Surface ozone concentrations typically increase with temperature, but emerging evidence indicates a decline at extreme-high temperatures. This reversal challenges the prevailing expectation of monotonic ozone increase under global warming and highlights a narrow temperature range in which peak ozone is most likely to occur, which is crucial for predicting extreme pollution events. Despite its importance, the global extent and underlying mechanism of this ozone-temperature reversal remain unclear. Using thousands of monitoring sites across the Northern Hemisphere, we demonstrate that this reversal is widespread and robust. Once temperatures exceed a threshold peak-ozone temperature, enhanced buoyancy-driven convection clears ozone and its precursors, thereby decreasing surface ozone. We show that this peak-ozone temperature corresponds closely to the theoretically derived onset of thermally driven convection and is governed by atmospheric thermodynamic conditions. Climate projections indicate that the peak-ozone temperature will rise due to enhanced convection inhibition under global warming. This upward shift delays the onset of ozone reversal, resulting in higher peak ozone levels and longer periods of elevated ozone exposure during heatwaves. Future mitigation efforts must anticipate the growing risk of co-occurrence of ozone pollution and heat extremes as surface ozone will peak both at higher temperatures and with higher values.

To tackle the planetary environmental and climate crisis and meet the United Nations’ Sustainable Development Goals (SDGs), we must fully leverage the potential of Earth observations (EO). This involves integrating globally sourced data on the atmosphere, hydrosphere, cryosphere, lithosphere, along with ecological and socio-economic information. By harmonizing and integrating these diverse data sources, we can more effectively incorporate observational data into multi-scale modeling and artificial intelligence (AI) frameworks. This paper is based on discussions from the “Towards Global Earth Observatory” workshop held from May 8–10, 2023, organized by the World Meteorological Organization (WMO) and the Atmosphere and Climate Competence Center (ACCC), in collaboration with the Institute for Atmospheric and Earth System Research (INAR) at the University of Helsinki. The current state of EO and data repositories is fragmented, highlighting the need for a more integrated approach to establish a new global Ground-Based Earth Observatory (GGBEO). Here, we summarize the current status of selected in-situ and ground-based remote sensing observation systems and outline future actions and recommendations to meet scientific, societal, and economic needs. In addition, we identify key steps to create a coordinated and comprehensive GGBEO system that leverages existing investments, networks, and infrastructures. This system would integrate regional and global ground-based in situ and remote sensing systems, marine, and airborne observational data. An integrated approach should aim for seamless coordination, interoperable and harmonized data repositories, easily searchable and accessible data, and sustainable long-term funding.

This study describes the application of a regional Earth system model with updated parameterizations for selected land–atmosphere exchange processes and multiplatform, multidisciplinary observations. We estimate reactive nitrogen (Nr = NOy+ NHx) emissions from various sources, surface and column nitrogen dioxide (NO2), and total and speciated Nr dry and wet deposition during 2018–2023 over the northeastern and mid-Atlantic US where nitrogen-oxide-limited or transitional chemical regimes dominate. The estimated Nr concentrations and deposition fluxes are related to ozone (O3) in terms of spatiotemporal variability and its key drivers as well as possible ecosystem impacts. Modeled surface O3 persistently agrees well with observations, with root mean square errors staying within 4–7 ppbv for individual years in May–June–July. Model-based surface O3–NO2 column correlation, which shows a dependency on column formaldehyde / NO2, is higher in 2020 (r=0.62) than in other years (r=0.47–0.56). Ozone vegetative uptake overall dropped by ∼10 % from 2018 to 2023, displaying clearer downward temporal changes than total Nr deposition as declining NOy emission and deposition competed with increasing NHx fluxes. It is highlighted that temporal variabilities 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 like irrigation. Deposition and biogenic emissions that are highly sensitive to interconnected environmental and plant physiological conditions, plus extra-regional sources (e.g., O3-rich stratospheric air and dense wildfire plumes from upwind regions), have been playing increasingly important roles in controlling pollutant budgets as local emissions decline owing to effective emission regulations and COVID lockdowns.

Biomass burning aerosol (BBA) from agricultural fires in southern Africa contributes about one-third of the global carbonaceous aerosol load. These particles have strong radiative effects in the southeast Atlantic (SEA), which depend in part on the radiative contrast between the aerosol layer in the free troposphere (FT) and the underlying cloud layer. However, there is large disagreement in model estimates of aerosol-driven climate forcing due to uncertainties in the vertical distribution, optical properties, and life cycle of these particles. This study applies a novel method combining remote sensing observations with regional model outputs to investigate the aging of the BBA and its impact on the optical properties during transatlantic transport from emission sources in Africa to the SEA. Results show distinct variations in extinction Ångström exponent (EAE) and single-scattering albedo (SSA) as aerosols age. Near the source, fresh aerosols are characterized by low mean SSA (0.84) and high EAE (1.85), indicating smaller, highly absorbing particles. By isolating marine contributions from the total column during BBA transport across the SEA, our analysis reveals an initial decrease in BBA absorptivity, with mean FT SSA of 0.87 after 6–7 d, followed by increased absorptivity with mean FT SSA of 0.84 after 10 d, suggesting enhanced absorption due to chemical aging. These findings indicate that BBA becomes more absorbing during extended transport across the SEA, with implications for reducing model uncertainties. Our remote-sensing-based results agree well with previous in situ studies and offer new insights into aerosol–radiation interactions and the energy balance over the SEA.

Heat and air pollution extremes are two leading global health stressors, both of which are particularly serious in China and India. It is well recognized that exposure to co-occurrence of heat and air pollution extremes will cause amplified health outcomes, yet century-long understanding of future co-occurrence is still lacking. On the basis of sophisticated regional coupled climate-chemistry modeling, we predict future individual and joint occurrences of heat and air pollution extremes in China and India in 2096-2100 relative to 2010-2014. We find intensified co-occurrences of heat and air pollution extremes in both China and India, despite reductions in projected emissions and improved air quality. Under the medium air pollution control of SSP245, the frequency of Tw&PM&O3 joint hazard increases by 382% in North India, and 729% in Beijing by the end of this century. Given the significant role of temperature changes in the co-occurrence and larger compounding health impacts, actions are urgently needed to reduce exposure to co-extreme events.

HEART is a university‐led alliance of institutions, agencies, and industries committed to voicing the needs of the states in the central land area of the US and advancing research to develop innovative environmental solutions that strengthen communities and support policymakers to improve the lives and livelihoods of people across the Heartland. The Heartland is the “Breadbasket” of the Nation and home to the nation's largest aquifer and largest river basin. However, the Heartland is highly vulnerable to the impacts of our changing environment. HEART aims to leverage and amplify the existing strengths of the members to develop robust, open, transformative solutions for mitigation of and resiliency to environmental changes threatening the region's water, food, and energy security. We are excited by the possibilities of HEART and welcome further engagement and suggestions.

The pioneering Sulfur Transport and Deposition Model, initially designed to simulate atmospheric sulfur, continues to find new applications and value in environmental science and policymaking.

Brown carbon (BrC) has been recognized as an important light-absorbing carbonaceous aerosol, yet understanding of its influence on regional climate and air quality has been lacking, mainly due to the ignorance of regional coupled meteorology-chemistry models. Besides, assumptions about its emissions in previous explorations might cause large uncertainties in estimates. Here, we implemented a BrC module into the WRF-Chem model that considers source-dependent absorption and avoids uncertainties caused by assumptions about emission intensities. To our best knowledge, we made the first effort to consider BrC in a regional coupled model. We then applied the developed model to explore the impacts of BrC absorption on radiative forcing, regional climate, and air quality in East Asia. We found notable increases in aerosol absorption optical depth (AAOD) in areas with high OC concentrations. The most intense forcing of BrC absorption occurs in autumn over Southeast Asia, and values could reach around 4 W m(-2). The intensified atmospheric absorption modified surface energy balance, resulting in subsequent declines in surface temperature, heat flux, boundary layer height, and turbulence exchanging rates. These changes in meteorological variables additionally modified near-surface dispersion and photochemical conditions, leading to changes of PM2.5 and O-3 concentrations. These findings indicate that BrC could exert important influence in specific regions and time periods. A more in-depth understanding could be achieved later with the developed model.

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.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). CORRESPONDING AUTHOR: Julie Winkler, winkler@msu.edu *Emeritus

High-resolution multi-component estimates of ground-level air pollutants are necessary for assessing their impacts to human health, agriculture, and ecosystems. We demonstrate a high-resolution fusion and downscaling approach over South Korea for May 2016 and May 2021. Daily 1 km fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) concentrations are calculated at ground level using a random forest machine learning (ML) algorithm, with predictors including reanalysis meteorology, satellite aerosol optical depth (AOD), and gridded surface fields from chemical transport models (CTM). The ML model is tested for May 2016, coinciding with the Korea-United States Air Quality Study (KORUS-AQ) intensive field campaign, and for May 2021, to allow incorporation of observations from the Geostationary Environment Monitoring Spectrometer (GEMS). In the tests for May 2016, the correlation coefficients (R) and root mean squared errors (RMSE) relative to withheld observations of daily-averaged pollutants in 10-fold cross-validation are promising: 0.93 (5.5 μg/m3), 0.90 (5.5 ppbv), and 0.95 (4.7 ppbv) for PM2.5, O3, and NO2, respectively. Relative performance is assessed for alternate choices of predictors: (a) 80-km global reanalysis Copernicus Atmosphere Monitoring Service (CAMS) vs. 4-km regional Weather Research and Forecasting model coupled with Chemistry (WRF-Chem); (b) AOD polar-orbiting Moderate Resolution Image Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) vs. geostationary GEMS; and (c) variations in surface observation density. This study is among the very first to incorporate both CTM and GEMS AOD for building surface high resolution multiple air pollution predictions over South Korea.

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. The datasets are available at https://doi.org/10.57760/sciencedb.13151 (Kong et al., 2023).

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.

The variability of total column ozone (TCO) and tropospheric column ozone (TrCO) was examined in Central Asia. Measurements were conducted at the Lidar Station Teplokluchenka in eastern Kyrgyzstan for one year, July 2008–July 2009. TCO was obtained using a handheld Microtops II Ozonometer (TCO-MII) and from the Aura OMI (TCO-OMI) satellite. Nitrogen dioxide (NO2) and formaldehyde concentrations also were obtained from the OMI satellite. Formaldehyde was used as a surrogate for volatile organic compounds. TrCO was estimated by the difference between TCO-OMI and stratospheric column ozone retrieved from the MLS satellite. Comparison of the ground-based TCO-MII with TCO-OMI showed good agreement (r2 = 0.93), and linear regression between these was used to estimate missing values in the TCO-MII dataset. The contribution of TrCO to TCO varied from 15% in summertime to 5% in winter. High values of TrCO were observed during summer (July: 45 DU) and low values during winter (December: 15 DU), as is typically observed. The average values of TrCO for summer, autumn, winter, and spring were equal to 42, 27, 20, and 30 DU, respectively. Seasonal variability of TrCO corresponded to solar intensity, indicating that TrCO is likely to form through photochemical means rather than stratospheric intrusion. The spatial distribution of NO2 and VOC were examined to better understand the regional sources of these ozone precursors. Transport from highly populated areas of the Ferghana Valley and Tashkent in Uzbekistan contributed to the TrCO concentrations observed in this work. The HCHO/NO2 ratio, an indicator of the ozone production rate, suggested that reducing NO2 would be more effective in reducing TrCO during most of the year, except summer, when reductions of both would likely be needed.

Abstract. 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.

precipitation) over six regions of China from January to February 2020, with calculated evaluation statistics summarised in Table S4.In general, the simulation can well capture the main feathers of the observed meteorological conditions in all regions for our simulation period.All variables exhibited small RMSE values in all regions, that are around 1 m/s for wind speed, 1℃ for T, 10% for RH and 0.08-2.38mmfor precipitation.Therefore, the WRF can generally well reproduce the meteorological conditions for all regions of China, which is adequate for our inversion estimates.

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.

. The unprecedented lockdown of human activities during the COVID-19 pandemic have significantly influenced 23 the social life in China. However, understanding of the impact of this unique event on the emissions of different species is 24 still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we 25 developed a