Assessing positive matrix factorization model fit: a new method to estimate uncertainty and bias in factor contributions at the daily time scale

International audienceA Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM2.5 concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A balanced bootstrap is used to create replicate datasets, with the same model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability yet also be very biased. These results are likely dependent on characteristics of the data

Assessing positive matrix factorization model fit: a new method to estimate uncertainty and bias in factor contributions at the measurement time scale

A Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM<sub>2.5</sub> concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A circular block bootstrap is used to create replicate datasets, with the same receptor model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across the model results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability across results yet also be very biased. These findings are likely dependent on characteristics of the data

Analysis of ozone and nitric acid in spring and summer Arctic pollution using aircraft, ground-based, satellite observations and MOZART-4 model: source attribution and partitioning

In this paper, we analyze tropospheric O_3 together with HNO_3 during the POLARCAT (Polar Study using Aircraft, Remote Sensing, Surface Measurements and Models, of Climate, Chemistry, Aerosols, and Transport) program, combining observations and model results. Aircraft observations from the NASA ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) and NOAA ARCPAC (Aerosol, Radiation and Cloud Processes affecting Arctic Climate) campaigns during spring and summer of 2008 are used together with the Model for Ozone and Related Chemical Tracers, version 4 (MOZART-4) to assist in the interpretation of the observations in terms of the source attribution and transport of O_3 and HNO_3 into the Arctic (north of 60° N). The MOZART-4 simulations reproduce the aircraft observations generally well (within 15%), but some discrepancies in the model are identified and discussed. The observed correlation of O_3 with HNO_3 is exploited to evaluate the MOZART-4 model performance for different air mass types (fresh plumes, free troposphere and stratospheric-contaminated air masses). Based on model simulations of O_3 and HNO_3 tagged by source type and region, we find that the anthropogenic pollution from the Northern Hemisphere is the dominant source of O3 and HNO3 in the Arctic at pressures greater than 400 hPa, and that the stratospheric influence is the principal contribution at pressures less 400 hPa. During the summer, intense Russian fire emissions contribute some amount to the tropospheric columns of both gases over the American sector of the Arctic. North American fire emissions (California and Canada) also show an important impact on tropospheric ozone in the Arctic boundary layer. Additional analysis of tropospheric O_3 measurements from ground-based FTIR and from the IASI satellite sounder made at the Eureka (Canada) and Thule (Greenland) polar sites during POLARCAT has been performed using the tagged contributions. It demonstrates the capability of these instruments for observing pollution at northern high latitudes. Differences between contributions from the sources to the tropospheric columns as measured by FTIR and IASI are discussed in terms of vertical sensitivity associated with these instruments. The first analysis of O_3 tropospheric columns observed by the IASI satellite instrument over the Arctic is also provided. Despite its limited vertical sensitivity in the lowermost atmospheric layers, we demonstrate that IASI is capable of detecting low-altitude pollution transported into the Arctic with some limitations

Identifying fire plumes in the Arctic with tropospheric FTIR measurements and transport models

We investigate Arctic tropospheric composition using ground-based Fourier transform infrared (FTIR) solar absorption spectra, recorded at the Polar Environment Atmospheric Research Laboratory (PEARL, Eureka, Nunavut, Canada, 80°05' N, 86°42' W) and at Thule (Greenland, 76°53' N, −68°74' W) from 2008 to 2012. The target species, carbon monoxide (CO), hydrogen cyanide (HCN), ethane (C_2H_6), acetylene (C_2H_2), formic acid (HCOOH), and formaldehyde (H_2CO) are emitted by biomass burning and can be transported from mid-latitudes to the Arctic. By detecting simultaneous enhancements of three biomass burning tracers (HCN, CO, and C_2H_6), ten and eight fire events are identified at Eureka and Thule, respectively, within the 5-year FTIR time series. Analyses of Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model back-trajectories coupled with Moderate Resolution Imaging Spectroradiometer (MODIS) fire hotspot data, Stochastic Time-Inverted Lagrangian Transport (STILT) model footprints, and Ozone Monitoring Instrument (OMI) UV aerosol index maps, are used to attribute burning source regions and travel time durations of the plumes. By taking into account the effect of aging of the smoke plumes, measured FTIR enhancement ratios were corrected to obtain emission ratios and equivalent emission factors. The means of emission factors for extratropical forest estimated with the two FTIR data sets are 0.40 ± 0.21 g kg^(−1) for HCN, 1.24 ± 0.71 g kg^(−1) for C_2H_6, 0.34 ± 0.21 g kg^(−1) for C_2H_2, and 2.92 ± 1.30 g kg^(−1) for HCOOH. The emission factor for CH_3OH estimated at Eureka is 3.44 ± 1.68 g kg^(−1). To improve our knowledge concerning the dynamical and chemical processes associated with Arctic pollution from fires, the two sets of FTIR measurements were compared to the Model for OZone And Related chemical Tracers, version 4 (MOZART-4). Seasonal cycles and day-to-day variabilities were compared to assess the ability of the model to reproduce emissions from fires and their transport. Good agreement in winter confirms that transport is well implemented in the model. For C_2H_6, however, the lower wintertime concentration estimated by the model as compared to the FTIR observations highlights an underestimation of its emission. Results show that modeled and measured total columns are correlated (linear correlation coefficient r > 0.6 for all gases except for H_2CO at Eureka and HCOOH at Thule), but suggest a general underestimation of the concentrations in the model for all seven tropospheric species in the high Arctic

Measuring Health Spillovers for Economic Evaluation: A Case Study in Meningitis

The health of carers and others close to the patient will often be relevant to economic evaluation, but it is very rarely considered in practice. This may reflect a lack of understanding of how the spillover effect of illness can be appropriately quantified. In this study we used three different approaches to quantify health spillovers resulting from meningitis. We conducted a survey of 1218 family networks affected by meningitis and used regression modelling to estimate spillover effects. The findings show that meningitis had long-term effects on family members' health, particularly affecting the likelihood of family members reporting anxiety and depression. These effects extended beyond a single close family member. These findings suggest that vaccinating against meningitis will bring significant health benefits not just to those that might have contracted the illness but also to their family networks. In methodological terms, different approaches for quantifying health spillovers provided broadly consistent results. The choice of method will be influenced by the ease of collecting primary data from family members in intervention contexts

A Model for the Analysis of Caries Occurrence in Primary Molar Tooth Surfaces

Recently methods of caries quantification in the primary dentition have moved away from summary ‘whole mouth’ measures at the individual level to methods based on generalised linear modelling (GLM) approaches or survival analysis approaches. However, GLM approaches based on logistic transformation fail to take into account the time-dependent process of tooth/surface survival to caries. There may also be practical difficulties associated with casting parametric survival-based approaches in a complex multilevel hierarchy and the selection of an optimal survival distribution, while non-parametric survival methods are not generally suitable for the assessment of supplementary information recorded on study participants. In the current investigation, a hybrid semi-parametric approach comprising elements of survival-based and GLM methodologies suitable for modelling of caries occurrence within fixed time periods is assessed, using an illustrative multilevel data set of caries occurrence in primary molars from a cohort study, with clustering of data assumed to occur at surface and tooth levels. Inferences of parameter significance were found to be consistent with previous parametric survival-based analyses of the same data set, with gender, socio-economic status, fluoridation status, tooth location, surface type and fluoridation status-surface type interaction significantly associated with caries occurrence. The appropriateness of the hierarchical structure facilitated by the hybrid approach was also confirmed. Hence the hybrid approach is proposed as a more appropriate alternative to primary caries modelling than non-parametric survival methods or other GLM-based models, and as a practical alternative to more rigorous survival-based methods unlikely to be fully accessible to most researchers

Iron Speciation in PM2.5 from Urban, Agriculture, and Mixed Environments in Colorado, USA

Atmospheric iron solubility varies depending on whether the particles are collected in rural or urban areas, with urban areas showing increased iron solubility. In this study, we investigate if the iron species present in different environments affects its ultimate solubility. Field data are presented from the Platte River Air Pollution and Photochemistry Experiment (PRAPPE), aimed at understanding the interactions between organic carbon and trace elements in atmospheric particulate matter (PM). 24-hr PM2.5 samples were collected during the summer and winter (2016–2017), at three different sites on the Eastern Colorado plains: an urban, agricultural, and a mixed site. Downtown Denver had an average total and water-soluble iron air concentration of 181.2 and 7.7 ng m−3, respectively. Platteville, the mixed site, had an average of total iron of 76.1 ng m−3, with average water-soluble iron concentration of 9.1 ng m−3. Jackson State Park (rural/agricultural) had the lowest total iron average of 31.5 ng m−3 and the lowest water-soluble iron average, 1.3 ng m−3. The iron oxidation state and chemical speciation of 97 samples across all sites and seasons was probed by X-ray absorption near edge structure (XANES) spectroscopy. The most common iron phases observed were almandine (Fe₃Al₂Si₃O₁₂) (Denver 21%, Platteville 16%, Jackson 24%), magnetite (Fe3O4) (Denver 9%, Platteville 4%, Jackson 5%) and Fe (III)dextran (Denver 5%, Platteville 13%, Jackson 5%), a surrogate for Fe-organic complexes. Additionally, native iron [Fe(0)] was found in significant amounts at all sites. No correlation was observed between iron solubility and iron oxidation state or chemical speciation

Ozone depletion events observed in the high latitude surface layer during the TOPSE aircraft program

During the Tropospheric Ozone Production about the Spring Equinox (TOPSE) aircraft program, ozone depletion events (ODEs) in the high latitude surface layer were investigated using lidar and in situ instruments. Flight legs of 100 km or longer distance were flown 32 times at 30 m altitude over a variety of regions north of 58° between early February and late May 2000. ODEs were found on each flight over the Arctic Ocean but their occurrence was rare at more southern latitudes. However, large area events with depletion to over 2 km altitude in one case were found as far south as Baffin Bay and Hudson Bay and as late as 22 May. There is good evidence that these more southern events did not form in situ but were the result of export of ozone-depleted air from the surface layer of the Arctic Ocean. Surprisingly, relatively intact transport of ODEs occurred over distances of 900–2000 km and in some cases over rough terrain. Accumulation of constituents in the frozen surface over the dark winter period cannot be a strong prerequisite of ozone depletion since latitudes south of the Arctic Ocean would also experience a long dark period. Some process unique to the Arctic Ocean surface or its coastal regions remains unidentified for the release of ozone-depleting halogens. There was no correspondence between coarse surface features such as solid ice/snow, open leads, or polynyas with the occurrence of or intensity of ozone depletion over the Arctic or subarctic regions. Depletion events also occurred in the absence of long-range transport of relatively fresh “pollution” within the high latitude surface layer, at least in spring 2000. Direct measurements of halogen radicals were not made. However, the flights do provide detailed information on the vertical structure of the surface layer and, during the constant 30 m altitude legs, measurements of a variety of constituents including hydroxyl and peroxy radicals. A summary of the behavior of these constituents is made. The measurements were consistent with a source of formaldehyde from the snow/ice surface. Median NOx in the surface layer was 15 pptv or less, suggesting that surface emissions were substantially converted to reservoir constituents by 30 m altitude and that ozone production rates were small (0.15–1.5 ppbv/d) at this altitude. Peroxyacetylnitrate (PAN) was by far the major constituent of NOy in the surface layer independent of the ozone mixing ratio

Steady state free radical budgets and ozone photochemistry during TOPSE

A steady state model, constrained by a number of measured quantities, was used to derive peroxy radical levels for the conditions of the Tropospheric Ozone Production about the Spring Equinox (TOPSE) campaign. The analysis is made using data collected aboard the NCAR/NSF C-130 aircraft from February through May 2000 at latitudes from 40° to 85°N, and at altitudes from the surface to 7.6 km. HO2 + RO2 radical concentrations were measured during the experiment, which are compared with model results over the domain of the study showing good agreement on the average. Average measurement/model ratios are 1.04 (σ = 0.73) and 0.96 (σ = 0.52) for the MLB and HLB, respectively. Budgets of total peroxy radical levels as well as of individual free radical members were constructed, which reveal interesting differences compared to studies at lower latitudes. The midlatitude part of the study region is a significant net source of ozone, while the high latitudes constitute a small net sink leading to the hypothesis that transport from the middle latitudes can explain the observed increase in ozone in the high latitudes. Radical reservoir species concentrations are modeled and compared with the observations. For most conditions, the model does a good job of reproducing the formaldehyde observations, but the peroxide observations are significantly less than steady state for this study. Photostationary state (PSS) derived total peroxy radical levels and NO/NO2ratios are compared with the measurements and the model; PSS-derived results are higher than observations or the steady state model at low NO concentrations