Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution

2016 ◽  
Vol 50 (7) ◽  
pp. 3695-3705 ◽  
Author(s):  
Mariel D. Friberg ◽  
Xinxin Zhai ◽  
Heather A. Holmes ◽  
Howard H. Chang ◽  
Matthew J. Strickland ◽  
...  
Keyword(s):  
Air Pollution ◽  
Observational Data ◽  
Transport Model ◽  
Chemical Transport ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Chemical Transport Model ◽  
Model Simulations

scholarly journals Record-breaking ozone loss in the Arctic winter 2010/2011: comparison with 1996/1997

2012 ◽  
Vol 12 (15) ◽  
pp. 7073-7085 ◽  
Author(s):  
J. Kuttippurath ◽  
S. Godin-Beekmann ◽  
F. Lefèvre ◽  
G. Nikulin ◽  
M. L. Santee ◽  
...  
Keyword(s):  
Transport Model ◽  
Chemical Transport ◽  
The Arctic ◽  
Altitude Range ◽  
Chemical Transport Model ◽  
Model Simulations ◽  
Ozone Loss ◽  
Elevated Ozone ◽  
Record Value ◽  
First Time

Abstract. We present a detailed discussion of the chemical and dynamical processes in the Arctic winters 1996/1997 and 2010/2011 with high resolution chemical transport model (CTM) simulations and space-based observations. In the Arctic winter 2010/2011, the lower stratospheric minimum temperatures were below 195 K for a record period of time, from December to mid-April, and a strong and stable vortex was present during that period. Simulations with the Mimosa-Chim CTM show that the chemical ozone loss started in early January and progressed slowly to 1 ppmv (parts per million by volume) by late February. The loss intensified by early March and reached a record maximum of ~2.4 ppmv in the late March–early April period over a broad altitude range of 450–550 K. This coincides with elevated ozone loss rates of 2–4 ppbv sh−1 (parts per billion by volume/sunlit hour) and a contribution of about 30–55% and 30–35% from the ClO-ClO and ClO-BrO cycles, respectively, in late February and March. In addition, a contribution of 30–50% from the HOx cycle is also estimated in April. We also estimate a loss of about 0.7–1.2 ppmv contributed (75%) by the NOx cycle at 550–700 K. The ozone loss estimated in the partial column range of 350–550 K exhibits a record value of ~148 DU (Dobson Unit). This is the largest ozone loss ever estimated in the Arctic and is consistent with the remarkable chlorine activation and strong denitrification (40–50%) during the winter, as the modeled ClO shows ~1.8 ppbv in early January and ~1 ppbv in March at 450–550 K. These model results are in excellent agreement with those found from the Aura Microwave Limb Sounder observations. Our analyses also show that the ozone loss in 2010/2011 is close to that found in some Antarctic winters, for the first time in the observed history. Though the winter 1996/1997 was also very cold in March–April, the temperatures were higher in December–February, and, therefore, chlorine activation was moderate and ozone loss was average with about 1.2 ppmv at 475–550 K or 42 DU at 350–550 K, as diagnosed from the model simulations and measurements.

scholarly journals Solar response in tropical stratospheric ozone: a 3-D chemical transport model study using ERA reanalyses

2011 ◽  
Vol 11 (24) ◽  
pp. 12773-12786 ◽  
Author(s):  
S. Dhomse ◽  
M. P. Chipperfield ◽  
W. Feng ◽  
J. D. Haigh
Keyword(s):  
Standard Model ◽  
Transport Model ◽  
Stratospheric Ozone ◽  
Chemical Transport ◽  
Stratospheric Aerosol ◽  
Data Sets ◽  
Chemical Transport Model ◽  
Model Simulations ◽  
Model Study ◽  
Significant Difference

Abstract. We have used an off-line 3-D chemical transport model (CTM) to investigate the 11-yr solar cycle response in tropical stratospheric ozone. The model is forced with European Centre for Medium-Range Weather Forecasts (ECMWF) (re)analysis (ERA-40/operational and ERA-Interim) data for the 1979–2005 time period. We have compared the modelled solar response in ozone to observation-based data sets that are constructed using satellite instruments such as Total Ozone Mapping Spectrometer (TOMS), Solar Backscatter UltraViolet instrument (SBUV), Stratospheric Aerosol and Gas Experiment (SAGE) and Halogen Occultation Experiment (HALOE). A significant difference is seen between simulated and observed ozone during the 1980s, which is probably due to inhomogeneities in the ERA-40 reanalyses. In general, the model with ERA-Interim dynamics shows better agreement with the observations from 1990 onwards than with ERA-40. Overall both standard model simulations are partially able to simulate a "double peak"-structured ozone solar response with a minimum around 30 km, and these are in better agreement with HALOE than SAGE-corrected SBUV (SBUV/SAGE) or SAGE-based data sets. In the tropical lower stratosphere (TLS), the modelled solar response with time-varying aerosols is amplified through aliasing with a volcanic signal, as the model overestimates ozone loss during high aerosol loading years. However, the modelled solar response with fixed dynamics and constant aerosols shows a positive signal which is in better agreement with SBUV/SAGE and SAGE-based data sets in the TLS. Our model simulations suggests that photochemistry contributes to the ozone solar response in this region. The largest model-observation differences occur in the upper stratosphere where SBUV/SAGE and SAGE-based data show a significant (up to 4%) solar response whereas the standard model and HALOE do not. This is partly due to a positive solar response in the ECMWF upper stratospheric temperatures which reduces the modelled ozone signal. The large positive upper stratospheric solar response seen in SBUV/SAGE and SAGE-based data can be reproduced in model runs with fixed dynamical fields (i.e. no inter-annual meteorological changes). As these runs effectively assume no long-term temperature changes (solar-induced or otherwise), it should provide an upper limit of the ozone solar response. Overall, full quantification of the solar response in stratospheric ozone is limited by differences in the observed data sets and by uncertainties in the solar response in stratospheric temperatures.

Using Fused Chemical Transport Models to Estimate Spatially and Temporally Resolved Ambient Air Pollution in Georgia and North Carolina

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Ted Russel ◽  
Ran Huang ◽  
Niru Senthilkumart ◽  
James Mulholland ◽  
Matthew Strickland ◽  
...  
Keyword(s):  
Air Pollution ◽  
North Carolina ◽  
Chemical Transport ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Transport Models ◽  
Chemical Transport Models

scholarly journals Nitrate transboundary heavy pollution over East Asia in winter

2017 ◽  
Vol 17 (6) ◽  
pp. 3823-3843 ◽  
Author(s):  
Syuichi Itahashi ◽  
Itsushi Uno ◽  
Kazuo Osada ◽  
Yusuke Kamiguchi ◽  
Shigekazu Yamamoto ◽  
...  
Keyword(s):  
Air Pollution ◽  
East Asia ◽  
Transport Model ◽  
Chemical Transport ◽  
First Episode ◽  
Chemical Transport Model ◽  
High Concentration ◽  
Air Mass ◽  
Transboundary Air Pollution ◽  
Western Japan

Abstract. High PM2. 5 concentrations of around 100 µg m−3 were observed twice during an intensive observation campaign in January 2015 at Fukuoka (33.52° N, 130.47° E) in western Japan. These events were analyzed comprehensively with a regional chemical transport model and synergetic ground-based observations with state-of-the-art measurement systems, which can capture the behavior of secondary inorganic aerosols (SO42−, NO3−, and NH4+). The first episode of high PM2. 5 concentration was dominated by NO3− (type N) and the second episode by SO42− (type S). The concentration of NH4+ (the counterion for SO42− and NO3−) was high for both types. A sensitivity simulation in the chemical transport model showed that the dominant contribution was from transboundary air pollution for both types. To investigate the differences between these types further, the chemical transport model results were examined, and a backward trajectory analysis was used to provide additional information. During both types of episodes, high concentrations of NO3− were found above China, and an air mass that originated from northeast China reached Fukuoka. The travel time from the coastline of China to Fukuoka differed between types: it was 18 h for type N and 24 h for type S. The conversion ratio of SO2 to SO42− (Fs) was less than 0.1 for type N, but reached 0.3 for type S as the air mass approached Fukuoka. The higher Fs for type S was related to the higher relative humidity and the concentration of HO2, which produces H2O2, the most effective oxidant for the aqueous-phase production of SO42−. Analyzing the gas ratio as an indicator of the sensitivity of NO3− to changes in SO42− and NH4+ showed that the air mass over China was NH3-rich for type N, but almost NH3-neutral for type S. Thus, although the high concentration of NO3− above China gradually decreased during transport from China to Fukuoka, higher NO3− concentrations were maintained during transport owing to the lower SO42− for type N. In contrast, for type S, the production of SO42− led to the decomposition of NH4NO3, and more SO42− was transported. Notably, the type N transport pattern was limited to western Japan, especially the island of Kyushu. Transboundary air pollution dominated by SO42− (type S) has been recognized as a major pattern of pollution over East Asia. However, our study confirms the importance of transboundary air pollution dominated by NO3−, which will help refine our understanding of transboundary heavy PM2. 5 pollution in winter over East Asia.

Relative effects of open biomass and crop straw burning on haze formation over central and eastern China: modelling study driven by constrained emissions

2020 ◽  
Author(s):  
Pengfei Li ◽  
Shaocai Yu ◽  
Yujie Wu ◽  
khalid Mehmood ◽  
Liqiang Wang ◽  
...  
Keyword(s):  
High Pressure ◽  
Transport Model ◽  
Eastern China ◽  
Chemical Transport ◽  
Ambient Air ◽  
Chemical Transport Model ◽  
Pressure System ◽  
Crop Straw ◽  
Satellite Retrievals ◽  
Straw Burning

<p><span>Open biomass burning (OBB) has large potential in triggering local and regional severe haze with elevated fine particulate matter (PM<sub>2.5</sub>) concentrations and could thus deteriorate ambient air quality and threaten human health. Open crop straw burning (OCSB), as a critical part of OBB, emits abundant gaseous and particulate pollutants, especially in fields with intensive agriculture, such as central and eastern China (CEC).  However, uncertainties in current OCSB and other types of OBB emissions in </span><span>chemical transport models (CTMs) lead to inaccuracies in evaluating their impacts on haze formations. Satellite retrievals provide </span><span>an alternative that can be used to simultaneously quantify emissions of </span><span>OCSB and other types of OBB, such as </span><span>the Fire INventory from NCAR version 1.5 (FINNv1.5), which, nevertheless, generally underestimate their magnitudes due to unresolved small fires. In this study, we selected June in 2014 as our study period, which exhibited a complete evolution process of OBB (from June 1 to 19) over CEC. During this period, OBB was dominated by OCSB in terms of the number of fire hotspot and associated emissions, most of which were located at Henan and Anhui with intensive enhancements from June 5 to 14. OCSB generally exhibits spatiotemporal correlation with regional haze over the central part of CEC (Henan, Anhui, Hubei, and Hunan), while other types of OBB emissions had influences on Jiangxi, Zhejiang, and Fujian. Based on these analyses, we establish a constraining method that integrates </span><span>ground-level PM<sub>2.5</sub> measurements with </span><span>a state-of-art fully coupled regional meteorological and chemical transport model (the two-way coupled WRF-CMAQ) in order to derive optimal OBB emissions based on FINNv1.5. It is demonstrated that these emissions allow the model to reproduce meteorological and chemical fields over CEC during the study period, whereas the original FINNv1.5 underestimated OBB emissions by 2 ~ 7 times, depending on specific spatiotemporal scales. The results show that OBB had substantial impacts on surface PM<sub>2.5</sub> concentrations over CEC. Most of the OBB contributions were dominated by OCSB, especially in Henan, Anhui, Hubei, and Hunan, while other types of OBB emissions also exerted influence in Jiangxi, Zhejiang, and Fujian. With the </span><span>concentration-weighted trajectory (CWT) method, potential OCSB sources leading to severe haze in Henan, Anhui, Hubei, and Hunan were pinpointed. The results show that the OCSB emissions in Henan and Anhui can cause haze not only locally but also regionally through regional transport. </span><span>Combining with meteorological analyses, we can find that surface weather patterns played a cardinal role in reshaping spatial and temporal characteristics of PM<sub>2.5</sub> concentrations. Stationary high-pressure systems over CEC enhanced local PM<sub>2.5</sub> concentrations in Henan and Anhui. Then, with the evolution of meteorological patterns, Hubei and Hunan in the low-pressure system were impacted by areas enveloped in the high-pressure system. These results suggest that policymakers should strictly undertake interprovincial joint enforcement actions to prohibit irregular OBB, especially OCSB over CEC. Constrained OBB emissions can, to a large extent, supplement estimations derived from satellite retrievals as well as reduce overestimates of bottom-up methods.</span></p>

Simulation of spread of air pollution plumes from forest fires with the use of COSMO-Ru7-ART chemical-transport model

2014 ◽  
Vol 27 (3) ◽  
pp. 268-274 ◽  
Author(s):  
G. V. Surkova ◽  
D. V. Blinov ◽  
A. A. Kirsanov ◽  
A. P. Revokatova ◽  
G. S. Rivin
Keyword(s):  
Air Pollution ◽  
Forest Fires ◽  
Transport Model ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Pollution Plumes

scholarly journals Production of lightning NOxand its vertical distribution calculated from three-dimensional cloud-scale chemical transport model simulations

2010 ◽  
Vol 115 (D4) ◽  
Author(s):  
Lesley E. Ott ◽  
Kenneth E. Pickering ◽  
Georgiy L. Stenchikov ◽  
Dale J. Allen ◽  
Alex J. DeCaria ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Transport Model ◽  
Three Dimensional ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Model Simulations
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