Search

A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system’s 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system.Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system. [ABSTRACT FROM AUTHOR]

Ozone is a pollutant formed in the atmosphere by photochemical processes involving nitrogen oxides (NO x) and volatile organic compounds (VOCs) when exposed to sunlight. Tropospheric boundary layer ozone is regularly measured at ground stations and sampled infrequently through balloon, lidar, and crewed aircraft platforms, which have demonstrated characteristic patterns with altitude. Here, to better resolve vertical profiles of ozone within the atmospheric boundary layer, we developed and evaluated an uncrewed aircraft system (UAS) platform for measuring ozone and meteorological parameters of temperature, pressure, and humidity. To evaluate this approach, a UAS was flown with a portable ozone monitor and a meteorological temperature and humidity sensor to compare to tall tower measurements in northern Wisconsin. In June 2020, as a part of the WiscoDISCO20 campaign, a DJI M600 hexacopter UAS was flown with the same sensors to measure Lake Michigan shoreline ozone concentrations. This latter UAS experiment revealed a low-altitude structure in ozone concentrations in a shoreline environment showing the highest ozone at altitudes from 20–100 m a.g.l. These first such measurements of low-altitude ozone via a UAS in the Great Lakes region revealed a very shallow layer of ozone-rich air lying above the surface. [ABSTRACT FROM AUTHOR]

The El Niño-Southern Oscillation (ENSO) is a major driver of interannual variability in both tropical and mid-latitudes and has been found to have a strong impact on the distribution of tropospheric ozone in the tropical Pacific in satellite observational datasets, chemical transport models, and chemistry-climate simulations. Here we analyze interannual variability in tropical tropospheric ozone by applying composite analysis, empirical orthogonal function (EOF) analysis and multiple linear regression to the Real-time Air Quality Modeling System (RAQMS) Aura chemical reanalysis. As shown in similar studies, the dominant mode of interannual variability in tropical tropospheric ozone is driven by ENSO. ENSO composites show that the ENSO signature in tropospheric ozone is strongest near the tropopause. We also show an enhancement in tropical ozone over the maritime continent below 700 hPa during El Niño that is dependent on the magnitude of the biomass burning emissions in the region. We reconstruct the ENSO variability in tropical tropospheric ozone through a multiple linear regression of principal components for precipitation and CO. The multiple linear regression quantifies that variability in biomass burning contributes to ENSO variability in tropical tropospheric ozone though the dominant driver is convection. [ABSTRACT FROM AUTHOR]

The quantification of aerosol size distributions is crucial for understanding the climate and health impacts of aerosols, validating models, and identifying aerosol sources. This work provides one of the first continuous measurements of aerosol size distribution from 1.02 to 8671 nm near the shore of Lake Michigan. The data were collected during the Lake Michigan Ozone Study (LMOS 2017), a comprehensive air quality measurement campaign in May and June 2017. The time-resolved (2-min) size distribution are reported herein alongside meteorology, remotely sensed data, gravimetric filters, and gas-phase variables. Mean concentrations of key aerosol parameters include PM2.5 (6.4 μg m-3), number from 1 to 3 nm (1.80x104 cm-3) and number greater than 3 nm (8x103 cm-3). During the field campaign, approximately half of days showed daytime ultrafine burst events, characterized by particle growth from sub 10 nm to 25–100 nm. A specific investigation of ultrafine lake spray aerosol was conducted due to enhanced ultrafine particles in onshore flows coupled with sustained wave breaking conditions during the campaign. Upon closer examination, the relationships between the size distribution, wind direction, wind speed, and wave height did not qualitatively support ultrafine particle production from lake spray aerosol; statistical analysis of particle number and wind speed also failed to show a relationship. The alternative hypothesis of enhanced ultrafine particles in onshore flow originating mainly from new particle formation activity is supported by multiple lines of evidence. [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.

We investigate the benefit of assimilating high spatial‐temporal resolution nitrogen dioxide (NO2) measurements from a geostationary (GEO) instrument such as Tropospheric Emissions: Monitoring of Pollution (TEMPO) versus a low‐earth orbit (LEO) platform like TROPOspheric Monitoring Instrument (TROPOMI) on the inverse modeling of nitrogen oxides (NOx) emissions. We generated synthetic TEMPO and TROPOMI NO2 measurements based on emissions from the COVID‐19 lockdown period. Starting with emissions levels prior to the lockdown, we use the Weather Research and Forecasting Model coupled with Chemistry/Data Assimilation Research Testbed (WRF‐Chem/DART) to assimilate these pseudo‐observations in Observing System Simulation Experiments to adjust NOx emissions and quantify how well the assimilation of TEMPO versus TROPOMI measurements recovers the lockdown‐induced emissions changes. We find that NOx emission biases can be ameliorated using half as many simulation days when assimilating GEO observations, and the estimated NOx emissions in 23 out of 29 major urban regions in the US are more accurate. The root mean square error and coefficient of determination of posterior NOx emissions are reduced by 12.5%–41.5% and 1.5%–17.1%, respectively, across different regions. We conduct sensitivity experiments that use different data assimilation (DA) configurations to assimilate synthetic GEO observations. Results demonstrate that the temporal width of the DA window introduces −10% to −20% biases in the emissions inversion and constraining both NOx concentrations and emissions simultaneously yields the most accurate NOx emissions estimates. Our work serves as a valuable reference on how to appropriately assimilate GEO observations for constraining NOx emissions in future studies. Plain Language Summary: Nitrogen oxides (NOx) are major air pollutants and precursors to tropospheric ozone and secondary inorganic aerosols. The diverse natural and anthropogenic sources of NOx pose a challenge for NOx emissions estimates. Inverse modeling techniques which use observations to infer emissions can be applied to improve our understanding of anthropogenic NOx emissions. This study aims to compare the ability of the new geostationary (GEO) instrument Tropospheric Emissions: Monitoring of Pollution (TEMPO) and the existing low‐earth orbit instrument TROPOspheric Monitoring Instrument (TROPOMI) to constrain NOx emissions. Synthetic TEMPO and TROPOMI NO2 measurements are generated and assimilated to constrain NOx emissions in an idealized experiment in which the "true" emissions are known. The results show the true NOx emissions can be retrieved using half as many simulation days when assimilating GEO NO2 observations. Moreover, the experiment that assimilates GEO NO2 observations improves the accuracy of estimated NOx emissions by 12.5%–41.5% and 1.5%–17.1% in terms of root mean square error and coefficient of determination, respectively, across different air quality regions. The NOx emissions in most urban regions are better constrained when assimilating GEO NO2 data. We also propose best practices for assimilating GEO NO2 observations, which can serve as reference for future research. Key Points: True NOx emissions can be recovered using half as many simulation days when assimilating synthetic Tropospheric Emissions: Monitoring of Pollution (TEMPO) observations rather than TROPOspheric Monitoring InstrumentAssimilating synthetic TEMPO observations improve emissions inversion accuracy by 13%–42% across different regions of USThe best estimates of NOx emissions are achieved by using short data assimilation window (e.g., 30 min) and updating concentrations/emissions jointly [ABSTRACT FROM AUTHOR]

Ozone is a pollutant formed in the atmosphere by photochemical processes involving nitrogen oxides (NOx) and volatile organic compounds (VOCs) when exposed to sunlight. Tropospheric boundary layer ozone is regularly measured at ground stations and sampled infrequently through balloon, lidar, and crewed aircraft platforms, which have demonstrated characteristic patterns with altitude. Here, to better resolve vertical profiles of ozone within the atmospheric boundary layer, we developed and evaluated an uncrewed aircraft system (UAS) platform for measuring ozone and meteorological parameters of temperature, pressure, and humidity. To evaluate this approach, an UAS was flown with a portable ozone monitor and a meteorological temperature and humidity sensor to compare to tall tower measurements in northern Wisconsin. In June 2020, as a part of the WiscoDISCO20 campaign, a DJI M600 hexacopter UAS was flown with the same sensors to measure Lake Michigan shoreline ozone concentrations. This latter UAS experiment revealed low-altitude structure in ozone concentrations in a shoreline environment showing highest ozone at altitudes from 20-100 mAGL. These first such measurements of lowaltitude ozone via UAS in the Great Lakes Region revealed a very shallow layer of ozone rich air lying above the surface. [ABSTRACT FROM AUTHOR]

We evaluate two high-resolution Lake Michigan air quality simulations during the 2017 Lake Michigan Ozone Study campaign. These air quality simulations employ identical chemical configurations but use different input meteorology. The AP-XM configuration follows the U.S. Environmental Protection Agency (EPA)-recommended modeling practices, whereas the YNT_SSNG employs different parameterization schemes and satellite-based inputs of sea surface temperatures, green vegetative fraction, and soil moisture and temperature. Overall, we find a similar performance in the model simulations of hourly and maximum daily average 8 h (MDA8) ozone, with the AP-XM and YNT_SSNG simulations showing biases of -11.42 and -13.54 ppbv (parts per billion by volume), respectively, during periods when the observed MDA8 was greater than 70 ppbv. However, for the two monitoring sites that observed high-ozone events, the AP-XM simulation better matched observations at Chiwaukee Prairie, and the YNT_SSNG simulation better matched observations at the Sheboygan Kohler-Andrae (KA) State Park. We find that the differences between the two simulations are largest for column amounts of ozone precursors, particularly NO 2. Across three high-ozone events, the YNT_SSNG simulation has a lower NO 2 column bias (0.17×1015 mol cm -2) compared to the AP-XM simulation (0.31×1015 mol cm -2). The YNT_SSNG simulation also has an advantage in that it better captures the structure of the boundary layer and lake breeze during the 2 June high-ozone event, although the timing of the lake breeze is about 3 h too early at Sheboygan. Our results are useful for informing an air quality modeling framework for the Lake Michigan area. [ABSTRACT FROM AUTHOR]

High-resolution simulations were performed to assess the impact of different parameterization schemes, surface initialization datasets, and analysis nudging on lower-tropospheric conditions near Lake Michigan. Simulations were run where climatological or coarse-resolution surface initialization datasets were replaced by high-resolution, real-time datasets depicting lake surface temperatures (SST), green vegetation fraction (GVF), and soil moisture and temperature (SOIL). Comparison of a baseline simulation employing a configuration similar to that used at the Environmental Protection Agency ("EPA") to another simulation employing an alternative set of parameterization schemes (referred to as "YNT") showed that the EPA configuration produced more accurate analyses on the outermost 12-km resolution domain, but that the YNT configuration was superior for higher-resolution nests. The diurnal evolution of the surface energy fluxes was similar in both simulations on the 12-km grid but differed greatly on the 1.3-km grid where the EPA simulation had much smaller sensible heat flux during the daytime and physically unrealistic ground heat flux. Switching to the YNT configuration led to substantial decreases in root mean square error for 2-m temperature and 2-m water vapor mixing ratio on the 1.3-km grid. Additional improvements occurred when the high-resolution satellite-derived surface datasets were incorporated into the modeling platform, with the SOIL dataset having the largest positive impact on temperature and water vapor. The GVF and SST datasets also produced more accurate temperature and water vapor analyses, but degradations in wind speed, especially when using the GVF dataset. The most accurate simulations were obtained when using the high-resolution SST and SOIL datasets and analysis nudging above 2 km AGL. [ABSTRACT FROM AUTHOR]

High-resolution simulations were performed to assess the impact of different parameterization schemes, surface initialization datasets, and analysis nudging on lower-tropospheric conditions near Lake Michigan. Simulations were run where climatological or coarse-resolution surface initialization datasets were replaced by high-resolution, real-time datasets depicting lake surface temperatures (SST), green vegetation fraction (GVF), and soil moisture and temperature (SOIL). Comparison of a baseline simulation employing a configuration similar to that used at the Environmental Protection Agency ("EPA") to another simulation employing an alternative set of parameterization schemes (referred to as "YNT") showed that the EPA configuration produced more accurate analyses on the outermost 12-km resolution domain, but that the YNT configuration was superior for higher-resolution nests. The diurnal evolution of the surface energy fluxes was similar in both simulations on the 12-km grid but differed greatly on the 1.3-km grid where the EPA simulation had much smaller sensible heat flux during the daytime and physically unrealistic ground heat flux. Switching to the YNT configuration led to substantial decreases in root mean square error for 2-m temperature and 2-m water vapor mixing ratio on the 1.3-km grid. Additional improvements occurred when the high-resolution satellite-derived surface datasets were incorporated into the modeling platform, with the SOIL dataset having the largest positive impact on temperature and water vapor. The GVF and SST datasets also produced more accurate temperature and water vapor analyses, but degradations in wind speed, especially when using the GVF dataset. The most accurate simulations were obtained when using the high-resolution SST and SOIL datasets and analysis nudging above 2 km AGL. [ABSTRACT FROM AUTHOR]

High concentrations of ozone along the coastline of Lake Michigan are a persistent air quality management challenge. Complementing observations during the 2017 Lake Michigan Ozone Study (LMOS 2017), WRF‐Chem modeling was used to quantify sensitivity of modeled ozone (O3) to anthropogenic nitrogen oxides (NOx) and volatile organic compound (VOC) emissions, including to changes in volatile chemical product (VCP). The daily maximum 8 hr average (MDA8) over the high ozone region of Lake Michigan decreased by 2.7 ppb with exclusion of VCP from the inventory, and was sensitive to both NOx and VOC changes, with greater sensitivity to NOx. Close to urban centers, MDA8 ozone was VOC‐sensitive. Clusters of coastal receptor sites were identified based on similarity in response to emission perturbations, with most clusters being NOx‐sensitive and NOx‐sensitivity increasing with distance from major emission sources. The 2 June 2017 ozone event, which has received considerable focus, is shown to be atypical due to unusually strong and spatially extended VOC‐sensitive behavior. WRF‐Chem integrated reaction rate analysis was used to compute radical termination rates due to NOx (LNOx) and to radical‐radical reactions (LROx). LROx/LNOx and formaldehyde to NO2 ratio (FNR) were shown to be predictive of modeled MDA8 ozone sensitivity, but with variation in predictive power as a function of time of day, which has implications for air quality management use of FNR from geostationary satellites. Plain Language Summary: Surface ozone is an air pollutant of concern due to human health impacts. In locations with elevated ozone concentrations, including coastal regions around Lake Michigan, ozone pollution is managed by controlling emissions of the two classes of chemicals that drive ozone chemistry: volatile organic compounds (VOCs) and nitrogen oxides (NOx). However, due to large reductions in emissions of NOx and VOC over the past 20 years, the leverage that future reductions will have is uncertain. Reductions of 4–5 ppb (∼7%) are needed in several locations, relative to 2017–2019 concentrations, to meet the 2015 ozone standard of 70 ppb. In this paper, we use simulations of atmospheric chemistry and airflow over the Midwestern US to address this issue. By comparing simulations based on different VOC and NOx emissions, we find that reductions in NOx emissions have more influence on ozone than reductions in VOC emissions, except for a small zone downwind of Chicago. On high ozone days over Lake Michigan, a 10% decrease in VOC (NOx) emissions can lower ozone in the key high ozone zone over southern Lake Michigan by 0.4% (0.8%). Volatile chemical products, an uncertain component of emission inventories, are responsible for 2.7 ppb (∼4%) of ozone. Key Points: Outside of a small (85 km) zone downwind of Chicago, ozone concentrations and production near Lake Michigan is generally NOx‐sensitiveOn event days 10% decrease in volatile organic compound emission can lower MDA8 by 0.4% and 10% decrease in nitrogen oxides emission can lower MDA8 by 0.8% over Lake MichiganVolatile chemical product emissions were modeled to produce an average 2.7 ppb ozone increase over the lake [ABSTRACT FROM AUTHOR]

Satellite observations of tropospheric NO2 columns can provide top-down observational constraints on emissions estimates of nitrogen oxides (NOx). Mass-balance based methods are often applied for this purpose, but do not isolate near surface emissions from those aloft, such as lightning emissions. Here, we introduce an inverse modeling framework that couples satellite chemical data assimilation to a chemical transport model and infers satellite-constrained emissions totals using the iterative finite-difference mass-balance method. The approach improves the finite-difference mass-balance inversion by isolating the near-surface emissions increment. We apply the framework to estimate lightning and anthropogenic NOx emissions over the Northern Hemisphere. Using overlapping observations from the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), we compare NOx emissions inferences from these satellite instruments, as well as the impacts of emissions changes on modeled NO2 and O3. OMI inferences of anthropogenic emissions consistently lead to larger emissions than TROPOMI inferences, attributed to a low bias in TROPOMI NO2 retrievals. Updated lightning NOx emissions from either satellite improve the chemical transport model's low tropospheric O3 bias. Combined lightning and anthropogenic updates inferred from satellite observations can improve the model's ability to represent background and ground-level O3 concentrations, an ongoing policy consideration in the U.S. as domestic and international emissions control strategies evolve. [ABSTRACT FROM AUTHOR]