journals.plos.org Open in urlscan Pro
35.190.43.188  Public Scan

Form analysis
1 forms found in the DOM

Name: searchForm — GET /climate/search

<form class="_4iXNe4FwH0ozktgSaeLylA==" id="searchForm" name="searchForm" action="/climate/search" method="get"><input id="search" type="text" name="q" placeholder="SEARCH" required="" aria-label="Search"><button id="headerSearchButton" type="submit"
    title="Submit search" aria-label="Submit search" class=" sRE36hvpQ5oc1svWlDAQnQ== uHAe9zLNKdlVEmzkDVZdfQ=="><span>Search</span></button><input type="hidden" name="filterJournals" value="PLOSClimate"></form>

Text Content

Skip to main content
Advertisement

 * plos.org
 * create account
 * sign in

 * * Publish
   * About
   * Browse
   
 * 
 * Search
   advanced search

 * 0
   Save
   
   Total Mendeley and Citeulike bookmarks.

 * 0
   Citation
   
   Paper's citation count computed by Dimensions.

 * 33
   View
   
   PLOS views and downloads.

 * 0
   Share
   
   Sum of Facebook, Twitter, Reddit and Wikipedia activity.

Open Access

Peer-reviewed

Research Article


POWERLESS IN THE STORM: SEVERE WEATHER-DRIVEN POWER OUTAGES IN NEW YORK STATE,
2017–2020

 * Nina M. Flores,
   
   Roles Conceptualization, Data curation, Formal analysis, Writing – original
   draft, Writing – review & editing
   
   Affiliation Department of Environmental Health Sciences, Mailman School of
   Public Health, Columbia University, New York, New York, United States of
   America
   
   https://orcid.org/0000-0002-2723-1728
   
   ⨯
 * Alexander J. Northrop,
   
   Roles Conceptualization, Data curation, Writing – review & editing
   
   Affiliations Department of Environmental Health Sciences, Mailman School of
   Public Health, Columbia University, New York, New York, United States of
   America, Vagelos College of Physicians and Surgeons, Columbia University, New
   York, New York, United States of America
   
   https://orcid.org/0000-0002-2132-6435
   
   ⨯
 * Vivian Do,
   
   Roles Conceptualization, Writing – review & editing
   
   Affiliation Department of Environmental Health Sciences, Mailman School of
   Public Health, Columbia University, New York, New York, United States of
   America
   
   ⨯
 * Milo Gordon,
   
   Roles Conceptualization, Writing – review & editing
   
   Affiliation Department of Environmental Health Sciences, Mailman School of
   Public Health, Columbia University, New York, New York, United States of
   America
   
   ⨯
 * Yazhou Jiang,
   
   Roles Conceptualization, Writing – review & editing
   
   Affiliation Department of Electrical and Computer Engineering, Clarkson
   University, Potsdam, New York, United States of America
   
   ⨯
 * Kara E. Rudolph,
   
   Roles Conceptualization, Methodology, Writing – review & editing
   
   Affiliation Department of Epidemiology, Mailman School of Public Health,
   Columbia University, New York, New York, United States of America
   
   https://orcid.org/0000-0002-9417-7960
   
   ⨯
 * Diana Hernández,
   
   Roles Conceptualization, Writing – review & editing
   
   Affiliation Department of Sociomedical Sciences, Mailman School of Public
   Health, Columbia University, New York, New York, United States of America
   
   ⨯
 * Joan A. Casey
   
   Roles Conceptualization, Funding acquisition, Methodology, Writing – review &
   editing
   
   * E-mail: jacasey@uw.edu
   
   Affiliations Department of Environmental Health Sciences, Mailman School of
   Public Health, Columbia University, New York, New York, United States of
   America, Department of Environmental and Occupational Health Sciences,
   University of Washington, Seattle, Washington, United States of America
   
   https://orcid.org/0000-0002-9809-4695
   
   ⨯


POWERLESS IN THE STORM: SEVERE WEATHER-DRIVEN POWER OUTAGES IN NEW YORK STATE,
2017–2020

 * Nina M. Flores, 
 * Alexander J. Northrop, 
 * Vivian Do, 
 * Milo Gordon, 
 * Yazhou Jiang, 
 * Kara E. Rudolph, 
 * Diana Hernández, 
 * Joan A. Casey

x
 * Published: May 1, 2024
 * https://doi.org/10.1371/journal.pclm.0000364
 * 


 * Article
 * Authors
 * Metrics
 * Comments
 * Media Coverage
 * Peer Review

 * Abstract
 * Introduction
 * Methods
 * Results
 * Discussion
 * Conclusion
 * Supporting information
 * References

 * Reader Comments
 * Figures





ABSTRACT

The vulnerability of the power grid to severe weather events is a critical issue
as climate change is expected to increase extreme events, which can damage
components of the power grid and/or lessen electrical power supply, resulting in
power outages. However, largely due to an absence of granular spatiotemporal
outage data, we lack a robust understanding of how severe weather-driven
outages, their community impacts, and their durations distribute across space
and socioeconomic vulnerability. Here, we pair hourly power outage data in
electrical power operating localities (n = 1865) throughout NYS with urbanicity,
CDC Social Vulnerability Index, and hourly weather (temperature, precipitation,
wind speed, lightning strike, snowfall) data. We used these data to characterize
the impact of extreme weather events on power outages from 2017–2020, while
considering neighborhood vulnerability factors. Specifically, we assess (a) the
lagged effect of severe weather on power outages, (b) common combinations of
severe weather types contributing to outages, (c) the spatial distribution of
the severe weather-driven outages, and (d) disparities in severe weather-driven
outages by degree of community social vulnerability. We found that across NYS,
39.9% of all outages co-occurred with severe weather. However, certain regions,
including eastern Queens, upper Manhattan and the Bronx of NYC, the Hudson
Valley, and Adirondack regions were more burdened with severe weather-driven
outages. Using targeted maximum likelihood estimation, we found that the
frequency of heat-, precipitation-, and wind-driven outages disproportionately
impacted vulnerable communities in NYC. When comparing durations of outages, we
found that in rural regions, precipitation- and snow-driven outages lasted the
longest in vulnerable communities. Under a shifting climate, anticipated
increases in power outages will differentially burden communities due to
regional heterogeneity in severe weather event severity, grid preparedness, and
population socioeconomic profiles/vulnerabilities. As such, policymakers must
consider these characteristics to inform equitable grid management and
improvements.


FIGURES

  

Citation: Flores NM, Northrop AJ, Do V, Gordon M, Jiang Y, Rudolph KE, et al.
(2024) Powerless in the storm: Severe weather-driven power outages in New York
State, 2017–2020. PLOS Clim 3(5): e0000364.
https://doi.org/10.1371/journal.pclm.0000364

Editor: Teodoro Georgiadis, Institute for BioEconomy CNR, ITALY

Received: October 31, 2023; Accepted: February 2, 2024; Published: May 1, 2024

Copyright: © 2024 Flores et al. This is an open access article distributed under
the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.

Data Availability: Meteorological and social variables were obtained from
publicly available datasets described in the methods section. Power outage data
was obtained from the NYS Department of Public Service:
https://dps.ny.gov/electric.

Funding: This work was supported by the National Institute for Environmental
Health Sciences (NIEHS) P30 ES009089 (JAC), 5T32ES007322-22 (NMF), and
P30ES007033 (JAC). The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests
exist.


INTRODUCTION

Electricity is a critical aspect of modern life, supporting everyday activities
like making a phone call, cooking a meal, and heating or cooling one’s home [1,
2]. Despite how central electricity is to daily life, having resilient and
reliable power systems remains a challenge in the United States (US) [2]. Power
outages (POs) are becoming increasingly common–in large part due to the age and
disrepair of the electrical grid and its vulnerability to severe weather events.
Severe weather, the leading cause of widespread power outages in the US [3–5],
can lead to cascading effects such as throwing key parameters of power quality
like frequency or voltage out of sync, overloading transmission lines, or even
complete voltage/frequency collapse [5]. A range of severe weather conditions
threaten the grid, including extreme heat, extreme cold, and tropical storms.
For example, the Chicago Heat Wave of 1995 led to a surge in power use and the
failure of three power transformers. This led to widespread outages for over two
days [6]. In 2012, Hurricane Sandy downed overhead lines and flooded underground
lines, leading to extensive outages, sometimes lasting weeks and affecting
millions of customers across 21 Northeastern states [7]. In Texas, Winter Storm
Uri in 2021 froze natural gas wells, power plants, and gathering lines, leaving
millions of customers without power for days to weeks [8].

The power grid’s vulnerability to severe weather events becomes even more
critical in the context of climate change, which is expected to increase weather
variability and prevalence of extreme events (e.g., storms, wildfires,
heatwaves, floods) [9]. Such events readily damage components of the power grid
including power plants, substations, distribution centers, and power lines. In
addition to causing more extreme events, climate change will result in rising
temperatures and increased temperature variability, affecting electricity
reliability and use. High temperatures can decrease the output from
thermoelectric plants and the carrying capacity of power lines, but also
increase the power demand as people run air conditioning to keep cool [10, 11].
The energy transition will result in greater reliance on electricity for
heating, cooking, and transit, making continuous access increasingly vital [12].
Outages, especially those occurring with very hot or cold weather have been tied
to adverse cardiovascular, respiratory, and renal outcomes [13]. Thus,
preventing prolonged outages or providing backup power sources is critical for
population health.

Climate-driven increases in power outages raise important environmental and
climate justice concerns. Persistently marginalized communities may already be
disproportionately burdened by severe weather-driven outages due to a confluence
of factors such as discriminatory housing practices [14], historic underfunding
in communities of color, inequitable restoration guidelines [15, 16], and the
concentration of low-income communities and communities of color high-risk areas
like flood zones [17] and hot neighborhoods [18, 19]. Researchers cataloged
exposure disparities during some outages [20, 21]. Outages in New York City
resulting from Tropical Storm Isaias were longer in regions that were lower
income and/or had higher percentages of non-white residents [22]. Thus,
documenting the dual burden of extreme events and power outage exposure is
necessary to promote health equity. However, previous assessments of severe
weather-driven outages (a) rarely included data at a sub-county level; and (b)
often failed to consider urban/rural differences for which outages may have
varying community impacts due to population/housing density, demographic
profiles and/or backup power accessibility [3, 4, 13]. Such analyses could
inform policies related to electrical grid reliability and restoration to
promote health equity.

New York State (NYS), however, collects power outage data statewide at a
granular (~zip code tabulation area) level, providing data availability to fill
this gap. Here, we use hourly power operating locality level power outage,
temperature, precipitation, wind speed, snowfall, lightning, urbanicity, and
social vulnerability data across NYS to characterize the impact of extreme
weather on power outage distributions and durations from 2017–2020. We also
consider inequitable exposure by community vulnerability factors. We conduct
analyses in three regions: NYC, non-NYC urban, and rural regions of NYS to
assess (a) the lagged effect of severe weather on power outages, (b) the most
prevalent combinations of severe weather types that contribute to outages, (c)
the spatial distribution of the severe weather driven outages, and (d)
disparities in severe weather-driven outages by community social vulnerability.


METHODS


STUDY OVERVIEW

In the present analysis, we use locality-level (n = 1,764) power outage,
weather, urbanicity, and social vulnerability data from January 1, 2017-December
31, 2020 to assess the impact of extreme weather on power outage distributions
and durations, while considering vulnerability factors.


POWER OUTAGE ASCERTAINMENT

We obtained information on customers without power in 30-minute increments
within localities from the NYS Department of Public Service from 2017–2020 [23].
We excluded localities with <30 customers or >5% temporal missingness over the
study period, resulting in 1,764 (94.6%) included localities. The dataset also
included locality boundaries in a shapefile format and the number of customers
served, and the electrical utilities operating in each locality. A power
operating locality is the smallest level at which outage data is reported to the
state and is comparable in size to zip code tabulation areas; the localities
serve ~11,000 customers, on average. These customers include residential,
commercial, and electrical meters. We aggregated the 30-minute data to the
hourly level to match our weather metric data.


WEATHER METRIC ASCERTAINMENT

We primarily sourced weather data from land-surface model estimates. We pulled
data on average temperature, windspeed, and precipitation at the hourly-level
from forcing data for Phase 2 of the North American Land Data Assimilation
System (NLDAS-2) [24]. NLDAS-2 provides gridded estimates of each of these
variables with ~14km2 resolution. We obtained hourly snowfall data from the
ERA5-Land reanalysis dataset, which is available hourly with ~11km2 resolution
[25]. We aggregated the gridded datasets to locality boundaries via areal
weighting using Google Earth Engine [26]. We collected lightning strike data
from the International Space Station Lightning Imaging Sensor, which records the
time and location of lightning strikes, starting in March 2017 [27]. To match
the spatial and temporal resolution of the other data, we calculated the hourly
number of lightning strikes in each locality. Since lightning data was only
available beginning March 2017, to preserve as much data as possible, we assumed
no lightning strikes occurred during the first two months of 2017. Lightning
strikes were most common from April to August (n = 1624 total strikes), and only
4 total strikes occurred in January and February 2018–2020 combined.


URBANICITY ASCERTAINMENT

Because of unique population, outage, and weather profiles, we ran all analyses
separately for NYC, non-NYC urban, and rural regions of NYS. To classify
localities into their respective regions, we first used 2010 US Census on the
percent of the total population classified as urban/rural at the block group. We
interpolated this to the locality-level with areal weighting. When localities
had >50% of inhabitants designated as rural, we assigned the locality a rural
classification [28]. We distinguished between NYC and non-NYC urban, using the
county indicators included in the power outage data. We assigned localities
listed in the New York, Bronx, Kings, Queens, and Richmond counties as NYC. The
final classification of each locality used in all further analysis is available
in Fig 1 and S1 Fig.

Download:
 * PPT
   PowerPoint slide
 * PNG
   larger image
 * TIFF
   original image

Fig 1. The spatial boundaries of each power operating locality in NYC, non-NYC
urban, and rural regions of NYS.



Republished from The New York State Department of Public Service under a CC BY
license, with permission from The New York State Department of Public Service
original copyright 2020.



https://doi.org/10.1371/journal.pclm.0000364.g001


THE LAGGED EFFECT OF SEVERE WEATHER ON POWER OUTAGES, BY SEVERE WEATHER METRIC
AND BY REGION

To assess the lagged and non-linear effects of weather on the proportion of
customers without power in a locality, we used negative binomial generalized
additive models, an extension of generalized linear models that allow for
smoothed or nonlinear fits [29]. We selected the negative binomial fit after
assessing regression diagnostics using the DHARMa package across a range of
model types including Poisson, negative binomial, Tweedie, and zero-inflated
Poisson [30]. We modelled weather using distributed lag nonlinear models
(DLNMs), which simultaneously allow for the modelling of nonlinear
exposure-outcome relationships and delayed events [31]. DLNMs are also
advantageous because they provide constrained lag terms which account for high
temporal autocorrelation, a feature of the hourly weather data. We adjusted for
seasonal and temporal trends by including a natural spline term with 6 knots per
year for the date [32]. We also included fixed effects for the utility that
serves each locality to account for a lack of spatial independence. We used the
Akaike information criterion to select the appropriate degrees of freedom for
both the exposure and the lag out of a range of 2–5. We used the Moran’s I to
assess spatial autocorrelation in the final models (S15–S17 Figs) [33].


OUTAGE, SEVERE WEATHER, AND SEVERE WEATHER-DRIVEN OUTAGE CLASSIFICATIONS

For the remainder of our analyses, we conceptualized outages, severe weather
events, and a severe weather-driven outage each as binary variables. We relied
on the severe weather and power outage literature, our weather and outage data’s
distribution, and the results from our first objective, which assessed the
lagged effect of severe weather on outages.

In previous literature, outages have been defined as hours where the proportion
of customers without power exceeds the 90th percentile of the hourly proportion
without power statewide [34, 35]. We adapted a similar definition for our
analyses, but instead defined the 90th percentile separately for each region
(NYC, urban non-NYC, and rural) to prevent the undercounting of outages in NYC,
where the population in a locality was much larger than in rural regions. The
90th percentile of customers without power was 0.04%, 3.4%, and 15.2%, for NYC,
non-NYC urban, and rural, respectively. As an example, for our binary outage
metric, any hour where the percentage of customers without power exceeded 3.4%
in a non-NYC urban power operating locality was classified as an outage.

We similarly used percentile classifications from our data distribution to
determine the presence of a severe weather event. We hoped to identify pertinent
thresholds in our first objective, but we observed the general pattern of
outages increasing at the extreme of weather metric distributions rather than
distinct thresholds. Thus, we used a statewide 97.5th percentile to define
severe weather events. We chose to keep this metric statewide for
interpretability. For most of the continuous metrics (precipitation, wind speed,
snowfall) we identified the 97.5th percentile of each metric from 2017–2020, and
then any hour above that threshold we defined as a severe-weather event. For
temperature, the definition slightly deviated. To define extremely hot hours, we
calculated the 97.5th percentile of temperatures during the during the hot
months (May-Sept) and to define extremely cold hours, we calculated the 2.5th
percentile during cold months (Oct-April). The presence of lighting was
collapsed to a binary depending on whether a locality experienced any lightning
strikes during that hour.

Finally, to create a severe weather-driven outage definition, we used the two
previously described definitions and a lag component. Using the results from our
first objective, it appeared that much of the effect of the extreme events was
immediate (within 8–12 hours of exposure). Therefore, we decided to use 8 hours
as our window of interest. Our final definition for a severe weather-driven
outage was an outage that started either within the same hour of a severe
weather event or within 8 hours following a severe weather event.


THE MOST PREVALENT AND HAZARDOUS COMBINATIONS OF SEVERE WEATHER TYPES THAT
CONTRIBUTE TO OUTAGES

Once we defined outages and severe weather-driven outages, we used these
definitions to achieve study objectives 2–4. We wanted to identify the severe
weather events (or combination of severe weather events) that lead to the most
significant power outages in frequency, duration, or proportion of customers
impacted. To do so, we first calculated the frequency, average duration, and
average proportion of customers impacted for each classification of severe
weather driven outage (e.g., wind + precipitation-driven, wind-driven). We
omitted snow-driven outages from this analysis to reduce the number of classes
and redundancies between groups, as snow is part of the precipitation estimates.

We then calculated a severe/non-severe outage ratio for each severe weather
combination. We calculated this using the following formula: where, for each
severe weather combination, we divided the number of outages from a severe
weather combination, by the number of hours with the severe weather combination.
Then to standardize this across non-severe outages, we divided the numerator by
the number of outages without any severe weather event divided by the hours
without any severe weather events. A severe/non-severe ratio > 1 indicates that
outages are more likely due to that severe weather combination, i, than times
without any severe weather events. The ratios can then be compared across severe
weather types to quantify which combinations are the most likely to cause
outages.


THE DISTRIBUTION OF THE SEVERE WEATHER DRIVEN OUTAGES ACROSS THE STATE

With outages, severe weather driven outages, and pertinent combinations of
severe weather types defined, we performed descriptive analyses by mapping the
frequency, average number of customers without power, and the duration of
outages during severe weather driven and non-severe weather driven outages for
each region. We also calculated the percentage of outages that were due to
severe weather events in each locality. Finally, we presented the frequency of
severe weather driven outages by type. For this mapping and for subsequent
analyses, to reduce the number of analyses, we present outages driven by each of
the six weather metrics of interest (cold, heat, lightning, precipitation, snow,
and wind). Thus, outages caused by both wind and precipitation would be counted
in both the wind and precipitation panels of Fig 4.


DISPARITIES IN THE FREQUENCY AND DURATION OF SEVERE WEATHER-DRIVEN OUTAGES

We then aimed to assess the association between social vulnerability and outage
exposure.


SOCIAL VULNERABILITY CLASSIFICATION

We used the 2020 Social Vulnerability Index (SVI) created by the CDC/ATSDR to
determine the social vulnerability of each power operating locality [36]. The
CDC/ATSDR designed the index to identify communities that may need support
during disasters like those driven by climate change. It incorporates 16 social
factors from the 2016–2020 American Community Survey that capture several
aspects of outage-related vulnerability (e.g., poverty, disability, housing
type, age, English-language proficiency). The final index score ranges from
0–100 where higher values indicate increased social vulnerability. We downloaded
the 2020 SVI at the census tract level, and used areal interpolation to
determine the scores using to the power operating locality using a
target-density weighting approach [37]. Finally, for each urbanicity region, we
grouped each locality into their respective SVI quartile resulting in the final
distribution displayed in S2 Fig.


STATISTICAL ANALYSIS–THE FREQUENCY AND DURATION OF SEVERE WEATHER-DRIVEN OUTAGES

To estimate disparities in the distribution of outages, we used targeted maximum
likelihood estimation (TMLE) [38], a doubly robust maximum-likelihood–based
approach. We estimate the average treatment effect of being in the highest
quartile of SVI versus all others on risk of a severe weather-driven outage. We
performed these analyses separately for each of the rural, non-NYC urban, and
NYC regions. We implemented TMLE using the ltmle [39] and SuperLearner [40]
packages in R. We ran three sets of sensitivity analyses. In the first, we
stratified the analyses by year. In the second, we re-ran the analyses raising
the thresholds for the number of severe-driven outages from n = 1+ to n = 3+ and
then n = 5+. In the third, we included latitude and longitude to account for
possible spatial confounding.

To understand disparities in the duration of outages, we present the duration of
each outage type by SVI quartile along with results from Kruskal−Wallis tests
[41]. All code for the conducted analyses are available on GitHub:
https://github.com/nina-flores/nys_severe_weather_outages.


RESULTS

From 2017–2020, we identified 40,646 electrical power outages, of which we
linked 16,236 (39.9%) to severe weather. Non-severe weather-driven outages
lasted 3.6 hours, on average, whereas outages due to severe weather events
lasted anywhere from 3 to 17 hours, on average (Table 1).

Download:
 * PPT
   PowerPoint slide
 * PNG
   larger image
 * TIFF
   original image

Table 1. The frequency, average duration, and average customers out during
severe weather driven outages, by weather types and by region.



https://doi.org/10.1371/journal.pclm.0000364.t001


THE LAGGED EFFECT OF SEVERE WEATHER ON POWER OUTAGES, BY TYPE AND BY REGION

Using DLNMs, we examined both nonlinear exposure-outcome relationships and
delayed events, for each weather metric of interest. An example of the output
from these analyses showing the lagged relationship between hourly temperatures
and the proportion of customers without power for non-NYC urban localities is
displayed in Fig 2. Here, we visualize the relative rate of customers without
power as temperatures increase or decrease away from the median of 9.8°C across
24 hours of lags. By focusing on the same hour of exposure, lag 0, we found that
an increase in temperature to 30°C in non-NYC urban localities leads to 3.8 (95%
CI: 3.6–4.1) times the rate of customers without power, during that same hour of
the temperature increase, compared to the median temperature. However, we
observed a 5–15-hour lag between extreme cold temperatures and an increased rate
of outages. We created a Shiny dashboard so that readers could view the 3D plots
(Fig 2A) and 2D plots across any number of lags up to 24 (Fig 2B) for each of
the weather metrics (precipitation, snowfall, temperature, and windspeed) and
each of the regions of analysis (NYC, non-NYC urban, rural;
https://oyb6ek-nina-flores.shinyapps.io/severe-weather-app/).

Download:
 * PPT
   PowerPoint slide
 * PNG
   larger image
 * TIFF
   original image

Fig 2.



The lagged relationship of hourly temperature and the proportion of customers
without power for non-NYC urban localities at all lags 0–24 (a), and at lag 0
(b). Panel b is constructed by slicing panel a where the lag = 0, as shown by
the light blue plane at lag 0. All rates are relative to the overall median
temperature of 9.8°C, shown by the dotted blue vertical line (b). To view these
figures for all other weather metrics (precipitation, snowfall, temperature, and
windspeed) and regions of analysis (NYC, non-NYC urban, rural), please visit our
shiny dashboard: https://oyb6ek-nina-flores.shinyapps.io/severe-weather-app/.



https://doi.org/10.1371/journal.pclm.0000364.g002

We found that the weather metrics most strongly associated with power outages
varied by region: in NYC and non-NYC urban areas, precipitation led to the
largest rate ratios whereas in rural NYS, extreme wind led to the largest rate
ratios. However, no region had a clear threshold at which any of the weather
metrics distinctly increased outages. Rather, there was a smooth trend that
outages increased as each weather metric became more extreme.

Lagged effects differed by weather metric and region. For instance, in non-NYC
urban areas, extremely hot temperatures (>30°C) were most strongly associated
with immediate increases in the rate of outages whereas extremely cold
temperatures (<-10°C) were most strongly associated with increases in the rate
of outages after 6–8 lagged hours. However, in rural regions of NYS, the impacts
of extremely hot and extremely cold temperatures were most observable at lag 0.
Though there was heterogeneity in the lagged effects, we observed that the
effect peaked across most weather metrics and regions after 8–12 lagged hours,
which influenced our final definition of weather-driven outages.


THE MOST PREVALENT AND HAZARDOUS COMBINATIONS OF SEVERE WEATHER TYPES THAT
CONTRIBUTE TO OUTAGES

We identified outages that exceeded the region (NYC, non-NYC urban, and rural)
specific 90th percentile of customers without power and described their summary
statistics (Table 1). By calculating the frequency of outages, their average
duration, and the average proportion of customers without power during outages,
by severe weather cause, we found that wind was the most frequent and the
strongest single predictor of prolonged outages across all 3 regions (Table 1).
Following wind, precipitation and heat alone also had consistently high
frequency and durations across regions. Extreme cold alone had varying impacts
across regions. For instance, in rural regions of NYS, outages driven by extreme
cold were less likely to induce prolonged outages than non-severe weather
conditions; however, in NYC, extreme cold conditions were more likely to cause
prolonged outages than non-severe weather conditions.

When comparing multiple severe weather metrics simultaneously, we found that
some combinations of multiple severe weather events led to longer or more
widespread outages than single causes alone. The combination of extreme
precipitation + wind led to the longest average duration for any severe weather
type across all three regions, with an average duration of 20.2 hours in NYC,
18.5 hours in non-NYC urban, and 12.5 hours in rural NYS.

Using a severe/non-severe weather ratio to understand the impact of each weather
metric on outages, we found that, though outages associated with lightning were
relatively infrequent, they were the most likely single severe weather event to
co-occur with outages across all 3 regions (Table 2). Following lightning,
precipitation and wind also had consistently high severe/non-severe weather
ratios across regions. Extreme cold alone had varying impacts across regions.
For instance, in rural regions of NYS, outages driven by extreme cold were less
likely to induce outages than non-severe weather conditions, however, in NYC,
extreme cold conditions were more likely to cause outages than non-severe
weather conditions.

Download:
 * PPT
   PowerPoint slide
 * PNG
   larger image
 * TIFF
   original image

Table 2. The frequency and severe/non-severe weather ratios for each weather
driven outage.



https://doi.org/10.1371/journal.pclm.0000364.t002

When comparing multiple severe weather metrics simultaneously, we found that,
generally, the combination of multiple severe weather events had higher ratios
than single events alone. Overall, the top 5 combinations driving outages of
multiple types, heat + lightning + precipitation, lightning + precipitation,
lightning + wind, heat + precipitation, and precipitation + wind, all had ratios
greater than lightning’s 105. Of note, heat + precipitation and precipitation +
wind were both frequent (caused 718 and 1,878 outages, respectively) and had
high severe/non-severe weather ratios.

Across NYS, 39.9% of all outages co-occurred with severe weather (Table 1).
However, there was heterogeneity in this percentage across regions and
localities. Severe weather contributed to over 50%, 85%, and 87% of all outages
in some NYC, non-NYC urban, and rural localities, respectively. Across all 3
regions, generally, severe weather-driven outages impacted larger percentages of
electrical customers and had longer durations than non-severe weather-driven
outages (S3–S6 Figs and Table 2). In maps of the frequency of severe
weather-driven outages overall and by weather metric, we found that certain
localities were vulnerable to outages across all severe weather metrics. In NYC,
localities in Queens, the Bronx, and Staten Island experienced the most severe
weather-driven outages, with localities in Queens experiencing outages driven by
each weather metric (cold, heat, lightning, precipitation, snow, and wind; Fig
3). In non-NYC urban regions, the most frequent severe weather outages occurred
on Long Island and in the Hudson Valley and in rural regions, the most frequent
severe weather outages occurred in North and Central NYS (S7 and S8 Figs).

Download:
 * PPT
   PowerPoint slide
 * PNG
   larger image
 * TIFF
   original image

Fig 3.



The frequency of (a) any severe weather driven outage and the frequency of
outages co-occurring with extreme (b) cold, (c), heat, (d) lightning, (e)
precipitation, (f) snow, and (g) wind in NYC, from 2017–2020. Republished from
The New York State Department of Public Service under a CC BY license, with
permission from The New York State Department of Public Service original
copyright 2020.



https://doi.org/10.1371/journal.pclm.0000364.g003

In analyses assessing whether differences in the distribution of outages were
due to social vulnerability, we found different effects across urbanicity (Fig
4). We estimated that in NYC, had all regions been in the 4th quartile of SVI
the number of heat-, precipitation-, and wind-driven outages would have been
12.1% (3.3%, 21.0%), 14.8% (-0.5%, 30.2%), and 19.1% (8.5%, 29.8%) higher,
respectively, versus if all localities had been in quartiles 1–3. We estimated
that in non-NYC urban NYS, had all regions been in the 4th quartile of SVI the
number of heat-driven outages would have been 7.5% lower (-16.3%, 1.3%), versus
if all localities had been in quartiles 1–3. Finally, in rural NYS, we estimated
that if all regions had been in the 4th quartile of SVI the number of
snow-driven outages would have been 5.6% lower (-13.2%, 2.0%), versus if all
localities had been in quartiles 1–3. Otherwise, SVI did not seem to have an
impact on outage frequency for non-NYC urban and rural localities. These results
were consistent with a sensitivity analysis that used higher counts of severe
weather outages (n = 3+ and 5+, rather than n = 1+) as the outcome (S9 Fig).
When stratifying analyses by year, in NYC we found significantly positive
associations between SVI and snow-driven outages for the year 2018–2019 as well
(S10 Fig). When adding latitude and longitude to the models to account for
possible spatial confounding, most estimates remained the same. However, in NYC
there was no longer a relationship between vulnerability and
precipitation-driven outages (S11 Fig).

Download:
 * PPT
   PowerPoint slide
 * PNG
   larger image
 * TIFF
   original image

Fig 4. The percent difference in the average treatment effect comparing the
highest quartile of SVI (most vulnerable) to all others, presented for each
outage cause and by urbanicity.



The percentages can be interpreted as the percent difference in the probability
of an outage of each severe weather type had all localities been in the highest
quartile of SVI (most vulnerable) versus if all localities had been in quartiles
1–3, presented for each outage cause and by urbanicity.



https://doi.org/10.1371/journal.pclm.0000364.g004

The duration of some outage types was also longer in regions with higher SVI;
for example, outages driven by wind and precipitation lasted the longest in
regions in the 4th quartile of SVI (SVI Q1 = 15.0, SVI Q2 = 17.2, SVI Q3 = 15.8,
and SVI Q4 = 18.0 hours, S1 and S2 Tables). However, this also varies by region
(S12–S14 Figs). In NYC, outages with the longest durations in high vulnerability
regions co-occurred with precipitation whereas in rural regions, these
co-occurred with precipitation and snow.


DISCUSSION

In this analysis, we assessed severe weather-driven outages in NYS from
2017–2020 by region and social vulnerability. We found that the frequency,
duration, and magnitude of outages depend on a combination of severe weather
type, urbanicity, and vulnerability status. In NYC, severe weather driven outage
were more common and lasted longer in marginalized communities. In rural
regions, outages were no more common in socially vulnerable communities but when
they occurred, lasted longer for socially vulnerable communities.

This paper is among the first to consider differences in severe weather-driven
outages across urbanicity. This stratification is important because differences
in housing stock (e.g., size, attached/detached), grid infrastructure (e.g.,
presence of overhead/buried distribution lines, sprawl), population
size/density, and behaviors surrounding energy often vary by urbanicity [42].
Furthermore, region-specific analyses are important in the context of climate
change because climate change drives unexpected weather event occurrences and
magnitudes that the grid may be ill-equipped to handle. Severe weather intensity
may be region and urbanicity specific due to the urban heat island effect,
proximity to water, and tree canopy. The NYC, non-NYC urban, and rural
stratification provided nuance to our analyses of outage prevalence and
disparities.

Our definition of severe weather-driven outages allows us to more precisely
understand the relationship between weather and outages. Including a temporal
component of severe weather-driven outages (i.e., considering lagged effects of
weather to determine severe-weather related outage) improves upon previous
definitions of severe weather-driven outages that use the co-occurrence of
outages on the same day to define outages [35]. The previous definition did not
(a) account for outages occurring earlier in the day than the extreme event or
(b) incorporate lagged effects of outages on the preceding day–two limitations
our definition overcomes. Our definition could be used in future papers or
further adapted to more accurately define outages or other adverse events caused
by severe weather.

By investigating the most prevalent and hazardous combinations of severe weather
types that contribute to outages, we found that extreme heat/precipitation and
extreme precipitation/wind were the most likely to precede outages while extreme
precipitation/wind and extreme wind alone led to the longest outage durations.
This was largely consistent across the three regions studied and with a national
assessment of severe weather and power outages, which found that 8+ hour outages
were the most likely to occur on county-days with heavy precipitation/cyclone
/heat and heavy precipitation/cyclone [35]. Our analyses revealed that, though
the likelihood of outages due to these events may be similar across urbanicity,
the restoration times differed. The average duration of outages due to extreme
precipitation/wind in rural regions was 12.5 hours compared to 18.5 hours in
non-NYC urban and 20.2 hours in NYC regions, respectively. Such information is
critical for utilities, policymakers, and electricity users preparing for
outages in a changing climate.

By investigating disparities in the frequency and duration of severe
weather-driven outages, we add to a growing literature identifying disparities
in power outage experiences, though we highlight that this varies across
urbanicity. Previous analyses of major severe weather-driven outages
demonstrated evidence of disparities, across a range of locations. Outages
during the 2021 Texas Power Crisis were more widespread and longer in counties
with a higher percentage of Hispanic residents [20]. Outages after Hurricane
Irma were longer in regions with more Hispanic residents, regions with more
residents with disabilities, and rural regions [21]. Outages in New York City
resulting from Tropical Storm Isaias were longer in regions that were lower
income and/or had higher percentages of non-White residents [22]. Previous work
posits that increased outage exposure in vulnerable communities may be the
result of historical and current discriminatory practices. Practices such as
redlining and zoning have had longstanding impacts, including (1)
underinvestment in marginalized communities and (2) the placement of
marginalized communities in disaster-prone regions–both of which may make these
communities more likely to experience outages. During outage events, many
electric utilities prioritize power restoration in regions with community
assets, such as mass transit, hospitals, police and fire stations, and sewage
and water stations. Regions with these assets were outlined as a priority for
Con Edison in NYS following Tropical Storm Ida in September of 2021 [43]. By
tying power restoration preferences to community assets, these guidelines can
lead to inequitable outage distributions and durations for underfunded and
under-resourced communities [16].

We found evidence of disparities in outages by community social vulnerability,
with variation by region and severe weather event. In NYC, we identified that
heat-, precipitation-, and wind-driven outages disproportionately impacted
vulnerable communities. We also found that in NYC, on average, the duration of
precipitation-driven outages was highest in localities with the highest social
vulnerability. In rural NYS, on average, the duration of precipitation- and
snow-driven outages were higher in localities with greater social vulnerability.
Given the centrality of electrical energy for daily life in the US, an imbalance
in electrical disruptions (in distribution, duration, or health impact) is
inherently an environmental justice and climate justice issue. Furthermore, the
energy transition will result in greater reliance on electricity for heating,
cooking, and transit, making electrical disruptions even more impactful [12]. We
add that plans to achieve grid reliability may look different across urbanicity.
For example, NYC may prioritize improvements that increase reliability during
extreme precipitation, as extreme precipitation-driven outages were both more
frequent and longer in vulnerable regions. While reliability remains a concern,
it is important to ensure that urban dwellers have safe backup power options.
Diesel generators, for example, are commonly used as backup power sources but
“emit pollutants, are prone to failure, can be difficult to operate and refuel”,
and have been linked to spikes in carbon monoxide poisonings observed with
natural disasters [44]. Cleaner backup power sources are becoming available
(e.g., solar + storage [44]) but may require further work to fully incorporate
urban and low-income communities. Residents in multiple unit housing face more
challenges in accessing backup power options than people living in single family
homes, a housing typology more common in suburban and rural areas. Such that
even if people living in low density housing setting are experiencing more
frequent outages, they are likely equipped with whole house generators that
reduce the likelihood of a full interruption in contrast to apartment dwellers.
Furthermore, any out-of-pocket expenses required for backup options may be
inhibitive for low-income renters. Thus, developing programs that can provide
these options for renters free of fees, as was done in a pilot program providing
Powerwall battery systems to customers in Vermont, could be one way to equitably
move toward resilient power [44]. Based on our results, rural NYS may instead
prioritize addressing the longer durations of outages in vulnerable communities
during extreme snow or precipitation. This could look like prioritizing power
restoration in regions with higher concentrations of low-income and/or medically
vulnerable individuals first.

In the 2011–2021 decade, the United States experienced a 78% increase in
weather-related power outages, compared to the previous decade [45]. Addressing
power outages, in the face of climate change and the energy transition is a
public health issue [45]. Power outages can directly impact health through a
variety of mechanisms. These include carbon monoxide poisoning, a common
consequence of using generators to cope with outages [46, 47], or the
exacerbation of underlying cardiovascular, respiratory, renal, and mental health
diseases due to sudden shifts in temperature, air filtration, stress, physical
activity (e.g., through using the stairs when an elevator in not powered), or
status of electricity-dependent medical devices [13, 44]. As such, increasing
electrical reliability, equitably, will be a key part of a just energy
transition and climate justice.

Our analysis had limitations. First, though the meteorological data was the most
temporally resolved available, it is still hourly averages (or totals).
Therefore, results from our first objective cannot be interpreted as the exact
values at which power outages occur, but rather show that generally, power
outages increase as each of the meteorological variables become more extreme.
Second, we added nuance to our analyses by focusing on urbanicity differences.
However, the urbanicity classifications were still quite coarse. Refining these
classifications by incorporating information on population density or region
(e.g., rural-central NYS) may provide deeper insights. Third, our choice to use
8 hours of lags rather than a larger value (e.g., 12, 24) may undercount the
number of outages that had a severe weather antecedent. We chose 8 hours to be
more conservative as many of the increased rates of outages due to severe
weather peaked near 8 hours. Fourth, we used the CDC’s social vulnerability
index as a metric of social vulnerability because it was designed to identify
communities that may need support during disasters like those driven by climate
change and it incorporates social variables that capture several aspects of
outage-related vulnerability like poverty, disability, housing type, age,
English-language proficiency. Though this index includes a comprehensive list of
outage-relevant variables, important variables may still be omitted. For
example, when creating plans for disaster or grid management, one may directly
want to know the number of individuals using electricity-dependent medical
devices. Though this may be partially captured by disability or correlate with
poverty and age, the use of the index alone may not fully identify regions or
households with more severe electricity vulnerabilities. Fifth, it is important
to note that a power outage does not equate to powerlessness for everyone
because it is customary in places where outages are more frequent to have backup
generators, and many houses that are equipped with them have an automatic switch
over in the context of an outage. For urban dwellers the lack of backup power
options may indeed render them without power for the duration of the outage
[48]. This is especially problematic for socially and medically vulnerable
groups, but we cannot decipher whether outages herein were directly related to
powerlessness. Finally, our results may not be generalizable outside of NYS.
Instead, our results highlight the importance of considering regional and social
differences to inform grid improvements. Such information can promote climate
justice in the modernization of the US electrical grid.


CONCLUSION

The US power grid is proven to be highly reliable in general; however, the
resilient and reliable grid operation is increasingly challenged by severe
weather events–events that are increasing in frequency and magnitude due to
climate change. Considering the adverse health impacts of power outages and the
increasing reliance on electricity, addressing severe weather-driven electrical
outages is critical for population health and environmental and climate justice.
Our NYS analysis provides a definition of severe weather-driven outages that
could be used to document the impacts of power outages further, especially those
co-occurring with severe weather. Here, we document that, regardless of region,
extreme heat/precipitation and extreme wind/precipitation were the most likely
to precede outages from 2017–2020. We also provided region-specific information,
highlighting that outage lasted the longest for vulnerable, rural communities
following snow or precipitation. Thus, we highlight the importance of
considering regional, social, and economic characteristics to inform equitable
grid management and improvements.


SUPPORTING INFORMATION

The spatial boundaries of each power operating locality in NYC, non-NYC urban,
and rural regions of New York State with a map of New York state’s location in
the contiguous United States for reference.

Showing 1/19: pclm.0000364.s001.pdf

Skip to figshare navigation
100 km
NYC
Urban,
non−NYC
Rural

1 / 19
ShareDownload

figshare


S1 FIG. THE SPATIAL BOUNDARIES OF EACH POWER OPERATING LOCALITY IN NYC, NON-NYC
URBAN, AND RURAL REGIONS OF NEW YORK STATE WITH A MAP OF NEW YORK STATE’S
LOCATION IN THE CONTIGUOUS UNITED STATES FOR REFERENCE.

Map of the contiguous United States was obtained from the US census:
https://www.census.gov/. Locality map republished from The New York State
Department of Public Service under a CC BY license, with permission from The New
York State Department of Public Service original copyright 2020.

https://doi.org/10.1371/journal.pclm.0000364.s001

(PDF)


S2 FIG. LOCALITY ASSIGNMENT TO QUARTILES OF THE CDC’S SOCIAL VULNERABILITY
INDEX, BY REGION.

Republished from The New York State Department of Public Service under a CC BY
license, with permission from The New York State Department of Public Service
original copyright 2020.

https://doi.org/10.1371/journal.pclm.0000364.s002

(PDF)


S3 FIG. THE PERCENTAGE OF ALL OUTAGES THAT WERE CAUSED BY SEVERE WEATHER IN NYC,
NON-NYC URBAN, AND RURAL NYS FROM 2017–2020.

Republished from The New York State Department of Public Service under a CC BY
license, with permission from The New York State Department of Public Service
original copyright 2020.

https://doi.org/10.1371/journal.pclm.0000364.s003

(PDF)


S4 FIG. COMPARISON OF THE FREQUENCY, PROPORTION OUT, AND DURATION OF POWER
OUTAGES FOR NON-SEVERE WEATHER DRIVEN (LEFT) AND SEVERE WEATHER DRIVEN OUTAGES
IN NYC FROM 2017–2020.

Republished from The New York State Department of Public Service under a CC BY
license, with permission from The New York State Department of Public Service
original copyright 2020.

https://doi.org/10.1371/journal.pclm.0000364.s004

(PDF)


S5 FIG. COMPARISON OF THE FREQUENCY, PROPORTION OUT, AND DURATION OF POWER
OUTAGES FOR NON-SEVERE WEATHER DRIVEN (LEFT) AND SEVERE WEATHER DRIVEN OUTAGES
IN NON-NYC URBAN NYS FROM 2017–2020.

Republished from The New York State Department of Public Service under a CC BY
license, with permission from The New York State Department of Public Service
original copyright 2020.

https://doi.org/10.1371/journal.pclm.0000364.s005

(PDF)


S6 FIG. COMPARISON OF THE FREQUENCY, PROPORTION OUT, AND DURATION OF POWER
OUTAGES FOR NON-SEVERE WEATHER DRIVEN (LEFT) AND SEVERE WEATHER DRIVEN OUTAGES
IN RURAL NYS FROM 2017–2020.

Republished from The New York State Department of Public Service under a CC BY
license, with permission from The New York State Department of Public Service
original copyright 2020.

https://doi.org/10.1371/journal.pclm.0000364.s006

(PDF)


S7 FIG.

The frequency of (a) any severe weather driven outage and the frequency of
outages driven by extreme (b) cold, (c), heat, (d) lightning, (e) precipitation,
(f) snow, and (g) wind in non-NYC urban NYS, from 2017–2020. Republished from
The New York State Department of Public Service under a CC BY license, with
permission from The New York State Department of Public Service original
copyright 2020.

https://doi.org/10.1371/journal.pclm.0000364.s007

(PDF)


S8 FIG.

The frequency of (a) any severe weather driven outage and the frequency of
outages driven by extreme (b) cold, (c), heat, (d) lightning, (e) precipitation,
(f) snow, and (g) wind in rural NYS, from 2017–2020.

https://doi.org/10.1371/journal.pclm.0000364.s008

(PDF)


S9 FIG. THE PERCENT DIFFERENCE IN THE AVERAGE TREATMENT EFFECT COMPARING THE
HIGHEST QUARTILE OF SVI (MOST VULNERABLE) TO ALL OTHERS, PRESENTED FOR EACH
OUTAGE CAUSE AND BY URBANICITY.

The percentages can be interpreted as the percent difference in the probability
of 1+ outage of each severe weather type had all localities been in the highest
quartile of SVI (most vulnerable) versus if all localities had been in quartiles
1–3, presented for each outage cause and by urbanicity. We also present the
results for 3+ and 5+ outages of each severe weather type.

https://doi.org/10.1371/journal.pclm.0000364.s009

(PDF)


S10 FIG. THE PERCENT DIFFERENCE IN THE AVERAGE TREATMENT EFFECT COMPARING THE
HIGHEST QUARTILE OF SVI (MOST VULNERABLE) TO ALL OTHERS, PRESENTED FOR EACH
OUTAGE CAUSE AND BY URBANICITY.

The percentages can be interpreted as the percent difference in the probability
of 1+ outage of each severe weather type had all localities been in the highest
quartile of SVI (most vulnerable) versus if all localities had been in quartiles
1–3, presented for each outage cause and by urbanicity. We also present the
results for by year.

https://doi.org/10.1371/journal.pclm.0000364.s010

(PDF)


S11 FIG. THE PERCENT DIFFERENCE IN THE AVERAGE TREATMENT EFFECT COMPARING THE
HIGHEST QUARTILE OF SVI (MOST VULNERABLE) TO ALL OTHERS, PRESENTED FOR EACH
OUTAGE CAUSE AND BY URBANICITY.

These models included latitude and longitude variables. The percentages can be
interpreted as the percent difference in the probability of 1+ outage of each
severe weather type had all localities been in the highest quartile of SVI (most
vulnerable) versus if all localities had been in quartiles 1–3, presented for
each outage cause and by urbanicity.

https://doi.org/10.1371/journal.pclm.0000364.s011

(PDF)


S12 FIG. VIOLIN PLOTS OF THE DURATION OF OUTAGES CAUSED BY EACH WEATHER METRIC
FROM 2017–2020 IN NYC.

https://doi.org/10.1371/journal.pclm.0000364.s012

(PDF)


S13 FIG. VIOLIN PLOTS OF THE DURATION OF OUTAGES CAUSED BY EACH WEATHER METRIC
FROM 2017–2020 IN NON-NYC URBAN NYS.

https://doi.org/10.1371/journal.pclm.0000364.s013

(PDF)


S14 FIG. VIOLIN PLOTS OF THE DURATION OF OUTAGES CAUSED BY EACH WEATHER METRIC
FROM 2017–2020 IN RURAL NYS.

https://doi.org/10.1371/journal.pclm.0000364.s014

(PDF)


S15 FIG. MAP OF THE RESIDUALS FOR NYC.

The Moran’s I was: -.05 with a p-value of .07.

https://doi.org/10.1371/journal.pclm.0000364.s015

(PDF)


S16 FIG. MAP OF THE RESIDUALS FOR NON-NYC, URBAN NYS.

The Moran’s I was: < .001 with a p-value of .87.

https://doi.org/10.1371/journal.pclm.0000364.s016

(PDF)


S17 FIG. MAP OF THE RESIDUALS FOR RURAL NYS.

The Moran’s I was: .003 with a p-value of .18.

https://doi.org/10.1371/journal.pclm.0000364.s017

(PDF)


S1 TABLE. THE FREQUENCY, AVERAGE DURATION, AND AVERAGE CUSTOMERS OUT DURING
SEVERE WEATHER DRIVEN OUTAGES, BY TYPE AND BY SVI QUARTILE.

https://doi.org/10.1371/journal.pclm.0000364.s018

(PDF)


S2 TABLE. THE FREQUENCY AND SEVERE/NON-SEVERE WEATHER RATIOS FOR EACH WEATHER
DRIVEN OUTAGE BY SVI QUARTILE.

https://doi.org/10.1371/journal.pclm.0000364.s019

(PDF)


REFERENCES

 1.  1. Use of electricity—U.S. Energy Information Administration (EIA). 2022;
     Available from:
     https://www.eia.gov/energyexplained/electricity/use-of-electricity.php.
 2.  2. Eckstrom S. Outing Power Outages: Real-time, Predictive, and Practical
     Socio-Demographic Analytics for New York City [M.S.]. Ann Arbor: New York
     University Tandon School of Engineering; 2022.
 3.  3. Mukherjee S, Nateghi R, Hastak M. A multi-hazard approach to assess
     severe weather-induced major power outage risks in the U.S. Reliability
     Engineering & System Safety. 2018 2018/07/01/;175:283–305.
     * View Article
     * Google Scholar
 4.  4. Kenward A, Raja U. Blackout: Extreme weather climate change and power
     outages. Climate central. 2014;10:1–23.
     * View Article
     * Google Scholar
 5.  5. Alhelou H, Hamedani-Golshan M, Njenda T, Siano P. A survey on power
     system blackout and cascading events: research motivations and challenges.
     Energies 12 (4): 682. 2019.
     * View Article
     * Google Scholar
 6.  6. Klinenberg E. Heat Wave: A Social Autopsy of Disaster in Chicago:
     University of Chicago Press; 2003.
 7.  7. Henry D, Ramirez-Marquez JE. On the Impacts of Power Outages during
     Hurricane Sandy—A Resilience-Based Analysis. Systems Engineering.
     2016;19(1):59–75.
     * View Article
     * Google Scholar
 8.  8. Busby JW, Baker K, Bazilian MD, Gilbert AQ, Grubert E, Rai V, et al.
     Cascading risks: Understanding the 2021 winter blackout in Texas. Energy
     Research & Social Science. 2021 2021/07/01/;77:102106.
     * View Article
     * Google Scholar
 9.  9. IPCC, 2022: Climate Change 2022: Impacts, Adaptation, and Vulnerability.
     Contribution of Working Group II to the Sixth Assessment Report of the
     Intergovernmental Panel on Climate Change [Pörtner H.-O., Roberts D.C.,
     Tignor M., Poloczanska E.S., Mintenbeck K., Alegría A., Craig M., Langsdorf
     S., Löschke S., Möller V., Okem A., Rama B. (eds.)]. Cambridge University
     Press. Cambridge University Press, Cambridge, UK and New York, NY, USA,
     3056 pp., https://doi.org/10.1017/9781009325844
 10. 10. Obringer R, Nateghi R, Maia-Silva D, Mukherjee S, CR V, McRoberts DB,
     et al. Implications of Increasing Household Air Conditioning Use Across the
     United States Under a Warming Climate. Earth’s Future.
     2022;10(1):e2021EF002434.
     * View Article
     * Google Scholar
 11. 11. Usgcrp. Fourth National Climate Assessment. 2022:1–470.
 12. 12. Bogdanov D, Ram M, Aghahosseini A, Gulagi A, Oyewo AS, Child M, et al.
     Low-cost renewable electricity as the key driver of the global energy
     transition towards sustainability. Energy. 2021 2021/07/15/;227:120467.
     * View Article
     * Google Scholar
 13. 13. Casey JA, Fukurai M, Hernández D, Balsari S, Kiang MV. Power Outages
     and Community Health: a Narrative Review. Curr Environ Health Rep. 2020
     Dec;7(4):371–83. pmid:33179170
     * View Article
     * PubMed/NCBI
     * Google Scholar
 14. 14. Rothstein R. The color of law: A forgotten history of how our
     government segregated America: Liveright Publishing; 2017.
 15. 15. Tormos-Aponte F, García-López G, Painter MA. Energy inequality and
     clientelism in the wake of disasters: From colorblind to affirmative power
     restoration. Energy Policy. 2021 2021/11/01/;158:112550.
     * View Article
     * Google Scholar
 16. 16. Maliszewski PJ, Larson EK, Perrings C. Environmental determinants of
     unscheduled residential outages in the electrical power distribution of
     Phoenix, Arizona. Reliability Engineering & System Safety. 2012
     2012/03/01/;99:161–71.
     * View Article
     * Google Scholar
 17. 17. Herreros-Cantis P, Olivotto V, Grabowski ZJ, McPhearson T. Shifting
     landscapes of coastal flood risk: environmental (in)justice of urban
     change, sea level rise, and differential vulnerability in New York City.
     Urban Transformations. 2020 2020/07/29;2(1):9.
     * View Article
     * Google Scholar
 18. 18. Fothergill A, Peek LA. Poverty and Disasters in the United States: A
     Review of Recent Sociological Findings. Natural Hazards. 2004
     2004/05/01;32(1):89–110.
     * View Article
     * Google Scholar
 19. 19. Hsu A, Sheriff G, Chakraborty T, Manya D. Disproportionate exposure to
     urban heat island intensity across major US cities. Nature Communications.
     2021 2021/05/25;12(1):2721. pmid:34035248
     * View Article
     * PubMed/NCBI
     * Google Scholar
 20. 20. Flores NM, McBrien H, Do V, Kiang MV, Schlegelmilch J, Casey JA. The
     2021 Texas Power Crisis: distribution, duration, and disparities. Journal
     of Exposure Science & Environmental Epidemiology. 2023
     2023/01/01;33(1):21–31.
     * View Article
     * Google Scholar
 21. 21. Mitsova D, Esnard A-M, Sapat A, Lai BS. Socioeconomic vulnerability and
     electric power restoration timelines in Florida: the case of Hurricane
     Irma. Natural Hazards. 2018 2018/11/01;94(2):689–709.
     * View Article
     * Google Scholar
 22. 22. How to Prevent a Power Outage From Becoming a Crisis. 2021 2021-07-20.
 23. 23. Service NYSDoP. Electric. Available from: https://dps.ny.gov/electric.
 24. 24. GSFC G, MD, USA, NASA Goddard Earth Sciences Data and Information
     Services Center (GES DISC). NLDAS-2: North American Land Data Assimilation
     System Forcing Fields. 2013.
 25. 25. Muñoz Sabater J. (2019): ERA5-Land monthly averaged data from 1950 to
     present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS).
     https://doi.org/10.24381/cds.68d2bb30
 26. 26. Google Earth Engine Reducer Overview. Available from:
     https://developers.google.com/earth-engine/guides/reducers_intro.
 27. 27. Station IS. Quality Controlled Lightning Imaging Sensor (LIS) on
     International Space Station (ISS) Science Data V2. 2020.
 28. 28. Deziel NC, Warren JL, Bravo MA, Macalintal F, Kimbro RT, Bell ML.
     Assessing community-level exposure to social vulnerability and isolation:
     spatial patterning and urban-rural differences. Journal of Exposure Science
     & Environmental Epidemiology. 2023 2023/03/01;33(2):198–206. pmid:35388169
     * View Article
     * PubMed/NCBI
     * Google Scholar
 29. 29. Hastie T, Hastie MT. Package ‘gam’. R package version. 2015:90124–3.
     * View Article
     * Google Scholar
 30. 30. Hartig F, Hartig MF. Package ‘DHARMa’. R package. 2017.
     * View Article
     * Google Scholar
 31. 31. Gasparrini A, Armstrong B, Kenward MG. Distributed lag non-linear
     models. Stat Med. 2010 Sep 20;29(21):2224–34. pmid:20812303
     * View Article
     * PubMed/NCBI
     * Google Scholar
 32. 32. Perperoglou A, Sauerbrei W, Abrahamowicz M, Schmid M. A review of
     spline function procedures in R. BMC Medical Research Methodology. 2019
     2019/03/06;19(1):46. pmid:30841848
     * View Article
     * PubMed/NCBI
     * Google Scholar
 33. 33. Anselin L. Global Spatial Autocorrelation (2). 2022.
 34. 34. Zhang W, Sheridan SC, Birkhead GS, Croft DP, Brotzge JA, Justino JG, et
     al. Power Outage: An Ignored Risk Factor for COPD Exacerbations. Chest.
     2020 2020/12/01/;158(6):2346–57. pmid:32502591
     * View Article
     * PubMed/NCBI
     * Google Scholar
 35. 35. Do V, McBrien H, Flores NM, Northrop AJ, Schlegelmilch J, Kiang MV, et
     al. Spatiotemporal distribution of power outages with climate events and
     social vulnerability in the USA. Nature Communications. 2023
     2023/04/29;14(1):2470. pmid:37120649
     * View Article
     * PubMed/NCBI
     * Google Scholar
 36. 36. Centers for Disease Control and Prevention/ Agency for Toxic Substances
     and Disease Registry/ Geospatial Research A, and Services Program.
     CDC/ATSDR Social Vulnerability Index. 2020.
 37. 37. Hallisey E, Tai E, Berens A, Wilt G, Peipins L, Lewis B, et al.
     Transforming geographic scale: a comparison of combined population and
     areal weighting to other interpolation methods. International Journal of
     Health Geographics. 2017 2017/08/07;16(1):29. pmid:28784135
     * View Article
     * PubMed/NCBI
     * Google Scholar
 38. 38. Luque-Fernandez MA, Schomaker M, Rachet B, Schnitzer ME. Targeted
     maximum likelihood estimation for a binary treatment: A tutorial. Stat Med.
     2018 Jul 20;37(16):2530–46. pmid:29687470
     * View Article
     * PubMed/NCBI
     * Google Scholar
 39. 39. Lendle SD, Schwab J, Petersen ML, van der Laan MJ. ltmle: An R Package
     Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data.
     Journal of Statistical Software. 2017 10/16;81(1):1–21.
     * View Article
     * Google Scholar
 40. 40. van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet
     Mol Biol. 2007;6:Article25. pmid:17910531
     * View Article
     * PubMed/NCBI
     * Google Scholar
 41. 41. Hoffman JIE. Chapter 25—Analysis of Variance. I. One-Way. In: Hoffman
     JIE, editor. Basic Biostatistics for Medical and Biomedical Practitioners
     (Second Edition): Academic Press; 2019. p. 391–417.
 42. 42. Ewing R, Rong F. The impact of urban form on U.S. residential energy
     use. Housing Policy Debate. 2008 2008/01/01;19(1):1–30.
     * View Article
     * Google Scholar
 43. 43. Relations, C. E. M. Con Edison Plans To Finish 95% Of Ida Restoration
     By Friday Night | Con Edison,
     <https://www.coned.com/en/about-us/media-center/news/20210902/con-edison-plans-to-finish-95-percent-ida-restoration-by-friday-night>
     (2021).
 44. 44. Mango M, Casey JA, Hernández D. Resilient Power: A home-based
     electricity generation and storage solution for the medically vulnerable
     during climate-induced power outages. Futures. 2021 2021/04/01/;128:102707.
     * View Article
     * Google Scholar
 45. 45. Central C. Surging Power Outages and Climate Change. 2022.
 46. 46. Fife CE, Smith LA, Maus EA, McCarthy JJ, Koehler MZ, Hawkins T, et al.
     Dying to Play Video Games: Carbon Monoxide Poisoning From Electrical
     Generators Used After Hurricane Ike. Pediatrics. 2009;123(6):e1035–e8.
     pmid:19482736
     * View Article
     * PubMed/NCBI
     * Google Scholar
 47. 47. Worsham CM, Woo J, Kearney MJ, Bray CF, Jena AB. Carbon Monoxide
     Poisoning during Major U.S. Power Outages. New England Journal of Medicine.
     2022;386(2):191–2. pmid:35020991
     * View Article
     * PubMed/NCBI
     * Google Scholar
 48. 48. Dominianni C, Ahmed M, Johnson S, Blum M, Ito K, Lane K. Power Outage
     Preparedness and Concern among Vulnerable New York City Residents. Journal
     of Urban Health. 2018 2018/10/01;95(5):716–26. pmid:30051238
     * View Article
     * PubMed/NCBI
     * Google Scholar


Download PDF
 
 * Citation
 * XML

Print
Share
 * Reddit
 * Facebook
 * LinkedIn
 * Mendeley
 * Twitter
 * Email
   

 

Advertisement



SUBJECT AREAS
?

FOR MORE INFORMATION ABOUT PLOS SUBJECT AREAS, CLICK HERE.

WE WANT YOUR FEEDBACK. DO THESE SUBJECT AREAS MAKE SENSE FOR THIS ARTICLE? CLICK
THE TARGET NEXT TO THE INCORRECT SUBJECT AREA AND LET US KNOW. THANKS FOR YOUR
HELP!

 * Snow  
   
   Is the Subject Area "Snow" applicable to this article? Yes No
   
   Thanks for your feedback.

 * New York  
   
   Is the Subject Area "New York" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Meteorology  
   
   Is the Subject Area "Meteorology" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Power grids  
   
   Is the Subject Area "Power grids" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Climate change  
   
   Is the Subject Area "Climate change" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Wind power  
   
   Is the Subject Area "Wind power" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Power distribution  
   
   Is the Subject Area "Power distribution" applicable to this article? Yes No
   
   Thanks for your feedback.

 * Storms  
   
   Is the Subject Area "Storms" applicable to this article? Yes No
   
   Thanks for your feedback.



 * Publications
 * PLOS Biology
 * PLOS Climate
 * PLOS Complex Systems
 * PLOS Computational Biology
 * PLOS Digital Health
 * PLOS Genetics
 * PLOS Global Public Health

 *  
 * PLOS Medicine
 * PLOS Mental Health
 * PLOS Neglected Tropical Diseases
 * PLOS ONE
 * PLOS Pathogens
 * PLOS Sustainability and Transformation
 * PLOS Water

 * Home
 * Blogs
 * Collections
 * Give feedback
 * LOCKSS

 * Privacy Policy
 * Terms of Use
 * Advertise
 * Media Inquiries
 * Contact

PLOS is a nonprofit 501(c)(3) corporation, #C2354500, based in San Francisco,
California, US





COOKIE PREFERENCE CENTER

Our website uses different types of cookies. Optional cookies will only be set
with your consent and you may withdraw this consent at any time. Below you can
learn more about the types of cookies PLOS uses and register your cookie
preferences.

Accept All Cookies


CUSTOMIZE YOUR COOKIE PREFERENCE

+Strictly Necessary

Always On
+Functional

OnOff
+Performance and Analytics

OnOff
+Marketing

OnOff

Save Selected Preferences and Close
For more information about the cookies and other technologies used by us, please
read our Cookie Policy.