Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

Objective: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. Methods: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London.

Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO2), 0% to +4% for ozone (O3), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 µm (PM10), and there was no response for PM2.5. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO2 pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.

Impact of lifetime air pollution exposure patterns on the risk of chronic disease.

Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20-40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .1.

Continuous Positive Airway Pressure Reduces Plasma Neurochemical Levels in Patients with OSA: A Pilot Study.

Obstructive sleep apnea (OSA) is a risk factor for neurodegenerative diseases. This study determined whether continuous positive airway pressure (CPAP), which can alleviate OSA symptoms, can reduce neurochemical biomarker levels. Thirty patients with OSA and normal cognitive function were recruited and divided into the control (n = 10) and CPAP (n = 20) groups. Next, we examined their in-lab sleep data (polysomnography and CPAP titration), sleep-related questionnaire outcomes, and neurochemical biomarker levels at baseline and the 3-month follow-up. The paired t-test and Wilcoxon signed-rank test were used to examine changes. Analysis of covariance (ANCOVA) was performed to increase the robustness of outcomes. The Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores were significantly decreased in the CPAP group. The mean levels of total tau (T-Tau), amyloid-beta-42 (Aß42), and the product of the two (Aß42 × T-Tau) increased considerably in the control group (ΔT-Tau: 2.31 pg/mL; ΔAß42: 0.58 pg/mL; ΔAß42 × T-Tau: 48.73 pg2/mL2), whereas the mean levels of T-Tau and the product of T-Tau and Aß42 decreased considerably in the CPAP group (ΔT-Tau: -2.22 pg/mL; ΔAß42 × T-Tau: -44.35 pg2/mL2). The results of ANCOVA with adjustment for age, sex, body mass index, baseline measurements, and apnea-hypopnea index demonstrated significant differences in neurochemical biomarker levels between the CPAP and control groups. The findings indicate that CPAP may reduce neurochemical biomarker levels by alleviating OSA symptoms.

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events.

Objectives: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods: We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results: The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions: The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

Associations between risk of Alzheimer's disease and obstructive sleep apnea, intermittent hypoxia, and arousal responses: A pilot study.

Objectives: Obstructive sleep apnea (OSA) may increase the risk of Alzheimer's disease (AD). However, potential associations among sleep-disordered breathing, hypoxia, and OSA-induced arousal responses should be investigated. This study determined differences in sleep parameters and investigated the relationship between such parameters and the risk of AD. Methods: Patients with suspected OSA were recruited and underwent in-lab polysomnography (PSG). Subsequently, blood samples were collected from participants. Patients' plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aß42) were measured using an ultrasensitive immunomagnetic reduction assay. Next, the participants were categorized into low- and high-risk groups on the basis of the computed product (Aß42 × T-Tau, the cutoff for AD risk). PSG parameters were analyzed and compared. Results: We included 36 patients in this study, of whom 18 and 18 were assigned to the low- and high-risk groups, respectively. The average apnea-hypopnea index (AHI), apnea, hypopnea index [during rapid eye movement (REM) and non-REM (NREM) sleep], and oxygen desaturation index (≥3%, ODI-3%) values of the high-risk group were significantly higher than those of the low-risk group. Similarly, the mean arousal index and respiratory arousal index (R-ArI) of the high-risk group were significantly higher than those of the low-risk group. Sleep-disordered breathing indices, oxygen desaturation, and arousal responses were significantly associated with an increased risk of AD. Positive associations were observed among the AHI, ODI-3%, R-ArI, and computed product. Conclusions: Recurrent sleep-disordered breathing, intermittent hypoxia, and arousal responses, including those occurring during the NREM stage, were associated with AD risk. However, a longitudinal study should be conducted to investigate the causal relationships among these factors.

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

Targeted Use of Sustainable Aviation Fuel to Maximize Climate Benefits.

Sustainable aviation fuel (SAF) can reduce aviation's CO2 and non-CO2 impacts. We quantify the change in contrail properties and climate forcing in the North Atlantic resulting from different blending ratios of SAF and demonstrate that intelligently allocating the limited SAF supply could multiply its overall climate benefit by factors of 9-15. A fleetwide adoption of 100% SAF increases contrail occurrence (+5%), but lower nonvolatile particle emissions (-52%) reduce the annual mean contrail net radiative forcing (-44%), adding to climate gains from reduced life cycle CO2 emissions. However, in the short term, SAF supply will be constrained. SAF blended at a 1% ratio and uniformly distributed to all transatlantic flights would reduce both the annual contrail energy forcing (EFcontrail) and the total energy forcing (EFtotal, contrails + change in CO2 life cycle emissions) by ∼0.6%. Instead, targeting the same quantity of SAF at a 50% blend ratio to ∼2% of flights responsible for the most highly warming contrails reduces EFcontrail and EFtotal by ∼10 and ∼6%, respectively. Acknowledging forecasting uncertainties, SAF blended at lower ratios (10%) and distributed to more flights (∼9%) still reduces EFcontrail (∼5%) and EFtotal (∼3%). Both strategies deploy SAF on flights with engine particle emissions exceeding 1012 m-1, at night-time, and in winter.

Dynamics and outcomes of binary collisions of equi-diameter picolitre droplets with identical viscosities.

The dynamics of binary collisions of equi-diameter picolitre droplets with identical viscosities, varying impact speeds and impact angles have been investigated experimentally and compared to collision outcome prediction models. Collisions between pairs of pure water droplets with a viscosity of 0.89 mPa s and pairs of aqueous-sucrose (40% w/w) droplets with a viscosity of 5.17 mPa s were examined. The colliding droplets were ∼38 µm in diameter, which is around ten times smaller than those previously investigated when examining the effect of viscosity on the outcome of binary droplet collisions. Varying the impact speed and angle resulted in different collision outcomes, including coalescence, reflexive separation and stretching separation. The collision outcomes were plotted on two viscosity dependent regime maps. The regime boundaries are generally in agreement with earlier literature for both high and low viscosity cases. The agreement between experiment and theory, for both fluids, gives more confidence in the models tested here to predict collision outcomes for droplets of this size and these viscosities.

Source terms for benchmarking models of SARS-CoV-2 transmission via aerosols and droplets.

There is ongoing and rapid advancement in approaches to modelling the fate of exhaled particles in different environments relevant to disease transmission. It is important that models are verified by comparison with each other using a common set of input parameters to ensure that model differences can be interpreted in terms of model physics rather than unspecified differences in model input parameters. In this paper, we define parameters necessary for such benchmarking of models of airborne particles exhaled by humans and transported in the environment during breathing and speaking.

Long-Term In-Use NOx Emissions from London Buses with Retrofitted NOx Aftertreatment.

Buses constitute a significant source of air pollutant emissions in cities. In this study, we present real-world NOx emissions from 97 diesel-hybrid buses measured using on-board diagnostic systems over 44 months and 6.35 million km in London. Each bus had previously been retrofitted with a selective catalytic reduction (SCR) aftertreatment system to reduce emissions of nitrogen oxides (NOx). On average, parallel hybrid (PH) and series hybrid (SH) buses emitted 3.80 g of NOx/km [standard deviation (SD) of 1.02] and 2.37 g of NOx/km (SD of 0.51), respectively. The SCR systems reduced engine-out emissions by 79.8% (SD of 5.0) and 87.2% (SD of 2.9) for the PHs and SHs, respectively. Lower ambient temperatures (0-10 °C) increased NOx emissions of the PHs by 24.2% but decreased NOx emissions of the SHs by 27.9% compared to values found at more moderate temperatures (10-20 °C). To improve emissions inventories, we provide new distance-based NOx emissions factors for different ranges of ambient temperature. During the COVID-19 pandemic, the emissions benefits of reduced congestion were largely offset by more frequent route layovers leading to lower SCR temperatures and effectiveness. This study shows that continuous in-service measurements enable quantification of real-world vehicle emissions over a wide range of operations that complements conventional testing approaches.

A novel multi-pollutant space-time learning network for air pollution inference.

Detailed information about air pollution in space and time is essential to manage risks to public health. In this paper we propose a multi-pollutant space-time learning network (Multi-AP learning network), which estimates pixel-wise (grid-level) concentrations of multiple air pollutant species based on fixed-station measurements and multi-source urban features, including land use information, traffic data, and meteorological conditions. We infer concentrations of multiple pollutants within one integrated learning network, which is applied to and evaluated on a case study in Chengdu (4900 km2, 26 April - 12 June 2019), where air pollutant (PM2.5, PM10 and O3) measurements from 40 monitoring sites are used to train the network to estimate pollutant concentrations in 4900 grid-cells (1 km2). The Multi-AP learning network allows us to estimate highly-resolved (1 km × 1 km, hourly) air pollution maps based on pollutant measurements which cover less than 1% of the grid-cells with better accuracy compared to other approaches, and with significant computational efficiency improvements. The time-cost is 1/3 of the time-cost of modelling each pollutant individually. Furthermore, we evaluate the relative importance of features and find that the meteorological feature set is the most important followed the land use features. The proposed Multi-AP method could be used to estimate air pollution exposure across a city using a limited set of air pollution monitoring sites.

Associations of the distance-saturation product and low-attenuation area percentage in pulmonary computed tomography with acute exacerbation in patients with chronic obstructive pulmonary disease.

Background: Chronic obstructive pulmonary disease (COPD) has high global health concerns, and previous research proposed various indicators to predict mortality, such as the distance-saturation product (DSP), derived from the 6-min walk test (6MWT), and the low-attenuation area percentage (LAA%) in pulmonary computed tomographic images. However, the feasibility of using these indicators to evaluate the stability of COPD still remains to be investigated. Associations of the DSP and LAA% with other COPD-related clinical parameters are also unknown. This study, thus, aimed to explore these associations. Methods: This retrospective study enrolled 111 patients with COPD from northern Taiwan. Individuals' data we collected included results of a pulmonary function test (PFT), 6MWT, life quality survey [i.e., the modified Medical Research Council (mMRC) scale and COPD assessment test (CAT)], history of acute exacerbation of COPD (AECOPD), and LAA%. Next, the DSP was derived by the distance walked and the lowest oxygen saturation recorded during the 6MWT. In addition, the DSP and clinical phenotype grouping based on clinically significant outcomes by previous study approaches were employed for further investigation (i.e., DSP of 290 m%, LAA% of 20%, and AECOPD frequency of ≥1). Mean comparisons and linear and logistic regression models were utilized to explore associations among the assessed variables. Results: The low-DSP group (<290 m%) had significantly higher values for the mMRC, CAT, AECOPD frequency, and LAA% at different lung volume scales (total, right, and left), whereas it had lower values of the PFT and 6MWT parameters compared to the high-DSP group. Significant associations (with high odds ratios) were observed of the mMRC, CAT, AECOPD frequency, and PFT with low- and high-DSP groupings. Next, the risk of having AECOPD was associated with the mMRC, CAT, DSP, and LAA% (for the total, right, and left lungs). Conclusion: A lower value of the DSP was related to a greater worsening of symptoms, more-frequent exacerbations, poorer pulmonary function, and more-severe emphysema (higher LAA%). These readily determined parameters, including the DSP and LAA%, can serve as indicators for assessing the COPD clinical course and may can serve as a guide to corresponding treatments.

The ventilation of buildings and other mitigating measures for COVID-19: a focus on wintertime.

The year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation.

An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data.

Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.

Modelling of instantaneous emissions from diesel vehicles with a special focus on NOx: Insights from machine learning techniques.

Accurate instantaneous vehicle emissions models are vital for evaluating the impacts of road transport on air pollution at high temporal and spatial resolution. In this study, we apply machine learning techniques to a dataset of 70 diesel vehicles tested in real-world driving conditions to: (i) cluster vehicles with similar emissions performance, and (ii) model instantaneous emissions. The application of dynamic time warping and clustering analysis by NOx emissions resulted in 17 clusters capturing 88% of trips in the dataset. We show that clustering effectively groups vehicles with similar emissions profiles, however no significant correlation between emissions and vehicle characteristics (i.e. engine size, vehicle weight) were found. For each cluster, we evaluate three instantaneous emissions models: a look-up table (LT) approach, a non-linear regression (NLR) model and a neural network multi-layer perceptron (MLP) model. The NLR model provides accurate instantaneous NOx predictions, on par with the MLP: relative errors in prediction of emission factors are below 20% for both models, average fractional biases are -0.01 (s.d. 0.02) and -0.0003 (s.d. 0.04), and average normalised mean squared errors are 0.25 (s.d. 0.14) and 0.29 (s.d. 0.16), for the NLR and MLP models respectively. However, neural networks are better able to deal with vehicles not belonging to a specific cluster. The new models that we present rely on simple inputs of vehicle speed and acceleration, which could be extracted from existing sources including traffic cameras and vehicle tracking devices, and can therefore be deployed immediately to enable fast and accurate prediction of vehicle NOx emissions. The speed and the ease of use of these new models make them an ideal operational tool for policy makers aiming to build emission inventories or evaluate emissions mitigation strategies.

Mitigating the Climate Forcing of Aircraft Contrails by Small-Scale Diversions and Technology Adoption.

The climate forcing of contrails and induced-cirrus cloudiness is thought to be comparable to the cumulative impacts of aviation CO2 emissions. This paper estimates the impact of aviation contrails on climate forcing for flight track data in Japanese airspace and propagates uncertainties arising from meteorology and aircraft black carbon (BC) particle number emissions. Uncertainties in the contrail age, coverage, optical properties, radiative forcing, and energy forcing (EF) from individual flights can be 2 orders of magnitude larger than the fleet-average values. Only 2.2% [2.0, 2.5%] of flights contribute to 80% of the contrail EF in this region. A small-scale strategy of selectively diverting 1.7% of the fleet could reduce the contrail EF by up to 59.3% [52.4, 65.6%], with only a 0.014% [0.010, 0.017%] increase in total fuel consumption and CO2 emissions. A low-risk strategy of diverting flights only if there is no fuel penalty, thereby avoiding additional long-lived CO2 emissions, would reduce contrail EF by 20.0% [17.4, 23.0%]. In the longer term, widespread use of new engine combustor technology, which reduces BC particle emissions, could achieve a 68.8% [45.2, 82.1%] reduction in the contrail EF. A combination of both interventions could reduce the contrail EF by 91.8% [88.6, 95.8%].

Real world CO2 and NOx emissions from 149 Euro 5 and 6 diesel, gasoline and hybrid passenger cars.

In this study CO2 and NOx emissions from 149 Euro 5 and 6 diesel, gasoline and hybrid passenger cars were compared using a Portable Emissions Measurement System (PEMS). The models sampled accounted for 56% of all passenger cars sold in Europe in 2016. We found gasoline vehicles had CO2 emissions 13-66% higher than diesel. During urban driving, the average CO2 emission factor was 210.5 (sd. 47) gkm-1 for gasoline and 170.2 (sd. 34) gkm-1 for diesel. Half the gasoline vehicles tested were Gasoline Direct Injection (GDI). Euro 6 GDI engines <1.4ℓ delivered ~17% CO2 reduction compared to Port Fuel Injection (PFI). Gasoline vehicles delivered an 86-96% reduction in NOx emissions compared to diesel cars. The average urban NOx emission from Euro 6 diesel vehicles 0.44 (sd. 0.44) gkm-1 was 11 times higher than for gasoline 0.04 (sd. 0.04) gkm-1. We also analysed two gasoline-electric hybrids which out-performed both gasoline and diesel for NOx and CO2. We conclude action is required to mitigate the public health risk created by excessive NOx emissions from modern diesel vehicles. Replacing diesel with gasoline would incur a substantial CO2 penalty, however greater uptake of hybrid vehicles would likely reduce both CO2 and NOx emissions. Discrimination of vehicles on the basis of Euro standard is arbitrary and incentives should promote vehicles with the lowest real-world emissions of both NOx and CO2.

Greenhouse Gas and Noxious Emissions from Dual Fuel Diesel and Natural Gas Heavy Goods Vehicles.

Dual fuel diesel and natural gas heavy goods vehicles (HGVs) operate on a combination of the two fuels simultaneously. By substituting diesel for natural gas, vehicle operators can benefit from reduced fuel costs and as natural gas has a lower CO2 intensity compared to diesel, dual fuel HGVs have the potential to reduce greenhouse gas (GHG) emissions from the freight sector. In this study, energy consumption, greenhouse gas and noxious emissions for five after-market dual fuel configurations of two vehicle platforms are compared relative to their diesel-only baseline values over transient and steady state testing. Over a transient cycle, CO2 emissions are reduced by up to 9%; however, methane (CH4) emissions due to incomplete combustion lead to CO2e emissions that are 50-127% higher than the equivalent diesel vehicle. Oxidation catalysts evaluated on the vehicles at steady state reduced CH4 emissions by at most 15% at exhaust gas temperatures representative of transient conditions. This study highlights that control of CH4 emissions and improved control of in-cylinder CH4 combustion are required to reduce total GHG emissions of dual fuel HGVs relative to diesel vehicles.

Global civil aviation black carbon emissions.

Aircraft black carbon (BC) emissions contribute to climate forcing, but few estimates of BC emitted by aircraft at cruise exist. For the majority of aircraft engines the only BC-related measurement available is smoke number (SN)-a filter based optical method designed to measure near-ground plume visibility, not mass. While the first order approximation (FOA3) technique has been developed to estimate BC mass emissions normalized by fuel burn [EI(BC)] from SN, it is shown that it underestimates EI(BC) by >90% in 35% of directly measured cases (R(2) = -0.10). As there are no plans to measure BC emissions from all existing certified engines-which will be in service for several decades-it is necessary to estimate EI(BC) for existing aircraft on the ground and at cruise. An alternative method, called FOX, that is independent of the SN is developed to estimate BC emissions. Estimates of EI(BC) at ground level are significantly improved (R(2) = 0.68), whereas estimates at cruise are within 30% of measurements. Implementing this approach for global civil aviation estimated aircraft BC emissions are revised upward by a factor of ~3. Direct radiative forcing (RF) due to aviation BC emissions is estimated to be ~9.5 mW/m(2), equivalent to ~1/3 of the current RF due to aviation CO2 emissions.