Commercial chemicals are used extensively across global urban centers, posing a potential exposure risk to 4.2 billion people, which accounts for 55% of the global population. Harmful chemicals are often assessed and regulated based on their environmental persistence, bioaccumulation, and toxic properties, under international and national initiatives such as the Stockholm Convention. However, current regulatory frameworks largely rely upon knowledge of the properties of the parent chemicals, with minimal consideration given to their atmospheric transformation products. This is mainly due to a signi cant lack of experimental data, as identifying transformation products in complex mixtures of airborne chemicals is an immense analytical challenge,hence making a comprehensive and reliable risk assessment for harmful chemicals currently unachievable. Here, we develop a novel framework, combining laboratory and eld experiments, non-target analysis techniques, and in-silico modelling, to identify and assess the hazards of airborne chemicals, which takes into account atmospheric chemical reactions. By applying this framework to organophosphate ame retardants, as representative chemicals of emerging concern, we nd that their transformation products are globally distributed across 18 megacities, representing a previously unrecognized exposure risk for the world's urban populations. Furthermore, the transformation products can be up to an order of magnitude more persistent, environmentally mobile, and toxic than the parent chemicals. The results indicate that the overall human and environmental risks associated with ame retardants could be signi cantly underestimated, while highlighting a strong need to include atmospheric transformations in the development of regulations for all harmful chemicals moving forward.

Abstract. Oil-sands (OS) operations in Alberta, Canada, are a large source of secondary organic aerosol (SOA). However, the SOA formation process from OS-related precursors remains poorly understood. In this work, a newly developed oxidation flow reactor (OFR), the Environment and Climate Change Canada OFR (ECCC-OFR), was characterized and used to study the yields and composition of SOA formed from OH oxidation of α-pinene, selected alkanes, and the vapors evolved from five OS-related samples (OS ore, naphtha, tailings pond water, bitumen, and dilbit). The derived SOA yields from α-pinene and selected alkanes using the ECCC-OFR were in good agreement with those of traditional smog chamber experiments but significantly higher than those of other OFR studies under similar conditions. The results also suggest that gas-phase reactions leading to fragmentation (i.e., C–C bond cleavage) have a relatively small impact on the SOA yields in the ECCC-OFR at high photochemical ages, in contrast to other previously reported OFR results. Translating the impact of fragmentation reactions in the ECCC-OFR to ambient atmospheric conditions reduces its impact on SOA formation even further. These results highlight the importance of careful evaluation of OFR data, particularly when using such data to provide empirical factors for the fragmentation process in models. Application of the ECCC-OFR to OS-related precursor mixtures demonstrated that the SOA yields from OS ore and bitumen vapors (maximum of ∼0.6–0.7) are significantly higher than those from the vapors from solvent use (naphtha), effluent from OS processing (tailings pond water), and from the solvent diluted bitumen (dilbit; maximum of ∼0.2–0.3), likely due to the volatility of each precursor mixture. A comparison of the yields and elemental ratios (H∕C and O∕C) of the SOA from the OS-related precursors to those of linear and cyclic alkane precursors of similar carbon numbers suggests that cyclic alkanes play an important role in the SOA formation in the OS. The analysis further indicates that the majority of the SOA formed downwind of OS facilities is derived from open-pit mining operations (i.e., OS ore evaporative emissions) rather than from higher-volatility precursors from solvent use during processing and/or tailings management. The current results have implications for improving the regional modeling of SOA from OS sources, for the potential mitigation of OS precursor emissions responsible for observed SOA downwind of OS operations, and for the understanding of petrochemical- and alkane-derived SOA in general.

Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail. INDEX TERMS Crack detection, image processing, deep learning, 3D imaging.

A conducting polymer-based hydrogel (PPy/CPH) with a polypyrrole-poly(vinyl alcohol) interpenetrating network was prepared by utilization of a chemical cross-linked poly(vinyl alcohol)-HSO hydrogel (CPH) film as flexible substrate followed by vapor-phase polymerization of pyrrole. Then an all-solid-state polymer supercapacitor (ASSPS) was fabricated by sandwiching the CPH film between two pieces of the PPy/CPH film. The ASSPS is mechanically robust and flexible with a tensile strength of 20.83 MPa and a break elongation of 377% which is superior to other flexible conducting polymer hydrogel-based supercapacitors owing to the strong hydrogen bonding interactions among the layers and the high mechanical properties of the PPy/CPH. It exhibits maximum volumetric specific capacitance of 13.06 F/cm and energy density of 1160.9 μWh/cm. The specific capacitance maintains 97.9% and 86.3% of its initial value after 10 000 folding cycles and 10 000 charge-discharge cycles, respectively. The remarkable electrochemical and mechanical performance indicates this novel ASSPS device is promising for flexible electronics.