Mountain Pine Beetle Impacts on Health through Lost Forest Air Pollutant Sinks

Abstract

The mountain pine beetle (MPB) destroys millions of coniferous trees annually throughout Western US forests. Coniferous forests are important air pollutant sinks, removing pollutants from the air such as PM_2.5 (particulate matter < 2.5 μm in diameter), O₃ (ozone), SO₂ (sulfur dioxide), NO₂ (nitrogen dioxide), and CO (carbon monoxide). In this paper, US Forest Service data on MPB tree mortality in the Western US is combined with a forest air pollution model (i-Tree Eco) and standard health impact functions to assess the human mortality and morbidity impacts of MPB-induced tree mortality. Modeling results suggest considerable spatial and temporal heterogeneity of impacts across the Western US. On average, MPB is associated with 10.0–15.7 additional deaths, 6.5–40.4 additional emergency room (ER) visits, and 2.2–10.5 additional hospital admissions per year over 2005–2011 due to lost PM_2.5 sinks. For every 100 trees killed by MPB, the average PM_2.5 mortality health costs are $418 (2019$). Impacts on other criteria pollutants are also estimated. Several sensitivity checks are performed on model inputs. These results have important policy implications for MPB management and on our understanding of the complex couplings between forest pests, forest health, and human health.

1. Introduction

The mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins, is a destructive insect pest in conifer forests throughout the Western United States (US) and British Columbia, Canada [1]. Though native to the region, MPB outbreaks have been exacerbated since the 1990s by warmer winters brought on by climate change and large, contiguous, overstocked stands of large-diameter trees throughout the region [2,3]. The US Forest Service estimates that MPB has affected >55 million acres of forest, or approximately 20% of all Western conifer forests by area since 1990, primarily impacting lodgepole pine (Pinus contorta Douglas ex Loudon) and ponderosa pine (Pinus ponderosa Douglas ex P. Lawson and C. Lawson) trees, making it one of the largest drivers of landscape-scale tree mortality in the US [4] (and see Appendix A for additional information on MPB growth dynamics).

Dead, rusty-colored conifer trees caused by MPB reduce the ecosystem services provided by forests, with economic impacts on recreation [5], property values [6], landscape aesthetics [7], and subjective well-being [8], in addition to direct market impacts on the forestry and logging industry [9]. For instance, Rosenberger et al. [5] found that moderate to severe MPB outbreaks can cause recreation losses totaling $5–$59 million in Rocky Mountain National Park in Colorado, USA.

However, there is increasing interest that in order to understand the overall impacts of forest-attacking pests, investigations of indirect, as opposed to direct, effects of forest loss on society must be conducted that recognize the coupled human and natural systems framework that connects human welfare to forest disturbance events (see discussions in [10,11,12,13] for the specific case of bark beetles).

One prominent indirect effect of MPB that has not received much attention is the impact of landscape-scale tree mortality on regional air quality and human health outcomes. Trees and forests are important air pollutant sinks. Gaseous air pollutants are removed by trees through leaf stomata uptake and plant surfaces, while particulate pollutants are intercepted on the surfaces of leaves, branches, and bark [14]. Air pollutants removed by trees include PM_2.5 (particulate matter < 2.5 μm in diameter), O₃ (ozone), SO₂ (sulfur dioxide), NO₂ (nitrogen dioxide), and CO (carbon monoxide), each of which are associated with human respiratory and cardiovascular outcomes [15,16,17]. Trees also remove carbon from the atmosphere and store it as biomass, though this does not directly impact human health. Additionally, PM₁₀ is removed by trees, but the focus is often on PM_2.5 instead. Thus, it would be anticipated that extensive losses of coniferous forests due to MPB would have demonstrable effects on regional air quality with associated human mortality and morbidity outcomes. However, the magnitude and spatial-temporal extent of such impacts are presently unknown.

In non-MPB contexts, estimates of the magnitude of tree impacts to air pollution and health have been made in the literature. Using i-Tree Eco, the same modeling software used in the present study, Nowak et al. [18] estimated that urban trees in the US remove between 4.7 to 64.5 tons of PM_2.5 per year, depending on the US city, at a value of $1.1–$60.1 million per year in avoided mortality and morbidity outcomes. In more recent work, Nowak et al. [19] calculated that trees and forests in the conterminous US removed a total of 17.4 million tons of air pollution (PM_2.5, O₃, SO₂, and NO₂) in 2010 with modeled mortality and morbidity benefits of $6.8 billion, on the basis of 850 avoided deaths and 670,000 avoided incidences of acute respiratory symptoms.

Of particular concern when considering the air pollution and health impacts of MPB-induced tree mortality is the fact that coniferous trees, which MPB exclusively attack, are more effective at intercepting PM_2.5 compared to broad-leafed deciduous trees by about 25% on average, due to differences in leaf morphology [20]. Additionally, MPB are primarily attracted to large diameter, mature trees, which tend to remove significantly more pollutants from the air than smaller, young trees, since pollutant removal depends on tree size and maturity [18]. This underscores the potential significant impacts that MPB may be having on regional air quality and human health across the Western US and beyond.

To the best of my knowledge, there are no prior studies explicitly investigating MPB impacts to air pollution and health. However, there are related studies that have looked at human health and, in some cases, air pollution consequences of anthropogenic deforestation [21,22] and invasive species-caused tree mortality [23,24,25,26]. By all accounts, this literature suggests worsening air pollution and worsening health outcomes after significant tree loss occurs. For instance, Jones and McDermott [23] showed that lost ash trees due to the invasive emerald ash borer resulted in increased air pollution concentrations ranging from 9.2 to 46.2% across the US, increasing rates of cardiovascular mortality by 6.2–32.5/yr. per 100,000 people.

There is also a related literature on the economic impacts of MPB, including reduced recreational opportunities [5], lower life satisfaction and happiness [8], and reduced residential property values [6]. Market impacts of MPB have also been estimated [9]. Others have also investigated the economic feasibility of using timber from MPB kills as a source of bioenergy [27], and bioeconomic models have been constructed for land management strategies during MPB outbreaks [28].

In this paper, I extend the literature on MPB impacts in three ways. First, by quantifying the air pollution and health consequences of MPB, I provide the literature and policymakers with a better understanding of the total economic scale of MPB impacts on society (a “scale contribution”). This is an important advancement over past economic cost studies of MPB, which have tended to focus on first-order direct impacts [5,6,9], whereas this work provides estimates of indirect costs on society. Second, since air pollutants disperse across geographic landscapes and over space, this work expands the scope of MPB welfare impacts to include those populations within the larger airshed, but who may not be directly affected by MPB impacts to recreation, aesthetics, or property values. By expanding the potential pool of affected individuals vis-à-vis the larger airshed, I provide for a more holistic assessment of the true burden of MPB on society (a “scope contribution”). Finally, this work provides new information on the expanded set of potential benefits of MPB management, thereby eliminating one potential source of downward bias in benefit-cost analyses of MPB prevention and control programs. This is important since a key MPB prevention mechanism is stand density reduction and crown thinning [29], which has co-benefits on wildfire risk and future wildfire severity [30]. Thus, more accurate benefit-cost analyses of MPB management, inclusive of human health considerations, could serve as admissible evidence for policies designed to restore overall forest health.

2. Materials and Methods

To estimate the air pollution impacts of MPB tree kill in the Western US, high-resolution MPB disturbance data will be combined with the US Forest Service’s i-Tree Eco software tool. Then, using the estimated MPB-induced air pollution impacts provided by i-Tree Eco, I will calculate impacts to various human health outcomes using standard US Environmental Protection Agency (EPA) health impact functions. Lastly, the estimated health impacts will be economically valued using economic cost estimates from the US EPA and the extant economics literature. Each component is described below.

2.1. MPB Disturbance Data

Data on annual tree mortality caused by MPB in the Western US (defined here as the states of Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming) was obtained from the US Forest Service Insect and Disease Survey (IDS) database for the 2005–2011 period. The Western US is focused on since this is the primary historical habitat range of MPB [4]. The IDS data obtained are specific to MPB tree mortality and were assembled by the Forest Service using the Digital Aerial Sketch Mapping (DASM) system. Additional information on the DASM data are provided in Appendix B.

Annual DASM data contain between 42,000 and 88,000 individual polygons, indicating extensive coverage across Western US conifer forests (see Figure 1). In what follows, I use information on the number of trees killed by MPB for all tree host species. To avoid over-counting tree mortality in cases where the same area or where a portion of the same area was surveyed more than once in a year, I remove all overlapping sections of polygons, keeping only the disjoint areas. Using GIS software, the total number of trees killed by MPB in each Western US county was calculated as the sum of the individual disjointed polygon attributes intersecting each county. This was carried out annually and separately by tree species. If a polygon intersected more than one county, the county with the greatest geographic extent of intersection was assigned to that polygon.

2.2. Using i-Tree Eco to Model Tree Pollution Removal

To estimate lost air pollution removal due to MPB tree mortality, the i-Tree Eco software tool was utilized. i-Tree Eco is a peer-reviewed modeling program developed in partnership with the US Forest Service that uses sample or inventory tree data to assess forest structure, forest health, and forest ecosystem services (including air pollution removal) for any tree population [31]. The original basis for the i-Tree program is the US Forest Service’s Urban Forest Effects (UFORE) model, developed in the 1990s to investigate the benefits of urban forests. Applications of i-Tree Eco for estimating tree pollutant removals include [32,33,34].

The software uses a gas exchange and particulate matter interception model at the individual-tree leaf level combined with hourly monitoring-station weather data from the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information and hourly pollution concentration monitoring station data from the US EPA AirData network to estimate hourly tree pollution removal of PM_2.5, O₃, SO₂, NO₂, and CO.

Pollution removal or pollution flux for pollutant $i$, $F_i$, is calculated as the product of the deposition velocity, $V_i$, and the $i^{th}$ pollutant’s hourly concentration in the atmosphere (as measured by a monitoring station), $C_i$,

$$F_i=V_i\times C_i$$

(1)

The deposition velocity is calculated as the inverted sum of the aerodynamic ($R_i^a$), quasi-laminar boundary layer ($R_i^b$), and canopy ($R_i^c$) resistances [13], and represents the velocity at which pollutants deposit to the leaf surface,

$$V_i={\left(R_i^a+R_i^b+R_i^c\right)}^{-1}$$

(2)

Values for $R_i^a$, $R_i^b$, and $R_i^c$ are calculated using hourly weather data (temperature, windspeed, humidity, precipitation, solar radiation) and big-leaf and multilayer canopy deposition models. Importantly, the $V_i$ values used for PM account for hourly resuspension of particulates off of the tree surface and back into the atmosphere due to wind, thus reducing potential upward bias in PM removal rates.

To obtain average hourly net pollution removal across an entire study area (e.g., city, county), $P_i$, i-Tree multiples the hourly pollutant flux ($F_i$) at the leaf level by total tree canopy coverage. Canopy coverage is determined using daily leaf area indices based on the percent of the area that is comprised of evergreen tree species and information on seasonal leaf on/leaf off dates in fall and spring. Field data are also used to increase the accuracy of these estimates (see discussion in [33]).

The $P_i$ calculated above is in units of mass and not volumetric concentration. To calculate the hourly change in pollutant concentration, $\Delta C_i$, the following equation is used,

$$\Delta C_i=\frac{\Delta P_i}{BL\times SA}$$

(3)

where $\Delta P_i$ is the hourly change in pollutant mass for the $i^{th}$ pollutant, $BL$ is the hourly atmospheric boundary layer height, and $SA$ is the geographic surface area of the study area. Boundary layer heights vary throughout the day and i-Tree uses radiosonde station data from NOAA’s Earth System Research Laboratory to determine these [31].

$\Delta P_i$ and $\Delta C_i$ are the main outcome variables of interest obtained from i-Tree Eco. They provide modeled estimates of the aggregate change in pollutant mass and pollutant concentration (in units ppb (parts per billion), ppm (parts per million), or $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$, depending on the pollutant), respectively, per pollutant type, across all inventoried trees in a given study area. i-Tree Eco reports monthly and annual averages of $\Delta P$ for each pollutant as part of its modeling results output. The software also produces estimates of the percentage improvement to air quality, using the following formula,

$$\% \mathrm{air} \mathrm{quality} \mathrm{improvement}=\frac{\Delta P_i}{\Delta P_i+\left(B_i\times BL\times SA\right)}$$

(4)

where $B_i$ is the hourly monitoring station measured pollutant concentration in the atmosphere. Using Equations (3) and (4), hourly estimates of $\Delta C$ can be calculated and then averaged over some desired time period.

Annual estimates of $\Delta P_i$ and $\Delta C_i$ for the specific case of MPB were obtained for each Western US county using the processed DASM data. The analysis was performed for each tree species identified in the DASM using tree diameter sizes from the Gymnosperm Database [35] and default species-specific i-Tree values for other tree characteristics. In the sensitivity checks later in the paper (Appendix D), other diameter sizes are used to allow for the possibility of small diameter tree kill.

Results from i-Tree are technically estimates of the counterfactual (what pollution removal would have been if the trees killed by MPB had still been alive). In actuality, these trees are no longer alive and thus the estimates obtained from i-Tree are used in what follows as representing the lost pollution removal due to MPB in a given county-year, i.e., the pollution removal that would have occurred in a county-year if MPB tree mortality had not happened and thus had not removed these pollutant sinks. While virtually no gaseous pollutants (O₃, SO₂, NO₂, CO) are removed by dead trees, some fraction of PM might continue to be deposited onto tree limbs, branches, and conifer needles. I ignore continued PM deposition here since the dead trees will be eventually removed as part of salvage harvesting operations, or for other purposes (e.g., aesthetics, hazard, etc.), or due to needle loss, or eventual tree collapse. Thus, in the long-run, PM removal by dead trees is non-existent. Given this, my estimates of lost PM_2.5 removal and associated health impacts should be considered as being representative of long-run effects. This is further explored in the sensitivity checks later in the paper.

2.3. Health Impact Functions and Economic Valuation

Health impact functions are used to estimate the human mortality and morbidity impacts associated with modeled changes in pollution concentrations due to MPB tree mortality.

The standard log-linear health impact function can be written following [36],

$$\Delta y_{ih}=POP\times y_{ih}^0\left(1-\frac{1}{\exp \left({\beta }_{ih}\times \Delta C_i\right)}\right)$$

(5)

where $\Delta y_{ih}$ is the change in health outcome $h$ associated with pollutant $i$, $POP$ is the population of the study area of interest, $y_{ih}^0$ is the baseline incidence rate of health outcome $h$, ${\beta }_{ih}$ is the epidemiological relationship between changes in the concentration of pollutant $i$, and health outcome $h$, and $\Delta C_i$ is the change in pollutant concentration estimated from i-Tree Eco in Equation (3).

MPB health impacts are estimated at the county-year level using Equation (5) for: (i) all-cause mortality, (ii) emergency room (ER) visits for asthma, and (iii) hospital admissions (HA) for all-respiratory outcomes. Epidemiological estimates for each ${\beta }_{ih}$ term were obtained from various sources (see footnote below Table 1).

Annual age-adjusted baseline incidence rates for all-cause mortality were obtained from the National Center for Health Statistics (NCHS) through the CDC WONDER website. Baseline incidence rates for ER asthma visits and HA all-respiratory outcomes were obtained from the Healthcare Cost and Utilization Project (HCUP) as made available by the Agency for Healthcare Research and Quality (AHRQ). Bridged-race population estimates were obtained from NCHS, again through the CDC WONDER website.

To economically value changes in mortality and morbidity, I apply a standard value of a statistical life (VSL) of $9.42 million (in inflation adjusted 2019 US dollars; 2019$) from [37] to all estimated mortality outcomes. A value of $481.44 (2019$) from [38] is applied to each ER asthma visit, and a value of $39,259 (2019$) from [36] is applied to each HA all-respiratory instance. The VSL and HA cost values used are the same as those employed by the US EPA in their regulatory impact analyses.

2.4. Summary Statistics and Parameter Values

Summary statistics and parameter values for key model inputs are presented in Table 1. Across the Western US, county-level MPB tree mortality averages 118,316 trees per year. For context, there are an estimated 228 billion trees in the US [39], implying that MPB kill is approximately 0.10% of total US live tree stocks per year, aggregated across all Western US counties.

Table 1. Summary statistics and parameter values of model inputs (annual county-level averages).

	Mean	Std. Dev.	Sample Size
Lodgepole pine (Pinus contorta) mortality	69,167.3	315,355.6	2016
Ponderosa pine (Pinus ponderosa) mortality	27,532.0	125,527.3	2016
Great Basin bristlecone pine (Pinus longaeva) mortality	35.49	161.8	2016
Rocky Mountain bristlecone pine (Pinus aristata) mortality	535.4	2427.5	2016
Monterey pine (Pinus radiata) mortality	0.292	1.33	2016
Limber pine (Pinus flexilis) mortality	4697.1	21,415.7	2016
SouthWestern white pine (Pinus strobiformis) mortality	4.09	18.65	2016
Sugar pine (Pinus lambertiana) mortality	1774.7	8091.6	2016
Western white pine (Pinus monticola) mortality	1549.9	7066.6	2016
Whitebark pine (Pinus albicaulis) mortality	13,014.7	59,338.2	2016
Tree mortality, all species	118,315.6	539,438.3	2016
Population	148,307.4	652,786.5	2016
Baseline mortality rate (per 100,000)	757.6	135.3	1949
Baseline ER asthma rate (per 100,000)	459.4	169.4	2016
Baseline HA all-respiratory rate (per 100,000)	2409.3	876.4	2016
Monitored PM2.5 ($\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$)	5.11	1.51	2016
Monitored O3 (ppm)	0.071	0.009	2016
Monitored SO2 (ppb)	41.07	32.98	2016
Monitored NO2 (ppb)	37.68	11.74	2016
Monitored CO (ppm)	2.10	0.464	2016
$\beta$ (mortality, PM2.5) = 0.0005826891
$\beta$ (mortality, O3) = 0.000507844
$\beta$ (mortality, SO2) = 0.000399202
$\beta$ (mortality, NO2) = 0.0074100012
$\beta$ (mortality, CO) = 0.0328431136
$\beta$ (ER asthma, PM2.5) = 0.005602959
$\beta$ (ER asthma, O3) = 0.00397574
$\beta$ (ER asthma, SO2) = 0.000049975
$\beta$ (ER asthma, NO2) = 0.0019802627
$\beta$ (ER asthma, CO) = 0.0099503309
$\beta$ (HA all-respiratory, PM2.5) = 0.00207
$\beta$ (HA all-respiratory, O3) = 0.007147005
$\beta$ (HA all-respiratory, SO2) = 0.0202669672
$\beta$ (HA all-respiratory, NO2) = 0.0028013898
$\beta$ (HA all-respiratory, CO) = 0.088156972
Num. states = 11
Num. counties = 2016

Notes: annual county-level averages shown for Western US counties over 2005–2011. Tree mortality is average number of dead trees. ER, emergency room; HA, hospital admissions; ppb, parts per billion; ppm, parts per million. Sources: US Forest Service DASM, NCHS, HCUP, US EPA, [15,40,41,42,43,44,45,46,47,48,49,50,51].

Table 1. Summary statistics and parameter values of model inputs (annual county-level averages).

	Mean	Std. Dev.	Sample Size
Lodgepole pine (Pinus contorta) mortality	69,167.3	315,355.6	2016
Ponderosa pine (Pinus ponderosa) mortality	27,532.0	125,527.3	2016
Great Basin bristlecone pine (Pinus longaeva) mortality	35.49	161.8	2016
Rocky Mountain bristlecone pine (Pinus aristata) mortality	535.4	2427.5	2016
Monterey pine (Pinus radiata) mortality	0.292	1.33	2016
Limber pine (Pinus flexilis) mortality	4697.1	21,415.7	2016
SouthWestern white pine (Pinus strobiformis) mortality	4.09	18.65	2016
Sugar pine (Pinus lambertiana) mortality	1774.7	8091.6	2016
Western white pine (Pinus monticola) mortality	1549.9	7066.6	2016
Whitebark pine (Pinus albicaulis) mortality	13,014.7	59,338.2	2016
Tree mortality, all species	118,315.6	539,438.3	2016
Population	148,307.4	652,786.5	2016
Baseline mortality rate (per 100,000)	757.6	135.3	1949
Baseline ER asthma rate (per 100,000)	459.4	169.4	2016
Baseline HA all-respiratory rate (per 100,000)	2409.3	876.4	2016
Monitored PM2.5 ($\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$)	5.11	1.51	2016
Monitored O3 (ppm)	0.071	0.009	2016
Monitored SO2 (ppb)	41.07	32.98	2016
Monitored NO2 (ppb)	37.68	11.74	2016
Monitored CO (ppm)	2.10	0.464	2016
$\beta$ (mortality, PM2.5) = 0.0005826891
$\beta$ (mortality, O3) = 0.000507844
$\beta$ (mortality, SO2) = 0.000399202
$\beta$ (mortality, NO2) = 0.0074100012
$\beta$ (mortality, CO) = 0.0328431136
$\beta$ (ER asthma, PM2.5) = 0.005602959
$\beta$ (ER asthma, O3) = 0.00397574
$\beta$ (ER asthma, SO2) = 0.000049975
$\beta$ (ER asthma, NO2) = 0.0019802627
$\beta$ (ER asthma, CO) = 0.0099503309
$\beta$ (HA all-respiratory, PM2.5) = 0.00207
$\beta$ (HA all-respiratory, O3) = 0.007147005
$\beta$ (HA all-respiratory, SO2) = 0.0202669672
$\beta$ (HA all-respiratory, NO2) = 0.0028013898
$\beta$ (HA all-respiratory, CO) = 0.088156972
Num. states = 11
Num. counties = 2016

Notes: annual county-level averages shown for Western US counties over 2005–2011. Tree mortality is average number of dead trees. ER, emergency room; HA, hospital admissions; ppb, parts per billion; ppm, parts per million. Sources: US Forest Service DASM, NCHS, HCUP, US EPA, [15,40,41,42,43,44,45,46,47,48,49,50,51].

3. Results

3.1. Lost Air Pollution Removal

The first set of model results shown are the annual lost pollution removal impacts of MPB, aggregated across the entire Western US (

Table 2. MPB air pollution impacts by year for Western US, 2005–2011.

	PM2.5		O3		SO2		NO2		CO
	Tonnes (t)	t/Lost Tree	Tonnes (t)	t/Lost Tree	Tonnes (t)	t/Lost Tree	Tonnes (t)	t/Lost Tree	Tonnes (t)	t/Lost Tree
2005	14,361	0.0009	43,457	0.003	16,476	0.001	28,257	0.002	5645	0.0005
2006	14,450	0.0009	49,440	0.003	17,834	0.001	28,482	0.002	7272	0.0004
2007	16,374	0.0008	67,214	0.003	19,110	0.0009	34,886	0.002	6495	0.0003
2008	44,592	0.0009	158,251	0.003	49,328	0.001	87,597	0.002	15,841	0.0003
2009	52,507	0.0009	175,803	0.003	55,478	0.0009	105,897	0.002	20,240	0.0003
2010	36,889	0.0009	119,277	0.003	38,712	0.0009	74,112	0.002	14,164	0.0003
2011	30,648	0.0009	111,125	0.003	33,885	0.0009	61,213	0.002	7202	0.0002
Average	29,974	0.0009	103,510	0.003	32,975	0.0009	60,063	0.002	10,979	0.0003

Notes: listed are the total annual air pollution impacts (lost pollutant mass) of MPB-induced tree mortality across all Western US counties, by year. Estimates were obtained from i-Tree Eco using annual MPB tree mortality data from the DASM.

). Impacts are presented separately by pollutant type and year. For PM_2.5, MPB tree mortality reduces conifer forest pollution removal by 14,361 to 52,507 tons per year, with an average loss of 29,974 tons/yr. between 2005 and 2011. The largest impacts are observed for O₃, with lost pollution removal ranging 43,457 to 175,803 tons/yr., or, 103,510 tons/yr. on average. Annual average impacts on other pollutants are, in order from highest to lowest: 60,063 tons/yr. (NO₂), 32,975 tons/yr. (SO₂), and 10,979 tons/yr. (CO). On a per lost tree basis, an average of 0.0009 tons of PM_2.5 and SO₂ removal, 0.003 tons of O₃ removal, 0.002 tons of NO₂ removal, and 0.0003 tons of CO removal are lost for each individual tree killed by MPB per year.

3.2. Mortality and Morbidity Impacts and Costs

Table 3. Aggregate MPB mortality and morbidity impacts and costs by pollutant type and year for Western US, 2005–2011.

	Mortality (All-Cause)		ER Visits (Asthma)		Hospital Admissions (All-Respiratory)
	Cases	Costs (Millions $)	Cases	Costs (Millions $)	Cases	Costs (Millions $)
PM2.5
2005	9.97	93.9	6.54	0.003	2.30	0.090
2006	11.0	104	6.96	0.003	2.15	0.084
2007	11.8	111	10.7	0.005	2.96	0.116
2008	12.9	122	19.5	0.009	5.63	0.222
2009	15.7	147	21.0	0.010	6.07	0.238
2010	14.2	134	19.4	0.009	5.33	0.209
2011	9.92	93.4	40.4	0.019	10.5	0.410
Average	12.2	115	17.8	0.008	4.99	0.196
O3
2005	0.967	9.10	0.468	0.0002	0.481	0.019
2006	0.930	8.76	0.520	0.0003	0.432	0.017
2007	1.57	14.8	0.901	0.0004	0.720	0.028
2008	3.58	33.8	1.95	0.0009	1.51	0.059
2009	4.09	38.6	2.37	0.001	1.89	0.074
2010	3.59	33.8	2.08	0.001	1.68	0.066
2011	7.44	70.1	4.75	0.002	3.61	0.142
Average	3.17	29.9	1.86	0.0008	1.47	0.058
SO2
2005	5.15	48.6	0.386	0.0002	4.36	0.171
2006	4.19	39.5	0.346	0.0002	3.53	0.139
2007	4.48	42.2	0.525	0.0003	4.99	0.196
2008	6.87	64.7	1.25	0.0006	11.5	0.451
2009	8.45	79.6	1.77	0.0009	15.6	0.612
2010	6.96	65.6	1.36	0.0007	12.4	0.486
2011	3.09	29.2	2.94	0.001	20.5	0.805
Average	5.60	52.8	1.23	0.0006	10.4	0.409
NO2
2005	4.96	46.7	1.42	0.0007	1.04	0.041
2006	6.46	60.9	1.93	0.0009	1.06	0.042
2007	5.24	49.4	3.68	0.002	2.10	0.082
2008	6.66	62.7	6.08	0.003	3.22	0.126
2009	6.94	65.4	7.11	0.003	3.95	0.155
2010	5.84	55.0	7.73	0.004	4.44	0.174
2011	5.09	48.0	18.8	0.009	10.8	0.422
Average	5.88	55.4	6.68	0.003	3.80	0.149
CO
2005	0.078	0.735	0.029	0.00001	0.153	0.006
2006	0.063	0.595	0.026	0.00001	0.122	0.005
2007	0.076	0.715	0.032	0.00002	0.138	0.005
2008	0.155	1.46	0.062	0.00003	0.266	0.010
2009	0.159	1.49	0.067	0.00003	0.287	0.011
2010	0.119	1.13	0.052	0.00003	0.219	0.009
2011	0.161	1.52	0.079	0.00004	0.287	0.011
Average	0.116	1.09	0.050	0.00002	0.210	0.008

Notes: listed are the total annual mortality and morbidity health impacts and costs of MPB-induced tree mortality across all Western US counties, by year. Estimates were obtained by combining versions of the air pollution estimates in Table 2 with the health impact function in Equation (5). Costs are in millions of 2019 USD.

presents modeled annual health impacts of lost pollution removal due to MPB tree mortality using Equation (5). Results are presented separately by year, pollutant type, and health outcome, and have been totaled across all counties. The largest mortality impacts are observed for PM_2.5, where between 10 and 15.7 deaths per year are estimated, at a cost ranging from $93.9 million to $147 million per year. Note that all costs presented are in inflation adjusted 2019 US dollars (2019$). Morbidity impacts of modeled changes in PM_2.5 are 6.5–40.4 additional ER asthma visits per year and 2.15–10.5 additional HA all-respiratory outcomes per year, with associated cost ranges of $3000–$19,000/yr. (ER asthma) and $84,000–$410,000/yr. (HA all-respiratory). Average annual MPB attributable deaths due to lost removal of O₃, SO₂, NO₂, and CO are 3.17/yr., 5.60/yr., 5.88/yr., and 0.12/yr., respectively, with annual economic impacts ranging $1.09–$55.4 million per year. ER asthma and HA all-respiratory impacts for O₃, SO₂, NO₂, and CO generally average one to two additional cases per year per pollutant, with notably higher exceptions for SO₂ (HA all-respiratory) and NO₂ (ER asthma), and notably lower exceptions for CO (ER asthma and HA all-respiratory). Morbidity economic costs range from $20/yr. to $8000/yr. (ER asthma) and $8000/yr. to $409,000/yr. (HA all-respiratory).

In Figure 2, I show the geographic and temporal heterogeneity of MPB health impacts across the Western US. The county-level PM_2.5 mortality health costs in millions of 2019$ for the years of 2005, 2007, 2009, and 2011 are plotted. PM_2.5 was selected as the representative pollutant here since it is associated with the highest health costs among all the pollutants investigated. There is a clear clustering of mortality impacts in three distinct areas: (i) along the Front Range of northern Colorado; (ii) in Western Montana; and (iii) along the Cascade Mountain range in central Washington and northcentral Oregon. Impacts in these areas are primarily driven by large MPB tree kill, as can be seen through a comparison of Figure 1 and Figure 2. Mortality health costs along the front range of northern Colorado are also likely due to high human population clustering around the Denver metro area, which significantly increases the costs of lost tree pollution removal since small pollution effects in or around high population centers have disproportionately large impacts on human health [19]. Mortality impacts are generally small throughout most of Utah, Nevada, northern Arizona, southern California, southern Colorado, and eastern Montana. This may be in part due to limited conifer stands in these areas or due to low endemic populations of MPB.

Note the significant temporal heterogeneity in mortality costs across the four time periods in Figure 2. As one example of this, consider Marion County, Oregon, located south of Portland and containing the town of Salem (the state capital), which has estimated mortality health costs of $55 million in 2005. By 2011, Marion County experiences only $11 million in mortality costs; an 80% decrease over 6 years. Another example is Albany County, Wyoming, where the University of Wyoming is located, which has $19 million in mortality costs in 2005 and $50 million in 2011 (an increase of 163%). While just two examples of temporal heterogeneity (and others can be found in Figure 2), they illustrate the changing dynamics of MPB impacts over the short-run, which are driven from year-to-year by tree host abundance (i.e., availability of live trees that MPB can attack), and also by weather conditions (especially temperature and precipitation, which affect tree resistance and MPB populations), annual wildfire patterns, and baseline pollution concentrations. Given this, one key takeaway from Figure 2 is that it demonstrates just how rapidly MPB health impacts, from PM_2.5-induced mortality in this case, can scale up or down over the span of only a few years. One policy implication of these results is the need for active informational campaigns targeted at communities in areas with significant MPB activity in a given year, that specifically provide pollution avoidance information so as to reduce potential mortality and morbidity health impacts. To increase their effectiveness, such public health campaigns might closely track with annual MPB activity so that the timing of greatest avoidance can coincide with the timing of greatest MPB kill.

Table A1. Health costs per 100 trees killed by MPB by pollutant type and year for the Western US, 2005–2011.

		Mortality (All-Cause)	ER Visits (Asthma)	Hospital Admissions (All-Respiratory)
	Trees Killed (Millions)	Costs Per 100 Trees Killed ($)	Costs Per 100 Trees Killed ($)	Costs Per 100 Trees Killed ($)
PM2.5
2005	15.2	618	0.020	0.592
2006	15.1	689	0.020	0.556
2007	20.3	547	0.025	0.571
2008	51.6	236	0.017	0.430
2009	59.4	247	0.017	0.401
2010	41.0	327	0.022	0.510
2011	35.9	260	0.053	1.14
Average	37.2	418	0.025	0.600
O3
2005	15.2	59.9	0.001	0.125
2006	15.1	58.0	0.002	0.113
2007	20.3	72.9	0.002	0.138
2008	51.6	65.5	0.002	0.114
2009	59.4	65.0	0.002	0.125
2010	41.0	82.4	0.002	0.161
2011	35.9	195.3	0.006	0.396
Average	37.2	85.6	0.002	0.167
SO2
2005	15.2	320	0.001	1.13
2006	15.1	262	0.001	0.921
2007	20.3	208	0.001	0.966
2008	51.6	125	0.001	0.874
2009	59.4	134	0.002	1.03
2010	41.0	160	0.002	1.19
2011	35.9	81.3	0.003	2.24
Average	37.2	184	0.002	1.19
NO2
2005	15.2	307	0.005	0.270
2006	15.1	403	0.006	0.278
2007	20.3	243	0.010	0.404
2008	51.6	122	0.006	0.244
2009	59.4	110	0.005	0.261
2010	41.0	134	0.010	0.424
2011	35.9	134	0.025	1.18
Average	37.2	208	0.010	0.437
CO
2005	15.2	4.84	0.00007	0.040
2006	15.1	3.94	0.00007	0.033
2007	20.3	3.52	0.00010	0.025
2008	51.6	2.83	0.00006	0.019
2009	59.4	2.51	0.00005	0.019
2010	41.0	2.76	0.00007	0.022
2011	35.9	4.23	0.00011	0.031
Average	37.2	3.52	0.00008	0.027

Notes: listed are the total annual health costs per 100 trees killed by MPB by pollutant type and year across all Western US counties for all-cause mortality, ER visits asthma, and HA all-respiratory. Costs are in 2019 USD.

in Appendix C presents the mortality and morbidity cost results per 100 trees killed by MPB per year by pollutant type. These results suggest that every 100 trees lost to MPB generates between $236 and $689 (average = $418) in PM_2.5 mortality health costs. Similarly, average mortality costs per 100 trees killed by MPB for O₃, SO₂, NO₂, and CO are $85.6, $184, $208, and $3.52, respectively. Note that a sensitivity analysis on key parameter inputs and modeling assumptions is provided in Appendix D.

4. Discussion

This paper estimated the air pollution and human health impacts of mountain pine beetle (MPB) tree mortality across its historical range in the Western US over the 2005–2011 period. The main result is that MPB conifer tree kill is associated with annual average mortality impacts of 12.2 deaths/yr. (from lost PM_2.5 removal), 3.2 deaths/yr. (from lost O₃ removal), 5.6 deaths/yr. (from lost SO₂ removal), 5.9 deaths/yr. (from lost NO₂ removal), and 0.12 deaths/yr. (from lost CO removal), with associated economic costs of $115 million/yr. (PM_2.5), $29.9 million/yr. (O₃), $52.8 million/yr. (SO₂), $55.4 million/yr. (NO₂) and $1.09 million/yr. (CO). Significant respiratory-related morbidity impacts were also found, but the economic costs associated with them are orders of magnitude lower than the observed mortality health costs due to large differences in per unit values (i.e., VSL vs. cost-of-illness).

Substantial county-level spatial and temporal heterogeneity was found across the Western US, with clear clustering occurring in at least three parts of the region where disproportionally high beetle kill occurred over the study period. The magnitude of impacts in a given county were also found to vary considerably from year-to-year due primarily to changes in MPB kill, which is driven in large part by weather and wildfire conditions that impact MPB populations and tree resilience. In addition to its avoidance behavior implications, the heterogeneity findings might also presage what could occur as MPB spreads outside of its historical range into the eastern US and eastern Canada. Specifically, that detection of MPB in an area is not a necessary and sufficient condition for meaningful pollution health impacts; rather, large health impacts likely emerge after an outbreak has occurred and around high population centers where lost pollutant sinks will be more impactful. Thus, from a health policy perspective, the focus and attention of forest management should be on reducing the likelihood of severe MPB outbreaks (especially those near urban areas).

My results can be compared to those from Nowak et al. [19] who estimated 8.29 × 10⁻⁴ avoided deaths per ton of PM_2.5 removed and 1.92 × 10⁻⁵ avoided deaths per ton of O₃ removed by trees in the conterminous US. By comparison, I estimate that an average of 4.07 × 10⁻⁴ and 3.06 × 10⁻⁵ deaths are associated with each MPB-induced lost ton of PM_2.5 and O₃, respectively. The small differences observed may be caused by differences in the types of tree species investigated (I focus exclusively on conifers and not all tree species found in the US) and study location (Western US vs. conterminous US). Overall, however, the results are reasonably comparable on key metrics to the extant literature.

While the focus on this work is on one direction of impact (the effect of MPB on air pollution and, by association, human health), it is important to note that other couplings likely exist between MPB, trees, and air pollution. Prior work has shown that air pollution itself can negatively affect insect species growth and interactions [52,53]. Additionally, trees’ natural defense mechanisms and stress tolerance are known to be a function of air pollution levels [54]. Elevated air pollution levels in MPB infested areas could therefore result in other impacts that might be investigated as a part of future work.

Since the investigation undertaken here included a complete record of all MPB tree mortality in the Western US, it is representative of the entire historical geographic range of MPB outbreaks in the US. Going forward in time, however, it is unclear how generalizable these results will be due to the unpredictable nature of MPB outbreaks from year-to-year. Over the study period from 2005 to 2012, several historically large MPB outbreaks occurred across the west, though outbreaks over the past few years (2013-onward) have been more limited in size and scale [4]. If the era of subdued MPB outbreaks continues into the future, then the pollution health impacts of the beetle will be lower in magnitude than those estimated here. However, by some accounts, the current limited period of outbreaks is likely only a short-term deviation, and on-going climate change and persistent droughts in the west are expected to contribute to increased MPB outbreaks in the future [55]. If this scenario is true, it would mean that the health impacts estimated here are potentially lower bounds on what future impacts may be, especially if MPB spreads outside of its historical range [56].

Along these same lines, there is concerted efforts by Federal, state, and local forest management officials to improve overall forest health conditions across the US, such as by thinning tree stands, removing undergrowth, and utilizing controlled burns. Such efforts not only can reduce the probability of a successful MPB outbreak [29], but can also reduce future wildfire risk and severity [30]. Depending on the success of future MPB control policies, it is therefore possible that the human health impacts of MPB are lower in the future than those estimated here.

Perhaps more importantly, is the need for continued financing mechanisms of forest health improvement programs. While the explosive growth of wildfire frequency and severity over the last decade certainly has created strong impetus for action, the “co-benefits” of MPB management (e.g., crown thinning, understory removal, etc.) on forest health cannot be overlooked. Thus, benefit-cost analyses of MPB management initiatives, inclusive of pollution and human health considerations, have the potential to be consequential going forward by increasing the expected benefits of action vis-à-vis the larger airshed. This could help increase resources directed towards forest health programs, which would affect both MPB and future wildfire risk.