Enhancing the Health and Well-Being of People with Chronic Diseases: Assessment and Sustainable Development Planning for Therapeutic Landscapes after Urban Expansion

Under the influence of economic, environmental, and social structural changes, urban space expands and contracts to varying degrees and the everyday urban landscape changes in response. Over the past 20 years, a large number of cities in China have undergone a brief but rapid urban expansion and are moving toward shrinking cities. Most of these cities are now facing social problems such as an aging population and a high prevalence of chronic diseases. Therefore, the “therapeutic” role and impact of everyday landscapes in these cities need to be examined in the context of urban development processes through appropriate assessment methods. Therefore, this study applies the ANP-mV model to examine the therapeutic nature of everyday urban landscapes in different development periods, with the aim of enhancing the health and well-being of people with chronic diseases. Firstly, this study uses the city of Jinzhou in Northeast China as an example to develop a framework for assessing the therapeutic nature of everyday urban landscapes based on the health care needs of people with chronic diseases; secondly, it examines the therapeutic nature of the former Jinzhou Suburban Riverfront Forest Park as it has developed and evolved over the past 16 years; finally, it explores place-making and regeneration strategies for therapeutic landscapes from the perspectives of dynamic impact and sustainable development to enhance chronic illness patients’ well-being. At the theoretical level, this study contributes by providing a methodology and research ideas for examining the “therapeutic” nature of everyday urban landscapes and proposing further development plans for renewal, constructing a framework for assessing therapeutic landscapes, and elucidating the relationship between networks of influence and the relative importance of various assessment dimensions/elements. At the practical application level, the contribution of this study is to provide local policymakers with a key decision basis for the future development planning of the East Lake Forest Park. The aim is to explore landscape creation and regeneration strategies for the East Lake Forest Park in the context of Jinzhou's progressive move toward a shrinking city, in order to sustain the well-being of the chronically ill.

Volcanic carbon cycling in East Lake, Newberry Volcano, Oregon, USA

The carbon cycle in East Lake (Newberry Volcano, Oregon, USA) is fueled by volcanic CO2 inputs with traces of Hg and H2S. The CO2 dissolves in deep lake waters and is removed in shallow waters through largely diffusive surface degassing and photosynthesis. Escaping gas and photosynthate have low δ13C values, leading to δ13C(DIC) (DIC—dissolved inorganic carbon) as high as +5.7‰ in surface waters, well above the common global lake range. A steep δ13C depth gradient is further established by respiration and absorption of light volcanic CO2 in bottom waters. The seasonal CO2 degassing starts at >100 t CO2/day after ice melting in the spring and declines to ~40 t/day in late summer, degassing ~11,700 t CO2/yr. Thus, volcano monitoring through gas fluxes from crater lakes should consider lacustrine processes that modulate the volcanic gas output over time. The flux contribution of a bubbling CO2 “hotspot” increased from 20% to >90% of the lake-wide CO2 flux from 2015 to 2019 CE, followed by a “toxic gas alert” in July 2020. East Lake is an active volcanic lake with a “geogenic” ecosystem driven primarily by hydrothermal inputs.

Supplemental Material: Volcanic carbon cycling in East Lake, Newberry Volcano, Oregon, USA

Details on the laboratory, data analysis, modeling methods, and equipment specifications, including methods and results of the two box models showing seasonal carbon cycling in East Lake, and raw data from the main text figures.<br>

Supplemental Material: Volcanic carbon cycling in East Lake, Newberry Volcano, Oregon, USA

Details on the laboratory, data analysis, modeling methods, and equipment specifications, including methods and results of the two box models showing seasonal carbon cycling in East Lake, and raw data from the main text figures.<br>

Research on Toughness Design Strategy of Wetland Park Based on the “Chain Theory” —— A case study of Wuhan East Lake Falling Wild Goose Scenic Spot

Wetland is the significant territorial resources and natural resources in China, whereas the establishment of wetland park is an effective measure to protect wetland resources. The research, taking the Wuhan East Lake Falling Wild Goose Scenic Spot as the example and pointing out the “chain theory”, focuses on the toughness plan and design of wetland park to build “Wetland Pond Chain System” and promote the capacity of nourishing and defending, which can be benefit for the sustainable development of wetland and provide certain references for wetland park plan and design in the future.

A full-view scenario model for urban waterlogging response in a big data environment

The emergence of big data is breaking the spatial and time limitations of urban waterlogging scenario description. The scenario data of different dimensions (e.g., administrative levels, sectors, granularities, and time) have become highly integrated. Accordingly, a structural and systematic model is needed to represent waterlogging scenarios for more efficient waterlogging response decision-making. In this article, a full-view urban waterlogging scenario is first defined and described from four dimensions. Next a structured representation of scenario element is given based on knowledge unit method. The full-view scenario model is then constructed by extracting the scenario correlation structures between different dimensions (called scenario nesting), i.e., inheritance nesting, feedback nesting, aggregation nesting, and selection nesting. Finally, a real-world case study in Wuhan East Lake High-tech Development Zone, China is evaluated to verify the reasonability of the full-view model. The results show that the proposed model effectively integrates scenario data from different dimensions, which helps generate the complete key scenario information for urban waterlogging decision-making. The full-view scenario model is expected to be applicable for other disasters under big data environment.