Hurricane Harvey brought more than 50 in. of rainfall to some areas of the Greater Houston Metro area (GHMA) starting on August 25, 2017; the Hurricane was also associated with damage to environmental infrastructure such as wastewater facilities, superfund sites, and leaks and spills from industrial and municipal facilities. This study collected post-Harvey water quality data in multiple streams for several weeks after the Hurricane. In addition to measuring impact, the study compared the observed concentrations of several physical, chemical, and microbial constituents and water properties to their historical counterparts in an effort to understand the water quality impacts of Harvey on the natural water systems within the GHMA. Unusual water quality findings such as low pH were observed that likely had acute and chronic effects on ecosystems including the loss of oyster populations in Galveston Bay. In-stream microbial concentrations, using E. coli as the indicator, were within historical norms typically reported for the GHMA. The observed levels of measured dissolved metals post Harvey, while relatively low, when multiplied by the significant volume of water discharged from bayous to Galveston Bay, meant the delivery of a substantial load of trace metals to the estuary. Specifically, the load in the particulate phase would be expected to accumulate and gradually repartition to the dissolved phase for a long period of time. Total metal concentrations, when elevated relative to their historical counterparts, could be associated with the presence of industrial activities. Overall, anthropogenic activities including the presence of hydraulic flood control structures, local runoff from industrialized areas, and active superfund sites were recognized as key factors affecting short-term acute water quality impacts. Watersheds with very little human alterations experienced minimal water quality changes and had relatively rapid recoveries post-Harvey.
Background
The spread of coronavirus in the United States with nearly five and half million confirmed cases and over 170,000 deaths has strained public health and health care systems. While many have focused on clinical outcomes, less attention has been paid to vulnerability and risk of infection. In this study, we developed a planning tool that examines factors that affect vulnerability to COVID-19.
Methods
Across 46 variables, we defined five broad categories: 1) access to medical services, 2) underlying health conditions, 3) environmental exposures, 4) vulnerability to natural disasters, and 5) sociodemographic, behavioral, and lifestyle factors. The developed tool was validated by comparing the estimated overall vulnerability with the real-time reported normalized confirmed cases of COVID-19.
Analysis
A principal component analysis was undertaken to reduce the dimensions. In order to identify vulnerable census tracts, we conducted rank-based exceedance and K-means cluster analyses.
Results
All of the 5 vulnerability categories, as well as the overall vulnerability, showed significant (P-values <<0.05) and relatively strong correlations (0.203<ρ<0.57) with the normalized confirmed cases of COVID-19 at the census tract level. Our study showed a total of 722,357 (~17% of the County population) people, including 171,403 between the ages of 45–65 (~4% of County’s population), and 76,719 seniors (~2% of County population), are at a higher risk based on the aforementioned categories. The exceedance and K-means cluster analysis demonstrated that census tracts in the northeastern, eastern, southeastern and northwestern regions of the County are at highest risk.
Conclusion
Policymakers can use this planning tool to identify neighborhoods at high risk for becoming hot spots; efficiently match community resources with needs, and ensure that the most vulnerable have access to equipment, personnel, and medical interventions.
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