The gas-phase reaction of ClONO2 with hydrogen chloride

The gas-phase reaction of ClONO₂ with HCl was investigated using two large-volume environmental chambers with analysis by in situ long pathlength Fourier transform infrared absorption spectroscopy. In these chambers the reaction was observed to proceed, at least in part, by heterogenous routes, and an upper limit to the rate constant for the homogeneous gas-phase reaction of geneous routes, and an upper limit to the rate constant for the homogeneous gas-phase reaction of $$k\left( {{\text{ClONO}}_{\text{2}} + {\text{HCl}}} \right) < 1.5 \times 10^{ - 19} {\text{ cm}}^{\text{3}} {\text{ molecule}}^{{\text{ - 1}}} {\text{ s}}^{{\text{ - 1}}}$$ Was derived at 298±2K. Assuming that this room-temperature upper limit to the rate constant is applicable to stratospheric temperatures, this homogeneous gas-phase reaction can be estimated to be of negligible importance as a ClONO₂ loss process in the stratosphere.     

The observed relation between the Kolmogorov and von Karman constants in the surface boundary layer

In steady, neutrally-stratified flow over uniform terrain, the Kolmogorov constant for the one-dimensional spectrum in the inertial subrange (α ₁) and the von Karman constant of the logarithmic profile (k) are shown to be related by $$\alpha _1 k^{{4 \mathord{\left/ {\vphantom {4 3}} \right. \kern-\nulldelimiterspace} 3}} = \left[ {\frac{{\sum \phi }}{{0.555}}} \right]\left[ {\frac{{nz}}{{\bar U_z }}} \right]^{{2 \mathord{\left/ {\vphantom {2 3}} \right. \kern-\nulldelimiterspace} 3}} \left[ {\frac{{\ln z_2 /z_1 }}{{\bar U_2 - \bar U_1 }}} \right]^2 \simeq 0.136,$$ , where the numerical value results from field measurements recorded in near-ideal conditions. This experimentally-observed Kolmogorov-von Karman ‘K-von K’ product is close to the value designated by a one-dimensional equivalent of the theoretical relation previously given by Roth (1970). More-over, it is in remarkably close agreement with new values of both constants independently proposed in recent years.     

Surface influence upon vertical profiles in the nocturnal boundary layer

Near-surface wind profiles in the nocturnal boundary layer, depth h, above relatively flat, tree-covered terrain are described in the context of the analysis of Garratt (1980) for the unstable atmospheric boundary layer. The observations at two sites imply a surface-based transition layer, of depth z _*, within which the observed non-dimensional profiles Φ _M ⁰ are a modified form of the inertial sub-layer relation \(\Phi _M \left( {{z \mathord{\left/ {\vphantom {z L}} \right. \kern-0em} L}} \right) = \left( {{{1 + 5_Z } \mathord{\left/ {\vphantom {{1 + 5_Z } L}} \right. \kern-0em} L}} \right)\) according to $$\Phi _M^{\text{0}} \simeq \left( {{{1 + 5z} \mathord{\left/ {\vphantom {{1 + 5z} L}} \right. \kern-\nulldelimiterspace} L}} \right)\exp \left[ { - 0.7\left( {{{1 - z} \mathord{\left/ {\vphantom {{1 - z} z}} \right. \kern-\nulldelimiterspace} z}_ * } \right)} \right]$$ , where z is height above the zero-plane displacement and L is the Monin-Obukhov length. At both sites the depth z _* is significantly smaller than the appropriate neutral value (z _*N ) found from the previous analysis, as might be expected in the presence of a buoyant sink for turbulent kinetic energy.     

Estimating a Lagrangian Length Scale Using Measurements of CO2 in a Plant Canopy

Analytical Lagrangian equations capable of predicting concentration profiles from known source distributions offer the opportunity to calculate source/sink distributions through inverted forms of these equations. Inverse analytical Lagrangian equations provide a practical means of estimating source profiles using concentration and turbulence measurements. Uncertainty concerning estimates of the essentially immeasurable Lagrangian length scale ( ${\mathcal{L}}$ ), a key input, impedes the operational practicality of this method. The present study evaluates ${\mathcal{L}}$ within a corn canopy by using field measurements to constrain an analytical Lagrangian equation. Measurements of net CO₂ flux, soil-to-atmosphere CO₂ flux, and in-canopy profiles of CO₂ concentration provided the information required to solve for ${\mathcal{L}}$ in a global optimization algorithm for 30-min time intervals. For days when the canopy was a strong CO₂ sink, the optimization frequently located ${\mathcal{L}}$ profiles that follow a convex shape. A constrained optimization then fit the profile shape to a smooth sigmoidal equation. Inputting the optimized ${\mathcal{L}}$ profiles in the forward and inverse Lagrangian equations leads to strong correlations between measured and calculated concentrations and fluxes. Coefficients of the sigmoidal equation were specific to each 30-min period and did not scale with any measured variable. Plausible looking ${\mathcal{L}}$ profiles were associated with negative bulk Richardson number values. Once the canopy senesced, a simple eddy diffusivity profile sufficed to relate concentrations and sources in the analytical Lagrangian equations.     

Hourly estimation of soil heat flux density at the soil surface with three models and two field methods

Heat flux density at the soil surface (G ₀) was evaluated hourly on a vegetal cover 0.08 m high, with a leaf area index of 1.07 m² m^?2, during daylight hours, using Choudhury et al. (Agric For Meteorol 39:283–297, 1987) ( $ G_0^{\text{rn}} $ ), Santanello and Friedl (J Appl Meteorol 42:851–862, 2003) ( $ G_0^{\text{s}} $ ), and force-restore ( $ G_0^{\text{fr}} $ ) models and the plate calorimetry methodology ( $ G_0^{\text{pco}} $ ), where the gradient calorimetry methodology (G _0R ) served as a reference for determining G ₀. It was found that the peak of G _0R was at 1 p.m., with values that ranged between 60 and 100 W m^?2 and that the G ₀/Rn relation varied during the day with values close to zero in the early hours of the morning and close to 0.25 in the last hours of daylight. The $ G_0^{\text{s}} $ model presented the best performance, followed by the $ G_0^{\text{rn}} $ and $ G_0^{\text{fr}} $ models. The plate calorimetry methodology showed a similar behavior to that of the gradient calorimetry referential methodology.     

Scaling fluctuation analysis and statistical hypothesis testing of anthropogenic warming

Although current global warming may have a large anthropogenic component, its quantification relies primarily on complex General Circulation Models (GCM’s) assumptions and codes; it is desirable to complement this with empirically based methodologies. Previous attempts to use the recent climate record have concentrated on “fingerprinting” or otherwise comparing the record with GCM outputs. By using CO₂ radiative forcings as a linear surrogate for all anthropogenic effects we estimate the total anthropogenic warming and (effective) climate sensitivity finding: ΔT _anth  = 0.87 ± 0.11 K, $\uplambda_{{2{\text{x}}{\text{CO}}_{2} ,{\text{eff}}}} = 3.08 \pm 0.58\,{\text{K}}$ . These are close the IPPC AR5 values ΔT _anth  = 0.85 ± 0.20 K and $\uplambda_{{2{\text{x}}{\text{CO}}_{2} }} = 1.5\!-\!4.5\,{\text{K}}$ (equilibrium) climate sensitivity and are independent of GCM models, radiative transfer calculations and emission histories. We statistically formulate the hypothesis of warming through natural variability by using centennial scale probabilities of natural fluctuations estimated using scaling, fluctuation analysis on multiproxy data. We take into account two nonclassical statistical features—long range statistical dependencies and “fat tailed” probability distributions (both of which greatly amplify the probability of extremes). Even in the most unfavourable cases, we may reject the natural variability hypothesis at confidence levels >99 %.     

Cross-Validation of Open-Path and Closed-Path Eddy-Covariance Techniques for Observing Methane Fluxes

Methane ( ${\mathrm {CH}}_{4}$ ) fluxes observed with the eddy-covariance technique using an open-path ${\mathrm {CH}}_{4}$ analyzer and a closed-path ${\mathrm {CH}}_{4}$ analyzer in a rice paddy field were evaluated with an emphasis on the flux correction methodology. A comparison of the fluxes obtained by the analyzers revealed that both the open-path and closed-path techniques were reliable, provided that appropriate corrections were applied. For the open-path approach, the influence of fluctuations in air density and the line shape variation in laser absorption spectroscopy (hereafter, spectroscopic effect) was significant, and the relative importance of these corrections would increase when observing small ${\mathrm {CH}}_{4}$ fluxes. A new procedure proposed by Li-Cor Inc. enabled us to accurately adjust for these effects. The high-frequency loss of the open-path ${\mathrm {CH}}_{4}$ analyzer was relatively large (11 % of the uncorrected covariance) at an observation height of 2.5 m above the canopy owing to its longer physical path length, and this correction should be carefully applied before correcting for the influence of fluctuations in air density and the spectroscopic effect. Uncorrected ${\mathrm {CH}}_{4}$ fluxes observed with the closed-path analyzer were substantially underestimated (37 %) due to high-frequency loss because an undersized pump was used in the observation. Both the bandpass and transfer function approaches successfully corrected this flux loss. Careful determination of the bandpass frequency range or the transfer function and the cospectral model is required for the accurate calculation of ${\mathrm {CH}}_{4}$ fluxes with the closed-path technique.     

Numerical simulation of particle trajectories in inhomogeneous turbulence,III: Comparison of predictions with experimental data for the atmospheric surface layer

It is shown that predictions of a numerical trajectory-simulation method agree closely with the Project Prairie Grass observations of the concentrations 100 m downwind of a continuous point source of sulphur dioxide if the height (z) dependence of the Lagrangian length scale Λ _L is chosen as: whereL is the Monin-Obukhov length. The value of 0.5 for Λ _L /z in neutral conditions is consistent with the findings of Reid (1979) for the Porton experiment, and is also shown to be the best choice for simulation of an experiment in which concentration profiles were measured a short distance (< 40 m) downwind of an elevated point source of glass beads (40 μn diameter). $$\begin{gathered} \Lambda _L = 0.5z\left( {1 - 6\frac{z}{L}} \right)^{{1 \mathord{\left/ {\vphantom {1 4}} \right. \kern-\nulldelimiterspace} 4}} L< 0 \hfill \\ \Lambda _L = 0.5z/\left( {1 + 5\frac{z}{L}} \right)L > 0 \hfill \\ \end{gathered} $$      

Impacts of Mixing Processes in Nocturnal Atmospheric Boundary Layer on Urban Ozone Concentrations

A number of open questions remain regarding the role of low-level jets (LLJs) and nocturnal mixing processes in the buildup of tropospheric ozone. The prevalence of southerly winds and LLJs in the U.S. Southern Great Plains during summer makes this region an ideal site for investigating the structure of the nocturnal boundary layer and its impacts on urban air quality. Ozone $(\mathrm{O}_{3})$ and nitrogen oxide concentrations measured at regulatory monitoring sites in the Oklahoma City (OKC) area and simulations with the Weather Research and Forecasting with Chemistry (WRF/Chem) model were analyzed to show how the nocturnal LLJ moderates boundary-layer mixing processes and air quality. Datasets collected during the Joint Urban 2003 campaign, which took place in July 2003 in OKC, provided detailed information about nocturnal boundary-layer structure and dynamics. In general, ${\mathrm{O}_{3}}$ time series show the expected behavior that urban ${\mathrm{O}_{3}}$ concentrations decrease at night due to nitrogen oxide titration reactions, but elevated ${\mathrm{O}_{3}}$ concentrations and secondary ${\mathrm{O}_{3}}$ peaks are also seen quite frequently after sunset. LLJs developed on most nights during the study period and were associated with strong vertical wind shear, which affected the boundary-layer stability and structure. Near-surface ${\mathrm{O}_{3}}$ concentrations are higher during less stable nights when active mixing persists throughout the night. The WRF/Chem model results agree well with the observations and further demonstrate the role of LLJs in moderating nocturnal mixing processes and air quality. The highest nocturnal ${\mathrm{O}_{3}}$ concentrations are linked to a strong LLJ that promotes both nocturnal long-range transport and persistent downward mixing of ${\mathrm{O}_{3}}$ from the residual layer to the surface.     

Role of atmospheric ammonia in the formation of inorganic secondary particulate matter: A study at Kanpur,India

Levels of fine Particulate Matter (PM_fine), SO₂ and NO_x are interlinked through atmospheric reactions to a large extent. NO_x, NH₃, SO₂, temperature and humidity are the important atmospheric constituents/conditions governing formation of fine particulate sulfates and nitrates. To understand the formation of inorganic secondary particles (nitrates and sulfates) in the atmosphere, a study was undertaken in Kanpur, India. Specifically, the study was designed to measure the atmospheric levels of covering winter and summer seasons and day and night samplings to capture the diurnal variations. Results showed are found to be significantly high in winter season compared to the summer season. In winter, the molar ratio of to was found to be greater than 2:1. This higher molar ratio suggests that in addition to (NH₄)₂SO₄, NH₄NO₃ will be formed because of excess quantity of present. In summer, the molar ratio was less than 2:1 indicating deficit of to produce NH₄NO₃. The nitrogen conversion ratio (NO₂ to NO₃) was found to be nearly 50% in the study area that suggested quick conversion of NO₂ into nitric acid. As an overall conclusion, this study finds that NH₃ plays a vital role in the formation of fine inorganic secondary particles particularly so in winter months and there is a need to identify and assess sources of ammonia emissions in India.     

A Semi-Analytical Approach for Parametrization of the Obukhov Stability Parameter in the Unstable Atmospheric Surface Layer

A semi-analytical scheme is proposed to parametrize the Obukhov stability parameter \(\zeta \) (= \(z/L\) ; \(z\) is the height above the ground and \(L\) is the Obukhov length) in terms of the bulk Richardson number ( \(R_{iB}\) ) in unstable conditions within the framework of Monin–Obukhov similarity (MOS) theory. The scheme involves, (i) a solution of a cubic equation in \(\zeta \) whose coefficients depend on the gradient Richardson number ( \(R_{i}\) ), and (ii) a relationship between \(R_{i}\) and \(R_{iB}\) . The proposed scheme is applicable for a wide range (i) \(-5\le R_{iB}\le 0\) , (ii) \(0\le \hbox {ln}(z_{0}/z_{h})\le 29.0\) , and (iii) \(10\le z/z_{0}\le 10^{5}\) and performs relatively better than all other schemes in terms of accuracy in computation of surface-layer transfer coefficients. The absolute errors in computing the transfer coefficients do not exceed 7 %. The analysis presented here is found to be valid for different \(\gamma _{m}\) and \(\gamma _{h}\) appearing in the expressions of the similarity functions \(\varphi _{m}\) and \(\varphi _{h}\) (representing non-dimensional wind and temperature profiles), so long as the ratio of \(\gamma _{m}\) to \(\gamma _{h} \ge 1\) . The improved scheme can be easily employed in atmospheric modelling for a comprehensive range of \(R_{iB}\) and a variety of surfaces.     

Chemistry of Precipitation Events and Inter-Relationship with Ambient Aerosols over a Semi-Arid Region in Western India

The precipitation events (n = 91), collected for 3 years (2000–2002) during the period of SW-monsoon (Jun–Aug) from an urban site (Ahmedabad, 23.0°N, 72.6°E) of a semi-arid region in western India, are found to exhibit characteristic differences in terms of their solute contents. The low solute (<700 μeq L⁻¹) events are either marked by heavy precipitation amount or successive events collected during an extended rain spell; whereas light precipitation events occurring after antecedent dry period are characterized by high solutes (>700 μeq L⁻¹). The ionic composition of low solute events show large variability due to varying contribution of anthropogenic species (: 1%–74%; : 1%–25%; and : 8%–68%) to the respective ion balance. In high solute events, ionic abundances are dominated by mineral dust (Ca²⁺ and ) and sea-salts (Na⁺ and Cl⁻). These differences are also reflected in the pH of low solute events (range: 5.2–7.4, VWM: 6.4) and high solute events (range: 6.6–8.2, VWM: 7.3). The comparison of Ca²⁺/Na⁺ and nss- ratios (on equivalent basis) in rain and aerosols suggests that the ionic composition of high solute events is influenced by below-cloud scavenging; whereas evidence for in-cloud scavenging is significantly reflected in low solute events. The annual wet-deposition fluxes of and are 330 and 480 mg m⁻² y⁻¹, respectively, in contrast to their corresponding dry-deposition fluxes (14 and 160 mg m⁻² y⁻¹); whereas wet and dry removal of Ca²⁺, Mg²⁺ and are comparable.     

Scalar Flux–Gradient Relationships Under Unstable Conditions over Water in Coastal Regions

The scalar flux–gradient relationships of temperature ( $\phi _{T}$ ? T ) and specific humidity ( $\phi _{q}$ ? q ) under unstable conditions are investigated using eddy-covariance measurements of air–sea turbulent fluxes and vertical profiles of temperature and specific humidity collected from a marine meteorological platform. The gradients of temperature and specific humidity are obtained from measurements at five heights above the sea surface using the log-square fitting method and the simpler first-order approximation method. The two methods yield similar results. The proposed flux–gradient relationships $\phi _{T}$ ? T and $\phi _{q}$ ? q covers a wide range of instability: the stability parameter $\zeta $ ζ ranges from $-$ ? 0.1 to $-$ ? 50. The functional form of the proposed flux–gradient relationships is an interpolation between the Businger–Dyer relation and the free convection relation, which includes the “ $-$ ? 1/2” and “ $-$ ? 1/3” scaling laws at two different stability regimes. The widely used COARE 3.0 algorithm, which is an interpolation between the integrals of the Businger–Dyer and the free convection relations, is also evaluated and compared. The analysis and comparisons show that both schemes generate reasonable values of $\phi _{q}$ ? q in the whole unstable regime. The COARE 3.0 algorithm, however, overestimates $\phi _{T}$ ? T values under very unstable conditions. The errors in the flux–gradient relationships induced by the random errors in the turbulence measurements are assessed. When the random errors are taken into account, the observations agree with predictions of various schemes fairly well, implying that the dominant transport mechanism is adequately captured by the Monin–Obukhov similarity theory. The study also shows that $\phi _{q}$ ? q is significantly ${>}\phi _{T}$ > ? T under unstable conditions and that the ratio $\phi _{q}/\phi _{T}$ ? q / ? T increases with $-\zeta $ ? ζ . The ratio of $\phi _{q}$ ? q to $\phi _{T}$ ? T and the ratio of turbulent transport efficiencies of heat and water vapour ( $R_{wT}/R_{wq}$ R wT / R wq ) suggest that heat is transported more efficiently than water vapour under unstable conditions.     

Observed and simulated sensitivities of summertime urban surface air temperatures to anthropogenic heat in downtown areas of two Japanese Major Cities,Tokyo and Osaka

In this study, the sensitivities of surface air temperatures to anthropogenic heat (AH) were investigated in downtowns of the two Japanese major cities, Tokyo and Osaka. First, meteorological measurements were made with the simultaneous monitoring of electricity demand in a contrastive couple of a downtown commercial area (C-area) and a residential area (R-area) within each city in summer 2007. From the measurements, the areal-mean surface air temperatures were obtained as \( {\overline{T}}_{\mathrm{C}} \) and \( {\overline{T}}_{\mathrm{R}} \) for each of the C-area and R-area, respectively. Using the actual electricity demand and the estimated motor fuels consumption, their areal total was evaluated as the energy-consumption-basis AH. The estimated C-areas' AH indicated greater values up to 220 W/m² on weekdays and remarkable decrease about by half on weekends, whereas that in the R-areas showed less values of 10–20 W/m² stably. Then, \( {\overline{T}}_{\mathrm{C}}-{\overline{T}}_{\mathrm{R}} \) on calm and fine days were found to be systematically decreased from weekdays to weekends in both cities roughly indicating a proportional relationship with the reductions in the C-areas' AH on weekends. The result suggested a common afternoon sensitivity for both C-areas of around 1.0°C/100 W/m², which indicated an intensity of the AH impact on surface air temperature there. Next, to simulate the observed AH impact, the authors' CM-BEM (a multilayer urban canopy model coupled with a building energy model) was newly implemented in the mesoscale Weather Research and Forecasting (WMF) model. This new system, WRF-CM-BEM, was applied to Tokyo and almost reasonably validated from the aspects of the reproducibility of urban surface air temperature and electricity demand in the observation areas. The simulations also suggested that WRF-CM-BEM underestimated the observed air temperature sensitivity to AH in the Tokyo C-area roughly by half but still in the same order of magnitude.     

Absolute OH quantum yields in the laser photolysis of nitrate,nitrite and dissolved H2O2 at 308 and 351 nm in the temperature range 278–353 K

Absolute quantum yields for the formation of OH radicals in the laser photolysis of aqueous solutions of NO₃ ^-, NO₂ ^- and H₂O₂ at 308 and 351 nm and as a function of pH and temperature have been measured. A scavenging technique involving the reaction between OH and SCN^- ions and the time resolved detection by visible absorption of the (SCN)₂ ^- radical ion was used to determine the absolute OH yields. The following results were obtained:

1. NO ₃ ^- -photolysis:% MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaqabeaacaaIWa% GaaGioaGGaaiab-bcaGiaab6gacaqGTbGaaeOoaiab-bcaGiabfA6a% gnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaaiikaiaaikdacaaI5a% GaaGioaiab-bcaGiab-P5aljaacMcacqGH9aqpcqWFGaaicqWFWaam% cqWFUaGlcqWFWaamcqWFXaqmcqWF3aWncqWFGaaicqGHXcqScqWFGa% aicqWFWaamcqWFUaGlcqWFWaamcqWFWaamcqWFZaWmcqWFGaaicaqG% MbGaae4BaiaabkhacaqGGaGaaeinaiaabccacqGHKjYOcaqGGaGaam% iCaiaabIeacaqGGaGaeyizImQaaeiiaiaabMdaaeaacqWFGaaicqWF% GaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicq% WFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqqHMoGr% daWgaaWcbaGae83Nd8Kae83LdGeabeaakiaacIcacaWGubGaaiykai% abg2da9iabfA6agnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaaiik% aiaaikdacaaI5aGaaGioaiab-bcaGiab-P5aljaacMcacqWFGaaica% qGLbGaaeiEaiaabchacaqGGaWaamWaaeaacaqGOaGaaeymaiaabIda% caqGWaGaaeimaiaabccacqGHXcqScaaI0aGaaGioaiaaicdacaqGPa% GaaeikamaalaaabaGaaeymaaqaaiaabkdacaqG5aGaaeioaaaacaqG% GaGaeyOeI0IaaeiiamaalaaabaGaaeymaaqaaiaadsfaaaGaaeykaa% Gaay5waiaaw2faaiaac6caaaaa!9673!\[\begin{gathered}08 {\text{nm:}} \Phi _{{\rm O}{\rm H}} (298 {\rm K}) = 0.017 \pm 0.003 {\text{for 4 }} \leqslant {\text{ }}p{\text{H }} \leqslant {\text{ 9}} \hfill \\\Phi _{{\rm O}{\rm H}} (T) = \Phi _{{\rm O}{\rm H}} (298 {\rm K}) {\text{exp }}\left[ {{\text{(1800 }} \pm 480{\text{)(}}\frac{{\text{1}}}{{{\text{298}}}}{\text{ }} - {\text{ }}\frac{{\text{1}}}{T}{\text{)}}} \right]. \hfill \\\end{gathered}\] Selected experiments at 351 nm indicate that these results are essentially unchanged.
2. NO ₂ ^- -photolysis:% MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaqabeaacaaIWa% GaaGioaGGaaiab-bcaGiaab6gacaqGTbGaaeOoaiab-bcaGiabfA6a% gnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaaiikaiaaikdacaaI5a% GaaGioaiab-bcaGiab-P5aljaacMcacqGH9aqpcqWFGaaicqWFOaak% cqWFWaamcqWFUaGlcqWFWaamcqWFXaqmcqWF3aWncqWFGaaicqGHXc% qScqWFGaaicqWFWaamcqWFUaGlcqWFWaamcqWFWaamcqWFXaqmcqWF% PaqkcqWFGaaicaqGMbGaae4BaiaabkhacaqGGaGaaeinaiaabccacq% GHKjYOcaqGGaGaamiCaiaabIeacaqGGaGaeyizImQaaeiiaiaabMda% caqGSaaabaGae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8% hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIa% e8hiaaIae8hiaaIaeuOPdy0aaSbaaSqaaiab-95apjab-D5aibqaba% GccaGGOaGaamivaiaacMcacqGH9aqpcqqHMoGrdaWgaaWcbaGae83N% d8Kae83LdGeabeaakiaacIcacaaIYaGaaGyoaiaaiIdacqWFGaaicq% WFAoWscaGGPaGae8hiaaIaaeyzaiaabIhacaqGWbGaaeiiamaadmaa% baGaaeikaiaabgdacaqG1aGaaeOnaiaabcdacaqGGaGaeyySaeRaae% iiaiaabodacaqG2aGaaeimaiaabMcacaqGOaWaaSaaaeaacaqGXaaa% baGaaeOmaiaabMdacaqG4aaaaiaabccacqGHsislcaqGGaWaaSaaae% aacaqGXaaabaGaamivaaaacaqGPaaacaGLBbGaayzxaaGaaiilaaqa% aiaaiodacaaI1aGaaGymaiaabccacaqGUbGaaeyBaiaabQdacqWFGa% aicqqHMoGrdaWgaaWcbaGae83Nd8Kae83LdGeabeaakiaacIcacaaI% YaGaaGyoaiaaiIdacqWFGaaicqWFAoWscaGGPaGaeyypa0Jae8hiaa% Iae8hkaGIae8hmaaJae8Nla4Iae8hmaaJae8hnaqJae8NnayJae8hi% aaIaeyySaeRae8hiaaIae8hmaaJae8Nla4Iae8hmaaJae8hmaaJae8% xoaKJae8xkaKIae8hiaaIaaeOzaiaab+gacaqGYbGaaeiiaiaabsda% caqGGaGaeyizImQaaeiiaiaadchacaqGibGaaeiiaiaab2dacaqGGa% GaaeioaiaabYcaaeaacqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWF% GaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicqWFGaaicq% WFGaaicqWFGaaicqWFGaaicqqHMoGrdaWgaaWcbaGae83Nd8Kae83L% dGeabeaakiaacIcacaWGubGaaiykaiabg2da9iabfA6agnaaBaaale% aacqWFFoWtcqWFxoasaeqaaOGaaiikaiaaikdacaaI5aGaaGioaiab% -bcaGiab-P5aljaacMcacqWFGaaicaqGLbGaaeiEaiaabchacaqGGa% WaamWaaeaacaqGOaGaaeymaiaabIdacaqGWaGaaeimaiaabccacqGH% XcqScaqGGaGaaeinaiaabcdacaqGWaGaaeykaiaabIcadaWcaaqaai% aabgdaaeaacaqGYaGaaeyoaiaabIdaaaGaaeiiaiabgkHiTiaabcca% daWcaaqaaiaabgdaaeaacaWGubaaaiaabMcaaiaawUfacaGLDbaaca% GGUaaaaaa!FC61!\[\begin{gathered}08 {\text{nm:}} \Phi _{{\rm O}{\rm H}} (298 {\rm K}) = (0.017 \pm 0.001) {\text{for 4 }} \leqslant {\text{ }}p{\text{H }} \leqslant {\text{ 9,}} \hfill \\\Phi _{{\rm O}{\rm H}} (T) = \Phi _{{\rm O}{\rm H}} (298 {\rm K}) {\text{exp }}\left[ {{\text{(1560 }} \pm {\text{ 360)(}}\frac{{\text{1}}}{{{\text{298}}}}{\text{ }} - {\text{ }}\frac{{\text{1}}}{T}{\text{)}}} \right], \hfill \\351{\text{ nm:}} \Phi _{{\rm O}{\rm H}} (298 {\rm K}) = (0.046 \pm 0.009) {\text{for 4 }} \leqslant {\text{ }}p{\text{H = 8,}} \hfill \\\Phi _{{\rm O}{\rm H}} (T) = \Phi _{{\rm O}{\rm H}} (298 {\rm K}) {\text{exp }}\left[ {{\text{(1800 }} \pm {\text{ 400)(}}\frac{{\text{1}}}{{{\text{298}}}}{\text{ }} - {\text{ }}\frac{{\text{1}}}{T}{\text{)}}} \right]. \hfill \\\end{gathered}\]
3. H₂O₂-photolysis:% MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGceaqabeaacaaIWa% GaaGioaGGaaiab-bcaGiaab6gacaqGTbGaaeOoaiab-bcaGiabfA6a% gnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaaiikaiaaikdacaaI5a% GaaGioaiab-bcaGiab-P5aljaacMcacqGH9aqpcqWFGaaicqWFOaak% cqWFWaamcqWFUaGlcqWF5aqocqWF4aaocqWFGaaicqGHXcqScqWFGa% aicqWFWaamcqWFUaGlcqWFWaamcqWFZaWmcqWFPaqkcqWFGaaicaqG% MbGaae4BaiaabkhacaqGGaGaamiCaiaabIeacaqGGaGaeyizImQaae% iiaiaabEdacaqGSaaabaGae8hiaaIae8hiaaIae8hiaaIae8hiaaIa% e8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaaIae8hiaa% Iae8hiaaIae8hiaaIae8hiaaIaeuOPdy0aaSbaaSqaaiab-95apjab% -D5aibqabaGccaGGOaGaamivaiaacMcacqGH9aqpcqqHMoGrdaWgaa% WcbaGae83Nd8Kae83LdGeabeaakiaacIcacaaIYaGaaGyoaiaaiIda% cqWFGaaicqWFAoWscaGGPaGae8hiaaIaaeyzaiaabIhacaqGWbGaae% iiamaadmaabaGaaeikaiaabAdacaqG2aGaaeimaiaabccacqGHXcqS% caqGGaGaaeymaiaabMdacaqGWaGaaeykaiaabIcadaWcaaqaaiaabg% daaeaacaqGYaGaaeyoaiaabIdaaaGaaeiiaiabgkHiTiaabccadaWc% aaqaaiaabgdaaeaacaWGubaaaiaabMcaaiaawUfacaGLDbaacaGGSa% aabaGaaG4maiaaiwdacaaIXaGaaeiiaiaab6gacaqGTbGaaeOoaiab% -bcaGiabfA6agnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaaiikai% aaikdacaaI5aGaaGioaiab-bcaGiab-P5aljaacMcacqGH9aqpcqWF% GaaicqWFOaakcqWFWaamcqWFUaGlcqWF5aqocqWF2aGncqWFGaaicq% GHXcqScqWFGaaicqWFWaamcqWFUaGlcqWFWaamcqWF0aancqWFPaqk% cqWFGaaicaqGMbGaae4BaiaabkhacaqGGaGaaeinaiaabccacqGHKj% YOcaqGGaGaamiCaiaabIeacaqGGaGaaeypaiaabccacaqG3aGaaeil% aaqaaiab-bcaGiab-bcaGiab-bcaGiab-bcaGiab-bcaGiab-bcaGi% ab-bcaGiab-bcaGiab-bcaGiab-bcaGiab-bcaGiab-bcaGiab-bca% Giab-bcaGiabfA6agnaaBaaaleaacqWFFoWtcqWFxoasaeqaaOGaai% ikaiaadsfacaGGPaGaeyypa0JaeuOPdy0aaSbaaSqaaiab-95apjab% -D5aibqabaGccaGGOaGaaGOmaiaaiMdacaaI4aGae8hiaaIae8NMdS% Kaaiykaiab-bcaGiaabwgacaqG4bGaaeiCaiaabccadaWadaqaaiaa% bIcacaqG1aGaaeioaiaabcdacaqGGaGaeyySaeRaaeiiaiaabgdaca% qG2aGaaeimaiaabMcacaqGOaWaaSaaaeaacaqGXaaabaGaaeOmaiaa% bMdacaqG4aaaaiaabccacqGHsislcaqGGaWaaSaaaeaacaqGXaaaba% GaamivaaaacaqGPaaacaGLBbGaayzxaaGaaiOlaaaaaa!F3D0!\[\begin{gathered}08 {\text{nm:}} \Phi _{{\rm O}{\rm H}} (298 {\rm K}) = (0.98 \pm 0.03) {\text{for }}p{\text{H }} \leqslant {\text{ 7,}} \hfill \\\Phi _{{\rm O}{\rm H}} (T) = \Phi _{{\rm O}{\rm H}} (298 {\rm K}) {\text{exp }}\left[ {{\text{(660 }} \pm {\text{ 190)(}}\frac{{\text{1}}}{{{\text{298}}}}{\text{ }} - {\text{ }}\frac{{\text{1}}}{T}{\text{)}}} \right], \hfill \\351{\text{ nm:}} \Phi _{{\rm O}{\rm H}} (298 {\rm K}) = (0.96 \pm 0.04) {\text{for 4 }} \leqslant {\text{ }}p{\text{H = 7,}} \hfill \\\Phi _{{\rm O}{\rm H}} (T) = \Phi _{{\rm O}{\rm H}} (298 {\rm K}) {\text{exp }}\left[ {{\text{(580 }} \pm {\text{ 160)(}}\frac{{\text{1}}}{{{\text{298}}}}{\text{ }} - {\text{ }}\frac{{\text{1}}}{T}{\text{)}}} \right]. \hfill \\\end{gathered}\] Together with the absorption coefficients and an assumed actinic flux within atmospheric droplets of twice the clear air value, the partial photolytic lifetimes (τ_OH) of these molecules at 298 K are estimated as 10.5 d, 5.4 h and 30.3 h for NO₃ ^-, NO₂ ^- and H₂O₂, respectively. These lifetimes will increase by a factor of two (NO₃ ^-, NO₂ ^-) and by 15% (H₂O₂) at T=278 K. Using average ambient concentrations in tropospheric aqueous droplets, the photolytic OH source strengths from these species are calculated to be 2.8×10^-11, 1.3×10^-11 and 1.4×10^-11 mol 1^-1 s^-1 for NO₃ ^-, NO₂ ^- and H₂O₂ respectively.

     

On the vorticity and energy budgets of the cold vortex in Northeast China: a case study

This study examines the vorticity budgets, turbulent extended exergy and kinetic energy evolution equations to investigate the major dynamical and energy conversion processes contributing to the initiation and intensification of the cold vortex over Northeast China that occurred during June 19–22, 2009. The results show that the cyclonic vorticity was initiated in the lower troposphere due to the intense convergence of horizontal winds. The growth of cyclonic vorticity in the middle troposphere is mainly due to the vertical transportation of the vorticity, yet the increase of cyclonic vorticity in the upper troposphere primarily results from the horizontal advection of vorticity. Of special interest in this study is the evaluation of the role of thermal advections in the baroclinic development of the cold vortex. The results indicate that the rising of the air over relatively warm areas and the sinking of the air in relatively cold regions are favorable for releasing turbulent extended exergy $ \left( {e_{\text{t}} } \right) $ , which is later converted to turbulent kinetic energy $ \left( {k_{\text{t}} } \right) $ , and this process occurs during the initiation and intensification of the cold vortex. In addition, barotropic energy conversion is another important process that contributes to the growth of k _t, and it strengthens gradually after the initiation of the cold vortex. Other than frictional consumption, the flux of k _t in the vertical direction also depletes some of k _t. The fluxes of e _t, baroclinic energy conversions and diabatic generations are favorable factors for the growth of e _t, whereas it decreases with time as a result of a large amount of e _t that is released. Most of the energy conversion processes, including the baroclinic and the barotropic energy transformations and the energy conversions from e _t to k _t, as well as the fluxes of e _t, are stronger in the lower troposphere than the other areas during the formation of the cold vortex. This accounts for the initiation of the cyclonic vorticity in the lower troposphere. Finally, the fact that the turbulent extended exergy releases primarily in the middle troposphere through the vertical thermal circulation is consistent with our understanding based on the vorticity budget analyses.     

Parametrization of orographical effects in the planetary boundary layer

Studies of the influence of orography on the dynamics of atmospheric processes usually assume the following relation as a boundary condition at the surface of the Earth, or at the top of the planetary layer: $$w = u\frac{{\delta z_0 }}{{\delta x}} + v\frac{{\delta z_0 }}{{\delta y}}$$ where u, v and w are the components of wind velocity along the x, y and z axes, respectively, and z ₀ = z₀(x, y) is the equation of the Earth's orography. We see that w, and consequently the influence of orography on the dynamics of atmospheric processes, depend on the wind (u, v) and on the slope of the obstacle (δz ₀/δx, δz₀/δy). In the present work, it is shown that the above relation for w is insufficient to describe the influence of orography on the dynamics of the atmosphere. It is also shown that the relation is a particular case of the expression: $$\begin{gathered} w_h = \left| {v_g } \right|\left[ {a_1 (Ro,s)\frac{{\delta z_0 }}{{\delta x}} + a_2 (Ro,s)\frac{{\delta z_0 }}{{\delta y}}} \right] + \hfill \\ + \frac{{\left| {v_g } \right|^2 }}{f}\left[ {b_1 (Ro,s)\frac{{\delta ^2 z_0 }}{{\delta x^2 }} + b_2 (Ro,s)\frac{{\delta ^2 z_0 }}{{\delta y^2 }} + b_3 (Ro,s)\frac{{\delta ^2 z_0 }}{{\delta x\delta y}}} \right] \hfill \\ \end{gathered} $$ where ¦vv _g¦ is the strength of the geostrophic wind, a ₁, a₂, b₁, b₂, b₃ are functions of Rossby number Ro and of the external stability parameter s. The above relation is obtained with the help of similarity theory, with a parametrization of the planetary boundary layer. Finally, the authors show that a close connection exists between the effects described by the above expression and cyclo- and anticyclogenesis.     

Chemical characterization of wet precipitation events and deposition of pollutants in coal mining region,India

The present study investigated the chemical composition of wet atmospheric precipitation in India’s richest coal mining belt. Total 418 samples were collected on event basis at six sites from July to October in 2003 and May to October in 2004 and analysed for pH, EC, F⁻, Cl⁻, , , Ca²⁺, Mg²⁺, Na⁺, K⁺ and . The average pH value (5.7) of the rainwater of the investigated area is alkaline in nature. However, the temporal pH variation showed the alkaline nature during the early phase of monsoonal rainfall but it trends towards acidic during the late and high rainfall periods. The rainwater chemistry of the region showed high contribution of Ca²⁺ (47%) and (21%) in cations and (55%) and Cl⁻ (23%) in anionic abundance. The high non seas salt fraction (nss) of Ca²⁺ (99%) and Mg²⁺ (96%) suggests crustal source of the ions, while the high nss (96%) and high ratio signifying the impact of anthropogenic sources and the source of the acidity. The ratio of varies from 0.03 to 3.23 with the average value of 0.84 suggesting that Ca²⁺ and play a major role in neutralization processes. The assessment of the wet ionic deposition rates shows no any specific trend, however Ca²⁺ deposition rate was highest followed by and _.     

An Approach to Minimizing Artifacts Caused by Cross-Sensitivity in the Determination of Air–Sea \text{ CO }_{2} Flux Using the Eddy-Covariance Technique

The air–sea $\text{ CO }_{2}$ flux was measured from a research vessel in the North Yellow Sea in October 2007 using an open-path eddy-covariance technique. In 11 out of 64 samples, the normalized spectra of scalars ( $\text{ CO }_{2}$ , water vapour, and temperature) showed similarities. However, in the remaining samples, the normalized $\text{ CO }_{2}$ spectra were observed to be greater than those of water vapour and temperature at low frequencies. In this paper, the noise due to cross-sensitivity was identified through a combination of intercomparisons among the normalized spectra of three scalars and additional analyses. Upon examination, the cross-sensitivity noise appeared to be mainly present at frequencies ${<}0.8\,\text{ Hz }$ . Our analysis also suggested that the high-frequency fluctuations of $\text{ CO }_{2}$ concentration (frequency ${>}0.8\,\text{ Hz }$ ) was probably less affected by the cross-sensitivity. To circumvent the cross-sensitivity issue, the cospectrum in the high-frequency range 0.8–1.5 Hz, instead of the whole range, was used to estimate the $\text{ CO }_{2}$ flux by taking the contribution of the high frequency to the $\text{ CO }_{2}$ flux to be the same as the contribution to the water vapour flux. The estimated air–sea $\text{ CO }_{2}$ flux in the North Yellow Sea was $-0.039\,\pm \,0.048\,\text{ mg } \text{ m }^{-2}\,\text{ s }^{-1},$ a value comparable to the estimates using the inertial dissipation method and Edson’s method (Edson et al., J Geophys Res 116:C00F10, 2011).     

The acid catalyzed nitration of methanol: formation of methyl nitrate via aerosol chemistry

The aqueous phase acid-catalyzed reaction of methanol (CH₃OH) with nitric acid (HNO₃) to yield methyl nitrate (CH₃ONO₂) under atmospheric conditions has been investigated using gas-phase infrared spectroscopy. Reactions were conducted in aqueous sulfuric acid solutions (50.5–63.6 wt.%) with [CH₃OH] = 0.00005–0.005 M and [HNO₃] = 0.02–0.21 M, at 278.2–328.6 K. Methyl nitrate production rates increased linearly with CH₃OH and HNO₃ concentrations and exponentially with sulfuric acid weight percent within the regime studied. Rates increased linearly with nitronium ion concentration, indicating that the reaction involves as the nitrating agent under these conditions. At 298 K, the rate of methyl nitrate production can be calculated from k _obs [CH₃OH][HNO₃], where k _obs  = 2.337 × 10⁻¹³(exp(0.3198*wt.% H₂SO₄)) when the solubility of CH₃ONO₂ in acidic solution is approximated by H* for pure water. The temperature dependence of the rate coefficient is related to solution composition, with activation energies of 59 and 49 kJ/mol at 51.1 and 63.6 wt.% H₂SO₄, respectively, when k is calculated from rate. The temperature dependence has also been parameterized for application to the atmosphere, but the small quantities of present in aerosol particles will result in methyl nitrate production rates too small to be of significance under most atmospheric conditions. An erratum to this article can be found at