Investigating thermal comfort and energy impact through microclimate monitoring- a citizen science approach
Introduction
Urban areas are becoming warmer than the surrounding rural countryside because of climate change and urban heat island phenomena. It is predicted that the frequency, magnitude and duration of heat waves will increase in the coming decades. A worldwide temperature increase of 1.8 K to 4 K between 1990 and 2010 was estimated by the International Panel on Climate Change [1]. Moreover, extreme heat events are one of the climate hazards causing deaths [2], [3] and major economic disruptions [4], [5]. Various studies [6], [7] have documented the effects of urban climate on peak electricity, thermal comfort and health as well as economy.
The impact of summertime overheating on new-build, existing and retrofitted domestic building stock is a growing public health issue across the world. Dense urban areas and inner-city neighbourhoods with limited open spaces and green areas increase urban dwellers' exposure to heat. The housing quality has significant implications for people’s health, therefore improving the conditions of housing and reducing health risks in the home is crucial [8]. Passive design strategies such as appropriate building materials, insulation, shading from direct sunlight and natural ventilation to cool indoor temperatures assist in protecting people from heat and associated illness. When the conditions exacerbate, fans and air conditioning can help to improve the indoor conditions. Air-conditioning use for space cooling is most cited as an adaptation technique in homes [9]. The market penetration of air conditioning has escalated in recent years causing increased demand in peak electricity and risk of black-outs. Although the proliferation of air-conditioning would aggravate the intensity of heatwaves [10], its uptake remains a popular choice in ensuring dwellings cope well with extreme heat [11]. The increased use of air conditioning leads to higher heat exposure levels as a result of which more air conditioning is required in urban areas with high population density [12], [13]. As demonstrated by a study in a semiarid climate, air conditioning systems’ cooling demand constitute up to 65% of total electricity demand on hot afternoons during extreme heat events [14].
Furthermore, many studies around the world including Australia have shown that heat waves in urban areas increase hospital admissions, morbidity and mortality, especially for low socio-economic groups [15], [16], [17], [18], [19]. Longer and hotter heatwaves increase the risk of heat-related illness and can also aggravate pre-existing conditions in vulnerable population including children, the elderly [20], and residents in older and highly dense housing stock where surrounding vegetation is limited [21]. Indeed, unconditioned residential units are subject to increased number of heat related deaths [19]. Additionally, highest land surface temperatures were noted in areas housing low-income households [4]. With high heat in the environment, vulnerable populations including elderly and children will experience heat stroke even when they are not involved in vigorous activities. The impacts of socio-economic factors on health outcomes due to extreme heat events (EHE) have been investigated by many researchers [22], [23], [24]. The elderly and those suffering from cardiovascular and pulmonary illnesses are more affected by the risk of high temperature. High temperatures during the night will also affect the sleep quality and general well-being. Previous studies, majority of them conducted on older people found correlation between higher indoor temperature and health symptoms such as poor sleep quality [25], [26], [27] and sleep disturbance [28].
In Australia, even though regulating energy efficiency and thermal comfort standards in dwellings are within the scope of energy rating systems, many studies argue that summer comfort has been overlooked in the Nationwide House Energy Rating Scheme (NatHERS) in Victoria, as the reduction of heating load in the design of buildings has been the main focus. Studies using building simulations showed that heat stress during heat waves in Melbourne could be lowered if the design focuses on summer comfort [29]. To analyse UHI implications for thermal comfort and energy performance of buildings, hourly weather data, including meteorological variables such as temperature, humidity, wind speed, wind direction and solar radiation, both historical and current data may be obtained from local meteorological stations. However, the challenges in taking urban observations result in the inadequate information on the impact of the UHI. Data collected by the Australian Bureau of Meteorology (BoM) generally corresponds to undisturbed urban environments which can be significantly different from that measured at city centres. Heating degree-days (HDDs) and cooling degree-days (CDDs) that measure the deviations of the daily mean ambient temperatures from heating and cooling temperatures, are the simplest methods used for energy analysis in buildings [30]. Because of the simplicity of calculations, HDDs and CDDs are commonly used for correlating and predicting building energy consumption [31], [32]. Weather data representing a typical year, such as the Typical Meteorological Years (TMY) or Test Reference Years (TRY) is generally required for performing building performance simulation. TMY and TRY datasets have been created with measurements obtained from historical sets of at least 15 years using internationally acknowledged statistical methods [33], [34], [35] and have been extensively used for predicting thermal energy performance of buildings. Such data is gathered from individual months that are derived from a number of years’ data set by using statistical methods, such as Finkelstein-Schafer statistic for identifying the most typical months using data from a number of years [36]. Generally typical future climate files are created using data generated from global climate models, therefore the climate variations and anomalies are usually neglected. TMY is not adequate to evaluate the thermal performance of buildings during extreme warm weather conditions [37]. Hong et al. [38] compared the energy consumption using Actual Meteorological Year (AMY) and TMY3 data to study the weather impact on peak electricity demand and energy for three types of office buildings across 17 climate zones. It was found that peak electricity demand has a greater dependence on annual weather in comparison to total energy use; and the weather impact is higher for buildings in colder climates in comparison to warm climates. Scientists across the world have been actively working to improve methodologies for predicting future weather accurately. At present, no universally accepted method is available for making summer weather data representing extreme weather for simulating building performance [36]. Researchers have introduced concepts such as Summer Reference Year (SRY) [36], morphing method [39] and dynamic downscaling method [40] to characterize near-extreme summer conditions in building simulations. However, the future change in disturbance which is essential for estimating peak demand is not taken into consideration in the daily weather disturbance. Previous studies in Australia [41], [42] investigated various methods for preparing future weather data for predicting residential energy consumption in relation to the NatHERS and highlighted the sensitivity of cooling load with regards to climate change.
In summary, most of the studies on heat related mortality and morbidity usually use temperature from weather stations, with the assumption that all persons who live in a specified geographical area experience the same exposure. The data from weather station which is generally located away from the city centres neglect UHI effects, leading to inaccurate predictions, which is particularly problematic during heat waves, when cooling loads can go up to double the average. Many studies highlight the importance of using weather data collected through field measurements in building simulations, taking into account local climate instead of weather data. Pyrgou et al. [33] note that if climate boundary conditions in terms of dry-bulb air temperature are not representative of the realistic conditions, cooling loads and indoor thermal comfort may not be properly calculated. However, collecting large scale climate data is a resource intensive work. The variability and complexity of outdoor environments demands extensive resources for scientific measurement of urban microclimate experiments. Citizen science has the ability to provide large scale data in a cost-effective way while providing public with real-world opportunities to learn and incorporate scientific methods to solve community-based challenges. Many crowd-sourced amateur networks (e.g., Wunderground, WOW) have issues with calibration, design flaws (unsuitable radiation shields), lack of metadata that makes data interpretation difficult, and have a reported bias of 2–3 °C against Met Office data [43]. Therefore, it is important to develop and pursue appropriate calibration and measurement protocols for collecting data through such methods. This paper discusses the results of a microclimate field study that used citizen science approach in conducting measurement across a number of states in Australia. The main aim of this paper is to demonstrate the use of citizen science data in determining comfort and cooling energy. The data collected by citizens are used to calculate outdoor comfort indices. Subsequently, energy simulations are performed on a low-income housing typology; and indoor heat stress and peak cooling load using different weather data are calculated and compared. The results are expected to provide useful data for citizens and policy makers for planning the urban built environment and open spaces.
Section snippets
Methodology
The methodology consists of three main steps as described below:
Comparison across various states
Outdoor microclimate field experiments involving citizen scientists were conducted across 22 councils in five states and two territories in Australia. The results of the measurements in 13 councils in Victoria, South Australia, Western Australia and ACT are reported here. The measurements resulted in 81 field experiments across 26 precincts. The experiments were typically conducted between 11:30am to 4:00 pm on different days. Average temperature observed from the nearest BoM weather stations
Discussion
Though the study is limited to a short period of time, it has demonstrated that citizen science has the potential to provide extensive and useful data required to understand, mitigate and adapt to extreme heat. Citizen science also has the potential to create a more scientifically literate society who can make informed choices. For example, the participation in this study helped citizens to understand how various materials behave in heat: for example, how hot artificial grass surfaces can
Conclusion
Summertime overheating in new-build, existing and retrofitted domestic building stock is a growing public health issue across the world and will get worse with the predicted rise in global temperatures and more frequent and long-lasting heat waves. For developing climate change mitigation and adaptation strategies, it is very important to assess the thermal comfort conditions and estimate the cooling and heating energy requirements of buildings using accurate climate data. Most of the studies
CRediT authorship contribution statement
Priyadarsini Rajagopalan: Conceptualization, Methodology, Software, Investigation, Formal analysis, Project administration. Mary Myla Andamon: Investigation, Formal analysis, Data curation, Writing - review & editing. Riccardo Paolini: Validation, Resources, Visualization.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements:
This project is funded through the Citizen Science Grants element of the Inspiring Australia – Science Engagement Programme (CSG55969) by the Department of Industry, Innovation and Scince, Australia, Innovation and Science. The authors would like to thank the Australian Government for this opportunity. The authors would also like to thank Prof Mat Santamouris and other members of the project team for contribution in the project. The authors would also like to thank Dr Jonathan Duverge for
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