Sub-irrigation and controlled drainage increase yields and mitigate acid loading in Finnish cultivated acid sulfate soils

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Secondary Organic Aerosol Formation from Healthy and Aphid-Stressed Scots Pine Emissions.

One barrier to predicting biogenic secondary organic aerosol (SOA) formation in a changing climate can be attributed to the complex nature of plant volatile emissions. Plant volatile emissions are dynamic over space and time, and change in response to environmental stressors. This study investigated SOA production from emissions of healthy and aphid-stressed Scots pine saplings via dark ozonolysis and photooxidation chemistry. Laboratory experiments using a batch reaction chamber were used to investigate SOA production from different plant volatile mixtures. The volatile mixture from healthy plants included monoterpenes, aromatics, and a small amount of sesquiterpenes. The biggest change in the volatile mixture for aphid-stressed plants was a large increase (from 1.4 to 7.9 ppb) in sesquiterpenes-particularly acyclic sesquiterpenes, such as the farnesene isomers. Acyclic sesquiterpenes had different effects on SOA production depending on the chemical mechanism. Farnesenes suppressed SOA formation from ozonolysis with a 9.7-14.6% SOA mass yield from healthy plant emissions and a 6.9-10.4% SOA mass yield from aphid-stressed plant emissions. Ozonolysis of volatile mixtures containing more farnesenes promoted fragmentation reactions, which produced higher volatility oxidation products. In contrast, plant volatile mixtures containing more farnesenes did not appreciably change SOA production from photooxidation. SOA mass yields ranged from 10.8 to 23.2% from healthy plant emissions and 17.8-26.8% for aphid-stressed plant emissions. This study highlights the potential importance of acyclic terpene chemistry in a future climate regime with an increased presence of plant stress volatiles

Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500-900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3-61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between -14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits.Peer reviewe

The effect of cloudiness on new-particle formation : investigation of radiation levels

Peer reviewe

Tools for environmental risk mitigation of acid sulphate soils

Project: Climate change adaptation tools for environmental risk mitigation of acid sulphate soils (CATERMASS) Funding: Life+ Environment policy and governance 2008-C1 Coordinator: Finnish Environment Institute (SYKE) Partners: Geological Survey of Finland (GTK), MTT Agrifood Research Finland, Finnish Game and Fisheries Research Institute (RKTL), Centre for Economic Development, Transport and the Environment, University of Helsinki, Åbo Academi University Action 3: Mitigation methods and their adaptation to changing climate condition