Focus on Satellite Remote Sensing of Atmospheric Environment over Asia

Recent studies demonstrated the difficulties to explain observed tropospheric nitrogen dioxide (NO₂) variabilities over the United States and Europe, but thorough analysis for the impacts on tropospheric NO₂ in China is still lacking. Here we provide a comparative analysis for the observed and modeled (Goddard Earth Observing System-Chem) tropospheric NO₂ in early 2020 in China. Both ozone monitoring instrument and surface NO₂ measurements show marked decreases in NO₂ abundances due to the 2019 novel coronavirus (COVID-19) controls. However, we find a large discrepancy between observed and modeled NO₂ changes over highly polluted provinces: the observed reductions in tropospheric NO₂ columns are about 40% lower than those in surface NO₂ concentrations. By contrast, the modeled reductions in tropospheric NO₂ columns are about two times higher than those in surface NO₂ concentrations. This discrepancy could be driven by the combined effects from uncertainties in simulations and observations, associated with possible inaccurate simulations of lower tropospheric NO₂, larger uncertainties in the modeled interannual variabilities of NO₂ columns, as well as insufficient consideration of aerosol effects and a priori NO₂ variability in satellite retrievals. In addition, our analysis suggests a small influence from free tropospheric NO₂ backgrounds in E. China in winter. This work demonstrates the challenge to interpret wintertime tropospheric NO₂ changes in China, highlighting the importance of integrating surface NO₂ observations to provide better analysis for NO₂ variabilities.

Trends of formaldehyde (HCHO) linked to anthropogenic activity over large cities located in the Asian continent are calculated for the period 2005–2019 using the Quality Assurance for Essential Climate Variables dataset from the Ozone Monitoring Instrument aboard the Aura satellite. Contributions due to anthropogenic emissions are isolated by applying a correction based on near-surface temperature in order to account for interference from local biogenic emissions. Strong positive trends are derived over the Middle East and the Indian subcontinent (up to 3.6% yr⁻¹ and 2.4% yr⁻¹ respectively) where regulations of anthropogenic non-methane volatile organic compound (NMVOC) emissions are currently limited. Weaker trends are observed over cities located in China, where the air pollution action plan (2013) may have mitigated NMVOC trends early on, but targeted legislature concerning VOC emissions was only recently introduced. HCHO trends for cities located in South and Equatorial Asia are mostly not significant or very uncertain. Cities located in Taiwan and Japan (regions in Asia where legislation has been in place since the early 2000s) display mostly negative trends.

Dust–cloud–surface radiation interactions are a complex nonlinear relation referring to the influences of both atmospheric dust and dust-on-snow on surface albedo. A 'Tiramisu' snow event occurred on 1 December 2018, in Urumqi, China, providing an excellent testbed for exploring the comprehensive effect induced by atmospheric dust and those deposited atop fresh snowpack on surface radiation. A detailed analysis indicates that the decrease of snow albedo by 0.17–0.26 (22%–34%) is contributed by the effects both the dust–cloud interactions and dust-on-snow at synoptic scale in this case. In particular, dust well mixed with ice clouds at altitudes of 2.5–5.5 km disrupted the 'seeder–feeder' structure of clouds and heterogeneous ice nucleation. Dust-induced changes in the low layer of ice clouds (3.3–5.5 km) under a low temperature of –20 °C resulted in a 31.8% increase in the ice particle radius and 84.6% increase in the ice water path, which acted to indirectly buffer the incident solar radiation reaching the surface. Dust particles deposited on the snow surface further caused snow darkening since the snow albedo was found to decrease by 11.8%–23.3%. These findings underscore the importance of considering the comprehensive effect of dust–cloud–radiation interactions in the future.

Over the past two decades, satellite instruments have provided unprecedented information on global air quality, and yet the remote sensing of surface ozone remains elusive. Here we propose a new method to infer spatial variability in surface ozone by combining multispectral ozone retrievals using radiances from the tropospheric emission spectrometer thermal infrared instrument and the ozone monitoring instrument ultratraviolet/visible instrument with a chemical reanalysis. We find that our inferred surface ozone in summertime China and the United States has regional biases of less than 4 ppb and a high spatial correlation when validated against independent surface measurements. Over the broader Asia region, our analysis results in a spatial pattern of summertime surface ozone that can largely be explained by a combination of the Asian monsoon circulation and ${\textrm{NO}}_x$ emissions. Our results show the potential of combining satellite measurements and chemical reanalyses to provide critical air quality information in regions of limited surface networks, thereby enhancing the global air quality observing system.

Emission inventory development for air pollutants, by compiling records from individual emission sources, takes many years and involves extensive multi-national effort. A complementary method to estimate air pollution emissions is in the use of satellite remote sensing. In this study, NO₂ observations from the Ozone Monitoring Instrument are combined with re-analysis meteorology to estimate urban nitrogen oxide (NO_X) emissions for 80 global cities between 2005 and 2019. The global average downward trend in satellite-derived urban NO_X emissions was 3.1%–4.0% yr⁻¹ between 2009 and 2018 while inventories show a 0%–2.2% yr⁻¹ drop over the same timeframe. This difference is primarily driven by discrepancies between satellite-derived urban NO_X emissions and inventories in Africa, China, India, Latin America, and the Middle East. In North America, Europe, Korea, Japan, and Australasia, NO_X emissions dropped similarly as reported in the inventories. In Europe, Korea, and Japan only, the temporal trends match the inventories well, but the satellite estimate is consistently larger over time. While many of the discrepancies between satellite-based and inventory emissions estimates represent real differences, some of the discrepancies might be related to the assumptions made to compare the satellite-based estimates with inventory estimates, such as the spatial disaggregation of emissions inventories. Our work identifies that the three largest uncertainties in the satellite estimate are the tropospheric column measurements, wind speed and direction, and spatial definition of each city.

Emissions of reactive nitrogen as ammonia (NH₃) and nitrogen oxides (NO_x), together with sulfur dioxide (SO₂), contribute to formation of secondary PM_2.5 in the atmosphere. Satellite observations of atmospheric NH₃, NO₂, and SO₂ levels since the 2000s provide valuable information to constrain the spatial and temporal variability of their emissions. Here we present a bottom-up Chinese NH₃ emission inventory combined with top-down estimates of Chinese NO_x and SO₂ emissions using ozone monitoring instrument satellite observations, aiming to quantify the interannual variations of reactive nitrogen emissions in China and their contributions to PM_2.5 air pollution over 2005–2015. We find small interannual changes in the total Chinese anthropogenic NH₃ emissions during 2005–2016 (12.0–13.3 Tg with over 85% from agricultural sources), but large interannual change in top-down Chinese NO_x and SO₂ emissions. Chinese NO_x emissions peaked around 2011 and declined by 22% during 2011–2015, and Chinese SO₂ emissions declined by 55% in 2015 relative to that in 2007. Using the GEOS-Chem chemical transport model simulations, we find that rising atmospheric NH₃ levels in eastern China since 2011 as observed by infrared atmospheric sounding interferometer and atmospheric infrared sounder satellites are mainly driven by rapid reductions in SO₂ emissions. The 2011–2015 Chinese NO_x emission reductions have decreased regional annual mean PM_2.5 by 2.3–3.8 μg m⁻³. Interannual PM_2.5 changes due to NH₃ emission changes are relatively small, but further control of agricultural NH₃ emissions can be effective for PM_2.5 pollution mitigation in eastern China.

Satellite-based inverse modeling has the potential to drive aerosol precursor emissions, but its efficacy for improving chemistry transport models (CTMs) remains elusive because of its likely inherent dependence on the error characteristics of a specific CTM used for the inversion. This issue is quantitively assessed here by using three CTMs. We show that SO₂ emissions from global GEOS-Chem adjoint model and OMI SO₂ data, when combined with spatial variation of bottom-up emissions, can largely improve WRF-Chem and WRF-CMAQ forecast of SO₂ and aerosol optical depth (in reference to moderate resolution imaging spectroradiometer data) in China. This suggests that the efficacy of satellite-based inversion of SO₂ emission appears to be high for CTMs that use similar or identical emission inventories. With the advent of geostationary air quality monitoring satellites in next 3 years, this study argues that an era of using top-down approach to rapidly update emission is emerging for regional air quality forecast, especially over Asia having highly varying emissions.

Tropospheric NO₂ columns retrieved from ozone monitoring instrument (OMI) are widely used, even though there is a significant loss of spatial coverage due to multiple factors. This work introduces a framework for reconstructing gaps in the OMI NO₂ data over China by using machine learning and an adaptive weighted temporal fitting method with NO₂ measurements from Global Ozone Monitoring Experiment–2B, and surface measurements. The reconstructed NO₂ has four important characteristics. First, there is improved spatial and temporal coherence on a day-to-day basis, allowing new scientific findings to be made. Second, the amount of data doubled, with 40% more data available. Third, the results are reliable overall, with a good agreement with Multi-AXis Differential Optical Absorption Spectroscopy measurements (R: 0.75–0.85). Finally, the mean of reconstructed NO₂ vertical columns during 2015 and 2018 is consistent with the original data in the spatial distribution, while the standard deviation decreases in most places over Mainland China. This novel finding is expected to contribute to both air quality and climate studies.