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Sea-spray aerosols (SSA) are an important part of the climate system because of their effects on the global radiative budget – both directly as scatterers and absorbers of solar and terrestrial radiation, and indirectly as cloud condensation nuclei (CCN) influencing cloud formation, lifetime, and precipitation. In terms of their global mass, SSA have the largest uncertainty of all aerosols. In this study we review 21 SSA source functions from the literature, several of which are used in current climate models. In addition, we propose a~new function. Even excluding outliers, the global annual SSA mass produced spans roughly 3–70 Pg yr−1 for the different source functions, for particles with dry diameter Dp < 10 μm, with relatively little interannual variability for a given function. The FLEXPART Lagrangian particle dispersion model was run in backward mode for a large global set of observed SSA concentrations, comprised of several station networks and ship cruise measurement campaigns. FLEXPART backward calculations produce gridded emission sensitivity fields, which can subsequently be multiplied with gridded SSA production fluxes in order to obtain modeled SSA concentrations. This allowed us to efficiently and simultaneously evaluate all 21 source functions against the measurements. Another advantage of this method is that source-region information on wind speed and sea surface temperatures (SSTs) could be stored and used for improving the SSA source function parameterizations. The best source functions reproduced as much as 70% of the observed SSA concentration variability at several stations, which is comparable with \"state of the art\" aerosol models. The main driver of SSA production is wind, and we found that the best fit to the observation data could be obtained when the SSA production is proportional to U103.5, where U10 is the source region averaged 10 m wind speed. A strong influence of SST on SSA production, with higher temperatures leading to higher production, could be detected as well, although the underlying physical mechanisms of the SST influence remains unclear. Our new source function with wind speed and temperature dependence gives a global SSA production for particles smaller than Dp < 10 μm of 9 Pg yr−1, and is the best fit to the observed concentrations.

Uptake of atmospheric CO 2 contributes to ocean acidification. Measurements of seawater chemistry reveal that the extreme acidity of the East Siberian Arctic Shelf is driven by terrestrial organic matter and freshwater inputs. Ocean acidification affects marine ecosystems and carbon cycling, and is considered a direct effect of anthropogenic carbon dioxide uptake from the atmosphere 1 , 2 , 3 . Accumulation of atmospheric CO 2 in ocean surface waters is predicted to make the ocean twice as acidic by the end of this century 4 . The Arctic Ocean is particularly sensitive to ocean acidification because more CO 2 can dissolve in cold water 5 , 6 . Here we present observations of the chemical and physical characteristics of East Siberian Arctic Shelf waters from 1999, 2000–2005, 2008 and 2011, and find extreme aragonite undersaturation that reflects acidity levels in excess of those projected in this region for 2100. Dissolved inorganic carbon isotopic data and Markov chain Monte Carlo simulations of water sources using salinity and δ 18 O data suggest that the persistent acidification is driven by the degradation of terrestrial organic matter and discharge of Arctic river water with elevated CO 2 concentrations, rather than by uptake of atmospheric CO 2 . We suggest that East Siberian Arctic Shelf waters may become more acidic if thawing permafrost leads to enhanced terrestrial organic carbon inputs and if freshwater additions continue to increase, which may affect their efficiency as a source of CO 2 .

Occupational exposure to airborne nickel is associated with an elevated risk for respiratory tract diseases including lung cancer. Therefore, the increased production of Ni-containing nanoparticles necessitates a thorough assessment of their physical, chemical, as well as toxicological properties. The aim of this study was to investigate and compare the characteristics of nickel metal (Ni) and nickel oxide (NiO) particles with a focus on Ni release, reactive oxygen species (ROS) generation, cellular uptake, cytotoxicity and genotoxicity. Four Ni-containing particles of both nano-size (Ni-n and NiO-n) and micron-size (Ni-m1 and Ni-m2) were tested. The released amount of Ni in solution was notably higher in artificial lysosomal fluid (e.g. 80-100 wt% for metallic Ni) than in cell medium after 24h (ca. 1-3 wt% for all particles). Each of the particles was taken up by the cells within 4 h and they remained in the cells to a high extent after 24 h post-incubation. Thus, the high dissolution in ALF appeared not to reflect the particle dissolution in the cells. Ni-m1 showed the most pronounced effect on cell viability after 48 h (alamar blue assay) whereas all particles showed increased cytotoxicity in the highest doses (20-40 μg cm2) when assessed by colony forming efficiency (CFE). Interestingly an increased CFE, suggesting higher proliferation, was observed for all particles in low doses (0.1 or 1 μg cm-2). Ni-m1 and NiO-n were the most potent in causing acellular ROS and DNA damage. However, no intracellular ROS was detected for any of the particles. Taken together, micron-sized Ni (Ni-m1) was more reactive and toxic compared to the nano-sized Ni. Furthermore, this study underlines that the low dose effect in terms of increased proliferation observed for all particles should be further investigated in future studies.

Aerosol-cloud interactions constitute the largest source of uncertainty in global radiative forcing estimates, hampering our understanding of climate evolution. Recent empirical evidence suggests surface tension depression by organic aerosol to significantly influence the formation of cloud droplets, and hence cloud optical properties. In climate models, however, surface tension of water is generally assumed when predicting cloud droplet concentrations. Here we show that the sensitivity of cloud microphysics, optical properties and shortwave radiative effects to the surface phase are dictated by an interplay between the aerosol particle size distribution, composition, water availability and atmospheric dynamics. We demonstrate that accounting for the surface phase becomes essential in clean environments in which ultrafine particle sources are present. Through detailed sensitivity analysis, quantitative constraints on the key drivers – aerosol particle number concentrations, organic fraction and fixed updraft velocity – are derived for instances of significant cloud microphysical susceptibilities to the surface phase. Aerosol-cloud interactions are a large source of uncertainty in radiative forcing estimates. Here, the authors show that the radiative effects of clouds are influenced by a combination of aerosol particle distribution, environmental conditions and atmosphere dynamics.

Black carbon (BC) in haze and deposited on snow and ice can have strong effects on the radiative balance of the Arctic. There is a geographic bias in Arctic BC studies toward the Atlantic sector, with lack of observational constraints for the extensive Russian Siberian Arctic, spanning nearly half of the circum-Arctic. Here, 2 y of observations at Tiksi (East Siberian Arctic) establish a strong seasonality in both BC concentrations (8 ng·m−3 to 302 ng·m−3) and dual-isotope–constrained sources (19 to 73% contribution from biomass burning). Comparisons between observations and a dispersion model, coupled to an anthropogenic emissions inventory and a fire emissions inventory, give mixed results. In the European Arctic, this model has proven to simulate BC concentrations and source contributions well. However, the model is less successful in reproducing BC concentrations and sources for the Russian Arctic. Using a Bayesian approach, we show that, in contrast to earlier studies, contributions from gas flaring (6%), power plants (9%), and open fires (12%) are relatively small, with the major sources instead being domestic (35%) and transport (38%). The observation-based evaluation of reported emissions identifies errors in spatial allocation of BC sources in the inventory and highlights the importance of improving emission distribution and source attribution, to develop reliable mitigation strategies for efficient reduction of BC impact on the Russian Arctic, one of the fastest-warming regions on Earth.

The reasons for amplified warming in the Arctic are not clear. Simulations with an Earth system model suggest that the decline in European aerosol emissions since 1980 explains a substantial fraction of the warming. The Arctic region is warming considerably faster than the rest of the globe 1 , with important consequences for the ecosystems 2 and human exploration of the region 3 . However, the reasons behind this Arctic amplification are not entirely clear 4 . As a result of measures to enhance air quality, anthropogenic emissions of particulate matter and its precursors have drastically decreased in parts of the Northern Hemisphere over the past three decades 5 . Here we present simulations with an Earth system model with comprehensive aerosol physics and chemistry that show that the sulfate aerosol reductions in Europe since 1980 can potentially explain a significant fraction of Arctic warming over that period. Specifically, the Arctic region receives an additional 0.3 W m −2 of energy, and warms by 0.5 °C on annual average in simulations with declining European sulfur emissions in line with historical observations, compared with a model simulation with fixed European emissions at 1980 levels. Arctic warming is amplified mainly in fall and winter, but the warming is initiated in summer by an increase in incoming solar radiation as well as an enhanced poleward oceanic and atmospheric heat transport. The simulated summertime energy surplus reduces sea-ice cover, which leads to a transfer of heat from the Arctic Ocean to the atmosphere. We conclude that air quality regulations in the Northern Hemisphere, the ocean and atmospheric circulation, and Arctic climate are inherently linked.

In this study we have analyzed whether tourist cruise ships have an influence on measured sulfur dioxide (SO2), ozone (O3), Aitken mode particle and equivalent black carbon (EBC) concentrations at Ny Ålesund and Zeppelin Mountain on Svalbard in the Norwegian Arctic during summer. We separated the measurement data set into periods when ships were present and periods when ships were not present in the Kongsfjord area, according to a long-term record of the number of passengers visiting Ny Ålesund. We show that when ships with more than 50 passengers cruise in the Kongsfjord, measured daytime mean concentrations of 60 nm particles and EBC in summer show enhancements of 72 and 45%, respectively, relative to values when ships are not present. Even larger enhancements of 81 and 72% were found for stagnant conditions. In contrast, O3 concentrations were 5% lower on average and 7% lower under stagnant conditions, due to titration of O3 with the emitted nitric oxide (NO). The differences between the two data subsets are largest for the highest measured percentiles, while relatively small differences were found for the median concentrations, indicating that ship plumes are sampled relatively infrequently even when ships are present although they carry high pollutant concentrations. We estimate that the ships increased the total summer mean concentrations of SO2, 60 nm particles and EBC by 15, 18 and 11%, respectively. Our findings have two important implications. Firstly, even at such a remote Arctic observatory as Zeppelin, the measurements can be influenced by tourist ship emissions. Careful data screening is recommended before summertime Zeppelin data is used for data analysis or for comparison with global chemistry transport models. However, Zeppelin remains as one of the most valuable Arctic observatories, as most other Arctic observatories face even larger local pollution problems. Secondly, given landing statistics of tourist ships on Svalbard, it is suspected that large parts of the Svalbard archipelago are affected by cruise ship emissions. Thus, our results may be taken as a warning signal of future pan-Arctic conditions if Arctic shipping becomes more frequent and emission regulations are not strict enough.

Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities.