scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

scholarly journals Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

2017 ◽  
Vol 17 (21) ◽  
pp. 13103-13118 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Health Effect ◽  
Model Performance ◽  
Performance Criteria ◽  
Ensemble Prediction ◽  
Daily Maximum ◽  
Observation Data ◽  
Emission Inventories

Abstract. Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

Air Quality Forecast Model Based on Model Products of CMAQ in Fuzhou City, China

2012 ◽  
Vol 518-523 ◽  
pp. 1442-1450
Author(s):  
Bin Bin Chen ◽  
Kai Yang ◽  
Chang Cheng Lin ◽  
Wen Lin ◽  
Hong Wang ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Forecast Model ◽  
Observation Data ◽  
Air Quality Model ◽  
Quality Model ◽  
Forecasting Accuracy ◽  
Air Quality Forecast ◽  
Index Level ◽  
Daily Forecast

Based on the forecast products of Community Multiscale Air Quality Model (CMAQ), observational data of air pollutants and the conventional meteorological observation data of surface from January 2007 to June 2010 in Fuzhou City, Fujian Province, China, the weather systems influencing on Fuzhou City are divided into 7 weather patterns, including continental high, subtropical high, shear, warm sectors convergence, upper trough, typhoon and tropical convergence. According to the forecast method of combination of dynamic and statistic, using multiple linear stepwise regression method, the forecasting models of daily pollutant concentration under different weather systems are established. The confidence level of the equations achieves 0.000 and so the models are statistically significant. Using the models, the prediction effect of the concentration of different air pollutants in Fuzhou City from July to December, 2010 is tested by back substitution. The forecasting accuracy on contamination index level of PM10 reaches to 71.3%, and the forecasting accuracy on SO2和NO2 both reaches to 100%. The comprehensive score of air quality daily forecast in the city comes to 88.8 points on average.

scholarly journals Climate-forced air-quality modeling at the urban scale: sensitivity to model resolution, emissions and meteorology

2015 ◽  
Vol 15 (13) ◽  
pp. 7703-7723 ◽  
Author(s):  
K. Markakis ◽  
M. Valari ◽  
O. Perrussel ◽  
O. Sanchez ◽  
C. Honore
Keyword(s):  
Air Quality ◽  
Emission Inventory ◽  
Horizontal Resolution ◽  
Anthropogenic Emissions ◽  
Vertical Resolution ◽  
Post Processing ◽  
Air Quality Model ◽  
Quality Model ◽  
Quality Modeling ◽  
Forced Air

Abstract. While previous research helped to identify and prioritize the sources of error in air-quality modeling due to anthropogenic emissions and spatial scale effects, our knowledge is limited on how these uncertainties affect climate-forced air-quality assessments. Using as reference a 10-year model simulation over the greater Paris (France) area at 4 km resolution and anthropogenic emissions from a 1 km resolution bottom-up inventory, through several tests we estimate the sensitivity of modeled ozone and PM2.5 concentrations to different potentially influential factors with a particular interest over the urban areas. These factors include the model horizontal and vertical resolution, the meteorological input from a climate model and its resolution, the use of a top-down emission inventory, the resolution of the emissions input and the post-processing coefficients used to derive the temporal, vertical and chemical split of emissions. We show that urban ozone displays moderate sensitivity to the resolution of emissions (~ 8 %), the post-processing method (6.5 %) and the horizontal resolution of the air-quality model (~ 5 %), while annual PM2.5 levels are particularly sensitive to changes in their primary emissions (~ 32 %) and the resolution of the emission inventory (~ 24 %). The air-quality model horizontal and vertical resolution have little effect on model predictions for the specific study domain. In the case of modeled ozone concentrations, the implementation of refined input data results in a consistent decrease (from 2.5 up to 8.3 %), mainly due to inhibition of the titration rate by nitrogen oxides. Such consistency is not observed for PM2.5. In contrast this consistency is not observed for PM2.5. In addition we use the results of these sensitivities to explain and quantify the discrepancy between a coarse (~ 50 km) and a fine (4 km) resolution simulation over the urban area. We show that the ozone bias of the coarse run (+9 ppb) is reduced by ~ 40 % by adopting a higher resolution emission inventory, by 25 % by using a post-processing technique based on the local inventory (same improvement is obtained by increasing model horizontal resolution) and by 10 % by adopting the annual emission totals of the local inventory. The bias of PM2.5 concentrations follows a more complex pattern, with the positive values associated with the coarse run (+3.6 μg m−3), increasing or decreasing depending on the type of the refinement. We conclude that in the case of fine particles, the coarse simulation cannot selectively incorporate local-scale features in order to reduce its error.

Resampling and extreme value statistics in air quality model performance evaluation

1985 ◽  
Vol 19 (9) ◽  
pp. 1503-1518 ◽  
Author(s):  
S.T. Rao ◽  
G. Sistla ◽  
V. Pagnotti ◽  
W.B. Petersen ◽  
J.S. Irwin ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Air Quality ◽  
Model Performance ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Air Quality Model ◽  
Quality Model ◽  
Model Performance Evaluation

scholarly journals Evaluation of European air quality modelled by CAMx including the volatility basis set scheme

2016 ◽  
Vol 16 (16) ◽  
pp. 10313-10332 ◽  
Author(s):  
Giancarlo Ciarelli ◽  
Sebnem Aksoyoglu ◽  
Monica Crippa ◽  
Jose-Luis Jimenez ◽  
Eriko Nemitz ◽  
...  
Keyword(s):  
Air Quality ◽  
Measurement Data ◽  
Model Performance ◽  
Monitoring And Evaluation ◽  
Basis Set ◽  
Air Quality Model ◽  
Quality Model ◽  
Aerosol Mass ◽  
Sensitivity Tests ◽  
Compensation Of Errors

Abstract. Four periods of EMEP (European Monitoring and Evaluation Programme) intensive measurement campaigns (June 2006, January 2007, September–October 2008 and February–March 2009) were modelled using the regional air quality model CAMx with VBS (volatility basis set) approach for the first time in Europe within the framework of the EURODELTA-III model intercomparison exercise. More detailed analysis and sensitivity tests were performed for the period of February–March 2009 and June 2006 to investigate the uncertainties in emissions as well as to improve the modelling of organic aerosol (OA). Model performance for selected gas phase species and PM2.5 was evaluated using the European air quality database AirBase. Sulfur dioxide (SO2) and ozone (O3) were found to be overestimated for all the four periods, with O3 having the largest mean bias during June 2006 and January–February 2007 periods (8.9 pbb and 12.3 ppb mean biases respectively). In contrast, nitrogen dioxide (NO2) and carbon monoxide (CO) were found to be underestimated for all the four periods. CAMx reproduced both total concentrations and monthly variations of PM2.5 for all the four periods with average biases ranging from −2.1 to 1.0 µg m−3. Comparisons with AMS (aerosol mass spectrometer) measurements at different sites in Europe during February–March 2009 showed that in general the model overpredicts the inorganic aerosol fraction and underpredicts the organic one, such that the good agreement for PM2.5 is partly due to compensation of errors. The effect of the choice of VBS scheme on OA was investigated as well. Two sensitivity tests with volatility distributions based on previous chamber and ambient measurements data were performed. For February–March 2009 the chamber case reduced the total OA concentrations by about 42 % on average. In contrast, a test based on ambient measurement data increased OA concentrations by about 42 % for the same period bringing model and observations into better agreement. Comparison with the AMS data at the rural Swiss site Payerne in June 2006 shows no significant improvement in modelled OA concentration. Further sensitivity tests with increased biogenic and anthropogenic emissions suggest that OA in Payerne was affected by changes in emissions from residential heating during the February–March 2009 whereas it was more sensitive to biogenic precursors in June 2006.

scholarly journals Forming High Ozone Concentration in the Ambient Air of Southern Taiwan under the Effects of Western Pacific Subtropical High

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Kuo-Cheng Lo ◽  
Chung-Hsuang Hung
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Model Simulation ◽  
Western Pacific Subtropical High ◽  
Ambient Air ◽  
Western Pacific ◽  
Subtropical High ◽  
Air Quality Model ◽  
Quality Model ◽  
Southern Taiwan

Due to the distinct geographical and meteorological conditions of Taiwan, air pollutants concentrations in the ambient air of it may vary with seasons. Accordingly, this study aimed to investigate the formation of high O3concentration in the ambient air of Southern Taiwan during summers. A high O3concentration case occurring between June 28 and July 2, 2013, was modeled and analyzed with WRF-Chem meteorological and air quality model. During the investigated period, a typical western Pacific subtropical high (WPSH) covered most East Asia, including Taiwan and its surrounding areas. The observations showed strong correlations between WPSH invasion and forming high O3concentrations. The dispersion of air pollutants in the ambient air is not sufficient to dilute their concentrations. In the afternoon of June 30, more than 60% of the air quality monitoring stations found O3concentrations exceeding 100 ppb, which were 2~3 times higher than their normal concentrations. Model simulation results verified that the presence of the WPSH hindered the dilution and transportation of air pollutants in ambient air. In addition, the air quality would be getting worse due to the leeward sides caused by the counter clockwise vertex formed in Southwestern Taiwan.

scholarly journals High Resolution Air Quality Forecasting over Prague within the URBI PRAGENSI Project: Model Performance during the Winter Period and the Effect of Urban Parameterization on PM

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 625 ◽  
Author(s):  
Jana Ďoubalová ◽  
Peter Huszár ◽  
Kryštof Eben ◽  
Nina Benešová ◽  
Michal Belda ◽  
...  
Keyword(s):  
Air Quality ◽  
Model Performance ◽  
Urban Canopy ◽  
Urban Environments ◽  
Winter Period ◽  
Air Quality Model ◽  
Quality Model ◽  
Boundary Layer Height ◽  
Project Model ◽  
Set Up

The overall impact of urban environments on the atmosphere is the result of many different nonlinear processes, and their reproduction requires complex modeling approaches. The parameterization of these processes in the models can have large impacts on the model outputs. In this study, the evaluation of a WRF/Comprehensive Air Quality Model with Extensions (CAMx) forecast modeling system set up for Prague, the Czech Republic, within the project URBI PRAGENSI is presented. To assess the impacts of urban parameterization in WRF, in this case with the BEP+BEM (Building Environment Parameterization linked to Building Energy Model) urban canopy scheme, on Particulate Matter (PM) simulations, a simulation was performed for a winter pollution episode and compared to a non-urbanized run with BULK treatment. The urbanized scheme led to an average increase in temperature at 2 m by 2 ∘ C, a decrease in wind speed by 0.5 m s − 1 , a decrease in relative humidity by 5%, and an increase in planetary boundary layer height by 100 m. Based on the evaluation against observations, the overall model error was reduced. These impacts were propagated to the modeled PM concentrations, reducing them on average by 15–30 μ g m − 3 and 10–15 μ g m − 3 for PM 10 and PM 2.5 , respectively. In general, the urban parameterization led to a larger underestimation of the PM values, but yielded a better representation of the diurnal variations.

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