The process by which a living organism becomes adapted to a change of climatic environment (AMS 2013). Acclimatisation is a relatively quick adaptation that occurs within the organism’s lifetime. Traditionally, acclimatisation studies in humans have focused on adapting to temperature changes (such as moving from a temperate climate to the tropics), but acclimatisation can also occur with a change in altitude, photoperiod and humidity, for instance.
Adaptation in human systems is defined by the Intergovernmental Panel on Climate Change (IPCC 2012b) as “The process of adjustment to actual or expected climate and its effects, in order to moderate harm or exploit beneficial opportunities”. In natural systems, the IPCC (2012b) defines adaptation as the process of adjustment to actual climate and its effects, and notes that human intervention may facilitate adjustment to the expected climate. In temperature-mortality modelling studies, adaptation includes physiological acclimatisation to warmer/colder temperatures, as well as a range of behavioural adaptations (e.g. dressing appropriately during hot/cold weather) and technological adaptations [e.g. air-conditioning, the introduction of heat health warning systems (HHWS), and the adaptation of infrastructure such as the construction of green roofs to reduce urban heat island (UHI) magnitude]. Most temperature-related mortality studies refer only to physiological acclimatisation and there is much debate as to how adaptation should be modelled (Gosling et al. 2009).
Air quality indices
An aggregated evaluation of the pollutant load of the atmosphere across multiple constituents, generally developed to compare environmental conditions to biological outcomes with an emphasis on human health. The indices also provide a concise method of informing the general public regarding atmospheric conditions by incorporating a suite of pollutants into one single measure. The method of aggregation varies from index to index, with some emphasising the single pollutant observed at the highest/most threatening concentration, whereas others consider the effects of all pollutants simultaneously (Kyrkilis et al. 2007). In the US, the Air Quality Index is determined by the maximum concentration of ozone, particulate pollution, carbon monoxide, sulphur dioxide, and nitrogen dioxide, where the concentration of each pollutant is normalized on a unitless 1–500 scale, where a value of 100 corresponds to the relevant national ambient air quality standard. The overall index value is set as that associated with the highest normalised score for any individual variable (Bishoi et al. 2009). A similar system based on the pollutant with the highest relative concentration is used in the UK, but values are converted to a 1–10 scale for communication to the public (Holgate 2011).
Air temperature distribution in urban areas
Similar to the UHI effect (urban versus rural), temperature is not distributed evenly within a single urban area. Air temperature is affected by such elements as surface cover (pavement vs grass, for example), building height, and anthropogenic heat. In general, rivers and parks are cooler than built-up areas during the day. Air temperature differences within the urban environment reach a peak during night-time (Petralli et al. 2011). Urban parks are often cooler than their surroundings (Spronken-Smith and Oke 1999) and the magnitude and timing of the park cool island (PCI) effect varies with park type and the extent to which the park differs from its surroundings (Spronken-Smith and Oke 1998).
Albedo (reflection coefficient) represents the reflective property of a surface. It is defined as the ratio of reflected shortwave radiation from the surface to incident shortwave radiation upon it. It is a dimensionless quantity that may also be expressed as a percentage, and it is measured on a scale from zero (surface with no reflecting power of a perfectly black surface) to 1 (perfect reflection of a white surface). Albedo is sometimes referred to as the reflection coefficient (α).
The majority of climate-health studies do not report raw mortality data but, in order to give an indication of the mortality attributable to climate, an excess mortality is estimated by subtracting the expected mortality from the observed mortality (Gosling et al. 2007). The expected mortality is often called the baseline mortality.
The International Society of Biometeorology (ISB) (2013) defines biometeorology as “An interdisciplinary science that considers the interactions between atmospheric processes and living organisms (plants, animals and humans).” The central question within the field is “how does weather and climate impact the well-being of all living creatures?” (ISB 2013). Please refer to McGregor (2012b) for the latest progress report on this field (at the time this glossary was published).
The rate (mass/time) of carbon transfer into, out of, or within environmental systems.
An environmental system that accumulates carbon over long time periods.
A process or system that releases more carbon into the atmosphere than it assimilates (Running 2008).
Cardiovascular and respiratory diseases related to air pollution
A number of stresses to the cardiovascular and respiratory systems have been linked to elevated atmospheric pollutant concentrations. Research to date has particularly emphasised relationships with ozone and particulate matter. Specific diseases linked with air pollution include those labelled as “Diseases of the circulatory system” and “Diseases of the respiratory system” in the ICD-10 classification (codes I00-I99 and J00-J99 respectively), especially heart failure, heart rhythm disturbances, cerebrovascular events, ischemic heart disease, peripheral vascular disease, chronic obstructive pulmonary disease, respiratory tract infections, pneumonia, and influenza (Dominici et al. 2006; Sunyer et al. 1996; Wong et al. 2002).
Conductive heat transfer
The rate at which heat energy is transferred between a unit area of two surfaces (e.g. human and chair) with the temperature difference being maintained. Usually expressed in units of Wm−2 °C−1 (IUPS Thermal Commission 2001).
Confounding factorsEpidemiological studies investigate the causes of particular health outcomes based on associations with different risk factors that are recorded in the study. The risk factors are often selected a priori. Whilst a given study will investigate particular exposures, the risk of developing the health outcome can be affected by other factors. In some cases, these factors may ultimately be responsible for some or all of the deduced relationship between health outcomes and exposure variables of interest. This distortion leads to an invalid comparison and the distorting factors are called confounding factors or confounding variables. As an example, with studies that investigate the relationship between increasing daily air temperature (the exposure variable) and heat-related mortality (the outcome variables), confounding factors that are often included are daily air pollution and relative humidity (Baccini et al. 2011; Ma et al. 2011; Porta 2008).
Correlation is a statistical method used to analyse the strength of association between variables (Snedecor and Cochran 1980). The measure used to describe this relationship is the correlation coefficient (r), a unitless number with values between −1 and 1. Positive values of r describe the tendency of two variables to increase together, while negative values describe the reverse behaviour. In the case where no relationship is found for two variables, the correlation coefficient is zero. Different methods exist to calculate the correlation coefficient. The most common is the Pearson product-moment correlation coefficient. A correlation describes only a statistical association between two variables, not cause and effect.
The difference in temperature between the outdoor mean temperature over a 24-h period and a given baseline (or threshold temperature) (ASHRAE 2005), which can differ depending on the purpose or study. Such purposes include calculations related to energy consumption (Arnell et al. 2013), building envelope requirements (ASHRAE 2005), fueling demand information (NOAA 2013), and extreme weather stress (Watts and Kalkstein 2004). The degree-day is the basis for calculating both the cooling degree-day(CDD), heating degree-day (HDD) and growing degree-day (GDD).
EuroHEAT was a project that quantified the health effects of heat in European cities and identified options for improving health systems’ preparedness for, and response to, the effects of heat-waves. The project, running from 2005 to 2007, was coordinated by WHO/Europe and co-funded by the European Commission (EC) Directorate-General for Health and Consumers (WHO 2013).
European climate assessment & dataset project
The objective of the European climate assessment & dataset (ECA&D) project is to combine collation of daily series of observations at meteorological stations, quality control, analysis of extremes and dissemination of both the daily data and the analysis results. The project has compiled a number of definitions and mathematical formulas of meteorological indices used in the ECA&D project (note, however, that these are not biometeorological indices). The ECA&D indices can be viewed at: http://eca.knmi.nl/indicesextremes/indicesdictionary.php.
Evaporative heat gain or loss (evaporative heat transfer)
Most commonly occurs as a loss, whereby heat is used for sweat to change phase from liquid to vapour. This consumes heat in the form of latent heat of vapourisation, with heat being transferred from the body to the air, causing cooling to occur. A gain occurs when heat is transferred from the air to the body when heat is released due to condensation on the skin. Expressed in units of Wm−2.
Generalised additive model
Generalised additive models (GAMs) assume that the mean of the dependent variable depends on an additive predictor through a nonlinear link function. GAMs permit the response probability distribution to be any member of the exponential family of distributions. For example, the non-linear link may be, for instance, Poisson or Gaussian in nature. This gives rise to methods such as Poisson regression modelling and loess smoothing, which are common in epidemiological studies on the association between temperature and mortality rates, for instance (Gouveia et al. 2003).
Greenhouse gases and the greenhouse effectMany chemical compounds present in Earth’s atmosphere are relatively poor absorbers of shortwave radiation but strong absorbers of longwave radiation, thus keeping the lower atmosphere and surface environment warmer than they otherwise would be given Earth’s solar constant. This radiative mechanism has been termed the “greenhouse effect” and responsible atmospheric constituents labelled “greenhouse gases.” According to the IPCC (2007), the most important greenhouse gases are water vapour and carbon dioxide, whilst nitrogen and oxygen—the two most abundant constituents of the atmosphere—have no effect. Many greenhouse gases occur naturally in the atmosphere (e.g. carbon dioxide, methane, water vapour) but others are synthetic, such as chlorofluorocarbons (CFCs), hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs). Human activity can intensify the greenhouse effect through the emission of greenhouse gases. For instance, the amount of carbon dioxide in the atmosphere has increased by about 35 % in the industrial era, due largely to the combustion of fossil fuels and removal of forests (IPCC 2007).
Growing degree-day (GDD) is a heat unit, expressed in units of °C-day. GDD aims to describe the heat energy received by a given plant over a given time period. It is often argued that the calculation of GDD in plant phenology and development science has greatly improved the description and prediction of phenological events compared to approaches such as number of days or time of year, for instance (Richardson et al. 2006). GDD is usually calculated by the following equation (McMaster and Wilhelm 1997):
GDD = [(Tmax + Tmin)/2]−Tbase
Tmax is the daily maximum air temperature (°C)
Tmin is the daily minimum air temperature (°C)
Tbase is the temperature below which the process of interest does not progress (°C).
In some cases, for simplification, [(Tmax + Tmin) / 2 ] is set equal to daily average temperature. Tbase varies among plant species and it is also likely to be dependent upon the growth stage being considered. Similarly the heat accumulation can also be measured at hourly frequency to generate growing degree hours (GDH). GDD and GDH can also be summed cumulatively over time as a measure of accumulated heat received by plants (Chuine 2000; Richardson et al. 2006).
Heat health warning system
A heat health warning system (HHWS) is a system, usually initiated by public health authorities, to reduce the health impacts of heat waves. It consists of two main components: one is responsible for the identification and forecasting of heat waves with serious health impacts, and a second initiates and coordinates public health measurements to mitigate the most serious health impacts of the heat wave effect (Kovats and Ebi 2006; WHO 2004).
An apparent temperature calculation designed to determine the temperature that the human body “feels” when its evaporative cooling mechanism (perspiration) is limited due to increased relative humidity. The variables required to calculate the heat index (HI) were established originally by Steadman (1979b), but the current operational equation was created through multiple regression statistical analyses by Rothfusz (1990):
Ta is air temperature (°C), R is the relative humidity (%).
In the United States, the National Weather Service uses the HI primarily to determine when to issue heat alerts.
Heating degree-day (HDD) is calculated by summing the number of degrees above an average hourly outdoor “threshold” temperature, for a 1-day (24-h) period. The ASHRAE (2001) standard is 18.3 °C, also used by NOAA (2013) for fuelling demand, and for building cooling requirements by ASHRAE (2005) in addition to a HDD at a 10 °C (HDD10) threshold. Annual HDDs represent the sum of the degree-daysthroughout one calendar year.
Heat load index
Gaughan et al. (2008) proposed this thermal comfort index for feedlot beef cattle in Australia. It can be calculated for two different environmental conditions:
For Tg < 25 °C: EQUATION
For Tg > 25 °C: EQUATION
where: Tg is the black globe temperature (°C) v is the wind speed (ms−1) R is the relative humidity (%).
Based upon these equations, the environment is classified as thermoneutral (HLI ≤ 70.0), warm (HLI is 70.1 to 77.0), hot (HLI is 77.1 to 86.0) or very hot (HLI > 86).
Heat-related mortality is conflating deaths occurring during the warm season and deaths attributed to heat. The latest ICD-10 codes (WHO 2011b) that consider heat-related causes of death are included in T67 (effects of heat and light), which incorporates T67.0 (heatstroke and sunstroke), T67.1 (heat syncope), T67.2 (heat cramp), T67.3 (heat exhaustion, anhydrotic), T67.4 (heat exhaustion due to salt depletion), T67.5 (heat exhaustion, unspecified), T67.6 (heat fatigue, transient), T67.7 (heat oedema), T67.8 (other effects of heat and light) and T67.9 (effect of heat and light, unspecified). These have been used in recent studies; e.g. Beggs and Vaneckova (2008) but the majority of studies tend to calculate “excess mortality” (Gosling et al. 2009) from time series of all-cause mortality, or from other causes (e.g. ischemic heart disease).
Heat stress index
A summer apparent temperature index (May–Sept) that computes relative heat stress comparisons of locations throughout the US, based on 30-year datasets and deviations from normal (Watts and Kalkstein 2004). It is different from the Index of Heat Stress. The heat stress index (HSI) uses the “shaded” Steadman Apparent Temperature index (Steadman 1984), cloud cover, cooling degree-days, and consecutive days of extreme heat to calculate a relative heat stress for individual locations at specific times of the warm season (Souch and Grimmond 2004). This is completed by evaluating the frequency distributions for 10-day intervals derived from the meteorological variables, with percentile values determined for each parameter. The final daily HSI (%) is based on the location of the daily summed value under the distribution curve (Watts and Kalkstein 2004). For example, a HSI of 97 % conveys that on the current date, only 3 % of days are likely to experience more stressful conditions than the day being reviewed.
Typically refers to classical heat stroke (CHS)—a dangerous and potentially fatal condition in which an individual’s core body temperature rises to a temperature above 40.0 °C as a result of exposure to high ambient air temperature. At this temperature, other key internal functions are compromised, including the central nervous system, which may result in hallucinations, convulsions, or coma. Exertional heat stroke (EHS) is diagnosed in cases where high core temperatures are caused by a high level of physical activity (potentially in combination with high environmental temperature) (Bouchama and Knochel 2002).
A heat wave is a period of extreme high temperature that lasts several days. Heat waves can be responsible for large numbers of weather-related deaths and diseases (Kovats and Ebi 2006). No globally accepted definition of a heat wave exists (Koppe et al. 2004; Robinson 2001). Three different approaches are usually applied to determine whether a period is defined as a “heat wave” (Gosling et al. 2009; Robinson 2001). Methods for identifying heat wave days include selecting the 95th percentile of daily temperature (Beniston 2004; Gosling et al. 2007; Hajat et al. 2002), selecting absolute temperatures (Koppe and Jendritzky 2005; Matzarakis et al. 2010) [this approach is mostly used with human biometeorological indices such as the Physiological Equivalent Temperature (PET) or the Universal Thermal Climate Index(UTCI), for which levels of thermal stress are defined (e.g. Matzarakis et al. (2010; 2009) for PET)], or approaches that identify a specific synoptic classification/air mass (Hondula et al. 2013). For farm animals, a heat wave has been defined as a period of at least 3 consecutive days during which recovery hours are less than 10, where the recovery hours are defined as hours with a temperature humidity index (THI) below 72 (Valtorta et al. 2002).
HeRATE (Health Related Assessment of the Thermal Environment) is a procedure that can be used with human biometeorological indices to consider short-term adaptation in thermal stress assessments (Koppe and Jendritzky 2005). It is based on levels of thermal stress and a conceptual model, which allows for short-term acclimatisation and adaptation through behavioural and societal measures by adjusting the levels of thermal stress based on the thermal conditions of the preceding weeks. In the heat health warning system (HHWS) of the German Weather Service (DWD), HeRATE is used together with the human biometeorological index, perceived temperature (PT).
The ability of an organism to maintain a state of internal stability or equilibrium within a dynamic ambient environment. In biometeorology, this is most often in reference to humans or other animals, and their ability to maintain a healthy body temperature in hot or cold conditions.
Human heat balance equation (energy budget)
A mathematical equation that describes the net flow rate of exchanges of heat (gains and losses) to a human in a given environment. The conceptual heat balance equation (Parsons 2010) is:
When the rate of heat storage is zero (S = 0), the body is in heat balance (i.e. constant temperature). If there is a net heat gain (loss) then storage will be positive (negative) and the body temperature will increase (decrease) (Parsons 2010).
The preferred apparent temperature calculation method in Canada. Unlike the heat index, which is used in the US, Humidex (short for “humidity index”) uses dewpoint temperature rather than relative humidity in the calculation (Masterson and Richardson 1979). The Humidex equation is:
Ta is air temperature (°C) Td is dew point temperature (°C).
The number of new cases of a disease or condition in a population within a specific time period. A sudden large increase in incidence would be classified as an epidemic.
If the index of heat stress is less than 100 %, sweat evaporation can match Ereq, and body temperature can be controlled. If the index of heat stress is greater than 100 %, sweat evaporation, and cooling, is limited to Emax (Brotherhood 2008). Hence, evaporative heat cannot be lost, and heat is stored in the body, core temperature rises, and heat stress is likely. The index of heat stress differs from the heat stress index (HSI).
Indoor air quality
Refers to the constituents of the air inside a building or enclosed space, which affects the health of the users. This is of increasing concern as humans are spending greater amounts of time indoors where harmful gases and particles can be produced and then accumulate. Pollutants are released into the air from a variety of source activities (e.g. building materials, work/home tasks and activities, heating, painting, cleaning) (US Environmental Protection Agency 2010). Locations of highest concern are those involving prolonged, continued exposure, such as home, school, and workplace (US Environmental Protection Agency 2010). Measuring and monitoring types of particles and gases of concern—such as moulds and allergens, radon, carbon monoxide, volatile organic compounds, asbestos fibres, NOx, carbon dioxide, and ozone—can determine indoor air quality. Computer modelling of ventilation and other airflow in buildings can predict air quality levels. Outdoor air may penetrate indoors via the ventilation systems of buildings, and thus must also be taken into consideration (Hang et al. 2009). In developing countries, carbon monoxide is a pollutant of high concern, affecting indoor air quality and occupant health in the home. This is due to the burning of any fuel such as gas, oil, kerosene, wood, or charcoal for cooking and heating (Duflo et al. 2008). Proper ventilation, filtration, and source control are the most used methods for diluting and improving indoor air quality and comfort, with control of high temperature and humidity also important (US Environmental Protection Agency 2010).
International Organization for Standardization standards
The International Organization for Standardization (ISO) is a network of national standards bodies that represents the world’s largest developer of voluntary International Standards. The British Standards Institution (BSI) is the UK’s National Standards Body and is responsible for producing and publishing British Standards and for representing UK interests in international and European standards organsations such as ISO. ISO documents are developed as international standards and documented as “ISO [Standard Number]”. Once ISO documents are published, they can be republished by individual countries as a national adoption. For example, when the EU adopt an ISO Standard, the letters “EN” are added to the document name, e.g. “EN ISO [Standard Number]”. Following adoption, the documents may be produced officially in different languages (e.g. English by the BSI), which adds an extra part to the document name, such as “BS EN ISO [Standard Number]” in the UK, or “DIN EN ISO [Standard Number]” in Germany, for instance. There are several ISO Standards for Assessing Thermal Environments.
International Organization for Standardization standards for assessing thermal environments
ISO standards for assessing thermal environments can be divided into three categories: hot, moderate and cold (Orosa and Oliveira 2012; Parsons 2006).
For hot environments, the ISO standards include:
BS EN ISO 11079:2007 (British Standards Institution 2008) for the determination and interpretation of cold stress when using required clothing insulation and local cooling effects.
BS EN ISO 9886:2004 (British Standards Institution 2004) for the evaluation of thermal strain by physiological measurements. Also applicable to hot and moderate environments.
BS EN ISO 13732-3:2008 (British Standards Institution 2009c) for methods for the assessment of human responses to contact with cold temperature surfaces.
International Classification of Diseases 10th revision: ICD-10
The international standard diagnostic classification used for all general epidemiological and many health management purposes as well as for clinical use. The 10th revision, published in 2006 by WHO, is the latest in a series, which has its origins in the 1850s. In ICD-10 an alpha-numerical system was introduced to code diseases and other health problems.
J: Diseases of the respiratory system
J30: Vasomotor and allergic rhinitis
J30.1: Allergic rhinitis due to pollen (hay fever, pollinosis).
Klima Michel model
The Klima Michel model (KMM) is an energy-balance-model for the human body, based on the predicted mean vote (PMV) equation of Fanger (1972) and the PMV correction of Gagge et al. (1986), that considers latent heat fluxes in a more appropriate way. Contrary to PMV, the KMM takes the complex outdoor radiant conditions into account (Jendritzky et al. 1979, 1990). In the KMM, the energy-balance (see Munich energy balance model for individuals; MEMI) is solved for a standard person of 35 years and 1.75 m tall, with a weight of 75 kg and a physical heat production of 135 W, which corresponds to walking at approximately 4 km h−1. Depending on the thermal conditions, the person adapts their clothing to between 0.5 and 1.75 clo.
Landsat is a NASA program, with the mission to provide repetitive acquisition of high-resolution multispectral data of the Earth’s surface on a global basis by remote sensing (NASA 2013). The data from the Landsat spacecraft constitute the longest record of the Earth’s continental surfaces as seen from space. The program has been providing data since 1972. According to NASA, no other current or planned remote-sensing system, public or private, fills the role of Landsat in global change research or in civil and commercial applications (NASA 2013). Landsat 8, is the latest mission in the Landsat series. Landsat 8 provides synoptic coverage of continental surfaces with spectral bands in the visible, near-infrared, short-wave, and thermal infrared regions of the electromagnetic spectrum and with moderate-resolution (15 m–100 m) (NASA 2013).
An approach to seasonal vegetation dynamics that integrates phenological patterns (mainly spatial) and processes (mainly temporal) within heterogeneous biophysical environments across multiple scales (Liang and Schwartz 2009).
Land surface phenologySeasonal pattern of variation in vegetated land surfaces observed from remote sensing (Friedl et al. 2006).
The energy required to change the state of a substance at a constant temperature (Thakore and Bhatt 2007). In atmospheric science, the three most common values are:
Latent heat of vaporisation (condensation) is equal to 2,257 kJ kg−1 at 100 °C (2,501 kJ kg−1 at 0 °C) and is the amount of energy absorbed (released) by water during evaporation (condensation) (Allaby 2002).
Latent heat of melting (fusion) is equal to 334 kJ kg−1 at 0 °C and is the amount of energy absorbed (released) by water during melting (freezing) (Allaby 2002).
Latent heat of sublimation is equal to 2,835 kJ kg−1 at 0 °C and is the amount of energy absorbed (released) by ice (water vapour) during sublimation (Allaby 2002).
Loess or lowess stands for “locally weighted smoothing”. It can be applied through the use of a generalised additive model (GAM). The technique can accommodate nonlinear and non-monotonic functions, thus offering a flexible nonparametric modelling tool. In loess, each observed value is replaced by a predicted value, generated by connecting the central point from a weighted regression for a given span (neighbourhood) of the data (Hastie and Tibshirani 1990). The polynomial is fitted using weighted least squares, giving more weight to points near the point whose response is being estimated and less weight to points further away.
Maximum evaporation (Emax) (evaporative capacity)
The maximum amount of evaporation possible from the body given the prevailing environmental conditions (measured in Wm−2). If Emax < Ereq (required evaporative heat loss) then sweat evaporation is limited and inefficient sweating occurs. If Emax > Ereq, evaporative cooling can occur.
Mean radiant temperature
The mean radiant temperature (Tmrt) is a parameter that combines all longwave and shortwave radiant fluxes to a single value. It is defined as the temperature of a surrounding black body that causes the same radiant heat fluxes as the complex radiant fluxes (Fanger 1972). In human biometeorology Tmrt is usually calculated for a standardised standing person. Since measurements are not available on many operational meteorological stations, different models exist, ranging from simple empirical models to full radiative-transfer models, which allow modelling of radiant fluxes based on standard meteorological measurements.
Total energy production in a human in unit time, commonly expressed in biometeorological studies based on the total body surface of a human in Wm−2. The standard BS EN ISO 8996:2004 (British Standards Institution 2005b) outlines the method for the estimation of metabolic heat production.
Minimum mortality temperature
The daily temperature (average, maximum, or minimum) associated with the fewest number of temperature-related mortality events in a particular city. The minimum mortality temperature (MMT) is particularly relevant in locations that display the characteristic “J-curve”, which shows that mortality ratesusually decrease as temperatures increase up to a certain point (the MMT). When temperatures increase above the MMT, mortality rates begin to increase again.
With respect to mitigation of disaster risk and disaster, mitigation is defined by the IPCC (2012a) as “The lessening of the potential adverse impacts of physical hazards (including those that are human-induced) through actions that reduce hazard, exposure, and vulnerability.” With respect to climate change, the IPCC (2012a) define mitigation as “A human intervention to reduce the sources or enhance the sinks of greenhouse gases.”
Morbidity is defined by the US CDC (CDC 2013) as “Any departure, subjective or objective, from a state of physiological or psychological well-being”. Morbidity may also refer to a count of the number of individuals afflicted by some illness or reduced health status, the affected proportion of a population, or the temporal extent of the prevalence of a given illness or disease (Porta 2008).
Mortality displacement / “harvesting”
Mortality displacement is a term used in the context of heat-related mortality. Mortality displacement is a phenomenon where the heat principally affects individuals whose health is already compromised and who would have died shortly anyway, regardless of the weather. Estimates of mortality displacement are often calculated for defined heat wave periods that include “before”, “during” and “after” the heat wave periods, lasting typically less than 2 months (Gosling et al. 2007; Le Tertre et al. 2006; Sartor et al. 1995). The effect of mortality displacement is usually calculated by dividing the mortality deficit (the number of “negative excess deaths” after the heat wave , i.e. the number of deaths below that expected after the heat wave ) by the total number of excess deaths during the heat wave (i.e. deaths above that expected during the heat wave ) and converting to a percentage. Estimates of mortality displacement calculated by this method include 15 % during the Belgium 1994 heat wave (Sartor et al. 1995), 6 % and 30 % in Paris and Lille respectively during the 2003 European heat wave in France (Le Tertre et al. 2006), and 71 %, 45 % and 59 % in Budapest, London and Sydney, during heat waves of 1991, 2003 and 2004, respectively (Gosling et al. 2007).
Mortality rateAccording to the US CDC (2013), a mortality rate is “A measure of the frequency of occurrence of death in a defined population during a specified interval of time”.
Munich energy balance model for individuals
The Munich energy balance model for individuals (MEMI) is an energy balance model of the human body based on the heat-balance equation for the human body (Matzarakis and Amelung 2008) and the two-node model of Gagge et al. (1972):
M + W + NR + C + ED + ERe + ESw + ST = 0
M is the metabolic rate (internal heat production) W is physical heat production
NR is net radiation of the body C is convective heat flow ED is latent heat flow to evaporate water into water vapour diffusing through the skin (imperceptible perspiration) ERe is respiratory heat flow ESw is heat flow due to evaporation of sweat
ST is a storage term.
Using the meteorological parameters Ta (air temperature), Tmrt (mean radiant temperature), VP (water vapour pressure), and v (wind speed), MEMI calculates the skin and core temperature of a standard person under light activity (80 W) by taking clothing insulation into account.
New wind chill equivalent temperature index
NOAA’s National Weather Service new Wind Chill Equivalent Temperature (WCET) index is based on a human face model and calculates the dangers from winter winds and temperatures below the freezing point (NOAA 2011). WCET is an apparent temperature index. WCET calculates wind speed at an average height of 5 feet, or 1.5 m (typical height of an adult human face) based on readings from the national standard height of 33 ft, or 10 m (typical height of an anemometer) and incorporates heat transfer theory, regarding heat loss from the body to its surroundings, during cold and breezy/windy days. It uses a consistent standard for skin tissue resistance and assumes no impact from the sun (i.e. clear night sky) (Osczevski and Bluestein 2005). In a recent evaluation of the WCET, Shitzer and Tikuisis (2012) suggest to improve the reliability of the values predicted by the WCET by applying a whole body thermoregulation model to evaluate human exposure to cold-windy conditions.
An abbreviation commonly used to describe the combined local atmospheric concentration of the compounds nitric/nitrous oxide (NO) and nitrogen dioxide (NO2). NOx is formed naturally in the atmosphere, especially at high altitudes, by the intense heat of lightning (Levine et al. 1984). Near the surface, NOx is formed largely by combustion (automobiles, industry, etc.), microbial activity in soils, and widespread application of nitrogen-rich, agricultural fertiliser (Harrison et al. 1995; Shepherd et al. 1991; Zhang et al. 2012). The reaction of NOx, particularly NO2, with other anthropogenic chemicals contributes to the formation of tropospheric ozone (O3). In the stratosphere, NOx has been shown to reduce ozone (O3) concentrations and most of this NOx is a result of N2O emissions (Revell et al. 2012). NO2, specifically, has been shown to have negative effects on the human respiratory system (Chauhan et al. 1998).
A molecule consisting of three oxygen atoms (O3). Most of the Earth’s ozone is in the stratosphere in what is often referred to as “the ozone layer”. Ozone is created by the interaction of diatomic oxygen (O2) with ultraviolet (UV) radiation. UV radiation splits O2 into two separate oxygen atoms that then react with another O2 molecule to form O3. Beginning in the late 1970s, scientists observed that stratospheric ozone concentrations were depleted, determined to be a result of the reaction of stratospheric ozone with industrial halogens such as CFCs and other artificial compounds. Since the ozone layer absorbs some bands of solar UV radiation, the most direct threat to human health from this depletion is an increase in skin cancers. The lowest levels of stratospheric ozone occurred in 1992–1993. Stratospheric ozone levels are now increasing, in part due to the 1987 Montreal Protocol and subsequent reductions of CFCs. Full regeneration of the ozone layer will take several decades. At ground level, ozone is formed by chemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOCs) in the presence of sunlight. Ozone in the troposphere is considered a secondary pollutant. Commonly cited negative impacts of tropospheric ozone include distress to the human respiratory system and decreased visibility, particularly in urban areas (Armstrong 1994; UN Environment Program 1998).
Perceived temperature (PT) is a human biometeorological parameter that describes the thermal perception of an individual, by the use of the air temperature of a reference environment (Staiger et al. 2011). This environment is defined as an indoor room, with the wind velocity reduced to a slight draught, a mean radiant temperature that equals the air temperature, and a water vapour pressure of 50 %. The thermo-physiological modelling is based on the Klima Michel model (KMM).
The PHEWE (assessment and prevention of acute health effects of weather conditions in Europe) project (Michelozzi et al. 2004) applied a standardised scientific approach to provide better knowledge on the human health effect of hot and cold atmospheric temperature and on the role of several effect modifiers. The general aim of the project was to assess the acute health effects of extreme weather, during the winter and summer season, in 16 European cities characterised by different climatic conditions, and to propose preventive strategies to reduce the health impact of weather conditions. The project lasted for 44 months between 2002 and 2006 and it was funded by the EU Fifth Framework Program.
Physiological equivalent temperature
Physiological equivalent temperature (PET) is a human biometeorological parameter that describes the thermal perception of an individual. It is defined as the air temperature at which, in a typical indoor setting (without wind and solar radiation), the heat budget of the human body is balanced with the same core and skin temperature as under the complex outdoor conditions to be assessed (Höppe 1999). The typical indoor setting is an indoor room, with windspeed (v) = 0.1 ms−1, vapour pressure (VP) = 12 hPa and mean radiant temperature (Tmrt) equal to the air temperature (Ta). For calculating the physiological parameters PET makes use of the Munich energy balance model for individuals (MEMI).
Planetary boundary layer
The bottom layer of the troposphere that is in contact with the surface of the Earth (AMS 2013). The planetary boundary layer (PBL) is sometimes also called the atmospheric boundary layer. The depth of the PBL varies in time and space (ranging from tens of metres to several kilometres).
PM2.5 (where PM is “particulate matter”) refers to particles with an aerodynamic diameter less than 2.5 μm. Unlike PM10, these particles can penetrate into the gas exchange regions of the lung and deposit in the alveoli (WHO 2006). A large number of particles in this fraction are formed from gases and combustion processes.
PM10 (where PM is “particulate matter”) refers to particles with an aerodynamic diameter less than 10 μm. These particles can penetrate the human respiratory system down to the lower thoracic region and cause serious health impacts (WHO 2006). PM10 is produced mainly mechanically by the breakdown of larger solid particles and by biological sources. Highest PM10 concentrations can be measured in regions with high traffic and heavy industry (Kappos et al. 2004). PM (PM10 as well as PM2.5 and PM1) is known to cause different health effects, mainly in the respiratory systems. In light of the negative health effects arising from particulates, many countries have defined measures to reduce PM emissions and increase air quality, e.g. by defining threshold values.
PM1 and ultra-fine particles
PM1 (where PM is “particulate matter”) and ultra-fine particles are those with an aerodynamic diameter less than 1 μm (PM1) or 0.1 μm (ultra-fine particles), respectively. These particles originate mainly from high-temperature combustion processes (WHO 2006). Due to their small diameter, measurement of these particles is complex, and the number of available long-time measurements is much smaller than for PM2.5 or PM10. Therefore, the health effect of ultrafine particles is less well known (Kappos et al. 2004; WHO 2003).
A statistical model that relates the values of one or more independent values to those of a dependent variable, where the dependent variable is represented by count data. The assumption of a Poisson regression model is that the count data in a particular unit of time follows the Poisson distribution (Loomis et al. 2005).
Predicted mean vote
An index used to predict the mean response of a large group of people, exposed to the same ambient conditions, according to the ASHRAE (American Society of Heating, Refrigerating, and Air-Conditioning Engineers) thermal sensation scale.
PMV was originally calculated by Fanger (1972) using the equation:
Fanger (1967) also developed a related index, called the predicted percentage dissatisfied (PPD), which is calculated from PMV, and predicts the percentage of people who are likely to be dissatisfied with a given thermal environment. BS EN ISO 7730:2005 (British Standards Institution 2006) uses both the PMV and PPD.
Predicted percent dissatisfied
Developed by Fanger (1967), the predicted percent dissatisfied (PPD) is an index that predicts the percentage of thermally dissatisfied people who feel too cool or too warm, and is calculated from the predicted mean vote (PMV). The PMV and PPD form are therefore closely related, and both indices take the form of a U-shaped relationship, where percentage dissatisfied increases for PMV values above and below zero (thermally neutral). At the neutral temperature as defined by the PMV index, PPD indicates that 5 % of occupants will still be dissatisfied with the thermal environment. The standard BS EN ISO7730:2005 (British Standards Institution 2006) uses both the PPD and PMV.
The proportion of occurrence of a particular health outcome within a given population, usually expressed as the number of impacted individuals divided by the total population (or an at-risk population) (Porta 2008).
Radiant heat exchangeHeat transfer per unit area (Wm−2) by the exchange of thermal radiation between the ambient environment and the human body. Based on four components of radiant energy: incoming shortwave radiation, reflected shortwave radiation, incoming longwave radiation, and emitted longwave radiation. A positive value indicates heat transfer to the environment; negative indicates heat absorption by the body (IUPS Thermal Commission 2001).
Relative air velocity
Considers the combined effects of wind speed, activity speed (speed at which an individual is moving), and the degree angle (α) between the wind direction and body movement for estimation of free and forced convective heat transfer due to wind (Vanos et al. 2012). Facing the wind, the relative air velocity (vr) is the sum of the walking velocity and air velocity. With the wind at back, the relative air velocity is the absolute value of the difference between walking and wind speeds (British Standards Institution 2009a). For all other angles, the relative air velocity may be calculated as follows (British Standards Institution 2009a; Havenith et al. 2012; Vanos et al. 2012):
v is the wind speed (m s−1) vw is the walking speed (m s−1)
α is the angle between walking and air directions (0° if both are in the same direction).
Relative air velocity can influence the predicted mean vote (PMV) and predicted percent dissatisfied (PPD) indices as the convective and evaporative heat exchanges are influenced by relative air velocity, and local thermal discomfort can occur due to draught of air velocity (British Standards Institution 2002b). To this end, relative air velocity is relevant to the standard BS EN ISO 7730:2005 (British Standards Institution 2006).
Required evaporative heat loss
Required evaporative heat loss (Ereq) is the evaporation that is required by the body to maintain a thermal balance. Represented by the sum of the net metabolic heat production by the human body, and exchanges of radiation and convection (Brotherhood 2008). Hence, to maintain energy balance, the sum of these components must equal evaporative cooling.
A characteristic of an individual or an element of their environment or behaviours that is associated with an elevated probability of contracting a certain disease or condition based on scientific evidence (Porta 2008). For example, smoking is considered a risk factor for contracting lung cancer.
Roughness sub-layer (RSL) corresponds to the air layers immediately above horizontally uniform surface types with tall roughness elements, where conventional flux–profile relationships and Monin-Obukhov similarity theory are likely to be invalid. In this layer flow consists of the interacting wakes and plumes (of heat, humidity and pollutants) introduced by individual roughness elements (Raupach 1979). The nature of the urban surface, with its rigid buildings of different heights and physical characteristics, separated by trees, urban canyons and open spaces, makes it particularly susceptible to the development of a RSL of significant depth, perhaps several times the average building height (Arnfield 2003; Roth 2000). Figure 2displays a schematic of the urban boundary layer (UBL) including its vertical layers and scales.
Sick building syndrome
A group of mucosal, skin, and general symptoms that are temporally related to working in particular buildings (Burge 2004). A recent review (Norbäck 2009) showed that sick building syndrome (SBS) is related to both personal and environmental factors, with increasing evidence for the role of personality traits and psychosocial work environment, reactive chemistry and the inflammatory properties of indoor particles for SBS. The review also noted that the link between indoor and outdoor air pollution should not be neglected when assessing SBS (Norbäck 2009).
A numerical measure of the overall success of a model or group of forecasts compared to a background or reference. The score is often expressed in terms of percentage improvement over the reference. Although skill scores often offer a more complete assessment of performance than simpler measures including percent correct and false alarm rate, no score can perfectly represent a model’s or forecast’s strengths and weaknesses. Examples of skill scores for binary forecast/observation data arranged into a 2-by-2 contingency table include the Heidke Skill Score, Pierce Skill Score, and Yule’s Q (Wilks 1995).
Steadman apparent temperature index
Not one, but several apparent temperature indices calculated by Steadman (1979a, 1979b, 1984). In an attempt to quantify “sultriness”, Steadman (1979a, b) incorporated temperature, humidity, clothing and human physiology into a number of algorithms, which were later simplified into regression equations for indoor, shaded and sunny conditions (Steadman 1984). The regression equations were later adapted by Rothfusz (1990) to create the heat index (HI).
Synoptic classification/air mass
An air mass is a large volume of air with homogeneous characteristics, especially with respect to temperature and moisture. Synoptic classification systems aim to discretise daily weather into subsets with similar regional-scale meteorology, and are commonly based on temperature or pressure fields at the surface (Kalkstein et al. 1996; Sheridan 2002). In recent years the terms have become more interchangeably used in the literature, as many classification systems aim to identify particular air mass types. Some investigators develop classification systems for particular applications (Kalkstein et al. 2011), while others adopt automated classifications available for certain geographies.
Temperature humidity index
Originally described by Thom (1959) for humans, this thermal comfort index is also widely used as a heat stress indicator for animals. There are several equations that can be used to calculate the temperature humidity index (THI) and these were compared by Bohmanova et al. (2007). They are listed below with details of their application (Bohmanova et al. 2007).
To monitor discomfort from temperature and humidity in humans (Bianca 1962)
Empirically determined in cattle exposed to heat stress conditions in climatic chambers (Bianca 1962):
To monitor the degree of discomfort in humans (Thom 1959):
The Oklahoma Mesonet Cattle Heat Stress Index, which is designed to indicate the level of heat stress of outdoor cattle (National Research Council 1971):
Developed by the United States Weather Bureau to describe discomfort in humans (National Research Council 1971):
Empirically determined in cattle exposed to heat stress conditions in climatic chambers (Yousef 1985):
Where: Ta is the dry bulb temperature (also known commonly as the air temperature) (°C) Tw is the wet bulb temperature (°C) Td is the dew point temperature (°C) R is the relative humidity (%).
Thermal comfort is the condition of mind that expresses satisfaction with the thermal environment; however, due to large physiological and psychological variations from one person to another, it is difficult to maintain thermal comfort in one given space for all (ASHRAE 2004), whether it be indoors or outdoors. It is crucial for human beings to maintain a constant core body temperature of 37 °C (98 °F). However, the temperature away from the core, such as on the skin and extremities for instance, can vary considerably with environmental and metabolic heat loads. To maintain the core body temperature, heat is exchanged with the environment by respiration (latent and sensible heat fluxes), radiation (longwave and shortwave), evaporation (latent heat flux), conduction (contact with solids), and convection (sensible heat flux) (Jendritzky and de Dear 2009). To this end, the human thermoregulatory system can be separated into active and passive interacting systems. The active system concerns the thermoregulatory response (e.g. shivering or perspiring) and the passive system deals with heat transfers at the body surface. When the body is under thermal comfort conditions, the body is under least strain because the active system is at its lowest activity level. However, increasing discomfort is associated with increasing strain. Research shows that people take action to improve their comfort conditions by modifying their clothing and metabolic rate when outdoors, or by interacting with the building when they are indoors, which are considered actions of adaptation (Nicol and Humphreys 2002). When adaptation opportunity is limited, departure from neutrality causes stress and dissatisfaction (Baker and Standeven 1996). According to Nikolopoulou et al. (2001), intrinsic factors such as past experience, expectations and time of exposure are also important for thermal comfort.
Thermal discomfort can be caused by unwanted local cooling or heating of the body due to radiant temperature asymmetry (cold or warm surfaces), draught (defined as a local cooling of the body caused by air movement), vertical air temperature difference, and cold or warm floors (British Standards Institution 2002b). It can be used to calculate the predicted percent dissatisfied (PPD).
Thermoregulation is the process of thermal energy control in any physical system (Da Silva and Maia 2013). Living organisms produce energy by metabolic processes and gain and lose energy from the environment. When there is no change in the metabolic heat production or any evaporative heat loss, it is possible to define the thermoneutral zone, limited by the lower critical temperature and the upper critical temperature. Here, the organism does not need to gain or lose energy to the environment. In the zone of homeothermy, the organism can maintain its body temperature within narrow limits.
Thermotolerance is “the ability of a cell or an organism to become resistant to heat stress” (Kregel 2002).
Temperature above or below at which significant elevations in morbidity or mortality are observed. In the case of heat-related mortality, methods for establishment of threshold temperatures include identification of an inflection point in the “J-shaped” or “U-shaped” relationship between temperature and mortality (Gosling et al. 2007), or a certain percentile of temperature associated with statistically significant increases in risk of mortality (Basu 2009). Threshold temperatures systematically vary geographically, such that those regularly exposed to high temperatures are less susceptible to heat than those that live in cooler locales (e.g. Davis et al. 2002).
A method for analysing data typically measured at successive times spaced at uniform intervals. Examples of time series are observations of weather and health indicators (Hajat et al. 2002) or crop production (Simelton et al. 2012). Its aim is to determine the pattern of progression by constructing a model. The simplest approach (descriptive-deterministic) considers the general direction of long-term development (trend), a cyclic drive within a certain time (e.g. seasonality) and an irregular component, that contains outliers or random fluctuation. By describing observations, regularity and changes can be detected and future developments estimated and forecast.
Town energy balance
A model developed by Masson (2000) to explain the turbulent flux interaction between the atmosphere and urban areas. The town energy balance (TEB) scheme uses urban canyon geography (a road lined by tall buildings). The model mimics the effect of buildings by finding a specific energy balance for three surface types: roof, wall, and road. The inputs are geometric, radiative and thermal parameters of the urban surface. The model output includes turbulent heat flux, outgoing longwave radiation, and outgoing shortwave radiation.
Tourism climate index
One of a suite of measures that aim to capture the relative appeal of different potential destinations for vacation and recreation. These measures are based largely on the weather and climate of a particular locale, and some include estimates of the energy balance to assess thermal comfort (Lin and Matzarakis 2008). The tourism climate index (TCI) of Mieczkowski (1985) includes measures of temperature, relative humidity, monthly rainfall, hours of sunshine, and wind speed. Recently developed climate indices, including the climate index for tourism (CIT) (de Freitas et al. 2008), incorporate a larger suite of variables to assess physiological stress and/or empirical data gleaned from visitors to various locales.
A t-test compares the means of two respective samples of data to suggest whether both samples come from the same population. If a given sample is compared with a known mean, then a single sample t-test is applied. If two samples need to be compared, and the samples were collected independently, then an independent sample t-test is applied. When two samples are not independent of each other and have some factor in common, the paired sample t-test can be applied. The test may also be used to compare linear regression slopes or correlation coefficients for significant differences.
Universal thermal climate index
The universal thermal climate index (UTCI) is an international standard performed by the European Cooperation in Science and Technology (COST) Action 730, based on recent research in human response-related thermo-physiological modelling (COST 2011). For any combination of air temperature, wind, radiation, and humidity (stress), UTCI is defined as the isothermal air temperature of the reference condition that would elicit the same dynamic response (strain) of the physiological model (Jendritzky et al. 2012). The associated assessment scale is developed from the simulated physiological responses and comprises ten categories: extreme cold stress; very strong cold stress; strong cold stress; moderate cold stress; slight cold stress; no thermal stress; moderate heat stress; strong heat stress; very strong heat stress; and extreme heat stress (“stress” refers to the physiological workload to maintain thermal equilibrium) (Błażejczyk et al. 2013). The International Journal of Biometeorology published a Special Issue for the UTCI in 2012 (McGregor 2012a). UTCI is an apparent temperature index.
Urban boundary layer
The urban boundary layer (UBL) is that portion of the planetary boundary layer (PBL) above the urban canopy layer (UCL) whose climatic characteristics are modified by the presence of a city at the surface (Oke 1976) (see Fig. 2). The UBL derives its characteristics from exchanges at its lower “surface”, a loosely defined interface located at roof level; the interface can be treated as a rough, flat, surface with generalised roughness, thermal and radiative characteristics (Arnfield 2003; Mills 1997).
Urban canopy layer
The urban canopy layer (UCL) is the layer in the vertical structure of the urban boundary layer (UBL) ranging from the surface to the top of buildings (Oke 1976; Roth 2000) (see Fig. 2). Within the UCL, the urban local scale climate is influenced greatly by the thermal properties of buildings and surfaces as well as local-scale flows arising from the geometry of buildings and streets (Oke 1987).
Open-air spaces between buildings in metropolitan areas that are located above streets, sidewalks, and alleys. Air movement through the city is controlled largely by the geometry of the open spaces, and the shapes of buildings and intersections create flows that significantly differ from the mean regional wind. The thermal climate of the urban canyon is impacted by the structural composition and shape of the buildings that surround it, especially their height and spacing in determining the patterns of sunlight and shading within the canyon (Oke 1987).
Urban climate zones
Discretisation of areas of the built environment based on the potential impact of buildings and other structures to modify the local atmosphere/environment. Urban climate zone (UCZ) classes are ranked approximately in order of their ability to modify the wind, thermal and moisture characteristics. These incorporate groups of Ellefsen’s zones (Ellefsen 1991), plus a simple measure of the urban structure, which has been shown to be closely related to flow, solar shading and the nocturnal heat island. Also included is a measure of the surface cover (% built) that is related to the degree of surface permeability (or an inverse measure using % open and vegetated; however, this does not increase with urban development as in the % built version) (Oke 2004). This classification has been detailed and improved by Stewart and Oke throughout the LCZ model (local climate zones) inheriting from Oke’s classification (Stewart and Oke 2009a, b; Stewart 2010).
Urban heat island
Increased temperature associated with a built environment, such as a city or town, with respect to near rural areas. The magnitude of the urban heat island (UHI) is typically higher at night, under clear and calm skies (Oke 1982). The UHI may pose a health risk for urban dwellers because of elevated ambient air temperatures (McGregor et al. 2007). Precipitation patterns have also been shown to be affected by some larger urban heat islands. Within urban areas complex temperature patterns arise from the variability in surface cover, building height, and anthropogenic heat sources, for instance. Urban areas have reduced sky view factors (SVFs) due to many tall buildings, which can contribute to the UHI effect.
Urban local scale
The urban local scale lies between the urban microscale and the urban mesoscale (see Fig. Fig.2).2). It represents hourly turbulence of sensible, latent, and storage heat fluxes within a spatial volume of 102–104 m3, reaching a height up to the inertial sub-layer (Grimmond and Oke 2002; Grimmond et al. 2010). The local scale is characteristic of neighbourhood responses and fluxes (as opposed to yard/house for urban microscale and region for urban mesoscale). The local scale is contained within the urban boundary layer (UBL), containing both the surface layer and roughness sub-layer (Grimmond and Oke 2002). Both microscale and mesoscale processes of turbulent sensible, latent and storage heat fluxes influence the local scale. Furthermore, the high variability in fluxes found in the urban microscale (roughness sub-layer)—generated by rough/varying topography and building/ground surfaces—are less variable at the height of the inertial sub-layer, and can thus be averaged with time. Therefore, there is a neighbourhood response, which is then affected by local weather patterns.
The urban mesoscale is an intermediate scale, ranging spatially from a few kilometres to several hundred kilometres horizontally, and tens of metres vertically, with a temporal scale of about 1 to 12 h (Pielke 1984) (see Fig. Fig.2).2). It lies between the scales of weather systems and of microclimates, within the atmospheric boundary layer (Hang et al. 2009). At this height, factors of urban roughness, terrain, and meteorological conditions are incorporated, while ignoring variability at the neighbourhood (urban local scale) and street-scales (urban microscale). Within mesoscale modelling, the urban-induced dynamical and thermal effects on surface energy budgets are key components in calculations (Arnfield 2003). There is a link in the urban microscales and urban mesoscales, where small effects become greater, particularly the effects of urban canyon radiation geometry and land surface dynamics affecting sensible heat flux (Arnfield 2003). At the mesoscale, interactions with the planetary boundary layer (PBL) are crucial, due to larger circulations, such as mountain/valley flows, land/sea breezes, and urban breezes (Martilli 2007). Mesoscale modelling involves weather forecasts, air quality, storms, and urban climates within the UBL.
The urban microscale deals with atmospheric phenomena at length and time scales less than 1 km and 1 day (AMS 2013). Urban microscale meteorology resides within the roughness sub-layer and smaller urban canopy layer (UCL), in which micrometeorological processes occur (see Fig. Fig.2).2). However, we are often interested in microscale measurements on the order of centimetres to metres, and seconds to hours. Within these scales, micrometeorologists are interested in partitioning of fluxes of heat, moisture, and gas exchanges from soil, vegetation, water, and ground/building surfaces, thus varying greatly within the same local climate (Oke 1987). Common urban microscale environments include parking lots, streets, open grass and treed green space, urban canyons, small lakes, and backyards. Within this scale, humans have the ability to modify properties so as to improve their thermal comfort. A very influential urban microscale effect is the three-dimensional radiative flux, which varies substantially with respect to thermal and reflective properties of urban surfaces (Grimmond et al. 2010). Within the built environment, three-dimensional streets and surface-plant-air interactions are increasingly dependent on microscale characteristics under calm, clear, nocturnal conditions, showing stronger dependence on the microscale site characteristics (Grimmond et al. 2010). Additionally, heat and water storage, plus anthropogenic heat fluxes, display significant spatial variability with respect to the morphology and type of the urban surface (Arnfield 2003).
In atmospheric science, a quantity that possesses a magnitude and a direction (e.g. wind, vorticity, etc.).
In biology, an organism that spreads infection by transporting pathogens from one host to another. For example, mosquitoes are vectors of malaria and ticks are vectors of Lyme disease.
Wet bulb globe temperature
Applied as an apparent temperature index to set safe limits for physical exertion in the heat. The indoor wet bulb globe temperature (WBGT) index is determined by two single readings: psychrometric wet bulb temperature (Tw) and black globe temperature (Tg), as follows:
For outdoors, WBGT is calculated with natural wet bulb temperature (Tnw), Tg and dry bulb temperature (Ta) as follows:
The WBGT is a commonly used index of thermal stress (Budd 2008) but it has recently been reviewed for its limitations and applicability (Budd 2008; Parsons 2006). The ergonomics of the thermal environment standard BS EN ISO 27243:1994 (British Standards Institution 1994) and ISO 7243:1989 (Parsons 2006) is based upon the WBGT.
The uncontrolled burning of plant matter, often in grasslands, bushlands, and woodlands. Wildfire may be caused by natural phenomena including lightning and volcanic debris as well as by anthropogenic actions. Burn characteristics are dependent on the atmospheric, ecological, and geological properties of the environment. Land use and climate significantly contribute to the fire potential of individual locales (Dominici et al. 2006; Hyndman and Hyndman 2010).
World Health Organization
The World Health Organization (WHO) is the directing and coordinating authority for health within the United Nations system. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries and monitoring and assessing health trends (WHO 2011a). When diplomats met to form the United Nations in 1945, they discussed establishing a global health organization. WHO’s Constitution came into force on 7 April 1948—a date we now celebrate every year as World Health Day. WHO fulfils its objectives through its core functions (WHO 2011a):
providing leadership on matters critical to health and engaging in partnerships where joint action is needed;
shaping the research agenda and stimulating the generation, translation and dissemination of valuable knowledge;
setting norms and standards and promoting and monitoring their implementation;
articulating ethical and evidence-based policy options;
providing technical support, catalysing change, and building sustainable institutional capacity;
monitoring the health situation and assessing health trends.