The largest and most variable step of selenium (Se) assimilation into aquatic ecosystems is the rapid uptake of aqueous Se by primary producers. These organisms can transfer more harmful forms of Se to higher trophic levels via dietary pathways, although much uncertainty remains around this step of Se assimilation due to site-specific differences in water chemistry, hydrological and biogeochemical characteristics, and community composition. Thus, predictions of Se accumulation are difficult, and boreal lake systems are relatively understudied. To address these knowledge gaps, five static-renewal field experiments were performed to examine the bioaccumulation of low, environmentally relevant concentrations of Se, as selenite, by naturally grown periphyton from multiple boreal lakes. Periphyton rapidly accumulated Se at low aqueous Se concentrations, with tissue Se concentrations ranging from 8.0 to 24.9 μg/g dry mass (dm) in the 1–2 μg Se/L treatments. Enrichment functions ranged from 2870 to 12 536 L/kg dm in the 4 μg Se/L treatment, to 11 867–22 653 L/kg dm in the 0.5 μg Se/L treatment among lakes. Periphyton Se uptake differed among the five study lakes, with periphyton from mesotrophic lakes generally accumulating more Se than periphyton from oligotrophic lakes. Higher proportions of charophytes and greater dissolved inorganic carbon in more oligotrophic lakes corresponded to less periphyton Se uptake. Conversely, increased proportions of bacillariophytes and total dissolved phosphorus in more mesotrophic lakes corresponded to greater periphyton Se uptake. Periphyton community composition and water chemistry variables were correlated, limiting interpretation of differences in periphyton Se accumulation among lakes. The results of this research provide insight on the biodynamics of Se assimilation at the base of boreal lake food webs at environmentally relevant concentrations, which can potentially inform ecological risk assessments in boreal lake ecosystems in North America.

The health effects of potentially toxic elements (PTEs) in airborne particulate matter (PM) are strongly dependent on their size distribution and dissolution. This study examined PTEs within nine distinct sizes of PM in a Chinese megacity, with a focus on their deposited and dissolved bioaccessibility in the human pulmonary region. A Multiple Path Particle Dosimetry (MPPD) model was used to estimate the deposited bioaccessibility, and an in-vitro experiment with simulated lung fluid was conducted for dissolved bioaccessibility. During the non-heating season, the dissolved bioaccessible fraction (DBF) of As, Cd, Co, Cr, Mn, Pb and V were greater in fine PM (aerodynamics less than 2.1 μm) than in coarse PM (aerodynamics between 2.1 and 10 μm), and vice versa for Ni. With the increased demand of heating, the DBF of Pb and As decreased in fine particle sizes, probably due to the presence of oxide/silicate compounds from coal combustion. Inhalation health risks based on the bioaccessible concentrations of PTEs displayed the peaks in <0.43 μm and 2.1–3.3 μm particulate sizes. The non-cancer risk was at an acceptable level (95th percentiles of hazard index (HI) was 0.49), but the cancer risk exceeded the threshold value (95th percentiles of total incremental lifetime cancer risk (TCR) was 8.91 × 10⁻⁵). Based on the results of uncertainty analysis, except for the exposure frequency, the total concentrations and DBF of As and Cr in <0.43 μm particle size segment have a greater influence on the uncertainty of probabilistic risk.

Gaining insight into the response of surface ozone (O₃) formation to its precursors plays an important role in the policy-making of O₃ pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O₃ and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O₃ and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O₃ prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O₃ and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O₃ concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O₃ formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O₃ formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O₃ and its precursors could be used to test and improve the simulation performance of modern models.

Organic fraction municipal solid waste (OFMSW) has a high potential for energy and value-added product recovery due to its carbon- and nutrient-rich composition; however, traditional value chains have treated OFMSW as an undesired by-product. This study focuses on value chain optimisation to assist the transition to resource recovery value chains. To achieve this, this work combined two stage stochastic mathematical optimisation with geographical spatial analysis and time series waste generation analysis. Existing infrastructure in England, including anaerobic digestion plants and road transportation networks, were included in the model. To account for uncertainty in waste generation, multiple scenarios and their associated probabilities were developed based on environmental variables. The optimisation problem was solved to further advance the understanding of economically optimal waste-to-resource value chains under waste generation variability. The pertinent decision variables included sizing, technology selection, waste flows and location of thermochemical treatment sites. The model highlights the potential reduction in system profitability as a result of different operating constraints, such as minimum plant operating capacity factors and landfill taxation. The latter was shown to have the largest impact on profitability as overconservative systems designs were implemented to hedge against the waste variability. Such computer-aided models offer opportunities to overcome the challenges posed by waste generation variability and waste to resource value chain transformation.

The determination of both stable nitrogen (δ¹⁵N–NO₃⁻) and stable oxygen (δ¹⁸O–NO₃⁻) isotopic signatures of nitrate in PM₂.₅ has shown potential for an approach of assessing the sources and oxidation pathways of atmospheric NOx (NO+NO₂). In the present study, daily PM₂.₅ samples were collected in the megacity of Beijing, China during the winter of 2017–2018, and this new approach was used to reveal the origin and oxidation pathways of atmospheric NOx. Specifically, the potential of field δ¹⁵N–NO₃⁻ signatures for determining the NOx oxidation chemistry was explored. Positive correlations between δ¹⁸O–NO₃⁻ and δ¹⁵N–NO₃⁻ were observed (with R² between 0.51 and 0.66, p < 0.01), and the underlying environmental significance was discussed. The results showed that the pathway-specific contributions to NO₃⁻ formation were approximately 45.3% from the OH pathway, 46.5% from N₂O₅ hydrolysis, and 8.2% from the NO₃+HC channel based on the δ¹⁸O-δ¹⁵N space of NO₃⁻. The overall nitrogen isotopic fractionation factor (εN) from NOx to NO₃⁻ on a daily scale, under winter conditions, was approximately +16.1‰±1.8‰ (consistent with previous reports). Two independent approaches were used to simulate the daily and monthly ambient NOx mixtures (δ¹⁵N-NOx), respectively. Results indicated that the monthly mean values of δ¹⁵N-NOx compared well based on the two approaches, with values of −5.5‰ ± 2.6‰, −2.7‰ ± 1.9‰, and −3.2‰ ± 2.2‰ for November, December, and January (2017–2018), respectively. The uncertainty was in the order of 5%, 5‰ and 5.2‰ for the pathway-specific contributions, the εN, and δ¹⁵N-NOx, respectively. Results also indicated that vehicular exhaust was the key contributor to the wintertime atmospheric NOx in Beijing (2017–2018). Our advanced isotopic perspective will support the future assessment of the origin and oxidation of urban atmospheric NOx.

Despite the global occurrence of microplastic contamination on sandy beaches, evidence of microplastic distribution within beaches remains contradictory. When conflicting evidence is used to inform sampling surveys, it increases uncertainty in resulting data. Moreover, it hampers spatially explicit risk characterization of microplastic pollution to intertidal fauna. We aimed to guide sampling designs for microplastic monitoring on beaches, and to quantify macroinfauna exposure to microplastics. Microplastic abundance, quantified between 5 mm–66 μm, lacked a significant zonation across the top sediment layer of sub-terrestrial, upper and lower midlittoral, and swash zones at two sites with varying anthropogenic influence on a microtidal dissipative beach in Uruguay. Microplastic abundance decreased exponentially with increasing grain size, as revealed by Bayesian Poisson regression, although the decrease was less steep compared to prior knowledge regarding sediment – plastic interactions obtained for large (millimeter-sized) industrial pellets. Significant differences in microplastic contamination between the two sites with varying anthropogenic influence likely related to their proximity to a freshwater canal. Corresponding field measurements of body burdens of fibers and irregular particles were significantly lower for the polychaete Euzonus (Thoracophelia) furcifera, despite its preference for finer sediments with higher microplastic loads, compared to the isopods Excirolana braziliensis and Excirolana armata. Results provide critical insights toward representative sampling of microplastics within beach sites. Specifically, we caution against sampling limited to the drift line, and instead recommend: 1) reporting beach morphodynamic characteristics; 2) using clearly defined, ecologically-informed zonation schemes; and 3) accounting for sediment grain size as a covariate to normalize among reported contamination levels. The results contribute valuable baseline data toward realistic exposure landscapes relative to the sediment grain size preferences of macroinfauna, needed to inform laboratory experiments.

Industrial boilers are a significant anthropogenic source of air pollutant emissions. In this study, a county-based atmospheric emission inventory of particulate matter (PM), PM₁₀, PM₂.₅, SO₂, NOx, organic carbon (OC) and elemental carbon (EC) from industrial boilers over mainland China in 2017 was developed for the first time, based on county-level activity data from ~61,000 coal-fired industrial boilers (CFIBs), ~44,000 biomass-fired industrial boilers (BFIBs), ~71,000 gas-fired industrial boilers (GFIBs) and ~9300 oil-fired industrial boilers (OFIBs), updated emission factors (EFs) and air pollution control device (APCD) efficiencies. The total national PM, PM₂.₅, PM₁₀, SO₂, NOx, OC and EC emissions from industrial boilers in 2017 were estimated to be 1,240, 347, 761, 1,648, 1,340, 13.1 and 15.8 kilotons (kt), respectively. Intensive air pollutant emissions from industrial boilers of more than 1000 kg/km² were predominantly in north-eastern, northern and eastern China. CFIBs contributed the most (77.6–94.0 %) to air pollutant emissions because of their high air pollutant EFs and the large amounts of coal consumed. BFIBs were the second-highest contributor to national air pollutant emissions, with the contribution of BFIBs to PM₂.₅, OC and EC emissions in central and southern China reaching up to 42.1 %, 61.7 % and 45.5 %, respectively. There were seasonal peaks in monthly air pollutant emissions in heating regions. The overall uncertainty realting to the new emission inventory was estimated as −25.9 %–22.7 %. Significant air pollutant emission reductions were obtained from 2017 to 2030, and by 2030 the PM, PM₁₀, PM₂.₅, SO₂ and NOx emissions were forecast to decrease by 40.1–84.0 %, 41.6–84.3 %, 44.5–75.2 %, 44.5–75.2 % and 19.5–46.8 % compared to 2017, respectively, under four proposed scenarios. The results of this study showed that differentiated industrial boiler management measures should be developed according to the actual emission characteristics.This work developed a county-based atmospheric emission inventory of PM, PM₁₀, PM₂.₅, SO₂, NOx, OC and EC from Chinese industrial boilers in 2017 for the first time.

Satellite-derived aerosol optical depth (AOD) has been widely used to predict ground-level fine particulate matter (PM₂.₅) concentrations, although its utility can be limited due to missing values. Despite recent attempts to address this issue by imputing missing satellite AOD values, the uncertainty associated with the AOD imputation and its impacts on PM₂.₅ predictions have been understudied. To fill this gap, we developed a missing data imputation model for the AOD derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and PM₂.₅ prediction models using several machine learning methods. We also examined how the uncertainty associated with the imputed AOD and a choice of machine learning algorithm were propagated to PM₂.₅ predictions. The application of the proposed imputation model to the data from New York State in the U.S. achieved a superior performance than those related studies, with a cross-validated R² of 0.94 and a Root Mean Square Error of 0.017. We also found that there was considerable uncertainty in PM₂.₅ predictions associated with the use of imputed AOD values, although it was not as high as the uncertainty from the machine learning algorithms used in PM₂.₅ prediction models. We concluded that the quantification of uncertainties for both AOD imputation and its propagation to AOD-based PM₂.₅ prediction is necessary for accurate and reliable PM₂.₅ predictions.

Plastic pollution has been a growing concern in recent decades due to the proliferation and ease of manufacturing of single use plastic products and inadequate waste and recycling management. Microplastic, and even smaller nanoplastic, particles are persistent pollutants in aquatic and terrestrial systems and are the subject of active and urgent research. This review will explore the current research on how exposure to plastic particles occurs and the risks associated from different exposure routes: ingestion, inhalation, and dermal exposure. The effects of microplastics on the cardiovascular system are of particular importance due to its sensitivity and ability to transport particles to other organ systems. The effects of microplastics and nanoplastics on the heart, platelet aggregation, and thrombus formation will all be explored with focus on how the particle characteristics modulate their effect. Plastic particle interactions are highly dependent on both their size and their surface chemistry and interesting research is being done with the interaction of particle characteristics and effect on thrombosis and the cardiovascular system. There is significant uncertainty surrounding some of the findings in this field as research in this area is still maturing. There are undoubtedly more physiological consequences than we are currently aware of resulting from environmental plastic exposure and more studies need to be conducted to reveal the full extent of pathologies caused by the various routes of microplastic exposure, with particular emphasis on longitudinal exposure effects. Further research will allow us to recognize the full extent of physiological impact and begin developing viable solutions to reduce plastic pollution and potentially design interventions to mitigate in-vivo plastic effects following significant or prolonged exposure.

Twenty-five years after the first look at polycyclic aromatic compounds (PACs) in Canada, this article presents current knowledge on Canadian PAC emission sources. The analysis is based on national inventories (the National Pollutant Release Inventory (NPRI) and the Air Pollutant Emissions Inventory (APEI)), an analysis of Canadian forest fires, and several air quality model-ready emissions inventories. Nationally, forest fires continue to dominate PAC emissions in Canada, however there is uncertainty in these estimates. Though forest fire data show a steady average in the total annual area burned historically, an upward trend has developed recently. Non-industrial sources (home firewood burning, mobile sources) are estimated to be the second largest contributor (∼6-8 times lower than forest fires) and show moderate decreases (25%–65%) in the last decades. Industrial point sources (aluminum production, iron/steel manufacturing) are yet a smaller contributor and have seen considerable reductions (90% +) in recent decades. Fugitive emissions from other industrial sources (e.g. disposals by the non-conventional oil extraction and wastewater sectors, respectively) remain a gap in our understanding of total PAC emissions in Canada. Emerging concerns about previously unrecognized sources such as coal tar-sealed pavement run-off, climate change are discussed elsewhere in this special issue. Results affirm that observations at the annual/national scale are not always reflective of regional/local or finer temporal scales. When determining which sources contribute most to human and ecosystem exposure in various contexts, examination at regional and local scales is needed. There is uncertainty overall in emissions data stemming in part from various accuracy issues, limitations in the scope of the various inventories, and inventory gaps, among others.