# Defining Heatwaves with Respect to Human Biometeorology. The Case of Attica Region, Greece

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

- A binary variable to capture if a percentile exceedance exists for each index (i.e., temperature, PET, and UTCI), with “0” signifying non-exceedance and “1” signifying exceedance of the percentile value, and
- Two binary heatwave variables, with “0” representing non-heatwave day and “1” representing heatwave day for durations that are greater than or equal to 2 and 3 days, respectively.

^{2}) interprets to what extent the variance of the heatwave variable explains the variance of the mortality. Lastly, in order to quantify the harvesting effect, we run a robust statistical analysis using the superposed Epoch analysis (SEA) as a means to observe when mortality peaks using different temperature percentiles. In the present section, the null H

_{0}and the alternative H

_{1}hypotheses are as follows:

**Hypothesis**

**1.**

**Hypothesis**

**2.**

**Hypothesis**

**3.**

## 3. Results

^{2}, 5 days after the event for the 97.5th percentile are equal to 0.044 for mean temperature, 0.039, for mean PET, and 0.033 for mean UTCI. In conclusion, the optimal and more robust definition of a heatwave for the case of Attica, concerning this analysis is “a period of at least 3 days when mean temperature is higher than the 97.5th percentile”. After defining a heatwave, we use the Superposed Epoch Analysis, a non-parametric statistical technique, using different percentiles of mean temperature, since as we show it is the optimal index to define a heatwave event and according to [17], it may best reflect heat stress For our estimation, the confidence interval is constructed using the preferred percentiles of the empirical distribution for a large number of resampled values. In particular, we use 10,000 resamples to construct the 95% confidence interval. Since our interest concentrates on the 15 days before and after an event, the superposed Epoch analysis constructs 10.000 randomly selected samples of size equal to 31 observations, which in turn are used to calculate the mean, the standard deviation, and finally, the confidence interval. The resampling method used in our analysis to construct the reported confidence intervals offer robustness for the results and thus minimizes the effects of data measurement errors.

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Saucy, A.; Ragettli, M.S.; Vienneau, D.; de Hoogh, K.; Tangermann, L.; Schäffer, B.; Röösli, M. The role of extreme temperature in cause-specific acute cardiovascular mortality in Switzerland: A case-crossover study. Sci. Total Environ.
**2021**, 790, 147958. [Google Scholar] [CrossRef] [PubMed] - López-Bueno, J.A.; Navas-Martín, M.A.; Linares, C.; Mirón, I.J.; Luna, M.Y.; Sánchez-Martínez, G.; Díaz, J. Analysis of the impact of heat waves on daily mortality in urban and rural areas in Madrid. Environ. Res.
**2020**, 195, 110892. [Google Scholar] [CrossRef] - Le Tertre, A.; Lefranc, A.; Eilstein, D.; Declercq, C.; Medina, S.; Blanchard, M.; Ledrans, M. Impact of the 2003 heatwave on all-cause mortality in 9 French cities. Epidemiology
**2006**, 17, 75–79. [Google Scholar] [CrossRef] - Rodrigues, M.; Santana, P.; Rocha, A. Modelling of Temperature-Attributable Mortality among the Elderly in Lisbon Metropolitan Area, Portugal: A Contribution to Local Strategy for Effective Prevention Plans. J. Urban Health
**2021**, 98, 516–531. [Google Scholar] [CrossRef] [PubMed] - Nairn, J.; Fawcett, R. Defining Heatwaves: Heatwave Defined as a Heat-Impact Event Servicing All Communiy and Business Sectors in Australia; CSIRO and the Bureau of Meteorology: Melbourne, Australia, 2013; CAWCR Technical Report No. 060. [Google Scholar]
- Tong, S.; FitzGerald, G.; Wang, X.Y.; Aitken, P.; Tippett, V.; Chen, D.; Guo, Y. Exploration of the health risk-based definition for heatwave: A multi-city study. Environ. Res.
**2015**, 142, 696–702. [Google Scholar] [CrossRef] [PubMed] - Xu, Z.; FitzGerald, G.; Guo, Y.; Jalaludin, B.; Tong, S. Impact of heatwave on mortality under different heatwave definitions: A systematic review and meta-analysis. Environ. Int.
**2016**, 89–90, 193–203. [Google Scholar] [CrossRef] - Nastos, P.T.; Matzarakis, A. Human bioclimatic conditions, trends, and variability in the athens university campus, Greece. Adv. Meteorol.
**2013**, 2013, 976510. [Google Scholar] [CrossRef] - Nastos, P.T.; Kapsomenakis, J. Regional climate model simulations of extreme air temperature in Greece. Abnormal or common records in the future climate? Atmos. Res.
**2015**, 152, 43–60. [Google Scholar] [CrossRef] - Perkins, S.E.; Alexander, L.V. On the measurement of heat waves. J. Clim.
**2013**, 26, 4500–4517. [Google Scholar] [CrossRef] - Kenney, W.L.; Craighead, D.H.; Alexander, L.M. Heat waves, aging, and human cardiovascular health. Med. Sci. Sport Exerc.
**2015**, 46, 1891–1899. [Google Scholar] [CrossRef] [Green Version] - Kuglitsch, F.G.; Toreti, A.; Xoplaki, E.; Della-Marta, P.M.; Zerefos, C.S.; Türkeş, M.; Luterbacher, J. Heat wave changes in the eastern mediterranean since 1960. Geophys. Res. Lett.
**2010**, 37, 1–5. [Google Scholar] [CrossRef] [Green Version] - D’Ippoliti, D.; Michelozzi, P.; Marino, C.; de’Donato, F.; Menne, B.; Katsouyanni, K.; Perucci, C.A. The impact of heat waves on mortality in 9 European cities: Results from the EuroHEAT project. Environ. Health A Glob. Access Sci. Source
**2010**, 9, 37. [Google Scholar] [CrossRef] [Green Version] - Park, J.; Kim, J. Defining heatwave thresholds using an inductive machine learning approach. PLoS ONE
**2018**, 13, e0206872. [Google Scholar] [CrossRef] - Dong, W.; Liu, Z.; Zhang, L.; Tang, Q.; Liao, H.; Li, X. Assessing heat health risk for sustainability in Beijing’s urban heat island. Sustainability
**2014**, 6, 7334–7357. [Google Scholar] [CrossRef] [Green Version] - Tong, S.; Wang, X.Y.; Yu, W.; Chen, D.; Wang, X. The impact of heatwaves on mortality in Australia: A multicity study. BMJ Open
**2014**, 4, 1–6. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Anderson, B.G.; Bell, M.L. Weather-Related Mortality: How Heat, Cold, and Heat Waves Affect Mortality in the United States. Epidemiology
**2009**, 20, 205. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Fischer, E.M.; Schär, C. Consistent geographical patterns of changes in high-impact European heatwaves. Nat. Geosci.
**2010**, 3, 398–403. [Google Scholar] [CrossRef] - Royé, D.; Codesido, R.; Tobías, A.; Taracido, M. Heat wave intensity and daily mortality in four of the largest cities of Spain. Environ. Res.
**2020**, 182, 109027. [Google Scholar] [CrossRef] [PubMed] - Robinson, P.J. On the definition of a heat wave. J. Appl. Meteorol.
**2001**, 40, 762–775. [Google Scholar] [CrossRef] - WMO; WHO. Heatwaves and Health: Guidance on Warning-System Development; World Meteorological Organization (WMO): Geneva, Switzerland, 2015; WMO, No. 1142. [Google Scholar]
- Dutta, A.; Bhattacharya, S.; Ak, K.; Pati, S.; Swain, S.; Nanda, L. At which temperature do the deleterious effects of ambient heat ‘kick-in’ to affect all-cause mortality? An exploration of this threshold from an eastern Indian city. Int. J. Environ. Health Res.
**2020**, 30, 187–197. [Google Scholar] [CrossRef] - Montero, J.C.; Miron, I.J.; Criado, J.J.; Linares, C.; Díaz, J. Difficulties of defining the term, heat wave, in public health. Int. J. Environ. Health Res.
**2013**, 23, 377–379. [Google Scholar] [CrossRef] - Basu, R.; Malig, B. High ambient temperature and mortality in California: Exploring the roles of age, disease, and mortality displacement. Environ. Res.
**2011**, 111, 1286–1292. [Google Scholar] [CrossRef] - Åström, D.O.; Bertil, F.; Joacim, R. Heat wave impact on morbidity and mortality in the elderly population: A review of recent studies. Maturitas
**2011**, 69, 99–105. [Google Scholar] [CrossRef] [PubMed] - Barnett, A.; Tong, S.; Clements, A. What Measure of Temperature is the Best Predictor of Mortality? Epidemiology
**2009**, 20, S13. [Google Scholar] [CrossRef] [Green Version] - Iñiguez, C.; Royé, D.; Tobías, A. Contrasting patterns of temperature related mortality and hospitalization by cardiovascular and respiratory diseases in 52 Spanish cities. Environ. Res.
**2021**, 192. [Google Scholar] [CrossRef] - Höppe, P. The physiological equivalent temperature—A universal index for the biometeorological assessment of the thermal environment. Int. J. Biometeorol.
**1999**, 43, 71–75. [Google Scholar] [CrossRef] - Matzarakis, A.; Rutz, F.; Mayer, H. Modelling the thermal bioclimate in urban areas with the RayMan model. In Proceedings of the International Conference on Passive and Low Energy Architecture, Geneva, Switzerland, 6–8 September 2006. [Google Scholar]
- Matzarakis, A.; Muthers, S.; Rutz, F. Application and comparison of UTCI and pet in temperate climate conditions. Finisterra
**2014**, 49, 21–31. [Google Scholar] [CrossRef] - Nastos, P.T.; Matzarakis, A. The effect of air temperature and human thermal indices on mortality in Athens, Greece. Theor. Appl. Climatol.
**2012**, 108, 591–599. [Google Scholar] [CrossRef] - Rodrigues, M.; Santana, P.; Rocha, A. Effects of extreme temperatures on cerebrovascular mortality in Lisbon: A distributed lag non-linear model. Int. J. Biometeorol.
**2019**, 63, 549–559. [Google Scholar] [CrossRef] [PubMed] - Ebi, K.L.; Schmier, J.K. A stitch in time: Improving public health early warning systems for extreme weather events. Epidemiol. Rev.
**2005**, 27, 115–121. [Google Scholar] [CrossRef] [PubMed] [Green Version]

**Figure 1.**Graphs of mortality for Attica: (

**a**) Graph of cardiological mortality during summer months; (

**b**) graph of respiratory mortality during summer months; (

**c**) graph of cardiorespiratory mortality during summer months.

**Figure 2.**Temperature graphs of mortality for Attica: (

**a**) Graph of mean temperature; (

**b**) graph of maximum temperature.

**Figure 5.**Map of mean values of cardiological, respiratory, and cardiorespiratory mortality (CR) for summer and winter for Attica. Note: The latitude and longitude coordinates are 37.983810 and 23.727539, respectively.

**Figure 6.**Superposed Epoch analysis for mortality due to cardiological diseases for duration of the heatwave event of at least 2 and 3 days: (

**a**) 95th percentile; (

**b**) 97.5th percentile; (

**c**) 99th percentile. Notes: (i) The shaded area shows the 95% Confidence Interval (CI), (ii) the vertical axis measures the mean number of deaths due to cardiological diseases, (iii) the horizontal axis depicts the time 15 days before and 15 days after the heatwave event. Day 0 is the first day of the event.

**Figure 7.**Superposed Epoch analysis for mortality due to respiratory diseases for duration of the heatwave event of at least 2 and 3 days: (

**a**) 95th percentile; (

**b**) 97.5th percentile; (

**c**) 99th percentile. Notes: (i) The shaded area shows the 95% Confidence Interval (CI), (ii) the vertical axis measures the mean number of deaths due to respiratory diseases, (iii) the horizontal axis depicts the time 15 days before and 15 days after the heatwave event. Day 0 is the first day of the event.

**Figure 8.**Superposed Epoch analysis for mortality due to cardiorespiratory diseases for duration of the heatwave event of at least 2 and 3 days: (

**a**) 95th percentile; (

**b**) 97.5th percentile; (

**c**) 99th percentile. Notes: (i) The shaded area shows the 95% Confidence Interval (CI), (ii) the vertical axis measures the mean number of deaths due to cardiorespiratory mortality, (iii) the horizontal axis depicts the time 15 days before and 15 days after the heatwave event. Day 0 is the first day of the event.

Mortality | Winter | Summer | ||||||
---|---|---|---|---|---|---|---|---|

Mean | Min. | Max. | Std. Deviation | Mean | Min. | Max. | Std. Deviation | |

Cardiological | 32.63 | 15.00 | 70.00 | 6.72 | 26.25 | 8.00 | 89.00 | 6.87 |

Respiratory | 10.10 | 1.00 | 36.00 | 5.00 | 7.94 | 0.00 | 31.00 | 4.19 |

Cardiorespiratory | 42.74 | 20.00 | 96.00 | 9.26 | 34.18 | 10.00 | 103.00 | 8.94 |

Percentiles | 90th | 92.5th | 95th | 97.5th | 99th |
---|---|---|---|---|---|

Mean Temperature | 28.67 | 29.23 | 30.00 | 31.00 | 32.15 |

Max. Temperature | 33.89 | 34.60 | 35.41 | 36.60 | 38.01 |

Mean PET | 28.61 | 29.50 | 30.58 | 31.39 | 33.46 |

Max. PET | 43.00 | 43.97 | 45.50 | 47.79 | 50.00 |

Mean UTCI | 28.35 | 29.17 | 30.02 | 31.25 | 32.55 |

Max. UTCI | 38.70 | 39.50 | 40.58 | 42.20 | 43.90 |

Temperature Percentile | 2 Days | |||||||||
---|---|---|---|---|---|---|---|---|---|---|

90th | 92.5th | 95th | 97.5th | 99th | ||||||

Days | Events | Days | Events | Days | Events | Days | Events | Days | Events | |

Mean Temp. | 858 | 142 | 635 | 114 | 404 | 81 | 196 | 51 | 82 | 24 |

Max Temp. | 11 | 5 | 7 | 3 | 2 | 1 | 0 | 0 | 0 | 0 |

Mean PET | 845 | 148 | 622 | 122 | 405 | 97 | 191 | 51 | 71 | 22 |

Max PET | 725 | 166 | 515 | 135 | 298 | 95 | 127 | 45 | 34 | 12 |

Mean UTCI | 841 | 147 | 617 | 121 | 402 | 93 | 186 | 51 | 74 | 23 |

Max UTCI | 766 | 169 | 536 | 129 | 340 | 99 | 142 | 44 | 46 | 16 |

Temperature Percentile | 3 Days | |||||||||
---|---|---|---|---|---|---|---|---|---|---|

90th | 92.5th | 95th | 97.5th | 99th | ||||||

Days | Events | Days | Events | Days | Events | Days | Events | Days | Events | |

Mean Temp. | 810 | 118 | 581 | 92 | 362 | 60 | 158 | 33 | 68 | 15 |

Max Temp. | 3 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |

Mean PET | 779 | 116 | 570 | 94 | 339 | 63 | 149 | 30 | 57 | 15 |

Max PET | 615 | 112 | 401 | 85 | 194 | 45 | 83 | 22 | 20 | 6 |

Mean UTCI | 767 | 108 | 561 | 91 | 348 | 64 | 150 | 33 | 56 | 14 |

Max UTCI | 658 | 112 | 438 | 83 | 250 | 54 | 106 | 26 | 36 | 11 |

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**MDPI and ACS Style**

Dimitriadou, L.; Nastos, P.; Zerefos, C.
Defining Heatwaves with Respect to Human Biometeorology. The Case of Attica Region, Greece. *Atmosphere* **2021**, *12*, 1100.
https://doi.org/10.3390/atmos12091100

**AMA Style**

Dimitriadou L, Nastos P, Zerefos C.
Defining Heatwaves with Respect to Human Biometeorology. The Case of Attica Region, Greece. *Atmosphere*. 2021; 12(9):1100.
https://doi.org/10.3390/atmos12091100

**Chicago/Turabian Style**

Dimitriadou, Lida, Panagiotis Nastos, and Christos Zerefos.
2021. "Defining Heatwaves with Respect to Human Biometeorology. The Case of Attica Region, Greece" *Atmosphere* 12, no. 9: 1100.
https://doi.org/10.3390/atmos12091100