The contribution of primary and secondary somatosensory cortices to the representation of body parts and body sides: an fMRI adaptation study

Although the somatosensory homunculus is a classically used description of the way somatosensory inputs are processed in the brain, the actual contributions of primary (SI) and secondary (SII) somatosensory cortices to the spatial coding of touch remain poorly understood. We studied adaptation of the fMRI BOLD response in the somatosensory cortex by delivering pairs of vibrotactile stimuli to the finger tips of the index and middle fingers. The first stimulus (adaptor) was delivered either to the index or to the middle finger of the right or left hand, whereas the second stimulus (test) was always administered to the left index finger. The overall BOLD response evoked by the stimulation was primarily contralateral in SI and was more bilateral in SII. However, our fMRI adaptation approach also revealed that both somatosensory cortices were sensitive to ipsilateral as well as to contralateral inputs. SI and SII adapted more after subsequent stimulation of homologous as compared with nonhomologous fingers, showing a distinction between different fingers. Most importantly, for both somatosensory cortices, this finger-specific adaptation occurred irrespective of whether the tactile stimulus was delivered to the same or to different hands. This result implies integration of contralateral and ipsilateral somatosensory inputs in SI as well as in SII. Our findings suggest that SI is more than a simple relay for sensory information and that both SI and SII contribute to the spatial coding of touch by discriminating between body parts (fingers) and by integrating the somatosensory input from the two sides of the body (hands).

Simulating the formation of secondary organic aerosol from the photooxidation of aromatic hydrocarbons

The formation and composition of secondary organic aerosol (SOA) from the photooxidation of benzene, p-xylene, and 1,3,5-trimethylbenzene has been simulated using the Master Chemical Mechanism version 3.1 (MCM v3.1) coupled to a representation of the transfer of organic material from the gas to particle phase. The combined mechanism was tested against data obtained from a series of experiments conducted at the European Photoreactor (EUPHORE) outdoor smog chamber in Valencia, Spain. Simulated aerosol mass concentrations compared reasonably well with the measured SOA data only after absorptive partitioning coefficients were increased by a factor of between 5 and 30. The requirement of such scaling was interpreted in terms of the occurrence of unaccounted-for association reactions in the condensed organic phase leading to the production of relatively more nonvolatile species. Comparisons were made between the relative aerosol forming efficiencies of benzene, toluene, p-xylene, and 1,3,5-trimethylbenzene, and differences in the OH-initiated degradation mechanisms of these aromatic hydrocarbons. A strong, nonlinear relationship was observed between measured (reference) yields of SOA and (proportional) yields of unsaturated dicarbonyl aldehyde species resulting from ring-fragmenting pathways. This observation, and the results of the simulations, is strongly suggestive of the involvement of reactive aldehyde species in association reactions occurring in the aerosol phase, thus promoting SOA formation and growth. The effect of NO, concentrations on SOA formation efficiencies (and formation mechanisms) is discussed.

Simulating the detailed chemical composition of secondary organic aerosol formed on a regional scale during the TORCH 2003 campaign in the southern UK

Following on from the companion study (Johnson et al., 2006), a photochemical trajectory model (PTM) has been used to simulate the chemical composition of organic aerosol for selected events during the 2003 TORCH (Tropospheric Organic Chemistry Experiment) field campaign. The PTM incorporates the speciated emissions of 124 nonmethane anthropogenic volatile organic compounds (VOC) and three representative biogenic VOC, a highly-detailed representation of the atmospheric degradation of these VOC, the emission of primary organic aerosol (POA) material and the formation of secondary organic aerosol (SOA) material. SOA formation was represented by the transfer of semi and non-volatile oxidation products from the gas-phase to a condensed organic aerosol-phase, according to estimated thermodynamic equilibrium phase-partitioning characteristics for around 2000 reaction products. After significantly scaling all phase-partitioning coefficients, and assuming a persistent background organic aerosol (both required in order to match the observed organic aerosol loadings), the detailed chemical composition of the simulated SOA has been investigated in terms of intermediate oxygenated species in the Master Chemical Mechanism, version 3.1 ( MCM v3.1). For the various case studies considered, 90% of the simulated SOA mass comprises between ca. 70 and 100 multifunctional oxygenated species derived, in varying amounts, from the photooxidation of VOC of anthropogenic and biogenic origin. The anthropogenic contribution is dominated by aromatic hydrocarbons and the biogenic contribution by alpha-and beta-pinene (which also constitute surrogates for other emitted monoterpene species). Sensitivity in the simulated mass of SOA to changes in the emission rates of anthropogenic and biogenic VOC has also been investigated for 11 case study events, and the results have been compared to the detailed chemical composition data. The role of accretion chemistry in SOA formation, and its implications for the results of the present investigation, is discussed.

Simulating regional scale secondary organic aerosol formation during the TORCH 2003 campaign in the southern UK

A photochemical trajectory model has been used to simulate the chemical evolution of air masses arriving at the TORCH field campaign site in the southern UK during late July and August 2003, a period which included a widespread and prolonged photochemical pollution episode. The model incorporates speciated emissions of 124 nonmethane anthropogenic VOC and three representative biogenic VOC, coupled with a comprehensive description of the chemistry of their degradation. A representation of the gas/aerosol absorptive partitioning of ca. 2000 oxygenated organic species generated in the Master Chemical Mechanism (MCM v3.1) has been implemented, allowing simulation of the contribution to organic aerosol (OA) made by semi- and non-volatile products of VOC oxidation; emissions of primary organic aerosol (POA) and elemental carbon (EC) are also represented. Simulations of total OA mass concentrations in nine case study events (optimised by comparison with observed hourly-mean mass loadings derived from aerosol mass spectrometry measurements) imply that the OA can be ascribed to three general sources: (i) POA emissions; (ii) a '' ubiquitous '' background concentration of 0.7 mu g m(-3); and (iii) gas-to-aerosol transfer of lower volatility products of VOC oxidation generated by the regional scale processing of emitted VOC, but with all partitioning coefficients increased by a species-independent factor of 500. The requirement to scale the partitioning coefficients, and the implied background concentration, are both indicative of the occurrence of chemical processes within the aerosol which allow the oxidised organic species to react by association and/or accretion reactions which generate even lower volatility products, leading to a persistent, non-volatile secondary organic aerosol (SOA). The contribution of secondary organic material to the simulated OA results in significant elevations in the simulated ratio of organic carbon (OC) to EC, compared with the ratio of 1.1 assigned to the emitted components. For the selected case study events, [OC]/[EC] is calculated to lie in the range 2.7-9.8, values which are comparable with the high end of the range reported in the literature.

Representation of model error in a convective-scale ensemble prediction system

In this paper ensembles of forecasts (of up to six hours) are studied from a convection-permitting model with a representation of model error due to unresolved processes. The ensemble prediction system (EPS) used is an experimental convection-permitting version of the UK Met Office’s 24- member Global and Regional Ensemble Prediction System (MOGREPS). The method of representing model error variability, which perturbs parameters within the model’s parameterisation schemes, has been modified and we investigate the impact of applying this scheme in different ways. These are: a control ensemble where all ensemble members have the same parameter values; an ensemble where the parameters are different between members, but fixed in time; and ensembles where the parameters are updated randomly every 30 or 60 min. The choice of parameters and their ranges of variability have been determined from expert opinion and parameter sensitivity tests. A case of frontal rain over the southern UK has been chosen, which has a multi-banded rainfall structure. The consequences of including model error variability in the case studied are mixed and are summarised as follows. The multiple banding, evident in the radar, is not captured for any single member. However, the single band is positioned in some members where a secondary band is present in the radar. This is found for all ensembles studied. Adding model error variability with fixed parameters in time does increase the ensemble spread for near-surface variables like wind and temperature, but can actually decrease the spread of the rainfall. Perturbing the parameters periodically throughout the forecast does not further increase the spread and exhibits “jumpiness” in the spread at times when the parameters are perturbed. Adding model error variability gives an improvement in forecast skill after the first 2–3 h of the forecast for near-surface temperature and relative humidity. For precipitation skill scores, adding model error variability has the effect of improving the skill in the first 1–2 h of the forecast, but then of reducing the skill after that. Complementary experiments were performed where the only difference between members was the set of parameter values (i.e. no initial condition variability). The resulting spread was found to be significantly less than the spread from initial condition variability alone.