ALBERT

Description: Microbial respiration depends on microclimatic variables and carbon (C) substrate availability, all of which are altered when ecosystems experience major disturbance. Widespread tree mortality, currently affecting piñon-juniper ecosystems in Southwestern North America, may affect C substrate availability in several ways; for example, via litterfall pulses and loss of root exudation. To determine piñon mortality effects on C and water limitation of microbial respiration, we applied field amendments (sucrose and water) to two piñon-juniper sites in central New Mexico, USA: one with a recent (〈 1 yr), experimentally-induced mortality event and a nearby site with live canopy. We monitored the respiration response to water and sucrose applications to the litter surface and to the underlying mineral soil surface, testing the following hypotheses: (1) soil respiration in a piñon-juniper woodland is water- and labile C-limited in both the litter layer and mineral soil; (2) water and sucrose applications increase temperature sensitivity of respiration; (3) the mortality-affected site will show a reduction in C limitation in the litter; (4) the mortality-affected site will show an enhancement of C limitation in the mineral soil. Litter respiration at both sites responded to increased water availability, yet surprisingly, mineral soil respiration was not limited by water. Temperature sensitivity was enhanced by some of the sucrose and water treatments. Consistent with hypothesis 3, C limitation of litter respiration was lower at the recent mortality site compared to the intact canopy site. Results following applications to the mineral soil suggest the presence of abiotic effects of increasing water availability, precluding our ability to measure labile C limitation in soil. Widespread piñon mortality may decrease labile C limitation of litter respiration, at least during the first growing season following mortality.

Description: Microbial respiration depends on microclimatic variables and carbon (C) substrate availability, all of which are altered when ecosystems experience major disturbance. Widespread tree mortality, currently affecting piñon–juniper ecosystems in southwestern North America, may affect C substrate availability in several ways, for example, via litterfall pulses and loss of root exudation. To determine piñon mortality effects on C and water limitation of microbial respiration, we applied field amendments (sucrose and water) to two piñon–juniper sites in central New Mexico, USA: one with a recent (〈 1 yr), experimentally induced mortality event and a nearby site with live canopy. We monitored the respiration response to water and sucrose applications to the litter surface and to the underlying mineral soil surface, testing the following hypotheses: (1) soil respiration in a piñon–juniper woodland is water- and labile C-limited in both the litter layer and mineral soil; (2) piñon mortality reduces the C limitation of litter respiration; and (3) piñon mortality enhances the C limitation of mineral soil respiration. Litter respiration at both sites responded to increased water availability, yet surprisingly, mineral soil respiration was not limited by water. Consistent with hypothesis 2, C limitation of litter respiration was lower at the recent mortality site compared to the intact canopy site. Applications to the mineral soil showed evidence of reduction in CO2 flux on the girdled site and a non-significant increase on the control. We speculate that the reduction may have been driven by water-induced carbonate dissolution, which serves as a sink for CO2 and would reduce the net flux. Widespread piñon mortality may decrease labile C limitation of litter respiration, at least during the first growing season following mortality.

Description: We present results from and evaluate the performance of a 12 month, 12 km horizontal resolution air pollution simulation for the contiguous United States using the WRF-Chem (Weather Research and Forecasting with Chemistry) meteorology and chemical transport model (CTM). We employ the 2005 US National Emissions Inventory, the Regional Atmospheric Chemistry Mechanism (RACM), and the Modal Aerosol Dynamics Model for Europe (MADE) with a Volatility Basis Set (VBS) secondary aerosol module. Overall, model performance is comparable to contemporary models used for regulatory and health-effects analysis, with an annual average daytime ozone (O3) mean fractional bias (MFB) of 12% and an annual average fine particulate matter (PM2.5) MFB of −1%. WRF-Chem, as configured here, tends to overpredict total PM2.5 at some high concentration locations, and generally overpredicts average 24 h O3 concentrations, with better performance at predicting average daytime and daily peak O3 concentrations. Predictive performance for PM2.5 subspecies is mixed: the model overpredicts particulate sulfate (MFB = 65%), underpredicts particulate nitrate (MFB = −110%) and organic carbon (MFB = −65%), and relatively accurately predicts particulate ammonium (MFB = 3%) and elemental carbon (MFB = 3%), so that the accuracy in total PM2.5 predictions is to some extent a function of offsetting over- and underpredictions of PM2.5 subspecies. Model predictive performance for PM2.5 and its subspecies is in general worse in winter and in the western US than in other seasons and regions, suggesting spatial and temporal opportunities for future WRF-Chem model development and evaluation.

Description: Mechanistic air pollution models are essential tools in air quality management. Widespread use of such models is hindered, however, by the extensive expertise or computational resources needed to run most models. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations – the air pollution outcome generally causing the largest monetized health damages – attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical and chemical information from the output of a state-of-the-science chemical transport model (WRF-Chem) within an Eulerian modeling framework, to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. InMAP uses a variable resolution grid that focuses on human exposures by employing higher spatial resolution in urban areas and lower spatial resolution in rural and remote locations and in the upper atmosphere; and by directly calculating steady-state, annual average concentrations. In comparisons run here, InMAP recreates WRF-Chem predictions of changes in total PM2.5 concentrations with population-weighted mean fractional error (MFE) and bias (MFB) 〈 10 % and population-weighted R2 ~ 0.99. Among individual PM2.5 species, the best predictive performance is for primary PM2.5 (MFE: 16 %; MFB: 13 %) and the worst predictive performance is for particulate nitrate (MFE: 119 %; MFB: 106 %). Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. Features planned for future model releases include a larger spatial domain, more temporal information, and the ability to predict ground-level ozone (O3) concentrations. The InMAP model source code and input data are freely available online.

Description: We present results from and evaluate the performance of a 12-month, 12 km horizontal resolution year 2005 air pollution simulation for the contiguous United States using the WRF-Chem (Weather Research and Forecasting with Chemistry) meteorology and chemical transport model (CTM). We employ the 2005 US National Emissions Inventory, the Regional Atmospheric Chemistry Mechanism (RACM), and the Modal Aerosol Dynamics Model for Europe (MADE) with a volatility basis set (VBS) secondary aerosol module. Overall, model performance is comparable to contemporary modeling efforts used for regulatory and health-effects analysis, with an annual average daytime ozone (O3) mean fractional bias (MFB) of 12% and an annual average fine particulate matter (PM2.5) MFB of −1%. WRF-Chem, as configured here, tends to overpredict total PM2.5 at some high concentration locations and generally overpredicts average 24 h O3 concentrations. Performance is better at predicting daytime-average and daily peak O3 concentrations, which are more relevant for regulatory and health effects analyses relative to annual average values. Predictive performance for PM2.5 subspecies is mixed: the model overpredicts particulate sulfate (MFB = 36%), underpredicts particulate nitrate (MFB = −110%) and organic carbon (MFB = −29%), and relatively accurately predicts particulate ammonium (MFB = 3%) and elemental carbon (MFB = 3%), so that the accuracy in total PM2.5 predictions is to some extent a function of offsetting over- and underpredictions of PM2.5 subspecies. Model predictive performance for PM2.5 and its subspecies is in general worse in winter and in the western US than in other seasons and regions, suggesting spatial and temporal opportunities for future WRF-Chem model development and evaluation.