Summary Background Air pollution in Beijing has been improving through implementation of the Air Pollution Prevention and Control Action Plan (2013–17), but its implications for respiratory morbidity have not been directly investigated. We aimed to assess the potential effects of air-quality improvements on respiratory health by investigating the number of cases of acute exacerbations of chronic obstructive pulmonary disease (COPD) advanced by air pollution each year. Methods Daily city-wide concentrations of PM 10 , PM 2·5 , PM coarse (particulate matter >2·5–10 μm diameter), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ) in 2013–17 were averaged from 35 monitoring stations across Beijing. A generalised additive Poisson time-series model was applied to estimate the relative risks (RRs) and 95% CIs for hospitalisation for acute exacerbation of COPD associated with pollutant concentrations. Findings From Jan 18, 2013, to Dec 31, 2017, 161 613 hospitalisations for acute exacerbation of COPD were recorded. Mean ambient concentrations of SO 2 decreased by 68% and PM 2·5 decreased by 33% over this 5-year period. For each IQR increase in pollutant concentration, RRs for same-day hospitalisation for acute exacerbation of COPD were 1·029 (95% CI 1·023–1·035) for PM 10 , 1·028 (1·021–1·034) for PM 2·5 , 1·018 (1·013–1·022) for PM coarse , 1·036 (1·028–1·044) for NO 2 , 1·019 (1·013–1·024) for SO 2 , 1·024 (1·018–1·029) for CO, and 1·027 (1·010–1·044) for O 3 in the warm season (May to October). Women and patients aged 65 years or older were more susceptible to the effects of these pollutants on hospitalisation risk than were men and patients younger than 65 years. In 2013, there were 12 679 acute exacerbations of COPD cases that were advanced by PM 2·5 pollution above the expected number of cases if daily PM 2·5 concentrations had not exceeded the WHO target (25 μg/m 3 ), whereas the respective figure in 2017 was 7377 cases. Interpretation Despite improvement in overall air quality, increased acute air pollution episodes were significantly associated with increased hospitalisations for acute exacerbations of COPD in Beijing. Stringent air pollution control policies are important and effective for reducing COPD morbidity, and long-term multidimensional policies to safeguard public health are indicated. Funding UK Medical Research Council.

Atmospheric chemical transport models (CTMs) have been widely used to simulate spatiotemporally resolved PM 2.5 concentrations. However, CTM results are usually prone to bias and errors. In this study, we improved the accuracy of PM 2.5 predictions by developing an ensemble deep learning framework to fuse model simulations with ground-level observations. The framework encompasses four machine-learning models, i.e., general linear model, fully connected neural network, random forest, and gradient boosting machine, and combines them by stacking approach. This framework is applied to PM 2.5 concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for China from 2014 to 2017, which has complete spatial coverage over the entirety of China at a 12-km resolution, with no sampling biases. The fused PM 2.5 concentration fields were evaluated by comparing with an independent network of observations. The R 2 values increased from 0.39 to 0.64, and the RMSE values decreased from 33.7 μg/m 3 to 24.8 μg/m 3 . According to the fused data, the percentage of Chinese population residing under the level II National Ambient Air Quality Standards of 35 μg/m 3 for PM 2.5 has increased from 46.5% in 2014 to 61.7% in 2017. The method is readily adapted to utilize near-real-time observations for operational analyses and forecasting of pollutant concentrations and can be extended to provide source apportionment forecasts as well.

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Abstract:Chinese cities are experiencing severe air pollution in particular, with extremely high PM 2.5 levels observed in cold seasons. Accurate forecasting of occurrence of such air pollution events in advance can help the community to take action to abate emissions and would ultimately benefit the citizens. To improve the PM 2.5 air quality model forecasts in China, we proposed a bias-correction framework that utilized the historic relationship between the model biases and forecasted and observational variables to post-process the current forecasts. The framework consists of four components: (1) a feature selector that chooses the variables that are informative to model forecast bias based on historic data; (2) a classifier trained to efficiently determine the forecast analogs (clusters) based on clustering analysis, such as the distance-based method and the classification tree, etc.; (3) an error estimator, such as the Kalman filter, to predict model forecast errors at monitoring sites based on forecast analogs; and (4) a spatial interpolator to estimate the bias correction over the entire modeling domain. One or more methods were tested for each step. We applied five combinations of these methods to PM 2.5 forecasts in 2014-2016 over China from the operational AiMa air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model. All five methods were able to improve forecast performance in terms of normalized mean error (NME) and root mean square error (RMSE), though to a relatively limited degree due to the rapid changing of emission rates in China. Among the five methods, the CART-LM-KF-AN (a Classification And Regression Trees-Linear Model-Kalman Filter-Analog combination) method appears to have the best overall performance for varied lead times. While the details of our study are specific to the forecast system, the bias-correction framework is likely applicable to the other air quality model forecast as well.

Background A small number of studies suggested that air pollution was associated with idiopathic pulmonary fibrosis (IPF) exacerbation, incidence and mortality. However, no studies to date were conducted in regions where air pollution is substantial. We aimed to investigate whether there are associations between acute increases in air pollution and hospitalization of patients with a confirmed primary diagnosis of IPF in Beijing. Methods Daily count of IPF hospitalizations (International Classification of Disease-10th Revision, J84.1) was obtained from an administrative database for 2013–2017 while daily city-wide average concentrations of PM10, PM2.5, NO2, Ozone, SO2 were obtained from 35 municipal monitoring stations for the same period. The association between daily IPF hospitalization and average concentration of each pollutant was analyzed with a generalized additive model estimating Poisson distribution. Results Daily 24-h mean PM2.5 concentration during 2013–2017 was 76.7 μg/m3. The relative risk (RR) of IPF hospitalization per interquartile range (IQR) higher (72 μg/m3) in PM2.5 was 1.049 (95% CI 1.024–1.074) and 1.031 (95% CI 1.007–1.056) for lag0 and moving averages 0–1 days respectively. No significant associations were observed for other lags. Statistically significant positive associations were also observed at lag0 with SO2, Ozone and NO2 (in men only). Positive associations were seen at moving averages 0–30 days for PM10 (RR per 86 μg/m3: 1.021, 95% CI 0.994–1.049), NO2 (RR per 30 μg/m3: 1.029, 95% CI 0.999–1.060), and SO2 (RR per 15 μg/m3: 1.060 (95% CI 1.025–1.097), but not with PM2.5 or Ozone. Conclusions Despite improvement in air quality since the implementation of clean air policy in 2013, acute exposure to higher levels of air pollution is significantly associated with IPF hospitalization in Beijing. Air quality policy should be continuously enforced to protect vulnerable IPF populations as well as the general public.