Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 35
Filtrar
Más filtros










Base de datos
Intervalo de año de publicación
1.
BMC Gastroenterol ; 20(1): 98, 2020 Apr 09.
Artículo en Inglés | MEDLINE | ID: mdl-32272891

RESUMEN

BACKGROUND: Statin may confer anticancer effect. However, the association between statin and risk of hepatocellular carcinoma (HCC) in patients with hepatitis B virus (HBV) or hepatitis C (HCV) virus infection remains inconsistent according to results of previous studies. A meta-analysis was performed to summarize current evidence. METHODS: Related follow-up studies were obtained by systematic search of PubMed, Cochrane's Library, and Embase databases. A random-effect model was used to for the meta-analysis. Stratified analyses were performed to evaluate the influences of study characteristics on the outcome. RESULTS: Thirteen studies with 519,707 patients were included. Statin use was associated with reduced risk of HCC in these patients (risk ratio [RR]: 0.54, 95% CI: 0.44 to 0.66, p < 0.001; I2 = 86%). Stratified analyses showed that the association between statin use and reduced HCC risk was consistent in patients with HBV or HCV infection, in elder (≥ 50 years) or younger (< 50 years) patients, in males or females, in diabetic or non-diabetic, and in those with or without cirrhosis (all p < 0.05). Moreover, lipophilic statins was associated with a reduced HCC risk (RR: 0.52, p < 0.001), but not for hydrophilic statins (RR: 0.89, p = 0.21). The association was more remarkable in patients with highest statin accumulative dose compared to those with lowest accumulative dose (p = 0.002). CONCLUSIONS: Satin use was independently associated with a reduced risk of HCC in patients with HBV or HCV infection.

2.
Artículo en Inglés | MEDLINE | ID: mdl-32324556

RESUMEN

Fiducial markers have been playing an important role in augmented reality (AR), robot navigation, and general applications where the relative pose between a camera and an object is required. Here we introduce TopoTag, a robust and scalable topological fiducial marker system, which supports reliable and accurate pose estimation from a single image. TopoTag uses topological and geometrical information in marker detection to achieve higher robustness. Topological information is extensively used for 2D marker detection, and further corresponding geometrical information for ID decoding. Robust 3D pose estimation is achieved by taking advantage of all TopoTag vertices. Without sacrificing bits for higher recall and precision like previous systems, TopoTag can use full bits for ID encoding. TopoTag supports tens of thousands unique IDs and easily extends to millions of unique tags resulting in massive scalability. We collected a large test dataset including in total 169,713 images for evaluation, involving in-plane and out-of-plane rotation, image blur, different distances and various backgrounds, etc. Experiments on the dataset and real indoor and outdoor scene tests with a rolling shutter camera both show that TopoTag significantly outperforms previous fiducial marker systems in terms of various metrics, including detection accuracy, vertex jitter, pose jitter and accuracy, etc.

3.
Biosci Rep ; 40(4)2020 Apr 30.
Artículo en Inglés | MEDLINE | ID: mdl-32162652

RESUMEN

BACKGROUND: Association between statin use and prognosis in patients with hepatocellular carcinoma (HCC) remains unknown. We performed a meta-analysis of follow-up studies to systematically evaluate the influence of statin use on clinical outcome in HCC patients. METHODS: Studies were obtained via systematic search of PubMed, Cochrane's Library, and Embase databases. A randomized-effect model was used to pool the results. Subgroup analyses were performed to evaluate the influence of study characteristics on the association. RESULTS: Nine retrospective cohort studies were included. Overall, statin use was associated with a reduced all-cause mortality in HCC patients (risk ratio [RR]: 0.81, 95% CI: 0.74-0.88, P < 0.001; I2 = 63%). Subgroup analyses showed similar results for patients with stage I-III HCC (RR: 0.83, 0.79, and 0.90 respectively, P all < 0.01) and patients after palliative therapy for HCC (RR: 0.80, P < 0.001), but not for patents with stage IV HCC (RR: 0.91, P = 0.28) or those after curative therapy (RR: 0.92, P = 0.20). However, the different between subgroups were not significant (both P > 0.05). Moreover, statin use was associated with reduced HCC-related mortality (RR: 0.78, P = 0.001) in overall patient population and HCC recurrence in patients after curative therapies (RR: 0.55, P < 0.001). CONCLUSIONS: Satin use is associated with reduced mortality and recurrence of HCC. These results should be validated in prospective cohort studies and randomized controlled trials.

4.
Environ Int ; 133(Pt A): 105167, 2019 12.
Artículo en Inglés | MEDLINE | ID: mdl-31634664

RESUMEN

We developed a hybrid chemical transport model and receptor model (CTM-RM) to conduct source apportionment of both primary and secondary PM2.5 (particulate matter ≤2.5 µm in diameter) at 36 km resolution throughout the U.S. State of Georgia for the years 2005 and 2007. This novel source apportionment model enabled us to estimate and compare associations of short-term changes in 12 PM2.5 source concentrations (agriculture, biogenic, coal, dust, fuel oil, metals, natural gas, non-road mobile diesel, non-road mobile gasoline, on-road mobile diesel, on-road mobile gasoline, and all other sources) with emergency department (ED) visits for pediatric respiratory diseases. ED visits for asthma (N = 49,651), pneumonia (N = 25,558), and acute upper respiratory infections (acute URI, N = 235,343) among patients aged ≤18 years were obtained from patient claims records. Using a case-crossover study, we estimated odds ratios per interquartile range (IQR) increase for 3-day moving average PM2.5 source concentrations using conditional logistic regression, matching on day-of-week, month, and year, and adjusting for average temperature, humidity, and holidays. We fit both single-source and multi-source models. We observed positive associations between several PM2.5 sources and ED visits for asthma, pneumonia, and acute URI. For example, for asthma, per IQR increase in the source contribution in the single-source model, odds ratios were 1.022 (95% CI: 1.013, 1.031) for dust; 1.050 (95% CI: 1.036, 1.063) for metals, and 1.091 (95% CI: 1.064, 1.119) for natural gas. These sources comprised 5.7%, 2.2%, and 6.3% of total PM2.5 mass, respectively. PM2.5 from metals and natural gas were positively associated with all three respiratory outcomes. In addition, non-road mobile diesel was positively associated with pneumonia and acute URI.


Asunto(s)
Contaminantes Atmosféricos/toxicidad , Servicio de Urgencia en Hospital/estadística & datos numéricos , Material Particulado/toxicidad , Trastornos Respiratorios/etiología , Adolescente , Contaminantes Atmosféricos/análisis , Niño , Carbón Mineral , Estudios Cruzados , Polvo , Femenino , Gasolina , Georgia , Humanos , Modelos Logísticos , Masculino , Oportunidad Relativa , Material Particulado/análisis
5.
Artículo en Inglés | MEDLINE | ID: mdl-31261860

RESUMEN

Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), is associated with adverse health effects. In order to quantify the impact of prescribed burning on human health, a general health impact function was used with exposure fields of PM2.5 from prescribed burning in Georgia, USA, during the burn seasons of 2015 to 2018, generated using a data fusion method. A method was developed to identify the days and areas when and where the prescribed burning had a major impact on local air quality to explore the relationship between prescribed burning and acute health effects. The results showed strong spatial and temporal variations in prescribed burning impacts. April 2018 exhibited a larger estimated daily health impact with more burned areas compared to Aprils in previous years, likely due to an extended burn season resulting from the need to burn more areas in Georgia. There were an estimated 145 emergency room (ER) visits in Georgia for asthma due to prescribed burning impacts in 2015 during the burn season, and this number increased by about 18% in 2018. Although southwestern, central, and east-central Georgia had large fire impacts on air quality, the absolute number of estimated ER asthma visits resulting from burn impacts was small in these regions compared to metropolitan areas where the population density is higher. Metro-Atlanta had the largest estimated prescribed burn-related asthma ER visits in Georgia, with an average of about 66 during the reporting years.


Asunto(s)
Contaminantes Atmosféricos/análisis , Asma/epidemiología , Servicio de Urgencia en Hospital/estadística & datos numéricos , Fuego , Agricultura Forestal/métodos , Material Particulado/análisis , Contaminación del Aire/análisis , Georgia/epidemiología , Humanos , Estaciones del Año
6.
Artículo en Inglés | MEDLINE | ID: mdl-31167440

RESUMEN

We have developed the Southern Integrated Prescribed Fire Information System (SIPFIS) to disseminate prescribed fire information, including daily forecasts of potential air quality impacts for southeastern USA. SIPFIS is a Web-based Geographic Information Systems (WebGIS) assisted online analysis tool that provides easy access to air quality and fire-related data products, and it facilitates visual analysis of exposure to smoke from prescribed fires. We have demonstrated that the information that SIPFIS provides can help users to accomplish several fire management activities, especially those related to assessing environmental and health impacts associated with prescribed burning. SIPFIS can easily and conveniently assist tasks such as checking residential community-level smoke exposures for personal use, pre-screening for fire-related exceptional events that could lead to air quality exceedances, supporting analysis for air quality forecasts, and the evaluation of prescribed burning operations, among others. The SIPFIS database is currently expanding to include social vulnerability and human health information, and this will evolve to bring more enhanced interactive functions in the future.


Asunto(s)
Contaminantes Atmosféricos/toxicidad , Contaminación del Aire/análisis , Fuego , Sistemas de Información Geográfica , Internet , Humo/efectos adversos , Contaminantes Atmosféricos/análisis , Predicción , Humanos , Humo/análisis , Sudeste de Estados Unidos
7.
Environ Sci Technol ; 53(13): 7306-7315, 2019 07 02.
Artículo en Inglés | MEDLINE | ID: mdl-31244060

RESUMEN

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.


Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , China , Aprendizaje Profundo , Monitoreo del Ambiente , Material Particulado
8.
Sensors (Basel) ; 19(4)2019 Feb 22.
Artículo en Inglés | MEDLINE | ID: mdl-30813310

RESUMEN

Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with these issues, the k-NN (k-Nearest Neighbor) fault detection method and extensions have been developed in recent years. Nevertheless, these methods are primarily used for fault detection, and few papers can be found that examine fault identification. In this paper, in order to extract effective fault information, the relationship between various faults and abnormal variables is studied, and an accurate "fault⁻symptom" table is presented. Then, a novel fault identification method based on k-NN variable contribution and CNN data reconstruction theories is proposed. When there is an abnormality, a variable contribution plot method based on k-NN is used to calculate the contribution index of each variable, and the feasibility of this method is verified by contribution decomposition theory, which includes a feasibility analysis of a single abnormal variable and multiple abnormal variables. Furthermore, to identify all the faulty variables, a CNN (Center-based Nearest Neighbor) data reconstruction method is proposed; the variables that have the larger contribution indices can be reconstructed using the CNN reconstruction method in turn. The proposed search strategy can guarantee that all faulty variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.

9.
J Air Waste Manag Assoc ; 69(4): 402-414, 2019 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-30499749

RESUMEN

Motor vehicles are major sources of fine particulate matter (PM2.5), and the PM2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NOx) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NOx estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM2.5, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects. Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.

10.
Environ Sci Technol ; 53(1): 242-250, 2019 01 02.
Artículo en Inglés | MEDLINE | ID: mdl-30500208

RESUMEN

Secondary organic aerosol (SOA) is a significant component of fine particulate matter, and it has increased during past drought periods in the U.S. Here, we use the Community Multiscale Air Quality (CMAQ) model to characterize the complex effects of drought on SOA through a case study comparing a drought period (June 2011) and a wet period (June 2013) over the southeast U.S. The model simulates a 68% (1.7 µg/m3) higher SOA concentration at the surface during drought and attributes 98% of this increase to biogenic SOA. Through model sensitivity simulations, the SOA increase associated with drought is attributed to 54% from accelerated gas-phase reactions oxidizing volatile organic compounds (VOCs) to SOA, 45% from higher emissions of biogenic VOCs, 18% from enhanced acid-catalyzed production of isoprene SOA in aerosol water due to changing sulfate, 3% from enhanced in-cloud aqueous phase chemistry. Because the higher SOA levels overwhelm the reduced precipitation, there is an increase in wet deposition flux in the drought month which offsets 20% of the total SOA increase. If anthropogenic emissions are held constant, anthropogenic SOA is 51% higher during drought, highlighting the importance of meteorological impacts on chemistry.


Asunto(s)
Contaminantes Atmosféricos , Aerosoles , Sequías , Material Particulado , Sudeste de Estados Unidos , Estados Unidos
11.
Drug Des Devel Ther ; 12: 3043-3049, 2018.
Artículo en Inglés | MEDLINE | ID: mdl-30271119

RESUMEN

Aims: Concerns have increased about the risk of fatal adverse events (FAEs) associated with molecular targeted agents (MTAs) in the treatment of advanced hepatocellular carcinoma (HCC). The purpose of this study is to investigate the overall incidence and risk of FAEs in advanced HCC with administration of MTAs by using a meta-analysis of available clinical trials. Materials and methods: Electronic databases were searched for relevant articles before March 2017. Eligible studies were selected according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Pooled incidence, Peto ORs and 95% CIs were calculated according to the heterogeneity of selected studies. Results: A total of 4,716 HCC participants from 10 randomized controlled trials (RCTs) were finally considered for this meta-analysis. The pooled incidence of death due to MTAs was 2.1% (95% CI 1.6%-2.8%) with a Peto OR of 1.79 (95% CI 1.07-3.01; p=0.027) in comparison with controlled groups. Subgroup analysis according to biological agents showed that brivanib treatment in HCC patients significantly increased the risk of developing FAEs (Peto OR 3.97; 95% CI 1.17-13.51; p=0.028) but not for sorafenib (Peto OR 1.78; 95% CI 0.54-5.89; p=0.34) and other MTAs (Peto OR 1.43; 95% CI 0.75-2.76; p=0.28). Sensitive analysis showed that the pooled results were influenced by removing each single trial. The most common causes of FAEs were hepatic failure (22.2%) and hemorrhage (13.3%), respectively. Conclusion: Clinicians should be aware of the risks of FAEs during the administration of MTAs in advanced HCC patients, especially for patients with abnormal liver function. However, the use of sorafenib remains justified in its approved indications due to their potential survival benefits and limited toxicities.


Asunto(s)
Alanina/análogos & derivados , Antineoplásicos/efectos adversos , Carcinoma Hepatocelular/tratamiento farmacológico , Neoplasias Hepáticas/tratamiento farmacológico , Terapia Molecular Dirigida , Triazinas/efectos adversos , Anciano , Alanina/efectos adversos , Alanina/uso terapéutico , Antineoplásicos/uso terapéutico , Humanos , Persona de Mediana Edad , Ensayos Clínicos Controlados Aleatorios como Asunto , Factores de Riesgo , Sorafenib/efectos adversos , Sorafenib/uso terapéutico , Triazinas/uso terapéutico
12.
Environ Int ; 113: 290-299, 2018 04.
Artículo en Inglés | MEDLINE | ID: mdl-29402553

RESUMEN

In China, rural migrant workers (RMWs) are employed in urban workplaces but receive minimal resources and welfare. Their residential energy use mix (REM) and pollutant emission profiles are different from those of traditional urban (URs) and rural residents (RRs). Their migration towards urban areas plays an important role in shaping the magnitudes and spatial patterns of pollutant emissions, ambient PM2.5 (fine particulate matter with a diameter smaller than 2.5 µm) concentrations, and associated health impacts in both urban and rural areas. Here we evaluate the impacts of RMW migration on REM pollutant emissions, ambient PM2.5, and subsequent premature deaths across China. At the national scale, RMW migration benefits ambient air quality because RMWs tend to transition to a cleaner REM upon arrival at urban areas-though not as clean as urban residents'. In 2010, RMW migration led to a decrease of 1.5 µg/m3 in ambient PM2.5 exposure concentrations (Cex) averaged across China and a subsequent decrease of 12,200 (5700 to 16,300, as 90% confidence interval) in premature deaths from exposure to ambient PM2.5. Despite the overall health benefit, large-scale cross-province migration increased megacities' PM2.5 levels by as much as 10 µg/m3 due to massive RMW inflows. Model simulations show that upgrading within-city RMWs' REMs can effectively offset the RMW-induced PM2.5 increase in megacities, and that policies that properly navigate migration directions may have potential for balancing the economic growth against ambient air quality deterioration. Our study indicates the urgency of considering air pollution impacts into migration-related policy formation in the context of rapid urbanization in China.


Asunto(s)
Contaminación del Aire/estadística & datos numéricos , Migración Humana , Material Particulado , Migrantes/estadística & datos numéricos , Urbanización , Contaminantes Atmosféricos , China , Ciudades , Vivienda , Humanos , Mortalidad Prematura , Salud Pública , Población Rural
13.
Environ Sci Technol ; 51(23): 13788-13796, 2017 Dec 05.
Artículo en Inglés | MEDLINE | ID: mdl-29110467

RESUMEN

Laboratory-based or in situ PM2.5 source profiles may not represent the pollutant composition for the sources in a different study location due to spatially and temporally varying characteristics, such as fuel or crustal element composition, or due to differences in emissions behavior under ambient versus laboratory conditions. In this work, PM2.5 source profiles were estimated for 20 sources using a novel optimization approach that incorporates observed concentrations with source impacts from a chemical transport model (CTM) to capture local pollutant characteristics. Nonlinear optimization was used to minimize the error between source profiles, CTM source impacts, and observations. In a 2006 U.S. application, spatial and seasonal variability was seen for coal combustion, dust, fires, metals processing, and other source profiles when compared to the reference profiles, with variability in species fractions over 400% (calcium in dust) compared to mean contributions of the same species. Revised profiles improved the spatial and temporal bias in modeled concentrations of several trace metal species, including Na, Al, Ca, Mn, Cu, As, Se, Br, and Pb. In an application of the CMB-iteration model for two U.S. cities, revised profiles estimated higher biomass burning and dust impacts for summer compared with previous studies. Source profile optimization can be useful for source apportionment studies that have limited availability of source profile data for the location of interest.


Asunto(s)
Contaminantes Atmosféricos , Monitoreo del Ambiente , Ciudades , Carbón Mineral , Polvo , Material Particulado
14.
Environ Sci Technol ; 51(23): 13797-13805, 2017 Dec 05.
Artículo en Inglés | MEDLINE | ID: mdl-29112386

RESUMEN

Ozone production efficiency (OPE), a measure of the number of ozone (O3) molecules produced per emitted NOX (NO + NO2) molecule, helps establish the relationship between NOX emissions and O3 formation. We estimate long-term OPE variability across the eastern United States using two novel approaches: an observation-based empirical method and a chemical transport model (CTM) method. The CTM approach explicitly controls for differing O3 and NOX reaction product (NOZ) deposition rates and separately estimates OPEs from on-road mobile and electricity generating unit sources across a broad spatial scale. We find lower OPEs in urban areas and that average July OPE increased over the eastern United States domain between 2001 and 2011 from 11 to 14. CTM and empirical approaches agree at low NOZ concentrations, but CTM OPEs are greater than empirical OPEs at high NOZ. Our results support that NOX emissions reductions become more effective at reducing O3 at lower NOZ concentrations. Electricity generating unit OPEs are higher than mobile OPEs except near emissions locations, meaning further utility NOX emissions reductions will have greater per unit impacts on O3 regionally.


Asunto(s)
Contaminantes Atmosféricos , Ozono , Monitoreo del Ambiente , Modelos Químicos , Estados Unidos
15.
Environ Sci Technol ; 51(7): 3852-3859, 2017 04 04.
Artículo en Inglés | MEDLINE | ID: mdl-28233499

RESUMEN

The traditional reduced-form model (RFM) based on the high-order decoupled direct method (HDDM), is an efficient uncertainty analysis approach for air quality models, but it has large biases in uncertainty propagation due to the limitation of the HDDM in predicting nonlinear responses to large perturbations of model inputs. To overcome the limitation, a new stepwise-based RFM method that combines several sets of local sensitive coefficients under different conditions is proposed. Evaluations reveal that the new RFM improves the prediction of nonlinear responses. The new method is applied to quantify uncertainties in simulated PM2.5 concentrations in the Pearl River Delta (PRD) region of China as a case study. Results show that the average uncertainty range of hourly PM2.5 concentrations is -28% to 57%, which can cover approximately 70% of the observed PM2.5 concentrations, while the traditional RFM underestimates the upper bound of the uncertainty range by 1-6%. Using a variance-based method, the PM2.5 boundary conditions and primary PM2.5 emissions are found to be the two major uncertainty sources in PM2.5 simulations. The new RFM better quantifies the uncertainty range in model simulations and can be applied to improve applications that rely on uncertainty information.


Asunto(s)
Contaminantes Atmosféricos , Material Particulado , Monitoreo del Ambiente , Modelos Teóricos , Incertidumbre
16.
Sci Total Environ ; 580: 283-296, 2017 Feb 15.
Artículo en Inglés | MEDLINE | ID: mdl-28024753

RESUMEN

In order to alleviate extreme haze pollution, understanding the origin of fine particulate matter (PM2.5) is crucial. In this study, we applied Particulate Matter Source Apportionment Technology (PSAT) in CAMx (Comprehensive Air Quality Model with Extensions) to quantify the impacts of emissions from different regions on PM2.5 concentrations in Beijing for haze episodes during January 6-23, 2013. Emission inventory was developed by Tsinghua University. Evolution of local and Regional contributions during local and non-local dominated haze episodes were discussed, separately. In the meanwhile, average contribution of other every city in Jing-Jin-Ji region to PM2.5 concentrations larger than 75µgm-3 in Beijing urban for each range of local contribution percent was analyzed. The results indicate that local emissions contributed 83.6% of PM2.5 at the urban center of Beijing, while regional transport from surrounding cities and parts of Shandong, Henan and Anhui provinces contributed 9.4%; long-range transport contributed the remaining 7.0% mainly from areas >750km away to the south of Beijing during this study period. Compared to non-local-dominated haze episodes, local-dominated heavy haze episodes in Beijing were easily resulted from unfavorable meteorological conditions with much lower PBL and wind velocity. Furthermore, local contribution is more easily to cause a sharp increase or sharp reduction of PM2.5 concentration in central Beijing, reflecting that Beijing local has much stronger potential to form extremely heavy haze episodes. The results indicated that controlling local emissions is a much more important measure to alleviate the extreme haze episodes in Beijing, like that on the night of Jan 12, 2013. Furthermore, emission control in Jing-Jin-Ji region, especially in Tangshan, Tianjin, Baoding, Langfang, Shijiazhuang and Cangzhou, as well as Henan and Shandong province, are important to reduce the PM2.5 concentrations and the occurrence of haze episodes in Beijing.

17.
Sci Total Environ ; 580: 235-244, 2017 Feb 15.
Artículo en Inglés | MEDLINE | ID: mdl-27986320

RESUMEN

The satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) is widely used to estimate ground-level fine ambient particulate matter (PM2.5) concentrations to evaluate their health effects. The associated estimation accuracy is often reduced by AOD missing values and by insufficiently accounting for the spatio-temporal PM2.5 variations. In this study, we aim to estimate ground-level PM2.5 concentrations at a fine resolution with improved accuracy by fusing fine-scale satellite and ground observations in the populated and polluted Beijing-Tianjin-Hebei (BTH) area of China in 2014. We employed a Bayesian-based statistical downscaler to model the spatio-temporal linear AOD-PM2.5 relationships. We used a 3km MODIS AOD product, which was resampled to a 4km resolution in a Lambert conic conformal projection, to assist comparison and fusion with predictions by atmospheric chemistry models. A two-step method was used to fill the missing AOD values to obtain a full AOD dataset with complete spatial coverage. The downscaler has a good performance in the fitting procedure (R2=0.75) and in the cross validation procedure (R2=0.58 by random method and R2=0.47 by city-specific method). The number of missing AOD values was serious and related to elevated PM2.5 concentrations. The gap-filled AOD values corresponded well with our understanding of PM2.5 pollution conditions in BTH. The prediction accuracy of PM2.5 concentrations were improved in terms of their annual and seasonal mean. As a result of its fine spatio-temporal resolution and complete spatial coverage, the daily PM2.5 estimation dataset could provide extensive and insightful benefits to related studies in the BTH area. This may include understanding the formation processes of regional PM2.5 pollution episodes, evaluating daily human exposure, and establishing pollution controlling measures.

18.
Environ Sci Technol ; 50(9): 4752-9, 2016 05 03.
Artículo en Inglés | MEDLINE | ID: mdl-27043852

RESUMEN

The accuracy in estimated fine particulate matter concentrations (PM2.5), obtained by fusing of station-based measurements and satellite-based aerosol optical depth (AOD), is often reduced without accounting for the spatial and temporal variations in PM2.5 and missing AOD observations. In this study, a city-specific linear regression model was first developed to fill in missing AOD data. A novel interpolation-based variable, PM2.5 spatial interpolator (PMSI2.5), was also introduced to account for the spatial dependence in PM2.5 across grid cells. A Bayesian hierarchical model was then developed to estimate spatiotemporal relationships between AOD and PM2.5. These methods were evaluated through a city-specific 10-fold cross-validation procedure in a case study in North China in 2014. The cross validation R(2) was 0.61 when PMSI2.5 was included and 0.48 when PMSI2.5 was excluded. The gap-filled AOD values also effectively improved predicted PM2.5 concentrations with an R(2) = 0.78. Daily ground-level PM2.5 concentration fields at a 12 km resolution were predicted with complete spatial and temporal coverage. This study also indicates that model prediction performance should be assessed by accounting for monitor clustering due to the potential misinterpretation of model accuracy in spatial prediction when validation monitors are randomly selected.


Asunto(s)
Teorema de Bayes , Monitoreo del Ambiente , Aerosoles , China , Material Particulado
19.
Environ Sci Technol ; 49(8): 5133-41, 2015 Apr 21.
Artículo en Inglés | MEDLINE | ID: mdl-25811418

RESUMEN

Impacts of emissions changes from four potential U.S. CO2 emission reduction policies on 2050 air quality are analyzed using the community multiscale air quality model (CMAQ). Future meteorology was downscaled from the Goddard Institute for Space Studies (GISS) ModelE General Circulation Model (GCM) to the regional scale using the Weather Research Forecasting (WRF) model. We use emissions growth factors from the EPAUS9r MARKAL model to project emissions inventories for two climate tax scenarios, a combined transportation and energy scenario, a biomass energy scenario and a reference case. Implementation of a relatively aggressive carbon tax leads to improved PM2.5 air quality compared to the reference case as incentives increase for facilities to install flue-gas desulfurization (FGD) and carbon capture and sequestration (CCS) technologies. However, less capital is available to install NOX reduction technologies, resulting in an O3 increase. A policy aimed at reducing CO2 from the transportation sector and electricity production sectors leads to reduced emissions of mobile source NOX, thus reducing O3. Over most of the U.S., this scenario leads to reduced PM2.5 concentrations. However, increased primary PM2.5 emissions associated with fuel switching in the residential and industrial sectors leads to increased organic matter (OM) and PM2.5 in some cities.


Asunto(s)
Dióxido de Carbono/análisis , Ambiente , Modelos Teóricos , Aire , Secuestro de Carbono , Ciudades , Clima , Política Ambiental/tendencias , Predicción , Material Particulado/análisis , Impuestos , Estados Unidos , Tiempo (Meteorología)
20.
Sci Total Environ ; 493: 544-53, 2014 Sep 15.
Artículo en Inglés | MEDLINE | ID: mdl-24973934

RESUMEN

Air quality forecasts generated with chemical transport models can provide valuable information about the potential impacts of fires on pollutant levels. However, significant uncertainties are associated with fire-related emission estimates as well as their distribution on gridded modeling domains. In this study, we explore the sensitivity of fine particulate matter concentrations predicted by a regional-scale air quality model to the spatial and temporal allocation of fire emissions. The assessment was completed by simulating a fire-related smoke episode in which air quality throughout the Atlanta metropolitan area was affected on February 28, 2007. Sensitivity analyses were carried out to evaluate the significance of emission distribution among the model's vertical layers, along the horizontal plane, and into hourly inputs. Predicted PM2.5 concentrations were highly sensitive to emission injection altitude relative to planetary boundary layer height. Simulations were also responsive to the horizontal allocation of fire emissions and their distribution into single or multiple grid cells. Additionally, modeled concentrations were greatly sensitive to the temporal distribution of fire-related emissions. The analyses demonstrate that, in addition to adequate estimates of emitted mass, successfully modeling the impacts of fires on air quality depends on an accurate spatiotemporal allocation of emissions.


Asunto(s)
Contaminantes Atmosféricos/análisis , Monitoreo del Ambiente , Fuego , Modelos Químicos , Humo/análisis
SELECCIÓN DE REFERENCIAS
DETALLE DE LA BÚSQUEDA