Developing a regional scale construction and demolition waste landfill landslide risk rapid assessment approach.

In recent years, construction and demolition waste (CDW) landfills landslide accidents have occurred globally, with consequences varying due to surrounding environmental factors. Risk monitoring is crucial to mitigate these risks effectively. Existing studies mainly focus on improving risk assessment accuracy for individual landfills, lacking the ability to rapidly assess multiple landfills at a regional scale. This study proposes an innovative approach utilizing deep learning models to quickly locate suspected landfills and develop risk assessment models based on surrounding environmental factors. Shenzhen, China, with significant CDW disposal pressure, is chosen as the empirical research area. Empirical findings from this study include: (1) the identification of 52 suspected CDW landfills predominantly located at the administrative boundaries within Shenzhen, specifically in the Longgang, Guangming, and Bao'an districts; (2) landfills at the lower risk of landslides are typically found near the northern borders adjacent to cities like Huizhou and Dongguan; (3) landfills situated at the internal administrative junctions generally exhibit higher landslide risks; (4) about 70 % of these landfills are high-risk, mostly located in densely populated areas with substantial rainfall and complex topographies. This study advances landfill landslide risk assessments by integrating computer vision and environmental analysis, providing a robust method for governments to rapidly evaluate risks at CDW landfills regionally. The adaptable models can be customized for various urban and broadened to general landfills by adjusting specific indicators, enhancing environmental safety protocols and risk management strategies effectively.

Immune characteristics of kidney transplant recipients with acute respiratory distress syndrome induced by COVID-19 at single-cell resolution.

BACKGROUND: COVID-19-induced acute respiratory distress syndrome (ARDS) can result in tissue damage and multiple organ dysfunction, especially in kidney transplant recipients (KTRs) receiving immunosuppressive drugs. Presently, single-cell research on COVID-19-induced ARDS is considerably advanced, yet knowledge about ARDS in KTRs is still constrained. METHODS: Single-cell RNA sequencing (scRNA-seq) analysis was performed to construct a comprehensive single-cell immune landscape of the peripheral blood mononuclear cells (PBMCs) of eight patients with COVID-19-induced ARDS, five KTRs with COVID-19-induced ARDS, and five healthy individuals. Subsequently, we conducted a comprehensive bioinformatics analysis, including cell clustering, enrichment analysis, trajectory analysis, gene regulatory network analysis, and cell-cell interaction analysis, to investigate the heterogeneity of the immune microenvironment in KTRs with ARDS. RESULT: Our study revealed that KTRs exhibit significant heterogeneity with COVID-19-induced ARDS compared with those of other individuals, with significant reductions in T cells, as well as an abnormal proliferation of B cells and monocytes. In the context of dual influences from immunosuppression and viral infection, KTRs exhibited more specific plasma cells, along with significant enrichment of dysfunctional GZMB and XAF1 double-positive effector T cells and IFI27-positive monocytes. Additionally, robust communication existed among T cells and monocytes in cytokine signaling. These effects impede the process of immune reconstitution in KTR patients. CONCLUSION: Our findings suggest that KTRs with COVID-19-induced ARDS show elevated antibody levels, impaired T cell differentiation, and dysregulation of innate immunity. In summary, this study provides a theoretical foundation for a comprehensive understanding of COVID-19-induced ARDS in KTRs.

Augmented reality-enabled human-robot collaboration to balance construction waste sorting efficiency and occupational safety and health.

Construction waste sorting (CWS) is highly recommended as a key step for construction waste management. However, current CWS involves humans' manual hand-picking, which poses significant threats to their occupational safety and health (OSH). Robotic sorting promises to change the situation by adopting modern artificial intelligence and automation technologies. However, in practice, it is usually challenging for robots to do an efficient job (e.g., measured by quickness and accuracy) owing to the difficulties in precisely recognizing compositions of the mixed and heterogeneous waste stream. Leveraging augmented reality (AR) as a communication interface, this research aims to develop a human-robot collaboration (HRC) approach to address the dilemmatic balance between CWS efficiency and OSH. Firstly, a model for human-robot collaborative sorting using AR is established. Then, a prototype for the AR-enable collaborative sorting system is developed and evaluated. The experimental results demonstrate that the proposed AR-enabled HRC method can improve the accuracy rate of CWS by 10% and 15% for sorting isolated waste and obscured waste, respectively, when compared to the method without human involvement. Interview results indicate a significant improvement in OSH, especially the reduction of contamination risks and machinery risks. The research lays out a human-robot collaborative paradigm for productive and safe CWS via an immersive and interactive interface like AR.

Documenting the immune response in patients with COVID-19-induced acute respiratory distress syndrome.

Introduction: Coronavirus disease 2019 (COVID-19) can lead to acute respiratory distress syndrome (ARDS) and life-threatening multi-organ failure with increased levels of inflammatory mediators and viral load; however, little is known about its pathophysiology. Methods: To better understand the cellular status of COVID-19-induced ARDS, we performed single-cell RNA sequencing on peripheral blood samples from patients with COVID-19-induced ARDS. Single-cell RNA sequencing combined with bioinformatics analysis was used to study dynamic changes in cell composition and transcriptional profiles. Results: The single-cell RNA sequencing data revealed significant phenotypic differences between patients with COVID-19-induced ARDS and controls, mainly in monocytes, and CD8+ T and B cells. B-cell and monocyte abundances were significant in COVID-19-induced ARDS patients compared to controls, while CD8+ T cells were depleted. These data suggest that there is an imbalance between lymphocytes and monocytes in the blood of COVID-19-induced ARDS patients. In addition, cytokine interactions between T cells, monocytes and B cells are enhanced as evidenced by the intercellular communication analysis. In particular, T cell subsets target receptors on other cells via CCL5 and may play an important role in patients with COVID-19-induced ARDS. Conclusion: Our analysis suggested that a dysregulated adaptive immune response exists in patients with COVID-19-induced ARDS. Overall, we provided a cellular picture of the peripheral immune response in patients with COVID-19-induced ARDS.

The Lesson Learned from the Unique Evolutionary Story of Avirulence Gene AvrPii of Magnaporthe oryzae.

Blast, caused by Magnaporthe oryzae, is one of the most destructive diseases affecting rice production. Understanding population dynamics of the pathogen's avirulence genes is pre-required for breeding and then deploying new cultivars carrying promising resistance genes. The divergence and population structure of AvrPii was dissected in the populations of southern (Guangdong, Hunan, and Guizhou) and northern (Jilin, Liaoning, and Heilongjiang) China, via population genetic and evolutionary approaches. The evolutionary divergence between a known haplotype AvrPii-J and a novel one AvrPii-C was demonstrated by haplotype-specific amplicon-based sequencing and genetic transformation. The different avirulent performances of a set of seven haplotype-chimeric mutants suggested that the integrity of the full-length gene structures is crucial to express functionality of individual haplotypes. All the four combinations of phenotypes/genotypes were detected in the three southern populations, and only two in the northern three, suggesting that genic diversity in the southern region was higher than those in the northern one. The population structure of the AvrPii family was shaped by balancing, purifying, and positive selection pressures in the Chinese populations. The AvrPii-J was recognized as the wild type that emerged before rice domestication. Considering higher frequencies of avirulent isolates were detected in Hunan, Guizhou, and Liaoning, the cognate resistance gene Pii could be continuously used as a basic and critical resistance resource in such regions. The unique population structures of the AvrPii family found in China have significant implications for understanding how the AvrPii family has kept an artful balance and purity among its members (haplotypes) those keenly interact with Pii under gene-for-gene relationships. The lesson learned from case studies on the AvrPii family is that much attention should be paid to haplotype divergence of target gene.

Cadmium, arsenic, and mineral nutrients in rice and potential risks for human health in South China.

Rice (Oryza sativa L.) is one of the most important staple food crops worldwide. For people fed on rice, toxic elements cadmium (Cd) and arsenic (As) and mineral nutrients in rice are pivotal to evaluate potential risks of harmful element intake and malnutrition. We collected rice samples of 208 cultivars (83 inbred and 125 hybrid) from fields in South China and determined Cd, As, As species, and mineral elements in brown rice. Chemical analysis shows that the average content of Cd and As in brown rice were 0.26 ± 0.32 and 0.21 ± 0.08 mg·kg-1, respectively. Inorganic As (iAs) was the dominative As species in rice. Rice Cd and iAs in 35.1% and 52.4% of the 208 cultivars exceeded rice Cd and iAs limits, respectively. Significant variations of rice subspecies and regions were found for Cd, As, and mineral nutrients in rice (P < 0.05). Inbred rice had lower As uptake and more balanced mineral nutrition than hybrid species. Significant correlation was observed between Cd, As versus mineral elements like Ca, Zn, B, and Mo (P < 0.05). Health risk assessment indicates that high risks of non-carcinogenic and carcinogenic of Cd and iAs, and malnutrition, in particular Ca, protein and Fe deficiencies, might be caused by rice consumption in South China.

Identifying the impacts of trading construction waste across jurisdictions: a simulation of the Greater Bay Area, China, using non-linear optimization.

Local authorities worldwide are actively encouraging waste material trading within their jurisdictions as a promising strategy to develop a more circular economy. Construction activities consume natural resources intensively and generate massive solid waste. With proper ex-post treatment, the waste materials can be recycled or even directly reused, hence contributing to the circular economy. Using the Hong Kong-Macao-Guangdong Greater Bay Area (GBA) as the context, we simulate the impacts of a construction waste trading market on the waste flows and the resulting monetary exchanges. Our model views each city as a representative agent that maximizes the benefit of conducting waste recycling. The interactions of their profit-seeking behavior will lead to optimized overall social costs. We then solve this problem using a non-linear optimization algorithm. The simulation shows that with a fully operational market, the traded waste materials amount to 1253.84 million m3, covering 82.36% of GBA's total construction waste generation in a typical year. The monetary transactions equal to US$38.41 billion. Such huge payments present a great opportunity for the GBA cities to develop their recycling industries. In addition, we argue that although increasing public pressure is effective in reducing inequalities in the final waste distribution, it also results in fewer financial transactions flowing to less-developed cities, which reduces their funding for developing the circular economy.

M2c Macrophages Protect Mice from Adriamycin-Induced Nephropathy by Upregulating CD62L in Tregs.

Regulatory T cells (Tregs) and M2c macrophages have been shown to exert potentially synergistic therapeutic effects in animals with adriamycin-induced nephropathy (AN), a model chronic proteinuric renal disease. M2c macrophages may protect against renal injury by promoting an increase in the number of Tregs in the renal draining lymph nodes of AN mice, but how they do so is unclear. In this study, we used an AN mouse model to analyze how M2c macrophages induce the migration of Tregs. Using flow cytometry, we found that M2c macrophages promoted the migration of Tregs from the peripheral blood to the spleen, thymus, kidney, and renal draining lymph nodes. At the same time, M2c macrophages significantly upregulated chemokine receptors and adhesion molecule in Tregs, including CCR4, CCR5, CCR7, CXCR5, and CD62L. Treating AN mice with monoclonal anti-CD62L antibody inhibited the migration of M2c macrophages and Tregs to the spleen, thymus, kidney, and renal draining lymph nodes. Taken together, our results suggest that M2c macrophages upregulate CD62L in Tregs and thereby promote their migration to inflammatory sites, where they exert renoprotective effects. These insights may aid the development of treatments against chronic kidney disease.

Validity of using ambient concentrations as surrogate exposures at the individual level for fine particle and black carbon: A systematic review and meta-analysis.

Exposure measurement error is an important source of bias in epidemiological studies. We assessed the validity of employing ambient (outdoor) measurements as proxies of personal exposures at individual levels focusing on fine particles (PM2.5) and black carbon (BC)/elemental carbon (EC) on a global scale. We conducted a systematic review and meta-analysis and searched databases (ISI Web of Science, Scopus, PubMed, Ovid MEDLINE®, Ovid Embase, and Ovid BIOSIS) to retrieve observational studies in English language published from 1 January 2006 until 5 May 2021. Correlation coefficients (r) between paired ambient (outdoor) concentration and personal exposure for PM2.5 or BC/EC were standardized as effect size. We used random-effects meta-analyses to pool the correlation coefficients and investigated the causes of heterogeneity and publication bias. Furthermore, we employed subgroup and meta-regression analyses to evaluate the modification of pooled estimates by potential mediators. This systematic review identified thirty-two observational studies involving 1744 subjects from ten countries, with 28 studies for PM2.5 and 11 studies for BC/EC. Personal PM2.5 exposure is more strongly correlated with ambient (outdoor) concentrations (0.63, 95% confidence interval [CI]: 0.57-0.68) than personal BC/EC exposure (0.49, 95% CI: 0.38-0.59), with significant differences in r (0.14, 95% CI: 0.03-0.25; p < 0.05). The results demonstrated that the health status of participants was a significant modifier of pooled correlations. In addition, the personal to ambient (P/A) ratio for PM2.5 and average ambient BC/EC levels were potential effect moderators of the pooled r. The funnel plots and Egger's regression test indicated inevident publication bias. The pooled estimates were robust through sensitivity analyses. The results support the growing consensus that the validity coefficient of proxy measures should be addressed when interpreting results from epidemiological studies to better understand how strong health outcomes are affected by different levels of PM2.5 and their components.

Elsholtzia splendens promotes phenanthrene and polychlorinated biphenyl degradation under Cu stress through enrichment of microbial degraders.

Co-contamination of heavy metals and organic pollutants is widespread in the environment. Metal-tolerant/hyperaccumulating plants have the advantage of enhancing co-operation between plants and rhizospheric microbes under heavy metal stress, but the underlying mechanism remains unclear. In the present study, the effects of Elsholtzia splendens and Lolium perenne on the rhizospheric microbial community and degraders of phenanthrene (PHE) and polychlorinated biphenyls (PCBs) were investigated. The results showed E. splendens could tolerate high Cu concentrations, while L. perenne was sensitive to Cu toxicity. Although Cu played the most important role in microbial community construction, both E. splendens and L. perenne caused shifts in the rhizospheric microbial community. For PHE and PCB degradation, L. perenne was more efficient under low Cu concentrations, whereas E. splendens performed better under high Cu concentrations. This difference can be attributed to shifts in the degrader community and key degradation genes identified by stable isotope probing. Moreover, higher abundances of various genes for organic pollutant degradation were observed in the rhizosphere of E. splendens than L. perenne based on gene prediction under high Cu stress. Our study reveals underlying mechanism of the advantages of heavy metal-tolerant plants for organic pollutant removal in soils co-contaminated with heavy metals.

Understanding loading patterns of construction waste hauling trucks: triangulation between big quantitative and informative qualitative data.

This research aims to understand the loading patterns of construction waste hauling trucks in Hong Kong and the factors shaping these patterns. It does so by triangulating the analytical results of big data collected from secondary sources and qualitative data from interviews. Firstly, based on the literature review and our engagement with the industry, four hypotheses on the nexus between "loading pattern" and the factors of (1) vehicle, (2) permitted gross vehicle weight, (3) commodity, and (4) ownership. Then, the hypotheses are tested with combined null hypothesis significance test and effect size measure using 13 million construction waste transportation records. Finally, the results are triangulated with interview data to empirically validate the nexus while providing sensible explanations to them. We find that the four hypotheses are all supported. Distinct loading patterns are presented by different types of (1) construction waste hauling trucks with different (2) permitted gross vehicle weights, (3) types of construction waste transported, and (4) ownership. These findings provide valuable evidence for more targeted interventions, e.g., introducing public policies or hauling operation optimization through the avoidance of excessive underloading or overloading.

Analyzing the Freight Characteristics and Carbon Emission of Construction Waste Hauling Trucks: Big Data Analytics of Hong Kong.

Unlike their counterparts that are used for container or municipal solid waste hauling, or their peers of taxies and other commercial vehicles, construction waste hauling trucks (CWHTs) are heterogeneous in that they transport construction waste from construction sites to designated disposal facilities. Depending on the intensity of the construction activities, there are many CWHTs in operation, imposing massive impacts on a region's transportation system and natural environment. However, such impacts have rarely been documented. This paper has analyzed CWHTs' freight characteristics and their carbon emission by harnessing a big dataset of 112,942 construction waste transport trips in Hong Kong in May 2015. It has been observed that CWHTs generate 4544 daily trips with 307.64 tons CO2-eq emitted on working days, and 553 daily trips emitting 28.78 tons CO2-eq on non-working days. Freight carbon emission has been found to be related to the vehicle type, transporting weight, and trip length, while the trip length is the most influential metric to carbon emission. This research contributes to the understanding of freight characteristics by exploiting a valuable big dataset and providing important benchmarking metrics for monitoring the effectiveness of policy interventions related to construction waste transportation planning and carbon emission.

Computer vision for solid waste sorting: A critical review of academic research.

Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little attention has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are introduced and compared. The distribution of academic research outputs is also examined from the aspects of waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were unevenly distributed in different sectors such as household, commerce and institution, and construction. Too often, researchers reported some preliminary studies using simplified environments and artificially collected data. Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested researchers to train and evaluate their CV algorithms.

Computer vision to recognize construction waste compositions: A novel boundary-aware transformer (BAT) model.

Recognition of construction waste compositions using computer vision (CV) is increasingly explored to enable its subsequent management, e.g., determining chargeable levy at disposal facilities or waste sorting using robot arms. However, the applicability of existing CV-enabled construction waste recognition in real-life scenarios is limited by their relatively low accuracy, characterized by a failure to distinguish boundaries among different waste materials. This paper aims to propose a novel boundary-aware Transformer (BAT) model for fine-grained composition recognition of construction waste mixtures. First, a pre-processing workflow is devised to separate the hard-to-recognize edges from the background. Second, a Transformer structure with a self-designed cascade decoder is developed to segment different waste materials from construction waste mixtures. Finally, a learning-enabled edge refinement scheme is used to fine-tune the ignored boundaries, further boosting the segmentation precision. The performance of the BAT model was evaluated on a benchmark dataset comprising nine types of materials in a cluttered and mixture state. It recorded a 5.48% improvement of MIoU (mean intersection over union) and 3.65% of MAcc (Mean Accuracy) against the baseline. The research contributes to the body of interdisciplinary knowledge by presenting a novel deep learning model for construction waste material semantic segmentation. It can also expedite the applications of CV in construction waste management to achieve a circular economy.

An oral GLP-1 and GIP dual receptor agonist improves metabolic disorders in high fat-fed mice.

Dual activation of the glucagon-like peptide 1 (GLP-1) receptor and the glucose-dependent insulinotropic polypeptide (GIP) receptor has potential as a novel strategy for treatment of diabesity. Here, we created a hybrid peptide which we named 19W, and show that it is more stable in presence of murine plasma than exendin-4 is. In vitro studies were performed to reveal that 19W could stimulate insulin secretion from INS-1 cells in a dose-dependent manner, just like the native peptide GIP and exendin-4 do. 19W effectively evoked dose-dependent cAMP production in cells targeting both GLP-1R and GIPR. In healthy C57BL/6J mice, the single administration of 19W significantly improved glucose tolerance. When administered in combination with sodium deoxycholate (SDC), its oral hypoglycemic activity was enhanced. Pharmacokinetics studies in Wistar rats revealed that 19W was absorbed following oral uptake, while SDC increased its bioavailability. A long-term (28 days) exposure study of twice-daily oral administration to high fat-fed (HFF) mice showed that 19W significantly reduced animal food intake, body weight, fasting blood glucose, total serum cholesterol (T-CHO), non-esterified free fatty acids (NEFA), and low-density lipoprotein cholesterol (LDL-C) levels. It also significantly improved glucose tolerance and the pancreatic ß/α cell ratio, and decreased the area of liver fibrosis. These results clearly demonstrate the beneficial action of this novel oral GLP-1/GIP dual receptor agonist to reduce adiposity and hyperglycemia in diabetic mice and to ameliorate liver fibrosis associated with obesity. This dual-acting peptide can be considered a good candidate for novel oral therapy to treat obesity and diabetes.

Estimating construction waste generation in the Greater Bay Area, China using machine learning.

Reliable construction waste generation data is a prerequisite for any evidence-based waste management effort, but such data remains scarce in many developing economies owing to their rudimentary recording systems. By referring to several models proposed for estimating waste generation, this study aims to develop a reliable and accessible method for estimating construction waste generation based on limited publicly available data. The study has two objectives. Firstly, it aims to estimate construction waste generation by focusing on the Greater Bay Area (GBA) in China, one of the world's most thriving regions in terms of construction activities. Secondly, it aims to compare the strengths and weaknesses of various waste quantification models. 43 sets of annual socio-economic, construction-related and C&D waste generation data ranging from 2005 to 2019 were collected from the local government authorities. By analyzing the data using four types of machine learning models, namely multiple linear regression, decision tree, grey models, and artificial neural network, it is found that all calibrated models, with their respective strengths and weaknesses, can produce acceptable results with the testing R2 ranging from 0.756 to 0.977. This study also reveals that the 11 cities in the GBA produced a total of about 364 million m3 of construction waste in 2018. The result can be used for monitoring the urban metabolism, quantifying carbon emission, developing a circular economy, valorizing recycled materials, and strategic planning of waste management facilities in the GBA. The research findings also contribute to the methodologies for estimating waste generation using limited data.

Tackling the "last mile" problem in renovation waste management: A case study in China.

Solid waste generated from building renovation, called "renovation waste" in this study, represents a major waste management problem. A particular difficulty is sourcing renovation waste arising sporadically from discrete sites for central processing. This can be characterized as a "last mile" problem in renovation waste management (RWM). This study reports good practice for dealing with the RWM last mile problem in a city in China. We conduct qualitative research comprising site investigations and interviews, organized in an in-depth case study. We discover that the city effectively solved the last mile problem by developing a multi-layer, nested waste management system, empowered further by various smart technologies and concerted collaboration from multi-stakeholders coordinated by a determined government. Nevertheless, the longevity of the RWM is contingent on confronting several challenges, including (a) achieving cost and benefit balance, (b) defining clearer standards and policies, and (c) raising stakeholders' awareness of waste management. A general RWM strategy is recommended to establish fluent channels through which to source and qualify renovation waste for central treatment. The study delves into the much neglected world of RWM and provides a valuable reference for tackling similar problems.

Quantifying the embodied carbon saving potential of recycling construction and demolition waste in the Greater Bay Area, China: Status quo and future scenarios.

Comparing with the enduring efforts to reduce carbon emissions in design, construction, and operation stages of a construction project, less attention has been paid to emission abatement potential in the end-of-life stage, particularly by recycling waste generated by construction and demolition (C&D) activities. This research aims to cover this knowledge void by quantifying the embodied carbon saving potential of recycling C&D waste. It does so by adopting a Life Cycle Assessment (LCA) and choosing the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in South China for a case study. The carbon emission is treated as embodied in construction materials, by recycling which the equivalent amount of carbon generated from the virgin materials can be saved. It is estimated that the GBA produced 128.49 Mt. of C&D waste in 2018, which implies an embodied carbon saving potential of 92.26 Mt. carbon emissions. The research goes further to understand the future C&D waste generation and their corresponding embodied carbon saving potential. A first-of-its-kind dynamic approach is developed to simulate the future 42-year saving potential under four construction development scenarios. Depending on different construction growth rates, the embodied carbon saving potential in 2060 can be up to 894.80 Mt. and down to 166.34 Mt. This research can help achieve China's 2060 carbon neutral goal by focusing on a non-negligible sector in an economically important region. Methods proposed in this paper are also applicable to other regions worldwide, especially where C&D waste data is insufficient.

Estimation of construction waste composition based on bulk density: A big data-probability (BD-P) model.

Estimating the composition of construction waste is crucial to the efficient operation of various waste management facilities, such as landfills, public fills, and sorting plants. However, this estimating task is often challenged by the desire of quickness and accuracy in real-life scenarios. By harnessing a valuable data set in Hong Kong, this research develops a big data-probability (BD-P) model to estimate construction waste composition based on bulk density. Using a saturated data set of 4.27 million truckloads of construction waste, the probability distribution of construction waste bulk density is derived, and then, based on the Law of Joint Probability, the BD-P model is developed. A validation experiment using 604 ground truth data entries indicates a model accuracy of 90.2%, Area Under Curve (AUC) of 0.8775, and speed of around 52 s per load in estimating the composition of each incoming construction waste load. The BD-P model also informed a linear model which can perform the estimation with an accuracy of 88.8% but consuming 0.4 s per case. The major novelty of this research is to harmonize big data analytics and traditional probability theories in improving the classic challenge of predictive analyses. In the practical sphere, it satisfactorily solves the construction waste estimation problem faced by many waste management facility operators. In the academic sphere, this research provides a vivid example that big data and theories are not adversaries, but allies.

Per- and polyfluoroalkyl substances (PFASs) in the soil-plant system: Sorption, root uptake, and translocation.

Per- and polyfluoroalkyl substances (PFASs) are ubiquitous in the environment but pose potential risks to ecosystems and human health. The soil-plant system plays an important role in the bioaccumulation of PFASs. Because most PFASs in the natural environment are anionic and amphiphilic (both lipophilic and hydrophilic), their sorption and accumulation behaviors differ from those of neutral organic and common ionic compounds. In this review, we discuss processes affecting the availability of PFASs in soil after analyzing the potential mechanisms underlying the sorption and uptake of PFASs in the soil-plant system. We also summarize the current knowledge on root uptake and translocation of PFASs in plants. We found that the root concentration factor of PFASs for plants grown in soil was not significantly correlated with hydrophobicity, whereas the translocation factor was significantly and negatively correlated with PFAS hydrophobicity regardless of whether plants were grown hydroponically or in soil. Further research on the cationic, neutral, and zwitterionic forms of diverse PFASs is urgently needed to comprehensively understand the environmental fates of PFASs in the soil-plant system. Additional research directions are suggested, including the development of more accurate models and techniques to evaluate the bioavailability of PFASs, the effects of root exudates and rhizosphere microbiota on the bioavailability and plant uptake of PFASs, and the roles of different plant organelles, lipids, and proteins in the accumulation of PFASs by plants.