Assessment of Health Effects of Fluctuating Concentrations Using Simplified Pharmacokinetic Algorithms

Abstract

The purpose of this study was to develop a simple, practical method to improve the accuracy of assessing health effects by determining the concentration patterns inside the body resulting from fluctuating external pollutant concentrations. Linear pharmacokinetic processes were assumed, and the attenuations of the high- and low-frequency components of the external pattern when entering through the "biological window" were determined. Similar attenuations also were determined for the "time-averaged sampling window." It was shown that when the averaging time was less than 1/4 of the biological half-life, no information of biological significance was lost. Thus, a simple arithmetic step equation was proposed to convert external time-averaged concentrations to "biologically effective concentrations" proportional to internal concentrations. Calculations could readily be made in real time by a monitoring instrument, or even with a pocket calculator. Another simple algorithm was proposed for determining a "biological damage parameter" representative of cumulative damage if the body repair process is slow. Finally, simple algorithms were proposed for calculating body burdens from total absorbed mass rates, a procedure that should be useful for pollutants such as lead that may enter the body through multiple pathways. Results were compared with experimental and hypothetical data to show their utility.     

Design of Sampling Schedules

Concern with the statistical precision associated with certain pollutant sampling schedules led to comparison of two sampling schemes, designated as “modified random” and “systematic” sampling methods. A nearly uninterrupted body of data representing six years of sampling provided a basis from which to perform hypothetical “sampling” by the two methods. Comparison of results shows that the systematic method generally gave better results. The method is very simple; ft entails the selection of a sampling interval (other than 7 days or multiple of 7), the selection (randomly) of an initial sampling date, and regular sampling at the selected interval thereafter.     

The representative day

The work presents a daily trend of pollutant concentrations, referred to as the “representative day”, i.e. the day for which the overal sum of the mean-square differences between its concentration, averaged within each hour, and the concentrations for all other days at the same hour, is a minimum. The approach also allows the identification of the “least representative day” (the daily series that maximises the mean sum of squared residuals), which usually indicates an anomalous situation in pollutant dispersion, characterised by concentration maxima at the ground.     

Analytical solutions to compartmental indoor air quality models with application to environmental tobacco smoke concentrations measured in a house

This paper derives the analytical solutions to multi-compartment indoor air quality models for predicting indoor air pollutant concentrations in the home and evaluates the solutions using experimental measurements in the rooms of a single-story residence. The model uses Laplace transform methods to solve the mass balance equations for two interconnected compartments, obtaining analytical solutions that can be applied without a computer. Environmental tobacco smoke (ETS) sources such as the cigarette typically emit pollutants for relatively short times (7-11 min) and are represented mathematically by a "rectangular" source emission time function, or approximated by a short-duration source called an "impulse" time function. Other time-varying indoor sources also can be represented by Laplace transforms. The two-compartment model is more complicated than the single-compartment model and has more parameters, including the cigarette or combustion source emission rate as a function of time, room volumes, compartmental air change rates, and interzonal air flow factors expressed as dimensionless ratios. This paper provides analytical solutions for the impulse, step (Heaviside), and rectangular source emission time functions. It evaluates the indoor model in an unoccupied two-bedroom home using cigars and cigarettes as sources with continuous measurements of carbon monoxide (CO), respirable suspended particles (RSP), and particulate polycyclic aromatic hydrocarbons (PPAH). Fine particle mass concentrations (RSP or PM3.5) are measured using real-time monitors. In our experiments, simultaneous measurements of concentrations at three heights in a bedroom confirm an important assumption of the model-spatial uniformity of mixing. The parameter values of the two-compartment model were obtained using a "grid search" optimization method, and the predicted solutions agreed well with the measured concentration time series in the rooms of the home. The door and window positions in each room had considerable effect on the pollutant concentrations observed in the home. Because of the small volumes and low air change rates of most homes, indoor pollutant concentrations from smoking activity in a home can be very high and can persist at measurable levels indoors for many hours.     

Chemical speciation of individual atmospheric particles using low-Z electron probe X-ray microanalysis:: characterizing “Asian Dust” deposited with rainwater in Seoul,Korea

Chemical speciation of individual microparticles is of much interest in environmental atmospheric chemistry; e.g. the determination of the elemental concentrations in individual atmospheric aerosol particles is important to study the chemical behavior of atmospheric pollution. Recently, an EPMA technique using an X-ray detector equipped with an ultra-thin window, allowing EPMA to determine concentrations of low-Z elements, such as C, N, and O, in individual particles of micrometer size, has been developed. This technique, called low-Z electron probe X-ray microanalysis (low-Z EPMA), is applied to characterize the water-insoluble part of “Asian Dust”, deposited by washout in the form of rainwater during an Asian Dust storm event and collected in Seoul, Korea. In this study, it was demonstrated that the single particle analysis using low-Z EPMA provided detailed information on various types of chemical species in the sample. In addition to aluminosilicates, silicon oxide, iron oxide, and calcium carbonate particles, which are expected to be present, carbonaceous particles are also observed in a significant fraction. This unexpected finding that particle sample originated from an arid area contains significant amount of carbonaceous particles is supported by the investigation of a “China Loess” sample. In addition, we also performed single particle analysis for a local soil sample, in order to check the possible influence from local sources on “Asian Dust”. The characteristics of the local soil particle sample, e.g. the types of aluminosilicate particles and the abdundance of particles with deviating chemical species, are clearly different from “Asian Dust” and “China Loess” samples, whereas those two are similar, implying that the “Asian Dust” sample was not much influenced by local sources.     

Accounting for averaging time in air pollution modeling

This paper examines the validity of a commonly used expression to estimate concentrations for averaging times that are much smaller than that corresponding to the model estimate. These “peak” concentrations, which can be several times larger than the model estimate, are required in applications such as odor assessment. We show that this expression cannot be justified, and that information on short-term concentrations is only contained in the probability distribution of time-averaged concentrations. The paper proposes a simple model of concentration time series to examine the effect of averaging time on concentration probability distributions. The results from the model are compared with previous theoretical and experimental studies.     

The Precision Associated with the Sampling Frequency of Log-Normally Distributed Air Pollutant Measurements

The frequency of air monitoring necessary to characterize an air pollutant for a given time period and area is an important problem. This paper deals with the precision of measuring an air pollutant concentration. Past research has shown that the distribution of many air pollutants can be described as log-normal. Using this result equations have been developed that predict the precision of the sample mean of the air pollutant as a function of: the frequency of sampling, the standard deviation of the logarithms of the air pollution measurements, and the level of confidence. An illustration is given to demonstrate their use. The equations are used to compare sampling plans. Tables are presented showing the precision associated with five sampling plans, for three geometric standard deviations, for three levels of confidence, and five periods of time over which the sampling plan is employed.

In an Appendix a mathematical development is presented showing the theoretical derivation of the equations. With these equations the precision of a sampling plan can be determined for any level of confidence or period of time. All that is needed is an estimate of the geometric standard deviation for the air pollution measurements.

Finally, the theoretical model is applied to air monitoring data that were collected at Roselawn School in Cincinnati, Ohio, between January 3, 1968, and April 1, 1968. The 90-day period was divided into three 30-day periods. All possible samples of size three were taken from each of the 30-day periods and their means and confidence intervals were calculated. The number of times the confidence intervals contained the true means was determined. The actual number of samples accepted as having contained the true mean, for the 80, 90, 95, and 9 9% level of confidence compared favorably with the theoretical. It is concluded that the model adequately described the behavior of air pollutants.     

Computation of uncertainty for atmospheric emission projections from key pollutant sources in Spain

Emission projections are important for environmental policy, both to evaluate the effectiveness of abatement strategies and to determine legislation compliance in the future. Moreover, including uncertainty is an essential added value for decision makers. In this work, projection values and their associated uncertainty are computed for pollutant emissions corresponding to the most significant activities from the national atmospheric emission inventory in Spain. Till now, projections had been calculated under three main scenarios: “without measures” (WoM), “with measures” (WM) and “with additional measures” (WAM). For the first one, regression techniques had been applied, which are inadequate for time-dependent data. For the other scenarios, values had been computed taking into account expected activity growth, as well as policies and measures. However, only point forecasts had been computed. In this work statistical methodology has been applied for: a) Inclusion of projection intervals for future time points, where the width of the intervals is a measure of uncertainty. b) For the WoM scenario, ARIMA models are applied to model the dynamics of the processes. c) In the WM scenario, bootstrap is applied as an additional non-parametric tool, which does not rely on distributional assumptions and is thus more general. The advantages of using ARIMA models for the WoM scenario including uncertainty are shown. Moreover, presenting the WM scenario allows observing if projected emission values fall within the intervals, thus showing if the measures to be taken to reach the scenario imply a significant improvement. Results also show how bootstrap techniques incorporate stochastic modelling to produce forecast intervals for the WM scenario.     

Fine Particulate Organic Material in the Los Angeles Basin - I: Assessment of the High-Volume Brigham Young University Organic Sampling System,BIG BOSS

ABSTRACT

A multi-system, high-volume, parallel plate diffusion dénuder Brigham Young University Organic Sampling System (BIG BOSS) was tested using collocated samplers at the Pico Rivera Monitoring Station of the South Coast Air Quality Management District, South Coast Air Basin, in September 1994. Six-hr daytime and 9-hr nighttime samples were collected with a flow of about 200 L/min through each of the three systems designed to collect particles smaller than 2.5, 0.8, and 0.4 mm in a diffusion denuder sampler. Efficiency for the removal of gas phase organic compounds by the diffusion denuder was evaluated using both theoretical predictions and field measurements. Both measured and calculated data indicate high denuder efficiency for the removal of gas phase aromatic and paraffinic compounds. The precision of the BIG BOSS was evaluated using collocated samplers. The precision of determination of total carbon and elemental carbon retained by a quartz filter or of semi-volatile carbonaceous material lost from particles during sampling averaged ±7%. The precision of determination of individual organic compounds averaged ±10%. An average of 42 and 62% of the particulate organic material was semi-volatile organic compounds (SVOCs) lost from particles during sampling for daytime and nighttime samples, respectively. This “negative” sampling artifact was an order of magnitude larger than the “positive” quartz filter artifact due to adsorption of gas phase organic material. Daytime concentrations of fine particulate elemental carbon and nonvolatile organic carbon were higher than nighttime concentrations, but nighttime fine particles contained more semi-volatile organic material than daytime.     

Automatic Air Monitoring and Telemetering to Central Points In Allegheny County

This year an automatic monitoring system will supplement existing air sampling stations in Allegheny County (Pittsburgh). This system will measure pollutant levels and weather conditions at several remote sites. “On-site” recorders are equipped to telemeter the data in analog form to a central station. At the central, a multipoint recorder is supplied for each pollutant measured. This basic system, purchased for a modest initial investment, has complete capabilities for future expansion. An estimated fifteen sites will provide representative pollution data for Allegheny County. However, the basic system can be expanded to include industry installed monitoring stations—a possibilty now being considered. Further expansion to a complete Data Management system including a computer based data logger to calculate emission inventory, peak averages, and other control data is also provided for.     

Turbulent Diffusion Behind Vehicles: Effect of Traffic Speed on Pollutant Concentrations

Recent theoretical and experimental investigations Indicate that turbulent diffusion behind moving vehicles Is Influenced by the speed of the vehicle. Vertical wake induced turbulent diffusion, explicitly treated in the numerical ROADWAY model, is proportional to the square of the wind speed relative to the moving vehicle. Hence, the model predictions of turbulent mixing and pollutant concentrations on and downwind of a roadway are dependent upon the traffic speed. It Is expected from theoretical considerations that the effect of vehicle speed on pollutant concentrations will be more significant during stable atmospheric conditions, because in neutral and unstable conditions the vehicle-wake turbulence is quickly masked by the ambient turbulence. In this study, experimental data are utilized to evaluate the theoretical predictions of the effects of traffic speed on the ambient pollutant concentrations. The effects of vehicle speed upon ambient concentrations are investigated through wind tunnel experiments and field studies that used dual tracers. Consistent with predictions of the ROADWAY model, data obtained near the Long Island Expressway indicate that the influence of traffic speed on the ambient pollutant concentrations Is not significant during unstable and neutral conditions. The Long Island experiment did not provide sufficient field data to assess the model predictions of the traffic speed effect during stable atmospheric conditions.     

Air quality measurements—From rubber bands to tapping the rainbow

It is axiomatic that good measurements are integral to good public policy for environmental protection. The generalized term for “measurements” includes sampling and quantitation, data integrity, documentation, network design, sponsorship, operations, archiving, and accessing for applications. Each of these components has evolved and advanced over the last 200 years as knowledge of atmospheric chemistry and physics has matured. Air quality was first detected by what people could see and smell in contaminated air. Gaseous pollutants were found to react with certain materials or chemicals, changing the color of dissolved reagents such that their light absorption at selected wavelengths could be related to both the pollutant chemistry and its concentration. Airborne particles have challenged the development of a variety of sensory devices and laboratory assays for characterization of their enormous range of physical and chemical properties. Advanced electronics made possible the sampling, concentration, and detection of gases and particles, both in situ and in laboratory analysis of collected samples. Accurate and precise measurements by these methods have made possible advanced air quality management practices that led to decreasing concentrations over time. New technologies are leading to smaller and cheaper measurement systems that can further expand and enhance current air pollution monitoring networks.

Implications: Ambient air quality measurement systems have a large influence on air quality management by determining compliance, tracking trends, elucidating pollutant transport and transformation, and relating concentrations to adverse effects. These systems consist of more than just instrumentation, and involve extensive support efforts for siting, maintenance, calibration, auditing, data validation, data management and access, and data interpretation. These requirements have largely been attained for criteria pollutants regulated by National Ambient Air Quality Standards, but they are rarely attained for nonroutine measurements and research studies.     

Effects of wind shear on pollution dispersion

Using an accurate numerical method for simulating the advection and diffusion of pollution puffs, it is demonstrated that point releases of pollution grow into a shape reflecting the vertical wind shear profile experienced by the puff within a time scale less than 4 h. For distances beyond several 10 s of kilometers from a release point, shear-related dispersion effects are probably the dominant mechanism affecting the area and magnitude of surface impacts. For assessing long-range pollutant dispersion, the common assumption that pollutants disperse as horizontally spherical “puffs” in the atmosphere is inherently inaccurate since shear-induced horizontal spreading of pollution is not a homogeneous “turbulent-like” diffusion process. A Lagrangian puff model can simulate an area impacted by a pollution puff only if larger shear-dependent horizontal puff dispersions are assumed. However, even if impacted areas are reasonably simulated, peak concentrations will be severely underestimated since atmospheric puffs influenced by even small amounts of wind shear are nonspherical. If horizontal dispersion coefficients in a Lagrangian puff model are adjusted so that peak concentrations are correctly simulated, then the calculated pollution impact area will be severely skewed. In shear environments, no choice of horizontal dispersion coefficients in a single-puff Lagrangian model will yield reasonable correlations with puffs that are skewed into nonspherical shapes by atmospheric wind shear.     

Spatial and temporal variations in the trace elemental data over the northeastern United States

Time series of trace elemental concentrations (14 species) of atmospheric fine particles measured in the northeastern United States between 1988 and 1998 are analyzed for spatial and temporal variations. The influence of synoptic-scale systems to the transport and accumulation of pollutants can be investigated successfully if the short-term fluctuations (i.e. weather-induced variations) embedded in the time series of pollutant concentrations are separated from the original time series. The spatial short-term correlation coefficients for Al, Br, Fe, Se, and Zn permit good predictions of pollutant concentrations up to distances of about 350 km from a given monitor. The species correlation matrices for individual sites reveal that As, Br, Se, and Zn are highly correlated in New York State, while Fe, Mn, and V show also strong correlations, suggesting the commonality of source regions for these industrial and urban pollutants impacting New York.     

Carbonaceous matter and PBDEs on indoor/outdoor glass window surfaces in Guangzhou and Hong Kong,South China

Organic films, collected from indoor and outdoor window surfaces in Guangzhou and Hong Kong of South China, were analyzed to quantify their organic carbon (OC), elemental carbon (EC), and polybrominated diphenyl ethers (PBDEs) content. The highest concentrations of OC, EC, and BDE-209 were found in Guangzhou with values of 10 000 μg m^?2, 2200 μg m^?2, and 4000 ng m^?2, respectively, and the highest concentration of Σ₇PBDE (sum of BDE-28, -47, -99, -100, -153, -154 and -183) was found in Hong Kong with a value of 25 ng m^?2. In most cases, the concentrations of PBDEs were higher in the exterior films than those in the interior films with BDE-209 as the predominant congener in both cities, suggesting that PBDEs mainly come from ambient environment, and deca-BDE accounts for major PBDE consumption. The growth rates of organic film on window surfaces were fast at the beginning, and reached a consistent level afterwards. The evolution rates ranged from 2.6 to 11 nm day^?1 for “bulk film”, while from 0.06 to 0.92 nm day^?1 for “pure film”. The concentrations of PBDEs on the window surfaces did not increase with the growth time, suggesting that the window surface may provide a good place for photo-degradation of PBDEs.     

Lead Concentrations in Detroit,New York,and Los Angeles Air

Lead concentrations in air were measured at 12 sites in Detroit, New York and Los Angeles as part of a program to relate automobile emissions and polynuclear aromatic hydrocarbons in air. The information on lead is reported separately because of the current interest in lead as an air pollutant. Sampling was conducted by means of a large “absolute” filter and equipment contained in a step-van truck. A portion of the filter was macerated in nitric acid and the lead determined spectrographically. The combined annual average lead concentration for four sites in metropolitan Los Angeles was approximately 40% higher than the combined averages of either the five sites in metropolitan New York or the three sites in metropolitan Detroit. Concentrations ranged from 0.4 ug/M³ at Santa Monica, to 18.4 ug/M³ at a Los Angeles Freeway Interchange. Concentrations were generally highest in freeway areas, intermediate in commercial areas, and lowest in residential areas. They were about 40% higher in daytime than at night. Average lead concentrations were highest during autumn in New York and winter in Los Angeles reflecting an inverse relationship with wind speed. Correlation coefficients between lead and carbon monoxide, at all sites, were statistically non-zero with 99% confidence and varied from 0.75 to 0.96. Lead concentrations in this study were higher than concentrations reported by others for Detroit, New York, and Los Angeles, presumably because sampling in this study was closer to traffic. However, concentrations in this study were lower than in-traffic concentrations given in the literature.     

Toward a biologically significant and usable standard for ozone that will also protect plants

Ozone remains an important phytotoxic air pollutant and is also recognized as a significant greenhouse gas. In North America, Europe, and Asia, incidence of high concentrations is decreasing, but background levels are steadily rising. There is a need to develop a biologically significant and usable standard for ozone. We compare the strengths and weaknesses of concentration-based, exposure-based and threshold-based indices, such as SUM60 and AOT40, and examine the O(3) flux concept. We also present major challenges to the development of an air quality standard for ozone that has both biological significance and practicality in usage.     

Passive sampling of ambient, gaseous air pollutants: an assessment from an ecological perspective

During some past two decades there has been a growing interest among air pollution-vegetation effects-scientists to use passive sampling systems for quantifying ambient, gaseous air pollutant concentrations, particularly in remote and wilderness areas. On the positive side, excluding the laboratory analysis costs, passive samplers are inexpensive, easy to use and do not require electricity to operate. Therefore, they are very attractive for use in regional-scale air quality assessments. Passive samplers allow the quantification of cumulative air pollutant exposures, as total or average pollutant concentrations over a sampling duration. Such systems function either by chemical absorption or by physical adsorption of the gaseous pollutant of interest onto the sampling medium. Selection of a passive sampler must be based on its known or tested characteristics of specificity and linearity of response to the chemical constituent being collected. In addition, the effects of wind velocity, radiation, temperature and relative humidity must be addressed in the context of absorbent/adsorbent performance and sampling rate. Because of all these considerations, passive samplers may provide under- or overestimations of the cumulative exposures, compared to the corresponding data from co-located continuous monitors or active samplers, although such statistical variance can be minimized by taking necessary precautions. On the negative side, cumulative exposures cannot identify short-term (相似文献   

An Air Quality Data Analysis System for Interrelating Effects,Standards, and Needed Source Reductions: Part 13— Applying the EPA Proposed Guidelines for Carcinogen Risk Assessment to a Set of Asbestos Lung Cancer Mortality Data

Abstract

The Clean Air Act Amendments of 1990 (CAAA-90) list 189 hazardous air pollutants (HAPs) for which “safe” ambient concentrations are to be determined. The primary purpose of this paper is to develop two mathematical models, lognormal and logarithmic, that effectively express excess lung cancer mortality as a function of asbestos concentration for an example set of data and also to suggest using these two models for additional HAPs. The secondary purpose of this paper is to calculate a “safe” asbestos concentration by first assuming a default linear extrapolation (to one excess death per million people, as specified for carcinogenic HAPs). The resulting “safe” concentration is an impossible-to-achieve 1/1000 of present background asbestos concentrations. A letter to the editor and a response in this Journal issue use additional asbestos data that suggest that the “safe” concentration should be about 730 times higher than first calculated here and that a default nonlinear extrapolation should be used instead, with the “safe” concentration proportional to the desired mortality level raised to the 0.39 power. These results suggest that the most important problem in setting a “safe” concentration for each carcinogenic HAP is to determine the correct nonlinear extrapolation to use for each HAP.     

Effects of Wind Direction on Coarse and Fine Particulate Matter Concentrations in Southeast Kansas

Abstract

Field data for coarse particulate matter ([PM] PM₁₀) and fine particulate matter (PM_2.5) were collected at selected sites in Southeast Kansas from March 1999 to October 2000, using portable MiniVol particulate samplers. The purpose was to assess the influence on air quality of four industrial facilities that burn hazardous waste in the area located in the communities of Chanute, Independence, Fredonia, and Coffeyville. Both spatial and temporal variation were observed in the data. Variation because of sampling site was found to be statistically significant for PM₁₀ but not for PM_2.5. PM₁₀ concentrations were typically slightly higher at sites located within the four study communities than at background sites. Sampling sites were located north and south of the four targeted sources to provide upwind and downwind monitoring pairs. No statistically significant differences were found between upwind and downwind samples for either PM₁₀ or PM_2.5, indicating that the targeted sources did not contribute significantly to PM concentrations. Wind direction can frequently contribute to temporal variation in air pollutant concentrations and was investigated in this study. Sampling days were divided into four classifications: predominantly south winds, predominantly north winds, calm/variable winds, and winds from other directions. The effect of wind direction was found to be statistically significant for both PM₁₀ and PM_2.5. For both size ranges, PM concentrations were typically highest on days with predominantly south winds; days with calm/variable winds generally produced higher concentrations than did those with predominantly north winds or those with winds from “other” directions. The significant effect of wind direction suggests that regional sources may exert a large influence on PM concentrations in the area.