[i] An inventory of air pollutant emissions in Asia in the year 2000 is developed to support atmospheric modeling and analysis of observations taken during the TRACE-P experiment funded by the National Aeronautics and Space Administration (NASA) and the ACE-Asia experiment funded by the National Science Foundation (NSF) and the National Oceanic and Atmospheric Administration (NOAA). Emissions are estimated for all major anthropogenic sources, including biomass burning, in 64 regions of Asia. We estimate total Asian emissions as follows: 34.3 Tg SO 2 , 26.8 Tg NO x , 9870 Tg CO 2 , 279 Tg CO, 107 Tg CH 4 , 52.2 Tg NMVOC, 2.54 Tg black carbon (BC), 10.4 Tg organic carbon (OC), and 27.5 Tg NH 3 . In addition, NMVOC are speciated into 19 subcategories according to functional groups and reactivity. Thus we are able to identify the major source regions and types for many of the significant gaseous and particle emissions that influence pollutant concentrations in the vicinity of the TRACE-P and ACE-Asia field measurements. Emissions in China dominate the signature of pollutant concentrations in this region, so special emphasis has been placed on the development of emission estimates for China. China's emissions are determined to be as follows: 20.4 Tg SO 2 , 11.4 Tg NO x , 3820 Tg CO 2 , 116 Tg CO, 38.4 Tg CH 4 , 17.4 Tg NMVOC, 1.05 Tg BC, 3.4 Tg OC, and 13.6 Tg NH 3 . Emissions are gridded at a variety of spatial resolutions from 1° × 1° to 30 s x 30 s, using the exact locations of large point sources and surrogate GIS distributions of urban and rural population, road networks, landcover, ship lanes, etc. The gridded emission estimates have been used as inputs to atmospheric simulation models and have proven to be generally robust in comparison with field observations, though there is reason to think that emissions of CO and possibly BC may be underestimated. Monthly emission estimates for China are developed for each species to aid TRACE-P and ACE-Asia data interpretation. During the observation period of March/ April, emissions are roughly at their average values (one twelfth of annual). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of ±16% for SO 2 to a high of ±450% for OC.

An inventory of gaseous and primary aerosol emissions in Asia in the year 2000 : NASA global tropospheric experiment transport and chemical evolution over the pacific (TRACE-P): Measurements and analysis (TRACEP1)

To estimate PM25 concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM25 concentrations over the conterminous United States in 2011 Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability Our results achieve an overall cross-validation (CV) R2 value of 080 Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 178 and 283 μg/m3, respectively, indicating a good agreement between CV predictions and observations The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales In addition, the 

Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach.

An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution

Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000–2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations

Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review

Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm

Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment.

A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO2 concentrations across China during 2013–2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R2 = 0.62 (RMSE = 13.3 μg/m3) for daily and R2 = 0.73 (RMSE = 6.5 μg/m3) for spatial predictions. The nationwide population-weighted multiyear average of NO2 was predicted to be 30.9 ± 11.7 μg/m3 (mean ± standard deviation), with a slowly but significantly decreasing trend at a rate of −0.88 ± 0.38 μg/m3/year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of −1.37 μg/m3/year, while the Beijing-Tianjin Metro did not show a temporal trend (P = 0.32). The population-weighted NO2 was predicted to be the highest in North China (40.3 ± 10.3 μg/m3) and lowest in Southwest China (24.9 ± 9.4 μg/m3). Approximately 25% of the population lived in nonattainment areas with annual...

Xunfei Deng

Papers

Spatiotemporal prediction of continuous daily PM2.5 concentrations across China using a spatially explicit machine learning algorithm

Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment.

Satellite-Based Estimates of Daily NO2 Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model.

Predictive geographical authentication of green tea with protected designation of origin using a random forest model

A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels.