# Correlacion de material particulado pm 2.5 en funcion a la humedad relativa -- precipitacion usando python, en la ciudad del cusco

## TL;DR

This study analyzes the relationship between PM 2.5 levels, relative humidity, and precipitation in Cusco using Python, applying correlation coefficients to understand their statistical connections during 2016-2018.

## Contribution

It introduces a Python-based method to evaluate the correlation between air quality and meteorological variables in Cusco, Peru, during 2016-2018.

## Key findings

- Identified significant positive and negative correlations between PM 2.5 and humidity.
- Demonstrated the use of Python for statistical analysis of environmental data.
- Validated correlations using Student's t-distribution at 5% significance level.

## Abstract

The study utilizes data corresponding to the average monthly concentration of particulate matter 2.5 (P.M. 2.5), relative humidity, and precipitation during the period 2016-2018 collected by the High Vol station of the Energy and Atmosphere Center, and the meteorological center SENAHMI (Kayra-UNSAAC). In order to estimate the statistical relationship between two quantitative variables, the Pearson and Spearman correlation coefficients are used, which were obtained using the Python programming language. The obtained t-value is compared with the Student's t-distribution table for a certain level of significance $(\alpha = 0.05)$. Positive and negative correlation values are established based on the obtained coefficients, and the interpretation of the Pearson and Spearman correlation values is used to determine if the data is related or not.