A new method for evaluating air quality using an ideal grey close function cluster correlation analysis method.
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ABSTRACT:
SUBMITTER: Ren X
PROVIDER: S-EPMC8639721 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIn recent years, increasing evidences have indicated that long non-coding RNAs (lncRNAs) are deeply involved in a wide range of human biological pathways. The mutations and disorders of lncRNAs are closely associated with many human diseases. Therefore, it is of great importance to predict potential associations between lncRNAs and complex diseases for the diagnosis and cure of complex diseases. However, the functional mechanisms of the majority of lncRNAs are still remain unclear. As a result, it remains a great challenge to predict potential associations between lncRNAs and diseases.ResultsHere, we proposed a new method to predict potential lncRNA-disease associations. First, we constructed a bipartite network based on known associations between diseases and lncRNAs/protein coding genes. Then the cluster association scores were calculated to evaluate the strength of the inner relationships between disease clusters and gene clusters. Finally, the gene-disease association scores are defined based on disease-gene cluster association scores and used to measure the strength for potential gene-disease associations.ConclusionsLeave-One Out Cross Validation (LOOCV) and 5-fold cross validation tests were implemented to evaluate the performance of our method. As a result, our method achieved reliable performance in the LOOCV (AUCs of 0.8169 and 0.8410 based on Yang's dataset and Lnc2cancer 2.0 database, respectively), and 5-fold cross validation (AUCs of 0.7573 and 0.8198 based on Yang's dataset and Lnc2cancer 2.0 database, respectively), which were significantly higher than the other three comparative methods. Furthermore, our method is simple and efficient. Only the known gene-disease associations are exploited in a graph manner and further new gene-disease associations can be easily incorporated in our model. The results for melanoma and ovarian cancer have been verified by other researches. The case studies indicated that our method can provide informative clues for further investigation.
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Project description:This paper describes the in situ monitoring of indoor air quality (IAQ) in two dwellings, using low-cost IAQ sensors to provide high-density temporal and spatial data. IAQ measurements were conducted over 2-week periods in the kitchen and bedroom of each home during the winter, spring, and summer seasons, characterized by different outside parameters, that were simultaneously measured. The mean indoor PM2.5 concentrations were about 15 μg m-3 in winter, they dropped to values close to 10 μg m-3 in spring and increased to levels of about 13 μg m-3 in summer. During the winter campaign, indoor PM2.5 was found mainly associated with particle penetration inside the rooms from outdoors, because of the high outdoor PM2.5 levels in the season. Such pollution winter episodes occur frequently in the study region, due to the combined contributions of strong anthropogenic emissions and stable atmospheric conditions. The concentrations of indoor volatile organic compounds (VOCs) and CO2 increased with the number of occupants (humans and pets), as likely associated with consequent higher emissions through breathing and metabolic processes. They also varied with occupants' daily activities, like cooking and cleaning. Critic CO2 levels above the limit of 1000 ppm were observed in spring campaign, in the weeks close to the end of the COVID-19 quarantine, likely associated with the increased time that the occupants spent at home.
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Project description:To effectively control air pollution, it is necessary to obtain a preliminary assessment of air quality. The purpose of this study was to introduce a cloud model method in air pollution assessment. First, the standard cloud models of air pollution indicators were obtained, and the calculating process of numerical characteristics employed by the standard cloud model was explained. Second, the levels of air pollution indicators were presented based on the qualitative and quantitative analysis of cloud models, which realized the uncertainty conversion between qualitative concepts and their corresponding quantitative values, as well as taking the fuzziness and randomness into account. Air quality assessment results including SO2, NO2, CO, O3, PM10 and PM2.5 were analysed. Third, the cloud model adopted in the assessment process of air quality was validated by grey relational analysis, and the results confirmed the validity of cloud model assessment. Fourth, the air pollution level of the air quality index (AQI) was determined, and the fuzziness and randomness of the assessment results were thoroughly analysed by taking entropy and hyper entropy into consideration. Fifth, seasonal variations in different air pollution indicators were analysed to proffer a series of recommendations for government policy decision-makers and travellers. The cloud model provided a new method for air quality assessment.
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Project description:This SuperSeries is composed of the SubSeries listed below.
2017-12-31 | GSE82233 | GEO
Project description:Air quality in European cities is still a challenge, with various urban areas frequently exceeding the PM2.5 and NO2 concentration levels allowed by the European Union Air Quality Standards. This is a problem both in terms of legislation compliance, but also in terms of health of citizens, as it has been recently estimated that 400 to 450 thousand people die prematurely every year due to poor air quality. Air quality in cities can be improved with a number of interventions, at different sectoral (industry, traffic, residential, etc …) and geographical (international, European, national, local, etc.) levels. In this paper we explore the potential of city level plans to improve mobility and air quality (excluding electro-mobility options, not considered in this study). We applied the "Sustainable Urban Mobility Plans" (SUMPs) framework to 642 cities in Europe and modelled how the measures they include may impact at first on mobility and emissions at urban level, and then on urban background concentrations of PM2.5 and NO2. Results show that annual averages moderately improve for both pollutants, with reductions of urban background concentrations up to 2% for PM2.5 and close to 4% for NO2. The impact on NO2 at street level (that will be higher than on urban background) is not evaluated in this work. The air quality improvement of the simulated SUMP would only partially alleviate air quality problems in urban areas, but such a reduction in the emissions of air pollutants should still be considered as a positive result of SUMPs, given that they correspond to a set of low-cost measures that can be implemented at local level. Furthermore, the introduction of electro-mobility options (not considered here) would increase the impact on air quality. Other types of benefits, such as reduced fuel consumption, greenhouse gas emissions, higher impact at street level or accident rates reduction further add to the overall positive impact.
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Project description:Following surgery for rectal cancer, two unfortunate outcomes for patients are permanent colostomy and local recurrence of cancer. We tested whether a quality-improvement strategy to change surgical practice would improve these outcomes.Sixteen hospitals were cluster-randomized to the intervention (Quality Initiative in Rectal Cancer strategy) or control (normal practice) arm. Consecutive patients with primary rectal cancer were accrued from May 2002 to December 2004. Surgeons at hospitals in the intervention arm could voluntarily participate by attending workshops, using opinion leaders, inviting a study team surgeon to demonstrate optimal techniques of total mesorectal excision, completing postoperative questionnaires, and receiving audits and feedback. Main outcome measures were hospital rates of permanent colostomy and local recurrence of cancer.A total of 56 surgeons (n = 558 patients) participated in the intervention arm and 49 surgeons (n = 457 patients) in the control arm. The median follow-up of patients was 3.6 years. In the intervention arm, 70% of surgeons participated in workshops, 70% in intraoperative demonstrations and 71% in postoperative questionnaires. Surgeons who had an intraoperative demonstration provided care to 86% of the patients in the intervention arm. The rates of permanent colostomy were 39% in the intervention arm and 41% in the control arm (odds ratio [OR] 0.97, 95% confidence interval [CI] 0.63-1.48). The rates of local recurrence were 7% in the intervention arm and 6% in the control arm (OR 1.06, 95% CI 0.68-1.64).Despite good participation by surgeons, the resource-intense quality-improvement strategy did not reduce hospital rates of permanent colostomy or local recurrence compared with usual practice.
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Project description:With the Goal-Establishment of "carbon compliance" and "carbon neutrality", enterprises should upgrade "green development" to a strategic position and implement it through all aspects of business operations. Green supplier selection is the initial phase of supply chain management, therefore a green supplier evaluation system is needed to achieve green development. Based on a literature analysis, We selected 45 metrics as candidates for evaluating suppliers. Then through expert interviews, some indicators were revised and supplemented, and finally a green supplier evaluation index system for high energy-consuming enterprises was constructed. A unique aspect of this paper is the introduction of rough number theory into the supplier evaluation process to improve the indicator assignment and the grey correlation TOPSIS method, which optimizes the processing of uncertain semantic information in the evaluation process. The rough number-grey correlation TOPSIS supplier evaluation model developed in this paper has been verified to be applicable and stable in case studies and successfully implemented.
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Project description:The Method Information Index (MII) is calculated from contraceptive users' responses to questions regarding counseling content-whether they were informed about methods other than the one they received, told about method-specific side effects, and advised what to do if they experienced side effects. The MII is increasingly reported in national surveys and used to track program performance, but little is known about its properties. Using additional questions, we assessed the consistency between responses and the method received in a prospective, multicountry study. We employed two definitions of consistency: (1) presence of any concordant response, and (2) absence of discordant responses. Consistency was high when asking whether users were informed about other methods and what to do about side effects. Responses were least consistent when asking whether side effects were mentioned. Adjusting for inconsistency, scores were up to 50 percent and 30 percent lower in Pakistan and Uganda, respectively, compared to unadjusted MII scores. Additional questions facilitated better understanding of counseling quality.
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Project description:Policy analysts and researchers often use models to translate expected emissions changes from pollution control policies to estimates of air pollution changes and resulting changes in health impacts. These models can include both photochemical Eulerian grid models or reduced complexity models; these latter models make simplifying assumptions about the emissions-to-air quality relationship as a means of reducing the computational time needed to simulate air quality. This manuscript presents a new database of photochemical- and reduced complexity-modelled changes in annual average particulate matter with aerodynamic diameter less than 2.5 μm and associated health effects and economic values for five case studies representing different emissions control scenarios. The research community is developing an increasing number of reduced complexity models as lower-cost and more expeditious alternatives to full form Eulerian photochemical grid models such as the Comprehensive Air-Quality Model with eXtensions (CAMx) and the Community Multiscale Air Quality (CMAQ) model. A comprehensive evaluation of reduced complexity models can demonstrate the extent to which these tools capture complex chemical and physical processes when representing emission control options. Systematically comparing reduced complexity model predictions to benchmarks from photochemical grid models requires a consistent set of input parameters across all systems. Developing such inputs is resource intensive and consequently the data that we have developed and shared (https://github.com/epa-kpc/RFMEVAL) provide a valuable resource for others to evaluate reduced complexity models. The dataset includes inputs and outputs representing 5 emission control scenarios, including sector-based regulatory policy scenarios focused on on-road mobile sources and electrical generating units (EGUs) as well as hypothetical across-the-board reductions to emissions from cement kilns, refineries, and pulp and paper facilities. Model inputs, outputs, and run control files are provided for the Air Pollution Emission Experiments and Policy Analysis (APEEP) version 2 and 3, Intervention Model for Air Pollution (InMAP), Estimating Air pollution Social Impact Using Regression (EASIUR), and EPA's source apportionment benefit-per-ton reduced complexity models. For comparison, photochemical grid model annual average PM2.5 output is provided for each emission scenario. Further, inputs are also provided for the Environmental Benefits and Mapping Community Edition (BenMAP-CE) tool to generate county level health benefits and monetized health damages along with output files for benchmarking and intercomparison. Monetized health impacts are also provided from EASIUR and APEEP which can provide these outside the BenMAP-CE framework. The database will allow researchers to more easily compare reduced complexity model predictions against photochemical grid model predictions.
| S-EPMC6911961 | biostudies-literature
Project description:Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.
| S-EPMC9957566 | biostudies-literature