[HTML][HTML] Correlating respiratory disease incidences with corresponding trends in ambient particulate matter and relative humidity
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 (PM 10 and PM 2.5). The …
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 (PM 10 and PM 2.5). The …
Abstract
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.
Elsevier
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