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Accurate long-term PM2.5 prediction is crucial for environmental management and public health. However, previous studies have mainly focused on short-term air quality point predictions, neglecting the importance of accurately predicting the long-term trends of PM2.5 and studying the uncertainty of PM2.5 concentration changes. The traditional approaches have limitations in capturing nonlinear relationships and complex dynamic patterns in time series, and they often overlook the credibility of prediction results in practical applications. Therefore, there is still much room for improvement in long-term prediction of PM2.5. This study proposes a novel long-term point and interval prediction framework for urban air quality based on multi-source spatial and temporal data, which further quantifies the uncertainty and volatility of the prediction based on the accurate PM2.5 point prediction. In this model, firstly, multi-source datasets from multiple monitoring stations are preprocessed. Subsequently, spatial clustering of stations based on POI data is performed to filter out strongly correlated stations, and feature selection is performed to eliminate redundant features. In this paper, the ConvFormer-KDE model is presented, whereby local patterns and short-term dependencies among multivariate variables are mined through a convolutional neural network (CNN), long-term dependencies among time-series data are extracted using the Transformer model, and a direct multi-output strategy is employed to realize the long-term point prediction of PM2.5 concentration. KDE is utilized to derive prediction intervals for PM2.5 concentration at confidence levels of 85%, 90%, and 95%, respectively, reflecting the uncertainty inherent in long-term trends of PM2.5. The performance of ConvFormer-KDE was compared with a list of advanced models. Experimental results showed that ConvFormer-KDE outperformed baseline models in long-term point- and interval-prediction tasks for PM2.5. The ConvFormer-KDE can provide a valuable early warning basis for future PM2.5 changes from the aspects of point and interval prediction. [ABSTRACT FROM AUTHOR]

Construction waste is an inevitable byproduct of urban renewal, causing severe pressure on the environment, health, and ecology. Accurately estimating the production of construction waste is crucial for assessing the consumption of urban renewal. However, traditional manual estimation methods rely heavily on statistical data and historical experience, which lack flexibility in practical applications and are time-consuming and labor-intensive. In addition, their accuracy and timeliness need to be improved urgently. Fortunately, with the advantages of high-resolution remote sensing images (HRSIs) such as strong timeliness, large amounts of information, and macroscopic observations, they are suitable for the large-scale dynamic change detection of construction waste. However, the existing deep learning models have a relatively poor ability to extract and fuse features for small and multi-scale targets, and it is difficult to deal with irregularly shaped and fragmented detection areas. Therefore, this study proposes a Multi-scale Target Attention-Enhanced Network (MT-AENet), which is used to dynamically track and detect changes in buildings and construction waste disposal sites through HRSIs and accurately estimate the annual production of urban construction waste. The MT-AENet introduces a novel encoder–decoder architecture. In the encoder, ResNet-101 is utilized to extract high-level semantic features. A depthwise separable-atrous spatial pyramid pooling (DS-ASPP) module with different dilation rates is constructed to address insufficient receptive fields, resolving the issue of discontinuous holes when extracting large targets. A dual-attention mechanism module (DAMM) is employed to better preserve positional and channel details. In the decoder, multi-scale feature fusion (MS-FF) is utilized to capture contextual information, integrating shallow and intermediate features of the backbone network, thereby enhancing extraction capabilities in complex scenes. The MT-AENet is used to extract buildings and construction waste at different periods in the study area, and the actual production and landfill volume of construction waste are calculated based on area changes, indirectly measuring the rate of urban construction waste resource conversion. The experimental results in Changping District, Beijing demonstrate that the MT-AENet outperforms existing baseline networks in extracting buildings and construction waste. The results of this study are validated according to government statistical standards, providing a promising direction for efficiently analyzing the consumption of urban renewal. [ABSTRACT FROM AUTHOR]

Accurate air quality prediction is paramount in safeguarding public health and addressing air pollution control. However, previous studies often ignore the geographic similarity among different monitoring stations and face challenges in dynamically capturing different spatial–temporal relationships between stations. To address this, an air quality predictive learning approach incorporating the Third Law of Geography with SAM–CNN–Transformer is proposed. Firstly, the Third Law of Geography is incorporated to fully consider the geographical similarity among stations via a variogram and spatial clustering. Subsequently, a spatial–temporal attention convolutional network that combines the spatial attention module (SAM) with the convolutional neural network (CNN) and Transformer is designed. The SAM is employed to extract spatial–temporal features from the input data. The CNN is utilized to capture local information and relationships among each input feature. The Transformer is applied to capture time dependencies across long-distance time series. Finally, Shapley's analysis is employed to interpret the model factors. Numerous experiments with two typical air pollutants (PM2.5, PM10) in Haikou City show that the proposed approach has better comprehensive performance than baseline models. The proposed approach offers an effective and practical methodology for fine-grained non-stationary air quality predictive learning. [ABSTRACT FROM AUTHOR]

The proportion of trips driving children to school has experienced a sharp increase throughout recent decades, which is accompanied by less physical activity among students, severe traffic congestion during peak hours, and extreme environmental pollution. This study aimed to test the Stage model of Self-regulated Behavioral Change (SSBC) to understand and explain parents' decision-making process about children's school travel mode shifting from car use to active school travel. A structural equation model and generalized ordinal logistic regression were used to test the parents' intention change process. The results show that the model accounted for 94%, 77%, and 69% of the variance in goal intention, behavioral intention, and implementation intention, respectively. Habitual behavior of private vehicle use would, to some extent, hinder parents' intentions to shift from driving to active school mode use. [ABSTRACT FROM AUTHOR]

Objective To develop a high performance liquid chromatography-tandem mass spectrometry （HPLC-MS/MS） method for the determination of the content of 4-fluoromethamphetamine （4-FMA） in rat plasma, and to provide a methodological basis for the study of the toxicokinetics of 4-FMA in rats. Methods Rat plasma samples were added into internal standard methamphetamine （MA）. Its proteins were precipitated with methanol and then separated with Poroshell 120 EC-C18 chromatographic column. A 0.1% formic acid aqueous solution and a 0.1% formic acid acetonitrile solution were used as the mobile phase at the flow rate of 0.4 mL/min. Electrospray ionization source was used for detection in the multiple reaction monitoring （MRM） mode. Results The linear relationship was good when the mass concentration of 4-FMA in plasma samples was in the range of 5-1 000 ng/mL （r>0.999）. The limit of detection （LOD） was 3 ng/mL and the limit of quantification （LOQ） was 5 ng/mL. The accuracy was expressed as relative error （RE）, and in the range of ±5%, the intra-day precision and inter-day precision （relative standard deviation, RSD） less than 9%, and the extraction recovery rate was more than 90%. The analysis and detection of plasma samples were completed within 2.5 min. Conclusion This study developed a HPLC-MS/MS method for the determination of 4-FMA in rat plasma samples. This method is accurate, rapid, simple and sensitive and can be applied to the study of toxicokinetics of 4-FMA.

Polycyclic aromatic hydrocarbons (PAHs) are known environmental pollutants. Studies are very limited regarding the impacts of paternal PAHs exposure on birth outcomes as well as the underpinning mechanisms in human. In this study, 302 reproductive-aged males (22–46 years old) were enrolled and demographic informatics data were obtained by questionnaires. The levels of urinary hydroxylated PAHs (OH-PAHs) were assessed by ultra-high performance liquid chromatography-tandem mass spectrometry; and methylation levels of the imprinting genes H19, Meg3, and Peg3 of sperm DNA were evaluated via bisulfite pyrosequencing. The analysis of the correlation between OH-PAHs levels and methylation levels of imprinting genes showed that OH-PAHs are correlated with some CpG sites in H19, Peg3, and Meg3. To further investigate an association of urinary OH-PAHs with birth outcomes, follow-up study of wives of these subjects has been performed for 1–3 years. As the result, a total of 157 babies were born. The birth outcomes parameters including birth weight (BW), length (BL), and ponderal index (PI) were recorded. The further analysis of generalized estimating equation indicated a negative correlation between urinary total OH-PAHs levels and newborn BW (β = −0.081, p = 0.020); but this association has not been found for BL and PI. Furthermore, a logistic regression analysis was employed for examining associations of the methylation of imprinting genes with birth outcomes parameters, which indicated a negative correlation between BW and H19, namely, each unit percent (%) elevation in methylation of H19 (but not Peg3 and Meg3) was significantly associated with a 0.135 g reduction of BW (β = −0.135; 95% CI 0.781–0.978). Putting together, these results show that paternal non-occupational environmental exposure to PAHs is associated with newborn BW. And imprinting gene H19 methylation may be involved in the underlying mechanisms. This study in human population adds a support for previous animal study and implies that environmental impact on the offspring through paternal pathway.

Ship dimensions are an important component of static AIS information, and are a key factor in identifying the risks of ship collisions. We describe a method of extracting and correcting ship contour information using inland waterway surveillance video combined with AIS information that does not depend on ship dimension data. A lightweight object detection model was used to determine the ship's position in an image. Dynamic AIS information was included to produce multigroup control points, solve the optimal homography matrix, and create a transformation model to map image coordinates onto water surface coordinates. A semantic segmentation DeepLabV3+ model was used to determine ship contours from the images, and the actual dimensions of the ship contours were calculated using homography matrix transformation. The mAP of the proposed object detection model and the MIoU of the semantic segmentation model were 86.73% and 91.07%, respectively. The calculation error of the ship length and width were 5.8% and 7.4%, respectively. These statistics indicate that the proposed method rapidly and accurately detected target ships in images, and that the model estimated ship dimensions within a reasonable range. [ABSTRACT FROM AUTHOR]

Outliers of ship trajectory from the Automatic Identification System (AIS) onboard a ship will affect the accuracy of maritime situation awareness, especially for a regular ship trajectory mixed with a spoofing ship, which has an unauthorized Maritime Mobile Service Identification code (MMSI) owned by a regular ship. As has been referred to in the literature, the trajectory of these spoofing ships would simply be removed, and more AIS data would be lost. The pre-processing of AIS data should aim to retain more information, which is more helpful in maritime situation awareness for the Maritime Safety Administration (MSA). Through trajectory feature mining, it has been found that there are obvious differences between the trajectory of a regular ship and that of a regular ship mixed with a spoofing ship, such as in terms of speed and distance between adjacent trajectory points. However, there can be a long update time interval in the results of severe missing trajectories of a ship, bringing challenges in terms of the identification of spoofing ships. In order to accurately divide the regular ship trajectory and spoofing ship trajectory, combined with trajectory segmentation by the update time interval threshold, the isolation forest was adopted in this work to train the labeled trajectory point of a regular ship mixed with a spoofing ship. The experimental results show that the average accuracy of the identification of spoofing ships using isolation forest is 88.4%, 91%, 93.1%, and 93.3%, corresponding to different trajectory segmentation by update time intervals (5 h, 10 h, 15 h, and 20 h). The research conducted in this study can almost eliminate the outliers of ship trajectory, and it also provides help for maritime situation awareness for the MSA. [ABSTRACT FROM AUTHOR]

Extracting roads from remote sensing images is of significant importance for automatic road network updating, urban planning, and construction. However, various factors in complex scenes (e.g., high vegetation coverage occlusions) may lead to fragmentation in the extracted road networks and also affect the robustness of road extraction methods. This study proposes a multi-scale road extraction method with asymmetric generative adversarial learning (MS-AGAN). First, we design an asymmetric GAN with a multi-scale feature encoder to better utilize the context information in high-resolution remote sensing images (HRSIs). Atrous spatial pyramid pooling (ASPP) and feature fusion are integrated into the asymmetric encoder–decoder structure to avoid feature redundancy caused by multi-level cascading operations and enhance the generator network's ability to extract fine-grained road information at the pixel level. Second, to maintain road connectivity, topologic features are considered in the pixel segmentation process. A linear structural similarity loss ( L S S I M ) is introduced into the loss function of MS-AGAN, which guides MS-AGAN to generate more accurate segmentation results. Finally, to fairly evaluate the performance of deep models under complex backgrounds, the Bayesian error rate (BER) is introduced into the field of road extraction for the first time. Experiments are conducted via Gaofen-2 (GF-2) high-resolution remote sensing images with high vegetation coverage in the Daxing District of Beijing, China, and the public DeepGlobe dataset. The performance of MS-AGAN is compared with a list of advanced models, including RCFSNet, CoANet, UNet, DeepLabV3+, and DiResNet. The final results show that (1) with respect to road extraction performance, the Recall, F1, and IoU values of MS-AGAN on the Daxing dataset are 2.17%, 0.04%, and 2.63% higher than the baselines. On DeepGlobe, the Recall, F1, and IoU of MS-AGAN improve by 1.12%, 0.42%, and 0.25%, respectively. (2) On road connectivity, the Conn index of MS-AGAN from the Daxing dataset is 46.39%, with an improvement of 0.62% over the baselines, and the Conn index of MS-AGAN on DeepGlobe is 70.08%, holding an improvement of 1.73% over CoANet. The quantitative and qualitative analyses both demonstrate the superiority of MS-AGAN in preserving road connectivity. (3) In particular, the BER of MS-AGAN is 20.86% over the Daxing dataset with a 0.22% decrease compared to the best baselines and 11.77% on DeepGlobe with a 0.85% decrease compared to the best baselines. The proposed MS-AGAN provides an efficient, cost-effective, and reliable method for the dynamic updating of road networks via HRSIs. [ABSTRACT FROM AUTHOR]

The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters. [ABSTRACT FROM AUTHOR]

Mercury (Hg) is a well-recognized environmental pollutant known by its toxicity of development and neurotoxicity, which results in adverse health outcomes. However, the mechanisms underlying the teratogenic effects of Hg are not well understood. Imprinting genes are emerging regulators for fetal development subjecting to environmental pollutants impacts. In this study, we examined the association between preconceptional Hg exposure and the alteration of DNA methylation of imprinting genes H19, Meg3, and Peg3 in human sperm DNA.A total of 616 men, aged from 22 to 59, were recruited from Reproductive Medicine Clinic of Maternal and Child Care Service Center and the Urologic Surgery Clinic of Shanxi Academy of Medical Sciences during April 2015 and March 2016. Demographic information was collected through questionnaires. Urine was collected and urinary Hg concentrations were measured using a fully-automatic double-channel hydride generation atomic fluorescence spectrometer. Methylation of imprinting genes H19, Meg3 and Peg3 of sperm DNA from 242 participants were examined by bisulfite pyrosequencing. Spearman's rank and multivariate regression analysis were used for correlation analysis between sperm DNA methylation status of imprinting genes and urinary Hg levels.The median concentration of Hg for 616 participants was 9.14μg/l (IQR: 5.56-12.52 μg/l; ranging 0.16-71.35μg/l). A total of 42.7% of the participants are beyond normal level for non-occupational exposure according to the criterion of Hg poisoning (≥10 μg/L). Spearman's rank analysis indicated a negative correlation between urinary Hg concentrations and average DNA methylation levels of imprinted genes H19 (rs = -0.346, p