Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model.

Accidental releases of untreated sewage into the environment, known as sewage spills, may cause adverse gastrointestinal stress to exposed populations, especially in young, elderly, or immune-compromised individuals. In addition to human pathogens, untreated sewage contains high levels of micropollutants, organic matter, nitrogen, and phosphorus, potentially resulting in aquatic ecosystem impacts such as algal blooms, depleted oxygen, and fish kills in spill-impacted waterways. Our Geographic Information System (GIS) model, Spill Footprint Exposure Risk (SFER) integrates fine-scale elevation data (1/3 arc-second) with flowpath tracing methods to estimate the expected overland pathways of sewage spills and the locations where they are likely to pool. The SFER model can be integrated with secondary measures tailored to the unique needs of decision-makers so they can assess spatially potential exposure risk. To illustrate avenues to assess risk, we developed risk measures for land and population health. The land risk of sewage spills is calculated for subwatershed regions by computing the proportion of the subwatershed's area that is affected by one modeled footprint. The population health risk is assessed by computing the estimated number of individuals who are within the modeled footprint using fine-scale (90 square meters) population estimates data from LandScan USA. In the results, with a focus on the Atlanta metropolitan region, potential strategies to combine these risk measures with the SFER model are outlined to identify specific areas for intervention.

Cascading effects of COVID-19 on population mobility and air quality: An exploration including place characteristics using geovisualization.

This study hypothesizes that public health responses to coronavirus disease 2019 (COVID-19), including a mandated restriction of activity (commonly called a 'lockdown') resulted in reduced transportation activities and changes in air quality in Texas, USA. This presented a natural experiment where population mobility and air quality before and after the lockdown could be compared. Changes in mobility were measured by SafeGraph mobility data (from opt-in smart phone applications that transmit location data) and air quality changes were based on NO2 concentrations measured by the European Space Agency's Sentinel-5 Precursor satellite (from the TROPOspheric Monitoring Instrument). The changes in population mobility and NO2 concentration between mid-March 2020 (lockdown initiated) and the end of 2020, as compared to the same time window in 2019, were the basis of exploring the lockdown hypothesis. Additionally, numerous socio-economic (place based) indicators were hypothesized to follow public health vulnerability assumptions based on COVID- 19 incidence patterns. This hypothesis was subjected to geovisualization techniques in order to find potential patterns and insights into the complex combinations of these place-based data. Our results suggest that simultaneously visualizing COVID-19, mobility, air quality and socio-economic data yields insights in underlying spatial processes related to public health policy decisions. The hypothesis that the lockdown resulted in reduced mobility and NO2 concentrations was found partially correct - this trend was observed in highly urbanized areas, but not in less populated areas. Data related public health vulnerability assumptions (e.g. a region's age, poverty, education, etc.) were agreed with in part, but disagreed with in part.

Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus.

BACKGROUND: This study postulates that underlying environmental conditions and a susceptible population's socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector's habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data. METHODS: Geovisualization was used to integrate an environmental model of the disease vector's habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster's data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters. RESULTS: Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

Prioritizing riparian corridors for ecosystem restoration in urbanizing watersheds.

BACKGROUND: Riparian corridors can affect nutrient, organic matter, and sediment transport, all of which shape water quality in streams and connected downstream waters. When functioning riparian corridors remain intact, they provide highly valued water quality ecosystem services. However, in rapidly urbanizing watersheds, riparian corridors are susceptible to development modifications that adversely affect those ecosystem services. Protecting high quality riparian corridors or restoring low quality corridors are widely advocated as watershed level water quality management options for protecting those ecosystem services. The two approaches, protection or restoration, should be viewed as complementary by watershed managers and provide a foundation for targeting highly functioning riparian corridors for protection or for identifying poorly functioning corridors for restoration. Ascertaining which strategy to use is often motivated by a specific ecosystem service, for example water quality, upon which watershed management is focused. We have previously reported on a spatially explicit model that focused on identifying riparian corridors that have specific characteristics that make them well suited for purposes of preservation and protection focused on water quality. Here we hypothesize that focusing on restoration, rather than protection, can be the basis for developing a watershed level strategy for improving water quality in urbanizing watersheds. METHODS: The model described here represents a geographic information system (GIS) based approach that utilizes riparian characteristics extracted from 40-meter wide corridors centered on streams and rivers. The model focuses on drinking water reservoir watersheds that can be analyzed at the sub-watershed level. Sub-watershed riparian data (vegetation, soil erodibility and surface slope) are scaled and weighted based on watershed management theories for water quality, and riparian restoration scores are assigned. Those scores are used to rank order riparian zones -the lower the score the higher the priority for riparian restoration. RESULTS: The model was applied to 90 sub-watersheds in the watershed of an important drinking water reservoir in north central Texas, USA. Results from this study area suggest that corridor scores were found to be most correlated to the amount of: forested vegetation, residential land use, soils in the highest erodibility class, and highest surface slope (r 2 = 0.92, p < 0.0001). Scores allow watershed managers to rapidly focus on riparian corridors most in need of restoration. A beneficial feature of the model is that it also allows investigation of multiple scenarios of restoration strategies (e.g.,  revegetation, soil stabilization, flood plain leveling), giving watershed managers a tool to compare and contrast watershed level management plans.

A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters.

BACKGROUND: The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity. METHODS: We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model. RESULTS: LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2 = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71). CONCLUSIONS: The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

A determination of the spatial concordance between Lyme disease incidence and habitat probability of its primary vector Ixodes scapularis (black-legged tick).

The spatial distribution of Ixodes scapularis, the most common tick vector of the bacterium Borrelia burgdorferi, the cause of Lyme disease in humans, has not been studied previously in Texas, United States of America. It has only rarely been reported in this state, so its local, spatial relationship to the distribution of this disease is unknown. From an epidemiological perspective, one would tend to hypothesise that there should be a high degree of spatial concordance between habitat suitability for the tick and incidence of the disease. Both maximum-entropy modelling of the tick's habitat probability and modelling of human incidence of Lyme disease using spatially adaptive filters provide reliable portrayals of the spatial distributions of these phenomena. Even though rates of human cases of Lyme disease as well as rates of Ixodes ticks infected with Borrelia bacteria are both relatively low in Texas, the best data currently available indicate that the assumption of high levels of spatial concordance would not be correct in Texas (Kappa coefficient of agreement = 0.039). It will take substantially more data to provide conclusive findings and to understand the results reported here, but this study provides an approach to begin understanding the discrepancy.

Modelling spatial concordance between Rocky Mountain spotted fever disease incidence and habitat probability of its vector Dermacentor variabilis (American dog tick).

The spatial distribution of Dermacentor variabilis, the most commonly identified vector of the bacterium Rickettsia rickettsii which causes Rocky Mountain spotted fever (RMSF) in humans, and the spatial distribution of RMSF, have not been previously studied in the south central United States of America, particularly in Texas. From an epidemiological perspective, one would tend to hypothesise that there would be a high degree of spatial concordance between the habitat suitability for the tick and the incidence of the disease. Both maximum-entropy modelling of the tick's habitat suitability and spatially adaptive filters modelling of the human incidence of RMSF disease provide reliable portrayals of the spatial distributions of these phenomenons. Even though rates of human cases of RMSF in Texas and rates of Dermacentor ticks infected with Rickettsia bacteria are both relatively low in Texas, the best data currently available allows a preliminary indication that the assumption of high levels of spatial concordance would not be correct in Texas (Kappa coefficient of agreement = 0.17). It will take substantially more data to provide conclusive findings, and to understand the results reported here, but this study provides an approach to begin understanding the discrepancy.

Borrelia, Ehrlichia, and Rickettsia spp. in ticks removed from persons, Texas, USA.

Data regarding the type, frequency, and distribution of tick-borne pathogens and bacterial agents are not widely available for many tick species that parasitize persons in the southern United States. We therefore analyzed the frequency and identity of pathogens and bacterial agents in ticks removed from humans and subsequently submitted to the Texas Department of State Health Services, Zoonosis Control Program, from October 1, 2004, through September 30, 2008. The data showed associations of bacterial agents and potential vectors. Tick-related illnesses may pose unidentified health risks in areas such as Texas, where incidence of human disease related to tick bites is low but well above zero and where ticks are not routinely suspected as the cause of disease. Cause, treatment, and prevention strategies can be better addressed through collecting sufficient data to establish baseline assessments of risk.

Near real-time monitoring and mapping of specific conductivity levels across Lake Texoma, USA.

A submersible sonde equipped with a specific conductivity probe, linked with a global positioning satellite receiver was developed, deployed on a small boat, and used to map spatial and temporal variations in specific conductivity in a large reservoir. 7,695 sample points were recorded during 8 sampling trips. Specific conductivity ranged from 442,uS/cm to 3,378,uS/cm over the nine-month study. The data showed five statistically different zones in the reservoir: 2 different riverine zones, 2 different riverine transition zones, and a lacustrine zone (the main lake zone). These data were imported to a geographic information system where they were spatially interpolated to generate 8 maps showing specific conductivity levels across the entire surface of the lake. The highly dynamic nature of water quality, due to the widely differing nature of the rivers that flow into the reservoir and the effect of large inflows of fresh water during winter storms is easily captured and visualized using this approach.