Accumulating evidence suggests that short- and long-term exposure to ambient fine particulate matter <= 2.5 mu m (PM2.5) during pregnancy is associated with preterm births, yet the results are inconsistent, and the shape of the exposure-response curve is unclear, partially due to the limited studies conducted in areas with high air pollution. Our study evaluated the association between ambient PM2.5 concentration and preterm births in Beijing, China.Daily preterm birth data were collected from a hospital in Beijing during 2006 to 2013; a time-series of daily PM2.5 concentrations during the same period is assembled with measured data at three monitoring sites in Beijing. An extension of the Poisson regression and a time-series model were applied to simultaneously estimate the acute and chronic effects of exposure to PM2.5, with mutual adjustment for short- and long-term exposure as well as for confounders.During the study period, the PM2.5 concentration was 70.4 +/- 60.6 mu g/m(3) and was found to be associated with an increased risk of preterm birth. In the study cohort, a 0.52% (95% confidence interval, CI: 0.09%, 0.96%) and 3.13% (95% CI: 1.92%, 4.35%) increase in preterm births was estimated for each 10-mu g/m(3) increase in short-and long-term exposure, respectively. This association was significantly modified by season (p < 0.05). With mutual adjustments for short- and long-term exposure, a more robust association (3.16%, 95% CI: 1.95%, 4.39%; per 10-mu g/m(3) increment in PM2.5) was observed for chronic effects. The exposure-response relationships for both short- and long-term exposure were linear, without a threshold, over the relatively low exposure range and flattened out at higher concentration levels. The maximum effect for long-term exposure to PM2.5 (33.6%) was much greater than that for short-term exposure (19.9%). These findings indicate that air quality improvements over a long period could yield significant health benefits. (C) 2018 Published by Elsevier B.V.
The adsorption of aqueous ions onto natural mineral surfaces controls numerous mineral–water interactions and is governed by, among other numerous factors, ion dehydration and hydrolysis. This work explored the extent to which dehydration and hydrolysis affect the adsorption of three metal cations, Al3+, Cr3+, and Mn2+, onto quartz (SiO2) and corundum (Al2O3) surfaces at pH 3.8 through the integration of flow microcalorimetry (FMC), quartz crystal microbalance with dissipation (QCM-D) measurements and density functional theory (DFT) calculations. At pH 3.8, negligible amounts of Mn2+ and Al3+ are hydrolyzed, while 78% of Cr3+ exist in hydrolyzed species. QCM-D and FMC measurements showed that Al3+ and Cr3+ adsorb to both surfaces, while Mn2+ adsorbed only to Al2O3. DFT bond energy calculations confirmed the favorable bonding between the mineral surfaces and Al3+ and Cr3+, and that Mn2+ adsorption onto SiO2 was unfavorable. Furthermore, FMC showed that on both surfaces, the adsorption of Al3+ was endothermic and reversible, while that of Cr3+ was exothermic and partially irreversible. Through the integration of experimental and computational methods, this work suggested that the reversible adsorption of unhydrolyzed cations (Mn2+ and Al3+) occurred through weak electrostatic interactions. The large energy cost required to dehydrate unhydrolyzed cations resulted in an endothermic adsorption process. Meanwhile, hydrolyzed Cr3+ species can adsorb on quartz and corundum through covalent-bond formation, and thus, their adsorption was partially irreversible. Furthermore, the hydrolysis of Cr3+ lowered the dehydration energy during adsorption, resulting in an exothermic adsorption. By using bond energies as a guide to indicate the possibility of thermodynamically favored adsorption, there was a strong agreement between the DFT and experimental techniques. The findings presented here contribute to understanding and predicting various mineral–water interfacial processes in the natural environment.
Fe(II) is an excellent promoter for advanced oxidation processes (AOPs) because of its environmental ubiquity and low toxicity. This study is among the first to characterize the reaction of peracetic acid (PAA) with Fe(II) ion and apply the Fe(II)/PAA AOP for degradation of micropollutants. PAA reacts with Fe(II) (k = 1.10 × 105–1.56 × 104 M–1 s–1 at pH 3.0–8.1) much more rapidly than H2O2 and outperforms the coexistent H2O2 for reaction with Fe(II). While PAA alone showed minimal reactivity with methylene blue, naproxen, and bisphenol-A, significant abatement (48–98%) of compounds was observed by Fe(II)/PAA at initial pH of 3.0–8.2. The micropollutant degradation by Fe(II)/PAA exhibited two kinetic phases (very rapid then slow) related to PAA and H2O2, respectively. Based on experimental evidence, formation of carbon-centered radicals (CH3C(O)O•, CH3C(O)•, and •CH3), •OH, and Fe(IV) reactive intermediate species from the PAA and Fe(II) reactions in the presence of H2O2 is hypothesized. The carbon-centered radicals and/or Fe(IV) likely played an important role in micropollutant degradation in the initial kinetic phase, while •OH was important in the second reaction phase. The transformation products of micropollutants showed lower model-predicted toxicity than their parent compounds. This study significantly advances the understanding of PAA and Fe(II) reaction and demonstrates Fe(II)/PAA to be a feasible advanced oxidation technology.
The potential environmental impact of air pollutants emitted from the oil sands industry in Alberta, Canada, has received considerable attention. The mining and processing of bitumen to produce synthetic crude oil, and the waste products associated with this activity, lead to significant emissions of gaseous and particle air pollutants. Deposition of pollutants occurs locally (i.e., near the sources) and also potentially at distances downwind, depending upon each pollutant's chemical and physical properties and meteorological conditions. The Joint Oil Sands Monitoring Program (JOSM) was initiated in 2012 by the Government of Canada and the Province of Alberta to enhance or improve monitoring of pollutants and their potential impacts. In support of JOSM, Environment and Climate Change Canada (ECCC) undertook a significant research effort via three components: the Air, Water, and Wildlife components, which were implemented to better estimate baseline conditions related to levels of pollutants in the air and water, amounts of deposition, and exposures experienced by the biota. The criteria air contaminants (e.g., nitrogen oxides [NOx], sulfur dioxide [SO2], volatile organic compounds [VOCs], particulate matter with an aerodynamic diameter <2.5 m [PM2.5]) and their secondary atmospheric products were of interest, as well as toxic compounds, particularly polycyclic aromatic compounds (PACs), trace metals, and mercury (Hg). This critical review discusses the challenges of assessing ecosystem impacts and summarizes the major results of these efforts through approximately 2018. Focus is on the emissions to the air and the findings from the Air Component of the ECCC research and linkages to observations of contaminant levels in the surface waters in the region, in aquatic species, as well as in terrestrial and avian species. The existing evidence of impact on these species is briefly discussed, as is the potential for some of them to serve as sentinel species for the ongoing monitoring needed to better understand potential effects, their potential causes, and to detect future changes. Quantification of the atmospheric emissions of multiple pollutants needs to be improved, as does an understanding of the processes influencing fugitive emissions and local and regional deposition patterns. The influence of multiple stressors on biota exposure and response, from natural bitumen and forest fires to climate change, complicates the current ability to attribute effects to air emissions from the industry. However, there is growing evidence of the impact of current levels of PACs on some species, pointing to the need to improve the ability to predict PAC exposures and the key emission source involved. Although this critical review attempts to integrate some of the findings across the components, in terms of ECCC activities, increased coordination or integration of air, water, and wildlife research would enhance deeper scientific understanding. Improved understanding is needed in order to guide the development of long-term monitoring strategies that could most efficiently inform a future adaptive management approach to oil sands environmental monitoring and prevention of impacts.Implications: Quantification of atmospheric emissions for multiple pollutants needs to be improved, and reporting mechanisms and standards could be adapted to facilitate such improvements, including periodic validation, particularly where uncertainties are the largest. Understanding of baseline conditions in the air, water and biota has improved significantly; ongoing enhanced monitoring, building on this progress, will help improve ecosystem protection measures in the oil sands region. Sentinel species have been identified that could be used to identify and characterize potential impacts of wildlife exposure, both locally and regionally. Polycyclic aromatic compounds are identified as having an impact on aquatic and terrestrial wildlife at current concentration levels although the significance of these impacts and attribution to emissions from oil sands development requires further assessment. Given the improvement in high resolution air quality prediction models, these should be a valuable tool to future environmental assessments and cumulative environment impact assessments.
To better understand the fate and transport of ferrihydrite nanoparticles (FNPs), which carry many contaminants in natural and engineered aquatic environments, the aggregation of FNPs was systematically investigated in this study. The pH isoelectric point (pHIEP), surface zeta potential, and particle size evolutions of FNPs were measured under varied aqueous conditions using dynamic light scattering (DLS). The influence of pH (5.0 ± 0.1 and 7.0 ± 0.1), ionic strength (IS), electrolytes (NaCl, CaCl2 and Na2SO4), and organics (humic acid, fulvic acid and CH3COONa) on the aggregation behaviors of FNPs were explored. Meanwhile, Derjaguin-Landau-Verwey-Overbeek (DLVO) theory was employed to better understand the controlling mechanisms of FNP aggregation. In the presence of sulfate, the surface charge of FNPs was neutralized under varied pH and ionic strength due to ion adsorption and FNPs phase transformation to schwertmannite based on FT-IR results. This phase transformation resulted in rapid aggregation in all water chemistries tested, whereas other salt species affected the aggregation primarily by ion adsorption and charge screening. Presence of increasing concentrations of the organic acids significantly shifted the pHIEP of FNPs (7.0 ± 0.2) to lower pH (< 4.0) due to adsorption of organics on FNPs surfaces making them negatively charged. The adsorption of HA/FA inhibited FNP aggregation significantly while CH3COONa did not, due to different effects on steric and/or electrosteric interactions among FNPs by organics with varied pKa values and molecular weights. After accounting for the important effects of pH, electrolytes, and organics in modifying FNPs’ surface charge, DLVO calculations agreed well with measured critical coagulation concentrations (CCC) values of FNPs at both pH 5.0 ± 0.1 and 7.0 ± 0.1 in the presence of NaCl. This study will hence be useful to better predict and control the fate and transport of FNPs in the presence of electrolytes and organics with different molecular weights, as well as the fate of the associated contaminants in natural and engineered systems.
This paper employs industrial robot installations that represent the level of smart manufacturing as the proxy variable of artificial intelligence(AI). Based on crosscountry panel data and China’s provincial panel data, we create a two-stage least square(2 SLS) regression model to examine the effect of an aging population on AI applications and AI’s effect on economic growth. In this manner, we aim to test whether AI has a substitutive effect on labor against the backdrop of an aging society and the kind of such a substitutive effect. Our findings suggest that the labor shortage arising from an aging society will prompt an economy to adopt smart manufacturing more broadly, i.e. an aging society is a driver of AI development. Smart manufacturing has a positive effect on local GDP and helps shore up the slowing economy resulting from an aging society. AI is an important tool for coping with the challenges of an aging society. Current AI development is an "induced innovation," and its substitutive relationship with labor is a "supplemental substitution" rather than "crowding-out substitution." If these characteristics are properly maintained, AI will contribute more to China’s economy against the backdrop of an aging society.
Abstract The mechanisms underlying elevation patterns in species and phylogenetic diversity remain a central issue in ecology and are vital for effective biodiversity conservation in the mountains. Gongga Mountain, located in the southeastern Qinghai–Tibetan Plateau, represents one of the longest elevational gradients (ca. 6,500 m, from ca. 1,000 to 7,556 m) in the world for studying species diversity patterns. However, the elevational gradient and conservation of plant species diversity and phylogenetic diversity in this mountain remain poorly studied. Here, we compiled the elevational distributions of 2,667 native seed plant species occurring in Gongga Mountain, and estimated the species diversity, phylogenetic diversity, species density, and phylogenetic relatedness across ten elevation belts and five vegetation zones. The results indicated that species diversity and phylogenetic diversity of all seed plants showed a hump-shaped pattern, peaking at 1,800–2,200 m. Species diversity was significantly correlated with phylogenetic diversity and species density. The floras in temperate coniferous broad-leaved mixed forests, subalpine coniferous forests, and alpine shrublands and meadows were significantly phylogenetically clustered, whereas the floras in evergreen broad-leaved forests had phylogenetically random structure. Both climate and human pressure had strong correlation with species diversity, phylogenetic diversity, and phylogenetic structure of seed plants. Our results suggest that the evergreen broad-leaved forests and coniferous broad-leaved mixed forests at low to mid elevations deserve more conservation efforts. This study improves our understanding on the elevational gradients of species and phylogenetic diversity and their determinants and provides support for improvement of seed plant conservation in Gongga Mountain.