High time resolution chemical characterization of submicron particles was carried out in the South American city of Santiago de Chile using the Aerosol Chemical Speciation Monitor (ACSM). The instrumentation operated for 100 days from August 17 th to November 23 rd 2011 in an urban station located inside the University of Santiago de Chile (USACH) campus. In addition, a semi-continuous OC/EC analyzer was also run in parallel with the ACSM for some of this time. Meteorological conditions varied along the studied period due to the transition from winter to spring time. Atmospheric temperature inversions were responsible for hourly average sub-micron particulate matter levels of up to 80 μg/m 3 , especially during the night time. The average submicron particle mass concentration (± standard deviation) for the whole period was 29.8 ± 25 μg/m 3 . Aerosol particles were composed mainly of organics 59%, followed by nitrate, ammonium, sulfate, black carbon and chloride with contributions of 14, 12, 8, 3 and 3%, respectively. Using positive matrix factorization, the organic fraction was divided into four distinct types of organic aerosol representing fresh automobile exhaust, biomass burning, and two oxygenated organic aerosol factors with different oxidation states. The transition from winter to spring was clearly seen in the composition of OA. The emissions from primary sources, such as vehicle and biomass burning, decreased in the period leading to spring, whereas the amount of oxygenated organic aerosol increased over the same time. This study shows that high time resolution measurements of aerosol chemical composition can lead to better characterizations of the evolution and sources of pollutants in an urban atmosphere.

Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM 10 and PM 2.5 ) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM 10 -PM 2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/ Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filterbased sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM 2.5 (normalized root-mean-square error 9-24%) and PM 10 (10-37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM 2.5 (R 2 0.47-0.86) than PM 10 (0.24-0.56). The correlations (R 2 ) between the 24-h PM 2.5 averages from the sensors and reference instruments were 0.63-0.87 for continuous monitoring and 0.69-0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM 2.5 averages after correcting for RH.

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Particulate matter (PM) from mining operations, engines, and ore processing may have adverse effects on health and well-being of workers and population living nearby. In this study, the characteristics of PM in an underground chrome mine were investigated in Kemi, Northern Finland. The concentrations and chemical composition of PM in size ranges from 2.5 nm to 10 mm were explored in order to identify sources, formation mechanisms, and post-emission processes of particles in the mine air. This was done by using several online instruments with high timeresolution and offline particulate sampling followed by elemental and ionic analyses. A majority of sub-micrometer particles (<1 mm in diameter, PM 1 ) originated from diesel engine emissions that were responsible for a rather stable composition of PM 1 in the mine air. Another sub-micrometer particle type originated from the combustion products of explosives (e.g., nitrate and ammonium). On average, PM 1 in the mine was composed of 62%, 30%, and 8% of organic matter, black carbon, and major inorganic species, respectively. Regarding the analyzed elements (e.g., Al, Si, Fe, Ca), many of them peaked at >1 mm indicating mineral dust origin. The average particle number concentration in the mine was (2.3 § 1.4) Ã 10 4 #/cm 3 . The maximum of particle number size distribution was between 30 and 200 nm for most of the time but there was frequently a distinct mode <30 nm. The potential origin of nano-size particles remained as challenge for future studies.

Globally, NO2 and PM2.5 declined while O3 increased during strict lockdown periods.

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The contributions to PM 2.5 from different emission sectors across central Chile and the Santiago metropolitan area during summer/fall and winter have been evaluated using a chemical transport model. The simulations generally underestimate the mean PM 2.5 concentrations compared to measurements conducted at stations in Santiago that belong to the Chilean National Air Quality Information System (SINCA). The potential reasons for this discrepancy include underestimated direct PM 2.5 emissions, missing emissions for semi-and intermediately volatile organic compounds (SVOCs and IVOCs) and overestimated wind speeds in the simulations. The simulated winter PM 2.5 concentrations in Santiago are lower and higher than the values observed during nighttime, and daytime and late evening, respectively, which may be related to excessive simulated wind speeds, as well as to uncertainties in the diurnal variation in the emissions. During summer/fall, the simulated diurnal variation better agrees with the observations, but the peak concentrations during the morning are underestimated, whereas those during the evening are overestimated. The simulated contributions of different aerosol components to the PM 2.5 at one station in Santiago are all lower than the observed values, except for elemental carbon equivalent black carbon (BC e ), which exhibit comparable or higher levels in the simulations. The absolute differences are the largest for the total organic matter, whereas the relative differences are the largest for BC e and ammonium. The simulated sector contributions indicate that emissions originating from transport and construction machinery dominate the PM 2.5 in Santiago; however, residential wood combustion is the primary source in other urban areas of central Chile, except near major point sources. Away from urban areas, traffic routes and major industrial sources, secondary inorganic aerosol (SIA) is estimated to be the largest component of the aerosol, whereas the simulated secondary organic aerosol (SOA) only contributes a small fraction.

The sources and characteristics of particulate matter (PM) were determined in a modern underground chrome mine in Finland. Measurements were conducted at five locations in the mine: the maintenance area, blasting area, ore pit dumping area, crushing station and conveyor belt. The measurement setup consisted of a Soot Particle Aerosol Mass Spectrometer (SP-AMS) for the particles' chemical composition; an Electrical Low Pressure Impactor, Nano Scanning Mobility Particle Sizer and Optical Particle Counter for the particle number and mass size distribution; and an Aethalometer for black carbon (BC). The particle number and mass concentration depended strongly on the measurement location and period. The PM 10 and the total number concentrations varied from 22 to 1100 µg m-3 and 1.7 × 10 3 to 2.3 × 10 5 # cm-3 , respectively, in the mine. In terms of the composition, the sub-micrometer particles (PM 1) consisted mostly of organic matter and BC, but at the blasting site, the fraction of sulfate was also significant. The SP-AMS data was analyzed with Positive Matrix Factorization (PMF) to identify and quantify the main sources of PM 1 in the mine. Based on the PMF analysis, the PM 1 originated mostly from diesel engines (35-84%) and blasting (7-60%). The impact of blasting on air quality in mines may become more pronounced in the future as the emissions from diesel engines decrease due to alternative fuels and better engine and aftertreatment technologies.