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

Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning

Importance

Medulloblastoma (MB) is the most common malignant brain tumor in children, with metastasis being the primary cause of recurrence and mortality. The tumor microenvironment (TME) plays a critical role in driving metastasis; however, the mechanisms underlying TME alterations in MB metastasis remain poorly understood.

Objective

To develop and validate machine learning (ML) models for predicting patient outcomes in MB and to investigate the role of TME components, particularly immune cells and immunoregulatory molecules, in metastasis.

Methods

ML models were constructed and validated to predict prognosis and metastasis in MB patients. Eight algorithms were evaluated, and the optimal model was selected. Lasso regression was employed for feature selection, and SHapley Additive exPlanations values were used to interpret the contribution of individual features to model predictions. Immune cell infiltration in tumor tissues was quantified using the microenvironment cell populations-counter method, and immunohistochemistry was applied to analyze the expression and distribution of specific proteins in tumor tissues.

Results

The ML models identified metastasis as the strongest predictor of poor prognosis in MB patients, with significantly worse survival outcomes observed in metastatic cases. High infiltration of CD8+ T cells and cytotoxic T lymphocytes (CTLs), along with elevated expression of the TGFB1 gene encoding transforming growth factor beta 1 (TGF-β1), were strongly associated with metastasis. Independent transcriptomic and immunohistochemical analyses confirmed significantly higher CD8+ T cell/CTL infiltration and TGF-β1 expression in metastatic compared to nonmetastatic MB samples. Patients with both high CD8+ T cell/CTL infiltration and elevated TGFB1 expression in the context of metastasis exhibited significantly worse survival outcomes compared to patients with low expression and no metastasis.

Interpretation

This study identifies metastasis as the key prognostic factor in MB and reveals the pivotal roles of CD8+ T cells, CTLs, and TGF-β1 within the TME in promoting metastasis and poor outcomes. These findings provide a foundation for developing future therapeutic strategies targeting the TME to improve MB patient outcomes.

Integrating D-S evidence theory and multiple deep learning frameworks for time series prediction of air quality

Accurate prediction of air quality time series data is helpful to identify and warn air pollution events in advance. Although the current air quality prediction models have made some progress in improving the accuracy of prediction, due to the impact of specific pollutants or complex meteorological conditions, these models still have the problems of low prediction accuracy, robustness and generalization ability in univariate prediction. In order to solve these problems, this study proposes a framework that integrates D-S evidence theory and a variety of deep learning models. The air quality data of three representative cities with climate characteristics in China are obtained and five indicators on air pollutants are collected. The preprocessed data are divided by time length to form short-term, medium-term and long-term input data, and MLP, RNN, CNN, LSTM, BI-LSTM and GRU models are established respectively. By comparing the performance indicators of the six models, three most suitable models are selected to predict the short, medium and long-term data respectively. Taking the prediction results and reliability as the three evidence bodies of the theory, a fusion model based on D-S evidence theory is established. For the three performance indicators MAE, RMSE and MAPE of the model, the best result of the fusion model increases the performance by 7.42%, 4.25% and 12.82% compared with the sub optimal architecture. This shows that integrating D-S evidence theory and a variety of deep learning algorithms provides an effective method to accurately predict the long-term air quality level in most urban areas.

Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning

Ozone pollution was widely reported along with PM2.5 reduction since 2013 in China. However, the meteorological drivers for ozone varying with different regions of China remains unknown using explainable machine learning, especially during the COVID-19 pandemic. Here we first analyzed spatiotemporal variations of PM2.5 and ozone from 2015 to 2022 in eleven urban agglomerations of China. PM2.5 decreased in all regions, with the largest drop in Beijing-Tianjin-Hebei (BTH). In contrast, ozone declined initially but rose during the pandemic in most regions, especially in Cheng-Yu. Probability density curves showed pronounced increase (24.7%) and slight change in the proportion of PM2.5 and ozone meeting the pollution criterions during the pandemic, respectively. Leveraging Random Forest with SHAP analysis, we further established ozone models in typical urban agglomerations with good performance (CV-R2 = 0.80-0.90; CV-RMSE = 8.52-19.20 μg/m3) during the pandemic, and compared their relative importance of meteorological variables. Particularly, temperature and incoming shortwave flux at top of atmosphere were identified with high importance in high-ozone regions such as Middle Plain and BTH. Increasing importance of PM (e.g., PM10) was found in southern China, e.g., Yangtze River Delta and Pearl River Delta regions. The western China was characterized with more importance of meteorology, especially in Tibet. Surface albedo and sensible heat flux from turbulence were noted distinctively with high importance in Tibet, partly due to their impacts on ozone formation by generating heat source and sink. In addition, sea level pressure (SLP) was revealed with the highest importance (25.2%) in Cheng-Yu, consistent with the fact that synoptic patterns characterized by SLP field could affect ozone pollution in Sichuan Basin. Our results not only provide an understanding of meteorological factors in regional ozone formation in China, but also highlight the feasibility of explainable machine learning in ozone studies.

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.

Deep learning bias correction of GEMS tropospheric NO2: A comparative validation of NO2 from GEMS and TROPOMI using Pandora observations

Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric column density of NO2 (TCDNO2) from the Geostationary Environment Monitoring Spectrometer (GEMS) during 2021-2023. Initially, we validate GEMS TCDNO2 against Pandora observations and compare its accuracy with measurements from the TROPOspheric Monitoring Instrument (TROPOMI). GEMS displays acceptable accuracy in TCDNO2 measurements, with a correlation coefficient (R) of 0.68, an index of agreement (IOA) of 0.79, and a mean absolute bias (MAB) of 5.73321 × 1015 molecules/cm2, though it is not highly accurate. The evaluation showcases moderate to high accuracy of GEMS TCDNO2 across all Pandora stations, with R values spanning from 0.46 to 0.80. Comparing TCDNO2 from GEMS and TROPOMI at TROPOMI overpass time shows satisfactory performance of GEMS TCDNO2 measurements, achieving R, IOA, and MAB values of 0.71, 0.78, and 6.82182 × 1015 molecules/cm2, respectively. However, these figures are overshadowed by TROPOMI's superior accuracy, which reports R, IOA, and MAB values of 0.81, 0.89, and 3.26769 × 1015 molecules/cm2, respectively. While GEMS overestimates TCDNO2 by 52 % at TROPOMI overpass time, TROPOMI underestimates it by 9 %. The deep learning bias corrected GEMS TCDNO2 (GEMS-DL) demonstrates a marked enhancement in the accuracy of original GEMS TCDNO2 measurements. The GEMS-DL product improves R from 0.68 to 0.88, IOA from 0.79 to 0.93, MAB from 5.73321 × 1015 to 2.67659 × 1015 molecules/cm2, and reduces MAB percentage (MABP) from 64 % to 30 %. This represents a significant reduction in bias, exceeding 50 %. Although the original GEMS product overestimates TCDNO2 by 28 %, the GEMS-DL product remarkably minimizes this error, underestimating TCDNO2 by a mere 1 %. Spatial cross-validation across Pandora stations shows a significant reduction in MABP, from a range of 45 %-105.6 % in original GEMS data to 24 %-59 % in GEMS-DL.

Air quality and health co-benefits of vehicle electrification and emission controls in the most populated United States urban hubs: Insights from New York, Los Angeles, Chicago, and Houston

Transitioning to electric vehicles (EVs) is a prominent strategy for reducing greenhouse gas emissions. However, given the complexity of atmospheric chemistry, the nuanced implications on air quality are yet to be fully understood. Our study delved into changes in PM2.5, ozone, and their associated precursors in major US urban areas, considering various electrification and mitigation scenarios. In the full electrification (FullE) scenario, PM2.5 reduction peaked at values between 0.34 and 2.29 μg.m-3 across distinct regions. Yet, certain areas in eastern Los Angeles exhibited a surprising uptick in PM2.5, reaching as much as 0.67 μg.m-3. This phenomenon was linked to a surge in secondary organic aerosols (SOAs), resulting from shifting NOx/VOCs (volatile organic compounds) dynamics and a spike in hydroxyl radical (OH) concentrations. The FullE scenario ushered in marked reductions in both NOx and maximum daily average 8-h (MDA8) ozone concentrations, with maximum levels ranging from 14.00 to 32.34 ppb and 2.58-9.58 ppb, respectively. However, certain instances revealed growths in MDA8 ozone concentrations, underscoring the intricacies of air quality management. From a health perspective, in the FullE scenario, New York, Chicago, and Houston stand to potentially avert 796, 328, and 157 premature deaths/month, respectively. Los Angeles could prevent 104 premature deaths/month in the HighE-BL scenario, representing a 29 % EV share for light-duty vehicles. However, the FullE scenario led to a rise in mortality in Los Angeles due to increased PM2.5 and MDA8 ozone levels. Economically, the FullE scenario projects health benefits amounting to 51-249 million $/day for New York, Chicago, and Houston. In contrast, Los Angeles may face economic downturns of up to 18 million $/day. In conclusion, while EV integration has the potential to improve urban air quality, offering substantial health and economic advantages, challenges persist. Our results emphasize the pivotal role of VOCs management, providing policymakers with insights for adaptable and efficient measures.

Evolution of surface ozone pollution pattern in eastern China and its relationship with different intensity heatwaves

With climate warming, eastern China has experienced a significant increase in temperature accompanied by intensified ozone pollution. We aimed to investigate the spatiotemporal patterns and relationships between ozone levels and temperature in eastern China using observation-based ozone data from 418 air quality monitoring stations and temperature data from ERA5. The summer maximum temperature and annual ozone concentration in eastern China increased significantly between 2015 and 2022, with increases rate of 10% and 2.84 μg/m3 yr-1, respectively. The baseline ozone concentration was increasing over time. The average difference in MDA8 O3 concentration in spring, summer, and autumn decreased, with more ozone pollution spreading into spring and autumn, indicating a trend of prolonging the ozone season. During the June-July-August (JJA) period of 2015-2022, heatwaves increased significantly in eastern China. The frequency of heatwave events >10 days played a vital role in exacerbating ozone pollution. During the JJA period, the increase rate in MDA8 O3 concentration was 9.31 μg/m3 yr-1 during heatwave periods, significantly higher than that during non-heatwave periods (4.01 μg/m3 yr-1). The correlation between MDA8 O3 concentration and temperature was as high as 0.99, indicating that temperature was vital in ozone formation during the JJA period in eastern China. This study suggests that more stringent actions are needed to control ozone-precursor compounds during frequent summertime heatwaves in eastern China.

A comprehensive approach combining positive matrix factorization modeling, meteorology, and machine learning for source apportionment of surface ozone precursors: Underlying factors contributing to ozone formation in Houston, Texas

Ozone concentrations in Houston, Texas, are among the highest in the United States, posing significant risks to human health. This study aimed to evaluate the impact of various emissions sources and meteorological factors on ozone formation in Houston from 2017 to 2021 using a comprehensive PMF-SHAP approach. First, we distinguished the unique sources of VOCs in each area and identified differences in the local chemistry that affect ozone production. At the urban station, the primary sources were n_decane, biogenic/industrial/fuel evaporation, oil and gas flaring/production, industrial emissions/evaporation, and ethylene/propylene/aromatics. At the industrial site, the main sources were industrial emissions/evaporation, fuel evaporation, vehicle-related sources, oil and gas flaring/production, biogenic, aromatic, and ethylene and propylene. And then, we performed SHAP analysis to determine the importance and impact of each emissions factor and meteorological variables. Shortwave radiation (SHAP values are ∼5.74 and ∼6.3 for Milby Park and Lynchburg, respectively) and humidity (∼4.87 and ∼4.71, respectively) were the most important variables for both sites. For the urban station, the most important emissions sources were n_decane (∼2.96), industrial emissions/evaporation (∼1.89), and ethylene/propylene/aromatics (∼1.57), while for the industrial site, they were oil and gas flaring/production (∼1.38), ethylene/propylene (∼1.26), and industrial emissions/evaporation (∼0.95). NOx had a negative impact on ozone production at the urban station due to the NOx-rich chemical regime, whereas NOx had positive impacts at the industrial site. The study's findings suggest that the PMF-SHAP approach is efficient, inexpensive, and can be applied to other similar applications to identify factors contributing to ozone-exceedance events. The study's results can be used to develop more effective air quality management strategies for Houston and other cities with high levels of ozone.