Previous studies have suggested that short-term exposure to ambient air pollution was associated with pediatric hospital admissions and emergency room visits for certain respiratory diseases; however, there is limited evidence on the association between short-term air pollution exposure and pediatric outpatient visits. Our aim was to quantitatively assess the short-term effects of ambient air pollution on pediatric outpatient visits for respiratory diseases. We conducted a time-series study in Yichang city, China between Jan 1, 2014 and Dec 31, 2015. Daily counts of pediatric respiratory outpatient visits were collected from 3 large hospitals, and then linked with air pollution data from 5 air quality monitoring stations by date. We used generalized additive Poisson models to conduct linear and nonlinear exposure-response analyses between air pollutant exposures and pediatric respiratory outpatient visits, adjusting for seasonality, day of week, public holidays, temperature, and relative humidity. Each interquartile range (IQR) increase in PM2.5 (lag 0), PM10 (lag 0), NO2 (lag 0), CO (lag 0), and O3 (lag 4) concentrations was significantly associated with a 1.91% (95% CI: 0.60%, 3.23%), 2.46% (1.09%, 3.85%), 1.88% (0.49%, 3.29%), 2.00% (0.43%, 3.59%), and 1.91% (0.45%, 3.39%) increase of pediatric respiratory outpatient visits, respectively. Similarly, the nonlinear exposure-response analyses showed monotonic increases of pediatric respiratory outpatient visits by increasing air pollutant exposures, though the associations for NO2 and CO attenuated at higher concentrations. These associations were unlikely modified by season. We did not observe significant association for SO2 exposure. Our results suggest that short-term exposures to PM2.5, PM10, NO2, CO, and O3 may account for increased risk of pediatric outpatient visits for respiratory diseases, and emphasize the needs for reduction of air pollutant exposures for children.

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management.

The Qinling subspecies of giant panda (Ailuropoda melanoleuca qinlingensis), is highly endangered; fewer than 350 individuals still inhabit Qinling Mountains. Previous research revealed captive pandas were exposed to bromine, so we hypothesized that captive pandas were exposed to and affected by polybrominated diphenyl ethers (PBDEs). To test this hypothesis, we tested blood and feces of captive and wild pandas, their drinking water, food (bamboo leaves) from SWARC (Shaanxi Wild Animal Research Center)and FNNR (Foping National Nature Reserve) and supplemental feedstuff given to captive panda at SWARC. We found 13 congeners of PBDEs in fecal samples, of which BDE47, BDE66, BDE71, BDE99, and BDE154 were the dominant, total PBDE concentration in feces of captive pandas was 255% higher than in wild pandas. We found nine PBDEs congeners in blood samples: BDE153 and BDE183 were the predominant congers. PBDEs in blood from captive pandas were significantly higher than in wild pandas. The total concentration of PBDEs were 5473 and 4835 (pg.g) in Fargesia qinlingensis, were 2192 and 1414 (pg.g) in Bashannia fargesii (2192, 1414 pg g), 0.066, 0.038 (pg/ml) in drinking water, and 28.8 (pg.g) in supplemental feedstuff for captive and wild pandas, which indicate that the PBDEs came from its bamboo feed, especially from Bashannia fargesii. Our results demonstrate that BDE99 and BDE47 could be threatening the pandas’ health especially for captive panda and there are potential health risks from PBDEs for pandas. In the short term, this risk may be ameliorated by strict control of food quality. In the long term, however, reducing air, water and soil contamination so as to improve environmental quality can best reduce these risks to meet the international standard such as Stockholm Convention.

Detailed chemical characterization of fine aerosols (PM2.5) is important for reducing air pollution in densely populated areas, such as the Yangtze River Delta region in China. This study systematically analyzed PM2.5 samples collected during November 2015 to April 2016 in urban Yangzhou using a suite of techniques, in particular, an Aerodyne soot particle aerosol mass spectrometry (SP-AMS). The techniques used here reconstructed the majority of total PM2.5 measured where extracted species comprised on average 91.2%. Source analyses of inorganic components showed that secondary nitrate, sulfate and chloride were the major species, while primary sources including biomass burning, coal combustion, traffic, industry and re-suspended dust due to nearby demolition activities, could contribute to other species. EC-tracer method estimated that the organic matter (OM) was composed of 65.4% secondary OM (SOM) and 34.6% primary OM (POM), while the SP-AMS analyses showed that the OM was comprised of 60.3% water-soluble OM (WSOM) and 39.7% water-insoluble OM (WIOM). Correlation analyses suggested that WSOM might be rich in secondary organic species, while WIOM was likely mainly comprised of primary organic species. We further conducted positive matrix factorization (PMF) analyses on the WSOM, and identified three primary factors including traffic, cooking and biomass burning, and two secondary factors. We found the secondary factors dominated WSOM mass (68.1%), and their mass contributions increased with the increase of WSOM concentrations. Relatively small contribution of primary sources to WSOM was probably due to their low water solubility, which should be investigated further in future. Overall, our findings improve understanding of the complex aerosol sources and chemistry in this region.

Human exposure to polybrominated diphenyl ethers (PBDEs) has been increasing over the last three decades in China and around the world. Animal studies suggest that PBDEs could reduce blood levels of thyroid hormones, but it is unclear whether PBDEs disrupt thyroid function in humans. We used data from a prospective birth cohort of 123 pregnant women who were enrolled between September 2010 and March 2011 in Shandong, China. We measured the concentrations of eight PBDE congeners (n = 106) and five thyroid hormones (n = 107) in cord serum samples. We examined the relationship between prenatal exposure to PBDEs and thyroid function (n = 90). Median concentrations of BDEs 47, 99, 100, and 153 (detection frequencies > 75%) were 3.96, 8.27, 3.31, and 1.89 ng/g lipid, respectively. A 10-fold increase in BDE-99 and Σ4 PBDEs (the sum of BDEs 47, 99, 100, and 153) concentrations was associated with a 0.41 μg/dL (95% confidence interval [CI]: 0.10 to 0.72) and 0.37 μg/dL (95% CI: 0.06 to 0.68) increase in total thyroxine levels (TT4), respectively. No associations were found between other individual congeners and any of the five thyroid hormones. Our study suggests that prenatal exposure to PBDEs may be associated with higher TT4 in cord blood. Given the inconsistent findings across existing studies, our results need to be confirmed in additional studies.

Little information exists on emission factors (EFs, quantities of pollutants emitted per unit of fuel consumed) for brick kilns in China, although brick kilns are important emission sources of many air pollutants, and 45% of the world's bricks are produced in China. In this study, EFs of carbon dioxide (CO2), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matters (PMs), black carbon (BC), organic carbon (OC), and polycyclic aromatic hydrocarbons (PAHs) for brick kilns were derived based on field measurements of a total of 18 brick kilns of major types in China. This was the first study to quantify EFs of both stack and fugitive sources based on a modified carbon balance method that was developed for this study. The EFs of most pollutants, especially the incomplete combustion products in fugitive emissions, were much higher than those for stack emissions, indicating a substantial underestimation of total emissions when leakage is not taken into consideration. This novel method can be applied to quantify emissions from other similar sources with both stack and fugitive emissions.

Ambient air pollution ranks high among the risk factors that increase the global burden of disease. Previous studies focused on assessing mortality risk and were sparsely performed in populous developing countries with deteriorating environments. We conducted a time-series study to evaluate the air pollution-associated years of life lost (YLL) and mortality risk and to identify potential modifiers relating to the season and demographic characteristics. Using linear (for YLL) and Poisson (for mortality) regression models and controlling for time-varying factors, we found that an interquartile range (IQR) increase in a three-day average cumulative (lag 0–2 day) concentrations of PM2.5, PM10, NO2 and SO2 corresponded to increases in YLL of 12.09 (95% confidence interval [CI]: 2.98–21.20), 13.69 (95% CI: 3.32–24.07), 26.95 (95% CI: 13.99–39.91) and 24.39 (95% CI: 8.62–40.15) years, respectively, and to percent increases in mortality of 1.34% (95% CI: 0.67–2.01%), 1.56% (95% CI: 0.80–2.33%), 3.36% (95% CI: 2.39–4.33%) and 2.39% (95% CI: 1.24–3.55%), respectively. Among the specific causes of death, cardiovascular and respiratory diseases were positively associated with gaseous pollutants (NO2 and SO2), and diabetes was positively correlated with NO2 (in terms of the mortality risk). The effects of air pollutants were more pronounced in the cool season than in the warm season. The elderly (>65 years) and females were more vulnerable to air pollution. Studying effect estimates and their modifications by using YLL to detect premature death should support implementing health risk assessments, identifying susceptible groups and guiding policy-making and resource allocation according to specific local conditions.

Although previous studies showed that children are widely exposed to phthalates, the sources of phthalate exposure for school-aged children in China are not well understood. This study aimed to assess phthalate metabolite levels and explore the factors influencing exposure in children. We collected demographic data and biological samples from 336 children aged 6–12 years. We calculated urinary concentrations of 14 mono-phthalate metabolites and conducted chi-square (χ2) tests and logistic regression analysis to determine the variables associated with phthalate levels. Mono-n-butyl phthalate (MnBP) and mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) were the most abundant urinary phthalate metabolites. In addition, housing type, decorating materials in the home, and frequency of canned food consumption were associated with exposure to low molecular weight phthalates. Water source, duration of time spent playing with toys, residential area, and frequency of canned food consumption were associated with exposure to high molecular weight phthalates. Based on these results, potential strategies to reduce exposure to phthalates include avoiding plastic food containers and chemical fragrances as well as eating fewer processed foods, especially canned foods, and foods in plastic packaging.

We developed a novel indicator, daily excessive concentration hours (DECH), to explore the acute mortality impacts of ambient fine particulate matter pollution (PM2.5) in Hong Kong. The DECH of PM2.5 was calculated as daily concentration-hours >25 μg/m3. We applied a generalized additive models to quantify the association between DECH and mortality with adjustment for potential confounders. The results showed that the DECH was significantly associated with mortality. The excess mortality risk for an interquartile range (565 μg/m3*hours) increase in DECH of PM2.5 was 1.65% (95% CI: 1.05%, 2.26%) for all natural mortality at lag 02 day, 2.01% (95% CI: 0.82%, 3.21%) for cardiovascular mortality at lag 03 days, and 1.41% (95% CI: 0.34%, 2.49%) for respiratory mortality at lag 2 day. The associations remained consistent after adjustment for gaseous air pollutants (daily mean concentration of SO2, NO2 and O3) and in alternative model specifications. When compared to the mortality burden of daily mean PM2.5, DECH was found to be a relatively conservative indicator. This study adds to the evidence by showing that daily excessive concentration hours of PM2.5 might be a new predictor of mortality in Hong Kong.

Primitive electronic waste (e-waste) recycling releases large amounts of organic pollutants and heavy metals into the environment. As crucial moderators of geochemical cycling processes and pollutant remediation, soil microbes may be affected by these contaminants. We collected soil samples heavily contaminated by e-waste recycling in China and Pakistan, and analyzed the indigenous microbial communities. The results of this work revealed that the microbial community composition and diversity, at both whole and core community levels, were affected significantly by polycyclic aromatic hydrocarbons (PAHs), polybrominated diphenyl ethers (PBDEs) and heavy metals (e.g., Cu, Zn, and Pb). The geographical distance showed limited impacts on microbial communities compared with geochemical factors. The constructed ecological network of soil microbial communities illustrated microbial co-occurrence, competition and antagonism across soils, revealing the response of microbes to soil properties and pollutants. Two of the three main modules constructed with core operational taxonomic units (OTUs) were sensitive to nutrition (total organic carbon and total nitrogen) and pollutants. Five key OTUs assigned to Acidobacteria, Proteobacteria, and Nitrospirae in ecological network were identified. This is the first study to report the effects of e-waste pollutants on soil microbial network, providing a deeper understanding of the ecological influence of crude e-waste recycling activities on soil ecological functions.