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Abstract Background Individual differences of mink, including color type, are speculated to affect the course of wound healing, thereby impacting wound assessment and management on the farms, as well as the assessment of wounds in forensic cases. In this study, we examined the effect of color type on early wound healing in farmed mink. Full thickness excisional wounds (2 × 2 cm) were made on the back in 18 mink of the color types Brown, Silverblue and Blue Iris. Gross and microscopic pathology of the wounds was evaluated 2 days post-wounding together with degree of wound size reduction, presence of bacteria and blood analyses. Results Pathological examination on day 2 showed the greatest mean wound size reduction in Brown mink (11.0%) followed by Blue Iris (7.9%) and Silverblue (1.6%). Bacteria were cultured from all wounds, and predominantly Staphylococcus species were recovered in mixed or pure culture. Histopathology from day 2 wounds showed a scab overlying necrotic wound edges, which were separated from underlying vital tissue by a demarcation zone rich in polymorphonuclear leukocytes. Fibroblasts and plump endothelial cells were more numerous in the deeper tissues. Complete blood count parameters were within normal ranges in most cases, however, the mink showed mildly to markedly decreased hematocrit and six mink of the color types Silverblue and Blue Iris showed moderately elevated numbers of circulating segmented neutrophils on day 2. There was a marked increase in concentration of serum amyloid A from day 0 to day 2 in all color types. Conclusions We have described differences in early wound healing between mink of the color types Brown, Silverblue and Blue Iris by use of an experimental wound model in farmed mink. The most pronounced difference pertained to the degree of wound size reduction which was greatest in Brown mink, followed by Blue Iris and Silverblue, respectively.

Ambient fine particulate matter (PM2.5) is the leading global environmental determinant of mortality. However, large gaps exist in ground-based PM2.5 monitoring. Satellite remote sensing of aerosol optical depth (AOD) offers information to help fill these gaps worldwide when augmented with a modeled PM2.5–AOD relationship. This study aims to understand the spatial pattern and driving factors of this relationship by examining η (PM2.5AOD) using both observations and modeling. A global observational estimate of η for the year 2019 is inferred from 6870 ground-based PM2.5 measurement sites and satellite-retrieved AOD. The global chemical transport model GEOS-Chem, in its high-performance configuration (GCHP), is used to interpret the observed spatial pattern of annual mean η. Measurements and the GCHP simulation consistently identify a global population-weighted mean η value of 96–98 µg m−3, with regional values ranging from 59.8 µg m−3 in North America to more than 190 µg m−3 in Africa. The highest η value is found in arid regions, where aerosols are less hygroscopic due to mineral dust, followed by regions strongly influenced by surface aerosol sources. Relatively low η values are found over regions distant from strong aerosol sources. The spatial correlation of observed η values with meteorological fields, aerosol vertical profiles, and aerosol chemical composition reveals that spatial variation in η is strongly influenced by aerosol composition and aerosol vertical profiles. Sensitivity tests with globally uniform parameters quantify the effects of aerosol composition and aerosol vertical profiles on spatial variability in η, exhibiting a population-weighted mean difference in aerosol composition of 12.3 µg m−3, which reflects the determinant effects of composition on aerosol hygroscopicity and aerosol optical properties, and a population-weighted mean difference in the aerosol vertical profile of 8.4 µg m−3, which reflects spatial variation in the column–surface relationship.

Atmospheric transport inversions are a powerful tool for independently estimating surface CO2 fluxes from atmospheric CO2 concentration measurements. However, additional tracers are needed to separate the fossil fuel CO2 (ffCO2) emissions from non-fossil CO2 fluxes. In this study, we focus on radiocarbon (14C), the most direct tracer of ffCO2, and the continuously measured surrogate tracer carbon monoxide (CO), which is co-emitted with ffCO2 during incomplete combustion. In the companion paper by Maier et al. (2024), we determined discrete 14C-based and continuous ΔCO-based estimates of the ffCO2 excess concentration (ΔffCO2) compared with a clean-air reference for the urban Heidelberg observation site in southwestern Germany. The ΔCO-based ΔffCO2 concentration was calculated by dividing the continuously measured ΔCO excess concentration by an average 14C-basedΔCO/ΔffCO2 ratio. Here, we use the CarboScope inversion framework adapted for the urban domain around Heidelberg to assess the potential of both types of ΔffCO2 observations to investigate ffCO2 emissions and their seasonal cycle. We find that, although they are more precise, 14C-based ΔffCO2 observations from almost 100 afternoon flask samples collected in the 2 years of 2019 and 2020 are not well suited for estimating robust ffCO2 emissions in the main footprint of this urban area, which has a very heterogeneous distribution of sources including several point sources. The benefit of the continuous ΔCO-based ΔffCO2 estimates is that they can be averaged to reduce the impact of individual hours with an inadequate model performance. We show that the weekly averaged ΔCO-based ΔffCO2 observations allow for a robust reconstruction of the seasonal cycle of the area source ffCO2 emissions from temporally flat a priori emissions. In particular, the distinct COVID-19 signal – with a steep drop in emissions in spring 2020 – is clearly present in these data-driven a posteriori results. Moreover, our top-down results show a shift in the seasonality of the area source ffCO2 emissions around Heidelberg in 2019 compared with the bottom-up estimates from the Netherlands Organization for Applied Scientific Research (TNO). This highlights the huge potential of ΔCO-based ΔffCO2 to validate bottom-up ffCO2 emissions at urban stations if the ΔCO/ΔffCO2 ratios can be determined without biases.

Measuring the 14C / C depletion in atmospheric CO2 compared with a clean-air reference is the most direct way to estimate the recently added CO2 contribution from fossil fuel (ff) combustion (ΔffCO2) in ambient air. However, as 14CO2 measurements cannot be conducted continuously nor remotely, there are only very sparse 14C-based ΔffCO2 estimates available. Continuously measured tracers, like carbon monoxide (CO), that are co-emitted with ffCO2 can be used as proxies for ΔffCO2, provided that the ΔCO / ΔffCO2 ratios can be determined correctly (here, ΔCO refers to the CO excess compared with a clean-air reference). In the present study, we use almost 350 14CO2 measurements from flask samples collected between 2019 and 2020 at the urban site Heidelberg, Germany, and corresponding analyses from more than 50 afternoon flasks collected between September 2020 and March 2021 at the rural ICOS site Observatoire pérenne de l'environnement (OPE), France, to calculate average 14C-based ΔCO / ΔffCO2 ratios for those sites. For this, we constructed a clean-air reference from the 14CO2 and CO measurements of Mace Head, Ireland. By dividing the hourly ΔCO excess observations by the averaged flask ratio, we calculate continuous proxy-based ΔffCO2 records. The mean bias between the proxy-based ΔffCO2 and the direct 14C-based ΔffCO2 estimates from the flasks is – with 0.31 ± 3.94 ppm for the urban site Heidelberg and −0.06 ± 1.49 ppm for the rural site OPE – only ca. 3 % at both sites. The root-mean-square deviation (RMSD) between proxy-based ΔffCO2 and 14C-based ΔffCO2 is about 4 ppm for Heidelberg and 1.5 ppm for OPE. While this uncertainty can be explained by observational uncertainties alone at OPE, about half of the uncertainty is caused by the neglected variability in the ΔCO / ΔffCO2 ratios at Heidelberg. We further show that modeled ratios based on a bottom-up European emission inventory would lead to substantial biases in the ΔCO-based ΔffCO2 estimates for both Heidelberg and OPE. This highlights the need for an ongoing observational calibration and/or validation of inventory-based ratios if they are to be applied for large-scale ΔCO-based ΔffCO2 estimates, e.g., from satellites.

Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM2.5) as major climate forcers. Yet the model performance for PM2.5 components remains little evaluated due in part to a lack of observational data. Here, we evaluate near-surface concentrations of PM2.5 and its five main components over China as simulated by 14 CMIP6 models, including organic carbon (OC; available in 14 models), black carbon (BC; 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM2.5 components. Seven models output total PM2.5 concentrations, and they all underestimate the observed total PM2.5 over eastern China, with GFDL-ESM4 (−1.5 %) and MPI-ESM-1-2-HAM (−1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM2.5 from the available component output, underestimate the total PM2.5 concentrations partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by −71.2 % to −37.8 % across the 14 models) and the smallest for BC (−47.9 % to −12.1 %). The multi-model mean (MMM) reproduces the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.74) fairly well, yet the agreement is poorer for BC (R = 0.39). The varying performances of ESMs on total PM2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing.

Abstract An integrated framework that tracks global stocks and flows of natural capital is needed to assess sustainable economic growth. Here, we develop a set of globally comprehensive monetary damages from particulate matter air pollution and greenhouse gas emissions in 165 countries from 1998 to 2018. Our results show that pollution intensity began to rise after a decade during which the global economy became less pollution-intensive from the late 1990s until the Great Recession. Larger economic production shares and higher pollution intensity in China and India drove this change. Deducting pollution damage from output from the late 1990s until the Great Recession yields higher growth estimates. After the Great Recession, this adjustment for pollution damage attenuated growth. We show that modeling monetary damages instead of physical measures of environmental quality affects inferences about sustainable development. Further, the monetary damages from exposure to particulate emissions peak earlier in the development path than damages due to carbon dioxide emissions. Monetary damages peak later than physical measures of both pollutants. For carbon dioxide, per capita emissions maximize at just over 60,000 dollars while monetary damages peak at nearly 80,000 dollars. In 2018, all but two countries were below this income level. Our results suggest that the global economy is likely to exhibit rising damages from particulates and carbon dioxide emissions in the years to come as nations grow and develop.

We present the first application of the ICOsahedral Nonhydrostatic model with Aerosols and Reactive Trace gases (ICON-ART) in inverse modeling in inverse modeling of greenhouse gas fluxes with an ensemble Kalman smoother. For this purpose, we extended ICON-ART to efficiently handle gridded emissions, generate an ensemble of perturbed emissions during runtime and use nudging on selected variables to keep the simulations close to analyzed meteorology. We show that the system can optimize total and anthropogenic European CH4 fluxes on a national scale in an idealized setup using pseudo-observations from a realistic network of measurement stations. However, we were unable to constrain the sum of the natural emission sources of comparatively low magnitude. Also regions with low emissions and regions with low observational coverage could not be optimized individually for lack of observational constraints. Furthermore, we investigated the sensitivities towards different inversion parameters and design choices with 15 sensitivity runs using the same idealized setup, demonstrating the robustness of the approach when regarding some minimal requirements of the setup (e.g., number of ensemble members). Subsequently, we applied the system to real in situ observations from 28 European stations for three years, 2008, 2013 and 2018. We used a priori anthropogenic fluxes from the EDGARv6 inventory and a priori natural fluxes from peatlands and mineral soils, inland waters, the ocean, biofuels and biomass burning, and geology. Our results for the year 2018 indicate that anthropogenic emissions may be underestimated in EDGARv6 by ca. 25 % in the Benelux countries and, to a smaller degree, in northwestern France and southern England. In the rest of the domain, anthropogenic fluxes are corrected downwards by the inversion, suggesting an overestimation in the a priori. For most countries, this means that the a posteriori country-total anthropogenic emissions are closer to the values reported to the United Nations Framework Convention on Climate Change (UNFCCC) than the a priori emissions from EDGARv6. Aggregating the a posteriori emissions across the EU27 + UK results in a total of 17.4 Tg yr−1, while the a priori emissions were 19.9 Tg yr−1. Our a posteriori is close to the total reported to the UNFCCC of 17.8 Tg yr−1. Natural emissions are reduced from their a priori magnitude almost everywhere, especially over Italy and Romania–Moldova, where a priori geological emissions are high, and over the United Kingdom and Scandinavia, where emissions from peatlands and wetlands were possibly unusually low during the hot and dry summer of 2018. Our a posteriori anthropogenic emissions for the EU27 + UK fall within the range estimated by global top-down studies but are lower than most other regional inversions. However, many of these studies have used observations from different measurement stations or satellite observations. The spatial pattern of the emission increments in our results, especially the increase in the Benelux countries, also agrees well with other regional inversions.

Abstract Robotic platforms for chemistry are developing rapidly but most systems are not currently able to adapt to changing circumstances in real-time. We present a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, we demonstrate the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures. We also show how the use of in-line spectroscopy such as HPLC, Raman, and NMR can be used for closed-loop optimization of reactions, exemplified using Van Leusen oxazole synthesis, a four-component Ugi condensation and manganese-catalysed epoxidation reactions, as well as two previously unreported reactions, discovered from a selected chemical space, providing up to 50% yield improvement over 25–50 iterations. Finally, we demonstrate an experimental pipeline to explore a trifluoromethylations reaction space, that discovers new molecules.

Abstract Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM2.5). The Flint Hills (FH) region of Kansas experiences extensive prescribed burning each spring (March‐May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM2.5 concentrations to the prescribed burning in the FH. To determine PM2.5 increases from local burning, we used low‐cost PM2.5 sensors (PurpleAir) and satellite observations. The FH were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24‐hr median PM2.5 to increase by 3.0–5.3 μg m−3 (based on different estimates) on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. The FH region was the most impacted by smoke PM2.5 compared to other regions of Kansas, as observed in satellite products and in situ measurements. Additionally, our study found that hourly PM2.5 estimates from a satellite‐derived product aligned with our ground‐based measurements. Satellite‐derived products are useful in rural areas like the FH, where monitors are scarce, providing important PM2.5 estimates.

Atmospheric measurements of the O2/N2 ratio and the CO2 mole fraction (combined into the conceptual tracer “Atmospheric Potential Oxygen”, APO) over continents have been proposed as a constraint on CO2 emissions from fossil-fuel burning. Here we assess the suitability of such APO data to constrain anthropogenic CO2 emissions in western Europe, with particular focus on their decadal trends. We use an inversion of atmospheric transport to estimate spatially and temporally explicit scaling factors on a bottom-up fossil-fuel emissions inventory. Based on the small number of currently available observational records, our CO2 emissions estimates show relatively large apparent year-to-year variations, exceeding the expected uncertainty of the bottom-up inventory and precluding the calculation of statistically significant trends. We were not able to trace the apparent year-to-year variations back to particular properties of the APO data. Inversion of synthetic APO data, however, confirms that data information content and degrees of freedom are sufficient to successfully correct a counterfactual prior. Larger sets of measurement stations, such as the recently started APO observations from the Integrated Carbon Observation System (ICOS) European research infrastructure, improve the constraint and may ameliorate possible problems with local signals or with measurement or model errors at the stations. We further tested the impact of uncertainties in the O2:CO2 stoichiometries of fossil-fuel burning and land biospheric exchange and found they are not fundamental obstacles to estimating decadal trends in fossil-fuel CO2 emissions, though further work on fossil-fuel O2:CO2 stoichiometries seems necessary.

Since 1999, Environment and Climate Change Canada (ECCC) has been coordinating a multi-laboratory comparison of measurements of long-lived greenhouse gases in whole air samples collected at the Global Atmosphere Watch (GAW) Alert Observatory located in the Canadian High Arctic (82∘28′ N, 62∘30′ W). In this paper, we evaluate the measurement agreement of atmospheric CO2, CH4, N2O, SF6, and stable isotopes of CO2 (δ13C, δ18O) between leading laboratories from seven independent international institutions. The measure of success is linked to target goals for network compatibility outlined by the World Meteorological Organization's (WMO) GAW greenhouse gas measurement community. Overall, based on ∼ 8000 discrete flask samples, we find that the co-located atmospheric CO2 and CH4 measurement records from Alert by CSIRO, MPI-BGC, SIO, UHEI-IUP, and ECCC versus NOAA (the designated reference laboratory) are generally consistent with the WMO compatibility goals of ± 0.1 ppm CO2 and ± 2 ppb CH4 over the 17-year period (1999–2016), although there are periods where differences exceed target levels and persist as systematic bias for months or years. Consistency with the WMO goals for N2O, SF6, and stable isotopes of CO2 (δ13C, δ18O) has not been demonstrated. Additional analysis of co-located comparison measurements between CSIRO and SIO versus NOAA or INSTAAR (for the isotopes of CO2) at other geographical sites suggests that the findings at Alert for CO2, CH4, N2O, and δ13C–CO2 could be extended across the CSIRO, SIO, and NOAA observing networks. The primary approach to estimate an overall measurement agreement level was carried out by pooling the differences of all individual laboratories versus the designated reference laboratory and determining the 95th percentile range of these data points. Using this approach over the entire data record, our best estimate of the measurement agreement range is −0.51 to +0.53 ppm for CO2, −0.09 ‰ to +0.07 ‰ for δ13C, −0.50 ‰ to +0.58 ‰ for δ18O, −4.86 to +6.16 ppb for CH4, −0.75 to +1.20 ppb for N2O, and −0.14 to +0.09 ppt for SF6. A secondary approach of using the average of 2 standard deviations of the means for all flask samples taken in each individual sampling episode provided similar results. These upper and lower limits represent our best estimate of the measurement agreement at the 95 % confidence level for these individual laboratories, providing more confidence for using these datasets in various scientific applications (e.g., long-term trend analysis).

Abstract Ambient fine particulate matter (PM2.5) is the world’s leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m3) to a peak in 2011 (38.9 μg/m3) and decreased steadily afterwards (34.7 μg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.

Abstract The discovery of new electroactive materials is slow due to the large combinatorial chemical space of possible experiments. Efficient exploration of redox‐active chemical space requires a machine learning assisted robotic platform with real‐time feedback. Here, we developed a closed‐loop robotic platform which is capable of synthesis and electrochemical characterisation controlled using a probabilistic algorithm. This was used to probe the redox behaviour of different polyoxometalates (POMs) precursors and explore the formation of redox‐active coordination complexes. The system can run accurate analytical electrochemical measurements whilst maintaining the performance and accuracy of both the working and reference electrodes. The platform successfully ran and analysed 336 coordination chemistry reactions by performing ca. 2500 cyclic voltammetry (CV) scans for analysis and electrode cleaning. Overall, the platform carried out over 9900 operations in 350 hours at a rate of 28 operations per hour, and we identified 24 complex solutions which showed significantly different redox activity. Experiments were performed using a universal chemical synthesis language (χDL) with variable inputs. The platform was used autonomously to investigate a range of POM precursor materials demonstrating 45 % increase in capacitance. The experiments ran for 36 hours with more than 6400 operations during which we analysed 200 POM precursor solutions.

Okadaic acid (OA) is an algae-produced lipophilic marine biotoxin that accumulates in the fatty tissue of filter-feeding shellfish. Ingestion of contaminated shellfish leads to the diarrheic shellfish poisoning syndrome. Furthermore, several other effects of OA like genotoxicity, liver toxicity and tumor-promoting properties have been observed, probably linked to the phosphatase-inhibiting properties of the toxin. It has been shown that at high doses OA can disrupt the physical barrier of the intestinal epithelium. As the intestine and the liver do not only constitute a physical, but also a metabolic barrier against xenobiotic exposure, we here investigated the impact of OA on the expression of cytochrome P450 (CYP) enzymes and transporter proteins in human HepaRG cells liver cells in vitro at non-cytotoxic concentrations. The interplay of OA with known CYP inducers was also studied. Data show that the expression of various xenobiotic-metabolizing CYPs was downregulated after exposure to OA. Moreover, OA was able to counteract the activation of CYPs by their inducers. A number of transporters were also mainly downregulated. Overall, we demonstrate that OA has a significant effect on xenobiotic metabolism barrier in liver cells, highlighting the possibility for interactions of OA exposure with the metabolism of drugs and xenobiotics.

An appropriate representation of point source emissions in atmospheric transport models is very challenging. In the Stochastic Time-Inverted Lagrangian Transport model (STILT), all point source emissions are typically released from the surface, meaning that the actual emission stack height plus subsequent plume rise is not considered. This can lead to erroneous predictions of trace gas concentrations, especially during nighttime when vertical atmospheric mixing is minimal. In this study we use two Weather Research and Forecasting (WRF)–STILT model approaches to simulate fossil fuel CO2 (ffCO2) concentrations: (1) the standard “surface source influence (SSI)” approach and (2) an alternative “volume source influence (VSI)” approach where nearby point sources release CO2 according to their effective emission height profiles. The comparison with 14C-based measured ffCO2 data from 2-week integrated afternoon and nighttime samples collected at Heidelberg, 30 m above ground level shows that the root-mean-square deviation (RMSD) between modelled and measured ffCO2 is indeed almost twice as high during the night (RMSD =6.3 ppm) compared to the afternoon (RMSD =3.7 ppm) when using the standard SSI approach. In contrast, the VSI approach leads to a much better performance at nighttime (RMSD =3.4 ppm), which is similar to its performance during afternoon (RMSD =3.7 ppm). Representing nearby point source emissions with the VSI approach could thus be a first step towards exploiting nocturnal observations in STILT. The ability to use nighttime observations in atmospheric inversions would dramatically increase the observational data and allow for the investigation of different source mixtures or diurnal cycles. To further investigate the differences between these two approaches, we conducted a model experiment in which we simulated the ffCO2 contributions from 12 artificial power plants with typical annual emissions of 1 million tonnes of CO2 and with distances between 5 and 200 km from the Heidelberg observation site. We find that such a power plant must be more than 50 km away from the observation site in order for the mean modelled ffCO2 concentration difference between the SSI and VSI approach to fall below 0.1 ppm during situations with low mixing heights smaller than 500 m.

CYP46A1 is a CNS-specific enzyme, which eliminates cholesterol from the brain and retina by metabolism to 24-hydroxycholesterol, thus contributing to cholesterol homeostasis in both organs. 2-Hydroxypropyl-β-cyclodextrin (HPCD), a Food and Drug Administration-approved formulation vehicle, is currently being investigated off-label for treatment of various diseases, including retinal diseases. HPCD was shown to lower retinal cholesterol content in mice but had not yet been evaluated for its therapeutic benefits. Herein, we put Cyp46a1−/− mice on high fat cholesterol-enriched diet from 1 to 14 months of age (control group) and at 12 months of age, started to treat a group of these animals with HPCD until the age of 14 months. We found that as compared with mature and regular chow-fed Cyp46a1−/− mice, control group had about 6-fold increase in the retinal total cholesterol content, focal cholesterol and lipid deposition in the photoreceptor-Bruch’s membrane region, and retinal macrophage activation. In addition, aged animals had cholesterol crystals at the photoreceptor-retinal pigment epithelium interface and changes in the Bruch’s membrane ultrastructure. HPCD treatment mitigated all these manifestations of retinal cholesterol dyshomeostasis and altered the abundance of six groups of proteins (genetic information transfer, vesicular transport, and cytoskeletal organization, endocytosis and lysosomal processing, unfolded protein removal, lipid homeostasis, and Wnt signaling). Thus, aged Cyp46a1−/− mice on high fat cholesterol-enriched diet revealed pathological changes secondary to retinal cholesterol overload and supported further studies of HPCD as a potential therapeutic for age-related macular degeneration and diabetic retinopathy associated with retinal cholesterol dyshomeostasis.

In situ CO2 and CO measurements from five Integrated Carbon Observation System (ICOS) atmosphere stations have been analysed together with footprint model runs from the regional Stochastic Time-Inverted Lagrangian Transport (STILT) model to develop a dedicated strategy for flask sampling with an automated sampler. Flask sampling in ICOS has three different purposes, namely (1) to provide an independent quality control for in situ observations, (2) to provide representative information on atmospheric components currently not monitored in situ at the stations, and (3) to collect samples for 14CO2 analysis that are significantly influenced by fossil fuel CO2 (ffCO2) emission areas. Based on the existing data and experimental results obtained at the Heidelberg pilot station with a prototype flask sampler, we suggest that single flask samples are collected regularly every third day around noon or in the afternoon from the highest level of a tower station. Air samples shall be collected over 1 h, with equal temporal weighting, to obtain a true hourly mean. At all stations studied, more than 50 % of flasks collected around midday will likely be sampled during low ambient variability (<0.5 parts per million (ppm) standard deviation of 1 min values). Based on a first application at the Hohenpeißenberg ICOS site, such flask data are principally suitable for detecting CO2 concentration biases larger than 0.1 ppm with a 1σ confidence level between flask and in situ observations from only five flask comparisons. In order to have a maximum chance to also sample ffCO2 emission areas, additional flasks are collected on all other days in the afternoon. To check if the ffCO2 component will indeed be large in these samples, we use the continuous in situ CO observations. The CO deviation from an estimated background value is determined the day after each flask sampling, and depending on this offset, an automated decision is made as to whether a flask shall be retained for 14CO2 analysis. It turned out that, based on existing data, ffCO2 events of more than 4–5 ppm that would allow ffCO2 estimates with an uncertainty below 30 % were very rare at all stations studied, particularly in summer (only zero to five events per month from May to August). During the other seasons, events could be collected more frequently. The strategy developed in this project is currently being implemented at the ICOS stations.

Abstract Background Treatment complexity rises in line with the number of drugs, single doses, and administration methods, thereby threatening patient adherence. Patients with multimorbidity often need flexible, individualised treatment regimens, but alterations during the course of treatment may further increase complexity. The objective of our study was to explore medication changes in older patients with multimorbidity and polypharmacy in general practice. Methods We retrospectively analysed data from the cluster-randomised PRIMUM trial (PRIoritisation of MUltimedication in Multimorbidity) conducted in 72 general practices. We developed an algorithm for active pharmaceutical ingredients (API), strength, dosage, and administration method to assess changes in physician-reported medication data during two intervals (baseline to six-months: ∆1; six- to nine-months: ∆2), analysed them descriptively at prescription and patient levels, and checked for intervention effects. Results Of 502 patients (median age 72 years, 52% female), 464 completed the study. Changes occurred in 98.6% of patients (changes were 19% more likely in the intervention group): API changes during ∆1 and ∆2 occurred in 414 (82.5%) and 338 (67.3%) of patients, dosage alterations in 372 (74.1%) and 296 (59.2%), and changes in API strength in 158 (31.5%) and 138 (27.5%) respectively. Administration method changed in 79 (16%) of patients in both ∆1 and ∆2. Simvastatin, metformin and aspirin were most frequently subject to alterations. Conclusion Medication regimens in older patients with multimorbidity and polypharmacy changed frequently. These are mostly due to discontinuations and dosage alterations, followed by additions and restarts. These findings cast doubt on the effectiveness of cross-sectional assessments of medication and support longitudinal assessments where possible. Trial registration. 1. Prospective registration: Trial registration number: NCT01171339; Name of registry: ClinicalTrials.gov; Date of registration: July 27, 2010; Date of enrolment of the first participant to the trial: August 12, 2010. 2. Peer reviewed trial registration: Trial registration number: ISRCTN99526053; Name of registry: Controlled Trials; Date of registration: August 31, 2010; Date of enrolment of the first participant to the trial: August 12, 2010.

Observations of aerosol scattering and absorption offer valuable information about aerosol composition. We apply a simulation of the Ultraviolet Aerosol Index (UVAI), a method of detecting aerosol absorption from satellite observations, to interpret UVAI values observed by the Ozone Monitoring Instrument (OMI) from 2005 to 2015 to understand global trends in aerosol composition. We conduct our simulation using the vector radiative transfer model VLIDORT with aerosol fields from the global chemical transport model GEOS-Chem. We examine the 2005–2015 trends in individual aerosol species from GEOS-Chem and apply these trends to the UVAI simulation to calculate the change in simulated UVAI due to the trends in individual aerosol species. We find that global trends in the UVAI are largely explained by trends in absorption by mineral dust, absorption by brown carbon, and scattering by secondary inorganic aerosol. Trends in absorption by mineral dust dominate the simulated UVAI trends over North Africa, the Middle East, East Asia, and Australia. The UVAI simulation resolves observed negative UVAI trends well over Australia, but underestimates positive UVAI trends over North Africa and Central Asia near the Aral Sea and underestimates negative UVAI trends over East Asia. We find evidence of an increasing dust source from the desiccating Aral Sea that may not be well represented by the current generation of models. Trends in absorption by brown carbon dominate the simulated UVAI trends over biomass burning regions. The UVAI simulation reproduces observed negative trends over central South America and West Africa, but underestimates observed UVAI trends over boreal forests. Trends in scattering by secondary inorganic aerosol dominate the simulated UVAI trends over the eastern United States and eastern India. The UVAI simulation slightly overestimates the observed positive UVAI trends over the eastern United States and underestimates the observed negative UVAI trends over India. Quantitative simulation of the OMI UVAI offers new insight into global trends in aerosol composition.

Atmospheric Δ14CO2 measurements are a well-established tool to estimate the regional fossil-fuel-derived CO2 component. However, emissions from nuclear facilities can significantly alter the regional Δ14CO2 level. In order to accurately quantify the signal originating from fossil CO2 emissions, a correction term for anthropogenic 14CO2 sources has to be determined. In this study, the HYSPLIT atmospheric dispersion model has been applied to calculate this correction for the long-term Δ14CO2 monitoring site in Heidelberg. Wind fields with a spatial resolution of 2.5° × 2.5°, 1° × 1°, and 0.5° × 0.5° show systematic deviations, with coarser resolved wind fields leading to higher mean values for the correction. The finally applied mean Δ14CO2 correction for the period from 1986–2014 is 2.3 ‰ with a standard deviation of 2.1 ‰ and maximum values up to 15.2 ‰. These results are based on the 0.5° × 0.5° wind field simulations in years when these fields were available (2009, 2011–2014), and for the other years they are based on 2.5° × 2.5° wind field simulations, corrected with a factor of 0.43. After operations at the Philippsburg boiling water reactor ceased in 2011, the monthly nuclear correction terms decreased to less than 2 ‰, with a mean value of 0.44 ± 0.32 ‰ from 2012 to 2014.

In complex and urban environments, atmospheric trace gas composition is highly variable in time and space. Point measurement techniques for trace gases with in situ instruments are well established and accurate, but do not provide spatial averaging to compare against developing high-resolution atmospheric models of composition and meteorology with resolutions of the order of a kilometre. Open-path measurement techniques provide path average concentrations and spatial averaging which, if sufficiently accurate, may be better suited to assessment and interpretation with such models. Open-path Fourier transform spectroscopy (FTS) in the mid-infrared region, and differential optical absorption spectroscopy (DOAS) in the UV and visible, have been used for many years for open-path spectroscopic measurements of selected species in both clean air and in polluted environments. Near infrared instrumentation allows measurements over longer paths than mid-infrared FTS for species such as greenhouse gases which are not easily accessible to DOAS.In this pilot study we present the first open-path near-infrared (4000–10 000 cm−1, 1.0–2.5 µm) FTS measurements of CO2, CH4, O2, H2O and HDO over a 1.5 km path in urban Heidelberg, Germany. We describe the construction of the open-path FTS system, the analysis of the collected spectra, several measures of precision and accuracy of the measurements, and the results a four-month trial measurement period in July–November 2014. The open-path measurements are compared to calibrated in situ measurements made at one end of the open path. We observe significant differences of the order of a few ppm for CO2 and a few tens of ppb for CH4 between the open-path and point measurements which are 2 to 4 times the measurement repeatability, but we cannot unequivocally assign the differences to specific local sources or sinks. We conclude that open-path FTS may provide a valuable new tool for investigations of atmospheric trace gas composition in complex, small-scale environments such as cities.

The isotopic composition of carbon (Δ14C and δ13C) in atmospheric CO2 and in oceanic and terrestrial carbon reservoirs is influenced by anthropogenic emissions and by natural carbon exchanges, which can respond to and drive changes in climate. Simulations of 14C and 13C in the ocean and terrestrial components of Earth system models (ESMs) present opportunities for model evaluation and for investigation of carbon cycling, including anthropogenic CO2 emissions and uptake. The use of carbon isotopes in novel evaluation of the ESMs' component ocean and terrestrial biosphere models and in new analyses of historical changes may improve predictions of future changes in the carbon cycle and climate system. We compile existing data to produce records of Δ14C and δ13C in atmospheric CO2 for the historical period 1850–2015. The primary motivation for this compilation is to provide the atmospheric boundary condition for historical simulations in the Coupled Model Intercomparison Project 6 (CMIP6) for models simulating carbon isotopes in the ocean or terrestrial biosphere. The data may also be useful for other carbon cycle modelling activities.

Fossil fuel CO2 (CO2ff) is the major contributor of anthropogenic CO2 in the atmosphere, and accurate quantification is essential to better understand the carbon cycle. Since October 2012, we have been continuously measuring the mixing ratios of CO, CO2, CH4, and H2O at five different heights at the Beromünster tall tower, Switzerland. Air samples for radiocarbon (Δ14CO2) analysis have also been collected from the highest sampling inlet (212.5 m) of the tower on a biweekly basis. A correction was applied for 14CO2 emissions from nearby nuclear power plants (NPPs), which have been simulated with the Lagrangian transport model FLEXPART-COSMO. The 14CO2 emissions from NPPs offset the depletion in 14C by fossil fuel emissions, resulting in an underestimation of the fossil fuel component in atmospheric CO2 by about 16 %. An average observed ratio (RCO) of 13.4 ± 1.3 mmol mol−1 was calculated from the enhancements in CO mixing ratios relative to the clean-air reference site Jungfraujoch (ΔCO) and the radiocarbon-based fossil fuel CO2 mole fractions. The wintertime RCO estimate of 12.5 ± 3.3 is about 30 % higher than the wintertime ratio between in situ measured CO and CO2 enhancements at Beromünster over the Jungfraujoch background (8.7 mmol mol−1) corrected for non-fossil contributions due to strong biospheric contribution despite the strong correlation between ΔCO and ΔCO2 in winter. By combining the ratio derived using the radiocarbon measurements and the in situ measured CO mixing ratios, a high-resolution time series of CO2ff was calculated exhibiting a clear seasonality driven by seasonal variability in emissions and vertical mixing. By subtracting the fossil fuel component and the large-scale background, we have determined the regional biospheric CO2 component that is characterized by seasonal variations ranging between −15 and +30 ppm. A pronounced diurnal variation was observed during summer modulated by biospheric exchange and vertical mixing, while no consistent pattern was found during winter.

Diabetic retinopathy (DR) is a complication secondary to diabetes and is the number one cause of blindness among working age individuals worldwide. Despite recent therapeutic breakthroughs using pharmacotherapy, a cure for DR has yet to be realized. Several clinical trials have highlighted the vital role dyslipidemia plays in the progression of DR. Additionally, it has recently been shown that activation of Liver X receptor (LXRα/LXRβ) prevents DR in diabetic animal models. LXRs are nuclear receptors that play key roles in regulating cholesterol metabolism, fatty acid metabolism and inflammation. In this manuscript, we show insight into DR pathogenesis by demonstrating an innovative signaling axis that unifies key metabolic regulators, Sirtuin 1 and LXR, in modulating retinal cholesterol metabolism and inflammation in the diabetic retina. Expression of both regulators, Sirtuin 1 and LXR, are significantly decreased in diabetic human retinal samples and in a type 2 diabetic animal model. Additionally, activation of LXR restores reverse cholesterol transport, prevents inflammation, reduces pro-inflammatory macrophages activity and prevents the formation of diabetes-induced acellular capillaries. Taken together, the work presented in this manuscript highlights the important role lipid dysregulation plays in DR progression and offers a novel potential therapeutic target for the treatment of DR.

Different carbon dioxide (CO2) emitters can be distinguished by their carbon isotope ratios. Therefore measurements of atmospheric δ13C(CO2) and CO2 concentration contain information on the CO2 source mix in the catchment area of an atmospheric measurement site. This information may be illustratively presented as the mean isotopic source signature. Recently an increasing number of continuous measurements of δ13C(CO2) and CO2 have become available, opening the door to the quantification of CO2 shares from different sources at high temporal resolution. Here, we present a method to compute the CO2 source signature (δS) continuously and evaluate our result using model data from the Stochastic Time-Inverted Lagrangian Transport model. Only when we restrict the analysis to situations which fulfill the basic assumptions of the Keeling plot method does our approach provide correct results with minimal biases in δS. On average, this bias is 0.2 ‰ with an interquartile range of about 1.2 ‰ for hourly model data. As a consequence of applying the required strict filter criteria, 85 % of the data points – mainly daytime values – need to be discarded. Applying the method to a 4-year dataset of CO2 and δ13C(CO2) measured in Heidelberg, Germany, yields a distinct seasonal cycle of δS. Disentangling this seasonal source signature into shares of source components is, however, only possible if the isotopic end members of these sources – i.e., the biosphere, δbio, and the fuel mix, δF – are known. From the mean source signature record in 2012, δbio could be reliably estimated only for summer to (−25.0 ± 1.0) ‰ and δF only for winter to (−32.5 ± 2.5) ‰. As the isotopic end members δbio and δF were shown to change over the season, no year-round estimation of the fossil fuel or biosphere share is possible from the measured mean source signature record without additional information from emission inventories or other tracer measurements.

Satellite observations of the ultraviolet aerosol index (UVAI) are sensitive to absorption of solar radiation by aerosols; this absorption affects photolysis frequencies and radiative forcing. We develop a global simulation of the UVAI using the 3-D chemical transport model GEOS-Chem coupled with the Vector Linearized Discrete Ordinate Radiative Transfer model (VLIDORT). The simulation is applied to interpret UVAI observations from the Ozone Monitoring Instrument (OMI) for the year 2007. Simulated and observed values are highly consistent in regions where mineral dust dominates the UVAI, but a large negative bias (−0.32 to −0.97) exists between simulated and observed values in biomass burning regions. We determine effective optical properties for absorbing organic aerosol, known as brown carbon (BrC), and implement them into GEOS-Chem to better represent observed UVAI values over biomass burning regions. The inclusion of absorbing BrC decreases the mean bias between simulated and OMI UVAI values from −0.57 to −0.09 over West Africa in January, from −0.32 to +0.0002 over South Asia in April, from −0.97 to −0.22 over southern Africa in July, and from −0.50 to +0.33 over South America in September. The spectral dependence of absorption after including BrC in the model is broadly consistent with reported observations for biomass burning aerosol, with absorbing Ångström exponent (AAE) values ranging from 2.9 in the ultraviolet (UV) to 1.3 across the UV–Near IR spectrum. We assess the effect of the additional UV absorption by BrC on atmospheric photochemistry by examining tropospheric hydroxyl radical (OH) concentrations in GEOS-Chem. The inclusion of BrC decreases OH by up to 30 % over South America in September, up to 20 % over southern Africa in July, and up to 15 % over other biomass burning regions. Global annual mean OH concentrations in GEOS-Chem decrease due to the presence of absorbing BrC, increasing the methyl chloroform lifetime from 5.62 to 5.68 years, thus reducing the bias against observed values. We calculate the direct radiative effect (DRE) of BrC using GEOS-Chem coupled with the radiative transfer model RRTMG (GC-RT). Treating organic aerosol as containing more strongly absorbing BrC changes the global annual mean all-sky top of atmosphere (TOA) DRE by +0.03 W m−2 and all-sky surface DRE by −0.08 W m−2. Regional changes of up to +0.3 W m−2 at TOA and down to −1.5 W m−2 at the surface are found over major biomass burning regions.

The continuous in situ measurement of δ18O in atmospheric CO2 opens a new door to differentiating between CO2 source and sink components with high temporal resolution. Continuous 13C–CO2 measurement systems have already been commercially available for some time, but until now, only few instruments have been able to provide a continuous measurement of the oxygen isotope ratio in CO2. Besides precise 13C/12C observations, the Fourier transform infrared (FTIR) spectrometer is also able to measure the 18O / 16O ratio in CO2, but the precision and accuracy of the measurements have not yet been evaluated. Here we present a first analysis of δ18O-CO2 (and δ13C-CO2) measurements with the FTIR analyser in Heidelberg. We used Allan deviation to determine the repeatability of δ18O-CO2 measurements and found that it decreases from 0.25‰ for 10 min averages to about 0.1‰ after 2 h and remains at that value up to 24 h. We evaluated the measurement precision over a 10-month period (intermediate measurement precision) using daily working gas measurements and found that our spectrometer measured δ18O-CO2 to better than 0.3‰ at a temporal resolution of less than 10 min. The compatibility of our FTIR-spectrometric measurements to isotope-ratio mass-spectrometric (IRMS) measurements was determined by comparing FTIR measurements of cylinder gases and ambient air with IRMS measurements of flask samples, filled with gases of the same cylinders or collected from the same ambient air intake. Two-sample t tests revealed that, at the 0.01 significance level, the FTIR and the IRMS measurements do not differ significantly from each other and are thus compatible. We describe two weekly episodes of ambient air measurements, one in winter and one in summer, and discuss what potential insights and new challenges combined highly resolved CO2, δ13C-CO2 and δ18O-CO2 records may provide in terms of better understanding regional scale continental carbon exchange processes.

A 2-month measurement campaign with a Fourier transform infrared analyser as a travelling comparison instrument (TCI) was performed at the Advanced Global Atmospheric Gases Experiment (AGAGE) and World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) station at Mace Head, Ireland. The aim was to evaluate the compatibility of atmospheric methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O) measurements of the routine station instrumentation, consisting of a gas chromatograph (GC) for CH4 and N2O as well as a cavity ring-down spectroscopy (CRDS) system for CH4 and CO2. The advantage of a TCI approach for quality control is that the comparison covers the entire ambient air measurement system, including the sample intake system and the data evaluation process. For initial quality and performance control, the TCI was run in parallel with the Heidelberg GC before and after the measurement campaign at Mace Head. Median differences between the Heidelberg GC and the TCI were well within the WMO inter-laboratory compatibility target for all three greenhouse gases. At Mace Head, the median difference between the station GC and the TCI were −0.04 nmol mol−1 for CH4 and −0.37 nmol mol−1 for N2O (GC-TCI). For N2O, a similar difference (−0.40 nmol mol−1) was found when measuring surveillance or working gas cylinders with both instruments. This suggests that the difference observed in ambient air originates from a calibration offset that could partly be due to a difference between the WMO N2O X2006a reference scale used for the TCI and the Scripps Institution of Oceanography (SIO-1998) scale used at Mace Head and in the whole AGAGE network. Median differences between the CRDS G1301 and the TCI at Mace Head were 0.12 nmol mol−1 for CH4 and 0.14 μmol mol−1 for CO2 (CRDS G1301 – TCI). The difference between both instruments for CO2 could not be explained, as direct measurements of calibration gases show no such difference. The CH4 differences between the TCI, the GC and the CRDS G1301 at Mace Head are much smaller than the WMO inter-laboratory compatibility target, while this is not the case for CO2 and N2O.

The mixing height (MH) is a crucial parameter in commonly used transport models that proportionally affects air concentrations of trace gases with sources/sinks near the ground and on diurnal scales. Past synthetic data experiments indicated the possibility to improve tracer transport by minimizing errors of simulated MHs. In this paper we evaluate a method to constrain the Lagrangian particle dispersion model STILT (Stochastic Time-Inverted Lagrangian Transport) with MH diagnosed from radiosonde profiles using a bulk Richardson method. The same method was used to obtain hourly MHs for the period September/October 2009 from the Weather Research and Forecasting (WRF) model, which covers the European continent at 10 km horizontal resolution. Kriging with external drift (KED) was applied to estimate optimized MHs from observed and modelled MHs, which were used as input for STILT to assess the impact on CO2 transport. Special care has been taken to account for uncertainty in MH retrieval in this estimation process. MHs and CO2 concentrations were compared to vertical profiles from aircraft in situ data. We put an emphasis on testing the consistency of estimated MHs to observed vertical mixing of CO2. Modelled CO2 was also compared with continuous measurements made at Cabauw and Heidelberg stations. WRF MHs were significantly biased by ~10–20% during day and ~40–60% during night. Optimized MHs reduced this bias to ~5% with additional slight improvements in random errors. The KED MHs were generally more consistent with observed CO2 mixing. The use of optimized MHs had in general a favourable impact on CO2 transport, with bias reductions of 5–45% (day) and 60–90% (night). This indicates that a large part of the found CO2 model–data mismatch was indeed due to MH errors. Other causes for CO2 mismatch are discussed. Applicability of our method is discussed in the context of CO2 inversions at regional scales.