Mercury (Hg) is a persistent and widespread heavy metal with neurotoxic effects in wildlife. While bioaccumulation of Hg has historically been studied in aquatic food webs, terrestrial consumers can become contaminated with Hg when they feed on aquatic organisms (e.g., emergent aquatic insects, fish, and amphibians). However, the extent to which dietary connectivity to aquatic ecosystems can explain patterns of Hg bioaccumulation in terrestrial consumers has not been well studied. Bats (Order: Chiroptera) can serve as a model system for illuminating the trophic transfer of Hg given their high dietary diversity and foraging links to both aquatic and terrestrial food webs. Here we quantitatively characterize the dietary correlates of long-term exposure to Hg across a diverse local assemblage of bats in Belize and more globally across bat species from around the world with a comparative analysis of hair samples. Our data demonstrate considerable interspecific variation in hair total Hg concentrations in bats that span three orders of magnitude across species, ranging from 0.04 mg/kg in frugivorous bats (Artibeus spp.) to 145.27 mg/kg in the piscivorous Noctilio leporinus. Hg concentrations showed strong phylogenetic signal and were best explained by dietary connectivity of bat species to aquatic food webs. Our results highlight that phylogeny can be predictive of Hg concentrations through similarity in diet and how interspecific variation in feeding strategies influences chronic exposure to Hg and enables movement of contaminants from aquatic to terrestrial ecosystems.

Air pollutant levels depend on emissions but can also be affected by the meteorological situation. We examined air pollutant trends (PM₁₀, NO₂, O₃ and SO₂) in Slovenia, where in the past the main issue were SO₂ levels. Now, the population is still exposed to PM₁₀ and ozone levels that are above the recommended levels.Our goal was to assess if the levels of air pollutants were decreasing from 2002 to 2017 due to emission ceilings or were more influenced by changes in the meteorological situation. We modelled the relationship between levels, meteorological parameters, and seasonality and then used the models with the best estimated generalisation to adjust levels for meteorology. Models showed a significant relationship between meteorological parameters and PM₁₀, NO₂, and O₃ levels, but not SO₂. We analysed trends of raw and adjusted levels and compared them. Trends of PM₁₀ and SO₂ were decreasing at all locations for raw and adjusted data. The largest decrease was observed in SO₂ levels where the largest decrease in emissions occurred. Trends of NO₂ were also significant and negative at most locations. Levels of O₃ did not exhibit a significant trend at most locations.Results show that changes in the meteorological situation affected PM₁₀ levels the most, especially where the entire period (2002–2017) could be observed. There is strong empirical evidence that changes in meteorological parameters contributed to the decrease in PM₁₀ levels while the decrease in NO₂ and SO₂ levels can be attributed to emission ceilings.

This study systematically explored the effect of humic acid (HA) (as model of natural organic matter) on the kinetics, products and transformation pathway of triclosan (TCS) by laccase-catalyzed oxidation. It was found that TCS could be effectively transformed by laccase-catalysis, with the apparent second-order rate constant being 0.056 U⁻¹ mL min⁻¹. HA inhibited the removal rate of TCS. HA-induced inhibition was negatively correlated with HA concentration in the range of 0–10 mg L⁻¹ and pH-dependent from 3.5 to 9.5. FT-IR and ¹³C NMR spectra showed a decrease of aromatic hydroxyl (phenolic) groups and an increase of aromatic ether groups, indicating the cross-linking of HA via C-O-C and C-N-C bonds during enzyme-catalyzed oxidation. Ten principle oxidative products, including two quinone-like products (2-chlorohydroquinone, 2-chloro-5-(2,4-dichlodichlorophenoxy)-(1,4)benzoquinone), one chlorinated phenol (2,4-dichlorophenol (2,4-DCP)), three dimers, two trimmers and two tetramers, were detected by gas chromatograghy/mass spectrometry (GC-MS) and high performance liquid chromatography/quadrupole time-of-flight/mass spectrometry (HPLC/Q-TOF/MS). The presence of HA induced significantly lesser generation of self-polymers and enhanced cross-coupling between HA and self-polymers via C-O-C, C-N-C and C-C coupling pathways. A plausible transformation pathway was proposed as follows: TCS was initially oxidized to form reactive phenoxyl radicals, which self-coupled to each other subsequently by C-C and C-O pathway, yielding self-polymers. In addition, the scission of ether bond was also observed. The presence of HA can promote scission of ether bond and further oxidation of phenoxyl radicals, forming hydroxylated or quinone-like TCS. This study shed light on the behavior of TCS in natural environment and engineered processes, as well provided a perspective for the water/wastewater treatment using enzyme-catalyzed oxidation techniques.

Ambient concentrations of fine particulate matter (PM₂.₅) concentration is often used as an exposure surrogate to estimate PM₂.₅ health effects in epidemiological studies. Ignoring the potential variations in the amount of outdoor PM₂.₅ infiltrating into indoor environments will cause exposure misclassification, especially when people spend most of their time indoors. As it is not feasible to measure the PM₂.₅ infiltration factor (Fᵢₙf) for each individual residence, we aimed to build models for residential PM₂.₅Fᵢₙf prediction and to evaluate seasonal Fᵢₙf variations among residences. We repeated collected paired indoor and outdoor PM₂.₅ filter samples for 7 continuous days in each of the three seasons (hot, cold and transitional seasons) from 48 typical homes of Shanghai, China. PM₂.₅-bound sulfur on the filters was measured by X-ray fluorescence for PM₂.₅Fᵢₙf calculation. We then used stepwise-multiple linear regression to construct season-specific models with climatic variables and questionnaire-based predictors. All models were evaluated by the coefficient of determination (R²) and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV). The 7-day mean (±SD) of PM₂.₅Fᵢₙf across all observations was 0.83 (±0.18). Fᵢₙf was found higher and more varied in transitional season (12–25 °C) than hot (>25 °C) and cold (<12 °C) seasons. Air conditioning use and meteorological factors were the most important predictors during hot and cold seasons; Floor of residence and building age were the best transitional season predictors. The models predicted 60.0%–68.4% of the variance in 7-day averages of Fᵢₙf, The LOOCV analysis showed an R² of 0.52 and an RMSE of 0.11. Our finding of large variation in residential PM₂.₅Fᵢₙf between seasons and across residences within season indicated the important source of outdoor-generated PM₂.₅ exposure heterogeneity in epidemiologic studies. Our models based on readily available data may potentially improve the accuracy of estimates of the health effects of PM₂.₅ exposure.

It is critical to estimate the exposure to indoor air pollution of residents spending most of their time in such microenvironments. However, the understanding regarding PM2.5 exposure in residential indoor environments is very limited. In this study, we collected participants' basic information and time–activity patterns, as well as details of other factors related to indoor air pollution exposure, through questionnaires presented to a large population in 37 counties of China. Continuous monitoring of ambient PM2.5 concentrations was performed using an environmental fixed-site monitoring network. Residential indoor PM2.5 concentrations were predicted using a mass balance model based on the data obtained. Evaluation of continuous daily average residential indoor PM2.5 exposure doses for large populations during winter revealed concentrations ranged from 67 to 195 μg/m3. Finally, differences in residential indoor PM2.5 exposure between northern and southern China were investigated. The results suggested that residential indoor PM2.5 concentrations in northern China, associated with heating, were higher than in the south. The established model could be important for improved understanding of human exposure to indoor PM2.5 air pollution.

Poultry-emitted air pollutants, including particulate matter (PM) and ammonia, have raised concerns due to potential negative effects on human health and the environment. However, developing and optimizing remediation technologies requires a better understanding of air pollutant concentrations, the emission plumes, and the relationships between the pollutants. Therefore, we conducted ten field experiments to characterize PM (total suspended particulate [TSP], particulate matter less than 10 μm in aerodynamic diameter [PM10], and particulate matter less than 2.5 μm in aerodynamic diameter [PM2.5]) and ammonia emission-concentration profiles from a typical commercial poultry house. The emission factors of the poultry house, which were calculated using the concentrations and fan speed, were 0.66 (0.29–0.99) g NH3-N bird−1d−1 for ammonia, 52 (44–168) g d−1AU−1 (AU = animal unit = 500 kg) for TSP, 3.48 (1.16–9.03) g d−1AU−1 for PM10, and 0.07 (0.00–0.36) g d−1AU−1 for PM2.5. PM and ammonia emission concentrations decreased as distance from the fan increased. Although emission concentrations were similar in the daytime and nighttime, diurnal and nocturnal plume shapes were different due to the increased stability of the atmosphere at night. Particle size distribution analysis revealed that, at a given height, the percentage of PM10 and PM2.5 was consistent throughout the plume, indicating that the larger particles were not settling out of the airstream faster than the smaller particles. Overall, the direction of the measured air pollutant emission plumes was dominated by the tunnel fan ventilation airflow rate and direction instead of the ambient wind speed and direction. This is important because currently-available air dispersion models use ambient or modeled wind speed and direction as input parameters. Thus, results will be useful in evaluating dispersion models for ground-level, horizontally-released, point sources and in developing effective pollutant remediation strategies for emissions.

A method was proposed to evaluate comprehensive effects of pHs and total metal concentration (TMC) variation for metal speciation human health risk in groundwater. The method used for the health assessment considered comprehensive and mutative effects caused by oral ingestion of groundwater based on human health risk assessment model and MINTEQ simulation. The results demonstrated that the dissolution rate of Ni2+ was affected by pH and Ni total concentration (total-Ni). With the increase of pH, the Ni2+ dissolved rate was smaller in the higher total-Ni at same pH. Ni2+ was dominant components contributed to health risk in groundwater. With the increase of pH in various total-Ni, HINi keep constant at first, and then decreased gradually. The HINi values of Ni speciation above acceptable level only in high total-Ni with alkaline conditions. The obtained results to verify that metals speciation were determined in health risk, and variation factors (pH and metal total concentration) played important role in risk estimation. These results provide basic information of heavy metal pollution control as well as remediation management.

Although several air pollution studies have examined the relationship between people living close to roadways and human health, we are unaware of studies that have examined the distance-decay of this effect based on a snapshot of congestion and focused on a micro-level traffic emission inventory. In this paper we estimate the distance-decay of premature mortality risk related to PM₂.₅ emitted by traffic congestion in Hamilton, Canada, in 2011 We employ the Stochastic User Equilibrium (SUE) traffic assignment algorithm to estimate congested travel times for each road link in our study area. Next, we used EPA's MOVES model to estimate mass of PM₂.₅, and then R-line dispersion model to predict concentration of PM₂.₅. Finally, we apply Integrated Exposure Response Function (IERF) to estimate PM₂.₅-related premature mortality at 100 m × 100 m grid resolution. We estimated total premature mortality over Hamilton to be 73.10 (95%CI: 39.05; 82.11) deaths per year. We observed that the proximity to a roadway increases the risk of premature mortality and the strength of this risk decreases as buffer sizes are increased. For example, we estimated that the premature mortality risk within buffer 0–100 m is 29.5% higher than for the buffer 101–200 m, 179.3% higher than for the buffer 201–300 m, and 566% higher than for the buffer 301–400 m. Our study provides a new perspective on exposure increments from traffic congestion. In particular, our findings show health effects gradients across neighborhoods, capturing microscale near-road exposure up to 2000 m of the roadway. Results from this research can be useful for policymakers to develop new strategies for the challenges of regulating transportation, land use, and air pollution.

PM₁ might be more hazardous than PM₂.₅ (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM₁ concentrations and its health effects are limited due to a lack of PM₁ monitoring data.To estimate spatial and temporal variations of PM₁ concentrations in China during 2005–2014 using satellite remote sensing, meteorology, and land use information.Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) data, Dark Target (DT) and Deep Blue (DB), were combined. Generalised additive model (GAM) was developed to link ground-monitored PM₁ data with AOD data and other spatial and temporal predictors (e.g., urban cover, forest cover and calendar month). A 10-fold cross-validation was performed to assess the predictive ability.The results of 10-fold cross-validation showed R² and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m³, respectively. For seasonal prediction, the R² and RMSE were 77% and 11.4 μg/m³, respectively. The predicted annual mean concentration of PM₁ across China was 26.9 μg/m³. The PM₁ level was highest in winter while lowest in summer. Generally, the PM₁ levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM₁ levels increased substantially in the South-Western Hebei and Beijing-Tianjin region.GAM with satellite-retrieved AOD, meteorology, and land use information has high predictive ability to estimate ground-level PM₁. Ambient PM₁ reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM₁.

Integrated compensatory responses of physiological systems towards homeostasis are generally overlooked when it comes to analysing alterations in biochemical parameters indicative of such processes. Here an hypothesis-driven multivariate analysis accounting for interactive multibiomarker responses was used to investigate effects of long-term exposure of Carcinus maenas to Hazardous and Noxious Substances (HNS). Adult male crabs were exposed to low and high post-spill levels of acrylonitrile (ACN) or aniline (ANL) for 21d. Bioaccumulation, feeding behaviour, and biomarkers related to mode-of-action (MoA) (detoxification, neurotransmission and energy production) were evaluated over time. Distinct temporal patterns of response to low and high exposure concentrations were depicted, with a main set of interactive multibiomarker predictors identified for each HNS (five for ACN and three for ANL), useful to follow coupled evolvement of biomarker responses. ACN caused peripheral neurotoxic effects coupled with enhanced biotransformation and significant oxidative damage particularly relevant in gills. ANL elicited alterations in central neurotransmission affecting ventilation coupled with very low levels of oxidative damage in gills. Results indicate chronic toxicity data are determinant to improve HNS hazard assessment if the aim is to obtain reliable risk calculations, and develop effective predictive models avoiding overestimation but sufficiently protective. Accounting for multibiomarker interactions brought otherwise overlooked information about C. maenas responses and MoA of ACN and ANL.