Comparison of summer and winter California central valley aerosol distributions from lidar and MODIS measurements

Aerosol distributions from two aircraft lidar campaigns conducted in the California Central Valley are compared in order to identify seasonal variations. Aircraft lidar flights were conducted in June 2003 and February 2007. While the ground PM_2.5 (particulate matter with diameter ≤ 2.5 μm) concentration was highest in the winter, the aerosol optical depth (AOD) measured from the MODIS and lidar instruments was highest in the summer. A multiyear seasonal comparison shows that PM_2.5 in the winter can exceed summer PM_2.5 by 68%, while summer AOD from MODIS exceeds winter AOD by 29%. Warmer temperatures and wildfires in the summer produce elevated aerosol layers that are detected by satellite measurements, but not necessarily by surface particulate matter monitors. Temperature inversions, especially during the winter, contribute to higher PM_2.5 measurements at the surface. Measurements of the mixing layer height from lidar instruments provide valuable information needed to understand the correlation between satellite measurements of AOD and in situ measurements of PM_2.5. Lidar measurements also reflect the ammonium nitrate chemistry observed in the San Joaquin Valley, which may explain the discrepancy between the MODIS AOD and PM_2.5 measurements.     

The Relation between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth and PM2.5 over the United States: A Geographical Comparison by U.S. Environmental Protection Agency Regions

Abstract

Aerosol optical depth (AOD) acquired from satellite measurements demonstrates good correlation with particulate matter with diameters less than 2.5 µm (PM_2.5) in some regions of the United States and has been used for monitoring and nowcasting air quality over the United States. This work investigates the relation between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM_2.5 over the 10 U.S. Environmental Protection Agency (EPA)-defined geographic regions in the United States on the basis of a 2-yr (2005–2006) match-up dataset of MODIS AOD and hourly PM_2.5 measurements. The AOD retrievals demonstrate a geographical and seasonal variation in their relation with PM_2.5. Good correlations are mostly observed over the eastern United States in summer and fall. The southeastern United States has the highest correlation coefficients at more than 0.6. The southwestern United States has the lowest correlation coefficient of approximately 0.2. The seasonal regression relations derived for each region are used to estimate the PM_2.5 from AOD retrievals, and it is shown that the estimation using this method is more accurate than that using a fixed ratio between PM_2.5 and AOD. Two versions of AOD from Terra (v4.0.1 and v5.2.6) are also compared in terms of the inversion methods and screening algorithms. The v5.2.6 AOD retrievals demonstrate better correlation with PM_2.5 than v4.0.1 retrievals, but they have much less coverage because of the differences in the cloud-screening algorithm.     

Particulate matter pollution in the coal-producing regions of the Appalachian Mountains: Integrated ground-based measurements and satellite analysis

This study integrates the relationship between measured surface concentrations of particulate matter 10 μm or less in diameter (PM₁₀), satellite-derived aerosol optical depth (AOD), and meteorology in Roda, Virginia, during 2008. A multiple regression model was developed to predict the concentrations of particles 2.5 μm or less in diameter (PM_2.5) at an additional location in the Appalachia region, Bristol, TN. The model was developed by combining AOD retrievals from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on board the EOS Terra and Aqua Satellites with the surface meteorological observations. The multiple regression model predicted PM_2.5 (r² = 0.62), and the two-variable (AOD-PM_2.5) model predicted PM_2.5 (r² = 0.4). The developed model was validated using particulate matter recordings and meteorology observations from another location in the Appalachia region, Hazard, Kentucky. The model was extrapolated to the Roda, VA, sampling site to predict PM_2.5 mass concentrations. We used 10 km x 10 km resolution MODIS 550 nm AOD to predict ground level PM_2.5. For the relevant period in 2008, in Roda, VA, the predicted PM_2.5 mass concentration is 9.11 ± 5.16 μg m^-3 (mean ± 1SD).

Implications: This is the first study that couples ground-based Particulate Matter measurements with satellite retrievals to predict surface air pollution at Roda, Virginia. Roda is representative of the Appalachian communities that are commonly located in narrow valleys, or “hollows,” where homes are placed directly along the roads in a region of active mountaintop mining operations. Our study suggests that proximity to heavy coal truck traffic subjects these communities to chronic exposure to coal dust and leads us to conclude that there is an urgent need for new regulations to address the primary sources of this particulate matter.     

A statistical model for predicting PM2.5 for the western United States

ABSTRACT

A new statistical model for predicting daily ground level fine scale particulate matter (PM_2.5) concentrations at monitoring sites in the western United States was developed and tested operationally during the 2016 and 2017 wildfire seasons. The model is site-specific, using a multiple linear regression schema that relies on the previous day’s PM_2.5 value, along with fire and smoke related variables from satellite observations. Fire variables include fire radiative power (FRP) and the National Fire Danger Rating System Energy Release Component index. Smoke variables, in addition to ground monitored PM_2.5, include aerosol optical depth (AOD) and smoke plume perimeters from the National Oceanic and Atmospheric Administration’s Hazard Mapping System. The overall statistical model was inspired by a similar system developed for British Columbia (BC) by the BC Center for Disease Control, but it has been heavily modified and adapted to work in the United States. On average, our statistical model was able to explain 78% of the variance in daily ground level PM_2.5. A novel method for implementation of this model as an operational forecast system was also developed and was tested and used during the 2016 and 2017 wildfire seasons. This method focused on producing a continuously-updating prediction that incorporated the latest information available throughout the day, including both updated remote sensing data and real-time PM_2.5 observations. The diurnal pattern of performance of this model shows that even a few hours of data early in the morning can substantially improve model performance.

Implications: Wildfire smoke events produce significant air quality impacts across the western United States each year impacting millions. We present and evaluate a statistical model for making updating predictions of fine particulate (PM_2.5) levels during smoke events. These predictions run hourly and are being used by smoke incident specialists assigned to wildfire operations, and may be of interest to public health officials, air quality regulators, and the public. Predictions based on this model will be available on the web for the 2019 western U.S. wildfire season this summer.     

Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization

A combination of in-situ PM_2.5, sunphotometers, upward pointing lidar and satellite aerosol optical depth (AOD) instruments have been employed to better understand variability in the correlation between AOD and PM_2.5 at the surface. Previous studies have shown good correlation between these measures, especially in the US east, and encouraged the use of satellite data for spatially interpolating between ground sensors. This work shows that cases of weak correlation can be better understood with knowledge of whether the aerosol is confined to the surface planetary boundary layer (PBL) or aloft. Lidar apportionment of the fraction of aerosol optical depth that is within the PBL can be scaled to give better agreement with surface PM_2.5 than does the total column amount. The study has shown that lidar combined with surface and remotely sensed data might be strategically used to improve our understanding of long-range or regionally transported pollutants in multiple dimensions.     

Effects of Wind Direction on Coarse and Fine Particulate Matter Concentrations in Southeast Kansas

Abstract

Field data for coarse particulate matter ([PM] PM₁₀) and fine particulate matter (PM_2.5) were collected at selected sites in Southeast Kansas from March 1999 to October 2000, using portable MiniVol particulate samplers. The purpose was to assess the influence on air quality of four industrial facilities that burn hazardous waste in the area located in the communities of Chanute, Independence, Fredonia, and Coffeyville. Both spatial and temporal variation were observed in the data. Variation because of sampling site was found to be statistically significant for PM₁₀ but not for PM_2.5. PM₁₀ concentrations were typically slightly higher at sites located within the four study communities than at background sites. Sampling sites were located north and south of the four targeted sources to provide upwind and downwind monitoring pairs. No statistically significant differences were found between upwind and downwind samples for either PM₁₀ or PM_2.5, indicating that the targeted sources did not contribute significantly to PM concentrations. Wind direction can frequently contribute to temporal variation in air pollutant concentrations and was investigated in this study. Sampling days were divided into four classifications: predominantly south winds, predominantly north winds, calm/variable winds, and winds from other directions. The effect of wind direction was found to be statistically significant for both PM₁₀ and PM_2.5. For both size ranges, PM concentrations were typically highest on days with predominantly south winds; days with calm/variable winds generally produced higher concentrations than did those with predominantly north winds or those with winds from “other” directions. The significant effect of wind direction suggests that regional sources may exert a large influence on PM concentrations in the area.     

A performance evaluation of the National Air Quality Forecast Capability for the summer of 2007

This paper provides a performance evaluation of the real-time, CONUS-scale National Air Quality Forecast Capability (NAQFC) that supported, in part, its transition into operational status. This evaluation focuses primarily on discrete forecasts for the maximum 8-h O₃ concentrations covering the 4-month period, June through September, 2007, using measurements obtained from EPA's AIRNow network. Results indicate that the 2007 NAQFC performed as well or better than previous configurations, despite the expansion of the forecast domain into the western half of the nation that is dominated by complex terrain. The mean, domain-wide, season-long correlation was 0.70. When examined over time, the domain-wide correlations exhibit a fairly consistent nature, with values exceeding 0.60 (0.70) over 90% (55%) of the days. The NAQFC systematically over-predicted the 8-h O₃ concentrations, continuing a trend established by earlier NAQFC configurations, though to a lesser degree. The summer-long mean forecast value of 53.2 ppb was 4.2 ppb higher than the observed value, resulting in a domain-wide Normalized Mean Bias (NMB) of 8.7%. Most of the over-prediction is associated with observed concentrations less than 50 ppb. In fact the model tends to under-predict when concentrations exceed 70 ppb. As with the bias, the error associated with the latest configuration was also lower. The summer-long Root Mean Square Error of 13.0 ppb (Normalized Mean Error (NME) = 20.4%) represented marked improvements over earlier forecasts. Examination of the spatial distribution of both the NMB and NME reveals that the NAQFC was generally within 25% for the NME and 25% for the NMB over a majority of the domain. Several areas of poorer performance, where the NMB and NME often exceed 25% and in some cases 50%, were noted. These areas include southern California, where the NAQFC tended to under-predict concentrations (especially on weekends) and the southeast Atlantic and Gulf coasts regions, where the model over-predicted. Subsequent analysis revealed that the incorrect temporal allocation of precursor emissions was likely the source of the under-prediction in southern California, while inaccurate simulation of PBL heights likely contributed to the over-prediction in the coastal regions.     

Forecasting Daily Maximum Surface Ozone Concentrations in Brunei Darussalam—An ARIMA Modeling Approach

Abstract

A time series approach using autoregressive integrated moving average (ARIMA) modeling has been used in this study to obtain maximum daily surface ozone (O₃) concentration forecasts. The order of the fitted ARIMA model is found to be (1,0,1) for the surface O₃ data collected at the airport in Brunei Darussalam during the period July 1998-March 1999. The model forecasts of one-day-ahead maximum O₃ concentrations have been found to be reasonably close to the observed concentrations. The model performance has been evaluated on the basis of certain commonly used statistical measures. The overall model performance is found to be quite satisfactory as indicated by the values of Fractional Bias, Normalized Mean Square Error, and Mean Absolute Percentage Error as 0.025, 0.02, and 13.14% respectively.     

Windblown Dust Contributes to High PM25 Concentrations

ABSTRACT

The revised National Ambient Air Quality Standards for PM include fine particulate standards based upon mass measurements of PM₂₅. It is possible in arid and semi-arid regions to observe significant coarse mode intrusion in the PM_2.5 measurement. In this work, continuous PM₁₀, PM_2.5, and PM_1.0 were measured during several windblown dust events in Spokane, WA. PM_{2 5} constituted ~30% of the PM₁₀ during the dust event days, compared with ~48% on the non-dusty days preceding the dust events. Both PM₁₀ and PM_2.5 were enhanced during the dust events. However, PM_1.0 was not enhanced during dust storms that originated within the state of Washington. During a dust storm that originated in Asia and impacted Spokane, PM_1.0 was also enhanced, although the Asian dust reached Washington during a period of stagnation and poor dispersion, so that local sources were also contributing to high particulate levels. The “intermodal” region of PM, defined as particles ranging in aerodynamic size from 1.0 to 2.5 um, was found to represent a significant fraction of PM₂₅ (~51%) during windblown dust events, compared with 28% during the non-dusty days before the dust events.     

Bayesian hierarchical modeling of personal exposure to particulate matter

In the US EPA's 1998 Baltimore Epidemiology-Exposure Panel Study, a group of 16 residents of a single building retirement community wore personal monitors recording personal fine particulate air pollution concentrations (PM_2.5) for 27 days, while other monitors recorded concurrent apartment, central indoor, outdoor and ambient site PM_2.5 concentrations. Using the Baltimore panel study data, we develop a Bayesian hierarchical model to characterize the relationship between personal exposure and concentrations of PM_2.5 indoors and outdoors. Personal exposure is expressed as a linear combination of time spent in microenvironments and associated microenvironmental concentrations. The model incorporates all available monitoring data and accounts for missing data and sources of uncertainty such as measurement error and individual differences in exposure. We discuss the implications of using personal versus ambient PM_2.5 measurements in characterization of personal exposure to PM_2.5.     

Spatial variation of particle number and mass over four European cities

The number of ultrafine particles may be a more health relevant characteristic of ambient particulate matter than the conventionally measured mass. Epidemiological time series studies typically use a central site to characterize human exposure to outdoor air pollution. There is currently very limited information how well measurements at a central site reflect temporal and spatial variation across an urban area for particle number concentrations (PNC).The main objective of the study was to assess the spatial variation of PNC compared to the mass concentration of particles with diameter less than 10 or 2.5 μm (PM₁₀ and PM_2.5).Continuous measurements of PM₁₀, PM_2.5, PNC and soot concentrations were conducted at a central site during October 2002–March 2004 in four cities spread over Europe (Amsterdam, Athens, Birmingham and Helsinki). The same measurements were conducted directly outside 152 homes spread over the metropolitan areas. Each home was monitored during 1 week. We assessed the temporal correlation and the variability of absolute concentrations.For all particle indices, including particle number, temporal correlation of 24-h average concentrations was high. The median correlation for PNC per city ranged between 0.67 and 0.76. For PM_2.5 median correlation ranged between 0.79 and 0.98. The median correlation for hourly average PNC was lower (range 0.56–0.66). Absolute concentration levels varied substantially more within cities for PNC and coarse particles than for PM_2.5. Measurements at the central site reflected the temporal variation of 24-h average concentrations for all particle indices at the selected homes across the urban area. A central site could not assess absolute concentrations across the urban areas for particle number.     

Measurements and modelling of PM2.5 concentrations near a major road in Kuopio,Finland

A particle measurement campaign was conducted in a suburban environment near a major road in Kuopio, Central Finland from 3 August to 9 September 1999. The mass concentrations of fine particles (PM_2.5) were measured simultaneously at distances of 12, 25, 52 and 87 m from the centre of a major road at a height of 1.8 m, using identical samplers. The concentration measurements were conducted during 16 daytime hours (from 6.00 a.m. to 10.00 p.m.) for 27 days. Traffic flows and relevant meteorological parameters were measured on-site; meteorological measurements from a nearby synoptic weather station were also utilised. We also suggest a preliminary model for predicting the concentrations of PM_2.5 and apply this model in order to analyse the measured data. The regionally and long-range transported contribution was evaluated on the basis of a semi-empirical mathematical model utilising as input values the daily sulphate, nitrate and ammonium measurements at the EMEP stations (Co-operative programme for monitoring and evaluation of the long-range transmission of air pollutants in Europe). The influence of primary vehicular emissions from the nearest roads was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. The contribution of non-exhaust particulate matter emissions (including resuspension of particulate matter from road surfaces) was estimated simply to be directly proportional to the concentrations originating from primary vehicular emissions. Comparison of the predicted results and measurements yields information on the relative importance of various source categories of the measured concentrations of PM_2.5. The regionally and long-range transported contribution, the primary and non-exhaust vehicular emissions, and other sources were estimated to contribute on average 41±6%, 33±6% and 26±7% of the observed PM_2.5 concentrations, respectively. The model presented could also be applied in other European cities for analysing the source contributions to measured fine particulate matter concentrations.     

Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD

Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R ² is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m³ for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R ² and RMSE are 0.81 and 27.46 μg/m³, respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.     

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.     

Development and application of a three-dimensional aerosol chemical transport model,PMCAMx

A three-dimensional chemical transport model (PMCAMx) is used to simulate PM mass and composition in the eastern United States for a July 2001 pollution episode. The performance of the model in this region is evaluated, taking advantage of the highly time and size-resolved PM and gas-phase data collected during the Pittsburgh Air Quality Study (PAQS). PMCAMx uses the framework of CAMx and detailed aerosol modules to simulate inorganic aerosol growth, aqueous-phase chemistry, secondary organic aerosol formation, nucleation, and coagulation. The model predictions are compared to hourly measurements of PM_2.5 mass and composition at Pittsburgh, as well as to measurements from the AIRS and IMPROVE networks. The performance of the model for the major PM_2.5 components (sulfate, ammonium, and organic carbon) is encouraging (fractional errors are in general smaller than 50%). Additional improvements are possible if the rainfall measurements are used instead of the meteorological model predictions. The modest errors in ammonium predictions and the lack of bias for the total (gas and particulate) ammonium suggest that the improved ammonia inventory used is reasonable. The significant errors in aerosol nitrate predictions are mainly due to difficulties in simulating the nighttime formation of nitric acid. The concentrations of elemental carbon (EC) in the urban areas are significantly overpredicted. This is a problem related to both the emission inventory but also the different EC measurement methods that have been used in the two measurement networks (AIRS and IMPROVE) and the actual development of the inventory. While the ability of the model to reproduce OC levels is encouraging, additional work is necessary to confirm that that this is due to the right reasons and not offsetting errors in the primary emissions and the secondary formation. The model performance against the semi-continuous measurements in Pittsburgh appears to be quite similar to its performance against daily average measurements in a wide range of stations across the Eastern US. This suggests that the skill of the model to reproduce the diurnal variability of PM_2.5 and its major components is as good as its ability to reproduce the daily average values and also the significant value of high temporal resolution measurements for model evaluation.     

Application of Satellite Remote-Sensing Data for Source Analysis of Fine Particulate Matter Transport Events

Abstract

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 μm (PM_2.5) event may indicate whether transport or local sources appear to be most responsible for high PM_2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.     

Regional scale modelling of particulate matter in the UK,source attribution and an assessment of uncertainties

The models HARM and ELMO are used to investigate the importance of different source categories contributing to total PM₁₀ (SIA, SOA and primary particulate matter) across the UK and the impact of uncertainties on both present day and future concentration estimates. Modelled concentrations of SIA (secondary inorganic aerosol) are compared against data from the UK's Nitric Acid and Aerosol Network and SOA (secondary organic aerosol) against measurements made at the Bush Estate, Edinburgh. These data indicate that the HARM/ELMO modelling approach comes close to achieving mass closure. Comparison with national maps of total PM₁₀ indicate that the models underestimate particulate matter concentrations around large conurbations, probably due to the localised nature of emissions of primary particulates in these areas and model scale. The models are used to attribute particulate matter to different source and size categories, assessing the relative importance of primaries, SIA and SOA; the contributions of anthropogenic and biogenic precursors of SOA; the relative importance of PM_coarse (PM₁₀–PM_2.5) and PM_fine (PM_2.5) and UK vs. other EMEP area sources. The implications of these attributions for emissions control policies are discussed. The impact of uncertainties in emissions of the sources of primaries, SIA and SOA are explored. For primary PM₁₀ and SOA this has been achieved through emissions scaling and for SIA using the GLUE (Generalised Likelihood Uncertainty Estimation) approach. The selection of acceptable model parameter sets has been based on the need to retain the capability to model deposition of S and N species. The impact of uncertainty on estimates of present day SIA concentrations is illustrated for sites in the Nitric Acid and Aerosol Network. A more limited assessment for 2010 has been carried out at the national scale, illustrating that inclusion of uncertainty can change modelled concentrations from no exceedance of current air quality objectives, to one of exceedance over large areas of south and east England.     

Aerosol Measurement: The Use of Optical Light Scattering for the Determination of Particulate Size Distribution,and Particulate Mass,Including the Semi-Volatile Fraction

Abstract

The GRIMM model 1.107 monitor is designed to measure particle size distribution and particulate mass based on a light scattering measurement of individual particles in the sampled air. The design and operation of the instrument are described. Protocols used to convert the measured size number distribution to a mass concentration consistent with U.S. Environmental Protection Agency protocols for measuring particulate matter (PM) less than 10 μm (PM₁₀) and less than 2.5 μm (PM_2.5) in aerodynamic diameter are described. The performance of the resulting continuous monitor has been evaluated by comparing GRIMM monitor PM_2.5 measurements with results obtained by the Rupprecht and Patashnick Co. (R&P) filter dynamic measurement system (FDMS). Data were obtained during month-long studies in Rubidoux, CA, in July 2003 and in Fresno, CA, in December 2003. The results indicate that the GRIMM monitor does respond to total PM_2.5 mass, including the semi-volatile components, giving results comparable to the FDMS. The data also indicate that the monitor can be used to estimate water content of the fine particles. However, if the inlet to the monitor is heated, then the instrument measures only the nonvolatile material, more comparable to results obtained with a conventional heated filter tapered element oscillating microbalance (TEOM) monitor. A recent modification of the model 180, with a Nafion dryer at the inlet, measures total PM_2.5 including the nonvolatile and semi-volatile components, but excluding fine particulate water. Model 180 was in agreement with FDMS data obtained in Lindon, UT, during January through February 2007     

Partitioning of nitrate and ammonium between the gas and particulate phases during the 1997 IMADA-AVER study in Mexico City

The partitioning of nitrate and ammonium between the gas and particulate phases is studied combining available equilibrium models and measurements taken in Mexico City during the 1997 IMADA-AVER field campaign. Based on this analysis, there are no significant differences in model predictions, but some discrepancies exist between predictions and observations. The inclusion of crustal elements in the modeling framework improves agreement of model predictions for particulate nitrate against measurements by approximately 5%. Although some equilibrium aerosol models do not explicitly treat crustal elements, these species can be treated as equivalent concentrations of sodium. Atmospheric equilibrium models predict daily average PM_2.5 nitrate concentrations within 20% of the IMADA-AVER measurements at the MER site. Six-hour average PM_2.5 nitrate concentrations are predicted within 30–50% on average except for the afternoon sampling periods (12:00–18:00 h). Investigating the possible sources of these discrepancies, it appears that a dynamic instead of an equilibrium approach is more suitable in reproducing aerosol behavior during these afternoon periods. By applying the Multicomponent Aerosol Dynamic Model (MADM), model performance in predicting concentrations of particulate nitrate significantly improves during the afternoon periods.     

Evaluation of Time-Resolved PM2.5 Data in Urban/Suburban Areas of New Jersey

ABSTRACT

Time-resolved data is needed for public notification of unhealthful air quality and to develop an understanding of atmospheric chemistry, including insights important to control strategies. In this research, continuous fine particulate matter (PM_2.5) mass concentrations were measured with tapered element oscillating microbalances (TEOMs) across New Jersey from July 1997 to June 1998. Data features indicating the influence of local sources and long-distance transport are examined, as well as differences between 1-hr maxima and 24-hr average concentrations that might be relevant to acute health effects. Continuous mass concentrations were not significantly different from filter-collected gravimetric mass concentrations with 95% confidence intervals during any season. Annual mean PM_2.5 concentrations from July 1997 to June 1998 were 17.3, 16.4, 14.1, and 15.3 μg/m³ at Newark, Elizabeth, New Brunswick, and Camden, NJ, respectively. Monthly averaged 24- and 1-hr daily maximum PM_2.5 concentrations suggest the existence of a high PM_2.5 (May-October) and a low PM_2.5 (November-April) season.

PM_2.5 magnitudes and temporal trends were very similar across the state during high PM_2.5 events. In fact, the between-site coefficients of determination (R²) for daily PM_2.5 measurements were 84-98% for June and July. Additionally, during the most pronounced PM_2.5 episode, PM_2.5 concentrations closely tracked the daily maximum 1-hr O₃ concentrations. These observations suggest the importance of transport and atmospheric chemistry (i.e., secondary formation) to PM_2.5 episodes in New Jersey. The influence of local sources was observed in diurnal concentration profiles and annual average between-site differences. Urban wintertime data illustrate that high 1-hr maximum PM_2.5 concentrations can occur on low 24-hr PM_2.5 days.