Uncertain penalties and compliance: experimental evidence

Environmental Economics and Policy Studies - We present the results of a series of economic laboratory experiments designed to study the compliance behavior of polluting firms when penalties are...     

An Epidemiological Study of Exposure to Coal Tar Pitch Volatiles Among Coke Oven Workers

Recent studies of mortality among coke plant workers indicate that there is an excess of respiratory cancer among men employed at the coke ovens and that the mortality is related to work areas and length of exposure to coal tar effluents, the body of information presented in this paper is directed to categorization of coke oven jobs into different work areas in terms of exposure to coal tar pitch volatiles developing an index of cumulative exposure to investigate the dose-response relationship between exposure and mortality. The exposure data have been taken from a study conducted by the Pennsylvania Department of Health, State Division of Occupational Health, and mortality data are based on a long-term study of steelworkers, conducted by the Department of Biostatistics, University of Pittsburgh. A summary index calculated for each worker combining the level of exposure and length of time exposed indicates that, as expected, both these factors are related to the development of cancer, particularly cancers of the respiratory system.

Conclusions of note are: Measured levels of coal tar pitch volatiles are 2-3 times higher for men who work at the top of coke ovens as compared to men employed at the side of the ovens. Even though the specific carcinogenic element or elements in the coke oven environment are not known at the present time, in-. creasing levels of exposure to coal tar pitch volatiles are related to art increased risk of dying from malignant neoplasms. Further coal tar pitch volatile measurements need to be made and analyses designed which will specify the dose-response relationship more precisely. This will provide information necessary to evaluate the recommended Threshold Limit Value.     

What Is the Potential Measurement Error in Occupational Exposure Studies?

ABSTRACT

This paper demonstrates statistical methods that estimate measurement error from available industrial hygiene data. Errors in measuring a continuous exposure variable may arise when all individuals in a work area are assigned the same exposure. An example is when the mean of exposure measurements obtained on a sample of individuals is assigned to all workers with similar jobs. This may lead to inaccurate point and interval estimates in exposure-response modeling. A method of simulating the distribution of true (i.e., unobserved) individual exposures is described in order to estimate the mean and variance of measurement error. The minimum variance unbiased estimator approximates the mean of lognormally distributed exposure measurements. The distribution of true individual exposures is approximated by the distribution of simulated estimates of mean exposure. The methodology is illustrated by exposure data from work areas manufacturing refractory ceramic fiber (RCF) and RCF products. Results show that exposure is slightly underestimated in work areas with between 25 and 113 exposure measurements; measurement error variance averages about 1.3% of the total variance.     

Binary logistic regression—Instrument for assessing museum indoor air impact on exhibits

This paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The prediction of the impact on the exhibits during certain pollution scenarios (environmental impact) was calculated by a mathematical model based on the binary logistic regression; it allows the identification of those environmental parameters from a multitude of possible parameters with a significant impact on exhibitions and ranks them according to their severity effect. Air quality (NO₂, SO₂, O₃ and PM_2.5) and microclimate parameters (temperature, humidity) monitoring data from a case study conducted within exhibition and storage spaces of the Romanian National Aviation Museum Bucharest have been used for developing and validating the binary logistic regression method and the mathematical model. The logistic regression analysis was used on 794 data combinations (715 to develop of the model and 79 to validate it) by a Statistical Package for Social Sciences (SPSS 20.0). The results from the binary logistic regression analysis demonstrated that from six parameters taken into consideration, four of them present a significant effect upon exhibits in the following order: O₃>PM_2.5>NO₂>humidity followed at a significant distance by the effects of SO₂ and temperature. The mathematical model, developed in this study, correctly predicted 95.1 % of the cumulated effect of the environmental parameters upon the exhibits. Moreover, this model could also be used in the decisional process regarding the preventive preservation measures that should be implemented within the exhibition space.

Implications: The paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The mathematical model developed on the environmental parameters analyzed by the binary logistic regression method could be useful in a decision-making process establishing the best measures for pollution reduction and preventive preservation of exhibits.     

Assessment of contamination from arsenical pesticide use on orchards in the Great Valley region, Virginia and West Virginia, USA

Lead arsenate pesticides were widely used in apple orchards from 1925 to 1955. Soils from historic orchards in four counties in Virginia and West Virginia contained elevated concentrations of As and Pb, consistent with an arsenical pesticide source. Arsenic concentrations in approximately 50% of the orchard site soils and approximately 1% of reference site soils exceed the USEPA Preliminary Remediation Goal (PRG) screening guideline of 22 mg kg(-1) for As in residential soil, defined on the basis of combined chronic exposure risk. Approximately 5% of orchard site soils exceed the USEPA PRG for Pb of 400 mg kg(-1) in residential soil; no reference site soils sampled exceed this value. A variety of statistical methods were used to characterize the occurrence, distribution, and dispersion of arsenical pesticide residues in soils, stream sediments, and ground waters relative to landscape features and likely background conditions. Concentrations of Zn, Pb, and Cu were most strongly associated with high developed land density and population density, whereas elevated concentrations of As were weakly correlated with high orchard density, consistent with a pesticide residue source. Arsenic concentrations in ground water wells in the region are generally <0.005 mg L(-1). There was no spatial association between As concentrations in ground water and proximity to orchards. Arsenic had limited mobility into ground water from surface soils contaminated with arsenical pesticide residues at concentrations typically found in orchards.