To elucidate the sorption affinity of biochars for neonicotinoid pesticides and the influence of biochar structure on sorption mechanisms therein, 24 biochar samples were obtained by pyrolyzing maize straw and pig manure at pyrolyzing temperatures (PTs) of 200–700 °C and by further deashing them using acids, and the sorption of three typical neonicotinoids, imidacloprid, clothianidin and thiacloprid on untreated and acid-deashed biochars were evaluated. All the biochar samples could efficiently adsorb the three neonicotinoids and multiple mechanisms were involved in sorption. With the increasing PTs, hydrophobic partition sorption increased, but had a declined contribution to the total sorption as revealed by a dual-mode model. Besides hydrophobic partition, specific interactions like cation-π electron donor acceptor (EDA) interactions (only for protonated IMI and CLO) and hydrogen bond and contributed much to the sorption on low-PT (≤500 °C) biochars, while the sorption on those high-PT (>500 °C) biochars mainly depended on pore-filling strengthened by cation-π and p/π-π EDA interactions. Thiacloprid showed stronger sorption on untreated biochars compared to imidacloprid and clothianidin, due to its greater ability to form hydrogen bond and hydrophobic interactions. Acid-deashing treatments increased the relative percentage contents of organic carbon, bulk O, aromaticity and O-containing functional groups, surface area and pore volume of biochars. The ash can bind neonicotinoids by specific interactions but played a negative role in the whole sorption on high-PT biochars by covering the inner sorption sites of organic moieties and blocking the micropores in biochars. The results acquired in the present study will help us to get deep insight in the comprehensive sorption mechanisms of polar pesticides on biochar and the effects of biochar structure.

Environmental contamination of the Arctic has widely been used as a worldwide pollution marker. Various classes of organic pollutants such as pesticides, personal care products, PAHs, flame retardants, biomass burning markers, and many others emerging contaminants have been regularly detected in Arctic samples. Although numerous papers have been published reporting data from the Canadian, Danish, and Norwegian Arctic regions, the environmental situation in Russian Arctic remains mostly underreported. Snow analysis is known to be used for monitoring air pollution in the regions with cold climate in both short-term and long-term studies. This paper presents the results of a nontargeted study on the semivolatile organic compounds detected and identified in snow samples collected at the Russian Artic Archipelago Novaya Zemlya in June 2016. Gas chromatography coupled to a high-resolution time-of-flight mass spectrometer enabled the simultaneous detection and quantification of a variety of pollutants including those from the US Environmental Protection Agency (EPA) priority pollutants list, emerging contaminants (plasticizers, flame retardants-only detection), as well as the identification of novel Arctic organic pollutants, (e.g., fatty acid amides and polyoxyalkanes). The possible sources of these novel pollutants are also discussed.GC-HRMS enabled the detection and identification of emerging contaminants and novel organic pollutants in the Arctic, e.g., fatty amides and polyoxyalkanes.

The contamination of agricultural crops by polycyclic aromatic hydrocarbons (PAHs) has drawn considerable attention due to their carcinogenicity, mutagenicity, and toxicity. However, the uptake process of PAHs in plant roots has not been clearly understood. In this work, we first study the radial uptake of phenanthrene in hydroculture wheat roots by vacuum-infiltration-centrifugation method. The concentration-dependent kinetics of apoplastic and symplastic uptake at phenanthrene concentrations of 0–6.72 μM for 4 h can be described with the Langmuir and Michaelis-Menten equations, respectively; whereas, their time-dependent kinetics at 5.60 μM phenanthrene for 36 h follow the Elovich equation. The apoplastic and symplastic uptake increases with temperature of 15–35 °C. The apparent Arrhenius activation energies for apoplastic and symplastic uptake are 77.5 and 9.39 KJ mol⁻¹, respectively. The symplastic uptake accounts for over 55% of total phenanthrene uptake, suggesting that symplast is the dominant pathway for wheat root phenanthrene uptake. Larger volume of symplast in roots and lower activation energy lead to the greater contribution of symplast to total uptake of phenanthrene. Our results provide not only novel insights into the mechanisms on the uptake of PAHs by plant roots, but also the help to optimize strategies for crop safety and phytoremediation of PAH-contaminated soil/water.

Several studies have indicated the presence of contaminants in Alpine aquatic ecosystems. Even if measured concentrations are far below those that cause acute effects, continuous exposure to sub-lethal concentrations may have detrimental effects on the aquatic species present in these remote environments. This may lead to a cascade of indirect effects at higher levels of the ecological hierarchy (i.e., the community). To improve the determination of ecologically relevant risk endpoints, behavioural alterations in organisms due to pollutants are increasingly studied in ecotoxicology. In fact, behaviour links physiological function with ecological processes, and can be very sensitive to environmental stimuli and chemical exposure. This is the first study on behavioural alteration in a wild population of an Alpine species. In the present study, a video tracking system was standardized and subsequently used to identify contaminant-induced behavioural alterations in Diamesa zernyi larvae (Diptera, Chironomidae). Diamesa zernyi larvae, collected in an Italian Alpine stream (Rio Presena, Trentino Region), were acclimated for 24 h and successively exposed to several aquatic contaminants (pesticides: chlorpyrifos, metolachlor, boscalid, captan; pharmaceuticals: ibuprofen, furosemide, trimethoprim) at concentrations corresponding to their Lowest Observed Effect Concentration (LOEC). After 24, 48, 72, and 96 h of exposure, changes in the distance moved, the average speed, and the frequency of body bends were taken to reflect contaminant- and time-dependent effects on larval behaviour. In general, metolachlor, captan, and trimethoprim tended to reduce all the endpoints under consideration, whereas chlorpyrifos, boscalid, ibuprofen, and furosemide seemed to increase the distances moved by the larvae. This could be related to the different mechanisms of action of the investigated chemicals. Independently of the contaminant, after 72 h a general slowing down of all the behavioural activities occurred. Finally, we propose a behavioural stress indicator to compare the overall behavioural effects induced by the various contaminants.

Reliable detection and attribution of changes in nitrogen (N) runoff from croplands are essential for designing efficient, sustainable N management strategies for future. Despite the recognition that excess N runoff poses a risk of aquatic eutrophication, large-scale, spatially detailed N runoff trends and their drivers remain poorly understood in China. Based on data comprising 535 site-years from 100 sites across China's croplands, we developed a data-driven upscaling model and a new simplified attribution approach to detect and attribute N runoff trends during the period of 1990–2012. Our results show that N runoff has increased by 46% for rice paddy fields and 31% for upland areas since 1990. However, we acknowledge that the upscaling model is subject to large uncertainties (20% and 40% as coefficient of variation of N runoff, respectively). At national scale, increased fertilizer application was identified as the most likely driver of the N runoff trend, while decreased irrigation levels offset to some extent the impact of fertilization increases. In southern China, the increasing trend of upland N runoff can be attributed to the growth in N runoff rates. Our results suggested that increased SOM led to the N runoff rate growth for uplands, but led to a decline for rice paddy fields. In combination, these results imply that improving management approaches for both N fertilizer use and irrigation is urgently required for mitigating agricultural N runoff in China.

Source samples including crustal dust, cement dust, coal combustion were sampled and ambient samples of PM₂.₅ and PM₁₀ were synchronously collected in Hefei from April to December 2014. The size distributions of the markers in the measured source profiles were incorporated into ME-2 solution to develop a new method, called the SDABB model (an advanced ABB three-way factor analysis model incorporating size distribution information). The performance of this model was investigated using three-way synthetic and ambient dataset. For the synthetic tests, the size distributions of markers estimated by the SDABB model were more consistent with true condition. The AAEs between estimated and observed contributions of the SDABB ranged from 15.2% to 29.0% for PM₁₀ and 19.9%–31.6% for PM₂.₅, which is lower than those of PMF2. For the ambient PM, six source categories were identified by SDABB for both sizes, although the profiles were different. The source contributions were sulphate (33.33% and 24.53%), nitrate and SOC (22.33% and 18.16%), coal combustion (19.01% and 18.23%), vehicular exhaust (12.99% and 12.07%), crustal dust (10.69% and 19.40%) and cement dust (1.65% and 5.39%) for PM₂.₅ and PM₁₀ respectively. In addition, the estimated ratios of Al, Si, Ti and Fe in CRD were 0.76, 0.84, 1.10 and 0.85; those of Al and Si in CC were 0.42 and 0.66; Ca and Si in CD were 0.95 and 1.10; NO₃⁻ and NH₄⁺ in nitrate were 1.11 and 1.01; and SO₄²⁻ and NH₄⁺ in sulphate were 0.96 and 1.16. These modeled ratios were consistent with the measured ratios. The size distribution of contributions also came close to reality. Thus, the advanced SDABB three-way model can better capture the characteristics of sources between sizes by effectively incorporating the size distributions of the markers as physical constraints.

High arsenic groundwater generally coexists with elevated Fe2+ concentrations (mg L−1 levels) under reducing conditions, but an explanation for the extremely high arsenic (up to ∼2690) concentrations at very low Fe2+ (i.e., μg L−1 levels) in groundwater of Datong Basin remains elusive. Field groundwater investigation and laboratory microcosm experiments were implemented in this study. The field groundwater was characterized by weakly alkaline (pH 7.69 to 8.34) and reducing conditions (Eh −221.7 to −31.9 mV) and arsenic concentration averages at 697 μg L−1. Acinetobacter (5.9–51.3%), Desulfosporosinus (4.6–30.2%), Brevundimonas (3.9–19%) and Pseudomonas (3.2–14.6%) were identified as the dominant genera in the bacterial communities. Bacterially mediated arsenate reduction, Fe(III) reduction, and sulfate reduction are processes occurring (or having previously occurred) in the groundwater. Results from incubation experiment (27 d) revealed that nitrate, arsenate, and Fe(III)/sulfate reduced sequentially with time under anoxic conditions, while Fe(III) and sulfate reduction processes had no obvious differences, occurring almost simultaneously. Moreover, low Fe2+ concentrations were attributed to initially high pH conditions, which relatively retarded Fe(III) reduction. In addition, arsenic behavior in relation to groundwater redox conditions, matrices, and solution chemistry were elaborated. Bacterial arsenate reduction process proceeded before Fe(III) and sulfate reduction in the incubation experiment, and the total arsenic concentration (dominated by arsenite) gradually increased from ∼7 to 115 μg L−1 as arsenate was reduced. Accordingly, bacterially mediated reductive desorption of arsenate is identified as the main process controlling arsenic mobility, while Fe(III) reduction coupled with sulfate reduction are secondary processes that have also contributed to arsenic enrichment in the study site. Overall, this study provide important insights into the mechanism controlling arsenic mobility under weakly alkaline and reducing conditions, and furnishes that arsenate reduction by bacteria play a major role leading to high accumulation of desorbed arsenite in groundwater.

Emissions from the transportation sector are rapidly changing worldwide; however, the interplay of such emission changes in the face of climate change are not as well understood. This two-part study examines the impact of projected emissions from the U.S. transportation sector (Part I) on ambient air quality in the face of climate change (Part II). In Part I of this study, we describe the methodology and results of a novel Technology Driver Model (see graphical abstract) that includes 1) transportation emission projections (including on-road vehicles, non-road engines, aircraft, rail, and ship) derived from a dynamic technology model that accounts for various technology and policy options under an IPCC emission scenario, and 2) the configuration/evaluation of a dynamically downscaled Weather Research and Forecasting/Community Multiscale Air Quality modeling system.By 2046–2050, the annual domain-average transportation emissions of carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), ammonia (NH3), and sulfur dioxide (SO2) are projected to decrease over the continental U.S. The decreases in gaseous emissions are mainly due to reduced emissions from on-road vehicles and non-road engines, which exhibit spatial and seasonal variations across the U.S. Although particulate matter (PM) emissions widely decrease, some areas in the U.S. experience relatively large increases due to increases in ship emissions. The on-road vehicle emissions dominate the emission changes for CO, NOx, VOC, and NH3, while emissions from both the on-road and non-road modes have strong contributions to PM and SO2 emission changes. The evaluation of the baseline 2005 WRF simulation indicates that annual biases are close to or within the acceptable criteria for meteorological performance in the literature, and there is an overall good agreement in the 2005 CMAQ simulations of chemical variables against both surface and satellite observations.

Herein, we utilize sequential extraction and high-throughput sequencing to investigate the effects of nanomaterial additives on As volatilization from flooded soils. We reveal that maximum volatilization is achieved in the fourth week and is followed by stabilization. The extent of volatilization decreased in the order of control > nano-zerovalent iron >40-nm hydroxyapatite > nano-Fe₃O₄ > 20-nm hydroxyapatite > multilayer graphene oxide > high-quality graphene oxide. The most abundant forms of As in soil corresponded to As-Fe and Al oxides. In soil with low levels of As pollution, the contents of these species increased after treatment with graphene oxides but decreased after treatment with other nanomaterials, with an opposite trend observed for soil with high levels of As pollution. The addition of nanomaterials influenced the activity of soil enzymes, e.g., hydroxyapatites affected the activities of urease and alkaline phosphatase, whereas graphene oxides significantly impacted that of peroxidase (P < 0.05). The addition of nanomaterials (which can potentially inhibit microbial growth) affected As levels by influencing the amount of As volatilized from polluted soil. Moreover, As volatilization, enzyme activity, and As speciation were observed to be mutually correlated (e.g., volatilization was negatively correlated to peroxidase activity and the contents of amorphous crystalline hydrous oxides of As-Fe and Al).