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Quantifying O impacts in urban areas due to wildfires using a Generalized Additive Model Xi Gong, Aaron S. Kaulfus, Udaysankar S Nair, and Daniel A. Jaffe Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b03130 • Publication Date (Web): 25 Oct 2017 Downloaded from http://pubs.acs.org on October 25, 2017

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Environmental Science & Technology

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Quantifying O3 impacts in urban areas due to wildfires using a

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Generalized Additive Model

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Xi Gong1,2, Aaron Kaulfus3, Udaysankar Nair3, Daniel A. Jaffe2,4* 1

School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China School of Science, Technology, Engineering and Mathematics, University of Washington Bothell, 18115 Campus Way NE, Bothell, WA 98011, USA 3 Department of Atmospheric Sciences, University of Alabama-Huntsville, Huntsville, AL 35899, USA 4 Department of Atmospheric Sciences, University of Washington Seattle, Seattle, WA 98195, USA * Corresponding author (University of Washington, [email protected]) 2

KEYWORDS. Ozone; Generalized Additive Model; Wildfires; Exceptional events

ACS Paragon Plus Environment


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ABSTRACT

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Wildfires emit O3 precursors but there are large variations in emissions, plume

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heights and photochemical processing. These factors make it challenging to model O3

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production from wildfires using Eulerian models. Here we describe a statistical approach

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to characterize the maximum daily 8-hour average O3 (MDA8) for 8 cities in the U.S. for

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typical, non-fire, conditions. The statistical model represents between 35-81% of the

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variance in MDA8 for each city. We then examine the residual from the model under

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conditions with elevated particulate matter (PM) and satellite observed smoke (“smoke

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days”). For these days, the residuals are elevated by an average of 3-8 ppb (MDA8)

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compared to non-smoke days. We found that while smoke days are only 4.1% of all days

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(May-Sept) they are 19% of days with an MDA8 greater than 75 ppb. We also show that

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a published method that does not account for transport patterns gives rise to large over-

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estimates in the amount of O3 from fires, particularly for coastal cities. Finally, we apply

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this method to a case study from August 2015, and show that the method gives results

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that are directly applicable to the EPA guidance on excluding data due to an

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uncontrollable source.

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TOC Art

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1.

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particulate organic carbon and many other compounds. The NOx and VOCs are

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precursors that can contribute to O3 formation.1 A review of more than 100 published

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studies found that wildfires usually result in O3 production downwind, with the amount of

INTRODUCTION Wildfires emit nitrogen oxides (NOx) and Volatile Organic Compounds (VOCs),


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O3 produced increasing with transport time away from the fire.2 Some studies have

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reported rapid O3 production, on the order of 50 ppb in 6 hours.3, 4 But there are large

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variations in amount and rate of O3 produced, likely associated with variability in

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emissions, plume heights and many other factors.

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In extra-tropical regions, wildfire emissions have a relatively high molar VOC/NOx

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ratio, around 10-100,1 which makes O3 production in smoke plumes very sensitive to

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NOx concentrations. Mixing of a wildfire plume into a NOx rich urban area can enhance

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O3 production.5-13 The VOC emissions also have a very high proportion of Oxygenated

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VOCs (OVOCs), which results in a different photochemical environment compared to

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typical urban chemistry.1 The high emissions of OVOCs, especially acetaldehyde, result

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in rapid sequestration of NOx, forming peroxyacetyl nitrate (PAN) or other organic

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nitrates.2, 14-17 In 5 fire plumes observed in the Pacific Northwest, PAN was found to

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average 44% of the observed NOy species.18 This is much higher than the 12-15% usually

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observed in urban plumes.19

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The large variability in emissions and plume heights, the sub-grid processes,

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chemical and physical processes with respect to aerosols and the large emissions of

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OVOCs make it challenging to model O3 production from wildfires with traditional

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Eulerian models. It is common for Eulerian models to over-predict O3 close to

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wildfires.20-22 In a rather extreme case, Baker et al23 report wildfire O3 production of up

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80 ppb, with simultaneous O3 over-predictions up to 60 ppb using the CMAQ model. Lu

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et al22, using the GEOS-Chem model, show significant over-predictions at sites close to

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the fires and under-predictions at sites further away. While there are many challenges in

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modeling wildfire plumes and O3 production, one of the most important factors is the

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rapid sequestration of NOx, which limits nearby O3 production. As this PAN moves

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downwind and decomposes, O3 production will be enhanced, in contrast to most other

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nitrogen oxides. Since the PAN can be transported from a region of high to low

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concentrations, this may have the effect of increasing overall O3 production. Thus

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accurately quantifying O3 production from wildfires is a major challenge.

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In 2015, the U.S. Environmental Protection Agency (EPA) tightened the National

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Ambient Air Quality Standards (NAAQS) for O3 to 70 ppb for the maximum daily 8-hour

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average (MDA8).24, 25 Because non-controllable sources can significantly impact O3 in



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some areas, the EPA has developed the “exceptional events” rule, which allows a state to

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petition the EPA to exclude data that have been substantially impacted by specific non-

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controllable sources. For wildfires, the EPA has developed a guidance document that

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makes recommendations on best practices to document wildfire impacts on O3.25 This

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guidance document presents several possible tools that can be used, but there are

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significant challenges with some of these. For example, the guidance document suggests

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that fire emissions divided by the distance from the fires (Q/D) is one indicator of a local

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wildfire influence, with higher values of Q/D indicative of stronger influence. However

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this method has not been extensively evaluated and data in the literature suggests that O3

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production continues for 1-3 days downwind of a large fire.2 A second tool mentioned in

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the guidance document is the use of statistical models. These relate O3 concentrations to a

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number of meteorological factors. Statistical models can predict O3 based on

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meteorological factors with R2 up to 0.8.26 The residual (observed O3 minus model fitted

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O3) can give information on additional or unusual sources of O3, which could include

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unusual industrial emissions, stratospheric intrusions or a contribution from wildfires.

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This approach has been used in a number of prior studies documenting O3 impacts from

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wildfires.6, 10, 27

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Generalized Additive Models (GAMs) are flexible regression models that can

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incorporate both a linear and non-linear dependence on numerical and categorical

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variables. These have been used previously to describe the relationship between

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meteorological variables and PM2.5 (particulate matter with an aerodynamic diameter less

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than 2.5µm), O3, SO2, and CO. 28-31 Camalier et al.26 demonstrated the use of a slightly

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different approach called GLM (Generalized Linear Model) to model urban O3 and

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showed that the daily maximum 8-hour average was related to temperature, humidity and

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other factors.

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To identify the presence of smoke we used the NOAA Hazard Mapping System-Fire

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and Smoke Product (HMS-FSP) combined with surface PM2.5 data. The HMS product is

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based on analysis of multiple satellite products, both geostationary and polar orbiting. As

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noted by previous researchers, the frequency of smoke detection at any site is likely an

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under-estimate.12, 32 Kaulfus et al33 combined surface PM data with the HMS product and

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examine the PM distributions for days in presence and absence of overhead HMS smoke.


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They found that on 20% of the days that were above the current 24-hour PM2.5 standard,

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the HMS smoke product showed smoke in the region.

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In this work, we apply GAMs to O3 concentrations for 8 cities in the U.S. and

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examine the residuals in the presence/absence of smoke. We then demonstrate how this

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approach can be used to estimate O3 production due to wildfires and relate this back to

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the EPA guidance document on exceptional events. As a case study we focus on the large

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wildfires that burned in the Pacific Northwest in the summer of 2015 and demonstrate

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substantial impacts on MDA8 O3 at distances up to 1200 km from the wildfires.

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2.

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2.1. Variables and Data Sources

MATERIALS AND METHODS

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Our analysis considers MDA8 O3 from May to September for 2008 to 2015 in 8

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cities in the western US (see Table S1 for locations). The presence of smoke is identified

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using the NOAA Hazard Mapping System-Fire and Smoke Product (FSP)

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(http://www.ospo.noaa.gov/Products/land/hms.html), combined with surface PM2.5 data.

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A variety of data sources were used for the analysis. The MDA8 O3 and PM2.5 data

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came from the EPA’s Air Data database and the National Park Service O3 database. The

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meteorological data came from the NCEP/NCAR reanalysis and AirNow tech dataset

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(http://www.airnowtech.org/). A full description of the meteorological variables used is

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given in Table S2. There is some missing data in the meteorological datasets for each site.

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We use series mean values in the GAMs for a small number of missing meteorological

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data. In all cases, we only include meteorological parameters that have at least 80% data

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completeness.

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The HYSPLIT model (v4.9) was run for each day using the GDAS 1ox1o dataset to

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calculate a 12-hour backward trajectory from each monitor site. Each trajectory was used

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to calculate the ending transport quadrant and direct point to point transport distance. For

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each city we examined the model performance for morning and afternoon trajectory times

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and choose the best O3 predictors. For all cities, we used the trajectories initialized at

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2pm local time. By using the direct distance and quadrant between starting and end

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points, we can incorporate a measure of air mass recirculation or stagnation in the

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statistical model. We also examined model performance using 24-hour backward



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trajectories, but found the 12-hour back trajectories slightly superior based on the

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Akaike’s Information Criterion (AIC) and R2 values for each analysis

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2.2. GAM Method and Model Development

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Generalized Additive Models are regression models involving a sum of smooth

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functions of covariates instead of linear coefficients.34, 35 The MDA8 O3 of each city has

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been modelled separately using the GAM in R software with the “mgcv package” .35 The

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equation can be written as:

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= + + + ⋯ + (1) where i indicates the ith day’s observation. fj(x) are smooth functions of the

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predictors or meteorological data. The element g(µi) is the “link” function, which

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specifies the relationship between the linear formulation on the right side of equation and

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the expected response µi. Xiθ represents an ordinary linear model component for

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predictors (elements of the vector Xi) not subject to nonlinear transformations, mainly

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categorical indicators. Non-linear functions fj(x) are used to represent the complex

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relationship between ozone and meteorological parameters.36 In this study, we found

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Gaussian distributions and the identity link function most appropriate because the

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distribution of ozone (or, more precisely, model residuals) is close to a normal

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distribution. In some analyses of ozone, non-Gaussian models and/or non-identity link

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functions, most commonly the log link that results in a multiplicative model, have been

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used. For example, Camalier et al.26 chose the unusual combination of a Gaussian model

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and a log link while Aldrin et al. 37and Pearce et al.38 modeled log-transformed pollutant

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concentrations with an additive model like that given above. In our work, we found very

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little difference in model results using the log-link function, and given the added

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computational time, we used only the identity link.

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Penalized cubic regression splines (CRS) were used for the smoothing functions fj(x)

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to allow a non-linear response between ozone and each meteorological parameter. The

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smoothness of each function f in the equation is controlled by the number of knots or the

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effective number of degrees of freedom. Models fit the observed ozone concentrations

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better as the number of knots increases but it will become less smooth and effectively

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over-fit the data. The number of knots are estimated with cross-validation.35 The default

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method of cross-validation was used to choose the degree of smoothing (penalization) of


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k=10 dimensional regression splines. Figure S1 shows an example of the O3 response

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function vs relative humidity and the resulting spline fit.

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As there are a large number of meteorological datasets, the challenge to building a

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succinct model is how to select the key parameters out of a large number of parameters.

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The same combination of parameters was used in the 8 cities of the US. The model

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considered only the main effects; the possible parameter interaction effects (such as wind

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speed and direction together) were ignored.37

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Various methods are available for selection of meteorological parameters, including

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“regularized” (e.g. “lasso”) methods and the default selection method (of the “select”

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argument) in the “mgcv” package. However, we chose to adopt a more structured,

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scientifically-motivated forward selection procedure based on AIC with a goal of

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obtaining a common model specification that works well across all of the cities.35, 38 We

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followed this process to build the model:

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1. Choose the common parameters that are important for all sites. We selected DOY

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(day of year), year and trajectory direction and trajectory distance as the basic

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parameters.

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2. Group the parameters with similar physical information (e.g. temperature

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variables, pressure variables, etc.) and add these variables one by one in the model to

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choose the ones have the greatest impact on the AIC.

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3. Identify the most important parameters for each city. The final model selection is

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a compromise of these processes so that all cities use the same variable set which

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includes either 18 or 10 variables models (Table S2). This allows us to compare the

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model residuals across cities for specific dates.

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As variables are added, the AIC decreases and R2 increases up to a point. Figure S2

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shows the R2 and AIC trend for Salt Lake City as variables are added. It was found that

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the AIC decreased as the adjusted R2 increased when the important parameters were

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included in the model. For the 8 cities, we evaluated a model with 18 and 10 parameters.

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Since the AIC decreased continuously up to 18 variables, we can be confident that the

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model has not been overfit.

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2.3. Model Quality Control



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For each model, we used graphical tools to assess the underlying assumptions of

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homogeneity, normality, and independence.35, 39 There are several methods we used: (1)

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Use the gam.check code in R software to check the QQ plots (sample quantiles against

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theoretical quantiles), scatterplots (residuals against linear predictor), histogram of the

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residuals and scatter plots (response against fitted values). These plots are shown in

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Figure S3; (2) Plot model residuals against fitted values; (3) Plot the residuals against

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each explanatory variable that was used in the model; (4) Examine the autocorrelation of

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both the original O3 data and residuals.

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We examined autocorrelation in the original O3 data and the model residuals. Figure

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S4 shows the autocorrelation functions for Provo, UT. For the Provo site, the original O3

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MDA8 dataset has a significant autocorrelation out to at least 15 days. This likely reflects

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the strong seasonality in O3 concentrations. The autocorrelation in the residuals is much

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less. The ACF (Auto Correlation Function) value for the residuals drops to a small value

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in a couple of days. Thus while there is still a small degree of autocorrelation in the

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results, it is not a large factor. This likely reflects the fact that the fit values include a day

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of year parameter that will remove the seasonality in residuals. Thus any autocorrelation

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may reflect temporal correlation in factors that cause high or low O3 that were not

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considered by the model (eg. a stratospheric intrusion or wildfire). Similar

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autocorrelation results are seen for the other sites examined.

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3.

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3.1. Summary of GAM Results for Each City

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RESULTS Table 1 shows the R2 for the model fits for all sites. Table S3 shows the model fits

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(R2) for both the 18- and 10-variable models. The change in the R2 when going from the

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18 parameters to the 10 parameter model is small. Figure 1 shows an example of the

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observed MDA8 vs model fit for Houston, TX.

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Table 1 shows that the GAM models have adjusted R2 values ranging from 0.35 to

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0.81. The residuals are normally distributed. The standard deviation, 95th and 97.5th

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percentiles of the residuals are shown in Table 1. The highest adjusted R2 is seen for

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Houston (0.81), and the lowest is seen for Yellowstone (0.35). This likely reflects the

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greater occurrence of high O3 days in Houston (and correspondingly, greater variance to


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be explained), and greater local meteorological control on O3.

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It is useful to contrast the ability of the GAM approach to capture variability in daily

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MDA8 values with typical Eulerian models. Simon et al40 summarized Eulerian model

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results for O3 from 69 Eulerian model studies reported in the literature and found typical

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R2 performance of between 0.56-0.73 for model calculated MDA8s, but much lower R2

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values (0.05 to 0.28) if only high MDA8 days were considered (>75 ppb). They also

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report that the Eulerian models tend to under-predict O3 on the highest days. For the

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Houston GAM results reported here, we find an R2 value (0.82) if all data are included

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But the GAM R2 values drop to 0.61, 0.52, 0.38 and 0.23 if only MDA8s greater than 40,

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50, 60 and 70 ppb, respectively, are included. Thus the GAM approach demonstrates

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comparable ability at capturing daily variability in MDA8 compared to most Eulerian

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models, keeping in mind the two approaches are very different and give different types of

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information.

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Table 1: Adjusted R2 and statistics on model residuals for the 8 cities using 18 meteorological parameters. Adjusted R2 One standard 97.5th 95th percentile of City (18 deviation of percentile of residuals (ppb) parametersa) residuals (ppb) residuals (ppb) Houston 0.81 8.1 13.5 17.8 Boise 0.55 5.6 9.3 11.1 Denver 0.54 8.0 11.4 14.2 Fort Collins 0.61 6.5 10.4 12.7 Yellowstone 0.35 5.7 9.6 11.3 Provo 0.52 6.1 9.6 11.7 Salt Lake 0.62 6.2 10.2 12.0 City Spokane 0.64 5.3 8.3 10.4 a 2 Adjusted R values for the 10-variable model are reported in Table S3.

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3.2. Interpretation of Residuals with HMS and PM Data

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A high residual alone does not identify the source of high O3. For this we used

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surface PM2.5 combined with the HMS satellite smoke product to identify “smoky” days

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in each city, similar to the approach used previously12. This analysis considers 6 out of

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the 8 sites, since Yellowstone and Provo do not have surface PM2.5 data. We calculated

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the standard deviation of daily PM2.5 values by month for all cities with data. These range



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from 3 to 7 µg-m-3 (shown in Table S4). We use the standard deviation for each site to

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identify whether the daily (24 hour mean) PM2.5 concentration is elevated or not for each

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city. We define Del PM2.5 as:

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Del PM2.5 = Daily PM2.5 – Monthly mean PM2.5 (2) In choosing the appropriate monthly mean to use, we want to consider that high fire

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periods will have enhanced monthly means, but also reductions in anthropogenic

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emissions may result in downward trends at some locations. So for locations with no

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significant downward trend, we use the monthly mean calculated using all years in the

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analysis for each month in equation 2. For sites that had months with a statistically

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significant downward trend in the monthly means, we use the monthly mean for that

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month-year in equation 2. Only Salt Lake City (May) and Houston (May and September)

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had statistically significant downward trends in PM2.5. If Del PM2.5 was higher than the

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standard deviation for that city and there was overhead smoke, as seen in the HMS

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product, these days were regarded as “smoke days” similar to a previous study.12 Table 2

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shows statistics for smoke and smoke-free days in each city, and Figure S5 shows the

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distribution of the residuals for one location (Salt Lake City). The distribution of

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residuals is nearly normal on smoke-free days, whereas for “smoke days” the distribution

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is skewed towards higher values, and the mean value of the residuals on smoke days is

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higher than on smoke-free days. Using a two sample t-test, we found a statistically

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significant difference (P

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