Correlations between 7Be, 210Pb, dust and PM10 concentrations in relation to meteorological conditions in northern Poland in 1998-2018

Applying the Kolmogorov-Zurbenko filter followed by random forest models to 7Be observations in Spain (2006-2021)

In this study, we analysed 7Be weekly surface measurements from six Spanish laboratories from 2006 to 2021. The Kolmogorov-Zurbenko filter was applied to the six 7Be time series, and following an iterative process, the original data were divided into two fractions: one related to variations characterized by periods above 33 days (including, among others, the seasonal cycle) and the second noisier fraction related to mechanisms originating from variations with periods below 33 days. Both fractions were independent at the six locations. The second machine-based step using random forest models was applied with the aim of identifying the most influential inputs to the observed 7Be concentrations, and machine learning-inspired regression models were fitted. With respect to seasonal components, the results indicated that the memory of the system was the most influential input, as expected by the large fraction of variance explained by the seasonal cycle, followed by that of humidity and wind-related variables. For the fraction corresponding to periods below 33 d, precipitation-, humidity-, and radiation-related variables were the most influential. This methodology has made it possible to successfully describe the major mechanisms known to be involved in the generation of the surface 7Be concentrations observed in Spain.

Temporal variations of 7Be and 210Pb activity concentrations in the atmospheric aerosols during 2018-2019 in Beijing, China and their correlations with meteorological parameters

In order to establish a regional database on natural radioactivity, a series of measurements of 713 atmospheric aerosol samples collected on filters over a two-year period (2018-2019) in center of Beijing, northeastern China have been performed to analyze ⁷Be and ²¹⁰Pb activity concentrations. The mean activity concentrations of ⁷Be and ²¹⁰Pb were found to be 7.10 ± 2.44 mBq m^-3 and 2.93 ± 1.52 mBq m^-3, respectively. Both the radionuclides exhibited strong seasonal variations, with maximum concentration of ⁷Be occurring in the spring and that of ²¹⁰Pb in the winter. The concentration of both the radionuclides was minimum in the rainy summer. Higher ⁷Be concentration in the spring was mainly caused by the stratosphere to troposphere exchange. Higher ²¹⁰Pb concentration during winter was maybe attributed to the combustion processes in heating systems and the ingression of continental air masses resulted from winds originating from northwest. The dependence of the activity concentrations of ⁷Be and ²¹⁰Pb with meteorological parameters such as rainfall, temperature, and humidity was studied through linear correlation analysis. Statistically significant negative correlations were observed between ⁷Be and ²¹⁰Pb activity concentrations with rainfall, respectively, which suggested that the removal mechanisms of these two radionuclides were similar. Lead-210 showed statistically significant correlations with temperature, humidity and PM₁₀. A comparison of the data obtained in the present study for Beijing with the northern hemisphere literature values of ⁷Be and ²¹⁰Pb in the atmospheric aerosols showed that the values were smaller than the ones observed in the present study. Overall, the study provides an improved understanding of the temporal variability and correlation of ⁷Be and ²¹⁰Pb concentrations in the atmosphere in center of Beijing, northeastern China.

7Be, 210Pb, airborne particulate matter and PM10 concentrations in relation to meteorological conditions in southern Poland in 1998-2016

An analysis of the concentration of ⁷Be in aerosol samples collected in one of the most polluted areas in Europe (Katowice and Krakow in southern Poland) indicated seasonal variability, with a maximum in the summer months. The average concentrations of ⁷Be were 4616.1 μBq m^-3 in Katowice and 3259.4 μBq m^-3 in Krakow, respectively, and they are among the highest values recorded in Poland in the studied period (1998-2016). These cities are also characterised by Poland's highest concentrations of ²¹⁰Pb (547.8 μBq m^-3 and 513.2 μBq m^-3). The highest radioactive concentrations of ²¹⁰Pb were observed in the winter and autumn, since in the case of these industrial areas, the combustion processes related to heating in the cold season of the year are an additional source of this isotope, next to its natural origin. The airborne particulate matter concentrations at both locations correlate with the concentrations of ²¹⁰Pb. The average values of PM10 concentrations (71.1 μg m^-3 in Krakow to 45.0 μg m^-3 in Katowice), were 2-3 times higher than the average ones recorded in northern Poland. It has been proven that air temperature is the key parameter affecting the transport of isotopes, especially in the warm season of the year, when its increase causes increased thermal convection, leading to intense vertical mixing and exchange in the troposphere. Analyses using the machine learning method allowed for an indication of the correlation between relative humidity and atmospheric precipitation, as well as higher wind speed and concentrations of ⁷Be which is inversely proportional. Geographical factors (the latitude of the station and the land elevation) have no impact on near-surface concentrations of ⁷Be in Poland.

Be-7 and Pb-210 in fallout and aerosols in 2000-2016 in central Europe - Deposition velocity and dependence on meteorological parameters

The main aim of this research was to determine the transport and deposition velocities of ⁷Be and ²¹⁰Pb based on a vast database containing the results of measurements of ⁷Be and ²¹⁰Pb in fallout and aerosol samples carried out at several stations located throughout Poland in the period from 2000 to 2016. The monthly deposition flux of ⁷Be and ²¹⁰Pb showed an unequivocal downward trend but was also subject to seasonal changes, with maximum values in the summer period. The same patterns were found in the case of the deposition rate, the average values of which were 0.7 cm s^-1 for ⁷Be and 0.5 cm s^-1 for ²¹⁰Pb. A strong, statistically significant dependence of the deposition rate on the amount of dust was demonstrated, whereby a 10 μg m^-3 decrease in dust increases the ⁷Be deposition rate by 0.1 cm s^-1. Reduction of the concentration of carrier particles reduces the share of dry deposition in favour of precipitation convection, which is much more significant for the transport of both isotopes to the surface. Study of the effect of meteorological parameters showed that the concentrations of ⁷Be in fallout and aerosol samples and ²¹⁰Pb in fallout increase with increasing temperature, indicating a significant share of convection processes in isotope transport. The concentrations of ²¹⁰Pb in aerosols did not show any significant statistical changes over time. Their maximum values were observed in the winter period, indicating an additional source of this isotope related to combustion processes in the heating season. The studies confirmed the dominant role of convective precipitation and large-scale precipitation processes in the elution of ⁷Be from the atmosphere by showing the monthly deposition of this isotope to be strongly dependent on the total precipitation (r = 0.618).

Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research—photovoltaic and wind energy, atmospheric physics and processes; in climate research—parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting.