Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), nitrogen monoxide (NO), ozone (O₃), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM₁₀) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R ² of more than 0.82 between modeled and observed hourly pollutant levels for CO, NO_x, NO₂, NO, and PM₁₀. However, predicted O₃ levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.     

Variation of surface ozone in Campo Grande,Brazil: meteorological effect analysis and prediction

The effect of meteorological variables on surface ozone (O₃) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O₃ concentration, relative humidity and solar radiation. The threshold model that considers two O₃ regimes was the one that correctly describes the effect of important meteorological variables in O₃ behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O₃ regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O₃ concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O₃ behaviour, being useful to define policy strategies for human health protection regarding air pollution.     

Assessment of indoor air in Austrian apartments with and without visible mold growth

Fungal spores are transported across great distances in the outdoor air and are also regularly found indoors. Building conditions and behavior-related problems in apartments may lead to massive growth of mold within a very short period of time.The aim of this study was to evaluate whether the visible growth of mold indoors influences the concentration of fungal spores in the air as well as the variety of their species. Samples were collected from 66 households in Austria. For each sampling, the corresponding outdoor air was measured as reference value. The size of the visible mold growth was categorized in order to correlate the extent of mold growth with the concentration of airborne spores as well as the fungal genera. In order to determine fungal spore concentrations in the air, the one-stage MAS-100^® air sampler was used. Malt extract agar (MEA) and dichloran glycerol agar (DG18) plates were used as culture media. The total colony forming units (CFU) per m³ were determined. The fungi were identified from the isolated colonies.The results show that in apartments visibly affected by mold, the median values were significantly higher than those of apartments without visible mold growth. The extent of visible mold growth is significantly correlated with both concentration of fungal spores (p<0.001) as well as the predominance of Penicillium sp. and Aspergillus sp. (p<0.001) in indoor air. The total fungal concentration of Penicillium and Aspergillus in the air of apartments is recommended for assessing fungal exposure.     

Impacts of Meteorological Factors on PM10: Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) Approaches

In this study, prediction capacities of multi-linear regression (MLR) and artificial neural networks (ANN) onto coarse particulate matter (PM₁₀) concentrations were investigated. Different meteorological factors on particulate pollution were chosen for operating variables in the model analyses. Two different regions (urban and industrial) were identified in the region of Kocaeli, Turkey. All data sets were obtained from air quality monitoring network of the Ministry of Environment and Urban Planning, and 120 data sets were used in the MLR and ANN models. Regression equations explained the effects of the meteorological factors in MLR analyses. In the ANN model, backpropagation network with two hidden layers has achieved the best prediction efficiency. Determination coefficients and error values were examined for each model. ANN models displayed more accurate results compared to MLR.     

Artificial neural network for the identification of unknown air pollution sources

Artificial neural networks (ANN), whose performances to deal with pattern recognition problems is well known, are proposed to identify air pollution sources. The problem that is addressed is the apportionment of a small number of sources from a data set of ambient concentrations of a given pollutant. Three layers feed-forward ANN trained with a back-propagation algorithm are selected. A test case is built, based on a Gaussian dispersion model. A subset of hourly meteorological conditions and measured concentrations constitute the input patterns to the network that is wired to recover relevant emission parameters of unknown sources as outputs. The rest of the model data are corrupted adding noise to some meteorological parameters and we test the effectiveness of the method to recover the correct answer. The ANN is applied to a realistic case where 24 h SO₂ concentrations were previously measured. Some of the limitations of the ANN approach, together with its capabilities, are discussed in this paper.     

Source strength of fungal spore aerosolization from moldy building material

The release of Aspergillus versicolor, Cladosporium cladosporioides, and Penicillium melinii spores from agar and ceiling tile surfaces was tested under different controlled environmental conditions using a newly designed and constructed aerosolization chamber. This study revealed that all the investigated parameters, such as fungal species, air velocity above the surface, texture of the surface, and vibration of contaminated material, affected the fungal spore release. It was found that typical indoor air currents can release up to 200 spores cm⁻² from surfaces with fungal spores during 30-min experiments. The release of fungal spores from smooth agar surfaces was found to be inadequate for accurately predicting the emission from rough ceiling tile surfaces because the air turbulence increases the spore release from a rough surface. A vibration at a frequency of 1 Hz at a power level of 14 W resulted in a significant increase in the spore release rate. The release appears to depend on the morphology of the fungal colonies grown on ceiling tile surfaces including the thickness of conidiophores, the length of spore chains, and the shape of spores. The spores were found to be released continuously during each 30-min experiment. However, the release rate was usually highest during the first few minutes of exposure to air currents and mechanical vibration. About 71–88% of the spores released during a 30-min interval became airborne during the first 10 min.     

Influence of growth form on the accumulation of airborne copper by lichens

Lichens are known to accumulate airborne elements. This characteristic makes them useful as biomonitors of air quality. However, direct correlations of element concentrations in the air with element concentrations in lichen thalli are generally unavailable. The purpose of this study was to quantitatively examine the relationship between concentrations of copper in ambient air samples and thalli of foliose and fruticose lichens. Lichen samples from four sites along an air copper gradient were collected and analyzed. Foliose specimens consistently accumulated more than twice as much copper as fruticose specimens at all four sites. The relationship between copper concentrations in foliose and fruticose lichens and ambient air samples along an airborne copper gradient was examined using a stepwise regression model. An R² value of (0.84) and an F-statistic of (182.5, p<0.001) for the model indicate that variability in lichen copper concentrations between sites is explained by airborne copper concentrations. Based on the regression analysis both foliose and fruticose growth forms appear to accurately predict airborne copper concentrations. A significant interaction between the airborne copper and growth form variables and large differences in the calculated slopes suggests that foliose lichens more efficiently accumulate airborne copper than fruticose lichens.     

Airborne fungi in low and high allergic prevalence child care centers

Fungi exposure has been linked to asthma and allergies among children. To determine the association between fungal exposure and wheeze and rhinitis symptoms, we examined concentrations of culturable indoor and outdoor fungi of various aerodynamic sizes in low and high allergic prevalence child care centers (CCCs) in Singapore. Environmental parameters were also performed for air temperature, relative humidity and ventilation rates, while information on CCC characteristics was collected via an inspection. Most commonly recovered fungi were Penicillium, Aspergillus, Geotrichum, Cladosporium and sterile mycelia with Geotrichum and sterile mycelia amounting to an average of 71.5% of the total airborne culturable fungi studied. Indoor and outdoor total culturable fungi concentrations and those in the size range of 1.1–3.3 μm were significantly higher in high allergic prevalence CCCs. When fungal types/genera were compared, indoor and outdoor Geotrichum and sterile mycelia of aerodynamic sizes 1.1–3.3 μm were found to be significantly elevated in high allergic prevalence CCCs. Indeed, average geometric mean diameters (D_{g, ave}) of indoor and outdoor culturable fungi were consistently smaller in CCCs with high prevalence of allergies than those with low prevalence. We found significant associations of higher fungal concentrations, especially those with smaller aerodynamic sizes in CCCs situated near parks. There were no differences in fungal levels between CCCs with respect to their dampness profile mainly due to high CCC ventilation rates. Since particle size is a factor that determines where a fungi particle deposits in the respiratory tract, this study provides useful information in the etiology of wheeze and rhinitis symptoms among the CCC attending children.     

Evaluation of the CAR-FMI model against measurements near a major road

A field measurement campaign was conducted near a major road in southern Finland from September 15 to October 30, 1995. The concentrations of NO, NO₂ and O₃ were measured simultaneously at three locations, at three heights (3.5, 6 and 10 m) on both sides of the road. Traffic densities and relevant meteorological parameters were also measured on-site. We have compared measured concentration data with the predictions of the road network dispersion model CAR-FMI, used in combination with a meteorological pre-processing model MPP-FMI. In comparison with corresponding results presented previously in the literature, the agreement of measured and predicted datasets was good, as measured using various statistical parameters. For all data (N=587), the index of agreement (IA) was 0.83, 0.82 and 0.89 for the measurements of NO_x, NO₂ and O₃, respectively. The IA is a statistical measure of the correlation of the predicted and measured time series of concentrations. However, the modelling system overpredicts NO_x concentrations with a fractional bias FB=+13%, and O₃ concentrations with FB=+8%, while for NO₂ concentrations FB=−2%. We also analyzed the difference between model predictions and measured data in terms of meteorological parameters. Model performance clearly deteriorated as the wind direction approached a direction parallel to the road, and for the lowest wind speeds. The range of variability concerning atmospheric stability, ambient temperature and the amount of solar radiation was modest during the measurement campaign. As expected, no clear dependencies of model performance were therefore detected in terms of these parameters. The experimental dataset is available for the evaluation of other roadside dispersion models.     

Assessing the genotoxicity of urban air pollutants using two in situ plant bioassays

Genotoxicity of urban air has been analysed almost exclusively in airborne particulates. We monitored the genotoxic effects of airborne pollutants in the urban air of Perugia (Central Italy). Two plant bioindicators with different genetic endpoints were used: micronuclei in meiotic pollen mother cells using Tradescantia-micronucleus bioassay (Trad-MCN) and DNA damage in nuclei of Nicotiana tabacum leaves using comet assay (Nicotiana-comet). Buds of Tradescantia clone # 4430 and young N. tabacum cv. Xanthi plants were exposed for 24 h at three sites with different pollution levels. One control site (indoor control) was also used. The two bioassays showed different sensitivities toward urban pollutants: Trad-MCN assay was the most sensitive, but DNA damage in N. tabacum showed a better correlation with the pollutant concentrations. In situ biomonitoring of airborne genotoxins using higher plants combined with chemical analysis is thus recommended for characterizing genotoxicity of urban air.     

Factors Influencing the Variability of SO2 Concentrations in Istanbul

ABSTRACT

The correlation between sulfur dioxide (SO₂) concentrations measured at the European and Asian sides of Istanbul and meteorological parameters is investigated using principal component analysis (PCA) and multiple regression analysis techniques. Several meteorological parameters are selected to represent the atmospheric conditions during two winter periods: 1993–1994 and 1994–1995. Six principal components are found to explain the majority of the observed meteorological variability. Surface pressure, 850-mb temperature, and surface zonal (east-west) and meridional (north-south) winds show high loadings on separate factors identified by PCA. We seek dominant meteorological parameters that control the SO₂ levels at each monitoring station. Several multiple regression analysis models are fitted to the data from each monitoring station using six principal components and previous day SO₂ concentrations as independent variables.

Results suggest that the most important parameters, highly correlated with SO₂ concentrations in the Istanbul metropolitan area, are atmospheric pressure and surface zonal and meridional winds. These components have more influence on the determination of the air pollution levels at the Asian side than at the European side.     

Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification

In recent years, the application of titanium dioxide (TiO₂) as a photocatalyst in asphalt pavement has received considerable attention for purifying ambient air from traffic-emitted pollutants via photocatalytic processes. In order to control the increasing deterioration of ambient air quality, urgent and proper risk assessment tools are deemed necessary. However, in practice, monitoring all process parameters for various operating conditions is difficult due to the complex and non-linear nature of air pollution-based problems. Therefore, the development of models to predict air pollutant concentrations is very useful because it can provide early warnings to the population and also reduce the number of measuring sites. This study used artificial neural network (ANN) and neuro-fuzzy (NF) models to predict NO_x concentration in the air as a function of traffic count (T_r) and climatic conditions including humidity (H), temperature (T), solar radiation (S), and wind speed (W) before and after the application of TiO₂ on the pavement surface. These models are useful for modeling because of their ability to be trained using historical data and because of their capability for modeling highly non-linear relationships. To build these models, data were collected from a field study where an aqueous nano TiO₂ solution was sprayed on a 0.2-mile of asphalt pavement in Baton Rouge, LA. Results of this study showed that the NF model provided a better fitting to NO_x measurements than the ANN model in the training, validation, and test steps. Results of a parametric study indicated that traffic level, relative humidity, and solar radiation had the most influence on photocatalytic efficiency.     

Annual and seasonal variability of ambient aerosols over an urban region in western India

Annual and seasonal variabilities in source contribution to total suspended particles (TSP) measured over an urban location in western India, Ahmedabad between May 2000 and January 2003 are examined in this study. Positive matrix factorization (PMF) resolved six factors including airborne regional dust, calcium carbonate rich dust, biomass burning/vehicular emissions, secondary nitrate/sulfate, marine aerosol, and smelter. In this study, non-parametric statistical tests including the Kruskal–Wallis analysis of variance (K–W ANOVA) and Spearman rank correlation (ρ) test were used to assess the annual and seasonal variations in factor contributions, and the influence of meteorology on these contributions, respectively. None of the factor contributions exhibited annual variations except airborne regional dust, and biomass burning/vehicular emissions factors. All of the factors exhibited seasonal variations. Several factor monsoon (July–September) median concentrations were significantly different from one or more of the other season medians. In general, it appeared that meteorological factors played a role in establishing the seasonal behavior of factor contributions. Factor contributions exhibited low to moderate correlations with meteorological parameters such as temperature, relative humidity, wind direction, and wind speed. Amongst all of the relationships, marine aerosol factor was reasonably well correlated with relative humidity (ρ = 0.73) and wind direction (ρ = 0.73) during the pre-monsoon season (March–May). This observation suggests that the aerosol transported by moisture laden winds from the Arabian sea contribute to this factor. The airborne regional dust factor was also moderately correlated with wind speed (ρ = 0.70) during the post-monsoon season. This relationship indicates that high regional dust concentrations are favored by high wind speeds and the resultant increase in dispersion.     

Fungi and bacteria in mould-damaged and non-damaged office environments in a subarctic climate

The fungi and bacterial levels of the indoor air environments of 77 office buildings were measured in winter and a comparison was made between the buildings with microbe sources in their structures and those without such sources. Penicillium, yeasts, Cladosporium and non-sporing isolates were the commonest fungi detected in the indoor air and in settled dust, in both the mould-damaged and control buildings. Aspergillus ochraceus, Aspergillus glaucus and Stachybotrys chartarium were found only in environmental samples from the mould-damaged buildings. Some other fungi, with growth requiring of water activity, a_w, above 0.85, occurred in both the reference and mould-damaged buildings, but such fungi were commoner in the latter type of buildings. The airborne concentrations of Penicillium, Aspergillus versicolor and yeasts were the best indicators of mould damage in the buildings studied. Penicillium species and A. versicolor were also the most abundant fungi in the material samples. This study showed that the fungi concentrations were very low (2–45 cfu m⁻³ 90% of the concentrations being <15 cfu m⁻³) in the indoor air of the normal office buildings. Although the concentration range of airborne fungi was wider for the mould-damaged buildings (2–2470 cfu m⁻³), only about 20% of the samples exceeded 100 cfu m⁻³. The concentrations of airborne bacteria ranged from 12 to 540 cfu m⁻³ in the control buildings and from 14 to 1550 cfu m⁻³ in the mould-damaged buildings. A statistical analysis of the results indicated that bacteria levels are generally <600 cfu m⁻³ in office buildings in winter and fungi levels are <50 cfu m⁻³. These normal levels are applicable to subarctic climates for urban, modern office buildings when measurements are made using a six-stage impactor. These levels should not be used in evaluations of health risks, but elevated levels may indicate the presence of abnormal microbe sources in indoor air and a need for additional environmental investigations.     

Effect of air-conditioner on fungal contamination

Air-conditioners (AC) produce much dew and wet conditions inside their apparatus, when in operation. We studied the fungal contamination in AC and found that the average fungal contamination of AC filters was about 5-fold greater than that of a carpet, and Cladosporium and Penicillium were predominant in AC filters. The fungal contamination inside AC, which were used everyday, increased more markedly than those not used daily, e.g. a few days per week or rarely. Moreover, the airborne fungal contamination in rooms during air-conditioning was about 2-fold greater than one in rooms without AC, and was highest when air-conditioning started and decreased gradually with time. We recognized that the airborne fungal contamination was controlled by the environmental condition of the rooms, in which AC were used. It is suggested that AC might promote mold allergies in users via airborne fungal spores derived from the AC. On the other hand, AC was estimated to remove moisture in the room atmosphere and carpets, and reduce the relative humidity in rooms. It was found that the average fungal contamination in the house dust of carpets with AC was suppressed by two-third of that in rooms without AC. The use of AC for suppressing fungal hazards was discussed.     

An application of parametric and nonparametric models to the assessment of fluoride levels in vegetation exposed to stack emissions of an aluminum reduction plant in Greece

Various statistical models were developed for assessing airborne fluoride (F) levels in natural vegetation near an aluminum reduction plant using as predictor variables the distance from the emission source, the predominating wind, and characteristic topography-geomorphology parameters. Results revealed that F concentrations in vegetation showed a predictable response to both wind conditions and landscape features. The linear model was found to give good estimations, taking advantage of the relatively strong linear correlation between concentration and distance. A nonlinear relationship between the F concentration in vegetation and the other variables was also found, while interactions between the variables were found to be non-first-order. The nonlinear relationship hypothesis was supported by the improved results of various nonlinear models that also indicated the importance of the area's topography-geomorphology and meteorology in model predictions. The application of an artificial neural network (ANN) model showed the closest agreement between predicted and observed values with a correlation coefficient of 0.92. The improved reliability of the ANN and a regression tree model (RTM) also were indicated by the normal distribution of their residuals. The RTM and the ANN were further investigated and found to be capable of identifying the importance of the variables in vegetation exposure to air emissions.     

Field controlled experiments on the physiological responses of maize (Zea mays L.) leaves to low-level air and soil mercury exposures

Thousands of tons of mercury (Hg) are released from anthropogenic and natural sources to the atmosphere in a gaseous elemental form per year, yet little is known regarding the influence of airborne Hg on the physiological activities of plant leaves. In the present study, the effects of low-level air and soil Hg exposures on the gas exchange parameters of maize (Zea mays L.) leaves and their accumulation of Hg, proline, and malondialdehyde (MDA) were examined via field open-top chamber and Hg-enriched soil experiments, respectively. Low-level air Hg exposures (<50 ng m^?3) had little effects on the gas exchange parameters of maize leaves during most of the daytime (p?>?0.05). However, both the net photosynthesis rate and carboxylation efficiency of maize leaves exposed to 50 ng m^?3 air Hg were significantly lower than those exposed to 2 ng m^?3 air Hg in late morning (p?<?0.05). Additionally, the Hg, proline, and MDA concentrations in maize leaves exposed to 20 and 50 ng m^?3 air Hg were significantly higher than those exposed to 2 ng m^?3 air Hg (p?<?0.05). These results indicated that the increase in airborne Hg potentially damaged functional photosynthetic apparatus in plant leaves, inducing free proline accumulation and membrane lipid peroxidation. Due to minor translocation of soil Hg to the leaves, low-level soil Hg exposures (<1,000 ng g^?1) had no significant influences on the gas exchange parameters, or the Hg, proline, and MDA concentrations in maize leaves (p?>?0.05). Compared to soil Hg, airborne Hg easily caused physiological stress to plant leaves. The effects of increasing atmospheric Hg concentration on plant physiology should be of concern.     

Temporal variability of mercury speciation in urban air

Semi-continuous measurements of ambient mercury (Hg) species were performed in Detroit, MI, USA for the calendar year 2003. The mean (±standard deviation) concentrations for gaseous elemental mercury (GEM), particulate mercury (HgP), and reactive gaseous mercury (RGM) were 2.2±1.3 ng m⁻³, 20.8±30.0, and 17.7±28.9 pg m⁻³, respectively. A clear seasonality in Hg speciation was observed with GEM and RGM concentrations significantly (p<0.001) greater in warm seasons, while HgP concentrations were greater in cold seasons. The three measured Hg species also exhibited clear diurnal trends which were particularly evident during the summer months. Higher RGM concentrations were observed during the day than at night. Hourly HgP and GEM concentrations exhibited a similar diurnal pattern with both being inversely correlated with RGM. Multivariate analysis coupled with conditional probability function analysis revealed the conditions associated with high Hg concentration episodes, and identified the inter-correlations between speciated Hg concentrations, three common urban air pollutants (sulfur dioxide, ozone, and nitric oxides), and meteorological parameters. This analysis suggests that both local and regional sources were major factors contributing to the observed temporal variations in Hg speciation. Boundary layer dynamics and the seasonal meteorological conditions, including temperature and moisture content, were also important factors affecting Hg variability.     

Spatiotemporal Features of Severe Air Pollution in Northern Taiwan (8 pp)

Background, Aims and Scope This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau. Methods Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events. Results and Discussion Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan. Conclusion According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms-1, 360 m and 800 m2s-1, respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed. Recommendation and Outlook By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.     

Determinants of airborne benzene concentrations in rural areas of western Canada

This study estimated the level and determinants of airborne benzene concentrations in rural western Canada. A multi-site, multi-month unbalanced two-factorial design was used to collect air samples at 1206 fixed sites across a geographic area associated with primary oil and gas industry in Canadian provinces of Alberta, north-eastern British Columbia, and central and southern Saskatchewan from April 2001 to December 2002. Benzene concentrations integrated over 1 calendar month were determined using passive organic vapour monitors. Linear mixed effects models were applied to identify the determinants of airborne benzene concentrations, in particular the proximity to oil and gas facilities. The observed geometric mean of benzene concentrations was 158 ng m⁻³, with large geometric standard deviation: 4.9. Benzene concentrations showed a seasonal variation with maxima in winter and minima in summer. Emissions from oil well (within 2 km) and compressor influenced monthly airborne benzene concentrations. However, in our study, being located in the general area of a gas plant seems to be the most important in determining monthly airborne benzene concentrations. These findings support the need for investigation of the impact of oil and gas industry on quality of rural air.