Characterizing the Temporal Variation of Airborne Particulate Matter in an Urban Area Using Variograms
This study aims to determine the optimal frequency for monitoring airborne pollutants in densely populated urban areas to effectively capture their temporal variations. While environmental organizations worldwide typically update air quality data hourly, there is no global consensus on the ideal mon...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Air |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2813-4168/3/1/7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This study aims to determine the optimal frequency for monitoring airborne pollutants in densely populated urban areas to effectively capture their temporal variations. While environmental organizations worldwide typically update air quality data hourly, there is no global consensus on the ideal monitoring frequency to adequately resolve pollutant (particulate matter) time series. By applying temporal variogram analysis to particulate matter (PM) data over time, we identified specific measurement intervals that accurately reflect fluctuations in pollution levels. Using January 2023 air quality data from the Joppa neighborhood of Dallas, Texas, USA, temporal variogram analysis was conducted on three distinct days with varying <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>PM</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> (particulate matter of size ≤ <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.5</mn><mo> </mo><mi mathvariant="sans-serif">μ</mi><mi mathvariant="normal">m</mi></mrow></semantics></math></inline-formula> in diameter) pollution levels. For the most polluted day, the optimal sampling interval for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>PM</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula> was determined to be 12.25 s. This analysis shows that highly polluted days are associated with shorter sampling intervals, highlighting the need for highly granular observations to accurately capture variations in PM levels. Using the variogram analysis results from the most polluted day, we trained machine learning models that can predict the sampling time using meteorological parameters. Feature importance analysis revealed that humidity, temperature, and wind speed could significantly impact the measurement time for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>PM</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>. The study also extends to the other size fractions measured by the air quality monitor. Our findings highlight how local conditions influence the frequency required to reliably track changes in air quality. |
|---|---|
| ISSN: | 2813-4168 |