Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.     

Freisetzung von Schwermetallen aus dem bodenbildenden Tonmineral Illit durch Aminosäuren

Kaolin W from Lohrheim (51% Illite, 31% Kaolinite, 18% Quarz, no Carbon) was loaded with various amounts of heavy metal ions (0.11–6.0 mmol/100g Cu, Ni, Cd) from their nitrate solutions. In the next step the release of the bound metals was tested with or without aminoacids (penicillamine and histidine) in solutions of weak acidity or alkalinity. It was found an evident influence of the aminoacids to the metal release in dependence of metalcoating, metal-aminoacid-ratio and experimental pH. Especially in alkali environment a multiplication of the copper release (to a factor of 39 (pen) or 27 (his)) was observed. An increased nickel (factor 53) and cadmium release (factor 4.5) was found with histidine. In this cases 80 to 100% of the heavy metals were removed from the clay-mineral-surface.     

Evaluation of noise environments during daily activities of university students

Noise conditions specifically in areas inside university facilities and its impact on the quality of life of university students are topics that have received little attention. This paper presents a study of the noise conditions in which university students of various institutions in Madrid, Spain, carry out their daily studies. A representative number of measurements was carried out using noise dosemeters and dataloggers in order to evaluate the levels of noise, noise dose and exposure to noise during school periods and extracurricular activities. The results were compared with the recommendations given by current environmental noise regulations. This paper was complemented with a survey to get to know how students perceive the exposure to noise in university environments.     

Assessing the long-term supply risks for mineral raw materials—a combined evaluation of past and future trends

This paper develops a method for identifying and assessing long-term supply risks for mineral raw materials. The method is based on a combined evaluation of past and future supply and demand trends. By analysing raw material boom and bust cycles over the past 50 years, we have quantified indicators and defined benchmarks for identifying critical market situations. By applying the method, risks for supply shortage may be identified at an early stage. In addition, a numerical evaluation model has been developed for better comparison between various mineral raw materials. Compared to other assessment methods this method uses specific benchmarks for each raw material to better assess supply risks. The method is embedded within a systematic and comprehensive analytical approach.