| 26 May 2026
Long-term BVOC Fluxes in a Suburban Tokyo Forest: Insights from Integrated Drone and Tower Observations
Abstract. Biogenic volatile organic compounds (BVOC) substantially impact regional photochemical air pollution, global climate change, and the carbon cycle. Although research on BVOC emissions is of paramount importance, only few studies have measured long-term BVOC fluxes from forest ecosystems. There are no long-term observational studies on BVOC emissions from suburban forests of major cities in Asia under a humid subtropical climate, which may have a major impact on urban air quality. We conducted long-term, multi-height BVOC observations at a 30 m flux tower in suburban Tokyo, evaluating isoprene emission flux from a Quercus serrata dominated mixed forest using the aerodynamic gradient method. Spatial variability was examined through integrated drone and tower observations, and Model of Emission Gases and Aerosols from Nature (MEGAN) estimates were compared with measurements. Isoprene volume mixing ratios increased significantly during the warm season (May–October), accounting for over 90% of BVOC composition in peak summer, while monoterpenes remained low with minimal vertical gradients. Isoprene exhibited distinct vertical volume mixing ratio gradients peaking within the canopy, with daily average emission flux ranging from −0.05 to 15.30 mg·m⁻²·h⁻¹. Horizontal volume mixing ratio variability within 30 m reached 10–30%, with enhanced heterogeneity in summer. Horizontal flux differed by approximately 30% between tower (height: 23–30 m) and drone (30–40 m) measurements. MEGAN systematically overestimated observations with maximum deviations in summer. These findings, derived from long-term observations, will contribute to assessing the impact of BVOCs on air quality and climate in cities worldwide, beyond temperate humid regions of Asia, and to reducing model uncertainties.
The article reports how the authors measured volatile organic compounds emitted by a suburban forest in Tokyo, predominantly composed of Quercus serrata. Their analyses used a flux tower to analyze isoprene and seven other monoterpenes. These analyses were also combined with drone measurements.
I carefully read the manuscript several times to try to understand the scientific nature of this research. I'm very interested in long-term BVOC measurements, so I tried to understand what you meant by long-term. In your research, you sampled a total of 34 days, spread over three years (10 days in 2023, 12 in 2024, and 12 in 2025). You write that you sampled 1-3 days each month (line 148) and between 10 a.m. and 4 p.m. (line 180). When you sampled three days in the same month only in July 2023, two days in the same month in October 2023, July 2024, May 2025, July 2025, and August 2025. The sampling times, however, are from 10:00 to 12:00 and from 14:00 to 16:00, with rare exceptions where sporadic sampling appears between 12:00 and 14:00. All this to reflect on whether this campaign can be treated as a long-term campaign. It seems not to me, I don't even understand how it's possible to make a comparison between seasons, group months in different years when there's only one day per month per year, and above all consider a campaign that covers 34 days over three years exhaustive. Especially at times that seem arbitrarily decided. So I suggest at least changing the title, even when talking about integrated observations between drone and tower. How can these observations be truly integrated if they aren't made during the same periods, where the tower makes a vertical profile while the drones, used only on rare summer days, conduct horizontal sampling for a maximum of 15 minutes due to drone limitations? Or perhaps I misunderstood that you only used drones on October 12, 2023, July 23, 2024, July 17, 2025, and July 29, 2025. What information can these samplings actually provide? At a distance of 15 to 30 meters from the tower, in a forest composed mostly of the same vegetation?
Perhaps your paper concerns the use of a particular methodology rather than the results of long-term sampling. The suggestion, therefore, would be to focus more on the technique used and reduce the portion of the results to the data supporting your technique, without relying too heavily on annual and monthly/seasonal comparisons. Push more on the potential of the technique, on how you have implemented it, on its potentials and limitations.
Going into the specifics of the paper, I suggest: