The present study for the first time demonstrated the interactions of metal oxide (MO) nano-pollutants (CuO and Al2O3-NPs) with tissues and cellular DNA of tomato plants grown in soil sand: silt: clay (667:190:143) and Hoagland-hydroponic system and assessed the hazardous effects of NPs on cell physiology and biochemistry. Results of SEM equipped with EDX revealed attachment of variably shaped CuO-NPs (18 nm) and Al2O3-NPs (21 nm) on roots, and internalization followed by translocation in plants by ICP-MS and TEM. Significant variations in foliage surface area, chlorophyll, proteins, LPO, and antioxidant enzymes were recorded. Roots and shoots accumulated 225.8 ± 8.9 and 70.5 ± 4 μgAl g−1 DW, whereas Cu accumulation was 341.6 ± 14.3 (roots) and 146.9 ± 8.1 μg g−1 DW (shoots) which was significant (p ≤ 0.0005) as compared to control. The total soluble protein content in roots, shoots, and leaves collected from Al2O3-NPs treated plants increased by 120, 80, and 132%, respectively while in CuO-NPs treatments, the increase was 68 (roots), 36 (shoots), and 86% (leaves) over control. The level of antioxidant enzymes in plant tissues was significantly (p ≤ 0.05) higher at 2000 μg ml−1 of MONPs over control. A dose-dependent increase in reactive oxygen species (ROS), biphasic change of lower and higher fluorescence in mitochondria due to dissipation of mitochondrial membrane potential (ΔΨm) and membrane defects using propidium iodide were observed. Comparatively, CuO-NPs induced higher toxicity than Al2O3-NPs. Perceptible changes in proteins (amide-I & II), cellulose, glucose, galactose and other carbohydrates were observed under FT-IR. The binding studies with TmDNA showed fluorescence quenching of EtBr-TmDNA and acridine orange-TmDNA complex only by CuO-NPs with -ΔG and +ΔH and +ΔS values. However, Al2O3-NPs induced lesser change in TmDNA conformation. Conclusively, the results are novel in better demonstrating the mechanistic basis of nano-phyto-toxicity and are important which could be used to develop strategies for safe disposal of Al2O3-NPs and CuO-NPs.

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).

Outdoor air pollution is considered as the most serious environmental problem for human health, associated with some million deaths worldwide per year. Cities have to cope with the challenges due to poor air quality impacting human health and citizen well-being. According to an analysis in the framework of this study, the annual mean concentrations of tropospheric ozone (O₃) have been increasing by on average 0.16 ppb year⁻¹ in cities across the globe over the time period 1995–2014. Green urban infrastructure can improve air quality by removing O₃. To efficiently reduce O₃ in cities, it is important to define suitable urban forest management, including proper species selection, with focus on the removal ability of O₃ and other air pollutants, biogenic emission rates, allergenic effects and maintenance requirements. This study reanalyzes the literature to i) quantify O₃ removal by urban vegetation categorized into trees/shrubs and green roofs; ii) rank 95 urban plant species based on the ability to maximize air quality and minimize disservices, and iii) provide novel insights on the management of urban green spaces to maximize urban air quality. Trees showed higher O₃ removal capacity (3.4 g m⁻² year⁻¹ on average) than green roofs (2.9 g m⁻² year⁻¹ as average removal rate), with lower installation and maintenance costs (around 10 times). To overcome present gaps and uncertainties, a novel Species-specific Air Quality Index (S-AQI) of suitability to air quality improvement is proposed for tree/shrub species. We recommend city planners to select species with an S-AQI>8, i.e. with high O₃ removal capacity, O₃-tolerant, resistant to pests and diseases, tolerant to drought and non-allergenic (e.g. Acer sp., Carpinus sp., Larix decidua, Prunus sp.). Green roofs can be used to supplement urban trees in improving air quality in cities. Urban vegetation, as a cost-effective and nature-based approach, aids in meeting clean air standards and should be taken into account by policy-makers.

We investigate the effects of natural variability of meteorological fields on surface PM₂.₅ concentration changes in East Asia during El Niño periods for the past three decades (1980–2014) through GEOS-Chem 3D global chemical transport model simulations. First, our evaluation of the model with anthropogenic emissions for 2006 and a comparison against observations show that the simulated results accurately reproduced the observed spatial distribution of annual mean aerosol concentrations for 2006–2007 including inorganic (sulfate, ammonium, and nitrate) and carbonaceous (organic and black carbon) aerosols in the surface air. Based on the Oceanic Niño Index, the assimilated meteorological data used in the model simulations indicate that 10 El Niño events occurred for the past three decades (1980–2014). We further classified the 10 El Niño events into 6 central Pacific El Niño (C-type) and 4 eastern Pacific El Niño (E-type) to examine the different roles of two El Niño types in determining seasonal surface PM₂.₅ concentrations in East Asia. We find opposite impacts on the seasonal surface PM₂.₅ concentrations depending on two El Niño types, such that the surface PM₂.₅ concentrations during the E-type period are higher than the climatological mean value, especially in northern East Asia. The peak increase of as much as 20% occurs in winter and is sustained until the following spring. However, the C-type period shows a decrease in seasonal PM₂.₅ concentrations in northern East Asia compare to the climatological mean, and the peak decrease of as much as 10% occurs in the following spring. The different of two El Niño types also have dissimilar impacts on surface PM₂.₅ concentrations in southeastern China. Natural variation of aerosol concentrations driven by the different of two El Niño types appears to be significant and would be an important factor in determining the inter-annual variation of aerosol concentrations in East Asia.

In this study, the molecular mechanisms involved in Ralstonia eutropha Q2-8-induced increased biomass and reduced cadmium (Cd) and arsenic (As) uptake in wheat plants (Triticum aestivum cv. Yangmai 16) were investigated in growth chambers. Strain Q2-8 significantly increased plant biomass (22–75%) without and with Cd (5 μM) + As (10 μM) stress and reduced plant above-ground tissue Cd (37%) and As (34%) contents compared to those in the controls. Strain Q2-8 significantly increased the proportions of Cd and As in wheat root cell walls. Under Cd and As stress, 109 root proteins were differentially expressed among which those involved in metabolisms, stress and defence, and energy were dominant in the presence of strain Q2-8. Furthermore, energy-, defence-, and cell wall biosynthesis-related proteins were found to be up-regulated. Notably, differentially expressed cell wall biosynthesis-related proteins in roots were only found in bacteria-inoculated plants under Cd and As stress. The results suggest that strain Q2-8 can alleviate Cd and As toxicity to wheat plant seedlings and reduce above-ground tissue Cd and As uptake by increasing the efficiency of root energy metabolism, defence, and cell wall biosynthesis under Cd and As stress.

Mg-Al-Ox supported monometallic (Ni) and bimetallic (Ni-Co) catalysts with different compositions of Mg and Al were investigated for CO₂ reforming of CH₄, using both coal and pure gas feeds, to limit the emission of these environmental pollutant gases into the atmosphere. Results showed that all the catalysts were active for dry reforming reaction using both feeds. Reactants conversion, stoichiometric product selectivity, and resistance to carbon deposition of catalysts remarkably improved when the Mg/Al ratio was greater than 1. Characterization results revealed changes in the bulk structure, textural and surface properties as the Mg/Al ratio and composition of catalysts changed. Improved active metal reduction, metal-support and metal-metal interaction (in the bimetallic) were also noted in the catalysts with Mg/Al ratio greater than 1. With respect to feed composition, less carbon deposition was recorded in the corresponding catalysts using coal gas compared to the pure gas. Ni-Co interaction and their interaction with MgO facilitated better basicity, increased metal dispersion and smaller particle size in Ni-Co-Mg₁.₇-Al₁-Ox, which showed best catalytic performance with no carbon deposition in both feeds. These interactions and properties stabilized the Ni site, which made the Ni-Co-Mg₁.₇-Al₁-Ox, catalyst resistant to sintering and carbon deposition.

The sources of aerosol ammonium (NH4+) are of interest because of the potential of NH4+ to impact the Earth's radiative balance, as well as human health and biological diversity. Isotopic source apportionment of aerosol NH4+ is challenging in the urban atmosphere, which has excess ammonia (NH3) and where nitrogen isotopic fractionation commonly occurs. Based on year-round isotopic measurements in urban Beijing, we show the source dependence of the isotopic abundance of aerosol NH4+, with isotopically light (−33.8‰) and heavy (0 to +12.0‰) NH4+ associated with strong northerly winds and sustained southerly winds, respectively. On an annual basis, 37–52% of the initial NH3 concentrations in urban Beijing arises from fossil fuel emissions, which are episodically enhanced by air mass stagnation preceding the passage of cold fronts. These results provide strong evidence for the contribution of non-agricultural sources to NH3 in urban regions and suggest that priority should be given to controlling these emissions for haze regulation. This study presents a carefully executed application of existing stable nitrogen isotope measurement and mass-balance techniques to a very important problem: understanding source contributions to atmospheric NH3 in Beijing. This question is crucial to informing environmental policy on reducing particulate matter concentrations, which are some of the highest in the world. However, the isotopic source attribution results presented here still involve a number of uncertain assumptions and they are limited by the incomplete set of chemical and isotopic measurements of gas NH3 and aerosol NH4+. Further field work and lab experiments are required to adequately characterize endmember isotopic signatures and the subsequent isotopic fractionation process under different air pollution and meteorological conditions.

Excess exposure to fluoride causes substantive health burden in humans and livestock globally. However, few studies have assessed the distribution and controls of variability of ambient background concentrations of fluoride in soil. Ambient background concentrations of fluoride in soil were collated for Greater Melbourne, Greater Geelong, Ballarat and Mitchell in Victoria, Australia (n = 1005). Correlation analysis and machine learning techniques were used to identify environmental and anthropogenic influences of fluoride variability in soil. Sub-soils (>0.3 m deep), in some areas overlying siltstone and sandstone, and to a lesser extent, overlying basalt, were naturally enriched with fluoride at concentrations above ecological thresholds for grazing animals. Soil fluoride enrichment was predominantly influenced by parent material (mineralogy), precipitation (illuviation), leaching during palaeoclimates and marine inputs. Industrial air pollution did not significantly influence ambient background concentrations of fluoride at a regional scale. However, agricultural practices (potentially the use of phosphate fertilisers) were indicated to have resulted in added fluoride to surface soils overlying sediments. Geospatial variables alone were not sufficient to accurately model ambient background soil fluoride concentrations. A multiple regression model based on soil chemistry and parent material was shown to accurately predict ambient background fluoride concentrations in soils and support assessment of fluoride enrichment in the environment.

Recent advances in research on algae inhibition by using low-cost straw proposed a possible mechanism that reactive oxygen species (ROS) generated by the solar irradiation of straw-derived dissolved organic matter (DOM) might contribute to cyanobacteria inhibition. However, this process is not clearly understood. Here, DOM from three types of straw (barley, rice, and wheat) and natural organic matter (NOM) isolates were investigated in terms of their photochemical properties and ROS generating abilities. Results demonstrated that the DOM derived from the aeration decomposition of barley straw (A-DOMbs) yielded the best formation efficiencies of hydrogen peroxide (H₂O₂) and hydroxyl radicals (•OH) under solar-simulated irradiation in all organic matter samples. Correlation analysis implies that optical parameters and phenolic hydroxyl group contents can signify ROS generating abilities of different DOM solutions. Bioassay results show that A-DOMbs possesses the highest inhibition performance for M. aeruginosa in all DOM samples, much higher than those of NOM isolates. The addition of catalase greatly relieves the inhibition performance, making the loss of chlorophyll a content decreased from 37.14% to 7.83% in 2 h for A-DOMbs, which implies that for cyanobacteria growth inhibition, photochemically-produced H₂O₂ from SOM is far more important than singlet oxygen (¹O₂), •OH, and even SOM itself. Our results show that H₂O₂ photochemically generated from straw-derived DOM is able to result in rapid inhibition of M. aeruginosa in a relatively short period, furthering the understanding of complicated mechanisms of cyanobacteria inhibition by using low-cost straw in eutrophic waters.

High concentrations of the fine particles (PM₂.₅) are frequently observed during all seasons over the North China Plain (NCP) region in recent years. In NCP, the contributions of regional transports to certain area, e.g. Beijing city, are often discussed and estimated by models when considering an effective air pollution controlling strategy. In this study, we selected three sites from southwest to northeast in NCP, in which the concentrations of air pollutants displayed a multi-step decreasing trend in space. An approach based on the measurement results at these sites has been developed to calculate the relative contributions of the minimal local emission (MinLEC) and the maximum regional transport (MaxRTC) to the air pollutants (e.g., SO₂, NO₂, CO, PM₂.₅) in Beijing. The minimal influence of local emission is estimated by the difference of the air pollutants' concentrations between urban and rural areas under the assumption of a similar influence of regional transport. Therefore, it's convenient to estimate the contributions of local emission from regional transport based on the selective measurement results instead of the complex numerical model simulation. For the whole year of 2013, the averaged contributions of MinLEC (MaxRTC) for NO₂, SO₂, PM₂.₅ and CO are 61.7% (30.7%), 46.6% (48%), 52.1% (40.2%) and 35.8% (45.5%), respectively. The diurnal variation of MaxRTC for SO₂, PM₂.₅ and CO shows an increased pattern during the afternoon and reached a peak (more than 50%) around 18:00, which indicates that the regional transport is the important role for the daytime air pollution in Beijing.