ENSO Modulation of PM<sub>2.5</sub> air pollution in Central Kalimantan, Indonesia revealed by a dense network of Purple Air sensors

Graham, Ailish M.; Spracklen, Dominick V.; McQuaid, James B.; Rigby, Richard; Nurrahmawati, Hanun; Ayona, Devina; Salmayenti, Resti; Kusin, Kitso; Jaya, Adi; Smith, Thomas E. L.; Alifindira, Annisa; Al Banna Choiruzzad, Shofwan; Pope, Richard

doi:10.5194/egusphere-2026-2746

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https://doi.org/10.5194/egusphere-2026-2746

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25 Jun 2026

| 25 Jun 2026

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

ENSO Modulation of PM_2.5 air pollution in Central Kalimantan, Indonesia revealed by a dense network of Purple Air sensors

Ailish M. Graham, Dominick V. Spracklen, James B. McQuaid, Richard Rigby, Hanun Nurrahmawati, Devina Ayona, Resti Salmayenti, Kitso Kusin, Adi Jaya, Thomas E. L. Smith, Annisa Alifindira, Shofwan Al Banna Choiruzzad, and Richard Pope

Abstract. Peatland fires in Indonesia drive severe air pollution. Studies have focused on El Niño years, therefore neutral and La Niña years remain uncharacterised. We deploy a dense network of PM_2.5 sensors across Central Kalimantan peatlands between August 2023 and October 2025 to quantify how El Niño–Southern Oscillation (ENSO) modulates the magnitude and spatial variability in dry season PM_2.5 concentrations. Sensors were installed at urban, rural, and remote locations, spanning El Niño (2023), neutral (2024), and La Niña (2025) dry seasons. During the 2023 El Niño dry season, low rainfall and deep water tables enhanced peatland flammability and supported extensive peat fires. Urban and rural sites exceeded the WHO 24‑hour PM_2.5 guideline on 99 % and 97 % of days. A remote site exceeded these guidelines on 85 % and 24 % of days, with fire smoke influence confirmed by low spatial variability and a dual‑peak diurnal cycle across sites, indicating regional pollution rather than local sources. As ENSO conditions shifted to neutral (2024) and La Niña (2025), increased rainfall and shallower water tables reduced fire activity and PM_2.5 concentrations. In 2024, WHO guideline exceedances fell to 41 % and 12 % at urban and rural sites. Our results indicate that even across non-El Niño years, dry season PM_2.5 is influenced by regional fire emissions, but the magnitude and spatial variability is modulated by ENSO-phase. Our results demonstrate that dense sensor networks can distinguish regional fire smoke from local pollution, enabling early detection of fire‑driven air quality degradation. Reducing peatland fires through restoration and fire management would deliver consistent air quality benefits.

Received: 13 May 2026 – Discussion started: 25 Jun 2026

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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CC1:
'Comment on egusphere-2026-2746', Nima Zafarmomen, 05 Jul 2026 reply
The manuscript presents a multi-year air-quality monitoring study examining how ENSO phase modulates PM2.5 pollution from Indonesian peatland fires in Central Kalimantan. The authors use a dense PurpleAir sensor network deployed across urban, rural, and remote sites from August 2023 to October 2025, covering El Niño, neutral, and La Niña dry seasons. The study shows that the 2023 El Niño dry season produced severe peat drying, extensive fire activity, and high PM2.5 exposure, while wetter neutral and La Niña conditions in 2024 and 2025 reduced fire activity and PM2.5 concentrations. The manuscript is timely and valuable because it demonstrates how dense low-cost sensor networks can distinguish regional fire-smoke influence from local pollution sources and provide practical evidence for early-warning and peatland management strategies.
The manuscript should more clearly state its main novelty. The study combines ENSO-phase comparison, peatland hydrology, fire activity, PM2.5 measurements, and dense low-cost sensor monitoring. The authors should clarify whether the primary contribution is the multi-year ENSO comparison, the dense sensor-network design, or the source-regime interpretation using spatial variability and diurnal cycles.

The sensor-network design should be discussed more explicitly because the number and location of sensors changed substantially across years. The authors should clarify how changes in sensor density, site composition, and data availability may affect comparisons among the 2023, 2024, and 2025 dry seasons.

The relative humidity correction is important for low-cost PM2.5 measurements in tropical environments. The authors should provide a clearer explanation of how the correction affects high-concentration fire-smoke periods and whether the same correction is appropriate across El Niño, neutral, La Niña, wet, and dry season conditions.

The use of coefficient of variation to distinguish regional smoke from local emissions is interesting, but the interpretation should be strengthened. The authors should explain more clearly what CoV threshold or pattern indicates regional fire-smoke dominance and how this metric behaves when sensor numbers change across the study period.

The manuscript links ENSO, rainfall, water-table depth, fire activity, and PM2.5 concentrations. This climate–hydrology–fire–air quality pathway is compelling, but the authors should be cautious with causal language. The analysis would be stronger if the authors more explicitly distinguish observed associations from mechanistic causation.

The authors are strongly recommended to cite recent work on high-spatiotemporal-resolution monitoring of traffic-related particulate pollution in complex urban environments. In particular, Yeganeh, B., Shakerdonyavi, A., Zafarmomen, N., and Taheri, A.: Comprehensive spatiotemporal analysis of long-term mobile monitoring for traffic-related particles in a complex urban environment, Atmospheric Pollution Research, 102870, 2025, is highly relevant. Citing this work would strengthen the manuscript’s discussion of dense monitoring strategies, spatial heterogeneity in PM2.5 exposure, and the broader importance of high-resolution particulate pollution observations.

Several figures are useful but visually dense, especially Figures 2, 3, and 4. The authors should improve readability by increasing font sizes, simplifying legends, and ensuring that dry-season shading, ENSO labels, and site categories are easily distinguishable.

Reply
Citation: https://doi.org/10.5194/egusphere-2026-2746-CC1
- CC2:
  'Reply on CC1', Zhiheng Liao, 06 Jul 2026 reply
  
  Dear Editor and Authors:
  I have serious doubts about the true purpose of Nima Zafarmomen's eponymous community comments on multiple ACPD articles (including this article). Nima Zafarmomen has recently posted numerous comments on papers covering very different topics (see attached comments), and in all of them, he strongly recommends citing his own article, "Comprehensive spatiotemporal analysis of long-term mobile monitoring for traffic-related particles in a complex urban environment," which is scheduled to appear in Atmospheric Pollution Research only in May 2026. In my humble opinion, that paper has no essential relevance to the articles he comments on. Moreover, Nima Zafarmomen's academic background (hydrometeorology) is far removed from the research directions relevant to the articles he comments on. Given these circumstances, I question the genuine intent behind Nima Zafarmomen's submitted eponymous community comments, and I would kindly ask the handling editor and the manuscript authors to give full consideration to the reliability of his comments.
  
  Zhiheng Liao
  Institute of Urban Meteorology, China Meteorological Administration, Beijing, China
  
  Reply
  
  Citation: https://doi.org/10.5194/egusphere-2026-2746-CC2
  - CC3: 'Reply on CC2', Nima Zafarmomen, 06 Jul 2026 reply
    
    Thank you for sharing your opinion. However, your comment appears to be based on assumptions about my background and motivations rather than on the scientific content of my community comment. Like you, I participated in the discussion by providing my scientific opinion. Whether any suggested reference is relevant is entirely for the authors and the handling editor to decide. If you believe there are scientific flaws in my comments, I would be happy to discuss those specific points.
    Otherwise, I believe it is more constructive to focus on the science rather than on personal assumptions about other contributors.
    
    Reply
    
    Citation: https://doi.org/10.5194/egusphere-2026-2746-CC3
    
    CC4: 'Reply on CC3', Zhiheng Liao, 06 Jul 2026 reply
    
    I have no right to decide; I am only offering my opinion to the handling editor and the authors. They will make the final judgment on the scientific soundness and reliability of your comments.
    
    Reply
    
    Citation: https://doi.org/10.5194/egusphere-2026-2746-CC4
    
    CC5: 'Reply on CC4', Nima Zafarmomen, 06 Jul 2026 reply
    
    Exactly. Then I believe the discussion should focus on the scientific merits of my comment, not on assumptions about my background or intentions. The handling editor and the authors will decide its relevance.
    
    Reply
    
    Citation: https://doi.org/10.5194/egusphere-2026-2746-CC5

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Short summary

Indonesia peatlands have been extensively drained for agriculture, but this makes them very flammable in periods of drought, causing widespread air pollution. We measured fine particulate pollution across Central Kalimantan from 2023 to 2025 using >40 sensors. Pollution was highest during dry El Niño conditions but still degraded air pollution in wetter years. Thus, showing that climate strongly controls fires and air quality, and that reducing fires could improve air quality every year.


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ENSO Modulation of PM2.5 air pollution in Central Kalimantan, Indonesia revealed by a dense network of Purple Air sensors

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