The Atmospheric Chemist’s Companion

Williams, Jonathan; Warneck, Peter

doi:10.1007/978-94-007-2275-0

Cited by 86 publications

(53 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another source of Cl -is the combustion of fossil fuels and waste incineration (Li et al, 2011;Wang et al, 2011). At our study site, the median Cl -/Na + ratios ranged from 2.1 (Event 1) to 10.3 (Event 6) which is substantially higher than the average ratio in seawater (1.16, Warneck and Williams, 2012). Potassium (K + ) is mainly emitted from biomass burning (Li et al, 2011) and was the highest in Event 4.…”

Section: Ionic Composition and Ecmentioning

confidence: 77%

Fog Water Chemical Composition on Ailaoshan Mountain, Yunnan Province, SW China

Nieberding

Breuer

Braeckevelt

et al. 2018

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

Between December 2015 and March 2016, fog water was collected at the subtropical mountain cloud forest site Ailaoshan in SW China at 2476 m above mean sea level. An active fog collector was employed to collect 117 samples during more than 140 hours of fog, covering 6 major fog events. The chemical analysis included acidity and inorganic ion concentrations. The median pH values of the fog events varied between 3.7 and 4.2, characterizing the fog water as acidic (pH < 5.0) to very acidic (pH < 4.0). The ion composition was dominated by H + , NH 4 + , SO 4 2-and NO 3 -, which made up more than 86% of the total ionic concentration (TIC). The generally rather high ion concentration levels cannot be explained by nearby emission sources but rather by long-range transport of air pollutants from various sources in East Asia. During one event on February 8, 2016, it is evident that it was the emissions from a large coal-fired power plant in Myanmar that led to high concentrations of sulphuric acid. This pilot study of fog chemistry in rural, mountainous SW China should be complemented by studies spanning the moist summer monsoon season and covering more chemical species.

show abstract

Section: Ionic Composition and Ecmentioning

confidence: 77%

Fog Water Chemical Composition on Ailaoshan Mountain, Yunnan Province, SW China

Nieberding

Breuer

Braeckevelt

et al. 2018

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

show abstract

“…The diabatic freezing time is determined by the rate of latent heat removal to the underlying rime substrate Inter-and extrapolated from Brownscombe and Hallett (1967); Macklin andPayne (1967, 1969). 2 Calculated for the corresponding LWCs for 3m s −1 (Macklin and Payne, 1967 Calculated at 0 • C and at the corresponding pH (Warneck and Williams, 2012;Barret et al, 2011;Johnson et al, 1996;Compernolle and Müller, 2014;Marsh and McElroy, 1985;Schwartz and White, 1981;Hales and Drewes, 1979;Maahs, 1982). 5 The mass accommodation coefficients (α at 273K) are taken from a review paper (Davidovits et al, 2006) or estimated as described elsewhere (Ervens et al, 2003).…”

Section: Model Descriptionmentioning

confidence: 99%

“…But also in urban regions carboxylic acids may contribute significantly to the free acidity in precipitation (Kawamura et al, 1996). Furthermore, they have a low photochemical reactivity in the atmospheric gas phase (photochemical lifetimes are more than a week), so that important sinks for these organic acids are dry and wet deposition (Chebbi and Carlier, 1996;Warneck, 2000;Warneck and Williams, 2012).…”

Section: Introductionmentioning

confidence: 99%

Chemistry of riming: the retention of organic and inorganic atmospheric trace constituents

Jost

Szakáll²,

Diehl³

et al. 2017

Atmos. Chem. Phys.

View full text Add to dashboard Cite

Abstract. During free fall in clouds, ice hydrometeors such as snowflakes and ice particles grow effectively by riming, i.e., the accretion of supercooled droplets. Volatile atmospheric trace constituents dissolved in the supercooled droplets may remain in ice during freezing or may be released back to the gas phase. This process is quantified by retention coefficients. Once in the ice phase the trace constituents may be vertically redistributed by scavenging and subsequent precipitation or by evaporation of these ice hydrometeors at high altitudes. Retention coefficients of the most dominant carboxylic acids and aldehydes found in cloud water were investigated in the Mainz vertical wind tunnel under dry-growth (surface temperature less than 0 • C) riming conditions which are typically prevailing in the mixed-phase zone of convective clouds (i.e., temperatures from −16 to −7 • C and a liquid water content (LWC) of 0.9±0.2 g m −3 ). The mean retention coefficients of formic and acetic acids are found to be 0.68 ± 0.09 and 0.63 ± 0.19. Oxalic and malonic acids as well as formaldehyde show mean retention coefficients of 0.97 ± 0.06, 0.98 ± 0.08, and 0.97 ± 0.11, respectively. Application of a semi-empirical model on the present and earlier wind tunnel measurements reveals that retention coefficients can be well interpreted by the effective Henry's law constant accounting for solubility and dissociation. A parameterization for the retention coefficients has been derived for substances whose aqueous-phase kinetics are fast compared to mass transport timescales. For other cases, the semi-empirical model in combination with a kinetic approach is suited to determine the retention coefficients. These may be implemented in high-resolution cloud models.

show abstract

“…We take the Ångström Law fit derived by Patat et al (2011) for Cerro Paranal in the optical wavelengths. We then decomposed this fit into several aerosol types given by Warneck & Williams (2012), and produced the phase function using a Mie scattering code for log normal distributions based on Bohren & Huffman (1983).…”

Section: Mie Scatteringmentioning

confidence: 99%

An advanced scattered moonlight model for Cerro Paranal

Jones

Noll

Kausch

et al. 2013

A&A

181

177

View full text Add to dashboard Cite

The largest natural source of light at night is the Moon, and it is the major contributor to the astronomical sky background. Being able to accurately predict the sky background, including scattered moonlight is important for scheduling astronomical observations. We have developed an improved scattered moonlight model, in which the components are computed with a better physical understanding as opposed to the simple empirical fit in the frequently used photometric model of Krisciunas & Schaefer (1991, PASP, 103, 1033). Our spectroscopic model can better trace the spectral trends of scattered moonlight for any position of the Moon and target observation. This is the first scattered moonlight model that we know of which is this physical and versatile. We have incorporated an observed solar spectrum, accurate lunar albedo fit, and elaborate scattering and absorption calculations that include scattering off of molecules and aerosols. It was designed for Cerro Paranal, but can be modified for any location with known atmospheric properties. Throughout the optical range, the uncertainty is less than 20%. This advanced scattered moonlight model can predict the amount of scattered moonlight for any given geometry of the Moon and target, and lunar phase for the entire optical spectrum.

show abstract

The Atmospheric Chemist’s Companion

Cited by 86 publications

References 0 publications

Fog Water Chemical Composition on Ailaoshan Mountain, Yunnan Province, SW China

Fog Water Chemical Composition on Ailaoshan Mountain, Yunnan Province, SW China

Chemistry of riming: the retention of organic and inorganic atmospheric trace constituents

An advanced scattered moonlight model for Cerro Paranal

Contact Info

Product

Resources

About