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.

The effects of exposure to different levels of ionising radiation were assessed on the genetic, epigenetic and microbiome characteristics of the “hologenome” of earthworms collected at sites within the Chernobyl exclusion zone (CEZ). The earthworms Aporrectodea caliginosa (Savigny, 1826) and Octolasion lacteum (Örley, 1881) were the two species that were most frequently found at visited sites, however, only O. lacteum was present at sufficient number across different exposure levels to enable comparative hologenome analysis. The identification of morphotype O. lacteum as a probable single clade was established using a combination of mitochondrial (cytochrome oxidase I) and nuclear genome (Amplified Fragment Length Polymorphism (AFLP) using MspI loci). No clear site associated differences in population genetic structure was found between populations using the AFLP marker loci. Further, no relationship between ionising radiation exposure levels and the percentage of methylated loci or pattern of distribution of DNA methylation marks was found. Microbiome structure was clearly site dependent, with gut microbiome community structure and diversity being systematically associated with calculated site-specific earthworm dose rates. There was, however, also co-correlation between earthworm dose rates and other soil properties, notably soil pH; a property known to affect soil bacterial community structure. Such co-correlation means that it is not possible to attribute microbiome changes unequivocally to radionuclide exposure. A better understanding of the relationship between radionuclide exposure soil properties and their interactions on bacterial microbiome community response is, therefore, needed to establish whether these the observed microbiome changes are attributed directly to radiation exposure, other soil properties or to an interaction between multiple variables at sites within the CEZ.

The potential adverse effects of nanoplastics, such as nanopolystyrene, have received the great attention recently. However, the molecular response of organisms to nanoplastics is still largely unknown. In this study, we employed Caenorhabditis elegans as an animal model to investigate the long non-coding RNAs (lncRNAs) in response to long-term exposure to low-dose nanopolystyrene (100 nm). Based on Hiseq 2000 sequencing and qRT-PCR confirmation, we identified 36 lncRNAs (21 down-regulated lncRNAs and 15 up-regulated lncRNAs) in response to nanopolystyrene (1 μg/L). Using intestinal reactive oxygen species (ROS) production and locomotion behavior as endpoints, we found that RNAi knockdown of linc-2, linc-9, or linc-61 induced a susceptibility to nanopolystyrene toxicity, and RNAi knockdown of linc-18 or linc-50 induced a resistance to nanopolystyrene toxicity. Meanwhile, nanopolystyrene (1 μg/L) increased expressions of linc-2, linc-9, linc-18, and linc-61 and decreased linc-50 expression, suggesting that these 5 lncRNAs mediated two different responses to nanopolystyrene exposure. Bioinformatical analysis implied that these 5 lncRNAs were associated with multiple biological processes and signaling pathways. Our results demonstrated the crucial roles of lncRNAs in response to long-term exposure to low-dose nanopolystyrene in organisms.

Pesticides guarantee us high productivity in agriculture, but the long-term costs have proved too high. Acute and chronic intoxication of humans and animals, contamination of soil, water and food are the consequences of the current demand and sales of these products. In addition, pesticides such as glyphosate are sold in commercial formulations which have inert ingredients, substances with unknown composition and proportion. Facing this scenario, toxicological studies that investigate the interaction between the active principle and the inert ingredients are necessary. The following work proposed comparative toxicology studies between glyphosate and its commercial formulation using the alternative model Caenorhabditis elegans. Worms were exposed to different concentrations of the active ingredient (glyphosate in monoisopropylamine salt) and its commercial formulation. Reproductive capacity was evaluated through brood size, morphological analysis of oocytes and through the MD701 strain (bcIs39), which allows the visualization of germ cells in apoptosis. In addition, the metal composition in the commercial formulation was analyzed by ICP-MS. Only the commercial formulation of glyphosate showed significant negative effects on brood size, body length, oocyte size, and the number of apoptotic cells. Metal analysis showed the presence of Hg, Fe, Mn, Cu, Zn, As, Cd and Pb in the commercial formulation, which did not cause reprotoxicity at the concentrations found. However, metals can bioaccumulate in soil and water and cause environmental impacts. Finally, we demonstrated that the addition of inert ingredients increased the toxic profile of the active ingredient glyphosate in C. elegans, which reinforces the need of components description in the product labels.

Natural dissolved organic matter (DOM) produced in algal blooms and overgrowths of macrophyte changes the elimination and ecotoxicity of estrogens in freshwater lakes. The complexation of 17α-ethinylestradiol (EE2) and various DOMs, including the water- and sediment-derived DOMs from the algal-dominant zone in Lake Taihu (TW and TS, respectively) and the macrophyte-dominant zone in Poyang Lake (PW and PS, respectively), and the humic acid (HA), was investigated along with the subsequent effects on EE2 biodegradation. Dialysis equilibrium experiments showed that binding to DOM significantly decreased the freely soluble concentrations of EE2. The binding capacity of the five DOMs followed the order of PW < TW < PS ≈ TS < HA. A negative correlation was found between the organic-carbon-normalized sorption coefficient (logKDOC) and the absorption ratio (E₂/E₃) of DOM, indicating that the large sized, aromatic molecules were involved in the complexation. The reduced freely soluble concentrations of EE2 did not inhibit its biodegradation by an EE2-degrading strain, Rhodobacter blasticus. Conversely, the autochthonous-dominated water-derived DOMs stimulated a more extensive biodegradation of EE2 than the sediment-derived DOMs, and the existence of HA resulted in the smallest increase in EE2 biodegradation. The promoting effect was associated with the increased concentration, activity, and transforming rate of R. blasticus by the bioavailable components in DOM. The present study suggests that the significant impact of natural DOM should be fully considered when assessing the fate and ecological risks of estrogens in eutrophic waters.

The trophic balance of freshwater aquaculture activities has traditionally been monitored by chemical analysis of water; however, the parameters measured are usually characterized by high temporal variability. Aquatic mosses can be used as biomonitors as they integrate both continuous and episodic contamination events. Here we report, for the first time, a method for monitoring N enrichment in the surroundings of fish farms by measuring the N content and isotopic signal (δ15N) of transplanted living and devitalized specimens of the aquatic moss Fontinalis antipyretica. For this purpose, moss samples (“moss bags”) were exposed at increasing distances (10, 100, 300 and 1000 m) up- and downstream of the effluent discharge points of four trout farms, for 10 and 30 days. The low natural (background) variability in δ15N in upstream samples enabled detection of outlier values, caused by aquaculture discharges, at distances of 10 and 100 m downstream, especially in devitalized moss and after 10 days of exposure. However, the unexpectedly low N contents of moss samples exposed close to the discharge points complicates interpretation of the high levels of N forms detected by conventional physicochemical analysis of water. Although the mechanisms that modify N parameters in moss tissues were not clear, measurement of the isotopic signal δ15N in devitalized moss exposed for 10 days proved useful for monitoring the N pollution associated with intensive freshwater aquaculture.

The abuse of antibiotics has caused the propagation of antibiotic resistance genes (ARGs) in aquaculture systems. Although the recirculating systems have been considered as a promising approach for preventing the coastal water pollution of antibiotics and ARG, rare information is available on the distribution and proliferation of ARGs in the recirculating mariculture system. This study firstly investigated the proliferation of ARGs in coastal recirculating mariculture systems. Ten subtypes of ARGs including tet (tetB, tetG, tetX), sul (sul1, sul2), qnr (qnrA, qnrB, qnrS), and erm (ermF, ermT) were detected. The absolute abundances of the ARGs detected in the mariculture farm were more than 1 × 10⁴ copies/mL. The sulfonamide resistance genes (sul1 and sul2) were the most abundant ARGs with the abundance of 3.5 × 10⁷–6.5 × 10¹⁰ copies/mL. No obvious correlation existed between the antibiotics and ARGs. Some bacteria were positively correlated with two or more ARGs to indicate the occurrence of multidrug resistance. The fluidized-bed biofilter for wastewater treatment in the recirculating system was the main breeding ground for ARGs while the UV sterilization process could reduce the ARGs. The highest flux of ARGs (6.5 × 10²¹ copies/d) indicated that the discharge of feces and residual baits was the main gateway for ARGs in the recirculating mariculture system to enter the environments.

Rapid urbanization created unique urban environment with a characteristic of dramatic modification of land cover, consequently causing profound perturbations in the transport and fate of pollutants in urban ecosystem. Taking a hyper-urbanization city (Shanghai) as an example to reveal the influence of urbanization development on pollutant footprint, this study reconstructed and compared historical evolutions of elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs) based on two lake sediment cores (DSL: Dianshan Lake; LXL: Luxun Lake) from early- and newly-urbanized areas, respectively. Historical fluxes of EC and total PAH (Σ₁₆PAHs) showed similar and sharply fluctuant increases occurring after the 1950s in the DSL core later than the LXL core after the 1900s. In modern times (after 2000), the mean fluxes of EC and Σ₁₆PAHs in the LXL core were 2.68- and 1.38-fold greater than those in the DSL core, respectively, indicating the stronger influence from more intensive human activities and longer industrial history in early urbanized area. Based on the significant correlations among socioeconomic factors with EC and Σ₁₆PAH fluxes, the extended STIRPAT (stochastic impacts by regression on population, affluence and technology) models were successfully constructed, revealing that significance of these driving factors were in the order of population > the proportion of heavy industry > coal consumption > gross domestic product (GDP) per capita > vehicle amount. In general, the obvious discrepancy in historical stage and intensity of sedimentary EC and PAH accumulations implied that some newly fast-developing cities still have a chance to adjust urban development strategy to avoid more serious pollution.