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The Exposure Uncertainty Analysis: The Association between Birth Weight and Trimester Specific Exposure to Particulate Matter (PM 2.5 vs. PM 10 )

View Article: PubMed Central - PubMed

ABSTRACT

Often spatiotemporal resolution/scale of environmental and health data do not align. Therefore, researchers compute exposure by interpolation or by aggregating data to coarse spatiotemporal scales. The latter is often preferred because of sparse geographic coverage of environmental monitoring, as interpolation method cannot reliably compute exposure using the small sample of sparse data points. This paper presents a methodology of diagnosing the levels of uncertainty in exposure at a given distance and time interval, and examines the effects of particulate matter (PM) ≤2.5 µm and ≤10 µm in diameter (PM2.5 and PM10, respectively) on birth weight (BW) and low birth weight (LBW), i.e., birth weight <2500 g in Chicago (IL, USA), accounting for exposure uncertainty. Two important findings emerge from this paper. First, uncertainty in PM exposure increases significantly with the increase in distance from the monitoring stations, e.g., 50.6% and 38.5% uncertainty in PM10 and PM2.5 exposure respectively for 0.058° (~6.4 km) distance from the monitoring stations. Second, BW was inversely associated with PM2.5 exposure, and PM2.5 exposure during the first trimester and entire gestation period showed a stronger association with BW than the exposure during the second and third trimesters. But PM10 did not show any significant association with BW and LBW. These findings suggest that distance and time intervals need to be chosen with care to compute exposure, and account for the uncertainty to reliably assess the adverse health risks of exposure.

No MeSH data available.


(a) Spatiotemporal autocorrelation and (b) semivariance of daily PM2.5 and PM10 in IL and Cleveland, OH. Time-space interval refers to diagonal interval; 1 = time interval ≤1 day and distance interval ≤0.025°, 2 = time interval ≤2 days and distance interval ≤0.05°, …, 15 = time interval ≤15 days and distance interval ≤0.375°.
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ijerph-13-00906-f002: (a) Spatiotemporal autocorrelation and (b) semivariance of daily PM2.5 and PM10 in IL and Cleveland, OH. Time-space interval refers to diagonal interval; 1 = time interval ≤1 day and distance interval ≤0.025°, 2 = time interval ≤2 days and distance interval ≤0.05°, …, 15 = time interval ≤15 days and distance interval ≤0.375°.

Mentions: Exposure uncertainty was examined with the aid of spatiotemporal autocorrelation and semivariance. Using the daily PM data of IL and OH from 2000 to 2014 autocorrelation and semivariance were computed at different distance and time intervals. In IL, there were 38 and 22 sites where PM2.5 and PM10 were monitored respectively, and in OH 39 and 44 sites, respectively. There more than 40,000 data points in each of these two states for each PM type, suggesting sufficiently large dataset to assess spatiotemporal analysis. The analysis of these data suggests that there is a sharp decline in spatiotemporal autocorrelation and steep rise in semivariance with respect to increase in distance and time intervals (Figure 2a,b; Table 1; see Supplementary Materials Tables S1 and S2, and Figure S1 for details). For example, autocorrelation of PM2.5 and PM10 in Illinois within 1 day and 0.025° distance is 0.99 and 1.0 respectively, which drops to 0.89 and 0.75 within 2 days and 0.05°. The regression analysis shows that the strength of autocorrelation declines by 0.258 and 0.26 with a unit increase in distance and time interval.


The Exposure Uncertainty Analysis: The Association between Birth Weight and Trimester Specific Exposure to Particulate Matter (PM 2.5 vs. PM 10 )
(a) Spatiotemporal autocorrelation and (b) semivariance of daily PM2.5 and PM10 in IL and Cleveland, OH. Time-space interval refers to diagonal interval; 1 = time interval ≤1 day and distance interval ≤0.025°, 2 = time interval ≤2 days and distance interval ≤0.05°, …, 15 = time interval ≤15 days and distance interval ≤0.375°.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036739&req=5

ijerph-13-00906-f002: (a) Spatiotemporal autocorrelation and (b) semivariance of daily PM2.5 and PM10 in IL and Cleveland, OH. Time-space interval refers to diagonal interval; 1 = time interval ≤1 day and distance interval ≤0.025°, 2 = time interval ≤2 days and distance interval ≤0.05°, …, 15 = time interval ≤15 days and distance interval ≤0.375°.
Mentions: Exposure uncertainty was examined with the aid of spatiotemporal autocorrelation and semivariance. Using the daily PM data of IL and OH from 2000 to 2014 autocorrelation and semivariance were computed at different distance and time intervals. In IL, there were 38 and 22 sites where PM2.5 and PM10 were monitored respectively, and in OH 39 and 44 sites, respectively. There more than 40,000 data points in each of these two states for each PM type, suggesting sufficiently large dataset to assess spatiotemporal analysis. The analysis of these data suggests that there is a sharp decline in spatiotemporal autocorrelation and steep rise in semivariance with respect to increase in distance and time intervals (Figure 2a,b; Table 1; see Supplementary Materials Tables S1 and S2, and Figure S1 for details). For example, autocorrelation of PM2.5 and PM10 in Illinois within 1 day and 0.025° distance is 0.99 and 1.0 respectively, which drops to 0.89 and 0.75 within 2 days and 0.05°. The regression analysis shows that the strength of autocorrelation declines by 0.258 and 0.26 with a unit increase in distance and time interval.

View Article: PubMed Central - PubMed

ABSTRACT

Often spatiotemporal resolution/scale of environmental and health data do not align. Therefore, researchers compute exposure by interpolation or by aggregating data to coarse spatiotemporal scales. The latter is often preferred because of sparse geographic coverage of environmental monitoring, as interpolation method cannot reliably compute exposure using the small sample of sparse data points. This paper presents a methodology of diagnosing the levels of uncertainty in exposure at a given distance and time interval, and examines the effects of particulate matter (PM) ≤2.5 µm and ≤10 µm in diameter (PM2.5 and PM10, respectively) on birth weight (BW) and low birth weight (LBW), i.e., birth weight <2500 g in Chicago (IL, USA), accounting for exposure uncertainty. Two important findings emerge from this paper. First, uncertainty in PM exposure increases significantly with the increase in distance from the monitoring stations, e.g., 50.6% and 38.5% uncertainty in PM10 and PM2.5 exposure respectively for 0.058° (~6.4 km) distance from the monitoring stations. Second, BW was inversely associated with PM2.5 exposure, and PM2.5 exposure during the first trimester and entire gestation period showed a stronger association with BW than the exposure during the second and third trimesters. But PM10 did not show any significant association with BW and LBW. These findings suggest that distance and time intervals need to be chosen with care to compute exposure, and account for the uncertainty to reliably assess the adverse health risks of exposure.

No MeSH data available.