Haze Levels Research Articles

Luminance-based methodology for assessment of low level haze in glazing

AbstractVisual defects, in particular haze, in glass and façade technologies can significantly impact the aesthetic quality and human experience of daylight and views in buildings. The glass and façade industry increasingly requires methods that can objectively predict and measure the subjective user experience of haze. This is required to appropriately inform the manufacturing process, ensuring optimal functionality and performance, and avoiding material waste and economic losses due to the replacement of defective glazing. Existing methods for measuring haze are not appropriate for assessing large samples, either at manufacturing sites or in-situ. However, haze defects are often only exhibited and visible when glazing is produced in large samples or installed under real luminous conditions. This paper introduces a novel luminance-based method that measures haze by evaluating the halo around a light source that is observed through the glazing. This method is initially tested in a controlled laboratory setting on small glazing samples with varying level of haze. The halo serves as a proxy for haze severity and it is quantified by using luminance-based measurements. In this work, the newly-proposed method is verified by comparing the ranking in haze severity of different samples as performed by means of a standard haze meter and the newly proposed method. Additionally, the paper examines the dependence of this ranking on factors such as camera setup distance and light intensity. It was found that the proposed method was able to effectively ranks samples differing in haze intensity by more than 0.1 orders of magnitude. Positioning the camera closer to the glazing and using higher light intensity yielded more accurate results. However, for haze levels below 1% and differences smaller than 0.1 orders of magnitude, the accuracy is insufficient. To define the expected level of accuracy of methods for haze characterisation in-situ, the sensitivity of the human eye to haze under varying luminous environments and view content needs to be quantified.

Anti‐glare performance of sol‐gel‐derived spray coatings prepared with various water‐to‐alkoxide ratios

AbstractWe fabricated anti‐glare (AG) coatings on glass sheets by spraying alkoxide‐derived silica sols and demonstrated that the water‐to‐alkoxide ratio is one of the key factors for improving AG performance and sol stability. We synthesized silica sols using tetraethylorthosilicate (TEOS), water, nitric acid, and denatured ethanol and sprayed them on sheets of chemically strengthened glass. The molar ratios of water to TEOS (r) were 3, 7, and 14. The sol with r = 7 provided a higher arithmetic‐mean height (Sa), smaller autocorrelation length (Sal), and better optical properties (lower gloss, higher haze, and lower sparkle level) than the sol with r = 3. An excessive amount of water at r = 14 yielded a large Sal and a high sparkle level. As the storage time of the sols increased, higher r values caused a more pronounced increase in Sa. Although none of the sols showed noticeable temporal changes during dynamic light scattering measurements, solutions with higher r values exhibited a more remarkable reduction in the retention time during liquid chromatography with a styrene‐divinylbenzene matrix. Hence, an excessive amount of water was thought to cause hydrophilization of the polymerized species during storage.

The dependence of PurpleAir sensors on particle size distribution and meteorology and their relationship to integrating nephelometers

The National Park Service (NPS) has a need to measure real-time PM2.5 and haze levels across its many park units to inform the public of unhealthy air and track visual degradation of park scenic vistas. To fulfill this need, the NPS deployed PurpleAir monitors (PA) in several park units. PA monitors are inexpensive monitors promoted as particle counters capable of measuring particle size distributions. Each PA is equipped with two Plantower (PMS) particle counters and a temperature, relative humidity (RH) and pressure sensor. In 2022, the NPS initiated a field study to evaluate PA limitations, uncertainties, and how the measurements relate to particle light scattering. The study was conducted in Fort Collins, CO where five nephelometers, particle sizing instruments, semi-continuous ion concentration measurements, and seven PAs were deployed. One nephelometer and two PA's sampled air from an RH controlled chamber with RH ≤ 40%. The PA temperatures were accurate, but the PA RH measurements were systematically underreported RH by ∼28% and it is recommended to scale these data by 1.35. The PMS reported size distributions are shown to be invalid. PA instruments mounted without protection from wind exhibited biases of ±160% for wind speeds exceeding 5 mph. Hourly CH1 data were precise, with errors of ∼100% at low concentrations of 30 #/dl and 15% for concentrations ≥1000 #/dl. Normalizing all PMS sensors to each other reduced the errors to 20% and 8% for low and higher particle concentrations respectively. The reported total particle count, i.e. ≥0.3 μm channel (CH1), was biased low, and the bias decreased linearly with mean scattering diameter (MSD). The ratio of measured ≥0.3 μm to PA CH1 channel increases linearly with MSD. The overall normalized mean deviation (NMD) between the two measurements is 121.8%. The CH1 channel also increases as a function of RH primarily due to the growth of hygroscopic aerosols. On average, the relationship between the atmospheric scattering coefficient (bsp) and the PMS ≥0.3 μm CH1 channel is described by the equation bsp = 0.015 × CH1 for MSD levels less than 0.35 μm. The PMS CH1 response decreases linearly with MSD. The NMD between ambient PMS derived bsp and measured ambient bsp is 27.0%. Recommendations are made for PA network deployment.

Nacreous Glass Composites with Superior Performance Engineered through Mechanical Vibration and Silanization

AbstractBioinspiration offers alternative solutions to overcome the inherent drawbacks of glass, such as low fracture toughness, strength, and impact resistance. Synthetic composites inspired by natural materials, such as nacre, have been recently introduced as an alternative to glasses. However, these have all suffered from trade‐offs between rigidity, optical clarity, fabrication scalability, and complexity. Here, two wave‐based fabrication techniques are presented to create a nacreous structure from glass flakes and polymethyl methacrylate. The glass's surface energy is controlled by adjusting the silane coverage on the glass surface, enabling high levels of structural compactness, mechanical properties, and optical clarity. The scalable glass composite, with a ≈60% glass volume fraction, possesses strength, fracture toughness, and impact resistance values, outperforming annealed glass by 4300%, 350%, and 400%, respectively. It also has a haze level of ≈18%, almost 60% less than that of the similar centrifuged‐based glass composite. This composite is proposed as a potential glass alternative in diverse applications.

China's environmental tax reform achieves the co-management of carbon and haze: The role of optimizing energy utilization

Environmental tax reform in China is an essential initiative to achieve carbon and haze co-management by shifting the Environmental Fee to Tax (EFT). Therefore, we employ a Coupling Coordination Degree (CCD) model to measure the level of carbon and haze co-management across three distinct scenarios in China. Treating the EFT policy as a quasi-natural experiment, the study explores its impact on carbon and haze co-management using data of 282 prefecture-level cities in China during 2015–2020. Furthermore, the study further analyzes the regional heterogeneity and the transmission mechanisms of optimizing energy utilization. The principal findings of this investigation are as follows. First, the co-management levels of carbon and haze in China show a discernible upward trend from 2015 to 2020. A heightened emphasis on carbon reduction is helpful in improving the co-management level of carbon and haze. Co-management levels of carbon and haze are lower within the treatment group compared to the control group, but their growth rates are higher. Second, the EFT policy in China has significantly enhanced the co-management level of carbon and haze. Third, the impact of China's EFT policy has a more pronounced effect in western China and a comparatively weaker effect in middle China, which may be attributed to variations in industrial infrastructure, supporting facilities, availability of clean energy resources, and prevailing policy inclinations across different regions of China. Finally, China's EFT policy is an instrumental factor in augmenting the co-management level of carbon and haze, primarily through pathways of upgrading energy structure and improving energy efficiency.

Advancing Light Scattering Control Through the Exploration of Temperature‐Sensitive Optical Modifiers

AbstractLight scattering holds immense significance in various applications, including LED lighting, display enhancement, and solar cell optimization, emphasizing the critical role of understanding scattering mechanisms, especially light–matter interactions, achieving effective optical diffusion. Consequently, the influence of stimuli on material properties, including refractive indices and thermal expansion behavior, has emerged as a pivotal consideration, leading to the development of stimuli‐responsive light scattering modifier. Here, a temperature‐sensitive light scattering modifier composed of poly(methyl methacrylate‐co‐styrene) and poly(ethylene‐co‐vinyl acetate) is presented as scattering particles and matrix, respectively, using a melt‐blending process. Optical properties are evaluated across controlled temperatures 0–70 °C. These findings demonstrate an increasing trend in diffuse transmission and haze as temperatures increased across all light scattering modifiers. Significantly, the optimized light scattering modifier, integrating a dual particle system, showcased uniform reduction gaps in parallel transmission and adjustable haze characteristics across the visible spectrum under temperature control. This optical modifier achieved a total transmission of 92% and adjustable haze levels ranging from 35% to 90% under temperature control. Furthermore, this fabrication of three‐layer films incorporating the light scattering modifier exhibited identical characteristics to the developed light scattering modifier, emphasizing their potential for seamless integration across diverse technological domains.

Self-supervised multi-task learning framework for safety and health-oriented road environment surveillance based on connected vehicle visual perception

Cutting-edge connected vehicle (CV) technologies have drawn much attention in recent years. The real-time traffic data captured by a CV can be shared with other CVs and data centers so as to open new possibilities for solving diverse transportation problems. The trajectory data of CVs have been well-studied and widely used. However, image data captured by onboard cameras in a connected environment, as being a kind of fundamental data source, are not sufficiently investigated, especially for safety and health-oriented visual perception. In this paper, a bidirectional process of image synthesis and decomposition (BPISD) approach is proposed, and thus a novel self-supervised multi-task learning framework, to simultaneously estimate depth map, atmospheric visibility, airlight, and PM2.5 mass concentration, in which depth map and visibility are considered highly associated with traffic safety, while airlight and PM2.5 mass concentration are directly correlated with human health. Both the training and testing phases of the proposed system solely require a single image as input. Due to the innovative training pipeline, the depth estimation network can automatically manage various levels of visibility conditions and overcome diverse inherent problems in current image-synthesis-based self-supervised depth estimation, thereby generating high-quality depth maps even in low-visibility situations and further benefiting accurate estimations of visibility, airlight, and PM2.5 mass concentration. Extensive experiments on the original and synthesized data from the KITTI dataset and real-world data collected in Beijing demonstrate that the proposed method can (1) achieve performance comparable in self-supervised depth estimation as compared with other state-of-the-art methods when taking clear images as input; (2) predict vivid depth map for images contaminated by various levels of haze when the network trained with previous framework fails; and (3) accurately estimate visibility, airlight, and PM2.5 mass concentrations. Beneficial applications can be developed based on the presented work to contribute to high-precise and dynamic geoinformation reconstruction, transportation, meteorology, and smart city.

Artificial intelligence based-prediction of energy efficiency and tailpipe emissions of soybean methyl ester fuelled CI engine under variable compression ratios

Biodiesel, a widely adopted substitute for conventional mineral diesel, is often derived from raw vegetable oils. The use of non-edible oils for the production of biodiesel has been favored for an extended period of time due to the potential impact on the availability of edible oil as a food source. The generation of soybean methyl ester biodiesel using the transesterification process. The primary objective of this study is to examine the potential use of soybean methyl ester biodiesel (referred to as "S") as a substitute for conventional diesel fuel in a standard light-duty compression ignition four-stroke, single-cylinder, water-cooled engine. The present study aimed to evaluate the impact of increased compression ratio (ranging from 14 to 18) on several combustion, performance, and emissions characteristics. This investigation was conducted using biodiesel blends consisting of 5%, 10%, 20%, and 40% soybean methyl ester, and the results were compared with those obtained using conventional diesel fuel. Additionally, in order to enhance the test results, a training process is being conducted on an artificial intelligence network (ANN). The analysis of the data indicated a positive correlation between the compression ratio (CR) and the in-cylinder pressure values at maximum load conditions. Additionally, a negative relationship was seen between the CR and the brake-specific fuel consumption (BSFC), suggesting a drop in BSFC as the CR rose. Upon evaluating the discharge numbers, it was seen that there was a notable reduction in other pollutants, a fall in NOx emissions, and an elevation in haze levels. Nevertheless, surpassing a sulphur content of 20% has an adverse impact on both emissions and the overall functioning of the engine. Based on the findings of the artificial neural network (ANN), the prediction results demonstrate a commendable capacity to accurately predict the experimental data with the R-value changing from 0.81919 to 0.95028 for all engine performance, combustion, and emission metrics.

End-to-End Detail-Enhanced Dehazing Network for Remote Sensing Images

Space probes are always obstructed by floating objects in the atmosphere (clouds, haze, rain, etc.) during imaging, resulting in the loss of a significant amount of detailed information in remote sensing images and severely reducing the quality of the remote sensing images. To address the problem of detailed information loss in remote sensing images, we propose an end-to-end detail enhancement network to directly remove haze in remote sensing images, restore detailed information of the image, and improve the quality of the image. In order to enhance the detailed information of the image, we designed a multi-scale detail enhancement unit and a stepped attention detail enhancement unit, respectively. The former extracts multi-scale information from images, integrates global and local information, and constrains the haze to enhance the image details. The latter uses the attention mechanism to adaptively process the uneven haze distribution in remote sensing images from three dimensions: deep, middle and shallow. It focuses on effective information such as haze and high frequency to further enhance the detailed information of the image. In addition, we embed the designed parallel normalization module in the network to further improve the dehazing performance and robustness of the network. Experimental results on the SateHaze1k and HRSD datasets demonstrate that our method effectively handles remote sensing images obscured by various levels of haze, restores the detailed information of the images, and outperforms the current state-of-the-art haze removal methods.

Using Haze Level Estimation in Data Cleaning for Supervised Deep Image Dehazing Models

Recently, supervised deep learning methods have been widely used for image haze removal. These methods rely on training data that are assumed to be appropriate. However, this assumption may not always be true. We observe that some data may contain hazy ground truth (GT) images. This can lead to supervised deep image dehazing (SDID) models learning inappropriate mapping between hazy images and GT images, which negatively affects the dehazing performance. To address this problem, two difficulties must be solved. One is to estimate the haze level in an image, and the other is to develop a haze level indicator to discriminate clear and hazy images. To this end, we proposed a haze level estimation (HLE) scheme based on dark channel prior and a haze level indicator accordingly for training data cleaning, i.e., to exclude image pairs with hazy GT images in the data set. With the data cleaning by the HLE, we introduced an SDID framework to avoid inappropriate learning and thus improve the dehazing performance. To verify the framework, using the RESIDE data set, experiments were conducted with three types of SDID models, i.e., GCAN, REFN and cGAN. The results show that our method can significantly improve the dehazing performance of the three SDID models. Subjectively, the proposed method generally provides better visual quality. Objectively, our method, using fewer training image pairs, was capable of improving PSNR in the GCAN, REFN, and cGAN models by 3.10 dB, 5.74 dB, and 6.44 dB, respectively. Furthermore, our method was evaluated using a real-world data set, KeDeMa. The results indicate that the better visual quality of the dehazed images is generally for models with the proposed data cleaning scheme. The results demonstrate that the proposed method effectively and efficiently enhances the dehazing performance in the given examples. The practical significance of this research is to provide an easy but effective way, that is, the proposed data cleaning scheme, to improve the performance of SDID models.

The impact of carbon-biased technological progress on carbon haze coordinated governance: insights from China.

Superficial or cosmetic measures are unlikely to solve the complex challenges of carbon haze governance. Carbon-biased technological progress plays a significant role in low-carbon technology and economic sustainable development. This study introduces carbon resources as a production factor in the transcendental logarithmic function to calculate the biased technological progress index of 30 provinces in China between 2010 and 2020. Subsequently, this study constructs the spatial Durbin model to empirically investigate the spatial spillover effect of carbon-biased technology progress on carbon haze coordinated governance. The findings reveal that China's technological progress is primarily characterized by carbon bias, particularly in terms of labor-using carbon-saving technological progress and capital-using carbon-saving technological progress. Additionally, both levels of carbon haze coordinated governance and carbon-biased technological progress have significant spatial agglomeration characteristics. Moreover, the carbon-biased technological progress can facilitate synergy degree of carbon haze governance through energy-saving effects but can also hinder the carbon haze coordinated governance through rebound effects. Therefore, it is imperative to improve labor productivity, augment green capital investment, and stimulate the induction of technological progress towards carbon saving to achieve sustainable and high-quality economic development. The primary contribution is identifying the uncertainty surrounding the impact of carbon-biased technological progress on coordinated governance of carbon haze, while also providing theoretical explanations for the influence channels of carbon-biased technological progress.

3D-Printed Polylactic Acid/Lignin Films with Great Mechanical Properties and Tunable Functionalities towards Superior UV-Shielding, Haze, and Antioxidant Properties.

Three-dimensional (3D) printing is regarded as a novel technique to realize the customized production of films. However, the relative lack of printable materials with excellent mechanical properties and tailored functionalities seriously restricts its wide application. Herein, a promising multifunctional 3D printing filament was fabricated by incorporating lignin into the polylactic acid (PLA) matrix and firstly applied to film production. The results indicate that lignin was an excellent mechanical reinforcement of the PLA matrix, especially for toughening. Only 0.5% lignin doping improved the toughness by 81.8%. Additionally, 3D-printed films with 0.5-5% lignin exhibited excellent ultraviolet (UV)-blocking capability of 87.4-99.9% for UVB and 65.6-99.8% for UVA, as well as remarkable antioxidant properties, ranging from 24.0% to 79.0%, and high levels of haze, ranging from 63.5% to 92.5%. Moreover, the prepared PLA/lignin (P/L) films based on 3D printing achieved the customization of film production and have potential applications in the fields of packaging, electronic products, medical care, and so forth. Overall, this work not only enriches the 3D printing composites with tailored multifunctionality but also brings the promising potential for the production of customized films.

A Multiple Scattering Theory Approach: Numerical Model for Haze Detection Using Horizontal Scanning LIDAR

Haze is hazardous to human health. Although horizontal scanning LIDARs have been used to monitor air pollution sources, they have not been used for haze detection. In this work, we present a numerical model for haze detection using horizontal scanning LIDAR by incorporating multiple scattering theory. This model could evaluate whether multiple scattering theory is suitable for analyzing horizontal scanning LIDAR readings to detect haze without assembling LIDARs. We demonstrate this using a hypothetical LIDAR that has maximum horizontal range of 1 km as case study example. In our assessment, Python™ was used to develop simulation algorithm based on single and multiple scattering LIDAR equations. Relative humidity was below 80% which corresponded to potential haze. Visibility data at different haze levels were incorporated for analysis. We then evaluated errors in hypothetical LIDAR readings associated with multiple scattering. Lastly, we calculated ratios of multiple scattering returns in haze relative to clear weather as haze level indicators. Numerical assessment showed that multiple scattering effects were negligible for both wavelength channels (532 nm and 808 nm) at receiver field-of-view (RFOV) of 1.5 mrad, but introduced significant errors when RFOV is 10 mrad. Average ratio of multiple scattering returns evaluated against threshold of 1 could directly reflect the severity of haze. Although this approach was not suitable for heavy and severe haze, it sufficed for practical applications. Overall, the case study demonstrated the feasibility of incorporating multiple scattering theory when analyzing horizontal scanning LIDAR readings to detect haze without experimental verification.

Object Detection for Mixed Traffic under Degraded Hazy Vision Condition

Vehicle detection in degraded hazy conditions poses significant challenges in computer vision. It is difficult to detect objects accurately under hazy conditions because vision is reduced, and color and texture information is distorted. This research paper presents a comparative analysis of different YOLO (You Only Look Once) methodologies, including YOLOv5, YOLOv6, and YOLOv7, for object detection in mixed traffic under degraded hazy conditions. The accuracy of object detection algorithms can be significantly impacted by hazy weather, so creating reliable models is critical. An open-source dataset of footage obtained from security cameras installed on traffic signals is used for this study to evaluate the performance of these algorithms. The dataset includes various traffic objects under varying haze levels, providing a diverse range of atmospheric conditions encountered in real-world scenarios. The experiments illustrate that the YOLO-based techniques are effective at detecting objects in degraded hazy conditions and give information about how well they perform in comparison. The findings help object detection models operate more accurately and consistently under adverse weather conditions.

All-Tunicate Cellulose Film with Good Light Management Properties for High-Efficiency Organic Solar Cells.

Tunicate nanocellulose with its unique properties, such as excellent mechanical strength, high crystallinity, and good biodegradability, has potential to be used for the preparation of light management film with tunable transmittance and haze. Herein, we prepared a whole tunicate cellulose film with tunable haze levels, by mixing tunicate microfibrillated cellulose (MFC) and tunicate cellulose nanofibrils (CNF). Then, the obtained whole tunicate cellulose film with updated light management was used to modify the organic solar cell (OSC) substrate, aiming to improve the light utilization efficiency of OSC. Results showed that the dosage of MFC based on the weight of CNF was an important factor to adjust the haze and light transmittance of the prepared cellulose film. When the dosage of MFC was 3 wt.%, the haze of the obtained film increased 74.2% compared to the pure CNF film (39.2%). Moreover, the optimized tunicate cellulose film exhibited excellent mechanical properties (e.g., tensile strength of 168 MPa, toughness of 5.7 MJ/m3) and high thermal stability, which will be beneficial to the workability and durability of OSC. More interestingly, we applied the obtained whole tunicate cellulose film with a high haze (68.3%) and high light transmittance (85.0%) as an additional layer to be adhered to the glass substrate of OSC, and a notable improvement (6.5%) of the power conversion efficiency was achieved. With the use of biodegradable tunicate cellulose, this work provides a simple strategy to enhance light management of the transparent substrate of OSC for improving power conversion efficiency.

Tuning the Solar Performance of Building Facades through Polymer 3D Printing: Toward Bespoke Thermo‐Optical Properties

AbstractFaçades are the primary interface controlling the flow of solar energy in buildings and affecting their energy balance and environmental impact. Recently, large‐scale 3D printing (3DP) of translucent polymers has been explored as a technique for fabricating façade components with bespoke properties and functionalities. Transmissivity is essential for building facades, as the response to solar radiation is crucial to obtaining comfort and greatly affects electricity and cooling demands. However, it is still unclear how 3DP parameters affect the optical properties of translucent polymers. This study establishes an experimental procedure to relate the optical properties of PETG components to design and 3DP parameters. It is observed that printing parameters control layer deposition, which governs internal light scattering in the layers and overall light transmission. Moreover, the layer resolution determines angle‐dependent properties. It is shown that printing parameters can be tuned to obtain tailored optical properties, from high normal transparency (≈90%) to translucency (≈60%), and with a range of haze levels (≈55–97%). These findings present an opportunity for large‐scale 3DP of bespoke façades, which can selectively admit or block solar radiation and provide uniform daylighting of a space. In the context of the building sector decarbonization, such components hold great potential for reducing emissions while ensuring occupant comfort.

Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm.

Single image dehazing has been a challenge in the field of image restoration and computer vision. Many model-based and non-model-based dehazing methods have been reported. This study focuses on a model-based algorithm. A popular model-based method is dark channel prior (DCP) which has attracted a lot of attention because of its simplicity and effectiveness. In DCP-based methods, the model parameters should be appropriately estimated for better performance. Previously, we found that appropriate scaling factors of model parameters helped dehazing performance and proposed an improved DCP (IDCP) method that uses heuristic scaling factors for the model parameters (atmospheric light and initial transmittance). With the IDCP, this paper presents an approach to find optimal scaling factors using the whale optimization algorithm (WOA) and haze level information. The WOA uses ground truth images as a reference in a fitness function to search the optimal scaling factors in the IDCP. The IDCP with the WOA was termed IDCP/WOA. It was observed that the performance of IDCP/WOA was significantly affected by hazy ground truth images. Thus, according to the haze level information, a hazy image discriminator was developed to exclude hazy ground truth images from the dataset used in the IDCP/WOA. To avoid using ground truth images in the application stage, hazy image clustering was presented to group hazy images and their corresponding optimal scaling factors obtained by the IDCP/WOA. Then, the average scaling factors for each haze level were found. The resulting dehazing algorithm was called optimized IDCP (OIDCP). Three datasets commonly used in the image dehazing field, the RESIDE, O-HAZE, and KeDeMa datasets, were used to justify the proposed OIDCP. Then a comparison was made between the OIDCP and five recent haze removal methods. On the RESIDE dataset, the OIDCP achieved a PSNR of 26.23 dB, which was better than IDCP by 0.81 dB, DCP by 8.03 dB, RRO by 5.28, AOD by 5.6 dB, and GCAN by 1.27 dB. On the O-HAZE dataset, the OIDCP had a PSNR of 19.53 dB, which was better than IDCP by 0.06 dB, DCP by 4.39 dB, RRO by 0.97 dB, AOD by 1.41 dB, and GCAN by 0.34 dB. On the KeDeMa dataset, the OIDCP obtained the best overall performance and gave dehazed images with stable visual quality. This suggests that the results of this study may benefit model-based dehazing algorithms.

Modeling study on the roles of the deposition and transport of PM2.5 in air quality changes over central-eastern China

The role of PM2.5 (particles with aerodynamic diameters ≤ 2.5 µm) deposition in air quality changes over China remains unclear. By using the three-year (2013, 2015, and 2017) simulation results of the WRF/CUACE v1.0 model from a previous work (Zhang et al., 2021), a non-linear relationship between the deposition of PM2.5 and anthropogenic emissions over central-eastern China in cold seasons as well as in different life stages of haze events was unraveled. PM2.5 deposition is spatially distributed differently from PM2.5 concentrations and anthropogenic emissions over China. The North China Plain (NCP) is typically characterized by higher anthropogenic emissions compared to southern China, such as the middle-low reaches of Yangtze River (MLYR), which includes parts of the Yangtze River Delta and the Midwest. However, PM2.5 deposition in the NCP is significantly lower than that in the MLYR region, suggesting that in addition to meteorology and emissions, lower deposition is another important factor in the increase in haze levels. Regional transport of pollution in central-eastern China acts as a moderator of pollution levels in different regions, for example by bringing pollution from the NCP to the MLYR region in cold seasons. It was found that in typical haze events the deposition flux of PM2.5 during the removal stages is substantially higher than that in accumulation stages, with most of the PM2.5 being transported southward and deposited to the MLYR and Sichuan Basin region, corresponding to a latitude range of about 24°N-31°N.

High-value copolyamide 6 materials with colorless transparent and low water absorption upgraded from upcycled and biomass comonomers

Polyamide 6 (PA6) is a semicrystalline polymer with excellent wide application for filament and packing materials. However, the use in filament and packing materials of PA6 with high crystallinity and rich hydrogen bonds is limited because of its low optical properties and poor anti-water absorption. In this work, a series of partially sustainable PA6 copolymers with high value were synthesized by melt polycondensation from fatty dimer acid (FDA) and upcycled chemical bis(6-aminohexyl) terephthalamide (BAHT). The chemical structure of partially sustainable PA6 copolymers was characterized by 1 H NMR, FT IR, relative viscosity, and amine value. The mechanical properties, optical properties, and anti-water absorption of high-value PA6 copolymers depended on the BAHT salt loading and the chain length of aliphatic dicarboxylic acids. The tensile strength of PA6 copolymers was decreased, but the tensile toughness, optical transmittance, and anti-water absorption of that were significantly improved. Overall, this easy method opens up new avenues for devising excellent optical transmittance and low water absorption of polyamide materials, whereas providing the new strategy for carbon-neutral through rPET upcycling and biomass use, and these PA6 copolymers have the potential and economy to be widely applied. • Combining both upcycling and biomass segments in polycaprolactam copolymers (CoPAs). • Bis(6-aminohexyl) terephthalamide (BAHT) upcycled from rPET bottle to promote spherulite shrinkage of CoPAs. • Fatty dimer acid influenced the optical behavior of the polyamide (transparency level > 80% and haze level < 10%). • Fatty dimer acid increased the anti-water absorption of CoPAs. • CoPAs prepared transparent monofilaments and films by twin-screw extrusion and hot-pressing.

The impact of land-sea breezes on the formation of Brown haze in an urban isthmus environment

In some international cities, winter air pollution can manifest into a local-scale brown air pollution haze which has been associated with negative health outcomes. Land-sea breezes are known to impact urban air quality through the recirculation of air pollution and the formation of internal boundary layers (IBLs). However, research into land-sea breezes has primarily focused on summer air pollution and little is known about the influence of land-sea breezes on local-scale winter brown haze. Using continuous data (including surface meteorology, surface air quality, satellite-derived sea surface temperatures, and ceilometer-derived boundary-layer depths) observed over seven winters from 2013 to 2019, we present a novel investigation of the influence of land-sea breezes on brown haze in Auckland, New Zealand. Severe brown haze days are significantly more likely to coincide with a land-sea breeze circulation simultaneously occurring at both the east and west coasts when compared with days on which brown haze is expected but not observed (based on favourable meteorology and high surface air pollution levels). Both severe brown haze and high surface level PM2.5 concentrations (previously associated with the presence of brown haze) are found to be associated with a high degree of horizontal recirculation at Auckland's east coast. No relationship is found between the occurrence of sea-breeze-induced IBLs and the formation of brown haze. The results presented in this study offer insights into the physical mechanisms that influence the formation and persistence of local-scale winter brown haze in a complex coastal setting with correspondingly complex land-sea breezes.