Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire PM2.5 Concentration Forecasting

Fine particulate matter, PM2.5, has been documented to have adverse health effects, and wildland fires are a major contributor to PM2.5 air pollution in the USA. Forecasters use numerical models to predict PM2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods

Statistical downscaling with spatial misalignment: Application to wildland fire PM2.5_{2.5} concentration forecasting

Fine particulate matter, PM$_{2.5}$, has been documented to have adverse health effects and wildland fires are a major contributor to PM$_{2.5}$ air pollution in the US. Forecasters use numerical models to predict PM$_{2.5}$ concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in the presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods

Repeating cardiopulmonary health effects in rural North Carolina population during a second large peat wildfire

Background Cardiovascular health effects of fine particulate matter (PM2.5) exposure from wildfire smoke are neither definitive nor consistent with PM2.5 from other air pollution sources. Non-comparability among wildfire health studies limits research conclusions. Methods We examined cardiovascular and respiratory health outcomes related to peat wildfire smoke exposure in a population where strong associations were previously reported for the 2008 Evans Road peat wildfire. We conducted a population-based epidemiologic investigation of associations between daily county-level modeled wildfire PM2.5 and cardiopulmonary emergency department (ED) visits during the 2011 Pains Bay wildfire in eastern North Carolina. We estimated changes in the relative risk cumulative over 0–2 lagged days of wildfire PM2.5 exposure using a quasi-Poisson regression model adjusted for weather, weekends, and poverty. Results Relative risk associated with a 10 μg/m3 increase in 24-h PM2.5 was significantly elevated in adults for respiratory/other chest symptoms 1.06 (1.00–1.13), upper respiratory infections 1.13 (1.05–1.22), hypertension 1.05 (1.00–1.09) and ‘all-cause’ cardiac outcomes 1.06 (1.00–1.13) and in youth for respiratory/other chest symptoms 1.18 (1.06–1.33), upper respiratory infections 1.14 (1.04–1.24) and ‘all-cause’ respiratory conditions 1.09 (1.01–1.17). Conclusions Our results replicate evidence for increased risk of cardiovascular outcomes from wildfire PM2.5 and suggest that cardiovascular health should be considered when evaluating the public health burden of wildfire smoke

Achievements and gaps in projection studies on the temperature-attributable health burden: Where should we be headed?

Future projection of the temperature-related health burden, including mortality and hospital admissions, is a growing field of research. These studies aim to provide crucial information for decision-makers considering existing health policies as well as integrating targeted adaptation strategies to evade the health burden. However, this field of research is still overshadowed by large uncertainties. These uncertainties exist to an extent in the future climate and population models used by such studies but largely in the disparities in underlying assumptions. Existing studies differ in the factors incorporated for projection and strategies for considering the future adaptation of the population to temperature. These differences exist to a great degree because of a lack of robust evidence as well as gaps in the field of climate epidemiology that still require extensive input from the research community. This narrative review summarizes the current status of projection studies of temperature-attributable health burden, the guiding assumptions behind them, the common grounds, as well as the differences. Overall, the review aims to highlight existing evidence and knowledge gaps as a basis for designing future studies on temperature-attributable health burden estimation. Finding a robust methodology for projecting the future health burden could be a milestone for climate epidemiologists as this would largely benefit the world when applying this technique to project the climate-attributable cause-specific health burden and adapt our existing health policies accordingly

Effects of Controlled Ozone Exposure on Circulating microRNAs and Vascular and Coagulation Biomarkers: A Mediation Analysis

Exposure to ozone (O3) is associated with adverse respiratory and cardiovascular outcomes. Alterations in circulating microRNAs (miRNAs) may contribute to the adverse vascular effects of O3 exposure through inter-cellular communication resulting in post-transcriptional regulation of messenger RNAs by miRNAs. In this study, we investigated whether O3 exposure induces alterations in circulating miRNAs that can mediate effects on downstream vascular and coagulation biomarkers. Twenty-three healthy male adults were exposed on successive days to filtered air and 300 ppb O3 for 2 h. Circulating miRNA and protein biomarkers were quantified after each exposure session. The data were subjected to mixed-effects model and mediation analyses for the statistical analyses. The results showed that the expression level of multiple circulating miRNAs (e.g., miR-19a-3p, miR-34a-5p) was significantly associated with O3 exposure. Pathway analysis showed that these miRNAs were predictive of changing levels of downstream biomarkers [e.g., D-dimer, C-reactive protein, tumor necrosis factor α (TNFα)]. Mediation analysis showed that miR-19a-3p may be a significant mediator of O3-exposure-induced changes in blood TNFα levels [0.08 (0.01, 0.15), p = 0.02]. In conclusion, this preliminary study showed that O3 exposure of healthy male adults resulted in changes in circulating miRNAs, some of which may mediate vascular effects of O3 exposure