As indoor smoking bans have become widely adopted, some U.S. communities are considering restricting smoking outdoors, creating a need for measurements of air pollution near smokers outdoors. Personal exposure experiments were conducted with four to five participants at six sidewalk bus stops located 1.5–3.3 m from the curb of two heavily traveled California arterial highways with 3300–5100 vehicles per hour. At each bus stop, a smoker in the group smoked a cigarette. Gravimetrically calibrated continuous monitors were used to measure fine particle concentrations (aerodynamic diameter ≤2.5 µm; PM2.5) in the breathing zones (within 0.2 m from the nose and mouth) of each participant. At each bus stop, ultrafine particles (UFP), wind speed, temperature, relative humidity, and traffic counts were also measured. For 13 cigarette experiments, the mean PM2.5 personal exposure of the nonsmoker seated 0.5 m from the smoker during a 5-min cigarette ranged from 15 to 153 µg/m3. Of four persons seated on the bench, the smoker received the highest PM2.5 breathing-zone exposure of 192 µg/m3. There was a strong proximity effect: nonsmokers at distances 0.5, 1.0, and 1.5 m from the smoker received mean PM2.5 personal exposures of 59, 40, and 28 µg/m3, respectively, compared with a background level of 1.7 µg/m3. Like the PM2.5 concentrations, UFP concentrations measured 0.5 m from the smoker increased abruptly when a cigarette started and decreased when the cigarette ended, averaging 44,500 particles/cm3 compared with the background level of 7200 particles/cm3. During nonsmoking periods, the UFP background concentrations showed occasional peaks due to traffic, whereas PM2.5 background concentrations were extremely low. The results indicate that a single cigarette smoked outdoors at a bus stop can cause PM2.5 and UFP concentrations near the smoker that are 16–35 and 6.2 times, respectively, higher than the background concentrations due to cars and trucks on an adjacent arterial highway.
Implications: Rules banning smoking indoors have been widely adopted in the United States and in many countries. Some communities are considering smoking bans that would apply to outdoor locations. Although many measurements are available of pollutant concentrations from secondhand smoke at indoor locations, few measurements are available of exposure to secondhand smoke outdoors. This study provides new data on exposure to fine and ultrafine particles from secondhand smoke near a smoker outdoors. The levels are compared with the exposure measured next to a highway. The findings are important for policies that might be developed for reducing exposure to secondhand smoke outdoors.相似文献
The transport behaviors of a suite of contaminants released from electronic waste (e-waste) recycling operations, including polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), and heavy metals, were evaluated by analyzing the contaminant residues in surface soils sampled in the surrounding area of an e-waste recycling site in South China. Concentrations of PBDEs and PCBs in the soil samples ranged from 0.565 to 2908 ng g(-1) dw and from 0.267 to 1891 ng g(-1) dw, respectively, while soil residues were 0.082-2.56, 3.22-287, and 16.3-162 μg g(-1) dw for Cd, Cu, and Pb, respectively. Concentrations of PBDEs and PCBs in soil decreased with increasing distance from the source of pollution, indicating possible PBDE and PCB contamination in the surrounding areas due to the short-range transport of these compounds from the e-waste recycling site. Although no significant difference in the short-range transport potential among PBDE and PCB congeners was observed, reductions in concentrations of the highly-brominated-BDEs and highly-chlorinated-CBs were slightly quicker than those of their less-halogen-substituted counterparts. Conversely, heavy metals showed the lowest transport potential due to their low vapor pressure, and results showed metals would remain near the pollution source instead of diffusing into the surrounding areas. Finally, mass inventories in areas near the e-waste site were 0.920, 0.134, 0.860, 4.68, 757, and 673 tons for BDE209, PBDEs (excluding BDE209), PCBs, Cd, Cu, and Pb, respectively. 相似文献
Biomass is one of the renewable energy sources on which policy makers are greatly dependent on since it is a flexible feedstock capable of conversion into electricity, transport liquid fuels and heat by chemical and biological processes on demand. Though numerous publications have examined the relationship of economic growth with renewable energy and other parameters, biomass energy has never been included in these studies. Then, this study examines the causal relationship within a multivariate panel cointegration/error correction framework which combines the cross-section and time series data while allowing for heterogeneity across different provinces. After employing panel data regression model ranging from 2003 through 2012 to examine the relationships of biofuels production with sustainable development in China, the paper concludes that the development of biofuel energy production integrated with the consideration of the improvement of income per capita, and the attraction of more capital investment, does make a significant contribution to economic growth. However, some negative side effects including the increase of greenhouse emissions and the decrease of marginal land still coexist with the economic development. Of course, the importance of these findings lies on their implications and their adoption on strategic policies. 相似文献
Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%. 相似文献