In south-central Chile, wood stoves have been identified as an important source of air pollution in populated areas. Eucalyptus (Eucalyptus globulus), Chilean oak (Nothofagus oblique), and mimosa (Acacia dealbata) were burned in a single-chamber slow-combustion wood stove at a controlled testing facility located at the University of Concepción, Chile. In each experiment, 2.7–3.1 kg of firewood were combusted while continuously monitoring temperature, exhaust gases, burn rate, and collecting particulate matter samples in Teflon filters under isokinetic conditions for polycyclic aromatic hydrocarbon and levoglucosan analyses. Mean particulate matter emission factors were 2.03, 4.06, and 3.84 g/kg dry wood for eucalyptus, oak, and mimosa, respectively. The emission factors were inversely correlated with combustion efficiency. The mean emission factors of the sums of 12 polycyclic aromatic hydrocarbons in particle phases were 1472.5, 2134.0, and 747.5 μg/kg for eucalyptus, oak, and mimosa, respectively. Fluoranthene, pyrene, benzo[a]anthracene, and chrysene were present in the particle phase in higher proportions compared with other polycyclic aromatic hydrocarbons that were analyzed. Mean levoglucosan emission factors were 854.9, 202.3, and 328.0 mg/kg for eucalyptus, oak, and mimosa, respectively. Since the emissions of particulate matter and other pollutants were inversely correlated with combustion efficiency, implementing more efficient technologies would help to reduce air pollutant emissions from wood combustion.
Implications: Residential wood burning has been identified as a significant source of air pollution in populated areas. Local wood species are combusted for home cooking and heating, which releases several toxic air pollutants, including particulate matter, carbon monoxide, and polycyclic aromatic hydrocarbons. Air pollutant emissions depend on the type of wood and the technology and operational conditions of the wood stove. A better understanding of emissions from local wood species and wood stove performance would help to identify better biomass fuels and wood stove technologies in order to reduce air pollution from residential wood burning. 相似文献
Abstract A neural fuzzy system was used to investigate the influence of environmental variables (time, aeration, moisture, and particle size) on composting parameters (pH, organic matter [OM], nitrogen [N], ammonium nitrogen [NH4+-N] and nitrate nitrogen [NO3--N]). This was to determine the best composting conditions to ensure the maximum quality on the composts obtained with the minimum ammonium losses. A central composite experimental design was used to obtain the neural fuzzy model for each dependent variable. These models, consisting of the four independent process variables, were found to accurately describe the composting process (the differences between the experimental values and those estimated by using the equations never exceeded 5–10% of the former). Results of the modeling showed that creating a product with acceptable chemical properties (pH, NH4+-N and NO3--N) entails operating at medium moisture content (55%) and medium to high particle size (3–5 cm). Moderate to low aeration (0.2 L air/min · kg) would be the best compromise to compost this residue because of the scant statistical influence of this independent variable. 相似文献