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Source apportionment of atmospheric PM1 is important for air quality control, especially in urban areas where high mass concentrations are often observed. Chemical analysis of molecular inorganic and organic tracer compounds and subsequently data analysis with receptor models give insight on the origin of the PM1 sources. In the present study, four source apportionment approaches were compared with an extended database containing inorganic and organic compounds that were measured during an intensive sampling campaign at urban traffic and urban background sites in Barcelona. Source apportionment of the combined database, containing both inorganic and organic compounds, was compared with more conventional approaches using inorganic and organic databases separately. Traffic emission sources were identified in all models for the two sites. The combined inorganic and organic databases provided higher discrimination capacity of emission sources. It identified aerosols generated by regional recirculation of biomass burning, secondary biogenic organic aerosols, harbor emissions, and specific industrial emissions. In this respect, this approach identified a relevant industrial source situated at NE Barcelona in which a waste incinerator plant, a combined-cycle power plant, and an industrial glass complex are located. Models using both inorganic and organic molecular tracer compounds improve the source apportionment of urban PM.

Fire danger indices are descriptors of fire potential in a large area, and combine a few variables that affect the initiation, spread and control of forest fires. The Canadian Fire Weather Index (FWI) is one of the most widely used fire danger indices in the world, and it is built upon instantaneous values of temperature, relative humidity and wind velocity at noon, together with 24 hourly accumulated precipitation. However, the scarcity of appropriate data has motivated the use of daily mean values as surrogates of the instantaneous ones in several studies that aimed to assess the impact of global warming on fire. In this paper we test the sensitivity of FWI values to both instantaneous and daily mean values, analyzing their effect on mean seasonal fire danger (seasonal severity rating, SSR) and extreme fire danger conditions (90th percentile, FWI90, and FWI>30, FOT30), with a special focus on its influence in climate change impact studies. To this aim, we analyzed reanalysis and regional climate model (RCM) simulations, and compared the resulting instantaneous and daily mean versions both in the present climate and in a future scenario. In particular, we were interested in determining the effect of these datasets on the projected changes obtained for the mean and extreme seasonal fire danger conditions in future climate scenarios, as given by a RCM. Overall, our results warn against the use of daily mean data for the computation of present and future fire danger conditions. Daily mean data lead to systematic negative biases of fire danger calculations. Although the mean seasonal fire danger indices might be corrected to compensate for this bias, fire danger extremes (FWI90 and specially FOT30) cannot be reliably transformed to accommodate the spatial pattern and magnitude of their respective instantaneous versions, leading to inconsistent results when projected into the future. As a result, we advocate caution when using daily mean data and strongly recommend the application of the standard definition for its calculation as closely as possible. Threshold-dependent indices derived from FWI are not reliably represented by the daily mean version and thus can neither be applied for the estimation of future fire danger season length and severity, nor for the estimation of future extreme events. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement 243888 (FUME Project). J.F. acknowledges nancial support from the Spanish R&D&I programme through grant CGL2010-22158-C02 (CORWES project). The ESCENA project (200800050084265) of the Spanish \Strategic action on energy and climate change" provided the WRF RCM simulation used in this study. We acknowledge three anonymous referees for their useful comments that helped to improve the original manuscript.

AbstractGlobal impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.

6 páginas.- 2 figura.- 29 referencias.- 1st Conference on Information Technology for Social Good, GoodIT 2021, Rome 9-11 September 2021 New challenges such as climate change and sustainability arise in society influencing not only environmental issues but human's health directly. To face these new challenges IT technologies and their application to environmental intelligent monitoring become into a powerful tool to set new policies and blueprints to contribute to social good. In the new H2020 project, WatchPlant will provide new tools for environmental intelligence monitoring by the use of plants as "well-being"sensors of the environment they inhabit. This will be possible by equipping plants with a net of communicated wireless self-powered sensors, coupled with artificial intelligence (AI) to become plants into "biohybrid organisms"to test exposure-effects links between plant and the environment. It will become plants into a new tool to be aware of the environment status in a very early stage towards in-situ monitoring. Additionally, the system is devoted to be sustainable and energy-efficient thanks to the use of clean energy sources such as solar cells and a enzymatic biofuel cell (BFC) together with its self-deployment, self-awareness, adaptation, artificial evolution and the AI capabilities. In this concept paper, WatchPlant will envision how to face this challenge by joining interdisciplinary efforts to access the plant sap for energy harvesting and sensing purposes and become plants into "biohybrid organisms"to benefit social good in terms of environmental monitoring in urban scenarios. © 2021 Owner/Author. Project WatchPlant has received funding from the European Union’s Horizon 2020 research and innovation program under the FET grant agreement, no. 101017899 Peer reviewed

Abstract. A 5-year greenhouse gas (GHG) exchange study of the three major gas species (CO2, CH4 and N2O) from an intensively managed permanent grassland in Switzerland is presented. Measurements comprise 2 years (2010 and 2011) of manual static chamber measurements of CH4 and N2O, 5 years of continuous eddy covariance (EC) measurements (CO2–H2O – 2010–2014), and 3 years (2012–2014) of EC measurement of CH4 and N2O. Intensive grassland management included both regular and sporadic management activities. Regular management practices encompassed mowing (three to five cuts per year) with subsequent organic fertilizer amendments and occasional grazing, whereas sporadic management activities comprised grazing or similar activities. The primary objective of our measurements was to compare pre-plowing to post-plowing GHG exchange and to identify potential memory effects of such a substantial disturbance on GHG exchange and carbon (C) and nitrogen (N) gains and losses. In order to include measurements carried out with different observation techniques, we tested two different measurement techniques jointly in 2013, namely the manual static chamber approach and the eddy covariance technique for N2O, to quantify the GHG exchange from the observed grassland site. Our results showed that there were no memory effects on N2O and CH4 emissions after plowing, whereas the CO2 uptake of the site considerably increased when compared to pre-restoration years. In detail, we observed large losses of CO2 and N2O during the year of restoration. In contrast, the grassland acted as a carbon sink under usual management, i.e., the time periods 2010–2011 and 2013–2014. Enhanced emissions and emission peaks of N2O (defined as exceeding background emissions 0.21 ± 0.55 nmol m−2 s−1 (SE = 0.02) for at least 2 sequential days and the 7 d moving average exceeding background emissions) were observed for almost 7 continuous months after restoration as well as following organic fertilizer applications during all years. Net ecosystem exchange of CO2 (NEECO2) showed a common pattern of increased uptake of CO2 in spring and reduced uptake in late fall. NEECO2 dropped to zero and became positive after each harvest event. Methane (CH4) exchange fluctuated around zero during all years. Overall, CH4 exchange was of negligible importance for both the GHG budget and the carbon budget of the site. Our results stress the inclusion of grassland restoration events when providing cumulative sums of C sequestration potential and/or global warming potential (GWP). Consequently, this study further highlights the need for continuous long-term GHG exchange observations as well as for the implementation of our findings into biogeochemical process models to track potential GHG mitigation objectives as well as to predict future GHG emission scenarios reliably.