PM10 Dispersion Modeling for Treasure Valley,Idaho

ABSTRACT

The recorded exceedances of the 24-hr PM₁₀ National Ambient Air Quality Standard (NAAQS) in Treasure Valley, Idaho, have been associated with prolonged stagnation periods during the winter. A comprehensive modeling study of PM₁₀ impact in Treasure Valley was performed to support the State Implementation Plan (SIP). The study included base-year and short-term episodic conditions. The ISCST3 (Industrial Source Complex Short Term 3) model, using the base-year meteorology and gridded emissions of mobile sources, point sources, and wood burning as input, generally agreed well with measurements in both temporal patterns and annual averages. The WYNDvalley model was evaluated using monitoring data and was used to simulate the PM₁₀ impact for episodic exceedances during stagnant winter conditions. An emission inventory was prepared for a base year (1995) and then extrapolated to the years 2000, 2005, 2010, and 2015 in order to determine air quality planning requirements. According to the simulations using base-year emissions and meteorology, exceedances are not expected. However, exceedances at some stations could be expected using projected emissions and episodic meteorology. Results from emission control strategies we developed indicate that mobile-source emissions have the most significant impact; reduction of 25% would be needed to eliminate the simulated exceedances in all projected years.     

Coarse–fine and day–night differences of water-soluble ions in atmospheric aerosols collected in Christchurch and Auckland,New Zealand

The water-soluble ions in fine (PM_<2.5) and coarse (PM_2.5−10) atmospheric aerosols collected in Christchurch during winter 2001, spring 2000 and summer 2001, and in Auckland during winter 2001 have been studied in terms of coarse–fine and day–night differences. Although the chemical characteristics of the coarse particles were similar in both cities, those of the fine particles collected in the Christchurch winter were significantly different, as manifested by higher concentrations of nss-K⁺, nss-Cl⁻, nss-Ca²⁺, nss-SO₄²⁻, NO₃⁻ and NH₄⁺. It was found that nighttime PM₁₀ and nss-K⁺ concentrations were much higher than their daytime concentrations in the Christchurch winter but a clear day–night difference was not apparent in the Auckland winter. Moreover, in the winter, sea-salt ions did not show a day–night difference; however, nss-SO₄²⁻ had opposite day–night variation in the two cities. An ion balance calculation has shown that in most samples, coarse particles can be neutral or alkaline, however, fine particles can be neutral or acidic. The possibility of ammonium salts existing in the fine particles collected in the Christchurch winter is discussed and it is concluded that a variety of ammonium salts were present. Equivalent ratios suggest that the fine particles may be significantly aged in the Christchurch winter.The evidence from our soluble ion study strongly suggests that wood and coal burning and secondary aerosols make a significant contribution to fine particulate mass in the Christchurch atmosphere. Thus, home-heating, a sheltered geographic location and relatively calm atmospheric condition are thought to be the major causes for the serious atmospheric particulate pollution in the Christchurch winter.     

Source apportionment of particulate matter (PM2.5) in an urban area using dispersion,receptor and inverse modelling

Air pollution emission inventories are the basis for air quality assessment and management strategies. The quality of the inventories is of great importance since these data are essential for air pollution impact assessments using dispersion models. In this study, the quality of the emission inventory for fine particulates (PM_2.5) is assessed: first, using the calculated source contributions from a receptor model; second, using source apportionment from a dispersion model; and third, by applying a simple inverse modelling technique which utilises multiple linear regression of the dispersion model source contributions together with the observed PM_2.5 concentrations. For the receptor modelling the chemical composition of PM_2.5 filter samples from a measurement campaign performed between January 2004 and April 2005 are analysed. Positive matrix factorisation is applied as the receptor model to detect and quantify the various source contributions. For the same observational period and site, dispersion model calculations using the Air Quality Management system, AirQUIS, are performed. The results identify significant differences between the dispersion and receptor model source apportionment, particularly for wood burning and traffic induced suspension. For wood burning the receptor model calculations are lower, by a factor of 0.54, but for the traffic induced suspension they are higher, by a factor of 7.1. Inverse modelling, based on regression of the dispersion model source contributions and the PM_2.5 concentrations, indicates similar discrepancies in the emissions inventory. In order to assess if the differences found at the one site are generally applicable throughout Oslo, the individual source category emissions are rescaled according to the receptor modelling results. These adjusted PM_2.5 concentrations are compared with measurements at four independent stations to evaluate the updated inventory. Statistical analysis shows improvement in the estimated concentrations for PM_2.5 at all sites. Similarly, inverse modelling is applied at these independent sites and this confirms the validity of the receptor model results.     

Dispersion modelling of a wintertime particulate pollution episode in Christchurch,New Zealand

This paper examines the inter-suburb dispersion of particulate air pollution in Christchurch, New Zealand, during a wintertime particulate pollution episode. The dispersion is simulated using the RAMS/CALMET/CALPUFF modelling system, with data from a detailed emissions inventory of home heating, motor vehicles and industry. During the period 27 July–1 August 1995, peak 1 h and 24 h PM₁₀ concentrations of 368 and 107 μg m⁻³, respectively, were observed. Peak concentrations occurred at night, when particulate emissions from wood- and coal-burning domestic heating appliances were at a maximum and emitted into a stable boundary layer. The model is generally able to reproduce the observed PM₁₀ time series recorded at surface monitors located throughout the urban area. For this simulation, the fractional gross error ranges between 0.69 and 0.99, and the fractional bias ranges between −0.17 and 0.30. Strong horizontal concentration gradients of 100 μg m⁻³ km⁻¹, both in the observational record and model predictions, are apparent. Three emission reduction options, designed to reduce the severity of particulate pollution episodes in Christchurch, are simulated. When both domestic open-hearth fires and all coal burning are removed, the 24 h average peak concentration is reduced by 55%. The number of guideline exceedences of PM₁₀ in the modelled period is reduced from five to one. Removing open-hearth fires results in 42% reduction in PM₁₀ concentration, resulting in three exceedences of the guideline, and removing coal-burning fires yields a 32% reduction in PM₁₀, resulting in four exceedences of the guideline.     

An empirical approach for the prediction of daily mean PM10 concentrations

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

The dispersion of the Buncefield oil fire plume: An extreme accident without air quality consequences

The dispersion of pollutants from the huge Buncefield oil depot fire that occurred on 11 December 2005 is simulated using a regional Eulerian chemistry-transport model. We analyse the transport and mixing of the fire plume. We show that the hot plume never reached the ground. Instead, it pierced the thin wintertime boundary layer and was injected into the free troposphere at higher altitudes. This is in agreement with data from many air quality stations. This high injection was fortunate because the fine aerosol particles (PM₁₀) mass column generated by the fire smoke exceeded that of ordinary pollution by an order of magnitude. Our regional chemistry-transport modelling is able to predict the early development of the plume dispersion, as shown by a qualitative comparison between simulated PM₁₀ columns and a satellite image obtained by the EOS-TERRA-MODIS sensor.If the accident had occurred in summer when boundary layers are much deeper and convective, a severe degradation in air quality due to PM₁₀ could have occurred, as shown by a sensitivity simulation assuming a similar fire during one of the hottest days of August 2003. The modelled impact of the fire on regional and European air quality levels strongly depends on the altitude reached by the buoyant plume, as shown by a set of sensitivity simulations with variable injection heights. However, in all cases we found that the fire only affected surface aerosol concentrations without increasing photochemical pollution.     

Wintertime PM2.5 and PM10 Source Apportionment at Sacramento,California

ABSTRACT

The chemical mass balance (CMB) model was applied to winter (November through January) 1991–1996 PM2.5 and PM10 data from the Sacramento 13th and T Streets site in order to identify the contributions from major source categories to peak 24-hr ambient PM_2.5 and PM₁₀ levels. The average monthly PM₁₀ monitoring data for the nine-year period in Sacramento County indicate that elevated concentrations are typical in the winter months. Concentrations on days of highest PM₁₀ are dominated by the PM2.5 fraction. One factor contributing to increased PM_2.5 concentrations in the winter is meteorology (cool temperatures, low wind speeds, low inversion layers, and more humid conditions) that favors the formation of secondary nitrate and sulfate aerosols. Residential wood burning also elevates fine particulate concentrations in the Sacramento area.

The results of the CMB analysis highlight three key points. First, the source apportionment results indicate that primary motor vehicle exhaust and wood smoke are significant sources of both PM_2.5 and PM₁₀ in winter. Second, nitrates, secondarily formed as a result of motor-vehicle and other sources of nitrogen oxide (NO_x), are another principal cause of the high PM_2.5 and PM₁₀ levels during the winter months. Third, fugitive dust, whether it is resuspended soil and dust or agricultural tillage, is not the major contributor to peak winter PM_2.5 and PM10 levels in the Sacramento area.     

Particulate matter source apportionment in a village situated in industrial region of Central Europe

The bilinear receptor model positive matrix factorization (PMF) was used to apportion particulate matter with an aerodynamic diameter of 1–10 μm (PM_1–10) sources in a village, B?ezno, situated in an industrial region of northern Bohemia in Central Europe. The receptor model analyzed the data sets of 90- and 60-min integrations of PM_1–10 mass concentrations and elemental composition for 27 elements. The 14-day sampling campaigns were conducted in the village in summer 2008 and winter 2010. Also, to ensure seasonal and regional representativeness of the data sets recorded in the village, the spatial-temporal variability of the 24-hr PM₁₀ and PM_1–10 within 2008–2010 in winter and summer across the multiple sites was evaluated. There were statistically significant interseasonal differences of the 24-hr PM data, but not intrasummer or intrawinter differences of the 24-hr PM_1–10 data across the multiple sites. PMF resolved seven sources of PM_1–10. They were high-temperature coal combustion; combustion in local heating boilers; marine aerosol; mineral dust; primary biological/wood burning; road dust, car brakes; and gypsum. The main summer factors were assigned to mineral dust (38.2%) and primary biological/wood burning (33.1%). In winter, combustion factors dominated (80%) contribution to PM_1–10. The conditional probability function (CPF) helped to identified local sources of PM_1–10. The source of marine aerosol from the North Sea and English Channel was indicated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT).

Implications: This is the first application of PMF to highly time/size resolved PM data in Czech Republic. The coarse aerosol fraction, PM_1–10, was chosen with regard to industrial character of the region, sampling site near the coal strip mine and coal power stations. Contrary to expectation, source apportionment did not show dominance of emissions from the coal strip mine. The results will enable local authorities and state bodies responsible for air quality assessment to focus on sources most responsible for air pollution in this industrial region.

Supplemental Materials:?Supplemental materials are available for this paper. Go to the publisher's online edition of the Journal of the Air & Waste Management Association for (1) details of measurement campaigns; (2) CPF for each of the sources contributing to PM_1–10; (3) factors contribution to PM_1–10 resolved by PMF; (4) diurnal pattern of road dust, car brake factor in summer and winter; (5) trajectories during the marine aerosol episode in winter 2010; and (6) temporal temperature, concentration, and wind speed relationships during the summer 2008 campaign and winter 2010 campaign.     

Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM_2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM_2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM_2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM_2.5 concentrations, a likely result of underestimating PM_2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.     

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

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

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

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.     

Biomagnetic monitoring of industry-derived particulate pollution

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

An integrated tool to assess the role of new planting in PM10 capture and the human health benefits: A case study in London

The role of vegetation in mitigating the effects of PM₁₀ pollution has been highlighted as one potential benefit of urban greenspace. An integrated modelling approach is presented which utilises air dispersion (ADMS-Urban) and particulate interception (UFORE) to predict the PM₁₀ concentrations both before and after greenspace establishment, using a 10 × 10 km area of East London Green Grid (ELGG) as a case study. The corresponding health benefits, in terms of premature mortality and respiratory hospital admissions, as a result of the reduced exposure of the local population are also modelled. PM₁₀ capture from the scenario comprising 75% grassland, 20% sycamore maple (Acer pseudoplatanus L.) and 5% Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) was estimated to be 90.41 t yr⁻¹, equating to 0.009 t ha⁻¹ yr⁻¹ over the whole study area. The human health modelling estimated that 2 deaths and 2 hospital admissions would be averted per year.     

A combined model–observation approach to estimate historic gridded fields of PM2.5 mass and species concentrations

This paper introduces a methodology for estimating gridded fields of total and speciated fine particulate matter (PM_2.5) concentrations for time periods and regions not covered by observational data. The methodology is based on performing long-term regional scale meteorological and air quality simulations and then integrating these simulations with available observational data. To illustrate this methodology, we present an application in which year-round simulations with a meteorological model (the National Center for Atmospheric Research/Penn State Mesoscale Model, hereafter referred to as MM5) and a photochemical air quality model (the Community Multiscale Air Quality Model, hereafter referred to as CMAQ) have been performed over the northeastern United States for 1988–2005. Model evaluation results for total PM_2.5 mass and individual species for the time period from 2000 to 2005 show that model performance varies by species, season, and location. Therefore, an approach is developed to adjust CMAQ output with factors based on these three variables. The adjusted model values for total PM_2.5 mass for 2000–2005 are compared against independent measurements not utilized for the adjustment approach. This comparison reveals that the adjusted model values have a lower root mean square error (RMSE) and higher correlation coefficients than the original model values. Furthermore, the PM_2.5 estimates from these adjusted model values are compared against an alternate method for estimating historic PM_2.5 values that is based on PM_2.5/PM₁₀ ratios calculated at co-located monitors. Results reveal that both methods yield estimates of historic PM_2.5 mass that are broadly consistent; however, the adjusted CMAQ values provide greater spatial coverage and information for PM_2.5 species in addition to total PM_2.5 mass. Finally, strengths and limitations of the proposed approach are discussed in the context of potential uses of this method.     

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

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

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

Fine particle concentrations and composition during wintertime inversions in Logan,Utah, USA

During Winter 2004, a series of elevated PM_2.5 events occurred in Logan, Utah, coinciding with strong winter inversions. This period resulted in 17 exceedances of the 24-h PM_2.5 standard, and some of the highest PM_2.5 mass loadings recorded in the United States, including 9 days of 24-h PM_2.5 measurements over 100 μg m⁻³. During the 3-month period, we monitored the size and mass concentrations of airborne particles using an aerosol mass spectrometer. PM_2.5 concentrations were dominated by the formation of ammonium nitrate, accounting for over 50% of the non-refractory aerosol matter throughout the study and 80% on the highest pollution days. Another 15–20% of the particulate matter was composed of organic carbon. The high particle concentration loadings in Utah's Cache Valley result from a combination of unfavorable meteorology dominated by a severe cold-temperature inversion, a mix of rural and urban emission sources, and a confined geographical area. As a rapidly growing formerly rural area, the Cache Valley is representative of future air pollution problems facing areas of the interior west undergoing rapid urbanization.     

Analysis of a Summertime PM2.5 and Haze Episode in the Mid-Atlantic Region

Abstract

Observations of the mass and chemical composition of particles less than 2.5 μm in aerodynamic diameter (PM_2.5), light extinction, and meteorology in the urban Baltimore-Washington corridor during July 1999 and July 2000 are presented and analyzed to study summertime haze formation in the mid-Atlantic region. The mass fraction of ammoniated sulfate (SO₄ ^2-) and carbonaceous material in PM_2.5 were each ～50% for cleaner air (PM_2.5 < 10 μg/m³) but changed to ～60% and ～20%, respectively, for more polluted air (PM_2.5 > 30 μg/m³). This signifies the role of SO₄ ^2- in haze formation. Comparisons of data from this study with the Interagency Monitoring of Protected Visual Environments network suggest that SO₄ ^2? is more regional than carbonaceous material and originates in part from upwind source regions. The light extinction coefficient is well correlated to PM_2.5 mass plus water associated with inorganic salt, leading to a mass extinction efficiency of 7.6 ± 1.7 m²/g for hydrated aerosol. The most serious haze episode occurring between July 15 and 19, 1999, was characterized by westerly transport and recirculation slowing removal of pollutants. At the peak of this episode, 1-hr PM_2.5 concentration reached ～45 μg/m³, visual range dropped to ～5 km, and aerosol water likely contributed to ～40% of the light extinction coefficient.     

WRF/Chem simulated springtime impact of rising Asian emissions on air quality over the U.S.

This paper examines the impact of tripled anthropogenic emissions from China and India over the base level (gaseous species and carbonaceous aerosols for 2000) on air quality over the U.S. using the WRF/Chem (Weather Research and Forecasting – Chemistry) model at 1° resolution. WRF/Chem is a state-of-the-science, fully coupled chemistry and meteorology system suitable for simulating the transport and dispersion of pollutants and their impacts. The analyses in this work were focused on MAM (March, April and May). The simulations indicate an extensive area of elevated pollutant concentrations spanning from the Arabian Sea to the Northern Pacific and to the Northern Atlantic. MAM mean contributions from the tripled Asian emissions over the U.S. are found to be: 6–12 ppbv for CO, 1.0–2.5 ppbv for O₃, and 0.6–1.6 μg m^?3 for PM_2.5 on a daily basis.     

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.     

Air quality modeling for effective environmental management in the mining region

Air quality in the mining sector is a serious environmental concern and associated with many health issues. Air quality management in mining regions has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanisms. A modeling approach called the mining air dispersion model (MADM) is developed to predict air pollutants concentration in the mining region while considering the deposition effect. The model takes into account the planet’s boundary conditions and assumes that the eddy diffusivity depends on the downwind distance. The developed MADM is applied to a mining site in Canada. The model provides values for the predicted concentrations of PM₁₀, PM_2.5, TSP, NO₂, and six heavy metals (As, Pb, Hg, Cd, Zn, Cr) at various receptor locations. The model shows that neutral stability conditions are dominant for the study site. The maximum mixing height is achieved (1280 m) during the evening in summer, and the minimum mixing height (380 m) is attained during the evening in winter. The dust fall (PM coarse) deposition flux is maximum during February and March with a deposition velocity of 4.67 cm/sec. The results are evaluated with the monitoring field values, revealing a good agreement for the target air pollutants with R-squared ranging from 0.72 to 0.96 for PM_2.5, from 0.71 to 0.82 for PM₁₀, and from 0.71 to 0.89 for NO₂. The analyses illustrate that the presented algorithm in this model can be used to assess air quality for the mining site in a systematic way. Comparisons of MADM and CALPUFF modeling values are made for four different pollutants (PM_2.5, PM₁₀, TSP, and NO₂) under three different atmospheric stability classes (stable, neutral, and unstable). Further, MADM results are statistically tested against CALPUFF for the air pollutants and model performance is found satisfactory.

Implications: The mathematical model (MADM) is developed by extending the Gaussian equation particularly when examining the settling process of important pollutants for the industrial region. Physical removal effects of air pollutants with field data have been considerred for the MADM development and for an extensive field case study. The model is well validated in the field of an open pit mine to assess the regional air quality. The MADA model helps to facilitate the management of the mining industry in doing estimation of emission rate around mining activities and predicting the resulted concentration of air pollutants together in one integrated approach.