Microplastics have been proven to be hotspots of bacterial pathogens and antibiotic resistance genes (ARGs). The enrichment of ARGs in microplastisphere, the specific niche for diverse microbial communities attached to the surface of microplastic, has attracted worldwide attention. By collecting 477 pairs of ARG abundance data belonging to 26 ARG types, based on the standardized mean difference (SMD) under the random effect model, we have performed the first meta-analysis of the ARG enrichment on microplastics in aquatic environments in order to quantitatively elucidate the enrichment effect, with comparison of non-microplastic materials. It was found that ARGs enriched on the microplastics were more abundant than that on the inorganic substrates (SMD = 0.26) and natural water environments (SMD = 0.10), but lower abundant than that on the natural organic substrates (SMD = −0.52). Furthermore, microplastics in freshwater tended to have a higher degree of ARG enrichment than those in saline water and sewage. The biofilm formation stage, structure, and component of microplastisphere may play a significant role in the enrichment of ARGs.

We provide a comprehensive and updated systematic review and meta-analysis of the association between air pollution exposure and depression, searching PubMed, Embase, and Web of Sciences for relevant articles published up to May 2021, and eventually including 39 studies. Meta-analyses were performed separately according to pollutant type [particulate matter with diameter ≤10 μm (PM₁₀) and ≤2.5 μm (PM₂.₅), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and carbon monoxide (CO)] and exposure duration [short- (<30 days) and long-term (≥30 days)]. Test for homogeneity based on Cochran's Q and I² statistics were calculated and the restricted maximum likelihood (REML) random effect model was applied. We assessed overall quality of pooled estimates, influence of single studies on the meta-analytic estimates, sources of between-study heterogeneity, and publication bias. We observed an increased risk of depression associated with long-term exposure to PM₂.₅ (relative risk: 1.074, 95% confidence interval: 1.021–1.129) and NO₂ (1.037, 1.011–1.064), and with short-term exposure to PM₁₀ (1.009, 1.006–1.012), PM₂.₅ (1.009, 1.007–1.011), NO₂ (1.022, 1.012–1.033), SO₂ (1.024, 1.010–1.037), O₃ (1.011, 0.997–1.026), and CO (1.062, 1.020–1.105). The publication bias affecting half of the investigated associations and the high heterogeneity characterizing most of the meta-analytic estimates partly prevent to draw very firm conclusions. On the other hand, the coherence of all the estimates after excluding single studies in the sensitivity analysis supports the soundness of our results. This especially applies to the association between PM₂.₅ and depression, strengthened by the absence of heterogeneity and of relevant publication bias in both long- and short-term exposure studies. Should further investigations be designed, they should involve large sample sizes, well-defined diagnostic criteria for depression, and thorough control of potential confounding factors. Finally, studies dedicated to the comprehension of the mechanisms underlying the association between air pollution and depression remain necessary.

Several epidemiological studies have evaluated the fractional exhaled nitric oxide (FeNO) of ambient air pollution but the results were controversial. We therefore conducted a systematic review and meta-analysis to investigate the associations between short-term exposure to air pollutants and FeNO level. We searched PubMed and Web of Science and included a total of 27 articles which focused on associations between ambient air pollutants (PM₁₀, PM₂.₅, black carbon (BC), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃)) exposure and the change of FeNO. Random effect model was used to calculate the percent change of FeNO in association with a 10 or 1 μg/m³ increase in air pollutants exposure concentrations. A 10 μg/m³ increase in short-term PM₁₀, PM₂.₅, NO₂, and SO₂ exposure was associated with a 3.20% (95% confidence interval (95%CI): 1.11%, 5.29%), 2.25% (95%CI: 1.51%, 2.99%),4.90% (95%CI: 1.98%, 7.81%), and 8.28% (95%CI: 3.61%, 12.59%) change in FeNO, respectively. A 1 μg/m³ increase in short-term exposure to BC was associated with 3.42% (95%CI: 1.34%, 5.50%) change in FeNO. The association between short-term exposure to O₃ and FeNO level was insignificant (P＞0.05). Future studies are warranted to investigate the effect of multiple pollutants, different sources and composition of air pollutants on airway inflammation.

As the most important gas-phase alkaline species, atmospheric ammonia (NH3) contributes considerably to the formation and development of fine-mode particles (PM2.5), which affect air quality and environmental health. Recent satellite-based observations suggest that the North China Plain is the largest agricultural NH3 emission source in China. However, our isotopic approach shows that the surface NH3 in the intraregional urban environment of Beijing-Tianjin-Shijiazhuang is contributed primarily by combustion-related processes (i.e., coal combustion, NH3 slip, and vehicle exhaust). Specifically, the Batch fractionation model was used to describe the partitioning of gaseous NH3 into particles and to trace the near-ground atmospheric NH3 sources. With the development of haze pollution, the dynamics of δ15N-NH4+ were generally consistent with the fractionation model. The simulated initial δ15N-NH3 values ranged from −22.6‰ to −2.1‰, suggesting the dominance of combustion-related sources for urban NH3. These emission sources contributed significantly (92% on hazy days and 67% on clean days) to the total ambient NH3 in urban cities, as indicated by a Bayesian mixing model. Based on the Batch fractionation model, we concluded the following: 1) δ15N-NH4+ can be used to model the evolution of fine-mode aerosols and 2) combustion-related sources dominate the near-ground atmospheric NH3 in urban cities. These findings highlight the need for regulatory controls on gaseous NH3 emissions transported from local and surrounding industrial sources.

The novel coronavirus disease (COVID-19) is primarily respiratory in nature, and as such, there is interest in examining whether air pollution might contribute to disease susceptibility or outcome. We merged data on COVID-19 cumulative prevalence and fatality rates as of May 31, 2020 with 2014–2019 pollution data from the US Environmental Protection Agency Environmental Justice Screen (EJSCREEN), with control for state testing rates, population density, and population covariate data from the County Health Rankings. Pollution data included three types of air emission concentrations (particulate matter<2.5 μm (PM2.5), ozone and diesel particulate matter (DPM)), and four pollution source variables (proximity to traffic, National Priority List sites, Risk Management Plan (RMP) sites, and hazardous waste treatment, storage and disposal facilities (TSDFs)). Results of mixed model linear multiple regression analyses indicated that, controlling for covariates, COVID-19 prevalence and fatality rates were significantly associated with greater DPM. Proximity to TSDFs was associated to greater fatality rates, and proximity to RMPs was associated with greater prevalence rates. Results are consistent with previous research indicating that air pollution increases susceptibility to respiratory viral pathogens. Results should be interpreted cautiously given the ecological design, the time lag between exposure and outcome, and the uncertainties in measuring COVID-19 prevalence. Areas with worse prior air quality, especially higher concentrations of diesel exhaust, may be at greater COVID-19 risk, although further studies are needed to confirm these relationships.

China continues to suffer from severe acid deposition, despite the government implying a series of policies to control air pollution. In this study, rainwater samples were collected from 2011 to 2016 in Sichuan province to measure the pH values and the concentrations of nine inorganic ions (SO₄²⁻, NO₃⁻, NH₄⁺, Cl⁻, Na⁺, Ca²⁺, K⁺, Mg²⁺, and F⁻), and then to investigate their spatiotemporal variations. Besides, the dominant sources for the acidic ions in the precipitation were also revealed by statistical model. The results showed that the rainwater continued to be highly acidic, and the Volume-Weighted Mean (VWM) pH value was calculated to be 5.18 during 2011 and 2016. NH₄⁺, Ca²⁺, NO₃⁻, and SO₄²⁻ were the dominant water-soluble inorganic ions, accounting for 79.2% of the total ions on average. The remarkable decrease in NO₃⁻ and SO₄²⁻ concentrations (from 75.9 to 54.3 μeq L⁻¹ and from 285 to 145 μeq L⁻¹, respectively) resulted in an increase in the pH value of rainwater from 5.24 in 2011 to 5.70 in 2016. The concentrations of SO₄²⁻, NO₃⁻, F⁻, Na⁺, and K⁺ showed remarkably seasonal variation, with the highest value observed in winter, followed by spring and autumn, and the lowest value observed in summer. High VWM concentration of these ions in winter were mainly due to adverse meteorological conditions (e.g., rare rainfall, lower planetary boundary height, and stagnant air) and intensive anthropogenic emissions. SO₄²⁻, NO₃⁻, and F⁻ ions peaked in the southeastern Sichuan province, which is a typical industrial region. NH₄⁺ concentrations decreased from 268 μeq L⁻¹ in the east to 10.4 μeq L⁻¹ in the western Sichuan province, which could be related to the development of agriculture in the eastern Sichuan province. Ca²⁺ peaked in southeastern Sichuan province due to intensive construction activities and severe stone desertification. On the basis of Positive Matrix Factorization (PMF) analysis, four sources of inorganic ions in rainwater were identified, including anthropogenic source, crust, biomass burning, and aging sea salt aerosol. Geographically Weighted Regression (GWR) was used to find the spatial correlations between the socio-economic factors and ions in the rainwater. At the regional scale, the influence of fertilizer consumption and Gross Agricultural Production (GAP) on NH₄⁺ increased from east to west; moreover the influence of Gross Industrial Production (GIP) on SO₄²⁻ and NO₃⁻ also increased.

Understanding how mercury (Hg) accumulates in the aquatic food web requires information on the factors driving methylmercury (MeHg) contamination. This paper employs data on MeHg in muscle tissue of three black bass species (Largemouth Bass, Spotted Bass, and Smallmouth Bass) sampled from 21 reservoirs in California. During a two-year period, reservoirs were sampled for total Hg in sediment, total Hg and MeHg in water, chlorophyll a, organic carbon, sulfate, dissolved oxygen, pH, conductivity, and temperature. These data, combined with land-use statistics and reservoir morphometry, were used to investigate relationships to size-normalized black bass MeHg concentrations. Significant correlations to black bass MeHg were observed for total Hg in sediment, total Hg and MeHg in surface water, and forested area. A multivariate statistical model predicted Largemouth Bass MeHg as a function of total Hg in sediment, MeHg in surface water, specific conductivity, total Hg in soils, and forested area. Comparison to historical reservoir sediment data suggested there has been no significant decline in sediment total Hg at five northern California reservoirs during the past 20 years. Overall, total Hg in sediment was indicated as the most influential factor associated with black bass MeHg contamination. The results of this study improve understanding of how MeHg varies in California reservoirs and the factors that correlate with fish MeHg contamination.

In this study, data collected from an urban air quality monitoring network are being used for the purpose of evaluating various methodologies used for spatial interpolation in the context of proposing an effective yet simple to apply scheme for PM spatial point estimations. The examined methods are the Inverse Distance Weighting, two linear regression models, the Multiple Linear Regression and the Linear Mixed Model, along with a Feed Forward Neural Network (FFNN) model. These schemes utilize daily PM₁₀ and PM₂.₅ concentrations collected from five and three air quality monitoring sites respectively. In order to obtain the resulted estimations, the leave-one-out cross-validation methodology is used for all methods. The evaluation of their predictive ability is performed by using a combination of difference and correlation statistical measures, scatter plots and statistical tests. The results indicate the usefulness of FFNNs as they are found to be statistically significantly superior for modelling the particulate matter spatial variability. The model performance statistics show that in most cases the error values are considerably lower for the FFNN model. Additionally, the rank and Wilcoxon rank tests reveal that the null hypothesis for equal predictive accuracy is rejected for the majority of monitoring sites and schemes (values lower than the critical t-value). According to the comparison results, the FFNN model is selected for forecasting air quality limit exceedances set by the European Union and World Health Organization air quality standards. For two monitoring sites in which the largest number of exceedances occurred, the probability of detection is high while the probability of false detection is very low, further establishing the neural networks’ predictive ability.

The associations between bisphenol analogues (BPs) exposure and oxidative damage was explored in this 3-year longitudinal study of 275 school children in East China. Nine BPs in first morning urine samples were measured to assess BPs exposure, and 8-hydroxydeoxyguanosine (8-OHdG) and 8-oxo-7,8-dihydroguanosine (8-OHG) were measured as biomarkers of oxidative DNA and RNA damage. Linear mixed model (LMM) was used for repeated measures analysis. School children were mainly exposed to BPA, BPS, BPF, and BPAF (detection frequencies: 97.9%, 42.2%, 13.3%, and 12.8%) with median concentrations of 1.55, 0.355, 0.236 and 0.238 μg g⁻¹Cᵣₑ, respectively. An interquartile range (IQR) increase in urinary BPA was significantly associated with 12.9% (95% CI: 6.1%, 19.6%) increase in 8-OHdG and 19.4% (95% CI: 11.7%, 27.1%) increase in 8-OHG, and for total of BPs (the sum of BPA, BPS, BPF, and BPAF), they were 17.4% (95% CI: 8.9%, 26.0%) for 8-OHdG and 25.9% (95% CI: 16.1%, 35.7%) for 8-OHG, respectively. BPS was positively associated with 8-OHG, but not with 8-OHdG. The study found positive associations of urinary levels of BPA and total BPs with 8-OHdG and 8-OHG and indicated that BPs exposure might cause oxidative RNA damage.