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The purpose of this study was to investigate the influence of increasing sulfate concentrations on chromium removal, to evaluate the effect of the presence of Cr(VI) on sulfate removal by Streptomyces sp. MC1 and to analyze the differential protein expression profile in the presence of this metal for the identification of proteins repressed or overexpressed. In the presence of Cr(VI) but in the absence of sulfate ions, bacterial growth was negligible, showing the Cr(VI) toxicity for this bacterium. However, the sulfate presence stimulated bacterium growth and Cr(VI) removal, regardless of its concentrations. Streptomyces sp. MC1 showed ability to remove chromium and sulfate simultaneously. Also, the sulfate presence favored the decrease of total chromium concentration from supernatants reaching a decrease of 50% at 48 h. In presence of chromium, seven proteins were down‐expressed and showed homology to proteins involved in protein biosynthesis, energy production and free radicals detoxification while two proteins involved in oxidation‐reduction processes identified as dihydrolipoamide dehydrogenase and S‐adenosyl‐l‐methionine synthase were overexpressed.

The 2012 Environmental Performance Index (EPI) ranks 132 countries on 22 performance indicators in the following 10 policy categories: environmental burden of disease, water (effects on human health), air pollution (effects on human health), air pollution (ecosystem effects), water resources (ecosystem effects), biodiversity and habitat, forestry, fisheries, agriculture and climate change. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. Each indicator has an associated environmental public health or ecosystem sustainability target. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The Pilot Trend Environmental Performance Index (Trend EPI) ranks countries on the change in their environmental performance over the last decade. As a complement to the EPI, the Trend EPI shows who is improving and who is declining over time. The 2012 EPI and Pilot Trend EPI were formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 27, 2012. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2012 EPI is at http://epi.yale.edu/.

Poor ambient air quality remains a serious public health issue throughout the world. Current air quality metrics insufficiently monitor exposure to air pollutants. This gap hinders the ability of decisionmakers to address the public health impacts of air pollution. To spur new emission control policies, and ensure implemented solutions realize meaningful gains in environmental health, we develop a framework of public-health-focused air quality indicators that quantifies over 200 countries’ trends in exposure to particulate matter, ozone, nitrogen oxides, sulfur dioxide, carbon monoxide, and volatile organic compounds. We couple population density to ground-level pollutant concentrations to derive population-weighted exposure metrics that quantify the pollutant levels experienced by the average resident in each country. These novel indicators and their applications enable regulators to identify their most critical pollutant exposure trends and allow countries to track the performance of their emissions control policies over time.

This dataset contains terrestrial fluxes of nitrous oxide (N2O), methane (CH4) and ecosystem respiration (carbon dioxide (CO2)) calculated from static chamber measurements in riparian buffers of oil palm plantations on mineral soil, in Riau, Sumatra, Indonesia. Measurements were made monthly, from January 2019 until September 2021, with a break from April 2019 to October 2019 to allow for felling and replanting, and another break from January 2021 to June 2021 due to Covid-19 restrictions. To help to reduce the environmental impact of oil palm plantations, riparian buffers are now required by regulations in many Southeast Asian countries. The experiments were conducted to investigate the impact of greenhouse gas emissions from the riparian buffers. Research was funded through NERC grant NE/R000131/1 Sustainable Use of Natural Resources to Improve Human Health and Support Economic Development (SUNRISE) Greenhouse gas concentrations were measured using static chambers, enclosed for 45 minutes. Multiple regressions (including linear and hierarchical multiple regression) were fitted to calculate the best fit flux, using the RCflux R package, written by Dr Peter Levy (UKCEH).

In developing an algal treatment system, selenium (Se) removal efficiency by Chlorella vulgaris was evaluated under various conditions such as Se concentration, algal density, temperature and pH. A maximum removal efficiency plateau of ∼90% was observed between 1000-3000 μg Se/L while the tolerance of Se toxicity was found at 6000 μg Se/L. C. vulgaris of 0.75 g DW/L showed the highest removal efficiency (84%), and volatilization was dominant below 1.37 g DW/L. Se volatilization was two times higher at 25 °C than at 20 °C in the first 24 h. Moreover, the highest removal efficiency (77%) was obtained at pH 8.0, compared to 66.5% at pH 6.5 and 40% at pH 10.0. To prevent ecotoxicity, Se laden algae were further burned to ashes or filtered out by Anodonta woodiana. After burning, biomass Se was reduced by 99%, with organo-Se entirely converted into inorganic Se, lowering Se bioavailability. A. woodiana removed 54% of Se in 24 h, leading to Se bioaccumulation in soft tissues, which may serve as dietary Se supplements for human health. Our results suggest the cleanup of Se-contaminated water from either agricultural runoff or industrial discharge could be achieved using an algal treatment system with minimum potential ecotoxicity.

Volatile organic compounds (VOCs) can lead to environmental pollution and threaten human health due to their toxic and carcinogenic nature. The emission of VOCs increases dramatically with the accelerated industrialization and economic growth. Adsorption is identified as one of the most promising recovery technologies owing to its cost-effectiveness, flexible operation, and low energy consumption. In particular, adsorption-based technologies have a high potential to recycle both adsorbents and adsorbates, typically to capture valuable aromatic VOCs from industrial exhaust. Porous materials such as carbon-based materials, zeolite-based materials, and organic polymers and their composites have been extensively developed for VOCs adsorption focusing in adsorption capacity, hydrophobic property, thermal stability and regenerability. Among them, porous carbons as VOCs adsorbents have attracted increasingly attention, because they can be regulated by tuning the pore structure for VOCs accessibility during the adsorption process. Moreover, porous carbons can adsorb target VOCs by controlling the pore structure and surface functional groups. Significantly, the pore size distribution of porous carbons mostly controls the VOCs sorption process. Micropores provide the main adsorption sites, while mesopores enhance the diffusion of VOCs. In this review, the adsorption mechanism of VOCs onto porous carbons was generally concluded. Porous carbons can be designed as a specific structure for adsorption of aromatic VOCs by controlling the pore structure, hydrophobic sites, π-electronic structure, and surface functional groups. Since there are limited review literatures on porous carbons derived from renewable resources for VOCs adsorption, this paper will provide an overview on the synthesis of porous carbons from biomass and other organic wastes for VOCs adsorption or integrated oxidation processes (e.g., photocatalysis, non-thermal plasma catalysis, chemical catalysis) under ambient conditions with the objective of guiding ...

This paper reports a study conducted by students as an independent research project under the mentorship of a research scientist at the National Institute of Education, Singapore. The aim of the study was to explore the relationships between local environmental stressors and physiological responses from the perspective of citizen science. Starting from July 2021, data from EEG headsets were complemented by those obtained from smartwatches (namely heart rate and its variability and body temperature and stress score). Identical units of a wearable device containing environmental sensors (such as ambient temperature, air pressure, infrared radiation, and relative humidity) were designed and worn, respectively, by five adolescents for the same period. More than 100,000 data points of different types—neurological, physiological, and environmental—were eventually collected and were processed through a random forest regression model and deep learning models. The results showed that the most influential microclimatic factors on the biometric indicators were noise and the concentrations of carbon dioxide and dust. Subsequently, more complex inferences were made from the Shapley value interpretation of the regression models. Such findings suggest implications for the design of living conditions with respect to the interaction of the microclimate and human health and comfort.

The present study was conducted in Benin to ascertain the association between exposure to combustion of solid fuel (coal and biomass) and tuberculosis.Cases were consecutive, sputum smear-positive tuberculosis patients never previously treated for tuberculosis for as long as 1 month. Two controls were selected from the neighbourhood of each case, matched by age and sex by a predefined procedure.A total of 200 new smear-positive cases and 400 neighbourhood controls were enrolled. In univariate analysis, using solid fuel for cooking (OR 1.7, 95% CI 1.1–2.8), ever smoking (OR 5.5, 95% CI 3.1–9.8), male sex (OR 10.5, 95% CI 1.6–71.1), daily use of alcoholic beverages (OR 2.3, 95% CI 1.2–4.2) and having a family member with tuberculosis in the previous 5 yrs (OR 30.5, 95% CI 10.8–85.8) were all significantly associated with tuberculosis cases. When all significant variables were entered into a multivariate conditional logistic regression model, the association between using solid fuel for cooking and tuberculosis cases was no longer statistically significant (adjusted OR 1.4, 95% CI 0.7–2.7).In conclusion, the association between exposure to combustion of solid fuel and tuberculosis was relatively weak and not statistically significant.

A chemical multisensor array is used in combination with an artificial neural network to estimate the biomass concentration and specific growth rate in a recombination Escherichia coli batch cultivation. It is shown that by providing sufficient information to the artificial neural network, an accuracy comparable to that of an established dry weight method can be achieved. The obtained prediction error (1 sigma) of 0.043 g l-1 for biomass compares well with the error of the dry weight method in this low biomass concentration range (0.1-3 g l-1). The prediction for the specific growth rate is accurate during important parts of the cell growth (1 sigma = 0.025 h-1). The results show that this non-invasive method is potentially useful for estimating biomass and specific growth rate on-line in bioprocesses.