Particulate Source Apportionment Technology Research Articles

Elucidating drivers of severe wintertime fine particulate matter pollution episodes in the Yangtze River Delta region of eastern China

Understanding the causes and sources responsible for severe fine particulate matter (PM2.5) pollution episodes that occur under conducive synoptic weather patterns (SWPs) is essential for regional air quality management. The Yangtze River Delta (YRD) region in eastern China has experienced recurrent severe PM2.5 episodes during the winters from 2013 to 2017. In this study, we employed an objective classification approach, the self-organizing map, to investigate the underlying impact of predominant SWPs on PM2.5 pollution in the YRD. We further conducted a series of source apportionment simulations using the Particulate Source Apportionment Technology (PSAT) tool integrated within the Comprehensive Air Quality Model with Extensions (CAMx) to quantify the source contributions to PM2.5 pollution under different SWPs. Here we identified six predominant SWPs over the YRD that are robustly connected to the evolution of the Siberian High. Considering the regional average PM2.5 anomalies, our results show that polluted SWPs favourable for the occurrence of regional PM2.5 pollution account for 61–78 %. The most conducive SWP, associated with the highest regional exceedance (46 %) of PM2.5 levels, is characterized by noticeable cyclonic anomalies at 850 hPa and stagnant surface weather conditions. Our source apportionment analysis emphasizes the pivotal role of local emissions and intra-regional transport within the YRD in shaping PM2.5 pollution in representative cities. Local emissions have the most significant impact on PM2.5 levels in Shanghai (32–48 %), while PM2.5 pollution in Nanjing, Hangzhou, and Hefei is more influenced by intra-regional transport (33–61 %). Industrial and residential emissions are the dominant sources, contributing 32–41 % and 24–38 % to PM2.5, respectively. Under specific SWPs associated with a stronger influence of inter-regional transport from northern China, there is a synchronously remarkable enhancement in the contribution of residential emissions. Our study pinpoints the opportunities for future air quality planning that would benefit from quantitative source attribution linked to prevailing SWPs.

Contributions of domestic sources to PM2.5 in South Korea

We use the CAMx (Comprehensive Air Quality Model with Extensions) chemical transport model (CTM) with 4-km horizontal resolution over the Korean Peninsula to investigate source contributions to PM2.5 in Korea from domestic and upwind sources. We modeled 2015 and 2016 to account for meteorological variation with Korean emissions from the Clean Air Policy Supporting System (CAPSS), meteorology from WRF (Weather, Research, and Forecasting) model, and regional boundary concentrations from the GEOS-Chem global CTM. The CAMx particulate source apportionment technology (PSAT) provided PM2.5 source contributions from 5 source sectors and 6 geographic regions within Korea, international sources, and boundary concentrations. PM2.5 contributions from outside Korea are important with boundary concentrations plus the “other” emissions sector (includes marine shipping, agricultural ammonia, and international emissions from North Korea and Japan within the CAMx domain) contributing 67% of annual average PM2.5 in Seoul in 2016 and 71% in 2015. The boundary concentrations contributed between 30% and 50% of PM2.5 at different Korean cities with contributions generally lower in 2016 than in 2015. For Korean sources, PM2.5 contributions from Electric Generating Unit (EGU) emissions were smaller than contributions from mobile and industrial emissions sources although there is considerable day-to-day variation in contributions. On an annual basis in 2016, the “other” category contributed 25% followed by mobile sources at 23%, industrial sources at 6%, and EGU sources at 3%. For 2015, the contributions were similar. Focusing on March when PM2.5 concentrations were higher than other months, the contributions from other, mobile, industrial, and EGUs were 21%, 18%, 4%, and 4%, respectively in 2016. For 2015, contributions from these four categories were 18%, 15%, 3%, and 3%, respectively.

Source contribution analysis of PM2.5 using Response Surface Model and Particulate Source Apportionment Technology over the PRD region, China

Identifying the emission source contributions to PM2.5 is essential for a sound PM2.5 pollution control policy. In this study, we conduct a comparative analysis of PM2.5 source contributions over the Pearl River Delta (PRD) region of China using two advanced source contribution modeling techniques: Response Surface Model (RSM) and Particulate Source Apportionment Technology (PSAT). Our comparative analyses show that RSM and PSAT can both reasonably predict the contribution of primary PM2.5 emission sources to PM2.5 formation due to its linear nature. For the secondary PM2.5 formed by the nonlinear reactions among PM2.5 precursors, however, our study shows that PSAT appears to have limitations in quantifying the nonlinear contribution of PM2.5 precursors to emission reductions, while RSM seems to better address the nonlinear relationship among PM2.5 precursors (e.g., PM2.5 disbenefits due to local NOx emission reductions in major cities with high NOx emissions). The pilot study case results show that for the ambient PM2.5 in the central cities (Guangzhou, Shenzhen, Foshan, Dongguan, and Zhongshan) of the PRD, the regional source emissions contribute the most by 42–66%; the dust emissions are the top contribution sources (29–34% by RSM and 27–31% by PSAT), and the mobile sources are listed as the secondary contributors accounting for 16–25% by RSM and 19–30% by PSAT among the anthropogenic emission sources. The city-scale cooperation on emission reductions and the enhancement of dust and mobile emission control are recommended to effectively reduce the ambient PM2.5 concentration in the PRD.

Status of Ambient PM2.5 Pollution in the Seoul Megacity (2020)

The Center for Air Quality & Control at the Seoul Research Institute of Public Health and the Environment (SIHE) has monitored changes in the concentration of fine dust in Seoul over the past 10 years and investigated meteorological factors as well as fine particulate matter (PM2.5), sulfur dioxide (SO2), and nitrogen dioxide (NO2) concentrations in northeastern China and its contribution to the PM2.5 concentration in Seoul. The concentration of fine dust in Seoul in 2020 was 21 µg/m3, which is down 16% from 2019 and the lowest since 2010. In 2020, China’s emissions of pollutants such as NO2 have decreased significantly due to regional blockades, social distancing, and factory shutdowns caused by COVID-19. As a results, the concentration of precursors such as SO2 and NO2, and PM2.5 in northeastern China are also decreased, which contributed to the reduction in PM2.5 concentration in Seoul caused by westerly winds blowing. In addition, the ratio of east and south winds that usually contain low concentrations of pollutants was more than 30% of the total air currents into Seoul, which is the highest in the last three years. Moreover, the mean wind velocity and the amount of precipitation were also the highest recorded values of 2.4 m/s and 1651.0 mm, respectively. Calculations using Comprehensive Air quality Model with eXtensions (CAMx)-Particulate Source Apportionment Technology (PSAT) show that the contribution of external inflows to the PM2.5 concentration in Seoul was 65%. We believe that the reasons for the low PM2.5 concentration in 2020 are due to meteorological factors and a decrease in air pollution in northeastern China. Meanwhile, the major contribution of emissions in Seoul (resuspended road dust and non-exhaust dust) was high. When the concentration of PM2.5 was high, the contribution of resuspended road dust was reduced due to an increase of secondary generating materials. Currently, data on emission reduction due to the COVID-19 cannot be assessed, which we believe will enable more accurate contribution calculations in the future.

High-time-resolution PM2.5 source apportionment based on multi-model with organic tracers in Beijing during haze episodes

Fine particulate matter (PM2.5) is a prominent atmospheric pollutant that poses serious adverse effects on air quality and human health. PM2.5 source apportionment based on receptor model suggests that Beijing is polluted by mixed emission sources, but the model is limited by a lack of organic tracers and an inability to distinguish between contributions from local and regional transport. In this study, positive matrix factorization (PMF) model with organic tracers was employed to analyze refined PM2.5 pollution sources at 1-h time resolution, and the contribution of regional transport was quantified using Particulate source apportionment technology (PSAT) in the Comprehensive Air Quality Model with Extensions (CAMx). The results identified nine source types using PMF model based on offline data for PM2.5 concentrations, organic carbon, elemental carbon, water-soluble ions, trace elements and organic species. Gasoline and diesel exhausts were distinguished by adding polycyclic aromatic hydrocarbons (PAHs), C19-C24 n-alkanes as key organic tracers. In addition, levoglucosan and hexadecanoic acid are important additions for identifying biomass burning and cooking, respectively. Furthermore, the contribution of specific sources and source regions, from the formation to dissipation of two typical haze episodes (EP1 and EP2) in Beijing, was quantitatively analyzed. EP1 was primarily caused by local emissions with an average contribution rate of 67.5%, characterized by secondary source, gasoline and diesel exhausts, as well as industrial source. EP2 was dominated by secondary source from regional transport contributing approximately 50%. Short-range transport from Baoding (9.1%) and Langfang (5.8%) in Hebei Province was the largest external contributor, and long-range transport contributed 20% of the PM2.5 concentration. This study suggests that combining receptor model-based source apportionment with air quality model has practical significance for understanding the causes of haze episodes, setting city-specific emission reduction measures and improving air quality in the Beijing-Tianjin-Hebei (BTH) region.

Numerical study of air pollution over a typical basin topography: Source appointment of fine particulate matter during one severe haze in the megacity Xi'an

Many cities are located in lands with typical basin topographies, which are not conducive to the spread of air pollutants. In the winter of 2016/2017, a severe haze happened in Xi’an, the main city in the Guanzhong Basin in central China. When the peak daily concentration of fine particulate matter (PM2.5) reaches 499 μg/m3, the source of the atmospheric pollution needs to be found urgently in order to take countermeasures. The comprehensive air quality model with extensions, coupled with the tracer tagging particulate source apportionment technology (PSAT) module, and an improved emission inventory, higher grid resolution, and bigger inner domain area, have been applied to quantify the contributions of local and regional emissions to the PM2.5 pollutions. The model performed well in time period considered in this study. The correlation of the simulated daily PM2.5 concentration data reaches 0.82, and the fraction of predictions within a factor of two of observations approaches 84%. With the PSAT module, the PM2.5 contributions from local and regional sources to the urban centre and rural areas during the severe winter haze event are analysed in detail. The PM2.5 concentrations in the urban centre in Xi’an is mainly originating from local emissions (60%), and Xianyang City is the largest contributor among the surrounding source regions (11.6%), while the transportation sector outside the Shaanxi Province (5.1%) also contributes significantly. Comparatively, the rural areas have lower local contributions and higher transport contributions. In particular, in the northern rural area Yanliang, the contribution from surrounding source regions approaches 82%. The results of this study suggest that to improve the air quality in a typical basin city, a regional-scale coordinated emissions control should be used, focusing on the emissions from both local and surrounding areas.

Investigating the Influence of the Implementation of an Energy Development Plan on Air Quality Using WRF-CAMx Modeling Tools: A Case Study of Shandong Province in China

In this paper, the Weather Research Forecast (WRF) Comprehensive Air Quality Model with Extensions (CAMx) modeling system with the particulate source apportionment technology (PSAT) module was used to study and analyze the spatial and temporal distribution of atmospheric pollutant concentrations and the source apportionment of fine particles (PM2.5) under the base year and an emission reduction scenario in the Shandong province, China. Our results show that industry is the largest contributor of PM2.5. In addition, the contribution of key energy-related industries was as high as 29.5%, with the thermal power industry being the largest individual contributor. In January, the largest contribution came from residents, reaching 41.3%. Moreover, loose coal burning in rural areas contributed up to 19.4% in winter. Our results also show that the emission reduction scenario had palpable effects on the reduction of air pollution. The more the emissions of SO2, NOx, PM2.5, and PM10 were reduced, the more the average concentration was decreased. The implementation of energy conservation and emission reduction policies by industry and resident is conducive to improving the quality of the atmospheric environment. In particular, a comprehensive control of loose coal burning in winter could significantly improve heavy pollution by particulate matter in winter.

Source and exposure apportionments of ambient PM2.5 under different synoptic patterns in the Pearl River Delta region

PM2.5 is one of the most notorious ambient pollutants in the Pearl River Delta (PRD) region during episodic conditions. In this work, the Comprehensive Air Quality Model with extension (CAMx) was used together with the Particulate Source Apportionment Technology (PSAT) module to analyze the influences of different sources on PM2.5 concentration in the PRD region under different synoptic patterns (sea high pressure, sub-tropical high pressure and equalizing pressure field). The result shows that the PM2.5 concentration increases to different degrees under the three synoptic patterns. The emissions outside the PRD region contribute more than 54% under episodic conditions. The source category contribution varies little under different synoptic patterns. Area (46%), mobile (21%) and industry point source (16%) are the major contributors over the three episodic cases. The regional source contributions (from other cities within the PRD) to Foshan, Zhongshan and Zhaoqing are larger and can reach up to 33%. People living in the PRD region are more exposed to pollutants produced from the area and mobile sources. About 80% of the population is exposed to PM2.5 levels exceeding the IT-3 standard during the pollution episodes.

Differences in concentration and source apportionment of PM2.5 between 2006 and 2015 over the PRD region in southern China

During China's 11th Five Year Plan (FYP) and 12th FYP (2006–2015), a series of air pollution control measures was implemented in the Pearl River Delta (PRD) region. Therefore, it is vital to determine how the concentration and sources of fine particulate matter (PM2.5) in this region changed between 2006 and 2015. In this work, using 2006 and 2015 emission inventories, the concentration and source apportionment of PM2.5 were simulated using the Weather Research and Forecast - Comprehensive Air Quality Model with Extensions (WRF-CAMx) for January, April, July and October in the PRD region. The PM2.5 in 10 cities and the contributions made by sources in six major categories were tracked using the Particulate Source Apportionment Technology (PSAT) module. The results showed that the PM2.5 concentration was lower across the entire PRD region in the 2015 emission scenario than in the 2006 scenario, and that the degree of this reduction exceeded 40 μg/m3 in some places. The PM2.5 contributed by mobile emissions decreased the most, especially in Guangzhou, Foshan and Shenzhen, where mobile contributions decreased from 15.0, 17.9 and 13.0 μg/m3 in 2006 to 2.6, 3.1 and 4.1 μg/m3 in 2015, respectively. The PM2.5 contributed by power plants also decreased, and in Dongguan and Guangzhou, the extent of this reduction reached 2.5 and 3.4 μg/m3 respectively. However, due to an increase in industrial production and population size, the PM2.5 from industrial point sources and area sources also increased between 2006 and 2015 in some of the cities. Investigation of the source apportionment for city centers yielded similar results. In addition to emissions within the PRD region, outside-PRD non-local contribution is still an important PM2.5 contributor. Hence, more stringent policies for controlling industrial and area sources and deepening province-to-province cooperation are urgently needed as the next step in PM2.5 control.

Source regions contributing to excess reactive nitrogen deposition in the Greater Yellowstone Area (GYA) of the United States

Abstract. Research has shown that excess reactive nitrogen (Nr) deposition in the Greater Yellowstone Area (GYA) of the United States has passed critical load (CL) thresholds and is adversely affecting sensitive ecosystems in this area. To better understand the sources causing excess Nr deposition, the Comprehensive Air Quality Model with Extensions (CAMx), using Western Air Quality Study (WAQS) emission and meteorology inputs, was used to simulate Nr deposition in the GYA. CAMx's Particulate Source Apportionment Technology (PSAT) was employed to estimate contributions from agriculture (AG), oil and gas (OG), fire (Fire), and other (Other) source sectors from 27 regions, including the model boundary conditions (BCs) to the simulated Nr for 2011. The BCs were outside the conterminous United States and thought to represent international anthropogenic and natural contributions. Emissions from the AG and Other source sectors are predominantly from reduced N and oxidized N compounds, respectively. The model evaluation revealed a systematic underestimation in ammonia (NH3) concentrations by 65 % and overestimation in nitric acid concentrations by 108 %. The measured inorganic N wet deposition at National Trends Network sites in the GYA was overestimated by 31 %–49 %, due at least partially to an overestimation of precipitation. These uncertainties appear to result in an overestimation of distant source regions including California and BCs and an underestimation of closer agricultural source regions including the Snake River valley. Due to these large uncertainties, the relative contributions from the modeled sources and their general patterns are the most reliable results. Source apportionment results showed that the AG sector was the single largest contributor to the GYA total Nr deposition, contributing 34 % on an annual basis. A total of 74 % of the AG contributions originated from the Idaho Snake River valley, with Wyoming, California, and northern Utah contributing another 7 %, 5 %, and 4 %, respectively. Contributions from the OG sector were small at about 1 % over the GYA, except in the southern Wind River Mountain Range during winter where they accounted for more than 10 %, with 46 % of these contributions coming from OG activities in Wyoming. Wild and prescribed fires contributed 18 % of the total Nr deposition, with fires within the GYA having the highest impact. The Other source category was the largest winter contributor (44 %) with high contributions from California, Wyoming, and northern Utah.

Air Pollutant Emission Inventory from Iron and Steel Industry in the Beijing-Tianjin-Hebei Region and Its Impact on PM2.5

The iron and steel industry, which discharges a large amount of pollutants including SO2, NOx, and PM2.5, is the main source of atmospheric pollution in the Beijing-Tianjin-Hebei region. Based on the bottom-up method, a high temporal and spatial resolution emission inventory of the iron and steel industry in the Beijing-Tianjin-Hebei region was developed, which took into account the multiple air pollutants released during coking, sintering, pelletizing, ironmaking, steelmaking, and the steel rolling process. As the emission inventory showed, the total emissions of SO2, NOx, TSP, PM10, PM2.5, CO, and VOC from the iron and steel industry in the Beijing-Tianjin-Hebei region in 2015 were 388.2, 272.3, 791.9, 531.5, 386.8, 8233.8, and 265.3 kilotons, respectively, among which, sintering and pelletizing were the two processes discharging the most pollutants (17.0%-72.0%), followed by the ironmaking process (4.6%-42.4%) and the steel rolling process (3.5%-35.7%); the iron and steel industry in Tangshan discharged the most pollutants (39.1%-63.5%) among those in all the 13 cities. The impact of the iron and steel industry on the regional PM2.5 concentration was simulated by a two-layer nested meteorology-air quality coupling model system (WRF-CMAx) with Particulate Source Apportionment Technology (PSAT). The simulation results showed that the iron and steel industry contributed 14.0%, 15.9%, 12.3%, and 8.7% of the PM2.5 concentrations of the Beijing-Tianjin-Hebei region in spring, summer, autumn, and winter, respectively, and that the iron and steel industry had the most significant impact on the PM2.5 concentrations in Tangshan among all the 13 cities, with a contribution rate up to 41.2%, followed by those in Qinhuangdao, Shijiazhuang, and Handan, with contributions of 19.3%, 15.3%, and 15.1%, respectively. The iron and steel industry has an important impact on the PM2.5 concentration of the Beijing-Tianjin-Hebei region to which the government should pay more attention, and take more effective control measures to address this problem.

Volatility-resolved source apportionment of primary and secondary organic aerosol over Europe

A three-dimensional regional chemical transport model (Particulate Matter Comprehensive Air Quality Model with Extensions, PMCAMx) was applied over Europe combined with a source apportionment algorithm, the Particulate Source Apportionment Technology (PSAT), in order to quantify the sources which contribute to the primary and secondary organic aerosol (OA) during different seasons. The PSAT algorithm was first extended to allow the quantification of the sources of OA as a function of volatility. The most significant OA sources during May were biogenic, while during February residential wood combustion and during September wildfires dominated. The contributions of the various sources have strong spatial dependence. Wildfires were significant OA sources (38% of the OA) for Russia during September, but had a much lower impact (5%) in Scandinavia. The above results are in general consistent with the findings of the CARBOSOL project for selected sites in Europe. For remote sites such as Finokalia in Crete, more than 90% of the OA has undergone two or more generations of oxidation for all seasons. This highly processed oxidized OA is predicted to also dominate over much of Europe during the summer and fall. The first generation SOA is predicted to represent 20–30% of the OA in central and northern Europe during these photochemically active periods.

Spatially and chemically resolved source apportionment analysis: Case study of high particulate matter event

This article presents the results of a detailed source apportionment study of the high particulate matter (PM) event in the Seoul Metropolitan Area (SMA), South Korea, during late February 2014. Using the Comprehensive Air Quality Model with Extensions with its Particulate Source Apportionment Technology (CAMx-PSAT), we defined 10 source regions, including five in China, for spatially and chemically resolved analyses. During the event, the spatially averaged PM10 concentration at all PM10 monitors in the SMA was 129 μg/m3, while the PM10 and PM2.5 concentrations at the BulGwang Supersite were 143 μg/m3 and 123 μg/m3, respectively. CAMx-PSAT showed reasonably good PM model performance in both China and the SMA. For February 23–27, CAMx-PSAT estimated that Chinese contributions to the SMA PM10 and PM2.5 were 84.3 μg/m3 and 80.0 μg/m3, respectively, or 64% and 70% of the respective totals, while South Korea's respective domestic contributions were 36.5 μg/m3 and 23.3 μg/m3. We observed that the spatiotemporal pattern of PM constituent concentrations and contributions did not necessarily follow that of total PM10 and PM2.5 concentrations. For example, Beijing-Tianjin-Hebei produced high nitrate concentrations, but the two most-contributing regions to PM in the SMA were the Near Beijing area and South Korea. In addition, we noticed that the relative contributions from each region changed over time. We found that most ammonium mass that neutralized Chinese sulfate mass in the SMA came from South Korean sources, indicating that secondary inorganic aerosol in the SMA, especially ammonium sulfates, during this event resulted from different major precursors originating from different regions.

Source-sector contributions to European ozone and fine PM in 2010 using AQMEII modeling data

Abstract. Source apportionment modeling provides valuable information on the contributions of different source sectors and/or source regions to ozone (O3) or fine particulate matter (PM2.5) concentrations. This information can be useful in designing air quality management strategies and in understanding the potential benefits of reducing emissions from a particular source category. The Comprehensive Air quality Model with Extensions (CAMx) offers unique source attribution tools, called the Ozone and Particulate Source Apportionment Technology (OSAT/PSAT), which track source contributions. We present results from a CAMx source attribution modeling study for a summer month and a winter month using a recently evaluated European CAMx modeling database developed for Phase 3 of the Air Quality Model Evaluation International Initiative (AQMEII). The contributions of several source sectors (including model boundary conditions of chemical species representing transport of emissions from outside the modeling domain as well as initial conditions of these species) to O3 or PM2.5 concentrations in Europe were calculated using OSAT and PSAT, respectively. A 1-week spin-up period was used to reduce the influence of initial conditions. Evaluation focused on 16 major cities and on identifying source sectors that contributed above 5 %. Boundary conditions have a large impact on summer and winter ozone in Europe and on summer PM2.5, but they are only a minor contributor to winter PM2.5. Biogenic emissions are important for summer ozone and PM2.5. The important anthropogenic sectors for summer ozone are transportation (both on-road and non-road), energy production and conversion, and industry. In two of the 16 cities, solvent and product also contributed above 5 % to summertime ozone. For summertime PM2.5, the important anthropogenic source sectors are energy, transportation, industry, and agriculture. Residential wood combustion is an important anthropogenic sector in winter for PM2.5 over most of Europe, with larger contributions in central and eastern Europe and the Nordic cities. Other anthropogenic sectors with large contributions to wintertime PM2.5 include energy, transportation, and agriculture.

Air Pollutant Emission Inventory and Impact of Typical Industries on PM2.5 in Chengde

In this study, detailed activity level of typical sector in Chengde in 2013 was obtained through a full-coverage investigation. A comprehensive emission inventory with country-level resolution in 2013 was developed based on guide of atmospheric pollutant emission inventory and updated emission factors. Then, the emission inventory within 1 km×1 km grid was generated using source-based spastial surrogates including population, road network and landuse date. Furthemore, meteorology-air quality modeling system (WRF-CMAx) including Particulate Source Apportionment Technology (PSAT) module was established in order to evaluate the impact of topical sector (e. g., electric power, the production of construction materials, the metallurgical industry, etc.) on PM2.5 concentration in January, April, July and October which were considered as the representative months of winter, spring, summer and autumn. The results showed the total emission of SO2, NOx, TSP, PM10, PM2.5, CO, VOCs and NH3 in Chengde in 2013 was respectively 81134 t, 72556 t, 368750 t, 119974 t, 51152 t, 1281371 t, 170642 t and 81742 t. Industrial source was the main emission contributor of SO2, NOx, CO, VOCs, accounting for 89.5%, 51.9%, 82.5% and 45.6% of total emissions, respectively. The major emission source of NOx also included on-road and non-road mobile source, respectively accounting for 26.7% and 10.8%. The major emission source of TSP, PM10 and PM2.5 was fugitive dust, accounting for 76.7%, 65.6% and 46.54%, respectively. Ammonia emissions from animals and farm accounted for 67.1% and 15.8% of total emissions, respectively. The numerical simulation result showed that the fugitive dust, the others, the metallurgical industry and boilers industry had relatively higher contributions to PM2.5 concentration, accounting for 23.1%, 20.6%, 13.3% and 11.2%, respectively. These emission sources should be paid more attention during the decision-making with respect to control strategies.

Source Apportionment of Sulfate and Nitrate over the Pearl River Delta Region in China

In this work, the Weather Research Forecast (WRF)–Sparse Matrix Operator Kernel Emission (SMOKE)–Comprehensive Air Quality Model with Extensions (CAMx) modeling system with particulate source apportionment technology (PSAT) module was used to study and analyze the source apportionment of sulfate and nitrate particulate matter in the Pearl River Delta region (PRD). The results show that superregional transport was an important contributor for both sulfates and nitrates in all 10 cities in this region in both February (winter) and August (summer). Especially in February, the average super-regional contribution of sulfate and nitrate reached up to 80% and 56% respectively. For the local and regional source category, power plant emissions (coal-fired and oil-fired) and industry emissions were important for sulfate formation in this region. Industry emissions and mobile emissions are important for nitrate formation in this region. In August, the sum of these two sources contributed around over 60% of local and regional nitrate. The contributions from power plant emissions and marine emissions became important in August due to the southerly prevailing wind direction. Area sources and biogenic emissions were negligible for sulfate and nitrate formation in this region. Our results reveal that cross-province cooperation is necessary for control of sulfates and nitrates in this region.

Source apportionment of PM2.5 in Tangshan, China—Hybrid approaches for primary and secondary species apportionment

The objective of this paper is to propose a hybrid approach for the source apportionment of primary and secondary species of PM2.5 in the city of Tangshan. The receptor-based PMF (Positive Matrix Factorization) is integrated with the emission inventory (EI) to form the first hybrid method for the source apportionment of the primary species. The hybrid CAMx-PSAT-CP (Comprehensive Air Quality Model with Extensions–Particulate Source Apportionment Technology–Chemical Profile) approach is then proposed and used for the source apportionment of the secondary species. The PM2.5 sources identified for Tangshan included the soil dust, the metallurgical industry, power plants, coalfired boilers, vehicles, cement production, and other sources. It is indicated that the PM2.5 pollution is a regional issue. Among all the identified sources, the metallurgy industry was the biggest contribution source to PM2.5, followed by coal-fired boilers, vehicles and soil dust. The other-source category plays a crucial role for PM2.5, particularly for the formation of secondary species and aerosols, and these other sources include non-specified sources such as agricultural activities, biomass combustion, residential emissions, etc. The source apportionment results could help the local authorities make sound policies and regulations to better protect the citizens from the local and regional PM2.5 pollution. The study also highlights the strength of utilizing the proposed hybrid approaches in the identification of PM2.5 sources. The techniques used in this study show considerable promise for further application to other regions as well as to identify other source categories of PM2.5. Open image in new window

Source contributions of urban PM2.5 in the Beijing–Tianjin–Hebei region: Changes between 2006 and 2013 and relative impacts of emissions and meteorology

Anthropogenic emissions in China have been controlled for years to improve ambient air quality. However, severe haze events caused by atmospheric aerosols with aerodynamic diameter less than or equal to 2.5 μm (PM2.5) have continued to occur, especially in the Beijing–Tianjin–Hebei (BTH) region. The Chinese government has set an ambitious goal to reduce urban PM2.5 concentrations by 25% in BTH by 2017 relative to the 2012 levels. Source apportionment (SA) is necessary to the development of the effective emission control strategies. In this work, the Comprehensive Air Quality Model with extensions (CAMx) with the Particulate Source Apportionment Technology (PSAT) is applied to the China domain for the years 2006 and 2013. Ambient surface concentrations of PM2.5 and its components are generally well reproduced. To quantify the contributions of each emission category or region to PM2.5 in BTH, the total emissions are divided into 7 emission categories and 11 source regions. The source contributions determined in this work are generally consistent with results from previous work. In 2013, the industrial (44%) and residential (27%) sectors are the dominant contributors to urban PM2.5 in BTH. The residential sector is the largest contributor in winter; the industry sector dominates in other seasons. A slight increasing trend (+3% for industry and +6% for residential) is found in 2013 relative to 2006, necessitating more attention to these two sectors. Local emissions make the largest contribution (40%–60%) for all receptors. Change of source contribution of PM2.5 in Beijing and northern Hebei are dominate by change of local emission. However, for Tianjin, and central and southern Hebei, change of meteorology condition are as important as change of emission, because regional inflow in these areas is more important than in Beijing and northern Hebei and can increase under unfavorable weather conditions, indicating a strong need for regional joint emission control efforts. The results in this study enhance the quantitative understanding of the source–receptor relationships and provide an important basis for policymaking to advance the control of PM2.5 pollution in China.

Local and distant source contributions to secondary organic aerosol in the Beijing urban area in summer

Quantification of local and distant source contributions to particulate matter is a key issue to improving air quality in large urban areas, but few studies have focused on secondary organic aerosol (SOA) source contributions in a large area, especially in China. In this study, we extended the Comprehensive Air Quality Model with Extensions (CAMX) version 5.4, replacing the two-product approach by the volatility basis-set (VBS) approach, with updated SOA yields based on smog chamber studies. The modules related to the computationally efficient particulate source apportionment technology (PSAT) used in CAMX v5.4 were extended based on the volatility basis set (VBS) approach. The updated version of the CAMX model was then used to calculate the local and distant source contributions to SOA in Beijing for the first time. The results indicated that the VBS approach substantially improved hourly, daily, and monthly SOA simulations, compared with the two-product approach and the observations. In August 2007, the local source contributions to anthropogenic and biogenic SOA in Beijing were 23.8% and 16.6%, respectively; distant sources dominated for both anthropogenic and biogenic SOA in Beijing: Northern Hebei, Middle Hebei, Northeast China, Inner Mongolia, Shandong, and Tianjin (including Xianghe) contributed 5.1% e18.2% to anthropogenic SOA in Beijing; whereas, Inner Mongolia, Northern Hebei, and Northeast China contributed 12.2%, 18.6%, and 10.1%, respectively, to biogenic SOA in Beijing. Additionally, other areas outside China respectively contributed 5.3% and 10.8% to anthropogenic and biogenic SOA in Beijing: this could be related to strong summer monsoon.

Rocky Mountain National Park reduced nitrogen source apportionment

AbstractExcess wet and dry deposition of nitrogen‐containing compounds are a concern at a number of national parks. The Rocky Mountain Atmospheric Nitrogen and Sulfur Study Part II (RoMANS II) campaign was conducted from November 2008 to November 2009 to characterize the composition of reactive nitrogen and sulfur deposited in Rocky Mountain National Park (RMNP). RoMANS II identified reduced nitrogen as the major contributor to reactive nitrogen deposition in RMNP, making up over 50% of the total. Motivated by this finding, the particulate source apportionment technology within the Comprehensive Air Quality Model with extensions was used here to estimate source apportionment of reduced nitrogen concentrations at RMNP. Source apportionment results suggest that approximately 40% of reduced nitrogen deposition to RMNP comes from ammonia sources within Colorado. However, the model evaluation also suggests that this number could be underrepresenting ammonia sources in eastern Colorado due to the difficulty of capturing upslope airflow on the eastern side of the Continental Divide with meteorological models. Emissions from California, the western model boundary, and the Snake River Valley in Idaho, the next three most influential sources, contribute approximately 15%, 8%, and 7%, respectively, to total reduced nitrogen measured in RMNP. Within Colorado, about 61%, 26%, and 13% of the total Colorado contribution comes from sources to the east of the Continental Divide, sources to the west of the Continental Divide, and from the park itself.