Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2603.02165

Investigating the short-term effects of particulate matter (PM) chemical components on mortality and the potential modifying effect of extreme temperature: A time-series analysis in London

Xiaolu Zhang·Anna Font·Anja Tremper·Max Priestman·Shawn Y. Lee·David C. Green·Dimitris Evangelopoulos·Gang I. Chen
View original source ↗Read source document ↗Export metadata ↓

Abstract

Particulate matter (PM) is linked to adverse health outcomes, yet the roles of specific PM components and their modification by extreme temperature remain unclear. We examined short-term associations between ten PM chemical components and daily mortality in Greater London (2015-2018). PM components include inorganic aerosols (black carbon from wood burning (BCwb) and traffic exhaust (BCtr), SO4, NO3, and NH4) and organic aerosols (hydrocarbon-like organic aerosol (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more and less oxidized oxygenated OA (MO-OOA and LO-OOA)). We applied quasi-Poisson generalized additive models and weighted quantile sum (WQS) regression to estimate single-pollutant, multi-pollutant, and mixture effects, respectively, and included interaction terms to test effect modification by heat waves and cold spells. All ten components showed positive associations with all-cause mortality in single-pollutant models with stronger estimated risks for respiratory mortality, particularly for NH4, NO3, SO4. In mixture analyses, the WQS index was significantly associated with all-cause mortality (RR = 1.015, 95% CI: 1.006-1.024 per 25th-percentile increase) and showed a marginally significance with respiratory mortality (RR = 1.018, 95% CI: 0.994-1.042). MO-OOA and COA contributed most to all-cause mortality, while BBOA and BC Wood dominated respiratory effects. Heat waves consistently amplified respiratory risks in both single-pollutant and mixture models with little evidence for cardiovascular mortality. Overall, MO-OOA demonstrated harmful associations across outcomes, suggesting potential toxicity link to secondary atmospheric oxidation processes. These findings support source-specific control strategies and highlight the importance of accounting for extreme temperature in air pollution mitigation policies.

Explore related subjects

physics.ao-phq-bio.QM

Keep this discovery

BibTeXRIS

Xiaolu Zhang, Anna Font, Anja Tremper, Max Priestman, Shawn Y. Lee, David C. Green, Dimitris Evangelopoulos, Gang I. Chen. 2026-03-02. Investigating the short-term effects of particulate matter (PM) chemical components on mortality and the potential modifying effect of extreme temperature: A time-series analysis in London. https://arxiv.org/abs/2603.02165

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↗