Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

In recent years, the frequency of extreme weather has increased, and urban waterlogging caused by sudden rainfall has occurred from time to time. With the development of urbanization, a large amount of land has been developed and the proportion of impervious area has increased, intensifying the risk of urban waterlogging. How to use the available meteorological data for accurate prediction and early warning of waterlogging hazards has become a key issue in the field of disaster prevention and risk assessment. In this paper, based on historical meteorological data, we combine domain knowledge and model parameters to experimentally extract rainfall time series related features for future waterlogging depth prediction. A novel waterlogging depth prediction model that applies only rainfall data as input is proposed by machine learning algorithms. By analyzing a large amount of historical flooding monitoring data, a “rainfall-waterlogging amplification factor” based on the geographical features of monitoring stations is constructed to quantify the mapping relationship between rainfall and waterlogging depths at different locations. After the model is trained and corrected by the measured data, the prediction error for short-time rainfall basically reaches within 2 cm. This method improves prediction performance by a factor of 2.5–3 over featureless time series methods. It effectively overcomes the limitations of small coverage of monitoring stations and insufficient historical waterlogging data, and can achieve more accurate short-term waterlogging prediction. At the same time, it can provide reference suggestions for the government to conduct waterlogging risk analysis and add new sensor stations by counting the amplification factor of other locations

Assessing the Impact of Different Agricultural Irrigation Charging Methods on Sustainable Agricultural Production

China is currently experiencing severe water scarcity issues in its agricultural production sector. To address this challenge, the Chinese government has taken steps towards implementing a nationwide reform in agricultural water pricing to accelerate the more sustainable management of the agricultural water resources sector. The present study adopted a multiple regression model to test four alternative irrigation water charging methodologies (charges based on ladder pricing, time, land area, and electricity) accompanied by supportive agricultural pricing policies to address the inherent conflicts between water conservation and agricultural development goals. This study focused on the Wei River Basin, which is recognized as a highly water-stressed region in China. This basin was chosen as a pilot area for comprehensive reform initiatives related to agricultural water pricing and served as the geographical scope for our research. Between June and July of 2022, we conducted comprehensive field surveys within the Wei River Basin, accumulating a dataset of 415 data points pertaining to the crop year of 2022. Our results showed that the ladder water price-based method exhibited remarkable potential in achieving substantial savings, with a minimum of 60.5239 m3/mu of irrigation water conserved for food crops and an impressive 67.8090 m3/mu for cash crops. However, regarding water-saving irrigation technologies, the estimation results indicated that electricity-based charging outperformed the other methods, resulting in an impressive 55.22% increase when ladder pricing served as the benchmark. In addition, regarding agricultural green production, the results for food crops and cash crops are different, with food crops being more sensitive to the ladder water price policies. Moreover, the results suggested that different water charging methods have significant heterogeneity effects from the perspective of the farmers’ scale, land fragmentation, and water price awareness capacity. This study forges an innovative path for water-stressed nations to execute agricultural water pricing reform and enhance agricultural production’s sustainable growth

Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification

The complex formation mechanism and numerous influencing factors of urban waterlogging disasters make the identification of their risk an essential matter. This paper proposes a framework for identifying urban waterlogging risk that combines multi-source data fusion with hydrodynamics (MDF-H). The framework consists of a source data layer, a model parameter layer, and a calculation layer. Using multi-source data fusion technology, we processed urban meteorological information, geographic information, and municipal engineering information in a unified computation-oriented manner to form a deep fusion of a globalized multi-data layer. In conjunction with the hydrological analysis results, the irregular sub-catchment regions are divided and utilized as calculating containers for the localized runoff yield and flow concentration. Four categories of source data, meteorological data, topographic data, urban underlying surface data, and municipal and traffic data, with a total of 12 factors, are considered the model input variables to define a real-time and comprehensive runoff coefficient. The computational layer consists of three calculating levels: total study area, sub-catchment, and grid. The surface runoff inter-regional connectivity is realized at all levels of the urban road network when combined with hydrodynamic theory. A two-level drainage capacity assessment model is proposed based on the drainage pipe volume density. The final result is the extent and depth of waterlogging in the study area, and a real-time waterlogging distribution map is formed. It demonstrates a mathematical study and an effective simulation of the horizontal transition of rainfall into the surface runoff in a large-scale urban area. The proposed method was validated by the sudden rainstorm event in Futian District, Shenzhen, on 11 April 2019. The average accuracy for identifying waterlogging depth was greater than 95%. The MDF-H framework has the advantages of precise prediction, rapid calculation speed, and wide applicability to large-scale regions

In Situ Photoacoustic Detection System for SO2 in High-Pressure SF6 Buffer Gas Using UV LED

Sulfur dioxide (SO2) is a key indicator for fault diagnosis in sulfur hexafluoride (SF6) gas-insulated equipment. In this work, an in situ photoacoustic detection system using an ultraviolet (UV) LED light as the excitation source was established to detect SO2 in high-pressure SF6 buffer gas. The selection of the SO2 absorption band is discussed in detail in the UV spectral regions. Based on the result of the spectrum selection, a UV LED with a nominal wavelength of 285 nm and a bandwidth of 13 nm was selected. A photoacoustic cell, as well as a high-pressure sealed gas vessel containing it, were designed to match the output optical beam and to generate a PA signal in the high-pressure SF6 buffer gas. The performance of the proposed system was assessed in terms of linearity and detection limit. An SO2 detection limit (1σ) of 0.17 ppm was achieved. Additionally, a correction method was supplied to solve PA signal derivation induced by pressure fluctuation. The method can reduce the derivation from about 5% to 1% in the confirmation experiment

Application of Excimer Lamp in Quantitative Detection of SF₆ Decomposition Component SO₂

Accurate quantitative detection for trace gas has long been the center of failure diagnosis for gas-insulated equipment. An absorption spectroscopy-based detection system was developed for trace SF6 decomposition SO2 detection in this paper. In order to reduce interference from other decomposition, ultraviolet spectrum of SO2 was selected for detection. Firstly, an excimer lamp was developed in this paper as the excitation of the absorption spectroscopy compared with regular light sources with electrodes, such as electrodeless lamps that are more suitable for long-term monitoring. Then, based on the developed excimer lamp, a detection system for trace SO2 was established. Next, a proper absorption peak was selected by calculating spectral derivative for further analysis. Experimental results indicated that good linearity existed between the absorbance and concentration of SO2 at the chosen absorption peak. Moreover, the detection limit of the proposed detection system could reach the level of 10−7. The results of this paper could serve as a guide for the application of excimer lamp in online monitoring for SF6-insulated equipment

Identification of Urban Rainstorm Waterlogging Based on Multi-source Information Fusion：A Case Study in Futian District, Shenzhen

Flood disasters have become one of the most threatening natural disasters in the world, in which waterlogging is the most common form in the context of highly urbanized megacities. The formation of flood disaster is related to many factors and involves information from multiple sources, making it difficult be predicted. This paper integrates multi-source information data, classifies the study area into different categories according to hydrological analysis results, and combines hydrodynamic theory and ArcGIS to get the quantitative prediction of the range and depth of waterlogging under different rainfall inputs. The evaluation results provide the government with accurate and timely information of waterlogging risks and locations in order to improve promptness of emergency management such as evacuation and managing traffics

Revealing the Effect of Photothermal Therapy on Human Breast Cancer Cells: A Combined Study from Mechanical Properties to Membrane HSP70

Hyperthermia-induced overexpression of heat shock protein 70 (HSP70) leads to the thermoresistance of cancer cells and reduces the efficiency of photothermal therapy (PTT). In contrast, cancer cell-specific membrane-associated HSP70 has been proven to activate antitumor immune responses. The dual effect of HSP70 on cancer cells inspires us that in-depth research of membrane HSP70 (mHSP70) during PTT treatment is essential. In this work, a PTT treatment platform for human breast cancer cells (MCF-7 cells) based on a mPEG-NH2-modified polydopamine (PDA)-coated gold nanorod core–shell structure (GNR@PDA-PEG) is developed. Using the force-distance curve-based atomic force microscopy (FD-based AFM), we gain insight into the PTT-induced changes in the morphology, mechanical properties, and mHSP70 expression and distribution of individual MCF-7 cells with high-resolution at the single-cell level. PTT treatment causes pseudopod contraction of MCF-7 cells and generates a high level of intracellular reactive oxygen species, which severely disrupt the cytoskeleton, leading to a decrease in cellular mechanical properties. The adhesion maps, which are recorded by aptamer A8 functional probes using FD-based AFM, reveal that PTT treatment causes a significant upregulation of mHSP70 expression and it starts to exhibit a partial aggregation distribution on the MCF-7 cell surface. This work not only exemplifies that AFM can be a powerful tool for detecting changes in cancer cells during PTT treatment but also provides a better view for targeting mHSP70 for cancer therapy

Metal/oxide interfacial effects on the selective oxidation of primary alcohols

A main obstacle in the rational development of heterogeneous catalysts is the difficulty in identifying active sites. Here we show metal/oxide interfacial sites are highly active for the oxidation of benzyl alcohol and other industrially important primary alcohols on a range of metals and oxides combinations. Scanning tunnelling microscopy together with density functional theory calculations on FeO/Pt(111) reveals that benzyl alcohol enriches preferentially at the oxygen-terminated FeO/Pt(111) interface and undergoes readily O–H and C–H dissociations with the aid of interfacial oxygen, which is also validated in the model study of Cu(2)O/Ag(111). We demonstrate that the interfacial effects are independent of metal or oxide sizes and the way by which the interfaces were constructed. It inspires us to inversely support nano-oxides on micro-metals to make the structure more stable against sintering while the number of active sites is not sacrificed. The catalyst lifetime, by taking the inverse design, is thereby significantly prolonged