Global estimation of CO emissions using three sets of satellite data for burned area

Using three sets of satellite data for burned areas together with the tree cover imagery and a biogeochemical component of the Integrated Science Assessment Model (ISAM) the global emissions of CO and associated uncertainties are estimated for the year 2000. The available fuel load (AFL) is calculated using the ISAM biogeochemical model, which accounts for the aboveground and surface fuel removed by land clearing for croplands and pasturelands, as well as the influence on fuel load of various ecosystem processes (such as stomatal conductance, evapotranspiration, plant photosynthesis and respiration, litter production, and soil organic carbon decomposition) and important feedback mechanisms (such as climate and fertilization feedback mechanism). The ISAM estimated global total AFL in the year 2000 was about 687 Pg AFL. All forest ecosystems account for about 90% of the global total AFL. The estimated global CO emissions based on three global burned area satellite data sets (GLOBSCAR, GBA, and Global Fire Emissions Database version 2 (GFEDv2)) for the year 2000 ranges between 320 and 390 Tg CO. Emissions from open fires are highest in tropical Africa, primarily due to forest cutting and burning. The estimated overall uncertainty in global CO emission is about ±65%, with the highest uncertainty occurring in North Africa and Middle East region (±99%). The results of this study suggest that the uncertainties in the calculated emissions stem primarily from the area burned data.     

Road grade estimation for on-road vehicle emissions modeling using light detection and ranging data

Vehicle-specific power (VSP) is useful for explaining a substantial portion of variability in real-world vehicle emissions, such as those measured with portable emissions monitoring systems (PEMS). VSP is a function of vehicle speed, acceleration, and road grade. Road grade is shown to significantly affect estimates of both VSP and of real-world emissions via sensitivity analysis and analysis of empirical data. However, road grade is difficult to measure reliably using PEMS. Therefore, alternative methods for estimating road grade were identified and compared. A preferred method for estimating road grade was explored in more detail based on light detection and ranging (LIDAR) data. The method includes buffering LIDAR data onto roadway maps using a geographic information system tool, defining segments of roadway based on criteria pertaining to vertical curvature, quantification of roadway elevations within the buffered segments, and estimation of road grade and banking by fitting a plane to each segment. Factors influencing errors in road grade estimates are discussed. The method was evaluated by application to selected interstate highways and comparison to design drawing data. The development and application of LIDAR-based road grade data are demonstrated via a case study using PEMS data collected in the Research Triangle Park, NC, area. LIDAR data are shown to be reliable and accurate for road grade estimation for vehicle emissions modeling.     

Fuel consumption, emissions estimation, and emissions cost estimates using global positioning data

The methodology laid out in this paper shows that typical operational data from vehicle fleets monitored by a global positioning system (GPS) can be used to estimate heavy-duty diesel vehicle (HDDV) emissions, thereby enabling waste managers and governing bodies to internalize the responsibility for socioenvironmental costs traditionally absorbed by external parties. Although municipal solid waste (MSW) collection vehicles are the subjects of this particular study, the methodology presented here can be applied to any fleet of vehicles monitored by GPS. This study indicates that MSW collection trucks may be considerably less fuel efficient in the field than published values for HDDV fuel efficiency suggest. The average fuel efficiency of one MSW collection truck was estimated as 0.90 +/- 0.44 km/L (2.12 +/- 1.03 mi/gal). This same truck would generate approximately 42 metric tons of CO2 equivalents/yr, which is comparable to the greenhouse gas emissions of a large sport utility vehicle driving six times the distance, in town, for a year. In terms of the impacts such emissions have, projections for the monetary cost of emissions are available but highly variable. They suggest that the external monetary costs of emissions range between 6 and 39% of the annual fuel costs for the studied MSW collection truck. The results of this study indicate a need for further research into valuation of the hidden, external costs of emissions, borne by local and global socioecological communities. The possible implications of this result include poorly advised fleet procurement decisions and underestimation of MSW collection fleet emissions.     

A comparative analysis of using trip-based versus link-based traffic data for regional mobile source emissions estimation

The current method used for calculating vehicle emissions integrates travel data and associated vehicle emission factors. Travel data from traditional travel demand models are normally link-based (e.g., volumes on roadway segments), while California emission factors are trip-based (i.e., average emission factors over an entire trip), creating a mismatch in the modeling interface. Using dynamic simulation for trip assignment, we present a new modeling framework that consistently provides both trip-based and link-based VMT-speed distributions. Using the Sacramento Metropolitan Area and Kern County in California, we demonstrate the feasibility of this new method and quantify the effects of using trip-based versus link-based travel data on regional peak period emission inventories. The comparison results indicate that for the base scenario in both studied regions, the link-based method generally results in higher emissions than the trip-based method. The sensitivities of the link and trip-based methods to road network variations also appear dissimilar. The link-based emissions are more sensitive to facility-related changes, while the trip-based emissions are more sensitive to demand-related changes. This suggests that greater care may need to be taken to specify the effects of this modeling interface issue within the transportation conformity process and subsequent mobile emissions analysis.     

Uncertainty estimation in the analysis of pesticide residues in olive oil using data from proficiency tests

In this work we report the results for estimating the measurement uncertainty (MU) following up the application of two different approaches, relatively the top-down procedure, by using proficiency test data. We have focused the estimation on the olive oil matrix. We used the analytical data obtained from five selected editions of the Proficiency Tests (PTs, from 2007 to 2011) on pesticide residues in olive oil to estimate the MU. These PTs have been organized by Istituto Superiore di Sanità annually in cooperation with International Olive Council (IOC) since 1997. The number of participants in each trial ranged from 10 to 43. We used a total of 34 pesticide results. The expanded uncertainty U _(c) was calculated using a covering factor k = 2 for a confidence interval of 95%. In the approach 1, the within–laboratory reproducibility standard deviation is combined with estimates of the method and laboratory bias using PTs data. In the approach 2, the way of estimating the MU is based only on the bias that the laboratory has obtained participating in a sufficient number of the IOC proficiency tests. Comparing the relative expanded uncertainty based on these different approaches we notice values quite constant and close, from 42% to 48%. Moreover, these calculated expanded uncertainties are less than the default value of 50% (corresponding to a 95% confidence level), adopted from European guidance document SANCO based on the fit-for-purpose relative standard deviation (FFP-RSD).     

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis.     

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in “land use” regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM_2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.     

基于大气环境监测数据的工业园区污染物排放总量实时反演核算方法

工业园区既是重要的社会经济发展单元,也是主要的大气污染排放单元。综合利用环境监测数据和数值模拟方法,开展工业园区污染排放的动态精准管控,有助于提升工业园区大气环境管控效果,统筹园区经济发展与环境优化。以某工业园区为例,探索了基于环境监测数据,利用高斯大气扩散模型和源参数反演技术,实现小时级别的园区大气污染物排放总量反演核算的技术可行性。结果表明：1) 监测站点布设方案显著影响反演核算精度,网格化布点方案有效提高了反演核算精度;2) 监测数据的随机误差显著影响反演核算精度,在简化布点方案下,园区监测站点对VOCs质量浓度监测误差应控制在0.1 mg∙m⁻³以下;3) 园区总排放强度对反演核算精度存在一定影响,总排放量大的园区反演核算精度较高;4) 反演核算精度与实时气象条件有关,当气象条件不适宜模型模拟污染物扩散或不适宜监测站点对污染物分布进行有效采样时,可能造成反演精度的下降。以上结果表明,在工业园区开展基于环境监测数据的大气污染物排放总量核算研究具有可行性,但需要在监测站点布设方案比选、监测站点数据质量控制等方面进行精细化管理,以达到最优反演核算效果。     

天津地区环境监测点PM2.5量化分析模型

为了研究天津地区PM2.5与5种日常监测的污染物——PM10、SO2、CO、O3和NO2的定量关系,通过天津环保物联网获取了11个环境监测点2013年8月—2014年5月的日监测数据,分别尝试了多元一次回归模型、二次回归模型和基于主成分分析回归模型进行建模与量化分析。相关性上,天津地区各站点PM2.5与5种污染物显著相关,其中与PM10、SO2和NO2相关系数分别达到0.89±0.03、0.63±0.09和0.69±0.06。模型评价结果表明,利用5种污染物监测指标预估PM2.5浓度基本可行,基于主成分分析模型效果最好,拟合度能够达到0.85以上,平均误差在20%左右。     

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.     

Odor emission rate estimation of indoor industrial sources using a modified inverse modeling method

Odor emission rates are commonly measured in the laboratory or occasionally estimated with inverse modeling techniques. A modified inverse modeling approach is used to estimate source emission rates inside of a postdigestion centrifuge building of a water reclamation plant. Conventionally, inverse modeling methods divide an indoor environment in zones on the basis of structural design and estimate source emission rates using models that assume homogeneous distribution of agent concentrations within a zone and experimentally determined link functions to simulate airflows among zones. The modified approach segregates zones as a function of agent distribution rather than building design and identifies near and far fields. Near-field agent concentrations do not satisfy the assumption of homogeneous odor concentrations; far-field concentrations satisfy this assumption and are the only ones used to estimate emission rates. The predictive ability of the modified inverse modeling approach was validated with measured emission rate values; the difference between corresponding estimated and measured odor emission rates is not statistically significant. Similarly, the difference between measured and estimated hydrogen sulfide emission rates is also not statistically significant. The modified inverse modeling approach is easy to perform because it uses odor and odorant field measurements instead of complex chamber emission rate measurements.     

Method of median semi-variance for the analysis of left-censored data: Comparison with other techniques using environmental data

In environmental monitoring, variables with analytically non-detected values are commonly encountered. For the statistical evaluation of these data, most of the methods that produce a less biased performance require specific computer programs. In this paper, a statistical method based on the median semi-variance (SemiV) is proposed to estimate the position and spread statistics in a dataset with single left-censoring. The performances of the SemiV method and 12 other statistical methods are evaluated using real and complete datasets. The performances of all the methods are influenced by the percentage of censored data. In general, the simple substitution and deletion methods showed biased performance, with exceptions for L/2, Inter and L/√2 methods that can be used with caution under specific conditions. In general, the SemiV method and other parametric methods showed similar performances and were less biased than other methods. The SemiV method is a simple and accurate procedure that can be used in the analysis of datasets with less than 50% of left-censored data.     

Retrieval of Carbon monoxide profiles over Indian region using MOPITT data

Vertical profiles of carbon monoxide (CO) over the Indian region have scarcely been monitored. Satellite sensor, Measurement Of Pollution In The Troposphere (MOPITT) provides profiles of CO using a global retrieval scheme, which converts measured radiances to CO mixing ratios. In this study we have developed a regional retrieval scheme, valid over the Indian region, which employs Line-By-Line (LBL) calculations over a tropical model atmosphere to generate a Look-Up-Table (LUT) forward model function and uses a regional a priori dataset of CO along with seasonally variable emissivity to invert the MOPITT radiances to CO profiles. This baseline study provides an approach to optimizing retrievals for specific regional applications. A case study was carried out over a forest fire prone region in Northern India from February to April 2005 to validate the retrieval algorithm. The results are in agreement with the fire maps generated from MODerate resolution Imaging Spectro-radiometer (MODIS). The shape of the CO profiles over the region matches quite well with the vertical structure of CO during the INDOEX campaign, especially during the polluted month of April. Inter-comparisons with the MOPITT data product indicate some discrepancies in the lower troposphere, especially during the forest fire season. Future studies with in-situ measurements may be able to diagnose these disparities.     

Analysis of air quality data near roadways using a dispersion model

We used a dispersion model to analyze measurements made during a field study conducted by the U.S. EPA in July–August 2006, to estimate the impact of traffic emissions on air quality at distances of tens of meters from an eight-lane highway located in Raleigh, NC. The air quality measurements consisted of long path optical measurements of NO at distances of 7 and 17 m from the edge of the highway. Sonic anemometers were used to measure wind speed and turbulent velocities at 6 and 20 m from the highway. Traffic flow rates were monitored using traffic surveillance cameras. The dispersion model [Venkatram, A., 2004. On estimating emissions through horizontal fluxes. Atmospheric Environment 38, 2439–2446] explained over 60% of the variance of the observed path averaged NO concentrations, and over 90% of the observed concentrations were within a factor of two of the model estimates.Sensitivity tests conducted with the model indicated that the traffic flow rate made the largest contribution to the variance of the observed NO concentrations. The meteorological variable that had the largest impact on the near road NO concentrations was the standard deviation of the vertical velocity fluctuations, σ_w. Wind speed had a relatively minor effect on concentrations. Furthermore, as long as the wind direction was within ±45° from the normal to the road, wind direction had little impact on near road concentrations. The measurements did not allow us to draw conclusions on the impact of traffic-induced turbulence on dispersion. The analysis of air quality and meteorological observations resulted in plausible estimates of on-road emission factors for NO.     

Characterization of regional transport and dispersion using Project MOHAVE tracer data

Perfluorocarbon tracers were released continuously from several surface locations and one power plant stack location during the winter (30 days) and summer (50 days) intensive studies as part of Project MOHAVE. Tracers were released in winter from the Mohave Power Plant (MPP) and Dangling Rope, UT, located on the shore of Lake Powell near Page, AZ; and in summer from MPP, the Tehachapi Pass between the Mojave Desert and the Central Valley in California, and El Centro, CA, on the California-Mexico border. At the Tehachapi tracer release site six-hour pulses of a separately identifiable perfluorocarbon tracer were released every four days in order to assess the time for the tracer to clear the monitoring network. Daily 24-hr integrated samples were collected at about 30 sites in four states. Limited tracer concentration data with higher time resolution is also available. Graphical displays and analyses identify several regional transport paths, including a convergence zone in the Mojave Desert, the importance of terrain channeling, especially in winter, and a relationship between 24-hr maximum influence function and distance that may prove useful as a scoping tool and to test regional scale air quality models. In winter, Dangling Rope tracer was routinely transported through the entire length of the Grand Canyon, while in summer, MPP tracer was routinely transported over most of Lake Mead.     

Sequential extraction analysis of heavy metals in sediments of variable composition using nitrilotriacetic acid to counteract resorption

Artificial sediments were made that contained variable amounts (up to 20% by weight) of feldspar, calcite, Fe-oxide or organic matter. Analysis of samples spiked with Pb and Zn in the presence and absence of nitrilotriacetic acid (NTA) showed that 400 mg l(-1) of chelating agent greatly reduced or eliminated sorption in each case. Further study showed that this NTA concentration did not cause significant mineral dissolution. Resorption during sequential extraction analysis of artificial sediments is indicated by the fact that with NTA, levels of metals are higher in the first step and lower during subsequent steps, compared with levels obtained without NTA. However, the addition of 400 mg l(-1) of NTA to each extracting solution in the sequence appears to be effective for counteracting resorption in feldspathic, calcareous, ferruginous and carbonaceous sediments.     

Errors in coarse particulate matter mass concentrations and spatiotemporal characteristics when using subtraction estimation methods

In studies of coarse particulate matter (PM_10-2.5), mass concentrations are often estimated through the subtraction of PM_2.5 from collocated PM₁₀ tapered element oscillating microbalance (TEOM) measurements. Though all field instruments have yet to be updated, the Filter Dynamic Measurement System (FDMS) was introduced to account for the loss of semivolatile material from heated TEOM filters. To assess errors in PM_10-2.5 estimation when using the possible combinations of PM₁₀ and PM_2.5 TEOM units with and without FDMS, data from three monitoring sites of the Colorado Coarse Rural–Urban Sources and Health (CCRUSH) study were used to simulate four possible subtraction methods for estimating PM_10-2.5 mass concentrations. Assuming all mass is accounted for using collocated TEOMs with FDMS, the three other subtraction methods were assessed for biases in absolute mass concentration, temporal variability, spatial correlation, and homogeneity. Results show collocated units without FDMS closely estimate actual PM_10-2.5 mass and spatial characteristics due to the very low semivolatile PM_10-2.5 concentrations in Colorado. Estimation using either a PM_2.5 or PM₁₀ monitor without FDMS introduced absolute biases of 2.4 µg/m³ (25%) to –2.3 µg/m³ (–24%), respectively. Such errors are directly related to the unmeasured semivolatile mass and alter measures of spatiotemporal variability and homogeneity, all of which have implications for the regulatory and epidemiology communities concerned about PM_10-2.5. Two monitoring sites operated by the state of Colorado were considered for inclusion in the CCRUSH acute health effects study, but concentrations were biased due to sampling with an FDMS-equipped PM_2.5 TEOM and PM₁₀ TEOM not corrected for semivolatile mass loss. A regression-based model was developed for removing the error in these measurements by estimating the semivolatile concentration of PM_2.5 from total PM_2.5 concentrations. By estimating nonvolatile PM_2.5 concentrations from this relationship, PM_10-2.5 was calculated as the difference between nonvolatile PM₁₀ and PM_2.5 concentrations.

Implications: Errors in the estimation of PM_10-2.5 concentrations using subtraction methods were shown to be related to the unmeasured semivolatile mass when using certain combinations of TEOM instruments. For the northeastern Colorado region, the absolute bias associated with this error significantly affects mean and 95th percentile values, which would affect assessment of compliance if PM_10-2.5 is regulated in the future. Estimating PM_10-2.5 mass concentrations using nonvolatile mass concentrations from collocated PM₁₀ and PM_2.5 TEOM monitors closely estimates the total PM_10-2.5 mass concentrations. A corrective model that removes the described error was developed and applied to data from two sites in Denver.

Supplemental Materials: Supplemental materials are available for this paper. Go to the publisher's online edition of the Journal of the Air & Waste Management Association.     

Top-down estimate of mercury emissions in China using four-dimensional variational data assimilation

An inverse modeling method using the four-dimensional variational data assimilation approach is developed to provide a top-down estimate of mercury emission inventory in China. The mercury observations on board the C130 aircraft during the Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) campaign in April 2001 are assimilated into a regional chemical transport model, STEM. Using a 340 Mg of elemental mercury emitted in 1999, the assimilation results in an increase in Hg⁰ emissions for China to 1140 Mg in 2001. This is an upper limit amount of the elemental mercury required in China. The average emission-scaling factor is ∼3.4 in China. The spatial changes in the mercury emissions after the assimilation are also evaluated. The largest changes are estimated on the China north-east coastal areas and the areas of north-center China. The influences of the observation and inventory uncertainties and the initial and boundary conditions on the emission estimates are discussed. Increasing the boundary conditions of Hg from 1.2 to 1.5 ng m⁻³, results in a top-down estimate of Hg⁰ emissions for China of 718 Mg, and leads the average scaling factor from 3.4 to 2.1.     

Verification of a three-dimensional transport model using tetroon data from project state

During August 1978, The Environmental Protection Agency (EPA) conducted a major field study at the Cumberland Steam Plant of the Tennessee Valley Authority. This study, known as the Tennessee Plume Study, was conducted as part of the EPA Sulfur Transport and Transformation in the Environment (STATE) Project. The field experiments included the release and tracking of tetroons from Cumberland during numerous intervals within the period of the study. On 15 August, 10 tetroons were released, traveling distances ranging from less than 25 km to in excess of 200 km. The tetroon position data were compared with three-dimensional (3-D) kinematic trajectory predictions from a 3-D regional-scale dynamic model. The average directional error was 7° where the maximum error was 14° and an error of less than 2° prevailed for 2 trajectories. The average displacement error was 9 % of the observed path of the tetroon, with the maximum being 30% and an error of 3% or less prevailing for 4 trajectories.