Mapping air contaminant concentrations using remote sensing FTIR

The health status of southern children: a neglected regional disparity

PURPOSE

Great variations exist in child health outcomes among states in the United States, with southern states consistently ranked among the lowest in the country. Investigation of the geographical distribution of children's health status and the regional factors contributing to these outcomes has been neglected. We attempted to identify the degree to which region of residence may be linked to health outcomes for children with the specific aim of determining whether living in the southern region of the United States is adversely associated with children's health status.

METHODS

A child health index (CHI) that ranked each state in the United States was computed by using state-specific composite scores generated from outcome measures for a number of indicators of child health. Five indicators for physical health were chosen (percent low birth weight infants, infant mortality rate, child death rate, teen death rate, and teen birth rates) based on their historic and routine use to define health outcomes in children. Indicators were calculated as rates or percentages. Standard scores were calculated for each state for each health indicator by subtracting the mean of the measures for all states from the observed measure for each state. Indicators related to social and economic status were considered to be variables that impact physical health, as opposed to indicators of physical health, and therefore were not used to generate the composite child health score. These variables were subsequently examined in this study as potential confounding variables. Mapping was used to redefine regional groupings of states, and parametric tests (2-sample t test, analysis of means, and analysis-of-variance F tests) were used to compare the means of the CHI scores for the regional groupings and test for statistical significance. Multiple-regression analysis computed the relationship of region, social and economic indicators, and race to the CHI. Simple linear-regression analyses were used to assess the individual effect of each indicator.

RESULTS

A geographic region of contiguous states, characterized by their poor child health outcomes relative to other states and regions of the United States, exists within the "Deep South" (Mississippi, Louisiana, Arkansas, Tennessee, Alabama, Georgia, North Carolina, South Carolina, and Florida). This Deep-South region is statistically different in CHI scores from the US Census Bureau-defined grouping of states in the South. The mean of CHI scores for the Deep-South region was >1 SD below the mean of CHI scores for all states. In contrast, the CHI score means for each of the other 3 regions were all above the overall mean of CHI scores for all states. Regression analysis showed that living in the Deep-South region is a stronger predictor of poor child health outcomes than other consistently collected and reported variables commonly used to predict children's health.

CONCLUSIONS

The findings of this study indicate that region of residence in the United States is statistically related to important measures of children's health and may be among the most powerful predictors of child health outcomes and disparities. This clarification of the poorer health status of children living in the Deep South through spatial analysis is an essential first step for developing a better understanding of variations in the health of children. Similar to early epidemiology work linking geographic boundaries to disease, discovering the mechanisms/pathways/causes by which region influences health outcomes is a critical step in addressing disparities and inequities in child health and one that is an important and fertile area for future research. The reasons for these disparities may be complex and synergistically related to various economic, political, social, cultural, and perhaps even environmental (physical) factors in the region. This research will require the use and development of new approaches and applications of spatial analysis to develop insights into the societal, environmental, and historical determinants of child health that have been neglected in previous child health outcomes and policy research. The public policy implications of the findings in this study are substantial. Few, if any, policies identify these children as a high-risk group on the basis of their region of residence. A better understanding of the depth and breadth of disparities in health, education, and other social outcomes among and within regions of the United States is necessary for the generation of policies that enable policy makers to address and mitigate the factors that influence these disparities. Defining and clarifying the regional boundaries is also necessary to better inform public policy decisions related to resource allocation and the prevention and/or mitigation of the effects of region on child health. The identification of the Deep South as a clearly defined subregion of the Census Bureau's regional definition of the South suggests the need to use more culturally and socially relevant boundaries than the Census Bureau regions when analyzing regional data for policy development.

Path concentration distribution of toluene using remote sensing FTIR and one-dimensional reconstruction method

A one-dimensional mapping technique coupled with Open-Path remote sensing FTIR was presented in this article. This technique was applied to one of the air toxic volatile organic compounds (VOCs)-toluene. The Path Integrated Concentrations (PICs) of toluene in different path lengths along one beam path were fitted by polynomial fitting method with degrees from 3 to 7. The 6th degree polynomial fitting showed the best fitting result. Moreover, the methods of reconstructing path concentration distribution along the beam path by applying a Gaussian model and the derivative of 6th degree polynomial fitting function were established in this article. The reconstructed concentrations of toluene along the beam path by the two methods were experimentally close. Results showed that the concentration peaks were at 1.05 and 2.40 m from the instrument by applying the Gaussian model with the maximum concentration of 0.85 and 3.19 ppm, and at 0.99 and 2.49 m from the instrument with the maximum concentration of 0.78 and 2.80 ppm by applying the 6th polynomial fitting function. It was obvious that the reconstruction results by these two methods were very close. This approach could be appreciated for its fast calculation, exact peak location orientation and concentration flow tendency mapping. In can be concluded that this method can provide the path concentration distributions of much more releasing gases in a briefly and intuitionistic way. The remote sensing FTIR coupled with these mathematical reconstruction techniques can be applied to the real world environmental and industrial hygiene monitoring, thus works as an alert system for the VOCs pollution.

Reconstruction of air contaminant concentration distribution in a two-dimensional plane by computed tomography and remote sensing FTIR spectroscopy

This research combined open path FTIR (OP-FTIR) technique and computed tomography (CT) to reconstruct air contaminant concentration distribution in a two-dimensional plane. Remote sensing FTIR instrument was used to scan radial beam geometry and obtain path integrated concentration (PIC) data of acetone gas in the measuring plane. Smooth basis function minimization (SBFM) algorithm was adopted to reconstruct gaseous concentration distribution. For the purpose of finding out the preferable number of Gaussians used in SBFM algorithm, single-Gaussian, double-Gaussian, and three-Gaussian models were used respectively. Experimental results showed that the reconstruction result of acetone concentration distribution by SBFM algorithm with double-Gaussian model agreed with real distribution more qualitatively and quantitatively than single-Gaussian and three-Gaussian. Also, it has been proved that simulated annealing algorithm used in the optimization process of SBFM reconstruction was feasible and effective. Although computed tomography and remote sensing FTIR technique (CT-RS-FTIR) is still at the laboratory study stage, with further improvement of SBFM algorithm and beam geometry, it promises to be used in air pollution monitoring widely.

Orthogonal signal correction used for noise elimination of open path Fourier transform infrared spectra

Open path Fourier transform infrared (OP-FTIR) spectroscopy is qualified in detecting mixtures by multivariate calibration methods such as partial least squares (PLS); however, its applications are still restricted by background noise, which is unavoidable for OP-FTIR spectra and cannot be resolved solely by multivariate calibration methods. Hence OP-FTIR spectra are often pretreated before the data are subjected to the multivariate calibration model. A new preprocessing technique, orthogonal signal correction (OSC), was presented in this paper. The principle of OSC is to remove the part in X orthogonal to Y, and it is implemented based on PLS and nonlinear iterative partial least squares (NIPALS) algorithm. The approach was applied to three different data sets of PLS model. It performed much better than classical PLS when handling data with noise but comparably when processing on the simulated data. Moreover, OSC could reduce the complexity of model, which would facilitate the interpretation of the models. The results reveal that the proposed method gives much better prediction than the classical PLS and is very promising for the wide use of OP-FTIR. The preprocessing technique, auto-scaling, and second-order derivatives (SOD) were also considered.