Testing the performance of field calibration techniques for low-cost gas sensors in new deployment locations: across a county line and across Colorado

We assessed the performance of ambient ozone (O3) and carbon dioxide (CO2) sensor field calibration techniques when they were generated using data from one location and then applied to data collected at a new location. This was motivated by a previous study (Casey et al., 2018), which highlighted the importance of determining the extent to which field calibration regression models could be aided by relationships among atmospheric trace gases at a given training location, which may not hold if a model is applied to data collected in a new location. We also explored the sensitivity of these methods in response to the timing of field calibrations relative to deployment periods. Employing data from a number of field deployments in Colorado and New Mexico that spanned several years, we tested and compared the performance of field-calibrated sensors using both linear models (LMs) and artificial neural networks (ANNs) for regression. Sampling sites covered urban and rural–peri-urban areas and environments influenced by oil and gas production. We found that the best-performing model inputs and model type depended on circumstances associated with individual case studies, such as differing characteristics of local dominant emissions sources, relative timing of model training and application, and the extent of extrapolation outside of parameter space encompassed by model training. In agreement with findings from our previous study that was focused on data from a single location (Casey et al., 2018), ANNs remained more effective than LMs for a number of these case studies but there were some exceptions. For CO2 models, exceptions included case studies in which training data collection took place more than several months subsequent to the test data period. For O3 models, exceptions included case studies in which the characteristics of dominant local emissions sources (oil and gas vs. urban) were significantly different at model training and testing locations. Among models that were tailored to case studies on an individual basis, O3 ANNs performed better than O3 LMs in six out of seven case studies, while CO2 ANNs performed better than CO2 LMs in three out of five case studies. The performance of O3 models tended to be more sensitive to deployment location than to extrapolation in time, while the performance of CO2 models tended to be more sensitive to extrapolation in time than to deployment location. The performance of O3 ANN models benefited from the inclusion of several secondary metal-oxide-type sensors as inputs in five of seven case studies.</p

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

Towards understanding the variability in biospheric CO2 fluxes:Using FTIR spectrometry and a chemical transport model to investigate the sources and sinks of carbonyl sulfide and its link to CO2

Understanding carbon dioxide (CO2) biospheric processes is of great importance because the terrestrial exchange drives the seasonal and interannual variability of CO2 in the atmosphere. Atmospheric inversions based on CO2 concentration measurements alone can only determine net biosphere fluxes, but not differentiate between photosynthesis (uptake) and respiration (production). Carbonyl sulfide (OCS) could provide an important additional constraint: it is also taken up by plants during photosynthesis but not emitted during respiration, and therefore is a potential means to differentiate between these processes. Solar absorption Fourier Transform InfraRed (FTIR) spectrometry allows for the retrievals of the atmospheric concentrations of both CO2 and OCS from measured solar absorption spectra. Here, we investigate co-located and quasi-simultaneous FTIR measurements of OCS and CO2 performed at five selected sites located in the Northern Hemisphere. These measurements are compared to simulations of OCS and CO2 using a chemical transport model (GEOS-Chem). The coupled biospheric fluxes of OCS and CO2 from the simple biosphere model (SiB) are used in the study. The CO2 simulation with SiB fluxes agrees with the measurements well, while the OCS simulation reproduced a weaker drawdown than FTIR measurements at selected sites, and a smaller latitudinal gradient in the Northern Hemisphere during growing season when comparing with HIPPO (HIAPER Pole-to-Pole Observations) data spanning both hemispheres. An offset in the timing of the seasonal cycle minimum between SiB simulation and measurements is also seen. Using OCS as a photosynthesis proxy can help to understand how the biospheric processes are reproduced in models and to further understand the carbon cycle in the real world

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&lt;sub&gt;2.5&lt;/sub&gt; 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

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

Revisiting global fossil fuel and biofuel emissions of ethane

Recent measurements over the Northern Hemisphere indicate that the long-term decline in the atmospheric burden of ethane (C2H6) has ended and the abundance increased dramatically between 2010 and 2014. The rise in C2H6 atmospheric abundances has been attributed to oil and natural gas extraction in North America. Existing global C2H6 emission inventories are based on outdated activity maps that do not account for current oil and natural gas exploitation regions. We present an updated global C2H6 emission inventory based on 2010 satellite-derived CH4 fluxes with adjusted C2H6 emissions over the U.S. from the National Emission Inventory (NEI 2011). We contrast our global 2010 C2H6 emission inventory with one developed for 2001. The C2H6 difference between global anthropogenic emissions is subtle (7.9 versus 7.2 Tg yr−1), but the spatial distribution of the emissions is distinct. In the 2010 C2H6 inventory, fossil fuel sources in the Northern Hemisphere represent half of global C2H6 emissions and 95% of global fossil fuel emissions. Over the U.S., unadjusted NEI 2011 C2H6 emissions produce mixing ratios that are 14–50% of those observed by aircraft observations (2008–2014). When the NEI 2011 C2H6 emission totals are scaled by a factor of 1.4, the Goddard Earth Observing System Chem model largely reproduces a regional suite of observations, with the exception of the central U.S., where it continues to underpredict observed mixing ratios in the lower troposphere. We estimate monthly mean contributions of fossil fuel C2H6 emissions to ozone and peroxyacetyl nitrate surface mixing ratios over North America of ~1% and ~8%, respectively