scholarly journals The Influence of Heat on Daily Police, Medical, and Fire Dispatches in Boston, Massachusetts: Relative Risk and Time-Series Analyses

2020 ◽  
Vol 110 (5) ◽  
pp. 662-668 ◽  
Author(s):  
Augusta A. Williams ◽  
Joseph G. Allen ◽  
Paul J. Catalano ◽  
Jonathan J. Buonocore ◽  
John D. Spengler
Keyword(s):  
Time Series ◽  
Relative Risk ◽  
Emergency Services ◽  
Extreme Heat ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Emergency Medical ◽  
Time Series Analyses ◽  
The Impact

Objectives. To examine the impact of extreme heat on emergency services in Boston, MA. Methods. We conducted relative risk and time series analyses of 911 dispatches of the Boston Police Department (BPD), Boston Emergency Medical Services (BEMS), and Boston Fire Department (BFD) from November 2010 to April 2014 to assess the impact of extreme heat on emergency services. Results. During the warm season, there were 2% (95% confidence interval [CI] = 0%, 5%) more BPD dispatches, 9% (95% CI = 7%, 12%) more BEMS dispatches, and 10% (95% CI = 5%, 15%) more BFD dispatches on days when the maximum temperature was 90°F or higher, which remained consistent when we considered multiple days of heat. A 10°F increase in daily maximum temperature, from 80° to 90°F, resulted in 1.016, 1.017, and 1.002 times the expected number of daily BPD, BEMS, and BFD dispatch calls, on average, after adjustment for other predictors. Conclusions. The burden of extreme heat on local emergency medical and police services may be agency-wide, and impacts on fire departments have not been previously documented. Public Health Implications. It is important to account for the societal burden of extreme heat impacts to most effectively inform climate change adaptation strategies and planning.

scholarly journals Building Resilience to Climate Change: Pilot Evaluation of the Impact of India’s First Heat Action Plan on All-Cause Mortality

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Jeremy J. Hess ◽  
Sathish LM ◽  
Kim Knowlton ◽  
Shubhayu Saha ◽  
Priya Dutta ◽  
...  
Keyword(s):  
Nonlinear Models ◽  
Action Plan ◽  
Incidence Rates ◽  
Extreme Heat ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
All Cause Mortality ◽  
Rate Ratios ◽  
The Impact

Background. Ahmedabad implemented South Asia’s first heat action plan (HAP) after a 2010 heatwave. This study evaluates the HAP’s impact on all-cause mortality in 2014–2015 relative to a 2007–2010 baseline. Methods. We analyzed daily maximum temperature (Tmax)-mortality relationships before and after HAP. We estimated rate ratios (RRs) for daily mortality using distributed lag nonlinear models and mortality incidence rates (IRs) for HAP warning days, comparing pre- and post-HAP periods, and calculated incidence rate ratios (IRRs). We estimated the number of deaths avoided after HAP implementation using pre- and post-HAP IRs. Results. The maximum pre-HAP RR was 2.34 (95%CI 1.98–2.76) at 47°C (lag 0), and the maximum post-HAP RR was 1.25 (1.02–1.53) estimated at 47°C (lag 0). Post-to-pre-HAP nonlagged mortality IRR for Tmax over 40°C was 0.95 (0.73–1.22) and 0.73 (0.29–1.81) for Tmax over 45°C. An estimated 1,190 (95%CI 162–2,218) average annualized deaths were avoided in the post-HAP period. Conclusion. Extreme heat and HAP warnings after implementation were associated with decreased summertime all-cause mortality rates, with largest declines at highest temperatures. Ahmedabad’s plan can serve as a guide for other cities attempting to increase resilience to extreme heat.

scholarly journals Social media reveal ecoregional variation in how weather influences visitor behavior in U.S. National Park Service units

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emily J. Wilkins ◽  
Peter D. Howe ◽  
Jordan W. Smith
Keyword(s):  
National Park ◽  
National Park Service ◽  
Spatial Behavior ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Daily Weather ◽  
Visitor Behavior ◽  
Park Service ◽  
The Impact

AbstractDaily weather affects total visitation to parks and protected areas, as well as visitors’ experiences. However, it is unknown if and how visitors change their spatial behavior within a park due to daily weather conditions. We investigated the impact of daily maximum temperature and precipitation on summer visitation patterns within 110 U.S. National Park Service units. We connected 489,061 geotagged Flickr photos to daily weather, as well as visitors’ elevation and distance to amenities (i.e., roads, waterbodies, parking areas, and buildings). We compared visitor behavior on cold, average, and hot days, and on days with precipitation compared to days without precipitation, across fourteen ecoregions within the continental U.S. Our results suggest daily weather impacts where visitors go within parks, and the effect of weather differs substantially by ecoregion. In most ecoregions, visitors stayed closer to infrastructure on rainy days. Temperature also affects visitors’ spatial behavior within parks, but there was not a consistent trend across ecoregions. Importantly, parks in some ecoregions contain more microclimates than others, which may allow visitors to adapt to unfavorable conditions. These findings suggest visitors’ spatial behavior in parks may change in the future due to the increasing frequency of hot summer days.

scholarly journals Monthly Temperature Observations for Uganda

2013 ◽  
Vol 52 (10) ◽  
pp. 2363-2372 ◽  
Author(s):  
John R. Christy
Keyword(s):  
Time Series ◽  
Public Access ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Record Keeping ◽  
Monthly Average ◽  
Weather Observations ◽  
Temperature Observations

AbstractThe International Surface Temperature Initiative is a worldwide effort to locate weather observations, digitize them for public access, and attach provenance to them. As part of that effort, this study sought documents of temperature observations for the nation of Uganda. Although scattered reports were found for the 1890s, consistent record keeping appears to have begun in 1900. Data were keyed in from images of several types of old forms as well as accessed electronically from several sources to extend the time series of 32 stations with at least 4 yr of data back as far as data were available. Important gaps still remain; 1979–93 has virtually no observations from any station. Because many stations were represented by more than one data source, a scheme is described to extract the “best guess” values for each station of monthly averages of the daily maximum, minimum, and mean temperature. A preliminary examination of the national time series indicates that, since the early twentieth century, it appears that Uganda experienced essentially no change in monthly-average daily maximum temperature but did experience a considerable rise in monthly-average daily minimum temperature, concentrated in the last three decades. Because there are many gaps in the data, it is hoped that readers with information on extant data that were not discovered for this study will contact the author or the project so that the data may be archived.

scholarly journals Time Series Construction of Summer Surface Temperatures for Alabama, 1883–2014, and Comparisons with Tropospheric Temperature and Climate Model Simulations

2016 ◽  
Vol 55 (3) ◽  
pp. 811-826 ◽  
Author(s):  
John R. Christy ◽  
Richard T. McNider
Keyword(s):  
Time Series ◽  
Climate Model ◽  
Spatial Scales ◽  
Heat Content ◽  
Time Frame ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Tropospheric Temperature

AbstractThree time series of average summer [June–August (JJA)] daily maximum temperature (TMax) are developed for three interior regions of Alabama from stations with varying periods of record and unknown inhomogeneities. The time frame is 1883–2014. Inhomogeneities for each station’s time series are determined from pairwise comparisons with no use of station metadata other than location. The time series for the three adjoining regions are constructed separately and are then combined as a whole assuming trends over 132 yr will have little spatial variation either intraregionally or interregionally for these spatial scales. Varying the parameters of the construction methodology creates 333 time series with a central trend value based on the largest group of stations of −0.07°C decade−1 with a best-guess estimate of measurement uncertainty from −0.12° to −0.02°C decade−1. This best-guess result is insignificantly different (0.01°C decade−1) from a similar regional calculation using NOAA’s divisional dataset based on daily data from the Global Historical Climatology Network (nClimDiv) beginning in 1895. Summer TMax is a better proxy, when compared with daily minimum temperature and thus daily average temperature, for the deeper tropospheric temperature (where the enhanced greenhouse signal is maximized) as a result of afternoon convective mixing. Thus, TMax more closely represents a critical climate parameter: atmospheric heat content. Comparison between JJA TMax and deep tropospheric temperature anomalies indicates modest agreement (r2 = 0.51) for interior Alabama while agreement for the conterminous United States as given by TMax from the nClimDiv dataset is much better (r2 = 0.86). Seventy-seven CMIP5 climate model runs are examined for Alabama and indicate no skill at replicating long-term temperature and precipitation changes since 1895.

Impact of mass human migration during Chinese New Year on Beijing urban heat island

2020 ◽  
Author(s):  
Jingjing Dou ◽  
Shiguang Miao
Keyword(s):  
Spatial Distribution ◽  
Urban Heat Island ◽  
Human Migration ◽  
Floating Population ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Heat Island ◽  
Urban Heat ◽  
The Impact

<p>The Chinese New Year (CNY, also called Spring Festival), which officially lasts for 7 days, is the most important holiday in China. Chinese people in large cities usually return to their hometowns for family reunions before the CNY holiday and return afterward. Nearly half of Beijing’s population has been reported to leave the city for family reunions before the CNY holidays in the past several years. Hourly automatic weather station (AWS) data during CNY 2010-2015 were used to analyze the changes in the temporal and spatial distribution of the Beijing urban heat island intensity (UHII) and the impact of mass human migration on urban temperature. Soil moisture, 10-m wind speed, and cloud cover were considered and indicated nearly no change during the pre-CNY period (2 to 4 weeks before CNY) and CNY week, which means that UHII variation was mainly affected by the mass human migration. Daily UHII during CNY week was lower than during pre-CNY period. UHII for daily maximum temperature decreased by 55% during CNY week than the pre-CNY period (0.6 °C during pre-CNY period vs. 0.27 °C during CNY week) due to mass human migration, which was much larger than the reduction in UHII for the daily maximum temperature (5%, 4.34 °C during the pre-CNY period vs. 4.11 °C during the CNY week). The spatial distribution of the UHII difference between CNY week and the pre-CNY period is closely related to the locations of functional population zones. UHII for daily maximum temperature decreases most (80%, 0.40 °C during the pre-CNY period vs. 0.08 °C during the CNY period) between the Third and Fourth Ring Roads (RRs), an area which experiences high human activity and has the highest floating population percentage. This study can provide suggestions for optimizing the layout of urban space and land-use structures.</p>

scholarly journals The impact of differences in large-scale circulation output from climate models on the regional modeling of ozone and PM

2012 ◽  
Vol 12 (20) ◽  
pp. 9441-9458 ◽  
Author(s):  
A. M. M. Manders ◽  
E. van Meijgaard ◽  
A. C. Mues ◽  
R. Kranenburg ◽  
L. H. van Ulft ◽  
...  
Keyword(s):  
Air Quality ◽  
Climate Models ◽  
Climate Model ◽  
Transport Model ◽  
Global Climate Model ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Climate Simulations ◽  
The Impact

Abstract. Climate change may have an impact on air quality (ozone, particulate matter) due to the strong dependency of air quality on meteorology. The effect is often studied using a global climate model (GCM) to produce meteorological fields that are subsequently used by chemical transport models. However, climate models themselves are subject to large uncertainties and fail to reproduce the present-day climate adequately. The present study illustrates the impact of these uncertainties on air quality. To this end, output from the SRES-A1B constraint transient runs with two GCMs, i.e. ECHAM5 and MIROC-hires, has been dynamically downscaled with the regional climate model RACMO2 and used to force a constant emission run with the chemistry transport model LOTOS-EUROS in a one-way coupled run covering the period 1970–2060. Results from the two climate simulations have been compared with a RACMO2-LOTOS-EUROS (RLE) simulation forced by the ERA-Interim reanalysis for the period 1989–2009. Both RLE_ECHAM and RLE_MIROC showed considerable deviations from RLE_ERA for daily maximum temperature, precipitation and wind speed. Moreover, sign and magnitude of these deviations depended on the region. The differences in average present-day concentrations between the simulations were equal to (RLE_MIROC) or even larger than (RLE_ECHAM) the differences in concentrations between present-day and future climate (2041–2060). The climate simulations agreed on a future increase in average summer ozone daily maximum concentrations of 5–10 μg m−3 in parts of Southern Europe and a smaller increase in Western and Central Europe. Annual average PM10 concentrations increased 0.5–1.0 μg m−3 in North-West Europe and the Po Valley, but these numbers are rather uncertain: overall, changes for PM10 were small, both positive and negative changes were found, and for many locations the two climate runs did not agree on the sign of the change. This illustrates that results from individual climate runs can at best indicate tendencies and should therefore be interpreted with great care.

scholarly journals Estimating the Impact of Global Warming on Aircraft Takeoff Performance in China

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1472
Author(s):  
Wei Yuan ◽  
Panxi Dai ◽  
Mengxiang Xu ◽  
Wei Song ◽  
Peng Zhang
Keyword(s):  
Global Warming ◽  
Critical Temperature ◽  
Carrying Capacity ◽  
East China ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Climate Projections ◽  
West China ◽  
The Impact

Aviation operations are significantly affected by weather conditions, such as high-temperature days. Under global warming, rising temperatures decrease the air density and thus, reduce the maximum takeoff weight of an aircraft. In this study, we investigate the impact of global warming on the aircraft takeoff performance in 53 airports in China by combining observational data and CMIP6 climate projections. There is a distinct geographic inhomogeneity of critical temperature, above which the takeoff weight decreases significantly with the increasing air temperature, mostly due to differences in airport elevations. By the end of the century, under the SSP5-8.5 scenario (with average warming of 5.2 °C in China), the daily maximum temperature for nearly all summer days in West China and for about half of the summer days in East China exceeds critical temperature, indicating that frequent weight restriction will be necessary. We further examine the reduction in carrying capacity due to climate change. By the end of the century, under the SSP5-8.5 scenario, the summer total carrying capacity will be reduced by about 2.8% averaged over all 53 airports. The impacts on airports in West China are nearly four times greater than those in East China, due to the higher vulnerability and stronger warming in West China.

scholarly journals The impact of differences in large-scale circulation output from climate models on the regional modeling of ozone and PM

2012 ◽  
Vol 12 (5) ◽  
pp. 12245-12285 ◽  
Author(s):  
A. M. M. Manders ◽  
E. van Meijgaard ◽  
A. C. Mues ◽  
R. Kranenburg ◽  
L. H. van Ulft ◽  
...  
Keyword(s):  
Air Quality ◽  
Climate Models ◽  
Climate Model ◽  
Transport Model ◽  
Global Climate Model ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Climate Simulations ◽  
The Impact

Abstract. Climate change may have an impact on air quality (ozone, particulate matter) due to the strong dependency of air quality on meteorology. The effect is often studied using a global climate model (GCM) to produce meteorological fields that are subsequently used by chemical transport models. However, climate models themselves are subject to large uncertainties and fail to adequately reproduce the present-day climate. The present study illustrates the impact of this uncertainty on air quality. To this end, output from the SRES-A1B constraint transient runs with two GCMs, i.e. ECHAM5 and MIROC-hires, has been dynamically downscaled with the regional climate model RACMO2 and used to force a constant emission run with the chemistry transport model LOTOS-EUROS in a one-way coupled run covering the period 1970–2060. Results from the two climate simulations have been compared with a RACMO2-LOTOS-EUROS (RLE) simulation forced by the ERA-Interim reanalysis for the period 1989–2009. Both RLE_ECHAM and RLE_MIROC showed considerable deviations from RLE_ERA in daily maximum temperature, precipitation and wind speed. Moreover, sign and magnitude of these deviations depended on the region. Differences in average concentrations for the present-day simulations were found of equal to (RLE_MIROC) or even larger than (RLE_ECHAM) the differences in concentration between present-day and future climate (2041–2060). The climate simulations agreed on a future increase in average summer ozone daily maximum concentrations (5–10 μg m−3) in parts of Southern Europe and a smaller increase in Western and Central Europe. Annual average PM10 concentrations increased (0.5–1.0 μg m−3) in North-West Europe and the Po Valley, but these numbers are rather uncertain. Overall, changes for PM10 were small, both positive and negative changes were found, and for many locations the two runs did not agree on the sign of the change. The approach taken here illustrates that results from individual climate runs can at best indicate tendencies and should therefore be interpreted with great care.

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