Particulate matter pollution and non-targeted analysis of polar compounds in three regions of Brazil

Atmospheric Particulate Matter (PM) is a pollutant with diverse origins, exhibiting varying chemical compositions, and undergoes several molecular transformations in the atmosphere. In this study, PM samples (PM2.5, PM10 and TSP) were collected in five Brazilian cities (Camboriú-SC; Catalão-GO; Florianópolis-SC; Limeira-SP and Novo Hamburgo-RS) during the four seasons of the year. Analysis of Variance (ANOVA) was used to evaluate the differences between each city and season in PM concentration. PM10 average concentrations were higher in the city of Limeira, compared to the other (ANOVA p-values and Tukey's test). Moreover, Tukey's test demonstrated differences between the average PM10 concentrations in summer and winter. Regarding TSP and PM2.5, Tukey's test showed differences between winter and warm seasons (spring and summer). Moreover, polar compounds from the samples collected in the summer (February) and winter (August) periods were analyzed (Ultra-High-Performance Liquid Chromatography coupled to a Quadrupole Time-of-Flight Mass Spectrometer) following a non-targeted approach and annotated. This is the first study to carry out this type of analysis in these five Brazilian cities. Despite the differences in PM concentrations, profiles of polar organic compounds, showed similarities between samples/and, in general, the same compounds were present, albeit with different intensities. The annotated compounds are associated with vehicle emissions and plastics, which are considered important global air polluters. Therefore, there is an urgent necessity for comprehensive studies aimed at investigating the non-targeted compounds existing in the atmosphere. Such research can provide invaluable insights to policymakers, enabling them to formulate effective guidelines and policies to mitigate particulate matter concentration and enhance overall air quality.

Quantitative and qualitative assessment of microbial aerosols in different indoor environments of a dental school clinic

In the indoor environment of dental clinics, dental staff and patients are exposed to various types of infectious agents transported by aerosols and particles, generated during dental procedures, promoting an increased risk of cross-infection. The aim of this study was to determine the levels and diversity of microbial aerosol in relation to particle load in five different departments of a dental school clinic. The air samples were collected by an active single-stage Andersen sampler during the treatment procedure. The mean concentrations of airborne bacteria were in the range of 52-1030 and 8-844 CFU/m³ at the distances of 0.5 and 2 m, respectively. Bacterial aerosols in pediatric, endodontics, and restorative wards and fungal aerosols in all the sampling wards were significantly higher at the distances of 0.5 m. The dominant bacteria and fungi were identified as Micrococcus, Bacillus, Streptococcus, Staphylococcus, Penicillium, Cladosporium, Aspergillus, Rhizopus, and Alternaria. The positive associations were also obtained between bacteria and fungi levels and particulate matter (PM) concentrations.

Commuter exposure to particulate matter in urban public transportation of Xi'an, China

PURPOSE

To investigate commuter exposures to particulate matter (PM) in urban public transportation buses and subways, PM concentrations were simultaneously monitored for these two modes, over the same routes, in Xi'an, China.

METHODS

The microenvironment variabilities in each stage of the total trip were analyzed. Exposure doses for the different commute processes were estimated based on the heart rates of volunteers. Experimental measurements were taken during peak traffic hours in July and October (summer and autumn) on two typical commute routes, for a total of 36 trips. One-way ANOVA was used to analyze the effects of different variables on commuter exposures.

RESULTS

On the same route, the average PM exposure concentration of bus commuters was higher than those of subway commuters. For example, on Route 1 in the case study, the average PM10, PM2.5, and PM1 exposure concentrations of bus commuters were 71.6%, 19%, and 10.4% higher, respectively, than those of subway commuters. In the ground transportation mode, the exposure concentration of bus commuters was affected by the type of vehicle. Particle concentrations were significantly higher inside compressed natural gas (CNG) buses, than in pure electric (PE) buses, and in summer, the PM10 concentration in a CNG bus was 4.3 times higher than that in a PE bus. In a CNG bus, commuters in the back door area suffered the highest PM10 exposure concentration (179.6 μg/m3), followed by those in the rear of the carriage (142.8 μg/m3), and then those in the front door area (105.4 μg/m3).

CONCLUSION

Commuters' avoidance of ground traffic sources, effective ventilation systems in buses, and the use of screens in subway systems can all help to lower the PM exposure of commuters. For all the modes of transportation in our study, the hottest spots for PM exposure appeared in the period when commuters were waiting for transit vehicles to arrive.

Particulate matter concentration from open-cut coal mines: A hybrid machine learning estimation

Particulate matter (PM) emission is one of the leading environmental pollution issues associated with the coal mining industry. Before any control techniques can be employed, however, an accurate prediction of PM concentration is desired. Towards this end, this work aimed to provide an accurate estimation of PM concentration using a hybrid machine-learning technique. The proposed predictive model was based on the hybridazation of random forest (RF) model particle swarm optimization (PSO) for estimating PM concentration. The main objective of hybridazing the PSO was to tune the hyper-parameters of the RF model. The hybrid method was applied to PM data collected from an open-cut coal mine in northern China, the Haerwusu Coal Mine. The inputs selected were wind direction, wind speed, temperature, humidity, noise level and PM concentration at 5 min before. The outputs selected were the current concentration of PM_2.5 (particles with an aerodynamic diameter smaller than 2.5 μm), PM₁₀ (particles with an aerodynamic diameter smaller than 10 μm) and total suspended particulate (TSP). A detailed procedure for the implementation of the RF_PSO was presented and the predictive performance was analyzed. The results show that the RF_PSO could estimate PM concentration with a high degree of accuracy. The Pearson correlation coefficients among the average estimated and measured PM data were 0.91, 0.84 and 0.86 for the PM_2.5, PM₁₀ and TSP datasets, respectively. The relative importance analysis shows that the current PM concentration was mainly influenced by PM concentration at 5 min before, followed by humidity > temperature ≈ noise level > wind speed > wind direction. This study presents an efficient and accurate way to estimate PM concentration, which is fundamental to the assessment of the atmospheric quality risks emanating from open-cut mining and the design of dust removal techniques.

Seasonal Characteristics and Particle-size Distributions of Particulate Air Pollutants in Urumqi

Urban particulate air pollution is a known cause of adverse human health effects worldwide. Urumqi is a large oasis city in which rapid urbanization has caused a series of eco-environmental problems including serious air pollution, water shortage, dense population, excess energy consumption, and the creation of an urban heat island, among others. Coal is the most important source of energy and air pollutants that are poorly dispersed into the natural surroundings are the main reasons for serious pollution in the Urumqi urban area. Using differential optical absorption spectroscopy (DOAS), aerosol levels were determined using the double optical path method. We found that aerosol concentrations in Urumqi increased rapidly in winter, and that the concentration of fine particles was much higher than that of coarse particles. The background aerosol concentration was highest in winter in the research area, and the air-flow speed had a significant impact on this because high speed surface winds that correspond to high air flows can transport the aerosol to other places. Some of the observed day-to-night differences may be caused by differing wind directions that transport air masses from different emission sources during the day and the night. Daily and seasonal differences in PM_1.0 concentrations of different grades of polluted air were statistically analyzed using average daily concentration data for particles smaller than 10, 2.5 and 1.0 microns (PM₁₀, PM_2.5 and PM_1.0), and meteorological observations for Urumqi, Tianshan District in 2010.