Study of indoor radon levels in high-rise air-conditioned office buildings

Artificial neural network modeling of meteorological and geological influences on indoor radon concentration in selected tertiary institutions in Southwestern Nigeria

Exposure to indoor radon, with no safe level, has been reported to bear the possible radiological risk to humans. The indoor radon level of a total of one hundred and thirty-two offices and sixty classrooms of tertiary institutions within different lithology and at varied meteorological values in southwestern Nigeria was measured using Electret Passive Environmental Radon Monitor (E-PERM). The meteorological parameters were obtained from the National Aeronautics and Space Administration (NASA) database. MATLAB scripts of code were used to develop the Artificial Neural Network (ANN) model. The measured parameters were subjected to both descriptive and inferential statistics. The highest mean radon concentration was observed in offices built on granitic bedrock with a value of 64.3 ± 1.7 Bq.m^-3 while the lowest was observed in alluvium bedrock with a value of 52.5 ± 1.4 Bq.m^-3. To enhance prediction involving erratic parametric patterns, the measured data were subjected to an optimized Artificial Neural Network architecture training, validation, and testing, leading to a model determined to have a Nash-Sutcliffe efficiency coefficient value of 0.997, Average Absolute Relative Error of 0.0115, and Mean Squared Error of 0.07. The predicted result was compared favorably with the measured data with 0.054 Average Validation Error, 0.027 Mean Absolute Error 3.64 Mean Absolute Percentage Error, and 83.7% Goodness-of-Prediction values. About 21.4% of the values were found to be higher than the 100 Bq.m^-3 limits specified by the World Health Organization. Measured radon concentration and predicted ANN data as obtained in this work, being novel in this study area is useful for immediate assessment of the level of risk associated with radon exposure as well as for future predictions. The ANN developed is effective and efficient in predicting indoor radon concentration.

Medical aspects of global warming

BACKGROUND

Global warming is caused by increased carbon dioxide (CO2)resulting in a greenhouse effect with enhanced warming of the earth. Measurements of CO2 show a steady increase over the past 30 years caused by the burning of fossil fuels and from the loss of natural CO2 sinks. A 100-year increase in global temperature by 0.3 to 0.6 degrees C is reflected in atmospheric warming, glacier shrinkage, and rising sea levels.

OBJECTIVES

Planetary ecosystem dynamics are being altered, challenging public health. It is predicted that morbidity and mortality will increase as a result of heat stress, as seen in recent heat waves in the U.S. Weather disaster effects will increase in number and magnitude, and both noninfectious and infectious diseases may flourish. A significant challenge will be the changes in life cycles of microbial species due to the warmer environs. Specific increases in incidence have been noted for vector-borne diseases, in addition to pulmonary findings, cardiovascular morbidity, neurological diseases, and occupational diseases.

CONCLUSIONS

Warming can be demonstrated by the observed changes that have already occurred in the environment, particularly the thinning of polar ice caps. The United States Global Research Program has been established to coordinate research activities, which responds to issues deemed important by the United Nations Framework Convention on Climate Change. Research issues pertain to the scientific uncertainties in the greenhouse effect, temperature measurements at various atmospheric levels and latitudes, and impact on biota redistribution. The Kyoto Protocol has mandated specific solutions, e.g., a 7% reduction in CO2 levels within 10 years. Future recommendations involve supporting new technologies that are available to decrease emissions as well as understanding the role that occupational and environmental specialists have in global warming recognition.