Estimating maximum concentrations for open path monitoring along a fixed beam path

Researchers have applied open path optical sensing techniques to a variety of workplace and environmental monitoring problems. Usually these data are reported in terms of a path-average (or path-integrated) concentration. When assessing potential human exposures along a beam path, this path-average value is not always informative, since concentrations along the path can vary substantially from the beam average. The focus of this research is to arrive at a method for estimating the upper-bound in contaminant concentrations over a fixed open beam path. The approach taken here uses a statistical model to estimate an upper-bound concentration based on a combination of the path-average and a measure of the spatial variability computed from point samples along the beam path. Results of computer simulations and experimental testing in a controlled ventilation chamber indicate that the model produced conservative estimates for the maximum concentration along the beam path. This approach may have many applications for open path monitoring in workplaces or wherever maximum concentrations are a concern.     

Estimating Maximum Concentrations for Open Path Monitoring Along a Fixed Beam Path

ABSTRACT

Researchers have applied open path optical sensing techniques to a variety of workplace and environmental monitoring problems. Usually these data are reported in terms of a path-average (or path-integrated) concentration. When assessing potential human exposures along a beam path, this path-average value is not always informative, since concentrations along the path can vary substantially from the beam average. The focus of this research is to arrive at a method for estimating the upper-bound in contaminant concentrations over a fixed open beam path. The approach taken here uses a statistical model to estimate an upper-bound concentration based on a combination of the path-average and a measure of the spatial variability computed from point samples along the beam path. Results of computer simulations and experimental testing in a controlled ventilation chamber indicate that the model produced conservative estimates for the maximum concentration along the beam path. This approach may have many applications for open path monitoring in workplaces or wherever maximum concentrations are a concern.     

Pollutant tomography using integrated concentration data from non-intersecting optical paths

We investigate the possibility of performing tomographic pollutant mapping using path-integral data from non-intersecting optical paths, and conclude that such a geometry does allow reconstruction of the pollutant distribution with the smooth basis function minimization method. The simulated optical data are derived from actual pollutant concentration distributions determined from previous experiments.     

An Air Quality Data Analysis System for Interrelating Effects,Standards, and Needed Source Reductions: Part 4. A Three-Parameter Averaging-Time Model

Urban air pollutant concentration data often tend to fit a two-parameter averaging-time model having three characteristics: (1) pollutant concentrations are (two-parameter) lognormally distributed for all averaging times; (2) median concentrations are proportional to averaging time raised to an exponent; and (3) maximum concentrations are approximately inversely proportional to averaging time raised to an exponent. Concentration data obtained near many isolated point sources and in some urban areas often do not fit a two-parameter lognormal distribution. An increment (either positive or negative) can be added to each such concentration in order to fit the data instead to a three-parameter lognormal distribution. This increment has been incorporated as the third parameter in a new three-parameter averaging-time model that can be used in both point-source and urban settings. Examples show how this new model can be used to analyze SO₂ concentration data obtained near a point source to determine the degree of emission reduction needed to achieve the national ambient air quality standards.     

Methods for sampling and analysis of tropospheric ethanol in gaseous and aqueous phases

In this paper, we report on techniques for sampling and measuring ethanol in both the gas and aqueous phases of the lower troposphere. In the gas phase, the best sampling conditions were ensured by adsorption on Hayesep Q with a Chromosorb W AW coated with LiCl dryer (method 1) or by cryogenic trapping (method 2). An intercomparison campaign showed good agreement between both methods under various conditions. Method 1 (adsorption on Hayesep Q with dryer) is easier to set up and to carry away from the laboratory. Method 2 (cryogenic trapping) requires longer sampling time (up to 60 min while method 1 requires only 10-15 min). Method 1 is adapted to high concentrations of ethanol (>20 ppb) and low relative humidity (<30%). Method 2 gives more accurate results than method 1 for low ethanol concentrations (1-20 ppb). Comparing these results to previous studies, it is clear that sampling with appropriate solid adsorbents or with stainless steel canisters (with appropriate humidified air and short storage time) is adapted to urban or industrial environments where ethanol concentrations are high. Cryogenic sampling must be preferred for remote places where ethanol concentrations are low. Three techniques were tested for sampling ethanol in the liquid phase, namely solid phase microextraction, purge and trap injection, and direct injection. Among those, the latter was chosen for field measurements of ethanol in rain samples at an urban location. These first ever results at an urban location show concentrations ranging from <1 to 5 microM in rains, which agree with the expected range of concentrations. However, the purge and trap method showed detection limits that were 50 times lower and should be preferred for liquid phase ethanol measurements in rural and remote locations. Combining cryogenic trapping for the gas phase (method 2) and direct injection for the liquid phase is convenient and well adapted for a multiphase study of ethanol in the atmosphere, where simultaneous measurements in both phases are needed.     

Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils

Mapping the spatial distribution of soil pollutants is essential for delineating contaminated areas. Currently, geostatistical interpolation, kriging, is increasingly used to estimate pollutant concentrations in soils. The kriging-based approach, indicator kriging (IK), may be used to model the uncertainty of mapping. However, a smoothing effect is usually produced when using kriging in pollutant mapping. The detailed spatial patterns of pollutants could, therefore, be lost. The local uncertainty of mapping pollutants derived by the IK technique is referred to as the conditional cumulative distribution function (ccdf) for one specific location (i.e. single-location uncertainty). The local uncertainty information obtained by IK is not sufficient as the uncertainty of mapping at several locations simultaneously (i.e. multi-location uncertainty or spatial uncertainty) is required to assess the reliability of the delineation of contaminated areas. The simulation approach, sequential indicator simulation (SIS), which has the ability to model not only single, but also multi-location uncertainties, was used, in this study, to assess the uncertainty of the delineation of heavy metal contaminated soils. To illustrate this, a data set of Cu concentrations in soil from Taiwan was used. The results show that contour maps of Cu concentrations generated by the SIS realizations exhausted all the spatial patterns of Cu concentrations without the smoothing effect found when using the kriging method. Based on the SIS realizations, the local uncertainty of Cu concentrations at a specific location of x', refers to the probability of the Cu concentration z(x') being higher than the defined threshold level of contamination (z(c)). This can be written as Prob(SIS)[z(x')>z(c)], representing the probability of contamination. The probability map of Prob(SIS)[z(x')>z(c)] can then be used for delineating contaminated areas. In addition, the multi-location uncertainty of an area A,delineated as contaminated based on the probability map of Prob(SIS)[z(x')>z(c)], can be calculated to assess the reliability of delineation. Multi-location uncertainty refers to the probability of Cu concentrations in several locations, x'(1), x'(2), em leader, x'(m,) in the area A, being higher than the threshold (z(c)) as denoted by Prob(SIS)[z(x'(1))>z(c), z(x'(2))>z(c), em leader, andz(x'(m))>z(c)] or Prob(SIS)[z(A)>z(c)]. The multi-location uncertainty Prob(SIS)[z(A)>z(c)], obtained from the SIS, can be used to assess the reliability of delineation for regions suspected of contamination, (A), which has been delineated as contaminated. Reliance on this information facilitates the decision making process in determining which areas are contaminated and require cleanup action.     

A new moss biomonitoring method for detecting sources of small scale pollution

A new simple method is proposed for identifying sources of small scale pollution, by determining the tissue concentrations of pollutants in terrestrial mosses. The method is based on spatial characterization of the processes that generate accumulation of pollutants in these organisms. The steps involved in implementing and applying the method are: (i) obtaining data on pollutants in moss samples collected at least at 35 pairs of sampling sites (SS) separated by 1 km distance; (ii) study of the distribution of the differences in concentration between the pairs of SS, eliminating pairs affected by foci that generate small scale processes; (iii) characterization of the resulting normal distributions and calculation, for different levels of significance, of the probability of a value lying within these distributions, and (iv) application of the calculated quantiles to data corresponding to the differences in concentrations between pairs of SS in the vicinities of foci of small scale pollution, to determine which elements can be considered as pollutants. Given the small number of data required once the distribution of the differences has been established, the method is an inexpensive, efficient way of establishing the probability that a pollutant is being emitted from a particular source. The method is very useful for verifying the results of pollution inventories, evaluating new technologies and improving the design of regional networks for biomonitoring of air quality with terrestrial mosses.     

Effective field sampling for predicting the spatial distribution of reindeer (Rangifer tarandus) with help of the Gibbs sampler

We describe an approach to estimation of the spatial distribution of reindeer (Rangifer tarandus). Spatial autocorrelation, inherent to the data describing the distribution of wildlife species, contains information that can be utilized to improve the effciency of field inventories. Our data included reindeer fecal pellet counts, satellite imagery and a digital terrain model. We applied ordinary logistic regression, autologistic regression, and the Gibbs sampler to predict spatial distribution of reindeer based on the combined data. A training set was used to compare the outcome for different field sampling designs for each method. Results suggested the possibility to reduce the number of plots by up to 75% with a 15% reduction in prediction accuracy (quality). We also showed that the Gibbs sampler outperformed, in terms of accuracy, the logistic regression. The outcome, however, was dependent on the spectral homogeneity of the area and on the relative position of the sampling design to the elevation curves. Our results justify the incorporation of spatial information when modeling the distribution of reindeer at finer scales (< 1 km).     

Further analysis and PMF application to the chemical composition data of IPCAJ's Kashima SPM study

The Industrial Pollution Control Association of Japan (IPCAJ) has conducted an air quality study. A brief summary has already appeared in previous papers. This set of data was not sufficiently analysed. This study attempts to examine the performance of the Positive Matrix Factorisation (PMF), a recently introduced technique in this field, on this multiple-site Kashima data set. The source apportionment study using the PMF model conducted for the Kashima data set identified six factors for each fine and coarse fraction. This result also seems to be appropriate, because spatial distribution and seasonal variation of the contributions for each emission source category could be explained reasonably. The PMF can analyse a data set that may be extracted from different populations, and it seems to be a powerful tool available for these kinds of complicated data sets.     

Workplace and Environmental Air Contaminant Concentrations Measured by Open Path Fourier Transform Infrared Spectroscopy: A Statistical Process Control Technique to Detect Changes from Normal Operating Conditions

Open path Fourier transform infrared (OP-FTIR) spectroscopy is a new air monitoring technique that can be used to measure concentrations of air contaminants in real or near-real time. OP-FTIR spectroscopy has been used to monitor workplace gas and vapor exposures, emissions from hazardous waste sites, and to track emissions along fence lines. This paper discusses a statistical process control technique that can be used with air monitoring data collected with an OP-FTIR spectrometer to detect departures from normal operating conditions in the workplace or along a fence line. Time series data, produced by plotting consecutive air sample concentrations in time, were analyzed. Autocorrelation in the time series data was removed by fitting dynamic models. Control charts were used with the residuals of the model fit data to determine if departures from defined normal operating conditions could be rapidly detected. Shewhart and exponentially weighted moving average (EWMA) control charts were evaluated for use with data collected under different room air flow and mixing conditions.

Under rapidly changing conditions the Shewhart control chart was able to detect a leak in a simulated process area. The EWMA control chart was found to be more sensitive to drifts and slowly changing concentrations in air monitoring data. The time series and statistical process control techniques were also applied to data obtained during a field study at a chemical plant. A production area of an acrylonitrile, 1,3-butadiene, and styrene (ABS) polymer process was monitored in near-real time. Decision logics based on the time series and statistical process control technique introduced suggest several applications in workplace and environmental monitoring. These applications might include signaling of an alarm or warning, increasing levels of worker respiratory protection, or evacuation of a community, when gas and vapor concentrations are determined to be out-of-control.     

Rapid measurements and mapping of tracer gas concentrations in a large indoor space

Rapid mapping of gas concentrations in air benefits studies of atmospheric phenomena ranging from pollutant dispersion to surface layer meteorology. Here we demonstrate a technique that combines multiple-open-path tunable-diode-laser spectroscopy and computed tomography to map tracer gas concentrations with approximately 0.5 m spatial and 7 s temporal resolution. Releasing CH₄ as a tracer gas in a large (7 m×9 m×11 m high) ventilated chamber, we measured path-integrated CH₄ concentrations over a planar array of 28 “long” (2–10 m) optical paths, recording a complete sequence of measurements every 7 s during the course of hour-long experiments. Maps of CH₄ concentration were reconstructed from the long path data using a computed tomography algorithm that employed simulated annealing to search for a best fit solution. The reconstructed maps were compared with simultaneous measurements from 28 “short” (0.5 m) optical paths located in the same measurement plane. On average, the reconstructed maps capture ∼74% of the variance in the short path measurements. The accuracy of the reconstructed maps is limited, in large part, by the number of optical paths and the time required for the measurement. Straightforward enhancements to the instrumentation will allow rapid mapping of three-dimensional gas concentrations in indoor and outdoor air, with sub-second temporal resolution.     

Soil concentrations and source apportionment of polybrominated diphenyl ethers (PBDEs) and trace elements around a heavily industrialized area in Kocaeli,Turkey

Air pollutants are transported by dry deposition, wet deposition, and gas exchange accumulated in soil. Therefore, soil is an important environmental medium reflecting the level and the spatial distribution of air pollutants such as polybrominated diphenyl ethers (PBDEs) and heavy metals. Soil concentrations of seven PBDE congeners and 21 trace elements were determined in a heavily industrialized region (Dilovasi) in Kocaeli, Turkey. At all sites, Σ₇PBDE concentrations ranged from 0.70 to 203 with a mean value of 26.3 μg kg^?1 (dry weight). The congener profiles and mass inventories of PBDEs and their interactions with soil organic matter (SOM) were also investigated. BDE-209 was the dominant congener at all sites, followed by BDE-99 and/or -47. The estimated inventory of PBDEs for the Dilovasi district was 310 kg. However, there are several additional industrial regions in Kocaeli city. Considering the total land area, the potential inventory would be much larger for this city. The relationship between the PBDE concentrations in soil and SOM content indicated that factors other than soil properties have a greater influence on soil concentrations. Crustal enrichment factors (EFs) were determined; correlation analysis and factor analysis (FA) were also applied to generated data set to identify and apportion the sources polluting the soil. Sn, Mn, Ca, As, Zn, Pb, and Cd had significantly high average EF values, indicating that their soil concentrations were mainly influenced by anthropogenic activities. In FA, six factors were extracted with a cumulative variance of 84.4 % and industrial activities and traffic were found to be the main factors affecting the soil profile.     

Modeling Inorganic Aerosols and Their Response to Changes in Precursor Concentration in Mexico City

Abstract

Based on data from the 1997 Investigación sobre Materia Particulada y Deterioro Atmosférico-Aerosol and Visibility Evaluation Research (IMADA-EVER) campaign and the inorganic aerosol model ISORROPIA, the response of inorganic aerosols to changes in precursor concentrations was calculated. The aerosol behavior is dominated by the abundance of ammonia and thus, changes in ammonia concentration are expected to have a small effect on particle concentrations. Changes in sulfate and nitrate are expected to lead to proportional reductions in inorganic fine particulate matter (PM_2.5). Comparing the predictions of ISORROPIA with the observations, the lowest bias and error are achieved when the aerosols are assumed to be in the efflorescence branch. Including crustal species reduces the bias and error for nitrate but does not improve overall model performance. The estimated response of inorganic PM_2.5 to changes in precursor concentrations is affected by the inclusion of crustal species in some cases, although average responses are comparable with and without crustal species. Observed concentrations of particle chloride suggest that gas phase concentrations of hydrogen chloride may not be negligible, and future measurement campaigns should include observations to test this hypothesis. Our ability to model aerosol behavior in Mexico City and, thus, design control strategies, is constrained primarily by a lack of observations of gas phase precursors. Future campaigns should focus in particular on better understanding the temporal and spatial distribution of ammonia concentrations. In addition, gas phase observations of nitric acid are needed, and a measure of particle water content will allow stable versus metastable aerosol behavior to be distinguished.     

Modeling inorganic aerosols and their response to changes in precursor concentration in Mexico City

Based on data from the 1997 Investigación sobre Materia Particulada y Deterioro Atmosférico-Aerosol and Visibility Evaluation Research (IMADA-EVER) campaign and the inorganic aerosol model ISORROPIA, the response of inorganic aerosols to changes in precursor concentrations was calculated. The aerosol behavior is dominated by the abundance of ammonia and thus, changes in ammonia concentration are expected to have a small effect on particle concentrations. Changes in sulfate and nitrate are expected to lead to proportional reductions in inorganic fine particulate matter (PM2.5). Comparing the predictions of ISORROPIA with the observations, the lowest bias and error are achieved when the aerosols are assumed to be in the efflorescence branch. Including crustal species reduces the bias and error for nitrate but does not improve overall model performance. The estimated response of inorganic PM2.5 to changes in precursor concentrations is affected by the inclusion of crustal species in some cases, although average responses are comparable with and without crustal species. Observed concentrations of particle chloride suggest that gas phase concentrations of hydrogen chloride may not be negligible, and future measurement campaigns should include observations to test this hypothesis. Our ability to model aerosol behavior in Mexico City and, thus, design control strategies, is constrained primarily by a lack of observations of gas phase precursors. Future campaigns should focus in particular on better understanding the temporal and spatial distribution of ammonia concentrations. In addition, gas phase observations of nitric acid are needed, and a measure of particle water content will allow stable versus metastable aerosol behavior to be distinguished.     

A new generic approach for estimating the concentrations of down-the-drain chemicals at catchment and national scale

A new generic approach for estimating chemical concentrations in rivers at catchment and national scales is presented. Domestic chemical loads in waste water are estimated using gridded population data. River flows are estimated by combining predicted runoff with topographically derived flow direction. Regional scale exposure is characterised by two summary statistics: PEC(works), the average concentration immediately downstream of emission points, and, PEC(area), the catchment-average chemical concentration. The method was applied to boron at national (England and Wales) and catchment (Aire-Calder) scales. Predicted concentrations were within 50% of measured mean values in the Aire-Calder catchment and in agreement with results from the GREAT-ER model. The concentration grids generated provide a picture of the spatial distribution of expected chemical concentrations at various scales, and can be used to identify areas of potentially high risk.     

Studies of spatial and temporal distribution characteristics of TSP-bound trace metals in Seoul, Korea

In the present study, TSP-bound metal concentrations were measured from seven different urbanized locations in Seoul, Korea for the period from March 2001 through May 2002. Our measurement data were analyzed to explore the possible influences of spatial and temporal factors on metal distribution characteristics. To determine the importance of those aspects, the measured concentrations were compared between different metals and between different sites in terms of several criteria: (1) absolute concentrations and enrichment factor (EF) values; (2) coefficient of variation (CV) values of metal concentrations; (3) relative patterns of temporal variations; and (4) relative abundance of strong correlations. According to our analysis of metal distribution characteristics in the study area, the main results of our study can be summarized as follows: (1) a number of metals (e.g., Cd, Cu, and Pb) are highly enriched relative to the average crustal ratios (to Fe); (2) the behavior of Cu is found to vary irregularly, while Fe, Mn, and Pb are tightly coupled both spatially and temporally in the study area; (3) for most elements except Cu and Cd, seasonal variations are observed in a systematic manner; and (4) when compared against those observed in other parts of the world, our Cu and Cd concentrations observed in many locations of Seoul are notably high. The overall results of our study suggest that the distribution characteristics of metals can be regulated strongly by spatial and temporal factors and that such controls are distinguished very clearly between different metal types.     

Efficient retrieval of vegetation leaf area index and canopy clumping factor from satellite data to support pollutant deposition assessments

Canopy leaf area index (LAI) is an important structural parameter of the vegetation controlling pollutant uptake by terrestrial ecosystems. This paper presents a computationally efficient algorithm for retrieval of vegetation LAI and canopy clumping factor from satellite data using observed Simple Ratios (SR) of near-infrared to red reflectance. The method employs numerical inversion of a physics-based analytical canopy radiative transfer model that simulates the bi-directional reflectance distribution function (BRDF). The algorithm is independent of ecosystem type. The method is applied to 1-km resolution AVHRR satellite images to retrieve a geo-referenced data set of monthly LAI values for the conterminous USA. Satellite-based LAI estimates are compared against independent ground LAI measurements over a range of ecosystem types. Verification results suggest that the new algorithm represents a viable approach to LAI retrieval at continental scale, and can facilitate spatially explicit studies of regional pollutant deposition and trace gas exchange.     

The effects of heavy metal contamination on the soil arthropod community of a shooting range

Soils in clay pigeon shooting ranges can be seriously contaminated by heavy metals. The pellets contained in ammunition are composed of Pb, Sb, Ni, Zn, Mn and Cu. The total concentrations of these metals in soils, and the effects of their increasing levels on the arthropod community were investigated at seven sampling sites in a clay pigeon shooting range and compared with two controls. Research revealed that the spatial distribution of Pb and Sb contamination in the shot-fall area was strongly correlated with the flight path of the pellets. Ordination obtained through Redundance Analysis showed that Collembola, Protura and Diplura were positively correlated with major detected contaminants (Pb, Sb), while Symphyla showed a negative correlation with these pollutants. Determination of the soluble lead fraction in soil, and of its bioaccumulation in the saprophagous Armadillidium sordidum (Isopoda) and the predator Ocypus olens (Coleoptera), showed that a significant portion of metallic Pb from spent pellets is bioavailable in the soil and can be bioaccumulated by edaphic organisms, entering the soil trophic network, but without biomagnification.     

区域大气环境中PM2．5／PM10空间分布研究

提出了一种利用移动监测技术研究区域大气环境中PM2.5／PM10空间分布的方法,并在2004年12月进行了宁波市全市域PM2.5／PM10空间分布的研究。数据显示：相同路径所代表的地区PM2.5和PM10具有很好的相关性,多数路径上PM2.5与PM10数据的相关系数平方在0．95以上,而不同路径上PM2.5与PM10的比值不同。文中给出了宁波市PM2.5／PM10污染的空间分布图,直观地显示出PM2.5／PM10污染的空间分布情况,突出了污染的重点点位和地区。