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Sci Total Environ ; 713: 136443, 2020 Apr 15.
Artículo en Inglés | MEDLINE | ID: mdl-31954251


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.

Integr Zool ; 15(1): 69-78, 2020 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-31305020


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.

Análisis por Conglomerados , Pandemias , Peste/epidemiología , Peste/historia , China/epidemiología , Historia del Siglo XVIII , Historia del Siglo XIX , Historia del Siglo XX , Humanos , Factores de Tiempo
R Soc Open Sci ; 6(6): 190216, 2019 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-31312490


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.

Lancet Planet Health ; 3(6): e270-e279, 2019 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-31229002


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.

Environ Sci Technol ; 53(13): 7306-7315, 2019 07 02.
Artículo en Inglés | MEDLINE | ID: mdl-31244060


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.

Contaminantes Atmosféricos , Contaminación del Aire , China , Aprendizaje Profundo , Monitoreo del Ambiente , Material Particulado