This study reports on the performance of electrochemical-based low-cost sensors and their use in a community application. CairClip sensors were collocated with federal reference and equivalent ...methods and operated in a network of sites by citizen scientists (community members) in Houston, Texas and Denver, Colorado, under the umbrella of the NASA-led DISCOVER-AQ Earth Venture Mission. Measurements were focused on ozone (O₃) and nitrogen dioxide (NO₂). The performance evaluation showed that the CairClip O₃/NO₂ sensor provided a consistent measurement response to that of reference monitors (r² = 0.79 in Houston; r² = 0.72 in Denver) whereas the CairClip NO₂ sensor measurements showed no agreement to reference measurements. The CairClip O₃/NO₂ sensor data from the citizen science sites compared favorably to measurements at nearby reference monitoring sites. This study provides important information on data quality from low-cost sensor technologies and is one of few studies that reports sensor data collected directly by citizen scientists.
The Korea-United States Air Quality (KORUS-AQ) field study was conducted during May-June 2016. The effort was jointly sponsored by the National Institute of Environmental Research of South Korea and ...the National Aeronautics and Space Administration of the United States. KORUS-AQ offered an unprecedented, multi-perspective view of air quality conditions in South Korea by employing observations from three aircraft, an extensive ground-based network, and three ships along with an array of air quality forecast models. Information gathered during the study is contributing to an improved understanding of the factors controlling air quality in South Korea. The study also provided a valuable test bed for future air quality-observing strategies involving geostationary satellite instruments being launched by both countries to examine air quality throughout the day over Asia and North America. This article presents details on the KORUS-AQ observational assets, study execution, data products, and air quality conditions observed during the study. High-level findings from companion papers in this special issue are also summarized and discussed in relation to the factors controlling fine particle and ozone pollution, current emissions and source apportionment, and expectations for the role of satellite observations in the future. Resulting policy recommendations and advice regarding plans going forward are summarized. These results provide an important update to early feedback previously provided in a Rapid Science Synthesis Report produced for South Korean policy makers in 2017 and form the basis for the Final Science Synthesis Report delivered in 2020.
The COVID-19 pandemic created an extreme natural experiment in which sudden changes in human behavior and economic activity resulted in significant declines in nitrogen oxide (NOx) emissions, ...immediately after strict lockdowns were imposed. Here we examined the impact of multiple waves and response phases of the pandemic on nitrogen dioxide (NO2) dynamics and the role of meteorology in shaping relative contributions from different emission sectors to NO2 pollution in post-pandemic New York City. Long term (> 3.5 years), high frequency measurements from a network of ground-based Pandora spectrometers were combined with TROPOMI satellite retrievals, meteorological data, mobility trends, and atmospheric transport model simulations to quantify changes in NO2 across the New York metropolitan area. The stringent lockdown measures after the first pandemic wave resulted in a decline in top-down NOx emissions by approx. 30 % on top of long-term trends, in agreement with sector-specific changes in NOx emissions. Ground-based measurements showed a sudden drop in total column NO2 in spring 2020, by up to 36 % in Manhattan and 19 %–29 % in Queens, New Jersey (NJ), and Connecticut (CT), and a clear weakening (by 16 %) of the typical weekly NO2 cycle. Extending our analysis to more than a year after the initial lockdown captured a gradual recovery in NO2 across the NY/NJ/CT tri-state area in summer and fall 2020, as social restrictions eased, followed by a second decline in NO2 coincident with the second wave of the pandemic and resurgence of lockdown measures in winter 2021. Meteorology was not found to have a strong NO2 biassing effect in New York City after the first pandemic wave. Winds, however, were favorable for low NO2 conditions in Manhattan during the second wave of the pandemic, resulting in larger column NO2 declines than expected based on changes in transportation emissions alone. Meteorology played a key role in shaping the relative contributions from different emission sectors to NO2 pollution in the city, with low-speed (< 5 m s-1) SW-SE winds enhancing contributions from the high-emitting power-generation sector in NJ and Queens and driving particularly high NO2 pollution episodes in Manhattan, even during – and despite – the stringent early lockdowns. These results have important implications for air quality management in New York City, and highlight the value of high resolution NO2 measurements in assessing the effects of rapid meteorological changes on air quality conditions and the effectiveness of sector-specific NOx emission control strategies.
Airborne and ground-based Pandora spectrometer NO2 column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound ...region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NO2 Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO2. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (r2= 0.92 and slope of 1.03), with the largest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250 m × 250 m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r2=0.96) than Pandora measurements are with TROPOMI (r2=0.84). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5∘) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. This factor causes a high bias in TROPOMI TrVCs of 4 %–11 %. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19 %–33 % during the LISTOS timeframe of June–September 2018. Part of this low bias is caused by coarse a priori profile input from the TM5-MP model; replacing these profiles with those from a 12 km North American Model–Community Multiscale Air Quality (NAMCMAQ) analysis results in a 12 %–14 % increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7 %–19 % low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets.
Background: In June 2008, burning peat deposits produced haze and air pollution far in excess of National Ambient Air Quality Standards, encroaching on rural communities of eastern North Carolina. ...Although the association of mortality and morbidity with exposure to urban air pollution is well established, the health effects associated with exposure to wildfire emissions are less well understood. Objective: We investigated the effects of exposure on cardiorespiratory outcomes in the population affected by the fire. Methods: We performed a population-based study using emergency department (ED) visits reported through the syndromic surveillance program NC DETECT (North Carolina Disease Event Tracking and Epidemiologie Collection Tool). We used aerosol optical depth measured by a satellite to determine a high-exposure window and distinguish counties most impacted by the dense smoke plume from surrounding referent counties. Poisson log-linear regression with a 5-day distributed lag was used to estimate changes in the cumulative relative risk (RR). Results: In the exposed counties, significant increases in cumulative RR for asthma 1.65 (95% confidence interval, 1.25-2.1), chronic obstructive pulmonary disease 1.73 (1.06-2.83), and pneumonia and acute bronchitis 1.59 (1.07-2.34) were observed. ED visits associated with cardiopulmonary symptoms 1.23 (1.06-1.43) and heart failure 1.37 (1.01-1.85) were also significantly increased. Conclusions: Satellite data and syndromic surveillance were combined to assess the health impacts of wildfire smoke in rural counties with sparse air-quality monitoring. This is the first study to demonstrate both respiratory and cardiac effects after brief exposure to peat wildfire smoke.
The COVID-19 pandemic created an extreme natural experiment in which sudden changes in human behavior and economic activity resulted in significant declines in nitrogen oxide (NO
) emissions, ...immediately after strict lockdowns were imposed. Here we examined the impact of multiple waves and response phases of the pandemic on nitrogen dioxide (NO
) dynamics and the role of meteorology in shaping relative contributions from different emission sectors to NO
pollution in post-pandemic New York City. Long term (> 3.5 years), high frequency measurements from a network of ground-based Pandora spectrometers were combined with TROPOMI satellite retrievals, meteorological data, mobility trends, and atmospheric transport model simulations to quantify changes in NO
across the New York metropolitan area. The stringent lockdown measures after the first pandemic wave resulted in a decline in top-down NO
emissions by approx. 30% on top of long-term trends, in agreement with sector-specific changes in NO
emissions. Ground-based measurements showed a sudden drop in total column NO
in spring 2020, by up to 36% in Manhattan and 19%-29% in Queens, New Jersey (NJ), and Connecticut (CT), and a clear weakening (by 16%) of the typical weekly NO
cycle. Extending our analysis to more than a year after the initial lockdown captured a gradual recovery in NO
across the NY/NJ/CT tri-state area in summer and fall 2020, as social restrictions eased, followed by a second decline in NO
coincident with the second wave of the pandemic and resurgence of lockdown measures in winter 2021. Meteorology was not found to have a strong NO
biassing effect in New York City after the first pandemic wave. Winds, however, were favorable for low NO
conditions in Manhattan during the second wave of the pandemic, resulting in larger column NO
declines than expected based on changes in transportation emissions alone. Meteorology played a key role in shaping the relative contributions from different emission sectors to NO with low-speed (
5 ms
) SW-SE winds enhancing contributions from the high-emitting power-generation sector in NJ and Queens and driving particularly high NO
pollution episodes in Manhattan, even during - and despite - the stringent early lockdowns. These results have important implications for air quality management in New York City, and highlight the value of high resolution NO
measurements in assessing the effects of rapid meteorological changes on air quality conditions and the effectiveness of sector-specific NO
emission control strategies.
NASA deployed the GeoTASO airborne UV–visible spectrometer in May–June 2017 to produce high-resolution (approximately 250m×250m) gapless NO2 datasets over the western shore of Lake Michigan and over ...the Los Angeles Basin. The results collected show that the airborne tropospheric vertical column retrievals compare well with ground-based Pandora spectrometer column NO2 observations (r2=0.91 and slope of 1.03). Apparent disagreements between the two measurements can be sensitive to the coincidence criteria and are often associated with large local variability, including rapid temporal changes and spatial heterogeneity that may be observed differently by the sunward-viewing Pandora observations. The gapless mapping strategy executed during the 2017 GeoTASO flights provides data suitable for averaging to coarser areal resolutions to simulate satellite retrievals. As simulated satellite pixel area increases to values typical of TEMPO (Tropospheric Emissions: Monitoring Pollution), TROPOMI (TROPOspheric Monitoring Instrument), and OMI (Ozone Monitoring Instrument), the agreement with Pandora measurements degraded, particularly for the most polluted columns as localized large pollution enhancements observed by Pandora and GeoTASO are spatially averaged with nearby less-polluted locations within the larger area representative of the satellite spatial resolutions (aircraft-to-Pandora slope: TEMPO scale =0.88; TROPOMI scale =0.77; OMI scale =0.57). In these two regions, Pandora and TEMPO or TROPOMI have the potential to compare well at least up to pollution scales of 30×1015 molecules cm-2. Two publicly available OMI tropospheric NO2 retrievals are found to be biased low with respect to these Pandora observations. However, the agreement improves when higher-resolution a priori inputs are used for the tropospheric air mass factor calculation (NASA V3 standard product slope =0.18 and Berkeley High Resolution product slope =0.30). Overall, this work explores best practices for satellite validation strategies with Pandora direct-sun observations by showing the sensitivity to product spatial resolution and demonstrating how the high-spatial-resolution NO2 data retrieved from airborne spectrometers, such as GeoTASO, can be used with high-temporal-resolution ground-based column observations to evaluate the influence of spatial heterogeneity on validation results.
Nitrogen oxides (NOx = NO + NO2) play a crucial role in the formation of ozone and secondary inorganic and organic aerosols, thus affecting human health, global radiation budget, and climate. The ...diurnal and spatial variations in NO2 are functions of emissions, advection, deposition, vertical mixing, and chemistry. Their observations, therefore, provide useful constraints in our understanding of these factors. We employ a Regional chEmical and trAnsport model (REAM) to analyze the observed temporal (diurnal cycles) and spatial distributions of NO2 concentrations and tropospheric vertical column densities (TVCDs) using aircraft in situ measurements and surface EPA Air Quality System (AQS) observations as well as the measurements of TVCDs by satellite instruments (OMI: the Ozone Monitoring Instrument; GOME-2A: Global Ozone Monitoring Experiment – 2A), ground-based Pandora, and the Airborne Compact Atmospheric Mapper (ACAM) instrument in July 2011 during the DISCOVER-AQ campaign over the Baltimore–Washington region. The model simulations at 36 and 4 km resolutions are in reasonably good agreement with the regional mean temporospatial NO2 observations in the daytime. However, we find significant overestimations (underestimations) of model-simulated NO2 (O3) surface concentrations during nighttime, which can be mitigated by enhancing nocturnal vertical mixing in the model. Another discrepancy is that Pandora-measured NO2 TVCDs show much less variation in the late afternoon than simulated in the model. The higher-resolution 4 km simulations tend to show larger biases compared to the observations due largely to the larger spatial variations in NOx emissions in the model when the model spatial resolution is increased from 36 to 4 km. OMI, GOME-2A, and the high-resolution aircraft ACAM observations show a more dispersed distribution of NO2 vertical column densities (VCDs) and lower VCDs in urban regions than corresponding 36 and 4 km model simulations, likely reflecting the spatial distribution bias of NOx emissions in the National Emissions Inventory (NEI) 2011.
The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument is a test bed for upcoming air quality satellite instruments that will measure backscattered ultraviolet, ...visible and near-infrared light from geostationary orbit. GeoTASO flew on the NASA Falcon aircraft in its first intensive field measurement campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Earth Venture Mission over Houston, Texas, in September 2013. Measurements of backscattered solar radiation between 420 and 465 nm collected on 4 days during the campaign are used to determine slant column amounts of NO2 at 250 m x 250 m spatial resolution with a fitting precision of 2.2 x 10(exp 15) molecules/sq cm. These slant columns are converted to tropospheric NO2 vertical columns using a radiative transfer model and trace gas profiles from the Community Multiscale Air Quality (CMAQ) model. Total column NO2 from GeoTASO is well correlated with ground-based Pandora observations (r = 0.90 on the most polluted and cloud-free day of measurements and r = 0.74 overall), with GeoTASO NO2 slightly higher for the most polluted observations. Surface NO2 mixing ratios inferred from GeoTASO using the CMAQ model show good correlation with NO2 measured in situ at the surface during the campaign (r = 0.85). NO2 slant columns from GeoTASO also agree well with preliminary retrievals from the GEO-CAPE Airborne Simulator (GCAS) which flew on the NASA King Air B200 (r = 0.81, slope = 0.91). Enhanced NO2 is resolvable over areas of traffic NOx emissions and near individual petrochemical facilities.
We improve the accuracy of daily ground-level fine particulate matter concentrations (PM2.5) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport ...model (GEOS-Chem) calculations of the relationship between AOD and PM2.5. This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM2.5, and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m3 + 67%) to ± (1 μg/m3 + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM2.5 estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m3 + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM2.5 as compared to day-specific relationships.
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