Carbon storage estimation and strategy optimization under low carbon objectives for urban attached green spaces.

In the context of rapid urbanization, scarce land resources have highlighted the importance of attached green spaces (AGS), which have received limited attention despite their critical importance in the urban carbon cycle. Analyzing the differences in carbon storage of different AGS types and proposing different optimization strategies can provide a reference for urban managers to scientifically enhance the carbon sinks of greenfield. This study estimated the carbon storage of AGS in central Shanghai using a series of detailed field survey data and remote sensing data. The results showed that AGS stored 296 Gg (1Gg = 109 g) of carbon, accounting for 56 % of the total carbon storage in all urban green spaces. While carbon density in AGS is lower than that in other green space types due to the dominance of small trees, simple vegetation structures and poor maintenance in some areas. Significant differences in carbon storage capacity were also found among different types of AGS, related to individual vegetation attributes and vegetation structure. Our findings emphasized that optimal design strategies should be different for various green space types, and proposed corresponding optimization strategies for different AGS types. Furthermore, the dual role of multi-layered planting structures in carbon sinks and biodiversity was also emphasized.

Explore the spatial pattern of carbon emissions in urban functional zones: a case study of Pudong, Shanghai, China.

It is crucial for the development of carbon reduction strategies to accurately examine the spatial distribution of carbon emissions. Limited by data availability and lack of industry segmentation, previous studies attempting to model spatial carbon emissions still suffer from significant uncertainty. Taking Pudong New Area as an example, with the help of multi-source data, this paper proposed a research framework for the amount calculation and spatial distribution simulation of its CO2 emissions at the scale of urban functional zones (UFZs). The methods used in this study were based on mapping relations among the locations of geographic entities and data of multiple sources, using the coefficient method recommended by the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions. The results showed that the emission intensity of industrial zones and transport zones was much higher than that of other UFZs. In addition, Moran's I test indicated that there was a positive spatial autocorrelation in high emission zones, especially located in industrial zones. The spatial analysis of CO2 emissions at the UFZ scale deepened the consideration of spatial heterogeneity, which could contribute to the management of low carbon city and the optimal implementation of energy allocation.

Polycyclic aromatic hydrocarbon contamination along roads based on levels on vehicle window films.

Vehicular emissions are known to be major contributors of airborne polycyclic aromatic hydrocarbons (PAHs) in cities. In order to assess the long-term contamination of PAHs along roads, we collected organic films from vehicle windows (26 private cars and 4 buses, in Shanghai, China) and used mathematical models to convert the film-bound PAH concentrations to the airborne PAH concentrations. The field measurements of airborne PAHs revealed that the partitioning and Level III fugacity model was suitable to estimate the airborne concentrations of high and low volatile PAHs (expect for naphthalene), respectively. The total airborne PAH concentrations along roads in Shanghai ranged from 0.83 to 3.37 µg m-3 and the incremental lifetime cancer risks (ILCRtotal) by exposure to PAHs along roads were greater than the USEPA lower guideline of 10-6, indicating non-negligible carcinogenic risks to drivers and passengers, especially via ingestion processes. This study provided a practicable method to investigate long-term air contamination of PAHs in vehicles and along roads based on film-bound PAH on vehicle windows. In addition, it was also possible to investigate the health risk in vehicles as a result of exposure to PAHs. Comparisons of PAHs between roads and shipping lanes also facilitated the delineation of vehicular and shipping PAH inventories. A capsule that summarizes the main finding of the work: Investigating film-bound PAH on vehicle windows is a practicable pathway to investigate the long-term contamination of PAHs in vehicles and along roads. This method can not only simplify the sampling processes, but the model calculations. The results also enabled investigations into ILCR in vehicles and specified source apportionment of traffic PAHs.

Transfer of antibiotic-resistant bacteria via ballast water with a special focus on multiple antibiotic-resistant bacteria: A survey from an inland port in the Yangtze River.

Ship ballast water can transfer harmful organisms, including antibiotic-resistant bacteria (ARB), among geographically isolated waters. In this study, the presence and composition of ARB and multiple ARB (MARB) were investigated in the ballast waters of 30 vessels sailing to the Port of Jiangyin (Jiangsu Province, China). ARB were detected in 83.3% of the ship's ballast water samples. Moreover, penicillin- and cephalothin-resistant bacteria were the most and least prevalent ARB in the ballast waters, respectively. Oxytetracycline-, chloramphenicol-, tetracycline-, and vancomycin-resistant bacteria were also detected at high concentrations. The multiple antibiotic resistance index demonstrated the presence of MARB, which exceeded 200% in the ballast waters of five ships. Furthermore, 15 species, including the human opportunistic pathogens Vibrio alginolyticus and Serratia nematodiphila, were resistant to at least three antibiotics. Therefore, the potential ecological risk of ARB warrants further attention because of their effective invasion by ballast water.

Polycyclic aromatic hydrocarbon contaminations along shipping lanes and implications of seafarer exposure: Based on PAHs in ship surface films and a film-air-water fugacity model.

Polycyclic aromatic hydrocarbons (PAHs) are one of the most toxic compounds in ship tailpipe exhausts. The long-term contamination of PAHs along shipping lanes and ports is difficult to assess using conventional methods such as AIS-EFs-data based (AIS, Automatic identification system; EFs, emission factors) or field sampling methods. To address this, we collected the organic films on ship surfaces and used a modified film-air-water fugacity model to convert the film-bound concentrations to the airborne (gaseous plus particulate) concentrations. Not surprisingly, concentrations of PAHs on organic films on ship surfaces were greater than those measured on films on residential buildings. The airborne total PAH concentrations along shipping lanes in Yangtze River Delta area ranged from 63.3-325 ng m-3, which were in the same order of magnitude to those in Beijing during haze days. The incremental lifetime cancer risks by exposure to PAHs in ship indoor air were higher than the US EPA lower guideline, indicating considerable carcinogenic risks to seafarers. Our study proposes an alternative method to estimate the long-term contaminations of PAHs along shipping lanes and highlights a notable health risk to seafarers.

A scientometric visualization analysis for global toxicology and pharmacology research of natural products from 1962 to 2018.

BACKGROUND: A growing number of studies have been focused on the medicinal potential of natural products since 1962, while few scholars have analyzed the existing documents comprehensively. PURPOSE: Aiming to visualize the researches on toxicology and pharmacology of natural products (TPNP) published between 1962 and 2018, as well as to reveal their spatiotemporal patterns, a scientometric analysis with 3210 relevant documents collected from Web of Science was conducted in this study. RESULTS: The most prominent contributors of TPNP research are mainly from the USA, China, Brazil, India and Germany. The knowledge domains of TPNP research focus mainly on the topics of (1) traditional Chinese medicine, (2) richardia grandiflora, (3) chemical conversion, (4) new generation, (5) modern medicine, (6) intelligent mixture, (7) hplc-based activity. Most countries have recognized the pharmaceutical potential of natural products, and have paid more attention to the pharmacological and toxicological characteristics of natural products in the past decade. Future TPNP research tends to focus more on complex analysis of mechanisms for diseases treatment, such as toxicology and pharmacology. CONCLUSION: This research has firstly demonstrated a comprehensive knowledge map for the existing toxicological and pharmacological researches of natural products, which offered essential instructions on medical application of natural products to future research.

Nitrogen diagnosis based on dynamic characteristics of rice leaf image.

Digital image processing is widely used in the non-destructive diagnosis of plant nutrition. Previous plant nitrogen diagnostic studies have mostly focused on characteristics of the rice canopy or leaves at some specific points in time, with the long sampling intervals unable to provide detailed and specific "dynamic features." According to plant growth mechanisms, the dynamic changing rate in leaf shape and color differ between different nitrogen supplements. Therefore, the objective of this study was to diagnose nitrogen stress levels by analyzing the dynamic characteristics of rice leaves. Scanning technology was implemented to collect rice leaf images every 3 days, with the characteristics of the leaves from different leaf positions extracted utilizing MATLAB. Newly developed shape characteristics such as etiolation area (EA) and etiolation degree (ED), in addition to shape (area, perimeter) and color characteristics (green, normalized red index, etc.), were used to quantify the process of leaf change. These characteristics allowed sensitive indices to be established for further model validation. Our results indicate that the changing rates in dynamic characteristics, in particular the shape characteristics of the first incomplete leaf (FIL) and the characteristics of the 3rd leaf (leaf color and etiolation indices), expressed obvious distinctions among different nitrogen treatments. Consequently, we achieved acceptable diagnostic accuracy (training accuracy 77.3%, validation accuracy 64.4%) by using the FIL at six days after leaf emergence, and the new shape characteristics developed in this article (ED and EA) also showed good performance in nitrogen diagnosis. Based on the aforementioned results, dynamic analysis is valuable not only in further studies but also in practice.

Identification of nitrogen, phosphorus, and potassium deficiencies in rice based on static scanning technology and hierarchical identification method.

Establishing an accurate, fast, and operable method for diagnosing crop nutrition is very important for crop nutrient management. In this study, static scanning technology was used to collect images of a rice sample's fully expanded top three leaves and corresponding sheathes. From these images, 32 spectral and shape characteristic parameters were extracted using an RGB mean value function and using the Regionprops function in MATLAB. Hierarchical identification was used to identify NPK deficiencies. First, the normal samples and non-normal (NPK deficiencies) samples were identified. Then, N deficiency and PK deficiencies were identified. Finally, P deficiency and K deficiency were identified. In the identification of every hierarchy, SVFS was used to select the optimal characteristic set for different deficiencies in a targeted manner, and Fisher discriminant analysis was used to build the diagnosis model. In the first hierarchy, the selected characteristics were the leaf sheath R, leaf sheath G, leaf sheath B, leaf sheath length, leaf tip R, leaf tip G, leaf area and leaf G. In the second hierarchy, the selected characteristics were the leaf sheath G, leaf sheath B, white region of the leaf sheath, leaf B, and leaf G. In the third hierarchy the selected characteristics were the leaf G, leaf sheath length, leaf area/leaf length, leaf tip G, difference between the 2nd and 3rd leaf lengths, leaf sheath G, and leaf lightness. The results showed that the overall identification accuracies of NPK deficiencies were 86.15, 87.69, 90.00 and 89.23% for the four growth stages. Data from multiple years were used for validation, and the identification accuracies were 83.08, 83.08, 89.23 and 90.77%.

[Leaf characteristics extraction of rice under potassium stress based on static scan and spectral segmentation technique].

The timing, convenient and reliable method of diagnosing and monitoring crop nutrition is the foundation of scientific fertilization management. However, this expectation cannot be fulfilled by traditional methods, which always need excessively work on sampling, detection and analysis and even exhibit lagging timing. In the present study, stable images for potassium-stressed leaf were acquired using stationary scanning, and object-oriented segmentation technique was adopted to produce image objects. Afterwards, nearest neighbor classifier integrated the spectral, shape and topologic information of image objects to precisely identify characteristics of potassium-stressed features. Diagnosing with image, the 3rd expanded leaves are superior to the 1st expanded leaves. In order to assess the result, 250 random samples and an error matrix were applied to undertake the accuracy assessment of identification. The results showed that the overall accuracy and kappa coefficient was 96.00% and 0.9453 respectively. The study offered an information extraction method for quantitative diagnosis of rice under potassium stress.

[Cotton identification and extraction using near infrared sensor and object-oriented spectral segmentation technique].

The real-time, effective and reliable method of identifying crop is the foundation of scientific management for crop in the precision agriculture. It is also one of the key techniques for the precision agriculture. However, this expectation cannot be fulfilled by the traditional pixel-based information extraction method with respect to complicated image processing and accurate objective identification. In the present study, visible-near infrared image of cotton was acquired using high-resolution sensor. Object-oriented segmentation technique was performed on the image to produce image objects and spatial/spectral features of cotton. Afterwards, nearest neighbor classifier integrated the spectral, shape and topologic information of image objects to precisely identify cotton according to various features. Finally, 300 random samples and an error matrix were applied to undertake the accuracy assessment of identification. Although errors and confusion exist, this method shows satisfying results with an overall accuracy of 96.33% and a KAPPA coefficient of 0.926 7, which can meet the demand of automatic management and decision-making in precision agriculture.