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. 2010 May;118(5):614-9.
doi: 10.1289/ehp.0901145. Epub 2010 Jan 4.

Economic activity and trends in ambient air pollution

Mary E Davis  1 , Francine Laden, Jaime E Hart, Eric Garshick, Thomas J Smith
Affiliations

Economic activity and trends in ambient air pollution

Mary E Davis et al. Environ Health Perspect. 2010 May.

Abstract

Background: One challenge in assessing the health effects of human exposure to air pollution in epidemiologic studies is the lack of widespread historical air pollutant monitoring data with which to characterize past exposure levels.

Objectives: Given the availability of long-term economic data, we relate economic activity levels to patterns in vehicle-related particulate matter (PM) over a 30-year period in New Jersey, USA, to provide insight into potential historical surrogate markers of air pollution.

Methods: We used statewide unemployment and county-level trucking industry characteristics to estimate historical coefficient of haze (COH), a marker of vehicle-related PM predominantly from diesel exhaust. A total of 5,920 observations were included across 25 different locations in New Jersey between 1971 and 2003.

Results: A mixed-modeling approach was employed to estimate the impact of economic indicators on measured COH. The model explained approximately 50% of the variability in COH as estimated by the overall R2 value. Peaks and lows in unemployment tracked negatively with similar extremes in COH, whereas employment in the trucking industry was positively associated with COH. Federal air quality regulations also played a large and significant role in reducing COH levels over the study period.

Conclusions: This new approach outlines an alternative method to reconstruct historical exposures that may greatly aid epidemiologic research on specific causes of health effects from urban air pollution. Economic activity data provide a potential surrogate marker of changes in exposure levels over time in the absence of direct monitoring data for chronic disease studies, but more research in this area is needed.

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Figures

Figure 1
Figure 1
COH trend in New Jersey (1971–2003).
Figure 2
Figure 2
COH and unemployment in New Jersey. The y-axis represents scaled COH (COH × 10) super-imposed with a measure of the percentage of the unemployment rate. Arrows indiate the relationship between unemployment rates and COH.
Figure 3
Figure 3
COH and trucking employment in New Jersey. The y-axis represents scaled COH (COH × 80) superimposed with a measure of the density of trucking employees per square mile.
Figure 4
Figure 4
Annual correlation coefficient of COH (log transformed) and trucking employment density.
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