The Impact of Iodide-Mediated Ozone Deposition and Halogen Chemistry on Surface Ozone Concentrations Across the Continental United States

Gantt, B.; Sarwar, Golam; Xing, Jia; Simon, Heather; Schwede, Donna; Hutzell, William T.; Mathur, Rohit; Saiz‐Lopez, Alfonso

doi:10.1021/acs.est.6b03556

Cited by 22 publications

(24 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chlorine chemistry was implemented into the WRF-Chem model (Lowe et al, 2015;Li et al, 2016) and into the Community Multi-scale Air Quality (CMAQ) model (Sarwar et al, 2014) to study the formation of nitryl chloride (ClNO 2 ) from the uptake of dinitrogen pentoxide (N 2 O 5 ) on aerosols containing chloride. Moreover, bromine and iodine chemistry was implemented in CMAQ in Gantt et al (2017) and Sarwar et al (2015), where the impact of iodide-mediated O 3 deposition on surface ozone concentrations was studied, and in the recent work of Muñiz-Unamunzaga et al (2018), which concluded that oceanic halogens and dimethyl sulfide (DMS) emissions need to be included into the regional models to accurately reproduce the air quality in coastal cities.…”

Section: Introductionmentioning

confidence: 99%

Importance of reactive halogens in the tropical marine atmosphere: a regional modelling study using WRF-Chem

et al. 2019

Self Cite

View full text Add to dashboard Cite

Abstract. This study investigates the impact of reactive halogen species (RHS, containing chlorine (Cl), bromine (Br) or iodine (I)) on atmospheric chemistry in the tropical troposphere and explores the sensitivity to uncertainties in the fluxes of RHS to the atmosphere and their chemical processing. To do this, the regional chemistry transport model WRF-Chem has been extended to include Br and I, as well as Cl chemistry for the first time, including heterogeneous recycling reactions involving sea-salt aerosol and other particles, reactions of Br and Cl with volatile organic compounds (VOCs), along with oceanic emissions of halocarbons, VOCs and inorganic iodine. The study focuses on the tropical east Pacific using field observations from the Tropical Ocean tRoposphere Exchange of Reactive halogen species and Oxygenated VOC (TORERO) campaign (January–February 2012) to evaluate the model performance. Including all the new processes, the model does a reasonable job reproducing the observed mixing ratios of bromine oxide (BrO) and iodine oxide (IO), albeit with some discrepancies, some of which can be attributed to difficulties in the model's ability to reproduce the observed halocarbons. This is somewhat expected given the large uncertainties in the air–sea fluxes of the halocarbons in a region where there are few observations of their seawater concentrations. We see a considerable impact on the inorganic bromine (Bry) partitioning when heterogeneous chemistry is included, with a greater proportion of the Bry in active forms such as BrO, HOBr and dihalogens. Including debromination of sea salt increases BrO slightly throughout the free troposphere, but in the tropical marine boundary layer, where the sea-salt particles are plentiful and relatively acidic, debromination leads to overestimation of the observed BrO. However, it should be noted that the modelled BrO was extremely sensitive to the inclusion of reactions between Br and the oxygenated VOCs (OVOCs), which convert Br to HBr, a far less reactive form of Bry. Excluding these reactions leads to modelled BrO mixing ratios greater than observed. The reactions between Br and aldehydes were found to be particularly important, despite the model underestimating the amount of aldehydes observed in the atmosphere. There are only small changes to the inorganic iodine (Iy) partitioning and IO when the heterogeneous reactions, primarily on sea salt, are included. Our model results show that tropospheric Ox loss due to halogens ranges between 25 % and 60 %. Uncertainties in the heterogeneous chemistry accounted for a small proportion of this range (25 % to 31 %). This range is in good agreement with other estimates from state-of-the-art atmospheric chemistry models. The upper bound is found when reactions between Br and Cl with VOCs are not included and, consequently, Ox loss by BrOx, ClOx and IOx cycles is high (60 %). With the inclusion of halogens in the troposphere, O3 is reduced by 7 ppbv on average. However, when reactions between Br and Cl with VOCs are not included, O3 is much lower than observed. Therefore, the tropospheric Ox budget is highly sensitive to the inclusion of halogen reactions with VOCs and to the uncertainties in current understanding of these reactions and the abundance of VOCs in the remote marine atmosphere.

show abstract

Section: Introductionmentioning

confidence: 99%

Importance of reactive halogens in the tropical marine atmosphere: a regional modelling study using WRF-Chem

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lu et al, 2016Lu et al, , 2018Zhou et al, 2015), atmospheric composition (e.g. Ganzeveld et al, 2009;Saiz-Lopez et al, 2014;Sherwen et al, 2016a), and air-quality prediction (e.g. Luhar et al, 2017Luhar et al, , 2018Sarwar et al, 2015;Sherwen et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

A machine-learning-based global sea-surface iodide distribution

Sherwen

Chance

Tinel

et al. 2019

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. Iodide in the sea-surface plays an important role in the Earth system. It modulates the oxidising capacity of the troposphere and provides iodine to terrestrial ecosystems. However, our understanding of its distribution is limited due to a paucity of observations. Previous efforts to generate global distributions have generally fitted sea-surface iodide observations to relatively simple functions using proxies for iodide such as nitrate and sea-surface temperature. This approach fails to account for coastal influences and variation in the bio-geochemical environment. Here we use a machine learning regression approach (random forest regression) to generate a high-resolution (0.125∘×0.125∘, ∼12.5km×12.5km), monthly dataset of present-day global sea-surface iodide. We use a compilation of iodide observations (1967–2018) that has a 45 % larger sample size than has been used previously as the dependent variable and co-located ancillary parameters (temperature, nitrate, phosphate, salinity, shortwave radiation, topographic depth, mixed layer depth, and chlorophyll a) from global climatologies as the independent variables. We investigate the regression models generated using different combinations of ancillary parameters and select the 10 best-performing models to be included in an ensemble prediction. We then use this ensemble of models, combined with global fields of the ancillary parameters, to predict new high-resolution monthly global sea-surface iodide fields representing the present day. Sea-surface temperature is the most important variable in all 10 models. We estimate a global average sea-surface iodide concentration of 106 nM (with an uncertainty of ∼20 %), which is within the range of previous estimates (60–130 nM). Similar to previous work, higher concentrations are predicted for the tropics than for the extra-tropics. Unlike the previous parameterisations, higher concentrations are also predicted for shallow areas such as coastal regions and the South China Sea. Compared to previous work, the new parameterisation better captures observed variability. The iodide concentrations calculated here are significantly higher (40 % on a global basis) than the commonly used MacDonald et al. (2014) parameterisation, with implications for our understanding of iodine in the atmosphere. We envisage these fields could be used to represent present-day sea-surface iodide concentrations, in applications such as climate and air-quality modelling. The global iodide dataset is made freely available to the community (https://doi.org/10/gfv5v3, Sherwen et al., 2019), and as new observations are made, we will update the global dataset through a “living data” model.

show abstract

“…The reaction of I − with ozone in the sea-surface micro-layer removes ozone from the atmosphere (dry deposition) (Ganzeveld et al, 2009) and results in the emission of inorganic iodine (HOI and I 2 ) into the atmosphere (Carpenter et al, 2013), which can subsequently catalytically destroy ozone (Chameides and Davis, 1980). A number of model studies have discussed the impact of oceansourced iodine on atmosphere composition in the context of air quality (Gantt et al, 2017;Sarwar et al, 2016;Sherwen et al, 2017b), climate (Sherwen et al, 2017b;Saiz-Lopez et al, 2012), aerosols (Sherwen et al, 2017a), and stratospheric ozone (Saiz-Lopez et al, 2015). These atmospheric modelling studies have used relatively simple parameterisations for predictions of sea-surface iodide.…”

Section: Introductionmentioning

confidence: 99%

A machine learning based global sea-surface iodide distribution

Sherwen¹,

Chance²,

Tinel³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Iodide in the sea-surface plays an important role in the Earth system. It modulates the oxidising capacity of the troposphere and provides iodine to terrestrial ecosystems. However, our understanding of its distribution is limited due to a paucity of observations. Previous efforts to generate global distributions have generally fitted sea-surface iodide observations to relatively simple functions of sea-surface temperature (Chance et al., 2014; MacDonald et al., 2014). This approach fails to account for coastal influences and variation in the bio-geochemical environment. Here we use a machine learning regression approach (Random Forest Regression) to generate a high resolution (0.125° x 0.125°, ∼ 12.5 km), monthly dataset of present-day global sea-surface iodide. We use a compilation of iodide observations (Chance et al., 2019b) that is 45 % larger than has been used previously (Chance et al., 2014) as the dependent variable and co-located ancillary parameters (temperature, nitrate, phosphate, salinity, shortwave radiation, topographic depth, mixed layer depth, and chlorophyll-a) from global climatologies as the independent variables. We investigate the regression models generated using different combinations of ancillary parameters and select the ten best-performing models to be included in an ensemble prediction. We then use this ensemble of models, combined with global fields of the ancillary parameters, to predict a new high resolution global sea-surface iodide field. Sea-surface temperature is the most important variable in all of the top ten models. We estimate a global average sea-surface iodide concentration of 106 nM (with an uncertainty of ∼ 20 %), which is within the range of previous estimates (60–130 nM). Similar to previous work, higher concentrations are predicted for the tropics than for the extra-tropics. Unlike the previous parameterisations, higher concentrations are also predicted for shallow areas such as coastal regions and the South China Sea. Compared to previous work, the new parameterisation better captures observed variability. The iodide concentrations calculated here are significantly higher (40 % on a global basis) than the commonly used MacDonald et al. (2014) parameterisation, with implications for our understanding of iodine in the atmosphere. The global iodide dataset is made freely available to the community (DOI: https://doi.org/10/gfv5v3) and as new observations are made, we will update the global dataset through a "living data" model.

show abstract

The Impact of Iodide-Mediated Ozone Deposition and Halogen Chemistry on Surface Ozone Concentrations Across the Continental United States

Cited by 22 publications

References 80 publications

Importance of reactive halogens in the tropical marine atmosphere: a regional modelling study using WRF-Chem

Importance of reactive halogens in the tropical marine atmosphere: a regional modelling study using WRF-Chem

A machine-learning-based global sea-surface iodide distribution

A machine learning based global sea-surface iodide distribution

Contact Info

Product

Resources

About