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AbstractPhenolic compounds are secondary metabolites involved in plant innate chemical defence against pests and diseases. Their concentration varies depending on plant tissue and also on genetic and environmental factors, e.g. availability of nutrient resources. This study examines specific effects of low (LN) and high (HN) nitrogen supply on organ (root, stem and leaf) growth and accumulation of major phenolics [chlorogenic acid (CGA); rutin; kaempferol rutinoside (KR)] in nine hydroponically grown tomato cultivars. LN limited shoot growth but did not affect root growth, and increased concentrations of each individual phenolic in all organs. The strength of the response was organ‐dependent, roots being more responsive than leaves and stems. Significant differences were observed between genotypes. Nitrogen limitation did not change the phenolic content in shoots, whereas it stimulated accumulation in roots. The results show that this trade‐off between growth and defence in a LN environment can be discussed within the framework of the growth–differentiation balance hypothesis (i.e. GDBH), but highlight the need to integrate all plant organs in future modelling approaches regarding the impact of nitrogen limitation on primary and secondary metabolism.

Abstract. The impact of multiphase reactions involving nitrogen dioxide (NO2) and aromatic compounds was simulated in this study. A mechanism (CAPRAM 2.4, MODAC Mechanism) was applied for the aqueous phase reactions, whereas RACM was applied for the gas phase chemistry. Liquid droplets were considered as monodispersed with a mean radius of 0.1 µm and a liquid content (LC) of 50 µg m-3. The multiphase mechanism has been further extended to the chemistry of aromatics, i.e. reactions involving benzene, toluene, xylene, phenol and cresol have been added. In addition, reaction of NO2 with dissociated hydroxyl substituted aromatic compounds has also been implemented. These reactions proceed through charge exchange leading to nitrite ions and therefore to nitrous acid formation. The strength of this source was explored under urban polluted conditions. It was shown that it may increase gas phase HONO levels under some conditions and that the extent of this effect is strongly pH dependent. Especially under moderate acidic conditions (i.e. pH above 4) this source may represent more than 75% of the total HONO/NO2 - production rate, but this contribution drops down close to zero in acidic droplets (as those often encountered in urban environments).

Even with the benefits of sustainability and climate change reporting, there is limited information on how municipalities are reporting on performance for external stakeholders in comparison with private sector organizations. The purpose of this research was to gain an understanding of the current state of sustainability and climate change reporting at the local level and to investigate the extent to which municipalities across Ontario, Canada, report. We used content analysis to identify the presence or non-presence of information on the websites of 38 municipalities and analyzed the results using descriptive statistics. Our analysis showed that the sample municipalities were not widely reporting on sustainability or climate change performance. Also, we identified a gap between the number of plans and reports produced by sample municipalities, with the latter being less common, indicating a need for an improved evaluation of plan implementation. Further, we found that a provincial regulation that required municipalities to make their energy conservation and demand management plans public did not guarantee publication of the plan on a municipality’s website. This study contributes to the growing field of sustainability and climate change planning and reporting by local governments and offers empirical evidence specific to Ontario, Canada.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Camptotheca acuminata is considered a natural medicinal plant with antitumor activity. The assessment of climate change impact on its suitable habitats is important for cultivation and conservation. In this study, we applied a novel approach to build ecological niche models with both climate and soil variables while the confounding effects between the variables in the two categories were avoided. We found that the degree-days below zero and mean annual precipitation were the most important climatic factors, while the basic soil saturation, soil gravel volume percentage, and clay content were the main soil factors, determining the suitable habitats of C. acuminata. We found that suitable habitats of this species would moderately increase in future climates under both the RCP4.5 and RCP8.5 climate change scenarios for the 2020s, 2050s, and 2080s. However, substantial shifts among levels of habitat suitability were projected. The dual high-suitable habitats would expand, which would be favorable for commercial plantations. Our findings contribute to a better understanding of the impact of climate change on this species and provide a scientific basis for the cultivation and conservation purposes.

Coupling between the UEDGE (edge fluid model), GINGRED (grid generation) and CAKE (equilibrium reconstruction) codes opens the door for automated interpretative scrape-off-layer (SOL) analysis over entire discharges, providing information that is essential in efforts to couple the SOL to core transport codes. In this work, we utilize new developments in the autoUEDGE code (Izacard et al. 2018) to investigate the behavior of the DIII-D SOL during the temporal evolution of an edge-localized mode (ELM) cycle. Modeled temperature and density profiles in UEDGE are automatically matched to experimental measurements by iteratively and self-consistently adjusting transport coefficient profiles in the plasma edge. This analysis is completed over multiple ELM cycles of a well-diagnosed discharge with long (∼100ms) inter-ELM periods. Directly after the ELM crash, a short period of high-density, low-temperature conditions is observed in Langmuir probe measurements at the outer divertor. This regime is associated with enhanced Dαemission and incident particle flux, suggesting that the divertor enters a period of high recycling after an ELM crash. After about ∼25ms, divertor conditions return to their pre-ELM conditions and remain there for several tens of milliseconds. Using the autoUEDGE code, the SOL is modeled as a function of ELM cycle using upstream profiles as input. The 2D modeling successfully reproduces both divertor Thomson scattering measurements and the experimentally observed divertor dynamics. Though the recycling is kept fixed throughout the modeling, changes in particle fluxes are consistent with local experimental recycling changes induced by ELMs. Agreement between modeling and observation suggests a strong link between upstream profiles and the high-recycling divertor conditions directly following large type-I ELMs.