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

<p>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 <i>R</i>² 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.</p> <p><i>Implications</i>: 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.</p

The degree of balkanization of all random trade networks.

<p>The degree of balkanization of all random trade networks.</p

The Pearson correlation values between GDP and weighted within-cluster distance of all random trade networks.

<p>The Pearson correlation values between GDP and weighted within-cluster distance of all random trade networks.</p

Dendrogram View.

<p>Two layouts to visualize the hierarchical structure of CONCOR results: the left one is a tree layout and the right one is a radial layout. Slider bar is used to control the level of CONCOR results.</p

Imports-exports relationship among partial countries in 2005.

<p>Flow1 means imports of importer1 from importer2 in current US millions of dollars, and flow2 means imports of importer2 from importer1 in current US millions of dollars.</p

Dendrogram view and choropleth map view.

<p>The choropleth map depicts GDP by country. Data are divided into quintiles (5 categories with an equal number of countries in each category) depicted by 5 sequentially ordered shades of green, from low GDP (very light green) to high GDP (very dark green). Each node in the dendrogram view corresponds to one country in the choropleth map view (The highlighted nodes in blue correspond to countries with borders highlighted in blue). The first run of CONCOR process reveals two positions in the 2005 ITN.</p

Validation results as a function of total connection numbers.

<p>Validation results as a function of total connection numbers.</p

The third run of the CONCOR process continues to subdivide groups.

<p>Figure 4A 4B, and 4C belong to the economically core countries, whereas Figure 4D, 4E, 4F, and 4G belong to the economic periphery countries.</p

CONCOR group level attribute data.

<p>*Mean GDP in 2005 for 4 groups identified at the second level of the CONCOR, mean GDP in 2005, mean distance, weighted distance by population for 7 groups at the third level of the CONCOR.</p

Validation results as a function of number of runs (N).

<p>Validation results as a function of number of runs (N).</p