Simulations of organic aerosol concentrations in Mexico City using the WRF-CHEM model during the MCMA-2006/MILAGRO campaign

Organic aerosol concentrations are simulated using the WRF-CHEM model in Mexico City during the period from 24 to 29 March in association with the MILAGRO-2006 campaign. Two approaches are employed to predict the variation and spatial distribution of the organic aerosol concentrations: (1) a traditional 2-product secondary organic aerosol (SOA) model with non-volatile primary organic aerosols (POA); (2) a non-traditional SOA model including the volatility basis-set modeling method in which primary organic components are assumed to be semi-volatile and photochemically reactive and are distributed in logarithmically spaced volatility bins. The MCMA (Mexico City Metropolitan Area) 2006 official emission inventory is used in simulations and the POA emissions are modified and distributed by volatility based on dilution experiments for the non-traditional SOA model. The model results are compared to the Aerosol Mass Spectrometry (AMS) observations analyzed using the Positive Matrix Factorization (PMF) technique at an urban background site (T0) and a suburban background site (T1) in Mexico City. The traditional SOA model frequently underestimates the observed POA concentrations during rush hours and overestimates the observations in the rest of the time in the city. The model also substantially underestimates the observed SOA concentrations, particularly during daytime, and only produces 21% and 25% of the observed SOA mass in the suburban and urban area, respectively. The non-traditional SOA model performs well in simulating the POA variation, but still overestimates during daytime in the urban area. The SOA simulations are significantly improved in the non-traditional SOA model compared to the traditional SOA model and the SOA production is increased by more than 100% in the city. However, the underestimation during daytime is still salient in the urban area and the non-traditional model also fails to reproduce the high level of SOA concentrations in the suburban area. In the non-traditional SOA model, the aging process of primary organic components considerably decreases the OH levels in simulations and further impacts the SOA formation. If the aging process in the non-traditional model does not have feedback on the OH in the gas-phase chemistry, the SOA production is enhanced by more than 10% compared to the simulations with the OH feedback during daytime, and the gap between the simulations and observations in the urban area is around 3 μg m[superscript −3] or 20% on average during late morning and early afternoon, within the uncertainty from the AMS measurements and PMF analysis. In addition, glyoxal and methylglyoxal can contribute up to approximately 10% of the observed SOA mass in the urban area and 4% in the suburban area. Including the non-OH feedback and the contribution of glyoxal and methylglyoxal, the non-traditional SOA model can explain up to 83% of the observed SOA in the urban area, and the underestimation during late morning and early afternoon is reduced to 0.9 μg m[superscript −3] or 6% on average. Considering the uncertainties from measurements, emissions, meteorological conditions, aging of semi-volatile and intermediate volatile organic compounds, and contributions from background transport, the non-traditional SOA model is capable of closing the gap in SOA mass between measurements and models.National Science Foundation (U.S.). Atmospheric Chemistry Program (ATM-0528227)National Science Foundation (U.S.). Atmospheric Chemistry Program (ATM-0810931)Molina Center for Energy and the Environmen

Chronic kidney-disease screening service quality: questionnaire survey research evidence from Taichung city

<p>Abstract</p> <p>Background</p> <p>Chronic kidney disease (CKD) is a serious public health problem in Taiwan and the world. The most effective, affordable treatments involve early prevention/detection/intervention, requiring screening. Successfully implementing CKD programs requires good patient participation, affected by patient perceptions of screening service quality. Service quality improvements can help make such programs more successful. Thus, good tools for assessing service quality perceptions are important. Aim: to investigate using a modified SERVQUAL questionnaire in assessing patient expectations, perceptions, and loyalty towards kidney disease screening service quality.</p> <p>Method</p> <p>1595 kidney disease screening program patients in Taichung City were requested to complete and return a modified kidney disease screening SERVQUAL questionnaire. 1187 returned them. Incomplete ones (102) were culled and 1085 were chosen as effective for use. Paired t-tests, correlation tests, ANOVA, LSD test, and factor analysis identified the characteristics and factors of service quality. The paired t-test tested expectation score and perception score gaps. A structural equation modeling system examined satisfaction-based components' relationships.</p> <p>Results</p> <p>The effective response rate was 91.4%. Several methods verified validity. Cronbach's alpha on internal reliability was above 0.902. On patient satisfaction, expectation scores are high: 6.50 (0.82), but perception scores are significantly lower 6.14 (1.02). Older patients' perception scores are lower than younger patients'. Expectation and perception scores for patients with different types of jobs are significantly different. Patients higher on education have lower scores for expectation (r = -0.09) and perception (r = -0.26). Factor analysis identified three factors in the 22 item SERVQUAL form, which account for 80.8% of the total variance for the expectation scores and 86.9% of the total variance for the satisfaction scores. Expectation and perception score gaps in all 22 items are significant. The goodness-of-fit summary of the SEM results indicates that expectations and perceptions are positively correlated, perceptions and loyalty are positively correlated, but expectations and loyalty are not positively correlated.</p> <p>Conclusions</p> <p>The results of this research suggest that the SERVQUAL instrument is a useful measurement tool in assessing and monitoring service quality in kidney disease screening services, enabling the staff to identify where service improvements are needed from the patients' perspectives.</p

Kinobead Profiling Reveals Reprogramming of BCR Signaling in Response to Therapy within Primary CLL Cells

Purpose: B-cell receptor (BCR) signaling is critical for the pathogenesis of chronic lymphocytic leukemia (CLL), promoting both malignant cell survival and disease progression. Although vital, understanding of the wider signaling network associated with malignant BCR stimulation is poor. This is relevant with respect to potential changes in response to therapy, particularly involving kinase inhibitors. In the current study, we describe a novel high-resolution approach to investigate BCR signaling in primary CLL cells and track the influence of therapy on signaling response. Experimental Design: A kinobead/mass spectrometry–based protocol was used to study BCR signaling in primary CLL cells. Longitudinal analysis of samples donated by clinical trial patients was used to investigate the impact of chemoimmunotherapy and ibrutinib on signaling following surface IgM engagement. Complementary Nanostring and immunoblotting analysis was used to verify our findings. Results: Our protocol isolated a unique, patient-specific signature of over 30 kinases from BCR-stimulated CLL cells. This signature was associated with 13 distinct Kyoto Encyclopedia of Genes and Genomes pathways and showed significant change in cells from treatment-naïve patients compared with those from patients who had previously undergone therapy. This change was validated by longitudinal analysis of clinical trials samples where BCR-induced kinome responses in CLL cells altered between baseline and disease progression in patients failing chemoimmunotherapy and between baseline and treatment in patients taking ibrutinib. Conclusions: These data comprise the first comprehensive proteomic investigation of the BCR signaling response within CLL cells and reveal unique evidence that these cells undergo adaptive reprogramming of this signaling in response to therapy

Global distribution of particle phase state in atmospheric secondary organic aerosols.

Shiraiwa M, Li Y, Tsimpidi AP, et al. Global distribution of particle phase state in atmospheric secondary organic aerosols. Nat Commun. 2017;8(1): 15002

Predictions of diffusion rates of large organic molecules in secondary organic aerosols using the Stokes-Einstein and fractional Stokes-Einstein relations

Information on the rate of diffusion of organic molecules within secondary organic aerosol (SOA) is needed to accurately predict the effects of SOA on climate and air quality. Diffusion can be important for predicting the growth, evaporation, and reaction rates of SOA under certain atmospheric conditions. Often, researchers have predicted diffusion rates of organic molecules within SOA using measurements of viscosity and the Stokes–Einstein relation (D∝1/η, where D is the diffusion coefficient and η is viscosity). However, the accuracy of this relation for predicting diffusion in SOA remains uncertain. Using rectangular area fluorescence recovery after photobleaching (rFRAP), we determined diffusion coefficients of fluorescent organic molecules over 8 orders in magnitude in proxies of SOA including citric acid, sorbitol, and a sucrose–citric acid mixture. These results were combined with literature data to evaluate the Stokes–Einstein relation for predicting the diffusion of organic molecules in SOA. Although almost all the data agree with the Stokes–Einstein relation within a factor of 10, a fractional Stokes–Einstein relation (D∝1/ηξ) with ξ=0.93 is a better model for predicting the diffusion of organic molecules in the SOA proxies studied. In addition, based on the output from a chemical transport model, the Stokes–Einstein relation can overpredict mixing times of organic molecules within SOA by as much as 1 order of magnitude at an altitude of ∼3 km compared to the fractional Stokes–Einstein relation with ξ=0.93. These results also have implications for other areas such as in food sciences and the preservation of biomolecules

Predictions of diffusion rates of large organic molecules in secondary organic aerosols using the Stokes-Einstein and fractional Stokes-Einstein relations

Information on the rate of diffusion of organic molecules within secondary organic aerosol (SOA) is needed to accurately predict the effects of SOA on climate and air quality. Diffusion can be important for predicting the growth, evaporation, and reaction rates of SOA under certain atmospheric conditions. Often, researchers have predicted diffusion rates of organic molecules within SOA using measurements of viscosity and the Stokes-Einstein relation (D ∝ 1/η, where D is the diffusion coefficient and η is viscosity). However, the accuracy of this relation for predicting diffusion in SOA remains uncertain. Using rectangular area fluorescence recovery after photobleaching (rFRAP), we determined diffusion coefficients of fluorescent organic molecules over 8 orders in magnitude in proxies of SOA including citric acid, sorbitol, and a sucrose-citric acid mixture. These results were combined with literature data to evaluate the Stokes-Einstein relation for predicting the diffusion of organic molecules in SOA. Although almost all the data agree with the Stokes-Einstein relation within a factor of 10, a fractional Stokes-Einstein relation (D ∝ 1/η ) with ξ = 0:93 is a better model for predicting the diffusion of organic molecules in the SOA proxies studied. In addition, based on the output from a chemical transport model, the Stokes-Einstein relation can overpredict mixing times of organic molecules within SOA by as much as 1 order of magnitude at an altitude of ∼ 3 km compared to the fractional Stokes-Einstein relation with ξ = 0.93. These results also have implications for other areas such as in food sciences and the preservation of biomolecules.

Predictions of diffusion rates of large organic molecules in secondary organic aerosols using the Stokes-Einstein and fractional Stokes-Einstein relations

Information on the rate of diffusion of organic molecules within secondary organic aerosol (SOA) is needed to accurately predict the effects of SOA on climate and air quality. Diffusion can be important for predicting the growth, evaporation, and reaction rates of SOA under certain atmospheric conditions. Often, researchers have predicted diffusion rates of organic molecules within SOA using measurements of viscosity and the Stokes-Einstein relation (D ∝ 1/η, where D is the diffusion coefficient and η is viscosity). However, the accuracy of this relation for predicting diffusion in SOA remains uncertain. Using rectangular area fluorescence recovery after photobleaching (rFRAP), we determined diffusion coefficients of fluorescent organic molecules over 8 orders in magnitude in proxies of SOA including citric acid, sorbitol, and a sucrose-citric acid mixture. These results were combined with literature data to evaluate the Stokes-Einstein relation for predicting the diffusion of organic molecules in SOA. Although almost all the data agree with the Stokes-Einstein relation within a factor of 10, a fractional Stokes-Einstein relation (D ∝ 1/ηξ) with ξ = 0:93 is a better model for predicting the diffusion of organic molecules in the SOA proxies studied. In addition, based on the output from a chemical transport model, the Stokes-Einstein relation can overpredict mixing times of organic molecules within SOA by as much as 1 order of magnitude at an altitude of ∼ 3 km compared to the fractional Stokes-Einstein relation with ξ = 0.93. These results also have implications for other areas such as in food sciences and the preservation of biomolecules

Global Distribution of the Phase State and Mixing Times within Secondary Organic Aerosol Particles in the Troposphere Based on Room-Temperature Viscosity Measurements

Information on the global distributions of secondary organic aerosol (SOA) phase state and mixing times within SOA is needed to predict the impact of SOA on air quality, climate, and atmospheric chemistry; nevertheless, such information is rare. In this study, we developed parameterizations for viscosity as a function of relative humidity (RH) and temperature based on room-temperature viscosity data for simulated pine tree SOA and toluene SOA. The viscosity parameterizations were then used together with tropospheric RH and temperature fields to predict the SOA phase state and mixing times of water and organic molecules within SOA in the troposphere for 200 nm particles. Based on our results, the glassy state can often occur, and the mixing times of water can often exceed 1 h within SOA at altitudes >6 km. Furthermore, the mixing times of organic molecules within SOA can often exceed 1 h throughout most of the free troposphere (i.e., ≳1 km in altitude). In most of the planetary boundary layer (i.e., ≲1 km in altitude), the glassy state is not important, and the mixing times of water and organic molecules are less than 1 h. Our results are qualitatively consistent with the results from Shiraiwa et al. (Nat. Commun., 2017), although there are quantitative differences. Additional studies are needed to better understand the reasons for these differences