In recent years, air pollution events have occurred frequently in China during the winter. Most studies have focused on the physical and chemical composition of polluted air. Some studies have examined the bacterial bioaerosols both indoors and outdoors. But few studies have focused on the relationship between air pollution and bacteria, especially pathogenic bacteria. Airborne PM samples with different diameters and different air quality index values were collected in Hangzhou, China from December 2014 to January 2015. High-throughput sequencing of 16S rRNA was used to categorize the airborne bacteria. Based on the NCBI database, the “Human Pathogen Database” was established, which is related to human health. Among all the PM samples, the diversity and concentration of total bacteria were lowest in the moderately or heavily polluted air. However, in the PM2.5 and PM10 samples, the relative abundances of pathogenic bacteria were highest in the heavily and moderately polluted air respectively. Considering the PM samples with different particle sizes, the diversities of total bacteria and the proportion of pathogenic bacteria in the PM10 samples were different from those in the PM2.5 and TSP samples. The composition of PM samples with different sizes range may be responsible for the variances. The relative humidity, carbon monoxide and ozone concentrations were the main factors, which affected the diversity of total bacteria and the proportion of pathogenic bacteria. Among the different environmental samples, the compositions of the total bacteria were very similar in all the airborne PM samples, but different from those in the water, surface soil, and ground dust samples. Which may be attributed to that the long-distance transport of the airflow may influence the composition of the airborne bacteria. This study of the pathogenic bacteria in airborne PM samples can provide a reference for environmental and public health researchers.

Analysis of observed PM₂.₅ in Beijing since 2009 reveals that winter haze over North China Plain (NCP) peaked in 2012 and 2013 and there was an improvement in air quality until 2016. The variation of wintertime PM₂.₅ from 2009 to 2016 is influenced by both emission changes and meteorology conditions, and we quantified the relative contributions from these two aspects. Sensitivity simulation by GEOS-Chem suggested that emission reductions over NCP in 2013–2017 caused 10% decrease of regional mean PM₂.₅ concentration in 2016 winter compared to 2012 winter level. We removed emission influence on PM₂.₅ to get PM₂.₅ that influenced by meteorology (met-influenced PM₂.₅). For met-influenced PM₂.₅, compared to original-observed PM₂.₅(the US Embassy data), percentage of clean days (daily PM₂.₅ ≤ 75 μg m⁻³) decreases while that of polluted (75 μg m⁻³ < daily PM₂.₅ ≤150 μg m⁻³) and heavily polluted (150 μg m⁻³ < daily PM₂.₅ ≤ 250 μg m⁻³) days increases. However, proportion of extremely polluted (daily PM₂.₅ > 250 μg m⁻³) days stays unchanged, even if emission reduction is doubled, indicating that the extremely polluted situation over NCP is dominated by meteorological conditions, and emission control from 2013 to 2017 has little effects on the extremely polluted days. We developed an effective haze day index (HDI) to represent the weather conditions conducive to haze days. HDI is constructed based on the normalized near surface meridional wind (V850), temperature difference (δT) between near surface (850 hPa) and upper atmosphere (250 hPa), and the relative humidity at 1000 hPa (RH1000). HDI is skillful to detect 72% of the severe haze days (daily PM₂.₅ > 150 μg m⁻³). On average, the anomalously high V850 is the main cause of severe haze, while in 2012 winter, RH1000 favorable for secondary aerosols’ formation is the largest contributor to haze.

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM₂.₅ can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM₂.₅, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM₂.₅ levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008–2011 and 2014–2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM₂.₅ concentrations varied from 0.98 to 54.2 μg m⁻³, temperature ranged from 8 to 26 °C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM₂.₅, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (p < .001) for 1 lag day between PM₂.₅ exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.

Polybrominated diphenyl ethers (PBDEs) are widely used as flame retardants for various products and have become ubiquitous pollutants in environmental media. However, little is known about PBDE levels in Shenzhen, a manufacturing center of electronic products. This study aimed to investigate spatiotemporal variability of PBDE concentration in atmospheric fine particles (PM2.5) and to estimate the daily inhalation exposure doses for local residents in Shenzhen, China. A total of 36 samples were collected and 8 PBDE compounds (BDE-28, 47, 99, 100, 153, 154, 183 and 209) were analyzed by isotope dilution high-resolution gas chromatograph/high-resolution mass spectrometer (HRGC/HRMS). Mean concentrations of Σ8PBDEs and BDE-209 in PM2.5 in Shenzhen were 33.47 pg/m3 and 24.75 pg/m3, respectively, which were lower than those for other reported cities from China. The mean concentration of Σ8PBDEs was higher in the winter + spring than that in summer + autumn, and both concentrations of BDE-28 and BDE-47 in PM2.5 were significantly higher in winter + spring than those in summer + autumn. Among the 8 congeners, BDE-209 was predominant, accounting for 73.9% of the Σ8PBDEs concentrations. Traffic area, industrial area, residential area and discharge of electronic industries had significant positive influences on PBDE concentrations in PM2.5. Both vegetation area and water area were significantly negatively correlated with PBDE levels in PM2.5. Significantly negative correlation was also found between PBDE concentrations in PM2.5 and the relative humidity. The ranking of estimated inhalation exposure doses of PBDEs via PM2.5 inhalation were toddlers (1.74 pg/kg b.w./day) > children (1.33 pg/kg b.w./day) > adults (1.26 pg/kg b.w./day) > teenagers (0.64 pg/kg b.w./day), and toddlers had a highest risk to expose to PBDEs by PM2.5. To our knowledge, the present study is the first to reveal the spatiotemporal variability of PBDEs in PM2.5 of Shenzhen, China.

Biomonitoring is one of the tools used to assess the mutagenic potential of the atmosphere. In this study, the mutagenicity of Tradescantia pallida, a species of plant largely present in urban environments, was investigated. The objectives of this study was to estimate the mutagenic potential of vehicular flow through the TRAD-MCN bioassay in cities located at different altitudes in the southwest mesoregion of Mato Grosso do Sul, Brazil, to infer possible abiotic agents that may contribute to the effects of atmospheric pollutants, and finally to map the cities with greater risks to the health of the local population. To achieve these objectives, the Tradescantia-micronucleus test was performed on young buds of T. pallida collected between August 2015 and August 2016 in nine cities of Mato Grosso do Sul. These buds were exposed to traffic flows of various intensities. The data collected consisted of measurements of meteorological parameters and vehicular traffic counts for each city. The variables considered were: mean ambient temperature; micronuclei frequency; vehicular flow; altitude; relative humidity; pluviosity. The application of the Trad-MCN bioassay, with the consideration of environmental variables and altitudes, and the use of the Kernel interpolation technique, allowed us to map the areas with significant pollution risks to the population. The highest frequency of exposure to mutagens occurred in the cities with the highest vehicular traffic intensity. The average ambient temperature failed to show a linear association with the frequency of the micronuclei in the samples analyzed (r = 0.11ns). A positive correlation was observed between micronuclei frequency and vehicular flow, (r = 0.67; p ≤ 0.001%) and between micronuclei frequency and altitude (r = 0.24; p ≤ 0.05). A negative correlation was found between relative humidity and micronuclei frequency (r = −0.19; p ≤ 0.05%). Thus, higher micronuclei frequency tended to be present in locations with low relative humidity and high altitudes and vehicular flow.

There have been few large multicity studies to evaluate the acute health effects of ambient air pollution on morbidity risk in developing counties. In this study, we examined the short-term associations of air pollution with daily hospital admissions in China. We conducted a nationwide time-series study in 218 Chinese cities between 2014 and 2016. Data on daily hospital admissions counts were obtained from the National Health Insurance Database for Urban Employees covering 0.28 billion enrollees. We used generalized additive model with Poisson regression to estimate the associations in each city, and we performed random-effects meta-analysis to pool the city-specific estimates. More than 60 million hospital admissions were analyzed in this study. At the national-average level, each 10 μg/m³ increase in PM₁₀, SO₂, and NO₂, and 1 mg/m³ increase in CO at lag 0 day was associated with a 0.29% (95% CI, 0.23%–0.36%), 1.16% (95% CI, 0.92%–1.40%), 1.68% (95% CI, 1.40%–1.95%), and 2.59% (95% CI, 1.69%–3.50%) higher daily hospital admissions, respectively. The associations of air pollution with hospital admissions remained statistically significant at levels below the current Chinese Ambient Air Quality Standards. The effect estimates were larger in cities with lower air pollutants levels or higher air temperatures and relative humidity, as well as in the elderly. In conclusion, our findings provide robust evidence of increased hospital admissions in association with short-term exposure to ambient air pollution in China.

Few studies have analyzed the health effects of temperature variability (TV) accounting for both interday and intraday variations in ambient temperature. In this study, TV was defined as the standard deviations of the daily minimum and maximum temperature during different exposure days. Distributed lag non-linear Poisson regression model was used to examine the city-specific effect of TV on mortality in 31 Chinese municipalities and provincial capital cities. The national estimate was pooled through a meta-analysis based on the restricted maximum likelihood estimation. To assess effect modification on TV-mortality association by individual characteristics, stratified analyses were further fitted. Potential effect modification by city characteristics was performed through a meta-regression analysis. In total, 259 million permanent residents and 4,481,090 non-accidental deaths were covered in this study. The effect estimates of TV on mortality were generally increased by longer exposure days. A 1 °C increase in TV at 0–7 days' exposure was associated with a 0.60% (95% CI: 0.25–0.94%), 0.65% (0.24–1.05%), 0.82% (0.29–1.36%), 0.86% (0.42–1.31%), 0.98% (0.57–1.39%) and 0.54% (−0.11-1.20%) increase in non-accidental, cardiovascular, IHD, stroke, respiratory and COPD mortalities, respectively. Those with lower levels of educational attainment were significantly susceptible to TV. Cities with dense population, higher mean temperatures, and relative humidity and lower diurnal temperature ranges also had higher mortality risks caused by TV. This study demonstrated that TV had considerable health effects. An early warning system to alert residents about large temperature variations is recommended, which may have a significant impact on the community awareness and public health.

This paper evaluates the UV photodegradation of 17β-estradiol (E2) on silica gel and in natural soil with different soil components. Silica gel was chosen as a stable and pure support to simulate the photochemical behavior of E2 on the surface of natural soil. Ultraviolet light, rather than visible light, was confirmed to play a decisive role in the photodegradation of E2 on silica gel. The effect of three soil components, including humic acid (HA), inorganic salts, and relative humidity (RH), on the photochemical behavior of E2 on silica gel or soil under UV irradiation was then evaluated. Two HA concentrations (10 and 20 mg g⁻¹) and three salts (ferric sulfate, copper sulfate and sodium carbonate) were observed to obviously inhibit the degradation of E2 on silica gel. Interestingly, nitrate was found to obviously improve the removal efficiency of E2. Both too-dry and too-wet conditions obviously reduced the removal rate of E2, and the optimum relative humidity (RH) value was found to be approximately about 35% (30 °C). Furthermore, twenty intermediate products and two major pathways were proposed to describe the transformation processes of E2 treated by UV irradiation, among which oligomers were found to be the major intermediate products before complete mineralization. The efficient UV removal of E2 on silica gel and natural soil suggested a feasible strategy to remediate E2 contaminated soil.

The generation of HONO and NO₂ by the photolysis of nitrates in the presence of humic acids (HA) was measured under various conditions. The photolysis experiments of HA, KNO₃ and KNO₃/HA under simulated sunlight was carried out by a flow tube reactor at ambient temperature and pressure. HONO and NO₂ were major products by the photolysis of KNO₃. By contrast, the photolysis of HA and KNO₃/HA mainly generated HONO. HA significantly enhanced the formation of HONO during the photolysis process of KNO₃. With increasing the KNO₃ mass, the HONO formation rate (RHONO) on KNO₃/HA increased while the photolysis rate normalized by the KNO₃ mass exhibited an opposite trend. RHONO on KNO₃/HA linearly increased with irradiation intensity (88–262 W/m²) and relative humidity (7–70%), whereas it linearly decreased with the pH (pH = 2–12). In addition, the reaction paths of the HONO formation by the photolysis of nitrates in the presence of HA were proposed according to experimental results. Finally, atmospheric implications of the enhanced HONO formation by the photolysis of nitrates in the presence of HA were discussed.

The effects of ambient temperature on stroke death in China have been well addressed. However, few studies are focused on the attributable burden for the incident of different types of stroke due to ambient temperature, especially in Beijing, China. We purpose to assess the influence of ambient temperature on hospital stroke admissions in Beijing, China. Data on daily temperature, air pollution, and relative humidity measurements and stroke admissions in Beijing were obtained between 2013 and 2014. Distributed lag non-linear model was employed to determine the association between daily ambient temperature and stroke admissions. Relative risk (RR) with 95% confidence interval (CI) and Attribution fraction (AF) with 95% CI were calculated based on stroke subtype, gender and age group. A total number of 147, 624 stroke admitted cases (including hemorrhagic and ischemic types of stroke) were documented. A non-linear acute effect of cold temperature on ischemic and hemorrhagic stroke hospital admissions was evaluated. Compared with the 25th percentile of temperature (1.2 °C), the cumulative RR of extreme cold temperature (first percentile of temperature, −9.6 °C) was 1.51 (95% CI: 1.08–2.10) over lag 0–14 days for ischemic type and 1.28 (95% CI: 1.03–1.59) for hemorrhagic stroke over lag 0–3 days. Overall, 1.57% (95% CI: 0.06%–2.88%) of ischemic stroke and 1.90% (95% CI: 0.40%–3.41%) of hemorrhagic stroke was attributed to the extreme cold temperature over lag 0–7 days and lag 0–3 days, respectively. The cold temperature's impact on stroke admissions was found to be more obvious in male gender and the youth compared to female gender and the elderly. Exposure to extreme cold temperature is associated with increasing both ischemic and hemorrhagic stroke admissions in Beijing, China.