用BaCl2滴定法快速测定硫酸盐的研究

用铬酸钡光度法、EDTA滴定法、间接原予吸收法、离子色谱法等方法测定SO4^2-检测上限都很低（12—200mg／L），且只适用于清洁水，若水样混浊则会引起较大的误差。有的地下水，SO4^2-浓度高迭44000mg／L，用BaCl2滴定法测定SO4^2-：测试水样，以溴酚蓝做酸碱指示剂，加1：50盐酸，调为pH=3．5，加入30ml异丙醇，以钍试荆做终点指示荆，滴加0．01mol／L BaCl2，砖红色为终点，根据BaCl2用量计算SO4^2-浓度。本方法快捷、准确、测定范围大，上限可迭2000mg／L，克服了光度法测定浑浊水样引起误差大的缺点。     

用BaCl2滴定法快速测定硫酸盐的研究

用铬酸钡光度法、EDTA滴定法、间接原子吸收法、离子色谱法等方法测定SO42-检测上限都很低(12-200mg/L),且只适用于清洁水,若水样混浊则全引起较大的误差.有的地下水,SO42-浓度高达44000mg/L,用BaCl2滴定法测定SO42-:测试水样,以溴酚蓝做酸碱指示剂,加1∶50盐酸,调为pH=3.5,加入30ml异丙醇,以钍试剂做终点指示剂,滴加0.01mol/L BaCl2,砖红色为终点,根据BaCl2用量计算SO42-浓度.本方法快捷、准确、测定范围大,上限可达2000mg/L,克服了光度法测定浑浊水样引起误差大的缺点.     

Statistical Considerations in Determining the Health Significance of Constituents of Airborne Particulate Matter

ABSTRACT

Because of the U.S. Environmental Protection Agency’s (EPA) new ambient air quality standard for fine particles, the need is likely to continue for more detailed scientific investigation of various types of particles and their effects on human health. Epidemiology studies have become the method of choice for investigating health responses to such particles and to other air pollutants in community settings. Health effects have been associated with virtually all of the gaseous criteria pollutants and with the major constituents of airborne particulate matter (PM), including all size fractions less than about 20 gm, inorganic ions, carbonaceous particles, metals, crustal material, and biological aerosols. In many of the more recent studies, multiple pollutants or agents (including weather variables) have been significantly associated with health responses, and various methods have been used to suggest which ones might be the most important. In an ideal situation, classical least-squares regression methods are capable of performing this task. However, in the real world, where most of the pollutants are correlated with one another and have varying degrees of measurement precision and accuracy, such regression results can be misleading. This paper presents some guidelines for dealing with such collinearity and model comparison problems in both single- and multiple-pollutant regressions. These techniques rely on mean effect (attributable risk) rather than statistical significance per se as the preferred indicator of importance for the pollution variables.     

Air Pollution and Mortality: Issues and Uncertainties

Abstract

Results from 31 epidemiology studies linking air pollution with premature mortality are compared and synthesized. Consistent positive associations between mortality and various measures of air pollution have been shown within each of two fundamentally different types of regression studies and in many variations within these basic types; this is extremely unlikely to have occurred by chance. In this paper, the measure of risk used is the elasticity, which is a dimensionless regression coefficient defined as the percentage change in the dependent variable associated with a 1% change in an independent variable, evaluated at the means. This metric has the advantage of independence from measurement units and averaging times, and is thus suitable for comparisons within and between studies involving different pollutants. Two basic types of studies are considered: time-series studies involving daily perturbations, and cross-sectional studies involving longer-term spatial gradients. The latter include prospective studies of differences in individual survival rates in different locations and studies of the differences in annual mortality rates for various communities.

For a given data set, time-series regression results will vary according to the seasonal adjustment method used, the covariates included, and the lag structure assumed. The results from both types of cross-sectional regressions are highly dependent on the methods used to control for socioeconomic and personal lifestyle factors and on data quality. Amajor issue for all of these studies is that of partitioning the response among collinear pollution and weather variables. Previous studies showed that the variable with the least exposure measurement error may be favored in multiple regressions; assigning precise numerical results to a single pollutant is not possible under these circumstances. We found that the mean overall elasticity as obtained from timeseries studies for mortality with respect to various air pollutants entered jointly was about 0.048, with a range from 0.01 to 0.12. This implies that about 5% of daily mortality is associated with air pollution, on average. The corresponding values from population-based cross-sectional studies were similar in magnitude, but the results from the three recent prospective studies varied from zero to about five times as much. Long-term responses in excess of short-term responses might be interpreted as showing the existence of chronic effects, but the uncertainties inherent in both types of studies make such an interpretation problematic.     

Daily Mortality in the Philadelphia Metropolitan Area and Size-Classified Particulate Matter

ABSTRACT

Time-series of daily mortality data from May 1992 to September 1995 for various portions of the seven-county Philadelphia, PA, metropolitan area were analyzed in relation to weather and a variety of ambient air quality parameters. The air quality data included measurements of size-classified PM, SO₄ ^2-, and H+ that had been collected by the Harvard School of Public Health, as well as routine air pollution monitoring data. Because the various pollutants of interest were measured at different locations within the metropolitan area, it was necessary to test for spatial sensitivity by comparing results for different combinations of locations. Estimates are presented for single pollutants and for multiple-pollutant models, including gaseous pollutants and mutually exclusive components of PM (PM_2.5 and coarse particles, SO₄ ^2- and non-SO₄ ^2- portions of total suspended particulate [TSP] and PM₁₀), measured on the day of death and the previous day.

We concluded that associations between air quality and mortality were not limited to data collected in the same part of the metropolitan area; that is, mortality for one part may be associated with air quality data from another, not necessarily neighboring, part. Significant associations were found for a wide variety of gaseous and particulate pollutants, especially for peak O₃. Using joint regressions on peak O₃ with various other pollutants, we found that the combined responses were insensitive to the specific other pollutant selected. We saw no systematic differences according to particle size or chemistry. In general, the associations between daily mortality and air pollution depended on the pollutant or the PM metric, the type of collection filter used, and the location of sampling. Although peak O₃ seemed to exhibit the most consistent mortality responses, this finding should be confirmed by analyzing separate seasons and other time periods.     

The Aerosol Research and Inhalation Epidemiology Study (ARIES): PM2.5 mass and aerosol component concentrations and sampler intercomparisons

The Aerosol Research and Inhalation Epidemiology Study (ARIES) was designed to provide high-quality measurements of PM2.5, its components, and co-varying pollutants for an air pollution epidemiology study in Atlanta, GA. Air pollution epidemiology studies have typically relied on available data on particle mass often collected using filter-based methods. Filter-based PM2.5 sampling is susceptible to both positive and negative errors in the measurement of aerosol mass and particle-phase component concentrations in the undisturbed atmosphere. These biases are introduced by collection of gas-phase aerosol components on the filter media or by volatilization of particle phase components from collected particles. As part of the ARIES, we collected daily 24-hr PM2.5 mass and speciation samples and continuous PM2.5 data at a mixed residential-light industrial site in Atlanta. These data facilitate analysis of the effects of a wide variety of factors on sampler performance. We assess the relative importance of PM2.5 components and consider associations and potential mechanistic linkages of PM2.5 mass concentrations with several PM2.5 components. For the 12 months of validated data collected to date (August 1, 1998-July 31, 1999), the monthly average Federal Reference Method (FRM) PM2.5 mass always exceeded the proposed annual average standard (12-month average = 20.3 +/- 9.5 micrograms/m3). The particulate SO4(2-) fraction (as (NH4)2SO4) was largest in the summer and exceeded 50% of the FRM mass. The contribution of (NH4)2SO4 to FRM PM2.5 mass dropped to less than 30% in winter. Particulate NO3- collected on a denuded nylon filter averaged 1.1 +/- 0.9 micrograms/m3. Particle-phase organic compounds (as organic carbon x 1.4) measured on a denuded quartz filter sampler averaged 6.4 +/- 3.1 micrograms/m3 (32% of FRM PM2.5 mass) with less seasonal variability than SO4(2-).     

Designing Systematic Conservation Assessments that Promote Effective Implementation: Best Practice from South Africa

Abstract:  Systematic conservation assessment and conservation planning are two distinct fields of conservation science often confused as one and the same. Systematic conservation assessment is the technical, often computer-based, identification of priority areas for conservation. Conservation planning is composed of a systematic conservation assessment coupled with processes for development of an implementation strategy and stakeholder collaboration. The peer-reviewed conservation biology literature abounds with studies analyzing the performance of assessments (e.g., area-selection techniques). This information alone, however, can never deliver effective conservation action; it informs conservation planning. Examples of how to translate systematic assessment outputs into knowledge and then use them for "doing" conservation are rare. South Africa has received generous international and domestic funding for regional conservation planning since the mid-1990s. We reviewed eight South African conservation planning processes and identified key ingredients of best practice for undertaking systematic conservation assessments in a way that facilitates implementing conservation action. These key ingredients include the design of conservation planning processes, skills for conservation assessment teams, collaboration with stakeholders, and interpretation and mainstreaming of products (e.g., maps) for stakeholders. Social learning institutions are critical to the successful operationalization of assessments within broader conservation planning processes and should include not only conservation planners but also diverse interest groups, including rural landowners, politicians, and government employees.     

An overview of cross-sectional studies of mortality and air pollution and related statistical issues

A heuristic framework is developed for assessing statistical studies of air pollution and mortality which use the General Linear Model as a principal tool for analysis. Arrays that systematically compare the information of each study are introduced. In addition, a number of studies are contrasted against several statistical criteria to assess the reliability of the study results. Reliability is considered in terms of statistical estimation theory and practice. Finally, there is a discussion of several points of statistical contention which can affect the results of these studies. The conclusion is that statistical studies that use the General Linear Model have made limited use of available methods and tests. Several issues are raised which will require additional research.     

Revisiting the Veterans Cohort Mortality Study: New results and synthesis

ABSTRACT

The Veterans Cohort Mortality Study began in 1999 in collaboration with Washington University in St. Louis, comprising ~70,000 male military veterans. We published six research papers on this cohort, considering the dynamics of all-cause mortality as the subjects aged and environmental parameters changed. This paper summarizes those results and presents new results by age group. Pollutants included monitored and modeled criteria pollutants, vehicular traffic density (annual km driven per unit of county land area), and modeled nationwide levels of hazardous species. In addition to spatial relationships, we examined the effects of exposure timing through separate analyses of sequential follow-up and exposure periods from 1976 to 2001. Risks associated with peak ozone decreased with lag between exposure and response, suggesting acute effects. Risks associated with traffic were invariant over time and consistent across five exposure databases. Associations with ozone were also coherent across databases; we found no consistent associations with particulate matter. Epidemiology considers both spatial and temporal relationships; most long-term studies focus on spatial gradients at a given time, thus masking effects of cohort aging and other trends during follow-up. Our new analyses distinguished between these temporal effects by analyzing age deciles for which separate mortality risks had been estimated for nationwide levels of nitrogen oxides (NO_x), benzene, and traffic density during four sequential follow-up subperiods, thus providing 40 sets of mortality risk coefficients. We used ordinary least squares regression to define relationships with subject age and follow-up year for the data set of 40 coefficients. We found strong nonlinear relationships between subject age and mortality coefficients for smoking, climate, poverty status, and air pollution; only smoking and climate coefficients changed over time as well. We concluded that these pollutant-mortality relationships reflected differences among the veterans’ residential locations rather than changes in their pollution exposures during follow-up. We saw no evidence that cleaner air reduced mortality.

Implications: Recent air pollution mortality studies emphasize PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm); we show associations with many other pollutants and a measure of traffic intensity. Control policies should thus be based on multipollutant analyses. We found no reduced risks with improved air quality after distinguishing cohort aging from purely temporal effects; longitudinal studies of accountability must thus account for changes in demography and exposures. Our studies of exposure timing indicate mainly coincident responses and no evidence for cumulative effects typical of smoking; we had no information on personal exposures. We found the strongest risks were associated with high-traffic locations rather than outdoor air quality per se.     

Note on a Method to Estimate Missing Air Pollution Data

An iterative regression procedure is presented to estimate missing air pollution measurements when the data are measured at two or more sampling stations in the same vicinity. The procedure utilizes the measurements taken at other stations, on neighboring days, and of other pollutants.

The procedure is applied to a set of Philadelphia pollution data with from five to seventeen per cent of the observations missing. The method is tested by comparing the known observed pollutant values, with their estimates given by the procedure. Correlations between the observations and their estimates are uniformly high, ranging from 0.87 to 0.91. These correlations compare favorably with those estimates given by a simple linear interpolation. The magnitude of the correlations suggests that estimates given by this iterative regression procedure may be used where missing observations pccur without fear of undesirable effects on subsequent work. Therefore, this procedure may be a valuable tool in handling the problem of missing observations in air pollution data.