Multi-Scale Atmospheric Emissions, Circulation and Meteorological Drivers of Ozone Episodes in El Paso-Juárez Airshed

Ozone pollution has been prevalent in the El Paso-Juárez Airshed (EPJA), especially in the past few decades, and it has been on the rise recently. The spatial and temporal distribution of the tropospheric ozone and several key meteorological factors that influence its concentration has not been adequately understood. Therefore, this investigation comprehensively examined 57 high and 48 low ozone episodes occurring in this region during 2013–2019. We found that the interannual ozone concentration in EPJA was strongly affected by anthropogenic emissions. On the other hand, seasonal ozone variations are due to meteorological variables (among them, solar radiation, planetary boundary layer, and winds) in addition to biogenic emission factors. High ozone events are characterized by calm winds, shallow planetary boundary layer (PBL), whereas low ozone events were marked with strong winds, precipitation, and deep PBL. Synoptic and mesoscale wind patterns for these ozone episodes were identified and characterized. Most of the high ozone episodes occurred when an anticyclonic circulation aloft was associated with a 500-mile middle and upper tropospheric high-pressure region over the EPJA. During these events, stable air masses with convective available potential energies (CAPE) values of less than 450 J/kg were found. The importance of surface topography is illustrated by the fact that stations close to the Rio Grande River show a bimodal distribution of wind direction according to the valley axis. High ozone episodes occur with a surface easterly wind that is decoupled from winds above the Franklin mountains.

Improving predictability of high-ozone episodes through dynamic boundary conditions, emission refresh and chemical data assimilation during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign

Although air quality in the United States has improved remarkably in the past decades, ground-level ozone (O3) often rises in exceedance of the national ambient air quality standard in nonattainment areas, including the Long Island Sound (LIS) and its surrounding areas. Accurate prediction of high-ozone episodes is needed to assist government agencies and the public in mitigating harmful effects of air pollution. In this study, we have developed a suite of potential forecast improvements, including dynamic boundary conditions, rapid emission refresh and chemical data assimilation, in a 3 km resolution Community Multiscale Air Quality (CMAQ) modeling system. The purpose is to evaluate and assess the effectiveness of these forecasting techniques, individually or in combination, in improving forecast guidance for two major air pollutants: surface O3 and nitrogen dioxide (NO2). Experiments were conducted for a high-O3 episode (28–29 August 2018) during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, which provides abundant observations for evaluating model performance. The results show that these forecast system updates are useful in enhancing the capability of this 3 km forecasting model with varying effectiveness for different pollutants. For O3 prediction, the most significant improvement comes from the dynamic boundary conditions derived from the NOAA operational forecast system, National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (R) from 0.81 to 0.93 and reduces the root mean square error (RMSE) from 14.97 to 8.22 ppbv, compared to that with the static boundary conditions (BCs). The NO2 from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, regardless of the BCs used, highlighting the importance of spatially resolved emission and meteorology inputs for the prediction of short-lived pollutants. The effectiveness of improved initial concentrations through optimal interpolation (OI) is shown to be high in urban areas with high emission density. The influence of OI adjustment, however, is maintained for a longer period in rural areas, where emissions and chemical transformation make a smaller contribution to the O3 budget than that in high-emission areas. Following the assessment of individual updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations and emission adjustment, to simulate a high-ozone episode during the 2018 LISTOS field campaign. The newly developed forecasting system significantly reduces the bias of surface NO2 prediction. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), this system is able to reproduce the NO2 VCD with a higher correlation (0.74), lower normalized mean bias (40 %) and normalized mean error (61 %) than NAQFC (0.57, 45 % and 76 %, respectively). The 3 km system captures magnitude and timing of surface O3 peaks and valleys better. In comparison with lidar, O3 profile variability of the vertical O3 is captured better by the new system (correlation coefficient of 0.71) than by NAQFC (correlation coefficient of 0.54). Although the experiments are limited to one pollution episode over the Long Island Sound, this study demonstrates feasible approaches to improve the predictability of high-O3 episodes in contemporary urban environments.

Ozone Induces Distress Behaviors in Fig Wasps with a Reduced Chance of Recovery

Among anthropogenic environmental risks, air pollution has the potential to impact animal and plant physiology, as well as their interactions and the long-term survival of populations, which could threaten the functioning of ecosystems. What is especially alarming is that the concentration of tropospheric ozone (O3) has dramatically increased since pre-industrial times. However, the direct effects of O3 on the behavior of pollinators themselves have not been investigated so far even though insect behavior is key to their ecological interactions, which underpin the stability of ecological networks responsible for species biodiversity in ecosystems. In this study, we aim to determine the potential effects of O3 episodes at different field-realistic concentrations (0, 40, 80, 120, and 200 ppb for 60 min) on the behavior of the fig wasp Blastophaga psenes by monitoring exposed individuals hourly for 5 h after exposure. We found that ozone episodes induced major changes in insect behavior, which were already significant at 80 ppb with individuals displaying abnormal motility. The tracking over time clearly showed that exposed individuals might only have a reduced chance of recovery, with a decreasing proportion of active fig wasps despite the cessation of an O3 episode. These findings illustrate that O3 episodes can affect pollinator behavior, which may have detrimental implications for pollination systems. It is, therefore, of importance to assess the effects of O3 on insect behavior in order to predict how it could modify ecological interactions and species biodiversity in ecosystems.

Overview of the Lake Michigan Ozone Study 2017

The Lake Michigan Ozone Study 2017 (LMOS 2017) was a collaborative multi-agency field study targeting ozone chemistry, meteorology, and air quality observations in the southern Lake Michigan area. The primary objective of LMOS 2017 was to provide measurements to improve air quality modeling of the complex meteorological and chemical environment in the region. LMOS 2017 science questions included spatiotemporal assessment of nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOC) emission sources and their influence on ozone episodes, the role of lake breezes, contribution of new remote sensing tools such as GeoTASO, Pandora, and TEMPO to air quality management, and evaluation of photochemical grid models. The observing strategy included GeoTASO on board the NASA UC-12 capturing NO2 and formaldehyde columns, an in situ profiling aircraft, two ground-based coastal enhanced monitoring locations, continuous NO2 columns from coastal Pandora instruments, and an instrumented research vessel. Local photochemical ozone production was observed on 2 June, 9–12 June, and 14–16 June, providing insights on the processes relevant to state and federal air quality management. The LMOS 2017 aircraft mapped significant spatial and temporal variation of NO2 emissions as well as polluted layers with rapid ozone formation occurring in a shallow layer near the Lake Michigan surface. Meteorological characteristics of the lake breeze were observed in detail and measurements of ozone, NOx, nitric acid, hydrogen peroxide, VOC, oxygenated VOC (OVOC), and fine particulate matter (PM2.5) composition were conducted. This article summarizes the study design, directs readers to the campaign data repository, and presents a summary of findings.

Improving predictability of high ozone episodes through dynamic boundary conditions, emission refresh and chemical data assimilation during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign

Although air quality in the United States improved remarkably in the past decades, ground-level ozone (O3) rises often in exceedance of the national ambient air quality standard in nonattainment areas, including the Long Island Sound (LIS) and its surrounding areas. Accurate prediction of high ozone episodes is needed to assist government agencies and the public in mitigating harmful effects of air pollution. In this study, we have developed a suite of potential forecast improvements, including dynamic boundary conditions, rapid emission refresh and chemical data assimilation, in a 3 km resolution Community Multi-scale Air Quality (CMAQ) modeling system. The purpose is to evaluate and assess the effectiveness of these forecasting techniques, individually or in combination, in improving forecast guidance for two major air pollutants: surface O3 and nitrogen dioxide (NO2). Experiments were conducted for a high O3 episode (August 28–29, 2018) during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, which provides abundant observations for evaluating model performance. The results show that these forecast system updates are useful in enhancing the capability of the forecasting model with varying effectiveness for different pollutants. For O3 prediction, the most significant improvement comes from the dynamic boundary conditions derived from NOAA National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (R) from 0.81 to 0.93 and reduces the Root Mean Square Error (RMSE) from 14.97 ppbv to 8.22 ppbv, compared to that with the static boundary conditions. The NO2 from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, highlighting the importance of spatially resolved emission and meteorology inputs for the prediction of short-lived pollutants. The effectiveness of improved initial concentrations through optimal interpolation (OI) is shown to be high in urban areas with high emission density. The influence of OI adjustment, however, is maintained for a longer period in rural areas where emissions and chemical transformation make a smaller contribution to the O3 budget than that in high emission areas. Following the assessment of individual forecast system updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations, and emission adjustment, to simulate the high ozone episode over the Long Island Sound region. The newly developed forecasting system significantly reduces the bias of surface NO2 concentration. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), the new system is able to reproduce the NO2 VCD with a higher correlation (0.74), lower normalized mean bias (40 %) and normalized mean error (61 %) than NAQFC (0.57, 45 % and 76 %, respectively). The new system captures magnitude and timing of surface O3 peaks and valleys better. In comparison with LIDAR O3 profile variability of the vertical O3 is captured better by the new system (correlation coefficient of 0.71) than by NAQFC (correlation coefficient of 0.54). Although the experiments are limited to one pollution episode over the Long Island Sound, this study demonstrates feasible approaches to improve the predictability of high O3 episodes in contemporary urban environments.