Environmental science
Recent Submissions
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Steps toward an integrated soil water tension and osmotic tension sensorThe two most important abiotic plant stressors that impact plant development and crop yields are water stress and salinity stress. These issues are particularly important in arid and semi-arid regions. According to a 2019 research paper, “thirty crop species provide 90% of our food, most of which display severe yield losses under moderate salinity.” Moderate salinity is defined as extracted pore-water salinity in the range of 4–8 dS m−1. Currently, commercially available soil moisture and bulk soil electrical conductivity sensors can estimate in situ soil pore-water electrical conductivity with suitably calibrated soil moisture and electrical conductivity models for a wide range of soil types and growing media. With knowledge of the pore-water electrical conductivity it is possible to estimate osmotic tension. Furthermore, there are commercially available dielectric tensiometers that provide soil water tension measurements from the water content of a porous matrix component that is in equilibrium with the wat
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An investigation into the impact of soil particle conductivity and percolation threshold on the Hilhorst model to estimate pore water conductivity in soilsThe aim of this work has been to assess the Hilhorst model, used for estimating in-soil pore water conductivity, against soil properties of percolation threshold and soil particle conductivity. The Hilhorst model has the benefit of requiring a single soil-specific parameter which makes this model easy to apply from soil permittivity and bulk conductivity measurements. However, the Hilhorst model requires that the bulk conductivity measurement is dominated by pore water conductivity, which is not always the case in many ‘‘real-world’’ settings. This work examines a mathematical framework derived from combining the Hilhorst and Ewing and Hunt models which allows the Hilhorst soil parameter to be derived for a range of soil particle conductivities (0 to 10 mS m−1) and percolation thresholds (0 to 0.1 m3 m−3). The analysis in this work indicates that the Hilhorst parameter is highly sensitive to both soil properties with respect to the default value of 4.1 that is often employed. This assessment indicates that: (
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A graphical derivation and statistical evaluation of simplified polynomials to determine vapour pressure deficit for use in ultra-low power microcontroller applicationsThe aim of this work has been to derive and statistically evaluate the accuracy of second-order and third-order polynomials to determine vapour pressure deficit (VPD). These polynomials take air temperature and relative humidity measurements to determine VPD without the use of an exponential function, as proposed by F W Murray in 1967. Replacing the exponential function with a 2nd or 3rd order polynomial may be beneficial in ultra-low power microcontroller-based measurement applications where; code size, memory usage and power requirements are critical design drivers. However, oversimplification may impact precision. This work presents alternative 2nd order and 3rd order equations that have been derived from a Murray equation dataset where VPD isothermal datasets were plotted against relative humidity. These linear relationships allow y = mx + c analysis where, (i) 'c' can be set to zero with a offset in the relative humidity data, and, (ii) 'm' can be derived from a 2nd or 3rd order polynomial where 'm' = f(
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Spatio-temporal patterns of rainfall variability in BangladeshBangladesh is experiencing a more rapid warming trend compared to the global average, facing significant climate-related risks. This study gave a comprehensive assessment of rainfall variability across the whole Bangladesh during 1989–2022. Annual rainfall in Bangladesh exhibited significant decreasing trends and high oscillation patterns. The multi-scale SPI and SPEI analysis revealed that Bangladesh experienced severe droughts in 1995, 1999, 2018, 2021 and 2022. The DFA revealed that rainfall evolution exhibited significant long-term positive correlation in almost Bangladesh. These results will support policymakers in Bangladesh to develop suitable strategies in mitigating climate change impacts.
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High-resolution precipitation prediction in Bangladesh via ensemble learningAs a developing agricultural country, Bangladesh is vulnerable to the effects of climate change, so accurate precipitation prediction is of great value to Bangladesh in achieving sustainable development. Traditional climate simulation models and prediction tools find it challenging to meet the growing needs on high spatial resolution. In this paper, we developed a XGBoost-based spatio-temporal precipitation prediction model and then generated high-resolution precipitation distribution maps in Bangladesh from 2025 to 2035, where the spatial resolution can reach 0.1° latitude and longitude. Finally, the EOF analysis reveals three leading modes in high-resolution precipitation evolution during 2025–2035.
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Effects of urban land-use planning on housing prices in Chiang Mai, ThailandChiang Mai is an emerging tourism-oriented city in Thailand. The booming tourism industry during the past decades has triggered significant expansion in its urban land area, resulting in a large number of newly-built residential communities appearing on unplanned land. In this study, we used multiscale geographically weighted regression (MGWR)-based hedonic price analysis to investigate 4624 housing transactions from 524 residential communities in Chiang Mai. This showed that the recent land-use planning in Chiang Mai has had unusual effects on housing prices; specifically, the effects of accessibility to hospitals, primary and secondary schools, green parks, and shopping malls could be ignored, demonstrating that local residents were well satisfied with land-use planning for high-quality medical and education sources and good living environments throughout the whole of Chiang Mai, and that no more land-use planning and investment on these facilities was needed. However, limited bus routes were only used for tourism and could not provide convenient routes for local residents, leading to their negative effects on housing prices in downtown areas, so the local government should lower the bus stop density in downtown areas and strengthen the transportation links between downtown areas and suburbs. Our study will not only support the urban land planning department of Chiang Mai to optimize residential communities and nearby facilities, but can also provide insights into housing price formation mechanisms in similar tourism-oriented cities in Thailand and beyond.
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Few-shot hyperspectral remote sensing image classification via an ensemble of meta-optimizers with update integrationHyperspectral images (HSIs) with abundant spectra and high spatial resolution can satisfy the demand for the classification of adjacent homogeneous regions and accurately determine their specific land-cover classes. Due to the potentially large variance within the same class in hyperspectral images, classifying HSIs with limited training samples (i.e., few-shot HSI classification) has become especially difficult. To solve this issue without adding training costs, we propose an ensemble of meta-optimizers that were generated one by one through utilizing periodic annealing on the learning rate during the meta-training process. Such a combination of meta-learning and ensemble learning demonstrates a powerful ability to optimize the deep network on few-shot HSI training. In order to further improve the classification performance, we introduced a novel update integration process to determine the most appropriate update for network parameters during the model training process. Compared with popular human-designed optimizers (Adam, AdaGrad, RMSprop, SGD, etc.), our proposed model performed better in convergence speed, final loss value, overall accuracy, average accuracy, and Kappa coefficient on five HSI benchmarks in a few-shot learning setting.
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Spatial patterns and evolution features of marine cold spells in the Arabian Sea during the past three decadesMarine Cold Spell (MCS) events are cold sea states with potentially devastating impacts on marine environments and ecosystems. In this study, we analyzed different MCS types with various severe categories in the Arabian Sea during 1994–2023. We found that all four types of MCS events shared a similar spatial pattern in terms of frequency, mean duration, mean intensity, and total days, but the frequency of 1-MCS events had a sharply decreasing trend compared with any other type of MCS events, indicating that ocean warming mainly led to the significant disappearance of short-period MCS events. Moreover, the MCS events in offshore Somalia had the highest occurring frequency, longest duration, largest intensity, and maximal total days, and were significantly different from those in other regions of the Arabian Sea. This is originated from that the cold–warm changes of the Somali current make larger fluctuations in the sea surface temperatures of the waters off Somalia, enhancing the occurring probability of MCS events, especially during the summers.
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The fragility of the ocean: from coral reef protection to deep-sea miningThe ocean environment around Pacific island countries and territories (PICTs) is fragile, from coral reefs along the coasts to deep-ocean habitats; 30% of the world’s reefs are important in the fishing and tourism sectors, with a total value estimated at nearly $36 billion. Global reef surveys during 2014–17 revealed that 80% of surveyed reefs had experienced significant coral bleaching and 35% had expe-rienced significant coral mortality. Observations demonstrate that the widespread damage to coral reefs caused by global warming is accelerating, underscoring the threat that anthropogenic climate change poses in terms of the irreversible transfor-mation of these essential ecosystems. But it is not only island coastal regions that face significant damage. There is also potential damage to the deep-sea (>200 m depth) areas around PICTs—where there are many gaps in our knowledge about biodiver-sity and ecosystems—owing to the encroaching possibility of mining for precious metals in the deep ocean. We describe an ecosystems services valuation approach to the cost–benefit analysis (CBA) of decision-making over deep-sea mining, and an economic approach to decision-making under uncertainty which supports a wait-and-see approach to allow more information to be obtained when there are high non-reversible costs at stake. We also explore relevant governing legal and financial options, such as mining trials and environmental financial assurance (EFA) bonds— as a prior condition of awarding a mining licence—to cover the costs of clean-up common in terrestrial mining. Protection of the coastal and deep-sea areas of PICTs is a major challenge that needs global cooperation.
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Multiscale impacts of land environmental features and planning on apartment resale prices in Jinan City, ChinaAs a typical city with a population of 5 to 10 million in China, Jinan has undergone significant increases in land supply during the past decade, resulting in substantial volatility in apartment sale/resale market prices. In this study, we investigated all second-hand apartment transactions from 826 communities of Jinan city and explored the multiscale impacts of land environmental features and planning on apartment resale prices throughout the city. Specifically, central and eastern regions had significantly positive impacts on apartment resale prices, while western regions had significantly negative impacts; education resources had consistently positive impacts throughout the city while shopping, business buildings, and medical resources had insignificant impacts; subway stations had insignificant impacts and bus stations had significant effects only in congestion points and northeastern edges. Our results revealed the formation mechanisms and spatial heterogeneity of apartment resale prices in Jinan. Our work will not only help in the decision making of potential apartment purchasers, but will also be conducive to enhancing the spatial justice of local governments in land supply and planning policies.
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Mitigating the impact of harmful algal blooms on aquaculture using technological interventions: case study on a South African farmSeafood, especially from the ocean, is now seen as a greener and more sustainable source of protein, causing an increase in its demand. This has also led to people making choices towards seafood as a replacement for carbon-intensive protein sources. As a result, the demand for seafood is growing, and as the aquaculture industry looks to increase production, keeping products safe and sustainable is imperative. There are many challenges faced by the aquaculture industry in meeting these increased demands. One such challenge is the presence of harmful algal blooms (HABs) in the ocean, which can have a major impact on aquatic life. In this paper, we look at the impact of this challenge on aquaculture and monitoring strategies whilst illustrating the potential for technological interventions to help mitigate the impact of an HAB. We will focus on Abagold Limited, a land-based marine aquaculture business that specialises in the large-scale production of abalone (Haliotis midae) based in Hermanus, South Africa. HABs are considered a threat to commercial-scale abalone farming along the South African coastline and require continuous monitoring. The most recent HAB was in February–April 2019, when the area experienced a severe red-tide event with blooms of predominantly Lingulodinium polyedrum. We present some of the monitoring strategies employing digital technologies to future-proof the industry. This article presents the development of a novel hybrid water quality forecasting model based on a TriLux multi-parameter sensor to monitor key water quality parameters. The actual experimental real water quality data from Abagold Limited show a good correlation as a basis for a forecasting model which would be a useful tool for the management of HABs in the aquaculture industry.
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High resolution temperature evolution maps of Bangladesh via data-driven learningAs a developing country with an agricultural economy as a pillar, Bangladesh is highly vulnerable to adverse effects of climate change, so the generation of high-resolution temperature maps is of great value for Bangladesh to achieve agricultural sustainable development. However, Bangladesh’s weak economy and sparse meteorological stations make it difficult to obtain such maps. In this study, by mining internal features and links inside observed data, we developed an efficient data-driven downscaling technique to generate high spatial-resolution temperature distribution maps of Bangladesh directly from observed temperature data at 34 meteorological stations with irregular distribution. Based on these high-resolution historical temperature maps, we further explored a data-driven forecast technique to generate high-resolution temperature maps of Bangladesh for the period 2025–2035. Since the proposed techniques are very low-cost and fully mine internal links inside irregular-distributed observations, they can support relevant departments of Bangladesh to formulate policies to mitigate and adapt to climate change in a timely manner.
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Leading modes and feature zones of sea ice concentration in North Pacific during Spring seasons of 2000–2020Under the influence of the Arctic amplification, sea ice variability in North Pacific is becoming a key indicator of global climate changes. Due to the widespread and complex impacts of North Pacific sea ice variations, the understanding of its dynamic changes, especially its spatial and temporal evolution patterns, has great significance. In this study, we used the rotated empirical orthogonal decomposition to divide the whole North Pacific into six feature zones on the evolution sea ice concentration under the recent global warming and highlighted the complexity and diversity of sea ice variations in the offshore waters.
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An overview study on management and implementation of WEEE in ChinaWaste electrical and electronic equipment (WEEE) which contains various valuable and harmful materials is an inevitable waste in modern society. In order to resolve the pollution problems associated with WEEE treatment, a WEEE management system has been established in China. The main role of importers and manufacturers of electrical and electronic equipment (EEE) is to pay the treatment fees to facilitate the WEEE recycling in China. The announced treatment and subsidy fee is given by set, not by the weight of WEEE. There is no lesser green treatment fee for the producers which can produce environmentally friendly EEE in China. Also, the recovery of refrigerants from the foaming agent of refrigerators is not required in China. In total, 45 million sets of recycled WEEE were certified in 2020, a year that contains the most updated data. Among them, 48%, 14%, 20%, 10% and 8% are for TV, refrigerator, washing machine, computer and air conditioners, respectively. The spatial analysis indicates that the WEEE recycling activities are mainly concentrated on the mid-east and east regions of China. It also can be concluded that the certified amount of each province has higher positive correlation with provincial population than provincial GDP per capita and green recovery rate. It also clearly notes that the amount of recycled air conditioner is the lowest for each province. Thus, more effort should be conducted to increase the recycling of scrapped air conditioner in China.
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Responses of extreme climates in South Asia under a G6sulfur scenario of climate engineeringUnder global warming scenarios, extreme climate events in South Asia will occur more frequently which will seriously threaten the safety of local residents. South Asia faces dual pressures of the obligation of carbon emissions reduction globally and the demand for a better life for huge populations. Stratospheric Aerosol Injection (SAI) climate engineering provides a potential solution to this dilemma. We compared the evolution of 12 climate extreme indices under historical scenarios, two future scenarios (SSP245, SSP585) and an implementation scenario of SAI climate engineering (G6sulfur). We showed that the intensity and frequency of extreme climates under a G6sulfur scenario would be significantly higher than those under historical scenarios, and that the difference in extreme climates under three scenarios (SSP245, SSP585, and G6sulfur) would be widely varying, with some indices being considerably mitigated while others would reflect a worse set of circumstances than would be the case without SAI climate engineering. Therefore, SAI climate engineering is not an effective tool to mitigate future climate extremes in South Asia under global warming scenarios.
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Impacts of marine cloud brightening scheme on climatic extremes in the Tibetan PlateauAs an ecologically fragile plateau and major water source in Asia, the Tibetan Plateau (TP) has grown warmer over recent decades, contributing to frequent occurrence of extreme climate events. It is urgently needed to find a suitable option to mitigate climate change impacts in the TP. The marine cloud brightening (MCB) scheme is proposed to mitigate global warming through the increasing cloud droplet number concentration of low marine clouds to reflect some solar radiation back into space. Until now, impacts of MCB scheme on the TP have not been investigated. In this study, we utilized 13 Expert Team on Climate Change Detection and Indices to assess the evolution of climate extremes over the TP with/without MCB implementation. We found that although the MCB is implemented over ocean only, it would cause significant changes on climate extremes in the TP which is very far from oceans and much higher than sea level. During 2030–2059, MCB implementation can decrease warm temperature extremes, leading to a significant decrease in the TXx index by 6–18°C, the TX90p index by 15–45 days, and the TN90p index by 15–50 days. MCB implementation would also have some cooling effects on cold temperature extremes, leading to an increase in the ID index by 30–80 days, the TX10p index by 22–32 days, and the TN10p index by about 12 days and a decrease in the TNn index by 0.5–1.5°C. Although MCB implementation would not have much impacts on precipitation extremes, it would significantly increase the area of the region with <10% drought frequency, and increase the drought intensity in the west of Lhasa city.
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Barriers and facilitators to genetic testing for breast and ovarian cancer amongst Black African women in Luton (UK)Evidence suggests that although Black African women have the lowest incidence of breast and ovarian cancer, they have the highest mortality rate and low rates of uptake for cancer screening services for these conditions in the United Kingdom (UK). This study aimed to explore the perceived barriers and facilitators to genetic testing for breast and ovarian cancer amongst Black African women in Luton (UK). We conducted a qualitative study that included one face-to-face and five telephone focus group discussions. Consistent with the health belief model, a focus group discussion guide was developed. A total of 24 participants, aged 23-57 who self-identified as Black African women and who were English speakers residing in Luton, took part in the focus group discussions. Purposive and snowballing sampling were used to recruit the participants for this study. The focus group discussions were recorded, transcribed per verbatim, coded and analyzed using an inductive thematic analysis approach, and the findings were classified. Nine themes emerged from the narratives obtained including six barriers and three facilitators. Barriers to genetic testing included (1) Cost and affordability, (2) Lack of knowledge, awareness, and family health history knowledge, (3) Language barrier, immigration, and distrust in western healthcare services, (4) Fear, (5) Cultural, religious, and intergenerational views and perceptions, and (6) Eligibility for genetic testing for the BRCA1/2 pathogenic variants and a lack of referral to specialist genetic clinics. Facilitators to genetic testing included (7) Availability of tests cost-free under the National Health Service (NHS) (8) Family members' health and (9) Awareness and education on genetic testing. The barriers and facilitators identified could enable policy makers and healthcare services alike to gain a better understanding of the factors influencing Black African women's decision-making process toward genetic testing. Ultimately, this work can inform interventions aiming to increase the uptake of genetic testing among this group.
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Can a chatbot enhance hazard awareness in the construction industry?Safety training enhances hazard awareness in the construction industry. Its effectiveness is a component of occupational safety and health. While face-to-face safety training has dominated in the past, the frequent lockdowns during COVID-19 have led us to rethink new solutions. A chatbot is messaging software that allows people to interact, obtain answers, and handle sales and inquiries through a computer algorithm. While chatbots have been used for language education, no study has investigated their usefulness for hazard awareness enhancement after chatbot training. In this regard, we developed four Telegram chatbots for construction safety training and designed the experiment as the treatment factor. Previous researchers utilized eye-tracking in the laboratory for construction safety research; most have adopted it for qualitative analyses such as heat maps or gaze plots to study visual paths or search strategies via eye-trackers, which only studied the impact of one factor. Our research has utilized an artificial intelligence-based eye-tracking tool. As hazard awareness can be affected by several factors, we filled this research void using 2-way interaction terms using the design of experiment (DOE) model. We designed an eye-tracking experiment to study the impact of site experience, Telegram chatbot safety training, and task complexity on hazard awareness, which is the first of its kind. The results showed that Telegram chatbot training enhanced the hazard awareness of participants with less onsite experience and in less complex scenarios. Low-cost chatbot safety training could improve site workers’ danger awareness, but the design needs to be adjusted according to participants’ experience. Our results oer insights to construction safety managers in safety knowledge sharing and safety training.
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ViMRT: a text-mining tool and search engine for automated virus mutation recognitionVirus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations.