Nearby Reference Research Articles

Per- and Poly-fluoroalkyl Substances (PFAS) in Sediment: a Source of PFAS to the Food Web?

Kent Lake is an impoundment of the Huron River in southeast lower Michigan. Fish collected from Kent Lake in 2017 had high concentrations of perfluorooctane sulfonate (PFOS) in fillets which resulted in a fish consumption advisory from the Michigan Department of Health and Human Services (MDHHS). In June 2018, a major source of PFOS was identified as the City of Wixom's wastewater treatment plant (WWTP) which discharges treated effluent to Norton Creek and the Huron River 5 miles upstream of Kent Lake. An industrial facility, which has been in operation since approximately 2000, was found to be the source of high levels of PFOS to Wixom's sanitary sewer. A granular activated carbon (GAC) system was installed at the facility, resulting in substantial reductions in PFOS concentrations in surface water and fish from Kent Lake. However, the PFOS decline in fish tissue reached a plateau at a level exceeding the "Do Not Eat" advisory threshold. This case study sought to explore the supposition that sediment is acting as an ongoing source of PFAS to the Kent Lake food web. We sampled PFAS in biota, sediment and water from Kent Lake and a nearby reference lake in 2021. Biota (benthos, forage fish and predator fish) were collected from both lakes and analyzed as whole-body composites for PFAS analytes. The results, including the patterns of PFAS contamination between the water, sediment and biota samples as well as partition coefficients and bioaccumulation factors, are consistent with PFAS in sediment acting as an ongoing source of contamination to the biota in Kent Lake. This study's results for PFOS (the predominant PFAS in Kent Lake sediment and biota) and 6:2 fluorotelomer sulfonic acid (6:2 FTS) provide additional lines of evidence that sediment acts as a source of PFAS to the aquatic food web.

Prevalence of antibiotic resistance genes in mining-impacted farmland environments.

Mining activities produce large quantities of tailings and acid mine drainage, which contain varieties of heavy metals, thereby affecting the downstream farmland soils and crops. Heavy metals could induce antibiotic resistance through co-selection pressure. However, the profiles of antibiotic resistance genes (ARGs) in the mining-affected farmland soils and crops are still unclear. Here we investigated contents of heavy metals, ARG abundances, mobile genetic elements (MGEs), and microbial community in mining-affected farmland soils and vegetables from Shangba village (SB), in comparison to a nearby reference village Taiping (TP). Results showed that in SB group, except for Cr, other metals were all above the Chinese Standards. When compared with the reference group, higher ARG abundances were detected in mining-affected farmland soils and vegetables, with great proportions of genes resistant to sulfonamides, chloramphenicols and tetracyclines. In addition, positive correlations were found between the above three ARG classes and heavy metals concentrations (especially Cu, Pb and Zn). Spearman's correlations revealed that there were positive correlations between sul1 and total nitrogen, as well as tetB/P and pH. Additionally, the Shannon index values were different for the samples from two villages (p < 0.05). Proteobacteria and Actinobacteria were dominant phyla in soil samples. Network analysis suggested that multiple genera (belonging to Proteobacteria and Actinobacteria) were positively associated with many ARGs (p < 0.05), implying they might be potential hosts for ARGs. To sum up, this study provided clear evidence that mining activities caused severe heavy metals pollution to the farmland, thus posing co-selection pressure on the persistence of ARGs in the affected farmland environments.

Relating Rainfall Retrieval Parameters to Network and Environmental Features to Improve Rainfall Estimates from Commercial Microwave Links in the Tropics

Abstract Potentially, the greatest benefit of commercial microwave links (CMLs) as opportunistic rainfall sensors lies in regions that lack dedicated rainfall sensors, most notably low- and middle-income countries. However, current CML rainfall retrieval algorithms are predominantly tuned and applied to (European) CML networks in temperate or Mediterranean climates. This study investigates whether local quantitative precipitation estimates from CMLs in a tropical region, specifically Sri Lanka, can be improved by optimizing two dominant parameters in the rainfall retrieval algorithm RAINLINK, namely, the wet antenna attenuation correction factor Aa and the relative contribution of minimum and maximum received signal levels α. Using a grid search, based on 10 months of CML data from 22 link–gauge clusters consisting of 105 sublinks that lie within 1 km of a daily rain gauge, the optimal values of Aa and α are first derived for the entire country and compared to the default RAINLINK values. Subsequently, the CMLs are grouped by link length, frequency, climate zone, and daily rainfall depth classes, and Aa and α are derived for each of these classes. Calibrating parameters on all clusters across the country only leads to minor improvements. The actual optimal Aa and α values depend on the performance metric favored. Calibrating on network properties, particularly short link length and high-frequency classes, does significantly improve rainfall estimates. By relating the optimal Aa and α values to known network metadata, the results from this study are potentially applicable to other tropical CML networks that lack nearby reference rainfall data. Significance Statement The purpose of this study is to improve rainfall estimates from commercial microwave links in Sri Lanka by optimizing two important rainfall retrieval algorithm parameters. Our results show that relating the optimal parameter values to operating frequency and pathlength improves rainfall estimates more than applying a single optimal parameter set to the entire network. By relating the optimal parameter values to readily known network properties, we aim to make these results applicable to other tropical countries, particularly low- and middle-income countries, that lack adequate reference rainfall data to calibrate rainfall estimates from commercial microwave links on.

Impact of stockpiling on soil fungal communities and their functions

Fungal communities are important components of soil biology and play crucial roles in multiple ecosystem functions and services; therefore, they can be used as effective indicators of soil function in stockpiled reclamation soils. We profiled the fungal communities of seven stockpiles of different ages (0.5, 1.5, 2, 5, 7, 11, and 28 years old) at various soil depths (0–300 cm) in two oil sand extraction locations in northern Alberta. Fungal communities of the stockpiles differed from the communities in the nearby undisturbed reference soils. The differences with the reference soils were more pronounced for the oldest and some of the younger stockpiled soils. Similar to previous studies, there was a decrease in fungal richness and overall diversity with increasing stockpile depth. Furthermore, soil stockpiling generated a shift in the functional composition based on inferred fungal guilds and trophic modes. Ectomycorrhizal fungi decreased and saprotrophic fungi increased in the stockpiles relative to the reference soils. Such a shift may have important implications for ecosystem functions and services associated with fungal communities, such as litter decomposition and plant growth promotion, when these stockpiles are used to reclaim ecosystems. Our results align with previous research identifying the negative effects of stockpiling on soil microbial communities, shed light on the soil functions that may be affected by this disturbance and their consequences for ecosystem functioning, and reflect the high sensitivity of fungal communities to soil disturbances like stockpiling.

Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks.

The rapid evolution of wireless communication systems necessitates advanced handover mechanisms for seamless connectivity and optimal network performance. Traditional algorithms, like 3GPP Event A3, often struggle with fluctuating signal strengths and dynamic user mobility, leading to frequent handovers and suboptimal resource utilization. This study proposes a novel approach combining Federated Learning (FL) and Long Short-Term Memory (LSTM) networks to predict Reference Signal Received Power (RSRP) and the strongest nearby Reference Signal Received Power (RSRP) signals. Our method leverages FL to ensure data privacy and LSTM to capture temporal dependencies in signal data, enhancing prediction accuracy. We develop a dynamic handover algorithm that adapts to real-time conditions, adjusting thresholds based on predicted signal strengths and historical performance. Extensive experiments with real-world data show our dynamic algorithm significantly outperforms the 3GPP Event A3 algorithm, achieving higher prediction accuracy, reducing unnecessary handovers, and improving overall network performance. In conclusion, this study introduces a data-driven, privacy-preserving approach that leverages advanced machine learning techniques, providing a more efficient and reliable handover mechanism for future wireless networks.

Active restoration after three decades: Seed addition increases native dominance compared to landscape‐scale secondary succession

Abstract Active restoration often aims to accelerate ecosystem recovery. However, active restoration may not be worthwhile if its effects are overwhelmed by changes that occur passively. Moreover, it can be challenging to separate the effects of passive processes, such as dispersal and natural succession, from active restoration efforts. We assess the 24‐year impact of actively restoring a Minnesota old‐field grassland via seed addition of native tallgrass prairie species. We compared the abundance of four functional plant groups in actively restored plots against abundances in three reference classes: (1) unrestored plots undergoing passive recovery within the same old field, (2) passively recovering plots in two nearby old fields of similar age and (3) a chronosequence of 21 old fields within the same landscape. Active restoration led to a higher abundance of native grasses and forbs in the 36 m2 treatment plots. Seed addition was more effective if the original vegetation was first removed using herbicide, burning and tilling. However, long‐term conclusions about the efficacy of active restoration varied widely depending on the choice of reference class. In our small‐scale restoration experiment, native abundance was similarly high in both the actively restored and reference plots after 24 years, suggesting either (1) passive recovery or (2) local dispersal of native species from nearby treatment plots (i.e. cross‐contamination). In contrast, a comparison with two nearby reference fields suggested active restoration resulted in much higher native abundance relative to passive recovery. A smaller, positive effect was detected when we compared actively restored plots to the chronosequence of old fields. In the chronosequence, many passively recovering old fields had transitioned to native grass dominance naturally, although active restoration appeared to increase native forb abundance. Synthesis and applications: Our findings highlight the importance of using scale‐appropriate references for assessing the efficacy and need for active restoration. Comparing actively restored plots with the surrounding landscape, we found that active restoration and passive recovery led to similar plant communities after 24 years. Because local dispersal from actively restored sites can nearby references, caution should be exercised when evaluating long‐term restoration projects using only small‐scale experiments.

Distribution and in situ bioaccumulation test of radioecologically relevant metals in boreal freshwater sediments

Sediments act as important sinks for metals and their radionuclides in aquatic environments and play a crucial role in their transfer and uptake to aquatic organisms. Traditional radioecological models use radionuclide concentrations in water to predict concentrations in aquatic organisms. In this study, we investigated the distribution of radioecologically important metals (Ba, Co, Ni, Sr, U) among sediment, porewater and hypolimnion over seasons. We also studied the uptake of these metals to benthic organisms and importance of sediment as an uptake source by conducting a 28-day in situ bioaccumulation experiment with oligochaete worms (Lumbriculus variegatus). The studied metals were chosen based on common occurrence of their radioactive isotopes in nuclear fuel cycle. Measurements of total elemental concentration were used as proxies to study the behavior of specific radionuclides. Sediment and water samples were collected from two small lakes connected to a former uranium mine in Eastern Finland, and from a nearby reference lake connected to a different drainage area. Environmental characteristics and concentrations measured from sediment, porewater and overlying water indicated only minor changes between seasons. Measured metals were highly associated with sediment particles, rather than porewater or hypolimnion. Both the distribution of metals and in situ experiment indicated the importance of sediment as the main source of bioaccumulation. Significant differences in Ba, Ni and U concentrations between treatments containing contaminated sediment and reference sediment were noted, regardless of water concentrations. Additionally, as U contaminated lakes lacked seasonal overturn during our monitoring period, metal distribution and environmental conditions remained unchanged in deeper parts of those lakes. Lastly, the results of this in situ bioaccumulation experiment are in line with the findings of our previous laboratory study using sediments from these same lakes.

Publisher Correction: Comparing long-term changes in cladoceran and diatom assemblages from a lake impacted by road salt seepage to a nearby reference lake near Toronto (Ontario, Canada)

Publisher Correction: Comparing long-term changes in cladoceran and diatom assemblages from a lake impacted by road salt seepage to a nearby reference lake near Toronto (Ontario, Canada)

Female-biased sex ratios and delayed puberty in two fish species with different Ecologies in an Anthropogenically affected urban lake

In aquatic ecosystems, endocrine-disrupting compounds (EDCs) pose a growing concern for their potential adverse effects on fish reproduction and development. In lake Pyhäjärvi, located in the urban boreal region of Tampere, Finland, a significant number of sexually immature pikeperch (Sander lucioperca) individuals have been identified in size and age categories that are expected to be sexually mature. To explore if this phenomenon is attributed to estrogenic endocrine disruption, we conducted a comprehensive study comparing fish from lake Pyhäjärvi with those from a nearby reference lake, lake Näsijärvi. Roach (Rutilus rutilus), known for its susceptibility to EDCs, was also included for comparison. We examined various parameters in both pikeperch and roach, including size, condition factor, age, reproductive indicators, biometric indices and gonadal histology. We also assessed liver vitellogenin mRNA levels and genetic sex in roach, and measured estrogen levels in lake waters and wastewater treatment plant effluents. Results revealed that approximately one-third of fish in both species exhibited sexual immaturity in lake Pyhäjärvi, with a female-biased sex ratio. Surprisingly, we found no signs of estrogenic endocrine disruption, indicated by the absence of intersex fish in both species. Furthermore, vitellogenin levels in roach closely resembled those in the reference lake. Estrogens were undetectable in the lake waters, suggesting that factors other than estrogenic EDCs, including other potential endocrine disruptors such as PCBs or heavy metals, may be influencing delayed sexual maturity and skewed sex ratios. Further inquiry is needed to pinpoint these underlying causes. Our study provides essential baseline information on fish sexual development in lake Pyhäjärvi, emphasizing the need for ongoing monitoring and research to understand delayed sexual maturity and biased sex ratios. This is vital given the increasing concern about EDC impacts on aquatic ecosystems and the necessity for effective management strategies to protect these ecosystems' health and integrity.

Long-term post-fire recovery of an oribatid mite assemblage: A case study from a temperate coniferous forest

Wildfire represents a significant natural disturbance factor in forest ecosystems expected to further increase in importance due to global climate change. It has a detrimental short-term impact on soil biota, but much less is known about its long-term effects, especially on soil mesofauna. Our study compared oribatid assemblages of the forest floor in moderately-burned forest sites along a post-fire chronosequence (8 fire history classes covering 0–110 years since fire) with near-by reference sites without fire history. All sites were situated on acidic soils in the Central European Elbe Sandstone Mountains (Bohemian Switzerland National Park, NW Czechia), mostly covered by pine and spruce forests. Data were analysed using linear mixed-effect models. We found a substantial impact of fire on oribatid assemblages. Whereas lower densities were observed for the first few years after a fire only, changes in assemblage feeding guilds persisted over at least four decades. Shifts towards smaller body size, parthenogenesis and fungivory at burned sites compared to larger body size, sexual reproduction and detritivory in unburned controls implied changes in assemblage functioning. The changes in functional traits, which correspond to previous research findings on the recovery of oribatid mites after clear-cutting, underscore a more universal pattern of post-disturbance development.

Inverse distance weight-assisted particle swarm optimized indoor localization

Indoor localization using wireless fidelity (WiFi) fingerprint has attracted considerable attention due to its extensive deployment and low cost. Most indoor fingerprint localization methods could be computationally inefficient and behave with unsatisfactory positioning accuracy. Therefore, an inverse distance weight (IDW) assisted particle swarm optimized (PSO) indoor localization (IDWPSOInLoc) algorithm is proposed. It is an optimized positioning process in the online stage rather than an offline optimization approach. The main idea is to find the optimal particle with the most similar generated fingerprint to the real-time testing one in the online stage. K nearest neighbors and boundary constraints are combined to initialize particles’ positions. The generated fingerprint of each particle is interpolated based on the IDW algorithm and nearby reference points within a specified range. A new fitness function, the sum of standard deviations between the generated and real-time testing fingerprints, is proposed to find the optimal particle. The position of the optimal particle with the minimal fitness value is taken as the estimated coordinate through iterations updating the particles’ velocities, positions, and generated fingerprints. IDWPSOInLoc achieves a mean positioning accuracy of 2.477 meters in the local environment experimental test and that of about 6 meters using selected floor in the UJIIndoorLoc dataset. Compared with state-of-the-art algorithms, positioning error is decreased by at least 11.9%. In addition, IDWPSOInLoc has the characteristic of low computational complexity. Experimental results show that the proposed IDWPSOInLoc outperforms the considered WiFi indoor positioning algorithms.

Measurement of the on-road wind environment in channelled roadways

A road test was undertaken to characterize the complex wind environment experienced by vehicles in channelled roadways. Channelled roadways consist of highways that are bordered by forests, hills, or other terrain features. A test vehicle was outfitted with three fast-response pressure probes in front of the vehicle located at 1 m, 2 m, and 3 m above the ground. Measurements were acquired on three highway routes in Canada with varying degrees of surrounding terrain, roadside forest cover, and in different wind conditions. Reference wind conditions were established using a network of federally-managed weather stations. The study identified that the effects of terrain and roadside forests can cause changes in wind direction of more than 50∘ to 90∘ relative to nearby reference weather stations. Directional channelling of winds was observed in forested and complex terrain, where changes in the wind direction mimicked the changes in highway alignment. The on-road video files were analysed using a machine-learning algorithm to quantitatively assess road-side forests and terrain and to identify fully-channelled sections of highway. Generalized channelled-flow trends were identified by considering the wind conditions in the channelled sections of all three routes. A linear relationship between the flow direction in the channel to that of the reference conditions was observed when the reference wind direction and the highway was within ±50∘. As the reference wind direction approached perpendicularity to the channelled segments, greater variations in the on-road wind direction were observed due to the unsteady flow within the channel. On-road wind speeds on each route were a maximum when the reference wind direction was approximately aligned with the highway and decreased to approximately 20% of the reference wind speed when the reference wind was perpendicular to the channel.

Comparing long-term changes in cladoceran and diatom assemblages from a lake impacted by road salt seepage to a nearby reference lake near Toronto (Ontario, Canada)

Comparing long-term changes in cladoceran and diatom assemblages from a lake impacted by road salt seepage to a nearby reference lake near Toronto (Ontario, Canada)

Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors

Abstract. It is essential to accurately assess and verify the effects of air pollution on human health and the environment in order to develop effective mitigation strategies. More accurate analysis of air pollution can be achieved by utilizing a higher-density sensor network. In recent studies, the implementation of low-cost sensors has demonstrated their capability to quantify air pollution at a high spatial resolution, alleviating the problem of coarse spatial measurements associated with conventional monitoring stations. However, the reliability of such sensors is in question due to concerns about the quality and accuracy of their data. In response to these concerns, active research efforts have focused on leveraging machine learning (ML) techniques in the calibration process of low-cost sensors. These efforts demonstrate promising results for automatic calibration, which would significantly reduce the efforts and costs of traditional calibration methods and boost the low-cost sensors' performance. As a contribution to this promising research field, this study aims to investigate the calibration transferability between identical low-cost sensor units (SUs) for NO2 and NO using ML-based global models. Global models would further reduce calibration efforts and costs by eliminating the need for individual calibrations, especially when utilizing networks of tens or hundreds of low-cost sensors. This study employed a dataset acquired from four SUs that were located across three distinct locations within Switzerland. We also propose utilizing O3 measurements obtained from available nearby reference stations to address the cross-sensitivity effect. This strategy aims to enhance model accuracy as most electrochemical NO2 and NO sensors are extremely cross-sensitive to O3. The results of this study show excellent calibration transferability between SUs located at the same site (Case A), with the average model performance being R2 = 0.90 ± 0.05 and root mean square error (RMSE) = 3.4 ± 0.9 ppb for NO2 and R2 = 0.97 ± 0.02 and RMSE = 3.1 ± 0.8 ppb for NO. There is also relatively good transferability between SUs deployed at different sites (Case B), with the average performance being R2 = 0.65 ± 0.08 and RMSE = 5.5 ± 0.4 ppb for NO2 and R2 = 0.82 ± 0.05 and RMSE = 5.8 ± 0.8 ppb for NO. Interestingly, the results illustrate a substantial improvement in the calibration models when integrating O3 measurements, which is more pronounced when SUs are situated in regions characterized by elevated O3 concentrations. Although the findings of this study are based on a specific type of sensor and sensor model, the methodology is flexible and can be applied to other low-cost sensors with different target pollutants and sensing technologies. Furthermore, this study highlights the significance of leveraging publicly available data sources to promote the reliability of low-cost air quality sensors.

Brook Trout population response to Brown Trout removal by electrofishing in a Wisconsin Driftless Area stream

AbstractObjectivePopulations of Brook Trout Salvelinus fontinalis have declined across their native range in North America due to a combination of habitat loss, exploitation, and introductions of nonnative salmonids. Brown Trout Salmo trutta have been widely introduced into streams across Brook Trout's native range and likely displace Brook Trout from suitable habitat. We evaluated the population response of Brook Trout to Brown Trout removal in Maple Dale Creek, a Wisconsin Driftless Area stream, relative to a nearby control stream with similar habitat and sympatric Brook Trout and Brown Trout. Variation in mean July stream temperature (11.6–16.4°C) among survey sites also allowed us to examine whether summer stream temperature was related to the population response of Brook Trout to Brown Trout removal.MethodsBetween 2019 and 2023, we completed 33.7 km of single‐pass electrofishing during 56 site visits and removed 20,495 Brown Trout from 7.1 km of Maple Dale Creek upstream of an existing fish passage barrier. Concurrently, annual salmonid population estimates were completed on Maple Dale Creek and a nearby reference stream.ResultBy 2023, biomass and density of Brown Trout age 1 and older (age 1+) had decreased to 1% or less of 2019 levels. Concurrently, age‐1+ Brook Trout biomass increased by a factor of 5.5 and density by a factor of 9.7. In contrast, age‐1+ Brook Trout density and biomass in our control stream remained low and relatively unchanged. Brook Trout population response to Brown Trout removal was positive across all four sites but exhibited a positive correlation to mean July stream temperature.ConclusionOur results add to the evidence that Brown Trout displace Brook Trout populations from stream habitat, highlight the utility of electrofishing removal of Brown Trout in restoring Brook Trout populations where downstream fish passage barriers are present, and suggest that Brown Trout suppression of Brook Trout is positively related to summer stream temperature.

Teasing out the Effects of Natural Stressors at Chemically Contaminated Sites.

Aquatic ecosystems are often impacted by a multitude of stressors, many of which are introduced by a combination of anthropogenic activities such as agricultural development, urbanization, damming, and industrial discharge. Determining the primary stressors responsible for ecological impairments at a site can be complex and challenging; however, it is crucial for making informed management decisions. Improper diagnosis of an impaired system can lead to misguided attempts at remediation, which can be both time consuming and costly. We focused on the development, implementation, and evaluation of methodologies that, in combination, allowed us to identify the primary stressors. These included a four-phase, weight-of-evidence (WOE) assessment including in situ Toxicity Identification and Evaluation (iTIE) testing, physicochemical and macrobenthos characterization, reciprocal sediment transplants, and laboratory and in situ toxicity testing. The contaminants of concern (COCs) at the site were elevated levels of ammonia, chloride, pH, and total dissolved solids in groundwater upwellings into a high-quality waterway. Reciprocal transplants of site sediments and nearby reference sediments and traditional benthic sampling showed impaired benthic indices and multiple stations around a contaminated industrial settling basin. Impaired stations had elevated COCs in groundwaters but exhibited a steep vertical concentration gradient, with concentrations decreasing near the sediment-surface water interface. We describe Phase 4 of the study, which focused on teasing out the role of dissolved oxygen sags in benthic macroinvertebrate responses. Extensive submerged and emergent macrophytes, algae, and cyanobacteria co-occurred at the impaired sites and increased throughout the summer. Laboratory testing suggested that ammonia and pH were possibly toxic at the sites, based on groundwater concentrations. The in situ toxicity testing, however, showed toxicity occurring even at stations with low levels of COCs concurrently with large diurnal fluxes in dissolved oxygen (DO). A final phase using a type of iTIE approach utilized limnocorrals with and without aeration and with in situ toxicity measures using Hyalella azteca. The Phase 4 assessment revealed that low DO levels were primarily responsible for impaired benthic communities, and COC upwellings were diluted at the sediment-water interface to nontoxic levels. These findings will allow for improved management decisions for more efficient and effective restoration activities. Environ Toxicol Chem 2024;43:1524-1536. © 2024 SETAC.

Monitoring the impact of forest changes on carbon uptake with solar-induced fluorescence measurements from GOME-2A and TROPOMI for an Australian and Chinese case study

Abstract. Reliable and robust monitoring tools are crucial to assess the effectiveness of land mitigation techniques (LMTs) in enhancing carbon uptake, enabling informed decision making by policymakers. This study, addressing one of the scientific goals of the EU Horizon 2020 Land Use Based Mitigation for Resilient Climate Pathways (LANDMARC) project, examines the feasibility of using satellite solar-induced fluorescence (SIF) in combination with other satellite data as a monitoring proxy to evaluate the effects of LMTs on carbon uptake. Two distinct cases are explored: (1) instantaneous vegetation destruction caused by a 2019 eucalyptus wildfire in southeast Australia and (2) gradual forest gain resulting from reforestation efforts in northern China in 2007–2012. The cases are monitored using SIF from the TROPOspheric Monitoring Instrument (TROPOMI) and Global Ozone Monitoring Experiment-2A (GOME-2A), respectively. Comparing the temporal variability in SIF across the affected areas and nearby reference areas reveals that vegetation dynamics changed as a consequence of the land-use changes in both cases. Specifically, in the Australia case, TROPOMI demonstrated an immediate reduction in the SIF signal of 0.6 mW m−2 sr−1 nm−1 (−72 %) over the eucalypt forest right after the fire. Exploiting the strong correspondence between TROPOMI SIF and gross primary productivity (GPP) at the nearby representative eddy covariance Tumbarumba site and through the FluxSat product, we estimate that the Australian fire led to a loss in GPP of 130–200 GgC in the first 8 months after the fire. Over the northern Chinese provinces of Gansu, Shaanxi, Sichuan, Chongqing, and Shanxi, we report an increase in GOME-2A summertime SIF of 0.1–0.2 mW m−2 sr−1 nm−1, coinciding with reforestation efforts between 2007 and 2012. This increase in the SIF signal is likely driven by a combination of increasingly favourable natural conditions and by the reforestation effort itself. A multivariate model that takes into account growth factors such as water availability and maximum temperature as well as satellite-derived forest-cover data explains the observed variability in GOME-2A SIF in the Chinese case reasonably well (R2=0.72). The model suggests that increases in both forest cover and soil moisture have led, in step, to the observed increase in vegetation activity over northern China. In that region, for every 100 km2 of additional forest cover, SIF increases by 0.1 mW m−2 sr−1 nm−1 between 2007 and 2012. Our study highlights that the use of satellite-based SIF, together with supporting in situ, modelled, and satellite data, allows us to monitor the impact of LMT implementation on regional carbon uptake as long as the scale of the LMT is of sufficient spatial extent.

GN-z11 in Context: Possible Signatures of Globular Cluster Precursors at Redshift 10

The first JWST spectroscopy of the luminous galaxy GN-z11 simultaneously established its redshift at z = 10.6 and revealed a rest-ultraviolet spectrum dominated by signatures of highly ionized nitrogen, which has so far defied clear interpretation. We present a reappraisal of this spectrum in the context of both detailed nebular modeling and nearby metal-poor reference galaxies. The N iv] emission enables the first nebular density measurement in an apparently predominantly star-forming galaxy at z > 10, revealing evidence for extremely high densities n e ≳ 105 cm−3. With a suite of photoionization models, we establish that regardless of the ionization mechanism and accounting for depletion and this density enhancement, gas substantially enriched in nitrogen ([N/O] = +0.52 assuming the nebular emission is dominated by star formation) is required to reproduce the observed lines. We compare the GN-z11 spectrum to local UV databases and highlight a unique nearby galaxy, Mrk 996, where a high concentration of Wolf–Rayet stars and their CNO-processed ejecta produce a UV spectrum remarkably similar in some respects to that of GN-z11 and the Sunburst Arc. Collating this evidence in the context of Galactic stellar abundances, we suggest that the peculiar nitrogenic features prominent in GN-z11 may be a unique signature of intense and densely clustered star formation in the evolutionary chain of the present-day globular clusters, consistent with in situ early enrichment with nuclear-processed stellar ejecta on a massive scale. Combined with insight from local galaxies, these and future JWST data open a powerful new window into the physical conditions of star formation and chemical enrichment at the highest redshifts.

Revitalizing the role of relative deprivation: An analysis of contemporary teacher protest strike emergence

This article examines the role of relative deprivation in contemporary teacher protest strikes. I specifically focus on the 2018–2019 unprecedented U.S. K-12 teacher strike wave. Through data generated from analyses of 35 semi-structured interviews with teachers who were also leaders of their local protest strike, I find that the role of relative deprivation in these strikes is complex. Specifically, I find that only relative deprivation experienced with nearby reference groups was described as leading to protest strike emergence but that all types of relative deprivation were used as an effective framing strategy to garner support and bring more teachers on board once the activism was underway. This paper revitalizes a consideration of relative deprivation as an important theory in understanding protest strike emergence and gives a better understanding of the impetus of this wave of teacher activism.

Source apportionment of polychlorinated biphenyls in the sediment of the Newtown Creek superfund site

Polychlorinated biphenyls (PCBs) are a primary contaminant of potential concern at the Newtown Creek superfund site. Measurements of PCBs in hundreds of samples of sediment (surface and cores) within Newtown Creek and at nearby reference locations were obtained from the Remedial Investigation (RI) databases. This data set was analyzed using Positive Matrix Factorization (PMF). A weight-of-evidence approach was used to attribute the PMF-generated fingerprints to sources. The PMF analysis generated eight factors (fingerprints or sources) that represent primary sources, such as Aroclors, as well as secondary sources, including the East River and Combined Sewer Outfalls (CSOs). In addition to the high-production volume Aroclors (1016/1242, 1248, 1254, and 1260), some less-widely used Aroclors (1232 and 1268) were found in Newtown Creek sediment. Aroclor 1268 is disproportionately abundant in the deepest sediments, while PCBs likely from CSOs are relatively more abundant in surface sediment.