Ambient PM_2.5 Exposure Modeling in LMICs: An Example from Peru.

Area of Science:

• Environmental Health
• Air Quality Monitoring
• Public Health Research

Background:

• Fine particulate matter (PM2.5) presents a significant public health risk, particularly in low- and middle-income countries (LMICs).
• Peru faces high ambient PM2.5 concentrations, exceeding WHO guidelines, with inadequate monitoring hindering research and policy.
• Existing air pollution monitoring gaps in LMICs necessitate innovative approaches for exposure assessment.

Purpose of the Study:

• To review strategies for creating national PM2.5 databases in LMICs.
• To detail efforts in Peru to establish a nationwide PM2.5 monitoring and exposure modeling system.
• To address data scarcity for health research and policy development in Peru.

Main Methods:

• Established a nationwide network of 176 low-cost sensors (LCS) across Peru, including urban and rural areas.
• Developed a hybrid modeling approach integrating LCS data, satellite remote sensing, chemical transport models, and machine learning.
• Collaborated with national environmental agencies for sensor deployment and data validation.

Main Results:

• The hybrid model demonstrated strong predictive performance in Lima (R² = 0.88) compared to regulatory monitors.
• The LCS network covers all 24 regions of Peru, with 62.5% in urban and 37.5% in rural settings.
• Ongoing work aims for daily, 5-km² resolution modeling across Peru for 2024-2026.

Conclusions:

• The hybrid approach provides a scalable solution for high-resolution PM2.5 exposure modeling in data-scarce regions.
• Sustainability hinges on local capacity building, long-term funding, and integration with regulatory networks.
• This initiative can inform public health interventions and environmental policy in Peru and other LMICs.