U.S. National PM2.5 Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

The U.S. Environmental Protection Agency (EPA) initiated the national PM_2.5 Chemical Speciation Monitoring Network (CSN) in 2000 to support evaluation of long-term trends and to better quantify the impact of sources on particulate matter (PM) concentrations in the size range below 2.5 μm aerodynamic diameter (PM_2.5; fine particles). The network peaked at more than 260 sites in 2005. In response to the 1999 Regional Haze Rule and the need to better understand the regional transport of PM, EPA also augmented the long-existing Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility monitoring network in 2000, adding nearly 100 additional IMPROVE sites in rural Class 1 Areas across the country. Both networks measure the major chemical components of PM_2.5 using historically accepted filter-based methods. Components measured by both networks include major anions, carbonaceous material, and a series of trace elements. CSN also measures ammonium and other cations directly, whereas IMPROVE estimates ammonium assuming complete neutralization of the measured sulfate and nitrate. IMPROVE also measures chloride and nitrite. In general, the field and laboratory approaches used in the two networks are similar; however, there are numerous, often subtle differences in sampling and chemical analysis methods, shipping, and quality control practices. These could potentially affect merging the two data sets when used to understand better the impact of sources on PM concentrations and the regional nature and long-range transport of PM_2.5. This paper describes, for the first time in the peer-reviewed literature, these networks as they have existed since 2000, outlines differences in field and laboratory approaches, provides a summary of the analytical parameters that address data uncertainty, and summarizes major network changes since the inception of CSN.

ImplicationsTwo long-term chemical speciation particle monitoring networks have operated simultaneously in the United States since 2001, when the EPA began regular operations of its PM_2.5 Chemical Speciation Monitoring Network (IMPROVE began in 1988). These networks use similar field sampling and analytical methods, but there are numerous, often subtle differences in equipment and methodologies that can affect the results. This paper describes these networks since 2000 (inception of CSN) and their differences, and summarizes the analytical parameters that address data uncertainty, providing researchers and policymakers with background information they may need (e.g., for 2018 PM_2.5 designation and State Implementation Plan process; McCarthy, 2013) to assess results from each network and decide how these data sets can be mutually employed for enhanced analyses. Changes in CSN and IMPROVE that have occurred over the years also are described.     

Application of polyacrylamide to reduce phosphorus losses from a Chinese purple soil: A laboratory and field investigation

Use of anionic polyacrylamide (PAM) to control phosphorus (P) losses from a Chinese purple soil was studied in both a laboratory soil column experiment and a field plot experiment on a steep slope (27%). Treatments in the column study were a control, and PAM mixed uniformly into the soil at rates of 0.02, 0.05, 0.08, 0.10, and 0.20%. We found that PAM had an important inhibitory effect on vertical P transport in the soil columns, with the 0.20% PAM treatment having the greatest significant reduction in leachate soluble P concentrations and losses resulting from nine leaching periods. Field experiments were conducted on 5 m wide by 21 m long natural rainfall plots, that allowed collection of both surface runoff and subsurface drainage water. Wheat was planted and grown on all plots with typical fertilizer applied. Treatments included a control, dry PAM at 3.9 kg ha^?1, dry PAM at 3.9 kg ha^?1 applied together with lime (CaCO₃ at 4.9 t ha^?1), and dry PAM at 3.9 kg ha^?1 applied together with gypsum (CaSO₄·2H₂O at 4 t ha^?1). Results from the field plot experiment in which 5 rainfall events resulted in measurable runoff and leachate showed that all PAM treatments significantly reduced runoff volume and total P losses in surface runoff compared to the control. The PAM treatments also all significantly reduced water volume leached to the tile drain. However, total P losses in the leachate water were not significantly different due to the treatments, perhaps due to the low PAM soil surface application rate and/or high experimental variability. The PAM alone treatment resulted in the greatest wheat growth as indicated by the plant growth indexes of wheat plant height, leaf length, leaf width, grain number per head, and dried grain mass. Growth indexes of the PAM with Calcium treatments were significantly lesser. These results indicate that the selection and use of soil amendments need to be carefully determined based upon the most important management goal at a particular site (runoff/nutrient loss control, enhanced plant growth, or a combination).     

PM2.5 speciation trends network: evaluation of whole-system uncertainties using data from sites with collocated samplers

The objectives of this paper are to contrast the relative variability of replicate laboratory measurements of selected chemical components of fine particulate matter (PM) with total variability from collocated measurements and to compare the magnitudes of the uncertainties determined from collocated sampler data with those currently being provided to U.S. Environmental Protection Agency (EPA)'s Air Quality System (AQS) database by RTI International (RTI). Pointwise uncertainty values are needed for modeling and data analysis and should include all the random errors affecting each data point. Total uncertainty can be decomposed into two primary components: analytical measurement uncertainty and sampling uncertainty. Analytical measurement uncertainties are relatively easy to calculate from routine quality control (QC) data. Sampling uncertainties, on the other hand, are comparatively difficult to measure. In this paper, the authors describe data from collocated samplers to provide a snapshot of whole-system uncertainty for several important chemical species. The components of uncertainty were evaluated for key species from each of the analytical methods employed by the PM2.5 Speciation Trends Network (STN) program: gravimetry, ion chromatography (IC), X-ray fluorescence (XRF), and thermal-optical analysis for organic carbon and elemental carbon. The results show that the laboratory measurement uncertainties are typically very small compared with uncertainties calculated from the differences between samples collected from collocated samplers. These differences are attributable to the "field" components uncertainty, which may include contamination and/or losses during shipping, handling, and sampling, as well as other distortions of the concentration level due to flow and sample volume variations. Uncertainties calculated from the collocation results were found to be generally similar to the uncertainties currently being loaded into EPA's AQS system, with some exceptions described below.     

Fine particulate matter source apportionment for the Chemical speciation Trends Network site at Birmingham, Alabama, using Positive Matrix Factorization

The Positive Matrix Factorization (PMF) receptor model version 1.1 was used with data from the fine particulate matter (PM2.5) Chemical Speciation Trends Network (STN) to estimate source contributions to ambient PM2.5 in a highly industrialized urban setting in the southeastern United States. Model results consistently resolved 10 factors that are interpreted as two secondary, five industrial, one motor vehicle, one road dust, and one biomass burning sources. The STN dataset is generally not corrected for field blank levels, which are significant in the case of organic carbon (OC). Estimation of primary OC using the elemental carbon (EC) tracer method applied on a seasonal basis significantly improved the model's performance. Uniform increase of input data uncertainty and exclusion of a few outlier samples (associated with high potassium) further improved the model results. However, it was found that most PMF factors did not cleanly represent single source types and instead are "contaminated" by other sources, a situation that might be improved by controlling rotational ambiguity within the model. Secondary particulate matter formed by atmospheric processes, such as sulfate and secondary OC, contribute the majority of ambient PM2.5 and exhibit strong seasonality (37 +/- 10% winter vs. 55 +/- 16% summer average). Motor vehicle emissions constitute the biggest primary PM2.5 mass contribution with almost 25 +/- 2% long-term average and winter maximum of 29 +/- 11%. PM2.5 contributions from the five identified industrial sources vary little with season and average 14 +/- 1.3%. In summary, this study demonstrates the utility of the EC tracer method to effectively blank-correct the OC concentrations in the STN dataset. In addition, examination of the effect of input uncertainty estimates on model results indicates that the estimated uncertainties currently being provided with the STN data may be somewhat lower than the levels needed for optimum modeling results.     

Contaminants of Emerging Concern in Fish from Western U.S. and Alaskan National Parks — Spatial Distribution and Health Thresholds

Remote national parks of the western U.S. and Alaska are not immune to contaminants of emerging concern. Semivolatile organic compounds (SOCs) such as pesticides and PCBs can selectively deposit from the atmosphere at higher rates in cold, high‐elevation and high‐latitude sites, potentially increasing risk to these ecosystems. In the environment, SOCs magnify up food chains and are known to increase health risks such as cancer and reproductive impairment. One hundred twenty‐eight fish in 8 national parks in Alaska and the western U.S. were analyzed for contaminant concentrations, assessed by region, and compared to human and wildlife health thresholds. SOC concentrations from an additional 133 fish from a previous study were also included, for a total of 31 water bodies sampled. PCBs, endosulfan sulfate, and p,p′‐DDE were among the most frequently detected contaminants. Concentrations of historic‐use pesticides dieldrin, p,p′‐DDE, and/or chlordanes in fish exceeded USEPA guidelines for human subsistence fish consumers and wildlife (kingfisher) health thresholds at 13 of 14 parks. Average concentrations in fish ranged from 0.6‐280 ng/g lipid (0.02‐7.3 μg/g ww). Contaminant loading was highest in fish from Alaskan and Sierra Nevada parks. Historic compounds were highest in Alaskan parks, while current‐use pesticides were higher in the Rockies and Sierra Nevada. This study provides a rigorous analysis of CECs in fish from national parks and identifies regions at potential risk.     

Emission study of a diesel engine fueled with higher alcohol-biodiesel blended fuels

This work examines the effect of butanol (higher alcohol) on the emission pattern of neat neem oil biodiesel (NBD100) fueled diesel engine. Single-cylinder, 4-stroke, research diesel engine was employed to conduct the trial. Blends comprising the mixture of biodiesel and higher alcohol were prepared by employing an ultrasonic agitator. Four test fuels such as neat neem oil biodiesel, diesel, and two blends of higher alcohol/neem oil biodiesel: 10% and 20% (by volume). Experimental result showed that increasing alcohol content to biodiesel brought down the various emissions such as Smoke, NO_x, HC, and CO by 6.8%, 10.4%, 8.6%, and 5.9%, respectively, at all loads. It was also concluded from the trail that a 20% higher alcohol/neem oil biodiesel blends show the promising signs in reducing all the emissions associated with biodiesel fuelled diesel engine.     

Perchlorate: Sources,uses, and occurrences in the environment

Perchlorate contamination of groundwater and soil continues to be a hot topic in many sectors, including industry, the federal Departments of Defense and Energy, regulators, and the general public. This article presents information on what is currently known regarding perchlorate uses, known and suspected naturally occurring versus anthropogenic perchlorate sources, and past and present manufacturing locations. It provides the reader with a baseline as well as background information to facilitate an understanding of perchlorate in the environment for the ultimate purpose of remediation. The article is based on research conducted by the Interstate Technology Regulatory Council for a forthcoming, more substantial perchlorate overview document scheduled for publication later in 2005. The document will include an overview of the chemical and discussions of remediation techniques and methods of analysis. © 2005 Wiley Periodicals, Inc.     

SOIL EROSION AND SEDIMENT YIELD PREDICTION ACCURACY USING WEPP1

ABSTRACT: The objectives of this paper are to discuss expectations for the Water Erosion Prediction Project (WEPP) accuracy, to review published studies related to WEPP goodness of fit, and to evaluate these in the context of expectations for WEPP's goodness of fit. WEPP model erosion predictions have been compared in numerous studies to observed values for soil loss and sediment delivery from cropland plots, forest roads, irrigated lands and small watersheds. A number of different techniques for evaluating WEPP have been used, including one recently developed where the ability of WEPP to accurately predict soil erosion can be compared to the accuracy of replicated plots to predict soil erosion. In one study involving 1,594 years of data from runoff plots, WEPP performed similarly to the Universal Soil Loss Erosion (USLE) technology, indicating that WEPP has met the criteria of results being “at least as good with respect to observed data and known relationships as those from the USLE,” particularly when the USLE technology was developed using relationships derived from that data set, and using soil erodibility values measured on those plots using data sets from the same period of record. In many cases, WEPP performed as well as could be expected, based on comparisons with the variability in replicate data sets. One major finding has been that soil erodibility values calculated using the technology in WEPP for rainfall conditions may not be suitable for furrow irrigated conditions. WEPP was found to represent the major storms that account for high percentages of soil loss quite well—a single storm application that the USLE technology is unsuitable for—and WEPP has performed well for disturbed forests and forest roads. WEPP has been able to reflect the extremes of soil loss, being quite responsive to the wide differences in cropping, tillage, and other forms of management, one of the requirements for WEPP validation. WEPP was also found to perform well on a wide range of small watersheds, an area where USLE technology cannot be used.