ALBERT

Description: The occurrence of nonliquid and liquid physical states of submicron atmospheric particulate matter (PM) downwind of an urban region in central Amazonia was investigated. Measurements were conducted during two intensive operating periods (IOP1 and IOP2) that took place during the wet and dry seasons of the GoAmazon2014/5 campaign. Air masses representing variable influences of background conditions, urban pollution, and regional- and continental-scale biomass burning passed over the research site. As the air masses varied, particle rebound fraction, an indicator of physical state, was measured in real time at ground level using an impactor apparatus. Micrographs collected by transmission electron microscopy confirmed that liquid particles adhered, while nonliquid particles rebounded. Relative humidity (RH) was scanned to collect rebound curves. When the apparatus RH matched ambient RH, 95 % of the particles adhered as a campaign average. Secondary organic material, produced for the most part by the oxidation of volatile organic compounds emitted from the forest, produces liquid PM over this tropical forest. During periods of anthropogenic influence, by comparison, the rebound fraction dropped to as low as 60 % at 95 % RH. Analyses of the mass spectra of the atmospheric PM by positive-matrix factorization (PMF) and of concentrations of carbon monoxide, total particle number, and oxides of nitrogen were used to identify time periods affected by anthropogenic influences, including both urban pollution and biomass burning. The occurrence of nonliquid PM at high RH correlated with these indicators of anthropogenic influence. A linear model having as output the rebound fraction and as input the PMF factor loadings explained up to 70 % of the variance in the observed rebound fractions. Anthropogenic influences can contribute to the presence of nonliquid PM in the atmospheric particle population through the combined effects of molecular species that increase viscosity when internally mixed with background PM and increased concentrations of nonliquid anthropogenic particles in external mixtures of anthropogenic and biogenic PM.

Description: During the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign, size-resolved cloud condensation nuclei (CCN) spectra were characterized at a research site (T3) 60 km downwind of the city of Manaus, Brazil, in central Amazonia for 1 year (12 March 2014 to 3 March 2015). Particle hygroscopicity (κCCN) and mixing state were derived from the size-resolved CCN spectra, and the hygroscopicity of the organic component of the aerosol (κorg) was then calculated from κCCN and concurrent chemical composition measurements. The annual average κCCN increased from 0.13 at 75 nm to 0.17 at 171 nm, and the increase was largely due to an increase in sulfate volume fraction. During both wet and dry seasons, κCCN, κorg, and particle composition under background conditions exhibited essentially no diel variations. The constant κorg of ∼ 0. 15 is consistent with the largely uniform and high O : C value (∼ 0. 8), indicating that the aerosols under background conditions are dominated by the aged regional aerosol particles consisting of highly oxygenated organic compounds. For air masses strongly influenced by urban pollution and/or local biomass burning, lower values of κorg and organic O : C atomic ratio were observed during night, due to accumulation of freshly emitted particles, dominated by primary organic aerosol (POA) with low hygroscopicity, within a shallow nocturnal boundary layer. The O : C, κorg, and κCCN increased from the early morning hours and peaked around noon, driven by the formation and aging of secondary organic aerosol (SOA) and dilution of POA emissions into a deeper boundary layer, while the development of the boundary layer, which leads to mixing with aged particles from the residual layer aloft, likely also contributed to the increases. The hygroscopicities associated with individual organic factors, derived from PMF (positive matrix factorization) analysis of AMS (aerosol mass spectrometry) spectra, were estimated through multivariable linear regression. For the SOA factors, the variation of the κ value with O : C agrees well with the linear relationship reported from earlier laboratory studies of SOA hygroscopicity. On the other hand, the variation in O : C of ambient aerosol organics is largely driven by the variation in the volume fractions of POA and SOA factors, which have very different O : C values. As POA factors have hygroscopicity values well below the linear relationship between SOA hygroscopicity and O : C, mixtures with different POA and SOA fractions exhibit a steeper slope for the increase in κorg with O : C, as observed during this and earlier field studies. This finding helps better understand and reconcile the differences in the relationships between κorg and O : C observed in laboratory and field studies, therefore providing a basis for improved parameterization in global models, especially in a tropical context.

Description: Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.

Description: The occurrence of non-liquid and liquid physical states of submicron atmospheric particulate matter (PM) downwind of an urban region in central Amazonia was investigated. Measurements were conducted during two Intensive Operating Periods (IOP1 and IOP2) that took place during the wet and dry seasons, respectively, of the GoAmazon2014/5 campaign. Air masses representing variable influences of background conditions, urban pollution, and regional and continental scale biomass burning passed over the research site. As the air masses varied, particle rebound fraction, which is an indicator of the mix of physical states in a sampled particle population, was measured in real time at ground level using an impactor apparatus. Micrographs collected by transmission electron microscopy confirmed that liquid particles adhered while non-liquid particles rebounded. Relative humidity (RH) was scanned to collect rebound curves. When the apparatus RH matched ambient RH, 95 % of the particles were liquid as a campaign average, although this percentage dropped to as low as 60 % during periods of anthropogenic influence. Secondary organic material, produced for the most part by the oxidation of volatile organic compounds emitted from the forest, was the largest source of liquid PM. Analyses of the mass spectra of the atmospheric PM by positive-matrix factorization (PMF) and of concentrations of carbon monoxide, total particle number, and oxides of nitrogen were used to identify time periods affected by anthropogenic influences, including both urban pollution and biomass burning. The occurrence of non-liquid PM correlated with these indicators of anthropogenic influence. A linear model having as output the rebound fraction and as input the PMF factor loadings explained up to 70 % of the variance in the observed rebound fractions. Anthropogenic influences appear to favor non-liquid PM by providing molecular species that increase viscosity when internally mixed with background PM, by contributing non-liquid particles in external mixtures of PM, and a by combination of these effects under real-world conditions.

Description: During the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign, size-resolved cloud condensation nuclei (CCN) spectra were characterized at a research site (T3) 60 km downwind of the city of Manaus, Brazil, in central Amazonia for one year (12 March 2014 to 3 March 2015). Particle hygroscopicity (κCCN) and mixing state were derived from the size-resolved CCN spectra, and the hygroscopicity of the organic component of the aerosol (κorg) was then calculated from κCCN and concurrent chemical composition measurements. The annual average κCCN increased from 0.13 at 75 nm to 0.17 at 171 nm, and the increase was largely due to an increase in sulfate volume fraction. During both wet and dry seasons, κCCN, κorg, and particle composition under background conditions exhibited essentially no diel variations. The constant κorg of ~ 0.15 is consistent with the largely uniform and high O : C value (~ 0.8), indicating that the aerosols under background conditions are dominated by the aged regional aerosol particles consisting of highly oxygenated organic compounds. For air masses strongly influenced by urban pollution and/or local biomass burning, lower values of κorg and organic O : C atomic ratio were observed during night, due to accumulation of freshly emitted particles, dominated by primary organic aerosol (POA) with low hygroscopicity, within a shallow nocturnal boundary layer. The O : C, κorg, and κCCN increased from the early morning hours and peaked around noon, driven by the formation and aging of secondary organic aerosol (SOA) and dilution of POA emissions into a deeper boundary layer, while the development of the boundary layer, which leads to mixing with aged particles from the residual layer aloft, likely also contributed to the increases. The hygroscopicities associated with individual organic factors, derived from PMF analysis of AMS spectra, were estimated through multi-variable linear regression. For the SOA factors, the variation of the κ value with O : C agrees well with the linear relationship reported from earlier laboratory studies of SOA hygroscopicity. On the other hand, the variation in O:C of ambient aerosol organics is largely driven by the variation in the volume fractions of POA and SOA factors, which have very different O : C values. As POA factors have hygroscopicity values well below the linear relationship between SOA hygroscopicity and O:C, mixtures with different POA and SOA fractions exhibit a steeper slope for the increase of κorg with O : C, as observed during this and earlier field studies. This finding helps better understand and reconcile the differences in the relationships between κorg and O : C observed in laboratory and field studies, therefore providing a basis for improved parameterization in global models, especially in a tropical context.

Description: Health risks connected with fine particulate matter (PM2.5) pollutants are well documented; increased risks of asthma, heart attack and heart failure are a few of the effects associated with PM2.5. Accurately forecasting PM2.5 is crucial for state agencies directed to devise State Implementation Plans (SIPS) to deal with National Ambient Air Quality Standards (NAAQS) exceedances. In previous work, we explored the application of multi-temporal data-driven neural networks (NNs) to forecasting PM2.5. Our work showed that under different input conditions, the NN approach achieves higher forecasting scores for local (12 km) resolution when compared to the other Chemical Transport Model forecast models, such as the Community Multi-Scale Air Quality system (CMAQ). Critical to our approach was the inclusion of prior PM2.5 concentrations, retrieved from ground monitoring stations, as part of the input dataset for the NN. The NN approach can provide high-level forecasting accuracy; however, because of the dependency on ground monitoring stations, the forecast coverage is sparse. Here, we extend our previous station-specific efforts by forecasting hourly PM2.5 values that are spatially continuous through the use of a deep neural network (DNN). The DNN approach combines spatial Kriging with additional local source variables to interpolate the measured PM2.5 concentrations across non-station locations. These interpolated PM2.5 values are used as inputs in the original forecasting NN. Cross-validation testing, using all New York State AirNow PM2.5 stations, showed that this forecast approach achieves accurate results, with a regression coefficient (R2) of 0.59, and a root mean square error (RMSE) of 2.22μgm3. Additionally, herein we demonstrate the usefulness of this approach on specific temporal events where significant dynamics of PM2.5 were observed; particularly, we show that even bias-corrected CMAQ forecasts do not track these transients and our NN method.