Comparison of the RADM2 and RACM chemical mechanisms in O3 simulations: effect of the photolysis rate constant

AbstractSince the photolysis rate plays an important role in any photoreaction leading to compound sink and radical formation/destruction and eventually O3 formation, its impact on the simulated O3 concentration was evaluated in the present study. Both RADM2 and RACM were adopted with and without updated photolysis rate constants. The newly developed photolysis rates were determined based on two major absorption cross-section and quantum yield data sources. CMAQ in conjunction with meteorological MM5 and emission data retrieved from Taiwan and East Asia were employed to provide spatial and temporal O3 predictions over a one-week period in a three-level nested domain [from 81 km × 81 km in Domain 1 (East Asia) to 9 km × 9 km in Domain 3 (Taiwan)]. Four cases were analyzed, namely, RADM2, with the original photolysis rates applied in Case 1 as a reference case, RADM2, with the updated photolysis rates applied in Case 2, and RACM, with and without the updated photolysis rates applied in Cases 3 and 4, respectively. A comparison of the simulation and observed results indicates that both the application of updated photolysis rate constants and RACM instead of RADM2 enhanced all three error analysis indicators (unpaired peak prediction accuracy, mean normalized bias error and mean absolute normalized gross error). Specifically, RADM2 with the updated photolysis rates resulted in an increase of 12 ppb (10%) in the daily maximum O3 concentration in southwestern Taiwan, while RACM without the updated photolysis rates resulted in an increase of 20 ppb (17%) in the daily maximum O3 concentration in the same area. When RACM with the updated photolysis rate constants was applied in the air quality model, the difference in the daily maximum O3 concentration reached up to 30 ppb (25%). The implication of Case 4 (RACM with the updated photolysis rates) for the formation and degradation of α-pinene and d-limonene was examined.

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Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

Remote Sensing ◽

10.3390/rs13112121 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2121

Author(s):

Changsuk Lee ◽

Kyunghwa Lee ◽

Sangmin Kim ◽

Jinhyeok Yu ◽

Seungtaek Jeong ◽

...

Keyword(s):

Cross Validation ◽

Ground Level ◽

Reanalysis Data ◽

Training Dataset ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Air Quality Model ◽

Quality Model ◽

Near Surface

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.

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Impact of a new condensed toluene mechanism on air quality model predictions in the US

Geoscientific Model Development Discussions ◽

10.5194/gmdd-3-2291-2010 ◽

2010 ◽

Vol 3 (4) ◽

pp. 2291-2314

Author(s):

G. Sarwar ◽

K. W. Appel ◽

A. G. Carlton ◽

R. Mathur ◽

K. Schere ◽

...

Keyword(s):

Air Quality ◽

Los Angeles ◽

Production Efficiency ◽

Sensitivity Study ◽

Chemical Mechanism ◽

Ozone Production ◽

Daily Maximum ◽

Air Quality Model ◽

Quality Model ◽

Mixing Ratios

Abstract. A new condensed toluene mechanism is incorporated into the Community Multiscale Air Quality Modeling system. Model simulations are performed using the CB05 chemical mechanism containing the existing (base) and the new toluene mechanism for the western and eastern US for a summer month. With current estimates of tropospheric emission burden, the new toluene mechanism increases monthly mean daily maximum 8-h ozone by 1.0–3.0 ppbv in Los Angeles, Portland, Seattle, Chicago, Cleveland, northeastern US, and Detroit compared to that with the base toluene chemistry. It reduces model mean bias for ozone at elevated observed ozone mixing ratios. While the new mechanism increases predicted ozone, it does not enhance ozone production efficiency. Sensitivity study suggests that it can further enhance ozone if elevated toluene emissions are present. While changes in total fine particulate mass are small, predictions of in-cloud SOA increase substantially.

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The Impact of Nudging in the Meteorological Model for Retrospective Air Quality Simulations. Part I: Evaluation against National Observation Networks

Journal of Applied Meteorology and Climatology ◽

10.1175/2007jamc1790.1 ◽

2008 ◽

Vol 47 (7) ◽

pp. 1853-1867 ◽

Cited By ~ 74

Author(s):

Tanya L. Otte

Keyword(s):

Air Quality ◽

Meteorological Conditions ◽

Daily Maximum ◽

Air Quality Model ◽

Quality Model ◽

Meteorological Model ◽

Model Simulations ◽

Dynamic Analyses ◽

The Impact ◽

Over Time

Abstract It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simulations to create “dynamic analyses” that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant fields are then used to characterize the meteorological conditions for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1-h ozone mixing ratio simulations. On average, the skill of the daily maximum 1-h ozone simulation deteriorates significantly over time when nonnudged MM5 fields are used in CMAQ. The daily maximum 1-h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves. These results affirm the advantage of using nudging in MM5 to create the meteorological characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5-day simulations.

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Impact of a new condensed toluene mechanism on air quality model predictions in the US

Geoscientific Model Development ◽

10.5194/gmd-4-183-2011 ◽

2011 ◽

Vol 4 (1) ◽

pp. 183-193 ◽

Cited By ~ 40

Author(s):

G. Sarwar ◽

K. W. Appel ◽

A. G. Carlton ◽

R. Mathur ◽

K. Schere ◽

...

Keyword(s):

Air Quality ◽

Los Angeles ◽

Production Efficiency ◽

Sensitivity Study ◽

Chemical Mechanism ◽

Ozone Production ◽

Daily Maximum ◽

Air Quality Model ◽

Quality Model ◽

The Us

Abstract. A new condensed toluene mechanism is incorporated into the Community Multiscale Air Quality Modeling system. Model simulations are performed using the CB05 chemical mechanism containing the existing (base) and the new toluene mechanism for the western and eastern US for a summer month. With current estimates of tropospheric emission burden, the new toluene mechanism increases monthly mean daily maximum 8-h ozone by 1.0–3.0 ppbv in Los Angeles, Portland, Seattle, Chicago, Cleveland, northeastern US, and Detroit compared to that with the base toluene chemistry. It reduces model mean bias for ozone at elevated observed ozone concentrations. While the new mechanism increases predicted ozone, it does not enhance ozone production efficiency. A sensitivity study suggests that it can further enhance ozone if elevated toluene emissions are present. While it increases in-cloud secondary organic aerosol substantially, its impact on total fine particle mass concentration is small.

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Impact of Aerosol Direct Effect on Wintertime PM2.5 Simulated by an Online Coupled Meteorology-Air Quality Model over East Asia

Aerosol and Air Quality Research ◽

10.4209/aaqr.2016.06.0282 ◽

2018 ◽

Vol 18 (4) ◽

pp. 1068-1079 ◽

Cited By ~ 7

Author(s):

Ami Sekiguchi ◽

Hikari Shimadera ◽

Akira Kondo

Keyword(s):

Air Quality ◽

East Asia ◽

Direct Effect ◽

Air Quality Model ◽

Quality Model ◽

Aerosol Direct Effect

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The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP: Part I Model Descriptions

10.5194/gmd-2017-306 ◽

2017 ◽

Author(s):

Jun Wang ◽

Partha S. Bhattacharjee ◽

Vijay Tallapragada ◽

Cheng-Hsuan Lu ◽

Shobha Kondragunta ◽

...

Keyword(s):

New York ◽

Biomass Burning ◽

Air Quality Model ◽

Quality Model ◽

Emission Data ◽

Aerosol Component ◽

Nasa Goddard Space ◽

Satellite Sst ◽

Satellite Applications ◽

Version 2.0

Abstract. The NEMS GFS Aerosol component (NGAC) version 2.0 for global multi-species aerosol forecast has been developed at the National Centers of Environment Prediction (NCEP) in collaboration with the NESDIS Center for Satellite Applications and Research (STAR), NASA Goddard Space Flight Center (GSFC), and University at Albany, State University of New York (SUNYA). This paper describes the continuous development of the NGAC system at NCEP after the initial global dust-only forecast implementation (NGAC version 1.0). With version 2, additional sea salt, sulfate, organic carbon and black carbon aerosol species were included. The smoke emissions are from the NESDIS STAR's Global Biomass Burning Product (GBBEPx), blended from the global biomass burning emission product from a constellation of geostationary satellites (GBBEP-Geo) and GSFC's Quick Fire Emission Data Version 2 from a polar orbiting sensor (QFED2). This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been used by many applications such as for regional air quality model lateral boundary conditions, satellite SST physical retrievals and the global solar insolation estimation. Positive impacts have been seen in these applications.

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The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 1: Model descriptions

Geoscientific Model Development ◽

10.5194/gmd-11-2315-2018 ◽

2018 ◽

Vol 11 (6) ◽

pp. 2315-2332 ◽

Cited By ~ 7

Author(s):

Jun Wang ◽

Partha S. Bhattacharjee ◽

Vijay Tallapragada ◽

Cheng-Hsuan Lu ◽

Shobha Kondragunta ◽

...

Keyword(s):

New York ◽

Biomass Burning ◽

State University ◽

Air Quality Model ◽

Quality Model ◽

Emission Data ◽

Aerosol Component ◽

Nasa Goddard Space ◽

Satellite Applications ◽

Version 2.0

Abstract. The NEMS GFS Aerosol Component Version 2.0 (NGACv2) for global multispecies aerosol forecast has been developed at the National Centers of Environment Prediction (NCEP) in collaboration with the NESDIS Center for Satellite Applications and Research (STAR), the NASA Goddard Space Flight Center (GSFC), and the University at Albany, State University of New York (SUNYA). This paper describes the continuous development of the NGAC system at NCEP after the initial global dust-only forecast implementation (NGAC version 1.0, NGACv1). With NGACv2, additional sea salt, sulfate, organic carbon, and black carbon aerosol species were included. The smoke emissions are from the NESDIS STAR's Global Biomass Burning Product (GBBEPx), blended from the global biomass burning emission product from a constellation of geostationary satellites (GBBEP-Geo) and GSFC's Quick Fire Emission Data Version 2 from a polar-orbiting sensor (QFED2). This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been used by many applications such as for regional air quality model lateral boundary conditions, satellite sea surface temperature (SST) physical retrievals, and the global solar insolation estimation. Positive impacts have been seen in these applications.

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Downscaling EDGAR emissions to local emission sectors for Switzerland

10.5194/egusphere-egu21-8208 ◽

2021 ◽

Author(s):

Jacinta Edebeli ◽

Curdin Spirig ◽

Julien Anet

Keyword(s):

Land Use ◽

Air Quality ◽

Measurement Data ◽

Pilot Project ◽

Air Quality Model ◽

Quality Model ◽

Emission Data ◽

Traffic Pattern ◽

Pollutant Concentrations ◽

The Impact

The fifth version of the Emission Database for Global Atmospheric Research (EDGAR 5.0) provides an impressive inventory of various pollutants. Pollutants from different emission sectors are available with daily, monthly and yearly temporal profiles at a high global resolution of 0.1&#176;&#215;0.1&#176;. Although this resolution has been sufficient for regional air quality studies, the emissions appeared to be too coarse for local air quality studies in areas with complex topography. With Switzerland as a case study, we present our approach for downscaling EDGAR emission data to a much finer resolution of 0.02&#176;&#215;0.02&#176; with the aim of modelling local air quality.We downscaled the EDGAR emissions using a combination of GIS tools including QGIS, ArcGIS, and a series of python scripts. We obtained the surface coverage of different land use features within the defined EDGAR emission sectors from Open Street Map (OSM) using the QuickOSM tool in QGIS. With the calculated local surface area coverage of the emissions sectors, we downscaled the EDGAR inventory data within ArcGIS using a set of developed Arcpy script tools.The outcome was a much finer resolved emission dataset which we fed into the WRF-CHEM air quality model within a pilot project. A comparison of the modelled pollutant concentrations using the two datasets (original EDGAR data and the downscaled data) shows an improved agreement between the downscaled dataset and the measurement data.Studies investigating the impact of urbanization, land use change or traffic pattern on air quality may benefit from our downscaling solution, which, thanks to the global coverage of OSM, can be globally applied.

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Solar “brightening” impact on summer surface ozone between 1990 and 2010 in Europe – a model sensitivity study of the influence of the aerosol–radiation interactions

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-9741-2018 ◽

2018 ◽

Vol 18 (13) ◽

pp. 9741-9765 ◽

Cited By ~ 3

Author(s):

Emmanouil Oikonomakis ◽

Sebnem Aksoyoglu ◽

Martin Wild ◽

Giancarlo Ciarelli ◽

Urs Baltensperger ◽

...

Keyword(s):

Air Quality ◽

Surface Ozone ◽

Biogenic Emissions ◽

Base Case ◽

Air Quality Model ◽

Quality Model ◽

Ozone Concentrations ◽

Photolysis Rates ◽

Ozone Trends ◽

The Impact

Abstract. Surface solar radiation (SSR) observations have indicated an increasing trend in Europe since the mid-1980s, referred to as solar “brightening”. In this study, we used the regional air quality model, CAMx (Comprehensive Air Quality Model with Extensions) to simulate and quantify, with various sensitivity runs (where the year 2010 served as the base case), the effects of increased radiation between 1990 and 2010 on photolysis rates (with the PHOT1, PHOT2 and PHOT3 scenarios, which represented the radiation in 1990) and biogenic volatile organic compound (BVOC) emissions (with the BIO scenario, which represented the biogenic emissions in 1990), and their consequent impacts on summer surface ozone concentrations over Europe between 1990 and 2010. The PHOT1 and PHOT2 scenarios examined the effect of doubling and tripling the anthropogenic PM2.5 concentrations, respectively, while the PHOT3 investigated the impact of an increase in just the sulfate concentrations by a factor of 3.4 (as in 1990), applied only to the calculation of photolysis rates. In the BIO scenario, we reduced the 2010 SSR by 3 % (keeping plant cover and temperature the same), recalculated the biogenic emissions and repeated the base case simulations with the new biogenic emissions. The impact on photolysis rates for all three scenarios was an increase (in 2010 compared to 1990) of 3–6 % which resulted in daytime (10:00–18:00 Local Mean Time – LMT) mean surface ozone differences of 0.2–0.7 ppb (0.5–1.5 %), with the largest hourly difference rising as high as 4–8 ppb (10–16 %). The effect of changes in BVOC emissions on daytime mean surface ozone was much smaller (up to 0.08 ppb, ∼ 0.2 %), as isoprene and terpene (monoterpene and sesquiterpene) emissions increased only by 2.5–3 and 0.7 %, respectively. Overall, the impact of the SSR changes on surface ozone was greater via the effects on photolysis rates compared to the effects on BVOC emissions, and the sensitivity test of their combined impact (the combination of PHOT3 and BIO is denoted as the COMBO scenario) showed nearly additive effects. In addition, all the sensitivity runs were repeated on a second base case with increased NOx emissions to account for any potential underestimation of modeled ozone production; the results did not change significantly in magnitude, but the spatial coverage of the effects was profoundly extended. Finally, the role of the aerosol–radiation interaction (ARI) changes in the European summer surface ozone trends was suggested to be more important when comparing to the order of magnitude of the ozone trends instead of the total ozone concentrations, indicating a potential partial damping of the effects of ozone precursor emissions' reduction.

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