Proteinaceous matter is an important component of PM₂.₅, which can cause adverse health effects and also influence the air quality and climate change. However, there is little attention to high time-resolved variations and potential role of aerosol proteins during haze pollution periods. In this study, PM₂.₅ samples were first collected by a medium flow sampler in autumn and winter in Xi'an, China. Then three high time-resolved monitoring campaigns during haze pollution periods were conducted to determine the evolving characteristics of total protein concentration and explore the interactive relationship between protein and other chemical compositions. The results showed that the average protein concentration in PM₂.₅ in Xi'an (5.46 ± 3.32 μg m⁻³) was higher than those in most cities of China, and varied by seasons and air pollution conditions. In particular, the protein concentration in PM₂.₅ increased with the increase of air quality index (AQI). The continuous variations of aerosol proteins during the haze pollution periods further showed that PM₂.₅, atmospheric humidity and long-distance air mass transport exerted the significant impacts on the protein components in aerosols. Based on the present observation, it is suggested that aerosol proteins might affect the generation of secondary aerosols under haze weather conditions. The present results may provide a new possible insight into the variations and the role of aerosol proteinaceous matter during the formation and development of haze pollution.

Heavy metals (HMs) in soil and water bodies greatly threaten human health. The wide separation of HMs urges the necessity to develop an expert system for HMs prediction and detection. In the current perspective, several propositions are discussed to design an innovative intelligence system for HMs prediction and detection in soil and water bodies. The intelligence system incorporates the Edge Cloud Server (ECS) data center, an innovative deep learning predictive model and the Federated Learning (FL) technology. The ECS data center is based on satellite sensing sources under human expertise ruling and HMs in-situ measurement. The FL system comprises a machine learning (ML) technique that trains an algorithm across multiple decentralized edge servers holding local data samples without exchanging them or breaching data privacy. The expected outcomes of the intelligence system are to quantify the soil and water bodies' HMs, develop new modified HMs pollution contamination indices and provide decision-makers and environmental experts with an appropriate vision of soil, surface water, and crop health.

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM₂.₅) in high-density cities, few studies have revealed the vertical differences in PM₂.₅ exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM₂.₅ exposure that combines a high-resolution LUR model with a vertical PM₂.₅ variation model to investigate the results of horizontal and vertical mobile PM₂.₅ monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM₂.₅ variation, respectively. Vertical measurements of PM₂.₅ from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM₂.₅ changes at a certain height relative to the ground. The vertical variation model for PM₂.₅ was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM₂.₅ exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM₂.₅, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

The contamination status of heavy metals in urban environment changes frequently with the industrial structure adjustment, energy conservation and emission reduction and thus requires timely investigation. Based on enrichment factor, multivariate statistical analysis and isotope fingerprinting, we assessed comprehensively the inputs and sources of heavy metals in different samples from an urban area that was less impacted by leaded gasoline exhaust. The road dust contained relatively high levels of Cr, Pb and Zn (with enrichment factor >2) that originated from both exhaust and non-exhaust traffic emissions, while the moss plants could accumulate high levels of Pb and Zn from the deposition of traffic exhaust emission. This suggest that the traffic emission is still an important source of metals in the urban area although gasoline is currently lead free. On the contrary, the occurrences of metals in the urban soils were controlled by natural sources and non-traffic anthropogenic emission. These findings revealed that different samples would receive different inputs of metals from different sources in the urban area, and the responsiveness and sensitiveness of these urban samples to metal inputs can be ranked as moss ≥ dust > soil. Taken together, our results suggested that in order to avoid generalizing and get detail source information, multi-samples and multi-measures must be adopted in the assessment of integrated urban environmental quality.

The short-term effects of ambient temperature on mortality have been widely investigated. However, the epidemiological evidence on the long-term effects of temperature on mortality is rare. In present study, we conducted a nationwide quasi-experimental design, which based on a variant of difference-in-differences (DID) approach, to examine the association between long-term exposure to ambient temperature and mortality risk in China, and to analyze the effect modification of population characteristics and socioeconomic status. Data on mortality were collected from 364 communities across China during 2006–2017, and environmental data were obtained for the same period. We estimated a 2.93 % (95 % CI: 2.68 %, 3.18 %) increase in mortality risk per 1 °C decreases in annual temperature, the greater effects were observed on respiratory diseases (5.16 %, 95 % CI: 4.53 %, 5.79 %) than cardiovascular diseases (3.43 %, 95 % CI: 3.06 %, 3.80 %), and on younger people (4.21 %, 95 % CI: 3.73 %, 4.68 %) than the elderly (2.36 %, 95 % CI: 2.06 %, 2.65 %). In seasonal analysis, per 1 °C decreases in average temperature was associated with 1.55 % (95 % CI: 1.23 %, 1.87 %), −0.53 % (95 % CI: −0.89 %, −0.16 %), 2.88 % (95 % CI: 2.45 %, 3.31 %) and 4.21 % (95 % CI: 3.98 %, 4.43 %) mortality change in spring, summer, autumn and winter, respectively. The effects of long-term temperature on total mortality were more pronounced among the communities with low urbanization, low education attainment, and low GDP per capita. In total, the decrease of average temperature in summer decreased mortality risk, while increased mortality risk in other seasons, and the associations were modified by demographic characteristics and socioeconomic status. Our findings suggest that populations with disadvantaged characteristics and socioeconomic status are vulnerable to long-term exposure of temperature, and targeted policies should be formulated to strengthen the response to the health threats of temperature exposure.

In this study, the organ distribution and exposure risk from dietary intake of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) were investigated for lotus collected from Ya-er Lake, a lake in Hubei Province, Central China that was historically polluted by the chlor-alkali industry. The highest concentrations of PCDD/Fs were found in the main and fibrous lotus roots, with mean values of 48.9 ± 90.1 pg/g and 94.6 ± 143 pg/g, respectively. In all the investigated samples, Octa-CDD (OCDD) and Octa-CDF (OCDF) were the predominant congeners, at 26% and 17% of Σ₁₇ PCDD/Fs, respectively, followed by 1,2,3,4,6,7,8-HpCDF (9%). The distribution ratios of PCDD/Fs in adjacent lotus organs indicated that PCDD/Fs accumulated easily in edible organs, such as lotus seeds, membrane and leaves. The WHO-TEQ in the edible lotus organs and the probable daily intake (PDI) of lotus products by residents were calculated: the toxic equivalents in the lotus fruit parts reached a mean of 2 pg WHO-TEQ₂₀₀₅/g dw, and the mean weekly intake of lotus products for adolescents living around Ya-er Lake was 2.3 pg WHO-TEQ/kg bw/week. These results suggested that long-term consumption of lotus products from Ya-er Lake presents a health hazard to residents.

Microplastics are a pollutant of growing concern, capable of harming aquatic organisms and entering the food web. While freshwater microplastic research has expanded in recent years, much remains unknown regarding the sources and delivery pathways of microplastics in these environments. This review aims to address the scientific literature regarding the spatial and temporal factors affecting global freshwater microplastic distributions and abundances. A total of 75 papers, published through June 2021 and containing an earliest publication date of October 2014, was identified by a Web of Science database search. Microplastic spatial distributions are heavily influenced by anthropogenic factors, with higher concentrations reported in regions characterized by urban land cover, high population density, and wastewater treatment plant effluent. Spatial distributions may also be affected by physical watershed characteristics such as slope and elevation (positive and negative correlations with microplastic concentrations, respectively), although few studies address these factors. Temporal variables of influence include precipitation and stormwater runoff (positive correlations) and water flow/discharge (negative correlations). Despite these overarching trends, variations in study results may be due to differing scales or contributing area delineations. Thus, more rigorous and standardized spatial analytical methods are needed. Future research could simultaneously evaluate both spatial and temporal factors and incorporate finer temporal resolutions into sampling campaigns.

Eyes convey important information about the external and internal worlds of animals. Individuals can follow the gaze of others to learn about the location of salient objects as well as assess eye qualities to evaluate the health, age or other internal states of conspecifics. Because of the increasing prevalence of artificial lighting at night (ALAN), urbanized individuals can potentially garner information from conspecific eyes under both daylight and ALAN. We tested this possibility using a visual modeling approach in which we estimated the maximum distance at which individuals could detect conspecific eyes under daylight and high levels of ALAN. We also estimated the minimum light level at which individuals could detect conspecific eyes. Great-tailed grackles (Quiscalus mexicanus) were used as our study species because they are highly social and are unusual among birds in that they regularly gather at nocturnal roosts in areas with high levels of ALAN. This visual modelling approach revealed that grackles can detect conspecific eyes under both daylight and ALAN, regardless of iris coloration. The grackles could detect conspecific eyes at farther distances in daylight compared to ALAN. Our results highlight the potential importance of lighting conditions in shaping social interactions.

Dinotefuran is a third-generation neonicotinoid pesticide and is increasingly used in agricultural production, which has adverse effects on nontarget organisms. However, the research on the impact of dinotefuran on nontarget organisms is still limited. Here the toxic effects of dinotefuran on an important economic species and a model lepidopteran insect, Bombyx mori, were investigated. Exposure to different doses of dinotefuran caused physiological disorders or death. Cytochrome P450, glutathione S-transferase, carboxylesterase, and UDP glycosyl-transferase activities were induced in the fat body at early stages after dinotefuran exposure. By contrast, only glutathione S-transferase activity was increased in the midgut. To overcome the lack of sensitivity of the biological assays at the individual organism level, RNA sequencing was performed to measure differential expressions of mRNA from silkworm larvae after dinotefuran exposure. Differential gene expression profiling revealed that various detoxification enzyme genes were significantly increased after dinotefuran exposure, which was consistent with the upregulation of the detoxifying enzyme. The global transcriptional pattern showed that the physiological responses induced by dinotefuran toxicity involved multiple cellular processes, including energy metabolism, oxidative stress, detoxification, and other fundamental physiological processes. Many metabolism processes, such as carbon metabolism, fatty acid biosynthesis, pyruvate metabolism, and the citrate cycle, were partially repressed in the midgut or fat body. Furthermore, dinotefuran significantly activated the MAPK/CREB, CncC/Keap1, PI3K/Akt, and Toll/IMD pathways. The links between physiological, biochemical toxicity and comparative transcriptomic analysis facilitated the systematic understanding of the integrated biological toxicity of dinotefuran. This study provides a holistic view of the toxicity and detoxification metabolism of dinotefuran in silkworm and other organisms.

Freshwater ecosystems are recognized as non-negligible sources of plastic contamination for the marine environment that is the final acceptor of 53 thousand tons of plastic per year. In this context, microplastic particles are well known to directly pose a great threat to freshwater organisms, they also indirectly affect the aquatic ecosystem by adsorbing and acting as a vector for the transport of other pollutants (“Trojan horse effect”). Polystyrene is one of the most widely produced plastics on a global scale, and it is among the most abundant microplastic particles found in freshwaters. Nevertheless, to date few studies have focused on the eco-genotoxic effects on freshwater organisms caused by polystyrene microplastic particles (PS-MPs) in combination with other pollutants such as pharmaceuticals and pesticides. The aim of this study is to investigate chronic and sub-chronic effects of the microplastic polystyrene beads (PS-MP, 1.0 μm) both as individual xenobiotic and in combination (binary/ternary mixtures) with the acicloguanosine antiviral drug acyclovir (AC), and the neonicotinoid broad-spectrum insecticide imidacloprid (IMD) in one of the most sensitive non-target organisms of the freshwater food chain: the cladoceran crustacean Ceriodaphnia dubia. Considering that the individually selected xenobiotics have different modes of action and/or different biological sites, the Bliss independence was used as reference model for this research. Basically, when C. dubia neonates were exposed for 24 h to the mixtures during Comet assay, mostly an antagonistic genotoxic effect was observed. When neonates were exposed to the mixtures for 7 days, mostly an additive chronic toxic effect occurred at concentrations very close or even overlapping to the environmental ones ranging from units to tens of ng/L for PS-MPs, from tenths/hundredths to units of μg/L for AC and from units to hundreds of μg/L for IMD, revealing great environmental concern.