Particulate Source Apportionment Technology Research Articles

An integrated PM2.5 source apportionment study: Positive Matrix Factorisation vs. the chemical transport model CAMx

Receptor and Chemical Transport Models are commonly used tools in source apportionment studies, even if different expertise is required. We describe an experiment using both approaches to apportion the PM2.5 (i.e., particulate matter with aerodynamic diameters below 2.5 μm) sources in the city of Genoa (Italy). A sampling campaign was carried out to collect PM2.5 samples daily for approximately six month during 2011 in three sites. The subsequent compositional analyses included the speciation of elements, major ions and both organic and elemental carbon; these data produced a large database for receptor modelling through Positive Matrix Factorisation (PMF). In the same period, a meteorological and air quality modelling system was implemented based on the mesoscale numerical weather prediction model WRF and the chemical transport model CAMx to obtain meteorological and pollutant concentrations up to a resolution of 1.1 km. The source apportionment was evaluated by CAMx over the same period that was used for the monitoring campaign using the Particulate Source Apportionment Technology tool. Even if the source categorisations were changed (i.e., groups of time-correlated compounds in PMF vs. activity categories in CAMx), the PM2.5 source apportionment by PMF and CAMx produced comparable results. The different information provided by the two approaches (e.g., real-world factor profile by PMF and apportionment of a secondary aerosol by CAMx) was used jointly to elucidate the composition and origin of PM2.5 and to develop a more general methodology. When studying the primary and secondary components of PM, the main anthropogenic sources in the area were road transportation, energy production/industry and maritime emissions, accounting for 40%–50%, 20%–30% and 10%–15%, of PM2.5, respectively.

A study of control policy in the Pearl River Delta region by using the particulate matter source apportionment method

In recent years, Mainland China, and in particular the industrial hotbed of the Pearl River Delta (PRD) has experienced an increasingly serious problem of high concentrations of airborne particulate matter. Following the tightening-up of China's air quality policies in recent years, and with especially fine particles now added to a new air quality objective, the identification of major source regions and major types of pollutants has become crucial. In this study, the Comprehensive Air Quality Model (CAMx), together with the Particulate Source Apportionment Technology (PSAT) source apportionment method, has been applied to analyze how different emission activities influence PM concentration in the PRD region. By using this method, a detailed source region and emission category contribution matrix is derived for all regions within the Hong Kong/PRD region and source appointment results show that, on average, for different cities super-regional transport and mobile vehicles are the two major fine particle sources, contributing 62% (34.3 μg m−3) and 21% (12.2 μg m−3) of the total figure in December, and 42% (13.1 μg m−3) and 28% (9.7 μg m−3) in April. Meanwhile, over the same period in Hong Kong, in addition to these two factors, marine proved another very significant source of particle pollutant, amounting to 18% of the total figure (4.7 μg m−3). Another important cause of high PM levels has been the transport of fine particles between cities within the PRD region, with three different regions selected for detailed analysis. Results show that for the Hong Kong/PRD region local reduction of mobile sources and collaboration between different areas could have succeeded in alleviating the air pollution problem.

The PSAT Technique for SO<sub>2</sub> Study in Beijing

In this study, a coupled MM5-CAMx air quality modeling system was proposed for simulating the SO2 concentration in Beijing, China during the heating season. The Particulate Source Apportionment Technology (PSAT) was introduced into the modeling system to investigate SO2 source apportionment from regions nearby Beijing and regions in Beijing. The results indicated that the influence from regions outside of Beijing including Hebei, Tianjin and Shanxi could not be ignored, and the total Emission Source Contribution Ratio (ESCR) from these regions was 10.8%. Metropolitan Beijing, including Chaoyang, Fengtai, the city center of Beijing, Haidian, and Shijingshan, with an ESCR of 82.5%, was the main contributing area in Beijing. The results obtained could provide useful information for Beijing’s SO2-emission control and abatement. This method could effectively identify SO2 pollution problem in a metropolis region and thus provide a scientific basis for air pollution control and mitigation.

A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2.5

Various approaches and tools exist to estimate local and regional PM2.5 impacts from a single emissions source, ranging from simple screening techniques to Gaussian based dispersion models and complex grid-based Eulerian photochemical transport models. These approaches either lack a realistic chemical and physical representation of the atmosphere for secondary PM2.5 formation or in the case of photochemical models may be too resource intensive for single source assessments. A simple non-linear regression model has been developed to estimate annual average downwind primary and secondarily formed PM2.5 nitrate and sulfate from single emissions sources. The statistical model is based on single emissions sources tracked with particulate source apportionment technology in a photochemical transport model. This non-linear regression model is advantageous in that the underlying data is based on single emissions sources modeled in a realistic chemical and physical environment of a photochemical model and provides downwind PM2.5 impact information with minimal resource burden. Separate regression models are developed for primary PM2.5, PM2.5 sulfate ion, and PM2.5 nitrate ion. Regression model inputs include facility emissions rates in tons per year and the distance between the source and receptor. An additional regression model input of receptor ammonia emissions is used to account for the variability in regional ammonia availability that is important for PM2.5 nitrate ion estimates.

Comparison of Source Apportionment and Sensitivity Analysis in a Particulate Matter Air Quality Model

Two efficient methods to study relationships between particulate matter (PM) concentrations and emission sources are compared in the three-dimensional comprehensive air quality model with extensions (CAMx). Particulate source apportionment technology (PSAT) is a tagged species method that apportions concentrations of PM components to their respective primary precursors, e.g., sulfate is apportioned to SOx, nitrate to NOx, etc. The decoupled direct method (DDM) calculates first-order sensitivities of PM concentrations to model inputs. Both tools were applied to two month long (February and July) PM modeling episodes and evaluated against changes in PM concentrations due to various emission reductions. The results show that source contributions calculated by PSAT start to deviate from the actual model responses as indirect effects from limiting reactants or nonprimary precursor emissions become important The DDM first-order sensitivity is useful for determining source contributions only if the model response to input changes is reasonably linear. For secondary inorganic PM, the response is linear for emission reductions of 20% in all cases considered and reasonably linear for reductions of 100% inthe case of on-road mobile sources. The model response for secondary organic aerosols and primary PM remains nearly linear to 100% reductions in anthropogenic emissions.

Development and application of a computationally efficient particulate matter apportionment algorithm in a three-dimensional chemical transport model

An on-line and an off-line version of a computationally efficient particulate matter source apportionment algorithm have been developed and compared using the three-dimensional chemical transport model PMCAMx. Both versions of the algorithm use source specific-species that track the contributions of source locations or source classes. The two versions showed a good agreement with each other and with more accurate, computationally demanding methods. The off-line algorithm (Particulate Source Apportionment Technology, PSAT) is simpler to implement, has a lower computational cost and is suitable for a range of source apportionment studies. As a first application, PSAT was used to investigate the contribution of power plant SO 2 emissions to particulate sulfate concentrations in the Eastern United States. The impact of the transport of SO 2 emissions from the Chicago, IL area and the impact of these emissions on particulate sulfate concentration in surrounding areas were also studied as a second application. The implementation of PSAT for the SO 2/particulate sulfate system only resulted in a 1% increase in computation time over the base simulation. The algorithm provides a computationally efficient platform for the study of pollutant transport and source contributions on regional scales.