Microplastics are continuously released into the terrestrial environment from sources where they are used and produced. These microplastics accumulate in soils, sediments, and freshwater bodies, and some are conveyed via wind and water to the oceans. The concentration gradient between terrestrial inland and coastal regions, the factors that influence the concentration, and the fundamental transport processes that could dynamically affect the distribution of microplastics are unclear. We analyzed microplastic concentration reported in 196 studies from 49 countries or territories from all continents and found that microplastic concentrations in soils or sediments and surface water could vary by up to eight orders of magnitude. Mean microplastic concentrations in inland locations such as glacier (191 n L⁻¹) and urban stormwater (55 n L⁻¹) were up to two orders of magnitude greater than the concentrations in rivers (0.63 n L⁻¹) that convey microplastics from inland locations to water bodies in terrestrial boundary such as estuaries (0.15 n L⁻¹). However, only 20% of studies reported microplastics below 20 μm, indicating the concentration in these systems can change with the improvement of microplastic detection technology. Analysis of data from laboratory studies reveals that biodegradation can also reduce the concentration and size of deposited microplastics in the terrestrial environment. Fiber percentage was higher in the sediments in the coastal areas than the sediments in inland water bodies, indicating fibers are preferentially transported to the terrestrial boundary. Finally, we provide theoretical frameworks to predict microplastics transport and identify potential hotspots where microplastics may accumulate.

Ambient fine particulate matter (PM₂.₅) can change the expression profile of microRNAs (miRs), which may play important roles in mediating inflammatory responses. The present study attempts to investigate the roles of miR-146a-5p in regulating cytokine expression in a human monocytic leukemia cell line (THP-1). Four types of PM₂.₅ extracts obtained from Beijing, China, were subjected to cytotoxic tests in THP-1 cells. These four PM₂.₅ extracts included two water extracts collected from non-heating and heating season (WN and WH), and two organic extracts from non-heating and heating season (DN and DH). Firstly, the four PM₂.₅ extracts caused cytotoxicity, oxidative stress responses, cytokine gene expressions and interleukin 8 (IL-8) release in THP-1 cells, with WH showing the highest cytotoxicity, WN showing the highest oxidative stress and inflammatory responses. Additionally, we observed expression of miR-146a-5p was significantly increased, with the maximal response of six folds in WN group. Cellular autophagy was initiated by PM₂.₅ indicated by related protein and gene expressions. Both RNA interference and autophagy inhibitor were applied to interrupt autophagy process in THP-1 cells. Autophagy dysfunction could alleviate IL-8 expression, suggesting autophagy process regulated cytokine expression and inflammatory response caused by PM₂.₅. A chemical inhibitor was applied to inhibit the function of miR-146a-5p, and then the expressions of IL-8 and autophagic genes were significantly aggravated. Meanwhile, two target genes of miR-146a-5p, interleukin-1 associated-kinase-1 (IRAK1) and tumor-necrosis factor receptor-associated factor-6 (TRAF6) were increased dramatically, which also played important roles in regulation of autophagy. These data suggested miR-146a-5p negatively modulated cytokine expression caused by PM₂.₅ via autophagy process through the target genes of IRAK1 and TRAF6. Our findings raised the concerns of the changes of miR expression profile and following responses caused by PM₂.₅.

Effects of commercial sand mining on aquatic diversity are of increasing global concern, especially in parts of some developing countries. However, understanding of this activity on the diversity of macroinvertebrates remains focused on the α component of species diversity, rather than community functioning. Thus, there remains much uncertainty regarding how each component of taxonomic (TD) and functional (FD) diversity respond to the activity both in freshwater and marine environments. Here, we assessed the effect of sand dredging on α, β and γ components of TD and FD during different dredging periods based on the response of macroinvertebrate communities over 4 years in the second largest freshwater lake in China. After three years of active dredging, substantial reductions in each component (α, β and γ) of TD and FD were observed within the dredged area. Moreover, after one year of natural recovery, a distinct restoration was observed with an obvious return in multiple facets of TD and FD indices. No such changes were observed within the adjacent and reference areas. Decreases in the multiple components of TD and FD within the dredged area were most likely associated with the direct extraction of substrate and associated benthic fauna and indirect variations of the water and sediment environment (e.g., increases in water depth and decreases in %Clay). Furthermore, dispersal processes and mass effects mainly contributed to the maintenance of TD and FD during the dredged and recovery stages. In addition, the fast recovery of TD and FD was also related to the simple taxonomic structure and highly connected nature of the study area. Our results suggest that a more precise experimental design (BACI) should be pursued to avoid potentially confounding effects (e.g., natural disturbance) because the sensitivity of diversity indices depends upon different experimental designs. Moreover, measurement of the impacts of sand dredging on macroinvertebrate diversity can be undertaken within a rigorous framework for better understanding the patterns and processes of each component of TD and FD under the sand dredging disturbance.

Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM₂.₅) concentrations in near-road environments. However, many sources of uncertainty and variability are associated with the process of near-road dispersion modelling, which renders a single-number estimate of concentration a poor indicator of near-road air quality. In this study, we propose an integrated traffic-emission-dispersion modelling chain that incorporates several major sources of uncertainty. Our approach generates PM₂.₅ probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM₂.₅ emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM₂.₅ levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM₂.₅ distributions at all locations along the road, indicating a high likelihood that the model can reproduce measured concentrations. A policy scenario expressing the impact of reductions in truck emissions revealed that a 30% reduction in near-road PM₂.₅ concentrations can be achieved by upgrading close to 55% of the current trucks circulating along the corridor. A speed limit reduction of 10 km/h could lead to statistically significant increases in PM₂.₅ concentrations at twelve out of the eighteen locations.

The acidogenic fermentation of dairy wastewater (DW) was evaluated for carboxylic acids (CA) production, investigating the influence of substrate/microorganism (S/X) ratio and applying different mathematical models to the bioproduct formation data. The experiments were performed in batch reactors for 28 days, and four S/X ratios were tested (0.8, 1.2, 1.6, and 1.9 gCOD gVSS⁻¹). The S/X ratio increase did not influence the percentage of DW conversion into carboxylic acids (42–44%), but the productivity was positively affected (100–200% in general). Acetic acid was the CA formed in the highest concentration for all experiments, followed by propionic and butyric acids. Exponential models were better suited to describe this kinetics process. Therefore, according to the estimated kinetic parameters, the S/X ratio 1.6 was more suitable for CA production from acidogenic fermentation of dairy wastewater, in which the concentrations of longer CA, such as propionate and butyrate, were formed in higher quantities. In addition, it was determined a correlation between the S/X ratio and kinetic parameters like degradation/production rate constant (K) and maximum productivity rate (μₘ).

The relationship between heavy metal(loid)s exposure and oxidative stress damage is a matter of research interest. Our study aimed to investigate the distribution patterns of the nine heavy metal(loid)s in blood of pregnant women, including four toxic heavy metal(loid)s [arsenic (As), cadmium (Cd), lead (Pb) and mercury (Hg)] and five typical heavy metal(loid)s [manganese (Mn), cobalt (Co), copper (Cu), zinc (Zn), and selenium (Se)] in blood. Blood samples of 348 women were collected and their concentrations in the serum (sr) and blood cells (bc) were measured, as well as serum heme oxygenase-1 (HO-1) (an oxidative stress marker). Total blood (tb) concentrations of these metal(loid)s and serum-to-blood cell concentration ratios (sr/bc) were further calculated. We found Cu mainly accumulated in the serum compared to the blood cells with Cuˢʳ/ᵇᶜ = 2.30, whereas Co, Se, and As evenly distributed between these two fractions. Other metal(loid)s mainly concentrated in the blood cells. Coˢʳ, Cuˢʳ, Cuᵇᶜ, Mnᵇᶜ, Znᵇᶜ, Cdᵇᶜ, Coᵗᵇ, Cuᵗᵇ, Mnᵗᵇ, Znᵗᵇ, Cdᵗᵇ, and Cuˢʳ/ᵇᶜ were negatively associated with serum HO-1, whereas Asˢʳ, Asᵇᶜ, Asᵗᵇ, Znˢʳ/ᵇᶜ, Cdˢʳ/ᵇᶜ, and Hgˢʳ/ᵇᶜ were positively, indicating of their potential toxicity. We concluded that the distribution patterns of blood heavy metal(loid)s, in particular for Cd, Hg and Zn, which either increased in serum or decreased in blood cells, might be associated with elevated serum oxidative stress, should be considered in environmental health assessments.

The availability of organic matters in vast quantities from the agricultural/industrial practices has long been a significant environmental challenge. These wastes have created global issues in increasing the levels of BOD or COD in water as well as in soil or air segments. Such wastes can be converted into bioenergy using a specific conversion platform in conjunction with the appropriate utilization of the methods such as anaerobic digestion, secondary waste treatment, or efficient hydrolytic breakdown as these can promote bioenergy production to mitigate the environmental issues. By the proper utilization of waste organics and by adopting innovative approaches, one can develop bioenergy processes to meet the energy needs of the society. Waste organic matters from plant origins or other agro-sources, biopolymers, or complex organic matters (cellulose, hemicelluloses, non-consumable starches or proteins) can be used as cheap raw carbon resources to produce biofuels or biogases to fulfill the ever increasing energy demands. Attempts have been made for bioenergy production by biosynthesizing, methanol, n-butanol, ethanol, algal biodiesel, and biohydrogen using different types of organic matters via biotechnological/chemical routes to meet the world’s energy need by producing least amount of toxic gases (reduction up to 20–70% in concentration) in order to promote sustainable green environmental growth. This review emphasizes on the nature of available wastes, different strategies for its breakdown or hydrolysis, efficient microbial systems. Some representative examples of biomasses source that are used for bioenergy production by providing critical information are discussed. Furthermore, bioenergy production from the plant-based organic matters and environmental issues are also discussed. Advanced biofuels from the organic matters are discussed with efficient microbial and chemical processes for the promotion of biofuel production from the utilization of plant biomasses.

Chlorinated paraffins (CPs) have been widely used as halogenated flame retardants and plasticizers since the mid-20th century. The prevalence of CPs in soil has been widely reported, but the distribution pattern of CPs in urbanized zones and their association with multiple socioeconomic variables have not been adequately explored. Herein, short-chain and medium-chain chlorinated paraffins (SCCPs and MCCPs) were investigated in surface soil samples from Tianjin, China, a typical urbanized area. The concentration distributions of SCCPs and MCCPs showed similar trends in different administrative divisions and land use types: urban areas > suburbs > outer suburbs (p < 0.001) and residential areas > greenbelts > agricultural areas (p < 0.001). The CP congeners in residential surface soils mainly included those with longer carbon chains and high degree of chlorination, while the CP congeners in agricultural surface soils mainly consisted of those with shorter carbon chains and fewer chlorine substituents. Multiple statistical approaches were used to explore the association between socioeconomic factors and CP distribution. CP concentration was significantly correlated to population density and gross domestic product (GDP) (p < 0.001), and structural equation models incorporating administrative regional planning showed an indirect impact on the distribution of MCCP concentration due to the influence of regional planning on population density. These results highlight the association between CP contamination and the degree of urbanization, and this paper provides useful information toward mitigating the exposure risk of CPs for urban inhabitants.

The construction of noise maps is of great significance for the development of urban sustainability and the protection of residents’ physical and mental health. The traditional noise map construction method is difficult to be widely used because of its low update frequency and high drawing cost. Based on the crowd-sensing technology and Latent Factor Model (LFM), this paper proposes a new noise map completion method called Spatial-Temporally Related LFM (STR-LFM) for solving the problem of data sparseness. First, the geographic information features including Point of Interest (POI), road network and building outline are fully excavated, and then combine the correlation of the samples in the time dimension to construct the similarity matrixes. After that, use the k-nearest neighbor algorithm to find out the similar samples of missing positions, and finally regard their weighted fusion as the predicted values. Experimental results show that the recovery error is lower than other commonly used methods, and the proposed method has better stability when faced with data sparseness problems at different levels.

The continuously increasing presence of micro- and nanoplastics contamination in numerous food products for human consumption is threatening and their potential health effects towards humans remain uncertain. At present, investigations on packaged beverages (e.g. bottled drinking water, beer, milk and refreshments) have received scientific attention and represent an important part of microplastic research as humans are orally exposed to these anthropogenic contaminants every day. Rapid and effective detection methods are important to quantify micro- and nanoplastic particles with a great accuracy as well as to identify their sources and characteristics. A number of methods are currently in use to assess microplastics in packaged beverages; however, the great variations in methods and data acquisition render difficulties when comparing the results and developing the protocols. Based on the challenges, this paper aims to provide a comprehensive understanding of emerging technological approaches, points out the current limitations from sample preparation to quantification and present recommendations. From the results of our analysis, we postulate an example framework that can be applied to different types of drinking products for investigating micro- and nanoplastics. Overall, this review will serve as a first step towards harmonization of micro- and nanoplastic monitoring efforts and a point of reference to help direct future researches focusing on drinking products intended for human consumption.