Impact of smoking on in-vehicle fine particle exposure during driving

Indoor smoking ban in public places can reduce secondhand smoke (SHS) exposure. However, smoking in cars and homes has continued. The purpose of this study was to assess particulate matter less than 2.5 μm (PM_2.5) concentration in moving cars with different window opening conditions. The PM_2.5 level was measured by an aerosol spectrometer inside and outside moving cars simultaneously, along with ultrafine particle (UFP) number concentration, speed, temperature and humidity inside cars. Two sport utility vehicles were used. Three different ventilation conditions were evaluated by up to 20 repeated experiments. In the pre-smoking phase, average in-vehicle PM_2.5 concentrations were 16–17 μg m^?3. Regardless of different window opening conditions, the PM_2.5 levels promptly increased when smoking occurred and decreased after cigarette was extinguished. Although only a single cigarette was smoked, the average PM_2.5 levels were 506–1307 μg m^?3 with different window opening conditions. When smoking was ceased, the average PM_2.5 levels for 15 min were several times higher than the US National Ambient Air Quality Standard of 35 μg m^?3. It took longer than 10 min to reach the level of the pre-smoking phase. Although UFP levels had a similar temporal profile of PM_2.5, the increased levels during the smoking phase were relatively small. This study demonstrated that the SHS exposure in cars with just a single cigarette being smoked could exceed the US EPA NAAQS under realistic window opening conditions. Therefore, the findings support the need for public education against smoking in cars and advocacy for a smoke-free car policy.     

Analysis of volatile organic compounds released during food decaying processes

A number of volatile organic compounds (VOCs) including acetone, methyl ethyl ketone, toluene, ethylbenzene, m,p-xylene, styrene, and o- xylene released during food decaying processes were measured from three types of decaying food samples (Kimchi (KC), fresh fish (FF), and salted fish (SF)). To begin with, all the food samples were contained in a 100-mL throwaway syringe. These samples were then analyzed sequentially for up to a 14-day period. The patterns of VOC release contrasted sharply between two types of fish (FF and SF) and KC samples. A comparison of data in terms of total VOC showed that the mean values for the two fish types were in the similar magnitude with 280 ± 579 (FF) and 504 ± 1,089 ppmC (SF), while that for KC was much lower with 16.4 ± 7.6 ppmC. There were strong variations in VOC emission patterns during the food decaying processes between fishes and KC that are characterized most sensitively by such component as styrene. The overall results of this study indicate that concentration levels of the VOCs differed significantly between the food types and with the extent of decaying levels through time.     

Removal of toxic ions (chromate, arsenate, and perchlorate) using reverse osmosis, nanofiltration, and ultrafiltration membranes

Rejection characteristics of chromate, arsenate, and perchlorate were examined for one reverse osmosis (RO, LFC-1), two nanofiltration (NF, ESNA, and MX07), and one ultrafiltration (UF and GM) membranes that are commercially available. A bench-scale cross-flow flat-sheet filtration system was employed to determine the toxic ion rejection and the membrane flux. Both model and natural waters were used to prepare chromate, arsenate, and perchlorate solutions (approximately 100 μg L⁻¹ for each anion) in mixtures in the presence of other salts (KCl, K₂SO₄, and CaCl₂); and at varying pH conditions (4, 6, 8, and 10) and solution conductivities (30, 60, and 115 mS m⁻¹). The rejection of target ions by the membranes increases with increasing solution pH due to the increasingly negative membrane charge with synthetic model waters. Cr(VI), As(V), and rejection follows the order LFC-1 (>90%) > MX07 (25–95%)  ESNA (30–90%) > GM (3–47%) at all pH conditions. In contrast, the rejection of target ions by the membranes decreases with increasing solution conductivity due to the decreasingly negative membrane charge. Cr(VI), As(V), and rejection follows the order CaCl₂ < KCl  K₂SO₄ at constant pH and conductivity conditions for the NF and UF membranes tested. For natural waters the LFC-1 RO membrane with a small pore size (0.34 nm) had a significantly greater rejection for those target anions (>90%) excluding (71–74%) than the ESNA NF membrane (11–56%) with a relatively large pore size (0.44 nm), indicating that size exclusion is at least partially responsible for the rejection. The ratio of solute radius (r_i,s) to effective membrane pore radius (r_p) was employed to compare ion rejection. For all of the ions, the rejection is higher than 70% when the r_i,s/r_p ratio is greater than 0.4 for the LFC-1 membrane, while for di-valent ions (, , and ) the rejection (38–56%) is fairly proportional to the r_i,s/r_p ratio (0.32–0.62) for the ESNA membrane.     

Peroxyacetyl nitrate (PAN) in the urban atmosphere

Peroxyacetyl nitrate (PAN) in air has been well known as the indicator of photochemical smog due to its frequent occurrences in Seoul metropolitan area. This study was implemented to assess the distribution characteristics of atmospheric PAN in association with relevant parameters measured concurrently. During a full year period in 2011, PAN was continuously measured at hourly intervals at two monitoring sites, Gwang Jin (GJ) and Gang Seo (GS) in the megacity of Seoul, South Korea. The annual mean concentrations of PAN during the study period were 0.64 ± 0.49 and 0.57 ± 0.46 ppb, respectively. The seasonal trends of PAN generally exhibited dual peaks in both early spring and fall, regardless of sites. Their diurnal trends were fairly comparable to each other. There was a slight time lag (e.g., 1 h) in the peak occurrence pattern between O₃ and PAN, as the latter trended to peak after the maximum UV irradiance period (16:00 (GJ) and 17:00 (GS)). The concentrations of PAN generally exhibited strong correlations with particulates. The results of this study suggest that PAN concentrations were affected sensitively by atmospheric stability, the wet deposition of NO₂, wind direction, and other factors.     

Kinetics of the oxidation of sucralose and related carbohydrates by ferrate(VI)

The kinetics of the oxidation of sucralose, an emerging contaminant, and related monosaccharides and disaccharides by ferrate(VI) (Fe(VI)) were studied as a function of pH (6.5-10.1) at 25°C. Reducing sugars (glucose, fructose, and maltose) reacted faster with Fe(VI) than did the non-reducing sugar sucrose or its chlorinated derivative, sucralose. Second-order rate constants of the reactions of Fe(VI) with sucralose and disaccharides decreased with an increase in pH. The pH dependence was modeled by considering the reactivity of species of Fe(VI), (HFeO(4)(-) and FeO(4)(2-)) with the studied substrates. Second-order rate constants for the reaction of Fe(VI) with monosaccharides displayed an unusual variation with pH and were explained by considering the involvement of hydroxide in catalyzing the ring opening of the cyclic form of the carbohydrate at increased pH. The rate constants for the reactions of carbohydrates with Fe(VI) were compared with those for other oxidant species used in water treatment and were briefly discussed.     

Source identification of particulate matter collected at underground subway stations in Seoul,Korea using quantitative single-particle analysis

Subway particle samples collected at four underground subway stations in Seoul, Korea were characterized by a single-particle analytical technique, low-Z particle electron probe X-ray microanalysis. To clearly identify indoor sources of subway particles, four sets of samples collected in tunnels, at platforms, near ticket offices, and outdoors were investigated. For the samples collected in tunnels, Fe-containing particles predominate, with relative abundances of 75–91% for the four stations. The amounts of Fe-containing particles decrease as the distance of sampling locations from the tunnel increases. In addition, samples collected at the platform in subway stations with platform screen doors (PSDs) that limit air-mixing between the platform and the tunnel showed marked decreases in relative abundances of Fe-containing particles, clearly indicating that Fe-containing subway particles are generated in the tunnel. PM₁₀ mass concentration levels are the highest in the tunnels, becoming lower as the distance of sampling locations from the tunnel increases. The extent of the decrease in PM₁₀ in stations with PSDs is also larger than that in stations without PSDs. The results clearly indicate that Fe-containing particles originating in tunnels predominate in the indoor microenvironment of subway stations, resulting in high indoor PM₁₀ levels, and that PSDs play a significant role in reducing Fe-containing particles at platforms and near ticket offices.     

Characteristics of aerosol types from AERONET sunphotometer measurements

By using observations from the Aerosol Robotic Network (AERONET), aerosol types are classified according to dominant size mode and radiation absorptivity as determined by fine-mode fraction (FMF) and single-scattering albedo (SSA), respectively. The aerosol type from anthropogenic sources is significantly different with regard to location and season, while dust aerosol is observed persistently over North Africa and the Arabian Peninsula. For four reference locations where different aerosol types are observed, time series and optical properties for each aerosol type are investigated. The results show that aerosol types are strongly affected by their sources and partly affected by relative humidity. The analysis and methodology of this study can be used to compare aerosol classification results from satellite and chemical transport models, as well as to analyze aerosol characteristics on a global scale over land for which satellite observations need to be improved.     

Long-term projections of non-fuel minerals: We were wrong,but why?

This article revisits global projections made in 1981 of eight metallic and fertilizer minerals for the year 2000. The principal objectives of the present study are to quantify the differences between the projected and observed levels of consumption for the year 2000 for eight of the 26 non-fuel minerals covered in the earlier study, and, then, to attempt to attribute these (often) large differences to the major determinants of minerals demand: income, technological, regulatory and other public policy changes, and changes in the recycling rates of the metallic minerals. The eight minerals are: aluminum, copper, iron, mercury, nickel, phosphate rock, potash and tin.This follow-up study begins with a discussion of the need for long-term projections of minerals. This section also includes a summary of the major determinants of the long-term demand for, and supply of, minerals, and a review of some of the earlier assessments of mineral needs and availability.Section 3 of the article begins with a short summary of the World Input–Output Model, the main methodological tool used in the earlier study that was developed by Prof. Wassily Leontief, the 1973 Nobel laureate in economics, and the way in which non-fuel minerals were represented in that system. This section also provides a summary of other global modeling efforts of non-fuel minerals that were carried out at a similar point in time for a similar interval.Section 4 presents the actual population, GDP and per capita GDP changes over the 1970–2000 time interval compared with the projected rates for these important determinants of mineral use, along with the projected and observed growth rates of minerals consumption for the eight non-fuel minerals included in this study. When the projections are compared to the observed global consumption rates for the year 2000, the differences range from +43% for nickel to +229% for potash.Section 5 discusses the apparent reasons for the differences between the projected and observed global consumption rates of these non-fuel minerals that include differences in the growth of GDP and GDP per capita, changes in recycling rates (for the metallic minerals), technological change, and regulatory or other public policy changes that have affected mineral use over the 30-year-interval ending in 2000.In light of the data and analysis presented in Sections sec# and sec#, the article concludes with some remarks, made almost a quarter of a century ago, by Prof. Leontief on the need and justification for long-term projections.