Search

The long‐term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). The Arctic is a challenging region to gather enough data to examine the changes in carbonate chemistry over sufficient scales. However, algorithms that calculate carbonate chemistry parameters from more frequently measured parameters, such as temperature and salinity, can be used to fill in data gaps. Here, these published algorithms were evaluated against in situ measurements using different data input types (data from satellites or in situ re‐analysis climatologies) across the Arctic Ocean. With the lowest uncertainties in the Atlantic influenced Seas (AiS), where re‐analysis inputs achieved total alkalinity estimates with Root Mean Squared Deviation (RMSD) of 21 μmol kg−1 and a bias of 2 μmol kg−1 (n = 162) and dissolved inorganic carbon RMSD of 24 μmol kg−1 and bias of −14 μmol kg−1 (n = 262). AiS results using satellite observation inputs show similar bias but larger RMSD, although due to the shorter time span of available satellite observations, more contemporary in situ data would provide further assessment and improvement. Synoptic‐scale observations of surface water carbonate conditions in the Arctic are now possible to monitor OA, but targeted in situ data collection is needed to enable the full exploitation of satellite observation‐based approaches. Plain Language Summary: The long‐term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). Sea surface salinity and temperature measurements from satellites or in situ re‐analysis products can be used as input to empirical algorithms to calculate OA parameters. This paper provides a first analysis of published Arctic Ocean empirical algorithms to estimate surface water OA parameters using observation‐based data sets. Results show promise in the Atlantic influenced Seas using both in situ re‐analysis and satellite products, but satellite salinity is relatively recent, and a paucity of in situ data in the satellite salinity era precludes a robust assessment. To fully exploit satellite‐based approaches, efforts need to focus on collecting in situ data while these satellites are overhead and operating in orbit. Key Points: Observation‐based data sets enable synoptic spatio‐temporal assessments of Arctic Ocean carbonate system parametersRe‐analysis and satellite products predict total alkalinity with accuracies of ∼21 μmol kg−1Targeted in situ data collection is needed to fully exploit satellite observations in the Arctic to enable carbonate system monitoring [ABSTRACT FROM AUTHOR]

Large rivers play an important role in transferring water and all of its constituents, including carbon in its various forms, from the land to the ocean, but the seasonal and inter-annual variations in these riverine flows remain unclear. Satellite Earth observation datasets and reanalysis products can now be used to observe synoptic-scale spatial and temporal variations in the carbonate system within large river outflows. Here, we present the University of Exeter (UNEXE) Satellite Oceanographic Datasets for Acidification (OceanSODA) dataset (OceanSODA-UNEXE) time series, a dataset of the full carbonate system in the surface water outflows of the Amazon (2010–2020) and Congo (2002–2016) rivers. Optimal empirical approaches were used to generate gridded total alkalinity (TA) and dissolved inorganic carbon (DIC) fields in the outflow regions. These combinations were determined by equitably evaluating all combinations of algorithms and inputs against a reference matchup database of in situ observations. Gridded TA and DIC along with gridded temperature and salinity data enable the calculation of the full carbonate system in the surface ocean (which includes pH and the partial pressure of carbon dioxide, pCO2). The algorithm evaluation constitutes a Type-A uncertainty evaluation for TA and DIC, in which model, input and sampling uncertainties are considered. Total combined uncertainties for TA and DIC were propagated through the carbonate system calculation, allowing all variables to be provided with an associated uncertainty estimate. In the Amazon outflow, the total combined uncertainty for TA was 36 µmolkg-1 (weighted root-mean-squared difference, RMSD, of 35 µmolkg-1 and weighted bias of 8 µmolkg-1 for n = 82), whereas it was 44 µmolkg-1 for DIC (weighted RMSD of 44 µmolkg-1 and weighted bias of - 6 µmolkg-1 for n = 70). The spatially averaged propagated combined uncertainties for the pCO2 and pH were 85 µatm and 0.08, respectively, where the pH uncertainty was relative to an average pH of 8.19. In the Congo outflow, the combined uncertainty for TA was identified as 29 µmolkg-1 (weighted RMSD of 28 µmolkg-1 and weighted bias of 6 µmolkg-1 for n = 102), whereas it was 40 µmolkg-1 for DIC (weighted RMSD of 37 µmolkg-1 and weighted bias of - 16 µmolkg-1 for n = 77). The spatially averaged propagated combined uncertainties for pCO2 and pH were 74 µatm and 0.08, respectively, where the pH uncertainty was relative to an average pH of 8.21. The combined uncertainties in TA and DIC in the Amazon and Congo outflows are lower than the natural variability within their respective regions, allowing the time-varying regional variability to be evaluated. Potential uses of these data would be the assessment of the spatial and temporal flow of carbon from the Amazon and Congo rivers into the Atlantic and the assessment of the riverine-driven carbonate system variations experienced by tropical reefs within the outflow regions. The data presented in this work are available at 10.1594/PANGAEA.946888 (Sims et al., 2023). [ABSTRACT FROM AUTHOR]

Warming of the Arctic due to climate change means the Arctic Ocean is now free from ice for longer, as sea ice melts earlier and refreezes later. Yet, it remains unclear how this extended ice-free period will impact carbon dioxide (CO 2) fluxes due to scarcity of surface ocean CO 2 measurements. Baseline measurements are urgently needed to understand spatial and temporal air–sea CO 2 flux variability in the changing Arctic Ocean. There is also uncertainty as to whether the previous basin-wide surveys are representative of the many smaller bays and inlets that make up the Canadian Arctic Archipelago (CAA). By using a research vessel that is based in the remote Inuit community of Ikaluqtuutiak (Cambridge Bay, Nunavut), we have been able to reliably survey p CO 2 shortly after ice melt and access previously unsampled bays and inlets in the nearby region. Here we present 4 years of consecutive summertime p CO 2 measurements collected in the Kitikmeot Sea in the southern CAA. Overall, we found that this region is a sink for atmospheric CO 2 in August (average of all calculated fluxes over the four cruises was - 4.64 mmol m -2 d -1), but the magnitude of this sink varies substantially between years and locations (average calculated fluxes of + 3.58, - 2.96, - 16.79 and - 0.57 mmol m -2 d -1 during the 2016, 2017, 2018 and 2019 cruises, respectively). Surface ocean p CO 2 varied by up to 156 µ atm between years, highlighting the importance of repeat observations in this region, as this high interannual variability would not have been captured by sparse and infrequent measurements. We find that the surface ocean p CO 2 value at the time of ice melt is extremely important in constraining the magnitude of the air–sea CO 2 flux throughout the ice-free season. However, further constraining the air–sea CO 2 flux in the Kitikmeot Sea will require a better understanding of how p CO 2 changes outside of the summer season. Surface ocean p CO 2 measurements made in small bays and inlets of the Kitikmeot Sea were ∼ 20–40 µ atm lower than in the main channels. Surface ocean p CO 2 measurements made close in time to ice breakup (i.e. within 2 weeks) were ∼ 50 µ atm lower than measurements made > 4 weeks after breakup. As previous basin-wide surveys of the CAA have focused on the deep shipping channels and rarely measure close to the ice breakup date, we hypothesize that there may be an observational bias in previous studies, leading to an underestimate of the CO 2 sink in the CAA. These high-resolution measurements constitute an important new baseline for gaining a better understanding of the role this region plays in the uptake of atmospheric CO 2. [ABSTRACT FROM AUTHOR]

In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO 2 (pCO 2), total alkalinity, dissolved inorganic carbon and pH) have been collated, quality-controlled and made publicly available. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO 2 , which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example, for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude- and projection-dependent (e.g. become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (e.g. a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching surface in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into "regions of interest": spatiotemporal cylinders consisting of circles on the Earth's surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All surface in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is applied to each of the non-in situ datasets in turn, producing a dataset of coincident matchups that are consistent in space and time. About 35 million in situ data points were matched with data from five satellite sources and five model and reanalysis datasets to produce a global matchup dataset of carbonate system data, consisting of ∼286000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 d in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is presented. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example, to enable regional studies. The matchup dataset provides users with a large multi-parameter carbonate system dataset containing data from different sources, in one consistent, collated and standardised format suitable for model–data intercomparisons and model evaluations. The OceanSODA-MDB data can be downloaded from 10.12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931 (Land and Piollé, 2022). [ABSTRACT FROM AUTHOR]

Large rivers play an important role in transferring water and all of its constituents including carbon in its various forms from the land to the ocean, but the seasonal and inter-annual variations in these riverine flows remain unclear. Satellite Earth observation datasets and reanalysis products can now be used to observe synoptic-scale spatial and temporal variations in the carbonate system within large river outflows. Here we present the OceanSODA-UNEXE time series, a dataset of the full carbonate system in the surface water outflows of the Amazon (2010-2020) and Congo Rivers (2002-2016). Optimal empirical approaches were used to generate gridded Total alkalinity (TA) and dissolved inorganic carbon (DIC) fields in the outflow regions. These combinations were determined by equitably evaluating all combinations of algorithms and inputs against a matchup database of in situ observations. Gridded TA and DIC along with gridded temperature and salinity data enable the calculation of the full carbonate system in the surface ocean. The algorithm evaluation constitutes a Type A uncertainty evaluation for TA and DIC where model, input and sampling uncertainties are considered. Total combined uncertainties for TA and DIC were propagated through the carbonate system calculation allowing all variables to be provided with an associated uncertainty estimate. In the Amazon outflow, the total combined uncertainty for TA was identified as 36 µmol kg-1 (weighted RMSD 35 µmol kg-1 and weighted bias 8 µmol kg-1 for n=82) and for DIC was 44 µmol kg-1 (weighted RMSD 44 µmol kg-1 and weighted bias -6 µmol kg-1 for n=70). The spatially averaged propagated uncertainties for the partial pressure of carbon dioxide (pCO2) and pH are 85 µatm and 0.08 respectively, where the pH uncertainty is relative to an average pH of 8.19. In the Congo outflow, the combined uncertainty for TA was identified as 29 µmol kg-1 (weighted RMSD 28 µmol kg-1 and weighted bias 6 µmol kg-1 for n=102) and for DIC was 40 µmol kg-1 (weighted RMSD 37 µmol kg-1and weighted bias -16 µmol kg-1 for n=77). The spatially averaged propagated uncertainties for pCO2 and pH are 74 µatm and 0.08 respectively, where the pH uncertainty is relative to an average pH of 8.21. The combined uncertainties in TA and DIC in the Amazon and Congo outflows are lower than the natural variability their respective regions allowing the time varying regional variability to be evaluated. Potential uses of these data would be for assessing the spatial and temporal flow of carbon from the Amazon and Congo rivers into the Atlantic and for assessing the riverine driven carbonate system variations experienced by tropical reefs within the outflow regions. [ABSTRACT FROM AUTHOR]

The carbonate chemistry of sea ice is known to play a role in global carbon cycles, but its importance is uncertain in part due to disparities in reported results. Variability in physical and biological drivers is usually invoked to explain differences between studies. In the Canadian Arctic Archipelago, "invisible polynyas" – areas of strong currents, thin ice, and potentially high biological productivity – are examples of extreme spatial variability. We used an invisible polynya as a natural laboratory to study the effects of inferred initial ice formation conditions, ice growth rate, and algal biomass on the distribution of carbonate species by collecting enough cores to perform a statistical comparison between sites located within, and just outside of, a polynya near Iqaluktuttiaq (Cambridge Bay, Nunavut, Canada). At both sites, the uppermost 10 cm ice horizon showed evidence of CO 2 off-gassing, while carbonate distributions in the middle and bottommost 10 cm horizons largely followed the salinity distribution. In the polynya, the upper ice horizon had significantly higher bulk total inorganic carbon (TIC), total alkalinity (TA), and salinity potentially due to freeze-up conditions that favoured frazil ice production. The middle ice horizons were statistically indistinguishable between sites, suggesting that ice growth rate is not an important factor for the carbonate distribution under mid-winter conditions. The thicker (non-polynya) site experienced higher algal biomass, TIC, and TA in the bottom horizon. Carbonate chemistry in the bottom horizon could largely be explained by the salinity distribution, with the strong currents at the polynya site potentially playing a role in desalinization; biology appeared to exert only a minor control, with some evidence that the ice algae community was net heterotrophic. We did see evidence of calcium carbonate precipitation but with little impact on the TIC:TA ratio and little difference between sites. Because differences were constrained to relatively thin layers at the top and bottom, vertically averaged values of TIC, TA, and especially the TIC:TA ratio were not meaningfully different between sites. This provides some justification for using a single bulk value for each parameter when modelling sea ice effects on ocean chemistry at coarse resolution. Exactly what value to use (particularly for the TIC:TA ratio) likely varies by region but could potentially be approximated from knowledge of the source seawater and sea ice salinity. Further insights await a rigorous intercomparison of existing data. [ABSTRACT FROM AUTHOR]

Warming of the Arctic due to climate change means the Arctic Ocean is now ice-free for longer as sea ice melts earlier and refreezes later. It remains unclear how the extended ice-free period will impact carbon dioxide (CO2) fluxes due to scarcity of surface ocean CO2 measurements. Baseline measurements are urgently needed to understand how air−sea CO2 fluxes will spatially and temporally vary in a changing Arctic Ocean. It is uncertain whether the previous basin-wide surveys are representative of the many smaller bays and inlets that make up the Canadian Arctic Archipelago. By using a research vessel that is based in the remote Inuit community of Cambridge Bay (Ikaluqtuutiak, Nunavut), we have been able to reliably survey p CO2 shortly after ice melt and access previously unsampled bays and inlets in the nearby region. We present four years of consecutive summertime p CO2 measurements collected in the Kitikmeot Sea in the southern Canadian Arctic Archipelago. Overall, we found that this region is a sink for atmospheric CO2 in August (average of all calculated fluxes over the four cruises was -8.3 mmol m-2 d-1) but the magnitude of this sink varies substantially between years and locations (average calculated fluxes of 0.41, -7.70, -21.26 and -2.08 mmol m-2 d-1 during the 2016, 2017, 2018 and 2019 cruises respectively). Surface ocean p CO2 varied by up to 142 μatm between years; this highlights the importance of repeat observations in the Arctic as this high interannual variability would not have been captured by sparse and infrequent measurements. We find that the p CO2 value of the surface ocean at the time of ice melt is extremely important in constraining the magnitude of the air−sea flux throughout the ice-free season. Further constraining the flux in the Kitikmeot Sea will require a better understanding of how p CO2 changes outside of the summer season. Surface ocean p CO2 measurements made in the bays and inlets in the Kitikmeot Sea were ~20–40 μatm lower than in the main channels, and p CO2 measurements made close to ice breakup (i.e. within 2 weeks) were 50–100 μatm lower than measurements made >4 weeks after breakup. As basin-wide surveys of the CAA have focused on the deeper shipping channels and rarely measure close to the ice break-up date, we hypothesize that there may be an observational bias in previous studies, leading to an underestimate of the CO2 sink in the Canadian Arctic Archipelago. These high-resolution measurements constitute an important new baseline for gaining a better understanding of the role this region plays in the uptake of atmospheric CO2. [ABSTRACT FROM AUTHOR]

Warming of the Arctic due to climate change means the Arctic Ocean is now ice-free for longer as sea ice melts earlier and refreezes later. It remains unclear how the extended ice-free period will impact carbon dioxide (CO2) fluxes due to scarcity of surface ocean CO2 measurements. Baseline measurements are urgently needed to understand how air-sea CO2 fluxes will spatially and temporally vary in a changing Arctic Ocean. It is uncertain whether the previous basin-wide surveys are representative of the many smaller bays and inlets that make up the Canadian Arctic Archipelago. By using a research vessel that is based in the remote Inuit community of Cambridge Bay (Ikaluqtuutiak, Nunavut), we have been able to reliably survey pCO2 shortly after ice melt and access previously unsampled bays and inlets in the nearby region. We present four years of consecutive summertime pCO2 measurements collected in the Kitikmeot Sea in the southern Canadian Arctic Archipelago. Overall, we found that this region is a sink for atmospheric CO2 in August (average of all calculated fluxes over the four cruises was -8.3 mmol m-2 d-1) but the magnitude of this sink varies substantially between years and locations (average calculated fluxes of 0.41, -7.70, -21.26 and -2.08 mmol m-2 d-1 during the 2016.2017,2018 and 2019 cruises respectively). Surface ocean pCO2 varied by up to 142 μatm between years; this highlights the importance of repeat observations in the Arctic as this high interannual variability would not have been captured by sparse and infrequent measurements. We find that the pCO2 value of the surface ocean at the time of ice melt is extremely important in constraining the magnitude of the air-sea flux throughout the ice-free season. Further constraining the flux in the Kitikmeot Sea will require a better understanding of how pCO2 changes outside of the summer season. Surface ocean pCO2 measurements made in the bays and inlets in the Kitikmeot Sea were ~20-40 μatm lower than in the main channels, and pCO2 measurements made close to ice breakup (i.e. within 2 weeks) were 50-100 μatm lower than measurements made >4 weeks after breakup. As basin-wide surveys of the CAA have focused on the deeper shipping channels and rarely measure close to the ice break-up date, we hypothesize that there may be an observational bias in previous studies, leading to an underestimate of the CO2 sink in the Canadian Arctic Archipelago. These high-resolution measurements constitute an important new baseline for gaining a better understanding of the role this region plays in the uptake of atmospheric CO2. [ABSTRACT FROM AUTHOR]

In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated, quality controlled and made publicly available. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude and projection dependent (e. g. become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (e. g. a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching surface in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into 'regions of interest': spatiotemporal cylinders consisting of circles on the Earth's surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All surface in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is applied to each of the nonin situ datasets in turn, producing a dataset of coincident matchups that are consistent in space and time. About 35 million in situ data points were matched with data from five satellite sources and five model and re-analysis datasets to produce a global matchup dataset of carbonate system data, consisting of ~286,000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 days in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is shown. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example to enable regional studies. The matchup dataset provides users with a large multi-parameter carbonate system dataset containing data from different sources, in one consistent, collated and standardised format suitable for model-data intercomparisons and model evaluations. The OceanSODA-MDB data can be downloaded from https://doi.org/10.12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931 (Land and Piollé, 2022). [ABSTRACT FROM AUTHOR]

Surface ocean carbon dioxide (CO2) measurements are used to compute the oceanic air–sea CO2 flux. The CO2 flux component from rivers and estuaries is uncertain due to the high spatial and seasonal heterogeneity of CO2 in coastal waters. Existing high-quality CO2 instrumentation predominantly utilises showerhead and percolating style equilibrators optimised for open-ocean observations. The intervals between measurements made with such instrumentation make it difficult to resolve the fine-scale spatial variability of surface water CO2 at timescales relevant to the high frequency variability in estuarine and coastal environments. Here we present a novel dataset with unprecedented frequency and spatial resolution transects made at the Western Channel Observatory in the south-west of the UK from June to September 2016, using a fast-response seawater CO2 system. Novel observations were made along the estuarine–coastal continuum at different stages of the tide and reveal distinct spatial patterns in the surface water CO2 fugacity (fCO2) at different stages of the tidal cycle. Changes in salinity and fCO2 were closely correlated at all stages of the tidal cycle and suggest that the mixing of oceanic and riverine endmembers partially determines the variations in fCO2. The correlation between salinity and fCO2 was different in Cawsand Bay, which could be due to enhanced gas exchange or to enhanced biological activity in the region. The observations demonstrate the complex dynamics determining spatial and temporal patterns of salinity and fCO2 in the region. Spatial variations in observed surface salinity were used to validate the output of a regional high-resolution hydrodynamic model. The model enables a novel estimate of the air–sea CO2 flux in the estuarine–coastal zone. Air–sea CO2 flux variability in the estuarine–coastal boundary region is influenced by the state of the tide because of strong CO2 outgassing from the river plume. The observations and model output demonstrate that undersampling the complex tidal and mixing processes characteristic of estuarine and coastal environment biases quantification of air–sea CO2 fluxes in coastal waters. The results provide a mechanism to support critical national and regional policy implementation by reducing uncertainty in carbon budgets. [ABSTRACT FROM AUTHOR]

Background. Stopping driving has significant negative consequences for older adults, but there is no simple, reliable screening tool to predict driving cessation. We sought to determine if self-rated health (SRH) was an independent predictor of driving cessation among older adults. Methods. Data on SRH (poor, fair, good, very good, or excellent), medical diagnoses, physical performance, visual acuity, driving status, and other relevant covariates were collected from 649 community-dwelling older Alabama drivers during in-home interviews. Using multivariable logistic regression analyses, we estimated the association of SRH with driving cessation 2 years later. Results. Participants had a mean age of 74 years; 43% were women, 41% African American, and 48% rural. Overall, 36% reported poor to fair SRH at baseline, and 11% had stopped driving after 2 years. Compared to 8% of drivers with good to excellent SRH, 17% with poor to fair health stopped driving (adjusted odds ratio [OR], 1.93; 95% confidence interval [CI], 1.09-3.41; p = .025). Lower Short Physical Performance Battery (SPPB) scores (adjusted OR, 0.86; 95% CI, 0.78-0.95; p = .001) and older age (adjusted OR, 1.06 per year; 95% CI, 1.01-1.11; p = .010) were also associated with driving cessation. Receiver operating characteristics curves documented similar predictive discrimination (c statistics) for SRH (0.72), the SPPB (0.70), and a count of comorbidities based on the Charlson Comorbidity Index (0.73). Conclusions. Poor to fair SRH predicted incident driving cessation after 2 years in a cohort of older adults. SRH can be easily obtained during clinic visits to identify at-risk drivers.

Sharlot M. Hall administered the initial growth of 'Mansion Museum' in 1864, and was the institution's director until she died in 1943. The museum, which was named after her, hosts major history and anthropology exhibits, and has received accreditation from the American Association of Museums and the Distinguished Organization Award in 2004 from the Arizona Humanities Council.

Purpose: To describe a pilot initiative sponsored by the Veterans Health Administration (VHA) to improve the health and community tenure of frail older veterans living in rural counties 50-100 miles from two host VHA medical centers. Design and Methods: Veterans aged 75 and older who scored at risk of repeated hospital admission on the PRA-Plus telephone questionnaire were targeted and visited by evaluators who administered a comprehensive health questionnaire prior to being assessed at home by the Coordination and Advocacy for Rural Elders (CARE) program clinical teams. Guided by current state-of-the-art practices, the nurse--social worker teams performed in-home standardized assessments using the MDS-HC, developed patient-specific care plans, and mobilized family, community, and VHA resources to implement plans. Results: On average, eight problems were identified for each patient, most commonly falls risk, social needs, pain, and needs related to IADL disability. As a result of initial assessment, two thirds of CARE participants received referral/linkage to formal services, more than half to medical providers. Implications: Through CARE, the VHA is learning more about the unmet needs of older rural veterans. Further development and evaluation should guide the VHA toward providing efficient, effective community-based services to all frail older veterans. Key Words: Case management, Rural aging, MDS-HC

Background. As the number of older adult drivers increases, distinguishing safe from unsafe older adult drivers will become an increasing public health concern. We report on the medical and functional factors associated with vehicle crashes in a cohort of Alabama drivers, 55 years old and older. Methods. This prospective study involved 174 older adults, on whom demographic, medical, functional, and physical performance data were collected in 1991. Subjects were then followed through 1996 for incident vehicle crashes. Results. Sixty-one subjects experienced between one and four police-reported vehicle crashes during the study period. Following adjustment for age, race, days driven per week, and gender, Cox proportional-hazards models showed the following variables to be associated with crash involvement: reported difficulty with yardwork or light housework (relative risk [RR] = 2.1; 95% confidence interval [CI] 1.1, 4.0; p = .02), or opening ajar (RR = 3.1; 95% CI 1.4, 6.7; p = .004); at least one crash before 1991 (RR = 2.1; 95% CI 1.2, 3.7;p = .008); using hypnotic medication (RR = 2.9; 95% CI 1.3, 6.6;p = .0 1); self-reported stroke or transient ischemic attack (RR = 2.7; 95% CI 1.1, 6.6; p = .03); scoring within the depressed range on the Geriatric Depression Scale (RR = 2.5; 95% CI 1.1, 6.0; p = .03), and failing the useful field-of-view test (RR = 1.9; 95% CI 1.0, 3.5; p = .05). Conclusions. Variables related to function, medication use, affect, neurological disease, and visuocognitive skills were associated with vehicle crash involvement in this cohort. Our findings suggest that multifactorial assessments are warranted to identify at-risk older drivers.

Copyright of Atmosphere -- Ocean (Taylor & Francis Ltd) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Surface ocean CO2 measurements are used to compute the oceanic air-sea CO2 flux. The CO2 flux component from rivers and estuaries is uncertain. Estuarine and coastal water carbon dioxide (CO2) observations are relatively few compared to observations in the open ocean. The contribution of these regions to the global air-sea CO2 flux remains uncertain due to systematic under-sampling. Existing high-quality CO2 instrumentation predominantly utilise showerhead and percolating style equilibrators optimised for open ocean observations. The intervals between measurements made with such instrumentation make it difficult to resolve the fine-scale spatial variability of surface water CO2 at timescales relevant to the high frequency variability in estuarine and coastal environments. Here we present a novel dataset with unprecedented frequency and spatial resolution transects made at the Western Channel Observatory in the south west of the UK from June to September 2016, using a fast response seawater CO2 system. Novel observations were made along the estuarine-coastal continuum at different stages of the tide and reveal distinct spatial patterns in the surface water CO2 fugacity (fCO2) at different stages of the tidal cycle. Changes in salinity and fCO2 were closely correlated at all stages of the tidal cycle and suggest that the mixing of oceanic and riverine end members determines the variations in fCO2. The observations demonstrate the complex dynamics determining spatial and temporal patterns of salinity and fCO2 in the region. Spatial variations in observed surface salinity were used to validate the output of a regional high resolution hydrodynamic model. The model enables a novel estimate of the air-sea CO2 flux in the estuarine-coastal zone. Air-sea CO2 flux variability in the estuarine-coastal boundary region is dominated by the state of the tide because of strong CO2 outgassing from the river plume. The observations and model output demonstrate that undersampling the complex tidal and mixing processes characteristic of estuarine and coastal environment bias quantification of air-sea CO2 fluxes in coastal waters. The results provide a mechanism to support critical national and regional policy implementation by reducing uncertainty in carbon budgets. [ABSTRACT FROM AUTHOR]

The alliance between Japan and France is not possible because neither government wants an alliance. France wants a simple military understanding whereby the deployment of Japanese troops in Korea would be coordinated with French operations in Taiwan and south China. For most of the 1883-1885 period, all that Japan stood to gain from France was indirect military and diplomatic support against China.

Background. Acute gastroenteritis is a cause of considerable morbidity and mortality in the elderly population. A prospective assessment of acute diarrhea in three community-based long-stay homes is described. Methods. A cohort study of acute gastroenteritis was performed in three community-based nursing homes, involving 572 residents over an 8-month period. Diarrhea cases were enrolled on the basis of the acute onset of loose stools of [greater than or equal to] 24 hours, as well as one of the following: a rectal temperature of [greater than or equal to] 100 [degrees] F, dehydration, positive occult blood, [greater than or equal to] 48 hours duration, or as a part of any outbreak. Stool cultures for Clostridium difficile were performed on all NH 1 patients. Results. Fifty-three gastroenteritis cases were ascertained, consistent with incidence rates of 14.6, 36.4, and 6.7 cases/100 patient years in NH 1, NH 2, and NH 3, respectively. Requiring a Foley catheter (OR = 2.57; 95% CI, 0.93, 7.09) increased diarrhea risk. Six Clostridium difficile enteritis cases and an episode attributable to Aeromonas/Pleisomonas species were diagnosed. One C. difficile diarrhea case was imported from hospital to NH 1. Ten of 12 fecal excretors resided in close geographic clusters in NH 1, where a majority of the latter were mobile and incontinent of stool. Conclusions. Acute gastroenteritis was a common disease in the study nursing homes, for which specific risk factors were identified. A predominant role for Clostridium difficile in the taxonomy of nursing home diarrheas was suggested.

Summary Whether or not age by itself increases susceptibility to pneumococcal infections separate from the effects of chronic disease is controversial. To address this issue, data collected for a retrospective [...]

Delirium is defined by criteria of the Diagnostic and Statistical Manual of the American Psychiatric Association (DSM-III) and its revised version (DSM-IIIR). These criteria include: acute onset with a fluctuating or changing course; memory deficits; disorientation; clouding of consciousness indicated by inability to shift, focus, or maintain attention; and abnormalities in psychomotor activities, perceptual abilities, speech, and sleep-wake cycle. Several studies have attempted to determine the prevalence, defined as disease frequency before admission, and incidence, which is number of cases developing in the hospital, of delirium, but these studies had various shortcomings. Older studies did not use a definition of delirium that was comparable to that of DSM-III and DSM-IIIR. When the DSM-II was used, the factors comprising the diagnosis of delirium were not described in operational terms. Finally, studies that use data from medical records to diagnose delirium are necessarily affected by the bias of the persons who recorded the information. An approach was developed to make the DSM-III criteria for delirium operational, and the prevalence and incidence of delirium were determined in 235 subjects aged 70 years and older. The patients were all admitted to general medicine and were identified by screening as having psychiatric disorders. They were seen by a psychiatrist, and the DSM-III criteria for delirium were applied to determine the prevalence and incidence of this delirium. The results show that delirium is prevalent among the elderly at admission, but the incidence of delirium developing in the hospital is lower than has been reported previously. (Consumer Summary produced by Reliance Medical Information, Inc.)

The Sharlot Hall Museum, founded in June 1928, is an accredited and honored institution that contains several historic buildings and it received the Distinguished Organization Award in 2004 from the Arizona Humanities Council. The Museum continues to work with regional and local partners and offers various public services, including major festivals, after-school programs, libraries and the Blue Rose Theater.