An application of probability density function for the analysis of PM2.5 concentration during the COVID-19 lockdown period

Area of Science:

• Environmental Science: Analysis of atmospheric pollutant concentrations.
• Public Health: Impact of air quality on human health.
• Data Science: Application of Probability Density Functions (PDF) for data distribution analysis.

Background:

• The COVID-19 pandemic led to unprecedented lockdowns globally, including India starting March 24, 2020.
• These restrictions on human activities provided a unique opportunity to study atmospheric pollutant levels.
• Particulate Matter 2.5 (PM 2.5) is a critical air pollutant affecting human health, necessitating regulatory measures.

Observation:

• PM 2.5 data from India, China, France, Brazil, and the USA during their respective 2020 lockdown periods were analyzed.
• A detailed study focused on India, dividing it into Central India, Coastal India, and the Indo-Gangetic Plain (IGP).
• Hourly PM 2.5 concentrations from March 24 to June 15, 2020, were compared with the same period in 2019.

Findings:

• The Probability Density Functions (PDF) method was employed to analyze PM 2.5 data distribution.
• Statistical indices, including the Chi-square test, were used to assess the 'goodness-of-fit' for the PDFs.
• A significant reduction in PM 2.5 concentrations was observed during the 2020 lockdown compared to 2019.

Implications:

• The study provides valuable data on background atmospheric pollutant concentrations during periods of reduced human activity.
• Findings can assist regulatory bodies in developing and implementing effective air quality control plans.
• Understanding PM 2.5 reduction dynamics informs strategies for mitigating air pollution and protecting public health.