Regional background ozone estimation for China through data fusion of observation and simulation.

Regional background ozone (O3_RBG) is an important component of surface ozone (O3). However, due to the uncertainties in commonly used Chemical Transport Models (CTMs) and statistical models, accurately assessing O3_RBG in China is challenging. In this study, we calculated the O3_RBG concentrations with the CTM - Brute Force Method (BFM) and constrained the results with site observations of O3 with the multiple linear regression (MLR) model. The annual average O3_RBG concentration in China region in 2020 is 35 ± 4 ppb, accounting for 81 ± 5 % of the maximum 8-h average O3 (MDA8 O3). We applied the random forest and Shapley additive explanations based on meteorological standardization techniques to separate the contributions of meteorology and natural emissions to O3_RBG. Natural emissions contribute more significantly to O3_RBG than meteorology in various Chineses regions (30-40 ppb), with higher contributions during the warm season. Meteorological factors show higher contributions in the spring and summer seasons (2-3 ppb) than the other seasons. Temperature and humidity are the primary contributors to O3_RBG in regions with severe O3 pollution in China, with their individual impacts ranging from 30 % to 62 % of the total impacts of all meteorological factors in different seasons. For policy implications, we tracked the contributions of O3_RBG and local photochemical reaction contributions (O3_LC) to total O3 concentration at different O3 levels. We found that O3_LC contribute over 45 % to MDA8 O3 on polluted days, supporting the current Chinese policy of reducing O3 peak concentrations by cutting down precursor emissions. However, as the contribution of O3_RBG is not considered in the policy, additional efforts are needed to achieve the control groal of O3 concentration. As the implementation of stringent O3 control measurements in China, the contribution of O3_RBG become increasingly significant, suggesting the need for attention to O3_RBG and regional joint prevention and control.

Ozone response modeling to NOx and VOC emissions: Examining machine learning models.

Current machine learning (ML) applications in atmospheric science focus on forecasting and bias correction for numerical modeling estimations, but few studies examined the nonlinear response of their predictions to precursor emissions. This study uses ground-level maximum daily 8-hour ozone average (MDA8 O3) as an example to examine O3 responses to local anthropogenic NOx and VOC emissions in Taiwan by Response Surface Modeling (RSM). Three different datasets for RSM were examined, including the Community Multiscale Air Quality (CMAQ) model data, ML-measurement-model fusion (ML-MMF) data, and ML data, which respectively represent direct numerical model predictions, numerical predictions adjusted by observations and other auxiliary data, and ML predictions based on observations and other auxiliary data. The results show that both ML-MMF (r = 0.93-0.94) and ML predictions (r = 0.89-0.94) present significantly improved performance in the benchmark case compared with CMAQ predictions (r = 0.41-0.80). While ML-MMF isopleths exhibit O3 nonlinearity close to actual responses due to their numerical base and observation-based correction, ML isopleths present biased predictions concerning their different controlled ranges of O3 and distorted O3 responses to NOx and VOC emission ratios compared with ML-MMF isopleths, which implies that using data without support from CMAQ modeling to predict the air quality could mislead the controlled targets and future trends. Meanwhile, the observation-corrected ML-MMF isopleths also emphasize the impact of transboundary pollution from mainland China on the regional O3 sensitivity to local NOx and VOC emissions, which transboundary NOx would make all air quality regions in April more sensitive to local VOC emissions and limit the potential effort by reducing local emissions. Future ML applications in atmospheric science like forecasting or bias correction should provide interpretability and explainability, except for meeting statistical performance and providing variable importance. Assessment with interpretable physical and chemical mechanisms and constructing a statistically robust ML model should be equally important.

Quantifying the wet deposition of reactive nitrogen over China: Synthesis of observations and models.

Accurate estimation on reaction nitrogen (Nr) deposition is highly demanded for assessing the impacts on the environment and human beings. This study investigated the wet deposition of inorganic nitrogen (IN) in mainland China by measurements from over 500 sites from five observational networks/databases and ensemble results of eleven chemical transport models (CTMs). Each data source has its focus and limitations and together formed a comprehensive view over China. But the inconsistency among different sources may hinder the appropriate usage of data. Model evaluation results demonstrated the models' deficiency in simulating the wet NO3- deposition over Southeast China (40 % underestimation) and showed an overall underestimation of wet NH4+ deposition over the hotspot regions (5-60 % underestimation). A synthesis of this study and twelve reference studies was conducted to quantify the national amount of wet IN deposition. The estimations by CTMs ranged 2.4-3.9 Tg(N) yr-1 for wet NOy deposition and 4-6.7 Tg(N) yr-1 for wet NHx deposition, after adjusting the results with 10-19 % underestimations in wet NOy deposition and 1-40 % underestimations in wet NHx deposition. The estimations by ground observations ranged 7.1-9 Tg(N) yr-1 for wet NOy deposition and 8-13.1 Tg(N) yr-1 for wet NHx deposition, which were 20-275 % higher than the estimation by CTMs, but the results were strongly influenced by the abundances and representative of measurements. Studies using statistical techniques to interpolate site observations predicted 3-5.5 Tg(N) yr-1 for wet NOy deposition and 3.9-7.2 Tg(N) yr-1 for wet NHx deposition. This approach benefited from high accuracy and good robustness of the statistical models, but the uncertainty in the interpolation methods could be a potential drawback.

Understanding the role of anthropogenic emissions in glaciers retreat in the central Andes of Chile.

Glaciers in Chilean Central Andes have significatively retreated, at least, in the last 60 years. From 2004 to 2014, the largest retreat in the area (-0.15 km2 yr-1) was observed at Olivares Alpha Glacier (OAG). Previous glacier fluctuation studies proposed that two open-pit mines distant 7 km from the glacier could be the cause of its enhanced retreat. However, this had not been yet tested due to the lack of measured data. Here, we investigated the impact that major air pollutants emitted by local mining activities could have on the differences observed in OAG glacial retreat compared with a glacier of similar size and altitude with no nearby anthropogenic sources: Bello Glacier (BG), which has a reported lower retreat (-0.02 km2 yr-1). Results revealed a link between anthropogenic air pollutants and glacial retreat rates, meaning that glacial retreat is decoupled from climatic and glaciological factors. Considering that both glaciers are located in the same climatic setting, the anthropogenic air pollutants deposited onto the OAG surface appear to be forcing positive feedback in which the pollutants deposition best explain the differences in the glacier retreat. With the results of this study, it has been calculated that the impact of mining in OAG could be responsible for 82% of its total retreat since between 2004 and 2014, and only the remaining 18% would correspond to the impact of climate change.

Localized energy burden, concentrated disadvantage, and the feminization of energy poverty.

Energy burden directly influences households' health and safety. Amid a growing literature on energy, poverty and gender remains relatively understudied. We evaluate socioeconomic, geographic, and health factors as multidimensions of concentrated disadvantage that magnify energy burden in the United States over time. We show that the energy burden is more pronounced in disadvantaged counties with larger elderly, impoverished, disabled people, and racialized populations where people do not have health insurance. Neighborhoods with households headed by women of color (especially Black women) are more likely to face a high energy burden, which worsened during the COVID-19 pandemic. Although energy costs are often regarded as an individual responsibility, these findings illustrate the feminization of energy poverty and indicate the need for an intersectional and interdisciplinary framework in devising energy policy directed to households with the most severe energy burden.

Projections of future wildfires impacts on air pollutants and air toxics in a changing climate over the western United States.

Wildfires emit smoke particles and gaseous pollutants that greatly aggravate air quality and cause adverse health impacts in the western US (WUS). This study evaluates how wildfire impacts on air pollutants and air toxics evolve from the present climate to the future climate under a high anthropogenic emission scenario at regional and city scales. Through employing multiple climate and chemical transport models, small changes in domain-averaged air pollutant concentrations by wildfires are simulated over WUS. However, such changes significantly increase future city-scale pollutant concentrations by up to 53 ppb for benzene, 158 ppb for formaldehyde, 655 µg/m3 for fine particulate matter (PM2.5), and 102 ppb for ozone, whereas that for the present climate are 104 ppb for benzene, 332 ppb for formaldehyde, 1,378 µg/m3 for PM2.5, and 140 ppb for ozone. Despite wildfires induce smaller changes in the future, the wildfire contribution ratios can increase by more than tenfold compared to the present climate, indicating wildfires become a more critical contributor to future air pollution in WUS. In addition, additional 6 exceedance days/year for formaldehyde and additional 3 exceedance days/year for ozone suggest increasing health impacts by wildfires in the future.

Improving Estimates of Sulfur, Nitrogen, and Ozone Total Deposition through Multi-Model and Measurement-Model Fusion Approaches.

Earth system and environmental impact studies need high quality and up-to-date estimates of atmospheric deposition. This study demonstrates the methodological benefits of multimodel ensemble and measurement-model fusion mapping approaches for atmospheric deposition focusing on 2010, a year for which several studies were conducted. Global model-only deposition assessment can be further improved by integrating new model-measurement techniques, including expanded capabilities of satellite observations of atmospheric composition. We identify research and implementation priorities for timely estimates of deposition globally as implemented by the World Meteorological Organization.

Quantifying spatial heterogeneity of vulnerability to short-term PM2.5 exposure with data fusion framework.

The current estimations of the burden of disease (BD) of PM2.5 exposure is still potentially biased by two factors: ignorance of heterogeneous vulnerabilities at diverse urbanization levels and reliance on the risk estimates from existing literature, usually from different locations. Our objectives are (1) to build up a data fusion framework to estimate the burden of PM2.5 exposure while evaluating local risks simultaneously and (2) to quantify their spatial heterogeneity, relationship to land-use characteristics, and derived uncertainties when calculating the disease burdens. The feature of this study is applying six local databases to extract PM2.5 exposure risk and the BD information, including the risks of death, cardiovascular disease (CVD), and respiratory disease (RD), and their spatial heterogeneities through our data fusion framework. We applied the developed framework to Tainan City in Taiwan as a use case estimated the risks by using 2006-2016 emergency department visit data, air quality monitoring data, and land-use characteristics and further estimated the BD caused by daily PM2.5 exposure in 2013. Our results found that the risks of CVD and RD in highly urbanized areas and death in rural areas could reach 1.20-1.57 times higher than average. Furthermore, we performed a sensitivity analysis to assess the uncertainty of BD estimations from utilizing different data sources, and the results showed that the uncertainty of the BD estimations could be contributed by different PM2.5 exposure data (20-32%) and risk values (0-86%), especially for highly urbanized areas. In conclusion, our approach for estimating BD based on local databases has the potential to be generalized to the developing and overpopulated countries and to support local air quality and health management plans.

The black carbon dispersion in the Southern Hemisphere and its transport and fate to Antarctica, an Anthropocene evidence for climate change policies.

Black carbon (BC) has been measured in Antarctica's air, and its global warming effect can potentially speed up the ice melting in the most solid water reservoir of the planet. However, the primary responsible sources are not well evidenced in this region. The dispersion of black carbon emissions from the Southern Hemisphere was conducting using atmospheric chemical transport model and we compared the results with satellite registries from March 1st to April 30th in 2014. The emission inventory considered the anthropogenic and biomass burning emissions from global datasets. The largest and most populated cities in Southern Hemisphere showed the higher emission of BC. As a result, the average daily concentrations of atmospheric BC were around 4 ng/m3 in most regions of Antarctica according to its pristine characteristics. We analyzed fifteen relevant sites in coastal zones of Antartica and some peaks registered by the satellite records were not replicated by model outputs and it was mainly associated with the lack of emissions. Finally, we made simulations in the same period without biomass burning emissions and we observed decreased concentrations of BC in the range of 20-50%. As a result, we show that the black carbon transportation from the continental land to the polar region took place in 17-24 days during the Austral summer and the biomass burning emissions were the primary source. Black Carbon deposition in Antarctica is not permanent, but the uncontrolled emissions from Southern Hemisphere can increase its transportation to the white continent and make its accumulation during the period when the weak polar vortex occurs.

Predicting U.S. Residential Building Energy Use and Indoor Pollutant Exposures in the Mid-21st Century.

The extent to which climate change and other factors will influence building energy use and population exposures to indoor pollutants is not well understood. Here, we develop and apply nationally representative residential energy and indoor pollutant model sets to estimate energy use, indoor pollutant concentrations, and associated chronic health outcomes across the U.S. residential building stock in the mid-21st century. The models incorporate expected changes in meteorological and ambient air quality conditions associated with IPCC RCP 8.5 and assumptions for changes in housing characteristics and population movements while keeping other less predictable factors constant. Site and source energy consumption for residential space-conditioning are predicted to decrease by ∼37-43 and ∼20-31%, respectively, in the 2050s compared to those in a 2010s reference scenario. Population-average indoor concentrations of pollutants of ambient origin are expected to decrease, except for O3. Holding indoor emission factors constant, indoor concentrations of pollutants with intermittent indoor sources are expected to decrease by <5% (PM2.5) to >30% (NO2); indoor concentrations of pollutants with persistent indoor sources (e.g., volatile organic compounds (VOCs)) are predicted to increase by ∼15-45%. We estimate negligible changes in disability-adjusted life-years (DALYs) lost associated with residential indoor pollutant exposures, well within uncertainty, although the attribution among pollutants is predicted to vary.

Evaluating the impact of mobility on COVID-19 pandemic with machine learning hybrid predictions.

COVID-19 pandemic had expanded to the US since early 2020 and has caused nationwide economic loss and public health crisis. Until now, although the US has the most confirmed cases in the world and are still experiencing an increasing pandemic, several states insisted to re-open business activities and colleges while announced strict control measures. To provide a quantitative reference for official strategies, predicting the near future trend based on finer spatial resolution data and presumed scenarios are urgently needed. In this study, the first attempted COVID-19 case predicting model based on county-level demographic, environmental, and mobility data was constructed with multiple machine learning techniques and a hybrid framework. Different scenarios were also applied to selected metropolitan counties including New York City, Cook County in Illinois, Los Angeles County in California, and Miami-Dade County in Florida to assess the impact from lockdown, Phase I, and Phase III re-opening. Our results showed that, for selected counties, the mobility decreased substantially after the lockdown but kept increasing with an apparent weekly pattern, and the weekly pattern of mobility and infections implied high infections during the weekend. Meanwhile, our model was successfully built up, and the scenario assessment results indicated that, compared with Phase I re-opening, a 1-week and a 2-week lockdown could reduce 4%-29% and 15%-55% infections, respectively, in the future week, while 2-week Phase III re-opening could increase 16%-80% infections. We concluded that the mandatory orders in metropolitan counties such lockdown should last longer than one week, the effect could be observed. The impact of lockdown or re-opening was also county-dependent and varied with the local pandemic. In future works, we expect to involve a longer period of data, consider more county-dependent factors, and employ more sophisticated techniques to decrease the modeling uncertainty and apply it to counties nationally and other countries.

Transient risk of ambient fine particulate matter on hourly cardiovascular events in Tainan City, Taiwan.

BACKGROUND: The association between daily changes in ambient fine particulate matter (PM2.5) and cardiovascular diseases have been well established in mechanistic, epidemiologic and exposure studies. Only a few studies examined the effect of hourly variations in air pollution on triggering cardiovascular events. Whether the current PM2.5 standards can protect vulnerable individuals with chronic cardiovascular diseases remain uncertain. METHODS: we conducted a time-stratified, case-crossover study to assess the associations between hourly changes in PM2.5 levels and the vascular disease onset in residents of Tainan City, Taiwan, visiting Emergency Room of Chi Mei Medical Center between January 2006 and December 2016. There were 26,749 cases including 10,310 females (38.5%) and 16,439 males (61.5%) identified. The time of emergency visit was identified as the onset for each case and control cases were selected as the same times on other days, on the same day of the week in the same month and year respectively. Residential address was used to identify the ambient air pollution exposure concentrations from the closest station. Conditional logistic regression with the stepwise selection method was used to estimate adjusted odds ratios (ORs) for the association. RESULTS: When we only included cases occurring at PM2.5>10 µg/m3 and PM2.5>25 µg/m3, very significant ORs could be observed for 10 µg/m3 increases in PM2.5 at 0 and 1 hour, implying fine particulate exposure could promptly trigger vascular disease events. Moreover, a very clear increase in risk could be observed with cumulative exposure from 0 to 48 hours, especially in those cases where PM2.5>25 µg/m3. CONCLUSIONS: Our study demonstrated that transient and low concentrations of ambient PM2.5 trigger adult vascular disease events, especially cerebrovascular disease, regardless of age, sex, and exposure timing. Warning and delivery systems should be setup to protect people from these prompt adverse health impacts.

The climate impact on atmospheric stagnation and capability of stagnation indices in elucidating the haze events over North China Plain and Northeast China.

In this study, the spatial pattern and temporal evolution of PM2.5 over North China Plain (NCP) and Northeast China (NEC) during 2014-2018 was investigated. The annual mean PM2.5 shows clear decreasing trends over time, but the seasonal mean PM2.5 as well as the seasonal total duration and frequency of haze days shows large inter-annual fluctuation. Based on the atmospheric stagnation index (ASI), this study examined the correlation between ASI and haze events over NCP and NEC. Detailed analysis indicates that location dependency exists of ASI in the capability of capturing the haze events, and the ability is limited in NCP. Therefore, we first propose two alternative methods in defining the ASI to either account for the lag effect or enlarge the threshold value of wind speed at 500 hPa. The new methods can improve the ability of ASI to explain the haze events over NEC, though marginal improvement was achieved in NCP. Furthermore, this study constructed the equation based on the boundary layer height and wind speed at 10-meter, apparently improving the ability in haze capture rate (HCR), a ratio of haze days during the stagnation to the total haze days. Based on a multi-model ensemble analyses under Representative Concentration Pathway (RCP) 8.5, we found that by the end of this century, climate change may lead to increases in both the duration and frequency of wintertime stagnation events over NCP. In contrast, the models predict a decrease in stagnant events and the total duration of stagnation in winter over NEC.

Ammonia emission abatement does not fully control reduced forms of nitrogen deposition.

Human activities and population growth have increased the natural burden of reactive nitrogen (N) in the environment. Excessive N deposition on Earth's surface leads to adverse feedbacks on ecosystems and humans. Similar to that of air pollution, emission control is recognized as an efficient means to control acid deposition. Control of nitrogen oxides (NOx = NO + NO2) emissions has led to reduction in deposition of oxidized nitrogen (NOy, the sum of all oxidized nitrogen species, except nitrous oxide [N2O]). Reduced forms of nitrogen (NHx = ammonia [NH3] + ammonium [NH4+]) deposition have, otherwise, increased, offsetting the benefit of reduction in NOy deposition. Stringent control of NH3 emissions is being considered. In this study, we assess the response of N deposition to N emission control on continental regions. We show that significant reduction of NHx deposition is unlikely to be achieved at the early stages of implementing NH3 emission abatement. Per-unit NH3 emission abatement is shown to result in only 60-80% reduction in NHx deposition, which is significantly lower than the demonstrated 80-120% benefit of controlling NOx emissions on NOy deposition. This 60-80% effectiveness of NHx deposition reduction per unit NH3 emission abatement reflects, in part, the effects of simultaneous reductions in NOx and SO2 emissions.

A first-principles investigation of α, ß, and γ-MnO2 as potential cathode materials in Al-ion batteries.

An inexpensive and eco-friendly alternative energy storage solution is becoming more in demand as the world moves towards greener technology. We used first principles calculations to investigate α, ß, and γ-MnO2 and their Al-ion intercalation mechanism in potential applications for aluminum batteries. We explored these complexes through investigating properties such as volume change, binding/diffusion energy, and band gap to gauge each material. α-MnO2 had almost no volume change. γ-MnO2 had the lowest binding energy and diffusion barrier. Our study gives insight into the feasibility of using MnO2 in aluminum batteries and guides investigation of the material within its different phases.

Associations of wildfire smoke PM2.5 exposure with cardiorespiratory events in Colorado 2011-2014.

BACKGROUND: Substantial increases in wildfire activity have been recorded in recent decades. Wildfires influence the chemical composition and concentration of particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5). However, relatively few epidemiologic studies focus on the health impacts of wildfire smoke PM2.5 compared with the number of studies focusing on total PM2.5 exposure. OBJECTIVES: We estimated the associations between cardiorespiratory acute events and exposure to smoke PM2.5 in Colorado using a novel exposure model to separate smoke PM2.5 from background ambient PM2.5 levels. METHODS: We obtained emergency department visits and hospitalizations for acute cardiorespiratory outcomes from Colorado for May-August 2011-2014, geocoded to a 4 km geographic grid. Combining ground measurements, chemical transport models, and remote sensing data, we estimated smoke PM2.5 and non-smoke PM2.5 on a 1 km spatial grid and aggregated to match the resolution of the health data. Time-stratified, case-crossover models were fit using conditional logistic regression to estimate associations between fire smoke PM2.5 and non-smoke PM2.5 for overall and age-stratified outcomes using 2-day averaging windows for cardiovascular disease and 3-day windows for respiratory disease. RESULTS: Per 1 µg/m3 increase in fire smoke PM2.5, statistically significant associations were observed for asthma (OR = 1.081 (1.058, 1.105)) and combined respiratory disease (OR = 1.021 (1.012, 1.031)). No significant relationships were evident for cardiovascular diseases and smoke PM2.5. Associations with non-smoke PM2.5 were null for all outcomes. Positive age-specific associations related to smoke PM2.5 were observed for asthma and combined respiratory disease in children, and for asthma, bronchitis, COPD, and combined respiratory disease in adults. No significant associations were found in older adults. DISCUSSION: This is the first multi-year, high-resolution epidemiologic study to incorporate statistical and chemical transport modeling methods to estimate PM2.5 exposure due to wildfires. Our results allow for a more precise assessment of the population health impact of wildfire-related PM2.5 exposure in a changing climate.

Nonlinear relationships between air pollutant emissions and PM2.5-related health impacts in the Beijing-Tianjin-Hebei region.

A direct and quantitative linkage of air pollution-related health effects to emissions from different sources is critically important for decision-making. While a number of studies have attributed the PM2.5-related health impacts to emission sources, they have seldom examined the complicated nonlinear relationships between them. Here we investigate the nonlinear relationships between PM2.5-related premature mortality in the Beijing-Tianjin-Hebei (BTH) region, one of the most polluted regions in the world, and emissions of different pollutants from multiple sectors and regions, through a combination of chemical transport model (CTM), extended response surface model (ERSM), and concentration-response functions (CRFs). The mortalities due to both long-term and short-term exposures to PM2.5 are most sensitive to the emission reductions of primary PM2.5, followed by NH3, nonmethane volatile organic compounds and intermediate volatility organic compounds (NMVOC+IVOC). The sensitivities of long-term mortality to emissions of primary organic aerosol (POA), NMVOC+IVOC and SO2 do not change much with reduction ratio, whereas the sensitivities to primary inorganic PM2.5 (defined as all chemical components of primary PM2.5 other than POA), NH3 and NOx increase significantly with the increase of reduction ratio. The emissions of primary PM2.5, especially those from the residential and commercial sectors, contribute a larger fraction of mortality in winter (57-70%) than in other seasons (28-42%). When emissions of multiple pollutants or those from both local and regional emissions are controlled simultaneously, the overall sensitivity of long-term mortality is much larger than the arithmetic sum of the sensitivities to emissions of individual pollutants or from individual regions. This implies that a multi-pollutant, multi-sector and regional joint control strategy should be implemented to maximize the marginal health benefits. For NOx emissions, we suggest a nationwide control strategy which significantly enhances the effectiveness for reducing mortality by avoiding possible side effects when only the emissions within the BTH region are reduced.

Spatial variation of modelled total, dry and wet nitrogen deposition to forests at global scale.

Forests are an important biome that covers about one third of the global land surface and provides important ecosystem services. Since atmospheric deposition of nitrogen (N) can have both beneficial and deleterious effects, it is important to quantify the amount of N deposition to forest ecosystems. Measurements of N deposition to the numerous forest biomes across the globe are scarce, so chemical transport models are often used to provide estimates of atmospheric N inputs to these ecosystems. We provide an overview of approaches used to calculate N deposition in commonly used chemical transport models. The Task Force on Hemispheric Transport of Air Pollution (HTAP2) study intercompared N deposition values from a number of global chemical transport models. Using a multi-model mean calculated from the HTAP2 deposition values, we map N deposition to global forests to examine spatial variations in total, dry and wet deposition. Highest total N deposition occurs in eastern and southern China, Japan, Eastern U.S. and Europe while the highest dry deposition occurs in tropical forests. The European Monitoring and Evaluation Program (EMEP) model predicts grid-average deposition, but also produces deposition by land use type allowing us to compare deposition specifically to forests with the grid-average value. We found that, for this study, differences between the grid-average and forest specific could be as much as a factor of two and up to more than a factor of five in extreme cases. This suggests that consideration should be given to using forest-specific deposition for input to ecosystem assessments such as critical loads determinations.

Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere.

Simplified representations of processes influencing forest biomass in Earth system models (ESMs) contribute to large uncertainty in projections. We evaluate forest biomass from eight ESMs outputs archived in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using the biomass data synthesized from radar remote sensing and ground-based observations across northern extratropical latitudes. ESMs exhibit large biases in the forest distribution, forest fraction, and mass of carbon pools that contribute to uncertainty in forest total biomass (biases range from -20 Pg C to 135 Pg C). Forest total biomass is primarily positively correlated with precipitation variations, with surface temperature becoming equally important at higher latitudes, in both simulations and observations. Relatively small differences in forest biomass between the pre-industrial period and the contemporary period indicate uncertainties in forest biomass were introduced in the pre-industrial model equilibration (spin-up), suggesting parametric or structural model differences are a larger source of uncertainty than differences in transient responses. Our findings emphasize the importance of improved (1) models of carbon allocation to biomass compartments, (2) distribution of vegetation types in models, and (3) reproduction of pre-industrial vegetation conditions, in order to reduce the uncertainty in forest biomass simulated by ESMs.

Modeling cold soak evaporative vapor emissions from gasoline-powered automobiles using a newly developed method.

Volatile organic compounds (VOCs) evaporate and vent from a vehicle's fuel tank to its evaporative control system when the vehicle is both driven and parked. VOCs making it past the control system are emissions. Driving and parking activity, fuel volatility, and temperature strongly affect vapor generation and the effectiveness of control technologies, and the wide variability in these factors and the sensitivity of emissions to these factors make it difficult to estimate evaporative emissions at the macro level. Established modeling methods, such as COPERT and MOVES, estimate evaporative emissions by assuming a constant in-use canister condition and consequently contain critical uncertainty when real conditions deviate from that standard condition. In this study, we have developed a new method to model canister capacity as a representative variable, and estimated emissions for all parking events based on semi-empirical functions derived from real-world activity data and laboratory measurements. As compared to chamber measurements collected during this study, the bias of the MOVES diurnal tank venting simulation ranges from -100% to 129%, while the bias for our method's simulation is 1.4% to 8.5%. Our modeling method is compared to the COPERT and MOVES models by estimating evaporative emissions from a Euro-3/4/5 and a Tier 2 vehicle in conditions representative for Chicago, IL, and Guangzhou, China. Estimates using the COPERT and MOVES methods differ from our method by -56% to 120% and -100% to 25%, respectively. The study highlights the importance for continued modeling improvement of the anthropogenic evaporative emission inventory and for tightened regulatory standards. Implications: The COPERT and MOVES methodologies contain large uncertainties for estimating evaporative emissions, while our modeling method is developed based on chamber measurements to estimate evaporative emissions and can properly address those uncertainties. Modeling results suggested an urgent need to complete evaporative emissions inventories and also indicated that tightening evaporative emission standards is urgently needed, especially for warm areas.