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Air pollution is an integral part of climatic, environmental, and socioeconomic current affairs and a cross-cutting component of certain United Nations Sustainable Development Goals (SDGs). Hence, reliable information on air pollution and human exposure is a crucial element in policy recommendations and decisions. At the same time, Earth Observation is steadily gaining confidence as a data input in the calculation of various SDG indicators. The current paper focuses on the usability of modern satellite remote sensing in the context of SDGs relevant to air quality. We introduce the socioeconomic importance of air quality and discuss the current uptake of geospatial information. The latest developments in Earth Observation provide measurements of finer spatial, temporal, and radiometric resolution products with increased global coverage, long-term continuation, and coherence in measurements. Leveraging on the two latest operational satellite technologies available, namely the Sentinel-5P and the Geostationary Environment Monitoring Spectrometer (GEMS) missions, we demonstrate two potential operational applications for quantifying air pollution at city and regional scales. Based on the two examples and by discussing the near-future anticipated geospatial capabilities, we showcase and advocate that the potential of satellite remote sensing as a, complementary to ground station networks, source of air pollution information is gaining confidence. As such, it can be an invaluable tool for quantifying global air pollution and deriving robust population exposure estimates. [ABSTRACT FROM AUTHOR]

In California, emission control strategies have been implemented to reduce air pollutants. Here we estimate the changes in nitrogen oxides (NOx = NO + NO2) emissions in 2005-2010 using a state-of-the-art four-dimensional variational approach. We separately and jointly assimilate surface NO2 concentrations and tropospheric NO2 columns observed by Ozone Monitoring Instrument (OMI) into the regional-scale Sulfur Transport and dEposition Model (STEM) chemical transport model on a 12 × 12 km2 horizontal resolution grid in May 2010. The assimilation generates grid-scale top-down emission estimates, and the updated chemistry fields are evaluated with independent aircraft measurements during the NOAA California Nexus (CalNex) field experiment. The emission estimates constrained only by NO2 columns, only by surface NO2, and by both indicate statewide reductions of 26%, 29%, and 30% from ~0.3 Tg N/yr in the base year of 2005, respectively. The spatial distributions of the emission changes differ in these cases, which can be attributed to many factors including the differences in the observation sampling strategies and their uncertainties, as well as those in the sensitivities of column and surface NO2 with respect to NOx emissions. The updates in California's NOx emissions reduced the mean error in modeled surface ozone in the Western U.S., even though the uncertainties in some urban areas increased due to their NOx-saturated chemical regime. The statewide reductions in NOx emissions indicated from our observationally constrained emission estimates are also reflected in several independently developed inventories: ~30% in the California Air Resources Board bottom-up inventory, ~4% in the 2008 National Emission Inventory, and ~20% in the annual mean top-down estimates by Lamsal et al. using the global Goddard Earth Observing System (GEOS)-Chem model and OMI NO2 columns. Despite the grid-scale differences among all top-down and bottom-up inventories, they all indicate stronger emission reductions in the urban regions. This study shows the potential of using space-/ground-based monitoring data and advanced data assimilation approach to timely and independently update NOx emission estimates on a monthly scale and at a fine grid resolution. The well-evaluated results here suggest that these approaches can be applied more broadly.

Dust loading in West and South Asia has been a major environmental issue due to its negative effects on air quality, food security, energy supply and public health, as well as on regional and global weather and climate. Yet a robust understanding of its recent changes and future projection remains unclear. On the basis of several high-quality remote sensing products, we detect a consistently decreasing trend of dust loading in West and South Asia over the last two decades. In contrast to previous studies emphasizing the role of local land use changes, here, we attribute the regional dust decline to the continuous intensification of Arctic amplification driven by anthropogenic global warming. Arctic amplification results in anomalous mid-latitude atmospheric circulation, particularly a deepened trough stretching from West Siberia to Northeast India, which inhibits both dust emissions and their downstream transports. Large ensemble climate model simulations further support the dominant role of greenhouse gases induced Arctic amplification in modulating dust loading over West and South Asia. Future projections under different emission scenarios imply potential adverse effects of carbon neutrality in leading to higher regional dust loading and thus highlight the importance of stronger anti-desertification counter-actions such as reforestation and irrigation management. [ABSTRACT FROM AUTHOR]

This study applies a regional Earth system model (NASA-Unified Weather Research and Forecasting with online chemistry) with updated parameterizations for selected land-air exchange processes and multi-platform observations, to first estimate reactive nitrogen (Nr = oxidized NOy + reduced NHx) emissions from anthropogenic and natural sources, nitrogen dioxide (NO2) column densities and surface concentrations, total and speciated Nr dry or/and wet deposition fluxes during 2018-2023 over the northeastern and Mid-Atlantic US most of which belong to nitrogen oxides-limited or transitional chemical regimes. The estimated multi-year Nr concentrations and deposition fluxes are then compared with and related to ozone (O3), in terms of their spatiotemporal variability and key drivers as well as possible ecosystem impacts. Finally, through three sets of case studies, we identify and discuss about 1) the capability of land data assimilation (DA) to reduce the uncertainty in modeled land surface states at daily-to-interannual timescales, that can propagate into atmospheric chemistry fields; 2) the impacts of irrigation on land surface and atmospheric fields as well as pollutants' ecosystem uptake and impacts; and 3) the impacts of transboundary air pollution during selected extreme events on pollutants' budgets and ecosystem impacts. With the updated model parameterizations and anthropogenic emission inputs, the eastern US surface O3 modeled by this tool persistently agrees better with observations (i.e., with root-mean-square errors staying within 4-7 ppbv for the individual years' May-June-July) than those in literature where model errors often exceed 20 ppbv. Based on model calculations, surface O3 correlates more strongly with early afternoon NO2 columns than formaldehyde columns (r=0.54 and 0.40, respectively). The O3 vegetative uptake overall dropped by ~10% from 2018 to 2023, displaying clearer downward temporal changes than the total Nr deposition due to the declining NOy emission and deposition fluxes competing with the increasing NHx fluxes. It is highlighted that, temporal variability of Nr and O3 concentrations and fluxes on subregional-to-local scales respond to hydrological variability that can be influenced by precipitation and controllable human activities such as irrigation. Deposition processes and biogenic emissions that are highly sensitive to interconnected environmental and plants' physiological conditions, as well as extra-regional sources (e.g., O3-rich stratospheric air and dense wildfire plumes from upwind regions), have been playing increasingly important roles in controlling pollutants' budgets in this area as local emissions go down owing to effective emission regulations and COVID lockdowns. To better inform the design of mitigation and adaptation strategies, it is recommended to continue evaluating and improving the model parameterizations and inputs relevant to these processes in seamlessly coupled multiscale Earth system models using laboratory and field experiments in combination with satellite DA which would in turn benefit remote sensing communities. [ABSTRACT FROM AUTHOR]

This study applies a regional Earth system model (NASA-Unified Weather Research and Forecasting with online chemistry) with updated parameterizations for selected land-air exchange processes and multi-platform observations, to first estimate reactive nitrogen (Nr = oxidized NOy + reduced NHx) emissions from anthropogenic and natural sources, nitrogen dioxide (NO2) column densities and surface concentrations, total and speciated Nr dry or/and wet deposition fluxes during 2018–2023 over the northeastern and Mid-Atlantic US most of which belong to nitrogen oxides-limited or transitional chemical regimes. The estimated multi-year Nr concentrations and deposition fluxes are then compared with and related to ozone (O3), in terms of their spatiotemporal variability and key drivers as well as possible ecosystem impacts. Finally, through three sets of case studies, we identify and discuss about 1) the capability of land data assimilation (DA) to reduce the uncertainty in modeled land surface states at daily-to-interannual timescales, that can propagate into atmospheric chemistry fields; 2) the impacts of irrigation on land surface and atmospheric fields as well as pollutants' ecosystem uptake and impacts; and 3) the impacts of transboundary air pollution during selected extreme events on pollutants' budgets and ecosystem impacts. With the updated model parameterizations and anthropogenic emission inputs, the eastern US surface O3 modeled by this tool persistently agrees better with observations (i.e., with root-mean-square errors staying within 4–7 ppbv for the individual years' May-June-July) than those in literature where model errors often exceed 20 ppbv. Based on model calculations, surface O3 correlates more strongly with early afternoon NO2 columns than formaldehyde columns (r =0.54 and 0.40, respectively). The O3 vegetative uptake overall dropped by ~10 % from 2018 to 2023, displaying clearer downward temporal changes than the total Nr deposition due to the declining NOy emission and deposition fluxes competing with the increasing NHx fluxes. It is highlighted that, temporal variability of Nr and O3 concentrations and fluxes on subregional-to-local scales respond to hydrological variability that can be influenced by precipitation and controllable human activities such as irrigation. Deposition processes and biogenic emissions that are highly sensitive to interconnected environmental and plants' physiological conditions, as well as extra-regional sources (e.g., O3-rich stratospheric air and dense wildfire plumes from upwind regions), have been playing increasingly important roles in controlling pollutants' budgets in this area as local emissions go down owing to effective emission regulations and COVID lockdowns. To better inform the design of mitigation and adaptation strategies, it is recommended to continue evaluating and improving the model parameterizations and inputs relevant to these processes in seamlessly coupled multiscale Earth system models using laboratory and field experiments in combination with satellite DA which would in turn benefit remote sensing communities. [ABSTRACT FROM AUTHOR]

During the operational phase of the ICARTT field experiment in 2004, the regional air quality model STEM showed a strong positive surface bias and a negative upper troposphere bias (compared to observed DC-8 and WP-3 observations) with respect to ozone. After updating emissions from NEI 1999 to NEI 2001 (with a 2004 large point sources inventory update), and modifying boundary conditions, low-level model bias decreases from 11.21 to 1.45 ppbv for the NASA DC-8 observations and from 8.26 to -0.34 for the NOAA WP-3. Improvements in boundary conditions provided by global models decrease the upper troposphere negative ozone bias, while accounting for biomass burning emissions improved model performance for CO. The covariances of ozone bias were highly correlated to NOz, NOy, and HNO3 biases. Interpolation of bias information through kriging showed that decreasing emissions in SE United States would reduce regional ozone model bias and improve model correlation coefficients. The spatial distribution of forecast errors was analyzed using kriging, which identified distinct features, which when compared to errors in postanalysis simulations, helped document improvements. Changes in dry deposition to crops were shown to reduce substantially high bias in the forecasts in the Midwest, while updated emissions were shown to account for decreases in bias in the eastern United States. Observed and modeled ozone production efficiencies for the DC-8 Were calculated and shown to be very similar (7.8) suggesting that recurring ozone bias is due to overestimation of NOx, emissions. Sensitivity studies showed that ozone formation in the United States is most sensitive to NOx, emissions, followed by VOCs and CO. PAN as a reservoir of NOx, can contribute to a significant amount of surface ozone through thermal decomposition. Copyright 2007 by the American Geophysical Union.

Although containing only single piece of information, aerosol layer height (ALH) indicates the altitude of aerosol layer in vertical coordinate which is essential for assessment of surface air quality and aerosol climate impact. Passive remote sensing measurements in oxygen (O2) absorption bands are sensitive to ALH, providing an opportunity to derive global or regional ALH information from satellite observations. In this study, we compare ALH products retrieved from near-infrared O2 absorption measurements from multiple satellite platforms including Geostationary Environment Monitoring Spectrometer (GEMS) focusing on Asia, Earth Polychromatic Imaging Camera (EPIC) in deep space, and polar orbiting satellite TROPOspheric Monitoring Instrument (TROPOMI), and validate them using spaceborne lidar (CALIOP) measurements for typical dust and smoke plumes. Adjustments have been made to account for the inherent variations in the definitions of ALH among different products, ensuring an apple-to-apple comparison. In comparison with CALIOP ALH, both EPIC and TROPOMI ALH display a high correlation coefficient (R) higher than 0.7 and an overestimation by ~ 0.8 km, whereas GEMS ALH exhibits minimal bias (0.1 km) but a slightly lower correlation with R of 0.64. Categorizing GEMS retrievals with UVAI ≥ 3 improves the agreement with CALIOP. GEMS ALH demonstrates a narrower range and lower mean value compared to EPIC and TROPOMI, and their correlation is further improved when UVAI ≥ 3. Furthermore, diurnal variation of GEMS and EPIC ALH, especially for UVAI ≥ 3, aligns with boundary layer development. Considering the important role of AOD in ALH retrieval, we found GEMS AOD at 680 nm correlates well with AERONET AOD (R ~ 0.9) but features a negative bias of -0.2. EPIC and TROPOMI tend to overestimate ALH by 0.33 km and 0.23 km, respectively, in dust cases. Finally, a dust and a smoke case are analysed in detail to explore the variation of ALH during plume transport from multiple data. [ABSTRACT FROM AUTHOR]

A new long-term emission inventory called the Inversed Emission Inventory for Chinese Air Quality (CAQIEI) was developed in this study by assimilating surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). This inventory contains the constrained monthly emissions of NOx, SO2, CO, primary PM2.5, primary PM10, and NMVOCs in China from 2013 to 2020, with a horizontal resolution of 15 km × 15 km. This paper documents detailed descriptions of the assimilation system and the evaluation results for the emission inventory. The results suggest that CAQIEI can effectively reduce the biases in the a priori emission inventory, with the normalized mean biases ranging from -9.1% to 9.5% in the a posteriori simulation, which are significantly reduced from the biases in the a priori simulations (-45.6% to 93.8%). The calculated RMSEs (0.3 mg/m³ for CO and 9.4-21.1 µg/m³ for other species, on the monthly scale) and correlation coefficients (0.76-0.94) were also improved from the a priori simulations, suggesting that CAQIEI can reasonably reproduce the magnitude and variation of emissions of different air pollutants in China. Based on CAQIEI, we estimated China's total emissions (including both natural and anthropogenic emissions) of the 6 species in 2015 to be as follows: 25.2 Tg of NOx, 17.8 Tg of SO2, 465.4 Tg of CO, 15.0 Tg of PM2.5, 40.1 Tg of PM10, and 46.0 Tg of NMVOCs. From 2015 to 2020, the total emissions reduced by 54.1% for SO2, 44.4% for PM2.5, 33.6% for PM10, 35.7% for CO, and 15.1% for NOx, but increased by 21.0% for NMVOCs. Larger emission reductions were achieved during the 2018-2020 action plan than during the 2013-2017 action plan for most species. In particular, NOx and NMVOC emissions were shown to increase during the 2013-2017 action plain, and there were obvious emission increases in the Fengwei Plain area over the Central China region. However, NOx and NMVOC emissions declined during the 2018-2020 action plan, and the emissions over the Fengwei Plain area also decreased. This suggests that the emission control policies were improved in the 2018-2020 action plan. We also compared CAQIEI with previous inventories, which verified our inversion results in terms of total emissions of NOx, SO2 and NMVOCs, and more importantly identified the potential uncertainties in our current understanding of China's air pollutant emissions. Firstly, CO emissions in China may be substantially underestimated by current inventories, with the CO emissions estimated by CAQIEI (426.8 Tg) being more than twice the amount in previous inventories (120.7-237.7 Tg). Significant underestimations for other air pollutant emissions may also exist over western and northeastern China. In addition, the NMVOC emissions were shown to be substantially underestimated over northern China but overestimated in southern China. Secondly, the emission reduction rates during 2015-2018 estimated by CAQIEI are generally smaller than those estimated by previous inventories, especially for NOx, PM10 and NMVOCs, suggesting that the mitigation effects of the air pollution control may be overestimated currently. In particular, China's NMVOC emissions were shown to have increased by 26.6% from 2015 to 2018, especially over the North China Plain by 38.0%), Northeast China (by 38.3%), and Central China (60.0%). In contrast, the emissions reduction rate of CO may be underestimated. Overall, our emissions inventory sheds new light on the complex variations of air pollutant emissions in China during its two recent clean air action periods, which could significantly improve our understanding of air pollutant emissions and related changes in air quality in China. [ABSTRACT FROM AUTHOR]

Long-range transport of sulfur dioxide (SO2) from east Asia to the central North Pacific troposphere was observed on transit flights during the NASA Transport and Chemical Evolution over the Pacific mission. A series of SO2-enhanced layers above the boundary layer was observed during these flights. The significant features included enhanced SO2 layers associated with low water vapor and low turbulence that were usually dynamically isolated from the marine boundary layer. This study shows that atmospheric dynamics were very important in determining the SO2 distributions in the central Pacific during March and April 2001. Trajectory studies revealed that SO2-enhanced layers could be connected to both volcanic and anthropogenic sources in east Asia. These trajectory studies also showed that the air parcels usually were lifted 2 km above the source regions and then progressed to the east in the midlatitudes (30° -60°N). The air parcels arrived in the central Pacific within 2-3 days. Sulfur dioxide transported at altitudes of 2-4 km dominated the SO2 distribution in the central Pacific. A comparison of SO 2 observations and results of chemical transport models indicated that SO2 was removed primarily by cloud processes. Therefore, in the absence of cloud, SO2 can be transported long distances if the trajectory is decoupled from the boundary layer. Another important observation was that the Miyake-jima volcano made a major contribution to the SO2 concentrations in the central Pacific troposphere during March and April 2001. At times, the volcanic SO2 had more influence in the central Pacific than the six largest anthropogenic SO2 source regions in east Asia. Copyright 2004 by the American Geophysical Union.

Online coupled meteorology-atmospheric chemistry models have greatly evolved in recent years. Although mainly developed by the air quality modeling community, these integrated models are also of interest for numerical weather [...]

Results from the Intergovernmental Panel on Climatic Change (IPCC) tropospheric photochemical model intercomparison (PhotoComp) are presented with a brief discussion of the factors that may contribute to differences in the modeled behaviors of HOx cycling and the accompanying O3 tendencies. PhotoComp was a tightly controlled model experiment in which the IPCC 1994 assessment sought to determine the consistency among models that are used to predict changes in tropospheric ozone, an important greenhouse gas. Calculated tropospheric photodissociation rates displayed significant differences, with a root-mean-square (rms) error of the reported model results ranging from about ±6–9% of the mean (for O3 and NO2) to up to ±15% (H2O2 and CH2O). Models using multistream methods in radiative transfer calculations showed distinctly higher rates for photodissociation of NO2 and CH2O compared to models using two-stream methods, and this difference accounted for up to one third of the rms error for these two rates. In general, some small but systematic differences between models were noted for the predicted chemical tendencies in cases that did not include reactions of nomnethane hydrocarbons (NMHC). These differences in modeled O3 tendencies in some cases could be identified, for example, as being due to differences in photodissociation rates, but in others they could not and must be ascribed to unidentified errors. O3tendencies showed rms errors of about ±10% in the moist, surface level cases with NOx concentrations equal to a few tens of parts per trillion by volume. Most of these model to model differences can be traced to differences in the destruction of O3 due to reaction with HO2. Differences in HO2, in turn, are likely due to (1) inconsistent reaction rates used by the models for the conversion of HO2 to H2O2 and (2) differences in the model-calculated photolysis of H2O2 and CH2O. In the middle tropospheric “polluted” scenario with NOx concentrations larger than a few parts per billion by volume, O3 tendencies showed rms errors of ±10–30%. These model to model differences most likely stem from differences in the calculated rates of O3 photolysis to O(1 D), which provides about 80% of the HOx source under these conditions. The introduction of hydrocarbons dramatically increased both the rate of NOx loss and its model to model differences, which, in turn, are reflected in an increased spread of predicted O3. Including NMHC in the simulation approximately doubled the rms error for O3concentration.

Limitations in the current capability of monitoring PM2.5 adversely impact air quality management and health risk assessment of PM2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, satellite remote sensing AODs are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM2.5 cannot be obtained with satellite remote sensing under cloudy/hazy conditions or during nighttime. In this work, we introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We quantified the quantitative impact of input variables on the results using sensitivity and visual analysis of the model. This technique provides ground-level PM2.5 concentrations with a high spatial resolution (0.01°) and 24-h temporal coverage, hour-by-hour, complete coverage. In central and eastern China, the 10-fold cross-validation results show that R2 is between 0.8 and 0.9, and RMSE is between 6 and 26 (µg m−3). The relative error varies in different concentration ranges and is generally less than 20%. Better constrained spatiotemporal distributions of PM2.5 concentrations will contribute to improving health effects studies, atmospheric emission estimates, and air quality predictions. [ABSTRACT FROM AUTHOR]