AI Summary & Key Takeaways
AI InsightMultiple linear regression analysis adequatelyexplained 99% of the variation in the RDI trends as a function of the trends in ambient PM2.5 and relativehumidity (RH); 77% of the variation was explained by the trends in PM2.5 and 22% by RH.
📌 3 Key Takeaways
Özet
Investigation over 14 months was undertaken at a representative rural location in the state of HimachalPradesh 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 seriesdata on RDI cases from public health centers of Jawali, the sampling location, was obtained along withthe corresponding time series data of ambient particulate matter (PM) concentrations in two sizefractions (PM10 and PM2.5). The time series of PM associated carbon forms d elemental carbon (EC), blackcarbon (BC), organic carbon (OC), and UV absorbing organic compounds (UVOC)d and meteorologicalfactors were taken into consideration as explanatory variables. De-composition of respective time seriesdata-sets using Empirical Ensemble Mode De-composition of separating trends from the multiple cyclicinfluences of variable periods enabled to establish a correlation in the RDI trends with trends in ambientPM2.5 concentrations and Relative Humidity (RH). Multiple linear regression analysis adequatelyexplained 99% of the variation in the RDI trends as a function of the trends in ambient PM2.5 and relativehumidity (RH); 77% of the variation was explained by the trends in PM2.5 and 22% by RH.