Air Pollution Source Apportionment Research Articles

The application of positive matrix factorization with diagnostics to BIG DATA

Over the past decade, Positive Matrix Factorization (PMF) has become the most commonly used tool for source apportionment of air pollutants with a large fraction of those studies using the implementation developed by the U.S. Environmental Protection Agency (EPA-PMF) and its very useful diagnostic tools. During the same period, more studies are utilizing monitoring tools that provide hourly or even higher time resolution data leading to large data sets (millions of data points). Because of the nature of the graphic user interface in EPA-PMF, it cannot handle data sets more than approximately half a million data points. Thus, alternative approaches are needed to permit these larger data sets to be analyzed and still be able to provide the diagnostic outputs that permit better interpretations of the results. We have developed a protocol to use the multilinear engine (ME-2) that is the solver used in EPA-PMF to make such analyses. Here we report how to use this approach and present results for a representative data set of particle size distributions obtained in Rochester, New York between 2008 and 2019.

Ensemble source apportionment of air pollutants and carbon dioxide based on online measurements

Air pollution and climate change have attracted worldwide attention due to their significant threats to human health and the environment. To maximize the co-benefits of clean air policies on greenhouse gas emissions and vice versa, it is imperative to coordinate emission control measures for air pollutants and carbon dioxide (CO2). To do this, we first need to better quantify the impacts of different sources to air pollutants and CO2 at once. Based on a 2-year observation of fine particulate matter (PM2.5), gaseous pollutants (including sulfur dioxide (SO2), nitrogen oxides (NOx), initial volatile organic compounds (In–VOCs)), as well as CO2, we apportioned the sources of criteria air pollutants and CO2 simultaneously. We then developed a new source apportionment method to quantitatively determine the relative impacts of different emission sources on (1) gaseous pollutants ensemble (SO2-NOx-In-VOCs) and (2) PM2.5 and CO2 ensemble (PM2.5-CO2). The results demonstrate that vehicle exhaust (35%), industrial emissions (31%), biomass burning (18%), and coal combustion (16%) were the main control targets for the SO2-NOx-In-VOCs ensemble, and they were also the dominant contributors to PM2.5-CO2 ensemble with similar contribution rates. Not surprisingly, the source impacts are substantially different for the two studied ensembles to those of individual air pollutant alone. This study provides a new source apportionment method to deliver scientific evidence for developing a coordinated strategy to maximize the benefits of clean air and carbon policies to air quality and the climate.

Source apportionment of air pollution in European urban areas: Lessons from the ClairCity project

Air pollution has become a major threat to human health in the last decades, with an increase of acute air pollution episodes in many cities worldwide. Source apportionment modelling provides valuable information on the contribution from different emission source sectors and source regions to distinct air pollutants concentrations. In this study, the CAMx model, with its PSAT tool, was applied to quantify the contribution of multiple source areas, categories and pollutant types to ambient air pollution, namely to PM and NO2 concentrations, over six European urban areas: Bristol (United Kingdom), Amsterdam (The Netherlands), Ljubljana (Slovenia), Liguria Region (Italy), Sosnowiec (Poland) and Aveiro Region (Portugal). Results indicate overall higher annual NO2 and PM concentrations located in the urban centres of the case studies. A comparison between the different areas showed that Liguria is the region with highest NO2 annual mean concentrations, while Ljubljana, Liguria Region and Sosnowiec are the case studies with the highest PM annual mean concentrations. The annual average contributions denote a major influence from road transport to NO2 concentrations, with up to 50%, except in Aveiro region, where road transport presents a lower contribution to NO2 concentrations, and the greatest contributor is the industrial combustion and processes sector with 45%. These results indicate a negligible contribution of the transboundary transport to NO2 concentrations, highlighting the relevance of local sources, while for PM concentrations the transboundary transport is the major contributor. The results highlight the relevance of long-range transport of PM across Europe. The transboundary transport reduces its importance during winter, when residential and commercial combustion increases its contribution. In the case of the Aveiro region, the industrial combustion and processes sector also plays an important contribution to PM concentrations.

Generalised non-negative matrix factorisation for air pollution source apportionment

Source Apportionment (SA) techniques are widely used for identifying key sources of air pollution, thereby providing critical inputs for policy measures. Positive Matrix Factorisation (PMF) (Paatero and Tapper, 1994) is a widely used SA technique. PMF uses the speciated concentration data (X) collected over several days and factorises it into source contribution (G) and source profile (F) matrices, albeit under positivity constraint. Towards this end, it involves solving an optimisation problem where the elements of X are weighted by the inverse of the standard deviations of the corresponding errors introduced during the sampling and chemical analysis process. Thus, PMF implicitly assumes that the errors in different elements of the X matrix are uncorrelated. This assumption may not hold since the sampling, and chemical analysis steps deployed in any data-collection campaign will inevitably lead to correlated errors. While there are other existing Non-Negative Matrix Factorisation (NMF) methods in literature that can be potentially used for SA, these also make various restrictive assumptions about the error covariance structure. In this work, we propose a new method called Generalised Non-Negative Matrix Factorisation (GNMF) to fill this gap. In particular, the proposed method is able to incorporate any error covariance matrix without making any restrictive assumptions on its structure. Towards this end, we integrate the full error covariance matrix in the objective function to be minimised to obtain F and G matrices. We derive the corresponding update rules for obtaining these matrices iteratively. To ensure non-negativity, we extend the multiplicative and projected gradient-based ideas available in NMF literature to the proposed GNMF approach. The proposed method subsumes various NMF methods available in literature as special cases. The utility of the proposed approach is demonstrated by comparing its performance with other methods on an SA problem using a dataset derived from field measurements.

Study on emission characteristics of tracer pollutants in cooking oil fumes

In this study, the following 5 kinds of tracer pollutants of cooking oil fumes were selected: palmitic acid, stearic acid, cholesterol, stigmasterol and β-sitosterol, and an effective method of their extraction, separation, enrichment and determination was established considering the conditions of the low-altitude exhaust and the condensation of cooking fumes. Five tracer pollutants were determined both in the PM2.5 and gas phases from college canteen exhaust and in waste oil condensed from fumes of different edible oils in households. It was found that the amount of palmitic acid was higher than that of stearic acid in the same sample; the emission characteristics of 5 substances in condensed grease samples from different oils varied considerably; compared with palmitic acid and stearic acid, 3 sterols (cholesterol, stigmasterol and β-sitosterol) are more characteristic as tracers of cooking oil fumes. The results provide technical and methodological references for the contribution and traceability analysis of catering source exhaust in air pollution source apportionment.

Results of Source Apportionment by Receptor Modeling of Ulaanbaatar City

Results of an air pollution source apportionment study using the Positive Matrix Factorization method (PMF) based on sampling of air pollution from a site of Nuclear Research Center, National University of Mongolia 2004-2009 and Zuun ail site 2008-2009 presented. From the statistical analysis of the data, it was possible to allocate factors to sources associated with coal combustion, motor vehicles, road dust, and soil.

Continuous PM2.5 Composition Measurements for Source Apportionment During Air Pollution Events

To explore the application of high-temporal-resolution data in PM2.5 source apportionment during air pollution events, ambient air PM2.5 components were continuously monitored in urban Nanjing from January to December, 2017. Commercially available instruments for continuous measurements were deployed to obtain hourly concentrations of elements, water-soluble ions, and carbonaceous components of PM2.5. Data for 15 elements and 5 bulk components during three pollution events(firework combustion during the Spring Festival, a spring sandstorm, and a winter haze event) and across the whole year comprised four datasets for source apportionment using positive matrix factorization(PMF), and the distribution of factor/source contributions and estimations of average concentrations of characteristic components were compared based on different input datasets(PMFfirework-sand-haze and PMFfull-year). The results showed that the identified factors/sources, factor profiles, and contributions differed largely between PMFfirework-sand-haze and PMFfull-year solutions. For example, the relative average contribution of the firework combustion factor derived from the PMFfull-year solution(was 1.50%) was far less than that of the PMFfirework solution. The dust factor had an average contribution of 8.51% in the PMFsand solution, which was approximately double that of the PMFfull-year solution. This might be explained by the fact that PMF assumes unvaried source compositions during the measurement campaign, meaning that the source apportionment results based on long-term observations will include bias due to changes in emission sources. Furthermore, during the firework combustion event, the estimated average concentration of K from the PMFfirework solution[(1.32±1.17) μg·m-3, P=0.64]was closer to measured value[(1.36±1.19) μg·m-3]than that of the PMFfull-year solution[(1.16±1.19) μg·m-3, P=0.0090]. For the sand storm event, the concentrations of Fe, Si, and Ti were significantly underestimated by the PMFfull-year solution[(0.061±0.042)-(1.06±0.65) μg·m-3, P<0.05], while their peak concentrations agreed well between the PMFsand estimations and the observations. During the winter haze event, all PM2.5 bulk components were well estimated by both the PMFfull-year and PMFhaze solutions. Based on these results, PMF source apportionment results based on continuous measurement data during pollution events can reasonably reflect short-term variations in characteristic PM2.5 components and their sources, which can improve the timeliness of air pollution source apportionment.

Influence of Highway Pavement on Metals in Road Dust: a Case Study in Houston, Texas

The elemental analysis of road dust can be used to determine the contribution of traffic-related metal emissions to the local environment. Previous studies have shown that brake wear, tire wear, and lead wheel weights release significant amounts of metals that lead to decreased biodiversity and degradation of urban streams. Highway pavement has not previously been considered as a source of metal inputs into the environment. To determine the extent that highway pavement contributes to metal loads in the local environment, road dust and pavement samples were collected for both asphalt and concrete pavement types on State Highway 59 in Houston, TX, USA. Samples were analyzed by ICP-MS to quantitate 9 metals: aluminum (Al), iron (Fe), vanadium (V), chromium (Cr), nickel (Ni), cobalt (Co), copper (Cu), zinc (Zn), and lead (Pb). A chemical mass balance receptor model that is commonly used for source apportionment of air pollution was applied to heavy metals in road dust, with brake dust, tire wear, wheel weights, and pavement materials as the emission sources. Pavement is shown to be the primary source of V, Cr, Ni, and Co in road dust, regardless of asphalt or concrete pavement type. Lead concentration in road dust is influenced by pavement type, with concrete pavement contributing approximately 45% of total lead in concrete road dust, while asphalt pavement only contributes 27% of total lead in asphalt road dust. Fly ash is the likely component of concrete pavement responsible for the higher contribution of Pb. An enrichment factor analysis indicates that Cr, Co, and V have low levels of enrichment compared to Cu, Zn, Pb, and Ni, indicating that pavement wear is generally a minor source of environmental metal contamination compared to brake and tire wear. Currently, brake and tire wear are the non-exhaust vehicle emissions of greatest concern; however, alternatives to fly ash should be considered to further reduce Pb and Ni concentrations in the environment.

Ambient VOCs Characteristics, Ozone Formation Potential, and Source Apportionment of Air Pollution in Spring in Zhengzhou

Ambient volatile organic compounds (VOCs) were determined by GC 5000 online gas chromatography in the urban site of Zhengzhou from April 15 to May 15, 2018. Based on chemical composition analysis, in this study, the concentrations, ozone formation potential (OFP), and source apportionment were studied. The results show that the averaged volume fraction of VOCs in Zhengzhou during spring was 40.26×10-9, which was 23% higher on polluted days (44.12×10-9) than on non-polluted days (35.82×10-9). The contribution of VOC species to OFP was in the order: alkenes > aromatics > alkanes > alkyne. The five factors identified by the PMF model were liquefied petroleum gas (LPG) volatilization sources (66.05%), motor vehicle exhaust sources (47.39%), industrial solvent sources (37.51%), fuel combustion sources (37.80%), and biogenic sources (11.25%). The contributions of LPG volatilization sources and biogenic sources on polluted days were higher by 22.92% and 68.50% than on non-polluted days, respectively.

Air Pollution Sources’ Contribution to PM2.5 Concentration in the Northeastern Part of the Czech Republic

This article focuses on the source apportionment of air pollution in a specific northeastern part of the Czech Republic. The research area, located around the city of Třinec, is significantly affected by a complex spectrum of air pollution sources, including local residential heating (coal and wood burning), heavy industry (mainly iron and steel production), road traffic, and regional and long-range air pollution transport from the nearby cities, Poland, and other countries. The main pollution sources contributing to the total concentration of fine suspended particles (PM2.5) were evaluated on the basis of the measurements at three sites and on subsequent positive matrix factorization modeling. The six major air pollution factors were identified, and their relative and absolute contributions were quantified. The result of the study is that the most important current task of air protection is to reduce the residential emissions from solid fuels, which are responsible for approximately 50–60% of PM2.5 concentration, followed by the regional primary and secondary aerosol sources (up to 40% of the total PM2.5 aerosol mass). Lower contributions have been identified in the case of resuspended mineral and biogenic particles (15–20%), long-range (trans-European) air pollution transport (up to 10%), and heavy industry (up to 10% in the most affected location). A detailed discussion has been provided considering specific regional EC (elemental carbon)–OC (organic carbon) relations in the region with traditional coal-burning for household heating which complicate the interpretation of the PMF (Positive Matrix Factorization) results, especially due to the interference between the traffic, residential heating, and biogenic aerosol factors.

Using Si, Al and Fe as Tracers for Source Apportionment of Air Pollutants in Lake Baikal Snowpack

The aim of this study was to select chemical species characterized by distinctly different proportions in natural and anthropogenic particulate matter that could be used as tracers for air pollutant sources. The end-member mixing approach, based on the observation that the chemical species in snow closely correlated with land use are those that exhibit differences in concentrations across the different types of anthropogenic wastes, was used for source apportionment. The concentrations of Si and Fe normalized to Al were used as tracers in the mixing equations. Mixing diagrams showed that the major pollution sources (in descending order) are oil, coal, and wood combustion. The traces of several minor sources, such as aluminum production plants, pulp and paper mills, steel rust, and natural aluminosilicates, were also detected. It was found that the fingerprint of diesel engines on snow is similar to that of oil combustion; thus, future research of the role of diesel engines in air pollution will be needed. The insufficient precision of source apportionment is probably due to different combinations of pollution sources in different areas. Thus, principles for the delineation of areas affected by different source combinations should be the subject of further studies.

Pollution emission characteristics, distribution of heavy metals, and particle morphologies in a hazardous waste incinerator processing phenolic waste

Secondary pollution emitted from hazardous waste incinerators (HWIs) can pose potential risks to the surrounding populations and environment. An investigation was conducted on pollutant emission status in a HWI combusting homogenized phenolic waste, woodchips, and electroplating sludge during the sampling period. Morphologies and elemental compositions of particles in flue gas and indoor air of the incinerator were characterized by TEM-EDS. Eight types of single particles were classified, as organic, soot, K-rich, S-rich, Na-rich, Fe-rich, mineral and fly ash particles. In the indoor air near the fly ash collector, organic and S-rich particles were the two most observed particles, taking 56 % and 30 %, respectively. While near the bottom ash collector, Fe-rich particles took approximately 30 %. Besides, the partitioning behavior of heavy metals in the incinerating process were investigated. Hg, Cd and Pb were mainly enriched in fly ash through evaporation, condensation, and adsorption; while Cr, Cu, Mn, and Ni were mostly remained in the bottom ash due to their low volatilities. This study provides information for regional air pollution source apportionment, but also helps understand the partitioning behavior of heavy metals for the secondary pollution control. Meanwhile, the visualized micro-compositions of indoor particles pave a way for occupational exposure risk assessment.

Source Apportionment of Air Pollutants for a Typical Pollution Event in Zhaoqing

Based on observational data for pollutants and meteorology, this study analyzed the pollution episode that occurred during Dec 17th to 23th in 2018 in Zhaoqing, Guangdong Province, China. Using the source apportionment model CMAQ-ISAM and the hybrid receptor model, the regional contributions to air pollution were examined. The results showed that low-pressure conditions had an adverse effect on the diffusion of pollutants during this pollution episode in Zhaoqing. Prior to the pollution episode, pollutants were mainly derived from Zhaoqing and Qingyuan, accounting for 19.2% and 10.7% of pollutants, respectively. As well as pollutants from Guangdong Province, long-distance transport of pollutants from Jiangxi, Hunan, Hubei, and Shaanxi accounted for approximately 64.5% of the total during the non-pollution period. During the polluted episode, major cities in Pearl River Delta and the eastern part of Guangdong Province contributed more pollutants as a surface high-pressure field moved southward. Zhaoqing, Foshan, Dongguan, Guangzhou, and Huizhou contributed 25.5%, 14.8%, 9.8%, 9.5%, and 5.3% of the pollutants, respectively. Cities in the eastern part of Guangdong Province including Heyuan, Meizhou, Shanwei, Jieyang, Shantou, and Chaozhou contributed 13.7% of the total pollutants. In addition, pollutants from Fujian, Jiangxi, and the Yangtze River Delta accounted for approximately 32.9%. Furthermore, pollutants transported under marine influences were one of the main causes of this pollution episode in Zhaoqing.

Determining the number of factors for non-negative matrix and its application in source apportionment of air pollution in Singapore

The non-negative matrix factorization has been used in many disciplines of research, where the number of factors plays a crucial role. However, a fully data-driven method for determining the number is yet not available in the literature. Based on the fact that the most appropriate number of factors should generate the best prediction, in this paper we propose a selection method using a two-step delete-one-out approach, called twice cross-validation. This method is easy to implement and is fully data-driven. It also works when constraints are imposed on the factorization including the sparsity. Intensive simulations and real data analyses suggest that the proposed method performs well in most cases and can select the number of factors correctly when the number of factors is much less than the dimension of variables and the sample size is reasonably large. As an important application, the proposed method is used for source apportionment of air pollution in Singapore, and provides physically reasonable source profiles.

Investigation and Source Apportionment of Air Pollutants in a Large Oceangoing Ship during Voyage.

The aims of this study were to determine compartmental air pollution during navigation of a large oceangoing ship and to identify preliminarily the major pollution sources. During the voyage of a bulk carrier ship, air samples were collected at 18 selected sites using a stratified sampling method. The concentrations of 15 pollutants were determined using gas chromatography. Results showed the concentrations of these pollutants varied significantly among the sampling sites, indicating major pollution sources at or nearby those locations. Five common factors extracted using factor analysis explained 89.092% of the total variance. Multivariate linear regression analysis showed the contributions to air pollution of these five common factors, i.e., the volatilization of ship paint, volatilization of ship-based oil, cooking activities, high-temperature release of rubber components on the ship and daily use of chemical products, and the application of deodorant and insecticide, were 41.07%, 25.14%, 14.37%, 11.78%, and 7.63%, respectively. Three significant groups were determined using cluster analysis based on their similarity, i.e., high, medium, and low pollution of sampling sites. This study established that the air of the bulk carrier ship was heavily polluted, and that effective identification of pollution sources could provide a scientific basis for its control.

PM2.5 Source Apportionment Using a Hybrid Environmental Receptor Model.

A hybrid environmental receptor model (HERM) that unifies the theory of effective-variance chemical mass balance (EV-CMB) and positive matrix factorization (PMF) models was developed to support the weight-of-evidence approach of air pollution source apportionment. The HERM software is capable of (1) conducting EV-CMB analysis for multiple samples in a single model run; (2) calculating EV-CMB and PMF source contributions, as well as middle grounds between the two (i.e., hybrid mode), using partial source information available for the study region; (3) reporting source contribution uncertainties and sample-/species-specific fitting performance measures; and (4) interfacing with MS Excel for convenient data inputs/outputs and analysis. Initial testing with simulated and real-world PM2.5 (fine particulate air pollutants with aerodynamic diameter <2.5 μm) data sets show that HERM reproduces EV-CMB results from existing software but with more tolerance to collinearity and better uncertainty estimates. It also shows that partial source information helps reduce rotational ambiguity in PMF, thus producing more accurate partitioning between highly correlated sources. Moreover, source profiles generated from the hybrid mode can be more representative of the study region than those acquired from other locales or calculated by PMF with no source information. Strategies to use HERM for source apportionment are recommended.

Source apportionment of aerosol particles at a European air pollution hot spot using particle number size distributions and chemical composition.

Ostrava in the Moravian-Silesian region (Czech Republic) is a European air pollution hot spot for airborne particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), and ultrafine particles (UFPs). Air pollution source apportionment is essential for implementation of successful abatement strategies. UFPs or nanoparticles of diameter <100nm exhibit the highest deposition efficiency in human lungs. To permit apportionment of PM sources at the hot-spot including nanoparticles, Positive Matrix Factorization (PMF) was applied to highly time resolved particle number size distributions (NSD, 14nm-10μm) and PM0.09-1.15 chemical composition. Diurnal patterns, meteorological variables, gaseous pollutants, organic markers, and associations between the NSD factors and chemical composition factors were used to identify the pollution sources. The PMF on the NSD reveals two factors in the ultrafine size range: industrial UFPs (28%, number mode diameter - NMD 45nm), industrial/fresh road traffic nanoparticles (26%, NMD 26nm); three factors in the accumulation size range: urban background (24%, NMD 93nm), coal burning (14%, volume mode diameter - VMD 0.5μm), regional pollution (3%, VMD 0.8μm) and one factor in the coarse size range: industrial coarse particles/road dust (2%, VMD 5μm). The PMF analysis of PM0.09-1.15 revealed four factors: SIA/CC/BB (52%), road dust (18%), sinter/steel (16%), iron production (16%). The factors in the ultrafine size range resolved with NSD have a positive correlation with sinter/steel production and iron production factors resolved with chemical composition. Coal combustion factor resolved with NSD has moderate correlation with SIA/CC/BB factor. The organic markers homohopanes correlate with coal combustion and the levoglucosan correlates with urban background. The PMF applications to NSD and chemical composition datasets are complementary. PAHs in PM1 were found to be associated with coal combustion factor.

Source Apportionment of Air Pollution and Characteristics of Volatile Organic Compounds in a Municipal Wastewater Treatment Plant, North Taiwan

ABSTRACTMonitoring air quality in the municipal wastewater treatment plants is an initial stage in preventing several problems related to air emissions. This study measured 103 volatile organic compounds (VOCs), total VOC (TVOC), and some prominent air pollutants (CO, CO2, NH3, H2S, PM1, PM2.5, PM7, PM10, TSP) in the municipal wastewater treatment plant X located in the north of Taiwan. Thirty-three VOCs were identified, which categorized as alkane, aromatic, alkene, ester, ether, haloalkane and ketone. Five dominant factors were determined from principal component analysis (PCA). The first factor involves indoor activities (such as particulate matter resuspension), and outdoor activities (such as vehicles exhausts), which explained 32.42% of total variance. Factor 2 was paint applications and domestic wastewater decomposition, with an explained variance of 17.23%. Factor 3 was solvent use, with an explained variance of 14.26%. Factor 4 was solvent use and road dust, with an explained variance of 8.54%. Factor 5 was a byproduct of the chlorination treatment process, with an explained variance of 6.92%. The five factors explained 79.37% of total variance. By applying absolute principal component scores (APCS), source apportionments were obtained having 35.21%, 26.04%, 16.13%, 7.03%, and 15.59% for factors 1, 2, 3, 4, and 5, respectively. Toluene had the highest ozone formation potential (OFP).

Source apportionment of air pollutants in the Greater Auckland Region of New Zealand using receptor models and elemental levels in the lichen, Parmotrema reticulatum

Some lichen species can provide environmental archives of atmospheric deposition of potentially harmful elements that can be investigated as records of environmental change. The use of lichens for air quality assessment in New Zealand has not been widely explored, despite their widespread application around the world. Due to this lack of attention, there is no lichen species that has been formally validated for use locally as an air pollution biomonitor although some species have been identified as being potentially useful. The most promising of these is Parmotrema reticulatum. This study assessed the performance of the lichen, P. reticulatum, as a biomonitor of elemental air pollutants by examining the extent to which its elemental composition can provide a meaningful pollution source apportionment modeling in Auckland's central business district (CBD). Using the elemental content in transplanted P. reticulatum, principal component analysis, cluster analysis and positive matrix factorisation receptor models, the major elemental air pollutants in Auckland's CBD were determined. Five sources explained 74.4% of the total variance contained in the lichen dataset. These sources represent elemental contributions from motor vehicles, port activities, biomass burning, marine aerosol and roadside dust or crustal matter. Motor vehicles, Auckland port activities, Biomass burning and marine aerosol were found to account for 11.8%, 39.8%, 10.2% and 6.9% of the overall elemental concentrations respectively. Emissions from roadside dust accounted for 5.6% of the overall elemental concentrations in the lichen data. Evidence from this study suggests that P. reticulatum elemental content be used to model pollution sources.

Source apportionment of PM2.5 for supporting control strategies in the Monterrey Metropolitan Area, Mexico

ABSTRACTThe Monterrey Metropolitan Area (MMA) in Northeast Mexico has shown high PM2.5 concentrations since 2003. The data shows that the annual average concentration exceeds from 2 to 3 times the Mexican PM2.5 annual air quality standard of 12 µg/m3. In a previous work we studied the chemical characterization of PM2.5 in two sites of the MMA during the winter season. Among the most important components we found ammonium sulfate and nitrate, elemental and organic carbon, and crustal matter. In this work we present the results of a second chemical characterization study performed during the summer time and the application of the chemical mass balance (CMB) model to determine the source apportionment of air pollutants in the region. The chemical analysis results show that the chemical composition of PM2.5 is similar in both sites and periods of the year. The results of the chemical analysis and the CMB model show that industrial, traffic, and combustion activities in the area are the major sources of primary PM2.5 and precursor gases of secondary inorganic and organic aerosol (SO2, NOx, NH3, and volatile organic compounds [VOCs]). We also found that black carbon and organic carbon are important components of PM2.5 in the MMA. These results are consistent with the MMA emission inventory that reports as major sources of particles and SO2 a refinery and fuel combustion, as well as nitrogen oxides and ammonium from transportation and industrial activities in the MMA and ammonium form agricultural activities in the state. The results of this work are important to identify and support effective actions to reduce direct emissions of PM2.5 and its precursor gases to improve air quality in the MMA. Implications: The Monterrey Metropolitan Area (MMA) has been classified as the most air-polluted area in Mexico by the World Health Organization (WHO). Effective actions need to be taken to control primary sources of PM2.5 and its precursors, reducing health risks on the population exposed and their associated costs. The results of this study identify the main sources and their estimated contribution to PM2.5 mass concentration, providing valuable information to the local environmental authorities to take decisions on PM2.5 control strategies in the MMA.