Acute respiratory infections and its Spearman correlation with meteorological and air pollutants data from Mexicali, BC, Mexico

Mexicali´s air presents high pollution mainly by breathable particulate matter (PM₁₀) and carbon monoxide (CO), although others like fine particle matter (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), and sulfur dioxide (SO₂) show important levels. It has seen that respiratory diseases of exposed populations are aggravated. To know for instance how those pollutants impact the acute respiratory infections (IRA) is important, because it helps to improve the environmental health policies. One way to know those impacts is by means of application of statistical models. The best results are gotten when the observed data sets are previously analyzed statistically. Spearman correlation offers an excellent methodology when the data sets to be used in the models are discrete and don´t show a normal distribution, such as the IRA data sets. This work presents Spearman analysis between IRA and meteorological data of temperature (temp), relative humidity (Rh), and air pollutants of PM₁₀, PM_2.5, CO, NO₂, O₃ y SO₂, from Mexicali. Also Pearson correlations between meteorological and air pollutants data are shown.