Concentration Hotspots Research Articles

State capital in a geoeconomic world: mapping state-led foreign investment in the global political economy

What are the consequences of the rise of foreign state-led investment for international politics? Existing research oscillates between a ‘geopolitical’ and a ‘commercial’ logic driving this type of investment and remains inconclusive about its wider international reverberations. In this paper, I suggest going beyond this dichotomy by analyzing its systemic consequences. To do so, I conceptually delineate a geoeconomic approach that emphasizes the globalized nature of foreign state investment. I argue that foreign state investment creates system-level patterns, which can be studied by observing similar sectoral and geographic investment behavior. I map this phenomenon globally for the first time, drawing on the largest dataset on foreign state investment. Empirically, I show how foreign state investment is highly concentrated in Europe, North America and East Asia, and is owned by a handful of dominant states. It is especially European geo-industrial clusters that represent the hotspots of such concentration. The findings also suggest that three global industries – energy production, high-tech manufacturing, and transportation and logistics – form the key areas for current and future state-led investment concentration. With these contributions, the paper illuminates the increasing presence of states as owners in the global political economy, and facilitates its study as a geoeconomic phenomenon.

Modelling local nanobiomaterial release and concentration hotspots in the environment

Nanobiomaterials (NBMs) are a special category of nanomaterials used in medicine. As applications of NBMs are very similar to pharmaceuticals, their environmental release patterns are likely similar as well. Different pharmaceuticals were detected in surface waters all over the world. Consequently, there exists a need to identify possible NBM exposure routes into the environment. As the application of many NBMs is only carried out at specific locations (hospitals), average predicted environmental concentrations (PECs) may not accurately represent their release to the environment. We estimated the local release of poly(lactic-co-glycolic acid) (PLGA), which is investigated for their use in drug delivery, to Swiss surface waters by using population data as well as type, size and location of hospitals as proxies. The total mean consumption of PGLA in Switzerland using an explorative full-market penetration scenario was calculated to be 770 kg/year. 105 hospitals were considered, which were connected to wastewater treatment plants and the receiving water body using graphic information system (GIS) modelling. The water body dataset contained 20,167 river segments and 210 lake polygons. Using the discharge of the river, we were able to calculate the PECs in different river segments. While we calculated high PLGA releases of 2.24 and 2.03 kg/year in large cities such as Geneva or Zurich, the resulting local PECs of 220 and 660 pg/l, respectively, were low due to the high river discharge (330 and 97 m3/s). High PLGA concentrations (up to 7,900 pg/l) on the other hand were calculated around smaller cities with local hospitals but also smaller receiving rivers (between 0.7 and 1.9 m3/s). Therefore, we conclude that population density does not accurately predict local concentration hotspots of NBMs, such as PLGA, that are administered in a hospital context. In addition, even at the locations with the highest predicted PLGA concentrations, the expected risk is low.

An investigation into seasonal variations of groundwater nitrate by spatial modelling strategies at two levels by kriging and co-kriging models

Nitrate pollution of groundwater through spatial models is investigated in this paper by using a sample of nitrate values at monitoring wells using the data from four seasons of a year, in which data are sparse. Two spatial modelling strategies are formulated at two levels, in which Strategy 1 comprises: three variations of kriging-based models (ordinary kriging, simple kriging and universal kriging), which are constructed at Level 1 to predict nitrate concentrations; and a Multiple Co-Kriging (MCoK) model is used at Level 2 to enhance the accuracy of the predictions. Strategy 2 is also at two levels but employs Indicator Kriging (IK) at Level 1 as a probabilistic spatial model to predict areas at risk of exceeding two thresholds of 37.5 mg/L and 50 mg/L of nitrate concentration, and Multiple Co-Indicator Kriging (MCoIK) at Level 2 for a better accuracy. The improvements at Level 2 for both strategies are remarkable and hence they are used to gain an insight into inherent problems. The results of a study delineate areas with excessive nitrate concentrations, which are in the vicinity of urban areas and hence reflect poor planning practices since the 1990s. The results further reveal the patterns on sensitivities to seasonal variations driven by aquifer recharge and strong dilution processes in spring times; and on the role of pumpage impacting aquifers giving rise to possible hotspots of nitrate concentrations.

Accounting for the Three-Dimensional Distribution of Escherichia coli Concentrations in Pond Water in Simulations of the Microbial Quality of Water Withdrawn for Irrigation

Evaluating the microbial quality of irrigation water is essential for the prevention of foodborne illnesses. Generic Escherichia coli (E. coli) is used as an indicator organism to estimate the microbial quality of irrigation water. Monitoring E. coli concentrations in irrigation water sources is commonly performed using water samples taken from a single depth. Vertical gradients of E. coli concentrations are typically not measured or are ignored; however, E. coli concentrations in water bodies can be expected to have horizontal and vertical gradients. The objective of this work was to research 3D distributions of E. coli concentrations in an irrigation pond in Maryland and to estimate the dynamics of E. coli concentrations at the water intake during the irrigation event using hydrodynamic modeling in silico. The study pond is about 22 m wide and 200 m long, with an average depth of 1.5 m. Three transects sampled at 50-cm depth intervals, along with intensive nearshore sampling, were used to develop the initial concentration distribution for the application of the environmental fluid dynamic code (EFDC) model. An eight-hour irrigation event was simulated using on-site data on the wind speed and direction. Substantial vertical and horizontal variations in E. coli concentrations translated into temporally varying concentrations at the intake. Additional simulations showed that the E. coli concentrations at the intake reflect the 3D distribution of E. coli in the limited pond section close to the intake. The 3D sampling revealed E. coli concentration hot spots at different depths across the pond. Measured and simulated 3D E. coli concentrations provide improved insights into the expected microbial water quality of irrigation water compared with 1D or 2D representations of the spatial variability of the indicator concentration.

Roving pharmacies: Modelling the dispersion of pharmaceutical contamination in estuaries

Abstract We characterized the transport pathways of pharmaceuticals released from wastewater outfalls in one of Europe’s largest estuarine systems (Tagus estuary, Portugal), to identify areas of ecological relevance prone to environmental degradation due to increased exposure to these emerging contaminants. Particle source, Lagrangian trajectories and arrivals over time (1, 6, and 10 days) were used as a proxy to determine the exposure potential and vulnerability of different habitats to an array of pharmaceuticals with different environmental degradation rates and half-lives. Then, exposure potential was combined with information on sensitive habitats to identify vulnerable areas in the estuary. Three different freshwater inflow scenarios were considered simulating Medium flow, No flow and flood conditions (High flow). Model results for areas with higher particle density were concordant with concentration hotspots of pharmaceuticals. Higher potential exposure in a larger area of the estuary was observed for the High freshwater flow; yet reduced particles export to adjacent coastal areas highlights the potential of estuaries and bays as particle sinks. In all modeled scenarios, vulnerable areas were predominantly located in the southern bays up to day 6, and later in eastern areas, which include a nature reserve. These areas are largely composed of intertidal soft substratum and salt marshes habitats, and thus warrant prioritization for monitoring and risk assessment of pharmaceuticals exposure in the Tagus estuary due to the higher risk of exposure of a wealth of vertebrate and invertebrate species that use them. This framework allowed the identification of vulnerable areas to pharmaceutical contamination and can be a valuable instrument to plan the location for wastewater treatment plants (WWTPs) in estuaries and coastal areas.

CARBON DIOXIDE AND METHANE FLUX MEASUREMENTS AT A LARGE UNSANITARY DUMPING SITE IN THE AMAZON REGION

Dumps are important anthropogenic sources of greenhouse gas emissions into the atmosphere, mostly CH4. However, few studies on the subject have been carried out in the Amazon region. Several factors affect the production and emission of dumps gas. The objective of this study was to quantify the spatial variation of CO2 and CH4 production in an Amazon dump and try to associate the relative importance of some environmental factors with the fluxes. This study was carried out in an open-air dump in the metropolitan region of Belém, where approximately 11.0 million Mg of waste was deposited in 25 years, of which 6.4 million Mg was organic. The emission rates of CH4 and CO2 from the surface of the dump were determined using the closed dynamic flux chamber technique. The study was conducted in three cells of different ages, sampled in two moments between the Amazon rainy and dry season. The Aura dump has an area of 30 ha and emits a total of 51.49 Mg CO2 ha-1 month-1 and 3.16 Mg CH4 ha-1 month-1 to the atmosphere. This results in an expressive production of 1,359,961.04 Mg CO2-e year-1, being that 58.54% is due to CH4 flux. The spatial variability in CO2 and CH4 flux is very large, especially for CH4, forming hotspots of high concentrations, and perhaps because of this, the flow has not been correlated with micrometeorological variations.

Targeted delivery of hydrogen for the bioremediation of aquifers contaminated by dissolved chlorinated compounds

Dihydrogen (H2) gas injection is a promising option to enhance the reductive biodehalogenation of contaminants in groundwater. However, it is challenging to ensure its targeted delivery at the right places in plumes, and for the long times required for bioremediation. In this paper, the ability of surfactant foam to retain H2 in the saturated zone and to enhance its release in the dissolved form was compared to traditional biosparging. H2 gas was injected, either alone, or as foam, in a 2D saturated cell packed with glass beads. This cell was continuously flushed with deoxygenated water to mimic aquifer circulation, and H2 was studied both in terms of gas distribution in the cell and dissolved concentrations downstream the injection zone. Experimental results are discussed in conjunction with simulations obtained using modeling. Both show that the viscous behavior of foam allows to efficiently retain greater volumes of H2 gas, 3.5 times higher than biosparging. Moreover, it is retained in a dense manner around the injection point, making possible the targeted delivery of this reagent. Besides, the gas dissolution in groundwater showed to be steadier and more persistent when gas was injected as foam, with dissolution rate constants observed to be 1.12 à 1.58 times lower. Finally, the retained foamed-gas persistently reduced water's relative permeability 1.7 to 5 times, diverting the groundwater flow from the treated zone despite the fast elution of the surfactant. Hence, when H2-foam injection is targeted to plume's contaminant concentration hotspots, on top of enhancing bioremediation, it can reduce contaminant diffusion to groundwater.

Long-Term Trends of Atmospheric CH4 Concentration across China from 2002 to 2016

Spatiotemporal variations of atmospheric CH4 from 2002 to 2016 across China were detected, based on the Atmospheric Infrared Sounder (AIRS) sixth-layer CH4 concentration. The CH4 concentration showed good consistency with the ground measurements of surface CH4 concentration from the World Data Centre for Greenhouse Gases (WDCGG) (R2 = 0.83, p < 0.01), indicating that the remotely-sensed CH4 reflected the spatial and temporal variations of surface CH4 concentration. Across China, three hotspots of CH4 concentration were found in northern Xinjiang, the northeast of Inner Mongolia/Heilongjiang, and the Norgay plateau in northwest Sichuan. The CH4 concentration showed obviously seasonal variations, with the maximum CH4 concentration occurring in summer, followed by the autumn, winter, and spring. Furthermore, the CH4 concentration showed significantly increasing trends across China, with the rate of increase ranging from ~0.29 to 0.62 ppb·month−1, which would bring a 0.0019~0.014 mK potential rise in surface temperature response over China. In particular, the most rapidly increasing rates occurred in the Qinghai-Tibet plateau, while relatively low rates occurred in southeast China.

Identification of hotspots of at-risk terrestrial vertebrate species in the south-central Great Plains of North America: A tool to inform and address regional-scale conservation

Biodiversity is being lost at an unprecedented rate, and resources for conservation efforts are limited. This is particularly problematic in the Great Plains of North America, where land-cover conversion for agriculture and energy production has reduced habitat for many species. In the U.S. portion of the Great Plains, a growing human population and a concomitant increasing need for food, fiber, and energy have caused landscape transformations that have resulted in over 700 vertebrate species currently being listed by state and federal conservation agencies as being at-risk in this region. Conservation efforts for such a large number of species will be most efficient when applied to areas with large numbers of these species, but such areas have never before been identified. We overlaid range maps created by the U.S. Geological Survey’s Gap Analysis Program for terrestrial vertebrate species to identify hotspots of high concentrations of U.S. state-defined Species of Greatest Conservation Need (SGCN; species that have identified as being rare or otherwise vulnerable enough to warrant conservation action in a given state) in the short- and mixed-grass prairie ecoregions of the southern and central Great Plains of the United States. We identified hotspots for species currently listed as SGCN as well as those pending designation, and a combined (current and pending) group. We then used data from the U.S. Geological Survey’s Protected Areas Database on land ownership and from the U.S. Department of Agriculture’s National Agricultural Statistics Service on land use/land cover to quantify the types of land ownership and land use/land cover types in hotspots to give land managers necessary information to address conservation of at-risk species in the Great Plains. Sufficient data were present for examination of 289 at-risk terrestrial vertebrate species. Hotspots of these species were located mostly on state- or federally-managed land in eastern New Mexico, Colorado, and west Texas. The current hottest hotspots were associated with areas with more natural/less anthropogenic forms of land use/land cover; areas with the lowest numbers of SGCNs had proportionately more cropland and less grassland than did hotspots. Identifying regional hotspots of at-risk biodiversity, and describing land use/land cover features associated with such areas, offers an opportunity to take a multi-species approach in more precisely establishing areas of conservation concern in the U.S.

High-Performance SERS Substrate Based on Hierarchical 3D Cu Nanocrystals with Efficient Morphology Control.

Cu nanocrystals of various shapes are synthesized via a universal, eco-friendly, and facile colloidal method on Al substrates using hexadecylamine (HDA) as a capping agent and glucose as a reductant. By tuning the concentration of the capping agent, hierarchical 3D Cu nanocrystals show pronounced surface-enhanced Raman scattering (SERS) through the concentrated hot spots at the sharp tips and gaps due to the unique 3D structure and the resulting plasmonic couplings. Intriguingly, 3D sword-shaped Cu crystals have the highest enhancement factor (EF) because of their relatively uniform size distribution and alignment. This work opens new pathways for efficiently realizing morphology control for Cu nanocrystals as highly efficient SERS platforms.

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

Integration of Landsat images and multisource data using spatial statistical analysis and geographical detector models can reveal the individual and interactive influences of anthropogenic activities and ecological factors on concentrations of atmospheric particulate matter less than 2.5 microns in diameter (PM2.5). This approach has been used in many studies to estimate biomass and forest disturbance patterns and to monitor carbon sinks. However, the approach has rarely been used to comprehensively analyze the individual and interactive influences of anthropogenic factors (e.g., population density, impervious surface percentage) and ecological factors (e.g., canopy density, stand age, and elevation) on PM2.5 concentrations. To do this, we used Landsat-8 images and meteorological data to retrieve quantitative data on the concentrations of particulates (PM2.5), then integrated a forest management planning inventory (FMPI), population density distribution data, meteorological data, and topographic data in a Geographic Information System database, and applied a spatial statistical analysis model to identify aggregated areas (hot spots and cold spots) of particulates in the urban area of Jinjiang city, China. A geographical detector model was used to analyze the individual and interactive influences of anthropogenic and ecological factors on PM2.5 concentrations. We found that particulate concentration hot spots are mainly distributed in urban centers and suburbs, while cold spots are mainly distributed in the suburbs and exurban region. Elevation was the dominant individual factor affecting PM2.5 concentrations, followed by dominant tree species and meteorological factors. A combination of human activities (e.g., population density, impervious surface percentage) and multiple ecological factors caused the dominant interactive effects, resulting in increased PM2.5 concentrations. Our study suggests that human activities and multiple ecological factors effect PM2.5 concentrations both individually and interactively. We conclude that in order to reveal the direct and indirect effects of human activities and multiple factors on PM2.5 concentrations in urban forests, quantification of fusion satellite data and spatial statistical methods should be conducted in urban areas.

Regional flow and deposition variability in adult female lungs: A numerical simulation pilot study

BackgroundDespite the promise of respiratory simulations improving diagnosis and treatment of pulmonary diseases, model predictions have yet to be translated into the clinical setting. Current state-of-the-art in silico models have not yet incorporated subject variability in their predictions of airflow distributions and extent of deposited particles. Until inter-subject variability is accounted for in lung modeling, it will remain impossible to translate model predictions into clinical practice. MethodsAirflow and particle trajectories (dp=1,3,5μm) are calculated in three subject-specific female adults by performing physiologically-based simulations. The computation framework features the ability to track air and particles throughout the respiration cycle and in the entire lung. Airway resistances, air velocities, and local deposition sites are correlated to airway anatomical features. FindingsSmaller airway diameters are correlated to larger airway resistances and pressure gradients in one subject compared to the other two. Irregular shape of the airway and flow direction (e.g. inspiration or expiration) correspond with peak velocities and secondary flow motions. Largest subject variability in deposition between conducting and respiratory zones is seen for 1 μm diameter particles. Little difference in total deposition is found among subjects. Localized deposited particle concentration hotspots are linked to airway anatomy and flow motion. InterpretationSimulation predictions provide a first look into the correlation of anatomical features with airflow characteristics and deposited particle concentrations. Global deposition percentages ranged (at most, by 20%) between subjects and variances in localized deposition hotspots are correlated to variances in flow characteristics.

Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015.

Satellite-based aerosol optical depth (AOD) is now comprehensively applied to estimate ground-level concentrations of fine particulate matter (PM2.5). This study aimed to construct the AOD-PM2.5 estimation models over Taiwan. The AOD-PM2.5 modeling in Taiwan island is challenging owing to heterogeneous land use, complex topography, and humid tropical to subtropical climate conditions with frequent cloud cover and prolonged rainy season. The AOD retrievals from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites were combined with the meteorological variables from reanalysis data and high resolution localized land use variables to estimate PM2.5 over Taiwan island from 2005 to 2015. Ten-fold cross validation was carried out and the residuals of the estimation model at various locations and seasons are assessed. The cross validation (CV) R2 based on monitoring stations were 0.66 and 0.66, with CV root mean square errors of 14.0μg/m3 (34%) and 12.9μg/m3 (33%), respectively, for models based on Terra and Aqua AOD. The results provided PM2.5 estimations at locations without surface stations. The estimation revealed PM2.5 concentration hotspots in the central and southern part of the western plain areas, particularly in winter and spring. The annual average of estimated PM2.5 concentrations over Taiwan consistently declined during 2005-2015. The AOD-PM2.5 model is a reliable and validated method for estimating PM2.5 concentrations at locations without monitoring stations in Taiwan, which is crucial for epidemiological study and for the assessment of air quality control policy.

HarmVect – a simulation‐based tool for pathway risk maps of invasive arthropods in Belgium

HarmVect is a tool simulating the introduction of arthropods into Belgium. The tool is designed to guide the user to divide the complex arrival of the arthropod into all its potential pathways, which in turn are further subdivided into individual pathway segments. These manageable pathway segments are then used to specify which detailed data the user needs to gather to input into the tool. This information is used to simulate the magnitude of the arthropod population during the successive pathway segments throughout each potential introduction pathway. The tool simulates the fluctuating propagule pressure throughout the successive segments of each pathway to calculate risk indices, which are then used to generate pathway risk maps of the arthropod for Belgium. The tool generates 2 types of pathway risk map: (a) a point of entry pathway (POE) risk map consisting of concentrated hotspots relating to the arthropod's entry at national borders, and (b) a point of appearance (POA) pathway risk map, succeeding the POE and visualizing the locations and level at which undetected propagules of the arthropod are exposed to the outside environment throughout Belgium. In conclusion, the tool produces an establishment risk map for the arthropods, which is generated by combining the POA with a map of climate suitability and host availability.

Modeling the spread of avian influenza viruses in aquatic reservoirs: A novel hydrodynamic approach applied to the Rhône delta (southern France)

Wild aquatic birds represent a natural reservoir of avian influenza viruses (AIV) that can be spread to poultry. AIV epizootics were associated with huge economic impacts during the last decades and are still of major concern. Within aquatic bird populations AIV are transmitted either by direct contact or through the ingestion of water that has been contaminated by infected individuals. This second route involving environmental transmission is of utmost importance in AIV dynamics, yet it has received far less attention than direct bird-to-bird contamination. Our objective was to combine a hydrodynamic model with data on mallard abundance and AIV infection rate within the population, so as to characterize virus dissemination within a complex wetland network. We chose the Vaccarès hydrosystem as a wetland model since it represents a large part of the Camargue region, which is a major wintering site for a large diversity of aquatic birds including AIV hosts. We aimed to identify the environmental parameters that drive AIV dynamics within this system and the spatio-temporal pattern of dispersion and persistence of viruses. Our results show that in a complex hydrosystem we can expect a great heterogeneity in AIV risk among wetlands. Our simulations underline how a simple “homogeneous box” approach could in the case of deltaic ecosystems minimize the expected risk by diluting it in the whole system. Moreover, such undermining of the risk perception could affect the predictions relative to risk duration. We present a new approach to identify hotspots of virus concentrations within deltaic areas that could take advantage of the duck count data, AIV surveys and hydrodynamic models that may already be available in several major duck wintering areas comprised of complex hydrosystems, such as the large European deltas. Our method could be of particular interest to optimize surveillance strategies in the current context of highly pathogenic AIV diffusion within wild bird populations.

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina.

To quantify the on-road PM2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM2.5 where the hybrid approach estimated 2.5 times more primary on-road PM2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM2.5 and suggesting that previous studies may have underestimated premature mortality due to PM2.5 from traffic-related emissions.

Real Time, Spatial, and Temporal Mapping of the Distribution of c-di-GMP during Biofilm Development

Bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP) is a dynamic intracellular signaling molecule that plays a central role in the biofilm life cycle. Current methodologies for the quantification of c-di-GMP are typically based on chemical extraction, representing end point measurements. Chemical methodologies also fail to take into consideration the physiological heterogeneity of the biofilm and thus represent an average c-di-GMP concentration across the entire biofilm. To address these problems, a ratiometric, image-based quantification method has been developed based on expression of the green fluorescence protein (GFP) under the control of the c-di-GMP-responsive cdrA promoter (Rybtke, M. T., Borlee, B. R., Murakami, K., Irie, Y., Hentzer, M., Nielsen, T. E., Givskov, M., Parsek, M. R., and Tolker-Nielsen, T. (2012) Appl. Environ. Microbiol. 78, 5060-5069). The methodology uses the cyan fluorescent protein (CFP) as a biomass indicator and the GFP as a c-di-GMP reporter. Thus, the CFP/GFP ratio gives the effective c-di-GMP per biomass. A binary mask was applied to alleviate background fluorescence, and fluorescence was calibrated against known c-di-GMP concentrations. Using flow cells for biofilm formation, c-di-GMP showed a non-uniform distribution across the biofilm, with concentrated hot spots of c-di-GMP. Additionally, c-di-GMP was found to be localized at the outer boundary of mature colonies in contrast to a uniform distribution in early stage, small colonies. These data demonstrate the application of a method for the in situ, real time quantification of c-di-GMP and show that the amount of this biofilm-regulating second messenger was dynamic with time and colony size, reflecting the extent of biofilm heterogeneity in real time.

Mercury content in agricultural soils (Vojvodina Province, Serbia)

The Vojvodina Province in northern Serbia is well known for its intensive field crops production. Over 90% of total arable land, which represents more than 1500.000ha, is used for field or vegetable crop production. A grid superimposed on Vojvodina land by means of a GIS tool (GIS ArcView 10) has divided land into 4×4km units, each representing an area of 1600ha. Total number of 1370 bulked soil samples were taken (0-30cm depth) from agricultural land and analysed for total mercury content THg. The samples were analysed using Direct Mercury Analyzer DMA 80 Milestone. Quality control was carried out with IRMM BCR reference materials 143R. The aim of this study was to determine the total content of Hg in agricultural soils and its spatial distributions in different parts of Vojvodina Province. The obtained results were within interval 0.008-0.974mgkg-1. The average concentration of Hg was 0.068, with median 0.048mgkg-1. The correlation was determined between Hg concentration and organic matter content in the soil. Content of Hg coincides with main geomorphological units of Vojvodina Province. Average values of Hg concentrations for soils formatted on different geomorphological units were 0.031 for sandy area with dune fields, 0.048 for alluvial terraces, 0.055 for upper Pleistocene terraces, 0.058 for loess plateaus, 0.083 for mountains and 0.092mgkg-1 for alluvial plains. Hg spatial distribution confirmed that most of Vojvodina Province area has geochemical origin of Hg. Higher concentration of Hg on alluvial plains indicated that the origin of Hg near rivers could be from anthropogenic source. The main rivers in Vojvodina have been dammed more than a century ago. Thus, higher concentrations of Hg in their alluvial plains out of narrow dammed zone around the rivers must be related to natural and anthropogenic sources located in their huge catchments. Higher content of Hg in mountain region can be explained by high clay content in these soils. Additional hotspots of Hg concentration of top soils are related to geographical locations of major towns. The obtained results also indicated that the measured levels of Hg in the soil are not limiting factors for production of safe food in Vojvodina.

Emissions of organic carbon and methane from petroleum and dairy operations in California's San Joaquin Valley

Abstract. Petroleum and dairy operations are prominent sources of gas-phase organic compounds in California's San Joaquin Valley. It is essential to understand the emissions and air quality impacts of these relatively understudied sources, especially for oil/gas operations in light of increasing US production. Ground site measurements in Bakersfield and regional aircraft measurements of reactive gas-phase organic compounds and methane were part of the CalNex (California Research at the Nexus of Air Quality and Climate Change) project to determine the sources contributing to regional gas-phase organic carbon emissions. Using a combination of near-source and downwind data, we assess the composition and magnitude of emissions, and provide average source profiles. To examine the spatial distribution of emissions in the San Joaquin Valley, we developed a statistical modeling method using ground-based data and the FLEXPART-WRF transport and meteorological model. We present evidence for large sources of paraffinic hydrocarbons from petroleum operations and oxygenated compounds from dairy (and other cattle) operations. In addition to the small straight-chain alkanes typically associated with petroleum operations, we observed a wide range of branched and cyclic alkanes, most of which have limited previous in situ measurements or characterization in petroleum operation emissions. Observed dairy emissions were dominated by ethanol, methanol, acetic acid, and methane. Dairy operations were responsible for the vast majority of methane emissions in the San Joaquin Valley; observations of methane were well correlated with non-vehicular ethanol, and multiple assessments of the spatial distribution of emissions in the San Joaquin Valley highlight the dominance of dairy operations for methane emissions. The petroleum operations source profile was developed using the composition of non-methane hydrocarbons in unrefined natural gas associated with crude oil. The observed source profile is consistent with fugitive emissions of condensate during storage or processing of associated gas following extraction and methane separation. Aircraft observations of concentration hotspots near oil wells and dairies are consistent with the statistical source footprint determined via our FLEXPART-WRF-based modeling method and ground-based data. We quantitatively compared our observations at Bakersfield to the California Air Resources Board emission inventory and find consistency for relative emission rates of reactive organic gases between the aforementioned sources and motor vehicles in the region. We estimate that petroleum and dairy operations each comprised 22% of anthropogenic non-methane organic carbon at Bakersfield and were each responsible for 8–13% of potential precursors to ozone. Yet, their direct impacts as potential secondary organic aerosol (SOA) precursors were estimated to be minor for the source profiles observed in the San Joaquin Valley.

Real‐Time Estimation of Pollution Emissions and Dispersion from Highway Traffic

AbstractTraffic‐related air pollution is a serious problem with significant health impacts in both urban and suburban environments. Despite an increased realization of the negative impacts of air pollution, assessing individuals' exposure to traffic‐related air pollution remains a challenge. Obtaining high‐resolution estimates are difficult due to the spatial and temporal variability of emissions, the dependence on local atmospheric conditions, and the lack of monitoring infrastructure. This presents a significant hurdle to identifying pollution concentration hot spots and understanding the emission sources responsible for these hot spots, which in turn makes it difficult to reduce the uncertainty of health risk estimates for communities and to develop policies that mitigate these risks. We present a novel air pollution estimation method that models the highway traffic state, highway traffic‐induced air pollution emissions, and pollution dispersion, and describe a prototype implementation for the San Francisco Bay Area. Our model is based on the availability of real‐time traffic estimates on highways, which we obtain using a traffic dynamics model and an estimation algorithm that augments real‐time data from both fixed sensors and probe vehicles. These traffic estimates combined with local weather conditions are used as inputs to an emission model that estimates pollutant levels for multiple gases and particulates in real‐time. Finally, a dispersion model is used to assess the spread of these pollutants away from the highway source. Maps generated using the output of the dispersion model allow users to easily analyze the evolution of individual pollutants over time, and provides transportation engineers and public health officials with valuable information that can be used to minimize health risks.