Nonlinear models for ground-level ozone forecasting

One of the main concerns in air pollution is excessive tropospheric ozone concentration. The aim of this work is to develop statistical models giving shortterm forecasts of future ground-level ozone concentrations. Since there are few physical insights about the dynamic relationship between ozone, precursor emissions and/or meteorological factors, a nonparametric and nonlinear approach seems promising in order to specify the forecast models. First, we apply four nonparametric procedures to forecast daily maximum 1-hour and maximum 8-hour averages of ozone concentrations in an urban area. Then, in order to improve the forecast performances, we combine the time series of the forecasts. This idea seems to give encouraging results. This work was supported by a MURST grant. The authors would like to thank two anonymous referees for their helpful comments.     

Sex-positive criminology: Possibilities for legal and social change

This article provides an introduction to sex-positive criminology and its goals for change. Sex-positive criminology draws from the “thick desire” organizing principle, which is a rights-based approach to human sexuality, as well as from positive sexuality approaches. It also draws from critical, queer, and feminist criminological traditions and abolitionist sensibilities. We discuss examples that pertain to key tenets of sex-positivity: consent and bodily autonomy, education, medical access, harm reduction, and ways to increase agency. Main topics of discussion include addressing deeply harmful and sex-negative laws and policies that perpetuate state violence, such as coerced or forced sterilization, criminalization of abortion and pregnancy loss, sexual and physical assault of sex workers by police, criminalization through medically inaccurate laws, and legislation such as Allow States and Victims to Fight Online Sex Trafficking Act and the Stop Enabling Sex Traffickers Act that puts marginalized populations at risk. Throughout, we reflect on possibilities for sex-positive laws and policies and the social impacts they would have, such as improving health and well-being.     

Major Factors Influencing the Health Impacts from Controlling Air Pollutants with Nonlinear Chemistry: An Application to China

Predicting the human‐health effects of reducing atmospheric emissions of nitrogen oxide (NO_x) emissions from power plants, motor vehicles, and other sources is complex because of nonlinearity in the relevant atmospheric processes. We estimate the health impacts of changes in fine particulate matter (PM_2.5) and ozone concentrations that result from control of NO_x emissions alone and in conjunction with other pollutants in and outside the mega‐city of Shanghai, China. The Community Multiscale Air Quality (CMAQ) Modeling System is applied to model the effects on atmospheric concentrations of emissions from different economic sectors and geographic locations. Health impacts are quantified by combining concentration‐response functions from the epidemiological literature with pollutant concentration and population distributions. We find that the health benefits per ton of emission reduction are more sensitive to the location (i.e., inside vs. outside of Shanghai) than to the sectors that are controlled. For eastern China, we predict between 1 and 20 fewer premature deaths per year per 1,000 tons of NO_x emission reductions, valued at $300–$6,000 per ton. Health benefits are sensitive to seasonal variation in emission controls. Policies to control NO_x emissions need to consider emission location, season, and simultaneous control of other pollutants to avoid unintended consequences.     

Multivariate spatial interpolation and exposure to air pollutants

We develop and apply an approach to the spatial interpolation of a vector-valued random response field. The Bayesian approach we adopt enables uncertainty about the underlying models to be représentés in expressing the accuracy of the resulting interpolants. The methodology is particularly relevant in environmetrics, where vector-valued responses are only observed at designated sites at successive time points. The theory allows space-time modelling at the second level of the hierarchical prior model so that uncertainty about the model parameters has been fully expressed at the first level. In this way, we avoid unduly optimistic estimates of inferential accuracy. Moreover, the prior model can be upgraded with any available new data, while past data can be used in a systematic way to fit model parameters. The theory is based on the multivariate normal and related joint distributions. Our hierarchical prior models lead to posterior distributions which are robust with respect to the choice of the prior (hyperparameters). We illustrate our theory with an example involving monitoring stations in southern Ontario, where monthly average levels of ozone, sulphate, and nitrate are available and between-station response triplets are interpolated. In this example we use a recently developed method for interpolating spatial correlation fields.     

食用菌制种关键性技术的新探讨

本文对食用菌制种技术进行了系统研究，特别是在没有专门制种设备和特殊无菌的环境条件下，如何培养出健壮的菌种。现已筛选出适合于大多数食用菌生长的良好培养基，并研究出培养基的常压灭菌法和简单易行的菌种分离纯化技术，解决了在农村大量发展食用菌的重大技术难题。     

A spatiotemporal model for Mexico City ozone levels

Summary.  We consider hourly readings of concentrations of ozone over Mexico City and propose a model for spatial as well as temporal interpolation and prediction. The model is based on a time-varying regression of the observed readings on air temperature. Such a regression requires interpolated values of temperature at locations and times where readings are not available. These are obtained from a time-varying spatiotemporal model that is coupled to the model for the ozone readings. Two location-dependent harmonic components are added to account for the main periodicities that ozone presents during a given day and that are not explained through the covariate. The model incorporates spatial covariance structure for the observations and the parameters that define the harmonic components. Using the dynamic linear model framework, we show how to compute smoothed means and predictive values for ozone. We illustrate the methodology on data from September 1997.     

纯天然菠萝饮料的研制

利用来源丰富、价廉的成熟菠萝为原料,研制出保留其营养成分和风味物质的纯天然饮料.将新鲜的成熟菠萝榨汁、过滤、微波灭菌制成纯天然菠萝饮料.以灭菌前后菠萝汁中维生素C的含量及灭菌后菠萝汁的外观性状为考核指标,采用正交试验优选出关键工序微波灭菌的最佳工艺条件.微波灭菌的最佳工艺条件是灭菌功率为700W,时间为5min.在最佳灭菌条件下菠萝汁中维生素C的保存率为82.09%.采用微波灭菌工艺制备纯天然菠萝饮料可行,其维生素C的保存率较高.     

Webs of empire: locating New Zealand's colonial past

This article reports on the views of non-medical interest groups about sterilization and intellectually disabled people in New Zealand. The rationale for undertaking a study in this sensitive area is presented. Notes about survey distribution method are included and participants’ responses outlined. The study found that a majority of participants agreed that intellectually disabled people should make their own decisions about reproductive capacity. Yet a clear tension between an overall endorsement of individual choice and a majority who also considered that sterilization could be warranted in at least some cases emerged from the data. Reasons for sterilization included: the best interests of intellectually disabled people and carers; concerns about sexual health/well-being; lack of sexual control; and concerns about sexual abuse. The extent of agreement with sterilization as a desirable practice for intellectually disabled boys/men was an unexpected finding. The article concludes with questions posed to promote further discussion.     

中法生物安全实验室标准应用体系对比分析

目的揭示中法两国生物安全实验室标准应用体系的状况。方法以中法两国的ISO／IEC生物安全实验室应用的标准体系为研究对象,收集和整理生物安全实验室相关标准信息,主要运用标准信息的文献计量方法,从标准的现行数量、分类、制定、修订、等效国际标准等方面的对比分析。结果与结论建议中国在生物安全实验室管理上应加强对医疗设备、卫生用品、洗涤设备等的灭菌消毒系列标准、实验室风险管理和持续改进方法等国际标准的等效采用。     

A robust functional time series forecasting method

Univariate time series often take the form of a collection of curves observed sequentially over time. Examples of these include hourly ground-level ozone concentration curves. These curves can be viewed as a time series of functions observed at equally spaced intervals over a dense grid. Since functional time series may contain various types of outliers, we introduce a robust functional time series forecasting method to down-weigh the influence of outliers in forecasting. Through a robust principal component analysis based on projection pursuit, a time series of functions can be decomposed into a set of robust dynamic functional principal components and their associated scores. Conditioning on the estimated functional principal components, the crux of the curve-forecasting problem lies in modelling and forecasting principal component scores, through a robust vector autoregressive forecasting method. Via a simulation study and an empirical study on forecasting ground-level ozone concentration, the robust method demonstrates the superior forecast accuracy that dynamic functional principal component regression entails. The robust method also shows the superior estimation accuracy of the parameters in the vector autoregressive models for modelling and forecasting principal component scores, and thus improves curve forecast accuracy.