Determining Temporal and Spatial Variations in Ozone Air Quality

Rao, S. Trivikrama; Zalewsky, Eric; Žurbenko, Igor G.

doi:10.1080/10473289.1995.10467342

Cited by 79 publications

(46 citation statements)

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“…Similarly diverse applications have been seen in studies of water pollution ( e.g., Baker et al, 1978;Lebowitz et al, 1992;Rao et al, 1995 ) and hazardous waste (e.g., Lagakos et al, 1986;Lampi et al, 1992;Gamble and Lewis, 1996;Molholland et al, 1998 ). Exposure indicators using residence location are easy and inexpensive to construct, and increased use is anticipated given advances in geographic information systems (GIS ) and databases that simplify identification of potentially polluted areas (Clark et al, 1982;Budnick et al, 1984;Geschwind et al, 1992;Dayal et al, 1995 ).…”

Section: Discussionmentioning

confidence: 99%

Residence location as a measure of environmental exposure: a review of air pollution epidemiology studies

Huang

Batterman

2000

J Expo Sci Environ Epidemiol

161

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Residence location has long been used to indicate environmental exposure in many epidemiological studies. This indicator is easy to establish, requires little exposure or monitoring data, and is potentially applicable to many types of investigations. The validity, accuracy and utility of residence location as an exposure indicator, however, is challenged by current concerns regarding multiple exposure pathways, persistent and toxic contaminants, and cumulative exposures from non -point, mobile and point sources. This paper reviews 45 epidemiological studies that use residence location to identify study populations and estimate air pollution exposures. Thirteen ( 29% ) of the studies determined environmental exposures based on``proximity'' measures, usually the distance from a subject's residence to a pollutant source. Other studies used``zones'' presumed to have equal pollutant levels. Several studies combined zone and proximity approaches. Exposures were quantified using monitoring data in 27 ( 60% ) studies and dispersion modeling in two ( 4% ) studies. Sixteen ( 36% ) studies did not use any environmental data to quantify exposure. A total of 31 ( 69% ) of the studies reported significant associations between health endpoints and the pollutant exposures represented by residence location. In general, comprehensive and systematic approaches to identify and estimate population exposures were not used, and the exposure estimates were therefore deemed likely to have great uncertainty. Unless exposure levels among groups are verified, it cannot be determined whether nonsignificant associations between exposures and health endpoints indicate a lack of measurable health effects, or are merely a result of exposure misclassification. Site -specific and quantitative exposure assessments are needed to better quantify and confirm exposures within such studies, as well as to permit interpretations and comparisons across studies. Journal of Exposure Analysis and Environmental Epidemiology (2000) 10, 66 ± 85.

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Section: Discussionmentioning

confidence: 99%

Residence location as a measure of environmental exposure: a review of air pollution epidemiology studies

Huang

Batterman

2000

J Expo Sci Environ Epidemiol

161

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“…Limited studies have indicated this is indeed the case. Ding et al (2008) analyzed O 3 data from the MOZAIC (measurement of ozone and water vapor by Airbus in-service aircraft) program and obtained a 2 % year −1 rate of increase in the daytime O 3 in the lower troposphere over Beijing and its surrounding areas for the period of 1995-2005 analyzed the TOR (tropospheric ozone residue) data over the period 1979-2005 and found a significant upward trend in tropospheric O 3 over the North China Plain for all seasons except for winter, with a maximum rate of increase of 1.10 DU per decade for summer. Wang et al (2009) found that surface O 3 at a regional station in Hong Kong increased at an average rate of 0.58 ppb year −1 from 1994 to 2007 and they associated the trend with an increase in tropospheric NO 2 .…”

Section: Introductionmentioning

confidence: 99%

Significant increase of surface ozone at a rural site, north of eastern China

et al. 2016

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Abstract. Ozone pollution in eastern China has become one of the top environmental issues. Quantifying the temporal trend of surface ozone helps to assess the impacts of the anthropogenic precursor reductions and the likely effects of emission control strategies implemented. In this paper, ozone data collected at the Shangdianzi (SDZ) regional atmospheric background station from 2003 to 2015 are presented and analyzed to obtain the variation in the trend of surface ozone in the most polluted region of China, north of eastern China or the North China Plain. A modified KolmogorovZurbenko (KZ) filter method was performed on the maximum daily average 8 h (MDA8) concentrations of ozone to separate the contributions of different factors from the variation of surface ozone and remove the influence of meteorological fluctuations on surface ozone. Results reveal that the short-term, seasonal and long-term components of ozone account for 36.4, 57.6 and 2.2 % of the total variance, respectively. The long-term trend indicates that the MDA8 has undergone a significant increase in the period of 2003-2015, with an average rate of 1.13 ± 0.01 ppb year −1 (R 2 = 0.92). It is found that meteorological factors did not significantly influence the long-term variation of ozone and the increase may be completely attributed to changes in emissions. Furthermore, there is no significant correlation between the longterm O 3 and NO 2 trends. This study suggests that emission changes in VOCs might have played a more important role in the observed increase of surface ozone at SDZ.

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“…[5][6][7]11,12 Several studies have used Pearson correlation coefficients to characterize spatial variability in ambient air pollutant concentrations. [13][14][15][16][17] Other studies have used analysis of variance to characterize variability in air pollutant concentrations between specific monitoring sites. 18 -20 Analysis of variance provides a measure of variation between monitoring sites relative to variation over time at one monitoring site and is particularly useful when several sites with different average concentrations are considered.…”

Section: Introductionmentioning

confidence: 99%