Receptor-based Source Apportionment Research Articles

Chemically speciated air pollutant emissions from open burning of household solid waste from South Africa

Abstract. Open burning of household solid waste is a large source of air pollutants worldwide, especially in the Global South. However, waste burning emissions are either missing or have large uncertainties in local, regional, or global emission inventories due to limited emission factor (EF) and activity data. Detailed particulate matter (PM) chemical speciation data are even less available. This paper reports source profiles and EFs for PM2.5 species as well as acidic and alkali gases measured from laboratory combustion of 10 waste categories that represent open burning in South Africa. Carbonaceous materials contributed more than 70 % of PM2.5 mass. Elemental carbon (EC) was most abundant from flaming materials (e.g., plastic bags, textiles, and combined materials), and its climate forcing exceeded the corresponding CO2 emissions by a factor of 2–5. Chlorine had the highest EFs among elements measured by X-ray fluorescence (XRF) for all materials. Vegetation emissions showed high abundances of potassium, consistent with its use as a marker for biomass burning. Fresh PM2.5 emitted from waste burning appeared to be acidic. Moist vegetation and food discards had the highest hydrogen fluoride (HF) and PM fluoride EFs due to fluorine accumulation in plants, while burning rubber had the highest hydrogen chloride (HCl) and PM chloride EFs due to high chlorine content in the rubber. Plastic bottles, plastic bags, rubber, and food discards had the highest EFs for polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs as well as their associated toxicities. Distinct differences between odd and even carbon preferences were found for alkanes from biological and petroleum-based materials: dry vegetation, paper, textiles, and food discards show preference for the odd-numbered alkanes, while the opposite is true for plastic bottles, plastic bags, and rubber. As phthalates are used as plasticizers, their highest EFs were found for plastic bottles and bags, rubber, and combined materials. Data from this study will be useful for health and climate impact assessments, speciated emission inventories, source-oriented dispersion models, and receptor-based source apportionment.

Apportionment of Vehicle Fleet Emissions by Linear Regression, Positive Matrix Factorization, and Emission Modeling

Real-world emission factors for different vehicle types and their contributions to roadside air pollution are needed for air-quality management. Tunnel measurements have been used to estimate emission factors for several vehicle types using linear regression or receptor-based source apportionment. However, the accuracy and uncertainties of these methods have not been sufficiently discussed. This study applies four methods to derive emission factors for different vehicle types from tunnel measurements in Hong Kong, China: (1) simple linear regressions (SLR); (2) multiple linear regressions (MLR); (3) positive matrix factorization (PMF); and (4) EMission FACtors for Hong Kong (EMFAC-HK). Separable vehicle types include those fueled by liquefied petroleum gas (LPG), gasoline, and diesel. PMF was the most useful, as it simultaneously seeks source profiles and source contributions. Diesel-, gasoline-, and LPG-fueled vehicle emissions accounted for 52%, 10%, and 5% of PM2.5 mass, respectively, while ammonium sulfate (~20%), ammonium nitrate (6%), and road dust (7%) were also large contributors. MLR exhibited the highest relative uncertainties, typically over twice those determined by SLR. EMFAC-HK has the lowest relative uncertainties due to its assumption of a single average emission factor for each pollutant and each vehicle category under specific conditions. The relative uncertainties of SLR and PMF are comparable.

Application of an improved gas-constrained source apportionment method using data fused fields: A case study in North Carolina, USA

A number of studies have found differing associations of disease outcomes with PM2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO2, NOx, O3, and SO2), total PM2.5 mass, and speciated PM2.5 (including crustal elements) over North Carolina for 2002–2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

Insights into the source-specific health risk of ambient particle-bound metals in the Pearl River Delta region, China

Quantification of source-specific health risks of PM2.5 plays an essential role in health-oriented air pollution control. However, there is limited evidence supporting the source-based risk apportionment of particle-bound metals. In this study, source-specific cancer and non-cancer risk characterization of 12 particle-bound metals was performed in the Pearl River Delta (PRD) region, China. A combination of health risk assessment model and receptor-based source apportionment modeling with positive matrix factorization (PMF) was applied for characterizing the spatial-temporal patterns for inhalation health risks of particle-bound metals in three main city clusters, inland area and coastal area in the region from December 2014 through July 2016. Results showed that the carcinogenic risk of particle-bound metals for adults (4.13 × 10-5) was higher than that for children (9.53 × 10-6) in the PRD region. The highest and significant non-carcinogenic risk was found in the northwest city cluster. Industrial emission (63.3%) were the dominant contributors to the cancer risk, while the main contributors to the non-cancer risk were the vehicle emission source (33.2%) in the dry season and industrial emission (30.8%) in the wet season. Our results provide important evidence for spatial source-specific health risks with temporal characteristics of particle-bound metals in most densely populated areas in the southern China, and suggest that reduction of industrial and vehicle emissions could facilitate more cost-effective PM2.5 control measures to improve human health.

Monitoring ambient air quality in Indian city airsheds

An understanding of sources contributing to a city’s air pollution problem is crucial for building an effective clean air action plan. All the non-attainment cities under the National Clean Air Programme (NCAP) are planning or conducting a source apportionment study. Irrespective of the approaches and equipment selected to conduct these studies, the initiation process requires an understanding of the pollution loads, mix of sources, and geography of the city to decide how much to monitor for better spatial representation and how many times to monitor for better temporal representation. In this report, we defined (a) The size of NCAP city airsheds (b) The recommended number of ambient air quality monitoring sites in an airshed (c) The operational sampling frequency to support receptor-based source apportionment studies. These resources are necessary for strengthening the monitoring needs of an airshed to track pollution levels, to conduct receptor-model-based source apportionment studies, and to support long-term air quality management plans. Together, 122 NCAP cities were clubbed into 94 airsheds representing a total of 154 cities and a total population of 290.6 million - 66 airsheds contain only one city; 16 airsheds contain two cities, nine airsheds contain three cities and four airsheds (Chandigarh, Delhi, Mumbai, and Kolkata) contain 11, 7, 7, and 4 cities respectively. For the 94 airsheds, a total of 1700 sampling sites are recommended to measure and analyse particulate matter, at an average of 18 per airshed. The Tier-1 cities on the NCAP list - Ahmedabad, Bengaluru, Delhi, Hyderabad, Kolkata, Mumbai, and Pune require at least 33, 41, 78, 36, 70, 68, and 32 sampling sites, respectively, which is more than the current operational monitoring capacity in the cities.

Recent changes in winter PM2.5 contributions from wood smoke, motor vehicles, and other sources in the Northwest U.S.

In the Northwest U.S. elevated measurements of PM2.5 from anthropogenic sources occur most often in winter. Major contributors to winter PM2.5 are direct primary emissions of wood smoke from residential wood combustion, primary emissions from motor vehicles, gaseous NOx emissions leading to particulate nitrate, and primary and secondary sources of particulate sulfate. A number of communities in the Northwest U.S. now have long data records of chemically speciated PM2.5 from which receptor-based source apportionment can be performed. This work uses receptor-based source apportionment on data from these monitoring sites to evaluate changes in the major contributors to winter PM2.5 over the available monitoring time span. Data from 9 sites are analyzed in this work using the Positive Matrix Factorization (PMF) source apportionment model. Each site was modeled individually rather than grouping the data from multiple sites. All sites had data through the summer of 2018, with most sites having 11 years of data and one site having 9 years of data. The number of PMF factors identified was between 5 to 10, depending on the site. Associations were made between PMF factors and PM2.5 sources based on comparison of PMF factor chemical profiles with published source test data and source profiles identified in other published studies. The most common factors found were: fresh wood smoke, aged wood smoke, soil dust, gas engines, mixed - gas engines and nitrate, ammonium sulfate, and ammonium nitrate. In this work, total wood smoke was identified as the combined contribution of fresh and aged wood smoke, and winter season data was defined as encompassing the last two months of a year and the first two months of the next year. To evaluate changes over time, average winter season PM2.5 measurements, major PM2.5 chemical components, and PMF factor results for the winter seasons of 2007 - 2009 were compared with the winter seasons of 2015 - 2017. The result for total 3-year average winter season PM2.5 was a decrease between 2% and 29% at the 9 sites, and the decreases were statistically significant at 3 sites. However, total winter season wood smoke contributions to PM2.5 decreased at every site between the two 3-year periods and the decreases were statistically significant at 8 of 9 sites, with decreases from 48% to 74% at those 8 sites. All PMF factors associated with ammonium nitrate (identified at 5 of 9 sites) decreased a statistically significant 11% to 54% between the two 3-year winter season periods. All PMF factors associated with ammonium sulfate (identified at 7 of 9 sites) decreased a statistically significant 27% to 81% between the two 3-year winter season periods. In contrast to the significant reductions in PM2.5 from PMF factors related to wood smoke, ammonium nitrate and ammonium sulfate, PMF factors associated with gas engines increased from 6% to 226% between the two 3-year winter season periods. Increases in PM2.5 contributions from gas engine related factors explain why overall average winter season PM2.5 had more modest percent reductions compared to the percent reductions for wood smoke, ammonium nitrate, and ammonium sulfate factors between the two 3-year winter season periods.

Visibility impacts at Class I areas near the Bakken oil and gas development

ABSTRACTOil and gas activities have occurred in the Bakken region of North Dakota and nearby states and provinces since the 1950s but began increasing rapidly around 2008 due to new extraction methods. Three receptor-based techniques were used to examine the potential impacts of oil and gas extraction activities on airborne particulate concentrations in Class I areas in and around the Bakken. This work was based on long-term measurements from the Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network. Spatial and temporal patterns in measured concentrations were examined before and after 2008 to better characterize the influence of these activities. A multisite back-trajectory analysis and a receptor-based source apportionment model were used to estimate impacts. Findings suggest that recent Bakken oil and gas activities have led to an increase in regional fine (PM2.5—particles with aerodynamic diameters <2.5 µm) soil and elemental carbon (EC) concentrations, as well as coarse mass (CM = PM10–PM2.5). Influences on sulfate and nitrate concentrations were harder to discern due to the concurrent decline in regional emissions of precursors to these species from coal-fired electric generating stations. Impacts were largest at sites in North Dakota and Montana that are closest to the most recent drilling activity.Implications: The increase in oil and gas activities in the Bakken region of North Dakota and surrounding areas has had a discernible impact on airborne particulate concentrations that impact visibility at protected sites in the region. However, the impact has been at least partially offset by a concurrent reduction in emissions from coal-fired electric generating stations. Continuing the recent reductions in flaring would likely be beneficial for the regional visual air quality.

Overview of receptor-based source apportionment studies for speciated atmospheric mercury

Abstract. Receptor-based source apportionment studies of speciated atmospheric mercury are not only concerned with source contributions but also with the influence of transport, transformation, and deposition processes on speciated atmospheric mercury concentrations at receptor locations. Previous studies applied multivariate receptor models including principal components analysis and positive matrix factorization, and back trajectory receptor models including potential source contribution function, gridded frequency distributions, and concentration–back trajectory models. Combustion sources (e.g., coal combustion, biomass burning, and vehicular, industrial and waste incineration emissions), crustal/soil dust, and chemical and physical processes, such as gaseous elemental mercury (GEM) oxidation reactions, boundary layer mixing, and GEM flux from surfaces were inferred from the multivariate studies, which were predominantly conducted at receptor sites in Canada and the US. Back trajectory receptor models revealed potential impacts of large industrial areas such as the Ohio River valley in the US and throughout China, metal smelters, mercury evasion from the ocean and the Great Lakes, and free troposphere transport on receptor measurements. Input data and model parameters specific to atmospheric mercury receptor models are summarized and model strengths and weaknesses are also discussed. Multivariate models are suitable for receptor locations with intensive air monitoring because they require long-term collocated and simultaneous measurements of speciated atmospheric Hg and ancillary pollutants. The multivariate models provide more insight about the types of Hg emission sources and Hg processes that could affect speciated atmospheric Hg at a receptor location, whereas back trajectory receptor models are mainly ideal for identifying potential regional Hg source locations impacting elevated Hg concentrations. Interpretation of the multivariate model output to sources can be subjective and challenging when speciated atmospheric Hg is not correlated with ancillary pollutants and when source emissions profiles and knowledge of Hg chemistry are incomplete. The majority of back trajectory receptor models have not accounted for Hg transformation and deposition processes and could not distinguish between upwind and downwind sources effectively. Ensemble trajectories should be generated to take into account the trajectory uncertainties where possible. One area of improvement that applies to all the receptor models reviewed in this study is the greater focus on evaluating the accuracy of the models at identifying potential speciated atmospheric mercury sources, source locations, and chemical and physical processes in the atmosphere. In addition to receptor model improvements, the data quality of speciated atmospheric Hg plays an equally important part in producing accurate receptor model results.

Impact of gas/particle partitioning of semivolatile organic compounds on source apportionment with positive matrix factorization.

To quantify and minimize the influence of gas/particle (G/P) partitioning on receptor-based source apportionment using particle-phase semivolatile organic compound (SVOC) data, positive matrix factorization (PMF) coupled with a bootstrap technique was applied to three data sets mainly composed of "measured-total" (measured particle- + gas-phase), "particle-only" (measured particle-phase) and "predicted-total" (measured particle-phase + predicted gas-phase) SVOCs to apportion carbonaceous aerosols. Particle- (PM2.5) and gas-phase SVOCs were collected using quartz fiber filters followed by PUF/XAD-4/PUF adsorbents and measured using gas chromatography-mass spectrometry (GC-MS). Concentrations of gas-phase SVOCs were also predicted from their particle-phase concentrations using absorptive partitioning theory. Five factors were resolved for each data set, and the factor profiles were generally consistent across the three PMF solutions. Using a previous source apportionment study at the same receptor site, those five factors were linked to summertime biogenic emissions (odd n-alkane factor), unburned fossil fuels (light SVOC factor), road dust and/or cooking (n-alkane factor), motor vehicle emissions (PAH factor), and lubricating oil combustion (sterane factor). The "measured-total" solution was least influenced by G/P partitioning and used as reference. Two out of the five factors (odd n-alkane and PAH factors) exhibited consistent contributions for "particle-only" vs "measured-total" and "predicted-total" vs "measured-total" solutions. Factor contributions of light SVOC and n-alkane factors were more consistent for "predicted-total" vs "measured-total" than "particle-only" vs "measured-total" solutions. The remaining factor (sterane factor) underestimated the contribution by around 50% from both "particle-only" and "predicted-total" solutions. The results of this study confirm that when measured gas-phase SVOCs are not available, "predicted-total" SVOCs should be used to decrease the influence of G/P partitioning on receptor-based source apportionment.

Positive matrix factorization of PM2.5 - eliminating the effects of gas/particle partitioning of semivolatile organic compounds.

Gas-phase concentrations of semi-volatile organic compounds (SVOCs) were calculated from gas/particle (G/P) partitioning theory using their measured particle-phase concentrations. The particle-phase data were obtained from an existing filter measurement campaign (27 January 2003-2 October 2005) as a part of the Denver Aerosol Sources and Health (DASH) study, including 970 observations of 71 SVOCs (Xie et al., 2013). In each compound class of SVOCs, the lighter species (e.g. docosane in n alkanes, fluoranthene in PAHs) had higher total concentrations (gas + particle phase) and lower particle-phase fractions. The total SVOC concentrations were analyzed using positive matrix factorization (PMF). Then the results were compared with source apportionment results where only particle-phase SVOC concentrations were used (particle only-based study; Xie et al., 2013). For the particle only-based PMF analysis, the factors primarily associated with primary or secondary sources (n alkane, EC/sterane and inorganic ion factors) exhibit similar contribution time series (r = 0.92-0.98) with their corresponding factors (n alkane, sterane and nitrate+sulfate factors) in the current work. Three other factors (light n alkane/PAH, PAH and summer/odd n alkane factors) are linked with pollution sources influenced by atmospheric processes (e.g. G/P partitioning, photochemical reaction), and were less correlated (r = 0.69-0.84) with their corresponding factors (light SVOC, PAH and bulk carbon factors) in the current work, suggesting that the source apportionment results derived from particle-only SVOC data could be affected by atmospheric processes. PMF analysis was also performed on three temperature-stratified subsets of the total SVOC data, representing ambient sampling during cold (daily average temperature < 10 °C), warm (≥ 10 °C and ≤ 20 °C) and hot (> 20 °C) periods. Unlike the particle only-based study, in this work the factor characterized by the low molecular weight (MW) compounds (light SVOC factor) exhibited strong correlations (r = 0.82-0.98) between the full data set and each sub-data set solution, indicating that the impacts of G/P partitioning on receptor-based source apportionment could be eliminated by using total SVOC concentrations.

A regional assessment of marine vessel PM2.5 impacts in the U.S. Pacific Northwest using a receptor-based source apportionment method

This work reports results from a receptor-based source apportionment analysis using the Positive Matrix Factorization (PMF) model on chemically speciated PM2.5 data from 36 urban and rural monitoring sites within the U.S. Pacific Northwest. The approach taken is to model each site independently, treats monitor datasets with a common data preparation protocol, and uses a common modeling protocol. Complementary data from two monitoring networks, the urban Chemical Speciation Network (CSN) and rural Interagency Monitoring of Protected Visual Environments (IMPROVE) Network, was modeled for the period of 2007–2011. 15 different factor types were found for CSN sites and 17 for IMPROVE sites, however many factors occurred at only a few locations. Only 3 factor types were common in both networks: sulfate/sulfur rich, nitrate rich, and soil. However, for coastal and near coastal monitoring sites there were three additional factors common in both networks: sea salt, aged sea salt, and residual fuel oil combustion (RFO). This work presents annual average PM2.5 mass impacts for all sites and factors found and the results for RFO are explored in greater depth. The association between RFO results and commercial marine vessel emissions is made based on similarities between factor chemical profiles and published emissions profiles, comparisons with emissions inventories, and the similarity in the spatial extent of RFO factor locations to that of the other marine aerosols identified in this study, sea salt and aged sea salt. All 14 monitoring sites with marine vessel RFO factors showed a seasonal cycle of mass impacts, with lower impacts in winter months (monthly average PM2.5 between 0.1 μg m−3 and 0.9 μg m−3 in January) and higher impacts in summer months (monthly average PM2.5 between 0.3 μg m−3 and 2.7 μg m−3 in August). These results set a baseline to measure progress in emissions reductions that are expected from implementation of the North American Emissions Control Area (ECA) beginning in August 2012.

Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques

Characterizing the spatio-temporal patterns and apportioning the pollution sources of water bodies are important for the management and protection of water resources. The main objective of this study is to describe the dynamics of water quality and provide references for improving river pollution control practices. Comprehensive application of neural-based modeling and different multivariate methods was used to evaluate the spatio-temporal patterns and source apportionment of pollution in Qiantang River, China. Measurement data were obtained and pretreated for 13 variables from 41 monitoring sites for the period of 2001–2004. A self-organizing map classified the 41 monitoring sites into three groups (Group A, B and C), representing different pollution characteristics. Four significant parameters (dissolved oxygen, biochemical oxygen demand, total phosphorus and total lead) were identified by discriminant analysis for distinguishing variations of different years, with about 80% correct assignment for temporal variation. Rotated principal component analysis (PCA) identified four potential pollution sources for Group A (domestic sewage and agricultural pollution, industrial wastewater pollution, mineral weathering, vehicle exhaust and sand mining), five for Group B (heavy metal pollution, agricultural runoff, vehicle exhaust and sand mining, mineral weathering, chemical plants discharge) and another five for Group C (vehicle exhaust and sand mining, chemical plants discharge, soil weathering, biochemical pollution, mineral weathering). The identified potential pollution sources explained 75.6% of the total variances for Group A, 75.0% for Group B and 80.0% for Group C, respectively. Receptor-based source apportionment was applied to further estimate source contributions for each pollution variable in the three groups, which facilitated and supported the PCA results. These results could assist managers to develop optimal strategies and determine priorities for river pollution control and effective water resources management.

The Denver Aerosol Sources and Health (DASH) study: Overview and early findings

Improved understanding of the sources of air pollution that are most harmful could aid in developing more effective measures for protecting human health. The Denver Aerosol Sources and Health (DASH) study was designed to identify the sources of ambient fine particulate matter (PM 2.5) that are most responsible for the adverse health effects of short-term exposure to PM 2.5. Daily 24-h PM 2.5 sampling began in July 2002 at a residential monitoring site in Denver, Colorado, using both Teflon and quartz filter samplers. Sampling is planned to continue through 2008. Chemical speciation is being carried out for mass, inorganic ionic compounds (sulfate, nitrate and ammonium), and carbonaceous components, including elemental carbon, organic carbon, temperature-resolved organic carbon fractions and a large array of organic compounds. In addition, water-soluble metals were measured daily for 12 months in 2003. A receptor-based source apportionment approach utilizing positive matrix factorization (PMF) will be used to identify PM 2.5 source contributions for each 24-h period. Based on a preliminary assessment using synthetic data, the proposed source apportionment should be able to identify many important sources on a daily basis, including secondary ammonium nitrate and ammonium sulfate, diesel vehicle exhaust, road dust, wood combustion and vegetative debris. Meat cooking, gasoline vehicle exhaust and natural gas combustion were more challenging for PMF to accurately identify due to high detection limits for certain organic molecular marker compounds. Measurements of these compounds are being improved and supplemented with additional organic molecular marker compounds. The health study will investigate associations between daily source contributions and an array of health endpoints, including daily mortality and hospitalizations and measures of asthma control in asthmatic children. Findings from the DASH study, in addition to being of interest to policymakers, by identifying harmful PM 2.5 sources may provide insights into mechanisms of PM effect.

Spatio-temporal patterns and source apportionment of coastal water pollution in eastern Hong Kong

The comprehensive application of different multivariate methods and geographic information systems (GIS) was used to evaluate the spatio-temporal patterns and source apportionment of coastal water pollution in eastern Hong Kong. Fourteen variables were surveyed at 27 sites monthly from 2000 to 2004. After data pretreatment, cluster analysis grouped the 12 months into two groups, June–September and the remaining months, and divided the entire area into two parts, representing different pollution levels. Discriminant analysis determined that NO 3 - – N , DO, and temperature and TN, SD, PO 4 3 - – P , and VSS were significant variables affecting temporal and spatial variations with 84% and 90% correct assignments, respectively. Five potential pollution sources were identified for each part by rotated principal component analysis, explaining 71% and 68% of the total variances, respectively. Receptor-based source apportionment revealed that most of the variables were primarily influenced by soil weathering and organic pollution, nutrient pollution (or agricultural runoff), and mineral pollution. Furthermore, GIS further facilitated and supported multivariate analysis results.

Vehicle-generated fugitive dust transport: analytic models and field study

Vehicle-generated road dust was measured under stable atmospheric conditions at a flat site in the Utah west desert, and the horizontal flux of the dust was calculated by integration of interpolation functions that are fit to the wind speed and dust concentration measurements at discrete heights. The site contained a uniformly spaced array of 2.5 m high×2.4 m×12.2 m roughness elements (cargo containers) simulating an urban setting. The dust measurement objective was to test the hypothesis that near-source removal of vehicle-generated dust may account for part of the reported systematic discrepancy between emission inventories based on AP-42 factors and receptor-based source apportionment studies. The horizontal flux of dust decreased to less than 15% of the initial value while traveling between measurement planes 3 and 95 m from the road, indicating significant near-source removal under the stable atmosphere and high surface roughness conditions of this field experiment. The magnitude of the flux was sensitive to assumptions about dust concentration and wind speed near the ground. The estimates for the flux at 3 m, obtained by integrating alternative interpolation functions, spanned the AP-42 value. Time-averaged data for meteorology, boundary layer turbulence, and dust concentration are provided to allow using the field observations for future theoretical analysis or numerical modeling studies.