Understorey communities can dominate forest plant diversity and strongly affect forest ecosystem structure and function. Understoreys often respond sensitively but inconsistently to drivers of ecological change, including nitrogen (N) deposition. Nitrogen deposition effects, reflected in the concept of critical loads, vary greatly not only among species and guilds, but also among forest types. Here, we characterize such context dependency as driven by differences in the amounts and forms of deposited N, cumulative deposition, the filtering of N by overstoreys, and available plant species pools. Nitrogen effects on understorey trajectories can also vary due to differences in surrounding landscape conditions; ambient browsing pressure; soils and geology; other environmental factors controlling plant growth; and, historical and current disturbance/management regimes. The number of these factors and their potentially complex interactions complicate our efforts to make simple predictions about how N deposition affects forest understoreys. We review the literature to examine evidence for context dependency in N deposition effects on forest understoreys. We also use data from 1814 European temperate forest plots to test the ability of multi-level models to characterize context-dependent understorey responses across sites that differ in levels of N deposition, community composition, local conditions and management history. This analysis demonstrated that historical management, and plot location on light and pH-fertility gradients, significantly affect how understorey communities respond to N deposition. We conclude that species' and communities' responses to N deposition, and thus the determination of critical loads, vary greatly depending on environmental contexts. This complicates our efforts to predict how N deposition will affect forest understoreys and thus how best to conserve and restore understorey biodiversity. To reduce uncertainty and incorporate context dependency in critical load setting, we should assemble data on underlying environmental conditions, conduct globally distributed field experiments, and analyse a wider range of habitat types.

Long- and short-term exposure to PM2.5 is of great concern in China due to its adverse population health effects. Characteristic of the severity of the situation in China is that in the Jing-Jin-Ji region considered in this work a total of 2725 excess deaths have been attributed to short-term PM2.5 exposure during the period January 10–31, 2013. Technically, the processing of large space-time PM2.5 datasets and the mapping of the space-time distribution of PM2.5 concentrations often constitute high-cost projects. To address this situation, we propose a synthetic modeling framework based on the integration of (a) the Bayesian maximum entropy method that assimilates auxiliary information from land-use regression and artificial neural network (ANN) model outputs based on PM2.5 monitoring, satellite remote sensing data, land use and geographical records, with (b) a space-time projection technique that transforms the PM2.5 concentration values from the original spatiotemporal domain onto a spatial domain that moves along the direction of the PM2.5 velocity spread. An interesting methodological feature of the synthetic approach is that its components (methods or models) are complementary, i.e., one component can compensate for the occasional limitations of another component. Insight is gained in terms of a PM2.5 case study covering the severe haze Jing-Jin-Ji region during October 1–31, 2015. The proposed synthetic approach explicitly accounted for physical space-time dependencies of the PM2.5 distribution. Moreover, the assimilation of auxiliary information and the dimensionality reduction achieved by the synthetic approach produced rather impressive results: It generated PM2.5 concentration maps with low estimation uncertainty (even at counties and villages far away from the monitoring stations, whereas during the haze periods the uncertainty reduction was over 50% compared to standard PM2.5 mapping techniques); and it also proved to be computationally very efficient (the reduction in computational time was over 20% compared to standard mapping techniques).

The relative impact of non-exhaust sources (i.e. road dust, tire wear, road wear and brake wear particles) on urban air quality is increasing. Among them, road dust resuspension has generally the highest impact on PM concentrations but its spatio-temporal variability has been rarely studied and modeled. Some recent studies attempted to observe and describe the time-variability but, as it is driven by traffic and meteorology, uncertainty remains on the seasonality of emissions. The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pavement characteristics, traffic intensity and speed, fleet composition, proximity to traffic lights, but also the presence of external sources. However, no parameterization is available as a function of these variables.We investigated mobile road dust smaller than 10 μm (MF10) in two cities with different climatic and traffic conditions (Barcelona and Turin), to explore MF10 seasonal variability and the relationship between MF10 and site characteristics (pavement macrotexture, traffic intensity and proximity to braking zone). Moreover, we provide the first estimates of emission factors in the Po Valley both in summer and winter conditions. Our results showed a good inverse relationship between MF10 and macro-texture, traffic intensity and distance from the nearest braking zone. We also found a clear seasonal effect of road dust emissions, with higher emission in summer, likely due to the lower pavement moisture. These results allowed building a simple empirical mode, predicting maximal dust loadings and, consequently, emission potential, based on the aforementioned data. This model will need to be scaled for meteorological effect, using methods accounting for weather and pavement moisture. This can significantly improve bottom-up emission inventory for spatial allocation of emissions and air quality management, to select those roads with higher emissions for mitigation measures.

PM₂.₅ is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM₂.₅. The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM₂.₅ were collected at Tianjin, a megacity in northern China. The ambient PM₂.₅ dataset, source information, and gas-to-particle ratios (such as SO₂/PM₂.₅, CO/PM₂.₅, and NOx/PM₂.₅ ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM₂.₅ was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.

Organophosphate flame retardants (OPFRs), as alternatives to polybrominated biphenyl ethers (PBDEs), are frequently detected in various environmental matrices. Owing to urbanization and industrial pollution, co-contamination of OPFRs and heavy metals is ubiquitous in the environment. The toxicity of OPFRs in aqueous phase is a significant concern, but uncertainty still exists regarding the co-toxicity to benthic organisms of OPFRs and metals in sediments. Hence, we explored the physiological response of Corbicula fluminea to OPFRs and Cd in sediments. The results indicated that the antioxidant system in the clams was stimulated in the presence of OPFRs and Cd, and the oxidative stress increased with increasing concentrations of OPFRs. In contrast, the cytochrome P450 (CYP450) content and acetylcholinesterase (AChE) activity were reduced by exposure to both OPFRs and Cd. The cytochrome P450 4 family (CYP4) mRNA expression and OPFR toxicity were lower than those in previously reported experiments conducted in the water phase. Moreover, the expression levels of metallothionein (MT) and AChE mRNA decreased when OPFRs and Cd were present together. The highest integrated biomarker response (IBR) index (IBR = 15.41) was observed in the presence of 45 mg kg⁻¹ Cd + 200 mg kg⁻¹ OPFRs, rather than the 45 mg kg⁻¹ Cd + 400 mg kg⁻¹ OPFRs treatment (IBR = 9.48). In addition, CYP450 and AChE in the digestive glands of C. fluminea exhibited significant correlations with the concentration of the OPFR/Cd mixture (p < 0.01) and could be effective biomarkers for OPFR and Cd co-contamination. The results potentially contribute to more realistic predictions and evaluations of the environmental risks posed by OPFRs in aquatic sediments contaminated with heavy metals, particularly with respect to the risk to benthic organisms.

Two hundred sixty-three fine particulate matter (PM2.5) samples collected on 3-day intervals over a 14-month period at two sites in the San Joaquin Valley (SJV) were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC), and organic molecular markers. A unique source profile library was applied to a chemical mass balance (CMB) source apportionment model to develop monthly and seasonally averaged source apportionment results. Five major OC sources were identified: mobile sources, biomass burning, meat smoke, vegetative detritus, and secondary organic carbon (SOC), as inferred from OC not apportioned by CMB. The SOC factor was the largest source contributor at Fresno and Bakersfield, contributing 44% and 51% of PM mass, respectively. Biomass burning was the only source with a statistically different average mass contribution (95% CI) between the two sites. Wintertime peaks of biomass burning, meat smoke, and total OC were observed at both sites, with SOC peaking during the summer months. Exceptionally strong seasonal variation in apportioned meat smoke mass could potentially be explained by oxidation of cholesterol between source and receptor and trends in wind transport outlined in a Residence Time Analysis (RTA). Fast moving nighttime winds prevalent during warmer months caused local emissions to be replaced by air mass transported from the San Francisco Bay Area, consisting of mostly diluted, oxidized concentrations of molecular markers. Good agreement was observed between SOC derived from the CMB model and from non-biomass burning WSOC mass, suggesting the CMB model is sufficiently accurate to assist in policy development. In general, uncertainty in monthly mass values derived from daily CMB apportionments were lower than that of CMB results produced with monthly marker composites, further validating daily sampling methodologies.Strong seasonal trends were observed for biomass and meat smoke OC apportionment, and monthly mass averages had lowest uncertainty when derived from daily CMB apportionments.

Currently the land use and land use change (LULUC) emits 1.3 ± 0.5 Pg carbon (C) year⁻¹, equivalent to 8% of the global annual emissions. The objectives of this study were to quantify (1) the impact of LULUC on greenhouse gas (GHG) emissions in a subtropical region and (2) the role of conservation agriculture to mitigate GHG emissions promoting ecosystem services. We developed a detailed IPCC Tier 2 GHG inventory for the Campos Gerais region of southern Brazil that has large cropland area under long-term conservation agriculture with high crop yields. The inventory accounted for historical and current emissions from fossil fuel combustion, LULUC and other minor sources. We used Century model to simulate the adoption of conservation best management practices, to all croplands in the region from 2017 to 2117. Our results showed historical (1930–2017) GHG emissions of 412 Tg C, in which LULUC contributes 91% (376 ± 130 Tg C), the uncertainties ranged between 13 and 36%. Between 1930 and 1985 LULUC was a major source of GHG emission, however from 1985 to 2015 fossil fuel combustion became the primary source of GHG emission. Forestry sequestered 52 ± 24 Tg C in 0.6 Mha in a period of 47 years (1.8 Tg C Mha⁻¹ year⁻¹) and no-till sequestered 30.4 ± 24 Tg C in 2 Mha in a period of 32 years (0.5 Tg C Mha⁻¹ year⁻¹) being the principal GHG mitigating activities in the study area. The model predictions showed that best management practices have the potential to mitigate 13 years of regional emissions (330 Tg C in 100 years) or 105 years of agriculture, forestry and livestock emissions (40 Tg C in 100 years) making the agriculture sector a net carbon (C) sink and promoting ecosystem services.

Exposure of wildlife to Active Pharmaceutical Ingredients (APIs) is likely to occur but studies of risk are limited. One exposure pathway that has received attention is trophic transfer of APIs in a water-fish-osprey food chain. Samples of water, fish plasma and osprey plasma were collected from Delaware River and Bay, and analyzed for 21 APIs. Only 2 of 21 analytes exceeded method detection limits in osprey plasma (acetaminophen and diclofenac) with plasma levels typically 2–3 orders of magnitude below human therapeutic concentrations (HTC). We built upon a screening level model used to predict osprey exposure to APIs in Chesapeake Bay and evaluated whether exposure levels could have been predicted in Delaware Bay had we just measured concentrations in water or fish. Use of surface water and BCFs did not predict API concentrations in fish well, likely due to fish movement patterns, and partitioning and bioaccumulation uncertainties associated with these ionizable chemicals. Input of highest measured API concentration in fish plasma combined with pharmacokinetic data accurately predicted that diclofenac and acetaminophen would be the APIs most likely detected in osprey plasma. For the majority of APIs modeled, levels were not predicted to exceed 1 ng/mL or method detection limits in osprey plasma. Based on the target analytes examined, there is little evidence that APIs represent a significant risk to ospreys nesting in Delaware Bay. If an API is present in fish orders of magnitude below HTC, sampling of fish-eating birds is unlikely to be necessary. However, several human pharmaceuticals accumulated in fish plasma within a recommended safety factor for HTC. It is now important to expand the scope of diet-based API exposure modeling to include alternative exposure pathways (e.g., uptake from landfills, dumps and wastewater treatment plants) and geographic locations (developing countries) where API contamination of the environment may represent greater risk.

Trace metal risk assessment and environmental quality standard definition require realistic models that quantify metal exposure and accumulation by biota. In the present study we propose a novel trait-based approach to predict whole body concentrations of metals in aquatic invertebrates from concentrations measured in different environmental compartments. Field data from a large riverine floodplain was used to calibrate and test the model. The prediction performance of the trait-based model was unbiased and uncertainty was below the twofold of measured concentrations for the four studied metals (Ni, Cu, Cd, Pb). The relative contribution of feeding, respiration and locomotion patterns as well as metal concentrations in three environmental compartments provided insights into the importance of different uptake pathways. The relation with the sediment (i.e., to what degree taxa live in or directly on the sediment) was shown to be the most important trait to predict metal accumulation. Overall, this study demonstrated the potential use of bioecological traits for the modeling of whole body metal concentrations of entire aquatic invertebrate communities.

During the last several decades, persistent organic pollutants and metals cause great concern for their toxicity in organisms as well as for their bioaccumulation and/or trophic transfer through the food chains in ecosystems. A large number of studies therefore have focused on the trophic levels of organisms to illustrate food web structure, as a critical component in the study of pollutant dynamics and biomagnification. The trends in biomagnification of pollutants in food webs indeed provide fundamental information about the properties and fates of pollutants in ecosystems. The trophic magnification supports the establishment of a reliable trophic structure, which can further aid the understanding of the transport and exposure routes of contaminants in accumulation and risk assessments. Recently, efforts to interpret the food web structure using carbon and nitrogen stable isotope ratios have contributed to better understanding of the fate of pollutants in the ecosystem. However, it is known that this isotope analysis of bulk ones has many weaknesses, particularly for uncertainties on the estimate of trophic levels and therefore of magnification factors for studied organisms, enough to support a regulatory interpretation. In this review, we collate studies that investigated biomagnification characteristics of pollutants in aquatic ecosystems, along with calculated trophic magnification factors. Moreover, we introduce a novel approach, compound-specific stable isotope analysis of nitrogen in amino acids, to establish reliable food web structures and accurate trophic levels for biomagnification studies. This method promises to provide sound results for interpreting the influence of the pollutant in organisms, along with their bioaccumulation and magnification characteristics, as well as that in ecosystem.