Statistical Linear Regression Model Research Articles

Daily Surface UV Exposure and Its Relationship to Surface Pollutant Measurements

For the past 30 years, the stratospheric ozone layer has decreased in the Northern Hemisphere. The main effect of this ozone decrease was an expected increase in the UV radiation at the Earth’s surface, but there has been no clear evidence of an increasing urban trend in surface UV. This study shows that specific air pollutants can reduce the increased surface levels of UV radiation and offers an explanation for why the expected surface UV increases have not been observed, especially in urban regions. A U.S. Environmental Protection Agency (EPA) UV monitoring site at the University of California at Riverside combined with air pollution data from a site operated by the California Air Resources Board in Rubidoux, CA, provided the basis of this study. The 1997 South Coast Ozone Study (SCOS-97) provided three key ingredients: black carbon, PM10 concentrations, and collocated radiometric measurements. The Total Ozone Mapping Spectrometer (TOMS) satellite data were used to provide the stratospheric ozone levels that were included in the statistical model. All of these input parameters would be used to test this study’s hypothesis: the expected increase of surface UV radiation, caused by decreases in stratospheric ozone, can be masked by increases in anthropogenic emissions. The values for the pollutants were 7:00 a.m.-5:00 p.m. averages of the instrument’s values taken during summer 1997. A statistical linear regression model was employed using the stratospheric ozone, black carbon, PM10 , and surface ozone concentrations, and the sin (Θ) and cos (Θ). The angle Θ is defined by Θ = 2π (Julian date/365). This model obtained a coefficient of determination of 0.94 with an uncertainty level (p value) of less than 0.3% for all of the variables in the model except ground-level ozone. The final model, regressed against a data set from a remote, western North Carolina site, resulted in a coefficient of determination of 0.92. The model shows that black carbon can reduce the Diffey-weighted UV levels that reach the surface by as much as 35%, depending on the season.

Using Continuous PM2.5 Monitoring Data to Report an Air Quality Index

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area’s (MSA’s) AQI.1 Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality. This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency’s (EPA’s) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity.2 The chosen measure of model adequacy is r2, the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greens-boro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r2 of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r2 of 0.86, based on 214 paired observations.

Blood volume in human bicuspid periodontal ligament determined by electron microscopy

The microvascular volume of periodontal ligament is reported to range from 1.63 to 3.5% in man, whereas that of animals varies from 7.5 to 11.5%. This transmission electron-microscopic investigation was undertaken to determine stereologically the volume in human periodontal ligament. The hypothesis tested was that the ligament blood volume in man is similar to that in animals. Left and right segments of mandible containing first and second premolars came from an adult burns’ victim who underwent jaw reconstruction. The segments were immersion-fixed in 2.5% glutaraldehyde, demineralized at 4°C in 0.1 M EDTA and processed for microscopy. Segments of distal periodontal ligament were sectioned at 150-μm intervals from the alveolar crest to the root apex and random tissue quadrats recorded for point counting and data analysis using a generalized linear-regression statistical model. Mean adjusted microvascular luminal volume was 9.52±2.28% (SEM) and the abluminal volume 12.91±2.76%; the wall volume was 3.39%. Significant differences existed between the luminal and abluminal volumes of the different vessel types ( p<0.05) and their distribution across the circumferential thirds of the ligament ( p<0.05). Total length density of the blood vessels was 149.84×10 3 cm/cm 3 and the surface density 330.19 cm 2/cm 3. Postcapillary-sized venules held 69.1% of the total blood volume and provided 49.3% of the luminal surface area. Venous capillaries were the most common vessel, comprising 48.5%, and they contributed 71.5% of the overall length density. This study confirmed the hypothesis for the blood volume in the periodontal ligament in man. Blood volumes do not reflect the configurations of microvascular beds.

Tide Prediction Using Neural Networks

Prediction of tides at a subordinate station located in the interior of an estuary or a bay is normally done by applying an empirical correction factor to observations at some standard or reference station. This paper presents an objective way to do so with the help of the neural network technique. In complex field conditions this approach may look more attractive to apply. Prediction of high water and low water levels as well as that of continuous tidal curves is made at three different locations. The networks involved are trained using alternative training algorithms. Testing of the networks indicated satisfactory reproduction of actual observations. This was further confirmed by a high value of the accompanying correlation coefficient. Such a correlation was better than the one obtained through use of the statistical linear regression model. The training algorithm of cascade correlation involved the lowest training time and hence is found to be more suitable for adaptive training purpose.