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In this work we will discuss the suitability of carbon isotope analysis in plants for the assessment of environmental changes and their effects on crops and natural systems. For C₃ species, carbon isotope composition (δ¹³C) of plant tissues constitutes an integrated record of the miscellaneous climatic and physiological factors that affect carbon assimilation and/or stomatal conductance. Here we present a literature review on the relationship between different environmental parameters and δ¹³C in both herbaceous plants and trees, including some examples and case studies. We will also consider the applicability of some of these relationships in palaeoecological studies, as well as for the assessment of climate change dynamics and its implications. Major advantages and limitations of this technique are further discussed.

Potato (Solanum tuberosum L. cv. Kara) was grown in Open‐Top Chambers (OTCs) in Northern Egypt at ambient (ca 350 ppm) or doubled CO₂ (ca 690 ppm) either in charcoal‐filtered air (15 nl l‐1) or in non‐filtered ambient air (78 nll‐1 O₃) to investigate the changes in physiology and yield under long‐term elevated CO₂ and/or O₃ throughout 100 days. Ambient O₃ level reduced net photosynthetic rates, number and weight of tubers by 18 per cent, 41 per cent and 21 per cent, respectively, whereas elevated CO₂ caused the opposite effect where it increased the same parameters by 44 per cent, 37 per cent and 20 per cent, respectively. Significant O₃ x CO₂ interactions were detected. However, O₃ caused an increase in GR and POD by 18 per cent and 35 per cent, respectively, while CO₂ caused an increase in POD only by 46 per cent, and there was no effect of O₃ and/or CO₂ on other enzymes. The results of this study are discussed in relation to predicted atmospheric changes.

Green roofs are strategies for the ecological intensification of cities and a measure of meeting some of the sustainable development goals (SDGs). They have widely been adopted as an adaptation strategy against an urban heat island (UHI). However, they are conventionally soil-based making it difficult and expensive to adopt as a strategy for greening existing buildings (GEB). This paper, therefore, develops a novel green roof system using climbers for thermal-radiative performance. The paper explores the vitality of climbing species as a nature-based strategy for GEB, and for the ecological improvement of the predominantly used cool roofs in sub-Saharan Africa (SSA).

Simulation for the same building Kejetia Central Market (KCM) Redevelopment; the existing aluminium roof (AL), soil-based extensive green roof (GR1) and the proposed green roof using climbing plants (GR2) were performed using ENVI-met. The AL and GR1 were developed as reference models to evaluate and compare thermal-radiative performance of the conceptual model (GR2). The long wave radiation emission (Q_lw), mean radiant temperature (MRT) and outdoor air temperature (T_a) of all three roofing systems were simulated under clear sky conditions to assess the performance and plant vitality considering water access, leaf temperature (T_f) and latent heat flux (LE₀) of GR1 and GR2.

There was no short wave radiation (Q_sw) absorption at the GR2 substrate since the climbers have no underlying soil mass, recording daily mean average Q_lw emission of 435.17 Wm⁻². The soil of GR1, however, absorbed Q_sw of 390.11 Wm⁻² and a Q_lw emission of 16.20 wm⁻² higher than the GR2. The AL recorded the lowest Q_lw value of 75.43 Wm⁻². Also, the stomatal resistance (r_s) was higher in GR1 while GR2 recorded a higher average mean transpiration flux of 0.03 g/sm³. This indicates a higher chance of survival of the climbers. The T_a of GR2 recording 0.45°C lower than the GR1 could be a good UHI adaptation strategy.

No previous research on climbers for green roof systems was found for comparison, so the KCM project provided a unique confluence of dynamic events including the opportunity for block-scale impact assessment of the proposed GEB strategy. Notwithstanding, the single case study allowed a focussed exploration of the novel theory of redefining green roof systems with climbers. Moreover, the simulation was computationally expensive, and engaging multiple case studies were found to be overly exhaustive to arrive at the same meaningful conclusion. As a novelty, therefore, this research provides an alternative theory to the soil-based green roof phenomenon.

The thermal-radiative performance of green roofs could be improved with the use of climbers. The reduction of the intensity of UHI would lead to improved thermal comfort and building energy savings. Also, very little dependence on the volume of soil would require little structural load consideration thereby leading not only to cheaper green roof construction but their higher demand, adoption and implementation in SSA and other low-income economies of the global south.

The reduction of the consumption of topsoil and water for irrigation could avoid the negative environmental impacts of land degradation and pollution which have a deleterious impact on human health. This fulfils SDG 12 which seeks to ensure responsible consumption of products. This requires the need to advance the research for improvement and training of local built environment practitioners with new skills for installation to ensure social inclusiveness in the combat against the intractable forces of negative climate impacts.

Climbers are mostly known for green walls, but their innovative use for green roof systems has not been attempted and adopted; it could present a cost-effective strategy for the GEB. The proposed green roof system with climbers apart from becoming a successful strategy for UHI adaptation was also able to record an estimated 568% savings on topsoil consumption with an impact on the reduction of pollution from excavation. The research provides an initial insight into design options, potentials and limitations on the use of climbers for green roofs to guide future research and experimental verification.

Incineration strengths of hazardous (2.8×10⁸μg/s) and nonhazardous (6.31×10⁸μg/s) materials were found from the quantities to be incinerated, chemical formulas, and the incineration time. The smoke stack geometry, exhaust dynamics, and different atmospheric stability conditions were used in the Gaussian model to predict the maximum concentration distances of 0.5 to about 6 km at the ground level in the downwind direction. However, trailing edges of some of exhaust distributions were found to extend beyond 120 km under some atmospheric stability conditions. The counties of Jefferson, Lonoke, Pulaski, Dallas, Cleveland, Calhoun, and Grant in the state of Arkansas are more likely to be affected than others. The possible major products in the hazardous incineration exhaust are chlorinated compounds. The results of this study are important to know the areas that fall under comparatively higher concentrations of incineration exhaust for further observations because of its inherent impact upon living beings, crop production, and environmental conditions.

The purpose of this paper is to provide an overview of smart agriculture systems and monitor and identify the technologies which can be used for deriving traditional agriculture system to modern agriculture system. It also provides the reader a broad area to work for the advancement in the field of agriculture and also explains the use of advanced technologies such as spectral imaging, robotics and artificial intelligence (AI) in the field of agriculture.

Smart uses of modern technologies were reviewed in the field of agriculture, which helps to monitor stress levels of plants and perform operations according to requirements. Operations can be irrigation, pests spray, monitoring crops, monitoring yield production, etc. Based on the literature review, a smart agriculture system is suggested. The parameters studied were spectral image processing, AI, unmanned aerial vehicle (UAVs) (fixed and rotatory), water or soil moisture, nutrients and pesticides.

The use of autonomous vehicles and AI techniques has been suggested through which the agriculture system becomes much more efficient. The world will switch to the smart agriculture system in the upcoming era completely. The authors conclude that autonomous vehicle in the field of science is time-saving and health efficient for both plants and workers in the fields. The suggested system increases the productivity of crops and saves the assets as well.

This review paper discusses the various contemporary technologies used in the field of agriculture and it will help future researchers to build on this research. This paper reveals that the UAVs along with multispectral, hyperspectral or red, green and blue camera (depends on the need) and AI are more suitable for the advancement of agriculture and increasing yield rate.

Mangroves constitute an important ecosystem because of their global extent and high productivity. These plants thrive in the intertidal zones of the tropics and subtropics that are characterized by regular tidal inundation and fluctuating salinity. Mangrove species are well adapted, both morphologically and physiologically, to survive under saline conditions, but in hypersaline environment their growth is reduced. The present chapter is a critical analysis on the impact of salinity on the growth of a common mangrove species (Hertiera fomes). The analysis has been carried out in the framework of Indian Sundarbans, which has contrasting salinity profiles in different segments owing to barrage discharge and siltation phenomena. Analysis of the decadal profile of salinity indicates a gradual lowering in the western Indian Sundarbans due to Farrakka barrage discharge and run-off from catchments. The central sector, however, exhibits a contrasting picture of increment of aquatic salinity through time, mainly due to disconnection of the Bidyadhari River with the Ganga–Bhagrirathi–Hooghly River system (in the western part). This has made the Matla River in the central Indian Sundarbans hypersaline in nature (that used to get water from the Bidyadhari River) finally leading to an insecure ecological condition for the growth and survival of mangroves. The possible remedial measures to combat the situation have also been listed considering the ecological framework of the study zone.

The purpose of this study is to examine the effects of nitrogen (N) deposition on clonal growth in a rhizome clonal plant, Leymus chinensis (Trin.) Tzvel.

The study established seven N concentration gradients (0, 2, 4, 8, 16, 32 and 64 g N m⁻²) to simulate the continuous increase in N deposition for the cultivation of L. chinensis seedlings and assess the response mechanism of the cloned L. chinensis plant at different N levels by analyzing the aboveground and belowground plant appearance traits, parent ramets and daughter ramets of resource allocation and biomass allocation.

The results of this study showed that the different N treatment levels could promote clonal growth and had certain regularity under the seven treatments. The addition of N could significantly increase the ramet number, rhizome length, rhizome spacer length, biomass of mother ramets, daughter ramets and belowground L. chinensis population when the N addition was greater than 4 g m⁻²; however, the clonal growth ability of L. chinensis decreased and the rhizome length, ramet number, stem and leaf biomass of daughter ramets and stem biomass of mother ramets significantly decreased when the N addition was greater than 32 g N m⁻².

With global warming, atmospheric N deposition is increasing and it is of great significance to explore the response mechanism of different N levels for the growth of clone plants. This study provides basic data and a theoretical basis for the survival prediction of cloned plants under the background of a global climate change strategy and has important theoretical and practical significance for the scientific management of grasslands in the future.

Drought stress can be considered as a dominant factor contributing to degradation of Mediterranean drylands. Therefore, there is a strong need to monitoring the level of water stress suffered by vegetation in the view of relating it to desertification risk of selected areas. In the present paper, some techniques for measuring and quantifying plant water stress are briefly reviewed and the advantages of using the pressure chamber technique to measure leaf water potential (Ψ_L) are highlighted. A new index (water stress impact on vegetation (WSIV)), based on the integral of the diurnal changes of Ψ_L, is proposed and its effectiveness for quantifying the amount of water stress suffered by vegetation is discussed on the basis of measurements performed in degraded areas of Turkey and Lebanon. The usefulness of WSIV as an ecophysiological tool for assessing the desertification risk of Mediterranean drylands is also discussed.

This study aimed to estimate the economic benefits of PM2.5 emission abatement by Red Pine, Pinus Koraiensis and Quercus, using a metering model analyzing the amount of PM2.5 absorption in Korea.

To estimate the economic effects of PM2.5 adsorptions by trees, the frequency of hospital visits resulting from respiratory and circulatory diseases was estimated using a Probit model based on the data from National Health and Nutrition Survey.

The results show that Quercus and Pinus Koraiensis absorb and eliminate the largest amount of PM2.5. Reducing 1 ton of PM2.5 emission through the planting of trees leads to lower incidences of respiratory and circulatory diseases equivalent to the amount of 95 million won. When the trees planted are 2-year-old Red Pine, Pinus Koraiensis and Quercus, the resulting economic benefits of the PM2.5 abatement would amount to 481 million won, 173 million won and 1,027 million won, respectively. If the trees are 80 years old, the economic benefits are estimated to be 73 billion won for Red Pine, 103 billion won for Pinus Koraiensis and 38 billion won for Quercus.

One limitation of this study is that the weight of PM2.5 adsorbed by each leaf area entirely depended on the experimental results from a prior study and the values are likely to be different from those actually absorbed in natural surroundings. In addition, because of the lack of data from a domestic survey on the surface of leaf area or the reload flow rate of PM2.5, this study referred to data from foreign research. Unfortunately, this specific data may not reflect climatic and terrain characteristics specific to the target country. We used the annual wind speed to calculate the reload flow rate and elimination volume; however, the figures could be more accurate with hourly or daily climate variations. When estimating the health benefits of changes in PM2.5 emissions on respiratory and circulatory diseases, more segmented access to patients' hospital visits and hospital admissions are desirable. Finally, the study focused on the three major tree species of Korea, however, a more detailed study of PM2.5 reduction by various tree types is needed in the future.

This paper quantitatively assessed the amount of PM2.5 adsorption by each of the three tree species. Then, the economic benefits were calculated in terms of how much money would be saved on hospital visits thanks to the reduced PM2.5 levels and lower incidences of respiratory and circulatory system diseases. The net contribution of this study was to prove the trees' function of reducing PM2.5 as it relates to human health. We focused on the most common trees in Korea and compared them to provide new information on the species.

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.