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Correlating respiratory disease incidences with corresponding trends in ambient particulate matter and relative humidity

机译:将呼吸系统疾病的发生率与周围颗粒物和相对湿度的相应趋势相关联

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摘要

Investigation over 14 months was undertaken at a representative rural location in the state of Himachal Pradesh to understand the putative correlation between the reported high Respiratory Disease Incidences (RDI) with air/particulate pollution exposure in a time series based investigations. Time series data on RDI cases from public health centers of Jawali, the sampling location, was obtained along with the corresponding time series data of ambient particulate matter (PM) concentrations in two size fractions (PM10 and PM2.5). The time series of PM associated carbon forms — elemental carbon (EC), black carbon (BC), organic carbon (OC), and UV absorbing organic compounds (UVOC)— and meteorological factors were taken into consideration as explanatory variables. De-composition of respective time series data-sets using Empirical Ensemble Mode De-composition of separating trends from the multiple cyclic influences of variable periods enabled to establish a correlation in the RDI trends with trends in ambient PM2.5 concentrations and Relative Humidity (RH). Multiple linear regression analysis adequately explained 99% of the variation in the RDI trends as a function of the trends in ambient PM2.5 and relative humidity (RH); 77% of the variation was explained by the trends in PM2.5 and 22% by RH.
机译:在一个基于喜马al尔邦的代表性农村地区进行了为期14个月的调查,以了解基于时间序列的调查中报告的高呼吸道疾病发病率(RDI)与空气/微粒污染暴露之间的假定相关性。获得了来自贾瓦利公共卫生中心(样本位置)RDI病例的时间序列数据,以及对应的两个大小分数(PM 10 和PM)中环境颗粒物(PM)浓度的时间序列数据。 2.5 )。与PM相关的碳形式(元素碳(EC),黑碳(BC),有机碳(OC)和吸收紫外线的有机化合物(UVOC))的时间序列和气象因素被视为解释变量。使用经验整合模式分解各个时间序列数据集将趋势与可变周期的多个周期性影响分离开来,可以建立RDI趋势与周围PM 2.5 趋势的相关性浓度和相对湿度(RH)。多元线性回归分析充分解释了RDI趋势中99%的变化是环境PM 2.5 和相对湿度(RH)趋势的函数; 77%的变化由PM 2.5 的趋势解释,而22%的由RH的趋势解释。

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