Chemical Composition of Emissions from Urban Sources of Fine Organic Aerosol

A dilution source sampling system was used to collect primary fine aerosol emissions from important sources of urban organic aerosol, including a boiler burning No. 2 fuel oil, a home fireplace, a fleet of catalyst-equipped and noncatalyst automobiles, heavy-duty diesel trucks, natural gas home appliances, and meat cooking operations. Alternative dilution sampling techniques were used to collect emissions from cigarette smoking and a roofing tar pot, and grab sample techniques were employed to characterize paved road dust, brake lining wear, tire wear, and vegetative detritus. Organic aerosol constituted the majority of the fine aerosol mass emitted from many of the sources tested. Fine primary organic aerosol emissions within the heavily urbanized western portion of the Los Angeles Basin were determined to total 29.8 metric tons/day. Over 40% of these organic aerosol emissions are from anthropogenic pollution sources that are expected to emit contemporary (nonfossil) aerosol carbon, in good agreement with the available ambient monitoring data

Mathematical modeling of atmospheric fine particle-associated primary organic compound concentrations

An atmospheric transport model has been used to explore the relationship between source emissions and ambient air quality for individual particle phase organic compounds present in primary aerosol source emissions. An inventory of fine particulate organic compound emissions was assembled for the Los Angeles area in the year 1982. Sources characterized included noncatalyst- and catalyst-equipped autos, diesel trucks, paved road dust, tire wear, brake lining dust, meat cooking operations, industrial oil-fired boilers, roofing tar pots, natural gas combustion in residential homes, cigarette smoke, fireplaces burning oak and pine wood, and plant leaf abrasion products. These primary fine particle source emissions were supplied to a computer-based model that simulates atmospheric transport, dispersion, and dry deposition based on the time series of hourly wind observations and mixing depths. Monthly average fine particle organic compound concentrations that would prevail if the primary organic aerosol were transported without chemical reaction were computed for more than 100 organic compounds within an 80 km × 80 km modeling area centered over Los Angeles. The monthly average compound concentrations predicted by the transport model were compared to atmospheric measurements made at monitoring sites within the study area during 1982. The predicted seasonal variation and absolute values of the concentrations of the more stable compounds are found to be in reasonable agreement with the ambient observations. While model predictions for the higher molecular weight polycyclic aromatic hydrocarbons (PAH) are in agreement with ambient observations, lower molecular weight PAH show much higher predicted than measured atmospheric concentrations in the particle phase, indicating atmospheric decay by chemical reactions or evaporation from the particle phase. The atmospheric concentrations of dicarboxylic acids and aromatic polycarboxylic acids greatly exceed the contributions that are due to direct emissions from primary sources, confirming that these compounds are principally formed by atmospheric chemical reactions

Molecular Marker Analysis as a Guide to the Sources of Fine Organic Aerosols

The molecular composition of fine particulate (D_p ≥ 2 µm) organic aerosol emissions from the most important sources in the Los Angeles area has been determined. Likewise, ambient concentration patterns for more than 80 single organic compounds have been measured at four urban sites (West Los Angeles, Downtown Los Angeles, Pasadena, and Rubidoux) and at one remote offshore site (San Nicolas Island). It has been found that cholesterol serves as a marker compound for emissions from charbroilers and other meat cooking operations. Vehicular exhaust being emitted from diesel and gasoline powered engines can be traced in the Los Angeles atmosphere using fossil petroleum marker compounds such as steranes and pentacyclic triterpanes (e.g., hopanes). Biogenic fine particle emission sources such as plant fragments abraded from leaf surfaces by wind and weather can be traced in the urban atmosphere. Using distinct and specific source organic tracers or assemblages of organic compounds characteristic for the sources considered it is possible to estimate the influence of different source types at any urban site where atmospheric data are available

Contribution of primary aerosol emissions from vegetation-derived sources to fine particle concentrations in Los Angeles

Field measurements of the n-alkanes present in fine atmospheric aerosols show a predominance of odd carbon numbered higher molecular weight homologues (C_(27)–C_(33)) that is characteristic of plant waxes. Utilizing a local leaf wax n-alkane profile in conjunction with an air quality model, it is estimated that, at most, 0.2–1.0 μg m^(−3) of the airborne fine particulate matter (d_p < 2.1 μm) present in the Los Angeles basin could originate from urban vegetative detritus; this corresponds to approximately 1–3% of the total ambient fine aerosol burden. However, some of the observed vegetation aerosol fingerprint in the Los Angeles air may be due in part to emissions from food cooking rather than plant detritus. Seasonal trends in the ambient n-alkane patterns are examined to seek further insight into the relative importance of anthropogenic versus natural sources of vegetation-derived fine particulate matter

3D flight route optimization for air-taxis in urban areas with evolutionary algorithms

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesElectric aviation is being developed as a new mode of transportation for the urban areas of the future. This requires an urban air space management that considers these aircraft and restricts the vehicles’ flight routes from passing nofly areas. Flight routes need to be determined that avoid the no-fly areas and are also optimally planned in regard to minimize the flight time, energy consumption and added noise. The no-fly areas and the flight routes can be best modelled as three-dimensional geographical objects. The problem of finding a good flight route that suits all three criteria is hard and requires an optimization technique. Yet, no study exists for optimizing 3D-routes that are represented as geographical objects while avoiding three-dimensional restricted areas. The research gap is overcome by optimizing the 3D-routes with the multi-criteria optimization technique called Nondominated Sorting Genetic Algorithm (II).We applied the optimization on the study area of Manhattan (New York City) and for two representatives of different electrical aircraft, the Lilium Jet and the Ehang 184. Special procedures are proposed in the optimization process to incorporate the chosen geographical representations. We included a seeding procedure for initializing the first flight routes, repair methods for invalid flight routes and a mutation technique that relocates points along a sine curve. The resulting flight routes are compromise solutions for the criteria flight time, energy emission and added noise. Compared to a least distance path, the optimized flight routeswere improved for all three objectives. The lowest observed improvementwas a noise reduction by 36% for the Ehang 184. The highest improvement was an energy consumption reduction by 90% for the Lilium Jet. The proposed representation caused high computation times, which lead to other limitations, e.g. a missing uncertainty analysis.With the proposed methods, we achieved to optimize 3D-routes with multiple objectives and constraints. A reproducibility self-assessment1 resulted in 2, 2, 2, 2, 1 (input data, preprocessing, methods, computational environment, results)

Acquisition of regional air quality model validation data for nitrate, sulfate, ammonium ion and their precursors

An intensive field study was conducted throughout California's South Coast Air Basin to acquire air quality model validation data for use with aerosol nitrate formation models. Aerosol nitrate, sulfate, ammonium, other major ionic aerosol species, nitric acid gas and ammonia were measured concurrently at ten sites for forty-eight consecutive hours during the period 30–31 August 1982. Ozone, NO and NO_x were measured at all locations, and PAN was measured at Pasadena and Riverside, completing a nitrogen balance on the air masses studied. The product of the measured nitric acid and ammonia concentrations ranged from less than 1 ppbv^2 to greater than 300 ppbv^2 during the experiment, providing a wide range of conditions over which comparisons can be drawn between chemical equilibrium calculations and experimental results. The ionic material in the aerosol phase was chemically more complex than is assumed by present theoretical models for the equilibrium between NH_3, HNO_3 and the aerosol phase, and included significant amounts of Na^+, Ca^(2+), Mg^(2+), K^+ and Cl^− in addition to NH_4^+, SO_4^(2−) and NO_3^−. Results of the experiment showed that aerosol nitrate levels in excess of 20 μm^(−3) accumulated in near-coastal locations in the morning of 31 August, followed by subsequent transport across the air basin. Trajectory analysis showed that the afternoon aerosol nitrate peak observed inland at Rubidoux near Riverside was associated with the same air mass that contained the high morning nitrate levels near the coast, indicating that description of both transport and atmospheric chemical reactions is important in understanding regional nitrate dynamics

Fighting viral infections and virus-driven tumors with cytotoxic CD4+ T cells

CD4+ T cells have been and are still largely regarded as the orchestrators of immune responses, being able to differentiate into distinct T helper cell populations based on differentiation signals, transcription factor expression, cytokine secretion, and specific functions. Nonetheless, a growing body of evidence indicates that CD4+ T cells can also exert a direct effector activity, which depends on intrinsic cytotoxic properties acquired and carried out along with the evolution of several pathogenic infections. The relevant role of CD4+ T cell lytic features in the control of such infectious conditions also leads to their exploitation as a new immunotherapeutic approach. This review aims at summarizing currently available data about functional and therapeutic relevance of cytotoxic CD4+ T cells in the context of viral infections and virus-driven tumors

Uncertainty in Pareto fronts of land use allocation caused by spatial input data

Pursuing the single goal of maximal agricultural production in land use management has proven to involve many drawbacks, including negative environmental, economic and social impacts. Finding an optimal land use allocation involves various contradicting short-term and long-term goals depending on the location and its demands. Existing multi-objective optimization techniques can incorporate multiple goals in land use allocation, but none addresses the uncertainty introduced by the necessary spatial input data. Land use classifications, soil maps, ground water level maps or biodiversity maps are exemplary spatial data products required for land use allocation problems. These spatial data can be imprecise or inaccurate, and these errors propagate through the optimization. Hence, the results of the optimization are uncertain. The degree of this uncertainty is not quantified by current optimization methods. Here, we aim to develop and test a strategy that considers the uncertainty in spatial input data and quantifies its impact on the resulting land use allocations. In a case study of land use allocation under two objectives, maximizing agricultural production and species richness, we define the uncertainty in spatial data input and accordingly make these inputs stochastic. A Monte Carlo simulation of an evolutionary algorithm is implemented to perform the optimization. Each individual Monte Carlo sample generates one Pareto front. By combining the calculated Pareto fronts, a median Pareto front with confidence intervals is formed, which represents the uncertain Pareto front. First results indicate that the confidence interval is narrower at the two ends of the Pareto front, because at these ends spatial data input of only one of the two objectives is relevant, such that the uncertainty in the other inputs does not affect the allocation. The uncertain Pareto front can be used to estimate the robustness of a certain land allocation choice given the uncertainty caused by spatial input data