Transboundary flow of pollutants in the atmosphere is a major global issue . This is very concerning in some parts such as East Asia where highly polluting regions can significantly impact the atmospheric composition at large distances downwind due to the transport of long-lived atmospheric pollutants. As well as the transport of primary pollutants, secondary, en route production of pollutants such as ozone (O3) and particulate matter (PM) can impact the concentration of these pollutants at long distances from their sources .

Ozone in the troposphere is produced from the oxidation of primary emitted compounds such as volatile organic compounds (VOCs), carbon monoxide (CO) and methane (CH4) in the presence of oxides of nitrogen (NOx). Thus increases in the emissions of these compounds can be expected to lead to increases in the concentration of O3 downwind of the emission sites. The rapid industrialization in East Asia has led to increased emissions of these primary compounds, and this in turn has led to increases in the concentration of O3 . Over the coming decades, there are predictions for this trend to continue . It is therefore important to measure the regional concentrations of atmospheric pollutants such as O3 and its precursors (VOCs, NOx, CO, etc.) in East Asian source regions and downwind over the coming years.

Finding suitable sites for making these long-term downwind measurements is, however, difficult. Sites need to be remote from local influences, yet sufficiently accessible for staff to visit for instrument maintenance and upgrades and for data to be transmitted back for processing, etc. Hateruma Island is a small island (12.7 km2) located off the coasts of both Taiwan and Japan's main islands and so is subject to East Asian outflow (Fig. a and b). It is the southernmost inhabited island (24.05∘ N, 123.80∘ E) in the Japanese archipelago, 500 km south-west of Japan's Okinawa Island, and is 250 km off the coast of Taiwan in the Pacific Ocean . As a part of its global monitoring effort, the Japanese National Institute for Environmental Studies' (NIES) Centre for Global Environmental Research (CGER) operates an atmospheric observatory on the island to carry out atmospheric measurements. CGER has made measurements of atmospheric constituents at the site over a number of years. During the winter the site is characterized by northerly winds and elevated pollution influenced by emissions from East Asian countries, whereas in the summer the air comes from the south and is typically cleaner . Observations of atmospheric composition at Hateruma Island have been used to analyse different problems. For example, and used observations from Hateruma to assess national emissions of hydrofluorocarbons (HFCs) and hydrochlorofluorocarbons (HCFCs) from China, Korea and Japan. continuously measured the atmospheric mixing ratios of perfluorocarbons (PFCs) at Hateruma Island and Cape Ochi-ishi since 2006, to infer their global and regional emissions. used observation of carbon monoxide (CO), carbon dioxide (CO2) and methane (CH4) to assess changing emissions from China and then went on to use a similar technique to assess the drop in CO2 emissions from China due to COVID-19 regulations .

A wider range of observations are made at the site than have been previously published. These include measurements of CO, ethane (C2H6), propane (C3H8), nitrogen oxides (NOx and NOy) and O3. Although these observations themselves are useful, the use of a chemical transport model allows observations to be put into the context of our wider understanding of atmospheric emissions – deposition, transport and chemistry allowing.

Here we use a chemical transport model (GEOS-Chem) together with observations of the key atmospheric gases (CO, C2H6, C3H8, NO, NO2, NOy and O3) measured at the observatory in Hateruma in 2018 to evaluate the model's ability to simulate the long-range transport of key species to the island of Hateruma and our understanding of emissions in the region. We start with a description of the observations made at the site (Sect. ) and provide a meteorological context for the observations using back trajectories. We then describe the GEOS-Chem model configuration in Sect.  and show an evaluation of the model performance in Sect. . Based on this evaluation we assess the sensitivity of the model to biomass burning emissions in Sect. , and in Sect.  we explore scaling anthropogenic Asian C2H6 and C3H8 emissions to better reflect the observations at the site. We draw conclusions in Sect. 7.

In this section, we describe the observations made at the site used for this study and the meteorological context of the air arriving at the site.

We use a chemical transport model, GEOS-Chem version 12.7.1 (10.5281/zenodo.3676008, ) to help analyse the measurements made at Hateruma. The model was first described by  but has had substantial improvements since then (https://geoschem.github.io/, last access: 11 August 2023). We use a regional simulation with a spatial resolution of 0.5∘ × 0.625∘ over the domain shown in Fig. a. The model includes a detailed HOx–NOx–VOC-ozone–halogen–aerosol tropospheric chemistry as described by  and is driven by offline meteorology from the NASA Global Modelling and Assimilation Office (http://gmao.gsfc.nasa.gov, last access: 5 September 2021) forward-processing product (GEOS-FP).

To generate restart files and boundary conditions, the model was run in a global configuration at a 4∘ × 5∘ horizontal resolution for 2 years (1 January 2017–1 January 2019) with the first year regarded as spin-up. We then run the model in its regional configuration using the boundary conditions derived from the global simulation and output the result every hour. Figures – in the Appendix show a comparison between the model simulations run at the native meteorological resolution of 0.25∘ × 0.3125∘ and the simulation run at 0.5∘ × 0.625∘. The differences between the two models are minimal, so we adopt the coarser 0.5∘ × 0.625∘ resolution version as it is substantially faster to run with only a small degradation in performance.

We run the model in a slightly different emission configuration than its default. We use the Community Emissions Data System (CEDS) emissions with applied monthly and diurnal variability as described in for all anthropogenic emissions other than for those from aircraft where we use the Aviation Emissions Inventory Code (AEIC) . This contrasts with the model default configuration which uses and for anthropogenic ethane and propane emissions but does makes our simulation consistent with recent Coupled Model Intercomparison Project (CMIP) model evaluations . The CEDS emissions were available at 0.1∘ resolution and the Harmonized Emissions Component (HEMCO) module ) interpolates all emissions to the grid resolution (0.5∘ × 0.625∘) of the model.

We also include geological emissions of ethane and propane that may represent a normally missing source . We assume a global total of 3.0 Tg yr-1 of ethane and 1.7 Tg yr-1 of propane . We spatially distribute these emissions using a geological methane emission dataset . This source represents only 10 %–20 % of the global emissions of both ethane and propane, and we do not believe that these geological emissions are of specific importance for this region.

Other emissions include offline soil NOx and online lightning NOx emissions . The PARANOX ship plume model by , which calculates the ageing of emissions in ship exhaust plumes for NOx, HNO3 and O3 was applied to the CEDS shipping emissions  . Biomass burning emissions use the Global Fire Emissions Database GFED 4.1 . Biogenic emissions follow the estimation from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) 2.1 . The natural emissions of acetaldehyde follow the calculation from . Natural sources of NH3 are adapted from the Global Emission InitiAtive (GEIA) as described by , with the inclusion of the Arctic seabird emissions .

Table  gives the list of simulations performed and the sections in which these simulations are discussed.

In order to assess the veracity of the GEOS-Chem model simulation we compare its performance: first against the meteorological observations made at the site and then against measurements of atmospheric chemical constituents. Since the model and observation data are available hourly and observation data capture is high in most cases, instances of missing data were dropped for both the observation and the model before comparison. The observation data were adjusted from Japan standard time (JST) to model default time (UTC). We compare hourly values from the observations and from the model for the physical and chemical variables except for NO, NO2 and NOy, where we compare local daylight averages due to high variability.

We use a number of standard metrics for describing the measurements and model (mean, median, standard deviation, 25th and 75th percentiles). When assessing the model performance we assess this in terms of the root mean square error (RMSE), mean bias and Pearson's correlation coefficient (r). We also calculate the slope of the best-fit lines using orthogonal distance regression (ODR).

In the case of wind direction, which is circular and not linear, the methodology for the estimation of RMSE and bias follows  and .

In order to understand the impact of biomass burning on the composition of the air arriving at the site, we conduct an additional simulation, switching off the biomass burning emissions from north Asia and South Asia separately as shown in Fig. . Two global simulations were run switching off the northern or southern Asian biomass burning to generate new boundary conditions, and then these were used for the two regional simulations which again switched off the biomass burning in either the north or south of Asia.

Figure a–f show the time series of the percentage change in modelled mixing ratios of C2H6, C3H8, CO, O3, NOx and NOy at the site when biomass burning emissions in either the northern or southern domain are switched off. Given the short lifetime of NOy and NOx, the site is less sensitive to biomass burning NOx than CO, C2H6 and C3H8, and Fig. d and f show the correlation between O3 and NOx when less ozone is formed due to switching off emissions, more NOx is accumulated.

During the winter months the contributions from north and South Asia are relatively small; however, in the summer, both sources can contribute significantly (20 %–35 %) to the modelled C2H6, C3H8 and CO. The contributions are smaller for O3 reaching a maximum at 10 % during events in the summer. There is a sharp dip around 10 August where CO, C2H6 and C3H8 levels drop by around 40 %–50 % due to switching off north Asian biomass burning emissions. After this sharp dip, the next decline from 16 August was more sustained and due to South Asian biomass burning emission. These sharp dips correspond to the passing of typhoons in the area, which can rapidly draw air from different direction for short period of time. Figure  shows the back-trajectory analysis for this period when the wind direction rapidly changed. There is also a tendency for increased Asian fire activity in the summer which explains why the strong and rapidly changing wind would bring in more emissions.

The periods of model overestimate in summer ethane, notably in August (Fig. b), correspond to periods with a high fraction of modelled ethane biomass burning (∼ 30 %). However, the model overestimate (see Fig. b) is much large than this (∼ 100 %). Thus, even if the biomass burning is switched off all together, the model overestimate during the summer would still remain. This suggests that the model overestimate of C2H6 in the summer is not primarily related to the modelled representation of biomass burning but likely results in uncertainties in the anthropogenic emissions of ethane south of Hateruma.

During the wintertime the model underestimates the observed ethane and propane. This is a period when the contribution from biomass burning is relatively low (<10 %). Thus the potential for the underestimate in wintertime ethane and propane to be related to errors in the biomass burning appears to be small. Instead, we now evaluate how much the Asian anthropogenic source of ethane and propane would have to be increased by in order to fit the observed mixing ratios.

The period between October to June (Figs. –) is characterized by elevated levels of pollution at the site due to flow from Russia, China, Korea and Japan (Fig. ). It is also a period when the model substantially underestimates the mixing ratios of ethane and propane. In this section we explore what scaling factor would have to be applied to the Asian anthropogenic emissions of ethane and propane within the high-resolution domain to better fit those observations.

Figure a and d show modelled ethane and propane plotted against observations in our base model. We separate the plot into two periods here as southern flow (blue, July–September) and northern flow (red, October–June). It is obvious from the comparison that during the northern-flow period, the model-measured slope is lower than expected (0.62 for ethane and 0.41 propane), whereas in the southern-flow period it is substantially overestimated for ethane (3.97) and to a smaller extent also for propane (1.51).

Given the site's largest exposure to relatively recent north Asian emissions in the northern-flow period, we focus on this. We multiply the Asian anthropogenic emission of ethane and propane within the high-resolution domain by a first rough estimate of a correction factor “A” (1.75 for ethane and 2.58 for propane, which are roughly the reciprocal of the model– measurement slopes show in Fig. , i.e. 1/0.62 and 1/0.41) aimed at making the slope of the linear fit in the northern-flow period become 1. This would represent a correction factor if all of the ethane and propane observed at the site were from within the high-resolution simulation domain and the oxidation lifetimes were correct.

The annual simulation was re-run with the anthropogenic emissions of ethane and propane increased by the A factor within the “Asian” high-resolution domain (Fig. b and e). Assuming a linear response, these two simulations allow the mixing ratios of ethane and propane in the base simulation to be represented as [X]=[X]Asian+[X]Restoftheworld and the mixing ratios in the simulation with increased emissions to be represented as [X]=1.75×[X]Asian+[X]Restoftheworld for ethane and [X]=2.58×[X]Asian+[X]Restoftheworld for propane. The base mixing ratios at each time point can then be decomposed to give an Asian component (although this is only the component within the higher-resolution domain) and a “Rest of the world” component. A scaling can then be applied, which optimizes the fit between the measurement and the model by finding an optimized multiplier for the Asian emission: Aoptimized.

The value of Aoptimized is found to be 2.22 and 3.17 for ethane and propane, respectively, if the best-fit line is optimized (Fig. ). This is consistent with the value found by optimizing the ratio of mean mixing ratios in the period when the wind flow is northerly (calculated as 2.24 and 3.14, for ethane and propane, respectively). Re-running the model with these increases in the ethane and propane emissions within the domain gives a scatter plot of ethane and propane measurements versus model values in Fig. c and f, a time series shown in Fig. , and a correlation between the whole dataset in Fig. . The correlation coefficient (r) is slightly reduced after the scaling (0.88 to 0.84 and 0.89 to 0.86 for C2H6 and C3H8, respectively).

For the northern-flow period (October to June), when the air is predominantly off north Asia, the modelled ethane now has a mean of 1443 ± 737 (1σ) pptv compared to an observed mean of 1449 ± 725 (1σ) pptv. The propane simulation has a mean of 493 ± 418 (1σ) pptv compared to an observed mean of 494 ± 407 (1σ) pptv. During the southern-flow period (July–September), the model now significantly overestimates the ethane (mean simulated = 587 ± 352 (1σ) pptv; mean observed = 308 ± 99 (1σ) pptv) and propane (mean simulated = 105 ± 88 (1σ) pptv; mean observed = 60 ± 37 (1σ) pptv).

The improvement in model performance during the northern flow and the degradation in performance during the southern flow point towards a non-uniformity in the correction factor. This could indicate a difference in the seasonality of the emissions compared to those used in with a significant reduction in the summer time emissions compared to the winter, or it could indicate a different reason for the increase in north Asia compared to south Asia, which is primarily sampled in the summer.

The comparison of ethane and propane observations with model results shows that a large increase in the prescribed emissions of these tracers is necessary, in agreement with previous studies. found it necessary to increase CEDS anthropogenic ethane emissions by a factor of roughly 2 and CEDS propane emission by a factor of nearly 3 to get agreement between their model and observations. These are very similar to the ratios found here. found it necessary to substantially increase anthropogenic ethane sources to fit observations. measured vertical profiles of VOCs (including C2H6 and C3H8) and reported that emission fluxes were up to 3 times larger than in the Multi-resolution Emission Inventory for China (MEIC) inventory estimates. It seems likely that current estimates of north Asian emissions of ethane and propane are currently underestimated.

Figure a and b show the impact of the increase (with the optimized factors) in Asian ethane and propane anthropogenic emissions on the mixing ratios of average hydroxyl radical and ozone in the region in July, respectively. This is the period of the highest ozone mixing ratios over China. The effect on the OH mixing ratios in July was small and patchy with up to a 2 % reduction in some places. On the other hand, O3 mixing ratios increased slightly, reaching a maximum over Beijing, where that amounts to a 2 % (around 1 ppbv) increase.

Measurements were made of a number of compounds in the air over the island of Hateruma (CO, C2H6, C3H8, NO, NO2, NOy and O3). We show that the site is mainly subject to clean Pacific air throughout the year. If the site is subject to polluted air masses, these are more likely to have originated from north Asia (Russia, China, Japan, Korea, etc.) in the winter months and from south Asia (Philippines, Borneo and other regions in South-East Asia including Vietnam, Indonesia, peninsular Malaysia and Thailand) in the summer months. This gives the site a significant seasonal cycle in the mixing ratios of pollutants considered in this study except NOx.

We have compared these observations to the output of the GEOS-Chem model, run in a regional configuration. We find that CO and O3 are well simulated; the model overestimates NOy observations but tends to underestimates NO, NO2, ethane and propane.

The underestimates in ethane and propane during the wintry months are unlikely to be reconciled by increases in biomass burning emissions but could be reconciled by substantial (factors of 2–3) increases in the Asian anthropogenic source of these compounds (consistent with previous studies). These large increases in emissions have negligible influence on the hydroxyl radical mixing ratios and very little impact on the ozone in the region's cities such as Beijing, where there is up to 1 ppbv change in ozone mixing ratios in July.

We do not believe the overestimates of NOy can be attributed to problems with the simulation of shipping NOx, as even switching off the shipping NOx emissions does not remove the overestimate. Also, biomass burning emissions can only contribute up to 10 % of NOx levels estimated at the site; therefore, the overestimates likely lies in the emissions of NOx from industrial regions close by, in issues with the chemistry or in the observations.

The site's location is unusual and is subject to air masses from a large number of sites and so can be used to understand emissions of compounds from a number of location. Future plans to enhance the measurement capability at the site with a gas-chromatography–mass-spectrometry system (GC-MS) should allow an evaluation of a large number of different organic compounds, further enhancing the capacity to understand sources of pollution in the region.