A machine learning approach to estimate hourly exposure to fine

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Environmental Modeling

A machine learning approach to estimate hourly exposure to fine particulate matter for urban, rural, and remote populations during wildfire seasons Jiayun Yao, Michael Brauer, Sean M. Raffuse, and Sarah Henderson Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01921 • Publication Date (Web): 24 Oct 2018 Downloaded from http://pubs.acs.org on October 25, 2018

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Environmental Science & Technology

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A machine learning approach to estimate hourly exposure to

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fine particulate matter for urban, rural, and remote

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populations during wildfire seasons

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Jiayun Yao1*, Michael Brauer1, Sean Raffuse2, Sarah B. Henderson1,3

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1. School of Population and Public Health, University of British Columbia, Vancouver, Canada,

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V6T 1Z3

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2. Air Quality Research Center, University of California, Davis, USA, 95616

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3. Environmental Health Services, British Columbia Centre for Disease Control, Vancouver,

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Canada, V5Z 4R4

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*Corresponding to Jiayun Yao at: School of Population and Public Health, University of British

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Columbia, 2206 East Mall, Vancouver, V6T 1Z3 Canada [email protected]

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Main text word count: 5037

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Abstract

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Exposure to wildfire smoke averaged over 24-hour periods has been associated with a wide range of

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acute cardiopulmonary events, but little is known about the effects of sub-daily exposures immediately

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preceding these events. One challenge for studying sub-daily effects is the lack of spatially and

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temporally resolved estimates of smoke exposures. Inexpensive and globally applicable tools to reliably

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estimate exposure are needed. Here we describe a Random Forests machine learning approach to

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estimate 1-hour average population exposure to fine particulate matter during wildfire seasons from

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2010 to 2015 in British Columbia, Canada, at a 5km by 5km resolution. The model uses remotely sensed

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fire activity, meteorology assimilated from multiple data sources, and geographic/ecological information.

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Compared with observations, model predictions had a correlation of 0.93, root mean squared error of

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3.2 µg/m3, mean fractional bias of 15.1%, and mean fractional error of 44.7%. Spatial cross-validation

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indicated an overall correlation of 0.60, with an interquartile range from 0.48 to 0.70 across monitors.

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This model can be adapted for global use, even in locations without air quality monitoring. It is useful for

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epidemiologic studies on sub-daily exposure to wildfire smoke, and for informing public health actions if

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operationalized in near-real-time.

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Table of Contents (TOC)/Abstract Art 2 ACS Paragon Plus Environment

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Introduction

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Wildfire frequency and intensity have significantly increased in North America over recent decades,1-5

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resulting in destruction of property and loss of human life. Smoke generated from wildfires can be

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transported to populations in regions distant from the actual fires, leading to degradation of air quality

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and threats to public health.6-9 Estimates from 1997-2006 suggest that deaths attributable to global

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landscape fire smoke exposure can range from 260,000 to 600,000 every year.10 Smoke is an important

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outdoor air pollution source that contributes to the health burden in many regions of the world,11 and

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the impacts are expected to increase in the future with the changing climate.12-14

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Exposure to wildfire smoke has been associated with a wide range of acute cardiopulmonary outcomes,

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from increased reporting of symptoms to elevated risk of mortality.15, 16 However, most of the

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epidemiologic evidence has been generated using exposures averaged over the 24-hour period on the

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day of the observed outcomes, and very little is known about the effects of sub-daily exposures in the

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hours before the outcome. As a result, it remains unclear how much of the cardiopulmonary effects of

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wildfire smoke should be attributed to short (i.e. hours) but extreme levels of exposure compared with

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longer (i.e. days) periods of cumulative exposure. Wildfire smoke levels within a community can change

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very rapidly, so such evidence is needed to develop air quality guidelines and optimal response plans

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based on relevant averaging periods. Knowledge about the potential impacts of short-term increases in

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exposure and the potential benefits of short-term interventions is critical for protecting public health.

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One of the biggest challenges for studying sub-daily smoke exposures is the ability to generate spatially

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and temporally resolved estimates of fine particulate matter (PM2.5) concentrations. Although 1-hour

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average measurements of PM2.5 from fixed air quality monitoring stations may be available, they are

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typically sparse in space, located in urban areas, and may fail during periods of extreme smoke.17

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Further, while wildfire smoke can affect well-monitored urban populations, it more frequently affects 3 ACS Paragon Plus Environment


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dispersed populations in rural and remote areas. In addition, assigning exposures from central

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monitoring stations assumes no spatial variability over potentially large geographic areas, which can

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introduce non-differential exposure misclassification and bias epidemiologic effect estimates towards

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the null.18 The one epidemiologic study that has used such data to evaluate sub-daily impacts of wildfire

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smoke reported a borderline positive association with out-of-hospital cardiac arrest.19

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The province of British Columbia, Canada, has urban, rural, and remote populations that are routinely

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affected by seasonal wildfire smoke. Our previous work in this context has shown that a spatially

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resolved model of 24-hour average wildfire smoke exposures produced more precise and robust

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epidemiologic effect estimates than measurements taken from the provincial air quality monitoring

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network, despite this network being relatively dense compared with other parts of the world.20 We

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therefore hypothesize that epidemiologic evidence on sub-daily, 1-hour exposures can be improved by

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developing a spatially resolved sub-daily exposure model. There are two possible ways to achieve this:

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deterministic and statistical modeling.

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Many air pollution models are deterministic, meaning that they use a variety of meteorological and

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other data inputs to simulate emissions and dispersion based on a set of equations describing the

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physical and chemical atmospheric processes. Deterministic wildfire smoke models usually consume

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information about the fires (location and intensity), fuel types, fuel loads, and meteorological conditions.

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This information is then used by different component models that simulate fire behavior, smoke

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emissions, and dispersion.21, 22 Such models require extensive expertise and considerable computing

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power to develop and maintain. Statistical models, on the other hand, estimate pollutant concentrations

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as a stochastic function of (1) the relevant variables that are currently available, and (2) the empirical

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relationship between those variables and the pollutant concentrations derived from historical data.23, 24

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Conventional approaches to statistical air pollution modeling have relied on linear regression, but rapid


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developments in the fields of data mining, machine learning, and computation have made these

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alternative methods more widely available in recent years.25, 26

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Machine learning is a range of computational methods that produce predictions based on algorithms

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trained using historical data, in an automated and iterative process without explicit programming.27-29

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Many machine learning approaches can accommodate complex relationships that are non-linear, or that

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are best explained by high-dimensional interactions. These properties have made them attractive for a

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wide range of applications, and several recent studies have used them to model air quality and pollutant

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exposures.30 Unlike deterministic models, statistical models derived from machine learning predict

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exposure based purely on the relationships between the response and predictive variables, without

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explicit knowledge of the complex and inter-dependant underlying physical processes. Although prior

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knowledge of the basic physical processes is necessary to offer these models a relevant set of predictive

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variables, achieving the most predictive model is the primary objective of machine learning approaches,

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not the interpretability of the output. These models tend to produce estimates that agree better with

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observations than their deterministic counterparts,31-34 while requiring less expertise and resources to

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operationalize.

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The Optimized Statistical Smoke Exposure Model (OSSEM) is a statistical model that we previously

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developed to estimate 24-hour average PM2.5 for the entire population of British Columbia applied linear

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regression to integrate data from multiple sources, including remote sensing images of fire and smoke,

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routine air quality monitoring, and meteorological models.35 It has been operationalized for public

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health surveillance since 2012.36 Here, we expand on the 24-hour model (OSSEM-24h) to build a 1-hour

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model (OSSEM-1h) that estimates PM2.5 exposures for the same population at the same resolution using

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more advanced statistical modeling techniques and data sources with higher temporal resolution. To

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identify relevant variables for this work, we also completed a thorough investigation of factors



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associated with the presence of smoke at the surface level, where populations are exposed.37 The

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OSSEM-1h model will be useful for conducting epidemiologic studies on sub-daily exposure to wildfire

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smoke, as well as informing public health actions once operationalized in near-real-time.

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Figure 1. Map of British Columbia, Canada. Grey squares indicate grid cells that cover the populated areas in the province, where predictions from the model are made. Orange crosses indicate large fires with high radiative power (>1000 MW) that occurred during the study period (2010-2015). The box with the dashed line indicates the central interior region for case study 1, while the box with solid line indicates the area for case study 2, which included the greater Vancouver area. The locations of the monitoring stations presented in case studies are also labeled. Base map data source: BC Stats and BC Ministry of Health

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Materials and Methods

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Study area and period

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British Columbia is the westernmost province of Canada, with a total land area of 925,186 km2, and a

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population of 4.8 million people.38 Over half of this population resides in the greater Vancouver area,

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while the vast majority of the landmass is sparsely populated39 (Figure 1). Almost two-thirds of the

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landmass is forested, making the province prone to seasonal wildfires.40 The study period covers the

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wildfire seasons (1 April to 30 September) of 2010 through 2015, during which the province experienced

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three severe fire seasons in 2010, 2014, and 2015, with nearly 3,300 km2 burned each year. The recent

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increase in extreme wildfire seasons has been attributed to global climate change and widespread forest

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damage caused by the mountain pine beetle over the last decades.41-43 Most large and intense wildfires

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occurred in the northern and central regions of the province during the study period (Figure 1).

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Data sources and variables

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The following data sources were used to derive the response variable and potentially predictive

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variables for the OSSEM-1h model, based on previous work to develop the OSSEM-24h35 and vertical

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height37 models. The spatial resolutions of the input datasets described below varied between 1 and

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50 km, so a prediction grid with a resolution of 5 km by 5 km was created for the province. Values for

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each of the potentially predictive variables were assigned to each cell. If more than one value was

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available for a single cell, the mean value was assigned for continuous variables and the closest value to

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the cell centroid was assigned for categorical variables. If no value was available within a cell, the value

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closest to the cell centroid was assigned.

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PM2.5 measurements: We obtained 1-hour average PM2.5 measurements from 72 air quality monitoring

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stations from the Provincial Air Data Archive Website maintained by the British Columbia Ministry of

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Environment and Climate Change Strategy. The total number of 1-hour observations available at each 7 ACS Paragon Plus Environment


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station during the study period ranged from 2263 to 19325 out of the 26352 hours. These 1-hour

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average data were used as the PM 1-hour response variable for model training, as well as the

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observations against which we compared the predictions for model evaluation. The response variable

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PM 1-hour was log-transformed due to its right-skewed distribution. The calendar month of the

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observation (Month) was included as a potentially predictive variable based on previous work 37.

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Ecozone: The provincial digital Biogeoclimatic Ecosystem Classification map (Version 10.0) was obtained

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from the GeoBC Data Discovery Service. The province was classified into 16 ecozones named by the

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major vegetation species present in each (Figure S1), which have similar growing conditions due to a

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broad, homogeneous macroclimate.44, 45 While not temporally variable over the study period, these data

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may indicate how smoke emissions factors vary by fuel type and climate.46-49 The potentially predictive

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variable Ecozone was assigned to each grid cell as the ecozone of the nearest fire.

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Fire activity: Data from the Moderate Resolution Spectroradiometer (MODIS) instruments aboard the

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polar orbit Aqua and Terra satellites were used to assign fire locations and intensity. Fire locations were

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taken as the centroids of all 1 km by 1 km fire pixels identified in the active fire product (MCD14ML,

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Collection 6), and fire intensities were approximated by the fire radiative power (FRP) variable. Because

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these two satellites only passed over the study area four times daily, information was not available for

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every hour. As a result, hotspots caused by presumed vegetation fires observed within the 12-hour

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window before or after the 1-hour prediction period were rasterized to the prediction grid cells. Five

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potentially predictive variables were derived from these data (Table 1): (1) FRP Within 500km as

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summed FRP values for all fires within 500 km of the grid cell; (2) Closest FRP Cluster as the summed FRP

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value for the closest fire cluster, where a cluster was defined as any group of grid cells with fire

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detections that shared any boundaries using the Queen’s case criterion50; (3) IDW FRP as the inverse

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distance weighted (IDW) average to the centroids of all fires within 500 km, which would reflect larger


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impacts from closer fires; and (4) Minimum Fire Distance as the distance to the nearest detected fire;

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and (5) Fire Direction as the direction in degrees bearing from the grid cell to the nearest fire. More

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details on the derivation of these variables can be found in previous work.37

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Meteorology: Hourly meteorological information was retrieved from the NASA Modern Era

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Retrospective-analysis for Research and Applications (MERRA) program, available at a spatial resolution

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of 2/3-degree longitude by 1/2-degree latitude. We used these data to generate potentially predictive

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variables for the planetary boundary layer height (PBLH) and the eastward and northward wind

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components at 50 m above the surface (E50m, N50m). We also extracted eastward and northward

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winds at the 500 hPa (E500hPa, N500hPa) and 250 hPa (E250hPa, N250hPa) pressure levels. These

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variables were predictive of the dispersion of wildfire smoke in previous studies.25, 37 More details about

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these variables can be found in previous work.37

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Elevation: The average elevation of the grid cell was calculated using data from the GTOPO30 product

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from the US Geological Survey Earth Resources Observation and Science Center. This is a global digital

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elevation model with a horizontal grid spacing of approximately 1km by 1km.

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Model building and evaluation

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Data reduction

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Given the large number of 1-hour PM2.5 observations available, using all observations for the modeling

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would be computationally expensive. In addition, a large proportion of the observations reflect low,

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background PM2.5 concentrations, which are not suited to the objective of modeling the air quality

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impacts of wildfire smoke. In consideration of these factors, we chose to train and evaluate OSSEM-1h

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using a reduced dataset. This included all observations with PM2.5 concentration > 15 µg/m3,

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approximately the 95th percentile of all 1-hour PM2.5 observations, and twice as many randomly-selected

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observations with PM2.5 concentrations  15 µg/m3 (Figure S2). 9 ACS Paragon Plus Environment


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Table 1. Data sources and variables. Variable

Description

Data source

1-hour PM2.5 from 72 monitoring stations

BC Ministry of Environment

Response variable PM 1-hour

Potentially predictive variables Month Ecozone FRP Within 500km Closest FRP IDW FRP Minimum Fire Distance

Categorical variable indicating the calendar month Ecozone from the Biogeoclimatic Ecosystem Classification map, where the closest fire is observed Summed fire radiative power (FRP) for all fire within 500 km Summed FRP for the closest fire cluster The inverse distance weighted averaged FRP for fire within 500 km Distance to the closest fire

E50m N50m E500hPa N500hPa E250hPa N250hPa

Direction as degrees bearing to the closest fire Planetary boundary layer height above land surface Eastward wind at 50 meters above surface Northward wind at 50 meters above surface Eastward wind at 500 hPa pressure level Northward wind at 500 hPa pressure level Eastward wind at 250 hPa pressure level Northward wind at 250 hPa pressure level

Elevation

Elevation of the land surface above sea level

Fire Direction PBLH

BC Ministry of Forests and Range

US National Aeronautics and Space Administration (NASA) data from the Moderate Resolution Imaging Spectroradiometer (MODIS)

NASA Modern Era Retrospectiveanalysis for Research and Applications (MERRA) program

US Geological Survey Earth Resources Observation and Science Center

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Model training and out-of-bag evaluation

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The reduced data were fitted with a Random Forests model, which is an ensemble of regression trees.51

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Each tree was trained with a random subset of the predictive variables on a random subset of the

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reduced data. We constructed the Random Forests models with a total of 500 trees using five predictive

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variables in each tree. To evaluate the model performance, predictions were made with the unsampled

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data from each tree, otherwise known as the “out-of-bag” data. Because a single observation could fall 10 ACS Paragon Plus Environment

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into the out-of-bag subset multiple times, all values predicted for the same PM 1-hour observation were

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averaged to obtain the final out-of-bag prediction. Four statistics were calculated to compare the out-of-

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bag predictions against the observations: (1) Pearson’s correlation coefficient; (2) root mean squared

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error (RMSE); (3) mean fractional bias (MFB); and (4) mean fractional error (MFE) (Table S1). The out-of-

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bag predictions were also examined on days with different fire activity levels. The provincial sum of FRP

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was calculated for each day of the study period, and days with FRP in 80 percentile were categorized as low, moderate and high fire days, respectively. Out-of-bag

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predictions from the model were compared with observations stratified by these groupings.

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The importance of each model variable was assessed by generating a random permutation of the

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variable, calculating the increase in model prediction error with the new values, and comparing it with

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the error in the model that used the real values. If a variable is important for prediction, the error will be

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dramatically increased when that variable is replaced with random values. A backward variable selection

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process was used based on variable importance. If a reduced model without the least important variable

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performed better or the same as the full model in out-of-bag prediction, that variable would be

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permanently removed from the final model. Otherwise, the variable would be kept, and the full model

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was regarded as the final model.

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Leave-region-out cross-validation

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To further test the model performance in areas away from the locations of the training data, a leave-

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region-out cross-validation was conducted. For the 72 monitoring stations used in the study, those

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within 50km of each other were clustered as a regional group. The 50km criterion was chosen based on

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a previous study that found the median distance from the populated grid cells of the model to the

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nearest monitor was approximately 50km.35 The clustering produced a total of 30 groups, each of which

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included 1-4 stations, except for the regional group in the greater Vancouver area, where 13 stations



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were clustered into one group. We then trained 16 models, 15 of which excluded data from a random

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selection of two regional groups, and one of which excluded data from the greater Vancouver regional

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group. Then we used the models to make predictions for the excluded stations. Using this spatial cross-

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validation design, we were able to test model performance when none of the training stations were

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within 50km of the testing locations. The same four evaluation statistics (Table S1) were calculated for

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each station and overall.

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Case studies

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In addition to the province-wide quantitative evaluation, the final model was qualitatively evaluated

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using two case studies. The first one examined an extreme fire and smoke event that occurred in August

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2010 across the central interior region of British Columbia52, which is frequently affected by seasonal

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wildfires and smoke53 (Figure 1). The second one focused on the southwestern region around greater

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Vancouver, where more than half of the population of the province resides (Figure 1). This region was

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more moderately impacted by smoke from wildfires burning north of the area in early July 2015.54 The

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monitoring stations used for evaluation in these case studies were chosen to represent impacts from the

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smoke events at different extents and timing. Predictions were made for populated grid cells (Figure 1)

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in the case study area during the case study period.

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Sensitivity Analysis

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Inclusion of PM lag 24-hour

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The OSSEM-24h model included PM2.5 concentrations from the previous day as its most important

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predictor35, but this limits its utility in areas without dense monitoring networks. Here we chose to

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exclude lagged PM2.5 concentrations from the main analyses, but to evaluate in a sensitivity analysis

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whether we could achieve a better model with these data. The 24-hour average of the PM2.5

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concentrations on each day were calculated using the 1-hour data. The values from the closest stations


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on the previous day were assigned to grid cells as the potentially predictive variable PM Lag 24-hour, to

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describe the general air quality in the time before the prediction was made. Evaluation statistics were

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calculated for out-of-bag predictions from model with PM Lag 24-hour and compared with those from

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the primary model.

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Inclusion of GOES AOD

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We originally intended to use the aerosol optical depth (AOD) product from the Geostationary

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Operational Environmental Satellite (GOES) West as a potentially predictive variable, because it was the

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only AOD product available at the timescale relevant to OSSEM-1h. These data are available at a 30-

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minute interval and 4 km X 4 km spatial resolution during the sunlit portion of the day, and have been

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correlated with ground-level PM2.5 concentrations in previous studies.25, 55-57 However, the variable was

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omitted because a large proportion (64%) of the data were missing. Given that MODIS AOD was an

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important contributor to OSSEM-24h, we tested OSSEM-1h models developed with and without GOES

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AOD using the subset of available data. We applied the cloud filter provided with the product to remove

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invalid retrievals and assigned the average of the two retrievals for each hour to the prediction grid. If

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the value at a grid cell was missing or invalid, the value from the closest cell with valid value within 50km

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was assigned, which was the same approach used for OSSEM-24h.35

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Results

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There were 789,337 1-hour observations with complete information for the response variable and all

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potentially predictive variables, among which 31,688 (4%) had PM2.5 concentrations higher than 15

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µg/m3. The PM2.5 observations had a median [25%ile, 75%ile] of 4 [2, 7] µg/m3. After the sampling

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procedure (Figure S2), a reduced dataset of 95,064 observations was used to train the final model. The

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PM2.5 observations in the reduced data had a median of 6 [3, 17] µg/m3.



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The final model included all 15 potentially predictive variables after the backward selection process. The

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eastward wind component at 50 m above surface (E50m) was the most important variable in the model,

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followed by Ecozone of the closest fire and other meteorological variables, and FRP Within 500km,

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which reflected the intensity of fire activity in area (Figure S3). The final model had an overall

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correlation of 0.93, RMSE of 9.34 µg/m3, MFB of -0.68%, and MFE of 45% between the out-of-bag

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predictions and observations. Some of the evaluation statistics were influenced by data reduction,

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where low PM2.5 values were under-represented relative to the entire distribution (Figure S2). When we

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applied the model to make predictions to the complete dataset (789,337 observations), the predictions

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had an overall correlation of 0.94, RMSE of 3.2 µg/m3, MFB of -15.1%, and MFE of 44.7% compared with

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observations. The model performed best on days with high fire activity (r = 0.94), compared with

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moderate fire activity (r = 0.90) and low fire activity (r = 0.77). The model tended to underestimate

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observed concentrations in all of these categories (Figure S4).

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The leave-region-out cross-validation resulted in an overall correlation of 0.60 between predictions and

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observations. Station-specific correlations ranged from -0.09 to 0.86, with an interquartile range from

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0.48 to 0.70. Some spatial patterns were observed in the distribution of the performance statistics

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across different stations (Figure 2). The model performed well around the greater Vancouver region

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with high correlations, low RMSE and MFE, and MFB close to zero. Stations in the central interior region

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also had high correlations, but they tended have large RMSE, negative MFB, and high MFE values (Figure

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2). This may have been due to the higher PM2.5 concentrations and thus larger variability in the central

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interior region compared with the greater Vancouver region. Stations with fewer observations and

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lower variability tended to have lower correlation: all stations with correlation below 0.3 had < 500

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observations and a PM2.5 interquartile range (IQR) of < 6 µg/m3, compared with the medians of 1326

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observations and 12 µg/m3 IQR among all stations.


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Figure 2. The (a) correlation (r), (b) root mean squared error (RMSE), (c) mean fractional bias (MFB), and (d) mean fractional error (MFE) between model predictions and observations in the leave-region-out cross-validation. The dashed box indicates the central interior area in case study 1, and the solid box with inset indicate the area in case study 2, which includes greater Vancouver. Base map data source: BC Stats and BC Ministry of Health

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Several large wildfires began burning near Williams Lake (Figure 1) in the central interior region on 28

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July 2010. These fires remained partially contained until 17 August 2010, when a wind event caused

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rapid fire growth and wide smoke dispersion across the region. Three of the PM2.5 monitoring stations in

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the area had distinctly different time series of observed 1-hour concentrations between 17 and 19

Case study 1



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August 2010, which were generally well-predicted by OSSEM-1h (Figure 3 a-c). However, the model

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tended to overestimate when PM2.5 concentrations were low and to underestimate when PM2.5

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concentrations were high. The spatial distribution of OSSEM-1h predictions reflected the rapid

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dispersion of smoke from Williams Lake to the surrounding area farther and farther removed from the

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fires (Figure 3 e-f).

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Case Study 2

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Starting 5 July 2015, the greater Vancouver area was affected by smoke from multiple fires at the north

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of the case study boundary (Figure 1). The 24-hour averaged PM2.5 concentrations across greater

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Vancouver were elevated over the provincial air quality objective (25 µg/m3) for multiple days, which is

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rare for the region. Stations in both Squamish and Chilliwack observed significant elevations in PM2.5

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concentrations, but with the peaks occurring at different times, while the Colwood station observed

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limited impacts (Figure 4). Similar to Case Study 1, OSSEM-1h predictions generally agreed well with

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observed 1-hour PM2.5 concentrations but consistently underestimated the high concentrations (Figure

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4).

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Sensitivity Analyses

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The model including the PM Lag 24-hour variable had a correlation of 0.93, RMSE of 9.41 µg/m3, MFB of

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-0.23%, and MFE of 44% between the out-of-bag predictions and observations. The correlations on days

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with high, moderate and low fire activity were 0.94, 0.98 and 0.76, respectively. This performance was

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very similar to the performance of the primary model without PM Lag 24-hour.

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There were 245,464 1-hour observations available across 66 monitoring stations with complete data for

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GOES AOD and all other predictive variables. Using the same approach described previously (Figure S2),

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34,758 observations were sampled to train the models with and without AOD. The distribution of the

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PM2.5 observations in this subset of data was similar to that of the full reduced dataset, with a median of 16 ACS Paragon Plus Environment

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7 [3, 17] µg/m3. The out-of-bag predictions made with the model including AOD had a correlation of

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0.90, RMSE of 10.7 µg/m3, MFB of 0.12% and MFE of 46% when compared with observations, while the

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model excluding AOD had a correlation of 0.91, RMSE of 9.7 µg/m3, MFB of 0.01% and MFE of 45%.

326 327



328 329 330 331 332 333 334 335 336 337

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Figure 3. Case study 1. On the left, the time series of 1-hour PM2.5 model predictions (grey bars) are compared with observations (black line) at monitoring stations in (a) Williams Lake, (b) Prince George, and (c) Burns Lake from 17 – 19 August 2010. The maps on the right show model predictions at (d) 17:00 on 17 August, (e) 10:00 on 18 August, and (f) 17:00 on 18 August in all populated grid cells. The time points correlating to the right-hand panels are labeled on each of the time series plots (color coded), and the locations correlating to the left-hand panels are labeled on each of the maps. Base map data: Google.


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338 339 340 341 342 343 344 345 346


Figure 4. Case study 2. On the left, the time series of PM2.5 model predictions (grey bars) are compared with observations (black line) at monitoring stations at (a) Chilliwack, (b) Colwood, and (c) Squamish from 05 – 07 July 2015. Maps on the right show model predictions at (d) 00:00 on 06 July 6, (e) 10:00 on 06 July, and (f) 05:00 on 07 July in all populated grid cells. The time points correlating to the right-hand panels are labeled on each of the time series plots (color coded), and the locations correlating to the left-hand panels are labeled on each of the maps. Base map data: Google.



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347

Discussion

348

A statistical model was developed to estimate 1-hour PM2.5 concentrations at 5 x 5 km resolution during

349

wildfire seasons over the entire province of British Columbia, Canada using a Random Forests model and

350

multiple data sources. The two case studies also showed that the model identified the temporal and

351

spatial peaks in smoke exposures. Predictions from the final model had a high correlation with

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observations, while spatial cross-validation revealed some variability in prediction performance. Overall,

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the model performed best closer to the coast, especially around the densely populated greater

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Vancouver area, where concentrations were generally lower. Although correlations remained high in the

355

interior region, the error and bias were increased further from the coast. To the best of our knowledge,

356

this is the first statistical model developed for smoke-related PM2.5 at a temporal resolution of 1-hour.

357

All other models that produce 1-hour estimates of smoke-related PM2.5 use a deterministic approach.

358

One example is the BlueSky framework, which is an operational smoke forecasting system run in both

359

the US and Canada. An evaluation of BlueSky performance at the 1-hour resolution in California

360

reported MFB values of −84.4% and −1.3% and MFE values of 100.8% and 91.8% for two case studies.58

361

Another example is the FireWork forecasting system developed by Environment Canada. An evaluation

362

of its performance during the 2015 fire season found a correlation of 0.49 and RMSE of 18 µg/m3

363

compared with observations in western Canada.22 Although the evaluation statistics for BlueSky and

364

FireWork were less favorable than OSSEM-1hr, these modeling systems were forecasting hours ahead of

365

time while the OSSEM-1hr model was retrospectively estimating concentrations using already-observed

366

data.

367

Deterministic and statistical models have been developed to estimate 1-hour PM2.5 concentrations due

368

to sources other than wildfire smoke. One study examined the PM2.5 estimates from ten deterministic air

369

quality modeling systems in regions of North America and Europe in 2006 and found that their 20 ACS Paragon Plus Environment

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370

correlations with observations were between 0.53 and 0.66.59 On the other hand, a statistical regression

371

model with remote sensing data built for southern Ontario, Canada resulted in a correlation of 0.8160 in

372

2004, while a neural network coupled with K-mean clustering approach developed in Auckland, New

373

Zealand, had a correlation of 0.7961 between 2008 and 2011. The out-of-bag evaluation suggests that

374

OSSEM-1hr performed well compared with these models, possibly due to the large training dataset, the

375

choice of modeling approach, the selection of relevant predictive variables, and the difference in

376

distribution of observations included in the analysis. The leave-region-out cross-validation analysis

377

showed that model performance can vary when applied to areas more than 50km from the locations of

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the training data. However, the model can still provide reasonable estimates for most of the exposed

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population in BC, because 80% of people reside within 10 km of a monitoring station.35

380

The inclusion of PM2.5 measurements from the previous day as a potentially predictive variable did not

381

improve the model performance in the out-of-bag evaluation, suggesting the potential of the model to

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be applied in regions without large air quality monitoring networks. When the data were subset to

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observations for which there were complete AOD data, the inclusion of AOD did not improve the model

384

performance. This was surprising, given the importance of AOD in previously-developed 24-hour

385

models.25, 55, 57 However, there are several factors to consider. First, the quality of the GOES AOD data

386

used here may be affected by the location of the study area, where the larger solar zenith angle at high

387

latitude can lead to larger bias in AOD retrieval.62 In addition, British Columbia is at edge of the GOES-

388

West image swath, and the larger satellite viewing angle results in a larger scattering angle, which

389

relates to larger bias compared with data retrieved at the centre of the image.63 Second, the standard

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cloud filter from the product may not be appropriate for the study period because heavy smoke may

391

have been incorrectly classified as cloud.25 Previous studies have shown that a less restrictive cloud filter

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derived from local estimates of surface brightness could improve the utility of AOD products from other

393

satellites during smoke events.64, 65 Third, the mountainous terrain in the province may result in more 21 ACS Paragon Plus Environment


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394

smoke aloft, leading to lower correlation between satellite AOD and surface concentrations. Fourth, the

395

variables used to represent fire activity and meteorological conditions may adequately explain the

396

response variable in the absence of AOD, leaving little room for improvement.

397

The OSSEM-1h model has some unique strengths. First, all data sources are publicly available and easy

398

to acquire. Second, most of the data have global coverage such that these methods could be locally

399

adapted and expanded to other parts of the world, and could be especially useful in fire-affected areas

400

without sufficient PM2.5 data from existing air quality monitoring networks. Third, the simplicity of the

401

model makes it relatively straightforward to operationalize for near-real-time surveillance. Finally,

402

OSSEM-1hr could also serve as a tool for more spatially comprehensive evaluation of smoke forecasting

403

models.

404

The OSSEM-1h model also has some important limitations. First, errors and uncertainty in the

405

measurement of the predictive variables, such as those from satellite data retrieval or meteorological

406

modeling, will be inherited by the model, affecting the model performance. Second, the fire activity

407

information is currently retrieved from MODIS, which is not updated hourly. As a result, the fire

408

information fed into the model may not reflect the situation at the exact hour of prediction, especially

409

when fire behavior is changing rapidly or there is active and effective effort to suppress fires. We did

410

explore the possibility of using the GOES fire product, which is updated every 30 minutes, but the fire

411

detection sensitivity is affected by the low spatial resolution66 and FRP measurements are not available

412

in the archived data. However, the launch of the GOES-R series of geostationary satellites since 2016 is

413

expected to improve the capacity of fire detection, potentially useful for developing similar models in

414

the future. The imaging instrument aboard of these satellites can provide three times more spectral

415

information, four times the spatial resolution, and more than five times faster temporal coverage than

416

the previous system.67 Third, this approach cannot fully account for smoke generated well beyond the


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417

British Columbia study area. Although smoke can travel to British Columbia from other regions,

418

especially the western United States and Siberia, a recent study found that most smoke affecting the

419

province originated from fires within the province.68 Fourth, while the machine learning approach can

420

make good estimates of the response variable, the precise nature of the relationships between the

421

response and predictive variables are impossible to ascertain. As such, users must simply trust the

422

output in context of its performance, in the absence of specific information about the model structure.

423

In addition, the model output may not be interpreted with the physical process of smoke dispersion.

424

Finally, the model selection process based on variable importance may not be adequate to select the

425

optimal model, as the permutation-based variable importance calculation may be influenced by

426

correlated variables in the model.69

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The OSSEM-1h model presented here can be used to temporally resolved estimates of population

428

exposure to PM2.5 during episodes of wildfire smoke. These can be useful for epidemiologic studies on

429

the very acute health effects of sub-daily exposure, given health outcome measures with sub-daily time

430

stamps such as ambulance dispatch data.19, 70 It can also be used as a tool for public health surveillance

431

of the exposure in near-real-time, which can be used inform timely actions to mitigate the adverse

432

population health impacts of wildfire smoke exposure.

433

Supporting Information

434

Table S1 and Figure S1-S4 in the Supporting Information, which contains additional information about

435

the variable input to the model, calculation of evaluation statistics, data sampling strategies, variable

436

importance and model evaluations.

437 438 23 ACS Paragon Plus Environment


439

Acknowledgement

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This work is supported by the Australian Research Council Linkage Program (LP130100146) and the

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British Columbia Lung Association. We would like to thank all the organizations that generate and

442

distribute the data used in the model, without which this work will not be possible. We also thank the

443

editor and reviewers for their valuable insights that make this work better.

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A machine learning approach to estimate hourly exposure to fine

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