Data assimilation of surface air pollutants (O3 and NO2) in the regional-scale air quality model AURORA

2012 ◽  
Vol 60 ◽  
pp. 99-108 ◽  
Author(s):  
Ujjwal Kumar ◽  
Koen De Ridder ◽  
Wouter Lefebvre ◽  
Stijn Janssen
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Air Pollutants ◽  
Regional Scale ◽  
Air Quality Model ◽  
Quality Model

scholarly journals A database and tool for boundary conditions for regional air quality modeling: description and evaluation

2014 ◽  
Vol 7 (1) ◽  
pp. 339-360 ◽  
Author(s):  
B. H. Henderson ◽  
F. Akhtar ◽  
H. O. T. Pye ◽  
S. L. Napelenok ◽  
W. T. Hutzell
Keyword(s):  
Carbon Monoxide ◽  
Air Quality ◽  
Boundary Conditions ◽  
Air Pollutants ◽  
Regional Scale ◽  
Air Quality Model ◽  
Ozone Monitoring Instrument ◽  
Quality Model ◽  
Uncertainty Estimates ◽  
Regional Air Quality

Abstract. Transported air pollutants receive increasing attention as regulations tighten and global concentrations increase. The need to represent international transport in regional air quality assessments requires improved representation of boundary concentrations. Currently available observations are too sparse vertically to provide boundary information, particularly for ozone precursors, but global simulations can be used to generate spatially and temporally varying lateral boundary conditions (LBC). This study presents a public database of global simulations designed and evaluated for use as LBC for air quality models (AQMs). The database covers the contiguous United States (CONUS) for the years 2001–2010 and contains hourly varying concentrations of ozone, aerosols, and their precursors. The database is complemented by a tool for configuring the global results as inputs to regional scale models (e.g., Community Multiscale Air Quality or Comprehensive Air quality Model with extensions). This study also presents an example application based on the CONUS domain, which is evaluated against satellite retrieved ozone and carbon monoxide vertical profiles. The results show performance is largely within uncertainty estimates for ozone from the Ozone Monitoring Instrument and carbon monoxide from the Measurements Of Pollution In The Troposphere (MOPITT), but there were some notable biases compared with Tropospheric Emission Spectrometer (TES) ozone. Compared with TES, our ozone predictions are high-biased in the upper troposphere, particularly in the south during January. This publication documents the global simulation database, the tool for conversion to LBC, and the evaluation of concentrations on the boundaries. This documentation is intended to support applications that require representation of long-range transport of air pollutants.

scholarly journals A database and tool for boundary conditions for regional air quality modeling: description and evaluation

2013 ◽  
Vol 6 (3) ◽  
pp. 4665-4704 ◽  
Author(s):  
B. H. Henderson ◽  
F. Akhtar ◽  
H. O. T. Pye ◽  
S. L. Napelenok ◽  
W. T. Hutzell
Keyword(s):  
Air Quality ◽  
Boundary Conditions ◽  
Air Pollutants ◽  
Regional Scale ◽  
Air Quality Model ◽  
Quality Model ◽  
Southern Boundary ◽  
Uncertainty Estimates ◽  
International Transport ◽  
Regional Air Quality

Abstract. Transported air pollutants receive increasing attention as regulations tighten and global concentrations increase. The need to represent international transport in regional air quality assessments requires improved representation of boundary concentrations. Currently available observations are too sparse vertically to provide boundary information, particularly for ozone precursors, but global simulations can be used to generate spatially and temporally varying Lateral Boundary Conditions (LBC). This study presents a public database of global simulations designed and evaluated for use as LBC for air quality models (AQMs). The database covers the contiguous United States (CONUS) for the years 2000–2010 and contains hourly varying concentrations of ozone, aerosols, and their precursors. The database is complimented by a tool for configuring the global results as inputs to regional scale models (e.g., Community Multiscale Air Quality or Comprehensive Air quality Model with extensions). This study also presents an example application based on the CONUS domain, which is evaluated against satellite retrieved ozone vertical profiles. The results show performance is largely within uncertainty estimates for the Tropospheric Emission Spectrometer (TES) with some exceptions. The major difference shows a high bias in the upper troposphere along the southern boundary in January. This publication documents the global simulation database, the tool for conversion to LBC, and the fidelity of concentrations on the boundaries. This documentation is intended to support applications that require representation of long-range transport of air pollutants.

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 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.

Networks of Air Quality Sensors and Their Use for High-resolution Mapping of Urban Air Quality

2020 ◽  
Author(s):  
Philipp Schneider ◽  
Nuria Castell ◽  
Paul Hamer ◽  
Sam-Erik Walker ◽  
Alena Bartonova
Keyword(s):  
Sensor Networks ◽  
Air Quality ◽  
High Resolution ◽  
Air Pollutants ◽  
Low Cost ◽  
Urban Air Quality ◽  
Urban Air ◽  
Sensor Systems ◽  
Air Quality Model ◽  
Quality Model

<p>One of the most promising applications of low-cost sensor systems for air quality is the possibility to deploy them in relatively dense networks and to use this information for mapping urban air quality at unprecedented spatial detail. More and more such dense sensor networks are being set up worldwide, particularly for relatively inexpensive nephelometers that provide PM<sub>2.5</sub> observations with often quite reasonable accuracy. However, air pollutants typically exhibit significant spatial variability in urban areas, so using data from sensor networks alone tends to result in maps with unrealistic spatial patterns, unless the network density is extremely high. One solution is to use the output from an air quality model as an a priori field and as such to use the combined knowledge of both model and sensor network to provide improved maps of urban air quality. Here we present our latest work on combining the observations from low-cost sensor systems with data from urban-scale air quality models, with the goal of providing realistic, high-resolution, and up-to-date maps of urban air quality.</p><p>In previous years we have used a geostatistical approach for mapping air quality (Schneider et al., 2017), exploiting both low-cost sensors and model information. The system has now been upgraded to a data assimilation approach that integrates the observations from a heterogeneous sensor network into an urban-scale air quality model while considering the sensor-specific uncertainties. The approach further ensures that the spatial representativity of each observation is automatically derived as a combination of a model climatology and a function of distance. We demonstrate the methodology using examples from Oslo and other cities in Norway. Initial results indicate that the method is robust and provides realistic spatial patterns of air quality for the main air pollutants that were evaluated, even in areas where only limited observations are available. Conversely, the model output is constrained by the sensor data, thus adding value to both input datasets.</p><p>While several challenging issues remain, modern air quality sensor systems have reached a maturity level at which some of them can provide an intra-sensor consistency and robustness that makes it feasible to use networks of such systems as a data source for mapping urban air quality at high spatial resolution. We present our current approach for mapping urban air quality with the help of low-cost sensor networks and demonstrate both that it can provide realistic results and that the uncertainty of each individual sensor system can be taken into account in a robust and meaningful manner.</p><p> </p><p>Schneider, P., Castell N., Vogt M., Dauge F. R., Lahoz W. A., and Bartonova A., 2017. Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment international, 106, 234-247.</p>

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 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 Evaluation of gas-particle partitioning in a regional air quality model for organic pollutants

2016 ◽  
Vol 16 (23) ◽  
pp. 15327-15345 ◽  
Author(s):  
Christos I. Efstathiou ◽  
Jana Matejovičová ◽  
Johannes Bieser ◽  
Gerhard Lammel
Keyword(s):  
Air Quality ◽  
Organic Pollutants ◽  
Heterogeneous Reaction ◽  
Regional Scale ◽  
Fate And Transport ◽  
Heterogeneous Reactions ◽  
Absorption Model ◽  
Air Quality Model ◽  
Adsorption Model ◽  
Quality Model

Abstract. Persistent organic pollutants (POPs) are of considerable concern due to their well-recognized toxicity and their potential to bioaccumulate and engage in long-range transport. These compounds are semi-volatile and, therefore, create a partition between vapour and condensed phases in the atmosphere, while both phases can undergo chemical reactions. This work describes the extension of the Community Multiscale Air Quality (CMAQ) modelling system to POPs with a focus on establishing an adaptable framework that accounts for gaseous chemistry, heterogeneous reactions, and gas-particle partitioning (GPP). The effect of GPP is assessed by implementing a set of independent parameterizations within the CMAQ aerosol module, including the Junge–Pankow (JP) adsorption model, the Harner–Bidleman (HB) organic matter (OM) absorption model, and the dual Dachs–Eisenreich (DE) black carbon (BC) adsorption and OM absorption model. Use of these descriptors in a modified version of CMAQ for benzo[a]pyrene (BaP) results in different fate and transport patterns as demonstrated by regional-scale simulations performed for a European domain during 2006. The dual DE model predicted 24.1 % higher average domain concentrations compared to the HB model, which was in turn predicting 119.2 % higher levels compared to the baseline JP model. Evaluation with measurements from the European Monitoring and Evaluation Programme (EMEP) reveals the capability of the more extensive DE model to better capture the ambient levels and seasonal behaviour of BaP. It is found that the heterogeneous reaction of BaP with O3 may decrease its atmospheric lifetime by 25.2 % (domain and annual average) and near-ground concentrations by 18.8 %. Marginally better model performance was found for one of the six EMEP stations (Košetice) when heterogeneous BaP reactivity was included. Further analysis shows that, for the rest of the EMEP locations, the model continues to underestimate BaP levels, an observation that can be attributed to low emission estimates for such remote areas. These findings suggest that, when modelling the fate and transport of organic pollutants on large spatio-temporal scales, the selection and parameterization of GPP can be as important as degradation (reactivity).

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