This study reviewed the prediction of fine particulate matter (PM 2.5 ) from satellite aerosol optical depth (AOD) and summarized the advantages and limitations of these predicting models. A total of 116 articles were included from 1436 records retrieved. The number of such studies has been increasing since 2003. Among these studies, four predicting models were widely used: Multiple Linear Regression (MLR) (25 articles), Mixed-Effect Model (MEM) (23 articles), Chemical Transport Model (CTM) (16 articles) and Geographically Weighted Regression (GWR) (10 articles). We found that there is no so-called best model among them and each has both advantages and limitations. Regarding the prediction accuracy, MEM performs the best, while MLR performs worst. CTM predicts PM 2.5 better on a global scale, while GWR tends to perform well on a regional level. Moreover, prediction performance can be significantly improved by combining meteorological variables with land use factors of each region, instead of only considering meteorological variables. In addition, MEM has advantages in dealing with the AOD data with missing values. We recommend that with the help of higher resolution AOD data, future works could be focused on developing satellite-based predicting models for the prediction of historical PM 2.5 and other air pollutants.

The perception of air quality significantly affects the acceptance of the public of the government’s environmental policies. The aim of this research is to explore the relationship between the perception of the air quality of parents and scientific monitoring data and to analyze the factors that affect parents’ perceptions. Scientific data of air quality were obtained from Wuhan’s environmental condition reports. One thousand parents were investigated for their knowledge and perception of air quality. Scientific data show that the air quality of Wuhan follows an improving trend in general, while most participants believed that the air quality of Wuhan has deteriorated, which indicates a significant difference between public perception and reality. On the individual level, respondents with an age of 40 or above (40 or above: OR = 3.252; 95% CI: 1.170–9.040), a higher educational level (college and above: OR = 7.598; 95% CI: 2.244–25.732) or children with poor healthy conditions (poor: OR = 6.864; 95% CI: 2.212–21.302) have much more negative perception of air quality. On the community level, industrial facilities, vehicles and city construction have major effects on parents’ perception of air quality. Our investigation provides baseline information for environmental policy researchers and makers regarding the public’s perception and expectation of air quality and the benefits to the environmental policy completing and enforcing.

PurposeThis study aimed to determine the factors that influence patient satisfaction with ecdemic medical care.Materials and methodsEight hundred and forty-four face-to-face interviews were conducted between October and November 2017 in two high-profile hospitals in Nanchang, China. Patient satisfaction was divided into lowest and highest satisfaction groups according the 80/20 rule. Demographic factors associated with patient satisfaction were identified by logistic regression models.ResultsRespondents’ main reasons for choosing a non-local hospital were “high level of medical treatment” (581/844), “good reputation of the hospital” (533/844), and “advanced medical equipment” (417/844). The top three items that dissatisfied the ecdemic patients were “long time to wait for treatment” (553/844), “complicated formalities” (307/844), and “poor overall service attitude” (288/844). Fewer female patients (adjusted odds ratio [AOR] =1.47, 95% confidence interval [CI] =1.03–2.11), patients with a family per-capita monthly income (FPMI) between 3,001 and 5,000 CNY (AOR =1.40, 95% CI =1.01–2.17), inpatients (AOR =1.46, 95% CI =1.01–2.13), and more patients with an FPMI >7,000 CNY (AOR =0.43, 95% CI =0.20–0.92) were detected in the lowest satisfaction group. Fewer patients with an associate’s or bachelor’s degree (AOR =2.40, 95% CI =1.37–4.20) and patients with an FPMI >7,000 CNY (AOR =3.02, 95% CI =1.10–8.33) were detected in the highest satisfaction group. Moreover, more inpatients (AOR =0.70, 95% CI =0.54–0.97) and those aged 46–65 years (AOR =0.63, 95% CI =0.33–0.98) were detected in the highest satisfaction group.ConclusionFindings suggested that managers of the medical facilities should note the importance of increasing their publicity through a rapidly developing media, as well as the necessity of creating a more patient-friendly medical care experience. Hospitals should also focus on the medical care experience of patients with relatively lower and higher income levels, male ecdemic patients, and ecdemic outpatients.

Objective: To understand parents' propensity to migrate and willingness to pay with respect to outdoor air pollution, and to explore related affecting factors.Methods: This study used a convenience sample and subjects were collected from a community in Wuchang District and Children's Hospital of Wuhan, respectively. A designed questionnaire was used for this study. Univariable and multivariable logistic regression models were applied to analyze the relationship between parents' individual and familial characteristics and related behavioral intensions to air quality improvement. Statistical analysis was done with SAS 9.1.Results: The questionnaire was completed by 865 subjects (response rate = 86.5%). The number of people with migrant intent was 150(36.4%) from hospital group, and 139(30.7%) from community group. In the hospital group, subjects with higher knowledge of air quality (OR = 6.268, p < 0.05) and higher average annual household income (AAHI), which was equal or more than 50,000 Yuan (OR = 2.045, p < 0.01), were found to be more intent to migrate. AAHI (OR = 1.939, p < 0.05) was also the affecting factor in the community group correspondingly. Those willing to pay for air quality improvement included 297 people (72.1%) from the hospital group and 333 people (73.5%) from the community group, and affecting factors was the public responsibility for air quality improvement (hospital group: OR = 3.380, p < 0.01; community group: OR = 4.436, p < 0.01).Conclusions: This study indicated high tendency of propensity to migrate for avoiding poor air condition and willingness to pay to improve air quality in Wuhan. Local governments should pay more attention to parents' knowledge of air pollution and attitudes towards government management of air quality, especially those willing to migrate.

Structural analysis methods (e.g., probing and feature attribution) are increasingly important tools for neural network analysis. We propose a new structural analysis method grounded in a formal theory of causal abstraction that provides rich characterizations of model-internal representations and their roles in input/output behavior. In this method, neural representations are aligned with variables in interpretable causal models, and then interchange interventions are used to experimentally verify that the neural representations have the causal properties of their aligned variables. We apply this method in a case study to analyze neural models trained on Multiply Quantified Natural Language Inference (MQNLI) corpus, a highly complex NLI dataset that was constructed with a tree-structured natural logic causal model. We discover that a BERT-based model with state-of-the-art performance successfully realizes the approximate causal structure of the natural logic causal model, whereas a simpler baseline model fails to show any such structure, demonstrating that neural representations encode the compositional structure of MQNLI examples. * equal contribution, randomized order Preprint. Under review.