There have been significant improvements in recent years in transportation and emissions modeling to allow better evaluations of transportation operational effects and associated vehicle emissions. In particular, instantaneous or modal emissions models have been developed for a variety of light-duty vehicles. To date, most of the effort has focused primarily on developing these models for light-duty vehicles with less effort devoted to heavy-duty diesel (HDD) vehicles. Although HDD vehicles currently make up only a fraction of the total vehicle population, they are major contributors to the emissions inventory. A description is provided of an HDD truck model that is part of a larger comprehensive modal emissions modeling (CMEM) program developed at the University of California (UC), Riverside. Several HDD truck submodels have been developed in the CMEM framework, each corresponding to a distinctive vehicletechnology category. The developed models use a parameterized physical approach in which the entire emission process is broken down into different components that correspond to physical phenomena associated with vehicle operation and emission production. A variety of trucks were extensively tested under a wide range of operating conditions at UC Riverside's Mobile Emissions Research Laboratory. The collected data were then used to calibrate the HDD models. Particular care was taken to investigate and implement the effects of varying grade and the use of variable fuel injection strategies. Results show good estimates for fuel use and the regulated emission species including nitrogen oxides, one of the key targets for HDD vehicles.

Mobile source emissions estimation techniques play a critical role for regional planning and development of emission control strategies. The primary models for mobile source emissions estimation have been the U.S. Environmental Protection Agency’s MOBILE model and the California Air Resources Board’s EMFAC model. These models work well for large regional areas but are not as well suited for “microscale” evaluation. Over the last several years, the College of Engineering–Center for Environmental Research and Technology (CE-CERT) has been evaluating in-use, light-duty vehicles as part of NCHRP Project 25-11, resulting in the development of a Comprehensive Modal Emissions Model (CMEM). An essential part of any model development process is validating the model. Various validation techniques have been applied to CMEM. This paper describes some of the latest validation work carried out in comparing CMEM results to independent emission testing results (independent in both vehicles and driving cycles). Further, CMEM has been compared with the latest versions of EMFAC and MOBILE. In general, compared with the independent emission measurements, CMEM predicts well. It has been found that CMEM is consistent with MOBILE and EMFAC at low to medium speeds. Greater deviations were found at very low speeds and very high speeds. At high speeds, CMEM tends to predict higher hydrocarbon (HC) emissions and lower oxides of nitrogen (NOx) emissions. At the very low speeds, CMEM tends to predict lower than EMFAC and MOBILE for all emissions. These comparisons are part of an ongoing validation process for development of CMEM.

Oxygenate content and fuel volatility (distillation) variables are important parameters affecting vehicle exhaust emissions, and data on their effects on the latest technology vehicles are quite limited. For this study, 12 California-certified LEV to SULEV vehicles were tested on a matrix of 12 fuels with varying levels of ethanol concentration (0, 5.7, and 10 vol %), T50 (195, 215, and 235 degrees F), and T90 (295, 330, and 355 degrees F). There were statistically significant interactions between ethanol and T90 for NMHC, ethanol, and T50 for CO and ethanol and T50 for NO(x). NMHC emissions increased with increasing ethanol content at the midpoint and high level of T90 but were unaffected at the low T90 level. CO emissions decreased as the ethanol content increased from the low to the midpoint level for all levels of T50, but between the 5.7 and 10% ethanol levels, CO showed only an increase for the high level of T50. NO(x) emissions increased with ethanol content for some conditions. Non-methane organic gases (NMOG) and toxic emissions were examined for only a subset of fuels with the highest T90 level, with NMOG, acetaldehyde, benzene, and 1-,3-butadiene all found to increase with increasing ethanol content.

Tomato (Solanum lycopersicum) has a unique resistance gene, Mi-1, that confers resistance to animals from distinct taxa, nematodes, and piercing and sucking insects. Mi-1 encodes a protein with a nucleotide-binding site and leucine-rich repeat motifs. Early in the potato aphid (Macrosiphum euphorbiae)--tomato interactions, aphid feeding induces the expression of the jasmonic acid (JA)-regulated proteinase inhibitor genes, Pin1 and Pin2. The jail-1 (jasmonic acid insensitive 1) tomato mutant, which is impaired in JA perception, was used to gain additional insight into the JA signaling pathway and its role in the Mi-1-mediated aphid resistance. The jail-1 mutant has a deletion in the Coil gene that encodes a putative F-box protein. In this study, aphid colonization, survival, and fecundity were compared on wild-type tomato and jail-1 mutant. In choice assays, the jail-1 mutant showed higher colonization by potato aphids compared with wild-type tomato. In contrast, no-choice assays showed no difference in potato aphid survival or fecundity between jail-1 and the wild-type parent. Plants homozygous for Mi-1 and for the jail mutation were not compromised in resistance to potato aphids, using either choice or no-choice assays. In addition, the accumulation of JA-regulated Pin1 transcripts after aphid feeding was Coil dependent. Taken together, these data indicate that, although potato aphids activate Coil-dependent defense response in tomato, this response is not required for Mi-1-mediated resistance to aphids.

NH3 emissions from motor vehicles have been the subject of a number of recent studies due to their potential impact on ambient particulate matter (PM). Highly time-resolved NH3 emissions can be measured and correlated with specific driving events utilizing a tunable diode laser (TDL). It is possible to incorporate NH3 emissions with this new information into models that can be used to predict emissions inventories from vehicles. The newer generation of modal models are based on modal events, with the data collected at second-by-second time resolution, unlike the bag-based emission inventory models such as EMFAC and MOBILE. The development of an NH3 modal model is described in this paper. This represents one of the first attempts to incorporate vehicle NH3 emissions into a comprehensive emissions model. This model was used in conjunction with on-road driving profiles to estimate the emissions of SULEV, ULEV, and LEV vehicles to be 9.4 +/- 4.1, 21.8 +/- 5.2, and 34.9 +/- 6.0 mg/mi, respectively. We also implement this new NH3 model to predict and evaluate the NH3 emission inventory in the South Coast air basin (SoCAB).

Research Program. This paper describes the initial phase of a longterm project with national implications for the improvement of transportation and air quality. The overall objective of the research is to develop and verify a comprehensive modal emissions model that accurately reflects the impacts of a vehicle's operating mode. The model is comprehensive in the sense that it will be able to predict emissions for a wide variety of light-duty vehicles (LDVs, i.e., cars and trucks) in various states of condition (e.g., properly functioning, deteriorated, malfunctioning). Other efforts and further background on modal emission modeling have been described elsewhere (1) and elsewhere in this Record by An et al.A specific modal emissions testing protocol has been developed that reflects both real-world driving and specific modal events associated with different levels of emissions. This testing protocol (described later in this paper) is being applied to more than 300 vehicles to provide the foundation for the modal emissions model. As a preliminary step, the test cycle has been applied to an initial fleet of 30 vehicles, where at least 1 vehicle falls into each of the 28 defined vehicle/technology categories. The preliminary analysis of the initial test fleet is described. VEHICLE/TECHNOLOGY CATEGORIZATIONThe conventional emission inventory models are based on bag emissions data (FTP) collected from certification tests of new cars, surveillance programs, and inspection/maintenance programs. These large sets of emissions data provide the basis for the conventional emission inventory models and are indexed primarily by model year. For LDVs, groupings are based on a few different vehicle classes and technology groups.In developing a modal emission model, we cannot base the model on these bag data and must collect second-by-second emissions data from a sample of vehicles to build a model that predicts emissions for the national fleet. The choice of vehicles for this sample is therefore crucial, since only a small sample (300+ vehicles) will be the basis for the model.The determination of the vehicle/technology categories in the modal model is a critical task, not only for vehicle recruitment and testing but also for the development of the model. Because the eventual output of the model is emissions, the vehicle/technology categories and the sampling proportions of the major vehicle/technology groups (normal versus high emitter, and carbureted versus fuel injected versus Tier 1) have been chosen based on each major category's contribution to total emissions, as opposed to a group's actual population in the national fleet. Recent results from both remote sensing and surveillance studies have indicated that a small population of vehicles contribute a substantial fraction of the total emissions The initial phase of a long-term project with national implications for the improvement of transportation and air quality is described. The overall objective of the research is to develop and verify a computer model that accurately esti...

Soils are considered important components of many pesticide contamination models and are frequently the direct or indirect targets of pesticides applied during agricultural activities. Soil texture is commonly referenced on pesticide labels as an important factor in the selection and application of pesticides and in identifying target areas that are vulnerable to leaching. In general, no guidelines exist for the common interpretation of generic soil texture terms found on pesticide labels, for example, coarse or coarse‐textured soils. In the present study, a significant logistic regression model (P = 0.017) was developed that is based on the soil particle‐size class composition of sections containing wells sampled for DBCP (1,3‐dibromochloropropane). Particle‐size class is a concept used in Soil Taxonomy to describe soil family texture and is a component of soil family names. The model contains terms for the sandy and fine particle‐size classes. The model was validated using data obtained from sources independent of those used to develop the model. Records in the California Soils Map Unit Inventory database that describes the soil map unit composition of >65 000 sections (1600 m2 or 1 mi2) were used to generate probability scores for >15 000 sections located in the San Joaquin Valley, CA. A geographic information system, an information management technique that is becoming an accepted tool for a wide range of regulatory agencies, was used to generate visual images of the probability scores. A map was developed that depicts four distinct probability classes of the DBCP‐contamination status of section‐sized areas and their distribution within the study area.

An EAFC enhances the efficiency and effectiveness of those who address elder abuse in one community, which in turn leads to improved outcomes. Continued analysis to identify strengths, weaknesses, and cost effectiveness of the EAFC model is ongoing.