scholarly journals Long-term probabilistic forecast of climate impact on air quality: Model development andt* distribution

2007 ◽  
Vol 112 (D19) ◽  
Author(s):  
Shao-Hang Chu
Keyword(s):  
Air Quality ◽  
Model Development ◽  
Climate Impact ◽  
Probabilistic Forecast ◽  
Air Quality Model ◽  
Quality Model

LONG-TERM SIMULATIONS OF PARTICULATE MATTER OVER EUROPE WITH EMPHASIS ON NORTH-RHINE-WESTPHALIA WITH A COMPREHENSIVE AIR QUALITY MODEL SYSTEM

2004 ◽  
Vol 35 ◽  
pp. S767-S768
Author(s):  
S. WURZLER ◽  
J. GEIGER ◽  
U. HARTMANN ◽  
V. HOFFMANN ◽  
U. PFEFFER ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model

scholarly journals Evaluation and Error Apportionment of an Ensemble of Atmospheric Chemistry Transport Modelling Systems: Multi-variable Temporal and Spatial Breakdown

2016 ◽  
Author(s):  
Efisio Solazzo ◽  
Roberto Bianconi ◽  
Christian Hogrefe ◽  
Gabriele Curci ◽  
Ummugulsum Alyuz ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Model Comparison ◽  
Regional Scale ◽  
Model Development ◽  
Model Performance ◽  
Strong Impact ◽  
Air Quality Model ◽  
Quality Model ◽  
Transport Modelling

Abstract. Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emissions and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high inter-dependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. The error embedded in the emissions is dominant for primary species (CO, PM, NO) and largely outweighs the error from any other source. The uncertainty in meteorological fields is most relevant to ozone. Some further aspects emerged whose interpretation requires additional consideration, such as, among others, the uniformity of the synoptic error being region and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

scholarly journals Sources of organic aerosols in Europe: A modelling study using CAMx with modified volatility basis set scheme

2019 ◽  
Author(s):  
Jianhui Jiang ◽  
Sebnem Aksoyoglu ◽  
Imad El-Haddad ◽  
Giancarlo Ciarelli ◽  
Hugo A. C. Denier van der Gon ◽  
...  
Keyword(s):  
Air Quality ◽  
Source Apportionment ◽  
Biomass Burning ◽  
Basis Set ◽  
Anthropogenic Sources ◽  
Air Quality Model ◽  
Quality Model ◽  
Wood Burning ◽  
Gasoline Vehicles

Abstract. Source apportionment of organic aerosols (OA) is of great importance to better understand the health impact and climate effects of particulate matter air pollution. Air quality models act as potential tools to identify OA components and sources at high spatial and temporal resolution, however, they generally underestimate OA concentrations, and comparisons of their outputs with an extended set of measurements are still rare due to the lack of long-term experimental data. In this study, we addressed such challenges at the European level. Using the regional air quality model Comprehensive Air Quality Model with Extensions (CAMx) and a volatility basis set (VBS) scheme which was optimized based on recent chamber experiments with wood burning and diesel vehicle emissions, and contained more source-specific sets compared to previous studies, we calculated the contribution of OA components and defined their sources over a whole-year period (2011). We modelled separately the primary and secondary OA contributions from old and new diesel and gasoline vehicles, biomass burning (mostly residential wood burning and agricultural waste burning excluding wildfires), other anthropogenic sources (mainly shipping, industry and energy production) and biogenic sources. An important feature of this study is that we evaluated the model results with measurements over a longer period than in the previous studies which strengthens our confidence in our modelled source apportionment results. Comparison against positive matrix factorization (PMF) analyses of aerosol mass spectrometric measurements at nine European sites suggested that the modified VBS scheme improved the model performance for total OA as well as the OA components, including hydrocarbon-like (HOA), biomass burning (BBOA) and oxygenated components (OOA). By using the modified VBS scheme, the mean bias of OOA was reduced from −1.3 μg m−3 to −0.4 μg m−3 corresponding to a reduction of mean fractional bias from −45 % to −20 %. The winter OOA simulation, which was largely underestimated in previous studies, was improved by 29 %–42 % among the evaluated sites compared to the default parameterization. Wood burning was the dominant OA source in winter (61 %) while biogenic emissions contributed ~55 % to OA during summer in Europe on average. In both seasons, the other anthropogenic sources comprised the second largest component (9 % in winter and 19 % in summer as domain average), while the average contributions of diesel and gasoline vehicles were rather small (~5 %) except for the metropolitan areas where the highest contribution reached 31 %. The results indicate the need to improve the emission inventory to include currently missing and highly uncertain local emissions, as well as further improvement of VBS parameterization for winter biomass burning. Although this study focused on Europe, it can be applied in any other part of the globe. This study highlights the ability of long-term measurements and source apportionment modelling to validate and improve emission inventories, and identify sources not yet properly included in existing inventories.

Impact of Long Transport on Taiwan’s Ozone Air Quality from East Asia

2014 ◽  
Vol 692 ◽  
pp. 13-21 ◽  
Author(s):  
Chien Hung Chen ◽  
Ken Hui Chang ◽  
Tu Fu Chen
Keyword(s):  
Air Quality ◽  
East Asia ◽  
Ozone Pollution ◽  
Air Quality Model ◽  
Quality Model ◽  
Case Scenario ◽  
Worst Case ◽  
Transboundary Transport ◽  
Pollution Area

To clarify the influence of air pollutants emitted from East Asia on the ozone air quality in Taiwan, this study performs a long-term simulation result for 4 months using Taiwan Air Quality Model. Influence from the current (2007) and future (2020) East Asian emissions on the ozone concentration in Taiwan were simulated. The date ranges simulated were February, May, August, and October of 2007, representing the seasons of winter, spring, summer, and autumn. Influence from transboundary transport on Taiwan was assessed based on simulations of these 4 months. The influence of transboundary transport on the monthly average of daily peak ozone concentrations in Taiwan is 15.5 ppb. Worst case scenarios in 2020 will contribute an additional 3.7 ppb. If the size of ozone pollution area (≧120 ppb) is considered, transboundary transport contributed to 72 % of the polluted area in 2007; the ozone pollution area in the worst case scenario in 2020 will further increase by 47 % from 2007 levels.

scholarly journals Air Quality Modeling with WRF-Chem v3.5 in East and South Asia: sensitivity to emissions and evaluation of simulated air quality

2015 ◽  
Vol 8 (10) ◽  
pp. 9373-9413 ◽  
Author(s):  
M. Zhong ◽  
E. Saikawa ◽  
Y. Liu ◽  
V. Naik ◽  
L. W. Horowitz ◽  
...  
Keyword(s):  
Air Quality ◽  
South Asia ◽  
Model Development ◽  
Model Performance ◽  
Ground Level ◽  
Performance Criteria ◽  
Weather Research And Forecasting ◽  
Air Quality Model ◽  
Quality Model ◽  
Mixing Ratios

Abstract. We conducted simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.5 to study air quality in East and South Asia at a spatial resolution of 20 km × 20 km. We find large discrepancies between two existing emissions inventories: the Regional Emission Inventory in Asia version 2 (REAS) and the Emissions Database for Global Atmospheric Research version 4.2 (EDGAR) at the provincial level in China, with maximum differences up to 500 % for CO emissions, 190 % for NO, and 160 % for primary PM10. Such differences in the magnitude and the spatial distribution of emissions for various species lead to 40–70 % difference in surface PM10 concentrations, 16–20 % in surface O3 mixing ratios, and over 100 % in SO2 and NO2 mixing ratios in the polluted areas of China. Our sensitivity run shows WRF-Chem is sensitive to emissions, with the REAS-based simulation reproducing observed concentrations and mixing ratios better than the EDGAR-based simulation for July 2007. We conduct further model simulations using REAS emissions for January, April, July, and October in 2007 and evaluate simulations with available ground-level observations. The model results show clear regional variations in the seasonal cycle of surface PM10 and O3 over East and South Asia. The model meets the air quality model performance criteria for both PM10 (mean fractional bias, MFB ≤ ± 60 %) and O3 (MFB ≤ ± 15 %) in most of the observation sites, although the model underestimates PM10 over Northeast China in January. The model predicts the observed SO2 well at sites in Japan, while it tends to overestimate SO2 in China in July and October. The model underestimates most observed NO2 in all four months. These findings suggest that future model development and evaluation of emission inventories and models are needed for particulate matter and gaseous pollutants in East and South Asia.

Use of a Dynamic Air Quality Model to Reduce the Uncertainties of Seasonal and Annual Atmospheric Depositions for Mercury TMDLs

2005 ◽  
Vol 2005 (3) ◽  
pp. 1393-1414
Author(s):  
Kuo-Liang Lai ◽  
Janet Kremer ◽  
Susan Sciarratta ◽  
R. Dwight Atkinson ◽  
Tom Myers
Keyword(s):  
Air Quality ◽  
Air Quality Model ◽  
Quality Model ◽  
Atmospheric Depositions
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