In vitro tests using human adenocarcinomic alveolar epithelial cell line A549 and small mouse monocyte-macrophage cell line J774A.1 were conducted to test toxicity of six PM (particulate matter) samples from Beijing. The properties of the samples differ significantly. The production of inflammatory cytokine (TNF-α for J774A.1) and chemokine (IL-8 for A549) and the level of intracellular reactive oxygen species (ROS) were used as endpoints. There was a positive correlation between water soluble organic carbon and DTT-based redox activity. Both cell types produced increased levels of inflammatory mediators and had higher level of intracelllar ROS, indicating the presence of PM-induced inflammatory response and oxidative stress, which were dose-dependent and significantly different among the samples. The releases of IL-8 from A549 and TNF-α from J774A.1 were significantly correlated to PM size, Zeta potential, endotoxin, major metals, and polycyclic aromatic hydrocarbons. No correlation between ROS and these properties was identified.

After particulate matter (PM) collection in Cotonou (Benin), a complete physicochemical characterization of PM2.5 and PM>2.5 was led. Then, their adverse health effects were evaluated by using in vitro culture of human lung cells. BEAS-2B (bronchial epithelial cells) were intoxicated during short-term exposure at increasing PM concentrations (1.5–96 μg/cm2) to determine global cytotoxicity. Hence, cells were exposed to 3 and 12 μg/cm2 to investigate the potential biological imbalance generated by PM toxicity. Our findings showed the ability of both PM to induce oxidative stress and to cause inflammatory cytokines/chemokines gene expression and secretion. Furthermore, PM were able to induce gene expression of enzymes involved in the xenobiotic metabolism pathway. Strong correlations between gene expression of metabolizing enzymes, proinflammatory responses and cell cycle alteration were found, as well as between proinflammatory responses and cell viability. Stress oxidant parameters were highly correlated with expression and protein secretion of inflammatory mediators.

Few prior cohort studies exist in developing countries examining the association of ambient particulate matter (PM) with mortality. We examined the association of particulate air pollution with mortality in a prospective cohort study of 71,431 middle-aged Chinese men. Baseline data were obtained during 1990–1991. The follow-up evaluation was completed in January, 2006. Annual average PM exposure between 1990 and 2005, including TSP and PM10, were estimated by linking fixed-site monitoring data with residential communities. We found significant associations between PM10 and mortality from cardiopulmonary diseases; each 10 μg/m3 PM10 was associated with a 1.6% (95%CI: 0.7%, 2.6%), 1.8% (95%CI: 0.8%, 2.9%) and 1.7% (95%CI: 0.3%, 3.2%) increased risk of total, cardiovascular and respiratory mortality, respectively. For TSP, we observed significant associations only for cardiovascular morality. These data contribute to the scientific literature on long-term effects of particulate air pollution for high exposure settings typical in developing countries.

This study evaluated whether short term exposures to NO2, O3, particulate matter <10 mm in diameter (PM10) were associated with higher risk of mortality. A total of 223,287 hypertensive patients attended public health-care services and newly prescribed at least 1 antihypertensive agent were followed-up for up to 5 years. A time-stratified, bi-directional case-crossover design was adopted. For all-cause mortality, significant positive associations were observed for NO2 and PM10 at lag 0–3 days per 10 μg/m3 increase in concentration (excess risks 1.187%–2.501%). Significant positive associations were found for O3 at lag 1 and 2 days and the excess risks were 1.654% and 1.207%, respectively. We found similarly positive associations between these pollutants and respiratory disease mortality. These results were significant among those aged ≥65 years and in cold seasons only. Older hypertensive patients are susceptible to all-cause and respiratory disease-specific deaths from these air pollutants in cold weather.

Zebrafish embryotoxicity and dioxin-like activity levels were tested for particulate air samples from an urban background site in Barcelona (Spain). Samples were collected during 14 months, and maximal values for both biological activities corresponded to samples collected during late autumn months, correlating with elevated PAH levels. Vehicle and combustion emissions appeared as the potentially most toxic sources, whereas total PM mass and mineral content appeared to be poor predictors of the biological activity of the samples. Samples simultaneously collected at different particle size cut-offs (10, 2.5, and 1 μm) did not differ significantly in dioxin-like PAH levels and biological activity, indicating that the sub-micron particle fraction (PM1) concentrated essentially all observed toxicity. Our results support the need for a tighter control on sub-micron particle emissions and show that total PM mass and, particularly, PM10, may not fully characterize the toxic potential of air samples.

Source apportionment study of aerosols over Bay of Bengal (BOB) were investigated during Integrated Campaign on Aerosol Radiation Budget (ICARB) in the pre–monsoon (March–April 2006) and winter (December–January 2008–09) seasons. Positive matrix factorization (PMF) was applied to identify sources of ambient particulate matter using daily chemical composition data collected in the pre–monsoon (total suspended particles, TSP) and winter season (particles with a diameter < 10μm, PM10). Sea salt (SS), secondary aerosol (SA), Si–dust, fossil fuel combustion (FFC), biomass burning (BB) sources have been identified in both seasons, however their relative contributions were different. The combined contribution of Si–dust, secondary aerosol and fossil fuel combustion, constitute ~67% of particulate matter in pre–monsoon, whereas, secondary aerosols and biomass burning were the major contributors (63.2%) to particulate matter in winter. The identified sources effectively predict the measured particulate concentration in the pre–monsoon (r2=0.74) and winter season (r2=0.82). Another receptor model, principal component analysis (PCA) was done to increase the plausibility of the results obtained by PMF. PCA resulted in the identification of the sources that were comparable to the PMF outputs. PCA of TSP in the pre–monsoon season resulted in the extraction of three components (crustal dust + secondary aerosol, biomass burning, fossil fuel combustion + industrial emissions) that explained the 83% of the variance in the data. Similarly, in winter season, PCA resulted in the extraction of four components (biomass burning + secondary aerosol, industrial emission, crustal dust, sea salt) that explained the 86% of the variance of the data.

Atmospheric circulation affects local concentrations of particulate matter with an aerodynamic diameter of 10μm or less (PM10) in different ways: Via the determination of local meteorological conditions favoring or suppressing the formation and the accumulation of PM10, and through its control on short–and long–range transport of particles and precursors. The quantitative assessment of the connections between the large–scale atmospheric circulation and local PM10 is relevant not only for the understanding of observed variations in PM10 concentrations. It is even more important for estimating the potential effects of projected future changes in large–scale atmospheric circulation on PM10. In this contribution, daily atmospheric circulation types (CTs), resulting from variants of three different classification methods, and their monthly occurrence frequencies have been utilized in three different downscaling approaches for estimating monthly indices of PM10 for the period 1980–2010 at 16 locations in Bavaria (Germany). All variants of approaches have been evaluated via a leave–one–out cross validation procedure in order to attain reliable performance ratings to detect the most suitable downscaling approaches. Results indicate that the highest performance of downscaling approaches is achieved in winter when the best performing models explain on average roughly 50% of the observed PM10 variance. From this it can be concluded that classification–based approaches are generally suitable for the downscaling of PM10, particularly during winter when PM10 concentrations in Bavaria reach maximum values. As preferable settings of the downscaling approaches, the usage of rather small spatial domains and a relatively high number of classes for circulation type classification and furthermore the utilization of multiple linear regression analyses or random forest analyses for relating CTs to PM10 have been ascertained. These findings provide the basis for further enhancements of the classification–based downscaling of monthly PM10 that will be realized in successive investigations.

Determining whether a reduction can be made in the total number of monitoring stations within the Air Quality Monitoring Network is very important since in case of necessity, the devices at one group of stations having similar air pollution characteristics can be transferred to another zone. This would significantly decrease the capital investment and operational cost. Therefore, the objective of this study was grouping the monitoring stations that share similar air pollution characteristics by using the methods of principal component analysis (PCA) and fuzzy c– means (FCM). In addition, this study also enables determining the emission sources, evaluating the performances of the methods and examining the zone in terms of pollution. In the classification of monitoring stations, different groups were formed depending on both the method of analysis and the type of pollutants. As a result of PCA, 5 and 3 classes have been determined for SO2 and PM10, respectively. This shows that the number of monitoring stations can be decreased. When reduced classes were analyzed, it was observed that a clear distinction cannot be made considering the affected source type. During the implementation of the FCM method, in order to facilitate comparison with the PCA, the monitoring stations were classified into 5 and 3 groups for SO2 and PM10, respectively. When the results were analyzed, it was seen that the uncertainty in PCA was reduced. When the two methods are compared, FCM was found to provide more significant results than PCA. The evaluation in terms of pollution, the results of the study showed that PM10 exceeded the limit values at all the monitoring stations, and SO2 exceeded the limit values at only 3 of the 22 stations.

The Petrochemical Complex of Duque de Caxias, Rio de Janeiro, is situated on a coastal strip between Guanabara Bay and a mountainous region covered with tropical forest. The complex comprises a refinery, a thermal power plant and several petrochemical industries. Higher rates of particulate–matter emissions are found in the region, mainly due to diesel emissions and the industrial activities of this area. In 2009 and 2010, samples were collected in three sites, and the 16 polycyclic aromatic hydrocarbons (PAHs) that are designated as priority pollutants by the US Environmental Protection Agency were determined. The sites are located in the vicinity of the Petrochemical Complex, one of them is on a roadside and the others are urban areas around the industrial complex. Multivariate analyses and diagnostic ratios show that the three studied areas were different, and the emissions seemed to be due to both gasoline and diesel vehicles. The carcinogenic PAHs represented the main fraction of the total PAHs determined in the particulate matter, and because the region is densely populated, these values may represent a health concern. The results indicate that regarding PAHs, the principal impact of the petrochemical complex is the high increase in the traffic of diesel vehicles and related tailpipe emissions.

The sources of PM10 in the Tahunanui airshed of Nelson, New Zealand were investigated using positive matrix factorization (PMF) on elemental data obtained from filters collected from September 2008-September 2009. Also, the source(s) of peak, non-winter PM10 concentrations that exceeded the National Environmental Standard for PM10 were investigated using PM10 and meteorological data from 2007–2012 and the PMF results. Seven PM10 sources were identified: biomass burning, motor vehicles, secondary sulfate, marine aerosol, crustal matter, protective coating activities and fertilizer. Overall, biomass burning was the dominant source contributor (35% of PM10). Analyses of PM10 concentration dependences on meteorological variables showed that peak, non-winter PM10 concentrations that occurred under moderate-to-high wind speeds from the southwest were the result of vehicular movements on unsealed roads in an industrial area. From this information, it is possible for Nelson City Council, who manages air quality at Tahunanui, to formulate mitigation strategies to reduce the impact of biomass burning and industrial vehicles on local air quality.