Predicted changes in summertime organic aerosol concentrations due to increased temperatures

Changes in summertime organic aerosol (OA) concentrations in the Eastern U.S. are investigated for different temperature change scenarios using the chemical transport model PMCAMx-2008. OA is simulated using the volatility basis set approach, assuming that the primary emissions are semi-volatile and that the intermediate volatile and semi-volatile organic compounds are oxidized in the gas phase, resulting in products with lower volatility. For the basic temperature change scenario where biogenic emissions are kept constant, ground-level OA decreases by −0.3% K⁻¹ on average. Increases in the north (+0.1% K⁻¹) and decreases in the south (−0.5% K⁻¹) are predicted. The effect of the uncertain temperature dependence of the aging rate constant is modest, changing the OA by only 0.1% K⁻¹ over the temperature-independent case. For the more realistic scenario in which biogenic OA precursor emissions are allowed to increase with temperature (up to 10% K⁻¹), however, average OA increases by 4.1% K⁻¹, with even higher increases in southern regions. These results suggest that as temperature increases, complicated changes in production, partitioning and chemical aging will take place. Nevertheless, the change in biogenic emissions and subsequent production of biogenic OA is more than an order of magnitude more important than the changes in the rates of chemical and physical atmospheric processes.     

Marginal PM25: Nonlinear Aerosol Mass Response to Sulfate Reductions in the Eastern United States

ABSTRACT

Reductions in airborne sulfate concentration may cause inorganic fine particulate matter (PM₂₅) to respond nonlinearly, as nitric acid gas may transfer to the aerosol phase. Where this occurs, reductions in sulfur dioxide (SO₂) emissions will be much less effective than expected at reducing PM_2.5. As a measure of the efficacy of reductions in sulfate concentration on PM , we define marginal PM_2.5 as the local change in PM_2.5 resulting from a small change in sulfate concentration. Using seasonal-average conditions and assuming thermodynamic equilibrium, we find that the conditions for PM_2.5 to respond nonlinearly to sulfate reductions are common in the eastern United States in winter, occurring at half of the sites considered, and uncommon in summer, due primarily to the influence of temperature. Accounting for diurnal and intraseasonal variability, we find that seasonal-average conditions provide a reasonable indicator of the time-averaged PM_2.5 response. These results indicate that reductions in sulfate concentration may be up to 50% less effective at reducing the annual-average PM_2.5 than if the role of nitric acid is neglected. Further, large reductions in sulfate will also cause an increase in aerosol nitrate in many regions that are the most acidic.     

Tail autotomy affects bipedalism but not sprint performance in a cursorial Mediterranean lizard

Running is essential in all terrestrial animals mainly for finding food and mates and escaping from predators. Lizards employ running in all their everyday functions, among which defense stands out. Besides flight, tail autotomy is another very common antipredatory strategy within most lizard families. The impact of tail loss to sprint performance seems to be species dependent. In some lizard species, tail shedding reduces sprint speed, in other species, increases it, and, in a few species, speed is not affected at all. Here, we aimed to clarify the effect of tail autotomy on the sprint performance of a cursorial lizard with particular adaptations for running, such as bipedalism and spike-like protruding scales (fringes) on the toepads that allow high speed on sandy substrates. We hypothesized that individuals that performed bipedalism, and have more and larger fringes, would achieve higher sprint performance. We also anticipated that tail shedding would affect sprint speed (though we were not able to define in what way because of the unpredictable effects that tail loss has on different species). According to our results, individuals that ran bipedally were faster; limb length and fringe size had limited effects on sprint performance whereas tail autotomy affected quadrupedal running only in females. Nonetheless, tail loss significantly affected bipedalism: the ability for running on hindlimbs was completely lost in all adult individuals and in 72.3% of juveniles.     

Modeling the formation of chlorination by-products in river waters with different quality

Water chlorination results in formation of a variety of organic compounds, known as chlorination by-products (CBPs), mainly trihalomethanes (THMs) and haloacetic acids (HAAs). Factors affecting their concentrations have been found to be organic matter content of water, pH, temperature, chlorine dose, contact time and bromide concentration, but the mechanisms of their formation are still under investigation. Within this scope, chlorination experiments have been conducted with river waters from Lesvos island, Greece, with different water quality regarding bromide concentration and organic matter content. The factors studied were pH, time and chlorine dose. The determination of CBPs was carried out by gas chromatography techniques. Statistical analysis of the results was focused on the development of multiple regression models for predicting the concentrations of total trihalomethanes and total HAAs based on the use of pH, reaction time and chlorine dose. The developed models, although providing satisfactory estimations of the concentrations of the CBPs, showed lower correlation coefficients than the multiple regression models developed for THMs only during previous study. It seems that the different water quality characteristics of the two river waters in the present study is responsible for this phenomenon. The results indicate that under these conditions the formation of THMs and HAAs in water has a more stochastic character, which is difficult to be described by the conventional regression techniques.     

Exposure Modeling of Benzene Exploiting Passive–Active Sampling Data

The objective of the present study is the exploitation of active sampling personal exposure data in assessing the factors that affect exposure to benzene in combination with the widely accepted scheme of passive sampling—time microenvironment–activity diaries (TMAD). The campaign included personal exposure measurements with both passive and active sampling in several microenvironments, evaluation of TMAD kept by the volunteers, and a variety of environmental data (ambient air benzene determination, traffic and meteorological observations). Due to the relatively elevated benzene traffic emissions, average personal exposure was determined to be equal to 8.9 μg/m³, ranging between 5 and 20 μg/m³, which is a value highly related to the average urban concentration (9.2 μg/m³). The information gained from TMAD was embedded (in terms of spatial and temporal distribution) into three zones respectively, in order to draw statistically significant conclusions about the exposure levels and the activity patterns. The contribution of the activities to the overall amount of exposure was further quantified and refined by active sampling measurements. These data revealed that driving in a traffic-congested road was the main activity leading to elevated exposure levels (up to 70 μg/m³), followed by walking on the roadside of a congested road (up to 35 μg/m³). Indoor exposure to benzene was in general lower than outdoor (indicating that traffic is the dominant source of benzene emissions in the wider area), and it was significantly affected by the presence of environmental tobacco smoke. The higher significance of the regression coefficients obtained by statistical analysis of the active sampling data was fundamental for the development of a regression-based prediction exposure model. The model was evaluated through comparison with the passive sampling data, which were considered as an unknown but realistic data exposure pattern. The model performed very well in terms of expressing the variance of the exposure data with an average score of R ² equal to 0.935. All of the above indicate that active sampling is a necessary albeit more laborious tool that needs to be used as a complement to passive sampling for precise quantification of the factors determining personal exposure patterns.     

The adoption of green energy technologies: The role of policies in Austria,Germany, and Switzerland

We contribute to the existing research about policy-induced technology adoption in several ways. First, we suggest a new survey design to measure the energy-related policy environment. Second, we simultaneously estimate the policy effects for the adoption propensity and the adoption intensity simultaneously and, third, we compare the policy effects in the three countries, Austria, Germany, and Switzerland. Based on a representative sample of firms for all three countries we find that policies essentially promote the adoption of technologies and they are practically ineffective for the intensity, which poses a great challenge to future policy designs. Voluntary agreements or demand-related factors are among the most important drivers for the adoption propensity of green energy technologies. Given the current institutional framework in the surveyed countries, subsidies are more effective in Austria, taxes are more effective in Germany, and demand-related factors are relatively more effective in Switzerland.     

Sources of Atmospheric Carbonaceous Particulate Matter in Pittsburgh,Pennsylvania

Abstract

The organic carbon (OC)/elemental carbon (EC) tracer method is applied to the Pittsburgh, PA, area to estimate the contribution of secondary organic aerosol (SOA) to the monthly average concentration of organic particu-late matter (PM) during 1995. An emissions inventory is constructed for the primary emissions of OC and EC in the area of interest. The ratio of primary emissions of OC to those of EC ranges between 2.4 in the winter months and 1.0 in the summer months. A mass balance model and ambient measurements were used to assess the accuracy of the emissions inventory. It is estimated to be accurate to within 50%. The results from this analysis show a strong monthly dependence on SOA contribution to the total organic PM concentration, varying from near zero during winter months to 50% or more of the total OC concentration in the summer.