scholarly journals Spatio-temporal analysis of the extent of an extreme heat event

Author(s):  
Ana C. Cebrián ◽  
Jesús Asín ◽  
Alan E. Gelfand ◽  
Erin M. Schliep ◽  
Jorge Castillo-Mateo ◽  
...  

AbstractEvidence of global warming induced from the increasing concentration of greenhouse gases in the atmosphere suggests more frequent warm days and heat waves. The concept of an extreme heat event (EHE), defined locally based on exceedance of a suitable local threshold, enables us to capture the notion of a period of persistent extremely high temperatures. Modeling for extreme heat events is customarily implemented using time series of temperatures collected at a set of locations. Since spatial dependence is anticipated in the occurrence of EHE’s, a joint model for the time series, incorporating spatial dependence is needed. Recent work by Schliep et al. (J R Stat Soc Ser A Stat Soc 184(3):1070–1092, 2021) develops a space-time model based on a point-referenced collection of temperature time series that enables the prediction of both the incidence and characteristics of EHE’s occurring at any location in a study region. The contribution here is to introduce a formal definition of the notion of the spatial extent of an extreme heat event and then to employ output from the Schliep et al. (J R Stat Soc Ser A Stat Soc 184(3):1070–1092, 2021) modeling work to illustrate the notion. For a specified region and a given day, the definition takes the form of a block average of indicator functions over the region. Our risk assessment examines extents for the Comunidad Autónoma de Aragón in northeastern Spain. We calculate daily, seasonal and decadal averages of the extents for two subregions in this comunidad. We generalize our definition to capture extents of persistence of extreme heat and make comparisons across decades to reveal evidence of increasing extent over time.

2018 ◽  
Vol 28 (5) ◽  
pp. 744-757 ◽  
Author(s):  
Shanyou Zhu ◽  
Yi Liu ◽  
Junwei Hua ◽  
Guixin Zhang ◽  
Yang Zhou ◽  
...  

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 280-280
Author(s):  
Mei Liu ◽  
Carol Buller ◽  
Barbara Polivka ◽  
Terri Woodburn ◽  
Mark Jakubauskas ◽  
...  

Abstract Studies have suggested that extreme weather events have differential effects by age. By leveraging electronic medical records, we aim to analyze the environmental influence of extreme heat on the health of older adults. From our healthcare system’s de-identified data warehouse, we extracted a retrospective cohort of 108,192 patients who were ≥65 years of age as of 1/1/2018 with pre-existing chronic conditions including diabetes, COPD, cardiovascular disease, or kidney disease. Extreme heat event period was defined as 5/1/2018 to 9/1/2018 (79 days with temperature ≥90o; 15 days of moderately poor/poor air quality index (AQI) [≥75] values) and the comparison period was defined as 5/1/2019 to 9/1/2019 (51 days with temperature ≥90o; 0 days with moderately poor/poor AQI values) in the Kansas City area. We randomly partitioned the study cohort into two sets and demonstrated the two patient sets were statistically similar (p>0.05) with respect to their demographic and underlying health conditions. Finally, we compared the respiratory, cardiovascular, and renal health outcomes between the 2018 and the 2019 cohorts. Most patients were Caucasians, female and had comorbid conditions. Results showed significantly higher number of all-cause emergency department visits (p=0.04) and outpatient visits (p=<.001) during the extreme heat event period in 2018. Analyses also showed significantly higher number of outpatient visits due to upper respiratory diseases (p=0.008) and acute renal failure (p=0.01) in 2018. In conclusion, extreme heat increased use of healthcare services in older adults with chronic conditions.


2018 ◽  
Vol 36 (2) ◽  
pp. 133-142 ◽  
Author(s):  
Ying Na ◽  
Riyu Lu ◽  
Bing Lu ◽  
Min Chen ◽  
Shiguang Miao

Author(s):  
Augusta A. Williams ◽  
John D. Spengler ◽  
Paul Catalano ◽  
Joseph G. Allen ◽  
Jose G. Cedeno-Laurent

In the Northeastern U.S., future heatwaves will increase in frequency, duration, and intensity due to climate change. A great deal of the research about the health impacts from extreme heat has used ambient meteorological measurements, which can result in exposure misclassification because buildings alter indoor temperatures and ambient temperatures are not uniform across cities. To characterize indoor temperature exposures during an extreme heat event in buildings with and without central air conditioning (AC), personal monitoring was conducted with 51 (central AC, n = 24; non-central AC, n = 27) low-income senior residents of public housing in Cambridge, Massachusetts in 2015, to comprehensively assess indoor temperatures, sleep, and physiological outcomes of galvanic skin response (GSR) and heart rate (HR), along with daily surveys of adaptive behaviors and health symptoms. As expected, non-central AC units (Tmean = 25.6 °C) were significantly warmer than those with central AC (Tmean = 23.2 °C, p < 0.001). With higher indoor temperatures, sleep was more disrupted and GSR and HR both increased (p < 0.001). However, there were no changes in hydration behaviors between residents of different buildings over time and few moderate/several health symptoms were reported. This suggests both a lack of behavioral adaptation and thermal decompensation beginning, highlighting the need to improve building cooling strategies and heat education to low-income senior residents, especially in historically cooler climates.


2021 ◽  
Author(s):  
Amin Sadeqi ◽  
Hossein Tabari ◽  
Yagob Dinpashoh

Abstract Climate change affects the energy demand in different sectors of the society. To investigate this possible impact, in this research, temporal trends and change points in heating degree-days (HDD), cooling degree-days (CDD), and their simultaneous combination (HDD+CDD) were analysed for a 60-year period (1960-2019) in Iran. The results show that less than 20% of the study stations had significant trends (either upward or downward) in HDD time series, while more than 80% of the stations had significant increasing trends in CDD and HDD+CDD time series. Abrupt changes in HDD time series mostly occurred in the early 1980s, but those in CDD time series were mostly observed in the 1990s. The cooling energy demand in Iran has dramatically increased as CDD values have raised up from 690 ºC-days to 1010 ºC-days in the last 60 years. HDD, however, almost remained constant in the same period. The results suggest that if global warming continues with the current pace, cooling energy demand in the residential sector will considerably increase in the future, calling for a change in residential energy consumption policies.


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Nancy VanStone ◽  
Adam Van Dijk ◽  
Paul Belanger ◽  
Kieran Moore

ObjectiveTo describe the lessons learned for public health decision-makers from an analysis of Acute Care Enhanced Surveillance (ACES) data for the heatwaves experienced in Ontario, Canada in the summer of 2018.IntroductionThe Acute Care Enhanced Surveillance (ACES) system provides syndromic surveillance for Ontario’s acute care hospitals. ACES receives over 99% of acute care records for emergency department (ED) visits; mean daily volume is 17,500 visits. ACES uses a maximum entropy classifier and generates more than 80 standard syndromes, fifteen of which are actively monitored for aberrational activity and are considered of higher public health relevance, including RESP (respiratory infection, non-croup), ILI (influenza-like illness), TOX (toxicological, chemical/drug exposure), AST (asthma), OPI (opioid exposure), CELL (cellulitis), GASTRO (gastroenteritis), ENVIRO (environmental, heat/cold exposure), MH (mental health), EOH (alcohol intoxication), DERM (rash), and SEP (bacteremia, sepsis).Syndromic surveillance provides a salient source of public health surveillance during extreme heat events; monitoring real-time ED visits can inform local public health authorities of health impacts, provide situation awareness to initiate and/or inform public health response, and help decision-makers allocate resources according to geographic (or demographic) vulnerability. While the use of syndromic surveillance has been well-characterized to monitor infectious disease outbreaks, its use to monitor the heat-health impacts is relatively novel for ACES users, specifically local public health authorities. This report describes the the data collected during an extended extreme heat event in Ontario, Canada, to highlight the value of syndromic surveillance during extreme heat events and make recommendations regarding incorporating ACES data into routine workflows.MethodsTemperature data were retrieved from Environment Canada historical databases for mid-June to mid-July 2018. Aggregate counts per day for total ED visits and and for individual syndromes were retrieved from ACES databases. Descriptive statistics were used to analyze all datasets.ResultsAn extreme heat event occurred in the southern region of Ontario in early summer, 2018. Environment Canada issues heat warnings for regions throughout Canada according to region-specific criteria; for southern Ontario, heat warnings are issued when 2 or more consecutive days of daytime maximum temperatures are expected to reach 31°C or when 2 or more consecutive days of humidex values are expected to reach 40. Extended heat warnings are issued when the event lasts beyond 2 days. An extended heat event occurred June 29 to July 5, 2018. Although the region is large, temperature data from Environment Canada’s climate monitoring station at Toronto’s Pearson Airport are shown (Figure 1) as an example of the temperatures observed for this time period in the region.ConclusionsLessons learned from an analysis of ACES data during an extreme heat event:1. The ENVIRO syndrome provides real-time monitoring of the health impacts during a heat event and may provide proxy for estimating the indirect effects of heat (e.g., impacts on chronic conditions). Public health authorities can monitor local health impacts during an extreme heat event.2. Patients seeking help at the ED do not appear to be skewed in acuity, sex nor age. This does not necessarily reflect the population that experiences the greatest impact from extreme heat, but rather those that are seeking help at the ED for the direct effects of heat. That said, an increase in ENVIRO counts does not indicate whether the increase is due to greater exposure to the heat (or sun), engaging in vigorous outdoor activity during the event (recreational or occupational), or lack of access to air conditioning.3. ED visits for ENVIRO can be geolocated to determine areas experiencing greater health impacts. This may allow allocation of resources to specifically address vulnerabilities. ACES has built-in mapping capabilities that allows a geovisualization of the home addresses for patients. Furthermore, aggregate counts for relevant syndromes are available for registered users on the Public Health Information Management System (PHIMS), a web-accessible GIS tool for situational awareness that gives public health decision-makers access to real time health impacts in concert with demographics, weather, and other emergency management information. 


Plant Disease ◽  
2001 ◽  
Vol 85 (3) ◽  
pp. 303-310 ◽  
Author(s):  
Y. Luo ◽  
S. K. Chong ◽  
O. Myers

Soybean roots were sequentially collected from two no-till fields from June 1997 through December 1998. Roots were ground to isolate and enumerate the fungus Fusarium solani f. sp. glycines, the causal agent of soybean sudden death syndrome (SDS), to obtain CFU per gram of root. The log CFU [log10(CFU + 1)] versus sampling time was used to produce the pathogen population curve, and the area under the pathogen population curve (AUPC) was calculated for each plot. The average log CFU from all plots for each sampling date was used to fit the logistic equation. Plot data of log CFU at each sampling time of the growing season, AUPC, and foliar disease index (FDX) were correlated with each other. Correlations among the log CFU in root residue in the winter of 1997, the log CFU and FDX in the 1998 growing season were also conducted. Geostatistics was applied to determine the spatial dependence in root colonization for different lag distance in the fields using semiviograms. Spatio-temporal autocorrelations of root colonization were studied using a computer model STAUTO. During the growing season, pathogen population in roots followed logistic growth in both fields. Pathogen populations in root residue decreased during the winter of 1997 and increased slightly in the spring of 1998 prior to planting. AUPC significantly correlated with FDX in both fields in 1997. Pathogen populations in root residue at three sampling dates in the winter of 1997 significantly correlated (r = 0.47 - 0.53) with FDX of the 1998 growing season in one field. No spatial dependence in root colonization was detected early in the growing season. However, some spatial dependence in certain directions of the fields was detected later in the growing seasons. Spatial dependence in AUPC in the across-rows direction was detected in both fields in 2 years. Spatial lag orders 0 and 1 were significantly correlated with temporal lag order 1 in both within-row and across-row directions in field 1 in 1997.


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