空气污染对信鸽比赛个体归巢速度的影响

空气污染作为一种有害的环境因素,对人类及动物的生理、心理均有影响.在鸟类中,信鸽(Columba livia)是研究空气污染影响的理想模型.为探究空气污染的行为学效应,通过收集并筛选2018和2019年成都市信鸽协会春秋两个季节举办的64场赛事共285羽参赛5场及以上的信鸽不同空距等级下的归巢速度,利用混合线性模型分析...     

Particulate Matter on Foliage of 13 Woody Species: Deposition on Surfaces and Phytostabilisation in Waxes – a 3-Year Study

Particulate matter (PM) as an air pollutant can be harmful for human health through allergic, mutagenic and carcinogenic effects. Although the main focus is on decreasing air pollution, after PM has been emitted to the atmosphere, one of the realistic options to decrease it's concentrations in urbanized area will be phytoremediation. This study compared the capacity to capture PM from air of seven tree species commonly cultivated in Poland (Catalpa bignonioides Walter, Corylus colurna L., Fraxinus pennsylvanica Marsh., Ginkgo biloba L., Platanus × hispanica Mill. ex Muenchh., Quercus rubra L., Tilia tomentosa Moench ‘Brabant’) and six shrub species (Acer tataricum subsp. ginnala (Maxim.) Wesm., Sambucus nigra L., Sorbaria sorbifolia (L.) A.Br., Spiraea japonica L.f., Syringa meyeri C.K. Schneid. ‘Palibin’, Viburnum lantana L.). Significant differences were found between species in mass of total PM accumulation for two PM categories and three size fractions determined and in amount of waxes. A positive correlation was found between in-wax PM of diameter 2.5–10 μm and amount of waxes, but not between amount of waxes and amount of total PM or of any size fraction.     

An informative Bayesian structural equation model to assess source-specific health effects of air pollution

A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles or particulate matter (PM). Quantifying source-specific risk is a challenge because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model (SEM) typically involves a large number of parameters, for small-sample settings, we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss the implications of our findings with respect to the design of future PM concentrator studies.     

A robust and readily implementable method for the meta-analysis of response ratios with and without missing standard deviations

The log response ratio, lnRR, is the most frequently used effect size statistic for meta-analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study-specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta-analyses of lnRR, regardless of ‘missingness’.     

A quantitative method for ranking the water quality tolerances of benthic species

This method compares relative tolerances of benthic organisms to levels of water pollutants using contingency table analyses. Ratings of the pollution tolerance of benthic species may thus be more accurately assigned, improving the previously proposed biotic indices. Preliminary application of this method utilizes six species of tubificid worms of the genera Limnodrilus and Tubifex, among which several trends in water tolerance seemed to have become apparent.Based on paper presented May 12, 1978 at the Twenty-sixth Annual Meeting of the North American Benthological Society, Winnipeg, Manitoba     

Particle Pollution Estimation Based on Image Analysis

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM_2.5 index. The results demonstrate that the image analysis method provides good prediction of PM_2.5 indexes, and different features have different significance levels in the prediction.     

The Probability Density Functions of the Contingency Coefficients,defined by KRAMER and PEARSON,in a 2-Dimensional Frequency Distribution of Grouped Bivariate Normal Variables. I

Two general measures for the degree of association in a contingency table are the contingency coefficients defined by PEARSON and KRAMER. In the case of a standardized bivariate normal distribution with correlation coefficient of the variables, whose realizations constitute the rows and columns of the table, the density functions of the two listed association measures are derived.     

Particulate matter exposure induces persistent lung inflammation and endothelial dysfunction

Epidemiologic and animal studies have shown that exposure to particulate matter air pollution (PM) is a risk factor for the development of atherosclerosis. Whether PM-induced lung and systemic inflammation is involved in this process is not clear. We hypothesized that PM exposure causes lung and systemic inflammation, which in turn leads to vascular endothelial dysfunction, a key step in the initiation and progression of atherosclerosis. New Zealand White rabbits were exposed for 5 days (acute, total dose 8 mg) and 4 wk (chronic, total dose 16 mg) to either PM smaller than 10 mum (PM(10)) or saline intratracheally. Lung inflammation was quantified by morphometry; systemic inflammation was assessed by white blood cell and platelet counts and serum interleukin (IL)-6, nitric oxide, and endothelin levels. Endothelial dysfunction was assessed by vascular response to acetylcholine (ACh) and sodium nitroprusside (SNP). PM(10) exposure increased lung macrophages (P<0.02), macrophages containing particles (P<0.001), and activated macrophages (P<0.006). PM(10) increased serum IL-6 levels in the first 2 wk of exposure (P<0.05) but not in weeks 3 or 4. PM(10) exposure reduced ACh-related relaxation of the carotid artery with both acute and chronic exposure, with no effect on SNP-induced vasodilatation. Serum IL-6 levels correlated with macrophages containing particles (P=0.043) and ACh-induced vasodilatation (P=0.014 at week 1, P=0.021 at week 2). Exposure to PM(10) caused lung and systemic inflammation that were both associated with vascular endothelial dysfunction. This suggests that PM-induced lung and systemic inflammatory responses contribute to the adverse vascular events associated with exposure to air pollution.     

Considering of certain relevant information in the analysis of contingency tables. A three-step procedure

A heuristic three-step procedure for analysing multidimensional contingency tables is given to meet the requirements of a mixed analysis from both hypotheses-ruled and data-ruled type. The first-step provides the structure of relationships among the attributes by fitting an appropriate unsaturated log-linear model to the data of the given contingency table. Restriction to elementary hierarchical models allows to get them by combining pairs of conditional independence. The result of the first step may be regarded as a certain validisation of real model ideas. In the second step the significant pairs of conditional dependence are analysed in regard to the levels of the condition complex. Only such significant pairs are to be considered, in general, where the condition complex does not include the response variable. The third-step may test special subtests in that significant two-dimensional tables found in step two or may extend the general statements by partitioning, the corresponding test statistics in additive components. Application examples demonstrate the general line of action.     

Accumulation of particulate matter,heavy metals,and polycyclic aromatic hydrocarbons on the leaves of Tilia cordata Mill. in five Polish cities with different levels of air pollution

Urbanized areas are struggling with the problem of air pollution and as the number of people living in cities is increasing, the situation is likely to deteriorate. One of the most harmful pollutants is particulate matter (PM). Increased levels of PM in the atmosphere are likely to have a negative impact on human health. Phytoremediation technology could be a solution. It involves plants acting as bio-filters by accumulating particles on, and in the leaves, thus removing the particles from the atmosphere. This study investigates the accumulation of PM including heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs), on the foliage of small-leaved lime (Tilia cordata Mill.) in five Polish cities. There were significantly different PM amounts found in the trees between the cities which related to the different quantities of PM in the atmosphere at these cities. Significant differences were found between cities for the amounts of the different particulate size fractions, and the HMs and PAHs in leaves. Strong winds reduced the amount of PM on leaves, especially the smallest fractions, but no relationship with precipitation was found. The results suggest that T. cordata improves the air quality in cities and can be used as an effective bioindicator for PM air pollution.     

A measurement error model for time-series studies of air pollution and mortality

One barrier to interpreting the observational evidence concerning the adverse health effects of air pollution for public policy purposes is the measurement error inherent in estimates of exposure based on ambient pollutant monitors. Exposure assessment studies have shown that data from monitors at central sites may not adequately represent personal exposure. Thus, the exposure error resulting from using centrally measured data as a surrogate for personal exposure can potentially lead to a bias in estimates of the health effects of air pollution. This paper develops a multi-stage Poisson regression model for evaluating the effects of exposure measurement error on estimates of effects of particulate air pollution on mortality in time-series studies. To implement the model, we have used five validation data sets on personal exposure to PM10. Our goal is to combine data on the associations between ambient concentrations of particulate matter and mortality for a specific location, with the validation data on the association between ambient and personal concentrations of particulate matter at the locations where data have been collected. We use these data in a model to estimate the relative risk of mortality associated with estimated personal-exposure concentrations and make a comparison with the risk of mortality estimated with measurements of ambient concentration alone. We apply this method to data comprising daily mortality counts, ambient concentrations of PM10measured at a central site, and temperature for Baltimore, Maryland from 1987 to 1994. We have selected our home city of Baltimore to illustrate the method; the measurement error correction model is general and can be applied to other appropriate locations.Our approach uses a combination of: (1) a generalized additive model with log link and Poisson error for the mortality-personal-exposure association; (2) a multi-stage linear model to estimate the variability across the five validation data sets in the personal-ambient-exposure association; (3) data augmentation methods to address the uncertainty resulting from the missing personal exposure time series in Baltimore. In the Poisson regression model, we account for smooth seasonal and annual trends in mortality using smoothing splines. Taking into account the heterogeneity across locations in the personal-ambient-exposure relationship, we quantify the degree to which the exposure measurement error biases the results toward the null hypothesis of no effect, and estimate the loss of precision in the estimated health effects due to indirectly estimating personal exposures from ambient measurements.     

Deep matrix factorization models for estimation of missing data in a low-cost sensor network to measure air quality

According to the WHO, pollution is a worldwide public health problem. In Colombia, low-cost strategies for air quality monitoring have been implemented using wireless sensor networks (WSNs), which achieve a better spatial resolution than traditional sensor networks for a lower operating cost. Nevertheless, one of the recurrent issues of WSNs is the missing data due to environmental and location conditions, hindering data collection. Consequently, WSNs should have effective mechanisms to recover missing data, and matrix factorization (MF) has shown to be a solid alternative to solve this problem. This study proposes a novel MF technique with a neural network architecture (i.e., deep matrix factorization or DMF) to estimate missing particulate matter (PM) data in a WSN in Aburrá Valley, Colombia. We found that the model that included spatial-temporal features (using embedding layers) captured the behavior of the pollution measured at each node more efficiently, thus producing better estimations than standard matrix factorization and other variations of the model proposed here.     

Effect of list errors on the estimation of population size

In many situations, it is possible to estimate the size of a closed population if some members of the population are recorded on one or more administrative lists. An important example is estimating the prevalence of a disease, where some members of the disease population may be recorded on lists such as disease registries, hospital admission records, and general practitioner records. An incomplete contingency table is formed by matching the lists and the missing cell count estimated by prediction based on a fitted model, which assumes that the matching is done without error. In practice, matching errors do occur, and in this article, we examine the effect of these errors on the estimation process and show how the standard models may be modified to allow for matching errors.     

Seasonal, daily and intradiurnal variation of PM10, NO2, NO and O3 in residential part of Zagreb, Croatia

Pollutants such as particulate matter, nitrogen oxides, carbon oxides, ground-level ozone, etc. are harmful to human health. Study of pollutant variation and its relationship with both dynamic and thermodynamic atmospheric boundary layer (ABL) structures is of importance not only for environmental protection but also for the public at large. The aim of this study was to analyze seasonal, daily and intradiurnal variation of PM10, NO2, NO and O3 in a residential part of an urban area, and the effect of some meteorological parameters. The study was conducted from January 1 till December 31, 2004 in the City of Zagreb using following methods: beta radiation absorption, chemiluminescence and UV photometry. The results presented in this article, show the dependence of air pollution levels upon traffic density, seasons and meteorological conditions. Considering the level of air pollution relative to the regulated limit and tolerated values, the measured 24-hour concentrations of all study pollutants exceeded the borderline values and/or tolerated values, however, the number of days with such pollutant concentrations did not exceed the allowed frequency. This is a preliminary study with the main objectives to point to the possible identification of the source of pollution and to assess the level of air contamination according to the new national legislation coordinated with European regulations. Future measurements and studies should evaluate in detail the causes of the concentration levels detected.     

森林植被与大气颗粒物的关系

近年来,大气颗粒物成为我国城市大气的主要污染物,其中细颗粒物（PM_2.5）粒径小、沉降困难,对环境的危害已成为亟待解决的问题。森林植被可显著消减空气颗粒物,有效改善空气环境质量。本文概述了植被对颗粒物的移除过程和方法,探讨了大气颗粒物与森林植被的相互关系。从单叶、单木及群落3个尺度,结合气象因素讨论了植被对移除大气颗粒物的影响,分析了颗粒物的后续再悬浮过程及对植被的危害。最后,从植被吸附颗粒物的能力测定和评价、本土高吸附PM_2.5能力植被的筛选及综合研究不同植被配置结构的吸附效应等方面提出了植被吸附颗粒污染物,尤其是细颗粒物的研究重点与趋势。     

The Decompositions for Point Symmetry Models in Two-way Contingency Tables

The decomposition for the complete point symmetry model in a rectangular contingency table is shown. Also the respective decompositions for the local point symmetry model and the reverse local point symmetry model in a square contingency table are given. Moreover test procedures for the decomposed models and an example are given.     

Use of log-linear analysis to construct explanatory models for TDBP- and AFB1-induced mutation spectra in lacI transgenic animals

Mutation spectra recovered from lacI transgenic animals exposed in separate experiments to tris-(2,3-dibromopropyl)phosphate (TDBP) or aflatoxin B1 (AFB1) were examined using log-linear analysis. Log-linear analysis is a categorical procedure that analyses contingency table data. Expected contingency table cell counts are estimated by maximum likelihood as effects of main variables and variable interactions. Evaluation of hierarchical models of decreasing complexity indicates when significant explanatory power is lost by the sequential omission of interactions between variables. Use of this technique allows construction of the most parsimonious models to account for mutation spectra obtained in the two experiments. The resulting statistical models are consistent with previous analyses of these data and with biological explanations for causes of the observed spectra.     

Oxidative and nitrosative stress markers in bus drivers

Exposure to ambient air pollution is associated with many diseases. Oxidative and nitrosative stress are believed to be two of the major sources of particulate matter (PM)-mediated adverse health effects. PM in ambient air arises from industry, local heating, and vehicle emissions and poses a serious problem mainly in large cities. In the present study we analyzed the level of oxidative and nitrosative stress among 50 bus drivers from Prague, Czech Republic, and 50 matching controls. We assessed simultaneously the levels of 15-F(2t)-isoprostane (15-F(2t)-IsoP) and 8-oxodeoxyguanosine (8-oxodG) in urine and protein carbonyl groups and 3-nitrotyrosine (NT) in blood plasma. For the analysis of all four markers we used ELISA techniques. We observed significantly increased levels of oxidative and nitrosative stress markers in bus drivers. The median levels (min, max) of individual markers in bus drivers versus controls were as follows: 8-oxodG: 7.79 (2.64-12.34)nmol/mmol versus 6.12 (0.70-11.38)nmol/mmol creatinine (p<0.01); 15-F(2t)-IsoP: 0.81 (0.38-1.55)nmol/mmol versus 0.68 (0.39-1.79)nmol/mmol creatinine (p<0.01); carbonyl levels: 14.1 (11.8-19.0)nmol/ml versus 12.9 (9.8-16.6)nmol/ml plasma (p<0.001); NT: 694 (471-3228)nmol/l versus 537 (268-13833)nmol/l plasma (p<0.001). 15-F(2t)-IsoP levels correlated with vitamin E (R=0.23, p<0.05), vitamin C (R=-0.33, p<0.01) and cotinine (R=0.47, p<0.001) levels. Vitamin E levels also positively correlated with 8-oxodG (R=0.27, p=0.01) and protein carbonyl levels (R=0.32, p<0.001). Both oxidative and nitrosative stress markers positively correlated with PM2.5 and PM10 exposure. In conclusion, our study indicates that exposure to PM2.5 and PM10 results in increased oxidative and nitrosative stress.