Exploratory Analyses for Water Quality of the Gulf of Mexico Basin, Texas, 2005-2014

With the increased demand for more water sources, the issues of water quality have become more important. This study presents exploratory data analyses on water quality issues related to the Gulf of Mexico Basin in Texas for the last decade: 2005-2014. Dissolved oxygen, water temperature, specific conductance, PH, transparency, sampling depth and Enterococci observations for the ten years from 2005 to 2014 were analyzed. Statistical testing showed that the observations followed similar distributions between the years. Thus they were combined for each of the variables. Throughout the ten years transparency, water temperature, specific conductance, and Enterococci showed higher variances. Pair-wise correlations were negligible but they had positive spatial autocorrelation. Sampling was constantly conducted in the four months of February, May, August, and November. Water temperature, dissolved oxygen and Enterococci concentrations varied significantly during these four months. Univariate spatial regressions showed that Enterococci is predicted to increase whereas dissolved oxygen, PH, water temperature, specific conductances were predicted to decrease.

Case Study: Reviewing Methods of Assessing Community Adaptive Capacity for Jefferson County, Texas

With the increased attention on community sustainability and resilience, different poles have developed voicing similarities and/or differences of the two concepts. This study quantifies adaptive capacity of Jefferson County, Texas, one of the coastal communities at the Gulf of Mexico having some of the worse adverse effects. Review of existing methods is presented. Analyses were conducted for the last ten years: from 2005 to 2014. Interestingly, statistical analyses showed that the County’s socio-economic profile or indicators have not changed throughout the ten years, but the environmental, institutional, and infrastructure indicators have. Focusing on one location magnifies the adaptive capacity of Jefferson County, the temporal aspect of both perspectives, and the relevance of existing methods to this community with its peculiarities. Future assessments need to be based on primary data collected through participatory engagement of all stakeholders. This calls for attempts to quantify adaptive capacity using the comparatively more challenging deductive reasoning, which would allow for incorporation of more risks and thus higher readiness.

Effect of Sea Level Rise and Groundwater Withdrawal on Seawater Intrusion in the Gulf Coast Aquifer: Implications for Agriculture

The two main factors contributing to depletion of freshwater resources are climate change and anthropological variables. This study presents statistical analyses that are local in its specifics yet global in its relevance. The decline in Gulf Coast aquifer water quality and quantity has been alarming especially with the increased demand on fresh water in neighboring non-coastal communities. This study used seawater levels, groundwater use, and well data to investigate the association of these factors on the salinity of water indicated by chloride levels. Statistical analyses were conducted pointing to the high significance of both sea water level and groundwater withdrawals to chloride concentrations. However, groundwater withdrawal had higher significance which points to the need of water management systems in order to limit groundwater use. The findings also point to the great impact of increased groundwater salinity in the Gulf Coast aquifer on agriculture and socioeconomic status of coastal communities. The high costs of desalinization point to the increased signification of water rerouting and groundwater management systems. Further investigation and actions are in dire need to manage these vulnerabilities of the coastal communities.

Female Head, Food Stamps, Ethnicity and Air Pollution: Confounders or Causes of Heart Disease in Texas

One in every four deaths in the United States is attributed to heart disease. While the ethnic variations have not been momentous, the socioeconomic disparities of heart disease incidence need to be further investigated. Moreover, exposure to air pollutants has been documented to cause heart disease. This secondary-data study investigates the effects of air pollutants as well as socioeconomic factors on hospitalization rate of heart disease in Texas. The rates for the five sub-diagnoses of cardiovascular disease, heart attack, stroke, hypertension and heart disease were linked to ozone, fine particulate matter, carbon monoxide, nitrogen dioxide, sulphur dioxide and socioeconomic status factors at the county level. These were college education attainment, households with female heads, percentage of users of food stamps, ethnicities, living near a park and living in houses with severe housing problems. Spatial lag modelling was conducted to estimate the statistical significance of the independent variables on the five sub-diagnoses of heart disease. Fine particulate matter, sulphur dioxide and being African American were significant to all the outcomes. Living in a household with female head was significant to stroke and hypertension. Using food stamps was significant to cardiovascular disease, heart attack and heart disease. Fine particulate matter and sulphur dioxide increase the risk of heart disease by a factor of three to twenty two times, respectively. Whereas low socioeconomic status increases the risk of heart disease by a factor of up to four times. The results of the effect of particulate air and sulphur dioxide pollution among people in low social class especially African Americans. The vicious cycle of heart disease and low socioeconomic status call for societal and policy makers’ attention through methodical interventions to address the two significant issues of industrial facilities site allocation and stationary emission resources.

The Effect of Air Pollutants and Socioeconomic Status on Asthma in Texas

Asthma prevalence in the United States and the world has been increasing, affecting millions including children (0 - 17 years old) and causing thousands of deaths every year at a societal cost of over billions of dollars. Further, it has been documented that asthma morbidity responds to socioeconomic variations. This study evaluates the relationship between asthma and five air pollutants along with socioeconomic status in Texas counties from 2005 to 2013. Air pollutants investigated were carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), fine particulate matter (PM2.5), and sulfur dioxide (SO2). Exploratory and spatial analyses produced consistent results. Asthma prevalence was positively associated with PM2.5, SO2, living near a park, and living in an urban area. Asthma childhood prevalence rates were positively associated with living in a household with a female head and negatively associated with ethnicity: Caucasian, Hispanic, and African American. Adult prevalence rates were positively associated with living in a household with female head, being on food stamps, and PM2.5. Both the overall and adult rates were positively associated with poverty. Asthma hospitalizations in Texas were positively associated with aerosol particles, sulfur dioxide, and low income. Moreover, the majority of air pollution in Texas is formed by stationary sources, which contradicts recent claims that mobile sources are the main emitters in Texas. Our findings are consistent with those from other geographical locations and suggest that additional studies and measures are required to fully explain the associations detected and underlying cause and effect paradigm.

Application of Multivariate Geostatistics in Environmental Epidemiology: Case Study from Houston, Texas

This study represents an example of investigating the associations between the joint exposure to ozone (O3) and particulate matter of sizes less than or equal to 2.5 micrometers in aerodynamic diameter (PM2.5) and cardiovascular disease (CVD) emergency room (ER) visits and chronic obstructive pulmonary disease (COPD) ER visits using multivariate geostatistics in Houston, Texas, from 2004 to 2009. Analyses showed lack of strong pair-wise association among the predictors of O3, PM2.5, wind speed, relative humidity, and temperature. Whereas CVD and COPD ER visits exhibited a strong positive correlation. Both outcomes drastically increased from 2006 possibly due to immigration from neighboring locations. Parametric testing showed that the data differed significantly between the years. Multivariate multiple regression results on the 2009 data showed that PM2.5, relative humidity, and temperature were significant to both CVD and COPD ER visits. Codispersion coefficients were constant which justified the assumption of intrinsic correlation. That is, our predictors had strong influence on the spatial variability of CVD and COPD ER visits. This multivariate geostatistics approach predicted an increase of 34% in CVD ER visits and 24% increase in COPD ER visits, which calls for more attention from policy makers. The use of multivariate geostatistics analyses enabled us to successfully detect the effects of risk factors on both outcomes.

Assessing the Association between Heart Attack, High Blood Pressure, and Heart Disease Mortality Rates and Particulate Matter and Socioeconomic Status Using Multivariate Geostatistical Model

This study addresses the public concerns of potential adverse health effects from ambient fine particulate matter as well as socioeconomic factors. Heart attack, high blood pressure, and heart disease mortality rates were investigated against fine particulate matter and socioeconomic status, for all counties in the United States in 2013. Multivariate multiple regressions as well as multivariate geostatistical predictions show that these are significant factors towards assessing the causal inferences between exposure to air pollution and socioeconomic status and the three mortality rates.

Spatiotemporal Analysis of the Effect of Ozone and Fine Particulate on CVD Emergency Room Visits in Harris County, Texas

Researchers have reported statistically significant associations between air pollutants and cardiovascular disease (CVD). However, few studies have investigated the acute cardiovascular effects of joint exposure to Ozone (O3) and fine particulate matter (PM2.5) in a location so distinctive like Harris County. The objective of this study was to investigate the association between the joint exposure to O3 and PM2.5, and emergency room diagnosis of CVD, in Harris County, Texas. Data used include all emergency room (ER) visits, and O3 and PM2.5 levels in the same years. Logistic regression modeled the effect of temperature, relative humidity, wind speed, wind, ozone, and fine particulate matter, averaged by day. Three models were estimated for all visits, visits during the months of April and September of 2005 and 2009, and for visits for patients from zip codes that are close to monitoring stations. A 1 μg/m3 increase in PM2.5 was associated with a 0.4% increase in the odds of cardiovascular disease emergency room diagnoses on the same day

Feasibility Study of a National Web-Based GIS Application to Assess the Risk of Pesticide Drift in the U.S.

Agricultural pollution is a major issue in the United States (U.S.) and the world. Biotic and abiotic farming byproducts adversely affect the ecosystem and human health. While pesticides and fertilizers are the primary sources of agricultural pollution, organic agriculture can help remediate the negative effects on humans and the ecosystem. However, many factors like chemical drift can limit this advantage. This paper presents a feasibility study of a web-based (Geografic Information System) GIS application which can model and predict the areas affected by agricultural chemicals drift. Other applications exist with limited assumptions that make their outcomes far from reality. A root definition and a rich picture are developed as well as a Strengths Weaknesses Opportunities and Threats (SWOT) analysis. Because of the huge geographical context, data requirements and analyses requirements are expected to be massive. Nonetheless, despite the expected challenges, the advantages of the proposed application outweigh the risks.

Age, Race and Gender Spatiotemporal Disparities of COPD Emergency Room Visits in Houston, Texas

Chronic obstructive pulmonary disease (COPD) is one of the major causes of morbidity and mortality in the United State. The investigation of the continuing increase in its prevalence and mortality has increased attempts to further understand its causes and how to manage it. Understanding the spatial and temporal distribution of COPD emergency room (ER) visits in Harris County (Houston) can guide these efforts in a uniform yet diverse setting like this one. The objectives of this study were to identify the temporal and spatial variations of COPD emergency room visits adjusted by age, gender, ethnicity, day of the week, month, and year, and to estimate the odds ratio of COPD emergency room visits adjusted by the six risk factors. The dataset used were extracted from two resources: ER Utilization Study and Harris County centroid coordinates. Exploratory statistical analyses were conducted to study the spatiotemporal disparities and investigate associations. Logistic regression was performed to estimate the odds ratio of COPD primary diagnosis adjusted for age, race, gender, day of the week, month, and year. The number of COPD ER visits kept increasing from 2004 throughout 2009 but there was a significant increase after the year 2005. Spring and summer had lower visits compared to winter and autumn. Lowest visits were during June and July and higher during December and January. Tuesdays had the highest number of visits compared to the remaining days of the week with Saturdays having had the lowest number of visits. Temporal analyses show the continuous increase in COPD ER visits in Houston as well as the consistent spatial disparities between zip regions. After adjustment for age, race, gender, day of the week, month, and year, there were statistically significant associations between emergency room chronic obstructive pulmonary disease diagnoses in Houston, Texas, with these six predictors.

Multivariate Geostatistical Model for Groundwater Constituents in Texas

Although many studies have explored the quality of Texas groundwater, very few have investigated the concurrent distributions of more than one pollutant, which provides insight on the temporal and spatial behavior of constituents within and between aquifers. The purpose of this research is to study the multivariate spatial patterns of seven health-related Texas groundwater constituents, which are calcium (Ca), chloride (Cl), nitrate (NO3), sodium (Na), magnesium (Mg), sulfate (SO4), and potassium (K). Data is extracted from Texas Water Development Board’s database including nine years: 2000 through 2008. A multivariate geostatistical model was developed to examine the interactions between the constituents. The model had seven dependent variables—one for each of the constituents, and five independent variables: altitude, latitude, longitude, major aquifer and water level. Exploratory analyses show that the data has no temporal patterns, but hold spatial patterns as well as intrinsic correlation. The intrinsic correlation allowed for the use of a Kronecker form for the covariance matrix. The model was validated with a split-sample. Estimates of iteratively re-weighted generalized least squares converged after four iterations. Matern covariance function estimates are zero nugget, practical range is 44 miles, 0.8340 variance and kappa was fixed at 2. To show that our assumptions are reasonable and the choice of the model is appropriate, we perform residual validation and universal kriging. Moreover, prediction maps for the seven constituents are estimated from new locations data. The results point to an alarmingly increasing levels of these constituents’ concentrations, which calls for more intensive monitoring and groundwater management.