A hybrid model for spatially and temporally resolved ozone exposures in the continental United States

Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. The authors propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. The authors used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. The authors trained the model with the Air Quality System (AQS) 8-hr daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R² on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km × 1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term.

IMPLICATIONS

Ozone monitors do not provide full data coverage over the United States, which is an obstacle to assess the health effect of ozone when monitoring data are not available. This paper used a hybrid approach to combine satellite-based ozone measurements, chemical transport model simulations, land-use terms, and other auxiliary variables to obtain spatially and temporally resolved ground-level ozone estimation.

The impacts of summer monsoons on the ozone budget of the atmospheric boundary layer of the Asia-Pacific region

The seasonal and inter-annual variations of ozone (O3) in the atmospheric boundary layer of the Asia-Pacific Ocean were investigated using model simulations (2001-2007) from the Model of Ozone and Related chemical Tracers, version 4 (MOZART-4). The simulated O3 and diagnostic precipitation are in good agreement with the observations. Model results suggest that the Asia-Pacific monsoon significantly influences the seasonal and inter-annual variations of ozone. The differences of anthropogenic emissions and zonal winds in meridional directions cause a pollutants' transition zone at approximately 20°-30°N. The onset of summer monsoons with a northward migration of the rain belt leads the transition zone to drift north, eventually causing a summer minimum of ozone to the north of 30°N. In years with an early onset of summer monsoons, strong inflows of clean oceanic air lead to low ozone at polluted oceanic sites near the continent, while strong outflows from the continent exist, resulting in high levels of O3 over remote portions of the Asia-Pacific Ocean. The reverse is true in years when the summer monsoon onset is late.

Impact of wildfires on ozone exceptional events in the Western u.s

Wildfires generate substantial emissions of nitrogen oxides (NOx) and volatile organic compounds (VOCs). As such, wildfires contribute to elevated ozone (O3) in the atmosphere. However, there is a large amount of variability in the emissions of O3 precursors and the amount of O3 produced between fires. There is also significant interannual variability as seen in median O3, organic carbon and satellite derived carbon monoxide mixing ratios in the western U.S. To better understand O3 produced from wildfires, we developed a statistical model that estimates the maximum daily 8 h average (MDA8) O3 as a function of several meteorological and temporal variables for three urban areas in the western U.S.: Salt Lake City, UT; Boise, ID; and Reno, NV. The model is developed using data from June-September 2000-2012. For these three locations, the statistical model can explain 60, 52, and 27% of the variability in daily MDA8. The Statistical Model Residual (SMR) can give information on additional sources of O3 that are not explained by the usual meteorological pattern. Several possible O3 sources can explain high SMR values on any given day. We examine several cases with high SMR that are due to wildfire influence. The first case considered is for Reno in June 2008 when the MDA8 reached 82 ppbv. The wildfire influence for this episode is supported by PM concentrations, the known location of wildfires at the time and simulations with the Weather and Research Forecasting Model with Chemistry (WRF-Chem) which indicates transport to Reno from large fires burning in California. The contribution to the MDA8 in Reno from the California wildfires is estimated to be 26 ppbv, based on the SMR, and 60 ppbv, based on WRF-Chem. The WRF-Chem model also indicates an important role for peroxyacetyl nitrate (PAN) in producing O3 during transport from the California wildfires. We hypothesize that enhancements in PAN due to wildfire emissions may lead to regional enhancements in O3 during high fire years. The second case is for the Salt Lake City (SLC) region for August 2012. During this period the MDA8 reached 83 ppbv and the SMR suggests a wildfire contribution of 19 ppbv to the MDA8. The wildfire influence is supported by PM2.5 data, the known location of wildfires at the time, HYSPLIT dispersion modeling that indicates transport from fires in Idaho, and results from the CMAQ model that confirm the fire impacts. Concentrations of PM2.5 and O3 are enhanced during this period, but overall there is a poor relationship between them, which is consistent with the complexities in the secondary production of O3. A third case looks at high MDA8 in Boise, ID, during July 2012 and reaches similar conclusions. These results support the use of statistical modeling as a tool to quantify the influence from wildfires on urban O3 concentrations.