Assessing long-term spatiotemporal patterns of PM2.5 using hybrid geostatistical modeling

Area of Science:

• Environmental Science
• Public Health
• Atmospheric Science

Background:

• Air pollution, specifically fine particulate matter (PM2.5), presents significant environmental and public health challenges globally, especially in rapidly urbanizing areas.
• Long-term spatiotemporal assessment of PM2.5 is crucial for effective air quality management and pollution mitigation strategies.
• Pakistan is identified as one of the world's most polluted countries, necessitating detailed investigation into its persistent particulate pollution patterns.

Purpose of the Study:

• To examine the long-term spatiotemporal distribution of PM2.5 concentrations in Pakistan over multiple decades.
• To investigate changes in PM2.5 patterns across successive decades using advanced modeling techniques.
• To provide a quantitative basis for improved air quality management and policy formulation in response to persistent pollution.

Main Methods:

• Employed a hybrid spatial interpolation (HSI) framework integrating machine learning algorithms with regression kriging for enhanced prediction accuracy.
• Conducted a decade-by-decade analysis to capture temporal changes in PM2.5 distribution.
• Utilized advanced geostatistical and hybrid modeling approaches for spatiotemporal analysis.

Main Results:

• PM2.5 concentrations in Pakistan remained consistently elevated, frequently exceeding WHO annual guidelines ($5\mu {\text{g,m}}^{-3}$).
• Evidence suggests increasing extremes and spatial expansion of pollution rather than a uniform rise in average levels.
• A persistent southwest to northeast pollution orientation was identified along the Indus corridor and extending to central plains, including major cities.

Conclusions:

• The study highlights the persistence and spatial expansion of PM2.5 concentrations in Pakistan.
• The developed HSI framework demonstrated improved interpolation performance for refined spatial PM2.5 pattern representation.
• Findings underscore the urgent need for targeted air quality management and policy interventions to address severe and expanding particulate pollution.