First Top-Down Diurnal Updates to NOx Emissions Inventory in Asia Informed by the Geostationary Environment Monitoring Spectrometer (GEMS) Tropospheric NO2 Columns

doi:10.21203/rs.3.rs-4283240/v1

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First Top-Down Diurnal Updates to NOx Emissions Inventory in Asia Informed by the Geostationary Environment Monitoring Spectrometer (GEMS) Tropospheric NO2 Columns

https://doi.org/10.21203/rs.3.rs-4283240/v1

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published 17 Oct, 2024

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Pioneering the use of the Geostationary Environment Monitoring Spectrometer’s (GEMS) observation data in air quality modeling, we updated Asia’s NO_x emissions inventory by leveraging its unprecedented sampling frequency. GEMS tropospheric NO₂ columns served as top-down constraints, guiding our Bayesian inversion to hourly update NO_x emissions in Asia during spring 2022. This effectively remedied the prior underrepresentation of daytime NO_x emissions, significantly improving simulation accuracy. The GEMS-informed update reduced the extent of model underestimation of surface NO₂ concentrations from 19.23–11.36% in Korea and from 12.85–4.42% in China, showing about 6% greater improvement compared to the update based on the sun-synchronous low earth orbit observation proxy. Improvements were more pronounced when larger amounts of observation data were available each hour. Our findings highlight the utility of geostationary observation data in fine-tuning the emissions inventory with fewer temporal constraints, thereby more effectively improving the accuracy of air quality simulations.

GEMS

geostationary

NOx

emissions inventory

top-down

inverse modeling

In Asia, regular emissions of nitrogen oxides (NO_x) have long posed concerns for public health, due to their detrimental impact on air quality and potential contribution to respiratory illnesses^1,2. Primarily originating from vehicles and industrial activities^3,4, NO_x emissions significantly contribute to urban air pollution, exacerbating environmental and health challenges in densely populated areas. In response to airborne hazards, Asia has seen extensive efforts to monitor air quality, leveraging a combination of ground-based monitoring stations and satellite-based observations^5–7. The advent of satellite instruments has been particularly beneficial in addressing the information gaps arising from ground-based sites’ limited geographic coverage.

The increasing availability of observation references has led to significant advancements in air quality research, particularly in estimating and simulating NO_x emission rates and ambient concentrations^8–12. Foremost among these research methods is the use of chemical transport models (CTMs), such as the Community Multiscale Air Quality (CMAQ) model¹³, which has been useful in understanding the complex behaviors of air pollutants and pollution mechanisms in Asia^14–17. However, this approach often encounters challenges in regions with traditionally sparse monitoring; the reliance on outdated emission inventory data – a critical input for CTMs – can compromise the accuracy of air quality simulations^18–20. The inherent uncertainties in bottom-up inventories of NO_x emissions, arising from limited knowledge of emission factors and broad extrapolations, further complicate the accuracy of these estimates^21–24. To address this challenge, extensive efforts have focused on utilizing satellite observation data to update bottom-up estimates of air pollutant emissions. A number of numerical modeling studies have effectively adjusted the extent of NO_x emissions in emission inventories by solving mathematical inverse problems, supported by top-down measurements from satellite instruments along sun-synchronous low earth orbits (LEO)^{8–12,25−28}. Instruments such as Ozone Monitoring Instrument (OMI), TROPOspheric Monitoring Instrument (TROPOMI), and Sentinel-5 Precursor (Sentinel-5P) have provided more current information on air pollutant loadings. The use of more up-to-date observation references has been confirmed to be effective in improving the emissions inventories, consequently enhancing the reliability of air quality simulations and laying a stronger basis for further air quality assessments.

Despite their significant contributions, the utilization of LEO satellite instruments encounters challenges in capturing the temporal dynamics of air pollutants^29–33. The orbiting routine of the instruments typically allows for only one snapshot of air pollutant loadings at each geographic location per day, leading to limited segments of daily observations³³. This often necessitates dependency on time-averaged observation data, which are typically averaged over biweekly, monthly, yearly, or even multi-year periods^{25–32,34−36}. This approach, although vastly practical, may not be sufficiently granular to accurately capture the highly variable nature of NO_x, which is characterized by their short lifetime both in the real world and CTM simulations^37–40. For example, NO_x emissions’ diurnal variations associated with the urban commuting cycle (i.e., dual peaks during morning and evening traffic)^41,42 remain underrepresented in LEO observations due to the instruments’ orbiting cycles. A shared insight among researchers is the pressing need for temporally more continuous satellite observations to effectively monitor the dynamic emission patterns⁴³.

In response to such a limitation, there has been a growing focus on utilizing observation data from satellite instruments in geostationary earth orbits (GEO)^43–45. GEO satellite instruments offer finer temporal resolutions, from several minutes to an hour during daylight hours, enabling more frequent and continuous observations. This advantage has been particularly well-exploited in aerosol research, where instruments like the Geostationary Ocean Color Imager (GOCI) and the Advanced Himawari Imager (AHI) have made substantial contributions to air quality modeling in East Asia^46–48. However, a gap remains in studies concerning gaseous air pollutants, particularly in terms of better inventorying the quantity of their emissions, owing to the historical absence of instruments capable of monitoring gas-phase species in a geostationary manner. In fulfilling such a need, the launch of the Geostationary Environment Monitoring Spectrometer (GEMS) in February 2020, the first instrument of its kind to measure the loadings of both gas-phase air pollutants and aerosols⁴⁵, set the stage for new research by providing enhanced monitoring capability over the Asia-Pacific region ^36,49–51. Deployed aboard the Geostationary Korean Multi-Purpose Satellite 2 (GEO-KOMPSAT-2), GEMS offers hourly measurements of trace gases, such as nitrogen dioxide (NO₂), sulfur dioxide, formaldehyde, and ozone, as well as aerosol properties during daylight hours in a consecutive manner⁴⁵ – capability that was previously unattainable with instruments on LEO platforms. Despite its higher orbital altitude compared to LEO instruments, GEMS's spatial resolution, at 3.5 km × 8 km, surpasses that of older LEO instruments, such as OMI at 13 km × 24 km at nadir, and is comparable to more recent LEO instruments like TROPOMI, which has offered a resolution of 5.5 km × 3.5 km since August 2019. This positions GEMS as even more valuable, providing observation data with superior temporal resolution and competitive spatial resolution, thereby underscoring its significant potential to advance our understanding and monitoring capabilities. Furthermore, upon the forthcoming completion of the GEO satellite instrument constellation, including NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO)⁴³ and ESA’s Sentinel-4⁴⁴, there emerges a critical need for more nuanced methodologies to better exploit the unprecedented quantity and quality of observation data becoming available.

Leveraging a wealth of top-down information from GEMS, our study aimed to establish a modeling approach to fine-tune the bottom-up estimates of air pollutant emissions, thereby yielding a more up-to-date and realistic emissions inventory. Focusing on the spring months of 2022, we evaluated the utility of GEMS tropospheric NO₂ columns as top-down constraints to update the NO_x emissions inventory in Asia. Informed by 8 to 10 observation references a day, our Bayesian inverse modeling provided hourly updates to the inventoried extent of hourly NO_x emissions. We conducted a series of CMAQ simulations of tropospheric NO₂ columns and surface NO₂ concentrations in Asia and assessed the model accuracies achieved with the original and updated NO_x emissions inventories. We then assessed the potential advantages of GEMS’s high-frequency observation data in addressing the temporal constraints of LEO platforms. This involved comparing the improvements in model performance achieved from using GEMS data and the proxy data of LEO satellite instrument observations.

2.1 Simulations using the original emissions inventory

We evaluated the model accuracy achieved using the original extent of NO_x emissions in the inventory. This involved comparing the tropospheric NO₂ columns and surface NO₂ concentrations observed during the months of March, April, and May (MAM) 2022 to those simulated by CMAQ using the a priori NO_x emissions.

Figures 1a and 1b illustrate the monthly averages of hourly NO₂ columns observed and modeled during daytime. Compared to GEMS NO₂ columns, the model tended to overestimate the columns across most regions of South Korea (hereafter referred to as Korea), with the exception of the Seoul metropolitan area (SMA), and in densely populated urban areas in China, including Beijing and Shenyang, and those across the southeast region. The model also overestimated the columns in major urban centers in Southeast Asian countries (e.g., Ho Chi Minh City and Hanoi in Vietnam, Bangkok in Thailand, and Kuala Lumpur in Malaysia, and Singapore), in India (particularly in the northeastern region), and in Japan (except for Tokyo) (see Figure S1 for geographic labels). Conversely, the model generally underestimated the columns in the SMA, across the North China Plain (NCP) and the plains in the northeast region of China, and in the landlocked urban centers of northcentral and southwestern China. The model underestimated the columns in the Yangtze River Delta (YRD) in March, and then overestimated the columns in April and May. Similar patterns of monthly spatial discrepancies, but to a greater extent, were noted between the NO₂ columns observed at 04:45 UTC (hereafter referred to as LEO proxy NO₂ columns; see Methods) and the corresponding modeled NO₂ columns (Figs. 1d and 1e).

Figure 2 shows a time series of the observed and modeled hourly surface NO₂ concentrations at ground-based monitoring stations in China during MAM 2022. The model generally underestimated the concentrations during daylight hours, aligning with GEMS retrieval times, and began overestimating in the subsequent hours leading up to nighttime. A similar tendency was observed in Korea (Figure S1), indicating a regional modeling challenge in East Asia when using the a priori emissions. Overall, there was a fair agreement between the observed and modeled hourly NO₂ concentrations, with correlation coefficients (R) ranging from 0.57 to 0.64 and indices of agreement (IOA) from 0.64 to 0.74 in China (Fig. 2) and those from 0.62 to 0.82 and 0.65 to 0.71, respectively, in Korea (Figure S1). The extent of overestimation was noticeable throughout the months, with normalized mean biases (NMB) ranging from 7.75–32.59%, mainly due to nighttime overestimation. However, during the daytime, the model severely underestimated NO₂ concentrations, with NMB from − 13.17% to -29.31% in Korea and from − 5.08% to -23.58% in China. This suggests a potential underrepresentation of daytime NO_x emissions in Asia, as discussed in several previous studies, which led to the overall underestimation of surface NO₂ concentrations in East Asia^27,34,36,52.

2.2. Top-down updates to the NO_x emissions inventory

After the top-down updates to the NO_x emissions inventory, we examined the subsequent changes in NO_x emissions in Asia during MAM 2022. This involved comparing the a priori emissions with the a posteriori NO_x emissions, which were obtained through the Bayesian inversions informed by the observation data from GEMS and LEO proxy (see Methods).

Figure 3 shows the monthly averages of hourly daytime NO_x emissions, comparing the updated emissions to the a priori emissions. The GEMS-informed update led to spatial adjustments in emission quantities, aiming to offset the model’s prior biases discussed earlier. NO_x emissions generally decreased in areas where the model previously underestimated the NO₂ columns, including most regions across Korea (except for the SMA), Beijing, Shenyang, the YRD (in April and May), as well as in highly populated urban areas in southeastern China, Southeast Asian countries, India, Japan (except for Tokyo). Specifically, in Beijing and Shenyang, the decreases in NO_x emissions at the pixels corresponding to the city centers were overwhelmed by the increased emissions in the surrounding greater metropolitan areas. Conversely, we observed substantial increases in NO_x emissions in the SMA, the NCP, the plains in northeastern China, the YRD (in March), and urban centers in northcentral and southwestern China. In areas where the model previously underestimated and overestimated NO₂ columns (Fig. 1c), we observed an average increase of 31.72% and a decrease of 22.78% in NO_x emissions, respectively, during MAM (Fig. 3c). The spatial extent of areas with decreased emissions was slightly smaller than that with increased emissions by a factor of 0.998, which led to a slight increase in domain-averaged NO_x emissions by 1.42%. This suggests that the localized increases in East Asia slightly outweighed the reductions elsewhere.

Upon observing similar spatial distributions, the LEO-informed update led to notably larger increases and decreases in NO_x emissions. In areas where the model previously underestimated and overestimated NO₂ columns (Fig. 1f), we noted an average increase of 35.39% and a decrease of 36.40% in NO_x emissions, respectively (Fig. 3e). The total area with decreased emissions was slightly larger than that with increased emissions by a factor of 1.023, resulting in an average decrease of 17.10% in NO_x emissions. This tendency towards more extensive adjustments, particularly towards decreasing the emissions, is attributed to the conventional inversion method practiced with LEO proxy NO₂ columns, which relies on monthly averaged, time-specific discrepancies between observed and modeled NO₂ columns (Fig. 1f) to adjust NO_x emissions throughout the entire day.

Figure 4 shows a time series of the monthly averages of hourly daytime NO_x emissions in Korea and China during MAM 2022 before and after the updates. We compared the absolute values of the a priori and a posteriori NO_x emissions and the extents of adjustments at each hour. This enabled us to assess how effectively the GEMS- and LEO-informed inversions captured daytime diurnal variations in the emissions.

The GEMS-informed update generally increased NO_x emissions in both Korea (Fig. 4a) and China (Fig. 4b), seemingly compensating for the previously underrepresented morning peak NO₂ concentrations (Figs. 2 and S1). Such a tendency towards increasing the emissions was less pronounced in China during the first hour of the day (7 AM local time), which is attributable to GEMS’s partial coverage that reaches only the eastern regions of China at that time. After the day progressed into the afternoon hours, we observed smaller extents of adjustments towards increasing the emissions in general, and even decreases in the emissions in Korea. This suggests that the a priori NO_x emissions for this midday time window, not associated with the morning or evening peaks, might have been overestimated. Such adjustments towards reducing the emissions were also noted after the LEO-informed update, but throughout the entire daylight hours. The LEO-informed update led to uniform decreases and increases in the emissions in Korea and China, respectively, the directions of which were determined based on the discrepancy between the observed and modeled NO₂ columns at 04:45 UTC (Fig. 1f) during the inversion process. These distinct responses of NO_x emissions to the GEMS- and LEO-informed inversions suggest the potential of geostationary observation data in refining the emissions in a temporally more nuanced manner, as further discussed in the following section.

2.3 Simulations using the updated NO_x emissions inventory

To assess the effectiveness of our top-down approach, we compared the model performances in simulating tropospheric NO₂ columns and surface NO₂ concentrations in Asia, achieved before and after updating the NO_x emissions inventory. This enabled us to determine the extent to which the model’s earlier biases could be reduced by exploiting more contemporary satellite observations.

Figures 5c and 5d illustrate the monthly averages of hourly daytime tropospheric NO₂ columns simulated after the GEMS-informed and LEO-informed updates. The GEMS-informed update generally remedied the model’s earlier underestimation (Figs. 5a and 5b), particularly in capturing the high peaks in East Asia, resulting in a closer alignment of the modeled columns with GEMS tropospheric NO₂ columns. NO₂ columns decreased after the update in areas where overestimation was once prevalent. Also, we noticed some instances of overcompensation. While effective in addressing underestimations of NO₂ columns, the GEMS-informed update introduced overestimations in some areas, such as the Yellow Sea, where the columns were already well-captured using the a priori emissions. Given that the inversion was not directly applied to NO_x emissions over sea surfaces, the increased NO₂ columns over the Yellow Sea are attributed to significant increases in nearby upwind inland areas, such as the NCP and the northeastern region of China.

The LEO-informed update also addressed the model’s prior biases, but with different spatial patterns across the domain. The update was generally more effective in constraining previously overestimated NO₂ columns but was less effective in capturing their high peaks in areas where underestimation was once prevalent, such as the southern half of the NCP. This was considered to be caused by the inversion, which was more substantially directed towards reducing the extent of NO_x emissions, as discussed earlier in Section 2.2. These spatial patterns closely reflected the discrepancies observed between LEO proxy at 04:45 UTC and the corresponding modeled NO₂ columns (Fig. 1c and 1f), reaffirming the ongoing challenge of preventing over- and under-correction in emissions inventories, especially when relying solely on time-averaged observation data^{11,36,52–55}.

Figures 6 and 7 compare the hourly daytime surface NO₂ concentrations observed and modeled in Korea and China, respectively, during MAM 2022. The GEMS-informed update improved R from 0.70 to 0.71 in Korea and from 0.73 to 0.78 in China on average during MAM (Table 1). To a lesser extent, the LEO-informed update also resulted in a slight improvement in correlation, maintaining R at 0.70 in Korea and advancing R to 0.74 in China. We also observed improvements in IOA, where the extents of improvement were more pronounced after the GEMS-informed update. The GEMS-informed update improved IOA from 0.78 to 0.81 in Korea and from 0.79 to 0.83 in China, while the LEO-informed update improved IOA to 0.79 in Korea and maintained IOA at 0.79 in China.

The model previously underestimated NO₂ concentrations by an average of 19.23% in Korea during MAM, and the GEMS-informed update moderated this negative bias to an average of 11.36% (Table 1). The smallest bias was achieved in April, with NMB of 0.02%, indicating a fairly close agreement between the modeled and observed concentrations. Similarly, in China, the extent of underestimation was substantially reduced from an average of 12.85–4.83% (Table 1) during MAM, with exceptional alignments in April and May, with NMB of 0.78% and 0.79%, respectively. The LEO-informed update was also generally effective in reducing the model’s prior biases but to a lesser extent. The extents of underestimation were slightly reduced to an average of 16.96% in Korea and 9.93% in China. Despite aiming to counterbalance the model’s prior biases, the LEO-informed update led to less pronounced improvements. This is attributable to the limited amount of information available from observation references, which were more beneficial towards constraining the overestimated extent of NO_x emissions as discussed in Section 2.2.

Table 1

Descriptive statistics comparing observed and modeled hourly surface NO₂ concentrations averaged at 459 AirKorea sites in Korea and 250 MEE sites in China during MAM 2022. Prior: modeled concentrations using the a priori emissions, LEO: modeled concentrations after the LEO-informed update, GEMS: modeled concentrations after the GEMS-informed update. R: Pearson’s correlation coefficient, IOA: index of agreement, NMB: normalized mean bias (%), and RMSE: root mean square error (ppb).
		Korea				China
		March	April	May	Average	March	April	May	Average
R	Prior	0.56	0.75	0.79	0.70	0.75	0.71	0.72	0.73
	LEO	0.58	0.74	0.77	0.70	0.76	0.72	0.74	0.74
	GEMS	0.55	0.77	0.80	0.71	0.79	0.77	0.77	0.78
IOA	Prior	0.66	0.84	0.85	0.78	0.78	0.77	0.81	0.79
	LEO	0.68	0.84	0.85	0.79	0.80	0.76	0.80	0.79
	GEMS	0.68	0.87	0.88	0.81	0.84	0.81	0.84	0.83
NMB (%)	Prior	-29.31	-13.17	-15.22	-19.23	-23.58	-9.88	-5.08	-12.85
	LEO	-27.75	-9.74	-13.38	-16.96	-19.28	-6.98	-3.52	-9.93
	GEMS	-24.84	0.02	-9.27	-11.36	-14.82	0.78	0.79	-4.42
MAE (ppb)	Prior	5.90	2.80	2.19	3.63	4.63	4.09	3.01	3.91
	LEO	5.74	2.77	2.11	3.54	4.36	3.90	3.27	3.84
	GEMS	5.39	2.68	1.89	3.32	3.70	3.15	2.69	3.18

Figure 8 illustrates the daytime mean surface NO₂ concentrations observed and modeled in Korea and China during MAM, aligned with the number of valid observations afforded by GEMS retrievals. While the GEMS-informed update was generally more effective in moderating the model’s prior biases, a noticeable aspect was the response of the a posteriori concentrations to the availability of valid observation references. On days with minimal valid data given (between 0 and 1 record on average), including March 13, 14, 17, 18, and April 13 in Korea, the GEMS-informed inversion led to minor adjustments in the corresponding NO₂ concentrations due to limited top-down information (Fig. 8a). Meanwhile, the LEO-informed inversion allowed the modeled concentrations to be adjusted in a continuous manner, taking advantage of the monthly-averaged observation data applied during the inversion, but with less pronounced improvements. A similar pattern was observed sporadically between the modeled and observed concentrations in China, but to a less severe extent (Fig. 8b).

Our study highlights the utility of geostationary observation data in fine-tuning an emissions inventory with the assistance of GEMS’s unprecedented sampling frequency, and ultimately in improving the accuracy of air quality simulations across Asia. The GEMS- and LEO-informed updates to the NO_x emissions inventory, despite both exploiting observed tropospheric NO₂ columns, adopted different approaches in response to the available top-down information. The GEMS-informed inversion effectively leveraged the fine temporal resolution of GEMS observation data to capture daytime diurnal variations in NO_x emissions more accurately, significantly enhancing the simulation accuracy of NO₂ loadings in Asia. The LEO-informed inversion was also generally effective in addressing the model biases, but it occasionally failed to fully adjust NO_x emissions. This is attributed to its reliance on limited top-down information, such as monthly-averaged discrepancies between prior simulations and observations, which may not accurately reflect the temporally dynamic nature of NO_x emissions.

It is important to note, however, that our model evaluations were largely confined to East Asia, due to the current availability of station measurement data. Nevertheless, our findings underscore the value of geostationary observation data in air quality research and advocate for the development of more advanced strategies to maximize the use of these observations. This will allow for more granular air quality analyses across Asia and potentially beyond, especially as we anticipate the complete deployment of the constellation of GEO satellite instruments across the Northern Hemisphere in the near future.

4.1 Modeling setup

Meteorology plays a critical role in air quality simulations by guiding the dispersion and transport of air pollutants. We simulated meteorological fields over the modeling domain (Figure S2) during MAM 2022 by using the Weather Research and Forecasting (WRF) model 3.8⁵⁶. This enabled us to simulate hourly meteorology over a 320 × 320 grid across 35 vertical layers at a spatial resolution of 27 km. The WRF outputs were then formatted for compatibility with CMAQ using the Meteorology-Chemistry Interface Processor (MCIP) 4.3⁵⁷.

CMAQ serves as a forward model in the emission adjustment process (see Section 4.4). Using established emissions input, CMAQ simulates corresponding air pollutant concentrations three-dimensionally within the atmosphere. This enables us to align our simulations with actual observed air quality, which is an essential step for refining emissions inventories. We simulated air quality and chemistry using CMAQ 5.2¹³ and its Decoupled Direct Method in Three Dimensions (CMAQ DDM-3D)⁵⁸. CMAQ DDM-3D calculates first-order coefficients that represent the locally semi-normalized sensitivity of modeled concentrations to changes in emissions. At the same spatial resolutions used for WRF simulations, we simulated hourly NO₂ concentrations over a 300 × 300 grid and computed their corresponding sensitivities to NO_x emissions. Both the WRF and CMAQ simulations began with a 10-day spin-up from February 19. Further technical details in our modeling setup are listed in Table S1.

4.2 Emissions in Asia

Emissions inventories inform CTMs about the sources and quantities of air pollutants, enabling the simulation of their emergence, behavior, and eventual concentrations in the atmosphere. To prepare anthropogenic emissions over the modeling domain, we used the Emissions Database for Global Atmospheric Research (EDGAR) 6.1⁵⁹. EDGAR provides annual figures (base year: 2018) of greenhouse gas and air pollutant emissions at a 0.1° spatial resolution. We processed these emissions into a format compatible with CMAQ using the Sparse Matrix Operator Kernel Emissions (SMOKE) 4.7⁶⁰. This included speciation of the inventory emissions, re-gridding the emissions into a 27 km resolution grid, and assigning the annual emissions into hourly emission estimates during MAM 2022 while accounting for time zones, weekday-weekend routines, and local diurnal variations.

To prepare biogenic and biomass burning emissions, we utilized the Model of Emissions of Gases and Aerosols from Nature (MEGAN) 3.0⁶¹ and the Fire Inventory from the National Center for Atmospheric Research (FINN) 1.5⁶², respectively. MEGAN estimates net emissions of gases and aerosols from terrestrial ecosystems, factoring in vegetation responses to meteorology. We obtained hourly biogenic emissions at a 27 km resolution, using the Reprocessed Moderate Resolution Imaging Spectroradiometer (MODIS) version 6 Leaf Area Index product⁶³ and the Visible Infrared Imaging Radiometer Suite (VIIRS) global Green Vegetation Fraction product⁶⁴, both at a 0.05° resolution, and WRF-simulated meteorology as key inputs to MEGAN. FINN provides emissions from open biomass burning, such as wildfires, agricultural fires, and prescribed burning, based on MODIS observations and fuel loading parameters. We obtained hourly biomass burning emissions over the modeling domain at a 27 km resolution. We then merged anthropogenic, biogenic, and biomass burning emissions altogether to prepare the a priori emissions input for CMAQ.

4.3 Satellite data: GEMS tropospheric NO₂ columns and LEO proxy

GEMS is the first UV-visible geostationary hyperspectral imager that measures the earth's radiance and solar irradiance within the 300 to 500 nm wavelength range, aiming to monitor atmospheric gases and aerosols in the broader Asia-Pacific region. We utilized hourly Level 2 GEMS data to gain a comprehensive overview of NO₂ pollution across Asia. This dataset, available since November 2022, includes observations from November 2020 to the present. Covering longitudes from 75° E to 145° E and latitudes from 5° S to 45° N, GEMS records 8 to 10 consecutive hourly snapshots of tropospheric NO₂ columns during daylight hours in MAM, at a spatial resolution of 3.5 km × 8 km⁴⁵. We selected GEMS’s tropospheric NO₂ columns observed from March 1 to May 31, 2022, to serve as the top-down observation references for updating the a priori emissions. To illustrate, GEMS recorded 8 observations every day from 00:45 to 06:45 and at 23:45 UTC in March, with this sampling frequency increasing to 10 observations from 00:45 to 07:45, and at 22:45 and 23:45 UTC during April and May. In local hours, the full temporal span of daytime observations extends from 07:45 AM to 4:45 PM in Korea and 06:45 AM to 03:45 PM in China. Beyond tropospheric NO₂ columns, we utilized several variables derived from the Level 2 data, including averaging kernel, cloud fraction, troposphere and stratosphere air mass factors, modeled pressures interpolated to GEMS layers, algorithm quality flags, and root mean square error. The averaging kernel and air mass factors, in conjunction with WRF-simulated pressure layers, were used to convert CMAQ-simulated NO₂ concentrations into columns³⁴. To ensure the quality of the observation references, we used pixels satisfying algorithm quality flags of 0 bit (good sample) and cloud fractions smaller than 0.3. We also considered the inherent bias in GEMS tropospheric NO₂ columns, observing an average discrepancy of -0.255 × 10¹⁵ molecules/cm² compared to Pandora spectrometer measurements in highly polluted grid cells (≥ 0.5×10¹⁶ molecules/cm²)⁴⁹, by selectively adjusting the observed values upwards.

To evaluate the effectiveness of using multi-hour observations from the GEO platform for emissions adjustment and to enable a comparison between the a posteriori emissions updated based on GEO and LEO observations, we created ‘LEO proxy’ data. This proxy uses GEMS’s 04:45 UTC tropospheric NO₂ columns as a stand-in for corresponding LEO observations, mimicking the temporal resolution of typical LEO satellite instruments. We selected the monthly averaged LEO proxy during MAM as benchmarks for updating the a priori emissions in each month. This approach enabled us to assess the utility of temporally more continuous observation data, rather than providing a direct comparison between the utilities of GEMS and other LEO instruments.

4.4 Top-down approach for the NO_x emissions adjustment

The extent of NO_x emissions is not directly observable through GEMS. Thereby, to establish quantitative constraints on NO_x emissions and provide updates accordingly, we employed a Bayesian approach for inverse modeling, suited for solving problems that are not grossly nonlinear⁶⁵. Recognizing the short lifespan of NO₂^37–40, we explicitly assumed a linear relationship between NO_x emissions and NO₂ columns. However, it is important to note that the availabilities of nighttime ozone and hydroxyl radicals (OH) can introduce nonlinearity between NO₂ concentrations and NO_x emissions²⁷ - a complexity that falls outside the scope of our study. Our method aimed to infer the most probable extent of NO_x emissions by minimizing the Bayesian-informed cost function in Eq. 1.

$$J\left(x\right)= \frac{1}{2}{\left(y-Fx\right)}^{T}{S}_{o}^{-1}\left(y-Fx\right)+\frac{1}{2}{\left(x-{x}_{a}\right)}^{T}{S}_{e}^{-1}\left(x-{x}_{a}\right) \left(1\right)$$

This process, based on Gaussian error assumptions, involved determining the a posteriori emissions $\varvec{x}$ given multiple sources of information, including the a priori emissions ${\varvec{x}}_{\varvec{a}}$, top-down observation references $\varvec{y}$ (either GEMS tropospheric NO₂ columns or LEO proxy NO₂ columns), and their counterpart $\varvec{F}$ from the forward model (CMAQ-simulated NO₂ columns). Given the 15-minute offset in GEMS retrievals from O’clock sharps (from 00:45 to 23:45 UTC), we aligned these observations to the nearest subsequent hour in the 00:00–23:00 UTC cycle. For example, GEMS observations at 04:45 UTC were used for constraining the emissions at 05:00 UTC. ${\varvec{S}}_{\varvec{e}}$ and ${\varvec{S}}_{\varvec{o}}$ represent the uncertainties in emissions and observations, which were set at 50%, 200%, and 100% for anthropogenic, biogenic, and biomass burning emissions^27,34,36, respectively, with observation error covariances sourced from the GEMS Level 2 data.

Upon reaching the minimum of the cost function, as identified by its first derivative, we applied the Gauss-Newton method as described in Eq. 2. This involved refinement of the estimate of $\varvec{x}$ (with each iteration denoted as $\varvec{i}$; $\varvec{i}=1$ in this study), gradually advancing towards a convergence of the solution. One limitation of this study is that we did not pursue extensive iterations, as our primary aim was to assess the potential of the newly available observation data, rather than to refine the model accuracy itself towards its finest. The Jacobian matrix $\varvec{K}$ explains the sensitivity relationship between NO_x emissions and NO₂ concentrations, which was quantified by CMAQ DDM-3D. Both $\varvec{F}$ and $\varvec{K}$ were updated in every iteration, guiding the inversion towards more accurate outcomes.

$${\widehat{x}}_{i+1} = {x}_{a}+{S}_{e}{K}_{i}^{T}{({K}_{i}{S}_{e}{K}_{i}^{T}+{S}_{o})}^{-1}\left[y-K{x}_{i}+{K}_{i}\left({x}_{i}-{x}_{a}\right)\right] \left(2\right)$$

Using GEMS NO₂ columns, this inversion was performed whenever the top-down constraints were available. This allowed us to update hourly NO_x emissions corresponding to GEMS’s daytime retrieval hours, while leaving the emissions unchanged during the hours without observations. To utilize LEO proxy NO₂ columns, we employed the inversion method from previous studies^27–28,36, which used time-averaged LEO observation data to update emissions inventories. Resolving the discrepancy between the monthly observed and modeled NO₂ columns during MAM, this inversion led hourly NO_x emissions to be uniformly updated throughout the entire day. After completing these inversions, we reran the model using the updated a posteriori emissions to simulate NO₂ concentrations over the modeling domain.

4.5 Ground-truth for model evaluation

Prior to proceeding with CMAQ simulations, we assessed the accuracy of the WRF-simulated meteorological fields at ground-based weather stations in Korea. We obtained hourly measurements of 2 m air temperature and 10 m wind U and V components observed at 95 sites during MAM 2022, sourced from the Korean Meteorological Administration. The modeled meteorology showed fair agreement with station measurements (Figure S3), with IOA ranging from 0.65 to 0.93.

To evaluate the accuracy of CMAQ simulations before and after updating the NO_x emissions inventory, we obtained hourly surface NO₂ concentrations (in ppb) observed at ground-based monitoring stations across Asia during MAM 2022, sourced from Korea’s Ministry of Environment (AirKorea) and China’s Ministry of Ecology and Environment (MEE). To ensure the quality of AirKorea measurements, from an original count of 515 stations, we excluded those with more than 50% missing data during the validation period^16,36, which resulted in a 9.93% data loss and retaining 459 stations. To assure the quality of MEE measurements, we applied data filtering methods^66–68 to the measurements at 250 control points across China. This included discarding negative concentrations and duplicate records (> 4 consecutive repeats) due to equipment failures, resulting in a 0.43% decrease in the number of data points. Note that we converted MEE’s native measurements in µg/m³ to ppb⁶⁸.

Acknowledgements

This work was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-2023-04-02-082). We thank the Research Computing Data Core at University of Houston for providing the supercomputing resources that supported this work.

Author contributions

JP took the lead in drafting the original manuscript. JP, YC, and KL designed the experiments. JP and JJ set up the modeling system with assistance from AKY. JP and JJ conducted the emissions adjustments. JP conducted model simulations and validated the results. KL provided the GEMS and AirKorea datasets. YC and KL, serving as the principal investigator and project manager, respectively, provided overall context and supervised the entire research process. All authors discussed the results, exchanged feedback on the manuscript draft, and contributed to the preparation of the final version.

Data availability

GEMS Level 2 products, including tropospheric NO₂ columns, are available through Open-API (in Korean) at https://nesc.nier.go.kr/ko/html/svc/openapi/explain.do (last accessed on April 11, 2024), provided by the Environmental Satellite Center under Korea’s National Institute of Environment Research (NIER). Quality-assured AirKorea measurement data are available on the AirKorea website (in Korean) at https://www.airkorea.or.kr/web/last_amb_hour_data?pMENU_NO=123 (last accessed on April 11, 2024). MEE measurement data can be accessed through the web archive (in Chinese) at https://quotsoft.net/air/ (last accessed on April 11, 2024), originally sourced from the China National Environmental Monitoring Center (CNEMC) database.

Competing interests

Authors declare no competing interests.

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No competing interests reported.

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You are reading this latest preprint version

First Top-Down Diurnal Updates to NOx Emissions Inventory in Asia Informed by the Geostationary Environment Monitoring Spectrometer (GEMS) Tropospheric NO2 Columns

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2 Results and discussion

2.1 Simulations using the original emissions inventory

2.2. Top-down updates to the NO_x emissions inventory

2.3 Simulations using the updated NO_x emissions inventory

3. Conclusion

4. Methods

4.1 Modeling setup

4.2 Emissions in Asia

4.3 Satellite data: GEMS tropospheric NO₂ columns and LEO proxy

4.4 Top-down approach for the NO_x emissions adjustment

4.5 Ground-truth for model evaluation

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1

First Top-Down Diurnal Updates to NOx Emissions Inventory in Asia Informed by the Geostationary Environment Monitoring Spectrometer (GEMS) Tropospheric NO2 Columns

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2 Results and discussion

2.1 Simulations using the original emissions inventory

2.2. Top-down updates to the NOx emissions inventory

2.3 Simulations using the updated NOx emissions inventory

3. Conclusion

4. Methods

4.1 Modeling setup

4.2 Emissions in Asia

4.3 Satellite data: GEMS tropospheric NO2 columns and LEO proxy

4.4 Top-down approach for the NOx emissions adjustment

4.5 Ground-truth for model evaluation

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1

2.2. Top-down updates to the NO_x emissions inventory

2.3 Simulations using the updated NO_x emissions inventory

4.3 Satellite data: GEMS tropospheric NO₂ columns and LEO proxy

4.4 Top-down approach for the NO_x emissions adjustment