A GIS Based Approach for Assessing the Association between Air Pollution and Asthma in New York State, USA

Studies on asthma have shown that air pollution can lead to increased asthma prevalence. The aim of this study is to examine the association between air pollution (fine particulate matter (PM2.5), sulfur dioxide (SO2) and ozone (O3)) and human health (asthma emergency department visit rate (AEVR) and asthma discharge rate (ADR)) among residents of New York, USA during the period 2005 to 2007. Annual rates of asthma were calculated from population estimates for 2005, 2006, and 2007 and number of asthma hospital discharge and emergency department visits. Population data for New York were taken from US Bureau of Census, and asthma data were obtained from New York State Department of Health, National Asthma Survey surveillance report. Data on the concentrations of PM2.5, SO2 and ground level ozone were obtained from various air quality monitoring stations distributed in different counties. Annual means of these concentrations were compared to annual variations in asthma prevalence by using Pearson correlation coefficient. We found different associations between the annual mean concentration of PM2.5, SO2 and surface ozone and the annual rates of asthma discharge and asthma emergency visit from 2005 to 2007. A positive correlation coefficient was observed between the annual mean concentration of PM2.5, and SO2 and the annual rates of asthma discharge and asthma emergency department visit from 2005 to 2007. However, the correlation coefficient between annual mean concentrations of ground ozone and the annual rates of asthma discharge and asthma emergency visit was found to be negative from 2005 to 2007. Our study suggests that the association between elevated concentrations of PM2.5 and SO2 and asthma prevalence among residents of New York State in USA is consistent enough to assume concretely a plausible and significant association

Chemical Complexity of Phosphorous-bearing Species in Various Regions of the Interstellar Medium

Phosphorus-related species are not known to be as omnipresent in space as hydrogen, carbon, nitrogen, oxygen, and sulfur-bearing species. Astronomers spotted very few P-bearing molecules in the interstellar medium and circumstellar envelopes. Limited discovery of the P-bearing species imposes severe constraints in modeling the P-chemistry. In this paper, we carry out extensive chemical models to follow the fate of P-bearing species in diffuse clouds, photon-dominated or photodissociation regions (PDRs), and hot cores/corinos. We notice a curious correlation between the abundances of PO and PN and atomic nitrogen. Since N atoms are more abundant in diffuse clouds and PDRs than in the hot core/corino region, PO/PN reflects <1 in diffuse clouds, MUCH LESS-THAN1 in PDRs, and >1 in the late warm-up evolutionary stage of the hot core/corino regions. During the end of the post-warm-up stage, we obtain PO/PN > 1 for hot core and <1 for its low-mass analog. We employ a radiative transfer model to investigate the transitions of some of the P-bearing species in diffuse cloud and hot core regions and estimate the line profiles. Our study estimates the required integration time to observe these transitions with ground-based and space-based telescopes. We also carry out quantum chemical computation of the infrared features of PH3, along with various impurities. We notice that SO2 overlaps with the PH3 bending-scissoring modes around similar to 1000-1100 cm(-1). We also find that the presence of CO2 can strongly influence the intensity of the stretching modes around similar to 2400 cm(-1) of PH3

Development of a machine vision system using the support vector machine regression (SVR) algorithm for the online prediction of iron ore grades

The mineral industry needs fast and efficient mineral quality monitoring equipment, and a machine vision system could be a suitable alternative to the traditional quality monitoring system. This study attempts to develop a machine vision-based expert system using support vector machine regression (SVR) model for the online quality monitoring of iron ores (hereafter known as ore grades). The images of the ore samples were captured during the run of condition on the fabricated conveyor belt transportation system. A total of 280 image features were extracted from each of the selected captured images in order to evaluate its suitability in object identification. A sequential forward floating selection (SFFS) algorithm was developed using the support vector machine regression (SVR) as a criterion function for selecting the optimum set of image features. The optimised feature subset was used as input, and the iron ore grade value was used as an output parameter for the model development. The grade of iron ore corresponding to each captured image was analysed in the laboratory using X-Ray Fluorescence (XRF) for grade estimation. The model was trained using 70% of the dataset and tested using 30% of the sample dataset. The model performance was evaluated using a test dataset with the five indices viz. the sum of squared errors (SSE), root mean squared error (RMSE), normalised mean squared error (NMSE), R-square (R2 ) and bias. The SSE, RMSE, NMSE and bias values of the model were obtained as 537.5367, 5.9863, 0.0063, and 0.8875, respectively. The R2 value of the model was obtained as 0.9402. The results indicate that the model performs satisfactorily for the iron ore grade prediction from the image collected in a controlled laboratory environment. The performance of the proposed model was compared with other models used in the previous studies. It was observed that the proposed model performs better than the other studied models (Gaussian Process Regression and Artificial Neural Network)