Abstract
In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.
Highlights
In recent years, increasing ground-level ozone concentrations (GOCs) have attracted worldwide attention because of their adverse effects on human health, climate, and vegetation [1,2]
We evaluated the predictive performance of the hybrid model for different seasons (Figure 8) and geographical regions (Figure 9)
GOCs are discussed in illustrated in Section 5.2, the ozone exposure analysis based on the GOCs are discussed in Section 5.3, and possible problems of the modelmodel are described
Summary
In recent years, increasing ground-level ozone concentrations (GOCs) have attracted worldwide attention because of their adverse effects on human health, climate, and vegetation [1,2]. To better understand these adverse effects, an accurate and high-resolution GOC distribution is urgently needed. In view of model construction (Table 1), two broad types of models have been employed to predict GOCs: deterministic models and statistical models Deterministic models, such as air quality models [2,3], weather research and forecasting models [4,5], and chemical transport models [6], can predict GOCs based on the theoretical description of ozone formation processes, but the computation process is relatively complex [7], and it is difficult to obtain high-resolution products (e.g., 1 km).
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