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Ostrava in the Moravian-Silesian region (Czech Republic) is a European air pollution hot spot for airborne particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), and ultrafine particles (UFPs). Air pollution source apportionment is essential for implementation of successful abatement strategies. UFPs or nanoparticles of diameter <100 nm exhibit the highest deposition efficiency in human lungs. To permit apportionment of PM sources at the hot-spot including nanoparticles, Positive Matrix Factorization (PMF) was applied to highly time resolved particle number size distributions (NSD, 14 nm-10 μm) and PM0.09-1.15 chemical composition. Diurnal patterns, meteorological variables, gaseous pollutants, organic markers, and associations between the NSD factors and chemical composition factors were used to identify the pollution sources. The PMF on the NSD reveals two factors in the ultrafine size range: industrial UFPs (28%, number mode diameter - NMD 45 nm), industrial/fresh road traffic nanoparticles (26%, NMD 26 nm); three factors in the accumulation size range: urban background (24%, NMD 93 nm), coal burning (14%, volume mode diameter - VMD 0.5 μm), regional pollution (3%, VMD 0.8 μm) and one factor in the coarse size range: industrial coarse particles/road dust (2%, VMD 5 μm). The PMF analysis of PM0.09-1.15 revealed four factors: SIA/CC/BB (52%), road dust (18%), sinter/steel (16%), iron production (16%). The factors in the ultrafine size range resolved with NSD have a positive correlation with sinter/steel production and iron production factors resolved with chemical composition. Coal combustion factor resolved with NSD has moderate correlation with SIA/CC/BB factor. The organic markers homohopanes correlate with coal combustion and the levoglucosan correlates with urban background. The PMF applications to NSD and chemical composition datasets are complementary. PAHs in PM1 were found to be associated with coal combustion factor.

The installations of weather-dependent renewable energy sources, such as wind turbines and solar plants, increased significantly in the last two decades. They are often built in remote areas without appropriate power grid connections. The construction of a new power transmission line or improving the old one requires significant financial and time costs. The grid operators prefer to limit the power production in order to keep the load of the power line under its rating. Instead of broadly-used static rating, the existing lines can be rated in real time using a dynamic thermal rating (DTR) system. DTR of power transmission lines can usually provide a significant increase of transmission capacity compared to the static rating. The main inputs to DTR systems are measured or forecast meteorological data. The relation of DTR to the renewable resources is obvious when we consider input parameters to the calculation scheme - wind speed, ambient temperature and shortwave radiation. Exactly same variables are determining production of wind and solar energy. A case study of virtual wind farm and corresponding power transmission line shows limits of renewable energy production at given site. The results demonstrate that the optimal size of wind farm is approximately triple when using DTR comparing to the static rating.

Characterizing Black Carbon (BC) at regional background areas is important for better understanding its impact on climate forcing and health effects. The variability and sources of Equivalent Black Carbon (EBC) in PM10 (atmospheric particles with aerodynamic diameter smaller than 10 μm) have been investigated during a 5-year measurement period at the National Atmospheric Observatory Košetice (NAOK), Czech Republic. Ground based measurements were performed from September 2012 to December 2017 with a 7-wavelength aethalometer (AE31, Magee Scientific). The contributions of fossil fuel (EBCff) and biomass burning (EBCbb) were estimated using the aethalometer model. Seasonal, diurnal and weekly variations of EBC were observed that can be related to the sources fluctuations and transport characteristic of pollutants predominantly associated with regional air masses recirculating over the Czech Republic and neighboring countries. The absorption Ångström exponent (α-value) estimated in summer (1.1 ± 0.2) was consistent with reported value for traffic, while the mean highest value (1.5 ± 0.2) was observed in winter due to increased EBCbb accounting for about 50% of the total EBC. This result is in agreement with the strong correlation between EBCbb and biomass burning tracers (levoglucosan and mannosan) in winter. During this season, the concentrations of EBCbb and Delta-C (proxy for biomass burning) reached a maximum in the evening when increasing emissions of wood burning in domestic heating devices (woodstoves/heating system) is expected, especially during the weekend. The diurnal profile of EBCff displays a typical morning peak during the morning traffic rush hour and shows a decreasing concentration during weekends due to lower the traffic emission.

The dynamics of air temperatures during cold half-year to a large extent affect all human activities. Most significant damages are caused by minimum air temperatures dropping to very low values. Due to the current ongoing climate change, air temperatures in winter in general are increasing, however, this does not necessarily mean the frequency of these very low temperatures decreases. It is not easy to express the course of air temperatures during cold periods using some simple characteristic. Meteorologists and climatologists use various characteristics, which can be referred to as frost indexes. These include the number of frost, ice or arctic days. Since these indexes not always perfectly reflect reaction of the nature to a particular temperature threshold, the analysis also included number of days with minimum air temperature below -3, -5, -7, -10, -15, -20 and -25°C. In the last 15 years (2001-2015), there has been a decrease in the frost indexes in comparison to the normal period 1961-1990, this trend however, is not always statistically significant. Significant changes were found in particular in case of the number of days with a higher temperature threshold (frost, ice days and minimum air temperature above -3 to -10°C). In contrast, days with extremely low temperatures are relatively rare (sometimes only observed once in several years), so the dominant factor here is rather variability. The paper also includes an analysis of possible future development based on the most recent climatological models.

This work examines the causal relationship between the value of the ground–air temperature offset and the precipitation changes for monitored 5-min data series together with their hourly and daily averages obtained at the Sporilov Geophysical Observatory (Prague). Shallow subsurface soil temperatures were monitored under four different land cover types (bare soil, sand, short-cut grass and asphalt). The ground surface temperature (GST) and surface air temperature (SAT) offset, ΔT(GST–SAT), is defined as the difference between the temperature measured at the depth of 2 cm below the surface and the air temperature measured at 5 cm above the surface. The results of the Granger causality test did not reveal any evidence of Granger causality for precipitation to ground–air temperature offsets on the daily scale of aggregation except for the asphalt pavement. On the contrary, a strong evidence of Granger causality for precipitation to the ground–air temperature offsets was found on the hourly scale of aggregation for all land cover types except for the sand surface cover. All results are sensitive to the lag choice of the autoregressive model. On the whole, obtained results contain valuable information on the delay time of ΔT(GST–SAT) caused by the rainfall events and confirmed the importance of using autoregressive models to understand the ground–air temperature relationship.

AbstractRadial tree growth is sensitive to environmental conditions, making observed growth increments an important indicator of climate change effects on forest growth. However, unprecedented climate variability could lead to non‐stationarity, that is, a decoupling of tree growth responses from climate over time, potentially inducing biases in climate reconstructions and forest growth projections. Little is known about whether and to what extent environmental conditions, species, and model type and resolution affect the occurrence and magnitude of non‐stationarity. To systematically assess potential drivers of non‐stationarity, we compiled tree‐ring width chronologies of two conifer species, Picea abies and Pinus sylvestris, distributed across cold, dry, and mixed climates. We analyzed 147 sites across the Europe including the distribution margins of these species as well as moderate sites. We calibrated four numerical models (linear vs. non‐linear, daily vs. monthly resolution) to simulate growth chronologies based on temperature and soil moisture data. Climate–growth models were tested in independent verification periods to quantify their non‐stationarity, which was assessed based on bootstrapped transfer function stability tests. The degree of non‐stationarity varied between species, site climatic conditions, and models. Chronologies of P. sylvestris showed stronger non‐stationarity compared with Picea abies stands with a high degree of stationarity. Sites with mixed climatic signals were most affected by non‐stationarity compared with sites sampled at cold and dry species distribution margins. Moreover, linear models with daily resolution exhibited greater non‐stationarity compared with monthly‐resolved non‐linear models. We conclude that non‐stationarity in climate–growth responses is a multifactorial phenomenon driven by the interaction of site climatic conditions, tree species, and methodological features of the modeling approach. Given the existence of multiple drivers and the frequent occurrence of non‐stationarity, we recommend that temporal non‐stationarity rather than stationarity should be considered as the baseline model of climate–growth response for temperate forests.

The Carpathian mountain region is one of the most significant natural refuges on the European continent. It is home to Europe’s most extensive tracts of montane forest, the largest remaining virgin forest and natural mountain beech-fir forest ecosystems. Adding to the biodiversity are semi-natural habitats such as hay meadows, which are the result of centuries of traditional land management. Like other mountain regions areas, the Carpathian mountain region provides important ecosystem goods and services such as water provision, food products, forest products and tourism. But these ecosystem services are feared to be under threat from climate change.This chapter reports on climate trends, impacts and adaptation options. Analysis of climate trends show an increase in annual mean temperature of 1.1–2.0 °C over the last 50 years (1961–2010), further increasing by 3.5–4.0 °C towards the end of the century. Precipitation changes are dispersed with an increase of 300–400 mm in the north and decrease of 100–150 mm in the south regions. Summer precipitation is projected to reduce by 20 %, whereas winter precipitation is projected to increase in most areas by 5–20 % by the year 2100. Both future scenarios and observations show high spatial variability and uncertainty. The same holds for the impacts on the investigated sectors water resources, forests, wetlands, grasslands, agriculture and tourism.The review of climate trends and adaptation options, inspired a strategic agenda on adaptation to be implemented under the regional Carpathian Convention. Planning for climate change adaptation benefits from transnational cooperation because many impacts relate to seasonal and geographical shifts across borders. This is true for the natural system (e.g. shifts in species distribution and snow cover) as well as for socio-economic activities like agriculture, forestry and tourism (e.g. shifting opportunities for growing crops and changes in the tourist season). Examples of adaptation exist, yet need to be communicated for wider adoption. Essential components of adaptation will be capacity building and information sharing, climate-proofing of infrastructure and investments, promotion of eco-system based adaptation measures and making biodiversity management more dynamic.

Climate change is the epitome of a global issue. Cities and their inhabitants face locally specific, yet still globally shared and interconnected problems from heat waves, storms, coastal or fluvia...