Alexandra Larsen, Shu Yang, Brian J Reich, Ana G Rappold
{"title":"A SPATIAL CAUSAL ANALYSIS OF WILDLAND FIRE-CONTRIBUTED PM<sub>2.5</sub> USING NUMERICAL MODEL OUTPUT.","authors":"Alexandra Larsen, Shu Yang, Brian J Reich, Ana G Rappold","doi":"10.1214/22-aoas1610","DOIUrl":null,"url":null,"abstract":"<p><p>Wildland fire smoke contains hazardous levels of fine particulate matter (PM<sub>2.5</sub>), a pollutant shown to adversely effect health. Estimating fire attributable PM<sub>2.5</sub> concentrations is key to quantifying the impact on air quality and subsequent health burden. This is a challenging problem since only total PM<sub>2.5</sub> is measured at monitoring stations and both fire-attributable PM<sub>2.5</sub> and PM<sub>2.5</sub> from all other sources are correlated in space and time. We propose a framework for estimating fire-contributed PM<sub>2.5</sub> and PM<sub>2.5</sub> from all other sources using a novel causal inference framework and bias-adjusted chemical model representations of PM<sub>2.5</sub> under counterfactual scenarios. The chemical model representation of PM<sub>2.5</sub> for this analysis is simulated using Community Multiscale Air Quality Modeling System (CMAQ), run with and without fire emissions across the contiguous U.S. for the 2008-2012 wildfire seasons. The CMAQ output is calibrated with observations from monitoring sites for the same spatial domain and time period. We use a Bayesian model that accounts for spatial variation to estimate the effect of wildland fires on PM<sub>2.5</sub> and state assumptions under which the estimate has a valid causal interpretation. Our results include estimates of the contributions of wildfire smoke to PM<sub>2.5</sub> for the contiguous U.S. Additionally, we compute the health burden associated with the PM<sub>2.5</sub> attributable to wildfire smoke.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181852/pdf/nihms-1846188.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Applied Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1214/22-aoas1610","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/9/26 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
Wildland fire smoke contains hazardous levels of fine particulate matter (PM2.5), a pollutant shown to adversely effect health. Estimating fire attributable PM2.5 concentrations is key to quantifying the impact on air quality and subsequent health burden. This is a challenging problem since only total PM2.5 is measured at monitoring stations and both fire-attributable PM2.5 and PM2.5 from all other sources are correlated in space and time. We propose a framework for estimating fire-contributed PM2.5 and PM2.5 from all other sources using a novel causal inference framework and bias-adjusted chemical model representations of PM2.5 under counterfactual scenarios. The chemical model representation of PM2.5 for this analysis is simulated using Community Multiscale Air Quality Modeling System (CMAQ), run with and without fire emissions across the contiguous U.S. for the 2008-2012 wildfire seasons. The CMAQ output is calibrated with observations from monitoring sites for the same spatial domain and time period. We use a Bayesian model that accounts for spatial variation to estimate the effect of wildland fires on PM2.5 and state assumptions under which the estimate has a valid causal interpretation. Our results include estimates of the contributions of wildfire smoke to PM2.5 for the contiguous U.S. Additionally, we compute the health burden associated with the PM2.5 attributable to wildfire smoke.
期刊介绍:
Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.