Air quality forecasting system based on chemical transport models

2019 ◽  
Vol 4 ◽  
pp. 203-218
Author(s):  
I.N. Kusnetsova ◽  
◽  
I.U. Shalygina ◽  
M.I. Nahaev ◽  
U.V. Tkacheva ◽  
...  
Keyword(s):  
Air Quality ◽  
Chemical Transport ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Forecasting System ◽  
Air Quality Forecasting

scholarly journals Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

2018 ◽  
Author(s):  
Scott D. Chambers ◽  
Elise-Andree Guérette ◽  
Khalia Monk ◽  
Alan D. Griffiths ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Diurnal Cycle ◽  
Transport Model ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Transport Models ◽  
Potential Health ◽  
Meteorological Model ◽  
Model Skill ◽  
Chemical Transport Models

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

scholarly journals Model Performance Differences in Sulfate Aerosol in Winter over Japan Based on Regional Chemical Transport Models of CMAQ and CAMx

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 488 ◽  
Author(s):  
Syuichi Itahashi ◽  
Kazuyo Yamaji ◽  
Satoru Chatani ◽  
Kunihiro Hisatsune ◽  
Shinji Saito ◽  
...  
Keyword(s):  
Air Quality ◽  
Deposition Velocity ◽  
Model Performance ◽  
Chemical Transport ◽  
Sulfate Aerosol ◽  
Performance Criteria ◽  
Second Phase ◽  
Transport Models ◽  
Chemical Transport Models ◽  
So2 Concentration

Sulfate aerosol (SO42−) is a major component of particulate matter in Japan. The Japanese model intercomparison study, J-STREAM, found that although SO42− is well captured by models, it is underestimated during winter. In the first phase of J-STREAM, we refined the Fe- and Mn-catalyzed oxidation and partly improved the underestimation. The winter haze in December 2016 was a target period in the second phase. The results from the Community Multiscale Air Quality (CMAQ) and Comprehensive Air quality Model with eXtentions (CAMx) regional chemical transport models were compared with observations from the network over Japan and intensive observations at Nagoya and Tokyo. Statistical analysis showed both models satisfied the suggested model performance criteria. CMAQ sensitivity simulations explained the improvements in model performance. CMAQ modeled lower SO42− concentrations than CAMx, despite increased aqueous oxidation via the metal catalysis pathway and NO2 reaction in CMAQ. Deposition explained this difference. A scatter plot demonstrated that the lower SO42− concentration in CMAQ than in CAMx arose from the lower SO2 concentration and higher SO42− wet deposition in CMAQ. The dry deposition velocity caused the difference in SO2 concentration. These results suggest the importance of deposition in improving our understanding of ambient concentration behavior.

Two models and two emission databases – evaluation of the PM10 and PM2.5 concentrations modelled with WRF-Chem and EMEP4PL

2020 ◽  
Author(s):  
Małgorzata Werner ◽  
Maciej Kryza ◽  
Justyna Dudek
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Model Performance ◽  
Correlation Coefficients ◽  
Chemical Transport ◽  
Population Exposure ◽  
Transport Models ◽  
Local Database ◽  
Chemical Transport Models ◽  
Pm10 And Pm2.5

<p>Some European countries in Eastern or Central Europe, such as Poland, have serious problems with air quality. High concentrations of particulate matter (PM) in winter are often related to high coal and wood combustion for residential heating. Meteorological conditions, i.e. low air temperature and anticyclones, provide favourable conditions for the accumulation of air pollution, rendering it harmful to people.  PM concentrations during the warmer period are much lower, however there are episodes with elevated concentrations related to e.g. long-range transport of pollutants from biomass burning areas. Policy makers in Poland put a lot of effort to improve air quality as well as inform and aware people on harmful effects of air pollution. One of the relevant tools which provides information on the past, current and future state of the air pollution are chemical transport models.</p><p>In this study we aim for validation of PM10 and PM2.5 concentrations from two different chemical transport models – WRF-Chem and EMEP4PL and two different emission databases – a) a regional EMEP database, and b) a local database provided by the Chief Inspectorate of Environmental Pollution. Modelled PM10 and PM2.5 concentrations were compared with observations from Polish stations for the year 2018. The results show a clear seasonal variation of the models performance with the lowest correlation coefficients in summer. Higher seasonal variability is observed for WRF-Chem than EMEP, which is probably related to differences in calculations of boundary layer height. Application of local database improves the results for both models. For several months, the performance of WRF-Chem and EMEP is clearly different, which shows that an ensemble approach with an application of these two models could improve the modelling results. The differences in the model performance significantly influence the results of the population exposure assessment.</p><p> </p>

scholarly journals Ensemble forecasts of air quality in eastern China – Part 1: Model description and implementation of the MarcoPolo–Panda prediction system, version 1

2019 ◽  
Vol 12 (1) ◽  
pp. 33-67 ◽  
Author(s):  
Guy P. Brasseur ◽  
Ying Xie ◽  
Anna Katinka Petersen ◽  
Idir Bouarar ◽  
Johannes Flemming ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Eastern China ◽  
Model Description ◽  
Transport Models ◽  
Ensemble Forecasts ◽  
Chemical Transport Models ◽  
Systematic Biases ◽  
Forecasting System ◽  
The Mean

Abstract. An operational multi-model forecasting system for air quality including nine different chemical transport models has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the 37 largest urban areas of China (population higher than 3 million in 2010). These individual forecasts as well as the mean and median concentrations for the next 3 days are displayed on a publicly accessible website (http://www.marcopolo-panda.eu, last access: 7 December 2018). The paper describes the forecasting system and shows some selected illustrative examples of air quality predictions. It presents an intercomparison of the different forecasts performed during a given period of time (1–15 March 2017) and highlights recurrent differences between the model output as well as systematic biases that appear in the median concentration values. Pathways to improve the forecasts by the multi-model system are suggested.

scholarly journals Systematic bias in evaluating chemical transport models with maximum daily 8 h average (MDA8) surface ozone for air quality applications: a case study with GEOS-Chem v9.02

2019 ◽  
Vol 12 (8) ◽  
pp. 3641-3648 ◽  
Author(s):  
Katherine R. Travis ◽  
Daniel J. Jacob
Keyword(s):  
Air Quality ◽  
Mixed Layer ◽  
Diurnal Cycle ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Systematic Bias ◽  
Transport Models ◽  
Ozone Deposition ◽  
Chemical Transport Models

Abstract. Chemical transport models frequently evaluate their simulation of surface ozone with observations of the maximum daily 8 h average (MDA8) concentration, which is the standard air quality policy metric. This requires successful simulation of the surface ozone diurnal cycle including nighttime depletion, but models often have difficulty simulating this diurnal cycle for a number of reasons, including (1) vertical grid structure in the surface layer, (2) timing of changes in mixed layer dynamics and ozone deposition velocity across the day–night transition, (3) poor representation of nighttime stratification, and (4) uncertainties in ozone nighttime deposition. We analyze the problem with the GEOS-Chem model, taking as a representative case study the Southeast US during the NASA SEAC4RS aircraft campaign in August–September 2013. The model is unbiased relative to the daytime mixed layer aircraft observations but has a mean +8 ppb bias at its lowest level (65 m) relative to MDA8 surface ozone observations. The bias can be corrected to +5 ppb by implicit sampling of the model at the 10 m altitude of the surface observations. The model does not capture frequent observed occurrences of <20 ppb MDA8 surface ozone on rainy days, possibly because of enhanced ozone deposition to wet surfaces that is unaccounted for. Restricting the surface ozone evaluation to dry days still shows inconsistencies with MDA8 ozone because of model errors in the ozone diurnal cycle. Restricting the evaluation to afternoon ozone completely removes the bias. We conclude that better representation of diurnal variations in mixed layer dynamics and ozone deposition velocities is needed in models to properly describe the diurnal cycle of ozone.

scholarly journals Analyzing experimental data and model parameters: implications for predictions of SOA using chemical transport models

2013 ◽  
Vol 13 (6) ◽  
pp. 15907-15947 ◽  
Author(s):  
K. C. Barsanti ◽  
A. G. Carlton ◽  
S. H. Chung
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
Chemical Transport ◽  
Basis Set ◽  
Base Case ◽  
Model Parameters ◽  
Smog Chamber ◽  
Modeling Framework ◽  
Transport Models ◽  
Chemical Transport Models

Abstract. Despite the critical importance for air quality and climate predictions, accurate representation of secondary organic aerosol (SOA) formation remains elusive. An essential addition to the ongoing discussion of improving model predictions is an acknowledgement of the linkages between experimental conditions, parameter optimization and model output, as well as the linkage between empirically-derived partitioning parameters and the physicochemical properties of SOA they represent in models. In this work, advantages of the volatility basis set (VBS) modeling approach are exploited to develop parameters for use in the computationally-efficient and widely-used two product (2p) SOA modeling framework, standard in chemical transport models such as CMAQ (Community Multiscale Air Quality) and GEOS-Chem (Goddard Earth Observing System–Chemistry). Calculated SOA yields and mass loadings obtained using the newly-developed 2p-VBS parameters and existing 2p and VBS parameters are compared with observed yields and mass loadings from a comprehensive list of published smog chamber studies to determine a "best available" set of SOA modeling parameters. SOA and PM2.5 levels are simulated using CMAQv.4.7.1; results are compared for a base case (with default 2p CMAQ parameters) and two "best available" parameter cases chosen to illustrate the high- and low-NOx limits of biogenic SOA formation from monoterpenes. Comparisons of published smog chamber data with SOA yield predictions illustrate that: (1) SOA yields for naphthalene and cyclic and > C5 alkanes are not well represented using either newly developed (2p-VBS) or existing (2p and VBS) parameters for low-yield aromatics and lumped alkanes, respectively; and (2) for 4 of 7 volatile organic compound + oxidant systems, the 2p-VBS parameters better represent existing data. Using the "best available" parameters (combination of published 2p and newly derived 2p-VBS), predicted SOA mass and PM2.5 concentrations increase by up to 10–15% and 7%, respectively, for the high-NOx case and up to 215% (~ 3 μg m−3) and 55%, respectively, for the low-NOx case. The ability to robustly assign "best available" parameters, however, is limited due to insufficient data for photo-oxidation of diverse monoterpenes and sesquiterpenes under a variety of atmospherically relevant NOx conditions. These results are discussed in terms of implications for current chemical transport model simulations and recommendations are provided for future measurement and modeling efforts.

scholarly journals Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

Atmosphere ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 25 ◽  
Author(s):  
Scott Chambers ◽  
Elise-Andree Guérette ◽  
Khalia Monk ◽  
Alan Griffiths ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Diurnal Cycle ◽  
Transport Model ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Transport Models ◽  
Potential Health ◽  
Meteorological Model ◽  
Model Skill ◽  
Chemical Transport Models

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

scholarly journals Systematic bias in evaluating chemical transport models with maximum daily 8-hour average (MDA8) surface ozone for air quality applications

2019 ◽  
Author(s):  
Katherine R. Travis ◽  
Daniel J. Jacob
Keyword(s):  
Air Quality ◽  
Diurnal Cycle ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Systematic Bias ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Aircraft Observations ◽  
Southeast Us ◽  
Quality Policy

Abstract. Chemical transport models typically evaluate their simulation of surface ozone with observations of the maximum daily 8-hour average (MDA8) concentration, which is the standard air quality policy metric. This requires successful simulation of the surface ozone diurnal cycle including nighttime depletion, but models are generally biased high at night because of difficulty in resolving the stratified conditions near the surface. We quantify the problem with the GEOS-Chem model for the Southeast US during the NASA SEAC4RS aircraft campaign in August–September 2013. The model is unbiased relative to the daytime mixed layer aircraft observations but has a +5 ppb bias relative to MDA8 surface ozone observations. The model also does not capture observed occurrences of

scholarly journals Ensemble Forecasts of Air Quality in Eastern China – Part 1. Model Description and Implementation of the MarcoPolo-Panda Prediction System

2018 ◽  
Author(s):  
Guy P. Brasseur ◽  
Ying Xie ◽  
A. Katinka Petersen ◽  
Idir Bouarar ◽  
Johannes Flemming ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Eastern China ◽  
Model Description ◽  
Transport Models ◽  
Ensemble Forecasts ◽  
Chemical Transport Models ◽  
Systematic Biases ◽  
Forecasting System ◽  
The Mean

Abstract. An operational multi-model forecasting system for air quality including 9 different chemical transport models has been developed and is providing daily forecasts of ozone, nitrogen oxides, and particulate matter for the 37 largest urban areas of China (population higher than 3 million in 2010). These individual forecasts as well as the mean and median concentrations for the next 3 days are displayed on a publicly accessible web site (http://www.marcopolo-panda.eu). The paper describes the forecasting system and shows some selected illustrative examples of air quality predictions. It presents an inter-comparison of the different forecasts performed during a given period of time (1–15 March 2017), and highlights recurrent differences between the model output as well as systematic biases that appear in the median concentration values. Pathways to improve the forecasts by the multi-model system are suggested.

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