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.

Impacts of TROPOMI-Derived NO X Emissions on NO2 and O3 Simulations in the NCP during COVID-19

NO₂ and O₃ simulations have great uncertainties during the COVID-19 epidemic, but their biases and spatial distributions can be improved with NO₂ assimilations. This study adopted two top-down NO _X inversions and estimated their impacts on NO₂ and O₃ simulation for three periods: the normal operation period (P1), the epidemic lockdown period following the Spring Festival (P2), and back to work period (P3) in the North China Plain (NCP). Two TROPOspheric Monitoring Instrument (TROPOMI) NO₂ retrievals came from the Royal Netherlands Meteorological Institute (KNMI) and the University of Science and Technology of China (USTC), respectively. Compared to the prior NO _X emissions, the two TROPOMI posteriors greatly reduced the biases between simulations with in situ measurements (NO₂ MREs: prior 85%, KNMI -27%, USTC -15%; O₃ MREs: Prior -39%, KNMI 18%, USTC 11%). The NO _X budgets from the USTC posterior were 17-31% higher than those from the KNMI one. Consequently, surface NO₂ levels constrained by USTC-TROPOMI were 9-20% higher than those by the KNMI one, and O₃ is 6-12% lower. Moreover, USTC posterior simulations showed more significant changes in adjacent periods (surface NO₂: P2 vs P1, -46%, P3 vs P2, +25%; surface O₃: P2 vs P1, +75%, P3 vs P2, +18%) than the KNMI one. For the transport flux in Beijing (BJ), the O₃ flux differed by 5-6% between the two posteriori simulations, but the difference of NO₂ flux between P2 and P3 was significant, where the USTC posterior NO₂ flux was 1.5-2 times higher than the KNMI one. Overall, our results highlight the discrepancies in NO₂ and O₃ simulations constrained by two TROPOMI products and demonstrate that the USTC posterior has lower bias in the NCP during COVD-19.

Impact of the COVID-19 outbreak on air pollution levels in East Asia

This study leverages satellite remote sensing to investigate the impact of the coronavirus outbreak and the resulting lockdown of public venues on air pollution levels in East Asia. We analyze data from the Sentinel-5P and the Himawari-8 satellites to examine concentrations of NO₂, HCHO, SO₂, and CO, and the aerosol optical depth (AOD) over the BTH, Wuhan, Seoul, and Tokyo regions in February 2019 and February 2020. Results show that most of the concentrations of pollutants are lower than those of February 2019. Compared to other pollutants, NO₂ experienced the most significant reductions by almost 54%, 83%, 33%, and 19% decrease in BTH, Wuhan, Seoul, and Tokyo, respectively. The greatest reductions in pollutants occurred in Wuhan, with a decrease of almost 83%, 11%, 71%, and 4% in the column densities of NO₂, HCHO, SO₂, and CO, respectively, and a decrease of about 62% in the AOD. Although NO₂, CO, and formaldehyde concentrations decreased in the Seoul and Tokyo metropolitan areas compared to the previous year, concentrations of SO₂ showed an increase in these two regions due to the effect of transport from polluted upwind regions. We also show that meteorological factors were not the main reason for the dramatic reductions of pollutants in the atmosphere. Moreover, an investigation of the HCHO/NO₂ ratio shows that in many regions of East China, particularly in Wuhan, ozone production in February 2020 is less NO_X saturated during the daytime than it was in February 2019. With large reductions in the concentrations of NO₂ during lockdown situations, we find that significant increases in surface ozone in East China from February 2019 to February 2020 are likely the result of less reaction of NO and O₃ caused by significantly reduced NO_X concentrations and less NO_X saturation in East China during the daytime.

An Estimation of Top-Down NOx Emissions from OMI Sensor Over East Asia

This study focuses on the estimation of top-down NOx emissions over East Asia, integrating information on the levels of NO2 and NO, wind vector, and geolocation from Ozone Monitoring Instrument (OMI) observations and Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model simulations. An algorithm was developed based on mass conservation to estimate the 30 km × 30 km resolved top-down NOx emissions over East Asia. In particular, the algorithm developed in this study considered two main atmospheric factors—(i) NOx transport from/to adjacent cells and (ii) calculations of the lifetimes of column NOx (τ). In the sensitivity test, the analysis showed the improvements in the top-down NOx estimation via filtering the data (τ ≤ 2 h). The best top-down NOx emissions were inferred after the sixth iterations. Those emissions were 11.76 Tg N yr−1 over China, 0.13 Tg N yr−1 over North Korea, 0.46 Tg N yr−1 over South Korea, and 0.68 Tg N yr−1 over Japan. These values are 34%, 62%, 60%, and 47% larger than the current bottom-up NOx emissions over these countries, respectively. A comparison between the CMAQ-estimated and OMI-retrieved NO2 columns was made to confirm the accuracy of the newly estimated NOx emission. The comparison confirmed that the estimated top-down NOx emissions showed better agreements with observations (R2 = 0.88 for January and 0.81 for July).

Bayesian geostatistical modelling of high-resolution NO2 exposure in Europe combining data from monitors, satellites and chemical transport models

Bayesian geostatistical regression (GR) models estimate air pollution exposure at high spatial resolution, quantify the prediction uncertainty and provide probabilistic inference on the exceedance of air quality thresholds. However, due to high computational burden, previous GR models have provided gridded ambient nitrogen dioxide (NO₂) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged NO₂ concentrations at 1 km² spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations. The tropospheric values of NO₂ derived from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration's (NASA's) Aura satellite were converted to near ground NO₂ concentration proxies using simulations from the 3-D global CTM (GEOS-Chem) at 0.5° × 0.625°spatial resolution and surface-to-column NO₂ ratios. Simulations from the Ensemble of regional CTMs at spatial resolution of 0.1° × 0.1°were extracted from the Copernicus atmosphere monitoring service (CAMS). The contribution of these covariates to the predictive capability of geostatistical models was for the first time evaluated here through a rigorous model selection procedure along with additional continental high-resolution satellite-derived products, including novel data from the pan-European Copernicus land monitoring service (CLMS). The results have shown that the conversion of columnar NO₂ values to surface quasi-observations yielded models with slightly better predictive ability and lower uncertainty. Nonetheless, the use of higher resolution CAMS-Ensemble simulations as covariates in GR models granted the most accurate surface NO₂ estimates, showing that, in 2016, 16.17 (95% C.I. 6.34-29.96) million people in Europe, representing 2.97% (95% C.I. 1.16% - 5.50%) of the total population, were exposed to levels above the EU directive and WHO air quality guidelines threshold for NO₂. Our estimates are readily available to policy makers and scientists assessing the burden of disease attributable to NO₂ in 2016.

Temporal Analysis of OMI-Observed Tropospheric NO2 Columns over East Asia during 2006–2015

The study analyzed temporal variations of Ozone Monitoring Instrument (OMI)-observed NO2 columns, interregional correlation, and comparison between NO2 columns and NOx emissions during the period from 2006 to 2015. Regarding the trend of the NO2 columns, the linear lines were classified into four groups: (1) ‘upward and downward’ over six defined geographic regions in central-east Asia; (2) ‘downward’ over Guangzhou, Japan, and Taiwan; (3) ‘stagnant’ over South Korea; and (4) ‘upward’ over North Korea, Mongolia, Qinghai, and Northwestern Pacific ocean. In particular, the levels of NO2 columns in 2015 returned to those in 2006 over most of the polluted regions in China. Quantitatively, their relative changes in 2015 compared to 2006 were approximately 10%. From the interregional correlation analysis, it was found that unlike positive relationships between the polluted areas, the different variations of monthly NO2 columns led to negative relationships in Mongolia and Qinghai. Regarding the comparison between NO2 columns and NOx emission, the NOx emissions from the Copernicus Atmosphere Monitoring Service (CAMS) and Clean Air Policy Support System (CAPSS) inventories did not follow the year-to-year variations of NO2 columns over the polluted regions. In addition, the weekly effect was only clearly shown in South Korea, Japan, and Taiwan, indicating that the amounts of NOx emissions are significantly contributed to by the transportation sector.

Changes in air quality and tropospheric composition due to depletion of stratospheric ozone and interactions with changing climate: implications for human and environmental health

UV radiation is an essential driver for the formation of photochemical smog, which includes ground-level ozone and particulate matter (PM).

Scaling relationship for NO2 pollution and urban population size: a satellite perspective

Concern is growing about the effects of urbanization on air pollution and health. Nitrogen dioxide (NO2) released primarily from combustion processes, such as traffic, is a short-lived atmospheric pollutant that serves as an air-quality indicator and is itself a health concern. We derive a global distribution of ground-level NO2 concentrations from tropospheric NO2 columns retrieved from the Ozone Monitoring Instrument (OMI). Local scaling factors from a three-dimensional chemistry-transport model (GEOS-Chem) are used to relate the OMI NO2 columns to ground-level concentrations. The OMI-derived surface NO2 data are significantly correlated (r = 0.69) with in situ surface measurements. We examine how the OMI-derived ground-level NO2 concentrations, OMI NO2 columns, and bottom-up NOx emission inventories relate to urban population. Emission hot spots, such as power plants, are excluded to focus on urban relationships. The correlation of surface NO2 with population is significant for the three countries and one continent examined here: United States (r = 0.71), Europe (r = 0.67), China (r = 0.69), and India (r = 0.59). Urban NO2 pollution, like other urban properties, is a power law scaling function of the population size: NO2 concentration increases proportional to population raised to an exponent. The value of the exponent varies by region from 0.36 for India to 0.66 for China, reflecting regional differences in industrial development and per capita emissions. It has been generally established that energy efficiency increases and, therefore, per capita NOx emissions decrease with urban population; here, we show how outdoor ambient NO2 concentrations depend upon urban population in different global regions.

Quantifying sources and sinks of trace gases using space-borne measurements: current and future science

We have been observing the Earth's upper atmosphere from space for several decades, but only over the past decade has the necessary technology begun to match our desire to observe surface air pollutants and climate-relevant trace gases in the lower troposphere, where we live and breathe. A new generation of Earth-observing satellites, capable of probing the lower troposphere, are already orbiting hundreds of kilometres above the Earth's surface with several more ready for launch or in the planning stages. Consequently, this is one of the most exciting times for the Earth system scientists who study the countless current-day physical, chemical and biological interactions between the Earth's land, ocean and atmosphere. First, I briefly review the theory behind measuring the atmosphere from space, and how these data can be used to infer surface sources and sinks of trace gases. I then present some of the science highlights associated with these data and how they can be used to improve fundamental understanding of the Earth's climate system. I conclude the paper by discussing the future role of satellite measurements of tropospheric trace gases in mitigating surface air pollution and carbon trading.