Verkenning van aanvullende maatregelen in het kader van de Programmatische Aanpak Stikstof : een verkenning van de gevolgen voor milieu en economie

Dit rapport is een verkenning van de gevolgen van mogelijke aanvullende stikstofmaatregelen die in het kader van de PAS kunnen worden genomen, om op die wijze ruimte te creëren voor economische ontwikkeling. Hierbij zijn de effectiviteit, kosten en de sociaaleconomische gevolgen van verschillende maatregelen geanalyseerd. De verkenning van de gevolgen van maatregelen voor de stikstofdepositie is uitgevoerd voor een selectie van 48 stikstofbelaste Natura 2000-gebieden. De Programmatische Aanpak Stikstof (PAS) beoogt een bijdrage te leveren aan het stoppen van de achteruitgang van de biodiversiteit. Tegelijkertijd wil de PAS de impasse rond vergunningverlening in het kader van de Natuurbeschermingswet doorbreken

Compressibility of ferropericlase at high-temperature: evidence for the iron spin crossover in seismic tomography

The iron spin crossover in ferropericlase, the second most abundant mineral in Earth's lower mantle, causes changes in a range of physical properties, including seismic wave velocities. Understanding the effect of temperature on the spin crossover is essential to detect its signature in seismic observations and constrain its occurrence in the mantle. Here, we report the first experimental results on the spin crossover-induced bulk modulus softening at high temperatures, derived directly from time-resolved x-ray diffraction measurements during continuous compression of (Mg0.8Fe0.2)O in a resistive-heated dynamic diamond-anvil cell. We present new theoretical calculations of the spin crossover at mantle temperatures benchmarked by the experiments. Based on our results, we create synthetic seismic tomography models to investigate the signature of the spin crossover in global seismic tomography. A tomographic filter is applied to allow for meaningful comparisons between the synthetic models and data-based seismic tomography models, like SP12RTS. A negative anomaly in the correlation between Vs variations and Vc variations (S-C correlation) is found to be the most suitable measure to detect the presence of the spin crossover in tomographic models. When including the effects of the spin crossover, the misfit between the synthetic model and SP12RTS is reduced by 63%, providing strong evidence for the presence of the spin crossover, and hence ferropericlase, in the lower mantle. Future improvement of seismic resolution may facilitate a detailed mapping of spin state using the S-C correlation, providing constraints on mantle temperatures by taking advantage of the temperature sensitivity of the spin crossover

Forest microclimates and climate change: importance, drivers and future research agenda

Forest microclimates contrast strongly with the climate outside forests. To fully understand and better predict how forests' biodiversity and functions relate to climate and climate change, microclimates need to be integrated into ecological research. Despite the potentially broad impact of microclimates on the response of forest ecosystems to global change, our understanding of how microclimates within and below tree canopies modulate biotic responses to global change at the species, community and ecosystem level is still limited. Here, we review how spatial and temporal variation in forest microclimates result from an interplay of forest features, local water balance, topography and landscape composition. We first stress and exemplify the importance of considering forest microclimates to understand variation in biodiversity and ecosystem functions across forest landscapes. Next, we explain how macroclimate warming (of the free atmosphere) can affect microclimates, and vice versa, via interactions with land-use changes across different biomes. Finally, we perform a priority ranking of future research avenues at the interface of microclimate ecology and global change biology, with a specific focus on three key themes: (1) disentangling the abiotic and biotic drivers and feedbacks of forest microclimates; (2) global and regional mapping and predictions of forest microclimates; and (3) the impacts of microclimate on forest biodiversity and ecosystem functioning in the face of climate change. The availability of microclimatic data will significantly increase in the coming decades, characterizing climate variability at unprecedented spatial and temporal scales relevant to biological processes in forests. This will revolutionize our understanding of the dynamics, drivers and implications of forest microclimates on biodiversity and ecological functions, and the impacts of global changes. In order to support the sustainable use of forests and to secure their biodiversity and ecosystem services for future generations, microclimates cannot be ignored.Peer reviewe

Space-based formaldehyde measurements as constrains on volatile organic compound emissions in east and south Asia and implications for ozone

We use a continuous 6-year record (1996–2001) of GOME satellite measurements of formaldehyde (HCHO) columns over east and south Asia to improve regional emission estimates of reactive nonmethane volatile organic compounds (NMVOCs), including isoprene, alkenes, HCHO, and xylenes. Mean monthly HCHO observations are compared to simulated HCHO columns from the GEOS-Chem chemical transport model using state-of-science, “bottom-up” emission inventories from Streets et al. (2003a) for anthropogenic and biomass burning emissions and Guenther et al. (2006) for biogenic emissions (MEGAN). We find that wintertime GOME observations can diagnose anthropogenic reactive NMVOC emissions from China, leading to an estimate 25% higher than Streets et al. (2003a). We attribute the difference to vehicular emissions. The biomass burning source for east and south Asia is almost 5 times the estimate of Streets et al. (2003a). GOME reveals a large source from agricultural burning in the North China Plain in June missing from current inventories. This source may reflect a recent trend toward in-field burning of crop residues as the need for biofuels diminishes. Biogenic isoprene emission in east and south Asia derived from GOME is 56 ± 30 Tg yr−1, similar to 52 Tg yr−1 from MEGAN. We find, however, that MEGAN underestimates emissions in China and overestimates emissions in the tropics. The higher Chinese biogenic and biomass burning emissions revealed by GOME have important implications for ozone pollution. We find 5 to 20 ppb seasonal increases in surface ozone in GEOS-Chem for central and northern China when using GOME-derived versus bottom-up emissions. Our methodology can be adapted for other regions of the world to provide top-down constraints on NMVOC emissions where multiple emission source types overlap in space and time.Earth and Planetary SciencesEngineering and Applied Science

Acute and Chronic Effects of Particles on Hospital Admissions in New-England

Background: Many studies have reported significant associations between exposure to $PM_{2.5}$ and hospital admissions, but all have focused on the effects of short-term exposure. In addition all these studies have relied on a limited number of $PM_{2.5}$ monitors in their study regions, which introduces exposure error, and excludes rural and suburban populations from locations in which monitors are not available, reducing generalizability and potentially creating selection bias. Methods Using our novel prediction models for exposure combining land use regression with physical measurements (satellite aerosol optical depth) we investigated both the long and short term effects of $PM_{2.5}$ exposures on hospital admissions across New-England for all residents aged 65 and older. We performed separate Poisson regression analysis for each admission type: all respiratory, cardiovascular disease (CVD), stroke and diabetes. Daily admission counts in each zip code were regressed against long and short-term $PM_{2.5}$ exposure, temperature, socio-economic data and a spline of time to control for seasonal trends in baseline risk. Results: We observed associations between both short-term and long-term exposure to $PM_{2.5}$ and hospitalization for all of the outcomes examined. In example, for respiratory diseases, for every10-µg/m$^3$ increase in short-term $PM_{2.5}$ exposure there is a 0.70 percent increase in admissions (CI = 0.35 to 0.52) while concurrently for every10-µg/m$^3$ increase in long-term $PM_{2.5}$ exposure there is a 4.22 percent increase in admissions (CI = 1.06 to 4.75). Conclusions: As with mortality studies, chronic exposure to particles is associated with substantially larger increases in hospital admissions than acute exposure and both can be detected simultaneously using our exposure models

Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data

© 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5

Effecten ontwerp Klimaatakkoord.

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