The gas-phase formation mechanism of iodic acid as an atmospheric aerosol source

Iodine is a reactive trace element in atmospheric chemistry that destroys ozone and nucleates particles. Iodine emissions have tripled since 1950 and are projected to keep increasing with rising O-3 surface concentrations. Although iodic acid (HIO3) is widespread and forms particles more efficiently than sulfuric acid, its gas-phase formation mechanism remains unresolved. Here, in CLOUD atmospheric simulation chamber experiments that generate iodine radicals at atmospherically relevant rates, we show that iodooxy hypoiodite, IOIO, is efficiently converted into HIO3 via reactions (R1) IOIO + O-3 -> IOIO4 and (R2) IOIO4 + H2O -> HIO3 + HOI + O-(1)(2). The laboratory-derived reaction rate coefficients are corroborated by theory and shown to explain field observations of daytime HIO3 in the remote lower free troposphere. The mechanism provides a missing link between iodine sources and particle formation. Because particulate iodate is readily reduced, recycling iodine back into the gas phase, our results suggest a catalytic role of iodine in aerosol formation.Peer reviewe

Synergistic HNO3_{3}–H2_{2}SO4_{4}–NH3_{3} upper tropospheric particle formation

New particle formation in the upper free troposphere is a major global source of cloud condensation nuclei (CCN)$^{1,2,3,4}$. However, the precursor vapours that drive the process are not well understood. With experiments performed under upper tropospheric conditions in the CERN CLOUD chamber, we show that nitric acid, sulfuric acid and ammonia form particles synergistically, at rates that are orders of magnitude faster than those from any two of the three components. The importance of this mechanism depends on the availability of ammonia, which was previously thought to be efficiently scavenged by cloud droplets during convection. However, surprisingly high concentrations of ammonia and ammonium nitrate have recently been observed in the upper troposphere over the Asian monsoon region5,6. Once particles have formed, co-condensation of ammonia and abundant nitric acid alone is sufficient to drive rapid growth to CCN sizes with only trace sulfate. Moreover, our measurements show that these CCN are also highly efficient ice nucleating particles—comparable to desert dust. Our model simulations confirm that ammonia is efficiently convected aloft during the Asian monsoon, driving rapid, multi-acid HNO$_{3}$–H$_{2}$SO$_{4}$–NH$_{3}$ nucleation in the upper troposphere and producing ice nucleating particles that spread across the mid-latitude Northern Hemisphere

Enhanced growth rate of atmospheric particles from sulfuric acid

In the present-day atmosphere, sulfuric acid is the most important vapour for aerosol particle formation and initial growth. However, the growth rates of nanoparticles (<10 nm) from sulfuric acid remain poorly measured. Therefore, the effect of stabilizing bases, the contribution of ions and the impact of attractive forces on molecular collisions are under debate. Here, we present precise growth rate measurements of uncharged sulfuric acid particles from 1.8 to 10 nm, performed under atmospheric conditions in the CERN (European Organization for Nuclear Research) CLOUD chamber. Our results show that the evaporation of sulfuric acid particles above 2 nm is negligible, and growth proceeds kinetically even at low ammonia concentrations. The experimental growth rates exceed the hard-sphere kinetic limit for the condensation of sulfuric acid. We demonstrate that this results from van der Waals forces between the vapour molecules and particles and disentangle it from charge–dipole interactions. The magnitude of the enhancement depends on the assumed particle hydration and collision kinetics but is increasingly important at smaller sizes, resulting in a steep rise in the observed growth rates with decreasing size. Including the experimental results in a global model, we find that the enhanced growth rate of sulfuric acid particles increases the predicted particle number concentrations in the upper free troposphere by more than 50 %

Rapid growth of new atmospheric particles by nitric acid and ammonia condensation

New-particle formation is a major contributor to urban smog$^{1,2}$, but how it occurs in cities is often puzzling$^{3}$. If the growth rates of urban particles are similar to those found in cleaner environments (1–10 nanometres per hour), then existing understanding suggests that new urban particles should be rapidly scavenged by the high concentration of pre-existing particles. Here we show, through experiments performed under atmospheric conditions in the CLOUD chamber at CERN, that below about +5 degrees Celsius, nitric acid and ammonia vapours can condense onto freshly nucleated particles as small as a few nanometres in diameter. Moreover, when it is cold enough (below −15 degrees Celsius), nitric acid and ammonia can nucleate directly through an acid–base stabilization mechanism to form ammonium nitrate particles. Given that these vapours are often one thousand times more abundant than sulfuric acid, the resulting particle growth rates can be extremely high, reaching well above 100 nanometres per hour. However, these high growth rates require the gas-particle ammonium nitrate system to be out of equilibrium in order to sustain gas-phase supersaturations. In view of the strong temperature dependence that we measure for the gas-phase supersaturations, we expect such transient conditions to occur in inhomogeneous urban settings, especially in wintertime, driven by vertical mixing and by strong local sources such as traffic. Even though rapid growth from nitric acid and ammonia condensation may last for only a few minutes, it is nonetheless fast enough to shepherd freshly nucleated particles through the smallest size range where they are most vulnerable to scavenging loss, thus greatly increasing their survival probability. We also expect nitric acid and ammonia nucleation and rapid growth to be important in the relatively clean and cold upper free troposphere, where ammonia can be convected from the continental boundary layer and nitric acid is abundant from electrical storms$^{4,5}$

Enhanced growth rate of atmospheric particles from sulfuric acid

In the present-day atmosphere, sulfuric acid is the most important vapour for aerosol particle formation and initial growth. However, the growth rates of nanoparticles (<10 nm) from sulfuric acid remain poorly measured. Therefore, the effect of stabilizing bases, the contribution of ions and the impact of attractive forces on molecular collisions are under debate. Here, we present precise growth rate measurements of uncharged sulfuric acid particles from 1.8 to 10 nm, performed under atmospheric conditions in the CERN (European Organization for Nuclear Research) CLOUD chamber. Our results show that the evaporation of sulfuric acid particles above 2 nm is negligible, and growth proceeds kinetically even at low ammonia concentrations. The experimental growth rates exceed the hard-sphere kinetic limit for the condensation of sulfuric acid. We demonstrate that this results from van derWaals forces between the vapour molecules and particles and disentangle it from charge-dipole interactions. The magnitude of the enhancement depends on the assumed particle hydration and collision kinetics but is increasingly important at smaller sizes, resulting in a steep rise in the observed growth rates with decreasing size. Including the experimental results in a global model, we find that the enhanced growth rate of sulfuric acid particles increases the predicted particle number concentrations in the upper free troposphere by more than 50 %.Peer reviewe

Role of iodine oxoacids in atmospheric aerosol nucleation

Iodic acid (HIO₃) is known to form aerosol particles in coastal marine regions, but predicted nucleation and growth rates are lacking. Using the CERN CLOUD (Cosmics Leaving Outdoor Droplets) chamber, we find that the nucleation rates of HIO₃ particles are rapid, even exceeding sulfuric acid–ammonia rates under similar conditions. We also find that ion-induced nucleation involves IO₃⁻ and the sequential addition of HIO₃ and that it proceeds at the kinetic limit below +10°C. In contrast, neutral nucleation involves the repeated sequential addition of iodous acid (HIO₂) followed by HIO₃, showing that HIO₂ plays a key stabilizing role. Freshly formed particles are composed almost entirely of HIO₃, which drives rapid particle growth at the kinetic limit. Our measurements indicate that iodine oxoacid particle formation can compete with sulfuric acid in pristine regions of the atmosphere

Automated, high-throughput image calibration for parallel-laser photogrammetry (advance online)

Parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject’s body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher meas-uring the same image twice will rarely return the same values both times (resulting in within-observer error), as is also the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques in ImageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data