The aggregation kinetics of fragmental polyethylene glycol terephthalate (PET) nanoplastics under various chemistry conditions in aqueous environment were firstly investigated in this work. The aggregation of PET nanoplastics increased with increasing electrolyte concentrations and decreasing solution pH, which became stronger with the presence of divalent cations (e.g. Ca²⁺ and Mg²⁺) than that of monovalent cations (e.g. Na⁺ and K⁺). The effect of cations with the same valence on the aggregation of PET nanoplastics was similar. The measured critical coagulation concentrations (CCC) for PET nanoplastics at pH 6 were 55.0 mM KCl, 54.2 mM NaCl, 2.1 mM CaCl₂ and 2.0 mM MgCl₂, which increased to 110.4 mM NaCl and 5.6 mM CaCl₂ at pH 10. In addition, the aggregation of PET nanoplastics was significantly inhibited with the presence of humic acid (HA), and the CCC values increased to 558.8 mM NaCl and 12.3 mM CaCl₂ (1 mg L⁻¹ HA). Results from this study showed that the fragmental PET nanoplastics had the quite higher CCC values and stability in aqueous environment. In addition, the aggregation behaviors of PET nanoplastics can be successfully predicted by the Derjguin Landau Verwey Overbeek (DLVO) theory.

Passive biomonitoring was applied in four Atlantic forest plots in southeast Brazil, affected by different levels of trace metal pollution (OP site located in Minas Gerais State and PEFI, PP and STG located in São Paulo State). Native tree species were selected as biomonitors according to their abundance in each plot and successional classification. Current trace metal concentrations in total suspended particles, leaves of non-pioneer (NPi) and pioneer (Pi) species, topsoil (0–20 cm) and litter and concentration ratios at the plant/soil interface were analyzed to verify the atmosphere-plant-soil interactions, basal concentrations, spatial variations and metal accumulation at the ecosystem level. Redundant analysis helped to identify similar characteristics of metal concentrations in PP and PEFI, which can be influenced by the high concentrations of elements related to anthropogenic inputs. Analysis of variance and multivariate statistics indicated that the trees of OP presented higher concentrations of Cr, Fe, Mn and Ni than those in the other sites. High enrichment of Cd, Fe, Ni in non-pioneer plants indicated that the PP forest (initially considered as the least polluted) has still been affected by metal pollution. Soil collected in STG was enriched by all elements, however these elements were low available for plant uptake. Metal deposited in leaves and litter was an important sink for soil cycling, nevertheless, these metals are not bioavailable in most cases. Non-pioneer tree species revealed to be more appropriate than pioneer species to indicate the current panorama of the contamination and bioavailability levels of trace metals in the tree community-litter-soil interface of the Atlantic forest remnants included in this study.

Plastic pollution continues to seep into natural and pristine habitats. Emerging laboratory-based research has evoked concern regarding plastic’s impact on ecosystem structure and function, the essence of the ecosystem services that supports our life, wellbeing, and economy. These impacts have yet to be observed in nature where complex ecosystem interaction networks are enveloped in environmental physical and chemical dynamics. Specifically, there is concern that environmental impacts of plastics reach beyond toxicity and into ecosystem processes such as primary production, respiration, carbon and nutrient cycling, filtration, bioturbation, and bioirrigation. Plastics are popularly regarded as recalcitrant carbon molecules, although they have not been fully assessed as such. We hypothesize that plastics can take on similar roles as natural recalcitrant carbon (i.e., lignin and humic substances) in carbon cycling and associated biogeochemistry. In this paper, we review the current knowledge of the impacts of plastic pollution on marine, benthic ecosystem function. We argue for research advancement through (1) employing field experiments, (2) evaluating ecological network disturbances by plastic, and (3) assessing the role of plastics (i.e., a carbon-based molecule) in carbon cycling at local and global scales.

Forest soils are among the world’s largest repositories for long-term accumulation of atmospherically deposited mercury (Hg), and understanding the potential for remobilization through gaseous emissions, aqueous dissolution and runoff, or erosive particulate transport to down-gradient aquatic ecosystems is critically important for projecting ecosystem recovery. Forestry operations, especially clear-cut logging where most of the vegetaiton is removed, can influence Hg mobility/fluxes, foodweb dynamics, and bioaccumulation processes. This paper measured surface-air Hg fluxes from catchments in the Pacific Northwest, USA, to determine if there is a difference between forested and logged catchments. These measurements were conducted as part of a larger project on the impact of forestry operations on Hg cycling which include measurements of water fluxes as well as impacts on biota. Surface-air Hg fluxes were measured using a commonly applied dynamic flux chamber (DFC) method that incorporated diel and seasonal variability in elemental Hg (Hg⁰) fluxes at multiple forested and harvested catchments. The results showed that the forested ecosystem had depositional Hg⁰ fluxes throughout most of the year (annual mean: −0.26 ng/m²/h). In contrast, the harvested catchments showed mostly emission of Hg⁰ (annual mean: 0.63 ng/m²/h). Differences in solar radiation reaching the soil was the primary driver resulting in a shift from net deposition to emission in harvested catchments. The surface-air Hg fluxes were larger than the fluxes to water as runoff and accounted for 97% of the differences in Hg sequestered in forested versus harvested catchments.

Since the early 2000s, an increasing number of power plants in the U.S. have switched from burning coal to burning gas and thus have released less SO₂ emissions into the atmosphere. We investigated whether stream chemistry (i.e., SO₄²⁻) also benefits from this transition. Using publicly available data from Pennsylvania (PA), a U.S. state with heavy usage of coal as fuel, we found that the impact of SO₂ emissions on stream SO₄²⁻ can be observed as far as 63 km from power plants. We developed a novel model that incorporates an emission-control technology trend for coal-fired power plants to quantify potentially avoided SO₂ emissions and stream SO₄²⁻ as power plants switched from coal to gas. The results show that, if 30% of the electricity generated by coal in PA in 2017 had been replaced by that from natural gas, a total of 20.3 thousand tons of SO₂ emissions could have been avoided and stream SO₄²⁻ concentrations could have decreased as much as 10.4%. Extrapolating the model to other states in the U.S., we found that as much as 46.1 thousand tons of SO₂ emissions per state could have been avoided for a similar 30% coal-to-gas switch, with potential amelioration of water quality near power plants. The emission-control technology trend model provides a valuable tool for policy makers to assess the benefits of coal-to-gas shifts on water quality improvements as well as the effectiveness of emission control technologies.

Freshwater ecosystems are negatively impacted by various pollutants, from agricultural, urban and industrial wastewater, with metals being one of the largest concerns. Moreover, freshwater ecosystems are often affected by alien species introductions that can modify habitats and trophic relationships. Accordingly, the threat posed by metals interacts with those by alien species, since the latter can accumulate and transfer these substances across the food web to higher trophic levels. How metals transfer within such communities is little studied. We analysed the concentration of 14 metals/metalloids (Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mg, Mn, Ni, Pb, Se, Zn, hereafter ‘metal(s)’) of eight fish and three crustacean species co-existing in the Arno River (Central Italy), most of which were alien. To assess the pathway of contaminants within the community, we coupled metal analysis with carbon and nitrogen stable isotope analysis derived from the same specimens. Crustaceans showed higher metal concentration than fish, except for Cd, Hg and Se that were higher in fish. We found evidence of trophic transfer for six metals (Cd, Cr, Hg, Mg, Se, Zn). Additionally, ontogenetic differences and differences among various fish tissues (muscle, liver, and gills) were found in metals concentration. Considerable biomagnification along the trophic chain was found for Hg, while other metals were found to biodilute. Using stable isotopes and Hg as a third diet tracer, we refined the estimations of consumed preys in the diet previously reconstructed with stable isotope mixing models. Alien species reach high biomass and can both survive to and accumulate high pollutants concentrations, potentially posing a risk for their predators and humans. A combined effect of environmental filtering and increased competition may potentially contribute to the disappearance of native species with lower tolerances.

Exposure to particulate air pollution has been associated with a variety of respiratory, cardiovascular and neurological problems, resulting in increased morbidity and mortality worldwide. Brake-wear emissions are one of the major sources of metal-rich airborne particulate pollution in roadside environments. Of potentially bioreactive metals, Fe (especially in its ferrous form, Fe²⁺) might play a specific role in both neurological and cardiovascular impairments. Here, we collected brake-wear particulate emissions using a full-scale brake dynamometer, and used a combination of magnetic measurements and electron microscopy to make quantitative evaluation of the magnetic composition and particle size of airborne emissions originating from passenger car brake systems. Our results show that the concentrations of Fe-rich magnetic grains in airborne brake-wear emissions are very high (i.e., ~100–10,000 × higher), compared to other types of particulate pollutants produced in most urban environments. From magnetic component analysis, the average magnetite mass concentration in total PM₁₀ of brake emissions is ~20.2 wt% and metallic Fe ~1.6 wt%. Most brake-wear airborne particles (>99 % of particle number concentration) are smaller than 200 nm. Using low-temperature magnetic measurements, we observed a strong superparamagnetic signal (indicative of ultrafine magnetic particles, < ~30 nm) for all of the analysed size fractions of airborne brake-wear particles. Transmission electron microscopy independently shows that even the larger size fractions of airborne brake-wear emissions dominantly comprise agglomerates of ultrafine (<100 nm) particles (UFPs). Such UFPs likely pose a threat to neuronal and cardiovascular health after inhalation and/or ingestion. The observed abundance of ultrafine magnetite particles (estimated to constitute ~7.6 wt% of PM₀.₂) might be especially hazardous to the brain, contributing both to microglial inflammatory action and excess generation of reactive oxygen species.

Air pollution could be a risk factor for the development of pterygium. This study aimed to investigate the potential associations between outpatient visits for pterygium and air pollutants. Using a time-stratified case-crossover design, the data of 3017 outpatients with pterygium visiting an eye center in Hangzhou, China, and the air pollution data of the Environmental Protection Department of Zhejiang Province between July 1, 2014, and November 30, 2019, were examined. The relationships between the air pollutants nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone, and fine particulate matter (PM) with median aerometric diameter <2.5 μm (PM₂.₅) and <10 μm (PM₁₀) and outpatient visits for primary pterygium were assessed using single- and multiple-pollutant models. Significant associations between outpatient visits for pterygium and air pollutants (PM₂.₅, PM₁₀, SO₂, and NO₂) were observed. Younger patients were found to be more sensitive to air pollution. Interestingly, the younger female patients with pterygium were more vulnerable to PM₂.₅ exposure during the warm season, while the younger male patients with pterygium were more sensitive to NO₂ during the cold season. Significant effects were also observed between the pterygium outpatients and PM₂.₅ (odds ratio [OR] = 1.06, P = 0.02), PM₁₀ (OR = 1.04, P = 0.01), and SO₂ (OR = 1.26, P = 0.01) during the warm season, as well as NO₂ (OR = 1.06, P = 0.01) during the cold season. Our study provides evidence that outpatient visits for pterygium are positively associated with increases in the air pollutants PM₂.₅, PM₁₀, SO₂, and NO₂, revealing the important role of air pollution in the occurrence and development of pterygium.

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.

Air quality forecasting for Hong Kong is a challenge. Even taking the advantages of auto-regressive integrated moving average and some state-of-the-art numerical models, a recently-developed hybrid method for one-day (two- and three-day) ahead forecasting performs similarly to (slightly better than) a simple persistence forecasting. Long-term forecasting also remains an important issue, especially for policy decision for better control of air pollution and for evaluation of the long-term impacts on public health. Given the well-recognized negative effects of PM₂.₅, NO₂ and O₃ on public health, we study their time series under the multi-scale framework with empirical mode decomposition and nonstationary oscillation resampling to explore the possibility of long-term forecasting and to improve short-term forecasts in Hong Kong. Applied to a dataset from January 2016 to December 2018, the long-term forecasting (with lead time about 100 days) of the multi-scale framework has the root-mean-square error (RMSE) comparable with that of the short-term (with lead time of one or two days) forecasting by the persistence method, while its improvement for short-term forecasting (with lead time of one, two or three days) is quite substantial over the persistence forecasting, with RMSEs reduced by respectively 44%–47%, 30%–45%, and 40%–60% for PM₂.₅, NO₂, and O₃. Compared to the hybrid method, it turns out that, for short-term forecasting for the same data, the multi-scale framework can reduce RMSE by about 25% (respectively 30%) for PM₂.₅ (respectively NO₂ and O₃). In addition, we find no significant difference in the forecasting performance of the multi-scale framework among different types of stations. The multi-scale framework is feasible for time series forecasting and applicable to other pollutants in other cities.