Measuring riverine macroplastic: Methods, harmonisation, and quality control.

River systems are a key environmental recipient of macroplastic pollution. Understanding the sources of macroplastic to rivers and the mechanisms controlling fate and transport is essential to identify and tailor measures that can effectively reduce global plastic pollution. Several guidelines exist for monitoring macroplastic in rivers; yet, no single method has emerged representing the standard approach. This reflects the substantial variability in river systems globally and the need to adapt methods to the local environmental context and monitoring goals. Here we present a critical review of methods used to measure macroplastic flows in rivers, with a specific focus on opportunities for methods testing, harmonisation, and quality assurance and quality control (QA/QC). Several studies have already revealed important findings; however, there is significant disparity in the reporting of methodologies and data. There is a need to converge methods, and their adaptations, towards greater comparability. This can be achieved through: i) methods testing to better understand what each method effectively measures and how it can be applied in different contexts; ii) incorporating QA/QC procedures during sampling and analysis; and iii) reporting methodological details and data in a more harmonised way to facilitate comparability and the utilisation of data by several end users, including policy makers. Setting this as a priority now will facilitate the collection of rigorous and comparable monitoring data to help frame solutions to limit plastic pollution, including the forthcoming global treaty on plastic pollution.

Boosting distributional copula regression.

Capturing complex dependence structures between outcome variables (e.g., study endpoints) is of high relevance in contemporary biomedical data problems and medical research. Distributional copula regression provides a flexible tool to model the joint distribution of multiple outcome variables by disentangling the marginal response distributions and their dependence structure. In a regression setup, each parameter of the copula model, that is, the marginal distribution parameters and the copula dependence parameters, can be related to covariates via structured additive predictors. We propose a framework to fit distributional copula regression via model-based boosting, which is a modern estimation technique that incorporates useful features like an intrinsic variable selection mechanism, parameter shrinkage and the capability to fit regression models in high-dimensional data setting, that is, situations with more covariates than observations. Thus, model-based boosting does not only complement existing Bayesian and maximum-likelihood based estimation frameworks for this model class but rather enables unique intrinsic mechanisms that can be helpful in many applied problems. The performance of our boosting algorithm for copula regression models with continuous margins is evaluated in simulation studies that cover low- and high-dimensional data settings and situations with and without dependence between the responses. Moreover, distributional copula boosting is used to jointly analyze and predict the length and the weight of newborns conditional on sonographic measurements of the fetus before delivery together with other clinical variables.

Learning from urban form to predict building heights.

Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.