Method For Delineation Research Articles

A method of delineating ecological red lines based on gray relational analysis and the minimum cumulative resistance model: a case study of Shawan District, China

The delineation of ecological red lines (ERLs) is of great significance to ensure ecological security and improve the quality of the ecological environment. However, most of the current ERL delineation only focuses on the natural environment, lacks consideration of the spatial pattern of ecological landscape, and there is no scientific standard for ERL delineation. From the perspective of ecological security patterns (ESPs), this study proposed an ERL delineation method based on gray relational analysis (GRA) and the minimum cumulative resistance (MCR) model. Then taking Shawan District as an example, the effectiveness of the method was verified by comparing the delineated ERL in this study with that of traditional evaluation methods. A total of 321.43 km2 of ERL was delineated, accounting for 67.75% of the total area of Shawan District, including 69.70 km2 of Grade I ERL zones, 251.73 km2 of Grade II ERL zones. The results show that ERLs based on GRA and MCR exhibit better connectivity and contain more ecological land. The method has guiding significance and reference value for regional ERL delineation, which is conducive to the decision-making of environmental protection departments. Conservation measures for different landscapes are more targeted to constrain people’s productive activities precisely.

Regional Rainfall Regimes Affect the Sensitivity of the Huff Quartile Classification to the Method of Event Delineation

The widely used Huff quartile approach classifies rainfall events according to which quarter of their duration contains the largest rainfall depth. The rainfall events themselves are often delineated by specifying a minimum rainless interevent time (MIT) that must precede and follow a period of rainfall for it to be identified as a separate event. However, there is no standard or universally applicable value of this MIT criterion. Some studies have stipulated as little as 15 rainless minutes to mark the start of a new event, whilst others have required 24 h or more. The present work investigates how the adoption of different values of the MIT criterion, based, for instance, on the response time of a catchment or the drying time of a vegetation canopy, affects the Huff quartile classification. To date, this has not been explored. To address this issue, the Huff classification is herein applied to data from two Australian ground observing stations, one arid continental and one wet tropical. For each location, rainfall events were delineated using values of the MIT criterion ranging from 30 min to 24 h. In comparison with the 6 h MIT adopted by Huff (1967) as being appropriate for locations in the eastern USA, results show that, for instance, the proportion of events classified as 4Q ranges from 70.9% larger when events are delineated with a MIT = 30 min to 50.8% smaller when events are delineated using a MIT = 24 h. Moreover, the changes in the Huff classification that result from the use of different MIT values were not the same in wet tropical and arid locations. It is argued here that these findings reflect complexity in the arrival of rainfall, including differences in event duration and intraevent intermittency, that cannot be captured in the Huff classification system. Relevant rainfall regime characteristics such as these are likely to vary geographically, and the differences shown between the eastern USA and the two Australian locations are only examples of what is likely a more general effect. The results show that there is no single and generally applicable ‘Huff classification’ process, and that rather than a 6 h MIT being applicable everywhere, different MIT durations are needed in locations having differing rainfall regimes.

Evaluation of three methods for delineation and attenuation estimation of the sinus region in MR-based attenuation correction for brain PET-MR imaging

BackgroundAttenuation correction is crucial in quantitative positron emission tomography-magnetic resonance (PET-MRI) imaging. We evaluated three methods to improve the segmentation and modelling of the attenuation coefficients in the nasal sinus region. Two methods (cuboid and template method) included a MRI-CT conversion model for assigning the attenuation coefficients in the nasal sinus region, whereas one used fixed attenuation coefficient assignment (bulk method).MethodsThe study population consisted of data of 10 subjects which had undergone PET-CT and PET-MRI. PET images were reconstructed with and without time-of-flight (TOF) using CT-based attenuation correction (CTAC) as reference. Comparison was done visually, using DICE coefficients, correlation, analyzing attenuation coefficients, and quantitative analysis of PET and bias atlas images.ResultsThe median DICE coefficients were 0.824, 0.853, 0.849 for the bulk, cuboid and template method, respectively. The median attenuation coefficients were 0.0841 cm−1, 0.0876 cm−1, 0.0861 cm−1 and 0.0852 cm−1, for CTAC, bulk, cuboid and template method, respectively. The cuboid and template methods showed error of less than 2.5% in attenuation coefficients. An increased correlation to CTAC was shown with the cuboid and template methods. In the regional analysis, improvement in at least 49% and 80% of VOI was seen with non-TOF and TOF imaging. All methods showed errors less than 2.5% in non-TOF and less than 2% in TOF reconstructions.ConclusionsWe evaluated two proof-of-concept methods for improving quantitative accuracy in PET/MRI imaging and showed that bias can be further reduced by inclusion of TOF. Largest improvements were seen in the regions of olfactory bulb, Heschl's gyri, lingual gyrus and cerebellar vermis. However, the overall effect of inclusion of the sinus region as separate class in MRAC to PET quantification in the brain was considered modest.

Individual Tree Crown Delineation Method Based on Multi-Criteria Graph Using Geometric and Spectral Information: Application to Several Temperate Forest Sites

Individual tree crown (ITC) delineation in temperate forests is challenging owing to the presence of broadleaved species with overlapping crowns. Mixed coniferous/deciduous forests with characteristics that differ with the type of tree thus require a flexible method of delineation. The ITC delineation method based on the multi-criteria graph (MCG-Tree) addresses this problem in temperate monospecific or mixed forests by combining geometric and spectral information. The method was used to segment trees in three temperate forest sites with different characteristics (tree types, species distribution, planted or natural forest). Compared with a state-of-the-art watershed segmentation approach, our method increased delineation performance by up to 25%. Our results showed that the main geometric criterion to improve delineation quality is related to the crown radius (performance improvement around 8%). Coniferous/deciduous classification automatically adapts the MCG-Tree criteria to the type of tree. Promising results are then obtained to improve delineation performance for mixed forests.

Comparison of Volumetric Quantitative PET Parameters Before and After a CT-Based Elastic Deformation on Dual-Time 18FDG-PET/CT Images: A Feasibility Study in a Perspective of Radiotherapy Planning in Head and Neck Cancer.

BackgroundThe use of 18FDG-PET/CT for delineating a gross tumor volume (GTV, also called MTV metabolic tumor volume) in radiotherapy (RT) planning of head neck squamous cell carcinomas (HNSCC) is not included in current recommendations, although its interest for the radiotherapist is of evidence. Because pre-RT PET scans are rarely done simultaneously with dosimetry CT, the validation of a robust image registration tool and of a reproducible MTV delineation method is still required.ObjectiveOur objective was to study a CT-based elastic registration method on dual-time pre-RT 18FDG-PET/CT images to assess the feasibility of PET-based RT planning in patients with HNSCC.MethodsDual-time 18FDG-PET/CT [whole-body examination (wbPET) + 1 dedicated step (headPET)] were selected to simulate a 2-times scenario of pre-RT PET images deformation on dosimetry CT. ER-headPET and RR-headPET images were, respectively, reconstructed after CT-to-CT rigid (RR) and elastic (ER) registrations of the headPET on the wbPET. The MTVs delineation was performed using two methods (40%SUVmax, PET-Edge). The percentage variations of several PET parameters (SUVmax, SUVmean, SUVpeak, MTV, TLG) were calculated between wbPET, ER-headPET, and RR-headPET. Correlation between MTV values was calculated (Deming linear regression). MTVs intersections were assessed by two indices (OF, DICE) and compared together (Wilcoxon test). Additional per-volume analysis was evaluated (Mann-Whitney test). Inter- and intra-observer reproducibilities were evaluated (ICC = intra-class coefficient).Results36 patients (30M/6F; median age = 65 y) were retrospectively included. The changes in SUVmax, SUVmean and SUVpeak values between ER-headPET and RR-headPET images were <5%. The variations in MTV values between ER-headPET and wbPET images were −6 and −3% with 40%SUVmax and PET Edge, respectively. Their correlations were excellent whatever the delineation method (R2 > 0.99). The ER-headPET MTVs had significant higher mean OF and DICE with the wbPET MTVs, for both delineation methods (p ≤ 0.002); and also when lesions had a volume > 5cc (excellent OF = 0.80 with 40%SUVmax). The inter- and intra-observer reproducibilities for MTV delineation were excellent (ICC ≥ 0.8, close to 1 with PET-Edge).ConclusionOur study demonstrated no significant changes in MTV after an elastic deformation of pre-RT 18FDG-PET/CT images acquired in dual-time mode. This opens possibilities for HNSCC radiotherapy planning improvement by transferring GTV-PET on dosimetry CT.

Use of Biodiversity Indicator and Reporting System (BIRS) for Biodiversity Assessment In Mining Landscape

The main aim of the study was to find out the critical biodiversity index of different habitats present within the state of Jharkhand and Odisha where Tata Steel Ltd carries out its Mining operations. The study has focused upon the biodiversity results discussion based on different biodiversity sites present within the areas. The positivism philosophy has been used for the conduction of the research. The research has focused on an inductive approach with a descriptive project design. The first-hand data collected by the Researcher has acted as the project outcomes to depict the clear image of the biodiversity within this range. The delineation method has been used to isolate the habitats and conducting the research. The biodiversity indexes found from this research have shown a greater than 1 value in every case. For Joda, Keonjhar, Odisha, it has been 4.5, contradictorily in Deojhar, Keonjhar, Odisha, it is 5.9, showing the high range of Diversity present in the ecologies of these places. The research outcome has also opened a future scope of finding out new unknown species that have been seen within the deep forests of this region. The knowledge about BIRS has been also gained with the help of this study that has focused on finding out the generic tendency of conducting biodiversity index analysis research.

GIS-automated delineation of hospital service areas in Florida: from Dartmouth method to network community detection methods

ABSTRACT Since the Dartmouth Hospital Service Areas (HSAs) were proposed three decades ago, there has been a large body of work using the unit in examining the geographic variation in health care in the U.S. for evaluating health care system performance and informing health policy. However, many studies question the replicability and reliability of the Dartmouth HSAs in meeting the challenges of an ever-changing and a diverse set of health care services. This research develops a reproducible, automated, and efficient GIS tool to implement Dartmouth method for defining HSAs. Moreover, the research adapts two popular network community detection methods to account for spatial constraints for defining HSAs that are scale flexible and optimize an important property such as maximum service flows within HSAs. A case study based on the state inpatient database in Florida from the Healthcare Cost and Utilization Project is used to evaluate the efficiency and effectiveness of the methods. The study represents a major step towards developing HSA delineation methods that are computationally efficient, adaptable for various scales (from a local region to as large as a national market) and automated without a steep learning curve for public health professionals.

Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

Deep Learning-Aided Automatic Contouring of Clinical Target Volumes for Radiotherapy in Breast Cancer After Modified Radical Mastectomy

Purpose: The aim of this study is to develop a practicable automatic clinical target volume (CTV) delineation method for radiotherapy of breast cancer after modified radical mastectomy.Methods: Unlike breast conserving surgery, the radiotherapy CTV for modified radical mastectomy involves several regions, including CTV in the chest wall (CTVcw), supra- and infra-clavicular region (CTVsc), and internal mammary lymphatic region (CTVim). For accurate and efficient segmentation of the CTVs in radiotherapy of breast cancer after modified radical mastectomy, a multi-scale convolutional neural network with an orientation attention mechanism is proposed to capture the corresponding features in different perception fields. A channel-specific local Dice loss, alongside several data augmentation methods, is also designed specifically to stabilize the model training and improve the generalization performance of the model. The segmentation performance is quantitatively evaluated by statistical metrics and qualitatively evaluated by clinicians in terms of consistency and time efficiency.Results: The proposed method is trained and evaluated on the self-collected dataset, which contains 110 computed tomography scans from patients with breast cancer who underwent modified mastectomy. The experimental results show that the proposed segmentation method achieved superior performance in terms of Dice similarity coefficient (DSC), Hausdorff distance (HD) and Average symmetric surface distance (ASSD) compared with baseline approaches.Conclusion: Both quantitative and qualitative evaluation results demonstrated that the specifically designed method is practical and effective in automatic contouring of CTVs for radiotherapy of breast cancer after modified radical mastectomy. Clinicians can significantly save time on manual delineation while obtaining contouring results with high consistency by employing this method.

Reassession of Topsoil Organic Carbon Density in China by Diagnostic Surface Horizon Method and Comparison of Two Topsoil Delineation Methods

Reassession of Topsoil Organic Carbon Density in China by Diagnostic Surface Horizon Method and Comparison of Two Topsoil Delineation Methods

An open‐source automated workflow to delineate storm events and evaluate concentration‐discharge relationships

AbstractThe advent of in situ optical sensors that can collect sub‐daily measurements of nutrients and turbidity in flowing water bodies has yielded comparatively much larger water quality data sets than were previously available. With these newly available data sets, there has been increased interest in studying event‐based concentration‐discharge (c‐Q) relationships to infer the sources and pathways of various watershed constituents during storms. With water quality data sets increasingly growing in size and scope, the need to automate the processing and analyses of such data has become apparent. However, consensus on storm event delineation methods as they pertain to c‐Q analysis is currently lacking, and methodological details, including parameter values, are sometimes unreported in the literature. Here, we present an open‐source workflow using the programming language R that automates the processing of sub‐daily c‐Q data to analyze event‐based hysteresis patterns. Briefly, the workflow accepts a time series of concentration and discharge data, extracts stormflow from streamflow, delineates storm events and then evaluates c‐Q relationships using widely applied metrics like the hysteresis index (HI) and the flushing index (FI). We applied the workflow to three watersheds in the mid‐Atlantic United States, including a 0.4‐km2 agricultural watershed, a 150‐km2 urbanizing watershed and a 29 940‐km2 mixed land use river basin. Sub‐daily sensor‐based nutrient concentrations and discharge data were collected in each watershed. Using the small agricultural watershed as an example, we demonstrate the step‐by‐step application of the workflow. We then present results from the larger watersheds as a means for comparison and to illustrate the flexibility of the code. We believe that this rapid approach to event‐based c‐Q analysis will allow scientists and practitioners more time to focus on interpreting results, and promote greater scientific reproducibility. Likewise, we conclude this Scientific Briefing with suggested future improvements to the workflow to increase the automation of data analyses and reproducibility.

Inter-observer variability of organ contouring for preclinical studies with cone beam Computed Tomography imaging.

Background and purposeIn preclinical radiation studies, there is great interest in quantifying the radiation response of healthy tissues. Manual contouring has significant impact on the treatment-planning because of variation introduced by human interpretation. This results in inconsistencies when assessing normal tissue volumes. Evaluation of these discrepancies can provide a better understanding on the limitations of the current preclinical radiation workflow. In the present work, interobserver variability (IOV) in manual contouring of rodent normal tissues on cone-beam Computed Tomography, in head and thorax regions was evaluated.Materials and methodsTwo animal technicians performed manually (assisted) contouring of normal tissues located within the thorax and head regions of rodents, 20 cases per body site. Mean surface distance (MSD), displacement of center of mass (ΔCoM), DICE similarity coefficient (DSC) and the 95th percentile Hausdorff distance (HD95) were calculated between the contours of the two observers to evaluate the IOV.ResultsFor the thorax organs, right lung had the lowest IOV (ΔCoM: 0.08 ± 0.04 mm, DSC: 0.96 ± 0.01, MSD:0.07 ± 0.01 mm, HD95:0.20 ± 0.03 mm) while spinal cord, the highest IOV (ΔCoM:0.5 ± 0.3 mm, DSC:0.81 ± 0.05, MSD:0.14 ± 0.03 mm, HD95:0.8 ± 0.2 mm). Regarding head organs, right eye demonstrated the lowest IOV (ΔCoM:0.12 ± 0.08 mm, DSC: 0.93 ± 0.02, MSD: 0.15 ± 0.04 mm, HD95: 0.29 ± 0.07 mm) while complete brain, the highest IOV (ΔCoM: 0.2 ± 0.1 mm, DSC: 0.94 ± 0.02, MSD: 0.3 ± 0.1 mm, HD95: 0.5 ± 0.1 mm).ConclusionsOur findings reveal small IOV, within the sub-mm range, for thorax and head normal tissues in rodents. The set of contours can serve as a basis for developing an automated delineation method for e.g., treatment planning.

A Modified U-Net Based Framework for Automated Segmentation of Hippocampus Region in Brain MRI

An accurate localization of the brain anatomical structure for correct and reliable diagnostic strategies is of great concern in many bio-medical applications. Towards this end, manual or semi-automated delineation methods used are found to be time consuming. Herein, to address this problem, we present an enhanced model for automated segmentation of two neighboring small structures of the brain in the Hippocampus region i.e., anterior and posterior. Our aim is to improve the segmentation performance, where the proposed architecture captures contextual information in encoding path and enables precise localization by utilizing the decoding path in a symmetric way. In particular, our proposed methodology enhances the original U-Net architecture with 3-dimensional (3D) data processing and employs spatial elastic deformation. Further, we evaluated the segmentation performance using recursive U-Net for comparison. The effectiveness of different optimization strategies are evaluated on a publicly available data comprising of 3D magnetic resonance imaging volumes from mono-modal hippocampus region. Our experimental results demonstrate the robustness of the proposed model by using patch-based augmentation technique for hippocampal segmentation.

An Effective Road Centerline Extraction Method From VHR

Road digitizing is a labor-intensive task. For reducing labor cost, this letter presents an effective semiautomatic road delineation method based on geodesic distance field (GDF) and piecewise polygon fitting. The main components of the approach are optimal circle calculation, soft road center kernel density estimation (KDE), fast GDF generation, and piecewise polygon fitting model. First, the adaptive circular template is proposed to automatically measure the road width. Next, the soft road center kernel density is estimated for fast GDF generation, which supports the extraction of road centerline between two adjacent seeds. Finally, piecewise polygon fitting is used to refine the road centerline. Extensive experiments demonstrate that the proposed algorithm is efficient and robust in road delineation. The proposed approach takes almost the same time to extract any length of road segment given fixed image size, and no hyperparameters needs to be set.

Toward comparative polycentricity scores: Assessing variations in regional delineation and subcenter identification

Urban spatial structure is increasingly characterized by polycentricity, the presence of multiple interconnected centers of similar size. Polycentricity indicators, which influence research and policies related to urban development, rely on three phases of analysis: (a) delineating regions, (b) identifying subcenters within these regions, and (c) operationalizing polycentricity; and each phase contains decision points for analysts. This paper argues that polycentricity methodologies should be thought of in terms of pathways, then systematically applies 15 such pathways to the case of German regional polycentricity and compares the results. Findings suggest that questions of polycentricity are more robustly measured by comparing across multiple regional delineation methods and selection of subcenters, then looking for signs of agreement or disagreement. When possible, constructing regions from larger areas through bottom-up methods tends to avoid the biases of administratively defined regions. When this is not possible, statistical approaches to subcenter identification can serve as a check to avoid forced selection of subcenters in poorly defined regions.

Spatial Delineation of Gold Resource Planning Regions Based on Multi-Objectives

Current gold resource management in China has faced dual challenge from guaranteeing metal supply (quantity) and resource upgrade and transformation (quality). Although China has proposed the setup of seven gold resource planning regions to resolve this challenge, how to spatially-delineate these regions has not been specified. In this study, we identify three criteria for the spatial delineation of these 7 planning regions, i.e., (i) geographic distance from any mining right to the center of its planning region is shorter than to the center of any adjacent planning region; (ii) the size of each planning region should be similar; (iii) the amount of gold production capacity in each planning region should reach a certain proportion threshold of the national total (namely the “goal value”). Referenced from the existing spatial delineation methods, Voronoi diagram and buffer analysis are used to calculate the spatial scope necessary to satisfy these three objectives, from both resource development (mining right-level) and management (administrative county-level) perspective. The results show that with the goal value rising from 40 to 60%, the planning region size, the number of mining right and the administrative county involved in the planning regions all increase substantially, but the growth rates are different. Our work integrates the mineral resource management and geographic analysis, and such methodology is also applicable on other commodities.

Effects of Tracer Uptake Time in Non-Small Cell Lung Cancer 18F-FDG PET Radiomics.

PET radiomics applied to oncology allow the measurement of intratumoral heterogeneity. This quantification can be affected by image protocols; hence, there is an increased interest in understanding how radiomic expression on PET images is affected by different imaging conditions. To address that interest, this study explored how radiomic features are affected by changes in 18F-FDG uptake time, image reconstruction, lesion delineation, and radiomic binning settings. Methods: Ten non-small cell lung cancer patients underwent 18F-FDG PET on 2 consecutive days. On each day, scans were obtained at 60 and 90 min after injection and reconstructed following EARL version 1 and with point-spread-function resolution modeling (PSF-EARL2). Lesions were delineated with an SUV threshold of 4.0, with 40% of SUVmax, and with a contrast-based isocontour. PET image intensity was discretized with both a fixed bin width (FBW) and a fixed bin number before the calculation of the radiomic features. Repeatability of features was measured with the intraclass correlation coefficient, and the change in feature value over time was calculated as a function of its repeatability. Features were then classified into use-case scenarios based on their repeatability and susceptibility to tracer uptake time. Results: With PSF-EARL2 reconstruction, 40% of SUVmax lesion delineation, and FBW intensity discretization, most features (94%) were repeatable at both uptake times (intraclass correlation coefficient > 0.9), 35% being classified for dual-time-point use cases as being sensitive to changes in uptake time, 39% were classified for cross-sectional studies with an unclear dependency on time, 20% were classified for cross-sectional use while being robust to uptake time changes, and 6% were discarded for poor repeatability. EARL version 1 images had 1 fewer repeatable feature (neighborhood gray-level different matrix coarseness) than PSF-EARL2; the contrast-based delineation had the poorest repeatability of the delineation methods, with 45% of features being discarded; and fixed bin number resulted in lower repeatability than FBW (45% and 6% of features were discarded, respectively). Conclusion: Repeatability was maximized with PSF-EARL2 reconstruction, lesion delineation at 40% of SUVmax, and FBW intensity discretization. On the basis of their susceptibility to uptake time, radiomic features were classified into specific non-small cell lung cancer PET radiomics use cases.

Segmentation of ore and waste rocks in borehole images using the multi-module densely connected U-net

During exploitation of metal mines, the delineation of orebody boundary is of great importance to control the dilution rate, ensure the mining efficiency and improve the economic benefits. At present, the delineation of orebody boundary is usually achieved by conventional methods, such as laboratory tests on rock powder, identification of ore samples under microscope and spectrum analysis etc. However, as the actual condition of orebody occurrence is extremely complex, the conventional delineation methods can hardly meet the requirements of full-length real-time efficient analysis for boreholes. As a result, the blasting design is out of step with the blasting mining process, leading to great economic loss. In this paper, a method for intelligent identification and automatic delineation of ore and waste rock boundary is proposed, based on the borehole imaging technology and the image segmentation method. The digital borehole imaging system is employed to obtain the borehole images in mine roadways. The Inception and DenseNet blocks are integrated to develop the multi-module densely connected U-net (MMDC-Unet), which can realize pixel-level semantic segmentation of borehole images by segmenting the borehole images of highly similar and highly randomly distributed ore according to the global and local image features. Based on the image segmentation results, the number of pixels for ore is counted to quantitatively analyze the distribution of ore and realize real-time rapid delineation of ore and waste rock boundary. By comparing the intelligent identification results with the chemical test results, it is found that the proposed method can greatly enhance the identification efficiency while satisfying the engineering requirements.

Land cover based simulation of urban stormwater runoff and pollutant loading

Urban stormwater models such as PCSWMM are important tools for evaluating urban stormwater quantity and quality. However, due to the lack of consideration of land covers, traditional catchment delineation methods have defects in model precision, parameter transferability and assessment of contribution from individual land cover types. This paper used PCSWMM model as a foundation, built a new land-cover based (LCB) model and made a systematic comparison with the traditional watershed delineation tool (WDT) model to study the impacts of land cover on the simulation of stormwater runoff and pollutant loading. The models were applied to two urban catchments in Calgary, Canada. The results revealed that the LCB model performed better than the WDT model in hydrological simulation, and land cover consideration can considerably improve model accuracy. The two models showed comparable performances in simulation of total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) loading. The LCB model parameters could be regionalized based on land cover types. The hydrologic-hydraulic parameters can be satisfactorily transferred from neighboring gauged catchments to similar ungauged catchments. The transferring of water quality parameters did not perform as satisfactory. The LCB model could quantitively evaluate the contribution to runoff and pollutant loads of different land covers. Roads and roofs were found to be the major contributors to urban runoff and pollutants in the two urban catchments. Green space became important only during large storms events and its contribution could be ignored during dryer years.

A modified delineation method of para-aortic nodal clinical target volume in patients with locally advanced cervical cancer.

PurposeTo validate the nodal center coverage (NCC) of the three mainstream delineation methods of para‐aortic nodal clinical target volume (CTV) and propose a modified delineation method of para‐aortic nodal CTV in prophylactic extended‐field irradiation (EFI) of cervical cancer.MethodsA total of 106 patients with para‐aortic lymph nodes (PALNs) identified on PET/CT were included at Peking Union Medical College Hospital between 2011 and 2020. PALNs were classified as left lateral para‐aortic (LLPA), aorto‐caval (AC), and right para‐caval (RPC). Distances from the nodal center to the aorta and inferior vena cava (IVC) were measured. The NCC of the three mainstream delineation methods of para‐aortic nodal CTV (CTV‐K, CTV‐S, and CTV‐D) and a modified CTV (CTV‐M) was calculated. Radiotherapy plans were created based on 4 CTVs for 10 selected patients who received prophylactic EFI. The chi‐squared test and the Student's t‐test were performed.ResultsWe identified 344 PALNs (216 LLPA, 101 AC, and 27 RPC) in 106 patients. Mean distance from the nodal center to the aorta was 9.6 mm in the LLPA and 7 mm in the AC and from the nodal center to the IVC was 5.6 mm in the AC and 5.6 mm in the RPC. CTV‐D improved the NCC of 98% compared with 92% for CTV‐K (p = 0.002) and 95% for CTV‐S (p = 0.046). CTV‐M provided the same satisfactory NCC as CTV‐D (97% vs. 98%, p = 0.485). The V50Gy to the duodenum, the Dmean to the bilateral kidneys, and the V45Gy to the small bowel were significantly lower on the CTV‐M‐based plan than on the CTV‐D‐based plan (p = 0.001, 0.011, and 0.001, respectively).ConclusionCTV‐D provided more satisfactory NCC than CTV‐K and CTV‐S. CTV‐M provided the same satisfactory NCC as CTV‐D and reduced the dose to the critical structures.