Source/receptor reconciliation of ambient organic fine particulate matter in Palm Beach County

Airborne particulate matter (PM) is of environmental concern not only in urban but also rural areas that are easily inhalable and have been considered responsible, together with gaseous pollutants, for possible health effects. The objectives of this research study is to generate an extensive data set for ambient PM collected at Belle Glade and Delray Beach that ultimately was used together with published source profiles to predict the contributions of major sources to the overall airborne particle burden in Belle Glade and Delray Beach. ^ The size segregated particle sampling was conducted for one entire year. The samples collected during the months of January and May were further subjected to chemical analysis for organic compounds by Gas Chromatography-Mass Spectrometry. Additional, PM10 sampling was conducted simultaneously with size segregated particle sampling during January and May to analyze for trace elements using Instrumental Neutron Activation Analysis technique. Elements and organic marker compounds were used in Chemical Mass Balance modeling to determine the major source contribution to the ambient fine particle matter burden. ^ Size segregated particle distribution results show bimodal in both sampling sites. Sugarcane pre-harvest burning in the rural site elevated PM10 concentration by about 30% during the sugarcane harvest season compared to sugarcane growing season. Sea salt particles and Saharan dust particles accounted for the external sources. ^ The results of trace element analysis show that Al, Ca, Cs, Eu, Lu, Nd, Sc, Sm, Th, and Yb are more abundant at the rural sampling site. The trace elements Ba, Br, Ce, Cl, Cr, Fe, Gd, Hf, Na, Sb, Ta, V, and W show high abundance at the urban site due to anthropogenic activities except for Na and Cl, which are from sea salt spray. On the other hand, size segregated trace organic compounds measurements show that organic compounds mainly from combustion process were accumulated in PM0.95. ^ In conclusion, major particle sources were determined by the CMB8.2 software as follows: road dust, sugarcane leaf burning, diesel-powered and gasoline powered vehicle exhaust, leaf surface abrasion particles, and a very small fraction of meat cooking. ^

An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO)

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment. The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks. The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.