Robust Estimation of Carbon Monoxide Measurements

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Statistical Analysis

Background:

• Air quality monitoring is crucial for public health.
• Carbon monoxide (CO) is a significant air pollutant.
• Long-term trend analysis of CO concentrations provides insights into emission patterns and control effectiveness.

Purpose of the Study:

• To analyze the trend of carbon monoxide (CO) concentrations at the Belisario air-quality monitoring station in Quito, Ecuador, from 2008 to 2019.
• To assess whether CO levels pose a health risk based on the Quito Air Quality Index.
• To apply robust statistical methods for reliable trend determination.

Main Methods:

• Utilized robust location and scale estimators for central tendency and dispersion.
• Employed classic, nonparametric, bootstrap, and robust confidence intervals for data categorization.
• Quantified differences between categories using confidence intervals to determine CO concentration trends.

Main Results:

• A statistically significant downward trend in CO concentration was observed at the Belisario station over the 12-year period (2008-2019).
• Nonparametric and robust statistical methods confirmed the precision of the downward trend.
• CO concentrations were found to be below the health risk threshold defined by the Quito Air Quality Index.

Conclusions:

• The long-term monitoring data robustly indicates a decreasing trend in CO pollution at the Belisario station.
• Current CO levels in this area do not represent a public health concern based on established air quality guidelines.
• Robust statistical techniques provide reliable evidence for environmental policy assessment and public health management.