scholarly journals Balance of Emission and Dynamical Controls on Ozone During the Korea‐United States Air Quality Campaign From Multiconstituent Satellite Data Assimilation

2019 ◽  
Vol 124 (1) ◽  
pp. 387-413 ◽  
Author(s):  
K. Miyazaki ◽  
T. Sekiya ◽  
D. Fu ◽  
K. W. Bowman ◽  
S. S. Kulawik ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Data Assimilation ◽  
Satellite Data

Satellite data assimilation for air quality forecast

2008 ◽  
Vol 16 (10) ◽  
pp. 1541-1545 ◽  
Author(s):  
H. Boisgontier ◽  
V. Mallet ◽  
J.P. Berroir ◽  
M. Bocquet ◽  
I. Herlin ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Satellite Data ◽  
Air Quality Forecast ◽  
Quality Forecast

scholarly journals A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

2017 ◽  
Vol 10 (12) ◽  
pp. 4743-4758 ◽  
Author(s):  
Youhua Tang ◽  
Mariusz Pagowski ◽  
Tianfeng Chai ◽  
Li Pan ◽  
Pius Lee ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Data Assimilation ◽  
Mean Squared Error ◽  
Optimal Interpolation ◽  
Air Quality Model ◽  
Quality Model ◽  
Multi Scale ◽  
Modis Aod ◽  
Environmental Prediction

Abstract. This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM2.5 (particle matter with diameter < 2.5 µm) leads to stronger increments in surface PM2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC compared to the surface PM2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.

scholarly journals Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1)

2021 ◽  
Author(s):  
Jason Edward Williams ◽  
Vincent Huijnen ◽  
Idir Bouarar ◽  
Mehdi Meziane ◽  
Timo Schreurs ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
The United States ◽  
Trace Gas ◽  
Chemically Induced ◽  
Quality Aspects ◽  
Tropospheric O3 ◽  
Aerosol Modeling

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) provides routine analyses and forecasts of trace gases and aerosols on a global scale. The core is ECMWF’s Integrated Forecast System (IFS), where modules for atmospheric chemistry and aerosols have been introduced, and which allows data-assimilation of satellite retrievals of composition. We have updated both the homogeneous and heterogeneous NOx chemistry applied in the three independent tropospheric-stratospheric chemistry modules maintained within CAMS, referred to as IFS(CB05BASCOE), IFS(MOCAGE) and IFS(MOZART). Here we focus on the evaluation of main trace gas products from these modules that are of interest as markers of air quality, namely lower tropospheric O3, NO2 and CO, with a regional focus over the contiguous United States without data assimilation. Evaluation against lower tropospheric composition reveals overall good performance, with chemically induced biases within 10 ppb across species across regions within the US with respect to a range of observations. The versions show overall equal or better performance than the CAMS Reanalysis. Evaluation of surface air quality aspects shows that annual cycles are captured well, albeit with variable seasonal biases. During wintertime conditions there is a large model spread between chemistry schemes in lower-tropospheric O3 (~10–35 %) and, in turn, oxidative capacity related to NOx lifetime differences. Analysis of differences in the HNO3 and PAN formation, which act as reservoirs for reactive nitrogen, revealed a general underestimate in PAN formation over polluted regions likely due to too low organic precursors. Particularly during wintertime, the fraction of NO2 sequestered into PAN has a variability of 100 % across chemistry modules indicating the need for further constraints. Notably a considerable uncertainty in HNO3 formation associated with wintertime N2O5 conversion on wet particle surfaces remains. In summary this study has indicated that the chemically induced differences in the quality of CAMS forecast products over the United States depends on season, trace gas, altitude and region. Whilst analysis of the three chemistry modules in CAMS provide a strong handle on uncertainties associated with chemistry modeling, the further improvement of operational products additionally requires coordinated development involving emissions handling, chemistry and aerosol modeling, complemented with data-assimilation efforts.

Monitoring air quality in class I wilderness areas of the northeastern United States using lichens and bryophytes

2016 ◽  
Author(s):  
Alison C. Dibble ◽  
James W. Hinds ◽  
Ralph Perron ◽  
Natalie Cleavitt ◽  
Richard L. Poirot ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Class I ◽  
Northeastern United States ◽  
Wilderness Areas

scholarly journals Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Santiago Lopez-Restrepo ◽  
Andres Yarce ◽  
Nicolás Pinel ◽  
O.L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Cost Model ◽  
Low Cost ◽  
Model Performance ◽  
Monitoring Network ◽  
Correlation Factor ◽  
Chemical Transport Model ◽  
High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

scholarly journals Association of temporary Environmental Protection Agency regulation suspension with industrial economic viability and local air quality in California, United States

2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Emily Chang ◽  
Kenneth Zhang ◽  
Margaret Paczkowski ◽  
Sara Kohler ◽  
Marco Ribeiro
Keyword(s):  
United States ◽  
Air Quality ◽  
Environmental Protection ◽  
Environmental Protection Agency ◽  
Manufacturing Industry ◽  
Economic Viability ◽  
Treatment Control ◽  
Oil Refineries ◽  
Pollution Levels ◽  
The Impact

Abstract Background This study seeks to answer two questions about the impacts of the 2020 Environmental Protection Agency’s enforcement regulation rollbacks: is this suspension bolstering the economic viability of industries as oil and manufacturing executives claim they will and are these regulations upholding the agency’s mission of protecting the environment? Results To answer the former question, we utilized 6 months of state employment level data from California, United States, as a method of gauging the economic health of agency-regulated industries. We implemented a machine learning model to predict weekly employment data and a t-test to indicate any significant changes in employment. We found that, following California's state-issued stay-at-home order and the agency’s regulation suspension, oil and certain manufacturing industries had statistically significant lower employment values. To answer the latter question, we used 10 years of PM2.5 levels in California, United States, as a metric for local air quality and treatment–control county pairs to isolate the impact of regulation rollbacks from the impacts of the state lockdown. Using the agency’s data, we performed a t-test to determine whether treatment–control county pairs experienced a significant change in PM2.5 levels. Even with the statewide lockdown—a measure we hypothesized would correlate with decreased mobility and pollution levels—in place, counties with oil refineries experienced the same air pollution levels when compared to historical data averaged from the years 2009 to 2019. Conclusions In contrast to the expectation that the suspension would improve the financial health of the oil and manufacturing industry, we can conclude that these industries are not witnessing economic growth with the suspension and state shutdown in place. Additionally, counties with oil refineries could be taking advantage of these rollbacks to continue emitting the same amount of PM2.5, in spite of state lockdowns. For these reasons, we ask international policymakers to reconsider the suspension of enforcement regulations as these actions do not fulfill their initial expectations. We recommend the creation and maintenance of pollution control and prevention programs that develop emission baselines, mandate the construction of pollution databases, and update records of pollution emissions.

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