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Air quality modeling (AQM) is often used to investigate gaseous pollution around industrial zones. However, this methodology requires accurate emission inventories, unbiased AQM algorithms and realistic boundary conditions. We introduce a new methodology for source apportionment of industrial gaseous emissions, which is based on a fuzzy clustering of ambient concentrations, along with a standard AQM approach. First, by applying fuzzy clustering, ambient concentration is expressed as a sum of non-negative contributions - each corresponding to a specific spatiotemporal pattern (STP); we denote this method as FUSTA (FUzzy SpatioTemporal Apportionment). Second, AQM of the major industrial emissions in the study zone generates another set of STP. By comparing both STP sets, all major source contributions resolved by FUSTA are identified, so a source apportionment is achieved. The uncertainty in FUSTA results may be estimated by comparing results for different numbers of clusters. We have applied FUSTA in an industrial zone in central Chile, obtaining the contributions from major sources of ambient SO 2 : a thermal power plant complex and a copper smelter, and other contributions from local and regional sources (outside the AQM domain). The methodology also identifies SO 2 episodes associated to emissions from the copper smelter., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Santiago is one of the largest cities in South America and has experienced high fine particulate matter (PM2.5) concentrations in fall and winter months for decades. To better understand the sources of fall and wintertime pollution in Santiago, PM2.5 samples were collected for 24 h every weekday from March to October 2013 for chemical analysis. Samples were analyzed for mass, elemental carbon (EC), organic carbon (OC), water soluble organic carbon (WSOC), water soluble nitrogen (WSTN), secondary inorganic ions, and particle-phase organic tracers for source apportionment. Selected samples were analyzed as monthly composites for organic tracers. PM2.5 concentrations were considerably higher in the coldest months (June-July), averaging (mean ± standard deviation) 62±15 μg/m(3) in these two months. Average fine particle mass concentration during the study period was 40±20 μg/m(3). Organic matter during the peak winter months was the major component of fine particles comprising around 70% of the particle mass. Source contributions to OC were calculated using organic molecular markers and a chemical mass balance (CMB) receptor model. The four combustion sources identified were wood smoke, diesel engine emission, gasoline vehicles, and natural gas. Wood smoke was the predominant source of OC, accounting for 58±42% of OC in fall and winter. Wood smoke and nitrate were the major contributors to PM2.5. In fall and winter, wood smoke accounted for 9.8±7.1 μg/m(3) (21±15%) and nitrate accounted for 9.1±4.8 μg/m(3) (20±10%) of fine PM. The sum of secondary inorganic ions (sulfate, nitrate, and ammonium) represented about 30% of PM2.5 mass. Secondary organic aerosols contributed only in warm months, accounting for about 30% of fine PM during this time., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Estimating contributions of anthropogenic sources to ambient particulate matter (PM) in desert regions is a challenging issue because wind erosion contributions are ubiquitous, significant and difficult to quantify by using source-oriented, dispersion models. A receptor modeling analysis has been applied to ambient PM(10) and PM(2.5) measured in an industrial zone ~20 km SE of Antofagasta (23.63°S, 70.39°W), a midsize coastal city in northern Chile; the monitoring site is within a desert region that extends from northern Chile to southern Perú. Integrated 24-hour ambient samples of PM(10) and PM(2.5) were taken with Harvard Impactors; samples were analyzed by X Ray Fluorescence, ionic chromatography (NO(3)(-) and SO(4)(=)), atomic absorption (Na(+), K(+)) and thermal optical transmission for elemental and organic carbon determination. Receptor modeling was carried out using Positive Matrix Factorization (US EPA Version 3.0); sources were identified by looking at specific tracers, tracer ratios, local winds and wind trajectories computed from NOAA's HYSPLIT model. For the PM(2.5) fraction, six contributions were found - cement plant, 33.7 ± 1.3%; soil dust, 22.4 ± 1.6%; sulfates, 17.8 ± 1.7%; mineral stockpiles and brine plant, 12.4 ± 1.2%; Antofagasta, 8.5 ± 1.3% and copper smelter, 5.3 ± 0.8%. For the PM(10) fraction five sources were identified - cement plant, 38.2 ± 1.5%; soil dust, 31.2 ± 2.3%; mineral stockpiles and brine plant, 12.7 ± 1.7%; copper smelter, 11.5 ± 1.6% and marine aerosol, 6.5 ± 2.4%. Therefore local sources contribute to ambient PM concentrations more than distant sources (Antofagasta, marine aerosol) do. Soil dust is enriched with deposition of marine aerosol and calcium, sulfates and heavy metals from surrounding industrial activities. The mean contribution of suspended soil dust to PM(10) is 50 μg/m(3) and the peak daily value is 104 μg/m(3). For the PM(2.5) fraction, suspended soil dust contributes with an average of 9.3 μg/m(3) and a peak daily value of 31.5 μg/m(3)., (Copyright © 2012 Elsevier B.V. All rights reserved.)

A receptor model analysis has been applied to ambient PM(2.5) measurements taken at Santiago, Chile (33.5°S, 70.7°W) in 2004 (117 samples) and in 1999 (95 samples) on a receptor site on the eastern side of the city. For both campaigns, six sources have been identified at Santiago and their contributions in 1999/2004 are: motor vehicles: 28 ± 2.5/31.2 ± 3.4%, wood burning: 24.8 ± 2.3/28.9 ± 3.3%, sulfates: 18.8 ± 1.7/16.2 ± 2.5%, marine aerosol: 13 ± 2.1/9.9 ± 1.5%, copper smelters: 11.5 ± 1.4/9.7 ± 3.3% and soil dust: 3.9 ± 1.5/4.0 ± 2.4%. Hence relative contributions are statistically the same but the absolute contributions have been reduced because ambient PM(2.5) has decreased from 34.2 to 25.1 μg/m(3) between 1999 and 2004 at Santiago. Similarity of results for both data sets - analyzed with different techniques at different laboratory facilities - shows that the analysis performed here is robust. Source identification was carried out by inspection of key species in source profiles, seasonality of source contributions, comparison with published source profiles and by looking at wind trajectories computed using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) from USA's National Oceanic and Atmospheric Administration (NOAA); for the wood burning sources the MODIS burned area daily product was used to confirm wildfire events along the year. Using this combined methodology we have shown conclusively that: a) marine air masses do reach Santiago's basin in significant amounts but combined with anthropogenic sources; b) all copper smelters surrounding Santiago - and perhaps coal-fired power plants as well - contribute to ambient PM(2.5); c) wood burning is the second largest source, coming from residential wood burning in fall and winter and from regional wildfires in spring and summer. The results of the present analysis can be used to improve emission inventories, air quality forecasting systems and cost-benefit analyses at local and regional scales., (Copyright © 2012 Elsevier B.V. All rights reserved.)