Limits...
Considerations in the use of ozone and PM(2.5) data for exposure assessment.

White WH - Air Qual Atmos Health (2009)

Bottom Line: The US national ambient-air monitoring network, created to verify compliance with health-based standards, now doubles as an important source of exposure data for the epidemiological analyses on which these standards increasingly rest, particularly in the case of ozone and PM(2.5).This paper was written for a workshop called to facilitate and inform the use of routine ozone and PM(2.5) data by the Environmental Public Health Tracking Network.It examines the fit between priorities that shape regulatory monitoring and modeling and the data needs of public health tracking.

View Article: PubMed Central - PubMed

Affiliation: Crocker Nuclear Laboratory, University of California, Davis, California, USA.

ABSTRACT
The US national ambient-air monitoring network, created to verify compliance with health-based standards, now doubles as an important source of exposure data for the epidemiological analyses on which these standards increasingly rest, particularly in the case of ozone and PM(2.5). This paper was written for a workshop called to facilitate and inform the use of routine ozone and PM(2.5) data by the Environmental Public Health Tracking Network. It examines the fit between priorities that shape regulatory monitoring and modeling and the data needs of public health tracking.

No MeSH data available.


Related in: MedlinePlus

Pollution climate of Line City. Blue bar maps the linear arrangement of emissions sources and monitor. Rectangles above the bar show x–z distributions of atmospheric concentrations under four different meteorological regimes. Curved arrows link individual meteorological regimes to individual points in graphs below the bar. These graphs plot city-average concentrations of the harmful agent X against monitored concentrations of X and the generic air quality indicator I
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Fig3: Pollution climate of Line City. Blue bar maps the linear arrangement of emissions sources and monitor. Rectangles above the bar show x–z distributions of atmospheric concentrations under four different meteorological regimes. Curved arrows link individual meteorological regimes to individual points in graphs below the bar. These graphs plot city-average concentrations of the harmful agent X against monitored concentrations of X and the generic air quality indicator I

Mentions: Increasing the number of species measured at a central monitoring station—adding species that are peculiar to particular source types, for example—can provide useful insights (Laden et al. 2000). The added value of such information for time series analyses can be limited, however, by the substantial temporal covariance typically observed among all species (e.g., Burnett et al. 2000, Table 17). Figure 3 sketches a general mechanism by which the localized health effects of specific primary emissions can be misattributed to more broadly distributed indicators such as PM2.5 or ozone, even when the toxic species is itself monitored. The idealized Line City is a collection of pollutant sources and two neighborhoods arrayed along an east–west axis. The two neighborhoods are bracketed by sources of the broadly distributed indicator species I. The neighborhoods themselves bracket the sole source of X, the primary pollutant actually affecting health. Each source generates a plume of effluent to the east or west depending on wind direction. Line City winds blow from the east on half of the days and from the west on the others. Both neighborhoods receive I emissions every day, but this I is mixed with unhealthful X in only one neighborhood at a time. Wind speed and mixing depth combine each day, independently of wind direction, to yield either good or poor synoptic ventilation. Only one of the two neighborhoods has air quality monitors. Because the I monitor always sees the effects of ventilation, even when the X monitor has nothing to measure, the I measurements give a better indication of overall community exposure to X than do the available measurements of X itself.Fig. 3


Considerations in the use of ozone and PM(2.5) data for exposure assessment.

White WH - Air Qual Atmos Health (2009)

Pollution climate of Line City. Blue bar maps the linear arrangement of emissions sources and monitor. Rectangles above the bar show x–z distributions of atmospheric concentrations under four different meteorological regimes. Curved arrows link individual meteorological regimes to individual points in graphs below the bar. These graphs plot city-average concentrations of the harmful agent X against monitored concentrations of X and the generic air quality indicator I
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2805789&req=5

Fig3: Pollution climate of Line City. Blue bar maps the linear arrangement of emissions sources and monitor. Rectangles above the bar show x–z distributions of atmospheric concentrations under four different meteorological regimes. Curved arrows link individual meteorological regimes to individual points in graphs below the bar. These graphs plot city-average concentrations of the harmful agent X against monitored concentrations of X and the generic air quality indicator I
Mentions: Increasing the number of species measured at a central monitoring station—adding species that are peculiar to particular source types, for example—can provide useful insights (Laden et al. 2000). The added value of such information for time series analyses can be limited, however, by the substantial temporal covariance typically observed among all species (e.g., Burnett et al. 2000, Table 17). Figure 3 sketches a general mechanism by which the localized health effects of specific primary emissions can be misattributed to more broadly distributed indicators such as PM2.5 or ozone, even when the toxic species is itself monitored. The idealized Line City is a collection of pollutant sources and two neighborhoods arrayed along an east–west axis. The two neighborhoods are bracketed by sources of the broadly distributed indicator species I. The neighborhoods themselves bracket the sole source of X, the primary pollutant actually affecting health. Each source generates a plume of effluent to the east or west depending on wind direction. Line City winds blow from the east on half of the days and from the west on the others. Both neighborhoods receive I emissions every day, but this I is mixed with unhealthful X in only one neighborhood at a time. Wind speed and mixing depth combine each day, independently of wind direction, to yield either good or poor synoptic ventilation. Only one of the two neighborhoods has air quality monitors. Because the I monitor always sees the effects of ventilation, even when the X monitor has nothing to measure, the I measurements give a better indication of overall community exposure to X than do the available measurements of X itself.Fig. 3

Bottom Line: The US national ambient-air monitoring network, created to verify compliance with health-based standards, now doubles as an important source of exposure data for the epidemiological analyses on which these standards increasingly rest, particularly in the case of ozone and PM(2.5).This paper was written for a workshop called to facilitate and inform the use of routine ozone and PM(2.5) data by the Environmental Public Health Tracking Network.It examines the fit between priorities that shape regulatory monitoring and modeling and the data needs of public health tracking.

View Article: PubMed Central - PubMed

Affiliation: Crocker Nuclear Laboratory, University of California, Davis, California, USA.

ABSTRACT
The US national ambient-air monitoring network, created to verify compliance with health-based standards, now doubles as an important source of exposure data for the epidemiological analyses on which these standards increasingly rest, particularly in the case of ozone and PM(2.5). This paper was written for a workshop called to facilitate and inform the use of routine ozone and PM(2.5) data by the Environmental Public Health Tracking Network. It examines the fit between priorities that shape regulatory monitoring and modeling and the data needs of public health tracking.

No MeSH data available.


Related in: MedlinePlus