building envelope or any available openings. For NO2, this was set to 1 throughout the entire year based on measured data (Emmerich and Persily, 1998)For PM2.5, P was set to 0.8 during the winter (1st October to 30th April) and 1 during the summer (1st May – 30th September) to account for increased availability of openings (Long et al., 2001) 3.3.2 Processing the energy outputs The Indoor CO2 levels are obtained at an hourly interval and are in ppm, whereas the NO 2 and PM2.5 are obtained at 15 minutes timestep. They outdoor dry bulb temperature, wind speed alongside the operative temperature is used in the processing of results. The NO 2 and PM

2.5

are in I/O ratio, thus

they need to be multiplied by the penetration factor as mentioned in section 3.3.1 and to get the indoor concentration vales, they are multiplied with the outdoor NO 2, PM2.5 values (35.4ug/m3 and 10.5ug/m3) respectively. The results for each simulation are analyzed based on basic descriptive statics. The relationship between environmental parameters and contaminants are tested using correlation and regression. Precipitation is not modelled in energy plus and the data analysis is done based on operative temperature, relative humidity, wind speed and pollutant concentrations.

3.3.3 Baseline vs Retrofitted/ refurbished model The model uses air flow network model in Energy plus, rather than using a fixed ventilation schedule of 8l/s/person. This air flow network model enables the designers to simulate multizone wind driven

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

24

KTKZ5

September 2021

models. The natural ventilation from the outdoors environment passes through the windows and enters the indoor environment.

The baseline model is simulated with the existing ASPIRE ‘seed’ model parameters as per table 3.3.3a.

The construction schedules and materials have been inserted based on the construction age and their relevant thermal values. The detailed set of materials and construction schedules are tabulated for both Baseline models 1919 and 1976 below. However, for refurbishment model, the existing building walls haven’t been replaced, instead mere addition of insulation to the existing buildings, The values are set to table 2.5.1 guidelines.

Table 3.3.3a Base model Inputs for simulation for Archetypes 19192

Element Wall - Exterior

Archetype 1919_Basemodel Composition Layer Inside to Outside

Brick- 225mm, Plaster board -13mm Plaster- 13mm

Properties U value

Element Wall - Exterior

U-value = 1.8W/(m2K)

Wall - Interior

Properties U value

Brick 105mm, XPS_polystyrene Co2 bowling 200mm, Concrete 105mm, Gypsum plaster 15mm

U-value = 0.15W/(m2K)

Plasterboard CIBSE 12mm, MW_Glasswool 70mm, Plasterboard CIBSE 12mm,

U- value - 0.38W/(m2K)

Wall - Interior

Plaster - 16mm, XPS - 70mm, Timber flooring - 30mm

U -value = 0.91W/(m2K)

Roof

Roof Asphalt 100mm, Cast concrete 100mm, Plasterboard 13mm, Plaster 16mm;

Asphalt 100mm, Cast concrete 100mm, XPS 70mm, Plasterboard 13mm, Plaster 16mm.

U-value = 2.9W/(m2K)

Ground floor

Ground floor Ground 6, Urea Formaldehyde foam, Cast concrete 100mm, Floor/ roof screed, Timber flooring

U -value = 1.5W/(m2K)

Interior floor

U -value = 0.15W/(m2K)

Ground 6, Urea Formaldehyde foam - 251 mm , Cast concrete 100mm, Floor/ roof screed - 70mm Timber flooring - 30mm

U -value = 1.5W/(m2K)

Plasterboard CIBSE 16mm, Rockwool_10-degree 70mm, Timber flooring 30mm

U -value = 0.32W/(m2K)

Timber flooring 30mm, Rockwool_10degree 70mm, Plasterboard CIBSE 16mm

U -value = 0.32W/(m2K)

Double glazing - LoE Clear 6mm glass, 13mm Argon gas, LoE Clear 6mm glass, U - value= 1.3W/(m2K)

SHGC: 0.6 U -value =1.3 W/(m2K)

Window curtains

Thickness = 0.004m

Interior floor Timber flooring 30mm, XPS 70mm, Plaster 16mm

U -value = 0.91W/(m2K)

Ceiling

Ceiling Plaster 16mm , XPS 70mm, Timber flooring 30mm,

U -value = 0.91W/(m2K)

Glazing

Glazing

Single plane glass 6mm

Internal shade

Archetype 1919_Refurbished/Retrofitted Composition Layer Inside to Outside

Window curtains

SHGC: 0.8 U -value = 0.91W/(m2K)

Thickness = 0.004m

Internal shade

2

The materials are created in energy plus and the U values are obtained from the CIBSE Guide A and Design builder database.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

25

KTKZ5

September 2021

Table 3.3.3b Retrofitted model Inputs for simulation for Archetypes 1976 Archetype 1976_Basemodel Element Wall - Exterior

Archetype 1976_Refurbished/Retrofitted

Composition Layer Inside to Outside

Properties U value

Brick 225mm, XPS 70mm, Plaster board 13mm Plaster 13mm,

U-value = 0.95W/(m2K)

Element Wall - Exterior

Wall - Interior

U -value = 0.91W/(m2K)

Roof

Brick 105mm, XPS_polystyrene Co2 bowling 200mm, Concrete 105mm, Gypsum plaster 15mm, Uvalue = 0.15W/(m2K)

U-value = 0.15W/(m2K)

Plasterboard CIBSE 12mm, MW_Glasswool 70mm, Plasterboard CIBSE 12mm, Uvalue - 0.38

U- value - 0.38W/(m2K)

Asphalt 100mm, Cast concrete 100mm, XPS 70mm, Plasterboard 13mm, Plaster 16mm.

U -value = 0.15W/(m2K)

U -value = 0.15W/(m2K)

Ground 6, Urea Formaldehyde foam, Cast concrete 100mm, Floor/ roof screed, Timber flooring

U -value = 1.5W/(m2K)

Roof Asphalt 100mm, Cast concrete 100mm, XPS 70mm, Plasterboard 13mm, Plaster 16mm; U -value = 0.91W/(m2K)

Ground 10, Underfloor bricks 350mm, Cast concrete 100mm, Flooring screed 70mm, Timber flooring 30mmU -value = 0.91W/(m2K)

U-value = 2.9W/(m2K)

Ground floor

U -value =1.5/(m2K)

Interior floor

Ceiling

Properties U value

Wall - Interior

Plasterboard 13mm, XPS 70mm, Plasterboard 13mm; U -value = 0.91W/(m2K)

Ground floor

Composition Layer Inside to Outside

Interior floor Timber flooring 30mm, XPS 70mm, Plaster 16mm

U -value = 0.91W/(m2K)

Plaster 16mm , XPS 70mm, Timber flooring 30mm,

U -value = 0.91W/(m2K)

Ceiling

Glazing

Plasterboard CIBSE 16mm, Rockwool_10-degree 70mm, Timber flooring 30mm U -value = 0.32W/(m2K) Timber flooring 30mm, Rockwool_10degree 70mm, Plasterboard CIBSE 16mm U value = 0.32W/(m2K)

U -value = 0.32W/(m2K)

U -value = 0.32W/(m2K)

Glazing

Single plane glass 6mm

Internal shade

Window curtains

SHGC: 0.8 U -value = 0.91W/(m2K)

Thickness = 0.004m

Internal shade

Double glazing - LoE Clear 6mm glass, 13mm Argon gas, LoE Clear 6mm glass, U - value= 1.3W/(m2K)

SHGC: 0.6 U -value = 0.91W/(m2K)

Window curtains

Thickness = 0.004m

For example, in the 1919 buildings as seen in table 3.3.3b Insulating materials like XPS_Polystrene with CO2 bowling of 100mm thickness is added to the 225mm thick brick wall, it was also noted that the insulation materials do no exceed 100mm, as they might increase the wall thickness and reduce the usable floor space. The internal plasterboard or plastering were changed accordingly to arrive the desired U value by changing them to Gypsum plastering or ‘CIBSE plasterboard’. It should also be noted that no major retrofitting is proposed, the ground floor is also left unaltered in both archetypes, however, the timber flooring in the interior floors.

3.3.4 Scenarios These scenarios are created by altering the following factors.

1. Window area – by altering the Window to Wall ratio (WWR) 2. Window placement – (centrally located, off centered, equally distributed) 3. Window glazing – Single, double, triple glazing and external shading features like sunshades, fins (h horizontal/ vertical).

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

26

KTKZ5

September 2021

W

Centrally located

Off centered located

Equally Distributed

The existing Baseline and Retrofitted model have centrally located windows with WWR 25% with single and double glazing respectively in both 1919 and 1976 archetype. Scenarios for window located off centered and equally distributed with triple glazing and altered WWR are generated below in table 3.3.2.

These scenarios are tested with weather files and the outcomes of each scenario are recorded and compared against one another.

After identifying the problematic orientation, several iterations are performed, 4 scenarios are tested with weather file for optimal outputs of CO2, NO2, PM2.5 levels and Operative temperature. Minor design interventions focusing on the building envelope façade windows are done. The WWR is altered from18% to 30% threshold to test the Department of Education (DfE) and ‘PassivHaus’ threshold as mentioned in table 2.5.1. Further the windows are replaced with triple glazing with an SHGC of 0.4 and U value of 0.78. Further, External vertical fins on the scenarios are added with an additional horizontal shade projection in scenario 4. The best performing scenario is chosen for overheating analysis and future climate resilience. The table 3.3.4 gives the model parameters adopted for final design interventions.

Table 3.3.4 Scenario 1,2,3,4 model inputs for Archetypes 1919 and 1976 Scenario 2

Scenario 1

Parameter

SIM001

SIM001a

Archetype

1919

1976

WWR Window

30% Located on the sides (Offset)

Placement Glazing

Parameter

SIM001

SIM001a

Archetype

Pre 1919

Post 1976

WWR

18%

Window

Central Window with narrow

Placement Triple glazing - LoE clear 3mm

Glazing

glass, Argon 13mm, Clear 3mm

U

value

glass, Argon 13mm, LoE clear

=0.78W/(m2/K),

3mm

SHGC= 0.4 Length x

1.5 x 1.2m; Sill - 0.75m

width x Sill

External

Length x width,

External 500mm Vertical metal fins

glass,

U

value

=0.78W/(m2/K), SHGC= 0.4

Sill height

height

Triple glazing - LoE clear 3mm gl ass, Argon 13mm, Clear 3mm

glass, Argon 13mm, LoE clear 3mm glass,

windows on the sides

Centre - 1.2 x 1.5m, sides 0.8 x 1.5m, Sill - 0.75m 500mm Vertical metal fins

shading

shading

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

27

KTKZ5

September 2021

Scenario 3

Scenario 4

Parameter

SIM002

SIM002a

Archetype

1919

1976

WWR Window

30% Equally distributed wide windows

Placement Glazing

Parameter Archetype

1919

1976 23%

Window

Equally distributed tall windows

Placement Triple glazing - LoE clear 3mm

Glazing

glass,

U

value

Triple glazing - LoE clear 3mm glass, Argon 13mm, Clear 3mm

glass, Argon 13mm, LoE clear 3mm

glass, Argon 13mm, LoE clear 3mm

=0.78W/(m2/K),

glass,

SHGC= 0.4

U value =0.78W/(m2/K),

SHGC= 0.4

1.5 x 1.2m; Sill - 0.75m

width x Sill

Length x

1.2 x 1.8m; Sill - 0.75m

width x Sill

height External

SIM003a

WWR

glass, Argon 13mm, Clear 3mm

Length x

SIM003

height 500mm Vertical metal fins

shading

External

500mm

shading

horizontal projection of 500mm

Vertical

metal

fins

&

3.3.5 Overheating analysis The school working hours/ occupancy schedule were set from morning 9am to evening 4pm, The Outdoor Running Mean temperature is taken in accordance with TM52. The operative temperature is calculated only for the occupied hours from May 1st to September 30th excluding the weekends but including the summer holidays. The weather file used is CIBSE 50th percentile London DSY.The BB101 says the dynamic simulation software should be capable of calculating the dry outdoor bulb temperature and running mean temperature. The dry outdoor bulb temperature is obtained from energy plus outputs, the running mean temperature as per TM52 is ‘The exponentially weighted running mean temperature, Trm, for any day is expressed in the series: Trm = (1 – α) (Tod – 1 + α Tod – 2 + α 2 Tod – 3 ....) where α is a constant (<1) and Tod–1, Tod–2, etc. are the daily mean temperatures for yesterday, the day before, and so on’, Here, α value for London is taken as 0.8.

3.3.6 Climate Resilience – Future climate files However, for Climate resilience / future weather scenarios, the various climate change model scenarios set out in the UK Climate Projections 2009 (UKCOP09) is taken into consideration. Thus, future weather files of the 2030s and 2050s London weather 50th percentile CIBSE TRY is used in the simulations (Eames and Mylona, 2018) . The final retrofitted scenario is tested with future files and hence a comparison is made for further recommendation and strategies.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

28

KTKZ5

September 2021

4 RESULTS & ANALYSIS The building performance simulations are consolidated below, the aim of the section is to provide an insight on the functioning of the building in terms of IAQ and thermal comfort by testing compliance with the guidelines recommended in table 2.5. The results are calculated for whole design year for all occupancy schedules.

4.1 CO2, Thermal comfort & Risk of overheating in Baseline and Refurbished model It is observed from the results that the annual mean CO2 concentrations of base model 1919 archetype had the highest CO2 concentrations compared to 1976 archetype. This is due to the low thermal efficiencies and lesser air tightness in the pre-1919 archetype. The North orientation of both archetypes had the highest whereas south had the least. This is due to the predominant wind direction and solar movement. The sun moves from the east to west via south. The probability of opening windows on the south for the purpose of ventilation, light and thermal gains are more as compared to North. Upon refurbishment, it can be observed that on an average the CO2 levels decreased by 60% in 1919 upon refurbishment and 76% in the 1976 archetype, thus indicating better ventilation as CO 2 is taken as a proxy for ventilation.

Table 4.1 and Fig 4.1 Annual Mean concentrations of CO2 concentrations in ppm across Base model and Retrofitted model in both Archetypes 1919 and 1976 Archetype

West

East

South

North

Base -1919

1053

1123

960

1251

Retro-

650

676

631

696

Base -1976

838

876

803

933

Retro-

641

678

642

677

1919

1976

Table 4.2 and Fig 4.3 Seasonal Mean concentrations of CO2 concentrations in ppm across Base model and Retrofitted m6odel in both Archetypes 1919 and 1976 Seasonal CO2 levels for Baseline 1976

Seasonal CO2 levels for Baseline 1919 West

East

South

North

West

East

South

North

1189

1259

1131

1305

Jan to March

1704

1849

1514

2131

Jan to March

April - June

592

705

665

787

April - June

558

635

602

712

Sep - Dec

1192

1209

1000

1317

Sep - Dec

961

966

868

1040

Seasonal CO2 levels for Retrofitted 1976

Seasonal CO2 levels for Retrofitted 1919

Jan to March

West

East

South

North

810

876

770

931

West

East

South

North

Jan to March

809

884

809

881

529

555

552

564

679

713

662

705

April - June

530

556

551

569

April - June

Sep - Dec

705

712

662

721

Sep - Dec

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

29

KTKZ5

September 2021

Moreover, on comparing the seasonal mean CO2 values in Table 4.2 and 4.3 ,baseline models of both 1919 and 1976 had the highest CO2 concentrations between January and March in all the four orientations which exceed the BB101 standard of 1500ppm. The North orientation had the highest CO2 concentrations and the South had a much lower concentration. Similarly the April to June months had the lowest <800ppm. However, The pre 1919 archetypes CO2 concentrations were much higher than Post 1976. This can be attributed to the higher thermal efficiency and air tightness of the 1976 archetypes. The refurbishment in both archetypes shows significant results. Their seasonal mean in all the seasons are below 1000ppm. Upon refurshiments the CO2 levels have decreased from 47% to 60% across January to December in the baseline model in pre 1919 archetype, whereas from a 67% to 70% decrease can be observed in post 1976.

Fig 4.2 Comparison of Optimal temperature in base model and retrofitted model in 1919 and 1976 archetypes across 4 different orientations

Table 4.2.3 Comparison of Optimal temperature in base model and retrofitted model in 1919 and 1976 archetypes across 4 different orientations Annual

Archetype

ClassroomsNO2by

West

East

South

North

Baseline Model_Pre 1919 in summer

26

26

25

24

Baseline Model_Pre 1919 in Winter

16

16

17

16

Baseline Model_Post 1976 in Summer

25

25

24

24

Baseline Model_Post 1976 in Winter

18

18

18

17

Retroffited Model_Pre 1919 in Summer

25

25

24

24

Retroffited Model_Pre 1919 in Winter

21

21

21

21

Retroffited Model_Post 1976 in Summer

25

25

24

24

Retroffited Model_Post 1976 in Winter

20

21

21

20

Orientaion

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

30

KTKZ5

September 2021

The Baseline model of 1919 and 1976 had temperatures below the optimal range of 20 ̊ C in winter , with West East and North orientation having the lowest temperature as seen in table 4.1.3. During summers the baseline model in 1919 in the west and east orientation had the highest , which was above the maximum optimal range of 25 ̊ C. The North orientation has the lowest temperatute in summer in both archetypes. The retrofitted model has increased the winter tempearture from 16 ̊ C to 21̊ C in Pre 1919, whereas from 17̊ C to 21 ͦ C. The summer temperatures in both archetypes are maintained, however, there is probability of overheating in the west and east orientation. North facing had the lowest risk of overheating, whereas the west and east had the highest.

4.2 Indoor vs outdoor concentrations of PM2.5 and NO2 The annual mean I/O ratios are simulated and multiplied with the averaged NO2 and PM2.5 outdoor concentrations and their penetration factor as mentioned in section 3.2.2. The South orientation had the highest PM2.5 and NO2 concentrations in both archetypes in the baseline model, but the retrofitted model gives similar results of 10.3ug/m3 of PM2.5 in 1919 archetype in West, East, and South direction and 4.7ug/m3 of NO2 concentrations in West and East orientation in pre-1919. Similar results are observed in West and East orientation of post 1976 archetype with respect to No2 concentrations.

Both archetypes showed that North orientation had the lowest NO2 and PM2.5 concentrations and I/O ratios in both archetypes and both base and Retrofitted models.

Table 4.2.1 Comparison of NO2 and PM2.5 Indoor levels in Base and Retrofitted model of both archetypes across 4 geometrical directions

Annual

Archetype

West

East

South

North

Archetype

West

East

South

North

Classrooms PM2.5 by

Classrooms NO2 by Orientation ug/m

Annual

Orientation in ug/m3

3

Baseline 1919

8.6

8.6

8.9

7.9

Baseline 1919

4.3

4.3

4.4

4.1

Baseline 1976

9.3

9.3

9.6

8.6

Baseline 1976

4.5

4.5

4.6

4.3

Retrofitted 1919

10.3

10.3

10.3

9.6

Retrofitted 1919

4.7

4.7

4.8

4.6

Retrofitted 1976

10.2

10.2

10.4

9.8

Retrofitted 1976

4.7

4.7

4.8

4.6

Table 4.2.2 Simulated annual PM2.5 and NO2 I/O ratios and their absolute average values across Baseline and Retrofitted model across 1919 and 1976 archetypes

Simulated annual PM2.5 I/O ratios and absolute

Simulated annual NO2 I/O ratios and absolute average

average values

values

I/O

Base

Base

Refurb_

pre-

Post

1919

1919

1976

0.44

0.46

Refurb_1976

µg/m

Base Post

Refurb_

Pre-

1976

1919

Refurb_1976

0.25

0.27

0.30

0.29

8.54

9.23

10.18

10.16

1919 0.49

0.49

I/O

4.76

µg/m3

ratio

ratio 3

Base

4.29

4.49

4.78

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

31

KTKZ5

September 2021

Graph 4.2.1Simulated annual PM2.5 and NO2 concentrations across Baseline and Retrofitted model across 1919 and 1976 archetypes compared against WHO guideline.

The refurbishment increases the I/O ratios of PM2.5 and NO2 in both archetypes. Higher thermal efficiencies permit prolonged window openings; thus, the outdoor concentrations penetrate inside faster. The South had the highest indoor pollutant concentration, and then the west and east, the North orientation had the lowest indoor pollutant concentration. However, the pre 1919 retrofitted archetype had the highest when compared to post 1976. From the tabulated results table 4.2.1 and the analysis the base model of 1919 has higher 16% higher NO2 and 4% higher PM2.5 indoor concentrations when compared to Baseline Post 1976. Moreover, upon refurbishment, average overall the PM2.5 and NO2 has increased by 11.3 % and 20 % in Baseline pre 1919 and 4 %and 9.6% in post 1976 archetype.

However, all the Indoor values were lesser than the WHO guidelines (Graph 4.2.1).

4.3 Comparison of scenarios From the above sections, the most problematic orientation of all based on the CO2, NO2, PM2.5 and Operative temperature are West and East. However, for the purpose of study east orientation is neglected as the receive the morning sun alone, thus, the risk of overheating is lesser as compared to west orientation. Thus, west orientation in the retrofitted model of both 1919 and 1976 archetype are considered for design interventions and iterations are performed. As mentioned in section 3.3.2, upon simulating the results from 4 different scenarios, it can be observed that the scenario 4 is the most optimized design as tabulated in Table 4.2.

All the scenarios have CO2, NO2 and PM2.5 levels below the targets. However, in the scenario 2 and 3 the 1919 model exceeds the optimal temperature range of 25 ͦ C by 9 % of times in summer and 19% of the time in 1976 archetype in the summer and 17% in the winter optimal temperature of 20 ͦ C. The scenario 4 maintains a mean optimal temperature of 24 ͦ C in the summer months (June to August) in both archetypes, but the Winter temperature in post 1976 has a low mean temperature of 18 ͦ C. It also exceeds the minimal optimal temperature range by 11% of the time between January and December months.

The NO2 and PM2.5 levels are highest in pre 1919 and post 1976 archetype when the WWR is 30%.

However, pre 1919 archetype has the highest on comparison. The post 1976 archetype of scenario 4 has the lowest indoor NO2 and PM2.5 concentration. (Refer Appendix 02, Graph 02a for I/O across all scenarios)

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

32

KTKZ5

September 2021

The table 4.3.1 further gives a detailed comparison of the seasonal I/O and Indoor concentrations of PM2.5 and NO2 in all scenarios. It can be noted that the PM2.5 and NO2 levels are highest in the summer months (June to August) and lowest in the winter (Dec to Feb). Scenario 4 is the most optimized, where in 1919 archetype is better.

Table 4.3.1 Comparison of Scenarios IAQ and thermal comfort in Base and Retrofitted model of both archetypes 1919 and 1976 Scenario 1 Parameters

Scenario 3

Scenario 4

SIM01a

SIM02

SIM02a

SIM03

SIM03a

SIM04

SIM04a

20%

20%

18%

18%

30%

30%

23%

23%

22 to 25

23

21

23

21

22

21

22

21

1000

676

738

713

817

614

659

658

704

WWR Annual mean Operative

Scenario 2

SIM01

Targets

temperature Annual CO2

ppm Annual NO2

40 ug/m3

11.1

10.3

11

10

11

11

10

9

Annual PM2.5

10 ug/m3

5.3

4.5

5

5

5

5

5

4

9

8

9

19

25

19

Nil

Nil

Nil

13

Nil

17

Nil

14

Nil

9

% Max Operative temp 25 and above %Min

operative

temp

below 20

CO2 Seasonal Jan to March

1000

883

998

969

883

766

844

847

928

538

557

558

538

514

519

621

553

721

804

742

721

647

720

702

762

25

25

25

25

25

25

24

24

21

18

21

18

20

18

21

18

1.8

1.8

1.8

1.8

1.8

1.8

1.8

1.8

NO

NO

NO

NO

NO

NO

YES

YES

ppm April to June

1000 ppm

September to Dec

1000 ppm

Temperature seasonal Summer (June to Aug) Winter (Dec to Feb) Wind speed m/s Most

1.5 m/s optimized

classroom

NO2 and PM2.5 Seasonal NO2 Summer

40 ug/m3

14.5

14.3

14.0

13.7

15.0

14.8

13.5

13.2

No2 Winter

40 ug/m3

8.4

7.3

8.1

7.0

8.7

7.9

7.7

6.8

PM2.5 Summer

10 ug/m3

7.1

5.6

7.0

6.9

7.2

7.2

6.5

6.4

PM2.5 Winter

10 ug/m3

3.9

3.8

3.8

3.5

3.9

3.7

3.4

3.1

I/O ratio NO2 Summer

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

No2 Winter

0.2

0.2

0.2

0.2

0.3

0.2

0.2

0.2

PM2.5 Summer

0.7

0.6

0.6

0.6

0.7

0.7

0.6

0.6

PM2.5 Winter

0.4

0.4

0.4

0.4

0.4

0.4

0.4

0.4

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

33

KTKZ5

September 2021

Thus, the scenario 4 SIM004 and SIM004a is chosen as the final optimized design. They have a WWR of 23% with tall windows (1.8m high) placed equidistance from one another. They also have triple glazing with windows shaded by both horizontal projection of 0.5m and vertical metal fins of 0.5m. The graphs show a comparison of the final retrofitted design of both archetypes against the base model, which has no external shading and, the windows are located centrally with WWR 25 % and single window glazing. It is observed that the NO2 and PM2.5 levels have increased in the final design. The pollutants level also vary when the Outdoor Air Wind speed varies. Upon performing a regression, Wind speed and Indoor pollutant concentrations are positively correlated.

Chart 4.3.1 Comparison of Baseline PM2.5 and NO2 with % of outdoor air flow in Base and Final model of both archetypes 1919 and 1976

It can be seen from chart 4.3.1 that the decrease in the percentage of wind speed decreased the NO2 and PM2.5 levels. This is evident as the pollutants are generated in the outdoor environments and are carried inside via the wind and enters the classroom through the window openings and infiltrations if any.

4.3.1 CO2 levels In the month of January and June, the CO2 levels in the final design are at their peak highest and peak lowest, the graph 4.3.1shows a typical week showing the hourly measure of CO2 levels inside the classroom an hour and after the occupied time (9am to 4pm). The percentage of air flow is inversely proportional to the CO2 levels. The wind speed increases in July whereas the CO2 levels are low.

Similarly, the CO2 levels are higher in January around 12am whereas the wind speed is 50% lower than the average outdoor speed. It also shows that the hourly levels are >1500ppm and is in accordance

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

34

KTKZ5

September 2021

with BB101. The CO2 levels in Winter increases from 8am and is at peak between 11am and 2pm, and then decreases slowly. The peak highest is at 12pm, where the wind speed is at its lowest.

Chart 4.3.2 A typical week showing the CO2 levels in Jan and July and the % of outside air flow in Archetype

4.4 Comparison of environmental parameters and Indoor contaminants There is a strong positive correlation between I/O ratio and operative temperature (chart 4.3.1). It is also noted that as the operative temperature varies the NO2 values vary accordingly. Thus, as the operative temperature increases the pollutant values increases. This can be attributed to the opening of windows during the summer where the temperature hits the maximum and the pollutants level reaches the maximum. The Appendix 02 , Graph 02 c shows correlation between I/O and wind speed, they show positive correlation.

Chart 4.3.1 Comparison of I/O ratio and operative temperature in Archetypes 1919 and 1976.

4.5 Overheating The overheating analysis on the final retrofitted archetypes as mentioned in section 3.3.4 suggest that the operative temperature in both the 1919 and post 1976 archetype fall below the T max range – acceptable temperature. Final design satisfies BB101 Criteria II - Normal expectation, according to which the ▲T value is + 3/-4 oC, where in for refurbished buildings, the minimum standard is Category IV. It thus performs better.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

35

KTKZ5

September 2021

However, from the graph 4.4 and 4.5, both the archetypes operative temperature is below the maximum temperature range of 28̊ C. However, in Week 15 the ▲T value reaches 1, where the operative temperature of both archetypes reaches above the T max value of 24 ̊ C , However, as per the adaptive thermal comfort range an upper threshold of +3 and -4 oC is given. Similarly in week 5, the ▲T value in archetype 1919 reaches -6, but the operative temperature is 26 ͦ C and is lesser than the Tmax value of 28̊ C. It can be concluded that the Final retrofitted archetypes have normal expectance level of overheating and are better performing in the peak summer in June, July, and August as it satisfies BB101 overheating criteria I and II. Table 4.5 Overheating assessment from May 1st to September 30th in final retrofitted archetypes 1919 and 1976 showing the Trm , Tmax and ▲T values

Months

TRM

Tmax

1,919

1,976

▲T

▲T

May

14

26

23

23

-3

-3

June

15

27

24

24

-2

-2

July

19

28

26

26

-2

-2

August

15

27

26

26

-1

-1

September

15

27

24

24

-3

-3

Graph 4.4 Adaptive thermal comfort _summertime overheating in west /final retrofitted archetypes_1919 & 1976

The tabulated weekly results are given in appendix 03 for further reference.

4.6 Climate resilience – Testing the final design with future weather files The final west retrofitted model scenario 4, SIM004 and SIM004a is tested for future climate weather files. The CIBSE 2030 and 2050, London 50th percentile TRY files are used as mentioned in section 3.3.5. From the tabulated results 4.5.1 and 4.5.2 it can be observed that in Final 1919 and 1976 archetype, the Annual Operative temperature (OT) from the present year increases in 2030 by an average of 2 ̊ C and 3 ̊ C in 2050 in the summer, moreover, the temperature in other months increased by 1 ̊ C in average in the 2030s and by another 1 ̊ C in the 2050s. The post 1976 archetype had higher

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

36

KTKZ5

September 2021

Operative temperatures at present and future when compared to Pre 1919. On comparing CO2 levels, the concentration decreased in 2030 and 2050 by 6.4 % and 6.9% in the pre-1919 archetype. Similarly, the CO2 levels in the post 1976 archetypes declined by 4.6 % in 2030 and by 11.8 % in 2050 on comparing with the present CO2 levels. The levels are <800ppm in 2050s in winter the 1976 archetype whereas >1000ppm in 2050s in 1919 archetype The wind speed however showed a steady increase from an annual average of 1.8m/s at present to 2.2m/s in 2030 and 2.26m/s in the 2050. The NO2 and PM2.5 levels in 1919 archetypes showed minor increase in in terms of I/O ratios. Similarly in the post 1976 archetype the PM2.5 and NO2 I/O ratios showed minor increase, but the NO2 ratios in 2030 dropped from 0.31 at present to 0.29 in 2030s and increased to 0.30 in 2050. The PM2.5 and NO2 ratios decreased significantly in the winter months (Dec to February) in 2030 and 2050 in both the archetype whereas it increased in the summer months (June to August), Refer Appendix 04.

Table 4.5.1 Comparison of monthly average of CO2 and thermal comfort factors in final 1919 & 1976 archetype for present, 2030 and 2050

Table 4.5.1 Comparison of monthly average of NO2 and PM2.5 in final 1919 & 1976 archetype for present, 2030 and 2050

In conclusion, the operative temperature and NO2 and PM2.5 level increases in the future at a slower and steadier pace whereas the CO2 level decreases significantly. The post 1976 final archetype performs in terms of IAQ (CO2 and NO2 and PM2.5 levels) but pre 1919 archetype performs better in terms of thermal comfort (maintaining ambient operative temperature) the future and at present.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

37

KTKZ5

September 2021

5 DISCUSSIONS 5.1 Summary of interpreted results From the results, it is evident that the North orientation of both the archetypes 1919 and 1976 has the lowest risk of overheating whereas the CO2 levels were highest in that orientation. It can also be noted that NO2 and PM2.5 values are least in that North, which is due to the sun’s movement across east west direction via south and the wind direction in London. Whereas the East, and West had the highest risk of overheating with higher indoor air pollutants and higher temperature but low levels of CO 2, indicating higher ventilation (graph 4.4.1 and 4.4.2). This can be attributed to the harsh low-angle sun on the east and west sides and the openings of windows based on user needs. Moreover, the summers are longer, and the post noon sun hits the west façade post noon. Thus, the post-lunch break will be hotter in the classrooms in the west orientation. From the scenarios, it can be assessed that in the winters or as the temperature decreases, the CO2 level increases, whereas the I/O ratios are lesser and it’s vice versa in the summer, with increased I/O ratios of NO 2 and PM2.5 and lower CO2 levels. The strong positive correlation link between operative temperature and I/O ratios and wind speed, whereas strong negative correlation link between CO2 levels and Operative temperature and wind speed. Upon selecting West orientation as problematic, the design interventions scenarios generated typically focuses on the window placement and sizes and the glazing schedule. The centrally located windows (base model & retrofitted archetypes) allowed more indoor air pollutants (NO 2 and PM2.5) compared to equally distributed windows (scenario 4). But the highest I/O ratio penetrated via off-centered windows.

The CO2 levels were however lowest in off-centered implying the ventilation is better, this contrasts with the Off-centered location where the amount of ventilation air decreases (Hassan, Shaalan and ElShazly, 2004). This can be true to other orientations as the wind direction plays a pivotal role in determining the inflow of outdoor particles. The inflow of wind is more in windows located in the center; thus, an ideal placement is an equal distribution.

But in the final retrofitted scenario, the WWR of 23% is achieved by equally distributing the windows, the windows were tall (1.2 x 1.8m) high. They had a lower CO2 level and Temperature as well NO2 and PM2.5 levels. Tall windows vs wider windows can be further studied.

Role of external shading devices: The evening sun are at their lowest angle, and they hit the west façade causing them to be harsh and causing glare. Thus, an external vertical shade will protect the classroom environment from the harsh post noon sun, the fins also act as a windbreak and helps in reducing the internal solar gains and breaking the wind pattern and speed. The horizontal projections are most effective on the southern orientation, however, they are provided in scenario 4, these can be made flexible or retractable during the winter months, as the classroom requires solar gains. Since the CO2 is highest in January and lowest in July, the I/O of NO 2 and PM 2.5 are highest in the summer (May to July) and lowest in (Jan to March). An integrated strategy based on the observation for west orientation can be made. But, in future weather scenarios peak temperatures are observed between July and August.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

38

KTKZ5

September 2021

The analyses revealed that every environmental factor, from indoor to outdoor, could be a potential predictor of PM2.5 and NO2. A study conducted by (Dawson, Adams, and Pandis.,2007) confirmed that PM2.5 concentration is sensitive to environmental conditions. They found higher ambient temperature increases the production of sulfate, that is a major component of PM2.5 (Huang et al., 2014). Moreover, high humidity level tends to increase nucleation and growth of particulate matter, thereby increases its concentrations (Wang et al., 2018). ‘High precipitation can lower the PM2.5 concentration due to the wet scavenging effect from rain’ (Liu et al., 2020) whilst ‘sunlight can enhance the photochemical conversion of gases’ (Holben et al., 2001).

5.2 Impact of Thermal efficiencies : Below table 5.2 is a summary of the U values used in the 1919 and 1976 archetypes in base model, refurbished / retrofitted and in the final intervention. It can be seen from the results that when the classrooms are insulated with carefully chosen U values, they provide significant results. The Upon refurbishment, it can be observed that on an average the CO 2 levels decreased by 60% in 1919 upon refurbishment and 76% in the 1976 archetype. These also helped in maintaining an ambient temperature level within the classroom. An ideal temperature ranges between 20 ̊ C to 22 ̊ C in Winter, whereas 22 ̊ C to 24 ̊ C in Summer(Salthammer et al., 2016), which helps the students overall learning performance and outcome. Study also showed that maintaining a CO2 level. Though BB101 suggests 1500ppm as CO2 threshold, 800ppm helps in children cognitive wellbeing. The final proposed design showed that altering U values and SHGC values enhanced the overall performance of the building not only at the present, but they were climate resilient as well. The CO2 levels in the final designed in both archetypes were maintained within 1000ppm in the winter season and were less than 800ppm in the summer. The overheating analysis also showed significant results as they comply Category II of BB101.The ambient temperature in the final design were maintained between 20 ̊ C to 22̊ ̊ C in 1919 archetype and 22̊ ̊ C to 24 ̊ C in both 1919 and 1976 archetypes. This enables the students to have a better cognitive ability and helps them in their concentration level. The ground floors of both archetypes were however left as it is, and the internal floorings weren’t insulated to DfE or PassivHaus standard. The glazing packages were also improved from single glazing to double glazing in the refurbished model to triple glazing in the final design. For further optimization in the future scenarios a high performing glass with reflective coating and an SHCG of 0.3 can be provisioned. The heat loss factor is lesser in the final design.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

39

KTKZ5

September 2021

5.2 Table Comparison of U values of Archetypes across Baseline, Retrofitted and Final design Dfe

Passiv Haus

Baseline

Retrofitted

Design Interventions on West orientation

Parameters

Standards

1919

1976

1919

1976

1919

1976

2

U- Values (W/m .K) Walls

0.18

0.17

1.8

0.95

0.15

0.15

0.15

0.15

Interior Wall

0.18

0.17

0.916

0.916

0.38

0.38

0.38

0.38

Ground Floor

0.18

0.14

1.5

0.95

NA

NA

NA

NA

Floor

0.18

0.14

0.91

0.91

0.28

0.28

0.28

0.28

Roof

0.15

0.13

2.9

0.97

0.15

0.15

0.15

0.15

Windows

1.6

0.8

6.1

6.1

1.3

1.3

0.78

0.78

Doors

1.6

0.8

13

1.3

1.3

1.3

1.3

1.3

Ventilation

Mixed

MVHR

Natural Ventilation

strategy

mode

Heat loss Form

4

3

North

20

15

25

25

5

25

East

30

20

25

25

25

25

South

35

25

25

25

25

25

West

30

20

25

25

25

25

23

23

2.16

factor

5.3 Outlining Limitations •

As mentioned in chapter 3, only 2 archetypes, the oldest and latest were considered. Using the Archetypes modeling approach only reflects a collective attribute of the stock and they do not give accurate results. The modeling results vary depending on the site location and its adjoining features and environmental parameters. Moreover, the ventilation type is restricted to only single-sided ventilation in this study. The I/O ratios penetration factors are subject to limitations as well.

•

Energy plus software does not consider the impacts of chemical reaction that occurs due to the impact of sunlight or other gases present in the outdoor environment. The study reveals that PM2.5 concentrations are triggered based on the amount of Sulfur content in the space.

Moreover, precipitation is an important environmental factor that hasn’t been considered in this study. (Chatzidiakou, Mumovic and Dockrell, 2014) explained that shifting direction affects the penetration ability of outdoor particulate matters and NO2 from traffic. Thus, room orientation towards the road could alter the indoor PM2.5 concentration. Precipitation alongside changing wind directions and wind speed alters the outdoor pollutants concentrations and levels.

Precipitation lowers the concentration of PM2.5 due to the scavenging effect.

•

Biophilia, impacts of vegetation, trees cannot be modeled in energy plus. Noise level integration in the model. Other pollutants and the chemical reaction that further triggers these indoor pollutant levels cannot be modeled. The model represents a typical scenario in London's urban area and is not site-specific.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

40

KTKZ5

September 2021

5.4 Recommendations 5.4.1 To the policy makers architects and consultants •

Co2 levels have been maintained below 800 ppm for 80 % of the time in both the archetypes.

A solar shading glass with a G value of 0.3 can be used to further optimize and future proof the building. A flexible movable external shading option rather than a fixed shading enables greater solar gain during the winter months (December to February). Since the scenario 4 used a fixed horizontal projection, the operative temperature in the winter in archetype 1976 can be enhanced to optimal range of 20 ̊ C to 22 ̊ C in winter with the help of retractable façade options or fins. This gives the opportunity for the classroom environment to receive the winter sun and retain solar thermal gains.

•

Shading plays a pivotal role in minimizing the shade as well as acts as a windbreaker. Due to global warming and UHI, the increased temperature level can be combatted with the aid of solar control strategy measures like movable external schedule-controlled shading.

•

Thermal efficiency strategies (U value and SHGC) are vital for retrofitting. U value guidelines like Dfe and BB101 can be made mandatory as a legislature rather than a guideline in the future can help in improving the IAQ as well as the energy performance of the building. Additionally, providing incentives to architects / built environment professionals can promote climate-resilient structures

5.4.2 For teachers/ student and management Clothing insulation plays a pivotal role in determining thermal comfort. Thus, wearing appropriate climate specific seasonal clothing like summer trousers, short sleeve shirts, socks and shoes and underwear with to clothing insulation of clo 0.5 to 0.58 can impact perceived thermal comfort levels.

Similarly, during the winters indoor clothing such as jacket, long sleeve trousers, socks, and shoes with clo ranging from 0.8 to 1.0 will impact the adaptive thermal comfort significantly. Windows opening strategy can make implicit effect on the IAQ. Thus, based on the results section, the side windows can be opened in the summer with restricted window openings to reduce the indoor pollutant concentrations in the west orientation. It is also observed that the NO2 and PM2.5 levels are low in the morning around 9 am to 10am and are at peak around 12 pm and steadily reduces thereby. Thus, the central window can be used for ventilation and CO2 elimination as the infiltration of air is more in central region. Using an electric fan and placing them near the windows to dilute the indoor air pollutant concentration and restricting the outdoor air pollutants from penetration through the windows as wind direction and speed is an important factor for driving the pollutant concentrations (Guerra et al., 2006). Investing in IEQ and healthy and integrated design features to ensure overall wellbeing of the environment and the students and teachers.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

41

KTKZ5

September 2021

5.4.3 Recommendations for future works •

According to the SINPHONIE project (Schools Indoor Pollution and Health: Observatory Network in Europe), schools located in the urban location were built in the pre-1919 era and the latest buildings existing post 1976 are in the sub urban context. Thus, an urban vs suburban scenario can be generated with appropriate weather files. This can further enable in identifying the issues and giving interventional strategies based on location. Moreover, all the archetypes can be considered and their impacts in different context like urban and suburban and their discrepancies can be further contrasted with socio economic inequalities.

•

Integrating the existing archetype model with IEQ such as lighting and noise as well other Indoor pollutants like VOCs and molds in the future can help in analyzing the models in a holistic manner. Also, integrating it with real time costing / value engineering to compare the cost of retrofitting a building against a new building as retrofitting helps in reducing the overall carbon footprint.

•

For future work, the studies can be integrated with a detailed healthy lighting in classrooms which focuses on daylighting factors like Annual Daylight Performance metrics (ASE), Spatial Daylight Autonomy (sDA) and Useful Daylight Illuminance (UDI) and glare mitigation to optimize and precisely develop a unified strategy that combines the effect of daylighting, artificial lights, Indoor air quality and thermal comfort for providing an optimized Indoor environmental quality.

•

Further research and advances can be done to address the questions on how the pollutants behave? How do they travel? Up or down? do they travel less in when the sill is increased? or do the travel less when openings are placed at the lintel, their behavior and nature of the pollutant composition can be further studied to ideally design a better optimized design.

•

The 2030 and 2050s CO2 levels have decreased, people studying in the future can take a detailed analysis on these future projections.

•

A cross ventilation strategy with other environmental factors like precipitation can enable in better understanding. Impact of VOCs upon refurbishment can be studied alongside.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

42

KTKZ5

September 2021

6 CONCLUSIONS The paper aimed at establishing a link between indoor air quality (IAQ) and thermal comfort prevailing in the school typology and their health and wellbeing repercussions by envisioning a retrofitting strategy and identifying the role of window placement in the classroom environment in London primary schools in urban areas. The results showed that refurbishment of archetypes can improve the IAQ and thermal comfort by reducing the CO2 by 60 % in 1919 and 76 % in 1976 archetype. The North orientation had the highest CO2 concentration but lowest risk of overheating in both archetypes. However, in terms of NO2 and PM2.5 I/O ratios, the south orientation had the highest alongside west and east, North had the lowest. In terms of overheating the west and east orientation in both archetypes had a higher risk. Thus, West orientation is considered for further design interventions as the sun angles are its lowest in the west orientation in the post noon, creating a greater risk of discomfort. Upon creating 4 different scenarios based on window placement, WWR and glazing and external shading (table 3.3.4), The final design intervention in the west orientation shows significant results in terms of CO2 level (<800ppm) and indoor operative temperature as well as NO2 and PM2.5 ratios. In both the archetypes CO2 level kept below 800ppm and an ambient indoor temperature range of 22 ̊ C to 25 ̊ Cis maintained. The NO2 and PM2.5 are kept below the WHO standards. They also fall under criteria II upon overheating analysis.

The design is tested for climate resilience / future proofing which has also resulted in an amicable manner, with less CO2 and outdoor pollutants level in the future, but the operative temperature increases by 2 ̊ C in 2030 and by 4 ̊ C in 2050. On comparing the window positioning, the equally distributed window performs better than the centered and off-centered, moreover, equally distributed tall narrow windows perform better than wide windows. The recommendations are given to combat these future scenarios as well by envisioning a retractable façade and a higher performing window with solar reflective and SHGC of 0.3. The health and cognitive wellbeing and performance of school children are improved through design interventions and recommendations provided for the school operation team, and policy makers and architects and for further work.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

43

KTKZ5

September 2021

REFERENCES Abdullah, H. K. and Alibaba, H. Z. (2020) ‘Window design of naturally ventilated offices in the mediterranean climate in terms of CO2 and thermal comfort performance’, Sustainability (Switzerland), 12(2). doi: 10.3390/su12020473.

Authority, G. L. et al. (no date) ‘No Title’.

Bakó-Biró, Z. et al. (2012) ‘Ventilation rates in schools and pupils’ performance’, Building and Environment, 48, pp. 215–223. doi: 10.1016/j.buildenv.2011.08.018.

Bhardwaj, S. and Belali, S. A. (2015) ‘A Review on Retrofitting’, International Journal of Civil Engineering, 2(3), pp. 7–10. doi: 10.14445/23488352/ijce-v2i3p103.

Boldo, E. et al. (2011) ‘Health impact assessment of a reduction in ambient PM(2.5) levels in Spain.’, Environment international, 37(2), pp. 342–348. doi: 10.1016/j.envint.2010.10.004.

Campano, M. A. et al. (2017) ‘Analysis of the Perceived Thermal Comfort in a Portuguese Secondary School: Methodology and Results’, International Journal of Engineering and Technology, 9(6), pp. 448– 455. doi: 10.7763/ijet.2017.v9.1015.

Chan, K. A. H. et al. (no date) The Effects of Air Pollution upon Public Health.

Chatzidiakou, L., Mumovic, D. and Dockrell, J. (2014) Chatzidiakou L, Mumovic D, Dockrell J. Chatzidiakou, L., Mumovic, D. and Summerfield, A. J. (2012) ‘What do we know about indoor air quality in school classrooms? A critical review of the literature’, Intelligent Buildings International, 4(4), pp. 228– 259. doi: 10.1080/17508975.2012.725530.

Chen, K. et al. (2017) ‘Impact of climate change on heat-related mortality in Jiangsu Province, China’, Environmental Pollution, 224, pp. 317–325. doi: https://doi.org/10.1016/j.envpol.2017.02.011.

CIBSE (no date) ‘Natura l vent il at i on i n non-dome s t i c bu il d i ng s C I B S E App li c at i on s Manua l A M10’.

CIBSE Guide (1999) ‘Guide A : Environmental Design Guide A : Environmental Design’.

CIBSE Tm52 (2013) ‘The limits of thermal comfort : avoiding overheating in European buildings’, CIBSE Tm52, pp. 1–25.

Constantin, D. et al. (2016) ‘Perception of cabin air quality among drivers and passengers’, Sustainability (Switzerland), 8(9). doi: 10.3390/su8090852.

Department for Environment Food and Rural Affairs (2007) ‘The Air Quality Strategy for England , Scotland

,

Wales

and

Northern

Ireland:

Volume

1’,

1,

pp.

1–56.

Available

at:

http://archive.defra.gov.uk/environment/quality/air/airquality/strategy/documents/air-qualitystrategyvol1.pdf.

Dong, J. et al. (2020) ‘London School Building Stock Model for Cognitive Performance Assessment’,

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

44

KTKZ5

September 2021

BSO20 - Building Simulation and Optimization Conference, (September).

Eames, M. and Mylona, A. (2018) ‘Thermal performance of buildings Find out more Exploiting UKCP18 data’, (2014). doi: 10.1016/j.buildenv.2010.12.018.https.

Emmerich, S. J. and Persily, A. K. (1998) ‘Energy Impacts of Infiltration and Ventilation in U.S. Office Buildings Using Multizone Airflow Simulation’, IAQ and Energy 98 Conference, (June), pp. 191–203.

ESFA (2016) ‘Guidelines on ventilation, thermal comfort and indoor air quality in schools. Building Bulletin 101. Draft for Public Consultation’, Department for Education, (June), p. 109. Available at: https://consult.education.gov.uk/capital/bb101-school-design-iaq-comfort-andventilation/supporting_documents/DfE

Ventilation

guide

consultation

draft

29

06

2016.pdf%0Ahttps://www.gov.uk/government/publications.

Fanger, P. O. (2006) ‘What is IAQ?’, Indoor Air, 16(5), pp. 328–334. doi: 10.1111/j.16000668.2006.00437.x.

Ferguson, A., Penney, R. and Solo-Gabriele, H. (2017) ‘A Review of the Field on Children’s Exposure to Environmental Contaminants: A Risk Assessment Approach’, International journal of environmental research and public health, 14(3), p. 265. doi: 10.3390/ijerph14030265.

Forns, J. et al. (2016) ‘Traffic-Related air pollution, noise at school, and behavioral problems in barcelona schoolchildren: A cross-sectional study’, Environmental Health Perspectives, 124(4), pp.

529–535. doi: 10.1289/ehp.1409449.

Friedlingstein, P. et al. (2019) ‘Global Carbon Budget 2019’, Earth System Science Data, 11(4), pp.

1783–1838. doi: 10.5194/essd-11-1783-2019.

Fromme, H. et al. (2007) ‘Particulate matter in the indoor air of classrooms—exploratory results from Munich

and

surrounding

area’,

Atmospheric

Environment,

41(4),

pp.

854–866.

doi:

https://doi.org/10.1016/j.atmosenv.2006.08.053.

Funding, E. and skills funding agency (2016) ‘Guidance on ventilation, thermal comfort and indoor air quality in schools’, Building Bulletin 101, (June).

Goyal, R. and Khare, M. (2009) ‘Indoor–outdoor concentrations of RSPM in classroom of a naturally ventilated school building near an urban traffic roadway’, Atmospheric Environment, 43(38), pp. 6026– 6038. doi: https://doi.org/10.1016/j.atmosenv.2009.08.031.

Guerra, S. A. et al. (2006) ‘Effects of wind direction on coarse and fine particulate matter concentrations in southeast Kansas’, Journal of the Air and Waste Management Association, 56(11), pp. 1525–1531. doi: 10.1080/10473289.2006.10464559.

Hassan, M. A., Shaalan, M. R. and El-Shazly, K. M. (2004) ‘Effects of Window Size and Location and Wind Direction on Thermal Comfort with Single-Sided Natural Ventilation’, World Renewable Energy Congress VIII (WREC 2004), (Wrec), p. 5.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

45

KTKZ5

September 2021

Honjo, T. (2009) ‘Thermal Comfort in Outdoor Environment’, (January 2009), pp. 43–47.

Kolokotroni, M. et al. (2012) ‘London’s urban heat island: Impact on current and future energy consumption

in

office

buildings’,

Energy

and

Buildings,

47,

pp.

302–311.

doi:

https://doi.org/10.1016/j.enbuild.2011.12.019.

Kukadia, V. and Upton, S. (2019) ‘Ensuring good indoor air quality in buildings’, (March).

Lai, A. C. K. et al. (2009) ‘An evaluation model for indoor environmental quality (IEQ) acceptance in residential buildings’, Energy and Buildings, 41(9), pp. 930–936. doi: 10.1016/j.enbuild.2009.03.016.

Lee, S. C. (1999) ‘Indoor air quality investigations at five classrooms’, Indoor Air, 9(2), pp. 134–138. doi: 10.1111/j.1600-0668.1999.t01-2-00008.x.

MacNaughton, P. et al. (2017) ‘The impact of working in a green certified building on cognitive function and

health’,

Building

and

Environment,

114,

pp.

178–186.

doi:

https://doi.org/10.1016/j.buildenv.2016.11.041.

Mahyuddin, N. and Awbi, H. (2012) ‘A Review of CO2 Measurement Procedures in Ventilation Research’,

International

Journal

of

Ventilation,

10(4),

pp.

353–370.

doi:

10.1080/14733315.2012.11683961.

Mendes, L. et al. (2016) ‘Mendes, et al. 2015’.

Montazami, A. and Nicol, F. (2013) ‘Overheating in schools: Comparing existing and new guidelines’, Building Research and Information, 41. doi: 10.1080/09613218.2013.770716.

Mumovic, D. et al. (2009) ‘Winter indoor air quality, thermal comfort and acoustic performance of newly built secondary schools in England’, Building and Environment, 44, pp. 1466–1477. doi: 10.1016/j.buildenv.2008.06.014.

NASA (2021) Atmospheric carbon dioxide measurement 2020-21.

Oikonomou, E. et al. (2020) ‘Assessing heat vulnerability in London care settings: case studies of adaptation to climate change’, 5th Building Simulation and Optimisation Virtual Conference.

Peng, Z., Deng, W. and Tenorio, R. (2017) ‘Investigation of indoor air quality and the identification of influential factors at primary schools in the north of China’, Sustainability (Switzerland), 9(7). doi: 10.3390/su9071180.

Petty, S. E. (2017) Indoor environmental quality, Forensic Engineering: Damage Assessments for Residential and Commercial Structures. doi: 10.1201/b14052.

‘Property data survey programme’ (no date), (January 2015).

Quality, A. I. R. and Group, E. (no date) ‘Nitrogen Dioxide in’.

REHVA, Federation of European Heating, V. and A. C. A. (2012) ‘REHVA Covid19 HVAC Guidance’, European University Institute, 2020(2), pp. 2–5. Available at: https://eur-lex.europa.eu/legal-

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

46

KTKZ5

September 2021

content/PT/TXT/PDF/?uri=CELEX:32016R0679&from=PT%0Ahttp://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52012PC0011:pt:NOT. Riggs, J. (2016) A practical guide to indoor air quality for the home and workplace / Dr. Julie Riggs.

First edition.

Salo, P. M., Sever, M. L. and Zeldin, D. C. (2009) ‘Indoor allergens in school and day care environments’, Journal of Allergy and Clinical Immunology, 124(2), pp. 185-192.e9. doi: https://doi.org/10.1016/j.jaci.2009.05.012.

Salthammer, T. et al. (2016) ‘Children’s well-being at schools: Impact of climatic conditions and air pollution’. doi: 10.1016/j.envint.2016.05.009.

Schwartz, Y. et al. (2021) ‘Developing a Data-driven School Building Stock Energy and Indoor Environmental

Quality

Modelling

Method’,

Energy

&

Buildings,

249,

p.

111249.

doi:

10.1016/j.enbuild.2021.111249.

Seppänen, O. A. (1999) ‘Association of ventilation rates and CO2 concentrations with health and other responses in commercial and institutional buildings’, Indoor Air, 9(4), pp. 226–252. doi: 10.1111/j.16000668.1999.00003.x.

Shendell, D. G. et al. (2004) ‘Associations between classroom CO2 concentrations and student attendance in Washington and Idaho’, Indoor Air, 14(5), pp. 333–341. doi: 10.1111/j.16000668.2004.00251.x.

Simion, M., Socaciu, L. and Unguresan, P. (2016) ‘Factors which Influence the Thermal Comfort Inside of Vehicles’, Energy Procedia, 85(November 2015), pp. 472–480. doi: 10.1016/j.egypro.2015.12.229.

Smedje, G., Mattsson, M. and Wålinder, R. (2011) ‘Comparing mixing and displacement ventilation in classrooms:

pupils’

perception

and

health’,

Indoor

Air,

21(6),

pp.

454–461.

doi:

https://doi.org/10.1111/j.1600-0668.2011.00725.x.

Stranger, M., Potgieter-Vermaak, S. S. and Van Grieken, R. (2008) ‘Characterization of indoor air quality in primary schools in Antwerp, Belgium’, Indoor air, 18(6), pp. 454–463. doi: 10.1111/j.16000668.2008.00545.x.

Sunyer, J. (2008) ‘The neurological effects of air pollution in children’, European Respiratory Journal, 32(3), pp. 535–537. doi: 10.1183/09031936.00073708.

Taylor, J. et al. (2015) ‘Understanding and mitigating overheating and indoor PM 2 . 5 risks using coupled temperature and indoor air quality models’. doi: 10.1177/0143624414566474.

Turanjanin, V. et al. (2014) ‘Indoor CO2 measurements in Serbian schools and ventilation rate calculation’, Energy, 77, pp. 290–296. doi: https://doi.org/10.1016/j.energy.2014.10.028.

US Department of Energy (2015) ‘EnergyPlus Documentation, Input Output Reference’, p. 1317.

Wargocki, P. and Wyon, D. P. (2007) ‘The effects of outdoor air supply rate and supply air filter condition

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

47

KTKZ5

September 2021

in classrooms on the performance of schoolwork by children (RP-1257)’, HVAC and R Research, 13(2), pp. 165–191. doi: 10.1080/10789669.2007.10390950.

Wargocki, P. and Wyon, D. P. (2017) ‘Ten questions concerning thermal and indoor air quality effects on the performance of office work and schoolwork’, Building and Environment, 112, pp. 359–366. doi: 10.1016/j.buildenv.2016.11.020. 张静 (2016) ‘美国EPA 2016/2017财年新有效成分评估计划’, 农药科学与管理, (5), p. 55.

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

48

KTKZ5

September 2021

APPENDIX Appendix 01 Refurbishment schedule Base model Item Space geometry

Description Width = 8m; Depth = 6.5m; Height = 3.5m

Archetype

Pre 1919

Exterior Wall construction

Brick 225mm, Plaster board 13mm and Plaster 13mm, U-value = 1.8W/(m2K)

Interior Wall

Plasterboard 13mm, XPS 70mm, Plasterboard 13mm; U -value = 0.91W/(m2K)

Roof Construction

Asphalt 100mm, Cast concrete 100mm, Plasterboard 13mm, Plaster 16mm; U-value = 2.9W/(m2K)

Ground floor construction

Ground 6, Urea Formaldehyde foam, Cast concrete 100mm, Floor/ roof screed, Timber flooring U -value = 1.5W/(m2K)

Interior floor

Plaster 16mm, XPS 70mm, Timber flooring 30mm U -value = 0.91W/(m2K)

Ceiling

Timber flooring 30mm, XPS 70mm, Plaster 16mm U -value = 0.91W/(m2K)

WWR

25%

Window glass

Single plane glass 6mm

Internal shade

Window curtains

Ventilation

Natural ventilation by window opening (Air flow network model)

Archetype Exterior Wall construction Interior Wall Roof Construction

Post 1976 Brick 225mm, XPS 70mm, Plaster board 13mm and Plaster 13mm, U-value = 0.95W/(m2K) Plasterboard 13mm, XPS 70mm, Plasterboard 13mm; U -value = 0.91W/(m2K) Asphalt 100mm, Cast concrete 100mm, XPS 70mm, Plasterboard 13mm, Plaster 16mm; U value = 0.91W/(m2K)

Ground floor construction

Ground 10, Underfloor bricks 350mm, Cast concrete 100mm, Flooring screed 70mm, Timber flooring 30mmU -value = 0.91W/(m2K)

Interior floor

Plaster 16mm, XPS 70mm, Timber flooring 30mm U -value = 0.91W/(m2K)

Ceiling

Timber flooring 30mm, XPS 70mm, Plaster 16mm U -value = 0.91W/(m2K)

WWR

25%

Window glass

Single plane glass 6mm, U -value = 6.1 W/(m2K)

Internal shade

Window curtains

Ventilation

Natural ventilation by window opening (Air flow network model)

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

49

KTKZ5

September 2021

Retrofitted/Refurbished Model Item Space geometry

Description Width = 8m; Depth = 6.5m; Height = 3.5m

Archetype Exterior Wall construction

1919 Brick 105mm, XPS_polystyrene Co2 bowling 200mm, Concrete 105mm, Gypsum plaster 15mm, U-value = 0.15W/(m2K)

Interior Wall Roof Construction

Plasterboard CIBSE 12mm, MW_Glasswool 70mm, Plasterboard CIBSE 12mm, U- value - 0.38 Asphalt 100mm, Cast concrete 100mm, XPS 70mm, Plasterboard 13mm, Plaster 16mm.

U -value = 0.15W/(m2K)

Ground floor construction

Ground 6, Urea Formaldehyde foam, Cast concrete 100mm, Floor/ roof screed, Timber flooring U -value = 1.5W/(m2K)

Interior floor

Plasterboard CIBSE 16mm, Rockwool_10-degree 70mm, Timber flooring 30mm U -value = 0.32W/(m2K)

Ceiling

Timber flooring 30mm, Rockwool_10degree 70mm, Plasterboard CIBSE 16mm U -value = 0.32W/(m2K)

WWR Window glass

25% Double glazing - LoE Clear 6mm glass, 13mm Argon gas, LoE Clear 6mm glass, U - value= 1.3W/(m2K)

Internal shade Ventilation

Window curtains of 4mm thick Natural ventilation by window opening (Air flow network model)

Archetype Exterior Wall construction

1976 Brick 105mm, XPS_polystyrene Co2 bowling 200mm, Concrete 105mm, Gypsum plaster 15mm, U-value = 0.15W/(m2K)

Interior Wall Roof Construction

Plasterboard CIBSE 12mm, MW_Glasswool 70mm, Plasterboard CIBSE 12mm, U- value - 0.38 Asphalt 100mm, Cast concrete 100mm, XPS 70mm, Plasterboard 13mm, Plaster 16mm.

U -value = 0.15W/(m2K)

Ground floor construction

Ground 6, Urea Formaldehyde foam, Cast concrete 100mm, Floor/ roof screed, Timber flooring U -value = 0.91W/(m2K)

Interior floor

Plasterboard CIBSE 16mm, Rockwool_10-degree 70mm, Timber flooring 30mm U -value = 0.32W/(m2K)

Ceiling

Timber flooring 30mm, Rockwool_10degree 70mm, Plasterboard CIBSE 16mm U -value = 0.32W/(m2K)

WWR Window glass

25% Double glazing - LoE Clear 6mm glass, 13mm Argon gas, LoE Clear 6mm glass, U - value= 1.3W/(m2K)

Internal shade Ventilation

Window curtains of 4mm thick Natural ventilation by window opening (Air flow network model)

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

50

KTKZ5

September 2021

Appendix 02 Graph 02 a I/O ratios of PM2.5 and NO2 across all scenarios

Graph 02 b I/O ratios of NO2 and Operative temperature across scenario 01 and 04

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

51

KTKZ5

September 2021

Graph 02 c Correlation between I/O ratios of NO2 and Wind speed

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

52

KTKZ5

September 2021

Appendix 03 Overheating Analysis in 1919 and 1976 Final Archetype Design week

TRM_Running Mean Temperature

Tmax-Maximum Comfort +acceptable temperature

Top_1976Operative Temperature

Top_1919Operative Temperature

▲T_1976

▲T_1919

1

11

25

23

23

-2.60

-2.84

2

13

26

22

22

-3.68

-4.04

3

17

27

23

22

-4.65

-5.49

4

13

26

24

24

-2.65

-2.51

5

17

27

25

25

-2.66

-2.66

6

14

26

24

24

-2.61

-2.42

7

13

26

23

23

-2.75

-2.74

8

18

28

26

26

-5.44

-5.80

9

15

27

24

24

-3.07

-2.92

10

18

28

25

25

-2.60

-2.52

11

18

28

25

26

-2.46

-2.38

12

20

28

26

26

-2.03

-2.03

13

20

29

27

27

-1.37

-1.38

14

20

29

27

27

-1.21

-1.19

15

14

27

27

27

0.79

0.87

16

14

26

25

25

-1.60

-1.46

17

16

27

25

25

-2.51

-2.49

18

16

27

23

24

-3.57

-3.37

19

11

25

23

23

-2.26

-2.16

20

17

27

24

24

-3.30

-3.27

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021

53

KTKZ5

September 2021

Appendix 04 Future Climate projections Table 03a 1919 Final Archetype future climate projections Months

OT_P

Co2_P

OT_2030

Co2_2030

OT_2050

Co2_2050

PM2.5

PM2.5 _2030

PM2.5 _2050

NO2

NO2_2030

NO2_2050

Jan

18

959

22

680

18

963

0.40

0.41

0.41

0.23

0.24

0.24

Feb

18

978

19

950

19

954

0.40

0.41

0.41

0.23

0.23

0.23

March April

19 21

847 609

20 21

799 636

20 21

706 600

0.42 0.45

0.42 0.46

0.45 0.49

0.24 0.26

0.24 0.27

0.26 0.29

May

23

553

23

493

23

501

0.55

0.57

0.59

0.34

0.37

0.38

June

24

497

25

474

25

475

0.58

0.61

0.60

0.38

0.41

0.40

July

24

487

26

471

27

472

0.60

0.61

0.61

0.40

0.41

0.42

August

24

475

26

476

27

474

0.60

0.61

0.62

0.40

0.41

0.42

Sept Oct

23 21

519 633

24 22

476 626

25 22

470 563

0.56 0.46

0.58 0.50

0.60 0.52

0.35 0.27

0.37 0.30

0.39 0.31

Nov

19

890

20

845

20

785

0.41

0.43

0.45

0.24

0.25

0.26

Dec

19

1007

18

987

19

910

0.37

0.38

0.38

0.21

0.21

0.22

Annual

21

704

22

659

22

656

0.48

0.50

0.51

0.30

0.31

0.32

Table 03a 1976 Final Archetype future climate projections Months

OT_P

Co2

OT_2030

Co2_2030

OT_2050

Co2_2050

PM2.5

PM2.5 _2030

PM2.5 _2050

NO2

NO2_2030

NO2_2050

Jan Feb March April May June July August Sept Oct Nov Dec

21 21 21 22 23 24 24 24 23 22 21 21 22.

891 889 760 571 532 491 484 475 496 574 821 916 658.

21 21 21 22 24 25 26 26 24 23 21 21 22.

853 837 674 574 490 474 471 476 474 579 736 902 628

23 23 22 22 23 25 27 27 25 23 21 21 24

607 607 640 553 495 474 472 474 470 527 677 789 565

0.37 0.37 0.38 0.41 0.52 0.57 0.59 0.60 0.52 0.42 0.38 0.34 0.45

0.38 0.37 0.39 0.40 0.55 0.60 0.61 0.61 0.56 0.46 0.39 0.35 0.47

0.38 0.37 0.40 0.44 0.57 0.60 0.61 0.62 0.58 0.44 0.39 0.35 0.48

0.24 0.24 0.25 0.28 0.36 0.39 0.41 0.42 0.37 0.31 0.25 0.22 0.31

0.20 0.20 0.22 0.24 0.35 0.40 0.41 0.41 0.35 0.28 0.22 0.19 0.29

0.21 0.20 0.23 0.26 0.36 0.39 0.42 0.42 0.37 0.27 0.23 0.19 0.30

UCL Institute of Energy and Environmental Engineering

BSEER

September 2021