The sensitivities of ozone and PM2.5 concentrations to the satellite-derived leaf area index over East Asia and its neighboring seas in the WRF-CMAQ modeling system

Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ

Short-term exposure to ground-level ozone (O3) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O3 forecasting to mitigate these risks, focusing on South Korea. We introduce Deep Bias Correction (Deep-BC), a novel framework leveraging Convolutional Neural Networks (CNNs), to refine hourly O3 forecasts from the Community Multiscale Air Quality (CMAQ) model. Our approach involves training Deep-BC using data from 2016 to 2019, including CMAQ's 72-hour O3 forecasts, 31 meteorological variables from the Weather Research and Forecasting (WRF) model, and previous days' station measurements of 6 air pollutants. Deep-BC significantly outperforms CMAQ in 2021, reducing biases in O3 forecasts. Furthermore, we utilize Deep-BC's daily maximum 8-hour average O3 (MDA8 O3) forecasts as input for the AirQ+ model to assess O3's potential impact on mortality across seven major provinces of South Korea: Seoul, Busan, Daegu, Incheon, Daejeon, Ulsan, and Sejong. Short-term O3 exposure is associated with 0.40 % to 0.48 % of natural cause and respiratory deaths and 0.67 % to 0.81 % of cardiovascular deaths. Gender-specific analysis reveals higher mortality rates among men, particularly from respiratory causes. Our findings underscore the critical need for region-specific interventions to address air pollution's detrimental effects on public health in South Korea. By providing improved O3 predictions and quantifying its impact on mortality, this research offers valuable insights for formulating targeted strategies to mitigate air pollution's adverse effects. Moreover, we highlight the urgency of proactive measures in health policies, emphasizing the significance of accurate forecasting and effective interventions to safeguard public health from the deleterious effects of air pollution.

A case study of surface ozone source contributions in the Seoul metropolitan area using the adjoint of CMAQ

To quantitatively investigate the transboundary behaviors and source attributions of ozone (O3) and its precursor species over East Asia, we utilize the adjoint technique in the CMAQ modeling system (the CMAQ adjoint). Our focus is on the Seoul Metropolitan Area (SMA) in South Korea, which is the receptor region of this study. We examine the contributions of both local and transported emissions to an O3 exceedance episode observed on June 3, 2019, estimating up to four days in advance. By using the CMAQ adjoint, we can determine the sensitivity of O3 remaining in the SMA to changes in O3 precursor emissions (emissions-based sensitivity) and concentrations (concentrations-based sensitivity) along the long-range transport pathways and emission source regions overseas. These include Beijing-Tianjin-Hebei (BTH), Shandong, Yangtze River Delta (YRD), and Central China. CMAQ adjoint-derived source attributions suggest that overseas precursor emissions and O3 contributed significantly to the O3 exceedance event in SMA. The emissions-based sensitivities revealed that precursor emissions originating from Shandong, YRD, Central China, and BTH contributed 11.42 ppb, 4.28 ppb, 1.24 ppb, 0.9 ppb, respectively, to the O3 exceedance episode observed in the SMA. Meanwhile, Korean emissions contributed 31.1 ppb. Concentrations-based sensitivities indicated that 19.3 ppb of contributions originated in regions beyond eastern China and directly affected the O3 level in the SMA in the form of background O3. In addition to capturing the transboundary movements of air parcels between the source and receptor regions, we performed HYSPLIT backward trajectory analyses. The results align with the trajectories of O3 and its precursors that we obtained from the adjoint method. This study represents a unique effort in employing the adjoint technique to examine the impacts of regional O3 on South Korea, utilizing a combination of emissions-based and concentrations-based sensitivities.Implications: This research brings to light the critical role of both local and regional precursor emissions in contributing to an ozone (O3) exceedance event in the Seoul Metropolitan Area (SMA), South Korea. Utilizing the CMAQ adjoint technique, a novel approach in the context of South Korea's O3 investigations, we were able to delineate the quantitative contributions of different regions, both within South Korea and from overseas areas such as Beijing, Shandong, Shanghai, and Central China. Importantly, the results underscore the substantial influence of transboundary pollutant transport, emphasizing the need for international collaboration in addressing air quality issues. As metropolitan areas around the globe grapple with similar challenges, the methodology and insights from this study offer a potent tool and framework for regions seeking to understand and mitigate the impacts of O3 on human health and the environment. By integrating different sensitivity types, coupled with HYSPLIT backward trajectory analyses, this research equips policymakers with comprehensive data to design targeted interventions, emphasizing the significance of collaborative efforts in tackling regional air pollution challenges. However, it's important to note the limitation of this study, which is a case study conducted over a short time period. This constraint may impact the generalizability of the findings and suggests a need for further research to validate and expand upon these results.

Localized biogenic volatile organic compound emission inventory in China: A comprehensive review

Volatile organic compounds (VOCs) are the precursors of forming ozone (O3) and fine particulate matter (PM2.5). Accurate estimates of biogenic VOC (BVOC) emissions is essential for understanding the formation mechanism of O3 and PM2.5 pollution and precise reduction on anthropogenic emissions and thereby mitigating O3 and PM2.5 pollution. To gain comprehensive knowledge of BVOC emissions and improve the accuracy of their estimation, this study reviewed localized national, regional, and municipal emission estimations in China. From their comparisons, BVOC emission characteristics and deficiencies in the inventory compilation methodology were also investigated. The estimated BVOC emissions in China ranged between 10 and 58.9 Tg yr-1 and 10.9-18.9 Tg C yr-1, with diverse contributions for different BVOC categories. The simulated historical and future BVOC emissions exhibited an increasing trend. The uncertainty of the BVOC estimates was mainly from the applications of incomplete emission models, less localized accurate emission factors, deficient vegetation cover information, and low-resolution meteorological data in the inventory compilation. The regional and municipal BVOC emission inventories mainly focused on the Beijing-Tianjin-Hebei, Pearl River Delta, Sichuan Basin, and Yangtze River Delta regions, as well as the cities therein. For the same area, different studies reported diverse BVOC emissions by a maximum of two orders of magnitude. There is usually a lack of basic data with more detailed investigations and higher precision for estimation of BVOC emissions. By summarizing the measurements on terrestrial and marine BVOC emission fluxes, they are mainly focused on the Guangdong, Zhejiang and Jiangxi provinces, and Yellow Sea, East China Sea, and South China Sea, respectively. Expanding the temporal and spatial scales of observations is encouraged to enhance our understanding on the emissions and improve the emission estimates.

Deep learning mapping of surface MDA8 ozone: The impact of predictor variables on ozone levels over the contiguous United States

The limited number of ozone monitoring stations imposes uncertainty in various applications, calling for accurate approaches to capturing ozone values in all regions, particularly those with no in-situ measurements. This study uses deep learning (DL) to accurately estimate daily maximum 8-hr average (MDA8) ozone and examines the spatial contribution of several factors on ozone levels over the contiguous U.S. (CONUS) in 2019. A comparison between in-situ observations and DL-estimated MDA8 ozone values shows a correlation coefficient (R) of 0.95, an index of agreement (IOA) of 0.97, and a mean absolute bias (MAB) of 2.79 ppb, highlighting the promising performance of the deep convolutional neural network (Deep-CNN) at estimating surface MDA8 ozone. Spatial cross-validation also confirms the high spatial accuracy of the model, which obtains an R of 0.91, and IOA of 0.96 and an MAB of 3.46 ppb when it is trained and tested on separate stations. To interpret the black-box nature of our DL model, we use Shapley additive explanations (SHAP) to generate a spatial feature contribution map (SFCM), the results of which confirm an advanced ability of Deep-CNN to capture the interactions between most predictor variables and ozone. For instance, the model shows that solar radiation (SRad) SFCM, with higher values, enhances the formation of ozone, particularly in the south and southwestern CONUS. As SRad triggers ozone precursors to produce ozone via photochemical reactions, it increases ozone concentrations. The model also shows that humidity, with its low values, increases ozone concentrations in the western mountainous regions. The negative correlation between humidity and ozone levels can be attributed to factors such as higher ozone decomposition resulting from increased levels of humidity and OH radicals. This study is the first to introduce the SFCM to investigate the spatial role of predictor variables on changes in estimated MDA8 ozone levels.

Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis

Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) unit to predict the O₃ and PM_2.5 levels in the atmosphere, as well as an air quality index (AQI). The prediction results given by the proposed model are compared with those from CNN-LSTM and LSTM models as well as random forest and support vector regression models. The proposed model achieves a correlation coefficient between the predicted and observed values of more than 0.90, outperforming the other four models. The model errors are also consistently lower when using the proposed approach. Sobol-based sensitivity analysis is applied to identify the variables that make the greatest contribution to the model prediction results. Taking the COVID-19 outbreak as the time boundary, we find some homology in the interactions among the pollutants and meteorological factors in the atmosphere during different periods. Solar irradiance is the most important factor for O₃, CO is the most important factor for PM_2.5, and particulate matter has the most significant effect on AQI. The key influencing factors are the same over the whole phase and before the COVID-19 outbreak, indicating that the impact of COVID-19 restrictions on AQI gradually stabilized. Removing variables that contribute the least to the prediction results without affecting the model prediction performance improves the modeling efficiency and reduces the computational costs.