Environmental Controls Research Articles

РОЗРОБКА КІБЕРФІЗИЧНОЇ СИСТЕМИ ДЛЯ АВТОМАТИЗАЦІЇ ТА КЕРУВАННЯ ІНТЕРНЕТОМ РЕЧЕЙ З ВИКОРИСТАННЯМ ПЛАТФОРМИ HOME ASSISTANT

The paper presents the development of a cyber-physical system based on the HomeAssistant platform for efficient automation and control of Internet of Things (IoT) devices. The architectural features, technical implementation and prospects for the development of the system are considered, with an emphasis on the ability to integrate a wide range of sensors and IoT devices into a single network to create adaptive and intelligent solutions. The main focus is on the implementation of automation scenarios for indoor climate control, which optimise living conditions depending on the internal needs of users and external weather conditions, significantly increasing energy efficiency and overall comfort. Data storage and analysis strategies are described in detail, including the use of NAS servers for backup, the use of MariaDB for storage and InfluxDB and Grafana for analytics and visualisation, ensuring a high level of reliability and availability of information. Testing was conducted to assess the delay of notifications transmitted via Telegram and the internal network of the cyber-physical system. The obtained results confirm the high efficiency of the implemented cyber-physical system in ensuring instant delivery of notifications, which is a key aspect for rapid response to critical situations in a dynamic environment. The Home Assistant platform has been found to have extensive capabilities to support artificial intelligence services. It integrates with a variety of services such as Google Assistant, TensorFlow, DeepStack, Amazon Alexa, and allows you to easily expand its functionality with additional components and plug-ins. Using intelligent algorithms and data analysis, the system can independently make decisions on the optimal use of resources, monitor devices in accordance with changing conditions and user needs, and respond to dangerous or unpredictable situations.

Dynamic regulation of water potential in Juniperus osteosperma mediates ecosystem carbon fluxes.

Some plants exhibit dynamic hydraulic regulation, in which the strictness of hydraulic regulation (i.e. iso/anisohydry) changes in response to environmental conditions. However, the environmental controls over iso/anisohydry and the implications of flexible hydraulic regulation for plant productivity remain unknown. In Juniperus osteosperma, a drought-resistant dryland conifer, we collected a 5-month growing season time series of in situ, high temporal-resolution plant water potential ( ) and stand gross primary productivity (GPP). We quantified the stringency of hydraulic regulation associated with environmental covariates and evaluated how predawn water potential contributes to empirically predicting carbon uptake. Juniperus osteosperma showed less stringent hydraulic regulation (more anisohydric) after monsoon precipitation pulses, when soil moisture and atmospheric demand were high, and corresponded with GPP pulses. Predawn water potential matched the timing of GPP fluxes and improved estimates of GPP more strongly than soil and/or atmospheric moisture, notably resolving GPP underestimation before vegetation green-up. Flexible hydraulic regulation appears to allow J. osteosperma to prolong soil water extraction and, therefore, the period of high carbon uptake following monsoon precipitation pulses. Water potential and its dynamic regulation may account for why process-based and empirical models commonly underestimate the magnitude and temporal variability of dryland GPP.

Optimizing photosynthetic photon flux density and light quality for maximizing space use efficacy in edamame at the vegetative growth stage

Compared with conventional crop cultivation in greenhouses or fields, plant factories with artificial light (PFAL) have advantages in the highly efficient use of space, energy, and resources available for cultivation. However, few studies on environmental controls for improving the space use efficacy (SUE) of PFAL in the production of edamame, a vegetable soybean, have been reported. Therefore, developing an environmental control method for high productivity with minimal space and energy requirements is of high priority. The aims of this study were to (1) identify the optimal photosynthetic photon flux density (PPFD) and light quality to enhance the SUE of edamame at the vegetative growth stage, and (2) examine the effects of PPFD, light quality, and their interaction on edamame plant growth at the vegetative stage. SUE is defined as the crop biomass produced per unit cubic volume of cultivation during the growth period. We examined three PPFD treatments (300, 500, and 700 μmol m−2 s−1) with three color temperature LED lamps (3,000, 5,000, and 6,500 K), for a total of nine treatments. The results demonstrated that, under the same light quality treatment, higher PPFDs resulted in larger fresh and dry weights of all organs, higher stem length, and lower specific leaf area. Under the same PPFD treatment, a high ratio of blue (400–499 nm) to red (600–699 nm) photon flux density increased the plant height but decreased the projected leaf area. The values of SUE at 700 μmol m−2 s−1 increased by 213, 163, and 92% with 3,000, 5,000, and 6,500 K, respectively compared with those at 300 μmol m−2 s−1. The values of SUE at 700 μmol m−2 s−1 increased by 34 and 23% in 5,000 and 6,500 K treatments, respectively compared with that in the 3,000 K treatment. In conclusion, a combination of 700 μmol m−2 s−1 PPFD and 5,000 K color temperature is the suitable condition to increase the SUE of edamame at the vegetative growth stage in a PFAL.

Humidity Diode Sensors Based on 1D Nanosized Silicon Structures

Introduction. Humidity measurement is essential in microelectronics, aerospace, biomedical, and food industries, as well as in households for climate control. Currently, various types of devices have been used as humidity sensors: capacitive, resistive, diode, gravimetric, optical structures, field-effect transistors and devices based on surface acoustic waves.Problem Statement. Today, there is a need to develop IC-compatible humidity sensors that have high sensitivityand low cost. To this end, silicon nanowires have been successfully used in resistive and capacitive humidity sensors. However, there is a lack of research on the nanowire effect on device parameters of diode-type humidity sensors.Purpose. To develop diode sensors based on silicon nanowires and to determine the effect of process parameters of synthesis and structural features of nanowires on the performance of humidity sensors.Materials and Methods. The process of sensor fabrication includes several steps: chemical cleaning of silicon wafer, synthesis of silicon nanowires using standard or modified metal-assisted chemical etching, phosphorus diffusion to create a p-n junction, front and back metallization. The surface morphology of the nanostructures has been studied by scanning electron microscopy. The humidity-sensitive characteristics have been studied with theuse of salt hygrostats.Results. It has been shown that the addition of one-dimensional silicon nanostructures to the diode-type sensor signifi cantly improves its characteristics. The rectification ratio increases from 161 to 1807, the response ups from 4.5 to 25, the sensitivity grows from 1.6 to 4.02 (%RH)–1, while the response time and recovery time are reduced from 85/90 to 25/30 s, the hysteresis value goes down from 75 to 16%, the signal deviation after cycling drops from 15to 3%, and the signal fluctuation during continuous device operation decreases from 17 to 15%.Conclusions. The results have shown that the use of a simple and cheap nanowire synthesis technology is effective to produce humidity sensors.

Working like Machines: Technological Upgrading and Labour in the Dutch Agri-food Chain

This article engages with the role of technological upgrading for work in agriculture, a sector commonly disregarded in debates about the future of work. Foregrounding migrant work in Dutch horticulture, it explores how technological innovation is connected to the scope and security of employment. Besides, it proposes a heuristic that connects workers’ experience to sectoral dynamics and the wider agri-food chain. Our analysis reads data from a small-scale qualitative study with different actors in the Dutch agri-food sector through the lens of the global value chain literature. Nuancing pessimistic predictions of widespread technological unemployment, we find product upgrading into high value-added products, and process upgrading, such as through climate control in greenhouses, to offer the potential for more and secure employment. However, higher work intensity and the dismantling of entitlements for rest and reproduction to ‘make people work like machines’ represent the underbelly of these dynamics.

Environmental controls on the distribution of brGDGTs and brGMGTs across the Seine River basin (NW France): implications for bacterial tetraethers as a proxy for riverine runoff

Abstract. Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are bacterial lipids that have been widely used as environmental proxies in continental paleorecords. Another group of related lipids, branched glycerol monoalkyl glycerol tetraethers (brGMGTs), has recently been proposed as a potential paleotemperature proxy. Nevertheless, the sources and environmental dependencies of both brGDGTs and brGMGTs along the river–sea continuum are still poorly understood, complicating their application as paleoenvironmental proxies in some aquatic settings. In this study, the sources of brGDGTs and brGMGTs and the potential factors controlling their distributions are explored across the Seine River basin (NW France), which encompasses the freshwater-to-seawater continuum. BrGDGTs and brGMGTs were analyzed in soils, suspended particulate matter (SPM), and sediments (n=237) collected along the land–sea continuum of the Seine basin. Both types of compounds (i.e., brGDGTs and brGMGTs) are shown to be produced in situ, in freshwater and saltwater, based on their high concentrations and distinct distributions in aquatic settings (SPM and sediments) vs. soils. Redundancy analysis further shows that both salinity and nitrogen dominantly control the brGDGT distributions. Furthermore, the relative abundance of 6-methyl vs. that of 5-methyl brGDGTs (the IR6Me ratio), the total nitrogen (TN), the δ15N, and the chlorophyll a concentration co-vary in a specific geographical zone with low salinity, suggesting that 6-methyl brGDGTs are preferentially produced under low-salinity and high-productivity conditions. In contrast to brGDGTs, the brGMGT distribution appears to be primarily regulated by salinity, with a distinct influence on the individual homologues. Salinity is positively correlated with homologues H1020a and H1020b and negatively correlated with compounds H1020c and H1034b in SPM. This suggests that bacteria living in freshwater preferentially produce compounds H1020c and H1034b, whereas bacteria that primarily grow in saltwater appear to be predominantly responsible for the production of homologues H1020a and H1020b. Based on the abundance ratio of the freshwater-derived compounds (H1020c and H1034b) vs. their saltwater-derived homologues (H1020a and H1020b), a novel proxy, the Riverine IndeX (RIX), is proposed to trace riverine organic matter inputs, with high values (>0.5) indicating a higher riverine contribution. We successfully applied RIX to the Godavari River basin (India) and a paleorecord across the upper Paleocene and lower Eocene from the Arctic Coring Expedition at Lomonosov Ridge, showing its potential applicability to both modern samples and paleorecords.

Thermal performance of packed-bed latent heat storage tank integrated with flat-plate collectors under intermittent loads of building heating

Packed-bed latent heat storage (PBLHS) system has been widely studied in the solar energy application area due to its properties of solving the time-space mismatch of energy supply and demand. The paper delves into the cyclic thermal performance of PBLHS tanks, seamlessly integrated with flat-plate collectors, to manage building heat loads effectively. A 1D transient mathematic model of PBLHS integrated with the solar collector for a building is established using MATLAB which is validated with the experimental results from the literature. Based on this model, the effect of different sizes, flow rates, inlet temperature thresholds and different heating months are discussed. The results show that: 1). Building heat load can have a significant influence on the thermal performance of the PBLHS system. 2). The optimal size of the tank for a 96 m2 house is 1.0*1.0 m and the optimal flow rate is 0.05 kg/s 3). The weather data can significantly affect the thermal performance of PBLHS, the storage tank heating durations for one week in different heating months are 108.4h, 73.6h,70.8h and 81.7h. These insights are pivotal for the optimal design of PBLHS tanks, paving the way for more efficient building heating solutions.

Eroding the Himalaya: Topographic and Climatic Control of Erosion Rates and Implications for Tectonics

Eroding the Himalaya: Topographic and Climatic Control of Erosion Rates and Implications for Tectonics

Decarbonization through smart energy management: Climate control in building-integrated rooftop greenhouses for urban agriculture across various climate conditions

This paper investigates the potential benefits of integrating rooftop greenhouses (RTGs) with buildings to reduce energy and CO2 costs, contributing to urban decarbonization efforts. This integration not only advances sustainable urban agriculture but also aligns with the United Nations Sustainable Development Goal 7 (SDG7) - Affordable and Clean Energy, and Sustainable Development Goal 11 (SDG11) - Sustainable Cities and Communities, through its energy-efficient, low-carbon approach. We hypothesize that this integration can significantly lower energy use in integrated RTGs (i-RTGs) and set out to validate this through dynamic models for both building and i-RTG climates, focusing on CO2 concentration, humidity, and temperature. Central to our approach is the implementation of a nonlinear model predictive control (NMPC) framework, which utilizes these dynamic models to make control decisions aimed at minimizing total control costs. This framework operates in a receding horizon procedure, regulating climate factors within the i-RTG and building using various actuators. We validate our approach by simulating an i-RTG atop a building in eleven different cities, demonstrating interactions such as energy, moisture, and CO2 exchange. Significantly, our study reveals that integrating i-RTG with buildings under the NMPC framework leads to a 15.2% reduction in control costs. Additionally, we find that the i-RTG model is adaptable to different climates, with colder regions showing greater cost reduction potential. Our study underscores the viability and advantages of integrating i-RTGs with buildings, offering a sustainable, decarbonizing solution for urban agriculture and building management that contributes to the fulfillment of SDG7 and SDG11.

Applications of Quantitative Microbial Risk Assessment to Respiratory Pathogens and Implications for Uptake in Policy: A State-of-the-Science Review.

Respiratory tract infections are major contributors to the global disease burden. Quantitative microbial risk assessment (QMRA) holds potential as a rapidly deployable framework to understand respiratory pathogen transmission and inform policy on infection control. The goal of this paper was to evaluate, motivate, and inform further development of the use of QMRA as a rapid tool to understand the transmission of respiratory pathogens and improve the evidence base for infection control policies. We conducted a literature review to identify peer-reviewed studies of complete QMRA frameworks on aerosol inhalation or contact transmission of respiratory pathogens. From each of the identified studies, we extracted and summarized information on the applied exposure model approaches, dose-response models, and parameter values, including risk characterization. Finally, we reviewed linkages between model outcomes and policy. We identified 93 studies conducted in 16 different countries with complete QMRA frameworks for diverse respiratory pathogens, including SARS-CoV-2, Legionella spp., Staphylococcus aureus, influenza, and Bacillus anthracis. Six distinct exposure models were identified across diverse and complex transmission pathways. In 57 studies, exposure model frameworks were informed by their ability to model the efficacy of potential interventions. Among interventions, masking, ventilation, social distancing, and other environmental source controls were commonly assessed. Pathogen concentration, aerosol concentration, and partitioning coefficient were influential exposure parameters as identified by sensitivity analysis. Most (84%, ) studies presented policy-relevant content including a) determining disease burden to call for policy intervention, b) determining risk-based threshold values for regulations, c) informing intervention and control strategies, and d) making recommendations and suggestions for QMRA application in policy. We identified needs to further the development of QMRA frameworks for respiratory pathogens that prioritize appropriate aerosol exposure modeling approaches, consider trade-offs between model validity and complexity, and incorporate research that strengthens confidence in QMRA results. https://doi.org/10.1289/EHP12695.

Optimized model predictive control for solar assisted earth air heat exchanger system in greenhouse

Agricultural greenhouses play a crucial role in addressing the threat of climate change to agricultural systems. The greenhouse systems control exhibits nonlinear characteristics and large lag, both affecting the regulation of the greenhouse thermal environment. In this paper, a control strategy was designed to set the dynamic reference value of the control objective of the greenhouse model, considering factors such as solar radiation and the heating capacity of water body. A complete greenhouse model was developed using TRNSYS, and then the model predictive control building and optimization were implemented through MATLAB. The control effects of traditional proportional-integral-differentiation control, initial model predictive control, and optimized model predictive control were compared. The results showed that the regulation of greenhouse temperature was improved by 10.6 % in the coldest mouth compared to the conventional proportional-integral-differentiation control by the optimized model predictive control. During a typical cold month, the violation of constraints was reduced by 29.7 % by dynamically setting the target of the greenhouse with the optimized model predictive control. The performance of greenhouse climate control is enhanced by the proposed optimized model predictive control method, ensuring a stable regulation of the crop growth environment.

Evaluating the impact of historical climate and early human groups in the Araucaria Forest of eastern South America

It has been hypothesized that the Araucaria Forest in southern Brazil underwent expansions in the past, driven either by human groups or by climate fluctuations of the Holocene and Pleistocene. Fossil pollen records of the Paraná pine Araucaria angustifolia, a dominant tree in that forest, provide some insights into when those may have occurred. Still, the timing of those expansions has never been estimated. To infer past range shifts and shed light on their main drivers, we employed next‐generation DNA sequencing (ddRADseq), machine learning, and a comprehensive database of fossil pollen records into a study of historical demographic inference and paleo‐distribution modeling of the Paraná pine. We found that A. angustifolia comprises two populations expanding at different times: one in the Mantiqueira mountain chain, the other in the southern Brazilian plateau. The southern population began to expand during the Last Glacial Period ~ 70 kya, long before human arrival in South America. Still, genetic analyses support that humans later impacted this population, resulting in lower genetic diversity, higher inbreeding, and high levels of gene flow over large distances with a weak pattern of isolation‐by‐distance. It is possible this resulted from human influence on seed dispersal and germination on the southern Brazilian plateau. The Mantiqueira population, in contrast, expanded only recently (~ 3 kya). This timing coincides with Holocene climatic changes and human settlements established further south, although, to date, there is little archeological evidence of human impact in the Mantiqueira. In addition, multitemporal species distribution models built from a combination of present‐day and pollen records infer a range expansion of the Araucaria Forest during glacial times until the cold humid HS1 event (~ 16 kya), when the forest was most widespread, with no evidence of glacial refugia. The combination of genomic and spatial analyses suggests that both human and climatic controls played a role in the dynamics of the Araucaria Forest.

Climatic controls on water-mass chemistry in a Paleocene lacustrine setting, Subei Basin, eastern China

The Paleocene epoch was characterized by global climate fluctuations and major carbon-cycle perturbations. During the greenhouse climate that characterized the early Cenozoic, a short-lived late Paleocene global cooling event has been recognized from marine records. However, the response of the terrestrial system to this climate cooling event is poorly understood. Here, we present major- and trace-element analyses, iron speciation systematics, carbonate carbon isotope data, and mineralogical observations from lacustrine sediments in Member II of the Paleocene Funing Formation (E1f2), utilizing well-preserved drill core from the Subei Basin, eastern China. Both chemical (chemical index of alteration [CIA], Al/K ratios) and mineralogical (mineralogical index of alteration, clay/feldspar ratios) proxies yield consistent weathering and paleoclimatic interpretations, suggesting a transition from cool and arid climatic conditions to warmer and more humid climatic conditions. Correlation of carbon isotopes between the Subei Basin and deep-sea records implies that this terrestrial setting records the short-lived Paleocene climate cooling event. The combination of climate records and paleosalinity proxies (B/Ga and S/total organic carbon [TOC]) indicates a relatively high-salinity water column (brackish to saline) under cool and arid climatic conditions in the Subei Basin, suggesting that elevated salinity was likely produced via net-evaporative conditions, rather than marine incursions. A shift toward less saline brackish conditions up section reflects an increase in precipitation and freshwater runoff under warmer and more humid climatic conditions. Iron speciation and redox-sensitive trace-metal systematics reveal fluctuating redox conditions, from oxic through to anoxic ferruginous, but with the distinct development of better ventilated conditions as freshwater inputs increased under more humid conditions. Our findings demonstrate the sensitivity of terrestrial climate to the late Paleocene climate cooling event and further reveal the chemical response of a lacustrine setting to a cooling episode in a greenhouse world.

Stenotrophomonas maltophilia Outbreak in an ICU: Investigation of Possible Routes of Transmission and Implementation of Infection Control Measures

Stenotrophomonas maltophilia, a non-fermentative, ubiquitous, gram-negative aerobic bacterium, is associated with high mortality rates, particularly in immunocompromised or debilitated patients. The prevalence rate of ICU-acquired pneumonia episodes caused by this microorganism has been found to be 2%. S. maltophilia has been identified as one of the top 10 microorganisms responsible for such infections in EU/EEA countries. This study describes an outbreak of S. maltophilia in an intensive care unit of a hospital in northern Italy. This includes an epidemiological investigation of the cases, the environmental microbiological controls carried out, a comparison of the strains by multilocus sequence typing (MLST), and the measures taken to prevent and control the outbreak. Among the seven clinical isolates of S. maltophilia analyzed herein, six demonstrated susceptibilities to trimethoprim–sulfamethoxazole. Conversely, one isolate of S. maltophilia exhibited resistance to first-line antibiotics. ST was found to be identical for six patients (ST 4), as well as in the environmental feedback on the trolley of Box 2. The analysis of the temporal and spatial progression of the outbreak has suggested that the transmission of S. maltophilia may have occurred through cross-transmission during care practices.

Environmental and Economic Clustering of Indonesian Provinces: Insights from K-Means Analysis

Indonesia's archipelago presents a distinctive opportunity for targeted sustainable development due to its complex interplay of economic advancement and environmental challenges. To better understand this dynamic and identify potential areas for focused intervention, this study applied K-means clustering to 2022 data on the Air Quality Index (AQI), electricity consumption, and Gross Regional Domestic Product (GRDP). The analysis aimed to delineate the provinces into three distinct clusters, providing a clearer picture of the varying levels of economic development and environmental impact across the nation's diverse islands. Each cluster reflects specific environmental and economic dynamics, suggesting tailored policy interventions. The results show that for provinces in Cluster 1, which exhibit moderate environmental quality and lower economic activity, the introduction of sustainable agricultural enhancements, eco-tourism, and renewable energy initiatives is recommended. Cluster 2, marked by higher economic outputs and moderate environmental conditions, would benefit from the implementation of smart urban planning, stricter environmental controls, and the adoption of clean technologies. Finally, Cluster 3, which includes highly urbanized areas with robust economic growth, requires expanded green infrastructure, improved sustainable urban practices, and enhanced public transportation systems. These recommendations aim to foster balanced economic growth while preserving environmental integrity across Indonesia’s diverse landscapes.

Predicting Air Quality

A list for revealing air quality is known as the air quality file (AQI). It measures the short-term effects of air pollution on a person's health. The motivation behind the AQI is to instruct general society on the negative well-being impacts of neighbourhood air contamination. How much air contamination in Indian urban communities has altogether expanded? The air quality index can be calculated mathematically in several different ways. Various examinations have found a connection between air contamination openness and unfavourable well-being influences in the populace. Data mining is one of the most intriguing methods for predicting and analysing AQI. This paper aims to determine the most efficient means by which AQI prediction can aid in climate control. The best technique can be enhanced to track down the most ideal arrangement. As a result, the work that is done in this paper requires extensive research as well as the addition of novel methods to guarantee that the best solution to the issue of poor air quality will be found. Another significant objective is to exhibit and show the specific measurements engaged with our work so that it is instructive and clever and subsequently gives legitimate examinations and helps future scientists. Keywords – Air Quality Index (AQI), Air pollution, Short-term effects, Health impacts, AQI prediction, Climate control, Novel methods, Extensive research, Comparative analysis, Future research.

Natural Ventilation to Manage Ammonia Concentration and Temperature in a Rabbit Barn in Central Mexico

The concentration of ammonia (NH3) and the temperature of the air surrounding the rabbit habitat in the farm condition basic health processes such as breathing and feeding. The indoor climate in a rabbit farm is largely conditioned by the ventilation system (air conditioning). The objective of this study was to build a numerical model based on computational fluid dynamics (CFD) in order to evaluate, by numerical simulations, the air dynamics of a rustic farm. After the validation of the computational model, the thermal gradient and ammonia concentration were analyzed under three wind incidence angles (0°, 45°, and 90° with respect to the horizontal Z axis of the facility). The results of the simulations showed that, in the area occupied by the rabbits (AOR), the concentration of ammonia with respect to the source was reduced by 37.3% in the most favorable case (wind direction at 45°), and 21.2% in the least favorable case (wind direction at 0°), and the indoor temperature presented a maximum difference of 2 °C with respect to the outside temperature. Climate control is a more expensive cost in rabbit farm exploitation; dynamics modulation can serve as an auxiliary tool for reducing health risks in rabbits. The use of models based on fluid dynamics allowed us to understand the efficiency of the ventilation system, which must be increased to reduce the found temperature gradient. Through numerical simulation it will be possible to find alternatives to increase the ventilation rate.

Genomic and environmental controls on Castellaniella biogeography in an anthropogenically disturbed subsurface

Castellaniella species have been isolated from a variety of mixed-waste environments including the nitrate and multiple metal-contaminated subsurface at the Oak Ridge Reservation (ORR). Previous studies examining microbial community composition and nitrate removal at ORR during biostimulation efforts reported increased abundances of members of the Castellaniella genus concurrent with increased denitrification rates. Thus, we asked how genomic and abiotic factors control the Castellaniella biogeography at the site to understand how these factors may influence nitrate transformation in an anthropogenically impacted setting. We report the isolation and characterization of several Castellaniella strains from the ORR subsurface. Five of these isolates match at 100% identity (at the 16S rRNA gene V4 region) to two Castellaniella amplicon sequence variants (ASVs), ASV1 and ASV2, that have persisted in the ORR subsurface for at least 2 decades. However, ASV2 has consistently higher relative abundance in samples taken from the site and was also the dominant blooming denitrifier population during a prior biostimulation effort. We found that the ASV2 representative strain has greater resistance to mixed metal stress than the ASV1 representative strains. We attribute this resistance, in part, to the large number of unique heavy metal resistance genes identified on a genomic island in the ASV2 representative genome. Additionally, we suggest that the relatively lower fitness of ASV1 may be connected to the loss of the nitrous oxide reductase (nos) operon (and associated nitrous oxide reductase activity) due to the insertion at this genomic locus of a mobile genetic element carrying copper resistance genes. This study demonstrates the value of integrating genomic, environmental, and phenotypic data to characterize the biogeography of key microorganisms in contaminated sites.

Tripartite interactions among free‐living, N‐fixing bacteria, arbuscular mycorrhizal fungi, and plants: Mutualistic benefits and community response to co‐inoculation

AbstractInteractions between arbuscular mycorrhizal (AM) fungi and free‐living nitrogen fixers (FLNF) occur in the rhizosphere where they can enhance plant nutrient acquisition, impact plant growth, and affect soil processes. Tripartite mutualism commonly occurs between nodule‐forming plants, symbiotic diazotrophs, and AM fungi, and can occur between non‐nodulating plants, FLNF, and AM fungi. However, information on the extent of, and controls on, tripartite mutualism in non‐nodulating plant systems is limited to a small number of crop plants and culturable microbial inoculum, mostly in greenhouse growing conditions. We conducted a systematic literature review to synthesize the current understanding of the responses of plants, AM fungi, and FLNF to co‐inoculation, as well as the conditions affecting tripartite mutualism and the magnitude and range of benefits conferred. Our review shows that plants generally benefit from co‐inoculation with AM fungi and FLNF taxa, but benefits are highly variable and context dependent, ranging from 94% reduction in plant shoot biomass to 255% increase in total plant biomass. Additionally, the presence of AM fungi can increase abundance of FLNF and the presence of FLNF can increase AM fungal root colonization, but these responses also vary widely. Major factors influencing variation in response to co‐inoculation by all organisms include plant phenology/age, soil type and nutrient availability, and partner pairing. There is potential for leveraging these tripartite mutualisms to improve plant productivity and soil microbial function, but successful application is more likely with a thorough understanding of the environmental and mechanistic controls on these relationships and testing of field‐scale implementation.

Optimizing the Implementation of Carbon Tax in Reducing the Impact of Environmental Pollution

Carbon taxes are one of the climate control tools that help achieve sustainable economic growth. It is a market-based instrument that aims to reduce greenhouse gas emissions by making it more expensive to emit carbon dioxide. The Indonesian government has shown its seriousness in reducing global warming by establishing carbon tax regulations, including a provision in Law No. 7 of 2021 concerning the Harmonization of Tax Regulations. Despite implementing carbon taxes in several countries, their implementation must be reconsidered to ensure the objective is achieved. Therefore, this research aims to optimize the implementation of a carbon tax to reduce the impact of environmental pollution. This paper will investigate whether or not the carbon tax has already reduced emissions or if it is not affected at all. Three factors could cause carbon emissions: Coal, vehicle, and greenhouse gas emissions. Therefore, conducting an expectation study encompassing these policymakers, stakeholders, and researchers can gain insights into the potential outcomes and impacts of optimizing the implementation of a carbon tax in reducing environmental pollution. It can inform decision-making processes and help guide the designing and refining adequate and equitable environmental policies.