Impact of Beijing's "Coal to Electricity" program on ambient PM2.5 and the associated reactive oxygen species (ROS).

The Beijing "Coal to Electricity" program provides a unique opportunity to explore air quality impacts by replacing residential coal burning with electrical appliances. In this study, the atmospheric ROS (Gas-phase ROS and Particle-phase ROS, abbreviated to G-ROS and P-ROS) were measured by an online instrument in parallel with concurrent PM2.5 sample collections analyzed for chemical composition and cellular ROS in a baseline year (Coal Use Year-CUY) and the first year following implementation of the "Coal to Electricity" program (Coal Ban Year-CBY). The results showed PM2.5 concentrations had no significant difference between the two sampling periods, but the activities of G-ROS, P-ROS, and cellular ROS in CBY were 8.72 nmol H2O2/m3, 9.82 nmol H2O2/m3, and 2045.75 µg UD /mg PM higher than in CUY. Six sources were identified by factor-analysis from the chemical components of PM2.5. Secondary sources (SECs) were the dominant source of PM2.5 in the two periods, with 15.90% higher contribution in CBY than in CUY. Industrial Emission & Coal Combustion sources (Ind. & CCs), mainly from regional transport, also increased significantly in CBY. The contributions of Aged Sea Salt & Residential Burning sources to PM2.5 decreased 5.31% from CUY to CBY. The correlation results illustrated that Ind. & CCs had significant positive correlations with atmospheric ROS, and SECs significantly associated with cellular ROS, especially nitrates (r = 0.626, p = 0.000). Therefore, the implementation of the "Coal to Electricity" program reduced PM2.5 contributions from coal and biomass combustion, but had little effect on the improvement of atmospheric and cellular ROS.

A cardiac MRI motion artifact reduction method based on edge enhancement network.

Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly, the four-plane normal vector adaptive fractional differential mask is applied to extract the edge features of blurred images. The four-plane normal vector method can reduce the noise information in the edge feature maps. The adaptive fractional order is selected according to the normal mean gradient and the local Gaussian curvature entropy of the images. Secondly, the extracted edge feature maps and blurred images are input into the de-artifact network. In this network, the edge fusion feature extraction network and the edge fusion transformer network are specially designed. The former combines the edge feature maps with the fuzzy feature maps to extract the edge feature information. The latter combines the edge attention network and the fuzzy attention network, which can focus on the blurred image edges. Finally, extensive experiments show that the proposed method can obtain higher peak signal-to-noise ratio and structural similarity index measure compared to state-of-art methods. The de-artifact images have clear texture edges.

Associations between source-resolved PM2.5 and airway inflammation at urban and rural locations in Beijing.

BACKGROUND: A large number of research studies have explored the health effects of exposure to atmospheric particulate matter. However, limited quantitative evidence has linked specific sources of personal PM2.5 directly to adverse health effects. This study was conducted in order to examine the association between airway inflammation and personal exposure to PM2.5 mass, components, and sources among two healthy cohorts living in both urban and rural areas of Beijing, China. METHODS: We conducted a follow-up study during the summer of 2016 and the winter of 2016/2017 among 92 students and 43 guards. 24-h personal and ambient exposure to PM2.5 and fractional exhaled nitric oxide (FeNO) were measured at least twice for each participant. Chemical components of 385 personal PM2.5 exposure samples were analyzed, and pollution sources were resolved by a positive matrix factorization (PMF) receptor model. We have constructed linear mixed effect models to evaluate the association between ambient/personal PM2.5 mass, chemical constituents, and source specific PM2.5 with FeNO after controlling for temperature, relative humidity, sites, season, and potential individual confounders. RESULTS: Interquartile range (IQR) increase in household heating sources was associated with increased FeNO (2.72%; 95% CI = 1.26-4.17%) across two sites. IQR increase in roadway transport was associated with increased FeNO (9.84%; 95% CI = 2.69-17%) in urban areas; IQR increase in Secondary inorganic sources and Industrial/Combustion sources were associated with increased FeNO (7.96%; 95% CI = 1.47-14.4%% and 7.85%; 95% CI = 0.0676-15.6%, respectively) in rural areas. Personal exposure to EC, OC, and some trace elements (Se, Pb, Bi, Cs) were also estimated to be significantly associated with the increase of FeNO. In addition, there was no significant difference (P > 0.05) between the effects of ambient and personal PM2.5 mass. CONCLUSIONS: Although personal PM2.5 mass was not significantly associated with the health effects, airway inflammation can be linked to source-resolved exposures.

Chemical composition and health risk indices associated with size-resolved particulate matter in Pearl River Delta (PRD) region, China.

Size-resolved particulate matter (PM) was collected at the Heshan Super-Station in the Pearl River Delta (PRD) region, China, to evaluate their chemical characteristics and potential health risks. The chemical mass closures illustrate that the dominant fraction in coarse (2.5 µm < Dp < 10 µm) PM was dust, while organic matter made up a substantial portion of both fine (0.1 < Dp < 2.5 µm) and ultra-fine (Dp < 0.10 µm) PM fractions. The size distribution of most PM components varied substantially. PM, NO3-, K+, Cl-, Na+ and most of the transition/redox metals displayed bimodal size distributions with the dominant peak at 0.32-0.56 µm plus a small peak at 1.8-3.2 µm. In contrast, unimodal size distributions were found for the rest of the species, such as water-soluble organic carbon (WSOC), NH4+, and SO42- and the majority of oxyanion metals with a single peak at 0.32-0.56 µm, and Mg2+, Ca2+, and dust tracer elements which mainly accumulated in coarse particles. Based on the crustal enrichment factor (CEF) analysis, Cd, Zn, Sb, Sn, As, Pb, Mo, Cu, and Cr primarily originated from anthropogenic activities, while Ti in all size fractions and Sr, Mg, Na, and Fe in fine and ultra-fine particles were mainly emitted from natural sources. The potential health risk assessment of trace metals was performed using the hazard quotient (HQ) and excess lifetime cancer risk (ELCR) indices. Although the adverse health effects of most metals were limited, significant potential carcinogenic risks were found for As and Cr in both fine and coarse particle size fractions, which contributed more than 95% of total ELCR. Therefore, considering that these two elements were mainly emitted from industrial processes, improvements in air quality and health risks in the PRD region can be largely achieved by reducing the emissions of local industrial sources.

Chemical characterization and source apportionment of PM2.5 personal exposure of two cohorts living in urban and suburban Beijing.

In the study, personal PM2.5 exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM2.5 monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM2.5 data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM2.5 exposures and produced poor correlations between personal and ambient PM2.5. These results demonstrate that individual behavior strongly affects personal PM2.5 exposure. Six primary sources of personal PM2.5 exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ±â€¯32.6%, AVG ±â€¯STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ±â€¯13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM2.5 exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.

Source apportionments of PM2.5 organic carbon during the elevated pollution episodes in the Ordos region, Inner Mongolia, China.

The Ordos region in the southwestern part of Inner Mongolia experiences frequent PM concentrations in excess of the national PM2.5 air quality standards. In order to determine the key sources of PM2.5 contributing to these pollution episodes, the main sources of PM2.5 OC during elevated PM episodes in the Inner Mongolia were analyzed and compared with non-polluted days. This will provide insight to the main sources of particulate matter pollution during the high-pollution episodes and the effective seasonal strategies to control sources of particulate matter during months and with the highest PM concentrations that need to be controlled. The PMF source contributions to OC demonstrated that the industrial/coal combustion (4762.77 ± 1061.54 versus 2726.49 ± 469.75 ng/m3; p < 0.001) and mobile source factors (4651.14 ± 681.82 versus 2605.55 ± 276.50 ng/m3; p value < 0.001) showed greater contributions to the elevated concentrations during the episode. The spatial analysis of secondary organic carbon (SOC) factors, regional biomass burning, and biogenic sources did not show significant difference in the pollution episodes and the non-polluted months. In addition, the bivariate polar plots and CWT maps of the industrial/coal combustion and mobile illustrated a regional long-range transport patterns from the external sources to the study area, however, adjacent areas were mostly controlling the contributions of these factors during the PM elevated episodes. The SOC sources, regional biomass burning, and biogenic sources illustrated a regional long-range transport with similar locations found during the elevated pollution episodes compared to the normal situations.

Acute changes in a respiratory inflammation marker in guards following Beijing air pollution controls.

The adverse respiratory health effects of PM2.5 have been studied. However, the epidemiological evidence for the association of specific PM2.5 sources with health outcomes is still limited. This study investigated the association between PM2.5 components and sources with a biomarker of acute respiratory inflammation (FeNO) in guards. Personal exposure was estimated by microenvironment samplers and FeNO measurements were carried out before, during and after the Victory Day Military Parade in Beijing. Four sources were determined by factor analysis, including urban pollution, dust, alloy steel abrasion and toxic metals. A mixed-effect model was used to estimate the associations of FeNO with PM2.5 sources and chemical constituents, controlling for age, BMI, smoke activity, physical activity, waist circumference, temperature and relative humidity. In summary, large concentration decreases in PM2.5 concentration and PM2.5 chemical constituents were observed in both roadside and indoor environments during the air control periods, immediately followed by statistically significant decreases in FeNO of roadside guards and patrol guards. Besides, statistically significant increases in FeNO were found to be associated with interquartile range (IQR) increases in some pollutants, with an increase of 1.45ppb (95% CI: 0.69, 2.20), 0.65ppb (95% CI: 0.13, 1.17), 1.48ppb (95% CI: 0.60, 2.35), 0.82ppb (95% CI: 0.44, 1.20), 0.77ppb (95% CI: 0.42, 1.11) in FeNO for mass, sulfate, BC, Ca2+ and Sm, respectively. In addition, compared to alloy steel abrasion and toxic metals, urban pollution and dust factors were more associated with acute airway inflammation for highly-exposed populations.

Sensitivity of source apportionment results to mobile source profiles.

The sensitivity of a source apportionment model to mobile source profiles was examined to determine the impact of using non-local mobile source profiles in chemical mass balance (CMB) models. We examined the impact of USA and Chinese mobile source profiles on source apportionment results in St. Louis, Missouri, and Beijing. The results showed that the use of non-local mobile source profiles did not impact the model apportionment results for vegetative detritus and biomass burning, but other primary source contributions were influenced by the use of non-local source profiles. Secondary organic carbon (SOC) contributions estimated by the CMB models with local and non-local profiles were compared to estimate of SOC from the EC tracer method and were found to be consistent with little bias. The results also showed that it is feasible to use the USA mobile profiles in China while model results were biased by using Chinese mobile profiles in the USA. Monthly and annual average concentrations of molecular markers in the source apportionment model showed lower sensitivity to source profiles than daily measurements, which has implications to the design of source apportionment studies.