Temporal and spatial analysis of anthropogenic mercury and CO2 emissions from municipal solid waste incineration in China: Implications for mercury and climate change mitigation.

The contribution of municipal solid waste incineration (MSWI) to anthropogenic mercury and CO2 emissions have become increasingly important over the past decade. This study developed an inventory of anthropogenic mercury emissions and CO2 emissions during the period of 2014-2020, of MSWI process in China using a bottom-up inventory at the plant level. Overall, national MSWI anthropogenic mercury emissions increased from 2014 to 2020 by province. It was estimated that total 8321.09 kg of anthropogenic mercury emissions from 548 MSWI plants were scattered in 31 provinces of mainland China in 2020. The average intensity of mercury emission in China was 0.06 g·t-1 in 2020, which was much lower than the pre-2010 level. Furthermore, the increased CO2 emission generated by MSWI from 2014 to 2020 is 1.97 times. Anthropogenic mercury emissions and CO2 emissions were concentrated mainly in developed coastal provinces and cities. The general uncertainty of national mercury emissions and CO2 emissions was estimated to be -123% to 323% and -130% to 335%, respectively. Furthermore, future emissions were predicted from 2030 to 2060 based on different scenarios of the independent and collaborative effects of control proposals, the results indicate that the enhancement of advanced air pollution control technologies and effective management of MSWI represent pivotal factors in realizing future reductions in CO2 and mercury emissions. The findings will supplement those for mercury and CO2 emissions, and be useful for relevant policy-making and to improve urban air quality, as well as human health.

Ultrasound image intelligent diagnosis in community-acquired pneumonia of children using convolutional neural network-based transfer learning.

Background: Studies show that lung ultrasound (LUS) can accurately diagnose community-acquired pneumonia (CAP) and keep children away from radiation, however, it takes a long time and requires experienced doctors. Therefore, a robust, automatic and computer-based diagnosis of LUS is essential. Objective: To construct and analyze convolutional neural networks (CNNs) based on transfer learning (TL) to explore the feasibility of ultrasound image diagnosis and grading in CAP of children. Methods: 89 children expected to receive a diagnosis of CAP were prospectively enrolled. Clinical data were collected, a LUS images database was established comprising 916 LUS images, and the diagnostic values of LUS in CAP were analyzed. We employed pre-trained models (AlexNet, VGG 16, VGG 19, Inception v3, ResNet 18, ResNet 50, DenseNet 121 and DenseNet 201) to perform CAP diagnosis and grading on the LUS database and evaluated the performance of each model. Results: Among the 89 children, 24 were in the non-CAP group, and 65 were finally diagnosed with CAP, including 44 in the mild group and 21 in the severe group. LUS was highly consistent with clinical diagnosis, CXR and chest CT (kappa values = 0.943, 0.837, 0.835). Experimental results revealed that, after k-fold cross-validation, Inception v3 obtained the best diagnosis accuracy, PPV, sensitivity and AUC of 0.87 ± 0.02, 0.90 ± 0.03, 0.92 ± 0.04 and 0.82 ± 0.04, respectively, for our dataset out of all pre-trained models. As a result, best accuracy, PPV and specificity of 0.75 ± 0.03, 0.89 ± 0.05 and 0.80 ± 0.10 were achieved for severity classification in Inception v3. Conclusions: LUS is a reliable method for diagnosing CAP in children. Experiments showed that, after transfer learning, the CNN models successfully diagnosed and classified LUS of CAP in children; of these, the Inception v3 achieves the best performance and may serve as a tool for the further research and development of AI automatic diagnosis LUS system in clinical applications. Registration: www.chictr.org.cn ChiCTR2200057328.

Study of the adsorption mechanism on the surface of a ceramic nanomaterial for gaseous Hg(II) removal.

Stable Hg(II)-containing flue gas has been successfully simulated by the plasma oxidation of Hg(0), and an effective solution for Hg(0) mercury fumes was obtained by combining the plasma with a ceramic nanomaterial. Characterization tests showed that the ceramic nanomaterial was mainly composed of silicon dioxide (SiO2) with other minor constituents, including potassium mica (KAl3Si3O11), iron magnesium silicate (Fe0.24Mg0.76SiO3) and dolomite (CaMg(CO3)2). The nanomaterial had many tube bank structures inside with diameters of approximately 8-10 nm. The maximum sorption capacity of Hg(II) was 5156 µg/g, and the nanomaterial can be regenerated at least five times. During the adsorption, chemical adsorption first occurred between Hg(II) and sulfydryl moieties, but these were quickly exhausted, and Hg(II) was then removed by surface complexation and wrapped into Fe moieties. The pseudo-first-order kinetic model and the Langmuir equation had the best fitting results for the kinetics and isotherms of adsorption. This work suggests that the ceramic nanomaterial can be used as an effective and recyclable adsorbent in the removal of gaseous Hg(II).