Impact of the COVID‐19 Economic Downturn on Tropospheric Ozone Trends: An Uncertainty Weighted Data Synthesis for Quantifying Regional Anomalies Above Western North America and Europe

This study quantifies the association between the COVID‐19 economic downturn and 2020 tropospheric ozone anomalies above Europe and western North America, and their impact on long‐term trends. Anomaly detection for an atmospheric time series is usually carried out by identifying potentially aberrant data points relative to climatological values. However, detecting ozone anomalies from sparsely sampled ozonesonde profiles (once per week at most sites) is challenging due to ozone's high temporal variability. We first demonstrate the challenges for summarizing regional trends based on independent time series from multiple nearby ozone profiling stations. We then propose a novel regional‐scale anomaly detection framework based on generalized additive mixed models, which accounts for the sampling frequency and inherent data uncertainty associated with each vertical profile data set, measured by ozonesondes, lidar or commercial aircraft. This method produces a long‐term monthly time series with high vertical resolution that reports ozone anomalies from the surface to the middle‐stratosphere under a unified framework, which can be used to quantify the regional‐scale ozone anomalies during the COVID‐19 economic downturn. By incorporating extensive commercial aircraft data and frequently sampled ozonesonde profiles above Europe, we show that the complex interannual variability of ozone can be adequately captured by our modeling approach. The results show that free tropospheric ozone negative anomalies in 2020 are the most profound since the benchmark year of 1994 for both Europe and western North America, and positive trends over 1994–2019 are diminished in both regions by the 2020 anomalies.

2020 is the only year that both Europe and western North America show strong negative tropospheric ozone anomalies since 1994

Positive free tropospheric ozone trends above Europe and western North America since 1994 are diminished by the 2020 anomalies

Data integration of multiple time series provides a better understanding of ozone variability compared to individual records

Meteorological modeling relevant to mesoscale and regional air quality applications: a review

The highest correlative relations for air pollution levels are often with meteorological variables such as temperature and wind speed. Today, sophisticated gridded high-resolution meteorological models are used to produce meteorological fields that drive chemical transport models for air quality management. Errors in specification of the physical atmosphere such as temperature, clouds and winds can affect the air quality predictions. Additionally, the efficiency and efficacy of emission control strategies can be compromised by errors in the meteorological fields. In this paper, the role of meteorology in air quality behavior, primarily from the viewpoint of regional ozone modeling as carried out in the U.S., is reviewed. Particular attention is given to physics and new techniques for improving meteorological model performance. Uncertainties in model turbulent mixing in the nighttime boundary layer, where large model differences exist, are examined. The role of spatial mesoscale features such as topography and land/water systems in models are discussed. The nocturnal low-level jet, a mesoscale temporal and spatial feature, and its impact on air quality are examined. Traditional air quality concerns have focused on synoptic conditions at the center of high-pressure systems. However, high ozone levels have also been associated with stationary fronts. The ability of models to capture mesoscale structure and yet retain synoptic structure and its timing is challenging. Data assimilation and its ability to improve model performance are examined. Particular attention is given to vertical nudging strategies that can affect formation of the nocturnal low-level jets. Finally, clouds can have a major impact on air quality since insolation impacts temperature, biogenic emissions and photolysis rates and extremes in stability. Traditional techniques, which attempt to insert cloud water where there is not dynamical support, can lead to additional errors. New dynamical approaches for improving model cloud performance are discussed.Implications: This article shows that there has been a considerable improvement in meteorological models used for air quality simulations. In particular, improvement in the tools for incorporating both traditional observations and new satellite data for retrospective studies has been beneficial to air quality community. However, while this trend is continuing, many challenges remain. As an example, due to having many options available in configuring a model simulation, there is a need to evaluate and recommend sets of options that provide important performance measures.

Modeling Ozone in the Eastern U.S. using a Fuel-Based Mobile Source Emissions Inventory

Recent studies suggest overestimates in current U.S. emission inventories of nitrogen oxides (NO _x = NO + NO₂). Here, we expand a previously developed fuel-based inventory of motor-vehicle emissions (FIVE) to the continental U.S. for the year 2013, and evaluate our estimates of mobile source emissions with the U.S. Environmental Protection Agency's National Emissions Inventory (NEI) interpolated to 2013. We find that mobile source emissions of NO _x and carbon monoxide (CO) in the NEI are higher than FIVE by 28% and 90%, respectively. Using a chemical transport model, we model mobile source emissions from FIVE, and find consistent levels of urban NO _x and CO as measured during the Southeast Nexus (SENEX) Study in 2013. Lastly, we assess the sensitivity of ozone (O₃) over the Eastern U.S. to uncertainties in mobile source NO _x emissions and biogenic volatile organic compound (VOC) emissions. The ground-level O₃ is sensitive to reductions in mobile source NO _x emissions, most notably in the Southeastern U.S. and during O₃ exceedance events, under the revised standard proposed in 2015 (>70 ppb, 8 h maximum). This suggests that decreasing mobile source NO _x emissions could help in meeting more stringent O₃ standards in the future.

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%.

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.