Northeim Kari, Marks Constant, Tiwari Chetan
Department of Biological Sciences, University of North Texas, Denton, TX, USA.
Department of Computer Science and Engineering, University of North Texas, Denton, TX, USA.
PeerJ. 2021 Apr 13;9:e11066. doi: 10.7717/peerj.11066. eCollection 2021.
In urban environments, environmental air pollution poses significant risks to respiratory health. Moreover, the seasonal spatial variability of the air pollutant ozone, and respiratory illness within Dallas-Fort Worth (DFW) is not well understood. We examine the relationships between spatial patterns of long-term ozone exposure and respiratory illness to better understand impacts on health outcomes. We propose that this study will establish an enhanced understanding of the spatio-temporal characteristics of ozone concentrations and respiratory emergency room visits (ERV) incidence.
Air pollution data (ozone) and ERV incidence data from DFW was used to evaluate the relationships between exposures and outcomes using three steps: (1) develop a geostatistical model to produce quarterly maps of ozone exposure for the DFW area; (2) use spatial analysis techniques to identify clusters of zip codes with high or low values of ozone exposure and respiratory ERV incidence; and (3) use concentration-response curves to evaluate the relationships between respiratory ERV incidence and ozone exposure.
Respiratory ERV incidence was highest in quarters 1 and 4, while ozone exposure was highest in quarters 2 and 3. Extensive statistically significant spatial clusters of ozone regions were identified. Although the maps revealed that there was no regional association between the spatial patterns of high respiratory ERV incidence and ozone exposure, the concentration-response analysis suggests that lower levels of ozone exposure may still contribute to adverse respiratory outcomes.
在城市环境中,环境空气污染对呼吸健康构成重大风险。此外,达拉斯 - 沃思堡(DFW)地区空气污染物臭氧的季节性空间变异性以及呼吸道疾病尚未得到充分了解。我们研究长期臭氧暴露的空间模式与呼吸道疾病之间的关系,以更好地理解对健康结果的影响。我们认为,本研究将增进对臭氧浓度和呼吸道急诊室就诊(ERV)发病率的时空特征的理解。
使用DFW的空气污染数据(臭氧)和ERV发病率数据,通过三个步骤评估暴露与结果之间的关系:(1)建立一个地理统计模型,生成DFW地区臭氧暴露的季度地图;(2)使用空间分析技术识别臭氧暴露和呼吸道ERV发病率高或低的邮政编码区域集群;(3)使用浓度 - 反应曲线评估呼吸道ERV发病率与臭氧暴露之间的关系。
呼吸道ERV发病率在第1季度和第4季度最高,而臭氧暴露在第2季度和第3季度最高。确定了广泛的具有统计学意义的臭氧区域空间集群。虽然地图显示呼吸道ERV高发病率的空间模式与臭氧暴露之间没有区域关联,但浓度 - 反应分析表明,较低水平的臭氧暴露仍可能导致不良的呼吸道结果。