The influence of temperature on the fate and transport of phthlates in indoor environments

textPhthalate esters are extensively used as plasticizers in building materials and consumer products, but are associated with serious health concerns. They are ubiquitous indoors, redistributing from their original source to all interior surfaces, including airborne particles, dust, and skin. The main objective of the research is to investigate the influence of temperature on the fate and transport of phthalates in indoor environments. In this study, the concentrations of benzyl butyl phthalate (BBzP) and di-2-ethylhexyl phthalate (DEHP) in indoor air, settled dust, and on different interior surfaces including mirror, glass, plate, cloth and wood were measured periodically in a test house. The measurements were conducted at temperatures of 21°C and 30°C, respectively. In addition, sorption kinetics was also monitored at the temperature of 21°C. The air concentrations of BBzP and DEHP at 21°C range from 141 ng/m₃ to 210 ng/m₃ and 66 ng/ m₃ to 100 ng/ m₃, respectively. For impervious surfaces such as dish plates, the surface concentrations reached steady-state concentrations in less than 24 hours, to the level between 2 and 8 [mu]g/m₂ for both BBzP and DEHP. In contrast, the time to reach steady state was much longer for porous surfaces such as hardwood (>1 week) and dust (> months). With the temperature increase to 30°C, the gas phase concentrations of BBzP and DEHP increased by about five times, and the surface concentrations on various surfaces also increased correspondingly. This investigation suggests that temperature has an important influence on the fate and transport of phthalates in indoor environments.Civil, Architectural, and Environmental Engineerin

Sorption of semi-volatile organic compounds to dust and other surfaces in indoor environments

Semi-volatile organic compounds (SVOCs) are ubiquitous in indoor environments. Because they partition strongly to dust other surfaces in the indoor environments, most SVOCs persist for years after the source is removed. Biomonitoring data based on blood and urine testing suggested the universal and significant human exposure to SVOCs, which may result in serious adverse health effects. However, because of the poor understanding of their transfer process from sources to indoor surfaces, significant uncertainties exist for the estimation of exposure to SVOCs through various pathways and effective strategies to limit such exposure remains hamstrung. The goal of this dissertation is to explicitly elucidate the sorption of important and emerging SVOCs to dust and other surfaces in the indoor environments. The specific research objectives are to 1) investigate the emission, sorption, and fate of phthalates in a residential test house; 2) characterize the direct transfer of SVOCs from sources to settled dust through systematic chamber study; and 3) measure SVOC levels in heating, ventilation, and air conditioning (HVAC) filter dust of U.S. lowincome homes and investigate their association with concentrations in settled dust, seasons, building characteristics, and childhood asthma. Strong sorption of phthalates was observed on interior surfaces, including dust, dish plates, windows, mirrors, fabric cloth, and wood, in a residential test house. In addition, when dust is in contact with the PVC floorings, equilibrium dust concentrations of phthalates are orders of magnitude higher than typical dust concentrations reported in the literature. And we found that the equilibrium concentrations of phthalates in dust can be predicted with the concentrations of phthalates within the gas layer in adjacent with the flooring materials. Finally, the results suggest that HVAC filter dust is a useful sampling media to monitor indoor SVOC concentrations with high sensitivity. When using settled dust, in addition to considering seasonal influences, it is very important to know the sampling location because the types and levels of SVOCs might be related to the local materials.Civil, Architectural, and Environmental Engineerin

Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data

Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress. Previous work primarily used 2D information as input, but multi-modal industrial anomaly detection based on 3D point clouds and RGB images is just beginning to emerge. The regular approach involves utilizing large pre-trained models for feature representation and storing them in memory banks. However, the above methods require a longer inference time and higher memory usage, which cannot meet the real-time requirements of the industry. To overcome these issues, we propose a lightweight dual-branch reconstruction network(DBRN) based on RGB-D input, learning the decision boundary between normal and abnormal examples. The requirement for alignment between the two modalities is eliminated by using depth maps instead of point cloud input. Furthermore, we introduce an importance scoring module in the discriminative network to assist in fusing features from these two modalities, thereby obtaining a comprehensive discriminative result. DBRN achieves 92.8% AUROC with high inference efficiency on the MVTec 3D-AD dataset without large pre-trained models and memory banks.Comment: 8 pages, 5 figure

Fatigue Driving Detection Method Based on IPPG Technology

Physiological signal index can accurately reflect the degree of fatigue, but the contact detection method will greatly affect the driver\u27s driving. This paper presents a non-contact method for detecting tired driving. It uses cameras and other devices to collect information about the driver\u27s face. By recording facial changes over a period and processing the captured video, pulse waves are extracted. Then the frequency domain index and nonlinear index of heart rate variability were extracted by pulse wave characteristics. Finally, the experiment proves that the method can clearly judge whether the driver is tired. In this study, the Imaging Photoplethysmography (IPPG) technology was used to realise non-contact driver fatigue detection. Compared with the non-contact detection method through identifying drivers\u27 blinking and yawning, the physiological signal adopted in this paper is more convincing. Compared with other methods that detect physiological signals to judge driver fatigue, the method in this paper has the advantages of being non-contact, fast, convenient and available for the cockpit environment

CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection

In the anomaly detection field, the scarcity of anomalous samples has directed the current research emphasis towards unsupervised anomaly detection. While these unsupervised anomaly detection methods offer convenience, they also overlook the crucial prior information embedded within anomalous samples. Moreover, among numerous deep learning methods, supervised methods generally exhibit superior performance compared to unsupervised methods. Considering the reasons mentioned above, we propose a self-supervised anomaly detection approach that combines contrastive learning with 2D-Flow to achieve more precise detection outcomes and expedited inference processes. On one hand, we introduce a novel approach to anomaly synthesis, yielding anomalous samples in accordance with authentic industrial scenarios, alongside their surrogate annotations. On the other hand, having obtained a substantial number of anomalous samples, we enhance the 2D-Flow framework by incorporating contrastive learning, leveraging diverse proxy tasks to fine-tune the network. Our approach enables the network to learn more precise mapping relationships from self-generated labels while retaining the lightweight characteristics of the 2D-Flow. Compared to mainstream unsupervised approaches, our self-supervised method demonstrates superior detection accuracy, fewer additional model parameters, and faster inference speed. Furthermore, the entire training and inference process is end-to-end. Our approach showcases new state-of-the-art results, achieving a performance of 99.6\% in image-level AUROC on the MVTecAD dataset and 96.8\% in image-level AUROC on the BTAD dataset.Comment: 6 pages,6 figure

Assessing Human Exposure to SVOCs in Materials, Products, and Articles : A Modular Mechanistic Framework

A critical review of the current state of knowledge of chemical emissions from indoor sources, partitioning among indoor compartments, and the ensuing indoor exposure leads to a proposal for a modular mechanistic framework for predicting human exposure to semivolatile organic compounds (SVOCs). Mechanistically consistent source emission categories include solid, soft, frequent contact, applied, sprayed, and high temperature sources. Environmental compartments are the gas phase, airborne particles, settled dust, indoor surfaces, and clothing. Identified research needs are the development of dynamic emission models for several of the source emission categories and of estimation strategies for critical model parameters. The modular structure of the framework facilitates subsequent inclusion of new knowledge, other chemical classes of indoor pollutants, and additional mechanistic processes relevant to human exposure indoors. The framework may serve as the foundation for developing an open-source community model to better support collaborative research and improve access for application by stakeholders. Combining exposure estimates derived using this framework with toxicity data for different end points and toxicokinetic mechanisms will accelerate chemical risk prioritization, advance effective chemical management decisions, and protect public health.Peer reviewe

Accumulation of di-2-Ethylhexyl Phthalate from Polyvinyl Chloride Flooring into Settled House Dust and the Effect on the Bacterial Community

Di-2-ethylhexyl phthalate (DEHP) is a plasticizer used in consumer products and building materials, including polyvinyl chloride flooring material. DEHP adsorbs from material and leaches into soil, water, or dust and presents an exposure risk to building occupants by inhalation, ingestion, or absorption. A number of bacterial isolates are demonstrated to degrade DEHP in culture, but bacteria may be susceptible to it as well, thus this study examined the relation of DEHP to bacterial communities in dust. Polyvinyl chloride flooring was seeded with homogenized house dust and incubated for up to 14 days, and bacterial communities in dust were identified at days 1, 7, and 14 using the V3–V4 regions of the bacterial 16S rRNA gene. DEHP concentration in dust increased over time, as expected, and bacterial richness and Shannon diversity were negatively correlated with DEHP concentration. Some sequence variants of Bacillus, Corynebacterium jeddahense, Streptococcus, and Peptoniphilus were relatively more abundant at low concentrations of DEHP, while some Sphingomonas, Chryseobacterium, and a member of the Enterobacteriaceae family were relatively more abundant at higher concentrations. The built environment is known to host lower microbial diversity and biomass than natural environments, and DEHP or other chemicals indoors may contribute to this paucity

GR(1)-Guided Deep Reinforcement Learning for Multi-Task Motion Planning under a Stochastic Environment

Motion planning has been used in robotics research to make movement decisions under certain movement constraints. Deep Reinforcement Learning (DRL) approaches have been applied to the cases of motion planning with continuous state representations. However, current DRL approaches suffer from reward sparsity and overestimation issues. It is also challenging to train the agents to deal with complex task specifications under deep neural network approximations. This paper considers one of the fragments of Linear Temporal Logic (LTL), Generalized Reactivity of rank 1 (GR(1)), as a high-level reactive temporal logic to guide robots in learning efficient movement strategies under a stochastic environment. We first use the synthesized strategy of GR(1) to construct a potential-based reward machine, to which we save the experiences per state. We integrate GR(1) with DQN, double DQN and dueling double DQN. We also observe that the synthesized strategies of GR(1) could be in the form of directed cyclic graphs. We develop a topological-sort-based reward-shaping approach to calculate the potential values of the reward machine, based on which we use the dueling architecture on the double deep Q-network with the experiences to train the agents. Experiments on multi-task learning show that the proposed approach outperforms the state-of-art algorithms in learning rate and optimal rewards. In addition, compared with the value-iteration-based reward-shaping approaches, our topological-sort-based reward-shaping approach has a higher accumulated reward compared with the cases where the synthesized strategies are in the form of directed cyclic graphs

GR(1)-Guided Deep Reinforcement Learning for Multi-Task Motion Planning under a Stochastic Environment

Motion planning has been used in robotics research to make movement decisions under certain movement constraints. Deep Reinforcement Learning (DRL) approaches have been applied to the cases of motion planning with continuous state representations. However, current DRL approaches suffer from reward sparsity and overestimation issues. It is also challenging to train the agents to deal with complex task specifications under deep neural network approximations. This paper considers one of the fragments of Linear Temporal Logic (LTL), Generalized Reactivity of rank 1 (GR(1)), as a high-level reactive temporal logic to guide robots in learning efficient movement strategies under a stochastic environment. We first use the synthesized strategy of GR(1) to construct a potential-based reward machine, to which we save the experiences per state. We integrate GR(1) with DQN, double DQN and dueling double DQN. We also observe that the synthesized strategies of GR(1) could be in the form of directed cyclic graphs. We develop a topological-sort-based reward-shaping approach to calculate the potential values of the reward machine, based on which we use the dueling architecture on the double deep Q-network with the experiences to train the agents. Experiments on multi-task learning show that the proposed approach outperforms the state-of-art algorithms in learning rate and optimal rewards. In addition, compared with the value-iteration-based reward-shaping approaches, our topological-sort-based reward-shaping approach has a higher accumulated reward compared with the cases where the synthesized strategies are in the form of directed cyclic graphs

Comparison of Raw Data-Based and Complex Image-Based Sparse SAR Imaging Methods

Sparse signal processing has already been introduced to synthetic aperture radar (SAR), which shows potential in improving imaging performance based on raw data or a complex image. In this paper, the relationship between a raw data-based sparse SAR imaging method (RD-SIM) and a complex image-based sparse SAR imaging method (CI-SIM) is compared and analyzed in detail, which is important to select appropriate algorithms in different cases. It is found that they are equivalent when the raw data is fully sampled. Both of them can effectively suppress noise and sidelobes, and hence improve the image performance compared with a matched filtering (MF) method. In addition, the target-to-background ratio (TBR) or azimuth ambiguity-to-signal ratio (AASR) performance indicators of RD-SIM are superior to those of CI-SIM in down-sampling data-based imaging, nonuniform displace phase center sampling, and sparse SAR imaging model-based azimuth ambiguity suppression