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[Display omitted] • Shiraz dust storm event mainly originated from the Saudi Arabia Peninsula. • The aerosol composition was affected by Persian Gulf sea salt and soil erosion in Iran and Iraq. • The target PTEs had the highest concentration in the central parts of the city. • These results were based on the mineralogy, δ18O and δ13C values, PMF and HYSPLIT modeling, and elemental ratios. Frontal dust storms are rare but important weather events in southern Iran affecting visibility and air quality, especially in urban areas. This study investigated the main characteristics of the 13 May 2018 frontal dust storm in Shiraz. The evaluation was based on geostatistical models, and mineralogical, elemental and isotopic data. The mean and median concentrations of major and trace elements followed the order Ca > Al > Fe > Mg > K > Na > Ti > Mn > Zn > V > Cr > Ni > Cu > Pb > Co > Cd, in agreement with the mineralogical composition of dust samples. The spatial distributions of the potentially toxic elements (PTEs) show that the concentrations were higher in the central sectors of city, likely due to the higher population density and traffic volume. Integrated source characterization coupled with positive matrix factorization (PMF) identified that the PTEs sources were geogenic, anthropogenic, and sea salt related. Geochemical isotopic methods, δ18O and δ13C values along with hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) analysis suggest that the dust storm mainly originated from Saudi Arabia. However, local lithological units and resuspended street dust also played an important role in the elemental distributions. These results also indicate that combining various datasets can be beneficial in characterizing the sources of dust storms and their components in southwestern Iran. [ABSTRACT FROM AUTHOR]

From late 1999 to early March 2000, measurements of particle number (particles 0.01–20 μm in aerodynamic diameter) concentrations were made inside of a townhouse occupied by two non-smoking adults and located in Reston, VA (approximately 25 miles northwest of Washington, DC). The particle size measurements were made using an SMPSTM and an APSTM as well as a Climet optical scattering instrument. In this study, positive matrix factorization (PMF) was used to study the indoor particle size distributions. The size distributions or profiles obtained were identified by relating the obtained source contributions to the source information provided by the occupants. Nine particle sources were identified, including two sources associated with gas burner use: boiling water and frying tortillas. Boiling water for tea or coffee was found to be associated only with the smallest particles, with a number mode close to the detection limit of the SMPS (i.e., 0.01 μm). Frying tortillas produced particles with a number mode at about 0.09 μm while broiling fish produced particles with a number mode at about 0.05 μm. A citronella candle was often burned during the study period, and this practice was found to produce a 0.2- μm modal number distribution. Other indoor particle sources identified included sweeping/vacuuming (volume mode at 2 μm); use of the electric toaster oven (number mode at 0.03 μm); and pouring of kitty litter (volume mode over 10 μm). Two outdoor sources were also resolved: traffic (number mode at about 0.15 μm) and wood smoke (major number mode at about 0.07 μm). The volume distributions showed presence of coarse particles in most of the resolved indoor sources probably caused by personal cloud emissions as the residents performed the various indoor activities. Practical Implications This study has shown that continuous measurements of indoor particle number and volume concentrations together with records of personal activities are useful for indoor source apportionment models. Each of the particle sources identified in this study produces distinct size distributions that may be useful in studying the mortality and morbidity effects of airborne particulate matter because they will have different penetrability and deposition patterns. [ABSTRACT FROM AUTHOR]

Conventional factor analyses can present problems in cases with changing numbers of sources and/or time-dependent source compositions. There is also lack of a reliable method to estimate uncertainties in the source contributions derived by positive matrix factorization (PMF). Applying a moving window evolving PMF to hourly PM 2.5 composition dataset from a field campaign in Tianjin China that included the Spring and Lantern Festivals and the start of COVID-19 pandemic has substantially improved the apportionment compared to a conventional analysis using the entire data. Festival-related sources (e.g., fireworks and residential burning here) have been clearly identified and estimated during both the Spring and Lantern Festivals. During this period, the sources changed because the time period overlaps with the outbreak of COVID-19 and related reductions in activity during the lockdown that began on Lunar New Year. Multiple PMF runs providing source contribution estimates made it possible to estimate the uncertainties in these values. Our results show that winds-dependent sources like dust and distant point sources have larger uncertainties than the other sources. Compared with conventional PMF analyses, the current method may better reflect the actual emissions as well as being able to estimate uncertainties. Thus, this approach appears to be an improvement if the appropriate data are available. [Display omitted] • A moving window evolving DN-PMF was applied to PM 2.5 composition dataset. • The applied method provides some uncertainty estimates for source contributions. • Wind dependent sources like dust and distant point sources have larger uncertainties than the other sources. Moving window evolving DN-PMF provides uncertainty estimates for source contributions. [ABSTRACT FROM AUTHOR]