Evaluation studies with a regional chemical transport model (EURAD) using air quality data from the EMEP monitoring network

Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, NO3−, NH4+, EC, OC, SO42−) over the contiguous United States on a daily basis for a period of ten years (2005–2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R2 values ranging from 0.84–0.98 across pollutants) and the spatial variation less well (R2 values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R2 values of 0.4 or more for all pollutants except SO2.

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Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽

10.3390/atmos12010091 ◽

2021 ◽

Vol 12 (1) ◽

pp. 91

Author(s):

Santiago Lopez-Restrepo ◽

Andres Yarce ◽

Nicolás Pinel ◽

O.L. Quintero ◽

Arjo Segers ◽

...

Keyword(s):

Air Quality ◽

Data Assimilation ◽

Transport Model ◽

Cost Model ◽

Low Cost ◽

Model Performance ◽

Monitoring Network ◽

Correlation Factor ◽

Chemical Transport Model ◽

High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

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A new diagnostic for tropospheric ozone production

10.5194/acp-2017-378 ◽

2017 ◽

Author(s):

Peter M. Edwards ◽

Mathew J. Evans

Keyword(s):

Air Quality ◽

Tropospheric Ozone ◽

Transport Model ◽

Chemical Transport ◽

Ozone Production ◽

Chemical Transport Model ◽

Organic Species ◽

Earth’S Climate ◽

Oxidation Of Organics

Abstract. Tropospheric ozone is important for the Earth’s climate and air quality. It is produced during the oxidation of organics in the presence of nitrogen oxides. Due to the range of organic species emitted and the chain like nature of their oxidation, this chemistry is complex and understanding the role of different processes (emission, deposition, chemistry) is difficult. We demonstrate a new methodology for diagnosing ozone production based on the processing of bonds contained within emitted molecules, the fate of which is determined by the conservation of spin of the bonding electrons. Using this methodology to diagnose ozone production in the GEOS-Chem chemical transport model, we demonstrate its advantages over the standard diagnostic. We show that the number of bonds emitted, their chemistry and lifetime, and feedbacks on OH are all important in determining the ozone production within the model and its sensitivity to changes. This insight may allow future model-model comparisons to better identify the root causes of model differences.

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Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

10.20944/preprints201811.0607.v1 ◽

2018 ◽

Author(s):

Scott D. Chambers ◽

Elise-Andree Guérette ◽

Khalia Monk ◽

Alan D. Griffiths ◽

Yang Zhang ◽

...

Keyword(s):

Air Quality ◽

Diurnal Cycle ◽

Transport Model ◽

Chemical Transport ◽

Chemical Transport Model ◽

Transport Models ◽

Potential Health ◽

Meteorological Model ◽

Model Skill ◽

Chemical Transport Models

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

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Coupling between surface ozone and leaf area index in a chemical transport model: strength of feedback and implications for ozone air quality and vegetation health

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-14133-2018 ◽

2018 ◽

Vol 18 (19) ◽

pp. 14133-14148 ◽

Cited By ~ 10

Author(s):

Shan S. Zhou ◽

Amos P. K. Tai ◽

Shihan Sun ◽

Mehliyar Sadiq ◽

Colette L. Heald ◽

...

Keyword(s):

Air Quality ◽

Leaf Area Index ◽

Leaf Area ◽

Dry Deposition ◽

Transport Model ◽

Surface Ozone ◽

Chemical Transport ◽

Chemical Transport Model ◽

Area Index ◽

Ozone Levels

Abstract. Tropospheric ozone is an air pollutant that substantially harms vegetation and is also strongly dependent on various vegetation-mediated processes. The interdependence between ozone and vegetation may constitute feedback mechanisms that can alter ozone concentration itself but have not been considered in most studies to date. In this study we examine the importance of dynamic coupling between surface ozone and leaf area index (LAI) in shaping ozone air quality and vegetation. We first implement an empirical scheme for ozone damage on vegetation in the Community Land Model (CLM) and simulate the steady-state responses of LAI to long-term exposure to a range of prescribed ozone levels (from 0 to 100 ppb). We find that most plant functional types suffer a substantial decline in LAI as ozone level increases. Based on the CLM-simulated results, we develop and implement in the GEOS-Chem chemical transport model a parameterization that computes fractional changes in monthly LAI as a function of local mean ozone levels. By forcing LAI to respond to ozone concentrations on a monthly timescale, the model simulates ozone–LAI coupling dynamically via biogeochemical processes including biogenic volatile organic compound (VOC) emissions and dry deposition, without the complication from meteorological changes. We find that ozone-induced damage on LAI can lead to changes in ozone concentrations by −1.8 to +3 ppb in boreal summer, with a corresponding ozone feedback factor of −0.1 to +0.6 that represents an overall self-amplifying effect from ozone–LAI coupling. Substantially higher simulated ozone due to strong positive feedbacks is found in most tropical forests, mainly due to the ozone-induced reductions in LAI and dry deposition velocity, whereas reduced isoprene emission plays a lesser role in these low-NOx environments. In high-NOx regions such as the eastern US, Europe, and China, however, the feedback effect is much weaker and even negative in some regions, reflecting the compensating effects of reduced dry deposition and reduced isoprene emission (which reduces ozone in high-NOx environments). In remote, low-LAI regions, including most of the Southern Hemisphere, the ozone feedback is generally slightly negative due to the reduced transport of NOx–VOC reaction products that serve as NOx reservoirs. This study represents the first step to accounting for dynamic ozone–vegetation coupling in a chemical transport model with ramifications for a more realistic joint assessment of ozone air quality and ecosystem health.

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Applying the delta tool to support the Air Quality Directive: evaluation of the TCAM chemical transport model

Air Quality Atmosphere & Health ◽

10.1007/s11869-014-0240-4 ◽

2014 ◽

Vol 7 (3) ◽

pp. 335-346 ◽

Cited By ~ 10

Author(s):

C. Carnevale ◽

G. Finzi ◽

A. Pederzoli ◽

E. Pisoni ◽

P. Thunis ◽

...

Keyword(s):

Air Quality ◽

Transport Model ◽

Chemical Transport ◽

Chemical Transport Model

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Chemical transport model simulations of organic aerosol in southern California: model evaluation and gasoline and diesel source contributions

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-4305-2017 ◽

2017 ◽

Vol 17 (6) ◽

pp. 4305-4318 ◽

Cited By ~ 21

Author(s):

Shantanu H. Jathar ◽

Matthew Woody ◽

Havala O. T. Pye ◽

Kirk R. Baker ◽

Allen L. Robinson

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Southern California ◽

Transport Model ◽

Chemical Transport ◽

Organic Aerosol ◽

Production Model ◽

Anthropogenic Sources ◽

Chemical Transport Model ◽

Mobile Sources

Abstract. Gasoline- and diesel-fueled engines are ubiquitous sources of air pollution in urban environments. They emit both primary particulate matter and precursor gases that react to form secondary particulate matter in the atmosphere. In this work, we updated the organic aerosol module and organic emissions inventory of a three-dimensional chemical transport model, the Community Multiscale Air Quality Model (CMAQ), using recent, experimentally derived inputs and parameterizations for mobile sources. The updated model included a revised volatile organic compound (VOC) speciation for mobile sources and secondary organic aerosol (SOA) formation from unspeciated intermediate volatility organic compounds (IVOCs). The updated model was used to simulate air quality in southern California during May and June 2010, when the California Research at the Nexus of Air Quality and Climate Change (CalNex) study was conducted. Compared to the Traditional version of CMAQ, which is commonly used for regulatory applications, the updated model did not significantly alter the predicted organic aerosol (OA) mass concentrations but did substantially improve predictions of OA sources and composition (e.g., POA–SOA split), as well as ambient IVOC concentrations. The updated model, despite substantial differences in emissions and chemistry, performed similar to a recently released research version of CMAQ (Woody et al., 2016) that did not include the updated VOC and IVOC emissions and SOA data. Mobile sources were predicted to contribute 30–40 % of the OA in southern California (half of which was SOA), making mobile sources the single largest source contributor to OA in southern California. The remainder of the OA was attributed to non-mobile anthropogenic sources (e.g., cooking, biomass burning) with biogenic sources contributing to less than 5 % to the total OA. Gasoline sources were predicted to contribute about 13 times more OA than diesel sources; this difference was driven by differences in SOA production. Model predictions highlighted the need to better constrain multi-generational oxidation reactions in chemical transport models.

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Evaluation of the LOTOS-EUROS NO<sub>2</sub> simulations using ground-based measurements and S5P/TROPOMI observations over Greece

10.5194/acp-2020-987 ◽

2020 ◽

Author(s):

Ioanna Skoulidou ◽

Maria-Elissavet Koukouli ◽

Astrid Manders ◽

Arjo Segers ◽

Dimitris Karagkiozidis ◽

...

Keyword(s):

Spatial Correlation ◽

Extreme Values ◽

Transport Model ◽

Monitoring Network ◽

Temporal Correlation ◽

Chemical Transport ◽

Pollution Monitoring ◽

Chemical Transport Model ◽

Air Pollution Monitoring ◽

Tropospheric No2

Abstract. The evaluation of chemical transport models, CTMs, is essential for the assessment of their performance regarding the physical and chemical parameterizations used. While regional CTMs have been widely used and evaluated over Europe, their validation over Greece is limited. In this study, we investigate the performance of the LOTOS-EUROS v2.2.001 regional chemical transport model in simulating nitrogen dioxide, NO2, over Greece from June to December 2018. In-situ NO2 measurements obtained from the National Air Pollution Monitoring Network are compared with surface simulations over the two major cities of Greece, Athens and Thessaloniki. The model reproduces well the spatial variability of the measured NO2 with a spatial correlation coefficient of 0.85 for the period between June and December 2018. About half of the 14 air quality monitoring stations show a good temporal correlation to the simulations, higher than 0.6, during daytime (12–15 p.m. local time), while the corresponding biases are negative. Most stations show stronger negative biases during winter than in summer. Furthermore, the simulated tropospheric NO2 columns are evaluated against ground-based MAX-DOAS NO2 measurements and space-borne Sentinel 5-Precursor TROPOMI tropospheric NO2 observations in July and December 2018. LOTOS-EUROS captures better the NO2 temporal variability in December (0.61 and 0.81) than in July (0.50 and 0.21) when compared to the corresponding measurements of the MAX-DOAS instruments in Thessaloniki and the rural azimuth viewing direction in Athens respectively. The urban azimuth viewing direction in Athens region however shows a better correlation in July than in December (0.41 and 0.19, respectively). LOTOS-EUROS NO2 columns over Athens and Thessaloniki agree well with the TROPOMI observations showing higher spatial correlation in July (0.95 and 0.82, respectively) than in December (0.82 and 0.66, respectively) while the relative temporal correlations are higher during winter. Overall, the comparison of the simulations with the TROPOMI observations shows a model underestimation in summer and an overestimation in winter both in Athens and Thessaloniki. Updated emissions for the simulations and model improvements when extreme values of boundary layer height are encountered are further suggested.

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Air quality impacts of the 2018 Mt. Kilauea Volcano eruption in Hawaii: A regional chemical transport model study with satellite-constrained emissions

Atmospheric Environment ◽

10.1016/j.atmosenv.2020.117648 ◽

2020 ◽

Vol 237 ◽

pp. 117648 ◽

Cited By ~ 1

Author(s):

Youhua Tang ◽

Daniel Q. Tong ◽

Kai Yang ◽

Pius Lee ◽

Barry Baker ◽

...

Keyword(s):

Air Quality ◽

Transport Model ◽

Chemical Transport ◽

Kilauea Volcano ◽

Chemical Transport Model ◽

Volcano Eruption ◽

Model Study ◽

Kīlauea Volcano

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A new diagnostic for tropospheric ozone production

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-13669-2017 ◽

2017 ◽

Vol 17 (22) ◽

pp. 13669-13680 ◽

Cited By ~ 4

Author(s):

Peter M. Edwards ◽

Mathew J. Evans

Keyword(s):

Air Quality ◽

Tropospheric Ozone ◽

Transport Model ◽

Chemical Transport ◽

Ozone Production ◽

Chemical Transport Model ◽

Organic Species ◽

Earth’S Climate ◽

Oxidation Of Organics

Abstract. Tropospheric ozone is important for the Earth's climate and air quality. It is produced during the oxidation of organics in the presence of nitrogen oxides. Due to the range of organic species emitted and the chain-like nature of their oxidation, this chemistry is complex and understanding the role of different processes (emission, deposition, chemistry) is difficult. We demonstrate a new methodology for diagnosing ozone production based on the processing of bonds contained within emitted molecules, the fate of which is determined by the conservation of spin of the bonding electrons. Using this methodology to diagnose ozone production in the GEOS-Chem chemical transport model, we demonstrate its advantages over the standard diagnostic. We show that the number of bonds emitted, their chemistry and lifetime, and feedbacks on OH are all important in determining the ozone production within the model and its sensitivity to changes. This insight may allow future model–model comparisons to better identify the root causes of model differences.

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