A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models

The current state of quantifying uncertainty in chemical transport models (CTM) is often limited and insufficient due to numerous uncertainty sources and inefficient or inaccurate uncertainty propagation methods. In this study, we proposed a feasible methodological framework for CTM uncertainty analysis, featuring sensitivity analysis to filter for important model inputs and a new reduced-form model (RFM) that couples the high-order decoupled direct method (HDDM) and the stochastic response surface model (SRSM) to boost uncertainty propagation. Compared with the SRSM, the new RFM approach is 64% more computationally efficient while maintaining high accuracy. The framework was applied to PM2.5 simulations in the Pearl River Delta (PRD) region and found five precursor emissions, two pollutants in lateral boundary conditions (LBCs), and three meteorological inputs out of 203 model inputs to be important model inputs based on sensitivity analysis. Among these selected inputs, primary PM2.5 emissions, PM2.5 concentrations of LBCs, and wind speed were identified as key uncertainty sources, which collectively contributed 81.4% to the total uncertainty in PM2.5 simulations. Also, when evaluated against observations, we found that there were systematic underestimates in PM2.5 simulations, which can be attributed to the two-product method that describes the formation of secondary organic aerosol

Evolution of anthropogenic air pollutant emissions in Guangdong Province, China, from 2006 to 2015

Guangdong Province (GD), one of the most prosperous and populous regions in China, still experiences haze events and growing ozone pollution in spite of the substantial air-quality improvement in recent years. Integrated control of fine particulate matter (PM2.5) and ozone in GD calls for a systematic review of historical emissions. In this study, emission trends, spatial variations, source-contribution variations, and reduction potentials of sulfur dioxide (SO2), nitrogen oxides (NO), PM2.5, inhalable particles (PM10), carbon monoxide (CO), ammonia (NH3), and volatile organic compounds (VOCs) in GD from 2006 to 2015 were first examined using a dynamic methodology, taking into account economic development, technology penetration, and emission controls. The relative change rates of anthropogenic emissions in GD during 2006-2015 are -48% for SO2, -0.5% for NO, -16% for PM2.5, -22% for PM10, 13% for CO, 3% for NH3, and 13% for VOCs. The declines of SO2, NO, PM2.5, and PM10 emissions in the whole province mainly resulted from the stringent emission control in the Pearl River delta (PRD) region, where most previous control measures were focused, especially on power plants (SO2 and NO), industrial combustion (SO2, PM2.5, PM10), on-road mobile sources (NO), and dust sources (PM2.5 and PM10). Emissions from other areas (non-PRD, NPRD), nevertheless, remain relatively stable due to the lax control measures and rapidly growing energy consumption. In addition, emission leaks of SO2 and NO from industries are observed from PRD to NPRD in 2010 and 2011. As a result, emissions in NPRD are increasingly important in GD, particularly those from industrial combustion. The contribution of NPRD to the total SO2 emissions in GD, for example, increased from 27% in 2006 to 48% in 2015. On-road mobile sources and solvent use are the two key sources that should receive more effective control measures in GD. Current control-driven emission reductions from on-road mobile sources are neutralized by the substantial growth of the vehicle population, while VOC emissions in GD steadily increase due to the growth of solvent use and the absence of effective control measures. Besides, future work could focus on power plants and industrial combustion in GD and industrial process sources in NPRD, which still have large emission reduction potentials. The historical emission inventory developed in this study not only helps to understand the emission evolution in GD, but also provides robust data to quantify the impact of emission and meteorology variations on air quality and unveil the primary cause of significant air-quality change in GD in the recent decade

An AIS-based high-resolution ship emission inventory and its uncertainty in Pearl River Delta region, China

Ship emissions contribute significantly to air pollution and impose health risks to residents along the coastal area. By using the refined data from the Automatic Identification System(AIS), this study developed a highly resolved ship emission inventory for the Pearl River Delta (PRD) region, China, home to three of ten busiest ports in the world. The region-wide SO2, NOX, CO, PM10, PM2.5, and VOC emissions in 2013 were estimated to be 61,484, 103,717, 10,599, 7155, 6605, and 4195 t, respectively. Ocean going vessels were the largest contributors of the total emissions, followed by coastal vessels and river vessels. In terms of ship type, container ship was the leading contributor, followed by conventional cargo ship, dry bulk carrier, fishing ship, and oil tanker. These five ship types accounted for \u3e 90% of total emissions. The spatial distributions of emissions revealed that the key emission hot spots all concentrated within the newly proposed emission control area (ECA) and ship emissions within ECA covered \u3e 80% of total ship emissions in the PRD, highlighting the importance of ECA in emissions reduction in the PRD. The uncertainties of emission estimates of pollutants were quantified, with lower bounds of − 24.5% to − 21.2% and upper bounds of 28.6% to 33.3% at 95% confidence intervals. The lower uncertainties in this study highlighted the powerfulness of AIS data in improving ship emission estimates. The AIS-based bottom-up methodology can be used for developing and upgrading ship emission inventory and formulating effective control measures on ship emissions in other port regions wherever possible

A New Combined Stepwise-Based High-Order Decoupled Direct and Reduced-Form Method To Improve Uncertainty Analysis in PM_2.5 Simulations

The traditional reduced-form model (RFM) based on the high-order decoupled direct method (HDDM), is an efficient uncertainty analysis approach for air quality models, but it has large biases in uncertainty propagation due to the limitation of the HDDM in predicting nonlinear responses to large perturbations of model inputs. To overcome the limitation, a new stepwise-based RFM method that combines several sets of local sensitive coefficients under different conditions is proposed. Evaluations reveal that the new RFM improves the prediction of nonlinear responses. The new method is applied to quantify uncertainties in simulated PM_2.5 concentrations in the Pearl River Delta (PRD) region of China as a case study. Results show that the average uncertainty range of hourly PM_2.5 concentrations is −28% to 57%, which can cover approximately 70% of the observed PM_2.5 concentrations, while the traditional RFM underestimates the upper bound of the uncertainty range by 1–6%. Using a variance-based method, the PM_2.5 boundary conditions and primary PM_2.5 emissions are found to be the two major uncertainty sources in PM_2.5 simulations. The new RFM better quantifies the uncertainty range in model simulations and can be applied to improve applications that rely on uncertainty information

High Gaseous Nitrous Acid (HONO) Emissions from Light-Duty Diesel Vehicles

Nitrous acid (HONO) plays an important role in the budget of hydroxyl radical (•OH) in the atmosphere. Vehicular emissions are a crucial primary source of atmospheric HONO, yet remain poorly investigated, especially for diesel trucks. In this study, we developed a novel portable online vehicular HONO exhaust measurement system featuring an innovative dilution technique. Using this system coupled with a chassis dynamometer, we for the first time investigated the HONO emission characteristics of 17 light-duty diesel trucks (LDDTs) and 16 light-duty gasoline vehicles in China. Emissions of HONO from LDDTs were found to be significantly higher than previous studies and gasoline vehicles tested in this study. The HONO emission factors of LDDTs decrease significantly with stringent control standards: 1.85 ± 1.17, 0.59 ± 0.25, and 0.15 ± 0.14 g/kg for China III, China IV, and China V, respectively. In addition, we found poor correlations between HONO and NOx emissions, which indicate that using the ratio of HONO to NOx emissions to infer HONO emissions might lead to high uncertainty of HONO source budget in previous studies. Lastly, the HONO emissions are found to be influenced by driving conditions, highlighting the importance of conducting on-road measurements of HONO emissions under real-world driving conditions. More direct measurements of the HONO emissions are needed to improve the understanding of the HONO emissions from mobile and other primary sources

High Gaseous Nitrous Acid (HONO) Emissions from Light-Duty Diesel Vehicles

Nitrous acid (HONO) plays an important role in the budget of hydroxyl radical (•OH) in the atmosphere. Vehicular emissions are a crucial primary source of atmospheric HONO, yet remain poorly investigated, especially for diesel trucks. In this study, we developed a novel portable online vehicular HONO exhaust measurement system featuring an innovative dilution technique. Using this system coupled with a chassis dynamometer, we for the first time investigated the HONO emission characteristics of 17 light-duty diesel trucks (LDDTs) and 16 light-duty gasoline vehicles in China. Emissions of HONO from LDDTs were found to be significantly higher than previous studies and gasoline vehicles tested in this study. The HONO emission factors of LDDTs decrease significantly with stringent control standards: 1.85 ± 1.17, 0.59 ± 0.25, and 0.15 ± 0.14 g/kg for China III, China IV, and China V, respectively. In addition, we found poor correlations between HONO and NOx emissions, which indicate that using the ratio of HONO to NOx emissions to infer HONO emissions might lead to high uncertainty of HONO source budget in previous studies. Lastly, the HONO emissions are found to be influenced by driving conditions, highlighting the importance of conducting on-road measurements of HONO emissions under real-world driving conditions. More direct measurements of the HONO emissions are needed to improve the understanding of the HONO emissions from mobile and other primary sources

High Gaseous Nitrous Acid (HONO) Emissions from Light-Duty Diesel Vehicles

Nitrous acid (HONO) plays an important role in the budget of hydroxyl radical (•OH) in the atmosphere. Vehicular emissions are a crucial primary source of atmospheric HONO, yet remain poorly investigated, especially for diesel trucks. In this study, we developed a novel portable online vehicular HONO exhaust measurement system featuring an innovative dilution technique. Using this system coupled with a chassis dynamometer, we for the first time investigated the HONO emission characteristics of 17 light-duty diesel trucks (LDDTs) and 16 light-duty gasoline vehicles in China. Emissions of HONO from LDDTs were found to be significantly higher than previous studies and gasoline vehicles tested in this study. The HONO emission factors of LDDTs decrease significantly with stringent control standards: 1.85 ± 1.17, 0.59 ± 0.25, and 0.15 ± 0.14 g/kg for China III, China IV, and China V, respectively. In addition, we found poor correlations between HONO and NOx emissions, which indicate that using the ratio of HONO to NOx emissions to infer HONO emissions might lead to high uncertainty of HONO source budget in previous studies. Lastly, the HONO emissions are found to be influenced by driving conditions, highlighting the importance of conducting on-road measurements of HONO emissions under real-world driving conditions. More direct measurements of the HONO emissions are needed to improve the understanding of the HONO emissions from mobile and other primary sources

High Gaseous Nitrous Acid (HONO) Emissions from Light-Duty Diesel Vehicles

Nitrous acid (HONO) plays an important role in the budget of hydroxyl radical (•OH) in the atmosphere. Vehicular emissions are a crucial primary source of atmospheric HONO, yet remain poorly investigated, especially for diesel trucks. In this study, we developed a novel portable online vehicular HONO exhaust measurement system featuring an innovative dilution technique. Using this system coupled with a chassis dynamometer, we for the first time investigated the HONO emission characteristics of 17 light-duty diesel trucks (LDDTs) and 16 light-duty gasoline vehicles in China. Emissions of HONO from LDDTs were found to be significantly higher than previous studies and gasoline vehicles tested in this study. The HONO emission factors of LDDTs decrease significantly with stringent control standards: 1.85 ± 1.17, 0.59 ± 0.25, and 0.15 ± 0.14 g/kg for China III, China IV, and China V, respectively. In addition, we found poor correlations between HONO and NOx emissions, which indicate that using the ratio of HONO to NOx emissions to infer HONO emissions might lead to high uncertainty of HONO source budget in previous studies. Lastly, the HONO emissions are found to be influenced by driving conditions, highlighting the importance of conducting on-road measurements of HONO emissions under real-world driving conditions. More direct measurements of the HONO emissions are needed to improve the understanding of the HONO emissions from mobile and other primary sources