DESCRIPCION DE LA ACCIÓN

Figure 5-4 shows the spatial distribution of computed correlations for the total PM2.5,

sulfate, and nitrate during the period of 2001-2010 across the CONUS. For these three air pollutants, correlations between simulated data and observed data are the highest in the EUS,

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indicating that the WRF-CMAQ model is more likely to excel in reproducing air pollutants in the EUS compared to the western US. Poor performance in the western US might be due to the coarse spatial resolution of the model, which constrains the representation of complex terrains such as urban areas and mountainous regions (Astitha et al. 2017). Another possible explanation is that the model is insufficient to describe the sporadic fire that occurs in the western US, especially in California (Park et al. 2006). Approximately 51.2%, 69.1%, and 1.5% of the stations of the CSN network have a Kendall’s Tau correlation coefficient of larger than 0.6 between simulated data and observed data for sulfate, nitrate, and total PM2.5 mass, respectively, whereas the

corresponding percentages of stations of IMPROVE network are 18.4%, 14.4%, and 2.4%. While the model works well for sulfate and nitrate, it does not generate similar good results for total PM2.5. This discrepancy in model bias is likely to be larger in urban stations than in rural stations,

reflecting the diversity and complexity of PM components in urban areas (Seinfeld 1989). Highly volatile and complicated relationships between climate and air pollutants in urban areas might also be a reason.

Figure 5-4. Spatial evaluation of the total PM2.5, SO4, and NO3. The colorbar shows the Kendall’s

Tau correlation coefficients calculated between monthly observed and modeled air pollutants. Stations with the statistical test not significant at the 95% level are removed.

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Annual trends and variations of total PM2.5 mass over the 2001-2010 period are depicted

in Figure 5-5. Total PM2.5 observed by CSN and IMPROVE networks and simulated by WRF-

CMAQ model are shown for the CONUS, and the NE, SE, and MW regions, separately. The model has considerable success in capturing the interannual variability and the declining decadal trends of total PM2.5 in the CONUS, with minimal bias in the trend slope. This result is consistent with

Seltzer et al. (2016). Spatially, the model reproduces annual means and annual trends of PM2.5

better in the MW region for both networks. By contrast, the model overestimates the annual means of PM2.5 in the NE region while it underestimates the annual means of PM2.5 in the SE region. The

model results for sulfate follow the same pattern as the total PM2.5 mass shows at urban stations.

However, the discrepancy between modeled data and observed data for sulfate is smaller than that for the total PM2.5 (see Figure S5-9). For nitrate, the WRF-CMAQ model tends to overestimate its

values throughout the EUS whereas smaller differences are found between simulated data and observed data in the CONUS, particularly at rural stations (see Figure S5-10). The underestimation of nitrate in the western US tends to negate these differences. These chemical matters are predominantly anthropogenic-related in urban areas where most human activities take place. Sharp decline in these matters as a result of EPA regulations on emission reduction from power generation and vehicular sources has greater benefit to urban areas. This is evidenced by the obvious decreasing trends in PM2.5, sulfate, and nitrate at CSN network (Ridley et al. 2018). The

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Figure 5-5. Comparison of annual and regional variations of WRF-CMAQ simulated total PM2.5

(in black solid line) against observed total PM2.5 (in red solid line) at CSN stations (in the first row)

and IMPROVE stations (in the second row). Shading areas are the annual standard deviation for these two datasets. * indicates that the slope is not significant at the 95% significance level.

Figure 5-6 compares the monthly cycles of total PM2.5 mass from simulations and

measurements at CSN and IMPROVE stations. Using area-averaged monthly means in each region enables a more representative evaluation of the spatiotemporal patterns inherent in PM2.5 data since

it smooths random errors (Buchard et al. 2016). On average, the model is effective in reproducing total PM2.5 in April and October over the CONUS. However, specific regional differences are also

found across the EUS. The model works well for data of May and September in the NE but its ability of simulation is not consistent for CSN and IMPROVE in the SE and MW regions. A negative bias is found for data in June-July-August across all regions. This is likely related to the underestimation of secondary organic aerosol (Zhang et al. 2014, Volkamer et al. 2006). Compared to PM2.5 concentrations measured at urban stations, measurements in rural areas appear to be lower

consistently. Despite differences in the two networks, monthly variations in CSN and IMPROVE are highly correlated for data in the NE and SE regions. Moreover, monthly evaluation of PM2.5

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chemical components indicates that while modeled sulfate matches well with measurements at CSN and IMPROVE over the CONUS, NE, and MW, nitrate concentrations are underestimated by the WRF-CMAQ for the months of June-July-August (see Figure S5-11 and Figure S5-12). The largest positive bias of nitrate is observed in data for November and February.

Figure 5-6. The same as Figure 5-5, but for monthly results.

The predicted seasonal averaged PM2.5 against the observed seasonal averaged PM2.5 in the

EUS is also presented in Figure 5-7. The seasonal variation of site-specific total PM2.5 is

reproduced reasonably well by the WRF-CMAQ, with larger values occurring in summer for the NE, autumn for the SE, and winter for the MW. The modeled concentration simulates well for both networks during spring and fall, but it underestimates PM2.5 concentrations during summer

for both networks and overestimates it during winter for IMPROVE, which is also reflected in the monthly variations. High performance on sulfate prediction is detected across the EUS with most of the points falling along the one-to-one line in Figure S5-13. Model performance on nitrate is somewhat worse than on sulfate so that nitrate is consistently over-predicted, especially at rural stations (see Figure S5-14). Similar model performance on sulfate and nitrate was reported by

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Seltzer et al. (2016) over the CONUS. While sulfate concentrations are expected to be high in summer due to oxidation, nitrate concentrations are expected to decrease due to volatilization (Tai, Mickley, and Jacob 2010).

Figure 5-7. Plots of seasonal mean values of total PM2.5 mass between CSN/IMPROVE observed

data and WRF-CMAQ predicted data at the same locations. MAM, March-April-May; JJA, June- July-August; SON, September-October-November; and DJF, December-January-February. The 1:1 line is plotted in black solid line.