Particulate Matter Concentration Levels Research Articles

Modeling the transport of Saharan dust toward the Mediterranean region: an important issue for its ecological implications

Airborne particulate matter (PM) is presently an environmental problem of primary concern, whose role in air quality, climatic and ecological issues is well recognized, though still a matter of extensive investigations (Intergovernmental Panel on Climate Change, 2007). It is of great scientific interest to detect sources of atmospheric particulate matter and quantify their influence on the global and local scales. Unfortunately, emissions are usually not directly available, while PM concentration time series are experimentally accessible, so that the problem often consists in inverting these data to determine the region of influence that caused the measured concentration. In this paper we are concerned with an alternative approach to inverse modeling based on backward trajectory analysis (BTA); this approach has the potential to overcome some limitations associated with traditional BTA. We apply this method to the analysis of PM time series from the Monte Cimone observatory, hereafter MCT, a high altitude station on the top of the Italian Northern Apennines, with the aim of estimate the contribution of Saharan dust transport on PM concentration levels registered in the Mediterranean region.

Modeling the transport of Saharan dust toward the Mediterranean region: an important issue for its ecological implications

Airborne particulate matter (PM) is presently an environmental problem of primary concern, whose role in air quality, climatic and ecological issues is well recognized, though still a matter of extensive investigations (Intergovernmental Panel on Climate Change, 2007).It is of great scientific interest to detect sources of atmospheric particulate matter and quantify their influence on the global and local scales. Unfortunately, emissions are usually not directly available, while PM concentration time series are experimentally accessible, so that the problem often consists in "inverting" these data to determine the region of influence that caused the measured concentration. In this paper we are concerned with an alternative approach to inverse modeling based on backward trajectory analysis (BTA); this approach has the potential to overcome some limitations associated with traditional BTA.We apply this method to the analysis of PM time series from the Monte Cimone observatory, hereafter MCT, a high altitude station on the top of the Italian Northern Apennines, with the aim of estimate the contribution of Saharan dust transport on PM concentration levels registered in the Mediterranean region.

The Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) field study methodology

The Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) field study examined indoor and outdoor exposure to traffic-generated air pollution by studying the individual processes of generation of traffic emissions, transport and dispersion of air contaminants along a roadway, and infiltration of the contaminants into a residence. Real-time instrumentation was used to obtain highly resolved time-series concentration profiles for a number of air pollutants. The B-TRAPPED field study was conducted in the residential Sunset Park neighborhood of Brooklyn, NY, USA, in May 2005. The neighborhood contained the Gowanus Expressway (Interstate 278), a major arterial road (4(th) Avenue), and residential side streets running perpendicular to the Gowanus Expressway and 4(th) Avenue. Synchronized measurements were obtained inside a test house, just outside the test house façade, and along the urban residential street canyon on which the house was located. A trailer containing Federal Reference Method (FRM) and real-time monitors was located next to the Gowanus Expressway to assess the source. Ultrafine particulate matter (PM), PM(2.5), nitrogen oxides (NO(x)), sulfur dioxide (SO(2)), carbon monoxide (CO), carbon dioxide (CO(2)), temperature, relative humidity, and wind speed and direction were monitored. Different sampling schemes were devised to focus on dispersion along the street canyon or infiltration into the test house. Results were obtained for ultrafine PM, PM(2.5), criteria gases, and wind conditions from sampling schemes focused on street canyon dispersion and infiltration. For comparison, the ultrafine PM and PM(2.5) results were compared with an existing data set from the Los Angeles area, and the criteria gas data were compared with measurements from a Vancouver epidemiologic study. Measured ultrafine PM and PM(2.5) concentration levels along the residential urban street canyon and at the test house façade in Sunset Park were demonstrated to be comparable to traffic levels at an arterial road and slightly higher than those in a residential area of Los Angeles. Indoor ultrafine PM levels were roughly 3-10 times lower than outdoor levels, depending on the monitor location. CO, NO(2), and SO(2) levels were shown to be similar to values that produced increased risk of chronic obstructive pulmonary disease hospitalizations in the Vancouver studies.

Particulate Matter Exposure during Domestic Work in Nepal

To measure particulate matter (PM) exposure of people involved in domestic work (i.e. housework by a resident, not paid work) in urban and rural Nepal, with exposure to biomass smoke in the rural areas, and to examine the performance of photometric devices in collecting these data. This paper details the results of these measurements and derives calibration factors for two photometric devices compared to gravimetric measures. Between April 2006 and February 2007, respirable dust and PM(2.5) levels were measured over a 24-h period in 490 households in a range of urban and rural settings in the Kathmandu valley of Nepal. Sampling was carried out by photometric and gravimetric methods with the co-located gravimetric data used to derive a calibration factor for the photometric devices. The time-weighted average (TWA) (24 h) respirable dust levels measured by gravimetric sampler ranged from 13 to 2600 microg m(-3) in the rural settings and 3 to 110 microg m(-3) in the urban settings. The co-located photometric and gravimetric devices indicate that the SidePak Personal Aerosol Monitor device required a calibration factor of 0.48 and 0.51 for rural and urban data, respectively, whereas the DustTrak device required a factor of 0.31 and 0.35 for rural and urban settings to correct for the particle size and density of the biomass smoke. The photometric devices provide time history data on PM concentration levels and generally indicate two distinct peaks around a morning and early evening cooking time. Those involved in domestic work in rural Nepal are exposed to average respirable dust concentrations of approximately 1400 microg m(-3). Converted to an 8-h TWA, this equates to more than the current UK limit for respirable dust (4000 microg m(-3)). Homemakers, primarily women, spend a large proportion of their lives indoors in these high respirable dust concentrations and these exposures are likely to produce respiratory illness. Exposure can be controlled by the use of different fuel types and/or the use of flued stoves.

Analyzing Regional Inluence of Particulate Matter on the City of Beijing, China

The concentration levels of particulate matter (PM) in the city of Beijing (39.92°N, 116.46°E), China are dependant on the long-range transport of PM in addition to local stationary and mobile sources. To analyze the regional influences of PM10 on the city of Beijing for the year 2004, 366 back trajectories were generated using Hybrid Single-Particle Langragian Integrated Trajectory modeling. The trajectories were then characterized by regions traversed 24 hrs back in time. The comparative impact scores of the trajectories were calculated based on the emission rates of PM10 and its precursors in each region the trajectories traversed as well its distance to Beijing. For the purpose of the subject analysis, the 366 days in 2004 were categorized as good (＜ 50 μg/m^3), moderate (50-150 μg/m^3), and poor (＞ 150 μg/m^3) air quality groups based on daily PM10 concentrations. Besides Hebei which surrounds Beijing in all directions, our results identified Inner Mongolia, Shanxi, and Mongolia as regions having greater influence on Beijing air quality due to the prevailing westerly and northwesterly winds. Overall, the higher the overall impact score of a particular trajectory cluster, the larger the probability of having poor air quality days and the smaller the probability of having good air quality days. The analysis also indicates that on an annual basis, when air masses travel from Shanxi, which is home to many coal-fired power plants, Beijing tends to have poor air quality due to high PM10 concentrations. In comparison, when air masses originating over Inner Mongolia, where anthropogenic emissions are low, Beijing tends to have good PM air quality. However, our case study showed that during the spring, air masses originating over Inner Mongolia and Mongolia tend to carry dust and sand to Beijing, leading to poor PM air quality.

Primary and secondary carbonaceous species in PM2.5 samples in Milan (Italy)

Seasonal elemental carbon (EC) and organic carbon (OC) concentration levels in PM2.5 samples collected in Milan (Italy) are presented and discussed, enriching the world-wide database of carbonaceous species in fine particulate matter (PM). High-volume PM2.5 sampling campaigns were performed from August 2002 through December 2003 in downtown Milan at an urban background site. Compared to worldwide average concentrations, in Milan warm-season OC and both warm- and cold-season EC are relatively low; conversely, cold-season OC concentrations are rather high. Consequently, high values for the OC/EC ratio are observed, especially in the winter period. The relation between OC/EC ratio values and wind direction is investigated, pointing out that the highest ratios are associated to winds blowing from those nearby areas where wood consumption for domestic heating is larger. Information on the OC partitioning between its primary and secondary fraction are derived by means of the EC-tracer method and principal component analysis. In the warm-season, OC is mainly of secondary origin, secondary organic aerosol (SOA) accounting for about 84% of the particulate organic matter and 25–28% of the PM2.5 mass. For the cold season the full application of the EC-tracer method was not possible and the primary organic aerosol deriving from traffic could only be estimated. However, principal component analysis (PCA) suggest a prevailing primary origin for OC, thus raising the attention on space heating emissions, and on wood combustion in particular, for air quality control. The role of traffic emissions on PM2.5 concentration levels, as a primary source, are also assessed: EC and primary organic matter from traffic account for a warm-season 30% and a cold-season 7% of the total carbon in PM2.5, that is for about 10% and 6% of PM2.5 mass, respectively. This latter small primary contribution estimated for the cold-season points out that stationary sources, which were not thought to play a significant role on PM concentration levels, may conversely be as much responsible for ambient particulate pollution.

Size distribution of atmospheric particulate matter at traffic exposed sites in the urban area of Milan (Italy)

Atmospheric particulate matter (PM) presence at four urban sites in the city of Milan (Italy) is characterised in terms of particle size distribution (number, surface, volume) for the cold and warm season. Simultaneous monitoring of particle number concentration (from 300 nm up to 20 μm of diameter) has been performed between August 2002 and December 2004 by means of a low-volume particle size laser analyser. The monitoring sites are characterised by a different exposure to traffic emissions, enabling for the assessment of the role of this source on both PM concentration levels and on particle size distributions. Data from an urban background site, not directly exposed to traffic emission, a site in a residential area of the city, and two kerbside sites (one at open air, one in a road tunnel) directly exposed to the traffic emissions are compared. Weekdays’ and weekends’ data from the urban background site are analysed for assessing the effect of the reduced traffic circulation on Sundays. Particle concentration is higher at the traffic exposed sites, (where a large increase in particles concentration is observed), especially in the size range between 0.5 and 5 μm, as a consequence of the primary particulate traffic emissions and of the resuspension of soil dust, whereas below 0.5 μm the particle concentrations are similar to those measured in the ambient air. The comparison of seasonal data points out that in the cold season the total number of particles is about two times greater than in the warm season, both on weekdays and on Sundays. In both seasons, particle size distributions present similar patterns, with 99.5% of the total number of particles characterised by a diameter smaller than 1 μm and with Sundays’ distributions shifted towards lower values compared to weekdays’, especially in the coarse size range. According to all these findings, the traffic emissions of the area mainly affect the presence of supermicron particles, while they play only a limited influence on the finest size fractions, characterised by a large background presence, deriving from secondary formation processes in the atmosphere.

PM 10 forecasting for Thessaloniki, Greece

The present research aims at developing an efficient and reliable module, for operational concentration levels of particulate matter with aerodynamic diameter up to 10 μm (PM 10) for the city of Thessaloniki. The Thessaloniki urban area is very densely built, with a high degree of motorisation and industrial activities concentration. The increase of emissions mainly from traffic and industry are responsible for the increase in atmospheric pollution levels during the last years. The air quality data sets examined in the current study are collected by a network of monitoring stations operated by the Municipality of Thessaloniki and correspond to PM 10 concentrations for the years 1994–2000. In order to provide with an operational air quality forecasting module for PM 10, statistical methods are investigated and applied. The presented results demonstrate that CART and Neural Network (NN) methods are capable of capturing PM 10 concentration trends, while CART may have a better performance concerning the index of agreement. Methods studied (including linear regression and principal component analysis) demonstrate promising operational forecasting capabilities.

Response of Inorganic PM to Precursor Concentrations

An inorganic aerosol equilibrium model is used to investigate the response of inorganic particulate matter (PM) concentrations with respect to the precursor concentrations of sulfuric acid, ammonia, and nitric acid over a range of temperatures and relative humidities. Diagrams showing regions of PM response to precursor concentrations are generated, thus allowing the qualification of assumptions concerning the response of PM to sulfate and overall sensitivity to ammonia and nitric acid availability. The PM concentration level responds nonlinearly to sulfate and shows overall sensitivity to ammonia and nitric acid availability for specific atmospheric conditions and precursor concentrations. The generated diagrams are applied as a means of approximating the PM response to precursor concentrations for two urban polluted areas. In both cases, reductions in ammonia emissions have the most significant impact on the total PM level. However, such a reduction will result in significant increases in atmospheric acidity.

Air Quality and Episodes of Acute Respiratory Illness in Taiwan Cities: Evidence from Survey Data

This paper reports on a unique study that records daily health status for over 900 residents of three urban areas in Taiwan and elicits their willingness to pay (WTP) to avoid episodes of illness. Incidence of illness is related to the ambient concentration levels of particulate matter, but the effects are much less pronounced than we would expect from earlier U.S. studies. Willingness to pay to avoid illness is considerably higher than that predicted by extrapolations of U.S. studies that rely on simple income adjustments. We argue that extrapolations from U.S. studies may be inadequate for predicting the benefits of reduced pollution levels in developing countries.