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

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.

Spectral Crop Coefficient Approach for Estimating Daily Crop Water Use

While the amount of water used by a crop can be measured using lysimeters or eddy covariance systems, it is more common to estimate this quantity based on weather data and crop-related factors. Among these approaches, the standard crop coefficient method has gained widespread use. A limitation of the standard crop coefficient approach is that it applies to “standard conditions” that are invariant from field to field. In this article, we describe a method for estimating daily crop water use (CWU) that is specific to individual fields. This method, the “spectral crop coefficient” approach, utilizes a crop coefficient numerically equivalent to the crop ground cover observed in a field using remote sensing. This “spectral crop coefficient” Ksp is multiplied by potential evapotranspiration determined from standard weather observations to estimate CWU. We present results from a study involving three farmers' fields in the Texas High Plains in which CWU estimated using the Ksp approach is compared to observed values obtained from eddy covariance measurements. Statistical analysis of the results suggests that the Ksp approach can produce reasonably accurate estimates of daily CWU under a variety of irrigation strategies from fully irrigated to dryland. These results suggest that the Ksp?approach could be effectively used in applications such as operational irrigation scheduling, where its field-specific nature could minimize over-irrigation and support water conservation.