Jianlei Lang

Air pollutant emissions from on-road vehicles in China, 1999–2011

The on-road vehicular emission in China from 1999 to 2011 was estimated, based on the emission factors of vehicles with different emission standards calculated by the COPERT model. The CO, NMVOC, NOX, BC and OC emissions changed from 19.7 Tg, 4.4 Tg, 2.3 Tg, 47.1 Gg and 74.4 Gg in 1999 to 32.7 Tg, 4.1 Tg, 7.6 Tg, 177.6 Gg and 101.5 Gg in 2011, respectively. The general trend for CO, NOX and BC was increasing, while the tendency for NMVOC and OC was firstly increase before 2002 and then decrease from 2003. The spatial analysis results showed that high emissions occurred in developed provinces (Guangdong, Shandong, Hebei, Jiangsu and Henan). The correlation between vehicular emissions and GDP were further investigated and good linear correlation was found. The not-obvious change of the inter-annual (1999-2011) fitted straight line slope and the sustained increasing emissions for NOX and BC suggested that the challenge of mitigating vehicular NOX and BC emissions is severe in China. The contribution from different vehicle types was also analyzed. Passenger car (PC) and motorcycle (MC) was the main contributor to the CO and NMVOC emissions. However, the contribution ratio of MC was decreasing from 36.6% and 68.8% in 1999 to 15.7% and 25.7% in 2011. Heavy duty truck (HDT) was the dominant contributor to NOX, BC and OC, with proportions of 58.9%, 57.6% and 52.8% in 2011, respectively. In addition, the uncertainty of the estimated emissions was also assessed based on the Monte Carlo simulation.

Application of MM5-CAMx-PSAT Modeling Approach for Investigating Emission Source Contribution to Atmospheric Pollution in Tangshan, Northern China

The MM5-CMAx-PSAT modeling approach was presented to identify the variation of emission contribution from each modeling grid to regional and urban air quality per unit emission rate change. The method was applied to a case study in Tangshan Municipality, a typical industrial region in northern China. The variation of emission contribution to the monthly atmospheric SO2 concentrations in Tangshan from each modeling grid of 9 × 9 km per 1000 t/yr of emission rate change was simulated for four representative months in 2006. It was found that the northwestern part of Tangshan region had the maximum contribution variation ratio (i.e., greater than 0.36%) to regional air quality, while the lowest contribution variation ratio (i.e., less than 0.3%) occurred in the coastal areas. Principal component analysis (PCA), canonical correlation analysis (CCA), and Pearson correlation analysis indicated that there was an obvious negative correlation between the grid-based variation of emission contribution to regional air quality and planetary boundary layer height (PBLH) as well as wind speed, while terrain data presented insignificant impacts on emission contribution variation. The proposed method was also applied to analyze the variation of emission contribution to the urban air quality of Tangshan (i.e., a smaller scale).

Scenario analysis to vehicular emission reduction in Beijing-Tianjin-Hebei (BTH) region, China

Motor vehicle emissions are increasingly becoming one of the important factors affecting the urban air quality in China. It is necessary and useful to policy makers to demonstrate the situation given the relevant pollutants reduction measures are taken. This paper predicted the reduction potentials of conventional pollutants (PM10, NOx, CO, HC) under different control strategies and policies in the Beijing-Tianjin-Hebei (BTH) region during 2011-2020. There are the baseline and 5 control scenarios designed, which presented the different current and future possible vehicular emissions control measures. Future population of different kinds of vehicles were predicted based on the Gompertz model, and vehicle kilometers travelled estimated as well. After that, the emissions reduction under the different scenarios during 2011-2020 could be estimated using emission factors and activity level data. The results showed that, the vehicle population in the BTH region would continue to grow up, especially in Tianjin and Hebei. Comparing the different scenarios, emission standards updating scenario would achieve a substantial reduction and keep rising up for all the pollutants, and the scenario of eliminating high-emission vehicles can reduce emissions more effectively in short-term than in long-term, especially in Beijing. Due to the constraints of existing economical and technical level, the reduction effect of promoting new energy vehicles would not be significant, especially given the consideration of their lifetime impact. The reduction effect of population regulation scenario in Beijing cannot be ignorable and would keep going up for PM10, CO and HC, excluding NOx. Under the integrated scenario considering all the control measures it would achieve the maximum reduction potential of emissions, which means to reduce emissions of PM10, NOx, CO, HC, by 56%, 59%, 48%, 52%, respectively, compared to BAU scenario for the whole BTH region in 2020.

Contribution of ship emissions to the concentration of PM2.5: A comprehensive study using AIS data and WRF/Chem model in Bohai Rim Region, China

Compared with on-road vehicles, emission from ships is one of the least-regulated anthropogenic emission sources and non-negligible source of primary aerosols and gas-phase precursors of PM2.5. The Bohai Rim Region in China hosts dozens of large ports, two of which ranked among the top ten ports in the world. To determine the impact of ship emissions on the PM2.5 concentrations over this region, two parts of works have been conducted in this study. First, a detailed ship emission inventory with high spatiotemporal resolution was developed based on Automatic Identification System (AIS) data. Then the WRF/Chem model was applied to modeling the impact of ship emissions by comparing two scenarios: with and without ship emissions. The results indicate that the total estimated ship emissions of SO2, NOX, PM10, PM2.5, CO, HC, and CO2 from Bohai Rim Region in 2014 are 1.9×105, 2.9×105, 2.6×104, 2.4×104, 2.5×104, 1.2×104, and 1.3×107tonnes, respectively. The modeling results indicate that the annual PM2.5 concentrations increased by 5.9% on land areas of Bohai Rim Region (the continent within 115.2°E-124.3°E and 36.1°N-41.6°N) due to ship emissions. The contributions show distinctive seasonal variations of contributions, presenting highest in summer (12.5%) followed by spring (6.9%) and autumn (3.3%), and lowest in winter (0.9%). The contribution reaches up to 10.7% along the shoreline and down to 1.0% 200km inland. After examining the statistics of the modeling results during heavy and non-heavy haze days in July, it was found that 6 out of 9 cities around the Bohai Rim Region were observed with higher contributions from ship emissions during heavy haze days compared with non-heavy haze days. These results indicate that the impacts of ship emissions on the ambient PM2.5 are non-negligible, especially for heavy haze days for most coastal cities in the Bohai Rim Region.

High-spatiotemporal-resolution ship emission inventory of China based on AIS data in 2014

Ship exhaust emissions have been considered a significant source of air pollution, with adverse impacts on the global climate and human health. China, as one of the largest shipping countries, has long been in great need of in-depth analysis of ship emissions. This study for the first time developed a comprehensive national-scale ship emission inventory with 0.005°×0.005° resolution in China for 2014, using the bottom-up method based on Automatic Identification System (AIS) data of the full year of 2014. The emission estimation involved 166,546 unique vessels observed from over 15billion AIS reports, covering OGVs (ocean-going vessels), CVs (coastal vessels) and RVs (river vessels). Results show that the total estimated ship emissions for China in 2014 were 1.1937×106t (SO2), 2.2084×106t (NOX), 1.807×105t (PM10), 1.665×105t (PM2.5), 1.116×105t (HC), 2.419×105t (CO), and 7.843×107t (CO2, excluding RVs), respectively. OGVs were the main emission contributors, with proportions of 47%-74% of the emission totals for different species. Vessel type with the most emissions was container (~43.6%), followed by bulk carrier (~17.5%), oil tanker (~5.7%) and fishing ship (~4.9%). Monthly variations showed that emissions from transport vessels had a low point in February, while fishing ship presented two emission peaks in May and September. In terms of port clusters, ship emissions in BSA (Bohai Sea Area), YRD (Yangtze River Delta) and PRD (Pearl River Delta) accounted for ~13%, ~28% and ~17%, respectively, of the total emissions in China. On the contrast, the average emission intensities in PRD were the highest, followed by the YRD and BSA regions. The establishment of this high-spatiotemporal-resolution ship emission inventory fills the gap of national-scale ship emission inventory of China, and the corresponding ship emission characteristics are expected to provide certain reference significance for the management and control of the ship emissions.

Chemical composition and sources of PM1 and PM2.5 in Beijing in autumn

Beijing, the capital of China, suffers from severe atmospheric aerosol pollution; nevertheless, a comprehensive study of the constituents and sources of PM1 is still lacking, and the differences between PM1 and PM2.5 are still unclear. In this study, an intensive observation was conducted to reveal the pollution characteristics of PM1 and PM2.5 in Beijing in autumn. Positive matrix factorization (PMF), backward trajectories and a potential source contribution function (PSCF) model were used to identify the source categories and source areas of PM1 and PM2.5. The results showed that the average concentrations of PM1 and PM2.5 reached 78.20μg/m3 and 95.47μg/m3 during the study period, respectively. PM1 contributed greatly to PM2.5. The PM1/PM2.5 value increased from 73.6% to 90.1% with PM1 concentration growing from <50μg/m3 to >150μg/m3. Higher secondary inorganic aerosol (SIA) proportions (31.3%-70.8%) were found in PM1. The higher fraction of SIA, OC, EC and typical elements in PM1 illustrated that anthropogenic components accumulated more in smaller size particles. Three typical weather patterns causing the heavy pollution in autumn were found as follows: (1) Siberian high and uniform high pressure field, (2) cold front and low-voltage system, and (3) uniform low pressure field. A PMF analysis indicated that secondary aerosols and coal combustion, vehicle, industry, biomass burning, and dust were the important sources of PM, accounting for 53.8%, 8.0%, 13.0%, 13.2% and 12.0% of PM1, respectively, and for 47.5%, 9.9%, 12.4%, 8.4% and 21.8% of PM2.5, respectively. The HYSPLIT and chemical components analysis indicated the potential contribution from biomass burning and fertilization ammonia emissions to PM1 in autumn. The source areas were similar for PM1 and PM1-2.5 under general polluted conditions, but during the heavily polluted periods, the source areas were distributed in farther regions from Beijing for PM1 than for PM1-2.5.

Investigating the contribution of shipping emissions to atmospheric PM2.5 using a combined source apportionment approach

Many studies have been conducted focusing on the contribution of land emission sources to PM2.5 in China; however, little attention had been paid to other contributions, especially the secondary contributions from shipping emissions to atmospheric PM2.5. In this study, a combined source apportionment approach, including principle component analysis (PCA) and WRF-CMAQ simulation, was applied to identify both primary and secondary contributions from ships to atmospheric PM2.5. An intensive PM2.5 observation was conducted from April 2014 to January 2015 in Qinhuangdao, which was close to the largest energy output port of China. The chemical components analysis results showed that the primary component was the major contributor to PM2.5, with proportions of 48.3%, 48.9%, 55.1% and 55.4% in spring, summer, autumn and winter, respectively. The secondary component contributed higher fractions in summer (48.2%) and winter (36.8%), but had lower percentages in spring (30.1%) and autumn (32.7%). The hybrid source apportionment results indicated that the secondary contribution (SC) of shipping emissions to PM2.5 could not be ignored. The annual average SC was 2.7%, which was comparable to the primary contribution (2.9%). The SC was higher in summer (5.3%), but lower in winter (1.1%). The primary contributions to atmospheric PM2.5 were 3.0%, 2.5%, 3.4% and 2.7% in spring, summer, autumn and winter, respectively. As for the detailed chemical components, the contributions of shipping emissions were 2.3%, 0.5%, 0.1%, 1.0%, 1.7% and 0.1% to elements & sea salt, primary organic aerosol (POA), element carbon (EC), nitrate, sulfate and secondary organic carbon (SOA), respectively. The results of this study will further the understanding of the implications of shipping emissions in PM2.5 pollution.

On-board measurements of gaseous pollutant emission characteristics under real driving conditions from light-duty diesel vehicles in Chinese cities

A total of 15 light-duty diesel vehicles (LDDVs) were tested with the goal of understanding the emission factors of real-world vehicles by conducting on-board emission measurements. The emission characteristics of hydrocarbons (HC) and nitrogen oxides (NOx) at different speeds, chemical species profiles and ozone formation potential (OFP) of volatile organic compounds (VOCs) emitted from diesel vehicles with different emission standards were analyzed. The results demonstrated that emission reductions of HC and NOx had been achieved as the control technology became more rigorous from Stage I to Stage IV. It was also found that the HC and NOx emissions and percentage of O2 dropped with the increase of speed, while the percentage of CO2 increased. The abundance of alkanes was significantly higher in diesel vehicle emissions, approximately accounting for 41.1%-45.2%, followed by aromatics and alkenes. The most abundant species were propene, ethane, n-decane, n-undecane, and n-dodecane. The maximum incremental reactivity (MIR) method was adopted to evaluate the contributions of individual VOCs to OFP. The results indicated that the largest contributors to O3 production were alkenes and aromatics, which accounted for 87.7%-91.5%. Propene, ethene, 1,2,4-trimethylbenzene, 1-butene, and 1,2,3-trimethylbenzene were the top five VOC species based on their OFP, and accounted for 54.0%-64.8% of the total OFP. The threshold dilution factor was applied to analyze the possibility of VOC stench pollution. The majority of stench components emitted from vehicle exhaust were aromatics, especially p-diethylbenzene, propylbenzene, m-ethyltoluene, and p-ethyltoluene.

Characterization of aircraft emissions and air quality impacts of an international airport

Beijing Capital International Airport (ZBAA) is the worlds second busiest airport. In this study, the emissions of air pollutants from aircraft and other sources at ZBAA in 2015 were estimated using an improved method, which considered the mixing layer height calculated based on aircraft meteorological data relay (AMDAR), instead of using the height (915m) recommended by ICAO. The yearly emissions of NOx, CO, VOCs, SO2, and PM2.5 at the airport were 8.76×103, 4.43×103, 5.43×102, 4.80×102, and 1.49×102ton/year, respectively. The spatial-temporal distribution of aircraft emissions was systematically analyzed to understand the emission characteristics of aircraft. The results indicated that NOx was mainly emitted during the take-off and climb phases, accounting for 20.5% and 55.5% of the total emissions. CO and HC were mainly emitted during the taxi phase, accounting for 91.6% and 92.2% of the total emissions. Because the mixing layer height was high in summer, the emissions of aircraft were at the highest level throughout the year. Based on the detailed emissions inventory, four seasons simulation using WRF-CMAQ model was performed over the domain surrounding the airport. The results indicated that the contribution to PM2.5 was relatively high in winter; the average impact was about 1.15μg/m3 within a radius of 1km around the airport. Meanwhile, the near surroundings and southwest areas of the airport are the most sensitive to PM2.5.

The impact of ship emissions on PM2.5 and the deposition of nitrogen and sulfur in Yangtze River Delta, China

Ship emissions contribute significantly to the deterioration of air quality, while their impacts on ambient PM2.5 and depositions have not been comprehensively evaluated. This is especially true for China because it has a long coastline, busy shipping routes and many large ports. To fill this gap, this study applied the SMOKE/WRF/CMAQ modeling system to quantifying the impacts of ships on PM2.5 compositions, annual and seasonal contribution to PM2.5 as well as the wet and dry deposition of nitrogen and sulfur compounds over the land areas in YRD region for 2014. The results showed that 4.0% of annual PM2.5 concentrations over the land areas could be explained by ship emissions and the largest contribution could reach up to 35.0% in port areas. Temporally, the contribution to PM2.5 exhibited an obviously seasonal variation. The highest contribution was predicted in autumn (6.2%), followed by summer (5.4%), spring (3.6%) and winter (1.2%) for the land areas. Spatially, the contribution reached up to 13.6% along the coastline and dropped to 2.1% 300 km inland. As for the impacts on PM2.5 components, the primary components were relatively small and increased mainly along the shipping routes and the Yangtze River, whereas the secondary components played a more important role in both water and land areas. The sulfur deposition due to ship emissions was occurred generally along the shipping routes and was dominated by the dry SO2 deposition. The nitrogen depositions, on the contrary, was observed not only along the shipping routes but also extend to wide land areas. Further investigation revealed that ship emissions have caused an evident increase of dry nitrogen deposition in NO2 and HNO3, while a slight decrease in NH3 over YRD region. These results indicated that comprehensive regulations of ship emissions are required considering their adverse effects on the ambient concentration of PM2.5 and the deposition of sulfur and nitrogen.