Evaluation of aromatic oxidation reactions in seven chemical mechanisms with an outdoor chamber

2013 ◽  
Vol 10 (3) ◽  
pp. 245 ◽  
Author(s):  
Harshal M. Parikh ◽  
Harvey E. Jeffries ◽  
Ken G. Sexton ◽  
Deborah J. Luecken ◽  
Richard M. Kamens ◽  
...  
Keyword(s):  
Air Quality ◽  
Aromatic Ring ◽  
Aromatic Hydrocarbons ◽  
Urban Areas ◽  
Ring Opening ◽  
Chamber Wall ◽  
Oxidation Reactions ◽  
Quality Models ◽  
Chemical Mechanisms ◽  
Wide Range

Environmental context Regulatory air quality models used to develop strategies to reduce ozone and other pollutants must be able to accurately predict ozone produced from aromatic hydrocarbons. In urban areas, major sources of aromatic hydrocarbons are gasoline and diesel-powered vehicles. Our findings show that the representation of aromatic hydrocarbon chemistry in air quality models is an area of high uncertainty Abstract Simulations using seven chemical mechanisms are intercompared against O3, NOx and hydrocarbon data from photooxidation experiments conducted at the University of North Carolina outdoor smog chamber. The mechanisms include CB4–2002, CB05, CB05-TU, a CB05 variant with semi-explicit aromatic chemistry (CB05RMK), SAPRC07, CS07 and MCMv3.1. The experiments include aromatics, unsaturated dicarbonyls and volatile organic compound (VOC) mixtures representing a wide range of urban environments with relevant hydrocarbon species. In chamber simulations the sunlight is characterised using new solar radiation modelling software. A new heterogeneous chamber wall mechanism is also presented with revised chamber wall chemical processes. Simulations from all mechanisms, except MCMv3.1, show median peak O3 concentration relative errors of less than 25% for both aromatic and VOC mixture experiments. Although MCMv3.1 largely overpredicts peak O3 levels, it performs relatively better in predicting the peak NO2 concentration. For aromatic experiments, all mechanisms except CB4–2002, largely underpredict the NO–NO2 crossover time and over-predict both the absolute NO degradation slope and the slope of NO2 concentration rise. This suggests a major problem of a faster and earlier NO to NO2 oxidation rate across all the newer mechanisms. Results from individual aromatic and unsaturated dicarbonyl experiments illustrate the unique photooxidation chemistry and O3 production of several aromatic ring-opening products. The representation of these products as a single mechanism species in CB4–2002, CB05 and CB05-TU is not adequate to capture the O3 temporal profile. In summary, future updates to chemical mechanisms should focus on the chemistry of aromatic ring-opening products.

scholarly journals The impact of different nitrous acid sources in the air quality levels of the Iberian Peninsula

2010 ◽  
Vol 10 (11) ◽  
pp. 28183-28230 ◽  
Author(s):  
M. Gonçalves ◽  
D. Dabdub ◽  
W. L. Chang ◽  
F. Saiz ◽  
O. Jorba ◽  
...  
Keyword(s):  
Air Quality ◽  
Iberian Peninsula ◽  
Gas Phase ◽  
Urban Areas ◽  
Nitrous Acid ◽  
Quality Models ◽  
Chemical Mechanisms ◽  
Gas Phase Chemistry ◽  
Secondary Pollutants ◽  
Phase Chemistry

Abstract. Hydroxyl radical (OH) is a primary oxidant in the atmosphere and affects both gas-phase pollutants and particulate matter levels. Nitrous acid (HONO) acts as an important source of OH in the urban atmosphere. Therefore it is important to account accurately for HONO sources within air quality models in order to predict air pollution dynamics. HONO observations in urban areas are characterized by high concentrations at night and low concentrations around midday. Existing gas-phase chemical mechanisms do not reproduce the observed HONO levels, suggesting a lack of sources, such as direct emissions or heterogeneous reactions. Specific HONO emission rates, heterogeneous chemical mechanisms leading to its formation and related kinetics are still unclear. Therefore, most air quality models consider exclusively gas-phase chemistry related to HONO. This work applies the WRF-ARW/HERMES/CMAQ modeling system to quantify the effect of the addition of HONO sources in the predictability of HONO profiles, and its subsequent effect on secondary pollutants formation (mainly O3 and PM2.5). The modeling episode is based on a 2004 severe summertime pollution event in the Iberian Peninsula, using high resolution of 4 × 4 km2. Two different parameterizations for emissions and the hydrolysis of NO2 on wet surfaces are added as HONO sources in the atmosphere. Emissions have the largest impact on HONO levels, especially in urban areas, where they can contribute from 66% to 94% to the HONO peak concentration. Additionally, in urban environments, NO2 hydrolysis on building and vegetation surfaces contributes up to 30% to the HONO peak. Both, the available surface area and the relative humidity must be included as parameters affecting the NO2 hydrolysis kinetics. As a result, NO2 hydrolysis is negligible on aerosol surfaces, due to the small surface area available for reaction, and it is more effective in producing HONO below high relative humidity conditions. The addition of HONO sources affects the concentration of secondary pollutants. In particular, major changes are produced in the early morning, due to the higher OH release via HONO photolysis. Significant changes in PM2.5 concentrations are predicted, that can be 16% (2.6 μg m−3) higher in the new scenarios. When accounting for HONO sources, nitrate levels increase especially in urban areas and sulfates in areas downwind from conventional power plants in the Iberian Peninsula. Also, O3 peak concentrations are slightly affected (from 0.7 to 4 ppb, 1% to 4.5%). The improvement of the HONO sources representation within air quality models produces changes in O3 peak predictions and significantly affects the reaction pathways leading to aerosols formation. Therefore, HONO sources other than gas-phase chemistry should be accurately included within modeling frameworks.

scholarly journals Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models

2021 ◽  
Vol 21 (9) ◽  
pp. 7373-7394
Author(s):  
Jérôme Barré ◽  
Hervé Petetin ◽  
Augustin Colette ◽  
Marc Guevara ◽  
Vincent-Henri Peuch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Gradient Boosting ◽  
Quality Models ◽  
Pollution Levels ◽  
Surface Station ◽  
Concentration Changes ◽  
Monitoring Service ◽  
No2 Pollution

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

Air quality modelling, simulation, and computational methods: a review

2013 ◽  
Vol 21 (3) ◽  
pp. 149-179 ◽  
Author(s):  
Mohanad El-Harbawi
Keyword(s):  
Air Quality ◽  
Computational Methods ◽  
Atmospheric Chemistry ◽  
Web Sites ◽  
Remote Sensing Data ◽  
Reference Source ◽  
Air Quality Modelling ◽  
Quality Models ◽  
Wide Range ◽  
Recent Developments

The objective of this paper is to provide a comprehensive theoretical review with regard to history, existing approaches, recent developments, major research, associated computational methods, and applications of air quality models. A wide range of topics is covered, focusing on sources of air pollution, primary and secondary pollutants, atmospheric chemistry, atmospheric chemical transport models, computer programs for dispersion modelling, online and offline air quality modelling, data assimilation, parallel computing, applications of geographic information system in air quality modelling, air quality index, as well as the use of satellite and remote sensing data in air quality modelling. Each of these elements is comprehensively discussed, covered, and reviewed with respect to various literature and methods related to air quality modelling and applications. Several major commercial and noncommercial dispersion packages are extensively reviewed and detailed advantages and limitations of their applications are highlighted. The paper includes several comparison summaries among various models used in air quality study. Furthermore, the paper provides useful web sites, where readers can obtain further information regarding air quality models and (or) software. Lastly, current generation of air quality models and future directions are also discussed. This paper may serve as a compendium for scientists who work in air quality modelling field. Some topics are generally treated; therefore, the paper may also be used as a reference source by many scientists working with air quality modelling.

scholarly journals Air Quality Modeling for Sustainable Clean Environment Using ANFIS and Machine Learning Approaches

2021 ◽  
Author(s):  
Osman Taylan ◽  
Abdulaziz Alkabaa ◽  
Mohammed Alamoudi ◽  
Abdulrahman Basahel ◽  
Mohammad Balubaid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Nonlinear Least Squares ◽  
Optimization Method ◽  
Learning Approaches ◽  
Quality Models ◽  
Neuro Fuzzy

Abstract Air quality monitoring and assessment are essential issues for sustainable environmental protection. The monitoring process is composed of data collection, evaluation, and decision making. Several important pollution factors, such as SO2, CO, PM10, O3, NOx, H2S, location, and many others, have detrimental effects on air quality. Air quality cannot be precisely recorded and measured due to the total effect of pollutants that usually cannot be collectively prescribed by a numerical value. Therefore, evolution is required to take into account the complex, poorly defined air quality problems in which several naive and noble modeling approaches are used to evaluate and solve. In this study, hybrid data-driven machine learning, and neuro-fuzzy methods are integrated for estimating the air quality in the urban area for public health concerns. 1771 data are collected during three years for each pollution factor, starting from June 1, 2016, till September 30, 2019. The Back-Propagation Multi-Layer Perceptron (BPMLP) algorithm was employed with the steepest descent approach to reduce the mean square error for training the algorithm of the neuro-fuzzy model. Levenberg-Marquardt (LM) approach was also employed as an optimization method with Artificial neural networks (ANNs) for solving nonlinear least-squares problems in this study. These approaches were evaluated by fuzzy quality charts and compared statistically with the US-EPA air quality standards. Due to the effectiveness and robustness of soft computing intelligent models, the public's early warning will be possible for avoiding the harmful effects of pollution inside the urban areas, which may reduce respiratory and cardiovascular mortalities. Consequently, the stability of air quality models was correlated with the absolute air quality index. The findings showed remarkable performance of ANFIS and ANN-based Air Quality models for High dimensional data assessment.

scholarly journals Model Performance Differences in Fine-Mode Nitrate Aerosol during Wintertime over Japan in the J-STREAM Model Inter-Comparison Study

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 511
Author(s):  
Syuichi Itahashi ◽  
Kazuyo Yamaji ◽  
Satoru Chatani ◽  
Kyo Kitayama ◽  
Yu Morino ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Model Performance ◽  
Comparison Study ◽  
Chemical Mechanisms ◽  
Quality Modeling ◽  
Western Japan ◽  
Fine Mode ◽  
The Difference ◽  
Heterogenous Reaction

In this study, the results for nitrate (NO3−) aerosol during winter from the first-phase model inter-comparison study of Japan’s Study for Reference Air Quality Modeling (J-STREAM) were analyzed. To investigate the models’ external and internal settings, the results were limited to Community Multiscale Air Quality (CMAQ) models. All submitted models generally underestimated NO3− over the urban areas in Japan (e.g., Osaka, Nagoya, and Tokyo); however, some model settings showed distinct behavior. The differences due to the model external settings were larger than the model internal settings. Emissions were an important factor, and emissions configured with lower NOx emissions and higher NH3 emissions led to a higher NO3− concentration as the NH3 was consumed under NH3-rich conditions. The model internal settings of the chemical mechanisms caused differences over China, and this could affect western Japan; however, the difference over Tokyo was lower. To obtain a higher NO3− concentration over the urban areas in Japan, the selection of the HONO option for the heterogenous reaction and the inline calculation of photolysis was desired. For future studies, the external settings of the boundary condition and the meteorological field require further investigation.

scholarly journals MOVEIM v1.0: Development of a bottom-up motor vehicular emission inventories for the urban area of Manaus in central Amazon rainforest

2018 ◽  
Author(s):  
Paulo R. Teixeira ◽  
Saulo R. de Freitas ◽  
Francis W. Correia ◽  
Antonio O. Manzi
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Spatial Scales ◽  
Transport Sector ◽  
Emission Inventories ◽  
Monitoring Networks ◽  
Weather Modification ◽  
Vehicular Emission ◽  
Bottom Up ◽  
Quality Models

Abstract. Emissions of gases and particulates in urban areas are associated with a mixture of various sources, both natural and anthropogenic. Understanding and quantifying these emissions is necessary in studies of climate change, local air pollution issues and weather modification. Studies have highlighted that the transport sector is key to closing the world’s emissions gap. Vehicles contribute substantially with the emission of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), non-methane hydrocarbon (NMHC), particulate matter (PM), methane (CH4), hydrofluorocarbon (HFC) and nitrous oxide (N2O). Several studies show that vehicle emission inventories are an important approach to providing a baseline estimate of on-road emissions in several scales, mainly in urban areas. This approach is essential to areas with incomplete or non-existent monitoring networks as well as for air quality models. Conversely, the direct downscale of global emission inventories in chemical transport and air quality models may not be able to reproduce the observed evolution of atmospheric pollution processes at finer spatial scales. To address this caveat, we developed a bottom-up vehicular emission inventory along the 258 main traffic routes from Manaus, based on local vehicle fleet data and emission factors (EFs). The results show that the light vehicles are responsible for the largest fraction of the pollutants, contributing 2.6, 0.87, 0.32, 0.03, 456 and 0.8 ton/h of CO, NOx, CH4, PM, CO2 and NMHC, respectively. Including the emissions of motorcycles, buses and trucks, our total estimation of the emissions is 4.1, 1.0, 0.37, 0.07, 63.5 and 2.56 ton/h, respectively. We also noted that light vehicles accounted for about 62.8 %, 84.7 %, 87.9 %, 45.1 %, 71.8 %, and 33.9 % and motorcycles in the order of 32.3 %, 6.5 %, 12.1 %, 6.2 %, 14.8 %, 8.7 %, respectively. Nevertheless, we can highlight the bus emissions which are around 35.7 % and 45.3 % for NMHC and PM. Our results indicate a better distribution over the domain reflecting the influences of standard behavior of traffic distribution per vehicle category. Finally, this inventory provides more detailed information to improving the current understanding of how vehicle emissions contribute to the ambient pollutant concentrations in Manaus and their impacts on regional climate changes. This work will also contribute to improved air quality numerical simulations, provide more accurate scenarios for policymakers and regulatory agencies to develop strategies for controlling the vehicular emissions, and, consequently, mitigate associated impacts on local and regional scales of the Amazon ecosystems.

scholarly journals Annual exposure to polycyclic aromatic hydrocarbons in urban environments linked to wintertime wood-burning episodes

2021 ◽  
Vol 21 (23) ◽  
pp. 17865-17883
Author(s):  
Irini Tsiodra ◽  
Georgios Grivas ◽  
Kalliopi Tavernaraki ◽  
Aikaterini Bougiatioti ◽  
Maria Apostolaki ◽  
...  
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Urban Areas ◽  
Fine Particulate Matter ◽  
Carcinogenic Risk ◽  
Urban Environments ◽  
Sampling Campaign ◽  
Wide Range ◽  
Local Sources ◽  
Polycyclic Aromatic

Abstract. Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants in fine particulate matter (PM) long known to have mutagenic and carcinogenic effects, but much is unknown about the importance of local and remote sources for PAH levels observed in population-dense urban environments. A year-long sampling campaign in Athens, Greece, where more than 150 samples were analyzed for 31 PAHs and a wide range of chemical markers, was combined with positive matrix factorization (PMF) to constrain the temporal variability, sources, and carcinogenic risk associated with PAHs. It was found that biomass burning (BB), a source mostly present during wintertime intense pollution events (observed for 18 % of measurement days in 2017), led to wintertime PAH levels that were 7 times higher than in other seasons and was as important for annual mean PAH concentrations (31 %) as diesel and oil (33 %) and gasoline (29 %) sources. The contribution of non-local sources, although limited on an annual basis (7 %), increased during summer, becoming comparable to that of local sources combined. The fraction of PAHs (12 members that were included in the PMF analysis) that was associated with BB was also linked to increased health risk compared to the other sources, accounting for almost half the annual PAH carcinogenic potential (43 %). This can result in a large number of excess cancer cases due to BB-related high PM levels and urges immediate action to reduce residential BB emissions in urban areas facing similar issues.

Automatic generation from MCM of reduced mechanisms to study the formation and evolution of SOA in 3D air quality models 

2021 ◽  
Author(s):  
Zhizhao Wang ◽  
Florian Couvidat ◽  
Karine Sartelet
Keyword(s):  
Molecular Structure ◽  
Air Quality ◽  
Large Scale ◽  
Automatic Generation ◽  
Oxidation Products ◽  
Chemical Mechanism ◽  
Aerosol Formation ◽  
Quality Models ◽  
Chemical Mechanisms ◽  
Formation And Evolution

&lt;p&gt;As secondary organic aerosols (SOA) largely contribute to the mass of particles and may strongly affect health, it is essential to represent them as accurately as possible in air quality models (AQM). Their formation and aging involve multi-generation oxidations of numerous volatile organic compounds (VOC) combined with gas-particle partitioning processes.&lt;br&gt;&amp;#160;&lt;br&gt;Tracking the non-linear relationship between VOC emissions and aerosol formation demands comprehensive chemical mechanisms, which take into account the whole complexity of the SOA precursor oxidation to simulate aerosols under various conditions.&lt;br&gt;However, the use of explicit gas-phase chemical mechanism (e.g., MCM, GECKO-A) or molecular structure-limited parameterization (e.g., VBS, SOM, FGOM) could be problematical in large-scale SOA modeling, as the former is overwhelmingly computational expensive while the latter loses tracks of VOC oxidation products after few generations and specific properties relying on aerosol formation.&lt;br&gt;&amp;#160;&lt;br&gt;Consequently, we have developed semi-explicit SOA chemical mechanisms designed to model the SOA formation and evolution in 3D AQM. These mechanisms are reduced based on simulations of the near-explicit master chemical mechanism (MCM) performed under various conditions representative of ambient conditions and different lumping strategies. The new mechanisms integrate the crucial SOA species/reactions with different mechanism complexities. The mechanisms, therefore, preserve the complexity of the oxidation chemistry (dependence on NOx of the SOA formation, the influence of radical concentrations, humidity, photolysis, etc..) as well as the molecular composition of the organic aerosol. The mechanisms are implemented in a novel 0D aerosol model SSH-aerosol, which can use the molecular structure of lumped compounds to estimate the influence of non-ideality on SOA formation.&lt;/p&gt;&lt;p&gt;The current application has been conducted on the MCM degradation scheme of beta-caryophyllene (C&lt;sub&gt;15&lt;/sub&gt;H&lt;sub&gt;24&lt;/sub&gt;), the most representative sesquiterpene. A reduction of the average 90% CPU time and up to 92% number of S/IVOCs species has been achieved compared to the original MCM mechanism.&lt;/p&gt;

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