Immobilisation remediation of arsenic-contaminated soils with promising CaAl-layered double hydroxide and bioavailability, bioaccessibility, and speciation-based health risk assessment.

Arsenic (As)-contaminated soil poses great health risk to human mostly through inadvertent oral exposure. We investigated CaAl-layered double hydroxide (CaAl-LDH), a promising immobilising agent, for the remediation of As-contaminated Chinese soils. The effects on specific soil properties and As fractionation were analyzed, and changes in the health risk of soil As were accurately assessed by means of advanced in vivo mice model and in vitro PBET-SHIME model. Results showed that the application of CaAl-LDH significantly increased soil pH and concentration of Fe and Al oxides, and effectively converted active As fractions into the most stable residual fraction, guaranteeing long-term remediation stability. Based on in vivo test, As relative bioavailability was significantly reduced by 37.75%. Based on in vitro test, As bioaccessibility in small intestinal and colon phases was significantly reduced by 25.65% and 28.57%, respectively. Furthermore, As metabolism (reduction and methylation) by the gut microbiota inhabiting colon was clearly observed. After immobilisation with CaAl-LDH, the concentration of bioaccessible As(Ⅴ) in the colon fluid was significantly reduced by 61.91%, and organic As (least toxic MMA(V) and DMA(V)) became the main species, which further reduced the health risk of soil As. In summary, CaAl-LDH proved to be a feasible option for immobilisation remediation of As-contaminated soils, and considerable progress was made in relevant health risk assessment.

Analytical model for organic contaminant transport in a cut-off wall and aquifer dual-domain system considering barrier arrangements.

This paper presents an analytical study of organic contaminants transport in a cut-off wall and aquifer dual-domain system, considering the effects of the inlet boundary conditions and cut-off structural arrangements. The comprehensive sensitivity analysis of parameters focusing on the breakthrough time, attenuation time and cumulative concentration are presented using the Monte Carlo simulation and Sobol global method. The simplified constant inlet boundary condition can lead to an excessively conservative prediction of the contaminant breakthrough compared to the 'finite mass' and 'decaying source' boundary conditions. The cut-off wall hydraulic performance can be enhanced by reducing the contaminant's head loss, shape factor, half-life and cut-off wall hydraulic conductivity while increasing the retardation factor. The contaminant's half-life can largely influence the maximum contaminant concentration, attenuation time and breakthrough time. For example, the maximum contaminant concentrations for T1/2 = 1.4 years and T1/2 = 100 years are 13 and 123 times greater than that for T1/2 = 0.1 year, respectively. The influence of the variation of shape factor on the breakthrough curve should be taken into consideration. Altering the structural arrangement of the cut-off wall to accommodate a smaller shape factor increases the contaminant breakthrough time. The proposed solution allows the analysis of a cut-off wall and aquifer system with different inlet boundary conditions and structural arrangements of the cut-off wall.

Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle.

Genetic selection for resilience is essential to improve the long-term sustainability of the dairy cattle industry, especially the ability of cows to maintain their level of production when exposed to environmental disturbances. Recording of daily milk yield provides an opportunity to develop resilience indicators based on milk losses and fluctuations in daily milk yield caused by environmental disturbances. In this context, our study aimed to explore milk loss traits and measures of variability in daily milk yield, including log-transformed standard deviation of milk deviations (Lnsd), lag-1 autocorrelation (Ra), and skewness of the deviations (Ske), as indicators of general resilience in dairy cows. The unperturbed dynamics of milk yield as well as milk loss were predicted using an iterative procedure of lactation curve modeling. Milk fluctuations were defined as a period of at least 10 successive days of negative deviations in which milk yield dropped at least once below 90% of the expected values. Genetic parameters of these indicators and their genetic correlation with economically important traits were estimated using single-trait and bivariate animal models and 8,935 lactations (after quality control) from 6,816 Chinese Holstein cows. In general, cows experienced an average of 3.73 environmental disturbances with a milk loss of 267 kg of milk per lactation. Each fluctuation lasted for 19.80 ± 11.46 days. Milk loss traits are heritable with heritability estimates ranging from 0.004 to 0.061. The heritabilities differed between Lnsd (0.135-0.250), Ra (0.008-0.058), and Ske (0.001-0.075), with the highest heritability estimate of 0.250 ± 0.020 for Lnsd when removing the first and last 10 days in milk in a lactation (Lnsd2). Based on moderate to high genetic correlations, lower Lnsd2 is associated with less milk losses, better reproductive performance, and lower disease incidence. These findings indicate that among the variables evaluated, Lnsd2 is the most promising indicator for breeding for improved resilience in Holstein cattle.

Pooled testing efficiency increases with test frequency.

Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around [Formula: see text] However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic.

Spatio-Temporal Evolution and Driving Factors of Rural Settlements in Low Hilly Region-A Case Study of 17 Cities in Hubei Province, China.

With the rapid development of the social economy, factors of social and economic development in China's rural areas have been continuously reorganized, and the pattern and distribution of rural residential areas have undergone significant changes. In rural areas, there have been many peculiar phenomena of "reducing people but not reducing land in rural areas, which has caused tremendous pressure on land resource protection. We used geographic detectors and a geographically temporally weighted regression model (GTWR) to explore the rural settlements' evolution and driving mechanism in Hubei Province from 1990 to 2015. The results show that the kernel density of rural settlements decreased from 1.62 villages/km2 in 1990 to 1.60 villages/km2 in 2015. The scale of rural residential patches has obvious regional differentiation characteristics. From southeast to northwest, there is a wave-like distribution structure of "high-low-high-low-high", and the clustering characteristics of "cold and hot spots" are strengthened with time. Based on GTWR analysis, the total rural population, total power of agricultural machinery, and rural electricity consumption have promoted the expansion of rural settlements, with the regression coefficients 0.096, 0.484, and 0.878, respectively. Cultivated land, agricultural output value, and rural labor force have negative impacts on the expansion, the regression coefficients of the village were -0.584, -0.510, and -0.109, respectively.

How do varying socio-economic driving forces affect China's carbon emissions? New evidence from a multiscale geographically weighted regression model.

The increase in carbon emissions has had great negative impacts on the healthy developments of the human environment and economic society. However, it is unclear how specific socio-economic factors are driving carbon emissions. Based on the multiscale geographically weighted regression (MGWR) model, this paper analyzes the impact mechanism of China's carbon emission data during 2010-2017. The results show that (1) during the study period, China's carbon emissions have obvious positive correlations in the spatial distribution, and the spatial autocorrelation of carbon emissions on the time scale has a further strengthening trend. (2) Compared with the results of the geographically weighted regression (GWR) model, the MGWR model is more robust, and the results are more realistic and reliable. The impacts of energy intensity, proportion of green coverage in built-up areas, and industrial structure on provincial carbon emissions are close to the global scale, and their spatial heterogeneity is weak. Other factors have spatially heterogeneous impacts on carbon emissions with different scale effects. (3) Except for proportion of green coverage in built-up areas, the industrial structure and trade openness have insignificant impacts on carbon emissions, but other variables have significant impacts. The total population, urbanization rate, energy intensity, and energy structure have positive impacts on carbon emissions, while the GDP per capita and foreign direct investment have negative impacts on it. This study shows that the main socio-economic factors have different degrees of impacts on carbon emissions with different scale, and we can refer to it to formulate more scientific measures to reduce carbon emissions.

Family socioeconomic position and abnormal birth weight: evidence from a Chinese birth cohort.

BACKGROUND: Birth weight is a strong determinant of infant short- and long-term health outcomes. Family socioeconomic position (SEP) is usually positively associated with birth weight. Whether this association extends to abnormal birth weight or there exists potential mediator is unclear. METHODS: We analyzed data from 14,984 mother-infant dyads from the Born in Guangzhou Cohort Study. We used multivariable logistic regression to assess the associations of a composite family SEP score quartile with macrosomia and low birth weight (LBW), and examined the potential mediation effect of maternal pre-pregnancy body mass index (BMI) using causal mediation analysis. RESULTS: The prevalence of macrosomia and LBW was 2.62% (n = 392) and 4.26% (n = 638). Higher family SEP was associated with a higher risk of macrosomia (OR 1.30, 95% CI 0.93-1.82; OR 1.53, 95% CI 1.11-2.11; and OR 1.59, 95% CI 1.15-2.20 for the 2nd, 3rd, and 4th SEP quartile respectively) and a lower risk of LBW (OR 0.69, 95% CI 0.55-0.86; OR 0.76, 95% CI 0.61-0.94; and OR 0.61, 95% CI 0.48-0.77 for the 2nd, 3rd, and 4th SEP quartile respectively), compared to the 1st SEP quartile. We found that pre-pregnancy BMI did not mediate the associations of SEP with macrosomia and LBW. CONCLUSIONS: Socioeconomic disparities in fetal macrosomia and LBW exist in Southern China. Whether the results can be applied to other populations should be further investigated.