Double trouble: The interaction of PM2.5 and O3 on respiratory hospital admissions.

The co-occurrence of fine particulate matter (PM2.5) and ozone (O3) pollution during the warm season has become a growing public health concern. The interaction between PM2.5 and O3 and its contribution to disease burden associated with co-pollution has not been thoroughly examined. We collected data on hospital admissions for respiratory diseases from a city-wide hospital discharge database in Beijing between 2013 and 2019. City-wide 24-h mean PM2.5 and daily maximum 8-h mean O3 were averaged from 35 monitoring stations across Beijing. Conditional Poisson regression was employed to estimate the interaction between warm-season PM2.5 and O3 on respiratory admissions. A model incorporating a tensor product term was used to fit the non-linear interaction and estimate the number of respiratory admissions attributable to PM2.5 and O3 pollution. From January 18, 2013 to December 31, 2019, 1,191,308 respiratory admissions were recorded. We observed multiplicative interactions between warm-season PM2.5 and O3 on upper respiratory infections (P = 0.004), pneumonia (P = 0.002), chronic obstructive pulmonary disease (P = 0.041), and total respiratory disease (P < 0.001). PM2.5-O3 co-pollution during warm season exhibited a super-additive effect on respiratory admissions, with a relative excess risk due to interaction of 1.65% (95%CI: 0.46%-2.84%). There was a non-linear pattern of the synergistic effect between PM2.5 and O3 on respiratory admissions. Based on the World Health Organization global air quality guidelines, 12,421 respiratory admissions would be reduced if both daily PM2.5 and O3 concentrations had not exceeded the target (PM2.5 15 µg/m3, O3 100 µg/m3). The number of respiratory admissions attributable to either PM2.5 or O3 pollution decreased by 48.7% from 2013 to 2019. Prioritizing O3 control during the warm season is a cost-effective strategy for Beijing. These findings underscore the significance of concurrently addressing both PM2.5 pollution and O3 pollution during the warm season to alleviate the burden of respiratory diseases.

Air pollution and hospitalization of patients with idiopathic pulmonary fibrosis in Beijing: a time-series study.

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.

Increased air pollution exposure among the Chinese population during the national quarantine in 2020.

The COVID-19 quarantine in China is thought to have reduced ambient air pollution. The overall exposure of the population also depends, however, on indoor air quality and human mobility and activities. Here, by integrating real-time mobility data and a questionnaire survey on time-activity patterns during the pandemic, we show that despite a decrease in ambient PM2.5 during the quarantine, the total population-weighted exposure to PM2.5 considering both indoor and outdoor environments increased by 5.7 µg m-3 (95% confidence interval, 1.2-11.0 µg m-3). The increase in population-weighted exposure was mainly driven by a nationwide urban-to-rural population migration before the Spring Festival coupled with the freezing of the migration backward due to the quarantine, which increased household energy consumption and the fraction of people exposed to rural household air pollution indoors. Our analysis reveals an increased inequality of air pollution exposure during the quarantine and highlights the importance of household air pollution for population health in China.

Global aerosol vertical structure analysis by clustering gridded CALIOP aerosol profiles with fuzzy k-means.

Aerosol vertical structures are critical to understanding distribution and source-sink patterns of aerosol on a large scale. In this study, we carried out spatial clustering analysis for 10-years long CALIOP aerosol profiles with a fuzzy k-means (FKM) method. Raw and normalized data sets were both classified into three representative clusters. Raw aerosol profiles of original data described both aerosol density and structure patterns, which were classified into polluted cluster, medium cluster and clean cluster with visual inspection. The mean aerosol extinction coefficient values in near surface from large to small respectively belonged to polluted cluster, medium cluster and clean cluster. As altitude increased, mean aerosol extinction coefficients of polluted cluster were in rapid decline trend from surface to upper atmosphere. In comparison, there was a slower decrease speed for the aerosol extinction coefficient values of mean aerosol profiles of the other two clusters. Aerosol profiles clusters using normalized data could be used to describe aerosol vertical structure patterns. Normalized aerosol profiles were classified into boundary-layer concentrated cluster (boundary cluster), vertically even distributed cluster (v-even cluster) and surface-layer concentrated cluster (surface cluster). The boundary cluster was stable in the low atmosphere with a decline trend upwards, which was spatially corresponds to strong anthropogenic emission and dust regions. The mean normalized extinction coefficient values of v-even cluster were relatively stable in a large vertical range (about 4 km) at regions with relatively weak wind fields. The coefficients of surface cluster were mainly distributed in the near surface with mostly in coastlines and low aerosol optical depth (AOD) regions. The cluster analysis of CALIOP aerosol profiles provided general patterns for global distributions of aerosol profile density and structure.

Improving the estimating accuracy of extinction coefficient of surface aerosol with a new layer-resolved model in China.

The aerosol extinction coefficient was an important factor for air quality. To estimate the aerosol extinction levels, widely used pure statistical models are generally not based on aerosol vertical structures. In this study, we estimated large-scale aerosol extinction coefficients by developing a new layer-resolved model with explicit inference for aerosol vertical distribution. The CALIOP aerosol profile, MODIS AOD and reanalysis boundary layer height data are used. The layer-resolved model was formulated by developing an explicit, steady and straightforward relationship between aerosol within boundary layer and corresponding AOD values. The estimated surface extinction coefficient from this model was compared against the values derived from station visibility observations in China in 2016. The results revealed that our model had outperformed the traditional one-layer model and the simplified two-layer model. Specifically, the numbers of ground stations with an NME value < 0.4 are enhanced by a percentage > 100%, with the NME values significantly decreased from 46%, 48% to 36% and RMSE values from 0.27, 0.25 to 0.21 km-1. Our model is easy for operational implementation thanks to its clear structure and input, and also informative to understand aerosol vertical distributions. Furthermore, this work will also be beneficial to air quality modeling studies to improve accuracy estimating ground-level PM2.5 concentrations.

Identifying the spatiotemporal clusters of plague occurrences in China during the Third Pandemic.

Plague, a devastating infectious disease caused by Yersinia pestis, has killed millions of people in the past and is still active in the natural foci of the world today. Understanding the spatiotemporal patterns of plague outbreaks in history is critically important, as it may help to facilitate prevention and control of potential future outbreaks. In this study, we explored spatiotemporal clusters of human plague occurrences in China using a machine-learning clustering method and reconstructed the potential transmission pattern during the Third Pandemic (1772-1964). We succeeded in identifying 6 clusters in the space domain (2D) and 13 clusters in the spatiotemporal domain (3D). Our results suggest that there were several temporal outbreaks and transmissions of plague in different spatial clusters. Together with the spatiotemporal nearest neighbor approach (ST-NNA), this method could allow us to have a clearer look at the spatiotemporal patterns of plague.

Human plague system associated with rodent diversity and other environmental factors.

Plague remains a threat to public health and is considered as a re-emerging infectious disease today. Rodents play an important role as major hosts in plague persistence and driving plague outbreaks in natural foci; however, few studies have tested the association between host diversity in ecosystems and human plague risk. Here we use zero-inflated generalized additive models to examine the association of species richness with human plague presence (where plague outbreaks could occur) and intensity (the average number of annual human cases when they occurred) in China during the Third Pandemic. We also account for transportation network density, annual precipitation levels and human population size. We found rodent species richness, particularly of rodent plague hosts, is positively associated with the presence of human plague. Further investigation shows that species richness of both wild and commensal rodent plague hosts are positively correlated with the presence, but only the latter correlated with the intensity. Our results indicated a positive relationship between rodent diversity and human plague, which may provide suggestions for the plague surveillance system.

Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014-2017.

Atmospheric chemical transport models (CTMs) have been widely used to simulate spatiotemporally resolved PM2.5 concentrations. However, CTM results are usually prone to bias and errors. In this study, we improved the accuracy of PM2.5 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 PM2.5 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 PM2.5 concentration fields were evaluated by comparing with an independent network of observations. The R2 values increased from 0.39 to 0.64, and the RMSE values decreased from 33.7 µg/m3 to 24.8 µg/m3. According to the fused data, the percentage of Chinese population residing under the level II National Ambient Air Quality Standards of 35 µg/m3 for PM2.5 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.

Associations between daily air quality and hospitalisations for acute exacerbation of chronic obstructive pulmonary disease in Beijing, 2013-17: an ecological analysis.

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 PM10, PM2·5, PMcoarse (particulate matter >2·5-10 µm diameter), nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), and ozone (O3) 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 SO2 decreased by 68% and PM2·5 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 PM10, 1·028 (1·021-1·034) for PM2·5, 1·018 (1·013-1·022) for PMcoarse, 1·036 (1·028-1·044) for NO2, 1·019 (1·013-1·024) for SO2, 1·024 (1·018-1·029) for CO, and 1·027 (1·010-1·044) for O3 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 PM2·5 pollution above the expected number of cases if daily PM2·5 concentrations had not exceeded the WHO target (25 µg/m3), 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.