GeoAnalitikSarawak had a DOE station over API 100 on 41 of the 45 days in DOE’s archive (38 on fixed stations only), Johor and Negeri Sembilan on 35, in the season Indonesia logged its second-highest count of fire detections in 15 years.
Counts are satellite detections, and a detection is not a fire. Day counts are floors: DOE’s archive is missing all of 4 Sep and 67 other hours. The study follows the 2026 haze through three places: Malaysia’s air, Indonesia’s burning and the path the air took between them.
Detections, 2026Under 1,300 7 provinces1,300–2,699 6 provinces2,700–7,199 7 provinces7,200–13,999 6 provinces14,000 or more 8 provinces
Days with a station over API 1000–9 4 states10–19 2 states20–29 3 states30–39 6 states40 or more 1 state
Where the haze landed
- Every one of the 45 days in DOE’s archive had at least one station over API 100, fixed stations included; on 32 days 20 or more stations did (28 on fixed stations only).
3 of 68 stations are mobile units, so every count is given both ways. Day counts are floors: the archive holds readings for 1,000 of its 1,091 hours (Where it landed).
- Sarawak had a station over API 100 on 41 of 45 recorded days, Selangor on 37, Johor and Negeri Sembilan on 35; counting fixed stations only, Sarawak falls to 38 and Selangor to 29.
Selangor moves most: 37 days counting its Johan Setia mobile unit, 29 on fixed stations only (station by station).
- On 16 September, 26.5 million of the 32.7 million residents linked to a station lived nearest one that read over API 100 (26.4 million counting fixed stations only).
People whose nearest DOE station, within 50 km, read over 100 that day. WorldPop 2020 residents, not citizens, and not a health outcome (residents day by day).
Where it burned
- Indonesia’s vegetation fires produced 518,186 S-NPP detections from 1 January to 15 October 2026, second in 15 years after 2015’s 648,333.
A detection is not a fire. S-NPP alone. 2026 is a lower bound: the satellite gaps hide about 4.0%. NOAA ended S-NPP delivery at 13:00 UTC on 2 Nov 2026, after this study’s window; the window is unaffected, but this S-NPP-only series cannot continue (Where it burned).
- Kalimantan’s 2026 detections reached 93.5% of its 2015 count; Sumatra’s reached 33.9%.
Kalimantan’s 271,027 detections against Sumatra’s 77,848, both lower bounds (Kalimantan, Sumatra).
- Papua and West Papua recorded 86,191 detections, 1.8 times their 2015 count; they are inside Indonesia’s total and shown apart.
This study does not trace where Papua’s smoke went (Papua and West Papua).
- West Kalimantan, which borders Sarawak, has already passed every full year on record with 78,346 detections, and so have Papua (72,002) and West Papua (14,189).
Filling the gaps only raises 2026, so every record stands (the records table).
How the air travelled
- On the 26 haze days, back-trajectories say the air reaching Johor Bahru and Singapore had passed low over both Sumatran and Kalimantan detections, Sumatra more often.
Of the haze-day arrivals the ensemble agreed on, 52% at both receptors had passed low over Sumatran detections, 39% at Johor Bahru and 40% at Singapore over Kalimantan’s. No area reaches the 70% rule set before the run. The ensemble agrees on 39–40% of arrivals. Trajectories show where the air passed, not how much smoke it carried. Window: arrivals 1 Sep – 15 Oct UTC (How it travelled).
- Weighted by fire contacts instead, the trajectories lean to Kalimantan (54–56%).
A diagnostic, not part of the rule: it measures something other than the arrival rule, and the 70% rule is unchanged (the diagnostic).
Why 2026
- July–September 2026 ran at +1.69 on CPC’s Relative ONI, second since 1950 to 1997’s +1.84.
RONI first; on the older ONI, 2026 ranks first. NOAA does not endorse this study (Why 2026).
- June–September 2026 was the driest of the 12 seasons since 2015 at 10 of the 12 fire points, and August–September the driest at all 12.
ERA5 rain starts in 2015, so 1997 compares on El Niño alone. The study sets the weather beside the detections; it does not test how much of the burning the weather explains (rain at the fire points).
Read the full study, six pages, open to everyone.Where the haze landed station by station, where it burned province by province, how the air travelled, why 2026, and the method with every credit and licence.
Data and credits
- Air quality: Department of Environment Malaysia (Jabatan Alam Sekitar), Air Pollutant Index Management System (APIMS), https://eqms.doe.gov.my/APIMS/main. “Data dan maklumat tertakluk di bawah Terma Penggunaan Data Terbuka Kerajaan Malaysia 1.0” (https://www.doe.gov.my/wp-content/uploads/2021/07/TermaPenggunaan.pdf).
- Detections: “We acknowledge the use of data and/or imagery from NASA's Fire Information for Resource Management System (FIRMS) (https://earthdata.nasa.gov/firms), part of NASA's Land, Atmosphere Near real-time Capability for Earth observations (LANCE) (https://earthdata.nasa.gov/lance) and NASA's Earth Science Data and Information System (ESDIS).”
- Residents: Bondarenko M., Kerr D., Sorichetta A., and Tatem, A.J. 2020, WorldPop, University of Southampton, UK. doi:10.5258/SOTON/WP00684 (CC BY 4.0). Summed to 1 km by the study. WorldPop’s data are “not intended, and should not be used, for purposes that discriminate against, exploit, surveil, or otherwise harm individuals or vulnerable populations”.
- Weather and modelled smoke: “Contains modified Copernicus Atmosphere Monitoring Service information 2026. Contains modified Copernicus Climate Change Service information 2026. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.”
- El Niño: ONI and RONI: NOAA Climate Prediction Center (public domain).
- Boundaries: geoBoundaries (Runfola et al. 2020, https://doi.org/10.1371/journal.pone.0231866), CC BY 4.0; provinces and states contain information from OpenStreetMap, under the ODbL.
- Boundary files under the ODbL, free online at geoBoundaries’ commit-pinned URLs: github.com/wmgeolab/geoBoundaries/
raw/9469f09/ releaseData/gbOpen/ followed by IDN/ADM1/geoBoundaries-IDN-ADM1.geojson, MYS/ADM1/geoBoundaries-MYS-ADM1.geojson, TLS/ADM0/geoBoundaries-TLS-ADM0.geojson, PNG/ADM0/geoBoundaries-PNG-ADM0_simplified.geojson and THA/ADM0/geoBoundaries-THA-ADM0_simplified.geojson. We repaired and simplified them (tolerance 0.0005°) and used them to assign fire detections to provinces, states and countries. - No credited source or agency endorses this study, and no figure here is official.
- Every licence, DOI and the method: Method and credits.