OMI-based emission source classification in East China and its spatial redistribution in view of pollution control measures.

This study aims to generate a satellite-based qualitative emission source characterization for the heavily polluted eastern part of China in the 2010-2016 time period. The applied source identification technique relies on satellite-based NOx and SO2 emission estimates by OMI, their SO2:NOx ratio, and the MIX anthropogenic emission inventory to distinguish emissions from different emission categories (urban, industrial, natural) and characterize the dominant source per 0.25° × 0.25° grid cell in East China. Overall, we find good agreement between the satellite- and emission inventory-based spatiotemporal distribution and characterization of the dominant emission sources in East China in 2010-2016. In 2010, the satellite measurements suggest an emission distribution less dominated by industrial areas, a somewhat larger role for urban/transportation areas and agricultural activities, and more natural emissions in the southern part compared to the bottom-up emission categorization. In 2016, more than half of the classified emission categories over East China have remained the same. At the same time, there is a notable increase of agricultural lands and decrease of areas dominated by industry/transportation in 2016, suggestive of an overall decrease in heavy air pollution in East China over the course of 7 years. This is likely attributed to the sustained efforts of the Chinese government to drastically improve the air quality, especially since 2013 when the National Air Pollution Prevention and Control Action Plan was enacted. However, signs of urban expansion (urbanization) and rural-urban migration ("Go West" motion) stemmed from China's rapid economic growth and labour demand are evident; escalating industrialization (even with cleaner means) and the urban population growth in East China resulted in stronger emissions from sources representing consumption and transportation which are strongly related to NO2 and PM10 pollution (rather than SO2) and are directly influenced by the population size. This resulted to a shift of the emissions from the east mainly to the north and northwest of East China. Overall, although the effectiveness of the Chinese environmental control policies has been successful, the air pollution problem remains an important concern.

Spaceborne Observations of Lightning NO2 in the Arctic.

The Arctic region is experiencing notable warming as well as more lightning. Lightning is the dominant source of upper tropospheric nitrogen oxides (NOx), which are precursors for ozone and hydroxyl radicals. In this study, we combine the nitrogen dioxide (NO2) observations from the TROPOspheric Monitoring Instrument (TROPOMI) with Vaisala Global Lightning Dataset 360 to evaluate lightning NO2 (LNO2) production in the Arctic. By analyzing consecutive TROPOMI NO2 observations, we determine the lifetime and production efficiency of LNO2 during the summers of 2019-2021. Our results show that the LNO2 production efficiency over the ocean is ∼6 times higher than over continental regions. Additionally, we find that a higher LNO2 production efficiency is often correlated with lower lightning rates. The summertime lightning NOx emission in the Arctic (north of 70° N) is estimated to be 219 ± 116 Mg of N, which is equal to 5% of anthropogenic NOx emissions. However, for the span of a few hours, the Arctic LNO2 density can even be comparable to anthropogenic NO2 emissions in the region. These new findings suggest that LNO2 can play an important role in the upper-troposphere/lower-stratosphere atmospheric chemical processes in the Arctic, particularly during the summer.

Source contributions to two super dust storms over Northern China in March 2021 and the impact of soil moisture.

Two extremely devastating super dust storms (SDS) hit Mongolia and Northern China in March 2021, causing many deaths and substantial economic damage. Accurate forecasting of dust storms is of great importance for avoiding or mitigating their effects. One of the most critical factors affecting dust emissions is soil moisture, but its value in desert exhibits significant uncertainty. In this study, model experiments were conducted to simulate dust emissions using four soil moisture datasets. The results were compared with observations to assess the effects of soil moisture on the dust emission strength. The Integrated Source Apportionment Method (ISAM) was used to track the dust sources and quantify the contribution from each source region to the dust load over the North China Plain (NCP), Korea peninsula, and western Japan. The results show large differences in the dust load depending on the soil moisture datasets used. The high soil moisture in the NCEP dataset results in substantial underestimation of the dust emission flux and PM10 concentration. Despite a minor overestimation of PM10 concentrations in many Northern China cities, the ERA5 dataset yields the best simulation performance. During the two SDS events, about 7.5 Mt dust was released from the deserts in Mongolia and 2.8 Mt from the deserts in China. Source apportionment indicates that the Mongolian Gobi Desert is the dominant source of PM10 in the NCP, Korea peninsula, and western Japan, accounting for 60 %-80 %, while Inner Mongolia contributed 10 %-20 %.

Satellite reveals a steep decline in China's CO2 emissions in early 2022.

Response actions to the coronavirus disease 2019 perturbed economies and carbon dioxide (CO2) emissions. The Omicron variant that emerged in 2022 caused more substantial infections than in 2020 and 2021 but it has not yet been ascertained whether Omicron interrupted the temporary post-2021 rebound of CO2 emissions. Here, using satellite nitrogen dioxide observations combined with atmospheric inversion, we show a larger decline in China's CO2 emissions between January and April 2022 than in those months during the first wave of 2020. China's CO2 emissions are estimated to have decreased by 15% (equivalent to -244.3 million metric tons of CO2) during the 2022 lockdown, greater than the 9% reduction during the 2020 lockdown. Omicron affected most of the populated and industrial provinces in 2022, hindering China's CO2 emissions rebound starting from 2021. China's emission variations agreed with downstream CO2 concentration changes, indicating a potential to monitor CO2 emissions by integrating satellite and ground measurements.

Satellite-based estimates of decline and rebound in China's CO2 emissions during COVID-19 pandemic.

Changes in CO2 emissions during the COVID-19 pandemic have been estimated from indicators on activities like transportation and electricity generation. Here, we instead use satellite observations together with bottom-up information to track the daily dynamics of CO2 emissions during the pandemic. Unlike activity data, our observation-based analysis deploys independent measurement of pollutant concentrations in the atmosphere to correct misrepresentation in the bottom-up data and can provide more detailed insights into spatially explicit changes. Specifically, we use TROPOMI observations of NO2 to deduce 10-day moving averages of NO x and CO2 emissions over China, differentiating emissions by sector and province. Between January and April 2020, China's CO2 emissions fell by 11.5% compared to the same period in 2019, but emissions have since rebounded to pre-pandemic levels before the coronavirus outbreak at the beginning of January 2020 owing to the fast economic recovery in provinces where industrial activity is concentrated.

Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19.

China's policy interventions to reduce the spread of the coronavirus disease 2019 have environmental and economic impacts. Tropospheric nitrogen dioxide indicates economic activities, as nitrogen dioxide is primarily emitted from fossil fuel consumption. Satellite measurements show a 48% drop in tropospheric nitrogen dioxide vertical column densities from the 20 days averaged before the 2020 Lunar New Year to the 20 days averaged after. This decline is 21 ± 5% larger than that from 2015 to 2019. We relate this reduction to two of the government's actions: the announcement of the first report in each province and the date of a province's lockdown. Both actions are associated with nearly the same magnitude of reductions. Our analysis offers insights into the unintended environmental and economic consequences through reduced economic activities.

A new method for deriving trace gas emission inventories from satellite observations: The case of SO2 over China.

A method is developed that allows the construction of spatial emission inventories. The method is applied for anthropogenic SO2 over China (0.25°×0.25°). The Enhancement Ratio Method (ERM) allows for the calculation of SO2 emissions using relationships between gridded satellite measurements of SO2 and NO2 at low wind speeds, and satellite-based NOx emission estimates. Here, we derive SO2 emissions for five years (2007-2011). A large decrease of emissions during 2007-2009 and a modest increase between 2010 and 2011 is observed. The evolution of emissions over time calculated here is in general agreement with bottom-up inventories, although differences exist, not only between the current inventory and other inventories but also among the bottom up inventories themselves. The ERM-derived emissions are consistent, spatially and temporally, with existing inventories.

NOx emission trends over Chinese cities estimated from OMI observations during 2005 to 2015.

Satellite NO2 observations have been widely used to evaluate emission changes. To determine trends in NOx emission over China, we used a method independent of chemical transport models to quantify the NOx emissions from 48 cities and 7 power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO2 observations during 2005 to 2015. We found that NOx emissions over 48 Chinese cities increased by 52% from 2005 to 2011 and decreased by 21% from 2011 to 2015. The decrease since 2011 could be mainly attributed to emission control measures in power sector; while cities with different dominant emission sources (i.e. power, industrial and transportation sectors) showed variable emission decline timelines that corresponded to the schedules for emission control in different sectors. The time series of the derived NOx emissions was consistent with the bottom-up emission inventories for all power plants (r=0.8 on average), but not for some cities (r=0.4 on average). The lack of consistency observed for cities was most probably due to the high uncertainty of bottom-up urban emissions used in this study, which were derived from downscaling the regional-based emission data to cities by using spatial distribution proxies.