What are the direct impacts of fires on air quality and aerosol transport?

Impacts on air quality and human health (UCLA)

For air quality impact, we focus on model/observation comparisons of two quantities of particular regulatory interest: daily maximum 8-h average ozone, and 24-h average PM2.5. The current National Ambient Air Quality Standards (NAAQS) are 35 !g m-3 for 24-h average PM2.5 and 75 ppbv for daily maximum 8-h average ozone, respectively. Daily maximum 8-h average ozone data and hourly updated PM2.5 data are available for many sites since 1980, from the EPA Aerometric Information Retrieval System (AIRS, http://www.epa.gov/ttn/airs/aqsdatamart). We will also calculate PM and ozone AQIs to indicate daily air quality and the associated health effects. We will focus on August-October each year when wildfires are typically strongest. We will first run WRF-Chem for each fire season with “typical” emissions, then rerun it on the inner domains with wildfire emissions added in. The difference between simulated air-quality fields from the above two runs is the contribution of emissions from wildfires. Health-related impacts of fire are influenced not only by the magnitude, intensity, and duration of fire, but also the proximity of the smoke plume to a population. Following Hu et al. (2008), we will estimate the total population of potential exposures caused by the wildfires by adding up the population (from census data) living in the grid cells that receives “wildfire emissions” contributions and have a predicted PM2.5 concentration higher than 35 !g m-3 (24-h average) and ozone concentrations higher than 75 ppbv (8-h average). We will also quantify the impact of fires on the increase of the admissions for respiratory diseases based on the findings by Delfino et al. (2009). Their study showed the associations of 2-day average PM2.5 with respiratory hospital admissions were stronger during than before or after the Southern California 2003 wildfires, and the strongest wildfire-related PM2.5 associations were for age groups 65-99 years (10% increase per 10 !g m-3) and 0-4 years (8% increase per 10 !g m-3). Based on these and other data given in Table 3 of Delfino et al. (2009), we will predict relative rates of asthma (and other respiratory) ‘admissions’ in relation to each 10 !g m-3 increase in 2-day moving average PM2.5 concentrations simulated by WRF-Chem, for different age groups, and then summarize the total with population and sociodemographics data at various levels such as zip- code level. We will first start with the 2007 and 2003 large fires, the foci of several air quality and human exposure studies (Delfino et al. 2009; Pfister et al. 2008; Kunzli et al. 2006; Phuleria et al. 2005). We will then expand our modeling and analysis to the past three decades for which reconstructed climate (section 3.4) and wildfire emissions will be available. Our goal here is to construct a climatology of PM2.5 and ozone air quality during fire seasons from multi-decade output of WRF-chem and to quantify the vulnerability of each community to air quality effects of fires, and then statistically identify which communities are most susceptible to air quality degradation due to wildfires.