Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2101.09757

Peculiarities in quantification of airborne particulate matter by means of Total Reflection X-ray Fluorescence

Yves Kayser·János Osán·Philipp Hönicke·Burkhard Beckhoff
View original source ↗Read source document ↗Export metadata ↓

Abstract

Knowledge on the temporal and size distribution of particulate matter (PM) in air as well as on its elemental composition is a key information for source appointment, for the investigation of their influence on environmental processes and for providing valid data for climate models. A prerequisite is that size fractionated sampling times of few hours must be achieved such that anthropogenic and natural emissions can be correctly identified. While cascade impactors allow for time- and size-resolved collection of airborne PM, total reflection X-ray fluorescence (TXRF) allows for element-sensitive investigation of low sample amounts thanks to its detection sensitivity. However, during quantification by means of TXRF it is crucial to be aware of the limits of TXRF in order to identify situations where collection times or pollution levels were exceedingly long or high. It will be shown by means of grazing incidence X-ray fluorescence (GIXRF), where different reflection conditions are probed, that a self consistent quantification of elemental mass depositions can be performed in order to validate or identify issues in quantification by means of TXRF. Furthermore, monitors of validity for a reliable quantification of the elemental composition of PM by means of TXRF will be introduced. The methodological approach presented can be transferred to tabletop instrumentation in order to guarantee a reliable quantification on an element sensitive basis of the PM collected. This aspect is highly relevant for defining appropriate legislation and measures for health and climate protection and for supporting their enforcement and monitoring.

Explore related subjects

physics.ao-ph

Keep this discovery

BibTeXRIS

Yves Kayser, János Osán, Philipp Hönicke, Burkhard Beckhoff. 2021-01-24. Peculiarities in quantification of airborne particulate matter by means of Total Reflection X-ray Fluorescence. https://arxiv.org/abs/2101.09757

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph↗
arXiv2026

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph↗