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

Role of export industries on ozone pollution and its precursors in China

This study seeks to estimate how global supply chain relocates emissions of tropospheric ozone precursors and its impacts in shaping ozone formation. Here we show that goods produced in China for foreign markets lead to an increase of domestic non-methane volatile organic compounds (NMVOCs) emissions by 3.5 million tons in 2013; about 13% of the national total or, equivalent to half of emissions from European Union. Production for export increases concentration of NMVOCs (including some carcinogenic species) and peak ozone levels by 20–30% and 6–15% respectively, in the coastal areas. It contributes to an estimated 16,889 (3,839–30,663, 95% CI) premature deaths annually combining the effects of NMVOCs and ozone, but could be reduced by nearly 40% by closing the technology gap between China and EU. Export demand also alters the emission ratios between NMVOCs and nitrogen oxides and hence the ozone chemistry in the east and south coast

Reconciling discrepancies in the source characterization of VOCs between emission inventories and receptor modeling

Emission inventory (EI) and receptor model (RM) are two of the three source apportionment (SA) methods recommended by Ministry of Environment of China and used widely to provide independent views on emission source identifications. How to interpret the mixed results they provide, however, were less studied. In this study, a cross-validation study was conducted in one of China's fast-developing and highly populated city cluster- the Pearl River Delta (PRD) region. By utilizing a highly resolved speciated regional EI and a region-wide gridded volatile organic compounds (VOCs) speciation measurement campaign, we elucidated underlying factors for discrepancies between EI and RM and proposed ways for their interpretations with the aim to achieve a scientifically plausible source identification. Results showed that numbers of species, temporal and spatial resolutions used for comparison, photochemical loss of reactive species, potential missing sources in EI and tracers used in RM were important factors contributed to the discrepancies. Ensuring the consensus of species used in EIs and RMs, utilizing a larger spatial coverage and longer time span, addressing the impacts of photochemical losses, and supplementing emissions from missing sources could help reconcile the discrepancies in VOC source characterizations acquired using both approaches. By leveraging the advantages and circumventing the disadvantages in both methods, the EI and RM could play synergistic roles to obtain robust SAs to improve air quality management practices

Hourly emissions of air pollutants and greenhouse gases from open biomass burning in China during 2016–2020

Abstract Open biomass burning (OBB) is a significant source of air pollutants and greenhouse gases that have contributed to air pollution episodes in China in recent years. An accurate emission inventory is critical for the precise control of OBB. Existing OBB emission datasets are commonly based on MODIS observations, and most only have a daily-scale temporal resolution. Daily OBB emissions, however, might not accurately represent diurnal variations, peak hours, or any open burning processes. The China Hourly Open Biomass Burning Emissions (CHOBE) dataset for mainland China from 2016 to 2020 was developed in this study using the spatiotemporal fusion of multiple active fires from MODIS, VIIRS S-NPP and Himawari-8 AHI detections. At a spatial resolution of 2 km, CHOBE provided gridded CO, NOx, SO2, NH3, VOCs, PM2.5, CO2, CH4 and N2O emissions from OBB. CHOBE will enhance insight into OBB spatiotemporal variability, improves air quality and climate modelling and forecasting, and aids in the formulation of precise OBB preventive and control measures

Using Cell Phone Location To Assess Misclassification Errors In Air Pollution Exposure Estimation

Air pollution epidemiologic and health impact studies often rely on home addresses to estimate individual subject\u27s pollution exposure. In this study, we used detailed cell phone location data, the call detail record (CDR), to account for the impact of spatiotemporal subject mobility on estimates of ambient air pollutant exposure. This approach was applied on a sample with 9886 unique simcard IDs in Shenzhen, China, on one mid-week day in October 2013. Hourly ambient concentrations of six chosen pollutants were simulated by the Community Multi-scale Air Quality model fused with observational data, and matched with detailed location data for these IDs. The results were compared with exposure estimates using home addresses to assess potential exposure misclassification errors. We found the misclassifications errors are likely to be substantial when home location alone is applied. The CDR based approach indicates that the home based approach tends to over-estimate exposures for subjects with higher exposure levels and under-estimate exposures for those with lower exposure levels. Our results show that the cell phone location based approach can be used to assess exposure misclassification error and has the potential for improving exposure estimates in air pollution epidemiology studies. Cell phone location-based exposure estimation has the potential for improving exposure estimates vs. home address-based approaches that are likely to have increased misclassification errors because it does not account for individual mobility

Modeling inorganic nitrogen deposition in Guangdong province, China

Atmospheric nitrogen deposition is an essential component of acid deposition and serves as one of main sources of nitrogen of the ecosystem. Along with rapidly developed economy, it is expected that the nitrogen deposition in Guangdong province is considerably large, due to substantial anthropogenic reactive nitrogen lost to the Pearl River Delta (PRD) region, one of the most developed region in China. However, characterization of chemical compositions of inorganic nitrogen (IN) deposition and quantification of nitrogen deposition fluxes in time and space in Guangdong province were seldom conducted, especially using a numerical modeling approach. In this study, we established a WRF/SMOKE-PRD/CMAQ model system and expanded 2006-based PRD regional emission inventories to Guangdong provincial ones, including SO2, NOx, VOC, PM10, PM2.5, and NH3 emissions for modeling nitrogen deposition in Guangdong province. Observations, including meteorological observed data, rainfall data, ground-level criteria pollutant measurements, satellite-derived data, and nitrogen deposition fluxes from field measurements were employed in the evaluation of model performance. Results showed that annual nitrogen deposition fluxes in the PRD region and Guangdong province were 31.01 kg N hm(-1) a(-1) and 26.03 kg N hm(-1) a(-1), dominated by NHx (including NH3 and NH4+), with a percentage of 63% and 71% of the total deposition flux of IN, respectively. The ratio of dry deposition to wet deposition was approximately 2:1 in the PRD region and about 3:2 in the whole Guangdong province. IN deposition was mainly distributed in the PRD region, Chaozhou, and Maoming, which was similar to the spatial distributions of NOx and NH3 emissions. The spatial distributions of chemical compositions of IN deposition implied that NH3-N and NOx-N tended to deposit in places close to emission sources, while spatial distributions of aerosol NH4+ - N and NO3- - N usually exhibited broader deposition areas, along with long-range transport of fine particles. Distinct temporal trends were found in IN components, especially for wet depositions, with peak values in August. (C) 2015 Elsevier Ltd. All rights reserved

A refined 2010-based VOC emission inventory and its improvement on modeling regional ozone in the Pearl River Delta Region, China

Accurate and gridded VOC emission inventories are important for improving regional air quality model performance. In this study, a four-level VOC emission source categorization system was proposed. A 2010-based gridded Pearl River Delta (PRD) regional VOC emission inventory was developed with more comprehensive source coverage, latest emission factors, and updated activity data. The total anthropogenic VOC emission was estimated to be about 117.4 x 10(4) t, in which on-road mobile source shared the largest contribution, followed by industrial solvent use and industrial processes sources. Among the industrial solvent use source, furniture manufacturing and shoemaking were major VOC emission contributors. The spatial surrogates of VOC emission were updated for major VOC sources such as industrial sectors and gas stations. Subsector-based temporal characteristics were investigated and their temporal variations were characterized. The impacts of updated VOC emission estimates and spatial surrogates were evaluated by modeling O-3 concentration in the PRD region in the July and October of 2010, respectively. The results indicated that both updated emission estimates and spatial allocations can effectively reduce model bias on O-3 simulation. Further efforts should be made on the refinement of source classification, comprehensive collection of activity data, and spatial-temporal surrogates in order to reduce uncertainty in emission inventory and improve model performance. (C) 2015 Elsevier B.V. All rights reserved