Publications·January 08, 2026
This ESCAP Policy Brief (2026) examines how geostationary satellite remote sensing, specifically the Geostationary Environment Monitoring Spectrometer (GEMS) onboard GEO-KOMPSAT-2B (GK-2B), can strengthen air pollution monitoring and policy in Southeast Asia. It builds on implementation experience from the PAPGAPi project (Building the Pan-Asia Partnership for Geospatial Air Pollution Information), conducted between 2021–2024 in collaboration with ESCAP, KOICA, NIER, and national agencies across eight Asia-Pacific countries
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1. Core Objective
The brief’s central aim is to demonstrate how combining:
Space-based observations (GEMS)
Ground-based hyperspectral spectrometers (Pandora instruments)
And regional cooperation mechanisms
can enhance geospatial air pollution intelligence, particularly for transboundary pollution, seasonal biomass burning, and urban-industrial emissions.
The project addresses a major structural challenge in Southeast Asia: insufficient ground-based monitoring density, limited integration of satellite data into operational air quality management, and weak coordination on cross-border pollution
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2. Technical Architecture: GEMS + Pandora
GEMS (Satellite System)
GEMS is a hyperspectral geostationary UV–visible spectrometer providing hourly daytime observations (6–10 times per day) across Asia
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NO₂ (tropospheric column)
AOD (Aerosol Optical Depth)
Other trace gases (SO₂, HCHO, O₃, etc.)
Because it is geostationary, GEMS provides:
High temporal resolution
Real-time daytime monitoring
Regional-scale coherence detection
However, limitations include:
No nighttime observations
CCD-related latitude-band data gaps (12°N artifacts)
Reduced accuracy under clouds, high humidity, and complex surface reflectance
Incomplete coverage in parts of Indonesia and Mongolia
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Pandora (Ground-based Network)
Between 2021–2024, 20 Pandora spectrometers were installed across participating countries
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Pandora instruments:
Are ground-based hyperspectral spectrometers
Measure column concentrations of NO₂, SO₂, HCHO, O₃
Are coordinated under the Pandonia Global Network (PGN)
They serve three functions:
Satellite validation (“ground truth”)
Long-term reference observations
Targeted campaign monitoring
Annexes show coverage tables (Annex I & II) indicating which Pandora sites fall under GEMS observational modes
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3. Country Case Studies (GEMS + Pandora)
The brief provides detailed analyses of NO₂ and AOD seasonality for:
Cambodia
Indonesia
Lao PDR
Mongolia
Philippines
Thailand
Viet Nam
The satellite maps (Figures 3–9) illustrate monthly mean NO₂ and AOD patterns
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Key Cross-Country Patterns
(A) Dual Air Quality Structure
The report identifies a dual structure of air quality drivers:
Persistent anthropogenic emissions
Urban centers (Bangkok, Hanoi, Phnom Penh, Metro Manila)
Industrial zones
Ports and transport corridors
→ Reflected in year-round elevated NO₂
Episodic biomass burning events
December–April (dry season)
Slash-and-burn agriculture
Forest fires
Peat fires (Indonesia)
→ Reflected in large-scale AOD spikes and seasonal NO₂ enhancement
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This dual structure is particularly visible in Thailand, Lao PDR, Cambodia, and Viet Nam.
(B) Transboundary Transport
The brief strongly emphasizes regional coherence of pollution patterns.
Examples:
Northern Thailand + Laos + Cambodia fire season
Indonesian peat smoke transport
Regional haze across maritime Southeast Asia
Dust transport affecting Mongolia
Air quality cannot be interpreted within national boundaries alone. Long-range transport, shared meteorology, and atmospheric circulation patterns drive cross-border pollution dynamics
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This is a major justification for regional monitoring frameworks.
(C) Meteorological Modulation
Satellite retrievals show:
Dry-season accumulation
Monsoon humidity-related AOD enhancement
Dust-driven aerosol peaks in Mongolia (March–July)
Reduced dispersion during stagnant atmospheric conditions
The brief stresses the interaction between:
Emission sources
Seasonal meteorology
Topography
Boundary-layer dynamics
4. Identified Limitations
The “Limitations” section (p. 19) clearly acknowledges several constraints
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Spatial coverage gaps (Indonesia, Mongolia)
CCD sensor artifacts
Cloud contamination
Surface reflectance challenges
Vertical profile assumptions in NO₂ retrieval
Absence of nighttime monitoring
Retrieval uncertainties during monsoon
This transparent discussion reinforces the need for integrated multi-platform observation systems.
5. Policy Recommendations
The final section provides three strategic pillars:
1️⃣ Densification of Ground Networks
Establish additional supersites
Create overlap sites in satellite transition zones
Deploy mobile platforms
Improve representativeness and validation
This improves:
Satellite validation
Diurnal understanding
Local emission source attribution
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2️⃣ Interoperable Multi-Satellite Integration
Combine geostationary (GEMS) and LEO satellites
Cross-calibration and harmonization
Structured integration frameworks
Use emerging constellations
This addresses:
Nighttime gaps
Spatial coverage
Uncertainty characterization
Long-term record continuity
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3️⃣ Institutionalization
Embed satellite monitoring into:
National mandates
Routine decision-making
Early warning systems
Regional data-sharing mechanisms
QA/QC frameworks
The brief argues that sustainable impact requires formal institutional embedding, not just project-based monitoring
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Key Words from the Report
GEMS, Pandora, PAPGAPi, Geostationary Environment Monitoring Spectrometer, NO₂, AOD, hyperspectral spectrometer, transboundary pollution, biomass burning, dust transport, satellite retrieval uncertainty, ground truth validation, densification of network, interoperability, institutionalization, early warning systems, cross-border emission dynamics, regional haze, geospatial air pollution information.