Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005-2016

Predicting long-term spatiotemporal characteristics of fine particulate matter (PM_2.5) is important in China to understand historical levels of PM_2.5, to support health effects research of both long-term and short-term exposures to PM_2.5, and to evaluate the efficacy of air pollution control policies. Satellite-retrieved aerosol optical depth (AOD) provides a unique opportunity to characterize the long-term trends of ground-level PM_2.5 at high spatial resolution. However, the missing rate of AOD in Northeastern China (NEC) is very high, especially in winter, and challenges the accuracy of long-term predictions of PM_2.5 if left unresolved. Using random forest algorithms, this study developed a gap-filling approach combing satellite AOD, meteorological data, land use parameters, population and visibility in the NEC during 2005-2016. The model, including all predictors, combined with a model without AOD was able to fill the gap of PM_2.5 predictions caused by missing AOD at 1-km resolution. The R² (RMSE) of the full-coverage predictions was 0.81 (18.5 μg/m³) at the daily level. Gap-filled PM_2.5 predictions on days with missing AOD reduced the relative prediction error from 28% to 2.5% in winter. The leave-one-year-out-cross-validation R² (RMSE) of the full-coverage predictions was 0.65 (16.3 μg/m³) at the monthly level, indicating relatively high accuracy of predicted historical PM_2.5 concentrations. Our results suggested that AOD helped increase the reliability of historical PM_2.5 prediction when ground PM_2.5 measurements were unavailable, even though predictions from the AOD model only accounted for approximate 37% of the whole dataset. Predicted PM_2.5 level in NEC have increased since 2005, reached its peak during 2013-2015, then saw a major decline in 2016. Our high-resolution predictions also showed a south to north gradient and many pollution hot spots in the city clusters surrounding provincial capitals, as well as within large cities. Overall, by combining predictions from the AOD model with higher accuracy and predictions from the non-AOD model to achieve full coverage, our modeling approach could produce long-term, full-coverage historical PM_2.5 levels in high-latitude areas in China, despite the widespread and persistent AOD missingness.

Estimations of benchmark dose for urinary metabolites of coke oven emissions among workers

Coke oven emissions (COEs), usually composed of polycyclic aromatic hydrocarbons (PAHs) and so on, may alter the relative telomere length of exposed workers and have been linked with adverse health events. However, the relevant biological exposure limits of COEs exposure has not been evaluated from telomere damage. The purpose of this study is to estimate benchmark dose (BMD) of urinary PAHs metabolites from COEs exposure based on telomere damage with RTL as a biomarker. A total of 544 exposed workers and 238 controls were recruited for participation. High-performance liquid chromatography and qPCR were used to detect concentrations of urinary mono-hydroxylated PAHs and relative telomere length in peripheral blood leukocytes for all subjects. The benchmark dose approach was used to estimate benchmark dose (BMD) and its lower 95% confidence limit (BMDL) of urinary OH-PAHs of COEs exposure based on telomere damage. Our results showed that telomere length in the exposure group (0.75 (0.51, 1.08)) was shorter than that in the control group (1.05 (0.76,1.44))(P < 0.05), and a dose-response relationship was shown between telomere damage and both 1-hydroxypyrene and 3-hydroxyphenanthrene in urine. The BMDL of urinary 1-hydroxypyrene from the optimal model for telomere damage was 1.96, 0.40, and 1.01 (μmol/mol creatinine) for the total, males, and females group, respectively. For 3-hydroxyphenanthrene, the BMDL was 0.94, 0.33, and 0.49 (μmol/mol creatinine) for the total, males, and females. These results contribute to our understanding of telomere damage induced by COEs exposure and provide a reference for setting potential biological exposure limits.

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.

Arsenic and fasting blood glucose in the context of other drinking water chemicals: a cross-sectional study in Bangladesh

GOAL

The goal of this study was to evaluate the association between groundwater arsenic and fasting blood glucose in the context of other groundwater chemicals, in Bangladesh.

METHODS

Fasting blood glucose, gender, body mass index, sociodemographic variables, and diabetes medication use were measured among adults ≥ 35 years of age (n = 6587) participating in the Bangladesh Demographic and Health Survey (BDHS) 2011. Groundwater chemicals in 3534 well water samples were measured in the British Geological Survey (BGS) and Department of Public Health Engineering (DPHE) 1998-99 survey. We assigned the nearest BGS-DPHE well's chemical exposure to each BDHS participant. We used survey-estimation linear regression methods to model natural log-transformed fasting blood glucose, among those using groundwater as their primary drinking-water source, as a function of groundwater arsenic. We considered possible interactions between categorical arsenic exposure and each of 14 other groundwater chemicals dichotomized at their medians. The chemicals considered as possible effect modifiers included: aluminum, barium, calcium, iron, potassium, lithium, magnesium, manganese, sodium, phosphorous, silicon, sulfate, strontium, and zinc.

RESULTS

Compared to persons exposed to groundwater arsenic ≤ 10 μg/L, the adjusted geometric mean ratio (GMR) of fasting blood glucose was 1.01 (95% confidence interval: 0.98, 1.04) for individuals exposed to groundwater arsenic concentrations > 10 μg/L and ≤ 50 μg/L, and was 1.01 (0.97, 1.03) for those with > 50 μg/L arsenic. There were no Bonferroni-significant interactions with other chemicals, after accounting for the large number of chemicals tested as modifiers.

CONCLUSIONS

In our analysis of groundwater chemistry data from 1998/99 and fasting blood glucose outcomes measured in nearby populations approximately a decade later, there was no overall association of fasting blood glucose with nearby historical groundwater arsenic. This null association was not significantly modified by the historical levels of other groundwater chemicals. These null results are inconclusive regarding shorter-term potential toxicity of arsenic for glucose regulation, if there are differences between the historical concentrations measured in nearby groundwater and the actual drinking water chemical exposures in the population during the etiologically relevant period for more acute phenotypes like fasting blood glucose. Drinking water supply-relevant, longitudinal exposure assessment with less measurement error is needed to more precisely evaluate the joint impacts of drinking water chemicals and establish if there is a sensitive time window for glycemic outcomes.

The association of wildfire smoke with respiratory and cardiovascular emergency department visits in Colorado in 2012: a case crossover study

BACKGROUND

In 2012, Colorado experienced one of its worst wildfire seasons of the past decade. The goal of this study was to investigate the relationship of local PM2.5 levels, modeled using the Weather Research and Forecasting Model with Chemistry, with emergency department visits and acute hospitalizations for respiratory and cardiovascular outcomes during the 2012 Colorado wildfires.

METHODS

Conditional logistic regression was used to assess the relationship between both continuous and categorical PM2.5 and emergency department visits during the wildfire period, from June 5(th) to July 6(th) 2012.

RESULTS

For respiratory outcomes, we observed positive relationships between lag 0 PM2.5 and asthma/wheeze (1 h max OR 1.01, 95 % CI (1.00, 1.01) per 10 μg/m(3); 24 h mean OR 1.04 95 % CI (1.02, 1.06) per 5 μg/m(3)), and COPD (1 h max OR 1.01 95 % CI (1.00, 1.02) per 10 μg/m(3); 24 h mean OR 1.05 95 % CI (1.02, 1.08) per 5 μg/m(3)). These associations were also positive for 2-day and 3-day moving average lag periods. When PM2.5 was modeled as a categorical variable, bronchitis also showed elevated effect estimates over the referent groups for lag 0 24 h average concentration. Cardiovascular results were consistent with no association.

CONCLUSIONS

We observed positive associations between PM2.5 from wildfire and respiratory diseases, supporting evidence from previous research that wildfire PM2.5 is an important source for adverse respiratory health outcomes.

Cytomegalovirus viral and antibody correlates in young children

Background

Young, healthy children shedding cytomegalovirus (CMV) in urine and saliva appear to be the leading source of CMV in primary infection of pregnant women.

Findings

We screened 48 children 6 months – 5 years old for CMV IgG and measured levels of CMV IgG, IgM and IgG avidity antibodies, frequency of CMV shedding, and viral loads in blood, urine, and saliva. Thirteen of the 48 children (27%) were CMV IgG positive, among whom 3 were also CMV IgM positive with evidence of recent primary infection. Nine of the 13 seropositive children (69%) were shedding 10²-10⁵ copies/ml of CMV DNA in one or more bodily fluid. Among seropositive children, low IgG antibody titer (1:20–1:80) was associated with the absence of shedding (p = 0.014), and enrollment in daycare was associated with the presence of CMV shedding (p = 0.037).

Conclusions

CMV antibody profiles correlated with CMV shedding. The presence of CMV IgM more often represents primary infection in children than in adults. Correlating antibodies with primary infection and viral shedding in healthy children adds to the understanding of CMV infection in children that can inform the prevention of CMV transmission to pregnant women.

Xylo-oligosaccharides and lactitol promote the growth of Bifidobacterium lactis and Lactobacillus species in pure cultures

The current screening study aimed at identifying promising prebiotic and synbiotic candidates. The fermentation of xylo-oligosaccharides, xylan, galacto-oligosaccharide, fructo-oligosaccharide, polydextrose, lactitol, gentiobiose and pullulan was investigated in vitro. The ability of these established and potential prebiotic candidates to function as a sole carbon source for probiotic (Bifidobacterium and Lactobacillus), intestinal and potential pathogenic microbes (Eubacterium, Bacteroides, Clostridium, Escherichia coli, Salmonella, and Staphylococcus) was assessed in pure cultures. Xylo-oligosaccharides were fermented with high specificity by the tested Bifidobacterium lactis strains and lactitol by lactobacilli, whereas galacto-oligosaccharides, fructo-oligosaccharides and gentiobiose were utilised by a larger group of microbes. Xylan, polydextrose and pullulan were utilised to a limited extent by only a few of the tested microbes. The results of this screening study indicate that xylo-oligosaccharides and lactitol support the growth of a limited number of beneficial microbes in pure cultures. Such a high degree of specificity has not been previously reported for established prebiotics. Based on these results, the most promising prebiotics and synbiotic combinations can be selected for further testing.

Xylo-oligosaccharides enhance the growth of bifidobacteria and Bifidobacterium lactis in a simulated colon model

A semi-continuous, anaerobic colon simulator, with four vessels mimicking the conditions of the human large intestine, was used to study the fermentation of xylo-oligosaccharides (XOS). Three XOS compounds and a xylan preparation were fermented for 48 hours by human colonic microbes. Fructo-oligosaccharides (FOS) were used as a prebiotic reference. As a result of the fermentation, the numbers of Bifidobacterium increased in all XOS and xylan simulations when compared to the growth observed in the baseline simulations, and increased levels of Bifidobacterium lactis were measured with the two XOS compounds that had larger distribution of the degree of polymerisation. Fermentation of XOS and xylan increased the microbial production of short chain fatty acids in the simulator vessels; especially the amounts of butyrate and acetate were increased. XOS was more efficient than FOS in increasing the numbers of B. lactis in the colonic model, whereas FOS increased the Bifidobacterium longum numbers more. The selective fermentation of XOS by B. lactis has been demonstrated in pure culture studies, and these results further indicate that the combination of B. lactis and XOS would form a successful, selective synbiotic combination.