What are Time Averages?

Understanding Time Averages in MyAir

The Time Average is the Interval of the alert/analysis, both averaging time and interval. So 60mins checks every 60mins for a 60min average

“Time Started” is the beginning of the hour. “Time Ending” is the end of the hour. i.e the Time Average is between 09.00 and 10.00

Where are Time Averages used In MyAir?

• You select both the Time Average (the averaging duration) and the Interval (how often data is checked).

• Example: a 60‑minute average means the system checks every 60 minutes based on data averaged over that period.

Purpose of Time Averages

• Reveal patterns: They help identify how pollution levels change over time.

• Compare with standards: Averaged values make it easier to assess compliance with health or regulatory guidelines.

• Support decision-making: They provide insights for industries, researchers, and policymakers in managing air quality.

Types of Time Averages

• Short-Term (≤ 60 minutes):Detect rapid fluctuations caused by traffic, domestic, or industrial activities. Useful for identifying short-term exposure peaks and generating responsive alerts.

• Long-Term (≥ 8 hours):Represent stable daily or workday averages, useful for tracking trends, assessing overall air quality, and checking compliance with daily health standards.

Why use Short Time Averages rather than Longer Term Averages?

• Captures Short-Term Variations: Fifteen-minute averages can detect rapid fluctuations in pollutant levels caused by traffic changes, weather conditions, or industrial activities, which a 24-hour average might miss.

• Improved Health Insights: Many health effects related to air pollution can occur due to short-term exposure to high concentrations of pollutants. Understanding these fluctuations can help identify peak exposure times and improve public health advisories.

• More Responsive Data: Frequent data collection allows for timely responses to air quality issues. If pollution levels spike, immediate action can be taken, such as issuing alerts or implementing traffic regulations.

• Enhanced Research Opportunities: Researchers can analyse the relationship between short-term air quality events and health or environmental impacts more effectively with high-resolution data.

• Informing Localised Strategies: Planners and policymakers can better target interventions or regulations in specific areas or times when pollution levels are highest, improving air quality management.

• Detailed Temporal Analysis: Shorter averaging periods allow for a more nuanced understanding of daily patterns, helping to identify trends throughout the day that could inform program implementation.

• Real-Time Monitoring Capabilities: Short intervals support the use of real-time air quality monitoring systems, providing instant feedback to communities and stakeholders.

Why use Longer Time Averages rather than Shorter Term Averages?

• Simplicity and Clarity: 24-hour averages provide a straightforward and easily understood measure of air quality over a complete day, making it easier for the public and policymakers to interpret the data.

• Smoothing of Variability: Daily averages can smooth out short-term fluctuations and anomalies that may occur within a day, offering a clearer indication of overall air quality trends without being overly influenced by transient spikes.

• Compliance with Standards: Many air quality standards and regulations are based on daily averages, so using this timeframe aligns with existing guidelines and compliance measures.

• Resource Efficiency: Monitoring systems may require more resources in terms of data storage, processing, and analysis for 15-minute data. Using 24-hour averages can reduce the volume of data that needs to be managed.

• Long-Term Analysis: Daily averages are often more suitable for long-term trend analysis and comparison over months or years, as they provide a stable dataset that can be more meaningful for assessing air quality changes over time.

• Focus on Health Outcomes: Certain health effects related to air pollution, such as chronic conditions, may be more closely associated with long-term exposure levels rather than short-term fluctuations. Daily averages may thus be more relevant for some health studies.

• Mitigating Noise: High-frequency data can sometimes introduce noise into analyses, making it harder to draw meaningful conclusions. Daily averages can help mitigate this issue by filtering out random variations.