Found 9746 publications. Showing page 358 of 390:
2023
Ixodes ricinus ticks are Scandinavia's main vector for tick-borne encephalitis virus (TBEV), which infects many people annually. The aims of the present study were (i) to obtain information on the TBEV prevalence in host-seeking I. ricinus collected within the Øresund-Kattegat-Skagerrak (ØKS) region, which lies in southern Norway, southern Sweden and Denmark; (ii) to analyse whether there are potential spatial patterns in the TBEV prevalence; and (iii) to understand the relationship between TBEV prevalence and meteorological factors in southern Scandinavia. Tick nymphs were collected in 2016, in southern Scandinavia, and screened for TBEV, using pools of 10 nymphs, with RT real-time PCR, and positive samples were confirmed with pyrosequencing. Spatial autocorrelation and cluster analysis was performed with Global Moran's I and SatScan to test for spatial patterns and potential local clusters of the TBEV pool prevalence at each of the 50 sites. A climatic analysis was made to correlate parameters such as minimum, mean and maximum temperature, relative humidity and saturation deficit with TBEV pool prevalence. The climatic data were acquired from the nearest meteorological stations for 2015 and 2016. This study confirms the presence of TBEV in 12 out of 30 locations in Denmark, where six were from Jutland, three from Zealand and two from Bornholm and Falster counties. In total, five out of nine sites were positive from southern Sweden. TBEV prevalence of 0.7%, 0.5% and 0.5%, in nymphs, was found at three sites along the Oslofjord (two sites) and northern Skåne region (one site), indicating a potential concern for public health. We report an overall estimated TBEV prevalence of 0.1% in questing I. ricinus nymphs in southern Scandinavia with a region-specific prevalence of 0.1% in Denmark, 0.2% in southern Sweden and 0.1% in southeastern Norway. No evidence of a spatial pattern or local clusters was found in the study region. We found a strong correlation between TBEV prevalence in ticks and relative humidity in Sweden and Norway, which might suggest that humidity has a role in maintaining TBEV prevalence in ticks. TBEV is an emerging tick-borne pathogen in southern Scandinavia, and we recommend further studies to understand the TBEV transmission potential with changing climate in Scandinavia.
Wiley-VCH
2023
Acoustic waves below the frequency limit of human hearing - infrasound - can travel for thousands of kilometres in the atmosphere. The global propagation signature of infrasound is highly sensitive to the wind structure of the stratosphere.
This work exploits processed continuous data from three high-latitude infrasound stations to characterize an aspect of the stratospheric polar vortex. Concretely, a mapping is developed which takes the infrasound data from these three stations as input and outputs an estimate of the polar cap zonal mean wind averaged over 60-90 degrees in latitude at the 1 hPa pressure level. This stratospheric diagnostic information is relevant to, for example, sudden stratospheric warming assessment and sub-seasonal prediction.
The considered acoustic data is within a low-frequency regime globally dominated by so-called microbarom infrasound, which is continuously radiated into the atmosphere due to nonlinear interaction between counter-propagating ocean surface waves.
We trained a stochastics-based machine learning model (delay-SDE-net) to map between a time series of five years (2014-2018) of processed infrasound data and the ERA5 (reanalysis-based) daily average polar cap wind at 1 hPa for the same period. The ERA5 data was hence treated as ground-truth. In the prediction, the delay-SDE-net utilizes time-lagged inputs and their dependencies, as well as the day of the year to account for seasonal differences. In the validation phase, the input was the 2019 and 2020 infrasound time series, and the model inference results in an estimate of the daily average polar cap wind time-series. This result was then compared to the ERA5 representation of the stratospheric diagnostic time-series for the same period.
The applied machine learning model is based on stochastics and allows for an interpretable approach to estimate the aleatoric and epistemic prediction uncertainties. It is found that the mapping, which is only informed of the trained model, the day of year, and the infrasound data from three stations, generates a 1 hPa polar cap average wind estimate with a prediction error standard deviation of around 10 m/s compared to ERA5.
Focus should be put on the winter months because this is when the coupling between the stratosphere and the troposphere can mostly influence the surface conditions and provide additional prediction skill, in particular during strong and weak stratospheric polar vortex regimes. The infrasound data is available in real-time, and we discuss how the developed approach can be extended to provide near real-time stratospheric polar vortex diagnostics.
2023
2023
Review of Interpreting Gaseous Pollution Data Regarding Heritage Objects
Pollutant gases pose a significant risk to some cultural heritage objects, and surveys have shown that the professionals involved consider themselves to lack knowledge to fully assess risk. Three approaches towards risk assessment, research results, standards and damage functions have been considered. An assessment tool has been developed, collating over 4000 research reports into a scheme for the impact on 22 materials of acetic and formic acids, nitrogen dioxide, ozone and reduced sulphur gases. The application of doses or concentrations has been considered, the impact of measurement time compared to annual exposure investigated and a simple tool derived.
MDPI
2023
2023
2023
2023
Quality-assured aerosol optical properties (AOP) with high spatiotemporal resolution are vital for the accurate estimation of direct aerosol radiative forcing and solar irradiance under clear skies. In this study, the sky information from an all-sky imager (ASI) is used with machine learning (ML) synergy to estimate aerosol optical depth (AOD) and the Ångström Exponent (AE). The retrieved AODs (AE) revealed good accuracy, with a dispersion error lower than 0.07 (0.15). The retrieved ML AOPs are used to estimate the DNI by applying radiative transfer modeling. The estimated ML DNI calculations revealed adequate accuracy to reproduce reference measurements with relatively low uncertainties.
2023
Black carbon emitted by incomplete combustion of fossil fuels and biomass has a net warming effect in the atmosphere and reduces the albedo when deposited on ice and snow; accurate knowledge of past emissions is essential to quantify and model associated global climate forcing. Although bottom-up inventories provide historical Black Carbon emission estimates that are widely used in Earth System Models, they are poorly constrained by observations prior to the late 20th century. Here we use an objective inversion technique based on detailed atmospheric transport and deposition modeling to reconstruct 1850 to 2000 emissions from thirteen Northern Hemisphere ice-core records. We find substantial discrepancies between reconstructed Black Carbon emissions and existing bottom-up inventories which do not fully capture the complex spatial-temporal emission patterns. Our findings imply changes to existing historical Black Carbon radiative forcing estimates are necessary, with potential implications for observation-constrained climate sensitivity.
Springer Nature
2023
This report presents European interim air quality maps for 2021, which are based on the non-validated up-to-date (UTD) measurement data and the CAMS Ensemble Forecast modelling results, together with other supplementary data. It contains maps of PM10 and NO2 annual averages and ozone indicator SOMO35.
ETC/HE
2023
2023
Review of methods that can be used in the assessment of atmospheric deposition
There are three main approaches for estimating the atmospheric deposition: 1) From measurements of air and precipitation chemistry combined with statistical interpolation, 2) Chemical transport models, 3) Combined observations and atmospheric model calculations. This report reviews these different approaches and come with some general recommendations on the different strategies and the way forward for Poland.
The report was made for the project "Strengthening of atmospheric deposition assessment in Poland based on Norwegian experience" under the program "Environment, Energy and Climate Change", financed by the European Economic Area Financial Mechanism 2014-2021".
NILU
2023
2023
2023
Increased contribution of biomass burning to haze events in Shanghai since China’s clean air actions
High levels of East Asian black carbon (BC) aerosols affect ecological and environmental sustainability and contribute to climate warming. Nevertheless, the BC sources in China, after implementing clean air actions from 2013‒2017, are currently elusive due to a lack of observational constraints. Here we combine dual-isotope-constrained observations and chemical-transport modelling to quantify BC’s sources and geographical origins in Shanghai. Modelled BC concentrations capture the overall source trend from continental China and the outflow to the Pacific. Fossil sources dominate (~70%) BC in relatively clean summer. However, a striking increase in biomass burning (15‒30% higher in a fraction of biomass burning compared to summer and 2013/2014 winter), primarily attributable to residential emissions, largely contributes to wintertime BC (~45%) pollution. It highlights the increasing importance of residential biomass burning in the recent winter haze associated with >65% emissions from China’s central-east corridor. Our results suggest clearing the haze problem in China’s megacities and mitigating climate impact requires substantial reductions in regional residential emissions, besides reducing urban traffic and industry emissions.
Springer Nature
2023
Fine-resolution spatio-temporal maps of near-surface urban air temperature (Ta) provide crucial data inputs for sustainable urban decision-making, personal heat exposure, and climate-relevant epidemiological studies. The recent availability of IoT weather station data allows for high-resolution urban Ta mapping using approaches such as interpolation techniques or machine learning (ML). This study is aimed at executing these approaches and traditional numerical modeling within a practical and operational framework and evaluate their practicality and efficiency in cases where data availability, computational constraints, or specialized expertise pose challenges. We employ Netatmo crowd-sourced weather station data and three geospatial mapping approaches: (1) Ordinary Kriging, (2) statistical ML model (using predictors primarily derived from Earth Observation Data), and (3) weather research and forecasting model (WRF) to predict/map daily Ta at nearly 1-km spatial resolution in Warsaw (Poland) for June–September and compare the predictions against observations from 5 meteorological reference stations. The results reveal that ML can serve as a viable alternative approach to traditional kriging and numerical simulation, characterized by reduced complexity and higher computational speeds within the domain of urban meteorological studies (overall RMSE = 1.06 °C and R2 = 0.94, compared to ground-based meteorological stations). The results have implications for identifying the urban regions vulnerable to overheating and evidence-based urban management in response to climate change. Due to the open-sourced nature of the applied predictors and input parsimony, the ML method can be easily replicated for other EU cities.
2023
2023
IOP Publishing
2023