Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000-2018.

Recent rapid warming has caused uneven impacts on the composition, structure, and functioning of northern ecosystems. It remains unknown how climatic drivers control linear and non-linear trends in ecosystem productivity. Based on a plant phenology index (PPI) product at a spatial resolution of 0.05° over 2000-2018, we used an automated polynomial fitting scheme to detect and characterize trend types (i.e., polynomial trends and no-trends) in the yearly-integrated PPI (PPIINT) for northern (> 30°N) ecosystems and their dependence on climatic drivers and ecosystem types. The averaged slope for the linear trends (p < 0.05) of PPIINT was positive across all the ecosystems, among which deciduous broadleaved forests and evergreen needle-leaved forests (ENF) showed the highest and lowest mean slopes, respectively. More than 50% of the pixels in ENF, arctic and boreal shrublands, and permanent wetlands (PW) had linear trends. A large fraction of PW also showed quadratic and cubic trends. These trend patterns agreed well with estimates of global vegetation productivity based on solar-induced chlorophyll fluorescence. Across all the biomes, PPIINT in pixels with linear trends showed lower mean values and higher partial correlation coefficients with temperature or precipitation than in pixels without linear trends. Overall, our study revealed the emergence of latitudinal convergence and divergence in climatic controls on the linear and non-linear trends of PPIINT, implying that northern shifts of vegetation and climate change may potentially increase the non-linear nature of climatic controls on ecosystem productivity. These results can improve our understanding and prediction of climate-induced changes in plant phenology and productivity and facilitate sustainable management of ecosystems by accounting for their resilience and vulnerability to future climate change.

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Temperature thresholds of ecosystem respiration at a global scale.

Ecosystem respiration is a major component of the global terrestrial carbon cycle and is strongly influenced by temperature. The global extent of the temperature-ecosystem respiration relationship, however, has not been fully explored. Here, we test linear and threshold models of ecosystem respiration across 210 globally distributed eddy covariance sites over an extensive temperature range. We find thresholds to the global temperature-ecosystem respiration relationship at high and low air temperatures and mid soil temperatures, which represent transitions in the temperature dependence and sensitivity of ecosystem respiration. Annual ecosystem respiration rates show a markedly reduced temperature dependence and sensitivity compared to half-hourly rates, and a single mid-temperature threshold for both air and soil temperature. Our study indicates a distinction in the influence of environmental factors, including temperature, on ecosystem respiration between latitudinal and climate gradients at short (half-hourly) and long (annual) timescales. Such climatological differences in the temperature sensitivity of ecosystem respiration have important consequences for the terrestrial net carbon sink under ongoing climate change.

A physiology-based Earth observation model indicates stagnation in the global gross primary production during recent decades.

Earth observation-based estimates of global gross primary production (GPP) are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. In this study, we attempt an ecosystem-level physiological approach of estimating GPP using an asymptotic light response function (LRF) between GPP and incoming photosynthetically active radiation (PAR) that better represents the response observed at high spatiotemporal resolutions than the conventional light use efficiency approach. Modelled GPP is thereafter constrained with meteorological and hydrological variables. The variability in field-observed GPP, net primary productivity and solar-induced fluorescence was better or equally well captured by our LRF-based GPP when compared with six state-of-the-art Earth observation-based GPP products. Over the period 1982-2015, the LRF-based average annual global terrestrial GPP budget was 121.8 ± 3.5 Pg C, with a detrended inter-annual variability of 0.74 ± 0.13 Pg C. The strongest inter-annual variability was observed in semi-arid regions, but croplands in China and India also showed strong inter-annual variations. The trend in global terrestrial GPP during 1982-2015 was 0.27 ± 0.02 Pg C year-1 , and was generally larger in the northern than the southern hemisphere. Most positive GPP trends were seen in areas with croplands whereas negative trends were observed for large non-cropped parts of the tropics. Trends were strong during the eighties and nineties but levelled off around year 2000. Other GPP products either showed no trends or continuous increase throughout the study period. This study benchmarks a first global Earth observation-based model using an asymptotic light response function, improving simulations of GPP, and reveals a stagnation in the global GPP after the year 2000.

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Recent divergence in the contributions of tropical and boreal forests to the terrestrial carbon sink.

Anthropogenic land use and land cover changes (LULCC) have a large impact on the global terrestrial carbon sink, but this effect is not well characterized according to biogeographical region. Here, using state-of-the-art Earth observation data and a dynamic global vegetation model, we estimate the impact of LULCC on the contribution of biomes to the terrestrial carbon sink between 1992 and 2015. Tropical and boreal forests contributed equally, and with the largest share of the mean global terrestrial carbon sink. CO2 fertilization was found to be the main driver increasing the terrestrial carbon sink from 1992 to 2015, but the net effect of all drivers (CO2 fertilization and nitrogen deposition, LULCC and meteorological forcing) caused a reduction and an increase, respectively, in the terrestrial carbon sink for tropical and boreal forests. These diverging trends were not observed when applying a conventional LULCC dataset, but were also evident in satellite passive microwave estimates of aboveground biomass. These datasets thereby converge on the conclusion that LULCC have had a greater impact on tropical forests than previously estimated, causing an increase and decrease of the contributions of boreal and tropical forests, respectively, to the growing terrestrial carbon sink.

Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite.

Plant water storage is fundamental to the functioning of terrestrial ecosystems by participating in plant metabolism, nutrient and sugar transport, and maintenance of the integrity of the hydraulic system of the plant. However, a global view of the size and dynamics of the water pools stored in plant tissues is still lacking. Here, we report global patterns of seasonal variations in ecosystem-scale plant water storage and their relationship with leaf phenology, based on space-borne measurements of L-band vegetation optical depth. We find that seasonal variations in plant water storage are highly synchronous with leaf phenology for the boreal and temperate forests, but asynchronous for the tropical woodlands, where the seasonal development of plant water storage lags behind leaf area by up to 180 days. Contrasting patterns of the time lag between plant water storage and terrestrial groundwater storage are also evident in these ecosystems. A comparison of the water cycle components in seasonally dry tropical woodlands highlights the buffering effect of plant water storage on the seasonal dynamics of water supply and demand. Our results offer insights into ecosystem-scale plant water relations globally and provide a basis for an improved parameterization of eco-hydrological and Earth system models.

Remotely sensed soil moisture to estimate savannah NDVI.

Satellite derived normalized difference vegetation index (NDVI) is a common data source for monitoring regional and global ecosystem properties. In dry lands it has contributed to estimation of inter-annual and seasonal vegetation dynamics and phenology. However, due to the spectral properties of NDVI it can be affected by clouds which can introduce missing data in the time series. Remotely sensed soil moisture has in contrast to NDVI the benefit of being unaffected by clouds due to the measurements being made in the microwave domain. There is therefore a potential in combining the remotely sensed NDVI with remotely sensed soil moisture to enhance the quality and estimate the missing data. We present a step towards the usage of remotely sensed soil moisture for estimation of savannah NDVI. This was done by evaluating the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture and three of its individual products with respect to their relative performance. The individual products are from the advance scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), and the Land Parameter Retrieval Model-Advanced Microwave Scanning Radiometer-Earth Observing System (LPRM-AMSR-E). Each dataset was used to simulate NDVI, which was subsequently compared to remotely sensed NDVI from MODIS. Differences in their ability to estimate NDVI indicated that, on average, CCI soil moisture differs from its individual products by showing a higher average correlation with measured NDVI. Overall NDVI modelled from CCI soil moisture gave an average correlation of 0.81 to remotely sensed NDVI which indicates its potential to be used to estimate seasonal variations in savannah NDVI. Our result shows promise for further development in using CCI soil moisture to estimate NDVI. The modelled NDVI can potentially be used together with other remotely sensed vegetation datasets to enhance the phenological information that can be acquired, thereby, improving the estimates of savannah vegetation phenology.

Effect of climate dataset selection on simulations of terrestrial GPP: Highest uncertainty for tropical regions.

Biogeochemical models use meteorological forcing data derived with different approaches (e.g. based on interpolation or reanalysis of observation data or a hybrid hereof) to simulate ecosystem processes such as gross primary productivity (GPP). This study assesses the impact of different widely used climate datasets on simulated gross primary productivity and evaluates the suitability of them for reproducing the global and regional carbon cycle as mapped from independent GPP data. We simulate GPP with the biogeochemical model LPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP, PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-based GPP product derived from eddy covariance measurements in combination with remotely sensed data. Our results show that all datasets tested produce relatively similar GPP simulations at a global scale, corresponding fairly well to the observation-based data with a difference between simulations and observations ranging from -50 to 60 g m-2 yr-1. However, all simulations also show a strong underestimation of GPP (ranging from -533 to -870 g m-2 yr-1) and low temporal agreement (r < 0.4) with observations over tropical areas. As the shortwave radiation for tropical areas was found to have the highest uncertainty in the analyzed historical climate datasets, we test whether simulation results could be improved by a correction of the tested shortwave radiation for tropical areas using a new radiation product from the International Satellite Cloud Climatology Project (ISCCP). A large improvement (up to 48%) in simulated GPP magnitude was observed with bias corrected shortwave radiation, as well as an increase in spatio-temporal agreement between the simulated GPP and observation-based GPP. This study conducts a spatial inter-comparison and quantification of the performances of climate datasets and can thereby facilitate the selection of climate forcing data over any given study area for modelling purposes.

Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model.

Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions.

Comparison between remote sensing and a dynamic vegetation model for estimating terrestrial primary production of Africa.

BACKGROUND: Africa is an important part of the global carbon cycle. It is also a continent facing potential problems due to increasing resource demand in combination with climate change-induced changes in resource supply. Quantifying the pools and fluxes constituting the terrestrial African carbon cycle is a challenge, because of uncertainties in meteorological driver data, lack of validation data, and potentially uncertain representation of important processes in major ecosystems. In this paper, terrestrial primary production estimates derived from remote sensing and a dynamic vegetation model are compared and quantified for major African land cover types. RESULTS: Continental gross primary production estimates derived from remote sensing were higher than corresponding estimates derived from a dynamic vegetation model. However, estimates of continental net primary production from remote sensing were lower than corresponding estimates from the dynamic vegetation model. Variation was found among land cover classes, and the largest differences in gross primary production were found in the evergreen broadleaf forest. Average carbon use efficiency (NPP/GPP) was 0.58 for the vegetation model and 0.46 for the remote sensing method. Validation versus in situ data of aboveground net primary production revealed significant positive relationships for both methods. A combination of the remote sensing method with the dynamic vegetation model did not strongly affect this relationship. CONCLUSION: Observed significant differences in estimated vegetation productivity may have several causes, including model design and temperature sensitivity. Differences in carbon use efficiency reflect underlying model assumptions. Integrating the realistic process representation of dynamic vegetation models with the high resolution observational strength of remote sensing may support realistic estimation of components of the carbon cycle and enhance resource monitoring, providing suitable validation data is available.

Annoyance, sleep and concentration problems due to combined traffic noise and the benefit of quiet side.

BACKGROUND: Access to a quiet side in one's dwelling is thought to compensate for higher noise levels at the most exposed façade. It has also been indicated that noise from combined traffic sources causes more noise annoyance than equal average levels from either road traffic or railway noise separately. METHODS: 2612 persons in Malmö, Sweden, answered to a residential environment survey including questions on outdoor environment, noise sensitivity, noise annoyance, sleep quality and concentration problems. Road traffic and railway noise was modeled using Geographic Information System. RESULTS: Access to a quiet side, i.e., at least one window facing yard, water or green space, was associated with reduced risk of annoyance OR (95%CI) 0.47 (0.38-0.59), and concentration problems 0.76 (0.61-0.95). Bedroom window facing the same environment was associated to reduced risk of reporting of poor sleep quality 0.78 (0.64-1.00). Railway noise was associated with reduced risk of annoyance below 55 dB(A) but not at higher levels of exposure. CONCLUSIONS: Having a window facing a yard, water or green space was associated to a substantially reduced risk of noise annoyance and concentration problems. If this window was the bedroom window, sleeping problems were less likely.

Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability.

The Dahra field site in Senegal, West Africa, was established in 2002 to monitor ecosystem properties of semiarid savanna grassland and their responses to climatic and environmental change. This article describes the environment and the ecosystem properties of the site using a unique set of in situ data. The studied variables include hydroclimatic variables, species composition, albedo, normalized difference vegetation index (NDVI), hyperspectral characteristics (350-1800 nm), surface reflectance anisotropy, brightness temperature, fraction of absorbed photosynthetic active radiation (FAPAR), biomass, vegetation water content, and land-atmosphere exchanges of carbon (NEE) and energy. The Dahra field site experiences a typical Sahelian climate and is covered by coexisting trees (~3% canopy cover) and grass species, characterizing large parts of the Sahel. This makes the site suitable for investigating relationships between ecosystem properties and hydroclimatic variables for semiarid savanna ecosystems of the region. There were strong interannual, seasonal and diurnal dynamics in NEE, with high values of ~-7.5 g C m(-2)  day(-1) during the peak of the growing season. We found neither browning nor greening NDVI trends from 2002 to 2012. Interannual variation in species composition was strongly related to rainfall distribution. NDVI and FAPAR were strongly related to species composition, especially for years dominated by the species Zornia glochidiata. This influence was not observed in interannual variation in biomass and vegetation productivity, thus challenging dryland productivity models based on remote sensing. Surface reflectance anisotropy (350-1800 nm) at the peak of the growing season varied strongly depending on wavelength and viewing angle thereby having implications for the design of remotely sensed spectral vegetation indices covering different wavelength regions. The presented time series of in situ data have great potential for dryland dynamics studies, global climate change related research and evaluation and parameterization of remote sensing products and dynamic vegetation models.

Crop yield gaps in Cameroon.

Although food crop yields per hectare have generally been increasing in Cameroon since 1961, the food price crisis of 2008 and the ensuing social unrest and fatalities raised concerns about the country's ability to meet the food needs of its population. This study examines the country's potential for increasing crop yields and food production to meet this food security challenge. Fuzzy set theory is used to develop a biophysical spatial suitability model for different crops, which in turn is employed to ascertain whether crop production is carried out in biophysically suited areas. We use linear regression to examine the trend of yield development over the last half century. On the basis of yield data from experimental stations and farmers' fields we assess the yield gap for major food crops. We find that yields have generally been increasing over the last half century and that agricultural policies can have significant effects on them. To a large extent, food crops are cultivated in areas that are biophysically suited for their cultivation, meaning that the yield gap is not a problem of biophysical suitability. Notwithstanding, there are significantly large yield gaps between actual yields on farmers' farms and maximum attainable yields from research stations. We conclude that agronomy and policies are likely to be the reasons for these large yield gaps. A key challenge to be addressed in closing the yield gaps is that of replenishing and properly managing soil nutrients.

Measured and modeled personal and environmental NO2 exposure.

BACKGROUND: Measured or modeled levels of outdoor air pollution are being used as proxies for individual exposure in a growing number of epidemiological studies. We studied the accuracy of such approaches, in comparison with measured individual levels, and also combined modeled levels for each subject's workplace with the levels at their residence to investigate the influence of living and working in different places on individual exposure levels. METHODS: A GIS-based dispersion model and an emissions database were used to model concentrations of NO2 at the subject's residence. Modeled levels were then compared with measured levels of NO2. Personal exposure was also modeled based on levels of NO2 at the subject's residence in combination with levels of NO2 at their workplace during working hours. RESULTS: There was a good agreement between measured façade levels and modeled residential NO2 levels (rs = 0.8, p > 0.001); however, the agreement between measured and modeled outdoor levels and measured personal exposure was poor with overestimations at low levels and underestimation at high levels (rs = 0.5, p > 0.001 and rs = 0.4, p > 0.001) even when compensating for workplace location (rs = 0.4, p > 0.001). CONCLUSION: Modeling residential levels of NO2 proved to be a useful method of estimating façade concentrations. However, the agreement between outdoor levels (both modeled and measured) and personal exposure was, although significant, rather poor even when compensating for workplace location. These results indicate that personal exposure cannot be fully approximated by outdoor levels and that differences in personal activity patterns or household characteristics should be carefully considered when conducting exposure studies. This is an important finding that may help to correct substantial bias in epidemiological studies.

Survey context and question wording affects self reported annoyance due to road traffic noise: a comparison between two cross-sectional studies.

BACKGROUND: Surveys are a common way to measure annoyance due to road traffic noise, but the method has some draw-backs. Survey context, question wording and answer alternatives could affect participation and answers and could have implications when comparing studies and/or performing pooled analyses. The aim of this study was to investigate the difference in annoyance reporting due to road traffic noise in two types of surveys of which one was introduced broadly and the other with the clearly stated aim of investigating noise and health. METHODS: Data was collected from two surveys carried out in the municipality of Malmö, southern Sweden in 2007 and 2008 (n = 2612 and n = 3810). The first survey stated an aim of investigating residential environmental exposure, especially noise and health. The second survey was a broad public health survey stating a broader aim. The two surveys had comparable questions regarding noise annoyance, although one used a 5-point scale and the other a 4-point scale. We used geographic information systems (GIS) to assess the average road and railway noise (LAeq,24h) at the participants' residential address. Logistic regression was used to calculate odds ratios for annoyance in relation to noise exposure. RESULTS: Annoyance at least once a week due to road traffic noise was significantly more prevalent in the survey investigating environment and health compared to the public health survey at levels > 45 dB(A), but not at lower exposure levels. However no differences in annoyance were found when comparing the extreme alternatives "never" and "every day". In the study investigating environment and health, "Noise sensitive" persons were more likely to readily respond to the survey and were more annoyed by road traffic noise compared to the other participants in that survey. CONCLUSIONS: The differences in annoyance reporting between the two surveys were mainly due to different scales, suggesting that extreme alternatives are to prefer before dichotomization when comparing results between the two. Although some findings suggested that noise-sensitive individuals were more likely to respond to the survey investigating noise and health, we could not find convincing evidence that contextual differences affected either answers or participation.

Work stress, worries, and pain interact synergistically with modelled traffic noise on cross-sectional associations with self-reported sleep problems.

OBJECTIVES: To examine the risk of sleep problems associated with work stress (job strain, job demands, and decision authority), worries and pain and to investigate the synergistic interaction between these factors and traffic noise. METHODS: Sleep problems and predictor variables were assessed in a cross-sectional public health survey with 12,093 respondents. Traffic noise levels were assessed using modelled A-weighted energy equivalent traffic sound levels at the residence. The risk of sleep problems was modelled using multiple logistic regression analysis. RESULTS: With regard to sleep problems not attributed to any external source (general sleep problems), independent main effects were found for traffic noise (women), decision authority (women), job strain, job demands, suffering from pain or other afflictions, worries about losing the job, experiencing bullying at work, having troubles paying the bills, and having a sick, disabled, or old relative to take care of (women). Significant synergistic effects were found for traffic noise and experiencing bullying at work in women. With regard to sleep problems attributed to traffic noise, strong synergistic interactions were found between traffic noise and, respectively, job demands (men), having pain or other afflictions, taking care of a sick, old, or disabled relative, and having troubles paying the bills. Main effects were found for worries about losing the job, experiencing bullying at work, job strain (men), and decision authority (men). Synergistic interactions could potentially contribute with 10-20% of the sleep problems attributed to traffic noise in the population. CONCLUSIONS: Work stress, pain, and different worries were independently associated with general sleep problems and showed in general no synergistic interaction with traffic noise. In contrast, synergistic effects between traffic noise and psychological factors were found with regard to sleep problems attributed to traffic noise. The synergy may contribute significantly to sleep problems attributed to traffic noise in the population.

Road traffic noise and hypertension: results from a cross-sectional public health survey in southern Sweden.

BACKGROUND: Results from studies of road traffic noise and hypertension are heterogeneous with respect to effect size, effects among males and females and with respect to effects across age groups. Our objective was to further explore these associations. METHODS: The study used cross-sectional public health survey data from southern Sweden, including 24,238 adults (18 - 80 years old). We used a geographic information system (GIS) to assess the average road noise (LAeq 24 hr) at the current residential address. Effects on self-reported hypertension were estimated by logistic regression with adjustment for age, sex, BMI, alcohol intake, exercise, education, smoking and socioeconomic status. RESULTS: Modest exposure effects (OR approximately 1.1) were generally noted in intermediate exposure categories (45 -64 dB(A)), and with no obvious trend. The effect was more pronounced at > 64 dB(A) (OR 1.45, 95% CI 1.04 - 2.02). Age modified the relative effect (p = 0.018). An effect was seen among middle-aged (40 - 59 years old) at noise levels 60 - 64 dB(A) (OR = 1.27, 95% CI 1.02 - 1.58)) and at > 64 dB(A) (OR = 1.91, 95% CI 1.19 - 3.06)). An effect was also indicated among younger adults but not among elderly. No apparent effect modification by gender, country of origin, disturbed sleep or strained economy was noted. CONCLUSION: The study supports an association between road traffic noise at high average levels and self-reported hypertension in middle-aged. Future studies should use age group -specific relative effect models to account for differences in prevalence.

Seasonal variation of carbon fluxes in a sparse savanna in semi arid Sudan.

BACKGROUND: Large spatial, seasonal and annual variability of major drivers of the carbon cycle (precipitation, temperature, fire regime and nutrient availability) are common in the Sahel region. This causes large variability in net ecosystem exchange and in vegetation productivity, the subsistence basis for a major part of the rural population in Sahel. This study compares the 2005 dry and wet season fluxes of CO2 for a grass land/sparse savanna site in semi arid Sudan and relates these fluxes to water availability and incoming photosynthetic photon flux density (PPFD). Data from this site could complement the current sparse observation network in Africa, a continent where climatic change could significantly impact the future and which constitute a weak link in our understanding of the global carbon cycle. RESULTS: The dry season (represented by Julian day 35-46, February 2005) was characterized by low soil moisture availability, low evapotranspiration and a high vapor pressure deficit. The mean daily NEE (net ecosystem exchange, Eq. 1) was -14.7 mmol d-1 for the 12 day period (negative numbers denote sinks, i.e. flux from the atmosphere to the biosphere). The water use efficiency (WUE) was 1.6 mmol CO2 mol H2O-1 and the light use efficiency (LUE) was 0.95 mmol CO2 mol PPFD-1. Photosynthesis is a weak, but linear function of PPFD. The wet season (represented by Julian day 266-273, September 2005) was, compared to the dry season, characterized by slightly higher soil moisture availability, higher evapotranspiration and a slightly lower vapor pressure deficit. The mean daily NEE was -152 mmol d-1 for the 8 day period. The WUE was lower, 0.97 mmol CO2 mol H2O-1 and the LUE was higher, 7.2 mumol CO2 mmol PPFD-1 during the wet season compared to the dry season. During the wet season photosynthesis increases with PPFD to about 1600 mumol m-2s-1 and then levels off. CONCLUSION: Based on data collected during two short periods, the studied ecosystem was a sink of carbon both during the dry and wet season 2005. The small sink during the dry season is surprising and similar dry season sinks have not to our knowledge been reported from other similar savanna ecosystems and could have potential management implications for agroforestry. A strong response of NEE versus small changes in plant available soil water content was found. Collection and analysis of flux data for several consecutive years including variations in precipitation, available soil moisture and labile soil carbon are needed for understanding the year to year variation of the carbon budget of this grass land/sparse savanna site in semi arid Sudan.