No abstract

Ammonia (NH3) is an important precursor of secondary inorganic aerosols and greatly impacts nitrogen deposition and acid rain. Previous studies have mainly focused on the agricultural NH3 emissions, while recent research has noted that industrial sources could be significant in China. However, detailed estimates of NH3 emitted from industrial sectors in China are lacking. Here, we established an unprecedented high-spatial-resolution data set of China’s industrial NH3 emissions using up-to-date measurements of NH3 and point source-level information covering eight major industries and 27 subdivided process categories. We found that China emitted 798 (90% confidence interval: 668–933) gigagrams of industrial NH3 into the atmosphere in 2019, equivalent to 44 ± 20% of the industrial emissions worldwide; this flux is 3-fold larger than that in 1998 and has fluctuated since 2014. Furthermore, although fertilizer production is responsible for approximately half of the emissions in China, the emissions from cement production and coal-fired power plants increased dramatically from near zero to 164 and 41 gigagrams, respectively, in the past two decades, primarily due to the NH3 escape caused by the large-scale application of the denitration process. Our results reveal that, unlike other major air pollutants, China’s industrial NH3 emission control is still in a critical period, and stricter NH3 emission standards and innovation in pollution control technologies are highly desirable.

No abstract

No abstract

Accurate cerebrovascular segmentation from Magnetic Resonance Angiography (MRA) and Computed Tomography Angiography (CTA) is of great significance in diagnosis and treatment of cerebrovascular pathology. Due to the complexity and topology variability of blood vessels, complete and accurate segmentation of vascular network is still a challenge. In this paper, we proposed a Vessel Oriented Filtering Network (VOF-Net) which embeds domain knowledge into the convolutional neural network. We design oriented filters for blood vessels according to vessel orientation field, which is obtained by orientation estimation network. Features extracted by oriented filtering are injected into segmentation network, so as to make use of the prior information that the blood vessels are slender and curved tubular structure. Experimental results on datasets of CTA and MRA show that the proposed method is effective for vessel segmentation, and embedding the specific vascular filter improves the segmentation performance.