RE: "ON THE USE OF GENERALIZED ADDITIVE MODELS IN TIME-SERIES STUDIES OF AIR POLLUTION AND HEALTH" AND "TEMPERATURE AND MORTALITY IN 11 CITIES OF THE EASTERN UNITED STATES"

Abstract Background Air pollution has been linked to increased susceptibility to SARS-CoV-2. Thus, it has been suggested that wildfire smoke events may exacerbate the COVID-19 pandemic. Objectives Our goal was to examine whether wildfire smoke from the 2020 wildfires in the western United States was associated with an increased rate of SARS-CoV-2 infections in Reno, Nevada. Methods We conducted a time-series analysis using generalized additive models to examine the relationship between the SARS-CoV-2 test positivity rate at a large regional hospital in Reno and ambient PM2.5 from 15 May to 20 Oct 2020. Results We found that a 10 µg/m3 increase in the 7-day average PM2.5 concentration was associated with a 6.3% relative increase in the SARS-CoV-2 test positivity rate, with a 95% confidence interval (CI) of 2.5 to 10.3%. This corresponded to an estimated 17.7% (CI: 14.4–20.1%) increase in the number of cases during the time period most affected by wildfire smoke, from 16 Aug to 10 Oct. Significance Wildfire smoke may have greatly increased the number of COVID-19 cases in Reno. Thus, our results substantiate the role of air pollution in exacerbating the pandemic and can help guide the development of public preparedness policies in areas affected by wildfire smoke, as wildfires are likely to coincide with the COVID-19 pandemic in 2021.

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Generalized Additive Models: Burning Rubber and Air Pollution in the United States

A Handbook of Statistical Analyses using SAS ◽

10.1201/9781584887850-13 ◽

2008 ◽

pp. 199-218

Keyword(s):

United States ◽

Air Pollution ◽

Generalized Additive Models ◽

The United States ◽

Additive Models

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Using spatio-temporal land use regression models to address spatial variation in air pollution concentrations in time series studies

Air Quality Atmosphere & Health ◽

10.1007/s11869-017-0500-1 ◽

2017 ◽

Vol 10 (9) ◽

pp. 1139-1149 ◽

Cited By ~ 8

Author(s):

Konstantina Dimakopoulou ◽

Alexandros Gryparis ◽

Klea Katsouyanni

Keyword(s):

Land Use ◽

Air Pollution ◽

Time Series ◽

Spatial Variation ◽

Regression Models ◽

Land Use Regression ◽

Time Series Studies ◽

Spatio Temporal ◽

Pollution Concentrations

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Generalized linear mixed models in time series studies of air pollution

Atmospheric Pollution Research ◽

10.5094/apr.2011.049 ◽

2011 ◽

Vol 2 (4) ◽

pp. 428-435 ◽

Cited By ~ 3

Author(s):

Ya–Hsiu Chuang ◽

Sati Mazumdar ◽

Taeyoung Park ◽

Gong Tang ◽

Vincent. C. Arena ◽

...

Keyword(s):

Air Pollution ◽

Time Series ◽

Mixed Models ◽

Generalized Linear Mixed Models ◽

Linear Mixed Models ◽

Time Series Studies

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Environmental and geographical effects on fog occurrence

10.5194/egusphere-egu21-5810 ◽

2021 ◽

Author(s):

Iva Hunova ◽

Marek Brabec ◽

Marek Malý ◽

Alexandru Dumitrescu ◽

Jan Geletič

Keyword(s):

Air Pollution ◽

Environmental Factors ◽

Tensor Product ◽

Generalized Additive Models ◽

Additive Models ◽

Low Rank ◽

Seasonal Component ◽

Explanatory Variables ◽

The Individual

Fog is a very complex phenomenon (Gultepe et al., 2007). In some areas it can contribute substantially to hydrological and chemical inputs and is therefore of high environmental relevance (Blas et al., 2010). Fog formation is affected by numerous factors, such as meteorology, air pollution, terrain (geomorphology), and land-use.In our earlier studies we addressed the role of meteorology and air pollution on fog occurrence (H&#367;nov&#225; et al., 2018) and long-term trends in fog occurrence in Central Europe (H&#367;nov&#225; et al., 2020). This study builds on earlier model identification of year-to-year and seasonal components in fog occurrence and brings an analysis of the deformation of the above components due to the individual explanatory variables. The aim of this study was to indicate the geographical and environmental factors affecting the fog occurrence.&#160;&#160;&#160;&#160;&#160;&#160; We have examined the data on fog occurrence from 56 meteorological stations of various types from Romania reflecting different environments and geographical areas. We used long-term records from the 1981&#8211;2017 period.&#160;&#160;&#160;&#160;&#160;&#160;&#160; We considered both the individual explanatory variables and their interactions. With respect to geographical factors, we accounted for the altitude and landform. With respect to environmental factors,&#160;&#160; we accounted for proximity of large water bodies, and proximity of forests. Geographical data from Copernicus pan-European (e.g. CORINE land cover, high resolution layers) and local (e.g. Urban Atlas) projects were used. Elevation data from EU-DEM v1.1 were source for morphometric analysis (Copernicus, 2020).&#160;&#160;&#160;&#160;&#160;&#160; &#160;We applied a generalized additive model, GAM (Wood, 2017; Hastie & Tibshirani, 1990) to address nonlinear trend shapes in a formalized and unified way. In particular, we employed penalized spline approach with cross-validated penalty coefficient estimation. To explore possible deformations of annual and seasonal components with various covariates of interest, we used (penalized) tensor product splines to model (two-way) interactions parsimoniously, Wood (2006).&#160;&#160;&#160;&#160;&#160;&#160; The fog occurrence showed significant decrease over the period under review. In general the selected explanatory variables significantly affected the fog occurrence and their effect was non-linear. Our results indicated that, the geographical and environmental variables affected primarily the seasonal component of the model. Of the factors which were accounted for, it was mainly the altitude showing the clear effect on seasonal component deformation (H&#367;nov&#225; et al., in press).&#160;&#160;&#160;&#160;&#160;&#160;&#160;References:Blas, M, Polkowska, Z., Sobik, M., et al. (2010). Atmos. Res. 95, 455&#8211;469.Copernicus Land Monitoring Service (2020). Accessed online at: https://land.copernicus.eu/.Gultepe, I., Tardif, R., Michaelidis, S.C., Cermak, J., Bott, A. et al. (2007). Pure Appl Geophys, 164, 1121-1159.Hastie, T.J., Tibshirani, R.J. (1990). Generalized Additive Models. Boca Raton, Chapman & Hall/CRC.H&#367;nov&#225;, I., Brabec, M., Mal&#253;, M., Dumitrescu, A., Geleti&#269;, J. (in press) Sci. Total Environ. 144359.H&#367;nov&#225;, I., Brabec, M., Mal&#253;, M., Valeri&#225;nov&#225;, A. (2018) Sci. Total Environ. 636, 1490&#8211;1499.H&#367;nov&#225;, I., Brabec, M., Mal&#253;, M., Valeri&#225;nov&#225;, A. (2020) Sci. Total Environ. 711, 135018.Wood, S.N. (2006) Low rank scale invariant tensor product smooths for generalized additive mixed models. Biometrics 62(4):1025-1036Wood, S.N. (2017). Generalized Additive Models: An Introduction with R (2nd ed). Boca Raton, Chapman & Hall/CRC.&#160;

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