Tropospheric ozone pollution increases the sensitivity of plant production to vapor pressure deficit across diverse ecosystems in the Northern Hemisphere

Tropospheric ozone (O3) pollution often accompanies droughts and heatwaves, which could collectively reduce plant productivity. Previous research suggested that O3 pollution can alter plant responses to drought by interfering with stomatal closure while drought can reduce stomatal conductance and provide protection against O3 stress. However, the interactions between O3 pollution and drought stress remain poorly understood at ecosystem scales with diverse plant functional types. To address this research gap, we used 10-year (2012-2021) satellite near-infrared reflectance of vegetation (NIRv) observations, reanalysis data of vapor pressure deficit (VPD), soil moisture (SM), and air temperature (Ta), along with O3 measurements and reanalysis data across the Northern Hemisphere to statistically disentangle the interconnections between NIRv, VPD, SM, and Ta under varying O3 levels. We found that high O3 concentrations significantly exacerbate the sensitivity of NIRv to VPD while have no notable impacts on the sensitivity of NIRv to Ta or SM for all plant functional types, indicating an enhanced combined impact of VPD and O3 on plants. Specifically, the sensitivity of NIRv to VPD increased by >75 % when O3 anomalies increased from the lowest 10 to the highest 10 percentiles across diverse plant functional types. This is likely because long-term exposure to high O3 concentrations can inhibit stomatal closure and photosynthetic enzyme activities, resulting in reduced water use efficiency and photosynthetic efficiency. This study highlights the need to consider O3 in understanding plant responses to climate factors and that O3 can alter plant responses to VPD independently of Ta and SM.

Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM)

Climate change and air pollution are emerging topics due to their possible enormous implications for health and social perspectives. In recent years, tropospheric ozone has been recognized as an important greenhouse gas and pollutant that is detrimental to human health, agriculture, and natural ecosystems, and has shown a trend of increasing interest. Machine-learning-based approaches have been widely applied to the estimation of tropospheric ozone concentrations, but few studies have included tropospheric ozone profiles. This study aimed to predict the Northern Hemisphere distribution of Lower-Stratosphere-to-Troposphere (LST) ozone at a pressure of 100 hPa to the near surface by employing a deep learning Long Short-Term Memory (LSTM) model. We referred to a history of all the observed parameters (meteorological data of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), satellite data, and the ozone profiles of the World Ozone and Ultraviolet Data Center (WOUDC)) between 2014 and 2018 for training the predictive models. Model–measurement comparisons for the monitoring sites of WOUDC for the period 2019–2020 show that the mean correlation coefficients (R2) in the Northern Hemisphere at high latitude (NH), Northern Hemisphere at middle latitude (NM), and Northern Hemisphere at low latitude (NL) are 0.928, 0.885, and 0.590, respectively, indicating reasonable performance for the LSTM forecasting model. To improve the performance of the model, we applied the LSTM migration models to the Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container (CARIBIC) flights in the Northern Hemisphere from 2018 to 2019 and three urban agglomerations (the Sichuan Basin (SCB), North China Plain (NCP), and Yangtze River Delta region (YRD)) between 2018 and 2019. The results show that our models performed well on the CARIBIC data set, with a high R2 equal to 0.754. The daily and monthly surface ozone concentrations for 2018–2019 in the three urban agglomerations were estimated from meteorological and ancillary variables. Our results suggest that the LSTM models can accurately estimate the monthly surface ozone concentrations in the three clusters, with relatively high coefficients of 0.815–0.889, root mean square errors (RMSEs) of 7.769–8.729 ppb, and mean absolute errors (MAEs) of 6.111–6.930 ppb. The daily scale performance was not as high as the monthly scale performance, with the accuracy of R2 = 0.636~0.737, RMSE = 14.543–16.916 ppb, MAE = 11.130–12.687 ppb. In general, the trained module based on LSTM is robust and can capture the variation of the atmospheric ozone distribution. Moreover, it also contributes to our understanding of the mechanism of air pollution, especially increasing our comprehension of pollutant areas.

Ozone Variability and Anomalies Observed during SENEX and SEAC4RS Campaigns in 2013

Tropospheric ozone variability occurs because of multiple forcing factors including surface emission of ozone precursors, stratosphere-to-troposphere transport (STT), and meteorological conditions. Analyses of ozonesonde observations made in Huntsville, AL, during the peak ozone season (May to September) in 2013 indicate that ozone in the planetary boundary layer was significantly lower than the climatological average, especially in July and August when the Southeastern United States (SEUS) experienced unusually cool and wet weather. Because of a large influence of the lower stratosphere, however, upper-tropospheric ozone was mostly higher than climatology, especially from May to July. Tropospheric ozone anomalies were strongly anti-correlated (or correlated) with water vapor (or temperature) anomalies with a correlation coefficient mostly about 0.6 throughout the entire troposphere. The regression slopes between ozone and temperature anomalies for surface up to mid-troposphere are within 3.0-4.1 ppbv·K-1. The occurrence rates of tropospheric ozone laminae due to STT are ≥50% in May and June and about 30% in July, August and September suggesting that the stratospheric influence on free-tropospheric ozone could be significant during early summer. These STT laminae have a mean maximum ozone enhancement over the climatology of 52±33% (35±24 ppbv) with a mean minimum relative humidity of 2.3±1.7%.

Global O3-CO Correlations in a Chemistry and Transport Model During July-August: Evaluation with TES Satellite Observations and Sensitivity to Input Meteorological Data and Emissions

We examine the capability of the Global Modeling Initiative (GMI) chemistry and transport model to reproduce global mid-tropospheric (618hPa) O₃-CO correlations determined by the measurements from Tropospheric Emission Spectrometer (TES) aboard NASA's Aura satellite during boreal summer (July-August). The model is driven by three meteorological data sets (fvGCM with sea surface temperature for 1995, GEOS4-DAS for 2005, and MERRA for 2005), allowing us to examine the sensitivity of model O₃-CO correlations to input meteorological data. Model simulations of radionuclide tracers (²²²Rn, ²¹⁰Pb, and ⁷Be) are used to illustrate the differences in transport-related processes among the meteorological data sets. Simulated O₃ values are evaluated with climatological ozone profiles from ozonesonde measurements and satellite tropospheric O₃ columns. Despite the fact that three simulations show significantly different global and regional distributions of O₃ and CO concentrations, all simulations show similar patterns of O₃-CO correlations on a global scale. These patterns are consistent with those derived from TES observations, except in the tropical easterly biomass burning outflow regions. Discrepancies in regional O₃-CO correlation patterns in the three simulations may be attributed to differences in convective transport, stratospheric influence, and subsidence, among other processes. To understand how various emissions drive global O₃-CO correlation patterns, we examine the sensitivity of GMI/MERRA model-calculated O₃ and CO concentrations and their correlations to emission types (fossil fuel, biomass burning, biogenic, and lightning NO_x emissions). Fossil fuel and biomass burning emissions are mainly responsible for the strong positive O₃-CO correlations over continental outflow regions in both hemispheres. Biogenic emissions have a relatively smaller impact on O₃-CO correlations than other emissions, but are largely responsible for the negative correlations over the tropical eastern Pacific, reflecting the fact that O₃ is consumed and CO generated during the atmospheric oxidation process of isoprene under low NO_x conditions. We find that lightning NO_x emissions degrade both positive correlations at mid-/high- latitudes and negative correlations in the tropics because ozone production downwind of lightning NO_x emissions is not directly related to the emission and transport of CO. Our study concludes that O₃-CO correlations may be used effectively to constrain the sources of regional tropospheric O₃ in global 3-D models, especially for those regions where convective transport of pollution plays an important role.

Ozone and carbon monoxide budgets over the Eastern Mediterranean

The importance of the long-range transport (LRT) on O3 and CO budgets over the Eastern Mediterranean has been investigated using the state-of-the-art 3-dimensional global chemistry-transport model TM4-ECPL. A 3-D budget analysis has been performed separating the Eastern from the Western basins and the boundary layer (BL) from the free troposphere (FT). The FT of the Eastern Mediterranean is shown to be a strong receptor of polluted air masses from the Western Mediterranean, and the most important source of polluted air masses for the Eastern Mediterranean BL, with about 40% of O3 and of CO in the BL to be transported from the FT aloft. Regional anthropogenic sources are found to have relatively small impact on regional air quality in the area, contributing by about 8% and 18% to surface levels of O3 and CO, respectively. Projections using anthropogenic emissions for the year 2050 but neglecting climate change calculate a surface O3 decrease of about 11% together with a surface CO increase of roughly 10% in the Eastern Mediterranean.

Atmospheric transport of ozone between Southern and Eastern Asia

This study describes the effect of pollution transport between East Asia and South Asia on tropospheric ozone (O3) using model results from the Task Force on Hemispheric Transport of Air Pollution (TF HTAP). Ensemble mean O3 concentrations are evaluated against satellite-data and ground observations of surface O3 at four stations in India. Although modeled surface O3 concentrations are 1020ppb higher than those observed, the relative magnitude of the seasonal cycle of O3 is reproduced well. Using 20% reductions in regional anthropogenic emissions, we quantify the seasonal variations in pollution transport between East Asia and South Asia. While there is only a difference of 0.05 to 0.1ppb in the magnitudes of the regional contributions from one region to the other, O3 from East Asian sources affects the most densely populated parts of South Asia while Southern Asian sources only partly affect the populated parts of East Asia. We show that emission changes over East Asia between 2000 and 2010 had a larger impact on populated parts of South Asia than vice versa. This study will help inform future decisions on emission control policy over these regions.

Ozone correlations between mid-tropospheric partial columns and the near-surface at two mid-atlantic sites during the DISCOVER-AQ campaign in July 2011

The current network of ground-based monitors for ozone (O₃) is limited due to the spatial heterogeneity of O₃ at the surface. Satellite measurements can provide a solution to this limitation, but the lack of sensitivity of satellites to O₃ within the boundary layer causes large uncertainties in satellite retrievals at the near-surface. The vertical variability of O₃ was investigated using ozonesondes collected as part of NASA's Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign during July 2011 in the Baltimore, MD/Washington D.C. metropolitan area. A subset of the ozonesonde measurements was corrected for a known bias from the electrochemical solution strength using new procedures based on laboratory and field tests. A significant correlation of O₃ over the two sites with ozonesonde measurements (Edgewood and Beltsville, MD) was observed between the mid-troposphere (7-10 km) and the near-surface (1-3 km). A linear regression model based on the partial column amounts of O₃ within these subregions was developed to calculate the near-surface O₃ using mid-tropospheric satellite measurements from the Tropospheric Emission Spectrometer (TES) onboard the Aura spacecraft. The uncertainties of the calculated near-surface O₃ using TES mid-tropospheric satellite retrievals and a linear regression model were less than 20 %, which is less than that of the observed variability of O₃ at the surface in this region. These results utilize a region of the troposphere to which existing satellites are more sensitive compared to the boundary layer and can provide information of O₃ at the near-surface using existing satellite infrastructure and algorithms.