Bullocksteenberg4007

5-methylbenzotriazole (5-TTri) and 5-chlorobenzotriazole (CBT) are two benzotriazole derivatives widely used in various industrial and domestic applications. This paper reports on the photochemical behaviour of 5-TTri and CBT in aqueous solutions under UV radiation at 254 nm and the influences of pH, salinity, metal species and humic acid (HA) on their photo-transformation processes. The photolysis of 5-TTri and CBT under the exposure to UV light were found to follow the first-order reaction kinetic in all cases with half-lives ranging from 7.1 h to 24.3 h for 5-TTri and 5.1 h-20.5 h for CBT in various aqueous solutions containing metal ions and HA. The photolysis rates for both 5-TTri and CBT were strongly dependent on the solution pH value, and decreased with increasing solution pH. Salinity, metal species Cu2+ and Fe3+, and especially HA had inhibitory effects on the photolysis of 5-TTri and CBT under UV light irradiation at 254 nm. We proposed the tentative photo transformation schemes for both 5-TTri and CBT, which involved two photoproducts (4-methylaniline and N, N-diethylaniline- p-toluidine) and three photoproducts (4-chloroaniline, Aniline and 2,6-diethylaniline), respectively, via N-N and N-NH bond scission and dechlorination process.A line is a common geometry for pollution sources, e.g., outdoor traffic pollution, and is thus useful for developing a suitable source term estimation (STE) method. Most existing methods regard the source as a single point that only includes location and strength parameters; however, limited attention has been paid to the geometric information of the source. This negligence may cause errors, or even failure, in the STE. Therefore, this paper proposes a line source estimation method that combines Bayesian inference with the super-Gaussian function. This function can approximate the shape of sources with several intuitive coefficients, which are adjusted to their true value through Bayesian inference. The performance of the proposed method was evaluated through estimation of a line source in two cases an ideal urban boundary layer, via simulation, and a complex urban square, via a wind tunnel experiment. The results demonstrate that this method is capable of identifying the source information without any prior geometric information regarding the source. Moreover, it was confirmed that the conventional point-based assumption method leads to failure in estimating the line source, which implies that geometry estimation is necessary for STE.

It is well known that air pollution causes respiratory morbidity and mortality by inducing airway inflammation. However, whether long-term exposure to air pollution is associated with increased incidence of chronic obstructive pulmonary disease (COPD) is controversial.

We conducted a systematic review and meta-analysis with a random-effects model to calculate the pooled risk estimates of COPD development per 10μg/m

increase in individual air pollutants. PubMed, Embase, and Cochrane Library were searched from the date of their inception to August 2019 to identify long-term (at least three years of observation) prospective longitudinal studies that reported the risk of COPD development due to exposure to air pollutants. The air pollutants studied included particulate matter (PM

and PM

) and nitrogen dioxide (NO

).

Of the 436 studies identified, seven met our eligibility criteria. Among the seven studies, six, three, and five had data on PM

, PM

, and NO

, respectively. The meta-analysis results showed that a 10μg/m

increase in PM

is associated with increased incidence of COPD (pooled HR 1.18, 95% CI 1.13-1.23). We also noted that a 10μg/m

increase in NO

is marginally associated with increased incidence of COPD (pooled HR 1.07, 95% CI 1.00-1.16). PM

seems to have no significant impact on the incidence of COPD (pooled HR 0.95, 95% CI 0.83-1.08), although the number of studies was too small. Meta-regression analysis found no significant effect modifiers.

Long-term exposure to PM

and NO

can be associated with increased incidence of COPD.

Long-term exposure to PM2.5 and NO2 can be associated with increased incidence of COPD.A circadian clock may underlie pesticide resistance mechanisms in organisms that are very important for humans, for example, in the honey bee (Apis mellifera). Using the gas chromatography, we evaluated the daily variability in the λ-cyhalothrin degradation rate in bodies of guards and forager bees, Apis mellifera. Additionally, using the RT-qPCR method, we studied expression levels of selected cytochrome P450 genes after exposure to λ-cyhalothrin. During 48-h-tests, we exposed bees to λ-cyhalothrin at four crucial times of the day at 0430 a.m., 1130 a.m., 0630 p.m., and 1130 p.m. The results obtained indicate that in bees the intensity of the λ-cyhalothrin degradation is the highest during first 6 h after intoxication, when it disappeared at the rate of 14.29% h-1, 11.43% h-1, 13.15% h-1, and 12.50% h-1 in bees treated at noon, sunset, midnight, and sunrise, respectively. In the later period (6-48 h of the experiment), the degradation stopped and its rate did not exceed 1.0% h-1. In the control group of bees we demonstrated that the increase in the Cyp9Q1 and Cyp9Q3 expression was the highest during the experiments started at 0430 a.m., while the highest elevation in the Cyp9Q2 expression was observed in the group for which the experiments started at 1130 p.m.In intoxicated honey bees, the highest increase in the Cyp9Q1 expression occurred in the group treated with the pesticide at 1130 a.m. In the case of genes encoding Cyp9Q2 and Cyp9Q3, the highest rise in the expression took place at 0630 p.m.The obtained results indicate that honey bees activate detoxifying mechanisms partly protecting them against the effects of hazardous substances absorbed from the environment more efficiently during foraging than at other times of the day.In this work, a novel cellulose aerogel (CNC-PVAm/rGO) was fabricated using cellulose nanocrystalline (CNC) modified with polyvinylamine (PVAm) and reduced graphene oxide (rGO). The resultant CNC-PVAm/rGO was then applied for the adsorption of diclofenac sodium (DCF), a typical non-steroidal anti-inflammatory drug. Characterization using ultra-high-resolution field emission scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and the Brunauer-Emmett-Teller surface area revealed that the obtained CNC-PVAm/rGO displayed an evident 3D porous structure, which had an ultralight weight, good recovery, abundant surface functional groups (e.g., -NH2 and -OH), and rGO nanosheets. In addition, the material presented a stable crystal structure and large specific surface area (105.73 m2 g-1). During the adsorption of DCF, the CNC-PVAm/rGO aerogel showed a rather excellent adsorption performance, with a maximum adsorption capacity (qmax) of 605.87 mg g-1, which was approximately 53 times larger than that of the bare CNC aerogel (11.45 mg g-1). The adsorption performance of CNC-PVAm/rGO was also better than that of other reported adsorbents. The adsorption of DCF to CNC-PVAm/rGO obeyed the Langmuir isotherm and pseudo-second-order kinetic models, and underwent a spontaneous exothermic process. Moreover, DCF was easily desorbed from CNC-PVAm/rGO with sodium hydroxide solution (0.1 mol L-1), and the absorbent could be reused four times. The introduction of PVAm and rGO to the CNC-PVAm/rGO aerogel also greatly enhanced electrostatic interactions, π-π interactions, and hydrophobic effects. These enhancements significantly promoted the hydrogen bonding interactions between the DCF molecules and CNC-PVAm/rGO, thus resulting in a large improvement in the adsorption performance of the aerogel.This study represents the first empirical estimation of threshold values between nitrogen dioxide (NO2) concentrations and COVID-19-related deaths in France. The concentration of NO2 linked to COVID-19-related deaths in three major French cities were determined using Artificial Neural Networks experiments and a Causal Direction from Dependency (D2C) algorithm. The aim of the study was to evaluate the potential effects of NO2 in spreading the epidemic. The underlying hypothesis is that NO2, as a precursor to secondary particulate matter formation, can foster COVID-19 and make the respiratory system more susceptible to this infection. Three different neural networks for the cities of Paris, Lyon and Marseille were built in this work, followed by the application of an innovative tool of cutting the signal from the inputs to the selected target. The results show that the threshold levels of NO2 connected to COVID-19 range between 15.8 μg/m3 for Lyon, 21.8 μg/m3 for Marseille and 22.9 μg/m3 for Paris, which were significantly lower than the average annual concentration limit of 40 μg/m³ imposed by Directive 2008/50/EC of the European Parliament.This study aims to find the association between short-term exposure to air pollutants, such as particulate matters and ground-level ozone, and SARS-CoV-2 confirmed cases. Generalized linear models (GLM), a typical choice for ecological modeling, have well-established limitations. These limitations include apriori assumptions, inability to handle multicollinearity, and considering differential effects as the fixed effect. We propose an Ensemble-based Dynamic Emission Model (EDEM) to address these limitations. EDEM is developed at the intersection of network science and ensemble learning, i.e., a specialized approach of machine learning. Generalized Additive Model (GAM), i.e., a variant of GLM, and EDEM are tested in Los Angeles and Ventura counties of California, which is one of the biggest SARS-CoV-2 clusters in the US. Donafenib GAM depicts that a 1 μg/m3, 1 μg/m3, and 1 ppm increase (lag 0-7) in PM 2.5, PM 10, and O3 is associated with 4.51% (CI 7.01 to -2.00) decrease, 1.62% (CI 2.23 to -1.022) decrease, and 4.66% (CI 0.85 to 8.47) increase in daily SARS-CoV-2 cases, respectively. Subsequent increment in lag resulted in the negative association between pollutants and SARS-CoV-2 cases. EDEM results in an R2 score of 90.96% and 79.16% on training and testing datasets, respectively. EDEM confirmed the negative association between particulates and SARS-CoV-2 cases; whereas, the O3 depicts a positive association; however, the positive association observed through GAM is not statistically significant. In addition, the county-level analysis of pollutant concentration interactions suggests that increased emissions from other counties positively affect SARS-CoV-2 cases in adjoining counties as well. The results reiterate the significance of uniformly adhering to air pollution mitigation strategies, especially related to ground-level ozone.Stress-induced deviations in central nervous system development has long-term effects on adult mental health. Previous research in humans demonstrates that prenatal or adolescent stress increases the risk for psychiatric disorders. Animal models investigating the effects of stress during prenatal or adolescent development produces behavioral outcomes analogous to those observed in humans. However, whether adolescent stress exposure potentiates the effects of prenatal stress is currently unknown. Thus, the current study tested whether adolescent stress increases the impact of prenatal stress on contextual and cued fear memory in adulthood. Male and female Sprague Dawley rats were exposed to a chronic variable stress schedule during the last week of gestation, during adolescence, or during both developmental periods before undergoing fear conditioning training in adulthood. Our hypothesis predicted that the combined effects of prenatal and adolescent stress on contextual and cued fear memory would be greater than the effects of stress during either time period alone.