Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence.

Nitrogen dioxide (NO2) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for the period 2019-2020 by combining surface NO2 measurements, satellite tropospheric NO2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO2 estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and root-mean-square error of 4.89 (9.95) µg/m3. The daily seamless high-resolution and high-quality dataset "ChinaHighNO2" allows us to examine spatial patterns at fine scales such as the urban-rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO2, especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within ±1 µg/m3). During the COVID-19 pandemic, surface NO2 concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO2 column, implying that the former can better represent the changes in NOx emissions.

Satellite evidence for changes in the NO2 weekly cycle over large cities.

Anthropogenic activities, by far the largest source of NOx into the atmosphere, induce a weekly cycle of NO2 abundances in cities. Comprehensive analysis of the 2005-2017 OMI NO2 dataset reveals significant weekly cycles in 115 of the 274 cities considered. These results are corroborated by a full year of high-resolution TROPOMI NO2 observations. The OMI dataset permits us to identify trends in the weekly cycle resulting from NOx emissions changes. The data show a clear weakening of the weekly cycle over European and U.S. cities, an evolution attributed to the decline in anthropogenic emissions and the resulting growing importance of background NO2, whereas NO2 lifetime changes also play a minor role. In particular, the Sunday NO2 columns averaged over all U.S. cities are found to increase, relative to the weekly average, from 0.72 during 2005-2007 to 0.88 in 2015-2017. The opposite tendency is recorded in regions undergoing rapid emission growth. Multiyear simulations over the U.S. and the Middle East using the chemistry-transport model MAGRITTEv1.1 succeed in capturing the observed weekly cycles over the largest cities, as well as the observed long-term trends in the weekly cycle.

Quantification of nitrogen oxides emissions from build-up of pollution over Paris with TROPOMI.

Nitrogen dioxide (NO2) is a regulated air pollutant that is of particular concern in many cities, where concentrations are high. Emissions of nitrogen oxides to the atmosphere lead to the formation of ozone and particulate matter, with adverse impacts on human health and ecosystems. The effects of emissions are often assessed through modeling based on inventories relying on indirect information that is often outdated or incomplete. Here we show that NO2 measurements from the new, high-resolution TROPOMI satellite sensor can directly determine the strength and distribution of emissions from Paris. From the observed build-up of NO2 pollution, we find highest emissions on cold weekdays in February 2018, and lowest emissions on warm weekend days in spring 2018. The new measurements provide information on the spatio-temporal distribution of emissions within a large city, and suggest that Paris emissions in 2018 are only 5-15% below inventory estimates for 2011-2012, reflecting the difficulty of meeting NOx emission reduction targets.

Balance of Emission and Dynamical Controls on Ozone During the Korea-United States Air Quality Campaign From Multiconstituent Satellite Data Assimilation.

Global multiconstituent concentration and emission fields obtained from the assimilation of the satellite retrievals of ozone, CO, NO2, HNO3, and SO2 from the Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment 2, Measurements of Pollution in the Troposphere, Microwave Limb Sounder, and Atmospheric Infrared Sounder (AIRS)/OMI are used to understand the processes controlling air pollution during the Korea-United States Air Quality (KORUS-AQ) campaign. Estimated emissions in South Korea were 0.42 Tg N for NO x and 1.1 Tg CO for CO, which were 40% and 83% higher, respectively, than the a priori bottom-up inventories, and increased mean ozone concentration by up to 7.5 ± 1.6 ppbv. The observed boundary layer ozone exceeded 90 ppbv over Seoul under stagnant phases, whereas it was approximately 60 ppbv during dynamical conditions given equivalent emissions. Chemical reanalysis showed that mean ozone concentration was persistently higher over Seoul (75.10 ± 7.6 ppbv) than the broader KORUS-AQ domain (70.5 ± 9.2 ppbv) at 700 hPa. Large bias reductions (>75%) in the free tropospheric OH show that multiple-species assimilation is critical for balanced tropospheric chemistry analysis and emissions. The assimilation performance was dependent on the particular phase. While the evaluation of data assimilation fields shows an improved agreement with aircraft measurements in ozone (to less than 5 ppbv biases), CO, NO2, SO2, PAN, and OH profiles, lower tropospheric ozone analysis error was largest at stagnant conditions, whereas the model errors were mostly removed by data assimilation under dynamic weather conditions. Assimilation of new AIRS/OMI ozone profiles allowed for additional error reductions, especially under dynamic weather conditions. Our results show the important balance of dynamics and emissions both on pollution and the chemical assimilation system performance.

Substantial Underestimation of Post-Harvest Burning Emissions in the North China Plain Revealed by Multi-Species Space Observations.

The large-scale burning of crop residues in the North China Plain (NCP), one of the most densely populated world regions, was recently recognized to cause severe air pollution and harmful health effects. A reliable quantification of the magnitude of these fires is needed to assess regional air quality. Here, we use an eight-year record (2005-2012) of formaldehyde measurements from space to constrain the emissions of volatile organic compounds (VOCs) in this region. Using inverse modelling, we derive that satellite-based post-harvest burning fluxes are, on average, at least a factor of 2 higher than state-of-the-art bottom-up statistical estimates, although with significant interannual variability. Crop burning is calculated to cause important increases in surface ozone (+7%) and fine aerosol concentrations (+18%) in the North China Plain in June. The impact of crop fires is also found in satellite observations of other species, glyoxal, nitrogen dioxide and methanol, and we show that those measurements validate the magnitude of the top-down fluxes. Our study indicates that the top-down crop burning fluxes of VOCs in June exceed by almost a factor of 2 the combined emissions from other anthropogenic activities in this region, underscoring the need for targeted actions towards changes in agricultural management practices.