Sustainable watershed management: an international multi-watershed case study

Global freshwater resources are being increasingly polluted and depleted, threatening sustainable development and human and ecosystem health. Utilizing case studies from 4 different watersheds in the United States, Japan, Switzerland, and Brazil, this paper identifies the most relevant sustainability deficits and derives general vectors for more sustainable water management. As a consequence of the demographic and economic developments experienced in the last few decades, each watershed has suffered declines in water quality, streamflow and biotic resources. However, the extent and the cultural perception of these water-related problems vary substantially in the different watersheds, leading to specific water-management strategies. In industrialized countries, exemplified by the US, Switzerland, and Japan, these strategies have primarily consisted of finance- and energy-intensive technologies, allowing these countries to meet water requirements while minimizing human health risks. But, from a sustainability point of view, such strategies, relying on limited natural resources, are not long-term solutions. For newly industrialized countries such as Brazil, expensive technologies for water management are often not economically feasible, thus limiting the extent to which newly industrialized and developing countries can utilize the expertise offered by the industrialized world. Sustainable water management has to be achieved by a common learning process involving industrialized, newly industrialized, and developing countries, following general sustainability guidelines as exemplified in this paper.     

The toxicological paradigms of aluminum and the etiology of Alzheimer's disease in the nematode Caenorhabditis elegans

This study examined behavioral and enzymatic changes of C. elegans from its exposure to aluminum, and the resulting relationship with Alzheimer's disease. After chronic and acute exposure to aluminum, the results indicated that it alters the cholinergic status and behavior parameters of the nematode, suggesting a relationship between exposure to aluminum and the etiology of AD.     

Spatial variance and assessment of nitrogen dioxide pollution in major cities of Pakistan along N5-Highway

This paper discusses the findings of the first car MAX-DOAS (multi-axis differential optical absorption spectroscopy) field campaign (300 km long) along the National Highway-05 (N5-Highway) of Pakistan conducted on 13 and 14 November, 2012. The main objective of the field campaign was to assess the spatial distribution of tropospheric nitrogen dioxide (NO₂) columns and corresponding concentrations along the N5-Highway from Islamabad to Lahore. Source identification of NO₂ revealed that the concentrations were higher within major cities along the highway. The highest NO₂ vertical column densities (NO₂ VCDs) were found around two major cities of Rawalpindi and Lahore. This study also presents a comparison of NO₂ VCDs measured by the ozone monitoring instrument (OMI) and car MAX-DOAS observations. The comparison revealed similar spatial distribution of the NO₂ columns with both car MAX-DOAS and satellite observations, but the car MAX-DOAS observations show much more spatial details. Maximum NO₂ VCD retrieved from car MAX-DOAS observations was up to an order of magnitude larger than the OMI observations in urban areas.     

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.