Infrared Measurement of Fluorocarbons,Carbon Tetrachloride,Carbonyl Sulfide,And Other Atmospheric Trace Gases

A cryogenic procedure for concentrating trace gases in the atmosphere has been developed and applied to the ambient air at Research Triangle Park, NC, Atlantic Beach, NC, and New York City. The concentrated gases have been analyzed by long path infrared absorption spectroscopy, with a detectability down to partial pressures of 10^?11 atmospheres. Carbonyl sulfide has been detected at partial pressures in the range 1 × 10^?10 atm. to 3 × 10^?10 atm. Carbon tetrachloride was always detected with a rather narrow partial pressure range of 0.7 × 10^?10 atm. to 1.1 × 10^?10 atm. Fluorocarbon-11 values ranged from about 1.3 × 10^?10 atm. at Research Triangle Park to 8 × 10^?10 atm. at New York City. Fluo-rocarbon-12 covered the range 1.8 × 10^?10 atm. to 1.0 × 10^?9 atm. Hydrocarbon pollutants varied widely in concentration, but were always detected, even in the clean rural air.     

Chemotaxis to atrazine and detection of a xenobiotic catabolic plasmid in Arthrobacter sp. DNS10

Introduction

A plasmid named pDNS10 was detected from an atrazine-degrading strain Arthrobacter sp. DNS10 which has been isolated previously in our laboratory.

Materials and methods

In this paper, a special plasmid-detecting method and drop assays experiments were mainly used to achieve research goals.

Results and discussion

pDNS10 exhibited an excellent stability because it also could be detected even when the strain DNS10 has been subcultured under nonselective conditions for eight times. Over a 48-h incubation period, the OD₆₀₀ of samples inoculated with strain DNS10 and strain DNS10-ST (both of them contained pDNS10) were 0.31 ± 0.042 and 0.305 ± 0.034, respectively ,whereas the OD₆₀₀ of samples inoculated strain without pDNS10 (strain DNS10-PE) was only 0.138 ± 0.018. No atrazine was detected in the inoculated strain DNS10 and strain DNS10-ST samples at this period. Contrarily, the atrazine-degrading rate of strain DNS10-PE was only 5.23 ± 0.71%. Furthermore, both the two types of strains containing pDNS10 confirmed the presence of known degrading genes such as trzN, atzB, and atzC. It suggests that pDNS10 is an atrazine catabolic plasmid. In drop assays experiments, the wild-type strain DNS10 cells were chemotactically attracted to atrazine, whereas strain DNS10-PE showed no chemotaxis to atrazine and hydroxyatrazine. There was some relationship between atrazine degradation and the chemotactic response towards atrazine in strain DNS10.

Conclusions

The biochemical characteristics of pDNS10 and the chemotaxis characteristics of strain DNS10 could help us in better understanding of the mechanism of atrazine degradation by strain DNS10.     

Biofilm formation and microbial activity in a biofilter system in the presence of MTBE, ETBE and TAME

Emerging water contaminants derived from unleaded gasoline such as methyl tert-butyl ether (MTBE), ethyl tert-butyl ether (ETBE) and tert-amyl methyl ether (TAME), are in need of effective bioremediation technologies for restoring water resources. In order to design the conditions of a future groundwater bioremediating biofilter, this work assesses the potential use of Acinetobacter calcoaceticus M10, Rhodococcus ruber E10 and Gordonia amicalis T3 for the removal of MTBE, ETBE and TAME in consortia or as individual strains. Biofilm formation on an inert polyethylene support material was assessed with scanning electron microscopy, and consortia were also analysed with fluorescent in situ hybridisation to examine the relation between the strains. A. calcoaceticus M10 was the best coloniser, followed by G. amicalis T3, however, biofilm formation of pair consortia favoured consortium M10-E10 both in formation and activity. However, degradation batch studies determined that neither consortium exhibited higher degradation than individual strain degradation. The physiological state of the three strains was also determined through flow cytometry using propidium iodide and 3′-dihexylocarbocyanine iodide thus gathering information on their viability and activity with the three oxygenates since previous microbial counts revealed slow growth. Strain E10 was observed to have the highest physiological activity in the presence of MTBE, and strain M10 activity with TAME was only maintained for 24 h, thus we believe that biotransformation of MTBE occurs within the active periods established by the cytometry analyses. Viable cell counts and oxygenate removal were determined in the presence of the metabolites tert-butyl alcohol (TBA) and tert-amyl alcohol (TAA), resulting in TBA biotransformation by M10 and E10, and TAA by M10. Our results show that A. calcoaceticus M10 and the consortium M10-E10 could be adequate inocula in MTBE and TAME bioremediating technologies.     

Identification of the sources of PM10 in a subway tunnel using positive matrix factorization

The level of particulate matter of less than 10 μm diameter (PM₁₀) at subway platforms can be significantly reduced by installing a platform screen-door system. However, both workers and passengers might be exposed to higher PM₁₀ levels while the cars are within the tunnel because it is a more confined environment. This study determined the PM₁₀ levels in a subway tunnel, and identified the sources of PM₁₀ using elemental analysis and receptor modeling. Forty-four PM₁₀ samples were collected in the tunnel between the Gireum and Mia stations on Line 4 in metropolitan Seoul and analyzed using inductively coupled plasma–atomic emission spectrometry and ion chromatography. The major PM₁₀ sources were identified using positive matrix factorization (PMF). The average PM₁₀ concentration in the tunnels was 200.8 ± 22.0 μg/m³. Elemental analysis indicated that the PM₁₀ consisted of 40.4% inorganic species, 9.1% anions, 4.9% cations, and 45.6% other materials. Iron was the most abundant element, with an average concentration of 72.5 ± 10.4 μg/m³. The PM₁₀ sources characterized by PMF included rail, wheel, and brake wear (59.6%), soil combustion (17.0%), secondary aerosols (10.0%), electric cable wear (8.1%), and soil and road dust (5.4%). Internal sources comprising rail, wheel, brake, and electric cable wear made the greatest contribution to the PM₁₀ (67.7%) in tunnel air.

ImplicationsWith installation of a platform screen door, PM₁₀ levels in subway tunnels were higher than those on platforms. Tunnel PM₁₀ levels exceeded 150 μg/m³ of the Korean standard for subway platform. Elemental analysis of PM₁₀ in a tunnel showed that Fe was the most abundant element. Five PM₁₀ sources in tunnel were identified by positive matrix factorization. Railroad-related sources contributed 68% of PM₁₀ in the subway tunnel.     

Soil water and particle size distribution influence laboratory-generated PM10

Management of soils to reduce the amount of PM₁₀ emitted during agricultural tillage operations is important for attainment of air quality standards in California's San Joaquin Valley (SJV). The purpose of this study was to improve and expand upon earlier work of predicting tillage-generated dust emissions based on soil properties. We focus on gravimetric soil water content (GWC) and soil texture. A mechanical laboratory dust generator was used to test 23 soils collected for this study. Averaged results showed PM₁₀ concentrations (mg m^?3) increased logarithmically as GWC decreased below soil water potentials of ?1500 kPa. Soils with clay contents less than about 10% by weight began to emit PM₁₀ at GWCs 1.5–4 times their GWC at ?1500 kPa. Soils with clay contents greater than about 10% began to emit PM₁₀ at GWC values closer to ?1500 kPa. We found no correlation between maximum PM₁₀ concentrations, measured at low GWC values, and the %sand, %silt, or %clay in a soil. However, there was a significant correlation between the %silt to %clay ratio and PM₁₀ concentrations. This not only suggests the dependence of dust emission magnitudes on the supply of particles of PM₁₀ size, but also the importance of clay in stabilizing aggregates and maintaining higher amounts of capillary water at lower water potentials. Based on modeled results of pooled data, PM₁₀ concentrations increased linearly (slope = 564) for every unit increase in the %silt to %clay ratio. However, when soils were separated into groups based on clay content, the slopes for PM₁₀ concentrations vs. %silt to %clay ratio were texture dependent. The slope for soils with <10% clay (slope = 727) was 3.3 times greater than for soils with >20% clay (slope = 221). Improved PM₁₀ emission prediction based on soil properties should improve management decisions aimed at reducing tillage-generated PM₁₀.     

Quantifying the effect of urban tree planting on concentrations and depositions of PM10 in two UK conurbations

Trees are efficient scavengers of particulate matter and are characterised by higher rates of dry deposition than other land types. To estimate the potential of urban tree planting for the mitigation of urban PM₁₀ concentrations, an atmospheric transport model was used to simulate the transport and deposition of PM₁₀ across two UK conurbations (the West Midlands and Glasgow). Tree planting was simulated by modifying the land cover database, using GIS techniques and field surveys to estimate reasonable planting potentials. The model predicts that increasing total tree cover in West Midlands from 3.7% to 16.5% reduces average primary PM₁₀ concentrations by 10% from 2.3 to 2.1 μg m⁻³ removing 110 ton per year of primary PM₁₀ from the atmosphere. Increasing tree cover of the West Midlands to a theoretical maximum of 54% by planting all available green space would reduce the average PM₁₀ concentration by 26%, removing 200 ton of primary PM₁₀ per year. Similarly, for Glasgow, increasing tree cover from 3.6% to 8% reduces primary PM₁₀ concentrations by 2%, removing 4 ton of primary PM₁₀ per year. Increasing tree cover to 21% would reduce primary PM₁₀ air concentrations by 7%, removing 13 ton of primary PM₁₀ per year.     

High Intensity lonization Applied to Venturi Scrubbing

The relationship between a standard for the class of particulate matter smaller than 10 μm (PM₁₀) and thoracic particle sampling has been explored over a wide range of ambient aerosol distributions characteristic of both the European and U.S. situations. As a general trend, the PM₁₀ sampling performance does meet the proposed U.S. EPA compliance criterion of ±10 percent. The major parameters determining compliance are the 50 percent cutpoint of the PM₁₀ inlet and the mass median diameter of the coarse particle mode.

Also an assessment has been made of a possible relationship between high volume sampling based air quality data and those based upon thoracic particle sampling. For average European situations the following indicative relation does hold: HVS ≈ 1.3 × TP ± 30 percent, and HVS ≈ 1.25 × PM₁₀. For the more arid U.S. situations: HVS ≈ 1.7 × TP and HVS ≈ 1.65 × PM₁₀, in realistic agreement with the experimentally observed U.S. findings of HVS ≈ 2.0 × PM₁₀. Under extreme situations with high winds both the compliance PM₁₀-TP and the relation HVS-TP drastically deteriorate.     

冬季沈阳市典型源排放PM_(10)浓度分布模拟分析

选取沈阳市7个典型的大气污染源2006年12月~2007年2月的PM10排放浓度资料,利用CALPUFF对PM10浓度月平均分布做模拟分析。模拟结果分析表明:冬季月平均PM10浓度分布的范围与风场、地形有直接的关系。地势平坦、风速大时,污染物扩散范围大,污染物浓度小;地势不平、风速小时,污染物扩散范围小,污染物浓度大。1月份是沈阳市冬季月平均大气污染最严重的月份,污染物分布主要集中在市区的北部、东部和南部地区,东部地区大气污染最为严重。     

Particulate matter sources on an hourly timescale in a rural community during the winter

Particulate matter (PM) sources at four different monitoring sites in Alexandra, New Zealand, were investigated on an hourly timescale. Three of the sites were located on a horizontal transect, upwind, central, and downwind of the general katabatic flow pathway. The fourth monitoring site was located at the central site, but at a height of 26 m, using a knuckleboom, when wind conditions permitted. Average hourly PM₁₀ (PM with an aerodynamic diameter <10 μm) concentrations in Alexandra showed slightly different diurnal profiles depending on the sampling site location. Each location did, however, feature a large evening peak and smaller morning peak in PM₁₀ concentrations. The central site in Alexandra experienced the highest PM₁₀ concentrations as a result of PM transport along a number of katabatic flow pathways. A significant difference in PM₁₀ concentrations between the central and elevated sites indicated that a shallow inversion layer formed below the elevated site, limiting the vertical dispersion of pollutants. Four PM₁₀ sources were identified at each of the sites: biomass combustion, vehicles, crustal matter, and marine aerosol. Biomass combustion was identified as the most significant source of PM₁₀, contributing up to 91% of the measured PM₁₀. Plots of the average hourly source contributions to each site revealed that biomass combustion was responsible for both the evening and morning peaks in PM₁₀ concentrations observed at each of the sites, suggesting that Alexandra residents were relighting their fires when they rose in the morning. The identification of PM sources on an hourly timescale can have significant implications for air quality management.

Implications: Monitoring the sources of PM₁₀ on an hourly timescale at multiple sites within an airshed provides extremely useful information for air quality management. Sources responsible for observed peaks in measured diurnal PM₁₀ concentration profiles can be easily identified and targeted for reduction. Also, hourly PM₁₀ sampling can provide crucial information on the role meteorology plays in the development of elevated PM₁₀ concentrations.     

The impacts of different kinds of dust events on PM10 pollution in northern China

In this study the frequencies of PM₁₀ (as key urban pollutant) in 14 key environmental protection cities in northern China were analyzed. It follows that the PM₁₀ concentration in the high-frequency period is higher with an extent 0.009–0.066 mg m⁻³ than in the low-frequency period of 2001–2002. Further the impacts of three kinds of dust events on the PM₁₀ concentration in four cities (Beijing, Hohhot, Xi’an and Lanzhou) were explored. The results showed that different kinds of dust events have different influences on variation of PM₁₀ concentration in these four cities. In Lanzhou and Hohhot, which are near the source areas of dust events, the contribution degree of these three dust events to the PM₁₀ is: floating dust>dust storm>blowing dust. Whereas, in Beijing and Xi’an situated in dust event passing areas, the mean value of PM₁₀ concentration is higher in blowing dust than in floating dust (no dust storm). In addition, the influences of dust events on PM₁₀ concentration are different in the cities on different dust event paths. In Beijing and Hohhot (on the northern path), the high PM₁₀ concentration is usually caused by blowing dust. But in both Lanzhou and Xi’an (on the western/northwestern path) the high PM₁₀ pollution concentration is usually caused by floating dust.     

Low emission zones reduce PM10 mass concentrations and diesel soot in German cities

In many European cities mass concentrations of PM₁₀ (particles less than 10 μm in size) are still exceeding air quality standards as set by the European Commission in 1999. As a consequence, many cities introduced low emission zones (LEZs) to improve air quality and to meet the limit values. In Germany currently 48 LEZs are in operation. By means of dispersion modeling, PM₁₀ concentrations were estimated to decrease up to 10%. Analysis of PM₁₀ levels conducted for Cologne, Berlin, and Munich some time after the LEZs were introduced showed reduction of PM₁₀ mass concentration in the estimated range. The PM₁₀ particle fraction is, however, composed of particles with varying toxicity, of which diesel soot is highly health relevant. An evaluation of air quality data conducted in Berlin showed that in 2010 traffic-related soot concentrations measured along major roads decreased by 52% compared to 2007. Diesel particle emissions in Berlin were reduced in 2012 by 63% compared to a business-as-usual scenario (reference year 2007). A strong reduction of the traffic-related particle fraction of PM_2.5 was also reported for Munich. Therefore, it is likely that the effects of LEZs are considerably more significant to human health than was anticipated when only considering the reduction of PM₁₀ mass concentrations.

Implications: The implementation of low emission zones in German cities might result in a reduction of PM₁₀ levels concentrations by up to 10%. However, it is difficult to show a reduction of PM₁₀ annual averages in this order of magnitude as meteorology has a large impact on the year-to-year variation of PM mass concentrations. Monitoring of other PM metrics such as black smoke (BS) or elemental carbon (EC) might be a better strategy for evaluating LEZs effects. The benefit of low emission zones on human health is far greater than is presently visible from routine measurements of PM₁₀.     

PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia, Spain

Levels of total suspended particles, PM10, PM2.5 and PM1 were continuously monitored at an urban kerbside in the Metropolitan area of Barcelona from June 1999 to June 2000. The results show that hourly levels of PM2.5 and PM1 are consistent with the daily cycle of gaseous pollutants emitted by traffic, whereas TSP and PM10 do not follow the same trend, at least in the diurnal period. The PM2.5/PM10 ratio is dependent on the traffic emissions, whereas additional contribution sources for the >10 μm fraction must be taken into account in the diurnal period. Different PM10 and PM2.5 source apportionment techniques were compared. A methodology based on the chemical determination of 83% of both PM10 and PM2.5 masses allowed us to quantify the marine (4% in PM10 and <1% in PM2.5), crustal (26% in PM10 and 8% in PM2.5) and anthropogenic (54% in PM10 and 73% in PM2.5) loads. Peaks of crustal contribution to PM10 (up to 44% of the PM10 mass) were recorded under Saharan air mass intrusions. A different seasonal trend was observed for levels of sulphate and nitrate, probably as a consequence of the different thermodynamic behaviour of these PM species and the higher summer oxidation rate of SO₂.     

Relationships between partition coefficient,uptake rate constant,clearance rate constant and time to equilibrium for bioaccumulation

Theoretical derivations together with published experimental data on bioaccumulation of lipophilic compounds by certain groups of fish indicates that the uptake and clearance rate constants have a fixed relationship to the octanol to water partition coefficient over the partition coefficient range 10^2.5 to 10⁶. This allows the calculation of times to establish effective equilibrium, and significant bioaccumulation of compounds in relation to the partition coefficient. By extrapolation superlipophilic compounds (partition coefficients > 10⁶) have been shown to require a minimum period of 0.5 years increasing to 10 years when P = 10⁸ to establish effective equilibrium and compounds with partition coefficients >10¹³ are not bioaccumulated to any significant extent. In practice then, a direct relationship between the bioaccumulation factor and the partition coefficient will not result with superlipophilic componds.     

Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan,China

Particulate matter with aerodynamic diameter below 10 μm (PM₁₀) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM₁₀ concentrations. It was evaluated by experiments using a 10-year data set of daily PM₁₀ concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM₁₀ concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM₁₀ concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM₁₀ concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM₁₀ concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM₁₀ forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction.

Implications: Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM₁₀ time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM₁₀ time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM₁₀ time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM₁₀ forecasting field.     

Hierarchical space-time modelling of PM10 pollution

In this paper, we propose a hierarchical spatio-temporal model for daily mean concentrations of PM₁₀ pollution. The main aims of the proposed model are the identification of the sources of variability characterising the PM₁₀ process and the estimation of pollution levels at unmonitored spatial locations. We adopt a fully Bayesian approach, using Monte Carlo Markov Chain algorithms. We apply the model on PM₁₀ data measured at 11 monitoring sites located in the major towns and cities of Italy's Emilia-Romagna Region. The model is designed for areas with PM₁₀ measurements available; the case of PM₁₀ level estimation from emissions data is not handled. The model has been carefully checked using Bayesian p-values and graphical posterior predictive checks. Results show that the temporal random effect is the most important when explaining PM₁₀ levels.     

Biomagnetic monitoring of industry-derived particulate pollution

Clear association exists between ambient PM₁₀ concentrations and adverse health outcomes. However, determination of the strength of associations between exposure and illness is limited by low spatial-resolution of particulate concentration measurements. Conventional fixed monitoring stations provide high temporal-resolution data, but cannot capture fine-scale spatial variations. Here we examine the utility of biomagnetic monitoring for spatial mapping of PM₁₀ concentrations around a major industrial site. We combine leaf magnetic measurements with co-located PM₁₀ measurements to achieve inter-calibration. Comparison of the leaf-calculated and measured PM₁₀ concentrations with PM₁₀ predictions from a widely-used atmospheric dispersion model indicates that modelling of stack emissions alone substantially under-predicts ambient PM₁₀ concentrations in parts of the study area. Some of this discrepancy might be attributable to fugitive emissions from the industrial site. The composition of the magnetic particulates from vehicle and industry-derived sources differ, indicating the potential of magnetic techniques for source attribution.     

Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain

The analysis of PM10 and TSP levels recorded in rural areas from Southern and Eastern Spain (1996–1999) shows that most of the PM10 and TSP peak events are simultaneously recorded at monitoring stations up to 1000 km apart. The study of the atmospheric dynamics by back-trajectory analysis and simulations with the SKIRON Forecast System show that these high PM10 and TSP events occur when high-dust Saharan air masses are transported over the Iberian Peninsula. In the January–June period, this dust transport is mainly caused by cyclonic activity over the West or South of Portugal, whereas in the summer period this is induced by anticyclonic activity over the East or Southeast Iberian Peninsula. Most of the Saharan intrusions which exert a major influence on the particulate levels occur from May to September (63%) and in January and October. In rural areas in Northeast Spain, where the PM10 annual mean is around 18 μg PM10 m⁻³, the Saharan dust accounts for 4–7 annual daily exceedances of the forthcoming PM10-EU limit value (50 μg PM10 m⁻³ daily mean). Higher PM10 background levels are recorded in Southern Spain (30 μg PM10 m⁻³ as annual mean for rural areas) and very similar values are recorded in industrial and urban areas. In rural areas in Southern Spain, the Saharan dust events accounts for 10–23 annual daily exceedances of the PM10 limit value, a high number when compared with the forthcoming EU standard, which states that the limit value cannot be exceeded more than 7 days per year. The proportion of Sahara-induced exceedances with respect to the total annual exceedances is discussed for rural, urban and industrial sites in Southern Spain.     

An empirical approach for the prediction of daily mean PM10 concentrations

An empirical model has been devised to predict concentrations of PM₁₀ at background and roadside locations in London. Factors to calculate primary PM₁₀ and PM_2.5 concentrations are derived from annual mean NO_X, PM_2.5 and PM₁₀ measurements across London and south east England. These factors are used to calculate daily means for the primary and non-primary PM₁₀ fractions for the London area. The model accurately predicts daily mean PM₁₀ and EU Directive Limit values across a range of sites from kerbside to rural. Predictions of future PM₁₀ can be made using the expected reductions in secondary PM₁₀ and site specific annual mean NO_X predicted from emission inventories and dispersion modelling. The model suggests that the EU Directive Limit values will be exceeded close to many of London's busiest roads, and perhaps at central background sites should there be a repeat of 1996 meteorological conditions during 2005. A repeat of 1997 meteorology conditions during 2005 would lead to the EU Limit Value being exceeded alongside the busiest central London roads only. The model is applicable for London and south east England but the methodology could be applied elsewhere at a city or regional level. The model relies on the currently observed ratio between NO_X and PM₁₀. This ratio has remained constant over the last 4 years but might change in the future. The NO_X:PM₁₀ ratio derived from measurements and used in this model, implies that emission inventories might over estimate primary PM₁₀ by more than 50%.     

Application of positive matrix factorization in characterization of PM(10) and PM(2.5) emission sources at urban roadside

The 24-h average coarse (PM₁₀) and fine (PM_2.5) fraction of airborne particulate matter (PM) samples were collected for winter, summer and monsoon seasons during November 2008-April 2009 at an busy roadside in Chennai city, India. Results showed that the 24-h average ambient PM₁₀ and PM_2.5 concentrations were significantly higher in winter and monsoon seasons than in summer season. The 24-h average PM₁₀ concentration of weekdays was significantly higher (12-30%) than weekends of winter and monsoon seasons. On weekends, the PM_2.5 concentration was found to slightly higher (4-15%) in monsoon and summer seasons. The chemical composition of PM₁₀ and PM_2.5 masses showed a high concentration in winter followed by monsoon and summer seasons.The U.S.EPA-PMF (positive matrix factorization) version 3 was applied to identify the source contribution of ambient PM₁₀ and PM_2.5 concentrations at the study area. Results indicated that marine aerosol (40.4% in PM₁₀ and 21.5% in PM_2.5) and secondary PM (22.9% in PM₁₀ and 42.1% in PM_2.5) were found to be the major source contributors at the study site followed by the motor vehicles (16% in PM₁₀ and 6% in PM_2.5), biomass burning (0.7% in PM₁₀ and 14% in PM_2.5), tire and brake wear (4.1% in PM₁₀ and 5.4% in PM_2.5), soil (3.4% in PM₁₀ and 4.3% in PM_2.5) and other sources (12.7% in PM₁₀ and 6.8% in PM_2.5).     

Evaluation of PM10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City,China

Primary fine particulate matters with a diameter of less than 10 µm (PM₁₀) are important air emissions causing human health damage. PM₁₀ concentration forecast is important and necessary to perform in order to assess the impact of air on the health of living beings. To better understand the PM₁₀ pollution health risk in Taiyuan City, China, this paper forecasted the temporal and spatial distribution of PM₁₀ yearly average concentration, using Back Propagation Artificial Neural Network (BPANN) model with various air quality parameters. The predicted results of the models were consistent with the observations with a correlation coefficient of 0.72. The PM₁₀ yearly average concentrations combined with the population data from 2002 to 2008 were given into the Intake Fraction (IF) model to calculate the IFs, which are defined as the integrated incremental intake of a pollutant released from a source category or a region over all exposed individuals. The results in this study are only for main stationary sources of the research area, and the traffic sources have not been included. The computed IFs results are therefore under-estimations. The IFs of PM₁₀ from Taiyuan with a mean of 8.5 per million were relatively high compared with other IFs of the United States, Northern Europe and other cities in China. The results of this study indicate that the artificial neural network is an effective method for PM₁₀ pollution modeling, and the Intake Fraction model provides a rapid population risk estimate for pollutant emission reduction strategies and policies.

Implications The PM₁₀ (particulate matter with an aerodynamic diameter ≤10 μm) yearly average concentration of Taiyuan, with a mean of 0.176 mg/m³, was higher than the 65 μg/m³ recommended by the U.S. Environmental Protection Agency (EPA). The spatial distribution of PM₁₀ yearly average concentrations showed that wind direction and wind speed played an important role, whereas temperature and humidity had a lower effect than expected. Intake fraction estimates of Taiyuan were relatively high compared with those observed in other cities. Population density was the major factor influencing PM₁₀ spatial distribution. The results indicated that the artificial neural network was an effective method for PM₁₀ pollution modeling.