scholarly journals Chemical transport models often underestimate inorganic aerosol acidity in remote regions of the atmosphere

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Benjamin A. Nault ◽  
Pedro Campuzano-Jost ◽  
Douglas A. Day ◽  
Duseong S. Jo ◽  
Jason C. Schroder ◽  
...  
Keyword(s):  
Fine Particles ◽  
Chemical Transport ◽  
Radiative Cooling ◽  
Large Uncertainty ◽  
Transport Models ◽  
Physicochemical Processes ◽  
Aerosol Acidity ◽  
Chemical Transport Models ◽  
Inorganic Aerosol ◽  
Inorganic Fraction

AbstractThe inorganic fraction of fine particles affects numerous physicochemical processes in the atmosphere. However, there is large uncertainty in its burden and composition due to limited global measurements. Here, we present observations from eleven different aircraft campaigns from around the globe and investigate how aerosol pH and ammonium balance change from polluted to remote regions, such as over the oceans. Both parameters show increasing acidity with remoteness, at all altitudes, with pH decreasing from about 3 to about −1 and ammonium balance decreasing from almost 1 to nearly 0. We compare these observations against nine widely used chemical transport models and find that the simulations show more scatter (generally R2 < 0.50) and typically predict less acidic aerosol in the most remote regions. These differences in observations and predictions are likely to result in underestimating the model-predicted direct radiative cooling effect for sulfate, nitrate, and ammonium aerosol by 15–39%.

Global Survey of Aerosol Acidity from Polluted to Remote Locations: Measurements and Comparisons with Global Models

2020 ◽  
Author(s):  
Benjamin A Nault ◽  
Pedro Campuzano-Jost ◽  
Duseong Jo ◽  
Doug Day ◽  
Roya Bahreini ◽  
...  
Keyword(s):  
Los Angeles ◽  
Major Ions ◽  
Water Concentration ◽  
Chemical Transport ◽  
Aerosol Composition ◽  
Transport Models ◽  
Large Variability ◽  
Cloud Properties ◽  
Aerosol Acidity ◽  
Chemical Transport Models

&lt;p&gt;The inorganic composition of aerosol impacts numerous chemical and physical processes and properties. However, many chemical transport models show large variability in both the concentration of the inorganic aerosols and their precursors (up to 3 orders of magnitude differences) and the inorganic aerosol composition. Different models predict very different properties (e.g., aerosol liquid water concentration and aerosol acidity) and outcomes (e.g., heterogeneous uptake of gases or aerosols&amp;#8217; direct and indirect impacts on climate). Here, we use airborne observations from campaigns conducted around the world to investigate how the inorganic fine aerosol (PM&lt;sub&gt;1&lt;/sub&gt;) composition, and one of its key parameters, aerosol acidity, changes from polluted regions (Mexico City, Los Angeles, Northeastern US, and Seoul) to remote ocean basins (the Atmospheric Tomography campaigns 1 and 2) in order to provide constraints for the chemical transport models. I find that the empirical ammonium balance with major ions (ammonium balance = mol NH&lt;sub&gt;4&lt;/sub&gt; / (2&amp;#215;mol SO&lt;sub&gt;4&lt;/sub&gt; + mol NO&lt;sub&gt;3&lt;/sub&gt;)) rapidly decreases from ~1 at the highest inorganic PM&lt;sub&gt;1&lt;/sub&gt; concentration to 0 at the lowest inorganic PM&lt;sub&gt;1&lt;/sub&gt;. The data indicate a robust trend for ammonium balance vs inorganic PM&lt;sub&gt;1&lt;/sub&gt; at all altitude levels in the troposphere, suggesting that NH&lt;sub&gt;3&lt;/sub&gt; emissions and subsequent neutralization of H&lt;sub&gt;2&lt;/sub&gt;SO&lt;sub&gt;4&lt;/sub&gt; becomes negligible in the most remote (lowest inorganic PM&lt;sub&gt;1&lt;/sub&gt;) regions. Further, a robust trend for PM&lt;sub&gt;1&lt;/sub&gt; pH (calculated with E-AIM) vs inorganic PM&lt;sub&gt;1&lt;/sub&gt; is observed at all levels for these campaigns, as well, decreasing from a pH of ~3 to a pH of ~ &amp;#8211;1 from the highest to lowest inorganic PM&lt;sub&gt;1&lt;/sub&gt;. The data overall implies very low NH&lt;sub&gt;3&lt;/sub&gt; (and NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;) throughout most of the atmosphere, contrary to predictions of some models, implying different physical properties than predicted in models. We compare these trends of ammonium balance and pH vs inorganic PM&lt;sub&gt;1&lt;/sub&gt; against 9 chemical transport models (CTMs), and we find that the CTMs show large variability for both the ammonium balance and pH vs inorganic PM&lt;sub&gt;&amp;#173;1&lt;/sub&gt;, compared to observations. Generally, we find a high bias in the ammonium balance and pH, likely due to too much NH&lt;sub&gt;&amp;#173;3&lt;/sub&gt; in model (possibly too high NH&lt;sub&gt;3&lt;/sub&gt; emissions over oceans or too long lifetime) and inclusion of externally mixed seasalt into the submicron pH calculation. These results overall would imply different aerosol properties in the models than observed, impacting the chemistry, optical properties, and cloud properties.&lt;/p&gt;

scholarly journals A Possible Pathway for Rapid Growth of Sulfate during Haze Days in China

2016 ◽  
Author(s):  
Guohui Li ◽  
Naifang Bei ◽  
Junji Cao ◽  
Rujin Huang ◽  
Jiarui Wu ◽  
...  
Keyword(s):  
Rapid Growth ◽  
Fine Particles ◽  
Control Strategies ◽  
Chemical Transport ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Underlying Basis ◽  
Haze Formation ◽  
Heterogeneous Formation ◽  
Beijing China

Abstract. Rapid industrialization and urbanization have caused frequent occurrence of haze in China during wintertime in recent years. The sulfate aerosol is one of the most important components of fine particles (PM2.5) in the atmosphere, contributing significantly to the haze formation. However, the heterogeneous formation mechanism of sulfate remains poorly characterized. Observed filter measurements of PM2.5, sulfate, and iron, and relative humidity in Xi'an, China have been employed to evaluate the mechanism and to develop a parameterization of the sulfate heterogeneous formation involving aerosol water for incorporation into atmospheric chemical transport models. Model simulations with the proposed parameterization can successfully reproduce the observed sulfate rapid growth and diurnal variations in Xi'an and Beijing, China. Reasonable representation of sulfate heterogeneous formation in chemical transport models considerably improves the PM2.5 simulations, providing the underlying basis for better understanding the haze formation and supporting the design and implementation of emission control strategies.

scholarly journals A possible pathway for rapid growth of sulfate during haze days in China

2017 ◽  
Vol 17 (5) ◽  
pp. 3301-3316 ◽  
Author(s):  
Guohui Li ◽  
Naifang Bei ◽  
Junji Cao ◽  
Rujin Huang ◽  
Jiarui Wu ◽  
...  
Keyword(s):  
Rapid Growth ◽  
Fine Particles ◽  
Control Strategies ◽  
Chemical Transport ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Underlying Basis ◽  
Haze Formation ◽  
Heterogeneous Formation ◽  
Beijing China

Abstract. Rapid industrialization and urbanization have caused frequent occurrence of haze in China during wintertime in recent years. The sulfate aerosol is one of the most important components of fine particles (PM2. 5) in the atmosphere, contributing significantly to the haze formation. However, the heterogeneous formation mechanism of sulfate remains poorly characterized. The relationships of the observed sulfate with PM2. 5, iron, and relative humidity in Xi'an, China have been employed to evaluate the mechanism and to develop a parameterization of the sulfate heterogeneous formation involving aerosol water for incorporation into atmospheric chemical transport models. Model simulations with the proposed parameterization can successfully reproduce the observed sulfate rapid growth and diurnal variations in Xi'an and Beijing, China. Reasonable representation of sulfate heterogeneous formation in chemical transport models considerably improves the PM2. 5 simulations, providing the underlying basis for better understanding the haze formation and supporting the design and implementation of emission control strategies.

Air quality forecasting system based on chemical transport models

2019 ◽  
Vol 4 ◽  
pp. 203-218
Author(s):  
I.N. Kusnetsova ◽  
◽  
I.U. Shalygina ◽  
M.I. Nahaev ◽  
U.V. Tkacheva ◽  
...  
Keyword(s):  
Air Quality ◽  
Chemical Transport ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Forecasting System ◽  
Air Quality Forecasting

Machine learning based bias correction for numerical chemical transport models

2021 ◽  
Vol 248 ◽  
pp. 118022
Author(s):  
Min Xu ◽  
Jianbing Jin ◽  
Guoqiang Wang ◽  
Arjo Segers ◽  
Tuo Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bias Correction ◽  
Chemical Transport ◽  
Transport Models ◽  
Chemical Transport Models

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

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 (&lt;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 &lsquo;natural complicating factors&rsquo; (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 &ldquo;rush hour&rdquo; 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.

scholarly journals The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

2016 ◽  
Vol 9 (7) ◽  
pp. 2753-2779 ◽  
Author(s):  
Steffen Beirle ◽  
Christoph Hörmann ◽  
Patrick Jöckel ◽  
Song Liu ◽  
Marloes Penning de Vries ◽  
...  
Keyword(s):  
Input Data ◽  
Polar Vortex ◽  
Chemical Transport ◽  
Estimation Algorithm ◽  
Satellite Observations ◽  
Satellite Measurements ◽  
Transport Models ◽  
Verification Algorithm ◽  
Chemical Transport Models ◽  
Total Column

Abstract. The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.

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