Estimating Ambient Ozone Effect of Kansas Rangeland Burning with Receptor Modeling and Regression Analysis

Prescribed rangeland burning in April is a long-standing practice in the Flint Hills region of eastern Kansas to maintain the tallgrass prairie ecosystem. The smoke plumes originating from these fires increases ambient PM2.5 concentrations and potentially contributes to ozone (O3) exceedances in dow...

Full description

Bibliographic Details
Main Authors: Zifei Liu, Yang Liu, James P. Murphy, Ronaldo Maghirang
Format: Article
Language:English
Published: MDPI AG 2017-02-01
Series:Environments
Subjects:
Online Access:http://www.mdpi.com/2076-3298/4/1/14
id doaj-f502543be15048369c28273cec90c70d
record_format Article
spelling doaj-f502543be15048369c28273cec90c70d2020-11-25T01:41:36ZengMDPI AGEnvironments2076-32982017-02-01411410.3390/environments4010014environments4010014Estimating Ambient Ozone Effect of Kansas Rangeland Burning with Receptor Modeling and Regression AnalysisZifei Liu0Yang Liu1James P. Murphy2Ronaldo Maghirang3Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USADepartment of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USADepartment of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USADepartment of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USAPrescribed rangeland burning in April is a long-standing practice in the Flint Hills region of eastern Kansas to maintain the tallgrass prairie ecosystem. The smoke plumes originating from these fires increases ambient PM2.5 concentrations and potentially contributes to ozone (O3) exceedances in downwind communities. Source apportionment research using Unmix modeling has been utilized to estimate contributions of Kansas rangeland burning to ambient PM2.5 concentrations. The objective of this study was to investigate the potential correlations between O3 and various sources of PM2.5 that are derived from receptor modeling, and then to specifically estimate contributions of Kansas rangeland burning to ambient O3 concentrations through regression analysis. Various daily meteorological data were used as predictor variables. Multiple regression models were developed for the eight-hour daily maximum O3 as well as the daily contributions of the five PM2.5 source categories that were derived from receptor modeling. Cross correlation was analyzed among residuals of the meteorological regression models for O3 and the daily contributions of the five PM2.5 source categories in order to identify the potential hidden correlation between O3 and PM2.5. The model including effects of meteorological variables and episodic contributions from fire and industrial emissions can explain up to 78% of O3 variability. For non-rainy days in April, the daily average contribution from prescribed rangeland burning to O3 was 1.8 ppb. On 3% of the days in April, prescribed rangeland burning contributed over 12.7 ppb to O3; and on 7% of the days in April, burning contributed more than 7.2 ppb to O3. When the intensive burning activities occur in days with high O3 background due to high solar radiation or O3 carryover from the previous day, the contributions from these episodic fire emissions could result in O3 exceedances of the National Ambient Air Quality Standards (NAAQS). The regression models developed in this study demonstrated that the most valuable predictors for O3 in the Flint Hills region include the O3 level on the previous day, total solar radiation, difference between daily maximum and minimum air temperature, and levels of episodic fire and industrial emissions. The long term goal is to establish an online O3 forecasting tool that can assist regulators and land managers in smoke management during the burning season so that the intensive burning activities can be planned to avoid forecasted high O3 days and thus prevent O3 exceedance.http://www.mdpi.com/2076-3298/4/1/14source apportionmentprescribed burningsmokeforecasting
collection DOAJ
language English
format Article
sources DOAJ
author Zifei Liu
Yang Liu
James P. Murphy
Ronaldo Maghirang
spellingShingle Zifei Liu
Yang Liu
James P. Murphy
Ronaldo Maghirang
Estimating Ambient Ozone Effect of Kansas Rangeland Burning with Receptor Modeling and Regression Analysis
Environments
source apportionment
prescribed burning
smoke
forecasting
author_facet Zifei Liu
Yang Liu
James P. Murphy
Ronaldo Maghirang
author_sort Zifei Liu
title Estimating Ambient Ozone Effect of Kansas Rangeland Burning with Receptor Modeling and Regression Analysis
title_short Estimating Ambient Ozone Effect of Kansas Rangeland Burning with Receptor Modeling and Regression Analysis
title_full Estimating Ambient Ozone Effect of Kansas Rangeland Burning with Receptor Modeling and Regression Analysis
title_fullStr Estimating Ambient Ozone Effect of Kansas Rangeland Burning with Receptor Modeling and Regression Analysis
title_full_unstemmed Estimating Ambient Ozone Effect of Kansas Rangeland Burning with Receptor Modeling and Regression Analysis
title_sort estimating ambient ozone effect of kansas rangeland burning with receptor modeling and regression analysis
publisher MDPI AG
series Environments
issn 2076-3298
publishDate 2017-02-01
description Prescribed rangeland burning in April is a long-standing practice in the Flint Hills region of eastern Kansas to maintain the tallgrass prairie ecosystem. The smoke plumes originating from these fires increases ambient PM2.5 concentrations and potentially contributes to ozone (O3) exceedances in downwind communities. Source apportionment research using Unmix modeling has been utilized to estimate contributions of Kansas rangeland burning to ambient PM2.5 concentrations. The objective of this study was to investigate the potential correlations between O3 and various sources of PM2.5 that are derived from receptor modeling, and then to specifically estimate contributions of Kansas rangeland burning to ambient O3 concentrations through regression analysis. Various daily meteorological data were used as predictor variables. Multiple regression models were developed for the eight-hour daily maximum O3 as well as the daily contributions of the five PM2.5 source categories that were derived from receptor modeling. Cross correlation was analyzed among residuals of the meteorological regression models for O3 and the daily contributions of the five PM2.5 source categories in order to identify the potential hidden correlation between O3 and PM2.5. The model including effects of meteorological variables and episodic contributions from fire and industrial emissions can explain up to 78% of O3 variability. For non-rainy days in April, the daily average contribution from prescribed rangeland burning to O3 was 1.8 ppb. On 3% of the days in April, prescribed rangeland burning contributed over 12.7 ppb to O3; and on 7% of the days in April, burning contributed more than 7.2 ppb to O3. When the intensive burning activities occur in days with high O3 background due to high solar radiation or O3 carryover from the previous day, the contributions from these episodic fire emissions could result in O3 exceedances of the National Ambient Air Quality Standards (NAAQS). The regression models developed in this study demonstrated that the most valuable predictors for O3 in the Flint Hills region include the O3 level on the previous day, total solar radiation, difference between daily maximum and minimum air temperature, and levels of episodic fire and industrial emissions. The long term goal is to establish an online O3 forecasting tool that can assist regulators and land managers in smoke management during the burning season so that the intensive burning activities can be planned to avoid forecasted high O3 days and thus prevent O3 exceedance.
topic source apportionment
prescribed burning
smoke
forecasting
url http://www.mdpi.com/2076-3298/4/1/14
work_keys_str_mv AT zifeiliu estimatingambientozoneeffectofkansasrangelandburningwithreceptormodelingandregressionanalysis
AT yangliu estimatingambientozoneeffectofkansasrangelandburningwithreceptormodelingandregressionanalysis
AT jamespmurphy estimatingambientozoneeffectofkansasrangelandburningwithreceptormodelingandregressionanalysis
AT ronaldomaghirang estimatingambientozoneeffectofkansasrangelandburningwithreceptormodelingandregressionanalysis
_version_ 1725040724777369600