airborne particulate matter
Recently Published Documents

AbstractFifteen airborne particulate matter-bound metals were analyzed at 14 sites in four large cities (Seoul, Incheon, Busan, Daegu) in South Korea, between August 2013 and June 2017. Among the seven sources resolved by positive matrix factorization, soil dust and marine aerosol accounted for the largest and second largest portions in the three cities; however, in Seoul, soil dust and traffic occupied the largest and the second largest, respectively. Non-carcinogenic risk assessed by inhalation of eight metals (Cd, Co, Ni, Pb, As, Al, Mn, and V) was greater than the hazard index (HI) of 1 at four sites located at or near the industrial complexes. Cumulative incremental lifetime cancer risk (ILCR) due to exposure to five metals (Cd, Co, Ni, Pb, and As) exceeded the 10−6 cancer benchmark at 14 sites and 10−5 at six sites, which includes four sites with HI greater than 1. The largest contributor to ILCR was coal combustion in Seoul, Incheon, and Daegu, and industry sources in Busan. Moreover, industry sources were the largest contributors to non-carcinogenic risk in Seoul, Busan, and Daegu, and soil dust was in Incheon. Incheon had the highest HI in spring because of the higher contribution of soil dust sources than in other seasons. The higher ILCR in Incheon in spring and winter and higher ILCR and HI in Daegu in autumn were mainly due to the influence of industry or coal combustion sources. Statistically significant differences in the ILCR and HI values among the sampling sites in Busan and Daegu resulted from the higher contribution of industry sources at a certain site in the respective city.

This paper focuses on the performance assessment of the green filtration system that incorporated with evaporative cooling that used to enhance indoor air quality. This system was invented in an attempt to thrive in a clean environment that becomes a solution for certain places. Indoor air quality (IAQ) and public health risk related to each other, it is due to the percentage of the city population that stays indoors rather than go outdoors. Indoor air contamination is originated from mixed origins such as volatile organic compounds (VOCs) and indoor airborne particulate matter (PM). The results show that the green filtration system manages to filter PM and VOCs in the air but not as efficient as modern air filters in the market. Furthermore, the evaporative cooling system possesses a huge deal in energy-saving within hot and arid climatic regions.

Ammonia from livestock manure reacts with chemical components discharged from various emission sources to produce airborne particulate matter. This study aimed to investigate a novel effective microbial agent to suppress ammonia gas emitted from manure. Both isolated L12I and 12III strains, identified as Pediococcus acidilactici (PA), were selected for their superior activity in assays performed with the evaluation criteria such as acid production, ammonia decomposition, and urease inhibition, which are key factors influencing ammonia excretion. The survivability of PA strains was confirmed by an increase in DNA abundance in the manure. PA strains lowered the pH of manure and suppressed the growth of hyper-ammonia-producing bacteria (HAB) possessing urease activity. The L12I and 12III treatment groups showed 23.58% and 38.00% emission reductions, respectively. Especially, the 12III strain was proven to be the more effective strain for reducing ammonia gas emission, with the best ability to reduce pH and inhibit HAB. The strains could have an additive effect in improving the manure quality as a nitrogen fertilizer by preserving the total nitrogen and urea content. These results suggest that PA strains can be used as unprecedented microbial agents to improve manure-derived environmental pollution and improve fertilizer quality.

Understanding the behavior of suspended pollutants in the atmosphere has become of paramount importance to determine air quality. For this purpose, a variety of simulation software packages and a large number of algorithms have been used. Among these techniques, recurrent deep neural networks (RNN) have been used lately. These are capable of learning to imitate the chaotic behavior of a set of continuous data over time. In the present work, the results obtained from implementing three different RNNs working with the same structure are compared. These RNNs are long-short term memory network (LSTM), a recurrent gated unit (GRU), and the Elman network, taking as a case study the records of particulate matter PM10 and PM2.5 from 2005 to 2019 of Mexico City, obtained from the Red Automatica de Monitoreo Ambiental (RAMA) database. The results were compared for these three topologies in execution time, root mean square error (RMSE), and correlation coefficient (CC) metrics.

Exposure to airborne particulate matter (PM) may affect neurodevelopmental outcomes in children. The mechanisms underlying these relationships are not currently known. We aim to assess whether PM affects the developing brains of schoolchildren in Poland, a country characterized by high levels of PM pollution. Children aged from 10 to 13 years (n = 800) are recruited to participate in this case–control study. Cases (children with attention deficit hyperactivity disorder (ADHD)) are being recruited by field psychologists. Population-based controls are being sampled from schools. The study area comprises 18 towns in southern Poland characterized by wide-ranging levels of PM. Comprehensive psychological assessments are conducted to assess cognitive and social functioning. Participants undergo structural, diffusion-weighted, task, and resting-state magnetic resonance imaging (MRI). PM concentrations are estimated using land use regression models, incorporating information from air monitoring networks, dispersion models, and characteristics of roads and other land cover types. The estimated concentrations will be assigned to the prenatal and postnatal residential and preschool/school addresses of the study participants. We will assess whether long-term exposure to PM affects brain function, structure, and connectivity in healthy children and in those diagnosed with ADHD. This study will provide novel, in-depth understanding of the neurodevelopmental effects of PM pollution.

With the acceleration of urbanization and industrialization, air pollution is becoming one of the most serious problems in cities. Urban green spaces, as “green infrastructure”, are an important part of urban ecosystems for air purification. Therefore, 10 typical green spaces of urban parks in the city of Xi’an, China, were selected as study areas according to vegetation structure and species composition. Considering meteorological factors and time changes, the effects of the selected green spaces with different vegetation structures of different heights on the reduction in airborne particulate matter concentration were explored. The results showed that the following: (1) Temperature, relative humidity, wind speed, and air pressure had significant correlation with the concentration of airborne particulate matter at the different heights, and the correlations were the same at 1.5 and 5 m. (2) After heating in winter, the concentration of airborne particulate matter with different particle sizes increased significantly. The concentration of airborne particulate matter showed different trends throughout the day, and the small particles (PM1 and PM2.5) had a trend of “lower in the morning and evening, and higher at noon”, while the large particles (PM10 and TSP) gradually decreased over time. (3) In the selected green spaces with different vegetation structure types, the concentration of airborne particulate matter below the canopy (1.5 m) was generally higher than that in the middle of the canopy (5 m), but the effects of reducing the concentration of airborne particulate matter were consistent at the different heights. (4) The adsorption capacity of PM1 and PM2.5 concentration was strong in the partially closed broad-leaved one-layered forest (PBO), and poor in the partially closed broad-leaved multi-layered forest (PBM). Partially closed broad-leaved multi-layered forest (PBM) and partially closed coniferous and broad-leaved mixed multi-layered forest (PMM) also had strong dust-retention effect on PM10 and TSP, while closed broad-leaved one-layered forest (CBO) had a poor dust-retention effect. The results showed that the reduction effects of urban green spaces with different spatial structures on air particles were different, and were restricted by various environmental factors, which could provide a theoretical basis for the optimization of urban green space structure and the improvement of urban air quality.

Reduction of non-exhaust airborne particulate matter (PM), leading to adverse effects in respiratory system, is an urgent task. In this work, we evaluated the impact of raw materials in friction materials on PM emission due to brake wear for passenger vehicle. Time- and temperature-dependent measurements using dynamometer were made for low-steel friction materials with varied abrasives and lubricant(graphite). The brake emission factor (BEF) for graphite of varied sizes ranged from 6.48 to 7.23 mg/km/vehicle. The number concentration indicates that smaller graphite (10 μm) produces more nano-sized particles than larger size (700 μm) by >50%. Depending on abrasives, BEF was found to be varied as large as by three-times, ranging from 4.37 to 14.41 mg/km/vehicle. As hardness of abrasive increases (SiC > Al2O3 > ZrSiO4), higher BEF was obtained, suggesting that abrasive wear directly contributes to emissions, evidenced by surface topology. Temperature-dependent data imply that particle emission for SiC abrasive is initiated at lower speed in WLTC cycle, where disc temperature (Tdisc) is ~100 °C, than that for ZrSiO4 (Tdisc >120 °C). Analysis of wear debris suggests that larger micron-sized particles include fragmented Fe lumps from disc, whereas smaller particles are, in part, formed by combination of oxidation and aggregation of nano-sized particles into small lumps.