The contribution of air temperature and ozone to mortality rates during hot weather episodes in eight German cities during the years 2000 and 2017

Hot weather episodes are globally associated with increased mortality. Elevated ozone concentrations occurring simultaneously contribute to mortality during these episodes, yet to what extent both stressors are linked to increased mortality rates varies from region to region. This study analyzes time series of observational data of air temperature and ozone concentrations for eight German cities during the years 2000 and 2017. By using an event-based risk approach, various air temperature thresholds were explored for 5 each city to detect hot weather episodes which are statistically associated with increased mortality. Multiple linear regressions were calculated to investigate the relative contribution of air temperature and ozone concentrations to mortality rates during these episodes, including their interaction. Results were compared for their similarities and differences among the investigated cities. In all investigated cities hot weather episodes, linked to increased mortality rates, were detected. Results of the multiple 10 linear regression further point towards air temperature as the major stressor explaining mortality rates during these episodes by up to 60 %, and ozone concentrations by up to 20 %. The strength of this association both for air temperature and ozone varies across the investigated cities. An interactive influence was found between both stressors, underlining their close relationship. For some cities, this interactive relationship explained more of the observed variance in mortality rates than each individual stressor alone. 15 We could show that during hot weather episodes, not only air temperature affects urban populations. Concurrently high ozone concentrations also play an important role for public health in German cities.

each city to detect hot weather episodes which are statistically associated with increased mortality. Multiple linear regressions were calculated to investigate the relative contribution of air temperature and ozone concentrations to mortality rates during these episodes, including their interaction. Results were compared for their similarities and differences among the investigated cities.
In all investigated cities hot weather episodes, linked to increased mortality rates, were detected. Results of the multiple 10 linear regression further point towards air temperature as the major stressor explaining mortality rates during these episodes by up to 60 %, and ozone concentrations by up to 20 %. The strength of this association both for air temperature and ozone varies across the investigated cities. An interactive influence was found between both stressors, underlining their close relationship.
For some cities, this interactive relationship explained more of the observed variance in mortality rates than each individual stressor alone. 15 We could show that during hot weather episodes, not only air temperature affects urban populations. Concurrently high ozone concentrations also play an important role for public health in German cities.

Introduction
Hot weather episodes (HWE) cause more human fatalities in Europe than any other natural hazard (EEA, 2019). HWE are typically characterized by elevated air temperature and can last for several days or weeks, depending on respective threshold 20 values that are used to identify such days. Numerous investigations found excessive mortality rates during days of elevated air temperature (Curriero et al., 2002;Anderson and Bell, 2009;Gasparrini and Armstrong, 2011;Gasparrini et al., 2015).
Increases in morbidity rates, hospital admissions and emergency calls are also associated with elevated air temperatures (Bassil et al., 2009;Karlsson and Ziebarth, 2018).
In addition, HWE are linked to increased tropospheric ozone concentrations (Shen et al., 2016;Schnell and Prather, 2017; 25 Phalitnonkiat et al., 2018). Zhang et al. (2017) and Schnell and Prather (2017), e.g., found for North America that the probability is up to 50 % that both air temperature and ozone concentrations reach their 95th percentile simultaneously. Ozone as a secondary air pollutant is formed by oxidation of volatile organic compounds. Increased air temperature and high solar radiation intensify this formation (Camalier et al., 2007;Varotsos et al., 2019). Correlations between both environmental stressors are mostly described as linear (Steiner et al., 2010). A variety of geographic and meteorological factors may influence this 30 relationship, such as the presence of precursors, local-specific wind patterns or the humidity content of the lower atmosphere (Steiner et al., 2010). At the upper end of the respective air temperature and ozone concentration distributions the direct linkage between the two stressors is discussed to be even more complex (Steiner et al., 2010;Shen et al., 2016). Despite this linkage, elevated ozone concentrations alone have also been associated with adverse health effects (Bell, 2004;Hůnová et al., 2013;Bae et al., 2015;Díaz et al., 2018;Vicedo-Cabrera et al., 2020). The close linkage of both environmental stressors makes it 35 necessary to account for their confounding influence on each other, in order to investigate distinctive health effects of each of these two stressors. But beyond the consideration of both environmental stressors as separated elements, their co-occurrence may lead to even higher rates of excess mortality (Burkart et al., 2013;Vanos et al., 2015;Scortichini et al., 2018;Krug et al., 2019). Some studies also indicate an interactive effect, which is larger than the sum of their individual effects (Cheng and Kan, 2012;Burkart et al., 2013;Analitis et al., 2018).

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Studies which investigate regional differences in the relation between HWE and ozone concentrations revealed differences in the air-temperature-ozone relationship (e.g., Shen et al., 2016;Schnell and Prather, 2017;Phalitnonkiat et al., 2018) and in terms of their individual and combined effects on mortality (Filleul et al., 2006;Burkart et al., 2013;Analitis et al., 2014;Breitner et al., 2014;Tong et al., 2015;Analitis et al., 2018;Scortichini et al., 2018). Some studies report a North-South gradient in the air-temperature-mortality relationship, indicating that populations of northern regions are more sensitive to heat 45 compared to southern regions, which are more affected by cold (e.g., Burkart et al., 2013;Scortichini et al., 2018). However, the influencing effect of elevated ozone concentrations is shown to be more differentiated. While some studies report a greater influence of elevated ozone concentrations for more heat-affected regions (Anderson and Bell, 2011;Scortichini et al., 2018), other studies discuss that regional differences are a result of location-specific physiological, behavioral, and social-economic characteristics, as well as the specific level of exposition across various cities Bell, 2009, 2011;Burkart et al., 50 2013;Breitner et al., 2014). For Germany, most studies investigated the effect of air temperature during HWE on mortality for different regions (Gabriel and Endlicher, 2011;Scherer et al., 2013;Muthers et al., 2017;an der Heiden et al., 2019). Breitner et al. (2014) investigated short-term effects of air temperature on mortality and modifications by ozone in three cities in southern Germany. But, to our knowledge, a national multi-city study exploring the impacts of HWE on mortality across different German cities has not been carried out so far. In addition, how ozone concentrations contribute to mortality rates 55 during HWE are inconclusive for different cities in Germany and world-wide, as described above.
A prior study for Berlin, Germany , identified HWE and episodes of elevated ozone concentrations with a risk-based approach for the period 2000 to 2014. Whereas ozone concentrations alone only showed a weak relationship to mortality rates, the co-occurrence with elevated air temperatures amplified mortality rates in Berlin. On the basis of these results, main focus of this study lies on the identification of HWE in multiple cities in Germany and to investigate how air 60 temperature and ozone concentrations contribute to mortality rates during these. Furthermore, the analysis period is extended up until 2017.
Main goals of this study are (a) to identify HWE that show statistical relations to mortality rates for eight of the largest German cities and (b) to compare these cities in terms of their location-specific relation of air temperature and ozone concentrations onto mortality rates. This study is structured by the following research questions: 1. Do other German cities, likewise Berlin, show a significant relationship between HWE and their specific mortality rates? 2. How does this relationship differ in terms of city-specific threshold values and the relative contribution of air temperature and ozone concentrations during HWE to the overall explained variance of the mortality rate?  Table 1). While the first six are the six most populous cities in Germany, the latter two were included in this study to ensure spatially relatively homogeneous distribution of the investigated cities in Germany. The analyzed cities comprise 10.3 million inhabitants at the end of 2017, which were 12.5 % of the entire German population at this time (DESTATIS, 2019). The smallest city in terms of population 75 (Hanover) has > 500 000 inhabitants, while the largest (Berlin) has > 3.5 million (Table 1).

Air temperature
Air temperature data at daily resolution was obtained from the German Weather Service (DWD, 2019). The selection of measurement sites was based on the availability of data covering the entire analysis period. For cities with more than one measurement site the site closest to the city center and to the co-located ozone measurement site was selected. An overview 80 of the selected measurement sites including their meta data is given in Table A1 and Fig. A1 in the appendix. We use daily average air temperature (TA) from each station. Previous studies found this to be a suitable predictor for the air-temperaturemortality relationship and a suitable indicator for the city's diurnal thermal conditions, compared to maximum or minimum air temperature (Hajat et al., 2006;Anderson and Bell, 2009;Vaneckova et al., 2011;Yu et al., 2010;Scherer et al., 2013;Chen et al., 2015).

Ozone concentrations
Data of hourly ozone concentrations were obtained from the German Environment Agency (UBA). These data stem from the air quality monitoring networks of the German federal states. To select one ozone monitoring station per city same criteria as for the selection of TA measurement sites were applied (closest to city center and to TA site). Only "urban background" stations were selected, as the ozone concentrations should be "representative of the exposure of the general urban population" 90 (EU, 2008). The daily maximum eight-hour moving average (MDA8) was calculated from hourly values for all selected sites, which is the widely used metric for ozone monitoring for human health purposes (WHO, 2006;EU, 2008).

Population data
Time series of annual population counts were obtained for each city from the German Federal Bureau of Statistics (DESTATIS, 2019). The German census of 2011 revealed an error between 1 % and 5 % to the previously available annually updated version 95 of the population time series for the selected cities, based on the prior census in 1990. Therefore, population time series were corrected based on the assumption that the error (a) increases over time and (b) correlates with the strength of the annual migration of each city. An error term was calculated for each year of the census period from 1990 to 2010 as the annual proportion of the total error (derived from the difference in the two census data in 2011). Each error term was further weighted by the proportion of the annual migration size from total migration size during the census period. This weighted error term 100 was then subtracted from the annual population size. Years 2012 to 2017 were likewise corrected based on afore-mentioned assumptions. Annual time series were then linearly interpolated to daily values for each city.

Mortality data
Daily values of deaths for each city were provided by the German Federal Bureau of Statistics (DESTATIS, 2019). We intentionally consider all-cause and all-age total death counts of the whole city in this study, as the main goal is to explore the 105 process which could have an effect (e.g. mortality) as a city-wide variable without any pre-assumption of disease-specific and heat-related health effects. For that reason, we do not want to exclude any death counts from the analysis that might be related to TA or MDA8. Mortality rates were calculated by dividing daily death counts by daily interpolated population counts.
Each time series of TA, MDA8, population and mortality rate were tested for a long-term annual trend. Whereas for TA and MDA8 no significant long-term trend could be detected over the analysis period, mortality rates in all cities showed a 110 significant (p < 0.05, double-sided t test) negative annual trend. This trend was corrected for to avoid any misinterpretation of the variance in the time series.

Methods
The methodological approach used in this study follows the concept of risk evaluation by the Intergovernmental Panel on Climate Change (IPCC) (IPCC, 2012). This concept was adopted for an explorative event-based risk analysis, which is explained 115 in detail by Scherer et al. (2013) and used in previous works to deduce two risk-based definitions of heat waves  or to quantify heat-related risks and hazards (Jänicke et al., 2018). This approach was also used to analyze the co-occurrence of HWE and episodes of elevated ozone concentrations in Berlin . The main advantage of this approach is that it explores time series without any pre-assumptions concerning threshold value, length or existing relation between potentially hazardous episodes (here described with TA) and an effect variable (here the mortality rate). In order to 120 identify HWE with a significant relation to mortality rates, the approach as described in  was applied. In this prior study, time series of TA and MDA8 were explored separately and the episodes, described as "events", were afterwards classified as temporally separated or co-occurring events of elevated TA and MDA8. Deviating from that approach, only HWE as characterized by elevated TA and identified by various threshold values are analyzed in this study. MDA8 is treated as an additional stressor during HWE and analyzed as described in Sect. 2.2.2.

Detection of HWE
Firstly, time series of TA for each city were searched for HWE as the occurrence of at least three consecutive days exceeding a certain TA threshold value (TA Thres ). TA Thres was iteratively increased in 0.5 K steps within the range 10°C to 30°C.
Secondly, at each TA Thres TA magnitude (TA Mag ) was calculated for each HWE as the accumulated sum of the difference of daily TA and respective TA Thres over the whole length of the HWE (sum of degree days above TA Thres ). Thirdly, univariate 130 linear regressions were calculated between TA Mag as predictor variable (logarithmized) and mean mortality rates during the HWE plus a maximum number of lag days (to account for possible lag effects in mortality rates after HWE) as the dependent variable over the whole study period. Regression models thus consist of a unique combination of TA Thres and maximum lag days. Models for each TA Thres were tested for a lag effect of maximum 0 to 7 days. Afterwards, the lag effect was fixed to four days, which was the mean lag effect across the analyzed cities. All presented results are based on this number (four). The base mortality rate for each model is provided as the mortality rate for zero TA Mag (y-intercept of the regression model) indicating conditions of no thermal stress. This approach was also sensitivity-tested for seasonal variances in the mortality rate by the use of a seasonal de-trended, LOESS-smoothed (Cleveland, 1979) mortality time series instead of the crude mortality rate.
Differences are negligible, which shows that the original approach chosen is insensitive to seasonal variances. In addition, HWE occur usually during the summer month when mortality rates are low. For each regression model, the explained variance (r 2 ) 140 was calculated. Error probabilities were calculated with a double-sided t-test. Regression models which were not statistically significant (p > 0.05) or comprised less than five HWE over the study period were discarded from further analyses. Error estimates for each regression model were calculated as the standard error of the regression coefficient (RE RC ) and of the base mortality rate (RE BR ). Regressions were also calculated for HWE with a minimum duration of consecutive days different from three (1 to 5 days). The chosen minimum duration of three days yielded best results in terms of r 2 , RE BR , and RE RC .

Multiple linear regressions
After detection of HWE, mean MDA8 (MDA8 M ) were calculated for the total duration of each HWE. Multiple linear regressions (MLR) were then calculated using the ordinary least square error method with TA Mag and MDA8 M of each HWE as predictor variables for mean mortality rates (as described in Sect. 2.2.1). The overall explained variance (r 2 ) and adjusted explained variance (r 2 adj ) as well as the explained variance for each single variable (r 2 TAMag and r 2 MDA8M ) were calculated. An 150 interaction term (r 2 TAMag,MDA8M ) was also estimated as a cross-product effect of both predictor variables. Statistical significance is assumed for an error probability of p < 0.05, calculated with a double-sided t-test. Table 1 shows statistics for TA and MDA8 during the analysis period for each city. The 50th percentile of TA ranges from 10.0°C

Results
in Hamburg to 11.9°C in Cologne. For the analyzed cities, the highest recorded maximum TA is 31.1°C in Cologne and  Table 1). Figure 2 presents results of the univariate regression analysis. For all cities, the analysis yields statistically significant results 160 between TA Mag and mean mortality rates during HWE for a variety of TA Thres . In all cities, statistically significant models are characterized by a minimum absolute TA Thres between 16°C and 18°C (Fig. 2, left panel). Results of all cities show generally increasing r 2 with increasing TA Thres . Yet, differences across cities can be seen in the range of TA Thres and r 2 of the regression models. Highest values for r 2 are obtained for Berlin, Cologne, Frankfurt and Stuttgart with values of more than 60 % for HWE of high TA Thres . Cities with generally high TA (Table 1) also yield highest values of r 2 . This may be a result of the   Whereas the range of absolute TA Thres of significant models varies across the cities, a percentile-based order reveals a more 170 similar pattern in terms of threshold-r 2 relationship across the cities (Fig. 2,

Multiple linear regression analysis 175
Results of the multiple linear regression and partitioning of r 2 are shown in Fig. 3 higher values compared to r 2 TAMag , and in addition increase with increasing TA Thres (Fig. 3(f)). Differences between cities are also observable for the interaction term between both variables (r 2 TAMag,MDA8M ). Whereas some cities show only marginal values (Hamburg, Hanover, Munich), the others show an increasing interaction term with increasing TA Thres , reaching up to 60 % in Frankfurt. A different pattern for the interaction term is visible for Berlin. Highest values of r 2 TAMag,MDA8M are obtained for medium TA Thres with declining trend towards higher TA Thres .

Relationship between TA Mag and mortality rates
The method used in this study allowed for an explorative identification and investigation of HWE, associated with an effect 190 on mortality. In contrast to other investigations in the field of environmental epidemiology, the aim of this study was not to estimate air temperature or ozone related deaths. One of the main goals of this study was to identify HWE in multiple German cities, that are associated with increased mortality. In all cities, the strength of this association (r 2 ) increases with increasing TA Thres . This is generally comparable with results from other investigations that show greater impact on mortality for more intense HWE (e.g., Anderson and Bell, 2011;Tong et al., 2015).

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However, the specific relationship between an absolute TA Thres and associated r 2 is affected by the specific TA distribution of each city and selected measurement site. Regression analyses were undertaken based on data of one selected measurement site per city, representing the atmospheric conditions of each city. Yet, it must be noted that data at these sites are not only influenced by city-wide characteristics, but also by characteristics of the closest environment at each site. Therefore, TA Thres is affected by the distinct air temperature distribution of the selected measurement site and might differ for other locations. The 200 usage of absolute TA Thres might thus be ambiguous for an inter-city comparison.
Throughout involved cities, an increase of r 2 was obtained around the 95th percentile of each city-specific TA distribution. This is also reported by the multi-city risk evaluation of various heat wave definitions for Australian cities (Tong et al., 2015).
The use of relative TA Thres to identify HWE is thus suggested for studies investigating multiple cities to take into account possible differences in TA distributions and acclimatization of the population to the local-specific air temperature distribution 205 Bell, 2009, 2011;Tong et al., 2015). The use of the 95th percentile could thus be interpreted as one possibility to identify HWE that capture most of the mortality effect. It has to be stressed, though, that results also reveal statistically significant regression models for HWE identified with TA Thres lower than the 95th percentile. Such HWE, identified via TA Thres < 95th percentile, should thus likewise be considered as health relevant.

Relative contribution of TA Mag and MDA8 M to mortality rates 210
Similar aspects as discussed above for the local dependence of air temperature measurements have to be noted also for ozone measurements. A comparison of regression analyses with the same method and based on data from different ozone measurement sites in Berlin was executed in . The ozone measurement site that was used in this study differs from the prior study. Yet, the data used here (Berlin Neukölln) revealed similar performance in terms of r 2 , but is the closest to the co-located TA measurement site (Berlin-Tempelhof).  The second goal of this study was to investigate how ozone concentration contributes to mortality rates during HWE. MLR results between the predictors TA Mag , MDA8 M and mean mortality rates show that the latter is explained across all cities by up to 60 % by the variance of TA Mag . This is in agreement with results of other studies which show that the effect of air temperature on mortality is stronger in comparison to the effect of ozone (e.g., Scortichini et al., 2018;Krug et al., 2019). MDA8 M alone partly explains mortality rates during HWE by up to 20 % in the investigated cities. Except of Frankfurt, this is mostly visible 220 for HWE that are identified with low TA Thres . Figure 4 shows that during HWE, MDA8 (per day) can reach values of up to 190 µg m −3 (e.g. Fig. 4(e), Cologne). This exceeds the target value of 120 µg m −3 set by the European Union to protect human health (EU, 2008). More than 50 % of the days during HWE identified via TA Thres < 20°C or even lower (depending on respective city) even fall below the ozone guideline value recommended by the World Health Organization (WHO) of 100 µg m −3 (WHO, 2006). Associated adverse mortality effects during days with MDA8 values lower than the WHO guideline 225 value for ozone were also found in the prior study focusing on Berlin  and in other studies and for other regions, e.g. Spain (Díaz et al., 2018) or cities in the United Kingdom (Atkinson et al., 2012;Powell et al., 2012).
However, the relative contribution of both MDA8 M and TA Mag varies between cities and different TA Thres . MDA8 M explains more of the mortality rate at low TA Thres than TA Mag . A lower TA Thres captures more HWE in which air temperature is relatively low, but ozone concentrations can reach high values. This may occur during dry, sunny days in early summer, which promote 230 the formation of ozone (Monks, 2000;Otero et al., 2016). This is also shown and discussed in . With increasing TA Thres a declining contribution of MDA8 M alone to the mortality rate is observable (particularly in Berlin and Cologne, Fig. 3(b) and (e), respectively). An increasing contribution of the interaction term (r 2 TAMag,MDA8M ) explaining mortality rates can be observed in all cities. This interaction is most pronounced in Berlin, Cologne, Frankfurt, Stuttgart and Leipzig and indicates that ozone contributes to mortality rates during HWE identified by higher TA Thres . Towards HWE identified by higher 235 TA Thres air temperature becomes the more dominant factor explaining mortality rates and the variance of MDA8 M is directly linked to the variance of TA Mag . Similar conclusions were drawn by (Burkart et al., 2013) and is basically comparable to results that the mortality effect of ozone is strengthened during days of elevated air temperature and HWE (Vanos et al., 2015;Analitis et al., 2018;Scortichini et al., 2018).

Inter-city differences 240
Strongest associations between TA Mag as well as MDA8 M and mortality rates were found for Berlin, Cologne, Frankfurt and Stuttgart. These cities are also those in which highest values of the 50th and 95th percentile and the maximum air temperature are recorded (Table 1). Based on absolute TA Thres , it is not clear if the lower effect observed in Hamburg, Hanover, Leipzig and Munich are reasoned by the absence of HWE with TA Thres > 24°C (Leipzig, Munich) or TA Thres > 22°C (Hamburg, Hanover), which occur in other cities and show strongest relationships to mortality rates.

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Heterogeneities across cities were obtained not only for city-specific absolute TA Thres but also for their respective values of r 2 , which is also reported in other studies investigating other cities across Europe (e.g., Filleul et al., 2006;Baccini et al., 2011;Burkart et al., 2013;Breitner et al., 2014;Analitis et al., 2018;Scortichini et al., 2018). City-specific peculiarities such as demographic or socio-economic characteristics at the community level may cause these differences ; people were shown to be more vulnerable to heat (Yu et al., 2010;Scherer et al., 2013;Benmarhnia et al., 2015). Thus, a higher ratio of elderly people may strengthen the mortality rate during HWE. The ratio of the elderly over 65 years are in fact heterogeneous among involved cities (Table 1) but a linkage to city-specific relation to the effect on mortality rates cannot be deduced. Heterogeneities across cities may also be caused by local-specific geographical characteristics. The close distance to the North and Baltic Sea, associated with a maritime climate, may prevent Hamburg from air temperatures that lead to higher 255 impacts on mortality rates as observed for other cities. Similarly, Munich is not only the city situated at the highest altitude in this study but also closest to the Alps, which may influence the local weather conditions and lead to weather characteristics resulting in weaker relations between high air temperature and mortality rates. However, these reasons remain hypothetical and do not explain the low impacts in Hanover and Leipzig. To sum up, differences between cities are conceivable to be an overlay of city-specific characteristics, such as demographic and geographic factors.

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Results of this study underline the complexity to find similarities across different cities to determine appropriate criteria to identify hazardous episodes in terms of a health-related adverse effect, if there was such an effort. Some cities show a strong relationship between TA Mag and mortality rates, but these are also the cities experiencing highest air temperatures in this study (Table 1). Moreover, the strength of this relationship also varies across cities for equal TA Thres values. However, most similarities arise by comparing results based on their local-specific percentile of the air temperature distribution rather than 265 using absolute thresholds. This further also includes the interactive contribution of ozone.
Further research is needed to investigate local characteristics in more detail such as geographic drivers, socio-economic or socio-demographic factors which may affect the air-temperature-ozone-mortality relationship. These may cause local heterogeneities. Further, some studies also identified other air pollutants that affect mortality during HWE. Especially, concentrations of particulate matter were also found to be increased during episodes of hot and dry weather (Tai et al., 2010;Schnell and 270 Prather, 2017; Kalisa et al., 2018). Enhanced emission of secondary fine particles during hot weather conditions accompanied with reduced air movement may lead to this increased concentration especially in urban areas. Further, particulate matter is also associated with adverse mortality effect and is thus additionally relevant to human health during HWE (Burkart et al., 2013;Analitis et al., 2014;Schnell and Prather, 2017;Analitis et al., 2018).

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This study investigated mortality rates during HWE in eight cities in Germany from 2000 to 2017. HWE were identified with a risk-based approach as a result of regressions between air temperature above a threshold and mean mortality rates during these episodes. HWE and thereby statistically significant regressions were detected in all selected cities for various air temperature thresholds. Results reveal a strong increase in the association around the 95th percentile of the local-specific air temperature distribution. Apart from air temperature, ozone concentrations were shown to contribute to mortality rates during HWE. While 280 air temperature was identified to be the dominant factor for elevated mortality rates, ozone concentrations alone contribute to those by up to 20 %. Additionally, results reveal that the effect of both stressors on mortality cannot be separated in many 13 https://doi.org/10.5194/nhess-2020-91 Preprint. Discussion started: 12 May 2020 c Author(s) 2020. CC BY 4.0 License. cases, highlighting their strong interaction. Especially for HWE identified via higher threshold values of air temperature, ozone mostly contributes to mortality rates as statistically inseparable interaction with air temperature. To which extend air temperature and ozone explain mortality rates differs across cities and for various air temperature thresholds. Some cities show 285 weak associations while the contributions of both stressors to mortality rates are more pronounced in others.
This study underlines the complexity to deduce one universal threshold value in order to identify potentially hazardous HWE in terms of a health effect. Yet, it also emphasizes that besides air temperature ozone contributes to mortality during HWE in German cities. Future research should focus on city-specific characteristics such as population characteristics or geographical peculiarities, which are likely leading to heterogeneities across cities and which may influence the respective air-temperature-290 ozone-mortality relationship. Figure A1. Location of selected air temperature and ozone measurement sites in the investigated cities. Land cover classification is based on CORINE 2018, v20 (EEA, 2019). Table A1. Selected air temperature and ozone measurement sites of each city. Air temperature data were obtained from the German Weather Service (DWD) (DWD, 2019), data of ozone concentrations were obtained by the German Environment Agency (UBA), based on originally measured data of air quality networks of the German federal states. City Air temperature measurements and produced the visualizations. All authors gave support in the writing process, discussed the results, and commented on the manuscript.
Dieter Scherer and Hans-Guido Mücke supervised the analysis.
Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. This research was funded by the Federal Ministry of Education and Research (BMBF), within the framework of Research for Sustainable Development (FONA), as part of the consortium "Three-dimensional Observation and Modeling of Atmospheric

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Processes in Cities" (www.uc2-3do.org), under grant no. 01LP1912. This study was further supported by the doctoral research program of the German Environment Agency (UBA). Daniel Fenner received funding by the Deutsche Forschungsgemeinschaft (DFG) as part of the research project "Heat waves in Berlin, Germany -urban climate modifications" under grant no. SCHE 750/15-1. We kindly thank the section "Air Quality Assessment" of the German Environment Agency (UBA) for providing ozone data. We further express our gratitude to the colleagues of the section "Environmental Medicine and Health Effects Assessment" for valuable discussions.