Air & waste : journal of the Air & Waste Management Association最新文献

Medical (biomedical) wastes pose numerous potential health and safety hazards. In addition to their infectious and toxic characteristics, the highly variable and inconsistent nature of medical waste streams has increased public concern about storage, treatment, transportation, and ultimate disposal. In recent years, techniques have been developed to reduce human exposure to the toxic and infectious components of medical wastes. The most commonly used techniques include internal segregation, containment, and incineration. Other common techniques include grinding, shredding, and disinfection, e.g., autoclaving and chemical treatment followed by landfilling. Of all the available technologies for medical waste treatment and disposal, incineration has been found to be the most effective method overall for destroying infectious and toxic material, volume reduction, and weight reduction in the medical waste stream. Incineration destroys the broadest variety of medical waste constituents and can recover energy from the medical waste stream. Incineration also is an appropriate alternative to burial of human pathological remains.

Measuring emissions from nonuniform area sources, such as waste repository sites, has been a difficult problem. A simple but reliable method is not available. An objective method of inverting downwind concentration measurements, utilizing an assumed form of atmospheric dispersion to reconstruct total emission rate and distribution, is described in this study. The Gaussian dispersion model is compared to a more realistic model based on K-theory and similarity expressions. A sensitivity analysis is presented indicating the atmospheric conditions under which a successful application of the method could be anticipated. Field releases of sulfur hexafluoride (SF6) from a simulated area source in flat terrain were conducted to check the method, ability to reconstruct source distribution, and total emission rate. The sensitivity analysis and the field study confirm that a few ground-level concentration measurements and a simple determination of the atmospheric dispersion characteristics are sufficient, under neutral to stable conditions, to obtain the total emission rate accurately. Reconstruction of the spatial pattern of the source is possible by utilizing concentration information from samplers located on two separate ground-level receptor lines, if a shift in the wind direction occurs and if it can be assumed that the total emission rate is time invariant. A method of cross-checking the accuracy of the reconstruction, using a simultaneous tracer release, is presented.

This paper presents a statistical method for filtering out or moderating the influence of meteorological fluctuations on ozone concentrations. Use of this technique in examining trends in ambient ozone air quality is demonstrated with ozone data from a monitoring location in New Jersey. The results indicate that this method can detect changes in ozone air quality due to changes in emissions in the presence of meteorological fluctuations. This method can be useful in examining the effectiveness of regulatory initiatives in improving ozone air quality.

Carbon monoxide (CO) exposures were measured inside a motor vehicle during 88 standardized drives on a major urban arterial highway, El Camino Real (traffic volume of 30,500-45,000 vehicles per day), over a 13-1/2 month period. On each trip (lasting between 31 and 61 minutes), the test vehicle drove the same 5.9-mile segment of roadway in both directions, for a total of 11.8 miles, passing through 20 intersections with traffic lights (10 in each direction) in three California cities (Menlo Park, Palo Alto, and Los Altos). Earlier tests showed that the test vehicle was free of CO intrusion. For the 88 trips, the mean CO concentration was 9.8 ppm, with a standard deviation of 5.8 ppm. Of nine covariates that were examined to explain the variability in the mean CO exposures observed on the 88 trips (ambient CO at two fixed stations, atmospheric stability, seasonal trend function, time of day, average surrounding vehicle count, trip duration, proportion of time stopped at lights, and instrument type), a fairly strong seasonal trend was found. A model consisting of only a single measure of traffic volume and a seasonal trend component had substantial predictive power (R2 = 0.68); by contrast, the ambient CO levels, although partially correlated with average exposures, contributed comparatively little predictive power to the model. The CO exposures experienced while drivers waited at the red lights at an intersection ranged from 6.8 to 14.9 ppm and differed considerably from intersection to intersection.(ABSTRACT TRUNCATED AT 250 WORDS)

Open path Fourier transform infrared (OP-FTIR) spectroscopy is a new air monitoring technique that can be used to measure concentrations of air contaminants in real or near-real time. OP-FTIR spectroscopy has been used to monitor workplace gas and vapor exposures, emissions from hazardous waste sites, and to track emissions along fence lines. This paper discusses a statistical process control technique that can be used with air monitoring data collected with an OP-FTIR spectrometer to detect departures from normal operating conditions in the workplace or along a fence line. Time series data, produced by plotting consecutive air sample concentrations in time, were analyzed. Autocorrelation in the time series data was removed by fitting dynamic models. Control charts were used with the residuals of the model fit data to determine if departures from defined normal operating conditions could be rapidly detected. Shewhart and exponentially weighted moving average (EWMA) control charts were evaluated for use with data collected under different room air flow and mixing conditions. Under rapidly changing conditions the Shewhart control chart was able to detect a leak in a simulated process area. The EWMA control chart was found to be more sensitive to drifts and slowly changing concentrations in air monitoring data. The time series and statistical process control techniques were also applied to data obtained during a field study at a chemical plant. A production area of an acrylonitrile, 1,3-butadiene, and styrene (ABS) polymer process was monitored in near-real time. Decision logics based on the time series and statistical process control technique introduced suggest several applications in workplace and environmental monitoring. These applications might include signaling of an alarm or warning, increasing levels of worker respiratory protection, or evacuation of a community, when gas and vapor concentrations are determined to be out-of-control.

As the field of epidemiology grows and multiple studies of the same topic are more frequently available, increased focus is placed on quantitative methods for synthesis of results to yield an overall conclusion. A major difficulty encountered in practice has been the lack of convenient methodology for addressing groups of studies which are similar, but not exactly alike, in features which may affect study results. The age group from which subjects were selected, the general health of subjects when selected, and the specific health endpoint examined are examples of such features. Some previous investigators have addressed the problem using iterative techniques, although most have opted for simpler models which assume that differences in the studies do not appreciably affect the outcome under investigation. That is, he studies are taken to be homogeneous in that the underlying effect being investigated is the same in each study. This paper presents a random-effects linear regression technique which allows differences in the individual study features. The proposed methodology does not require iterative or other complicated procedures, making it more readily accessible to the applied researcher. We demonstrate this technique on a set of studies of the health effects of indoor NO2 exposure in children. It is seen that odds ratios from these studies vary considerably according to subject age, the study location, and the health endpoint considered. A simple synthesis which does not account for these differences may be misleading.

Estimates of human exposure to soil are often needed to investigate potential risks to public health from toxicants released into the environment. Using the results of two recent tracer studies, estimates of average daily soil ingestion in young children and over a lifetime were ascertained. After establishing the distribution of the recoveries of the tracers in adults, the most reliable tracers were identified using an analysis of variance and Tukey's multiple comparison procedure. The identified reliable tracers were then employed to derive estimates of mean daily soil ingestion in young children. Ingestion rates were first adjusted to address the age differences of the children enrolled in the studies. A mean daily intake and variance were then determined. Estimates of soil ingestion over a lifetime were established based on levels determined in children.

Desirable qualities of a risk assessment procedure for use in routine assessment of the impact of new and modified stationary sources of carcinogenic emissions are: (1) readily available analysis techniques and (2) simplicity when applied to small sources. Regulatory Gaussian models have these qualities but are limited by their accuracy at large distances and the difficulty of calculating the cancer incidence. Calculation of risk for the maximally exposed individual (MEI risk) and cancer incidence is discussed, and the relations found among MEI risk, de minimus individual risk, cancer incidence, population density, carcinogenic source strength, release conditions, maximum distance to de minimus individual risk, release period, and distance to nearest receptor found from application of these models to a typical situation are described. Suggestions for setting values for maximum allowable MEI risk, maximum allowable cancer incidence and de minimus individual risk are also presented. Several types of carcinogenic sources are examined for their cancer impact. The effect of various maximum allowable exposure parameter values on the source's acceptability is also examined. Screening methods for both MEI risk and cancer incidence is discussed. Application of the analysis method to numerous sources is presented, including use of an empirical equation for cancer incidence.