The warming on earth is having a profound impact on human survival. With the improvement of people' living standard, the consumption of energy in residential sector has raised swiftly, leading to a rapid increase in corresponding CO2 emissions. To effectively mitigate household emissions, taking the Yangtze River Delta (YRD) region in China as a case study, this paper proposes a novel intelligent model combining driving forces exploration and prediction. The work first estimates the residential energy-related CO2 emissions precisely, and then the bivariate correlation analysis will be applied to analyze region discrepancy in main affecting factors of emissions based on 13 preliminary indicators. To obtain the principal information of above influencing factors as the input of prediction model, the kernel principal component analysis (KPCA) is introduced innovatively. Besides, butterfly optimization algorithm (BOA) is enhanced to better optimize the parameters of least square support vector machine (LSSVM). The new proposed hybrid model, hereafter called as EBOA-LSSVM, will be utilized to predict residential CO2 emissions in the YRD. Ultimate simulation results present the new model's prominent performance through comparing prediction accuracy with other models. Finally, this article provides some advice for policy makers to guide CO2 emissions reduction from residents department.Stable isotopes in wood lignin methoxyl groups (δ2HLM and δ13CLM values) have been suggested as valuable complementary paleoclimate proxies. In permafrost forests, tree growth is influenced by multiple factors, however temperature appears to have the strongest impact on tree growth and, therefore, on carbon cycling. To test whether δ2HLM and δ13CLM values of trees from permafrost regions might record climate parameters, two dominant tree species (Larix gmelinii, larch, and Pinus sylvestris var.  https://www.selleckchem.com/products/Eloxatin.html  mongolica, pine) collected from a permafrost forest in China's Greater Hinggan Mountains, were investigated. The two tree species larch and pine covered time spans of 1940 to 2013 and 1870 to 2013, respectively. Results showed significant correlations of pine and larch δ2HLM values and larch δ13CLM values with temperatures and in particular with the mean temperature of the growing season from April to August. However, only weak correlations of δ2HLM and δ13CLM values with moisture conditions, such as precipitation amount and relative humidity were observed. In addition, species specificity in the climate response was most obvious for δ13CLM values. Compared to a temperature reconstruction based on tree ring width, pine δ2HLM-based reconstruction showed strongest spatial correlations with regional temperature. Therefore, δ2HLM values might be a promising proxy to reconstruct growing-season temperatures in permafrost regions.There is uncertainty if current models for the Covid-19 pandemic should already take into account seasonality. That is because current environmental factors do not provide a powerful explanation of such seasonality, especially given climate differences between countries with moderate climates. It is hypothesized that one major factor is overlooked pollen count. Pollen are documented to invoke strong immune responses and might create an environmental factor that makes it more difficult for flu-like viruses to survive outside a host. This Dutch study confirms that there is a (highly) significant inverse correlation between pollen count and weekly changes in medical flu consults, and that there is a highly significant inverse correlation between hay fever incidence, as measured by prescribed medication revenues, and weekly flu consults. This supports the idea that pollen are a direct or indirect factor in the seasonality of flu-like epidemics. If seasonality will be observed during the covid-19 spread as well, it is not unlikely that pollen play a role.Anthropogenic climate change and the recent increase of Saharan dust deposition has had substantial effects on Mediterranean alpine regions. We examined changes in diatom assemblage composition over the past ~180 years from high-resolution, dated sediment cores retrieved from six remote lakes in the Sierra Nevada Mountains of Southern Spain. In all lakes, changes in diatom composition began over a century ago, but were more pronounced after ~1970 CE, concurrent with trends in rising regional air temperature, declining precipitation, and increased Saharan dust deposition. Temperature was identified as the main predictor of diatom assemblage changes, whereas both Saharan dust deposition drivers, the Sahel precipitation index and the winter North Atlantic Oscillation, were secondary explanatory variables. Diatom compositional shifts are indicative of lake alkalinization (linked to heightened evapoconcentration and an increase in calcium-rich Saharan dust input) and reduced lake water turbulence (linked to lower water levels and reduced inflows to the lakes). Moreover, decreases in epiphytic diatom species were indicative of increasing aridity and the drying of catchment meadows. Our results support the conclusions of previous chlorophyll-a and cladoceran-based paleolimnological analyses of these same dated sedimentary records which show a regional-scale response to climate change and Saharan dust deposition in Sierra Nevada lakes and their catchments during the 20th century. However, diatom assemblages seem to respond to different atmospheric and climate-related effects than cladoceran assemblages and chlorophyll-a concentrations. The recent impact of climate change and atmospheric Saharan deposition on lake biota assemblages and water chemistry, as well as catchment water availability, will have important implications for the valuable ecosystem services that the Sierra Nevada provides.In this work, the climatic impacts of modifying urban surface characteristics are examined for the medium-sized city of Vantaa, Finland, in the current climate and in a projected future climate of 2040-2069. In simulations with the SURFEX air-surface interaction model with a horizontal resolution of 500 m, the fraction of green spaces and relatively sparsely built suburban-type land use was increased at the expense of more densely built commercial and industrial areas. The influence of this land use intervention was found to be rather modest but comparable to the effects of the expected climate change under the RCP8.5 greenhouse gas scenario. For temperature, the climate change is the dominating effect, while wind speed is mainly controlled by surface characteristics. For relative humidity, climate change and the imposed intervention are of comparable importance. The results of this sensitivity study are intended to support policy makers by assessing the potential impact of altering the urban layout in order to improve thermal comfort or as a countermeasure to climate warming in a high-latitude city.