Characterizing the Indoor-Outdoor Relationship of Fine Particulate Matter in Non-Heating Season for Urban Residences in Beijing

<div><p>Objective</p><p>Ambient fine particulate matter (PM_2.5) pollution is currently a major public health concern in Chinese urban areas. However, PM_2.5 exposure primarily occurs indoors. Given such, we conducted this study to characterize the indoor-outdoor relationship of PM_2.5 mass concentrations for urban residences in Beijing.</p><p>Methods</p><p>In this study, 24-h real-time indoor and ambient PM_2.5 mass concentrations were concurrently collected for 41 urban residences in the non-heating season. The diurnal variation of pollutant concentrations was characterized. Pearson correlation analysis was used to examine the correlation between indoor and ambient PM_2.5 mass concentrations. Regression analysis with ordinary least square was employed to characterize the influences of a variety of factors on PM_2.5 mass concentration.</p><p>Results</p><p>Hourly ambient PM_2.5 mass concentrations were 3–280 μg/m³ with a median of 58 μg/m³, and hourly indoor counterpart were 4–193 μg/m³ with a median of 34 μg/m³. The median indoor/ambient ratio of PM_2.5 mass concentration was 0.62. The diurnal variation of residential indoor and ambient PM_2.5 mass concentrations tracked with each other well. Strong correlation was found between indoor and ambient PM_2.5 mass concentrations on the community basis (coefficients: r≥0.90, p<0.0001), and the ambient data explained ≥84% variance of the indoor data. Regression analysis suggested that the variables, such as traffic conditions, indoor smoking activities, indoor cleaning activities, indoor plants and number of occupants, had significant influences on the indoor PM_2.5 mass concentrations.</p><p>Conclusions</p><p>PM_2.5 of ambient origin made dominant contribution to residential indoor PM_2.5 exposure in the non-heating season under the high ambient fine particle pollution condition. Nonetheless, the large inter-residence variability of infiltration factor of ambient PM_2.5 raised the concern of exposure misclassification when using ambient PM_2.5 mass concentrations as exposure surrogates. PM_2.5 of indoor origin still had minor influence on indoor PM_2.5 mass concentrations, particularly at 11:00–13:00 and 22:00–0:00. The predictive models suggested that particles from traffic emission, secondary aerosols, particles from indoor smoking, resuspended particles due to indoor cleaning and particles related to indoor plants contributed to indoor PM_2.5 mass concentrations in this study. Real-time ventilation measurements and improvement of questionnaire design to involve more variables subject to built environment were recommended to enhance the performance of the predictive models.</p></div

Additional file 1: of Perceptions of overweight by primary carers (mothers/grandmothers) of under five and elementary school-aged children in Bandung, Indonesia: a qualitative study

Kartu Menuju Sehat (KMS- Health Card) for boys aged 0–24 months. Source: http://www.depkes.go.id/resources/download/info-terkini/Kartu Menuju Sehat KMS.pdf . (PDF 2475 kb

The information of variables subject to building characteristics and human behavior.

<p>The information of variables subject to building characteristics and human behavior.</p

Pearson correlation between indoor and ambient PM_2.5 mass concentrations.

<p>Pearson correlation between indoor and ambient PM_2.5 mass concentrations.</p

Descriptive statistics of the concentrations of pollutants.

<p>a. Valid ambient and indoor PM_2.5 mass concentrations were simultaneously available for 33 residences.</p><p>Descriptive statistics of the concentrations of pollutants.</p

Diurnal variation of pollutant concentrations.

<p>(Note: error bars in the Fig stand for standard deviation of the normalized pollutant concentrations).</p

Predictive model of indoor PM_2.5 mass concentrations.

<p>a. the levels of <i>distance to road</i>: 1- ≤ 193 m, 2–194–270 m, 3–271–425 m, 4- >425 m.</p><p>b. the levels of <i>frequency of furniture cleaning</i>: 1- everyday, 2- some times per week, 3- one time for several weeks.</p><p>c. the levels of <i>smoking</i>: 0- no smoking, 1- yes, 1time, 2- yes, multiple times.</p><p>d. the levels of <i>indoor plants</i>: 1- no plants, 2- a few, 3- many.</p><p>e. the levels of <i>frequency of floor cleaning</i>: 1- everyday, 2- some times per week.</p><p>f. the levels of <i>floor cleaning method</i>: 1- only mop, 2- with mop and broom/vacuum.</p><p>g. the levels of <i>traffic route</i>: 1-arterial road (ring roads and express ways), 2-urban highway, 3-quiet street.</p><p>Predictive model of indoor PM_2.5 mass concentrations.</p

Regression analysis of indoor and ambient PM_2.5 mass concentrations.

<p>Regression analysis of indoor and ambient PM_2.5 mass concentrations.</p

Predictive model of ambient PM_2.5 mass concentrations.

<p>Predictive model of ambient PM_2.5 mass concentrations.</p

Effect of Humic Acids with Different Characteristics on Fermentative Short-Chain Fatty Acids Production from Waste Activated Sludge

Recently, the use of waste activated sludge to bioproduce short-chain fatty acids (SCFA) has attracted much attention as the sludge-derived SCFA can be used as a preferred carbon source to drive biological nutrient removal or biopolymer (polyhydroxyalkanoates) synthesis. Although large number of humic acid (HA) has been reported in sludge, the influence of HA on SCFA production has never been documented. This study investigated the effects on sludge-derived SCFA production of two commercially available humic acids (referred to as SHHA and SAHA purchased respectively from Shanghai Reagent Company and Sigma-Aldrich) that differ in chemical structure, hydrophobicity, surfactant properties, and degree of aromaticity. It was found that SHHA remarkably enhanced SCFA production (1.7-3.5 folds), while SAHA had no obvious effect. Mechanisms study revealed that all four steps (solubilization, hydrolysis, acidification, and methanogenesis) involved in sludge fermentation were unaffected by SAHA. However, SHHA remarkably improved the solubilization of sludge protein and carbohydrate and the activity of hydrolysis enzymes (protease and α-glucosidase) owing to its greater hydrophobicity and protection of enzyme activity. SHHA also enhanced the acidification step by accelerating the bioreactions of glyceradehyde-3P → d-glycerate 1,3-diphosphate, and pyruvate → acetyl-CoA due to its abundant quinone groups which served as electron acceptor. Further investigation showed that SHHA negatively influenced the activity of acetoclastic methanogens for its competition for electrons and inhibition on the reaction of acetyl-CoA → 5-methyl-THMPT, which caused less SCFA being consumed. All these observations were in correspondence with SHHA significantly enhancing the production of sludge derived SCFA