Local Point Sources Research Articles

Ventilation effectiveness and incomplete mixing in air distribution design for airborne transmission

How ventilation should be arranged to be effective at reasonable air change rates is one key question as ventilation criteria and standard airborne disease transmission models are based on the well-mixed assumption, but air distribution patterns lead to non-uniform spatial concentrations. In this study a new method for ventilation effectiveness application in ventilation design for airborne transmission was developed and tested with tracer gas measurements in 22 rooms. Contrary to existing ventilation effectiveness values measured with distributed source, the developed method uses a couple of point source locations corresponding to an infector to quantify infection risk for each occupant. Novelty of the method is new ventilation effectiveness indicator that makes it possible to describe the effect of spatial variation of concentration and risk with single parameter. Quanta were used as input data to calculate the ventilation rate supplied by air distribution system corresponding to a specified risk level, but the differences between studied cases do not significantly depend on the quanta values. Application of the method to measured rooms showed that simple ventilation effectiveness calculation from average concentration at the breathing height, not requiring quanta data, provided lower ventilation effectiveness and higher ventilation rate in all cases. In many cases the difference in required ventilation rates was only a few percent, but in some large spaces it exceeded 10% with maximum of 39% in large open plan office with high concentration differences. Measured ventilation effectiveness values ranging from 0.5 to 1.4 indicate a substantial improvement potential in many cases.

Early fluid migration and alteration fronts in the CM chondrite Reckling Peak 17085

AbstractReckling Peak (RKP) 17085 is a newly classified Antarctic CM chondrite that preserves a complex alteration history characterized by mild aqueous alteration (CM2.7), overprinted by a short‐lived thermal metamorphic event (heating stage III [<750°C]), and affected by low‐grade terrestrial weathering. This meteorite contains abundant Fe‐rich bands within the fine‐grained matrix, composed of micron‐scale Fe‐oxyhydroxide minerals. They are interpreted as “alteration fronts” arising due to the dissolution and transport of Fe (typically <500 μm) before being abruptly deposited. This alteration texture is relatively rare among hydrated carbonaceous chondrites, with only five reported instances to date (Murchison, Murray, Allan Hills 81002, Miller Range 07687, and Northwest Africa 5958). Evidence from RKP 17085 suggests that early aqueous alteration operated as multiple geochemically isolated microenvironments, which moved outwards from local point sources within the matrix. Low permeability fine‐grained rims on chondrules appear to have acted as barriers to fluid flow, controlling the migration of fluid across the parent body. Furthermore, the higher porosity regions within the altered fine‐grained matrix represent either void space generated by the dehydration of hydrated minerals during post‐hydration metamorphism and/or sites of ice accretion (water‐ice or C‐bearing ices) preserved within a mildly altered primitive matrix.

Pronounced declines in heavy metal burdens of Minnesotan mammals over the last century

Humans have drastically altered the ecology of heavy metals, which can have negative effects on animal development and neural functioning. Many species have shown the ability to adapt to anthropogenic increases in metal pollution, but such evolutionary responses will depend on the extent of metal variation over space and time. For terrestrial vertebrates, it is unclear how metal exposure has changed over time: some studies suggest metal content peaked with the enactment of policies controlling lead emissions, while other studies suggest metal levels peaked at least a century earlier. We used 162 specimens of four mammal species (a mouse, shrew, bat, and squirrel) to ask how metal content of the fur and skin has changed over a 90-year time period, and impacts on individual performance (body size and cranial capacity). Using ICP-MS, we show that for lead, cadmium, copper, and chromium, there were significant declines in metal content in mammal tissue over the 90-year time period, with lead levels five times lower now than in the early 1900s. Importantly, metal content began to drop well before the pollution regulation of the 1970s. Effects of time greatly outweighed any effects of an individual living near a human population center. Surprisingly, there were no effects of body metal content on body size, and only manganese was negatively related to relative cranial capacity. Taken together, these results suggest that present day populations of mammals are experiencing levels of heavy metal exposure that are less stressful than they were 100 years ago. In addition, temporal decreases in metal loads likely partly reflect global patterns of pollution decline that affect atmospheric metal deposition rather than local point sources of exposure.

Inertial Algorithm with Dry Fraction and Convolutional Sparse Coding for 3D Localization with Light Field Microscopy

Light field microscopy is a high-speed 3D imaging technique that records the light field from multiple angles by the microlens array(MLA), thus allowing us to obtain information about the light source from a single image only. For the fundamental problem of neuron localization, we improve the method of combining depth-dependent dictionary with sparse coding in this paper. In order to obtain higher localization accuracy and good noise immunity, we propose an inertial proximal gradient acceleration algorithm with dry friction, Fast-IPGDF. By preventing falling into a local minimum, our algorithm achieves better convergence and converges quite fast, which improves the speed and accuracy of obtaining the locolization of the light source based on the matching depth of epipolar plane images (EPI). We demonstrate the effectiveness of the algorithm for localizing non-scattered fluorescent beads in both noisy and non-noisy environments. The experimental results show that our method can achieve simultaneous localization of multiple point sources and effective localization in noisy environments. Compared to existing studies, our method shows significant improvements in both localization accuracy and speed.

Radioactive source localization employing resistive electrode array (REA) detector.

Objective.In this feasibility study, we explore an application of a Resistive Electrode Array (REA) for localization of a radioactive point source. The inverse problem posed by multichannel REA detection is studied from mathematical perspective and involves the questions of the minimal configuration of the conductive leads that can achieve this goal. The basic configuration consists of a circularly shaped REA with four opposite electrical lead-pairs at its perimeter.Approach.A robust mathematical reconstruction method for a 3D radioactive source relative to the REA is presented. The characteristic empirical Green's function for the detector response of the REA is determined by numerically solving Laplace equations with appropriate boundary conditions. Based on this model, Monte Carlo simulations of the inverse problem with Gaussian noise are performed and the overall accuracy of the localization is investigated.Main results.The results show a 3D error distribution of localization which is uniform in the (x,y)-plane of the REA and strongly correlated in the orthogonalz-axis. The overall accuracy decreases with higher distance of the source to the detector which is intuitive due to approximate flux dependence following the inverse square law. Further, a saturation in accuracy regarding the number of electrical leads and a linear dependence of the reconstruction error on the measurement noise level are observed.Significance.A broad range of REA detector configurations and their characteristics are investigated by this study for radioactive source localization allowing diverse practical applications with detector diameters ranging from millimeters to meters.

Background Influence of PM2.5 in Dallas–Fort Worth Area and Recommendations for Source Apportionment

Source apportionment of observed PM2.5 concentrations is of growing interest as communities seek ways to improve their air quality. We evaluated publicly available PM2.5 data from the USEPA in the Dallas–Fort Worth metropolitan area to determine the contributions from various PM2.5 sources to the total PM2.5 observed. The approach combines interpolation and fixed effect regression models to disentangle background from local PM2.5 contributions. These models found that January had the lowest total PM2.5 mean concentrations, ranging from 5.0 µg/m3 to 6.4 µg/m3, depending on monitoring location. July had the highest total PM2.5 mean concentrations, ranging from 8.7 µg/m3 to 11.1 µg/m3, depending on the location. January also had the lowest mean local PM2.5 concentrations, ranging from 2.6 µg/m3 to 3.6 µg/m3, depending on the location. Despite having the lowest local PM2.5 concentrations, January had the highest local attributions [51–57%]. July had the highest mean local PM2.5 concentrations, ranging from 2.9 µg/m3 to 4.1 µg/m3, depending on the location. Despite having the highest local PM2.5 concentrations, July had the lowest local attributions [33–37%]. These results suggest that local contributions have a limited effect on total PM2.5 concentrations and that the observed seasonal changes are likely the result of background influence, as opposed to modest changes in local contributions. Overall, the results demonstrate that in the Dallas–Fort Worth metropolitan area, approximately half of the observed total PM2.5 is from background PM2.5 sources and half is from local PM2.5 sources. Among the local PM2.5 source contributions in the Dallas–Fort Worth metropolitan area, our analysis shows that the vast majority is from non-point sources, such as from the transportation sector. While local point sources may have some incremental site-specific local contribution, such contributions are not clearly distinguishable in the data evaluated. We present this approach as a roadmap for disentangling PM2.5 concentrations at different spatial levels (i.e., the local, regional, or state level) and from various sectors (i.e., residential, industrial, transport, etc.). This roadmap can help decision-makers to optimize mitigatory, regulatory, and/or community efforts towards reducing total community PM2.5 exposure.

Different volatile organic compounds in local point source air pollution pose distinctive elevated risks for respiratory disease-associated emergency room visits

Air pollution increases risk of respiratory disease but prior research has focused on particulate matter and criteria air pollutants, and there are few studies on respiratory effects of volatile organic compounds (VOC). We examined zip code level relationships between emergency room (ER) visits for respiratory illness and VOC pollution in New York State from 2010 to 2018. Detailed information on VOC pollution was derived from the National Emissions Inventory, which provides point source information on VOC emissions at the zip code level. We considered four respiratory diseases: asthma, acute upper respiratory infections, chronic obstructive pulmonary disease (COPD), and lower respiratory disease, using mixed effects regression with a random intercept to account for county level variability in single pollutant models, and Random Forest Regression (RFR) to assess relative importance of VOC exposures when considered together in multipollutant models.Single pollutant models show associations between respiratory-related ER visits with all pollutants of interest across all study years, even after adjusting for poverty and smoking by zip code. The largest relative single pollutant effect sizes considered included benzene, ethylbenzene, and total (summed) VOCs. Results from RFR including all VOC exposures indicate that ethylbenzene has the greatest variable importance for asthma, acute upper respiratory infections, and COPD, with toluene and benzene most important for lower respiratory ailments. RFR results also demonstrate presence of pairwise interactive effects between VOC pollutants. Our findings show that local VOC pollution may offer a significant contribution to the risk of respiratory disease-related ER visits, and that effects vary by illness and by VOC compound. ER visit rates for respiratory illness were elevated in high poverty zip codes, although this may be attributable to the fact that the poor lack basic access to health care and use ERs more frequently for routine care.

Divide-and-conquer DNN approach for the inverse point source problem using a few single frequency measurements

We consider the inverse problem to determine the number and locations of acoustic point sources from single low-frequency partial data. The problem is particularly challenging in the sense that the data is available only at a few locations which span a small aperture. Integrating the deep neural networks (DNNs) and Bayesian inversion, we propose a divide-and-conquer approach by dividing the inverse problem into three subproblems. The first subproblem is to determine the number of point sources, which is formulated as a common machine learning task—classification. A simple DNN is proposed and trained to predict the numbers of the point sources. The second subproblem is to reconstruct the (approximate) locations of the point sources. We formulate the problem as a nonlinear function with the input being the measured data and the output being a carefully elaborated location vector. Then a second DNN is proposed to learn the mapping and predict the location vector effectively. The location vector is post-processed to provide an indicator (image) function for the (approximate) locations of the point sources. The third subproblem is to improve the accuracy of the location prediction, for which we employ a Bayesian inversion algorithm. This divide-and-conquer approach can effectively treat both phase and phaseless data as demonstrated by various examples.

Covariance models and Gaussian process regression for the wave equation. Application to related inverse problems

In this article, we consider the general task of performing Gaussian process regression (GPR) on pointwise observations of solutions of the 3 dimensional homogeneous free space wave equation. In a recent article, we obtained promising covariance expressions tailored to this equation: we now explore the potential applications of these formulas. We first study the particular cases of stationarity and radial symmetry, for which significant simplifications arise. We next show that the true-angle multilateration method for point source localization, as used in GPS systems, is naturally recovered by our GPR formulas in the limit of the small source radius. Additionally, we show that this GPR framework provides a new answer to the ill-posed inverse problem of reconstructing initial conditions for the wave equation from a limited number of sensors, and simultaneously enables the inference of physical parameters from these data. We finish by illustrating this “physics informed” GPR on a number of practical examples.

Sediment source and sink identification using Sentinel-2 and a small network of turbidimeters on the Vjosa River

Abstract. Measurement of suspended sediment concentration (SSC) at a basin outlet yields a basin-integrated picture of sediment fluxes; however, it does not give a full spatial perspective on possible sediment pathways within the catchment. Spatially resolved estimates of SSC along river networks are needed to identify sediment sources and sinks, to track erosion gradients, and quantify anthropogenic effects on catchment-scale sediment production, e.g., by dam construction or erosion control. Here we explore the use of high-resolution Sentinel-2 satellite images for this purpose in narrow and morphologically complex mountain rivers, combined with ground station turbidity sensing for calibration and supported by a Lagrangian kayak-derived river profile measurement. The study is carried out on the Vjosa River in Albania, which is one of the last intact large river systems in Europe. We developed a workflow to estimate river turbidity profiles from Sentinel-2 images including atmospheric, cloud cover, and deepwater corrections for the period May 2019 to July 2021 (106 images). In situ turbidity measurements from four turbidity sensors located along the Vjosa River provided ground truthing. A multivariate linear regression model between turbidity and reflectance was fitted to this data. The extracted longitudinal river turbidity profiles were qualitatively validated with two descents of the river with a turbidity sensor attached to a kayak. The satellite-derived river profiles revealed variability in turbidity along the main stem with a strong seasonal signal, with the highest mean turbidity in winter along the entire length of the river. Most importantly, sediment sources and sinks could be identified and quantified from the river turbidity profiles, both for tributaries and within the reaches of the Vjosa. The river basin and network acted as a sediment source most of the time and significant sediment sinks were rare. Sediment sources were mostly tributaries following basin-wide rainfall, but within-reach sources in river beds and banks were also possible. Finally, we used the data to estimate the mean annual fine sediment yield at Dorez at ∼2.5±0.6 Mt yr−1, in line with previous studies, which reveals the importance of the Vjosa River as an important sediment source of the Adriatic Sea. This work presents a proof of concept that open-access high-resolution satellite data have potential for suspended sediment quantification not only in large waterbodies but also in smaller rivers. The potential applications are many, including identifying erosion hotspots, sediment activation processes, local point sources, glacial sediment inputs, and sediment fluxes in river deltas, with a necessary future research focus on improving accuracy and reducing uncertainty in such analyses.

Techno-economic assessment of different aviation fuel supply pathways including LH2 and LCH4 and the influence of the carbon source

Sustainable Jet A-1 and radical technological changes are required to decrease the CO2 emissions of the aviation industry. While the CO2 reduction potential of electric aircraft is limited, the implementation of LH2 and LCH4 are exemplary radical technological changes with large potential impact. Recent studies have evaluated conceptual designs for planes utilizing LH2 and LCH4. Also, the costs of sustainable Jet A-1 and green hydrogen have been evaluated in different studies. However, most studies focus on one fuel and its production costs and do not include the production at a sweet spot and the subsequent logistic to the destination. To address this issue and present novel research results, we conducted a holistic techno-economic assessment for the different fuels that could be implemented in the aviation industry; LH2, LCH4 and sustainable Jet A-1. Based on renewable hydrogen produced in Morocco, we compared the aspects of a local fuel synthesis with the synthesis in Germany. CO2 is supplied from ambient air in Morocco and from a local point source in Germany. For the latter, we considered a hydrogen transport via LH2 or LOHC, with a direct integration of the LOHC dehydrogenation into the fuel synthesis plant. We outlined the cost composition of every fuel supply chain and retrieved LH2 import costs of 157 €/MWh while the cost range for LCH4 is 217–228 €/MWh. SAF has the highest net production costs of 279–302 €/MWh.

The development of a framework for the selection of optimal sites for the location of municipal solid waste to value‐added facilities through the integration of a geographical information system and a fuzzy analytic hierarchy process

AbstractThis study develops a framework for quantification of the available municipal solid waste (MSW), defines the geographical point source locations for the MSW feedstock, and determines optimal locations for potential waste‐to‐value‐added (W2VA) facility sites. The framework developed here is applied to Canada. To determine optimal sites for W2VA facilities, a three‐stage decision‐making model comprising exclusion analysis, preference analysis, and network analysis was developed. The fuzzy analytic hierarchy process (FAHP) and geographic information systems (GIS) were used in an integrated decision‐making network to prioritize the preference factors and facility locations; with these, a land suitability map (LSM) was developed and suitable candidate sites for W2VA facilities were obtained for Canada. The identified candidate sites were then used in a network analysis to select optimal sites with minimal travel distances. This study was used to determine 10 and 15 optimal sites for W2VA facilities in Western Canada and Eastern Canada, respectively. This study prioritized optimal sites based on the minimization of transportation distances. The highest priority site in Western Canada is Site 1, located in Lethbridge County, Alberta. It is connected with 74 TSs and can receive 2.42 million tonnes of MSW. The highest priority site in Eastern Canada is Site 11, which is located in West Nipissing, Ontario, and is connected with 42 transport stations (TSs) and can receive 1 million tonnes of MSW. The adaptability of the applied decision‐making model, competency of the developed LSM, and flexibility of the network analysis provide a competent supporting tool for authorities to identify optimal locations for W2VA facilities.

Mapping high-resolution energy consumption CO2 emissions in China by integrating nighttime lights and point source locations

High-resolution CO2 emission inventories are essential to accurately assess spatiotemporal patterns of carbon emissions, analyze factors affecting carbon emissions, and develop sound emission reduction policies. The top-down approach is often used to map CO2 emissions from energy consumption due to its simplicity. However, the spatial proxy variables commonly used in this method, such as nighttime light (NL), land use, and population, are difficult to reflect the spatial distribution of CO2 emissions from large point sources. Therefore, this study uses the active fire product provided by Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite to extract the location of industrial heat sources in China, and then develops an improved CO2 emission estimation model by integrating industrial heat sources, Global Energy Monitor (GEM) power plant location and nighttime lights. The model is used to map CO2 emissions from energy consumption at a resolution of 1 km*1 km from 2012 to 2019 in China. It is found that the overall accuracy of the model is greatly improved at the provincial level, the R2 value is >0.75, and RMSE is distributed in 40–110 Mt. At the grid level, the improved model allocates more carbon emissions to the grid where the point source is located, which makes the spatial distribution of CO2 emissions more reasonable.

An in–vivo validation of ESI methods with focal sources

Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements of the electric field on the scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to the ill-posedness of the underlying mathematical problem. However, it is difficult to find systematic comparisons involving a wide variety of methods. Further, existing comparisons rarely take into account the variability of the results with respect to the input parameters. Finally, comparisons are typically performed using either synthetic data, or in-vivo data where the ground-truth is only roughly known. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are precisely known. We compare ten different ESI methods, using their implementation in the MNE-Python package: MNE, dSPM, LORETA, sLORETA, eLORETA, LCMV beamformers, irMxNE, Gamma Map, SESAME and dipole fitting. We perform comparisons under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of such parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with most accurate methods hitting an average localization error of 1.2 cm and outperforming least accurate ones erring by 2.5 cm. As expected, dipolar and sparsity-promoting methods tend to outperform distributed methods. For several distributed methods, the best regularization parameter turned out to be the one in principle associated with low SNR, despite the high SNR of the available dataset. Depth weighting played no role for two out of the six methods implementing it. Sensitivity to input parameters varied widely between methods. While one would expect high variability being associated with low localization error at the best solution, this is not always the case, with some methods producing highly variable results and high localization error, and other methods producing stable results with low localization error. In particular, recent dipolar and sparsity-promoting methods provide significantly better results than older distributed methods. As we repeated the tests with “conventional” (32 channels) and dense (64, 128, 256 channels) EEG recordings, we observed little impact of the number of channels on localization accuracy; however, for distributed methods denser montages provide smaller spatial dispersion. Overall findings confirm that EEG is a reliable technique for localization of point sources and therefore reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.

A method for the geometric calibration of ultrasound transducer arrays with arbitrary geometries

Geometric calibration of ultrasound transducer arrays is critical to optimizing the performance of photoacoustic computed tomography (PACT) systems. We present a geometric calibration method that is applicable to a wide range of PACT systems. We obtain the speed of sound and point source locations using surrogate methods, which results in a linear problem in the transducer coordinates. We characterize the estimation error, which informs our choice of the point source arrangement. We demonstrate our method in a three-dimensional PACT system and show that our method improves the contrast-to-noise ratio, the size, and the spread of point source reconstructions by 80±19%, 19±3%, and 7±1%, respectively. We reconstruct the images of a healthy human breast before and after calibration and find that the calibrated image reveals vasculatures that were previously invisible. Our work introduces a method for geometric calibration in PACT and paves the way for improving PACT image quality.

Location and Activity Characterization of Gamma-Ray Point Sources Concealed in Shipping Containers Using Iterative Reconstruction and Modeling Cargo-Specific Attenuation

Simulated measurements of traditional shipping container screening infrastructure based on large-area polyvinyl-toluene (PVT) or sodium-iodide detectors (NaI) are used alongside an iterative reconstruction algorithm to characterize the activity and location of a radioactive point source concealed within a shipping container loaded with cargo. A maximum likelihood expectation maximization reconstruction method is employed to reconstruct the source distribution under the assumption that there exists a single point source in the scenario. To account for shielding by the cargo, it is assumed that the encompassing cargo, which was chosen to represent iron cargo, such as scrap metal or machine parts, is homogeneously distributed throughout the 32.2-m3 container at realistic loaded container densities of 0.0, 0.2, or 0.6 gcm−3. When the material properties of the cargo are assumed known and provided to the algorithm, the method is capable of localizing the source to within 40.5 cm and estimating the activity to the correct order of magnitude for cases with no cargo and 0.2 gcm−3 iron cargo completely filling the 32.2-m3 volume. With iron cargo at a density of 0.6 gcm−3, the localization and activity estimation is significantly worse, which is attributed to the method of accounting for attenuation in the cargo, a decreased signal-to-noise ratio, and the use of gross-count data that include the effect of buildup radiation. Using 662-keV photopeak data from a NaI-based radiation portal monitor (RPM) achieves better results than gross-count data from a PVT- or NaI-based RPM with the correct order of magnitude activity estimates for all cargo densities. For scenarios where the material of the cargo is unknown, but its density and distribution are known, a brute-force search is performed to find the optimum mass attenuation coefficient that describes the cargo. From the range of mass attenuation coefficients obtained, the method is not capable of differentiating between different types of common cargo, but demonstrates the principle of the method for characterizing shipping container cargo. Ultimately, the largest limiting factor in this method is the use of a simple average to estimate the path length traveled from a point in the container through the cargo in the direction of a detector. The large area detectors result in a high variance in this path length, and the degree of attenuation is exponentially dependent on this value. Despite the simple method of accounting for attenuation in the cargo, the maximum likelihood expectation maximization point source (MLEM PS) method is able to characterize a concealed point source well in the case with a PVT-based RPM and 0.2 gcm−3, which is cargo above the average density of a shipping container, drastically reducing the search area in secondary screening processes. The MLEM PS algorithm, therefore, represents a means of enhancing shipping container screening procedures without requiring significant changes in infrastructure and hardware.

Local-to-regional methane emissions from the Upper Silesian Coal Basin (USCB) quantified using UAV-based atmospheric measurements

Abstract. Coal mining accounts for ∼12 % of the total anthropogenic methane (CH4) emissions worldwide. The Upper Silesian Coal Basin (USCB), Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coal bed CH4 emissions into the atmosphere are poorly characterized. As part of the carbon dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May–June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speed, and surface wind direction. We used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2=0.7–0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified. As an alternative, we have developed three upscaling approaches, i.e., by scaling the European Pollutant Release and Transfer Register (E-PRTR) annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate, which are derived from the hourly emission inventory. These estimates are in the range of 256–383 kt CH4 yr−1 for the inverse Gaussian (IG) approach and 228–339 kt CH4 yr−1 for the mass balance (MB) approach. We have also estimated the total CO2 emissions from coal mining ventilation shafts based on the observed ratio of CH4/CO2 and found that the estimated regional CO2 emissions are not a major source of CO2 in the USCB. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions.

Analysis of Trace Elements in Tree Rings of Pines Growing Nearby Steelwork in Southern Poland during the Industrial and Post-Industrial Periods

The current study explores for the first time the contrasts and similarities between the elemental (Na, Mg, Fe, Ni, Cu, Zn, and Pb) composition of pines that grow in a polluted industrialized area, located close to a steelworks, and that of pines growing in a comparative site, far from industry. Radial trace element profiles were determined by LA-ICPMS. The results are compared with the rainfall load at the monitoring station in Katowice, the nearest one to sampling sites, over the years 1999–2012, received from the Chief Inspectorate of Environmental Protection (GIOS). The results show that in annual tree rings, there is no direct linear correlation between rainfall load and concentration of the studied elements in wood of the annual rings. The element concentrations in trees may reflect the sum of different factors that impact the ecosystem, including pollution from large sources and local point sources, immission, load of the rainfall level, and also specific plant physiology processes.

Nitrate and Ammonium Deposition in the Midwestern Fragmented Forest

Whereas the impacts of N deposition on forest ecosystems have been well studied in remote areas in predominantly forested landscapes, we know relatively less about the impacts of N deposition on forests in heavily human-modified landscapes. We studied the influence of adjacent land use, local point sources, and woodlot stand structure on subcanopy N transport and enrichment via throughfall in three woodlot fragments in southern Lower Michigan, USA. We found that one site had markedly higher TF N concentrations compared to the other two; however, our data indicate that elevated TF concentrations resulted from differences in tree species composition, rather than differences in surrounding land use. Specifically, we observed that the local abundance of basswood (Tilia americana) was positively associated and the local abundance of northern red oak (Quercus rubra) was negatively associated with TF N concentrations. One site had markedly greater TF N fluxes compared to the other two, which was driven by a lack of understory vegetation, possibly due to higher deer browsing at this site. Together, the results of this study demonstrated that TF N concentrations and fluxes were more strongly influenced by the internal characteristics of fragmented woodlots, such as forest structure and species composition, than by the surrounding land use.

Geographic and demographic variability in serum PFAS concentrations for pregnant women in the United States

BackgroundWhile major pathways of human PFAS exposure are thought to be drinking water and diet, other pathways and sources have also been shown to contribute to a person’s cumulative exposure. However, the degree of contribution of these other sources to PFAS body burdens is still not well understood and occurrence data for PFAS in conssumer products and household materials are sparse. Questionnaire data concordant with biomonitoring may improve understanding of associations between other PFAS exposure pathways and exposure in human populations.ObjectiveThis study aims to better understand maternal and early-life exposures to PFAS from various potential sources and pathways in the context of household and community level characteristics.MethodsPFAS data from the National Children’s Study (NCS) Vanguard Data and Sample Archive Access System were analyzed from serum of 427 pregnant women residing in 7 counties throughout the United States. Location and self-reported questionnaire responses were used to analyze variability in serum concentrations based on demographics, housing characteristics, behaviors, and geography. Spatial mapping analyses incorporated publicly available data to further hypothesize potential sources of exposure in two NCS counties.ResultsLocation was associated with serum concentrations for all PFAS chemicals measured. Questionnaire responses for race/ethnicity, income, education level, number of household members, drinking water source, home age, and fast-food consumption were associated with PFAS levels. Statistical differences were observed between participants with the same questionnaire responses but in different locations. Spatial mapping analyses suggested that participants’ proximity to local point sources can overshadow expected trends with demographic information.SignificanceBy increasing understanding of maternal and early-life PFAS exposures from various potential sources and pathways, as well as highlighting the importance of proximity to potential sources in identifying vulnerable populations and locations, this work reveals environmental justice considerations and contributes to risk management strategies that maximize public health protection.ImpactThis work increases understanding of maternal and early-life PFAS exposures, reveals environmental justice considerations, and contributes to study design and risk management strategies.