Novel approach for identifying VOC emission characteristics based on mobile monitoring platform data and deep learning: Application of source apportionment in a chemical industrial park.

Refined volatile organic compound (VOC) emission characteristics are crucial for accurate source apportionment in chemical industrial parks. The data from mobile monitoring platforms in chemical industrial parks contain pollution information that is not intuitively displayed, requiring further excavation. A novel approach was proposed to identify VOC emission characteristics using the class activation map (CAM) technology of convolutional neural network (CNN), which was applied on the mobile monitoring platform data (MD) derived from a typical fine chemical industrial park.

Quantifying the energy-material-pollution nexus in a typical fine chemical industry: A sustainable development-oriented support for collaborative emission reduction.

The fine chemical industry is currently facing challenges in energy saving, material conservation, and pollution reduction due to the dual policy pressure of precise system management and collaborative pollution and carbon reduction. However, the interweaving of materials and energy input-output was not well understood due to the incomplete coverage and the lack of a generic framework. Therefore, a methodology based on the energy-material-pollution (E-M-P) coupling nexus was proposed to quantitatively assess multi-level coupling.

Flame Retardancy and Thermal Behavior of an Unsaturated Polyester Modified with Kaolinite-Urea Intercalation Complexes.

Organic modified kaolinite-urea intercalation complex (KUIC) was prepared using dimethyl sulfoxide (DMSO) as the precursor of kaolinite intercalation. Its structure was characterized by Fourier transform infrared (FTIR) and X-ray diffraction (XRD). Subsequently, as a synergistic agent, KUIC was combined with flame retardant ammonium polyphosphate (APP) to improve the flame retardant and smoke suppression performance of unsaturated polyester (UP) resin.