Climate warming is expected to destabilize permafrost carbon (PF‐C) by thaw‐erosion and deepening of the seasonally thawed active layer and thereby promote PF‐C mineralization to CO2 and CH4. A similar PF‐C remobilization might have contributed to the increase in atmospheric CO2 during deglacial warming after the last glacial maximum. Using carbon isotopes and terrestrial biomarkers (Δ14C, δ13C, and lignin phenols), this study quantifies deposition of terrestrial carbon originating from permafrost in sediments from the Chukchi Sea (core SWERUS‐L2‐4‐PC1). The sediment core reconstructs remobilization of permafrost carbon during the late Allerød warm period starting at 13,000 cal years before present (BP), the Younger Dryas, and the early Holocene warming until 11,000 cal years BP and compares this period with the late Holocene, from 3,650 years BP until present. Dual‐carbon‐isotope‐based source apportionment demonstrates that Ice Complex Deposit—ice‐ and carbon‐rich permafrost from the late Pleistocene (also referred to as Yedoma)—was the dominant source of organic carbon (66 ± 8%; mean ± standard deviation) to sediments during the end of the deglaciation, with fluxes more than twice as high (8.0 ± 4.6 g·m−2·year−1) as in the late Holocene (3.1 ± 1.0 g·m−2·year−1). These results are consistent with late deglacial PF‐C remobilization observed in a Laptev Sea record, yet in contrast with PF‐C sources, which at that location were dominated by active layer material from the Lena River watershed. Release of dormant PF‐C from erosion of coastal permafrost during the end of the last deglaciation indicates vulnerability of Ice Complex Deposit in response to future warming and sea level changes.

In this study, an Air Quality Health Index (AQHI) for Stockholm is introduced as a tool to capture the combined effects associated with multi-pollutant exposure. Public information regarding the expected health risks associated with current or forecasted concentrations of pollutants and pollen can be very useful for sensitive persons when planning their outdoor activities. For interventions, it can also be important to know the contribution from pollen and the specific air pollutants, judged to cause the risk. The AQHI is based on an epidemiological analysis of asthma emergency department visits (AEDV) and urban background concentrations of NOx, O3, PM10 and birch pollen in Stockholm during 2001–2005. This analysis showed per 10 µg·m–3 increase in the mean of same day and yesterday an increase in AEDV of 0.5% (95% CI: −1.2–2.2), 0.3% (95% CI: −1.4–2.0) and 2.5% (95% CI: 0.3–4.8) for NOx, O3 and PM10, respectively. For birch pollen, the AEDV increased with 0.26% (95% CI: 0.18–0.34) for 10 pollen grains·m–3. In comparison with the coefficients in a meta-analysis, the mean values of the coefficients obtained in Stockholm are smaller. The mean value of the risk increase associated with PM10 is somewhat smaller than the mean value of the meta-coefficient, while for O3, it is less than one fifth of the meta-coefficient. We have not found any meta-coefficient using NOx as an indicator of AEDV, but compared to the mean value associated with NO2, our value of NOx is less than half as large. The AQHI is expressed as the predicted percentage increase in AEDV without any threshold level. When comparing the relative contribution of each pollutant to the total AQHI, based on monthly averages concentrations during the period 2015–2017, there is a tangible pattern. The AQHI increase associated with NOx exhibits a relatively even distribution throughout the year, but with a clear decrease during the summer months due to less traffic. O3 contributes to an increase in AQHI during the spring. For PM10, there is a significant increase during early spring associated with increased suspension of road dust. For birch pollen, there is a remarkable peak during the late spring and early summer during the flowering period. Based on monthly averages, the total AQHI during 2015–2017 varies between 4 and 9%, but with a peak value of almost 16% during the birch pollen season in the spring 2016. Based on daily mean values, the most important risk contribution during the study period is from PM10 with 3.1%, followed by O3 with 2.0%.

Anthropogenic activities have increased the selenium (Se) concentration in the biosphere, but the overall impact on the ocean has not been examined. While Se is an essential nutrient for microorganisms, there is little information on the impact of biological processes on the concentration and speciation of Se in the ocean. Additionally, other factors controlling the distribution and concentration of Se species are poorly understood. Here we present data gathered in the subtropical Pacific Ocean during a cruise in 2011 and we used these field data and the literature, as well as laboratory photochemical experiments examining the stability and degradation of inorganic Se (both Se (IV) and Se (VI)) and dimethyl selenide, to further constrain the cycling of Se in the upper ocean. We also developed a multi‐box model for the biosphere to examine the impact of anthropogenic emissions on the concentration and distribution of Se in the ocean. The model concurs with the field data indicating that the Se concentration has increased in the upper ocean waters over the past 30 years. Our observational studies and model results suggest that Se (VI) is taken up by phytoplankton in the surface ocean, in contrast to the results of laboratory culture experiments. In conclusion, while anthropogenic inputs have markedly increased Se in the atmosphere (42%) and net deposition to the ocean (38%) and terrestrial landscape (41%), the impact on Se in the ocean is small (3% increase in the upper ocean). This minimal response reflects its long marine residence time.