Fig 1.
Map of 91 federal, state, and local parks and protected area (PPA) study sites overlaid on Level 2 ecoregion.
Point locations are used in lieu of polygons for visual effect. Base layers source: Esri. “Light Gray Canvas” [vector]. Scale Not Given. “Light Gray Canvas Basemap.” Feb 19, 2025. https://www.arcgis.com/home/item.html?id=979c6cc89af9449cbeb5342a439c6a76. (February 24, 2025).
Table 1.
Counts of parks and protected areas included in this study by state and managerial jurisdiction.
Table 2.
Descriptions of variables used in the generalized estimation equations models for federal, state, and local park and protected area (PPA) settings. Sample sizes relate to the weekly measurement for each study site over the time period and are as follows: federal (n = 3,258), state (n = 3,331) and local (n = 3,169). U.S. EPA = U.S. Environmental Protection Agency, U.S.G.S. = U.S. Geological Survey; QGIS = Q Geographic Information System.
Table 3.
Statistical summary for weekly weather and air quality variables in federal parks and protected areas (PPA) over the study period (2017 - 2021) in the Pacific Northwest, United States; (n = 3,258 weekly measurements). AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.
Table 4.
Statistical summary for weekly weather and air quality variables in state parks and protected areas (PPA) over the study period (2017 - 2021) in the Pacific Northwest, United States; (n = 3,331 weekly measurements). AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.
Table 5.
Statistical summary for weekly weather and air quality variables in local parks and protected areas (PPA) over the study period (2017 - 2021) in the Pacific Northwest, United States; (n = 3,169 weekly measurements). AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.
Fig 2.
The effect of weekly weather and wildfire smoke on visitation at 91 federal, state, and local parks and protected areas (PPA) in the Pacific Northwest, United States derived from generalized estimating equation models.
Visitation effect, calculated as -1*(1-Odds Ratio)*100, describes the percent change in response variable (proportion of weekly visitation) when continuous predictors increase by one unit (units shown on y-axis) or when the condition of categorical predictors is true. PM 2.5 = particulate matter with diameter <2.5 μm.
Table 6.
Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to federal parks and protected areas (PPA), including national parks, national monuments, and national forests included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with a diameter of 2.5 microns.
Fig 3.
Proportion of weekly visitation (response variable) for each managerial jurisdiction – federal, state, and local – over a 22-week visitation season from 2017 to 2021.
The response variable is calculated as the proportion of total weekly visitation compared to the five-year average (mean) total visitation for each site. The sample sizes are as follows: federal (n = 3,258), state (n = 3,331) and local (n = 3,169).
Table 7.
Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to state parks and protected areas (PPA), including state parks and state recreation areas included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.
Table 8.
Generalized estimating equation (GEE) model results on the influence of weekly weather conditions and air quality on weekly visitation to local parks and protected areas (PPA), including city parks, county parks, and regional parks included in this study. The odds ratio, calculated as exp(Standard Estimate), indicates the magnitude and direction of the predictors’ effect on the response variable (proportional weekly visitation). The visitation effect, calculated as -1*(1-Odds Ratio)*100 indicates the percent change in the response variable when the predictor increases by one unit (for continuous variables) or when the condition is true (for categorical variables). Significant results (p<0.05) in bold. AQI = air quality index; PM 2.5 = particulate matter with diameter <2.5 μm.