Quinolone antibiotics (QNs) pollution in lake environments is increasingly raising public concern due to their potential combined toxicity and associated risks. However, the spatiotemporal distribution and trophodynamics of QNs in transit-station lakes for water diversion are not well documented or understood. In this study, a comprehensive investigation of QNs in water, sediment, and aquatic fauna, including norfloxacin (NOR), ciprofloxacin (CIP), enrofloxacin (ENR), and ofloxacin (OFL), was conducted in Luoma Lake, a major transit station for the eastern route of the South-to-North Water Diversion Project in China. The target QNs were widely distributed in the water (∑QNs: 70.12 ± 62.79 ng/L) and sediment samples (∑QNs: 13.35 ± 10.78 ng/g dw) in both the non-diversion period (NDP) and the diversion period (DP), where NOR and ENR were predominant. All the QNs were detected in all biotic samples in DP (∑QNs: 80.04 ± 20.59 ng/g dw). The concentration of ∑QNs in the water in NDP was significantly higher than those in DP, whereas the concentration in the sediments in NDP was comparable to those in DP. ∑QNs in the water-sediment system exhibited decreasing trends from northwest (NW) to southeast (SE) in both periods; however, the Kₒc (organic carbon normalized partition coefficients) of individual QNs in DP sharply rose compared with those in NDP, which indicated that water diversion would alter the environmental fate of QNs in Luoma Lake. In DP, all QNs, excluding NOR, were all biodiluted across the food web; whereas their bioaccumulation potentials in the SE subregion were higher than those in the NW subregion, which was in contrast to the spatial distribution of their exposure concentrations. The estimated daily QN intakes via drinking water and aquatic products suggested that residents in the SE side were exposed to greater health risks, despite less aquatic pollution in the region.

Four most concerned heavy metal pollutants, arsenic, cadmium, lead, and mercury may share common mechanisms to induce metabolic syndrome (MetS). However, recent studies exploring the relationships between MetS and metal exposure presented inconsistent findings. We aimed to clarify the relationship between heavy metal exposure biomarkers and MetS using a meta-analysis and systematic review approach. Literature search was conducted in international and the Chinese national databases up to June 2020. Of selected studies, we extracted the relevant data and evaluated the quality of each study’s methodology. We then calculated the pooled effect sizes (ESs), standardized mean differences (SMDs), and their 95% confidence intervals (CIs) using a random-effect meta-analysis approach followed by stratification analyses for control of potential confounders. Involving 55,536 participants, the included 22 articles covered 52 observational studies reporting ESs and/or metal concentrations on specific metal and gender. Our results show that participants with MetS had significantly higher levels of heavy metal exposure [pooled ES = 1.16, 95% CI: 1.09, 1.23; n = 42, heterogeneity I² = 75.6%; and SMD = 0.22, 95% CI: 0.15, 0.29; n = 32, I² = 94.2%] than those without MetS. Pooled ESs in the subgroups stratified by arsenic, cadmium, lead, and mercury were 1.04 (95% CI: 0.97, 1.10; n = 8, I² = 61.0%), 1.10 (0.95, 1.27; 11, 45.0%), 1.21 (1.00, 1.48; 12, 82.9%), and 1.26 (1.06, 1.48; 11, 67.7%), respectively. Pooled ESs in the subgroups stratified by blood, urine, and the other specimen were 1.22 (95% CI: 1.08, 1.38; n = 26, I² = 75.8%), 1.06 (1.00, 1.13; 14, 58.1%), and 2.41 (1.30, 4.43; 2, 0.0%), respectively. In conclusion, heavy metal exposure was positively associated with MetS. Further studies are warranted to examine the effects of individual metals and their interaction on the relationship between MetS and heavy metals.

Human exposure to glyphosate has become ubiquitous because of its increasing agricultural use. Recent studies suggest endocrine disrupting effects of glyphosate. Specifically, in our work in rodents, low-dose early-life exposure to Roundup® (glyphosate-based herbicide) lengthened anogenital distance (AGD) in male and female offspring. AGD is a marker of the prenatal hormone milieu in rodents and humans. The relationship between glyphosate exposure and AGD has not been studied in humans. We conducted a pilot study in 94 mother-infant pairs (45 female and 49 male) from The Infant Development and the Environment Study (TIDES). For each infant, two AGD measurements were collected after birth; the anopenile (AGD-AP) and anoscrotal (AGD-AS) distances for males, and anoclitoral (AGD-AC) and anofourchette distances (AGD-AF) for females. We measured levels of glyphosate and its degradation product aminomethylphosphonic acid (AMPA) in 2nd trimester maternal urine samples using ultra-high-performance liquid chromatography-tandem mass spectrometry. We assessed the relationship between exposure and AGD using sex-stratified multivariable linear regression models. Glyphosate and AMPA were detected in 95% and 93% of the samples (median 0.22 ng/mL and 0.14 ng/mL, respectively). Their concentrations were moderately correlated (r = 0.55, p = 5.7 × 10⁻⁹). In female infants, high maternal urinary glyphosate (above the median) was associated with longer AGD-AC (β = 1.48, 95%CI (−0.01, 3.0), p = 0.05), but this was not significant after covariate adjustment. Increased AMPA was associated with longer AGD-AF (β = 1.96, 95%CI (0.44, 3.5), p = 0.01) after adjusting for infant size and age at AGD exam. No associations were detected in male offspring. These preliminary findings partially reproduce our previous results in rodents and suggest that glyphosate is a sex-specific endocrine disruptor with androgenic effects in humans. Given the increasing glyphosate exposures in the US population, larger studies should evaluate potential developmental effects on endocrine and reproductive systems.

Ambient fine particulate matter (PM2.5) pollution has been implicated in the development of hypertensive disorders of pregnancy. However, evidence on the effects of PM2.5-derived chemical constituents on gestational blood pressure (BP) is limited, and the potential mechanisms underlying the association remain unclear. In this study, we repeated three consecutive 72-h personal air sampling and BP measurements in 215 pregnant women for 590 visits during pregnancy. Individual PM2.5 exposure level was assessed by gravimetric method and 28 PM2.5 chemical constituents were analyzed by ED-XRF method. Plasma biomarkers of endothelial function and inflammation were measured using multiplexed immunoassays. Robust multiple linear regression models were used to estimate the associations among personal PM2.5 exposure and chemical constituents, BP changes (compared with pre-pregnancy BP) and plasma biomarkers. Mediation analyses were performed to evaluate underlying potential pathways. Result showed that exposure to PM2.5 was significantly associated with increases in systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP) in the early second trimester. Meanwhile, elevated concentration of lead (Pb) constituent in PM2.5 was significant associated with increases in DBP and MAP after adjusting for PM2.5 total mass. PM2.5 and Pb constituent also presented positive associations with plasma biomarkers of endothelial function (ET-1, E-selectin, ICAM-1) and inflammation (IL-1β, IL-6, TNFα) significantly. After multiple adjustment, elevated ET-1 and IL-6 were significantly correlated with increased gestational BP, and respectively mediated 1.24%–25.06% and 7.01%–10.69% of the increased BP due to PM2.5 and Pb constituent exposure. In conclusion, our results suggested that personal exposure to PM2.5 and Pb constituent were significantly associated with increased BP during pregnancy, and the early second trimester might be the sensitive window of PM2.5 exposure. The endothelial dysfunction and elevated inflammation partially mediated the effect of PM2.5 and Pb constituent on BP during pregnancy.

Anthropogenic heat emission (AHE) is an important driver of urban heat islands (UHIs). Further, both urban thermal environment research and sustainable development planning require an efficient estimation of anthropogenic heat flux (AHF). Therefore, this study proposed an improved multi-source AHF model, which was constructed using diverse data sources and small-scale samples, to better represent the spatiotemporal distribution of AHF. The performances of three machine learning algorithms (Cubist, gradient boosting decision tree, and simple linear regression) were quantitatively evaluated, and the impact of spatiotemporal heterogeneity on AHF estimation was considered for the first time. The results showed that multi-source datasets and sophisticated algorithms could more effectively reduce the estimation error and improve the accuracy of the spatiotemporal distribution of AHF than simple linear regression. In practical applications, the Cubist model performed better, with prediction errors being less than 0.9 W⋅m−2. Further, the characteristics of different heat sources from the model outputs varied widely, and the building metabolic heat exhibited significant seasonal spatiotemporal variations, which were largely determined by the regional climate. In contrast, industrial and transportation heat showed marginal monthly fluctuations. Similarly, spatiotemporal heterogeneity significantly affected the estimation of building metabolic heat (0.62 W⋅m−2), but it did not affect other heat sources. The proposed improved AHF model was verified to effectively capture the spatiotemporal variations of building heat and solve the issue of overestimation of industrial heat in urban regions. This study provides new methods and ideas for the accurate spatiotemporal quantification of AHF that can supplement future studies on climate warming, UHI, and air pollution.

Cadmium (Cd) exposure is recognized as an important risk factor for psychological health, but suitable physical activity may relieve depression. However, it remains unknown whether physical activity (PA) can reduce the effect of cadmium exposure on depression. Therefore, a cross-sectional data from National Health and Nutrition Examination Survey (NHANES) 2015–2018 was used. The Nine-item Patient Health Questionnaire (PHQ-9) was used to assess depression among the participants. PA was calculated according to the metabolic equivalent (MET), weekly frequency, and duration of each activity. Logistic regression and restricted cubic spline models were used to examine the associations of Cd and depression. A total of 5560 adults aged 20 years and above were finally included in this study. The results indicated a positive correlation between blood Cd and depression. The multivariate-adjusted ORs (95% CI) of the highest quartile were 2.290 (1.754–2.990) for depression, which was still significant after controlling other heavy metals (P < 0.05). Under Cd exposure, the high intensity of physical activity group had the lowest risk of depression (OR = 2.226, 95%CI: 1.447–3.425), while the group with no physical activity had the highest risk (OR = 2.443, 95%CI: 1.382–4.318). Our results indicate that inner Cd exposure may be a risk factor for depression, and physical activity can moderate this relationship to some degree.

Most chemical plant wastewater contains both organic and inorganic pollutants, which are easy to diffuse along with surface runoff. The combined pollution of nonylphenol (NP) and cadmium (Cd) in soil is a serious problem that has not attracted enough attention. Based on the effects of selenium (Se) and Pseudomonas aeruginosa (P. aeruginosa) on plant and soil microbial communities, we speculated that the application of Se and P. aeruginosa in soil could improve the phytoremediation efficiency of ryegrass on contaminated soil. In this study, pot experiments with Cd and NP co-contaminated soil were conducted, and the results showed that application of P. aeruinosa alone could improve the removal rates of NP and Cd by ryegrass, and the supplementary of Se further enhanced the effect of micro-phyto remediation, with the highest removal rates of NP and Cd were 79.6% and 49.4%, respectively. The application of P. aeruginosa plus Se reduced the adsorption of Cd and NP through C–O and Si–O–Fe of the soil, changed the enzyme activity, and also affected the changing trend of the microbial community in soil. Pseudomonas, Sphingomonadales, Nitrospira, and other beneficial bacteria were enriched after a 60-day period with P. aeruginosa and Se treatment, thus promoting the removal of NP and Cd. In light of the above results, we suggest that P. aeruginosa application can efficiently facilitate the phytoremediation of ryegrass on Cd-NP co-contaminated soil, and Se supplementation in soil showed the synergistic effect on the remediation.

Kitchen emissions are mixed indoor air pollutants with adverse health effects, but the large-scale assessment is limited by costly equipment and survey methods. This study aimed to discuss the application of backpropagation (BP) neural network models in the assessment of kitchen emissions based on the exposure marker. A total of 3686 participants were recruited for the kitchen survey, and their sleep quality was measured by the Pittsburgh sleep quality index (PSQI). After excluding the confounders, 365 participants were selected to assess their urinary hydroxy polycyclic aromatic hydrocarbons (OH-PAHs) concentrations by ultra-high-performance liquid chromatography/tandem mass spectrometry. Two BP neural network models were then set up using the survey and detection data from the 365 participants and used to predict the total urinary OH-PAHs concentrations of all participants. The total urinary OH-PAHs and 1-hydroxy-naphthalene (1-OHNap) concentrations were significantly higher among the 365 participants with poor sleep quality (global PSQI score > 5; P < 0.05). Results from internal and external validation showed that our model has high credibility (model 2). Further, the participants with higher predicted total urinary OH-PAHs concentrations were associated with the global PSQI score of >5 (odds ratio (OR) = 1.284, 95% confidence interval (CI) = 1.082–1.525 for participants with predicted total urinary OH-PAHs concentrations of over 1.897 μg/mmol creatinine in model 1, and OR = 1.467, 95% CI = 1.240–1.735 for participants with predicted total urinary OH-PAHs concentrations of over 2.253 μg/mmol creatinine in model 2) after adjusting for the confounders. Findings suggest that the BP neural network model is suitable for assessing kitchen emissions, and the urinary OH-PAHs concentrations can be taken as the model outlay.

Systemic neonicotinoid insecticides such as imidacloprid are increasingly applied against insect pest infestations on forest trees. However, leaves falling from treated trees may reach nearby surface waters and potentially represent a neonicotinoid exposure source for aquatic invertebrates. Given imidacloprid's susceptibility towards photolysis and high water solubility, it was hypothesized that the leaves' toxicity might be modulated by UV-irradiation during decay on the forest floor, or by leaching and re-mobilization of the insecticide from leaves within the aquatic ecosystem. To test these hypotheses, the amphipod shredder Gammarus fossarum was fed (over 7 d; n = 30) with imidacloprid-contaminated black alder (Alnus glutinosa) leaves that had either been pre-treated (i.e., leached) in water for up to 7 d or UV-irradiated for 1 d (at intensities relevant during autumn in Central Europe) followed by a leaching duration of 1 d. Gammarids' feeding rate, serving as sublethal response variable, was reduced by up to 80% when consuming non-pretreated imidacloprid-contaminated leaves compared to imidacloprid-free leaves. Moreover, both leaching of imidacloprid from leaves (for 7 d) as well as UV-irradiation reduced the leaves' imidacloprid load (by 46 and 90%) thereby mitigating the effects on gammarids' feeding rate to levels comparable to the respective imidacloprid-free controls. Therefore, natural processes, such as UV-irradiation and re-mobilization of foliar insecticide residues in water, might be considered when evaluating the risks systemic insecticide applications in forests might pose for aquatic organisms in nearby streams.

One of the fundamentals in the ecotoxicological studies is the need of data comparison, which can be easily reached with the help of a standardized biological model. In this context, any biological model has been still proposed for the biomonitoring and risk evaluation of freshwaters until now. The aim of this review is to illustrate the ecotoxicological studies carried out with the zebra mussel Dreissena polymorpha in order to suggest this bivalve species as possible reference organism for inland waters. In detail, we showed its application in biomonitoring, as well as for the evaluation of adverse effects induced by several pollutants, using both in vitro and in vivo experiments. We discussed the advantages by the use of D. polymorpha for ecotoxicological studies, but also the possible limitations due to its invasive nature.