Levels and patterns of organochlorine pesticides in agricultural soils in an area of extensive historical cotton cultivation in Henan province, China

Organochlorine pesticides (OCPs) have attracted widespread concern because of their environmental persistence and toxicity. The historical influence of different agricultural land use types on soil concentrations of OCP residues was investigated by collecting a total of 52 surface soil samples from long-term cotton fields and fields with other crops in Lvdian township, Henan province, eastern central China. The concentration, composition, and possible sources of 16 OCPs were determined and a health risk assessment of these soils was conducted. Hexachlorocyclohexane (HCH), heptachlor, chlordane, and dichloro diphenyl trichloroethane plus its main metabolites (DDTs) were the most frequently detected OCPs with concentrations of 2.9-56.4 ng g(-1), 4.3-14.0 ng g(-1), 18.0-1254.4 ng g(-1), and below detection limit (BDL) -206.1 ng g(-1), respectively. Analysis of variance of p,p-DDE shows significant (P < 0.05) differences while other OCPs show no significant differences between historical cotton fields and fields containing other crops. Compositional analysis suggests that the HCH is derived mainly from the use of lindane and that there are recent inputs. Analysis of variance and compositional analysis indicate that the p,p-DDE in surface soil from long-term cotton fields is derived mainly from the aerobic biodegradation of historical residues. The sum of carcinogenic risk values of OCPs for soil samples were found to be 1.58 x 10(-6), posing a low cancer risk to the inhabitants of the region studied

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Data-driven Analysis and Modeling of Passenger Flows and Service Networks for Public Transport Systems

Public transport (PT) plays an increasingly important role in solving mobility challenges, especially in densely populated metropolitan areas. Further improving PT systems requires more advanced planning and operations. Fortunately, the considerable amount of data that have become increasingly available for PT systems offer an opportunity to address this challenge. However, how these data can be effectively used to achieve this goal still remains as an unresolved question in the scientific literature. More research is therefore needed to bridge this gap in order to advance PT systems for addressing mobility challenges. To this end, this dissertation is focused on developing methods and models for translating high-volume data from various sources into novel knowledge and insights that can be used to improve PT planning and operations. This dissertation first examines how to obtain onboard occupancy of PT vehicles by integrating all the three different data sources mentioned above. Second, this dissertation deals with the issue of high-dimensionality in large-scale passenger flows. Third, we propose a k-means-based method to cluster PT stops for constructing zone-to-zone OD matrices. Fourth, this dissertation presents a new method for analyzing the accessibility of PT service networks based on a novel network science approach. Last, we investigate whether passenger flow distribution can be estimated solely based on network properties in PT systems.TRAIL Thesis Series no. T2020/2, the Netherlands Research School TRAILTransport and Plannin

Using passenger flows to determine key interchange connections for public transport synchronization

For large urban networks and hubs, optimizing transfer synchronization becomes computationally challenging. The objective of this paper is therefore to develop a generic, data-driven methodology to determine the key line/direction-combinations to synchronize based on passenger flows. We developed an approach to detect communities of directional lines based on passenger transfer flows, by calculating modularity using a C-space inspired network representation. Our results show intuitive clusters to prioritize for synchronization on a network level for tactical planning, and on the hub level for real-time coordination.Transport and Plannin

Can passenger flow distribution be estimated solely based on network properties in public transport systems?

We present a pioneering investigation into the relation between passenger flow distribution and network properties in public transport systems. The methodology is designed in a reverse engineering fashion by utilizing passively measured passenger flow dynamics over the entire network. We quantify the properties of public transport networks using a range of centrality indicators in the topological representations of public transport networks with both infrastructure and service layers considered. All the employed indicators, which originate from complex network science, are interpreted in the context of public transport systems. Regression models are further developed to capture the correlative relation between passenger flow distribution and several centrality indicators that are selected based on the correlation analysis. The primary finding from the case study on the tram networks of The Hague and Amsterdam is that the selected network properties can indeed be used to approximate passenger flow distribution in public transport systems to a reasonable extent. Notwithstanding, no causality is implied, as the correlation may also reflect how well the supply allocation caters for the underlying demand distribution. The significance and relevance of this study stems from two aspects: (1) the unraveled relation provides a parsimonious alternative to existing passenger assignment models that require many assumptions on the basis of limited data; (2) the resulting model offers efficient quick-scan decision support capabilities that can help transport planners in tactical planning decisions.Transport and Plannin

Constructing Transit Origin-Destination Matrices using Spatial Clustering

So-called tap-in–tap-off smart card data have become increasingly available and popular as a result of the deployment of automatic fare collection systems on transit systems in many cities and areas worldwide. An opportunity to obtain much more accurate transit demand data than before has thus been opened to both researchers and practitioners. However, given that travelers in some cases can choose different origin and destination stations, as well as different transit lines, depending on their personal acceptable walking distances, being able to aggregate the demand of spatially close stations becomes essential when transit demand matrices are constructed. With the aim of investigating such problems using data-driven approaches, this paper proposes a k-means-based station aggregation method that can quantitatively determine the partitioning by considering both flow and spatial distance information. The method was applied to a case study of Haaglanden, Netherlands, with a specified objective of maximizing the ratio of average intra-cluster flow to average inter-cluster flow while maintaining the spatial compactness of all clusters. With a range of clustering of different K performed first using the distance feature, a distance-based metric and a flow-based metric were then computed and ultimately combined to determine the optimal number of clusters. The transit demand matrices constructed by implementing this method lay a foundation for further studies on short-term transit demand prediction and demand assignment.Transport and Plannin

Analysis of network-wide transit passenger flows based on principal component analysis

Transit networks are complex systems in which the passenger flow dynamics are difficult to capture and understand. While there is a growing ability to monitor and record travelers' behavior in the past decade, knowledge on network-wide passenger flows, which are essentially high-dimensional multivariate data, is still limited. This paper describes how Principal Component Analysis (PCA) can be leveraged to develop insight into such multivariate time series transformed from raw individual tapping records of smart card data. With a one-month data set of the Shenzhen metro system used in this study, it is shown that a great amount of variance contained in the original data can be effectively retained in lower-dimensional sub-spaces composed of top few Principal Components (PCs). Features of such low dimensionality, PCs and temporal stability of the flow structure are further examined in detail. The results and analysis provided in this paper make a contribution to the understanding of transit flow dynamics and can benefit multiple important applications for transit systems, such as passenger flow modeling and short-term prediction.Transport and Plannin

Unraveling the Hierarchy of Public Transport Networks

Hierarchy is regarded as a natural phenomenon of public transport networks (PTN). The imbalanced distribution of passenger flow result in a hierarchical structure of PTN and it is also related to the development of technology and the introduction of new modes. However, there is still a lack of knowledge on how to identify the hierarchical structure of the multi-layer PTN. This study proposes a three-step passenger transfer flow based methodology for separating and ranking the PTN: (1) using passenger journey data to derive transfer flow matrix; (2) applying network representation with Louvain method of community detection to separate the PTN layers; (3) performing ranking method, separating inner-transfer and inter-transfer flow. To demonstrate our method, we use one-month smart card data of The Hague, the Netherlands provided by the PTN operator HTM. Our results show that our method is able to, regardless of the geographic location and the mode of transportation, identify the hierarchy of PTN based on the passenger transfer flow pattern. Temporal attributes are also discussed to illustrate how hierarchy is time-dependent, e.g. with respect to the day of the week and the time of the day. Our method supports public transport (PT) operators during design and optimization of PTN and in determining which sets of higher-level service to prioritize during different time periods.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and PlanningPolicy Analysi

Can Passenger Flow be Explained by Network Topology in Public Transport?

It has been rarely investigated in the field of public transport whether passenger ow can be explained by network topology. Based on the rich data sets from The Hague, The Netherlands, we conduct this study try- ing to shed light upon this question. The relation between passenger ow and topological measures in different public transport network representations are investigated in detail. Our preliminary results show promising evidence that the passenger ow is indeed correlated to topological measures in the case study network. Several linear regression models are also constructed to quan- tify the explanatory power of these topological measures.Transport and Plannin

Correction to: Are green buildings more liveable than conventional buildings? An examination from the perspective of occupants (Journal of Housing and the Built Environment, (2022), 10.1007/s10901-022-09983-9)

In the original publication of the article, the first affiliation “School of Public Administration, Hunan University, Changsha, China” was incorrectly repeated as fourth affiliation. The fourth affiliation should be “Building and Real Estate Dept. Hong Kong Polytechnic University, Hong Kong SAR”. The original article has been corrected.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Technology, Policy and ManagementManagement in the Built EnvironmentHousing SystemsDesign & Construction Managemen