Temperature changes between neighboring days and childhood asthma: a seasonal analysis in Shanghai, China

Lei, Xiaoning; Liu, Lijuan; Chen, Renjie; Liu, Cong; Hong, Jian‐Guo; Cao, Lu; Lu, Yanming; Dong, Xiaoyan; Chen, Xinchang; Qiu, Xiaoqing; Xia, Min; Ding, Bo; Qian, Liling; Wang, Libo; Zhou, Wenhao; Gui, Yonghao; Kan, Haidong; Zhou, Yu‐Feng; Zhang, Xiaobo

doi:10.1007/s00484-020-02057-x

Cited by 22 publications

(7 citation statements)

References 38 publications

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“…We considered several covariates as possible confounders of the association between thunderstorms in the presence of high pollen and severe asthma. These include daily weather (mean high temperature, mean relative humidity, and maximum wind speed), [50][51][52][53] and air pollution (PM 2.5 , ozone). [54][55][56][57][58] We collected daily weather data from Automated Surface Observation Systems or Automated Weather Observation Systems and downloaded from Iowa Mesonet.…”

Section: Covariates and Confounding Variablesmentioning

confidence: 99%

Thunderstorms, Pollen, and Severe Asthma in a Midwestern, USA, Urban Environment, 2007–2018

et al. 2022

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Background: Previous research has shown an association between individual thunderstorm events in the presence of high pollen, commonly called thunderstorm asthma, and acute severe asthma events, but little work has studied risk over long periods of time, using detailed measurements of storms and pollen. Methods: We estimated change in the risk of asthma-related emergency room visits related to thunderstorm asthma events in the Minneapolis-St. Paul metropolitan area over the years 2007-2018. We defined thunderstorm asthma events as daily occurrence of two or more lightning strikes during high pollen periods interpolating weather and pollen monitor data and modeling lightning counts. We acquired daily counts of asthma-related emergency department visits from the Minnesota Hospital Association and used a quasi-Poisson time-series regression to estimate overall relative risk of emergency department visits during thunderstorm asthma events. Results: We observed a 1.047 times higher risk (95% confidence interval = 1.012, 1.083) of asthma-related emergency department visits on the day of thunderstorm asthma event. Our findings are robust to adjustment for temperature, humidity, wind, precipitation, ozone, PM 2.5 , day of week, and seasonal variation in asthma cases. Occurrence of lightning alone or pollen alone showed no association with the risk of severe asthma. A two-stage analysis combining individual zip code-level results shows similar RR, and we see no evidence of spatial correlation or spatial heterogeneity of effect. Discussion: Our results support an association between co-occurrence of lightning and pollen and risk of severe asthma events. Our approach incorporates lightning and pollen data and small-spatial area exposure and outcome counts.

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Section: Covariates and Confounding Variablesmentioning

confidence: 99%

Thunderstorms, Pollen, and Severe Asthma in a Midwestern, USA, Urban Environment, 2007–2018

et al. 2022

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“…A day-to-day temperature change is estimated as the difference between the daily average temperatures on two neighboring days (Gough, 2008;Zhan et al 2017;Lei et al 2020):…”

Section: Extreme Temperature Change Between Neighboring Day Event Ind...mentioning

confidence: 99%

Evaluation of events of extreme temperature change between neighboring days in CMIP6 models over China

Song

Yan

2022

Theor Appl Climatol

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In the context of global warming, the frequency and intensity of extreme weather and climate events are increasing. However, the impact of these changes that is directly felt by people is the day-to-day temperature change. Extreme temperature changes between neighboring days (ETCNs) carry substantial disease risks and socioeconomic impacts. Evaluative studies of ETCN events with global circulation models (GCMs) remain unknown in China. This study quantitatively evaluates the performances of 36 GCMs and the multi-model ensemble (MME) of the Coupled Model Intercomparison Project 6 (CMIP6) in simulating the extreme cooling (EC) and extreme warming (EW) events of two consecutive days as defined by relative thresholds. Moreover, we select the optimal models in different regions at the seasonal and annual scales in China, providing theoretical support for the frequency projection and modeling improvement of ETCN events. The results showed that from 1981 to 2013, the annual average EW events and EC events in China showed increasing but not statistically significant trends, and the frequency of EW events was higher than that of EC events. EW events mostly occurred in spring, while EC events mostly occurred in autumn.Additionally, the performances of the CMIP6 models are quite different between EC and EW events.The simulations of EC events are generally more reliable than those of EW events, and the models can also capture the annual cycle of EC events well. Furthermore, the CMIP6 models overestimate the frequencies of EW and EC events but underestimate the frequency of EC events in autumn. The CMIP6 models exhibit poor performance in simulating the trend of and interannual variability in ETCN events and can only simulate the decreasing trend in autumn. Finally, according to the overall ranking of the CMIP6 models, GFDL-ESSM4 and EC-Earth3-Veg-LR achieve the best performance in simulating EW and EC events, respectively. The CMIP6 MME only effectively improved the capabilities of the models to simulate winter EC events in the WNW region. In terms of the trend of and interannual variability in ETCN events, individual models exhibited better performances than the CMIP6 ensemble.

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“…A day-to-day temperature change is estimated as the difference between the daily average 136 temperatures on two neighboring days (Gough, 2008;Zhan et al 2017;Lei et al 2020):…”

Section: Extreme Temperature Change Between Neighboring Day Event Index Definitions and 134 Calculations 135mentioning

confidence: 99%

Evaluation of Events of Extreme Temperature Change between Neighboring Days in CMIP6 Models over China

Song

Yan

2021

Preprint

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In the context of global warming, the frequency and intensity of extreme weather and climate events are increasing. However, the impact of these changes that is directly felt by people is the day-to-day temperature change. Extreme temperature changes between neighboring days (ETCNs) carry substantial disease risks and socioeconomic impacts. Evaluative studies of ETCN events with global circulation models (GCMs) remain unknown in China. This study quantitatively evaluates the performances of 36 GCMs and the multi-model ensemble (MME) of the Coupled Model Intercomparison Project 6 (CMIP6) in simulating the extreme cooling (EC) and extreme warming (EW) events of two consecutive days as defined by relative thresholds. Moreover, we select the optimal models in different regions at the seasonal and annual scales in China, providing theoretical support for the frequency projection and modeling improvement of ETCN events. The results showed that from 1981 to 2013, the annual average EW events and EC events in China showed increasing but not statistically significant trends, and the frequency of EW events was higher than that of EC events. EW events mostly occurred in spring, while EC events mostly occurred in autumn. Additionally, the performances of the CMIP6 models are quite different between EC and EW events. The simulations of EC events are generally more reliable than those of EW events, and the models can also capture the annual cycle of EC events well. Furthermore, the CMIP6 models overestimate the frequencies of EW and EC events but underestimate the frequency of EC events in autumn. The CMIP6 models exhibit poor performance in simulating the trend of and interannual variability in ETCN events and can only simulate the decreasing trend in autumn. Finally, according to the overall ranking of the CMIP6 models, GFDL-ESSM4 and EC-Earth3-Veg-LR achieve the best performance in simulating EW and EC events, respectively. The CMIP6 MME only effectively improved the capabilities of the models to simulate winter EC events in the WNW region. In terms of the trend of and interannual variability in ETCN events, individual models exhibited better performances than the CMIP6 ensemble.

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Temperature changes between neighboring days and childhood asthma: a seasonal analysis in Shanghai, China

Cited by 22 publications

References 38 publications

Thunderstorms, Pollen, and Severe Asthma in a Midwestern, USA, Urban Environment, 2007–2018

Thunderstorms, Pollen, and Severe Asthma in a Midwestern, USA, Urban Environment, 2007–2018

Evaluation of events of extreme temperature change between neighboring days in CMIP6 models over China

Evaluation of Events of Extreme Temperature Change between Neighboring Days in CMIP6 Models over China

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