Tropospheric ozone (O 3 ) is a greenhouse gas which can absorb heat and make the weather even hotter during extreme heatwaves. Besides, it is an influential ground-level air pollutant which can severely damage the environment. Thus evaluating the importance of various factors related to the O 3 formation process is essential. However, O 3 simulated by the available climate models exhibits large variance in different places, indicating the insufficiency of models in explaining the O 3 formation process correctly. In this paper, we aim to identify and understand the impact of various factors on O 3 formation and predict the O 3 concentrations under different pollution-reduced and climate change scenarios. We employ six supervised methods to estimate the observed O 3 using fourteen meteorological and chemical variables. We find that the deep neural network (DNN) and long short-term memory (LSTM) based models can predict O 3 concentrations accurately. We also demonstrate the importance of several variables in this prediction task. The results suggest that while Nitrogen Oxides negatively contributes to predicting O 3 , solar radiation makes a significantly positive contribution. Furthermore, we apply our two best models on O 3 prediction under different global warming and pollution reduction scenarios to improve the policy-making decisions in the O 3 reduction. Ozone (O 3 ) plays an essential role in the stratosphere to prevent organisms in the biosphere from exposing to excessive ultraviolet (UV) rays (Seinfeld & Pandis, 2016). However, it is also a greenhouse gas and a severe air pollutant at the ground-level. Ground-level O 3 can absorb longwave radiation from the earth, further shifting the radiation balance and even heating the surrounding atmosphere (Stevenson et al., 2013). High concentrations of the ground-level O 3 can also severely damage the ecological community. For instance, 4  15% of global wheat yields are lost because of O 3 pollution (Ainsworth, 2017). Therefore, the Environmental Protection Agency (EPA) of the United States set a (NAAQS) for six principal pollutants including ground-level O 3 . According to the latest 2015 NAAQS, the standard for ground-level O 3 is 0.070 ppm for an eight-hour average. Considering that tropospheric O 3 is produced through complicated reactions, understanding the importance of different variables and their interactions that produce O 3 is necessary. However, as an obvious model-observation disparity of tropospheric O 3 still exists in current global-scale chemical models developed based on theoretical studies (Young et al., 2018), analyzing its formation with new data-driven methods becomes essential. Several popular machine learning algorithms have been applied in the real-time prediction and down-scaling of O 3 concentration (Eslami et al., 2019;Watson et al., 2019). These methods are also used to simplify the O 3 prediction process in climate models and reduce the computational expense of fully interactive atmospheric chemistry schemes (Nowack et al., 2018). However, most of them focus only on the prediction task and ignore the comparison of the importance of the features with earlier theoretical studies. It is essential to understand the importance of the factors in the complex O 3 formation process to help improve policy-making and progress towards a healthier environment. In this study, we predict ground-level O 3 with different machine learning models and measure the importance of several factors involved in O 3 formation. The availability of enormous data such as satellite observation images and uninterrupted surface measurements helps in understanding tropospheric O 3 formation. The present modern techniques (e.g., deep learning methods) are also compatible with large-scale data and highly effective in making predictions. In this paper, we utilize large scale data and modern deep learning techniques to understand O 3 formation. The meteorological parameters and the concentration of pollutants are adequate for ground-level O 3 prediction. We use fourteen such variables in our analysis. The observed O 3 is regarded as true values for the prediction. We aim to learn a prediction function f that takes these fourteen variables as input features (X) and predict the value (y) of the observed O 3 . Our main contributions are as follows:  We collect a large dataset on observed O 3 and corresponding important weather factors. We build several supervised learning methods to accurately predict O 3 concentrations. In addition to the observed data, monthly historical simulation (2000-2014) (Danabasoglu, 2019a) and future projection (2015-2100) (Danabasoglu, 2019b;c;d;e) from CESM2 are used to evaluate We demonstrate three types of results. First, we describe the performance of all the proposed models via out of sample tests. Second, we present the importance of the input variables in predicting O 3 . Third, we evaluate the impact of climate change and pollution on the ground level O 3 and explain how these results would help in better policy making. We compare the performance of all six models in this experiment. predictions of the DNN and LSTM models, we compare the values of predicted and observed O 3 in Fig. 2b. Note that the predicted O 3 by the DNN and LSTM model is correlated with the observed ones and this justifies the good performance of the model. The tropospheric O 3 formation process is complex and is influenced by many variables. One of our main goals is to understand the influence of individual variables on O 3 prediction. Thus, we perform a permutation importance (Altmann et al., 2010) study with the two best performing models DNN and LSTM. The idea is to make one feature randomly unavailable and then compute the drop in model's performance. Note that this method is also model agnostic. We measure the increase in RMSE ( RMSE) as the drop in model's performance. The results shown in Fig. 3a suggest that solar radiation is the most significant variable among uncontrollable variables identified by both models. On the other hand, the DNN model emphasizes the significance of PM 10 while the LSTM model presents the importance of nitrogen monoxide (NO) among variables that are related to human activities and controllable.
These results also show that NO 2 is another major important anthropogenic variable in O 3 prediction. The similarity of two analyses signifies the influence of solar radiation and NO 2 in the prediction of O 3 . Other components having high importance according to the permutation analyses include relative humidity (RH), carbon monoxide (CO), and air temperature. Note that NO, NO 2 , CO and PM 10 can be reduced or controlled with better policies such as reduction in fuel combustion and increasing usage of electric vehicles. We further recognize the nature (positive or negative) of the contribution of each variables in predicting O 3 via a recent technique. We employ SHapley Additive exPlanations (SHAP) analysis (Lundberg & Lee, 2017) on DNN and LSTM models. The idea is to use a game theoretic approach called Shapley Values to compute contribution of each individual variable. As displayed in Fig. 3b, the results indicate that while NO and NO 2 have strong negative impact, the radiation has extremely positive influence on model-predicted O 3 . These results shed some light on the important mechanisms related to O 3 formation, as further illustrated in Figure 3c. An interesting observation is that the negative contribution of NO and NO 2 might imply that ground-level O 3 in Taiwan is mainly constrained by the "NOx-saturated" condition (Sillman, 1999). In other words, reducing the emission of the volatile organic compounds (VOCs), such as benzene, formaldehyde, methanol, and isoprene, might be more efficient than NOx for curtailing the surface O 3 concentration in Taiwan. However, the slightly negative contribution of CH 4 and non-methane hydrocarbon indicate that reducing these VOCs might not effectively reduce ground-level O 3 neither. As a result, the contradiction to the traditional theory could provide a hint for further interesting research directions towards the unrevealed mechanisms of O 3 formation. Besides, the high significance of PM 10 in the permutation analysis of DNN model could not only present the high correlation between PM 10 and O 3 but also be a notion to further explore the possible mechanism for O 3 formation on the surface of PM 10 . Accurate O 3 projection is an important task to help in improving environment related policies that include pollutant emission reduction and damage mitigation. In particular, monitoring and evaluating the impact of O 3 on agricultural crops are important since agricultural production losses might cause food crisis and even famine around the world. Here we aim to perform the O 3 prediction for different scenarios by applying our proposed DNN and LSTM models. The DNN and LSTM models are able to predict the monthly average data O 3 concentration quite accurately . Thus, we apply them for analyzing the impact of the pollution reduction and climate change on predicted O 3 . Climate change scenarios "Shared Socioeconomic Pathways" project four different climate change scenarios that are referred as ssp126, ssp245, ssp370, and ssp585 (O'Neill et al., 2016). The ssp126 scenario presumes people to "take the green road" that the world shifts gradually toward a more sustainable path. The ssp245 scenario is a "middle of the road" that the world nearly follows their historical patterns. The ssp370 scenario assumes a "regional rivalry" that weak action is taken on mitigating climate and reducing air pollutant emissions. The ssp585 suspects that the world chooses to accelerate their growth in economic output and energy use. The model simulations based on these four scenarios show that surface temperature over Taiwan is expected to raise 1.0, 1.6, 2.5, and 3.7  C in by the end of 2100 (2091-2100) compared to 2014-2018 (the period this study focuses). In addition, the water vapor content is supposed to increase 5%, 11%, 17% and 24% in different scenarios. The relative humidity has less change compared to near-surface temperature and water vapor content, as presented in As advised by the model simulation results, we apply the mentioned temperature and water vapor content increase to our test data (the observation during 2018) separately and together with the remaining variables unchanged and predict their effect on O 3 concentration. Figure 4a presents the results. The DNN and LSTM models both indicate that increasing temperature could raise O 3 concentration while increasing water vapor content could lower O 3 concentration. While the positive contribution of temperature increase could significantly raise O 3 , the negative contribution of water content vapor increase is able to offset the effect of temperature increase. Consequently, the change in O 3 becomes negligible when considering the perturbation of these two variables together (as shown in Figure 4a). The above results indicate that the reduction of anthropogenic pollutants might be more crucial for controlling O 3 in the future. Reducing pollution is always an important policy move in most countries for improving public health in general. However, controlling different pollutants can have a distinct impact on O 3 concentration. Thus, we study the effect of reducing 10% of each anthropogenic pollutant value on the predicted O 3 in test dataset applying DNN and LSTM models. Figure 4b shows the results which demonstrate that reducing 10 percent CO would have the most apparent effect on decreasing ground-level O 3 among all anthropogenic variables found by both models. Again, though controlling the emission of CO could contribute to lower O 3 , reducing the amount of NO and NO 2 might lead to an increment of O 3 . Therefore, simulations with different pollution control strategies by global climate models are still necessary to have a more comprehensive evaluation. Discussion As presented in Fig. 4b, reducing CO, CH 4 , and PM 2.5 could be important for decreasing O 3 concentration. Anthropogenic CO comes from the incomplete combustion of carbon-based fuel, and major CO sources include transportation and industrial activity. The generation of CH 4 , another major greenhouse gas, is also highly co-related to human activity, such as agriculture, fossil fuel extraction, wildfire, and biomass burning.
PM 2.5 are aerosols with complicated composition and can be directly emitted or formed via sophisticated chemical reactions of gases including NOx, SO 2 , and VOCs. To reduce CO, CH 4 , and PM 2.5 , it will be important to decrease the use of fuel vehicles and carbon-containing fuel and raise the percentage of renewable energy. However, reducing anthropogenic gases means that the concentrations of NO and NO 2 would also decrease. The negative contribution of NO and NO 2 must be carefully studied to evaluate the total effect of reducing anthropogenic gases to the future O 3 concentration. These results clearly show the various kinds of actions where the government should have a stricter policy to make a better environment for the future. In this study, we have predicted tropospheric O 3 which is one of the greenhouse gas and an influential ground-level air pollutant that can severely damage the environment. We have compared six methods to estimate the tropospheric O 3 concentration and understand the importance of some meteorological variables, trace gases and pollutants in forming the ground-level O 3 . The importance of solar radiation is emphasized in the best two models, DNN and LSTM models, which conform to the theoretical study. However, all NOx and volatile organic compounds (VOCs) are presented to contribute negatively to O 3 prediction, which contradicts the O 3 and NOx-VOCs relationship. This would promote a direction for future research about undiscovered O 3 formation mechanisms. Moreover, the study regarding the importance of the variables or factors will lead to better policy makings to control the production of such materials or pollutants. We have further investigated the O 3 concentration under different scenarios and shown that controlling anthropogenic gases, especially CO, could be critical for reducing O 3 in the future considering the facts that the surface temperature and water vapor content may increase. Our findings clearly show the various kind of actions that the government should have stricter policies on, to make a better environment for the future.