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Disparities in disruptions to public drinking water services in Texas communities during Winter Storm Uri 2021 [1]

['Brianna Tomko', 'School Of Earth Sciences', 'The Ohio State University', 'Columbus', 'Oh', 'United States Of America', 'Christine L. Nittrouer', 'Department Of Management', 'Rawls College Of Business', 'Texas Tech University']

Date: 2023-07

2.1. Geospatial analysis

To gather data on boil water advisories issued by public water systems, a request for information was placed with the Texas Commission of Environmental Quality (TCEQ) under the Texas Public Information Act. We limited the request to community public water systems, defined as those that have the potential to serve at least fifteen residential connections or twenty-five residents on a year-round basis [23], and excluded other public water systems such as schools, hospitals, and seasonal communities. Most of the Texas population (roughly 27 million people, or 93%) is served by community public water systems [24] and therefore is represented in the geospatial component of this study. Roughly 5% of Texans depend on private well water [25] and are not included in the geospatial analysis.

TCEQ provided a list of 2,080 community public water systems that reported issuing a boil water advisory related to Winter Storm Uri. The list includes the name of the public water system, a unique identifier code, the county, the issue date of the boil water notice, the rescind date of the boil water notice, the population served, and the number of connections served. Retail service areas for community public water systems were obtained from the Texas Water Development Board’s (TWDB) water service boundary viewer in November of 2021 [26]. It is worth noting that as of 2022, Texas is one of only 24 states with geospatial data products for community water system service area boundaries [27]. The Texas dataset includes 4,572 out of 4,641 community public water systems [28]. Fourteen of the 2,080 public water systems that issued boil water advisories did not have a service area polygon, so they were excluded from the analysis. The remaining 2,066 records were screened for completeness and to ensure that the dates of boil water advisories were consistent with Winter Storm Uri. A small number of records [28] were removed from the analysis because they were incomplete, or the reported advisory was not conclusively connected to Winter Storm Uri. The excluded records fit at least one of these exclusion criteria: 1) the reported advisory was issued and rescinded prior to Winter Storm Uri; 2) the reported advisory was issued before the storm hit, and local minimum temperatures never fell below freezing; 3) no rescind date was provided. In total, 2,038 public water systems were retained for analysis.

To relate information on boil water advisories to weather, daily climate summaries were retrieved from the National Oceanic and Atmospheric Administration (NOAA) from February 1, 2021 to February 28, 2021 for 360 weather stations in the state of Texas [29]. Some of the longest boil water advisories were not lifted until March, but the month of February fully encompassed the climatological phenomenon of Winter Storm Uri; thus, we restrict our investigation of the climatological phenomenon (for example, how long temperatures stayed below freezing) to February. A point feature class shapefile was created in ArcGIS for the weather stations with attributes containing the minimum and maximum recorded temperatures for each day in February. We also calculated the sum of the number of days in February that the maximum or minimum daily temperature was below freezing. Some stations had missing data for maximum and minimum temperatures on select days. No attempt was made to interpolate missing data because it is possible that data gaps are temperature-dependent or biased towards frozen temperatures. Our estimation of the number of frozen days is therefore conservative, meaning that the number of days below freezing may be underestimated, and the minimum daily recorded temperature may be overestimated. Weather data from the nearest station was attributed to each public water system using a spatial join with the nearest neighbor in ArcGIS.

Information on urban and rural households, population, race, and housing tenure were obtained for the state of Texas from the 2010 decennial United States Census using the R package tidycensus [30]. We opted for the 2010 census instead of the more recent 2020 census because the results of the 2020 decennial census and American Community Survey were impacted by the COVID-19 pandemic, and income data were only available as experimental estimates [24]. We acknowledge that Texas has experienced substantial growth and demographic change since 2010, which introduces additional uncertainty to our analysis. Medium income and its margin of error (MOE) were taken from the 2010 American Community Survey. The U.S. Census Bureau organizes data based on spatial hierarchy, ranging from states down to blocks, the smallest measurement scale. Income data are not available at the block level, but they are at the next largest block group level. Initial analyses showed that block group-level calculations compared well with block-level calculations for public water systems [31]. We, therefore, chose to analyze demographic information for block groups because there is simplicity and advantage to working at one consistent spatial level for demographic and income data.

In ArcGIS Pro, areas were calculated for both block groups and public water systems. Overlapping areas between the census block groups and public water systems were then used to compute aerially-weighted average demographics for each public water system. Further information is provided in Section 1 in S1 Text.

To explore relationships between public water system characteristics, we performed inferential statistical analyses, including Pearson correlation coefficients (which provide a measure of linear correlation between two variables) and principal component analysis (PCA) using MATLAB. The goal of PCA is to reduce the dimensionality of the data set by finding the combination of variables that best explain the total variance [32]. Variables that displayed strong positive skewness were logarithmically transformed (specifically, number of advisory days, service area, homes served, and median income). All variables were then scaled to have a mean of 0 and a standard deviation of 1. We chose 15 variables to be included in the final matrix of correlation coefficients, detailed in the Results. These variables were selected to represent a range of conditions (meteorological: extent and duration of freezing temperatures; geographic: latitude and longitude, degree of urbanization; scale of the system: size of service area and number of homes served; and demographics: race, ethnicity, homeownership, and income), with the goals of understanding which public water systems took the longest to recover, who was affected, and for how long. We explored different combinations of variables (e.g., minimum of daily minimum temperature versus minimum of daily maximum temperature; population served versus homes served; fraction of White individuals versus fraction of White families) and found negligible differences; duplicated variables were removed. Last, the dataset was subjected to PCA to test whether there were any underlying patterns in the public water systems that issued short or long boil water advisories. We removed the length of the boil water advisory as a variable from the analysis, so that public water systems were only described by geographic, meteorological, and demographic variables. We also chose to eliminate elevation, a variable strongly correlated with longitude and latitude, which was found in preliminary analysis to have a negligible effect on the amount of variance explained by the first two components in the PCA analysis, leaving a total of 13 remaining variables for the final statistical analysis.

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[1] Url: https://journals.plos.org/water/article?id=10.1371/journal.pwat.0000137

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