Nano silver and nano zinc-oxide monthly concentrations in surface waters across Europe were modeled at ∼6 × 9 km spatial resolution. Nano-particle loadings from households to rivers were simulated considering household connectivity to sewerage, sewage treatment efficiency, the spatial distribution of sewage treatment plants, and their associated populations. These loadings were used to model temporally varying nano-particle concentrations in rivers, lakes and wetlands by considering dilution, downstream transport, water evaporation, water abstraction, and nano-particle sedimentation. Temporal variability in concentrations caused by weather variation was simulated using monthly weather data for a representative 31-year period. Modeled concentrations represent current levels of nano-particle production. Two scenarios were modeled. In the most likely scenario, half the river stretches had long-term average concentrations exceeding 0.002 ng L−1 nano silver and 1.5 ng L−1 nano zinc oxide. In 10% of the river stretches, these concentrations exceeded 0.18 ng L−1 and 150 ng L−1, respectively. Predicted concentrations were usually highest in July.

Numerous studies reported that BPA could cause oxidative damage to different tissues in rats/mice. This study aimed to perform a systematic review and meta-analysis of BPA exposure on oxidative damage in rats/mice. A comprehensive literature search was conducted using PubMed, Embase, and Web of Science databases from their inception date until July 18, 2020. 20 eligible articles were included in this study. The results showed that BPA could significantly increase the level of MDA (SMD, 16.88; 95%CI, 12.06–21.71), but there was a significant reduction in the contents of antioxidants, such as GR (−10.46, −13.91 ∼ −7.02), CAT (−8.48, −11.66 ∼ −5.30), GPx (−9.37, −11.95 ∼ −6.80), GST (−7.59, −14.51 ∼ −0.67), GSH (−10.64, −13.96 ~ −7.33), and SOD (−6.48, −8.37 ∼ −4.58) in rats/mice. Our study provided clear evidence that BPA exposure could significantly induce oxidative damage in rats/mice. And we also found that the degree of oxidative damage was related to BPA dose, target tissue, intervention means, and exposure duration of BPA.

Evidence about the adverse effects of methamphetamine (METH) on invertebrates is scarce. Hence, C. elegans, a representative invertebrate model, was exposed to METH at environmental levels to estimate chronic and transgenerational toxicity. The results of chronic exposure were integrated into an underlying toxicity framework of METH in invertebrates (e.g., benthos) at environmentally relevant concentrations. The induction of cellular oxidative damage-induced apoptosis and fluctuation of ecologically important traits (i.e., feeding and locomotion) might be attributed by the activation of the longevity regulating pathway regulated by DAF-16/FOXO, and detoxification by CYP family enzymes. The adverse effects to the organism level included impaired viability and decreased fecundity. The results from transgenerational exposure elucidated the cumulative METH-induced damage in invertebrates. Finally, a new risk assessment method named toxicity indicator sensitivity distribution (TISD) analysis was proposed by combining multiple toxicity indicator test data (ECₓ) to derive the hazardous concentration for 10% indicators (C₁₀) of one species. The risk quotient (RQ) values calculated by measured environmental concentrations and C₁₀ in southern China, southeastern Australia, and the western US crossed the alarm line (RQ = 5), suggesting a need for long-term monitoring.

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.

The interest in tire wear particles (TWPs), generated from abrasion of tires, have gained traction over the past few years, both in regards to quantifying particulate emissions, leaching of different compounds, toxicity, and analytical methods. The life of a tire, from cradle to end-of-life, crosses over different scenarios during its lifetime and transcends environmental compartments and legislative areas, underlining the need for a collective approach. Sustainability for a tire encompasses the use of raw materials, recycling of raw materials, circular economy and material sourcing. The tire industry is currently making significant efforts towards a greener and more sustainable production considering reduction of CO₂-emissions, recycling, material sources and implementing the use of biomass from plants rather than oil-derived alternatives. In this paper, we aim to analyze and discuss the need for environmental regulation of tires in order to provide a series of targeted recommendations for future legislation. Our study shows that the numerous regulations related to tires focus on chemicals, manufacturing, raw materials, use of tires on roads, waste handling, safety and polycyclic aromatic hydrocarbons (PAHs) in different life cycle stages of a tire. However, none directly addresses the contribution of TWPs to the environment. Despite the overall good intentions of the existing regulations, there is a lack of focus on the compounds that partition from the tire and disperse in the environment, their mixture effects, and the transformative products from the parent compounds in the environment. Therefore, a renewed focus is needed on risk assessment of complex mixtures like TWPs. Thus, transparency in regard to use of chemicals in TWP, mixtures, minimization of emissions, and capture of particulate pollution should be a priority.

Road traffic noise is the most pervasive source of ambient outdoor noise pollution in Europe. Traffic noise prediction models vary in parameterisation and therefore may produce different estimates of noise levels depending on the geographical setting in terms of emissions sources and propagation field. This paper compares three such models: the European standard, Common Noise Assessment Methods for the EU Member States (hereafter, CNOSSOS), Nord2000 and Traffic Noise Exposure (TRANEX) model based on the UK methodology, in terms of their source and propagation characteristics. The tools are also compared by analysing estimated noise (LAₑq) from CNOSSOS, Nord2000 (2006 version), and TRANEX for more than one hundred test cases (N = 111) covering a variety of source and receiver configurations (e.g. varying source to receiver distance). The main aim of this approach was to investigate the potential pattern in differences between models’ performance for certain types of configurations. Discrepancies in performance may thus be linked to the differences in parameterisations of the CNOSSOS, Nord2000, and TRANEX (e.g. handling of diffraction, refraction). In most cases, both CNOSSOS and TRANEX reproduced LAₑq levels of Nord2000 (2006 version) within three to five dBA (CNOSSOS: 87%, TRANEX: 94%). The differences in LAₑq levels of CNOSSOS, compared to Nord2000, can be related to several shortcomings of the existing CNOSSOS algorithms (e.g. ground attenuation, multiple diffractions, and mean ground plane). The analyses show that more research is required in order to improve CNOSSOS for its implementation in the EU. In this context, amendments for CNOSSOS proposed by an EU Working Group hold significant potential. Overall, both CNOSSOS and TRANEX produced similar results, with TRANEX reproducing Nord2000 LAₑq values slightly better than the CNOSSOS. The lack of measured noise data highlights one of the significant limitations of this study and needs to be addressed in future work.

Emissions trading in the European Union (EU), covering the least uncertain emission sources of greenhouse gas emission inventories (CO₂ from combustion and selected industrial processes in large installations), began in 2005. During the first commitment period of the Kyoto Protocol (2008-2012), the emissions trading between Parties to the Protocol will cover all greenhouse gases (CO₂, CH₄, N₂O, HFCs, PFCs, and SF₆) and sectors (energy, industry, agriculture, waste, and selected land-use activities) included in the Protocol. In this paper, we estimate the uncertainties in different emissions trading schemes based on uncertainties in corresponding inventories. According to the results, uncertainty in emissions from the EU15 and the EU25 included in the first phase of the EU emissions trading scheme (2005-2007) is ±3% (at 95% confidence interval relative to the mean value). If the trading were extended to CH₄ and N₂O, in addition to CO₂, but no new emissions sectors were included, the tradable amount of emissions would increase by only 2% and the uncertainty in the emissions would range from -4 to +8%. Finally, uncertainty in emissions included in emissions trading under the Kyoto Protocol was estimated to vary from -6 to +21%. Inclusion of removals from forest-related activities under the Kyoto Protocol did not notably affect uncertainty, as the volume of these removals is estimated to be small.