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In recent years, China has implemented several measures to improve air quality. The Beijing-Tianjin-Hebei (BTH) region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years. How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future. To evaluate the changes in major air pollutant emissions over this region, this paper employs ensemble Kalman filtering (EnKF) for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System (NAQPMS) simulation data to inversely estimate the emission rates of SO2, NOX, CO, and primary PM2.5 over BTH region in February from 2014 to 2019. The results show that SO2, NOX, CO, and primary PM2.5 emissions in the BTH region decreased in February from 2014 to 2019 by 83%, 37%, 41%, and 42%, while decreases in Beijing during this period were 86%, 67%, 59%, and 65%, respectively. Compared with the prior emission inventory, the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region, with simulated root mean square errors of the monthly average concentrations of SO2, NOX, PM2.5, and CO reduced by 41%, 30%, 31%, and 22%, respectively. The average uncertainties of SO2, NOX, PM2.5, and CO inversion emissions in 2014–19 are ±14.03% yr−1, ±28.91% yr−1, ±126.15% yr−1, and ±43.58% yr−1. Compared with the uncertainty of MEIC emission, the uncertainties of all species changed by +2% yr−1, −2% yr−1, −26% yr−1, and −4% yr−1, respectively. The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions. The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014. The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory. [ABSTRACT FROM AUTHOR]

Long-term exposure to ambient particulate pollutants (PM2.5 and PM10) may increase the risk of chronic kidney disease (CKD), but the results of previous research were limited and inconsistent. The purpose of this study was to assess the relationships of PM2.5 and PM10 with CKD. This study was a cohort study based on the physical examination data of 2082 Beijing residents from 2013 to 2018 in the Beijing Health Management Cohort (BHMC). A land-use regression model was used to estimate the individual exposure concentration of air pollution based on the address provided by each participant. CKD events were identified based on self-report or medical evaluation (estimated glomerular filtration rate, eGFR less than 60 ml/min/1.73 m2). Finally, the associations of PM2.5 and PM10 with CKD were calculated using univariate and multivariate logistic regression models. During the research period, we collected potentially confounding information. After adjusting for confounders, each 10 μg/m3 increase in PM2.5 and PM10 exposure was associated with an 84% (OR: 1.84; 95% CI: 1.45, 2.33) and 37% (OR: 1.37; 95% CI: 1.15, 1.63) increased risk of CKD. Adjusting for the four common gaseous air pollutants (CO, NO2, SO2, O3), the effect of PM2.5 and PM10 on CKD was significantly enhanced, but the effect of PM10 was no longer significant in the multi-pollutant model. The results of the stratified analysis showed that PM2.5 and PM10 were more significant in males, middle-aged and elderly people over 45 years old, smokers, drinkers, BMI ≥ 24 kg/m2, and abnormal metabolic components. In conclusion, long-term exposure to ambient PM2.5 and PM10 was associated with an increased risk of CKD. [ABSTRACT FROM AUTHOR]

Vehicle emissions have become a major source of air pollution in urban areas, especially for near-road environments, where the pollution characteristics are difficult to capture by a single-scale air quality model due to the complex composition of the underlying surface. Here we developed a hybrid model CMAQ-RLINE_URBAN to quantitatively analyze the effects of vehicle emissions on urban roadside NO 2 concentrations at a high spatial resolution of 50 m × 50 m. To estimate the influence of various street canyons on the dispersion of air pollutants, a machine-learning-based street canyon flow (MLSCF) scheme was established based on computational fluid dynamics and two machine learning methods. The results indicated that compared with the Community Multi-scale Air Quality (CMAQ) model, the hybrid model improved the underestimation of NO 2 concentration at near-road sites with the mean bias (MB) changing from -10 to 6.3 µ g m -3. The MLSCF scheme obviously increased upwind concentrations within deep street canyons due to changes in the wind environment caused by the vortex. In summer, the relative contribution of vehicles to NO 2 concentrations in Beijing urban areas was 39 % on average, similar to results from the CMAQ-ISAM (Integrated Source Apportionment Method) model, but it increased significantly with the decreased distance to the road centerline, especially on urban freeways, where it reached 75 %. [ABSTRACT FROM AUTHOR]

Air pollutants' effect on ischemic stroke (IS) has been widely reported. But the effect of high-level concentrations during people's outdoor periods among hypertension patients was unknown. Peak-hour concentrations were defined considering air pollutants' high concentrations as well as people's outdoor periods. We conducted a time-series study and used the generalized additive model to analyze peak-hour concentrations' acute effect. A total of 315,499 IS patients comorbid with hypertension were admitted to secondary and above hospitals in Beijing from 2014 to 2018. A 10 µg/m3 (CO: 1 mg/m3) increase of the peak-hour concentrations was positively associated with IS hospital admissions among hypertension patients. The maximum effect sizes were as follows: for PM2.5, 0.17% (95% confidence interval [CI]: 0.10–0.24%) at Lag0 and 0.22% (95% CI: 0.12–0.33%) at Lag0–5; for PM10, 0.09% (95% CI: 0.05–0.13%) at Lag5 and 0.17% (95% CI: 0.09–0.26%) at Lag0–5; for SO2, 0.87% (95% CI: 0.46–1.29%) at Lag5; for NO2, 0.83% (95% CI: 0.62–1.04%) at Lag0 and 0.86% (95% CI: 0.59–1.13%) at Lag0–1; for CO 1.23% (95% CI: 0.66–1.80%) at Lag0 and 1.33% (95% CI: 0.33–2.35%) at Lag0–5; for O3 0.23% (95% CI: 0.12–0.35%) at Lag0 and 0.20% (95% CI: 0.05–0.34%) at Lag0–1. The effect sizes of PM2.5, NO2, and O3 remained significant after adjusting daily mean. Larger effect sizes were observed for PM2.5 and PM10 in cool season and for O3 in warm season. As significant exposure indicators of air pollution, peak-hour concentrations exposure increased the risk of IS hospital admissions among hypertension patients and it is worthy of consideration in relative environmental standard. It is suggested for hypertension patients to avoid outdoor activity during peak hours. More relevant searches are required to further illustrate air pollutant's effect on chronic disease population. [ABSTRACT FROM AUTHOR]

Background: Few studies have considered the interplay between commuting mode and air pollution on obesity. The aim of this study was to examine whether workplace air pollutants exposure modifying the associations between different commuting mode and obesity. Methods: A cross-sectional study of workers in Beijing was conducted in 2016. The study sample comprised 10,524 participants aged 18 to 65 years old. Outcomes were defined as overall obesity (BMI≥ 28 kg/m2) and abdominal obesity (WC ≥ 85 cm in men and WC ≥ 80 cm in women). Commuting modes were divided into walking, cycling, bus, subway, and car or taxi. Logistic regression models were used to estimate odds ratios relating commuting mode to overall and abdominal obesity and stratified by gender, controlling for covariates. Results: The association between commuting mode and obesity was more strongly in men than women. In the fully adjusted models, compared with car or taxi commuters, cycling (men: OR=0.37, 95% CI=0.20 to 0.68) or bus (men: OR=0.58, 95% CI=0.36 to 0.94) counterparts had a lower risk of overall obesity. Compared with car or taxi commuters, walking (men: OR=0.57, 95% CI=0.36 to 0.91), bus (men: OR=0.59, 95% CI=0.39 to 0.89), or subway (men: OR=0.59, 95% CI=0.39 to 0.89) counterparts had a lower risk of abdominal obesity. We observed significant interactions between exposure PM10 and cycling on overall obesity in men. After adjusting for air pollutants, the association between commuting mode and obesity was slightly strengthened. Conclusion: This study findings indicate that active (walking or cycling) or public (bus or subway) commuting modes were protected factors for overall and abdominal obesity among men. Air pollutants do not obscure the benefits of active or public commuting for obesity. These associations support the policy for increasing active or public commuting as a strategy to reduce the prevalence of obesity. [ABSTRACT FROM AUTHOR]

In order to improve the forecasting accuracy of atmospheric pollutant concentration, a prediction model of atmospheric PM2.5 and nitrogen dioxide (NO2) concentration based on support vector regression (SVR) is established. Quantum-behaved particle swarm optimization (QPSO) algorithm is used to select the optimal parameters influencing the performance of SVR. And in order to improve the problem that the fixed SVR model is difficult to adapt to the highly nonlinear process, a simple online SVR based on re-modeling method is proposed instead of the fixed one. According to hourly PM2.5 and NO2 concentrations and meteorological conditions from May 2014 to April 2015 in Wanliu Monitoring Station of Beijing in China, the experiment is carried out based on the data of 3 months. Meanwhile, PM2.5 concentration is predicted by three different prediction methods, including the recursive prediction method, direct prediction method, and online direct prediction method. The results show that the online direct prediction method is the most accurate in the three prediction methods. In addition, compared with original particle swarm optimization (PSO) algorithm, QPSO algorithm is tested more efficiently for the improvement of global search ability and robustness during the procedure of parameter selection. Moreover, the hybrid QPSO-SVR model proposed in this paper has higher prediction accuracy and less computational time compared with the PSO-SVR model, genetic algorithm (GA)-SVR model, and grid search (GS)-SVR model, which indicates reliability and effectiveness of the QPSO-SVR model in prediction of these two pollutant concentrations. [ABSTRACT FROM AUTHOR]

Ambient ultrafine particles (UFPs: particles of diameter less than 100 nm) cause significant adverse health effects. As people spend most time indoors, the outdoor-to-indoor transport of UFPs plays a critical role in the accuracy of personal exposure assessments. Herein, a strategy was proposed to measure and analyze the infiltration factor (F inf) of UFPs, an important parameter quantifying the fraction of ambient air pollutants that travel inside and remain suspended indoors. Ninety-three measurements were conducted in 11 residential rooms in all seasons in Beijing, China, to investigate F inf of UFPs and its associated influencing factors. A multilevel regression model incorporating eight possible factors that influence infiltration was developed to predict F inf and F infSOA (defined as the ratio of indoor to outdoor UFP concentrations without indoor sources, but with indoor secondary organic aerosol (SOA) formation). It was found that the air change rate was the most important factor and coagulation was considerable, while the influence of SOA formation was much smaller than that of other factors. Our regression model accurately predicted daily-average F inf. The annually-averaged F inf of UFPs was 0.66 ± 0.10, which is higher than that of PM 2.5 and PM 10 , demonstrating the importance of controlling indoor UFPs of outdoor origin. Image 1 • Indoor and outdoor UFPs and influencing factors were measured in 93 experiments. • A multilevel regression model was developed to predict UFP infiltration factors. • The coagulation effects on the infiltration of UFPs can't be neglected. • Indoor UFP concentrations originated outdoor can be accurately predicted. • A larger fraction of outdoor UFPs remain suspended indoors than PM 2.5 and PM 10. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5) pollution has become a worldwide environmental concern because of its adverse impacts on human health. This study aimed to explore the spatiotemporal variations and influencing factors of PM 2.5 concentrations in Beijing during the 2013–2018 period, and further analyzed the impacts of environmental protection policies implemented in recent years. Notably, this study employed various statistical methods, i.e., ordinary Kriging interpolation, spatial autocorrelation analysis, time-series analysis and the Bonferroni test, to evaluate the regional and seasonal differences of PM 2.5 concentrations based on long-term monitoring data. The results illustrated that PM 2.5 concentrations decreased on a yearly basis, demonstrating that air pollution control policies have achieved initial success. Furthermore, PM 2.5 concentrations were higher in the winter and in the southern regions. Diurnal variation presented a bimodal distribution, which varied slightly with the season. Relative humidity and wind speed were the principal meteorological factors affecting the distribution of PM 2.5 concentrations, while precipitation had essentially no effect. A high positive correlation between PM 2.5 and gaseous pollutants (SO 2 , NO 2 , and CO) indirectly reflected the contribution of automobile exhaust and coal-fired emissions. Generally, PM 2.5 concentrations demonstrated strong spatiotemporal variations, and meteorological factors and pollutant emissions played an important role in this. Image 1 • PM 2.5 concentrations decreased during 2013–2018. • Wind speed was the major factor affecting the distribution of PM 2.5. • Trace of precipitation had essentially no effect on reducing PM 2.5. • Vehicle exhaust emissions were an important source of PM 2.5 pollution. [ABSTRACT FROM AUTHOR]

Background: Grass pollen is the most prevalent sensitizing aeroallergen to cause respiratory allergies in northern China. Air pollutants have a substantial effect on respiratory health and some pollens. This study aimed to investigate relationships among airborne grass pollen, air pollutants and allergic diseases, in order to determine their effects on patients with grass pollen allergies in Beijing, China, during the period from 2013 to 2016. Methods: Data regarding autumnal grass pollens and air pollutants measured in Beijing from 2012 to 2016 were obtained from local governmental agencies. Patient data regarding specific immunoglobulin E (IgE) analyses from 2013 to 2016 were obtained from the Department of Allergy in Beijing Tongren Hospital. Spearman's rank correlation analysis was used to assess associations between the daily number of grass pollen allergen–positive patients and the following parameters: 3 clinically‐relevant grass pollen genera (Artemisia, Humulus, and Chenopodium) and inhalable pollutants. Results: Correlation analysis indicated that the daily number of grass pollen‐positive patients was significantly associated with the peak period of grass pollens, as well as pollutants SO2 and NOx. Moreover, concentrations of air pollutants (eg, ozone, oxides of nitrogen [NOx], and SO2) were consistently and significantly associated with concentrations of grass pollens; particulate matter 2.5 µm in diameter was negatively associated with Artemisia and Chenopodium pollens. Conclusion: Grass pollens exhibited substantial impact on allergic disease morbidity. Air pollutants impacted allergic disease and grass pollen. Thus, public health and clinical approaches to anticipate and reduce allergic disease morbidity from pollen and pollutants are needed. [ABSTRACT FROM AUTHOR]

Beijing suffered from serious air pollution in October, 2011 with the occurrence of three continuous episodes. Here we analyze the pollution status of particulate matter, the relationship between the gaseous pollutants, physical and chemical properties of single particles, and the profile of water-soluble ions in PM 2.5 during the three episodes. Regional and photochemically aged air masses, which were characterized as having high values of O 3 and SO 2 , were hypothesized to have played a dominant role in the first episode. After mixing local air masses with freshly-emitted primary pollutants, the concentration of NO x continued to increase and the size of SO 4 2– NO 3 − and NH 4 + in the particle population continued to become smaller. The amount of elemental carbon-rich and organic carbon-rich particles in the scaled single particles (0.2–2 μm) and water-soluble K + in PM 2.5 also increased in the episodes. All the available information suggests that the biomass or fuel burning sources in or around Beijing may have had a huge impact on the last two episodes. [ABSTRACT FROM AUTHOR]

Abstract: To investigate the lag effects and seasonal differences of urban air quality change on human health, we carried out a time-series analysis of daily general practitioner consultations for allergic rhinitis (AR) in Beijing, China. In earlier publications we had assessed the relative risks of air pollutants on AR in Beijing during the period 2009–2010. This article analyzes for the same time period the lag effects of air pollutants and the seasonal effects on daily number of outpatient with allergic rhinitis; differences among age groups and gender groups were also taken into consideration. The results show that, the largest effect RRs of PM10 were at lag01, lag0, lag01, lag01, and lag01 for the total population, male, female, young/middle aged people (20–60years age group), and old people (more than 60years age group), respectively. The largest associations of SO2 were observed at lag3, lag1, lag3, lag0 and lag0 for the total population, male, female, young people/middle aged people (20–60years age group), and old people (more than 60years age group), respectively. The strongest effects of NO2 were found at lag03 for the total population, female and the young/middle aged people; at lag0 for male and lag03 for elder people. RRs of the three pollutants for AR have shown a decreasing relevance from NO2 to PM10 to SO2. The seasonal effect models show that PM10, SO2, and NO2 had higher risk for AR in the warm season than in the cold season. [Copyright &y& Elsevier]

Exposure to ambient particulate matter with a diameter of <2.5 μm (PM 2.5) has been linked to increases in blood pressure. The aim of this study was to assess the effects of short-term exposure to PM 2.5 on blood pressure in office workers in Beijing, China. A total of 4801 individuals aged 18–60 years underwent an annual medical examination between 2013 and 2017. Levels of air pollutants were obtained from 35 fixed monitoring stations and correlated with the employment location of each participant to predict personal exposure via kriging interpolation. Linear mixed-effects models were used to estimate the changes in blood pressure associated with PM 2.5 exposure at various lag times. After adjusting for personal characteristics and other potential confounders, each interquartile range increase in PM 2.5 was associated with a 0.413-mmHg (95% confidence interval [CI]: 0.252–0.573), 0.171-mmHg (95% CI: 0.053–0.288), 0.278-mmHg (95% CI: 0.152–0.404), and 0.241-mmHg (95% CI: 0.120–0.362) increase in systolic blood pressure, diastolic blood pressure, pulse pressure, and mean arterial pressure, respectively (p < 0.05). Men, individuals previously diagnosed with hypertension, and subjects working in the northern districts of Beijing had larger changes in blood pressure, and the effect sizes were 0.477-mmHg (95% CI: 0.286–0.669), 0.851-mmHg (95% CI: 0.306–1.397, and 0.672-mmHg (95% CI: 0.405–0.940). The findings suggested that exposure to PM 2.5 had adverse effects on blood pressure, especially among males and hypertensive patients. Image 1 • Individual PM 2.5 exposure was measured by Kriging interpolation method. • Blood pressure increased significantly with elevations in PM 2.5 for lag 4–6 days. • Stronger effects were found among males and hypertensive patients. [ABSTRACT FROM AUTHOR]

Large areas of mainland China have been suffering frequently from heavy haze pollution during the past years, which feature high concentrations of fine particulate matter (PM 2.5 , particulate matters with aerodynamic diameters less than 2.5 μm) and low visibility. Moreover, these areas manifested strong regional complex pollution characteristics, particularly in North China including Beijing and the five surrounding provinces (BSFP). In this study, by using the localized comprehensive emission inventory of BSFP region in 2012 as an input, the Comprehensive Air Quality Model with Extensions-Particulate Matter Source Apportionment Technology (CAMx/PSAT) was used to assess the seasonal variations and source apportionment of PM 2.5 in the highly polluted BSFP region, with a specific focus on the sectoral and sub-regional contributions to PM 2.5 in Beijing during winter and summer. Results showed that the PM 2.5 concentrations of BSFP region was higher in winter than that in summer. And the heavily polluted area in BSFP region shrinked noticeably in summer, compared with winter. As for source apportionment of PM 2.5 , residential and remaining industrial sectors constituted the top two sources of PM 2.5 mass concentrations in Beijing. In addition, agricultural source represented a major contributor to ammonium, whereas transportation and power sectors constituted major sources to nitrates. In terms of contributions from sub-regions, the local sources ranked as the dominant contributors to PM 2.5 in Beijing, while the main external contributions originated from the surrounding areas, such as Hebei and Shandong. Results of daily source apportionment to PM 2.5 in Beijing showed that sub-regional long-distance transport became stronger when haze pollution was severe, in which contribution from remaining industrial sector would be higher than that of other periods. The results will allow for an improved understanding of the causes and origins of heavy regional PM 2.5 pollution, and thus will benefit the development of effective joint control policies and identification of key polluting emission categories in North China and ultimately serve as references for other highly polluted megacities in the world. Image 1 • Local sources represented the dominant contributors to ambient PM 2.5. • Residential and industrial sectors were main contributors to PM 2.5 in Beijing. • Lowering emission intensity from industrial sources should be prioritized. • Contributions from iron & steel and cement plants to PM 2.5 should be highlighted. • Contribution of regional transport became stronger during severe haze days compared to normal days. [ABSTRACT FROM AUTHOR]

Secondary aerosol (SA) is considered to be an increasingly dangerous air pollutant. However, quantification of SA has suffered from considerable uncertainty due to its poorly understood formation mechanism. In this study, a simplified approach based on characterizing coal combustion emission indicators polonium-210 (210Po), selenium (Se) and arsenic (As) was used to estimate SA loading (increment) during a heavy air pollution event in December 2016 in Beijing. Due to the insignificant input and output of air pollutants as well as the assumption of non-local emissions during this heavy air pollution event, a steady-state method of severe air pollution has been proposed to estimate SA increment (the sum of newly formed secondary inorganic aerosols and secondary organic aerosols) during this typical air pollution event. Corresponding algorithm of the method was developed depending on the characteristics of coal combustion related pollutants (210Po, Se, and As). The results indicated that the median contribution of newly formed SA to total suspended particulates (TSP) during this heavy air pollution event was 42.4% (interquartile range [IQR]: 36.6%–48.8%), which quantitatively confirms the dominant contribution of substantial enhancement of SA to heavy air pollution. Although further verification is needed, this steady-state method of evaluating severe air pollution has potential for quantification of the SA increment. Image 1 • 210Po, Se, and As can be used to estimate the SA increment in heavy pollution events. • 210Po activity concentration reached a plateau once the TSP concentration exceeded ~300 μg m−3. • The stagnant accumulated closed box model was proposed to quantify the SA increment. • The input and output of 210Po was negligible during stagnant accumulated heavy air pollution. • The explosive growth of SA was the dominant contributor to heavy air pollution. [ABSTRACT FROM AUTHOR]