Modeling stormwater management at the city district level in response to changes in land use and low impact development

Mitigating the impact of increasing impervious surfaces on stormwater runoff by low impact development (LID) is currently being widely promoted at site and local scales. In turn, the series of distributed LID implementations may produce cumulative effects and benefit the stormwater management at larger regional scales. However, the potential of multiple LID implementations to mitigate the broad-scale impacts of urban stormwater is not yet fully understood, particularly among different design strategies to reduce directly connected impervious areas (DCIA). In this study, the hydrological responses of stormwater runoff characteristics to four different land use conversion scenarios at the city scale were explored using GIS-based Stormwater Management Model (SWMM). Model simulation results confirmed the effectiveness of LID controls; however, they also indicated that even with the most beneficial scenarios hydrological performance of developed areas was still not yet up to the pre-development level, especially with pronounced changes from pervious to impervious land

Modulatory effect of Althaea officinalis L root extract on cisplatin-induced cytotoxicity and cell proliferation in A549 human lung cancer cell line

Purpose: To explore the modulatory effect of an Althaea officinalis root extract (AORE) on cisplatininduced cytotoxicity and cell proliferation in a lung cancer cell line.Methods: Aqueous AORE was obtained from peeled and powdered roots. The effect of cisplatin on cytotoxicity and cell proliferation was studied. The cisplatin concentrations tested ranged from 0 - 30 mg/mL. Cell viability and proliferation were studied using trypan blue and MTT assays, respectively. The cells were also exposed to a combination of cisplatin and the AORE.Results: Cisplatin yielded a 50 % inhibitory concentration at 25 mg/mL and exhibited a dose-dependent cell proliferation loss. Combined use of the root extract and cisplatin had significant (R2 = 0.8305) modulatory effects on cytotoxicity and antiproliferative activities. The best inhibitory effects were observed in cells exposed to a combination of 8 or 10 % AORE and 25 mg/mL cisplatin. The optimal effect on cell proliferation was obtained using 25 mg/mL cisplatin and 10 % v/v AORE.Conclusion: The enhanced activity of cisplatin in combination with AORE was more pronounced for cell proliferation than cytotoxicity, indicating that AOREs may be used to control tumor progression and metastasis.Keywords: A549 cells, Althaea officinalis, lung cancer, cell proliferation, cell viability, cisplatin, modulatory effec

Energy saving potential of fragmented green spaces due to their temperature regulating ecosystem services in the summer

Urban green spaces help to moderate the urban heat island (UHI) effects, and can provide important temperature regulating ecosystem services and opportunities for savings in cooling energy. However, because explicit market values for these benefits are still lacking, they are rarely incorporated into urban planning actions. Green spaces can generate a three-dimensional (3D) cool island that may reduce the cooling energy requirements within and around urban areas, but such 3D cooling effect has not been considered in previous studies quantifying energy savings from green spaces. This study presents a new and simple approach to quantify potential energy savings due to the temperature regulating ecosystem services of small-scale fragmented green spaces using the 3D simulation of the summer-day outdoor thermal environment in Nanjing, China. Field survey data and the microclimate model ENVI-met were applied to examine the outdoor 3D thermal environmental patterns at Gulou Campus of Nanjing University under two different scenarios: “with” and “without” green spaces. Modeling results were applied to quantify potential cooling energy savings based on the effect of green spaces on the outdoor urban environment and to calculate the cumulative temperature reduction due to green spaces using a regression model. The results show that, in the horizontal direction, the simulated distribution of wind speed and mean air temperature at 1.5 m height were closely related to the spatial distribution of the underlying surface types. Removal of green spaces increased mean air temperature by 0.5 °C (33.1 °C vs. 33.6 °C). In the vertical direction, removal of green spaces had little effect on the near-surface wind field; however, above the surface, the turbulence perpendicular to the main wind direction significantly increased. Quantification of the cooling benefits of green spaces in relation to the mean height of buildings on Gulou Campus yielded 5.2 W/m2 cooling energy, saving totally 1.3 × 104 kW h during a single daytime hot summer period. This case study corroborates the importance of green space for cooling and informs city planners and decision-makers on how microclimate is impacted by the loss of green spaces. These findings will facilitate preservation, planning, and design of green spaces to increase urban environmental benefits and to improve the microclimate of urban areas at neighborhood, city, and regional scales

Changes of Urban Green Spaces and Their Driving Forces : a Case Study of Jinan City, China <Article>

Urban green spaces are looked upon as the last remnant of nature in urbanized areas. They play a pivotal role in environmental and ecological changes, and furthermore can decide whether we can have a sustainable development in the urban area. With the urban growth, green spaces varied in the area, type and spatial pattern. To optimize urban green space in the future, it is necessary to understand the driving forces on the processes of urban green space changes. In this study we chose Jinan City as a case study. We analyzed the process of urban green spaces dynamic from 1996 to 2004. During this period, a significant increase of the total area of urban green spaces took place. However, the growth rate of different green space types was not the same. To better link the spatial pattern change with the process, a gradient analysis was conducted in 4 directions combined with landscape metrics. Based on the spatiotemporal change of urban green spaces, it could be concluded that urban greening policies and urban sprawl were the main driving forces

Simulating urban growth processes incorporating a potential model with spatial metrics

Urbanization is one phenomena that drives land use pattern change. Persistent rapid urbanization is associated with depletion of natural resources and worsening conditions in the urban environment. Monitoring urban development is, therefore, an absolute necessity in order to assure sustainable cities in the future. The main objective of this paper is to develop and apply an urban growth potential model incorporating spatial metrics. The model has been tested in Jinan City, China. Firstly, two satellite images (1989 and 2004 SPOT) were used to extract the land-cover. A general land use spatial pattern analysis, based on landscape metrics and a transformation matrix analysis, was conducted. Secondly, a moving window method was used to identify and capture the urbanization process through the PLAND landscape metric. The remote satellite data have been further processed:first to produce an initial state of the land-cover surface, and second to perform a time-series analysis and to assess the potential accuracy of the model application. In the second step, the calibrated model was used to predict the location of the urban growth over 16 years (2004–2020). The results indicated there will be a significant land use change until 2020. However, the spatial distribution of the potential growth areas is not homogenous. The study has confirmed the usefulness of a growth potential model incorporating the moving window method to predict urban growth trends and examining the impacts of urban development on natural resources. The results can provide decision support documents for urban planners and stakeholders with spatially explicit information for future planning and monitoring plans

Prognostic and immune microenvironment analysis of cuproptosis-related LncRNAs in breast cancer

Breast cancer is the most common tumor and the leading cause of cancer death in women. Cuproptosis is a new type of cell death, which can induce proteotoxic stress and eventually lead to cell death. Therefore, regulating copper metabolism in tumor cells is a new therapeutic approach. Long non-coding RNAs play an important regulatory role in immune response. At present, cuproptosis-related lncRNAs in breast cancer have not been reported. Breast cancer RNA sequencing, genomic mutations, and clinical data were downloaded from The Cancer Genome Atlas (TCGA). Patients with breast cancer were randomly assigned to the train group or the test group. Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-related lncRNAs and to construct a risk prognostic model. The prediction performance of the model is verified and recognized. In addition, the nomogram was used to predict the prognosis of breast cancer patients. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immunoassay were used to detect the differences in biological function. Tumor mutation burden (TMB) was used to measure immunotherapy response. A total of 19 cuproptosis genes were obtained and a prognostic model based on 10 cuproptosis-related lncRNAs was constructed. Kaplan-Meier survival curves showed statistically significant overall survival (OS) between the high-risk and low-risk groups. Receiver operating characteristic curve (ROC) and principal component analysis (PCA) show that the model has accurate prediction ability. Compared with other clinical features, cuproptosis-related lncRNAs model has higher diagnostic efficiency. Univariate and multivariate Cox regression analysis showed that risk score was an independent prognostic factor for breast cancer patients. In addition, the nomogram model analysis showed that the tumor mutation burden was significantly different between the high-risk and low-risk groups. Of note, the additive effect of patients in the high-risk group and patients with high TMB resulted in reduced survival in breast cancer patients. Our study identified 10 cuproptosis-related lncRNAs, which may be promising biomarkers for predicting the survival prognosis of breast cancer

Causal identification of transit-induced property value uplift in Canada\u27s Waterloo Region: A spatio-temporal difference-in-differences method application

Renewed interest in light-rail transit (LRT) in North America has heightened the need for an improved understanding of transit impacts on land value uplift (LVU). A number of studies have investigated the relationship, with findings varying with local contexts and estimation methods. Most of these studies focus on the aggregate effects of transit using cross-sectional models, but do not examine the spatial and temporal heterogeneity in transit impacts through quasi-experimental approaches. To bridge this gap, we build a set of spatio-temporal difference-in-differences (STDID) models for the causal identification of transit-induced land-value uplift, taking the new LRT line in Canada\u27s Waterloo Region as the case study. The study contributes to the transit-induced LVU literature in several ways. First, we account for the space-time influence of recent comparable sales in price determination, a transaction data-generating process often excluded in hedonic studies. Second, our models reveal the disaggregate effects of transit policies in different station areas and transit phases. Third, we provide a pre-LRT analysis in a car-dependent mid-sized urban area, which offers insight into speculative investment in TOD areas and/or resident anticipation of future accessibility benefits. The findings can provide guidance for value-capture programs and cost-benefit analysis for transit-oriented development in mid-sized cities

The Impact of Greenspace on Thermal Comfort in a Residential Quarter of Beijing, China

With the process of urbanization, a large number of residential quarters, which is the main dwelling form in the urban area of Beijing, have been developed in last three decades to accommodate the rising population. In the context of intensification of urban heat island (UHI), the potential degradation of the thermal environment of residential quarters can give rise to a variety of problems affecting inhabitants’ health. This paper reports the results of a numerical study of the thermal conditions of a residential quarter on a typical summertime day under four greening modification scenarios, characterized by different leaf area density (LAD) profiles. The modelling results demonstrated that vegetation could evidently reduce near-surface air temperature, with the combination of grass and mature trees achieving as much as 1.5 °C of air temperature decrease compared with the non-green scenario. Vegetation can also lead to smaller air temperature fluctuations, which contribute to a more stable microclimate. The Universal Thermal Climate Index (UTCI) was then calculated to represent the variation of thermal environment of the study area. While grass is helpful in improving outdoor thermal comfort, trees are more effective in reducing the duration and expansion of suffering from severe heat stress. The results of this study showed that proper maintenance of vegetation, especially trees, is significant to improving the outdoor thermal environment in the summer season. In consideration of the deficiency of the current code in the management of greenspace in residential areas, we hope the results reported here will help promote the improvement of the code and related regulations for greenspace management

HNO: High-Order Numerical Architecture for ODE-Inspired Deep Unfolding Networks

Recently, deep unfolding networks (DUNs) based on optimization algorithms have received increasing attention, and their high efficiency has been confirmed by many experimental and theoretical results. Since this type of networks combines model-based traditional optimization algorithms, they have high interpretability. In addition, ordinary differential equations (ODEs) are often used to explain deep neural networks, and provide some inspiration for designing innovative network models. In this paper, we transform DUNs into first-order ODE forms, and propose a high-order numerical architecture for ODE-inspired deep unfolding networks. To the best of our knowledge, this is the first work to establish the relationship between DUNs and ODEs. Moreover, we take two representative DUNs as examples, apply our architecture to them and design novel DUNs. In theory, we prove the existence, uniqueness of the solution and convergence of the proposed network, and also prove that our network obtains a fast linear convergence rate. Extensive experiments verify the effectiveness and advantages of our architecture