Critical Source Research Articles

Priority in removing diseased fruits for the control of cacao frosty pod rot (Moniliophthora Roreri (Cif. & Par.) Evans et al)

Cacao frosty pod rot is an exclusive disease of the fruit that is considered one of the main causes of low yields of the crop in Central and South America. Its control is based on the timely removal of critical sources of infection, especially fruits in a state of recent sporulation (one to three weeks after the process begins) and with symptoms of brown spot, which are the ones that generate the main epidemic outbreaks and, as a consequence, significantly decreasing production and the quality of the grain.

Revealing microbial community assembly patterns and succession process in the blackening process of black-odor water

Human-imported pollutants could induce water black, changing microbial community structure and function. Employed 16S rRNA high-throughput sequencing, field-scale investigations and laboratory-scale experiments were successively conducted to reveal mechanistic insights into microbial community assembly and succession of black-odor waters (BOWs). In the field-scale investigation, livestock breeding wastewater (56.7 ± 3.2%) was the most critical microbial source. Moreover, fermentation (27.1 ± 4.4%) was found to be the dominant function. Combined with laboratory experiments, the critical environmental factors, such as total organic carbon (30–100 mg/L), ammonia nitrogen (2.5–9 mg/L), initial dissolved oxygen (2–8 mg/L) and chlorophyll a (0–90 mg/L), impacted the intensity of blackening. The differentiation of ecological niches within the microbial community played a significant role in driving the blackening speed. In laboratory-scale experiments, the microbial ecological niche determined the blackening timing and dominations of the stochastic processes in the microbial assembly process (88 - 51%). The three stages, including the anaerobic degradation stage, blackening stage and slow recovery stage, were proposed to understand the assembly of the microbial communities. These findings enhance our understanding of microorganisms in BOWs and provide valuable insights for detecting and managing heavily organic polluted waters.

KLF4 inhibited the senescence-associated secretory phenotype in ox-LDL-treated endothelial cells via PDGFRA/NAMPT/mitochondrial ROS.

Inflammation is one of the significant consequences of ox-LDL-induced endothelial cell (EC) dysfunction. The senescence-associated secretory phenotype (SASP) is a critical source of inflammation factors. However, the molecular mechanism by which the SASP is regulated in ECs under ox-LDL conditions remains unknown. The level of SASP was increased in ox-LDL-treated ECs, which could be augmented by KLF4 knockdown whereas restored by KLF4 knock-in. Furthermore, we found that KLF4 directly promoted PDGFRA transcription and confirmed the central role of the NAPMT/mitochondrial ROS pathway in KLF4/PDGFRA-mediated inhibition of SASP. Animal experiments showed a higher SASP HFD-fed mice, compared with normal feed (ND)-fed mice, and the endothelium of EC-specific KLF4-/- mice exhibited a higher proportion of SA-β-gal-positive cells and lower PDGFRA/NAMPT expression. Our results revealed that KLF4 inhibits the SASP of endothelial cells under ox-LDL conditions through the PDGFRA/NAMPT/mitochondrial ROS. Ox-LDL-treated ECs and HFD-fed mice were used as endothelial senescence models in vitro and in vivo. SA-β-gal stain, detection of SAHF and the expression of inflammatory factors determined SASP and senescence of ECs. The direct interaction of KLF4 and PDGFRA promotor was analyzed by EMSA and fluorescent dual luciferase reporting analysis.

Study on Venture Capital Investments and its ability to stimulate Innovation and spur Economic Growth in India

Objective: This paper examines the impact of Venture Capital Investments (VCI) on Innovation and Economic Growth in India. Venture Capital has emerged as a critical source of funding for startups, enabling them to develop innovative ideas, scale their operations, and contribute to economic growth. India, with its vibrant entrepreneurial ecosystem, has witnessed a significant increase in venture capital investments over the past decade. Research Design and Methods used: This paper gathers key information from the market, varying from various reports and relevant data published by private players like E&Y, Bain & Company, Hurun, Venture Intelligence, Indian Venture Capital Association (IVCA); besides, governmental reports of SIDBI, SEBI, Economic Surveys, MSME Annual Reports and ‘Intellectual Property India’ report published by the Controller General of Patents, Design, Trademarks and Geographical Indications, Ministry of Commerce, Government of India. This is qualitative research. For the purpose of this research, the data pertains to last ten years. Using Python language, data analysis has been done by finding Correlation and Regression Analysis. The model has been run on Jupyter Notebook for Python. Findings: There is, visible upsurge in the VC investments in the last few years, much credited to the pro-active approach of the Government in creating the right ambience and the infrastructure. The flagship programs of the Government, like, Startup Mission and Make in India have yielded positive results. Pandemic did its own damage, but, post Covid-19 pandemic, there has been a considerable upward shift in the VC investments as they venture into new terrains, seeding new technologies and nurturing Startups. AS a result of this, there has been a significant jump in the number of Unicorns. There has been a quantum jump in number of VCs that have invested in India and also the deal size is consistently on the rise. Implications and Recommendations: By studying the existing research and data available, it is observed that there is asymmetry between the literature and the industry. The venture capital industry is inward cautious and there is dearth of exact deal data because of the secrecy and privacy issue, coupled with demographic and geographical shortcoming to collect the data. The role played by IVCA and other private players is, undoubtedly, worth a praise. It is recommended that further research should alienate with industry and be more methodical. Contribution and Value Added: This study will benefit, immensely, to both researchers, intending to do further research in this area, and, also to the industry in building a nexus with academia. This research has taken one step forward in building the bridge between the two. This paper fills the gap by identifying the relationship between key variables and apply statistical tools like Correlation and Regression analysis to assess the trends and contribution of venture capital investments in terms of key variables innovation and economic growth. The outcomes of this study will offer a comprehensive understanding of the fundamental elements of venture capital investments (VCI) and their influence on both innovation and the growth of the Indian economy.

Simulating the land use change effects on non-point source pollution in the Duliujian River Basin.

The uncertainty in the generation and formation of non-point source pollution makes it challenging to monitor and control this type of pollution. The SWAT model is frequently used to simulate non-point source pollution in watersheds and is mainly applied to natural watersheds that are less affected by human activities. This study focuses on the Duliujian River Basin (Xiqing section), which is characterized by a dense population and rapid urbanization. Based on the calibrated SWAT model, this study analyzed the effects of land use change on non-point source pollution both temporally and spatially. It was found that nitrogen and phosphorus non-point source pollution load losses were closely related to land use type, with agricultural land and high-density urban land (including rural settlements) being the main contributors to riverine nitrogen and phosphorus pollution. This indicates the necessity of analyzing the impact of land use changes on non-point source pollution loads by identifying critical source areas and altering the land use types that contribute heavily to pollution in these areas. The simulation results of land use type changes in these critical source areas showed that the reduction effect on non-point source pollution load is in the order of forest land > grassland > low-density residential area. To effectively curb surface source pollution in the study area, strategies such as modifying urban land use types, increasing vegetation cover and ground infiltration rate, and strictly controlling the discharge of domestic waste and sewage from urban areas can be implemented.

A Comprehensive Northern Hemisphere Particle Microphysics Data Set From the Precipitation Imaging Package

AbstractMicrophysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher‐order products including an approximation of volume‐weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self‐describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase‐changes at high temporal resolution. Bulk precipitation characteristics from a multi‐site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high‐quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics.

Impact of COVID-19 Lockdown on Inhaled Toxic Elements in PM2.5 in Beijing: Composition Characterization and Source-Specific Health Risks Assessment

In early 2020, China experienced a mass outbreak of a novel coronavirus disease (COVID-19). With an aim to evaluate the impact of emission variations on toxic element species in PM2.5 and the health risks associated with inhalation exposure during COVID-19, we collected PM2.5 filter samples in Beijing from January 1 to February 28, 2020. Positive matrix factorization (PMF) and a health risk (HR) assessment model were used to assess the health risks of the toxic elements and critical risk sources. The total concentration of eight toxic elements (Se, Cd, Pb, Zn, As, Cu, Ni, and Cr) in Beijing showed a trend of first increasing and then decreasing: full lockdown (322.9 ng m−3) > pre-lockdown (264.2 ng m−3) > partial lockdown (245.3 ng m−3). During the lockdown period, stringent control measures resulted in significant reductions (6−20%) in Zn, Pb, Cd, and Ni levels, while concentrations of Se, As, Cu, and Cr were unexpectedly elevated (14−348%). A total of five sources was identified: traffic emission, coal combustion, dust emission, industrial emission and mixed source of biomass burning and firework combustion. Total carcinogenic risk (TCR) of the selected toxic elements exceeded the US EPA limits for children and adults. As and Cr (IV) were the main contributors to non-carcinogenic and carcinogenic risks, respectively. For source-resolved risks, coal combustion was the main contributor to HI (43%), while industrial emissions were the main cause of TCR (45%). Additionally, increased contributions from coal combustion, biomass burning, and firework combustion during the full lockdown elevated the HI and TCR values.

Temporal dynamics, key influencing factors and source tracking of antibiotics in the biofloc zero-change aquaculture system

As environmental pollutants, antibiotic residues have become a research hotspot. The source, transfer, and influencing factors of antibiotics in the biofloc zero-change aquaculture system remain uncertain. The biofloc zero-change shrimp system during the cycle of shrimp production of a typical aquaculture enterprise in China were investigated. Sulfadiazine and erythromycin-H2O were the dominant antibiotics in source water, pond water, biofloc, and feed. The total concentrations of antibiotics (TCAs) in source water and pond water increased, and were significantly higher (1.28–3.91 times) in source water than those in pond water at the corresponding stage (p<0.05). Pearson correlation showed that positive correlations were observed between suspended solids (SS), chemical oxygen demand (COD) and erythromycin-H2O, with correlation coefficients of 0.84 and 0.82, respectively (p<0.01). Multivariable linear regression analysis showed that COD was positively correlated with the TCAs. RDA demonstrated that COD and SS were positively correlated with erythromycin-H2O. We found that the total amount of antibiotics from feed input throughout the rearing period was 12.24 mg, significantly less than those at the late-stage in the pond water and bioflocs (28.66–29.17 mg) in a shrimp pond. The total amount of antibiotics in the source water of the same volume as the aquaculture pond was 27.90–35.07 mg. This study demonstrates that COD and SS are critical factors affecting the transfer and distribution of antibiotics in the biofloc zero-change aquaculture system, and that source water is the most critical source of antibiotics in the aquaculture system.

Smallholder farmers’ dissatisfaction with contract farming: a gendered perspective

ABSTRACT This study investigates the drivers of farmers’ dissatisfaction with market-specification contracts for finger millet crop in Kenya. The researcher analysed data from 336 farmers contracted by USOMI, out of which a sub-sample of 146 farmers who grew millet in 2021 was analysed using a probit model and descriptive analysis approach, which showed that asymmetric information related to the role of the contracting firm, lack of adequate understanding of the contract, poor pricing, and input market imperfections are some of the critical constraints and sources of dissatisfaction in the commercialisation of millet farming in Western Kenya. On the other hand, farmers’ satisfaction is driven by access to market and reasonable prices offered by the spot market traders. While the study found out that productivity and pricing were not gendered, it was observed that women were generally satisfied with contract farming. Consequently, contractual schemes should integrate complementary factors such as input provision. While the results do not show explicit benefits to women farmers, they do allow for the backing of contract farming in rural development programs to reduce gender inequalities.

Assessing groundwater quality in semi-arid conditions: a geographic information systems-integrated approach using water quality index

Meeting freshwater demands in water-scarce regions is imperative, given challenges like hydric stress, urbanization, aridity, and population growth. This study focuses on the Tebessa aquifer system, a critical water source facing escalating demands. A primary objective is to identify actionable management strategies to improve groundwater quality dynamics in semi-arid regions. The methodology involves a comprehensive assessment of groundwater health using the Water Quality Index (WQI) approach. This method integrates diverse parameters influenced by intensive agricultural and industrial activities. Additionally, spatial relationship analysis, facilitated by Geographic Information System (GIS) technology, is employed to gain insights into complex hydrogeological processes. The multi-parameter strategy implemented results in water quality maps that highlight various ions, revealing spatial disparities and assigning quality priority classes from 'Good' to 'Poor' across the groundwater. The research, conducted in the semi-arid Tebessa-Morsott Plain, encompasses field and laboratory investigations to delineate hydrochemical traits, including alkalinity, salinity, and heightened ion concentrations. The results of the study contribute to scientific understanding by uncovering interrelationships and spatial intricacies, offering valuable insights for water resource governance. The effectiveness of the methodology, incorporating WQI and GIS, is demonstrated in the comprehensive evaluation of groundwater quality. Ultimately, this research provides a foundation for informed decision-making and sustainable management of groundwater resources in semi-arid regions.

Transport and risk of airborne pathogenic microorganisms in the process of decentralized sewage discharge and treatment

Sewage treatment processes are a critical anthropogenic source of bioaerosols and may present significant health risks to plant workers. Compared with the specialization and scale of urban sewage treatment, many decentralized treatment models are flexible and extensive. These treatment facilities are usually close to residential areas owing to the pipe network layout and other restrictions. Bioaerosols generated by these facilities may present a serious and widespread occupational and non-occupational exposure risk to nearby residents, particularly the elderly and children. An understanding of the characteristics and exposure risks of bioaerosols produced during decentralized sewage treatment is lacking. We compared bioaerosol emission characteristics and potential exposure risks under four decentralized sewage discharge methods and treatment models: small container collection (SCC), open-channel discharge (OCD), single household/combined treatment (SHCT), and centralized treatment (CT) in northwest China. The OCD mode had the highest bioaerosol production, whereas the CT mode had the lowest. The OCD model contained the most pathogenic bacterial species, up to 43 species, including Sphingomonas, Pseudomonas, Cladosporium, and Alternaria. Risk assessments indicated bioaerosol exposure was lower in the models with sewage treatment (SHCT and CT) than in those without (SCC and OCD). Different populations exhibited large variations in potential risks owing to differences in time spent indoors and outdoors. The highest risk was observed in males exposed to the SCC model. This study provides a theoretical basis and theories for the future joint prevention and control of the bioaerosol exposure risk from decentralized sewage treatment.

Critical mineral source potential from oil & gas produced waters in the United States

The volume of produced water, a by-product of oil & gas operations and other energy processes, has been growing across the United States (U.S.) along with the need to manage or recycle this wastewater. Produced water contains many naturally occurring elements of varying concentrations, including critical minerals which are essential to the clean energy transition. However, the current understanding of critical mineral concentrations in produced water and the associated volumes across the U.S. is limited. This study has assessed available databases and literature to gain insight into the presence and concentration of five high priority critical minerals, namely cobalt, lithium, magnesium, manganese, and nickel. The U.S. Geological Survey's National Produced Waters Geochemical Database was the main data source used for determining average critical mineral concentrations in produced water from the major oil and gas reservoirs in the U.S. The volumes of produced water for these major reservoirs were coupled with these concentrations to provide insights into where critical minerals are likely to have high abundance and therefore more recovery options. The analysis indicated the highest recovery potential for lithium and magnesium from produced water in the Permian basin and the Marcellus shale region. However, these assessments should be considered conservative due to the limited availability of reliable concentration data. It is expected more critical mineral recovery options could emerge with comprehensive characterization data from more recent and representative sources of produced water.

Optimizing bearing health condition monitoring: exploring correlation feature selection algorithm

Vibration signals are a critical source of information for detecting and diagnosing bearing faults, making this research particularly relevant to the condition monitoring of industrial machinery, particularly bearings using vibration signals. This study delves into how feature selection can be done using Pearson’s Correlation Co-efficient within the context of monitoring bearing health conditions, utilizing two distinct approaches. Approach-1 involves feature selection without considering labels, while Approach-2 incorporates labels for feature selection. Comparative analysis is conducted against outcomes obtained when all features are selected. The research scrutinizes the impact of feature selection on classifier performance, accuracy, and execution times, utilizing various machine learning algorithms such as Decision Tree (DT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB). The findings underscore that feature selection significantly enhances classifier accuracy while reducing execution times. Specifically, only DT and KNN with 50 neighbors achieved 100% accuracy when all features were considered. However, with feature selection using Approach-1 (without labels), DT, KNN, SVM (excluding 100 neighbors), and NB (with Normal/Gaussian kernel) attained 100% accuracy. Employing Approach-2 (with labeled features), DT with 0.7 and 0.9 thresholds, SVM-G with all thresholds (0.6, 0.7, and 0.9), KNN with all thresholds (except 100 neighbors), and NB-n (with all thresholds) achieved 100% accuracy. The study emphasizes the pivotal role of feature selection using Pearson’s Correlation Coefficient in enhancing machine learning classifier performance, offering promising avenues for future research and practical applications across diverse domains.

DLLog: An Online Log Parsing Approach for Large-Scale System

Syslog is a critical data source for analyzing system problems. Converting unstructured log entries into structured log data is necessary for effective log analysis. However, existing log parsing methods demonstrate promising accuracy on limited datasets, but their generalizability and precision are uncertain when applied to diverse log data. Enhancements in these areas are necessary. This paper proposes an online log parsing method called DLLog, which is based on deep learning and has the longest common subsequence. DLLog utilizes the GRU neural network to mine template words and applies the longest common subsequence to parse log entries in real-time. In the offline stage, DLLog combines multiple log features to accurately extract the template words, creating a log template set to assist online log parsing. In the online stage, DLLog parses log entries by calculating the matching degree between the real-time log entry and the log template in the log template set. This method also supports the incremental update of the log template set to handle new log entries generated by systems. We summarized the previous works and validated DLLog using real log data collected from 16 systems. The results demonstrate that DLLog achieves high parsing accuracy, universality, and adaptability.

Knowledge Management: The Case of Xerox

Recently, the concept of knowledge has been universally recognized as a critical source of achieving competitive advantage for organizations and as an extremely important intangible asset. In today's complex, ever-changing and competitive environment, organizations are challenged in order to successfully manage a wide range of fluctuations, to continuously innovate and deliver value to their customers. To achieve all of the above, they must be open to new ideas and develop a clear understanding of the ways in which knowledge is created, transferred and applied within organizations. Knowledge has been approached as the understanding of a subject or phenomenon, on a theoretical or practical basis. Knowledge management (KM) has been frequently used in recent years as a scientific term, while it has been applied to a range of activity sectors and disciplines. The purpose of KM is to manage, create and enhance the knowledge and intellectual assets of organizations. The effective use of KM can provide a number of benefits to organizations, such as enhancing the quality of services provided, increasing the response time developing innovations, offering above the norm customer value etc. The development of organizational knowledge must be sufficient, so that it can diffuse within the organization and applied where it is needed. Knowledge is increasingly recognized as a critical, strategic resource of organizations. In this paper, secondary data were utilized in order to adequately fulfill the set research objectives developed by the researcher, based on the critical evaluation of existing knowledge. Secondary data was used, while the case study research technique was utilized to develop valuable lessons for the organizations. The organization studied (XEROX) is a rare case of successful implementation of KM and was chosen for this reason. The research objective was to conceptually clarify the concept of KM, link it to organizational performance and provide suggestions in relation to strengthening KM and consolidating organizational processes of knowledge development and transfer.

Study on thermal error modeling of high-speed motorized spindle considering bearing inner ring temperature

In addressing the challenge of unattainable temperature data at critical heat source points in bearings, this study solves for the temperatures of the bearing inner ring and rollers by merging thermal resistance network calculations with experimental data, referencing finite element simulation outcomes. The impact of temperature data from various positions within bearings on model accuracy is assessed using a BP prediction model. Subsequently, the training dataset of the thermal error model is optimized to enhance its predictive performance. To further boost the predictive capabilities of the model, a whale optimization algorithm is employed to optimize the BP neural network, which is then compared against both the conventional BP model and the GPR model. The findings reveal that a model optimized with the input of inner ring temperatures and the WOA algorithm achieves an accuracy rate of 97.748 %, outperforming both the BP model and the GPR model. This study provides a new idea for the field of thermal error modeling of motorized spindle.

From art to health action: lessons from a community-based, culturally tailored arts-meets-health educational campaign in Hispanic communities.

Robust digital and community-led approaches are needed to combat health misinformation, as highlighted by the COVID-19 pandemic. Such gaps in public health outreach, compounded by systemic health barriers, contributed to higher rates of COVID-19 infection, mortality, and mental health effects among Hispanics during the peak of the pandemic. Thus, we conducted a community-based art-meets-health intervention [Stay Connected Los Angeles (SCLA)] to address the impacts of the COVID-19 pandemic in Hispanic communities. Led by local artists in collaboration with public health specialists and community members, SCLA used multimedia to promote infection mitigation behaviors and psychological well-being among the 120,000 residents of Eastern Los Angeles. Campaign materials were designed with input from community representatives and included digital media, large-scale murals, and comic-book style pieces. Two semi-structured focus groups (one in English and another in Spanish) were conducted to solicit participants' views on attributes of the campaign. Independent coders analyzed transcripts and applied thematic analysis to summarize key learnings regarding central health and mitigation messages, media modalities, how health information would be communicated, and the ideal spokespersons for delivering health-related messages. Focus group participants emphasized the effectiveness of social media, GIFs, and references to popular media. Further, youth involvement in the creative process was deemed to be important. Participants highlighted the need for clarity in public health messaging and adaptation of visual campaigns to the preferences of diverse age groups through different art styles. Finally, community leaders were found to be critical health information sources. As a model of a culturally tailored arts-meets health public education campaign, SCLA yielded valuable information on how to structure future public health messaging and media to create a meaningful improvement in health knowledge, mental well-being, and compliance with mitigation behaviors in communities that are often overlooked. Contributions from local artists can heighten appeal and acceptability of messages.

Grazing strategies for reducing contaminant losses in surface runoff from winter forage crop fields located in hill country and grazed by sheep

ABSTRACT A paired catchment study approach was used to quantify fluxes of Nitrogen (N), Phosphorus (P), sediment and Escherichia coli (E. coli) in surface runoff from fields where sheep were wintered on swede (Brassica napus) and kale (Brassica oleracea) crops. The effectiveness of a strategic grazing approach that protected critical source areas (CSAs) was examined to determine if these fluxes could be reduced. Averaged over two years, estimated fluxes of N, P and sediment in surface runoff recorded in winter and early spring (June-September) were up to 0.64, 0.22 and 51 kg ha−1, respectively, and up to 2 × 1011 MPN ha−1 for E. coli. With the exception of E. coli, these fluxes were low compared to those reported in other studies and largely attributed to the low amounts of surface runoff recorded. This, in turn, can be explained by relatively benign weather conditions recorded during the study period and the relatively light grazing pressures imposed by sheep. Compared to standard grazing practice, protection of CSAs reduced N, P, sediment, and E. coli fluxes by 38%, 48%, 55% and 63%, respectively. These reductions can largely be attributed to lower concentrations of contaminants in surface runoff when the CSA was protected.

Nanoplastics impair growth and nitrogen fixation of marine nitrogen-fixing cyanobacteria

Nanoplastics pollution is a growing environmental problem worldwide. Recent research has demonstrated the toxic effects of nanoplastics on various marine organisms. However, the influences of nanoplastics on marine nitrogen-fixing cyanobacteria, a critical nitrogen source in the ocean, remained unknown. Here, we report that nanoplastics exposure significantly reduced growth, photosynthetic, and nitrogen fixation rates of Crocosphaera watsonii (a major marine nitrogen-fixing cyanobacterium). Transcriptomic analysis revealed that nanoplastics might harm C. watsonii via downregulation of photosynthetic pathways and DNA damage repair genes, while genes for respiration, cell damage, nitrogen limitation, and iron (and phosphorus) scavenging were upregulated. The number and size of starch grains and electron-dense vacuoles increased significantly after nanoplastics exposure, suggesting that C. watsonii allocated more resources to storage instead of growth under stress. We propose that nanoplastics can damage the cell (e.g., DNA, cell membrane, and membrane-bound transporters), inhibit nitrogen and carbon fixation, and hence lead to nutrient limitation and impaired growth. Our findings suggest the possibility that nanoplastics pollution could reduce the new nitrogen input and hence affect the productivity in the ocean. The impact of nanoplastics on marine nitrogen fixation and productivity should be considered when predicting the ecosystem response and biogeochemical cycling in the changing ocean.

The Spatial–Temporal Emission of Air Pollutants from Biomass Burning during Haze Episodes in Northern Thailand

Air pollutants from biomass burning, including forest fires and agricultural trash burning, have contributed significantly to the pollution of the Asian atmosphere. Burned area estimates are variable, making it difficult to measure these emissions. Improving emission quantification of these critical air pollution sources requires refining methods and collecting thorough data. This study estimates air pollutants from biomass burning, including PMs, NOX, SO2, BC, and OC. Machine learning (ML) with the Random Forest (RF) method was used to assess burned areas in Google Earth Engine. Forest emissions were highest in the upper north and peaked in March and April 2019. Air pollutants from agricultural waste residue were found in the lower north, but harvesting seasons made timing less reliable. Biomass burning was compared to the MODIS aerosol optical depth (AOD) and Sentinel-5P air pollutants, with all comparisons made by the Pollution Control Department (PCD) Thailand air monitoring stations. Agro-industries, mainly sugar factories, produce air pollutants by burning bagasse as biomass fuel. Meanwhile, the emission inventory of agricultural operations in northern Thailand, including that of agro-industry and forest fires, was found to have a good relationship with the monthly average levels of ambient air pollutants. Overall, the information uncovered in this study is vital for air quality control and mitigation in northern Thailand and elsewhere.