A multibiomarker approach to assess lead toxicity on the black clam,Villorita cyprinoides (Gray, 1825), from Cochin estuarine system (CES), southwest coast,India

Environmental Science and Pollution Research - The southwest coast of India along the Cochin region is seriously affected by metal contamination from increasing industrial activities. This...     

Appraisal of kinetin spraying strategy to alleviate the harmful effects of UVC stress on tomato plants

Environmental Science and Pollution Research - Increasing ultraviolet (UV) radiation is causing oxidative stress that accounts for growth and yield losses in the present era of climate change....     

A Physical Explanation of the Lognormality of Pollutant Concentrations

Investigators In different environmental fields have reported that the concentrations of various measured substances have frequency distributions that are lognormal, or nearly so. That is, when the logarithms of the observed concentrations are plotted as a frequency distribution, the resulting distribution is approximately normal, or Gaussian, over much of the observed range. Examples include radionuclides in soil, pollutants in ambient air, Indoor air quality, trace metals In streams, metals in biological tissue, calcium In human remains. The ubiquity of the lognormal distribution in environmental processes is surprising and has not been adequately explained, since common processes in nature (for example, computation of the mean and the analysis of error) usually give rise to distributions that are normal rather than lognormal. This paper takes the first step toward explaining why lognormal distributions can arise naturally from certain physical processes that are analogous to those found in the environment. In this paper, these processes are treated mathematically, and the results are illustrated in a laboratory beaker experiment that Is simulated on the computer.     

An Air Quality Modeling Study Comparing Two Possible Sites for the New International Airport for Mexico City

Abstract

Using an air quality model, two future urban scenarios induced by the construction of the new international airport for Mexico City are compared at a regional level. The air quality model couples the meteorology model MM5 and state-of-the-art photochemistry. The air quality comparison is made using metrics for the criterion gases selected for the study. From the two urban scenarios compared, the option for Tizayuca is moderately better than the option for Texcoco, because relative reductions in O₃ and other photochemical pollutants are achieved over highly populated areas. Regardless of the site, the air quality for the central region of Mexico in the future will deteriorate. In the region of central Mexico, SO₂ and NO₂ will become important pollutants.     

Atmospheric Factors Influencing Daily Sulfate Concentrations in Chicago Air

Abstract

Confidence interval construction for central tendency is a problem of practical consequence for those who must analyze air contaminant data. Determination of compliance with relevant ambient air quality criteria and assessment of associated health risks depend upon quantifying the uncertainty of estimated mean pollutant concentrations. The bootstrap is a resampling technique that has been steadily gaining popularity and acceptance during the past several years. A potentially powerful application of the bootstrap is the construction of confidence intervals for any parameter of any underlying distribution. Properties of bootstrap confidence intervals were determined for samples generated from lognormal, gamma, and Weibull distributions. Bootstrap t intervals, while having smaller coverage errors than Student's t or other bootstrap methods, under-cover for small samples from skewed distributions. Therefore, we caution against using the bootstrap to construct confidence intervals for the mean without first considering the effects of sample size and skew. When sample sizes are small, one might consider using the median as an estimate of central tendency. Confidence intervals for the median are easy to construct and do not under-cover. Data collected by the Northeast States for Coordinated Air Use Management (NESCAUM) are used to illustrate application of the methods discussed.