Compilation and application of a primary PM2.5 emissions inventory with high sectoral resolution in Japan

To elucidate the macro-structure of the PM_2.5 emissions generated by Japan's economic activities, this paper presents an emission inventory of primary particles of PM_2.5 with high sectoral resolution based on the Japanese Input–Output Tables, comprising some 400 sectors. These primary PM_2.5 emissions were estimated by multiplying the estimated energy consumption associated with each fuel type by a PM₁₀ emission factor incorporating the technological level of dust collection in each sector and the mass ratio of PM_2.5 to PM₁₀. Non-energy emissions from agricultural open burning were also determined. Total PM_2.5 emissions in 2000 were 252 kt, 49% of which were due to mobile emission sources. Changes in total PM_2.5 emissions between 1990 and 2000 were also calculated. This showed that a substantial increase in energy sector emissions due to rising coal consumption was offset by a sharp decline in emissions from road vehicles and shipping vessels, resulting in an overall decrease in total emissions. In addition, the emissions induced by economic demand in each sector were quantified by means of input–output analysis, which revealed that demand for construction, foods and communications and services constituted the principal causes of real domestic emissions. An assessment of sectoral contributions to PM_2.5 emissions that takes into account the effects of human exposure, expressed as external costs, suggests that the contribution of transportation is greater than indicated on the grounds of direct emissions alone.     

Estimation of wind blown dust emissions in Europe and its vicinity

The assessment of the wind blown dust emission for Europe and selected regions of North Africa and Southwest Asia was carried out using a mesoscale model. The mesoscale model was parameterized based on the current literature review. The model provides data on PM₁₀ emission from several dust reservoirs (anthropogenic, agriculture, semi- and natural) with spatial resolution of 10 × 10 km and temporal resolution of 1 h. The spatial variability of PM₁₀ emission depends on soil texture, land cover/land use as well as meteorological conditions. Lands covered with water or permanently wet were excluded from the model. The land covered with vegetation is treated as dust reservoir whose dust emission capacity depends on the type of vegetation and cover. The dust reservoirs are divided into reservoirs with stable and unstable surface. The changes of emission in time depend on meteorological parameters.The wind blown dust emission should be treated as a non-continuous spatio-temporal process. The emissions are estimated with high uncertainty. The estimated PM₁₀ yearly total load emitted by wind from the European territory is highly differentiated in space and time and is equal to 0.74 Tg. The total load of PM₁₀ emitted by wind from North African and Southwest Asian land surface located in the vicinity of European boundaries is assessed as nearly 50% (0.43 Tg) of the total load estimated for the whole Europe.The average yearly PM₁₀ emission factor for Europe was estimated at 0.139 Mg km^?2.The PM₁₀ emission from agricultural areas is estimated at 52% of the total wind blown emission from the domain of the European Union project “Improving and applying methods for the calculation of natural and biogenic emissions and assessment of impacts to the air quality” - NatAir.PM₁₀ emission factor for natural areas of Europe is estimated at 0.021 Mg km^?2. Appropriate factors for agricultural areas and anthropogenic areas are 0.157 Mg km^?2 and 0.118 Mg km^?2, respectively. The latter two factors are probably underestimated due to omitting in the model of other dust emission mechanisms than aeolian erosion.     

Non-exhaust emission measurement system of the mobile laboratory SNIFFER

In this paper we describe and quality assure the sampling system of a mobile research laboratory SNIFFER which was shown to be a useful tool for studying emission levels of respirable dust from street surfaces. The dust plume had bimodal structure; another mode rising to higher altitudes whereas the other mode remained at lower altitudes. The system was tested on a route in Helsinki, Finland, during spring 2005 and 2006. The PM_2.5 and PM₁₀ were positively correlated and the PM levels increased with the vehicle speed. SNIFFER was able to identify the characteristic emission levels on different streets. A clear downward trend in the concentrations was observed in all street locations between April and June. The composition of the street dust collected by SNIFFER was compared with springtime PM₁₀ aerosol samples from the air quality monitoring stations in Helsinki. The results showed similarities in the abundance and composition of the mineral fraction but contained significantly more salt particles.     

The Relationship between Cross-Sectional and Time Series Studies

Abstract

There is a dearth of information on dust emissions from sources that are unique to the U.S. Department of Defense testing and training activities. However, accurate emissions factors are needed for these sources so that military installations can prepare accurate particulate matter (PM) emission inventories. One such source, coarse and fine PM (PM₁₀ and PM_2.5) emissions from artillery backblast testing on improved gun positions, was characterized at the Yuma Proving Ground near Yuma, AZ, in October 2005. Fugitive emissions are created by the shockwave from artillery pieces, which ejects dust from the surface on which the artillery is resting. Other contributions of PM can be attributed to the combustion of the propellants. For a 155–mm howitzer firing a range of propellant charges or zones, amounts of emitted PM₁₀ ranged from ～19 g of PM₁₀ per firing event for a zone 1 charge to 92 g of PM₁₀ per firing event for a zone 5. The corresponding rates for PM_2.5 were ～9 g of PM_2.5 and 49 g of PM_2.5 per firing. The average measured emission rates for PM₁₀ and PM_2.5 appear to scale with the zone charge value. The measurements show that the estimated annual contributions of PM₁₀ (52.2 t) and PM_2.5 (28.5 t) from artillery backblast are insignificant in the context of the 2002 U.S. Environment Protection Agency (EPA) PM emission inventory. Using national–level activity data for artillery fire, the most conservative estimate is that backblast would contribute the equivalent of 5 x 10^–4% and 1.6 x 10^–3% of the annual total PM₁₀ and PM_2.5 fugitive dust contributions, respectively, based on 2002 EPA inventory data.     

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.     

Development and Preliminary Evaluation of a Particulate Matter Emission Factor Model for European Motor Vehicles

ABSTRACT

Although modeling of gaseous emissions from motor vehicles is now quite advanced, prediction of particulate emissions is still at an unsophisticated stage. Emission factors for gasoline vehicles are not reliably available, since gasoline vehicles are not included in the European Union (EU) emission test procedure. Regarding diesel vehicles, emission factors are available for different driving cycles but give little information about change of emissions with speed or engine load. We have developed size-specific speed-dependent emission factors for gasoline and diesel vehicles. Other vehicle-generated emission factors are also considered and the empirical equation for re-entrained road dust is modified to include humidity effects. A methodology is proposed to calculate modal (accelerating, cruising, or idling) emission factors. The emission factors cover particle size ranges up to 10 um, either from published data or from user-defined size distributions.

A particulate matter emission factor model (PMFAC), which incorporates virtually all the available information on particulate emissions for European motor vehicles, has been developed. PMFAC calculates the emission factors for five particle size ranges [i.e., total suspended particulates (TSP), PM₁₀, PM₅, PM₂₅, and PM₁] from both vehicle exhaust and nonexhaust emissions, such as tire wear, brake wear, and re-entrained road dust. The model can be used for an unlimited number of roads and lanes, and to calculate emission factors near an intersection in user-defined elements of the lane. PMFAC can be used for a variety of fleet structures. Hot emission factors at the user-defined speed can be calculated for individual vehicles, along with relative cold-to-hot emission factors. The model accounts for the proportions of distance driven with cold engines as a function of ambient temperature and road type (i.e., urban, rural, or motorway).

A preliminary evaluation of PMFAC with an available dispersion model to predict the airborne concentration in the urban environment is presented. The trial was on the A6 trunk road where it passes through Loughborough, a medium-size town in the English East Midlands. This evaluation for TSP and PM₁₀ was carried out for a range of traffic fleet compositions, speeds, and meteorological conditions. Given the limited basis of the evaluation, encouraging agreement was shown between predicted and measured concentrations.     

Personal exposure to particulate air pollution in transport microenvironments

Personal measurements of exposure to particulate air pollution (PM₁₀, PM_2.5, PM₁) were simultaneously made during walking and in-car journeys on two suburban routes in Northampton, UK, during the winter of 1999/2000. Comparisons were made between concentrations found in each transport mode by particle fraction, between different particle fractions by transport mode, and between transport microenvironments and a fixed-site monitor located within the study area. High levels of correlation were seen between walking and in-car concentrations for each of the particle fractions (PM₁₀: r²=0.82; PM_2.5: r²=0.98; PM₁: r²=0.99). On an average, PM₁₀ concentrations were 16% higher inside the car than for the walker, but there were no difference in average PM_2.5 and PM₁ concentrations between the two modes. High PM_2.5:PM₁₀ ratios (0.6–0.73) were found to be associated with elevated sulphate levels. The PM_2.5:PM₁₀ and PM₁:PM_2.5 ratios were shown to be similar between walking and in-car concentrations. Concentrations of PM₁₀ were found to be more closely related between transport mode than either mode was with concentrations recorded at the fixed-site (roadside) monitor. The fixed-site monitor was shown to be a poor marker for PM₁₀ concentrations recorded during walking and in-car on a route over 1 km away.     

Mesoscale modeling of wintertime particulate matter episodes in eastern Washington, USA

The Spokane, Washington area is classified as a non-attainment area for the 24-h PM₁₀ standard due to a history of high particulate matter concentrations. A Eulerian regional air quality model (CALMET/CALGRID) has been used to characterize the emission, transport and dispersion of PM₁₀ and PM_2.5 in Spokane. Observations from a residential site (Rockwood, RW) and an industrial site (Crown Zellerbach, CZ), spanning July 1994–August 1996 were used to evaluate the current emission inventory. Two major tasks were devised to conduct the objectives of this investigation. First, a simple and efficient urban dispersion model (WYNDValley) was used to simulate important episodes characterized by the highest PM₁₀ and PM_2.5 concentrations. The selected episodes included four days with wet conditions for which no roads would have been emitting and seven days with dry conditions for which roads would emit. In the second step, a single road-emitting event was selected from the previous predicted results for further analysis using the Eulerian regional air quality model to examine the emission inventory. The urban and regional models predicted the observed concentration distributions reasonably well for the source emissions inventoried in Spokane. The mass concentrations of PM₁₀ were well predicted for the roads emitting case examined by both models indicating that the emission inventory based primarily upon area sources including roads is reasonably well characterized, at least at the RW site. The area sources around CZ are less well characterized, so that the PM₁₀ concentrations are underpredicted at CZ. The models appear unable to reach an equilibrium mass balance status at the beginning of the simulation, and the urban model seems unable to properly resolve the nocturnal boundary layer.     

Street-level concentrations of nitrogen dioxide and suspended particulate matter in Hong Kong

The concentrations of respirable suspended particulates (PM₁₀), fine suspended particulates (PM_2.5) and nitrogen dioxide (NO₂) were measured in various locations over the territory of Hong Kong. In order to study the contributions of these pollutants from motor vehicles and their characteristics, the attention was focused on the roadside, street-level concentrations. A statistical analysis of the sampling results was conducted to obtain general characteristics of the roadside particulate and nitrogen dioxide pollution and to investigate the effects of traffic volume and meteorological factors on the pollution levels. High correlation coefficients are found between PM₁₀, PM_2.5 and NO₂ concentration.     

Indoor particulate matter in urban residences of Alexandria,Egypt

Indoor particulate matter samples were collected in 17 homes in an urban area in Alexandria during the summer season. During air measurement in all selected homes, parallel outdoor air samples were taken in the balconies of the domestic residences. It was found that the mean indoor PM_2.5 and PM₁₀ (particulate matter with an aerodynamic diameter ≤2.5 and ≤10 μm, respectively) concentrations were 53.5 ± 15.2 and 77.2 ± 15.1 µg/m³, respectively. The corresponding mean outdoor levels were 66.2 ± 16.5 and 123.8 ± 32.1 µg/m³, respectively. PM_2.5 concentrations accounted, on average, for 68.8 ± 12.8% of the total PM₁₀ concentrations indoors, whereas PM_2.5 contributed to 53.7 ± 4.9% of the total outdoor PM₁₀ concentrations. The median indoor/outdoor mass concentration (I/O) ratios were 0.81 (range: 0.43–1.45) and 0.65 (range: 0.4–1.07) for PM_2.5 and PM₁₀, respectively. Only four homes were found with I/O ratios above 1, indicating significant contribution from indoor sources. Poor correlation was seen between the indoor PM₁₀ and PM_2.5 levels and the corresponding outdoor concentrations. PM₁₀ levels were significantly correlated with PM_2.5 loadings indoors and outdoors and this might be related to PM₁₀ and PM_2.5 originating from similar particulate matter emission sources. Smoking, cooking using gas stoves, and cleaning were the major indoor sources contributed to elevated indoor levels of PM₁₀ and PM_2.5.

Implications: The current study presents results of the first PM_2.5 and PM₁₀ study in homes located in the city of Alexandria, Egypt. Scarce data are available on indoor air quality in Egypt. Poor correlation was seen between the indoor and outdoor particulate matter concentrations. Indoor sources such as smoking, cooking, and cleaning were found to be the major contributors to elevated indoor levels of PM₁₀ and PM_2.5.     

Optimizing emission inventory for chemical transport models by using genetic algorithm

Air pollutant emission inventory is an important input parameter for chemical transport models (CTMs). Since great uncertainties exist in the emission inventory, further improvements and refinements are required. In this paper, genetic algorithm (GA), a global search and optimization method, was applied to optimize the emission inventory for the Models-3/Community Multiscale Air Quality (CMAQ) model. An emission optimizing system based on GA was developed and embedded to the CMAQ through the design of several core modules, which implemented the basic functions such as emission adjusting, GA population initializing, CMAQ results evaluating and GA operating. Hypothetical and real-data experiments were respectively performed to examine the validity of GA for emission calibrating. GA showed good performance in both experiments and was always able to find the global minimum. The emission optimizing system was then used to calibrate seasonal PM₁₀ emission inventories of Beijing. Results revealed that PM₁₀ emission in Beijing was underestimated in 2002, an average of 62.74% higher adjustment factor should be imposed on the original emission in target months of different seasons. With the calibrated emission inventories, CMAQ model errors were decreased by 6.46% on average in different seasons. It was concluded that GA was a promising search technique in calibrating emission inputs for CTMs.     

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.     

Estimation and validation of PM2.5/PM10 exhaust and non-exhaust emission factors for practical street pollution modelling

In order to carry out efficient traffic and air quality management, validated models and PM emission estimates are needed. This paper compares current available emission factor estimates for PM₁₀ and PM_2.5 from emission databases and different emission models, and validates these against eight high quality street pollution measurements in Denmark, Sweden, Germany, Finland and Austria.The data sets show large variation of the PM concentration and emission factors with season and with location. Consistently at all roads the PM₁₀ and PM_2.5 emission factors are lower in the summer month than the rest of the year. For example, PM₁₀ emission factors are in average 5–45% lower during the month 6–10 compared to the annual average.The range of observed total emission factors (including non-exhaust emissions) for the different sites during summer conditions are 80–130 mg km⁻¹ for PM₁₀, 30–60 mg km⁻¹ for PM_2.5 and 20–50 mg km⁻¹ for the exhaust emissions.We present two different strategies regarding modelling of PM emissions: (1) For Nordic conditions with strong seasonal variations due to studded tyres and the use of sand/salt as anti-skid treatment a time varying emission model is needed. An empirical model accounting for these Nordic conditions was previously developed in Sweden. (2) For other roads with a less pronounced seasonal variation (e.g. in Denmark, Germany, Austria) methods using a constant emission factor maybe appropriate. Two models are presented here.Further, we apply the different emission models to data sets outside the original countries. For example, we apply the “Swedish” model for two streets without studded tyre usage and the “German” model for Nordic data sets. The “Swedish” empirical model performs best for streets with studded tyre use, but was not able to improve the correlation versus measurements in comparison to using constant emission factors for the Danish side. The “German” method performed well for the streets without clear seasonal variation and reproduces the summer conditions for streets with pronounced seasonal variation. However, the seasonal variation of PM emission factors can be important even for countries not using studded tyres, e.g. in areas with cold weather and snow events using sand and de-icing materials. Here a constant emission factor probably will under-estimate the 90-percentiles and therefore a time varying emission model need to be used or developed for such areas.All emission factor models consistently indicate that a large part (about 50–85% depending on the location) of the total PM₁₀ emissions originates from non-exhaust emissions. This implies that reduction measures for the exhaust part of the vehicle emissions will only have a limited effect on ambient PM₁₀ levels.     

Coarse Particulate Matter Apportionment around a Steel Smelter Plant

Abstract

The results from a study carried out in the urban area of Genoa, Italy, where a large steel smelter recently shut down are presented. We had the opportunity to sample particulate matter (PM) before and after plant closure and, therefore, to measure the changes in concentration and composition of PM₁₀ (atmospheric PM with aerodynamic diameter <10 µm). Elemental concentrations of Na to Pb were obtained through energy dispersive X-ray fluorescence (ED-XRF), and the contributions of specific sources of PM₁₀ were calculated by positive matrix factorization (PMF). The PM₁₀ average concentration turned out to be surprisingly similar before and after closing of the smelter. Nevertheless, the comparison among data collected in the two periods (plant operating and closed), even with the limited information provided by ED-XRF, allowed us to single out two sources of PM related to the smelter activities, to extract their emission profile, and to quantify the impact of the plant on PM₁₀ levels.     

Determination and analysis of PM10 source apportionment during episodes of air pollution in Central Eastern European urban areas: The case of wintertime 2006

Source apportionment of air pollution due to particulate matter with an aerodynamic diameter <10 μm (PM₁₀) was investigated in Central Eastern European urban areas. A combination of four methods was developed to distinguish long-range transport (LRT) and regional transport (RT) from local pollution (LP) sources as well as to discern the involvement of traffic or residential sources in LP. Sources of PM₁₀ events of pollution were determined in January 2006 in representative Polish cities using monitored air quality and meteorological data, backward air mass trajectories, correlation and principal component analysis (PCA). Daily patterns of PM₁₀ levels show that several peak episodes were registered in Poland; January 21–30th being the most polluted days. Air mass back-trajectory analysis shows that all cities were under the influence of LRT from North-eastern origins (Russia–Belarus–Ukraine), most were also under LRT from Southern origin (Slovakia, Czech Republic), and northern cities were under national RT influence. PCA analysis shows that ion-sums of secondary inorganic aerosols account for LRT pollution while arsenic and chromium represents markers of RT (industrial) and LP (residential) sources of PM₁₀, respectively. Determination of several ratios (REG/UB, REG/TRAF, TRAF/UB) calculated between PM₁₀ levels measured at regional background (REG); urban background (UB) and traffic (TRAF) monitoring sites shows that, with ratios REG/UB ≥ 0.57, PM₁₀ episodes in both Szczecin and Warsaw bore a marked RT origin. The lower REG/UB ≤ 0.35 in the Southern cities of Cracow and Zabrze indicates that LP was the main contributor to the observed episodes. Only PM₁₀ episodes in Southern-western Poland (Jelenia Góra) were clearly of LP origin as characterized, by the lowest REG/UB ratio (<0.2). The high TRAF/UB ratios obtained for all cities (close to 1) indicate that there was a great uniformity of PM levels on an urban scale owing to the meteorologically stagnant conditions. A high correlation between PM₁₀, NO₂ and CO confirms that traffic emission represented a common and an important LP source of urban pollution in most Polish cities during January 2006. On the other hand PM₁₀ which is also highly correlated with SO₂ in 4 cities out of 6, indicates that coal combustion through domestic heating or industrial activities was also an important LP source of PM₁₀. Finally, extremely unfavourable meteorological conditions caused by the influence of a Siberian high-pressure system were found to be associated with the occurrence of severe PM₁₀ episodes of pollution.     

Application of MM5 and CAMx4 to local scale dispersion of particulate matter for the city of Christchurch,New Zealand

A numerical model, Mesoscale Model version 5 (MM5), is used in conjunction with a three-dimensional Eulerian/Lagrangian dispersion model (CAMx4) to model PM₁₀ dispersion for a period of 48 h for the city of Christchurch, New Zealand. In a typical winter, Christchurch usually experiences severe degradation in air quality. The formation of a nocturnal temperature inversion layer during stagnant synoptic conditions, and the emissions of particulate matter (PM₁₀) mainly from solid fuel home heating appliances (the ‘Domestic’ factor) leads to severe smog episodes on about 30 nights each winter. The modelling results from the highest resolution computational grid are compared with observed meteorology and air pollution dispersion for winter 2000, when the Christchurch Air Pollution Study (CAPS2000) was underway. The numerical modelling system is able to simulate surface-layer meteorology and PM₁₀ spatial distribution with a good level of skill, with the Index of Agreement and Pearson's correlation coefficient greater than 0.8 for PM₁₀.     

Particulate Emissions from Construction Activities

Abstract

Although it has long been recognized that road and building construction activity constitutes an important source of particulate matter (PM) emissions throughout the United States, until recently only limited research has been directed to its characterization. This paper presents the results of PM₁₀ and PM_2.5 (particles ≤10 μm and ≤2.5 μm in aerodynamic diameter, respectively) emission factor development from the onsite testing of component operations at actual construction sites during the period 1998 –2001. Much of the testing effort was directed at earthmoving operations with scrapers, because earthmoving is the most important contributor of PM emissions across the construction industry. Other sources tested were truck loading and dumping of crushed rock and mud and dirt carryout from construction site access points onto adjacent public paved roads. Also tested were the effects of watering for control of scraper travel routes and the use of paved and graveled aprons at construction site access points for reducing mud and dirt carryout. The PM₁₀ emissions from earthmoving were found to be up to an order of magnitude greater than predicted by AP-42 emission factors drawn from other industries. As expected, the observed PM_2.5:PM₁₀ emission factor ratios reflected the relative importance of the vehicle exhaust and the resuspended dust components of each type of construction activity. An unexpected finding was that PM_2.5 emissions from mud and dirt carryout were much less than anticipated. Finally, the control efficiency of watering of scraper travel routes was found to closely follow a bilinear moisture model.     

Responses of future air quality to emission controls over North Carolina,Part II: Analyses of future-year predictions and their policy implications

The MM5/CMAQ system evaluated in Part I paper is applied to study the impact of emission control on future air quality over North Carolina (NC). Simulations are conducted at a 4-km horizontal grid resolution for four one-month periods, i.e., January, June, July, and August 2009 and 2018. Simulated PM_2.5 in 2009 and 2018 show distribution patterns similar to those in 2002. PM_2.5 concentrations over the whole domain in January and July reduced by 5.8% and 23.3% in 2009 and 12.0% and 35.6% in 2018, respectively, indicating that the planned emission control strategy has noticeable effects on PM_2.5 reduction in this region, particularly in summer. More than 10% and 20% of 1-h and 8-h O₃ mixing ratios are reduced in July 2009 and 2018, respectively, demonstrating the effectiveness of emission control for O₃ reduction in summer. However, O₃ mixing ratios in January 2009 and 2018 increase by more than 5% because O₃ chemistry is VOC-limited in winter and the effect of NO_x reduction dominates over that of VOC reduction under such a condition. The projected emission control simulated at 4-km will reduce the number of sites in non-attainment for max 8-h O₃ from 49 to 23 in 2009 and to 1 in 2018 and for 24-h average PM_2.5 from 1 to 0 in 2009 and 2018 based on the latest 2008 O₃ and 2006 PM_2.5 standards. The variability in model predictions at different grid resolutions contributes to 1–3.8 ppb and 1–7.9 μg m^?3 differences in the projected future-year design values for max 8-h O₃ and 24-h average PM_2.5, respectively.     

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.