Concerns regarding microplastic contamination have spread from aquatic environments to terrestrial systems with a growing number of studies have been reported. Notwithstanding, the potential effects on soil ecosystems remain largely unexplored. In this study, the effects of polyethylene microplastics on soil enzymatic activities and the bacterial community were evaluated, and the microbiota colonizing on microplastics were also investigated. Microplastic amendment (2000 fragments per kg soil) significantly increased the urease and catalase activities in soil after 15 days, and no discernible alteration of invertase activities was detected. Results from high-throughput sequencing of 16S rRNA revealed that the alpha diversities (richness, evenness, and diversity) of the microbiota in soil were not obviously changed by the PE amendment, whereas the diversity indexes of microbiota on plastic fragments were significantly lower than those in the control and amended soils. Different taxonomic composition was observed in between the control and amended soils after 90 days of incubation. Bacterial assemblages with distinct community structure colonized the PE microplastics. Additionally, several taxa including plastic-degrading bacteria and pathogens were more abundant on microplastics. Simultaneously, the predicted functional profiles showed that the pathways of amino acid metabolism and xenobiotics biodegradation and metabolism were higher on the microplastics. These results indicated that microplastics in soil, compared with those in aquatic environments, can also act as a distinct microbial habitat, potentially altering the ecological functions of soil ecosystems.

Microplastics (synthetic polymers <5 mm) have been recently recognized as a big environmental concern, as their ubiquity is an undeniable fact. Their wide variety regarding shapes, sizes, and materials turn them into an intrinsically risky pollutant capable of causing several environmental impacts. Textile microfibers (MF) are a microplastic sub-group. These are mostly shed when a normal laundry of any garment takes place. Special attention has been put onto them, as high concentrations have been found in products for human consumption as shellfish and tap water. However, as there is no consensus on the methodologies to quantify and report the results of MFs detached from textile garments, the degree of similarity between published studies is very low. Hence, the aim of this research was to evaluate the microfibers’ detachment rates of finished garments and to provide a set of comparable units to report the results. These were found to range between 175 and 560 MF/g or 30000–465000 MF/m² of garment. In addition, there was a high correlation between the MF detachment and the textile article superficial density. Finally, our results were compared with a recent paper that estimated the annual mass flow of MFs to the oceans. This previous publication is 30 times higher when related to the mass but 40 times lower if related to the number of MFs.

Reservoirs can be an important environmental compartment for microplastic pollution. Previous investigations have found that surface waters and sediments in the Three Gorges Reservoir (TGR) have had high microplastic abundance, and the Xiangxi River, which is one of the largest primary tributaries of the TGR, has had much higher microplastic abundance than several marine and freshwater systems in China. A strip of land on the bank of the reservoir area, which is called the hydro-fluctuation belt (HFB), is periodically exposed due to the special hydrodynamic conditions in the TGR. The HFB may be an important source and/or sink of microplastics in TGR. In this study, microplastic occurrence in sediments from the Xiangxi River HFB was investigated to reflect the local microplastic pollution status and to evaluate its potential to serve as a source/sink of microplastics in the TGR. Seven sampling sites were selected, and sediments within the HFB and above the belt were collected in summer when the water level was low. The results showed that the microplastic abundance ranged from 0.55 ± 0.12 × 10³ to 14.58 ± 5.67 × 10³ particles m⁻², which was one to two orders of magnitude higher than that in sediments from the Xiangxi River in our previous study (80–846 particles m⁻²). Statistical analysis revealed that the microplastic abundance within the HFB was significantly higher than that of the area above the HFB. The results indicate that the HFB can be an important microplastic sink when the water level is low, and the belt can turn into a potential source when the water level is high. Cluster analysis was applied to reveal the characteristics of the microplastics collected at different sites, and the results suggest that the cluster analysis may be a useful tool in elucidating the source and fate of microplastics.

The effects of arsenic (As), cadmium (Cd) and zinc (Zn) on each other's uptake and oxidative stress in As-hyperaccumulator Pteris vittata were investigated. P. vittata plants were exposed to 50 μM As, Cd and/or Zn for 15 d in 0.2-strength Hoagland solution. When applied alone, P. vittata accumulated 185 mg kg⁻¹ As, 164 mg kg⁻¹ Cd and 327 mg kg⁻¹ Zn in the fronds. While Cd and Zn did not impact each other's uptake, As affected Cd and Zn uptake. Whereas As decreased Zn uptake, Zn affected As speciation in P. vittata fronds, with more arsenate (AsV) than arsenite (AsIII) being present. At 50 μM As, 75 μM Zn increased As accumulation in P. vittata fronds by 10 folds to 2363 mg kg⁻¹ compared to 50 μM Zn. Although AsV was the predominant As species in all tissues, Cd enhanced AsIII levels in the fronds but increased AsV in the roots. Co-exposure of Cd + Zn elevated oxidative stress basing on thiobarbituric acid reactive substances, H₂O₂ content, Evans blue dye uptake, membrane injury index and reactive oxygen species (ROS) relative to single metal. By lowering Cd and Zn concentrations in P. vittata fronds, As reduced the associated stress comparative to Cd or Zn treatment. The results enhance our understanding of the mechanisms underlying the interactions between As, Cd and Zn in As-hyperaccumulator P. vittata.

Atmospheric mercury (Hg) poses human health and ecological risks once deposited and bio-accumulated through food chains. Source contribution analysis of Hg deposition is essential to formulating emission control strategies to alleviate the adverse impact of Hg release from anthropogenic sources. In this study, a Hg version of California Puff Dispersion Modeling (denoted as CALPUFF-Hg) system with added Hg environmental processes was implemented to simulate the Hg concentration and deposition in the central region of the Pearl River Delta (cPRD) at 1 km × 1 km resolution. The contributions of eight source sectors to Hg deposition were evaluated. Model results indicated that the emission from cement production was the largest contributor to Hg deposition, accounting for 13.0%, followed by coal-fired power plants (6.5%), non-ferrous metal smelting (5.4%), iron and steel production (3.5%), and municipal solid waste incineration (3.4%). The point sources that released a higher fraction of gaseous oxidized mercury, such as cement production and municipal solid waste incineration, were the most significant contributors to local deposition. In this intensive industrialized region, large point sources contributed 67–94% of total Hg deposition of 6 receptors which were the nearest grid-cells from top five Hg emitters of the domain and the largest municipal solid waste incinerator in Guangzhou. Based on the source apportionment results, cement production and the rapidly growing municipal solid waste incineration are identified as priority sectors for Hg emission control in the cPRD region.

Urban wastewater effluents bring large amounts of nutrients, organic matter and organic microcontaminants into freshwater ecosystems. The effects of this complex mixture of pollutants on freshwater invertebrates have been studied mainly in temperate rivers and streams with high dilution capacities. In contrast, Mediterranean streams and rivers have lower dilution capacities especially during the seasonal drought, and are therefore exposed to high concentrations of pollutants. Here, we assess the effects of urban wastewater pollution on invertebrate communities from Mediterranean streams under different hydrological conditions. Specifically, we assessed the invertebrates taxonomic composition and functional biological traits in 12 streams, differing in stream and substrate size (sand or cobbles), under low (2 surveys) and baseflow (1 survey) conditions. In each stream, we selected reaches both upstream and downstream of the wastewater discharge point. Our results indicate that urban wastewater pollution favours the most tolerant invertebrate taxa and homogenises functional trait composition over time. Changes in functional traits were more evident during the seasonal drought, when the low flow conditions at the upstream and downstream sites were more severe and, pollutant concentrations downstream were at their highest. However, the effects of urban wastewater pollution were not uniform in the downstream sites; as local invertebrate communities differed in according to the river substrate and stream size (i.e., width and discharge). Overall, urban pollution caused by wastewater enhanced both, taxonomic and functional differences between the invertebrate communities. Such an absence of homogenisation among wastewater pollution impacted sites was probably related to the relevant role of stream substrate-size as well flow conditions in the rivers receiving the impact. These are attributes that need to be considered when setting the pollutant discharge limits in rivers and streams receiving effluents.

In recent decades, most big cities in China have experienced severe air pollution accompanied by rapid economic and social development. Analysis of measurements of air pollutants form a fundamental basis for understanding the characteristics of air pollution and are important references for policy-making. In this study, five-year measurements of air pollutants at 6 sites in Lhasa, a typical high altitude big city in southwestern China, were analyzed from January 2013 to December 2017. Air pollutants at all the 6 sites in Lhasa generally displayed similar patterns of both diurnal and monthly variations, indicating the mixed atmospheric environment and the overall effect of the meteorological conditions in the city. Spatially, the air pollutant concentrations at the 6 sites were generally characterized by high concentrations of SO₂, NO₂, CO, PM₁₀ and PM₂.₅ at urban sites and high O₃ concentrations at suburban sites. In comparison with other provincial capital cities in China, Lhasa has low concentrations of air pollutants, except for O₃, and thus, better air quality. Although Lhasa has experienced rapid urbanization and economic development, air pollution conditions have remained rather stable and even decreased slightly in term of particular air pollutants. We suggested that the relatively isolated location, low air pollutant emissions associated with its industrial structure and renewable energy consumption, and effective air pollution control measures, collectively contributed to the synchronous improvement of the economy and air quality in Lhasa. Such “Lhasa pattern” may serve as a positive example for other regional hub cities in China and beyond that experience socioeconomic development and simultaneously seek to improve air quality.

Changzhou, an industrial city in the Yangtze River Delta, has been experiencing serious haze pollution, particularly in winter. However, studies pertaining to the haze in Changzhou are very limited, which makes it difficult to understand the characteristics and formation of winter haze in this area, and develop effective control measures. In this study, we carried out continuous online observation of particulate matter, chemical components, and meteorology in Changzhou in February 2017. Our results showed that haze pollution occurred frequently in Changzhou winter and exhibited two patterns: dry haze with low relative humidity (RH) and wet haze with high RH. Water-soluble inorganic ions (SO₄²⁻, NO₃⁻, and NH₄⁺) accounted for ∼52.2% of the PM₂.₅ mass, of which sulfate was dominant in wet haze periods while nitrate was dominant in other periods. With the deterioration of haze pollution, the proportion of nitrate in PM₂.₅ increased, while sulfate proportion increased under wet haze and decreased under dry haze. Dry haze and wet haze appeared under slow north wind and south wind, respectively, and strong north wind or sea breeze scavenged pollution. We found that formation of nitrate occurred rapidly in daytime with high concentrations of odd oxygen (Oₓ = O₃ + NO₂), whereas formation of sulfate occurred rapidly during nighttime with high RH, indicating that photochemistry and heterogeneous reaction were the major formation mechanisms for nitrate and sulfate, respectively. Through the cluster analysis of 36-h backward trajectories, five sources of air masses from three directions were identified. High PM₂.₅ concentrations (84.1 μg m⁻³ on average) usually occurred under the influence of two clusters (46%) from the northwest, indicating that regional transport from northern China aggravated the winter haze pollution in Changzhou. Emission reduction, particularly the mobile sources, and regional joint prevention and control can help to mitigate the winter haze in Changzhou.

Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV Rˆ2 (0.677). Random forest was the second best performing algorithm with an LOLO CV Rˆ2 of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM₂.₅ (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM₂.₅ estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM₂.₅ concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM₂.₅ observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM₂.₅ concentration greater than 80 μg/m³ in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM₂.₅ were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM₂.₅ estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated.