Low-Cost Indoor Sensor Deployment for Predicting PM2.5 Exposure.

Indoor air quality is critical to human health, as individuals spend an average of 90% of their time indoors. However, indoor particulate matter (PM) sensor networks are not deployed as often as outdoor sensor networks. In this study, indoor PM2.5 exposure is investigated via 2 low-cost sensor networks in Pittsburgh. The concentrations reported by the networks were fed into a Monte Carlo simulation to predict daily PM2.5 exposure for 4 demographics (indoor workers, outdoor workers, schoolchildren, and retirees). Additionally, this study compares the effects of 4 different correction factors on reported concentrations from the PurpleAir sensors, including both empirical and physics-based corrections. The results of the Monte Carlo simulation show that mean PM2.5 exposure varied by 1.5 µg/m3 or less when indoor and outdoor concentrations were similar. When indoor PM concentrations were lower than outdoor, increasing the time spent outdoors on the simulation increased exposure by up to 3 µg/m3. These differences in exposure highlight the importance of carefully selecting sites for sensor deployment and show the value of having a robust low-cost sensor network with both indoor and outdoor sensor placement.

Influence of rainfall on size-resolved bioaerosols around a livestock farm.

Bioaerosols, capable of transporting microorganisms, can impact human health and agriculture by spreading to nearby communities. Their transmissions are influenced by various factors, including weather conditions and human activities. However, the scarcity of detailed, taxon-specific data on bioaerosols' sizes limits our ability to assess risks associated with bioaerosols' generation and spread. This study examined the composition and size of bioaerosols at a livestock farm and a non-agricultural site, focusing on how bioaerosols evolve at different locations and meteorological conditions. The location had an impact on bioaerosol samples. We conducted 16S rRNA gene amplicon sequencing to identify bacteria genera in bioaerosols. We observed consistently higher concentrations of bioaerosols across all sizes at the livestock farm, and samples from the livestock farm exhibited greater bacterial diversity, where we identified Staphylococcus and Corynebacterium as the most abundant species. The effects of rainfall on bioaerosol diversity are complex, suggesting a dynamic interplay between bioaerosol removal and generation. After rainfall, the bioaerosol fraction of particles larger than 2.5 µm increased by nearly 400% compared to post-rain levels. Conversely, for bioaerosols below 1 µm size, the fraction decreased by 50%. Furthermore, the sequencing results showed that precipitation differentially responded to the abundance of various genera in the bioaerosols. Moreover, even for the same genus, the response to precipitation varied depending on the size of the bioaerosols. Our research reveals how size, location, and environmental conditions influence bioaerosol dynamics, enhancing our understanding of bioaerosol formation and transmission.

Morphological and chemical classification of fine particles over the Yellow Sea during spring, 2015-2018.

Airborne fine particles can affect climate change and human health; moreover, they can be transported over significant distances. However, studies on characteristics of individual particles and their morphology, elemental composition, aging processes, and spatial distribution after long-range transport over the Yellow Sea are limited. Therefore, in this study, we conducted shipborne measurements of fine particulate matter of less than 2.5 µm in diameter (PM2.5) over the Yellow Sea and classified the individual particles into seven types based on their morphology and composition. Overall, the percentage of organic-rich particles was the highest, followed by that of sea spray, sulfur-rich, dust, metals, fly ash, soot, and other particles. Near Shandong, China, the percentage of fly ash and sulfur-rich particles increased, while an increased percentage of only sulfur-rich particles was observed near the Korean Peninsula. In the open sea, the PM2.5 concentrations were the lowest, and sea spray particles predominated. During the cruises, three types (Types 1, 2, and 3) of events with substantially increased PM2.5 concentrations occurred, each with different dominant particles. Type 1 events frequently featured air masses from northern China and Mongolia with high wind speeds and increased dust particles. Type 2 events involved air masses from China with high wind speeds; fly ash, soot, organic-rich particles, and the sulfate percentage in PM2.5 increased. Type 3 events displayed stagnant conditions and local transport (from Korea); soot, dust particles, and the secondary sulfate and nitrate percentages in PM2.5 increased. Thus, different types of transport affected concentrations and dominant types of fine particles over the Yellow Sea during spring.

Application of Single-Particle Mass Spectrometer to Obtain Chemical Signatures of Various Combustion Aerosols.

A single-particle mass spectrometer (SPMS) with laser ionization was constructed to determine the chemical composition of single particles in real time. The technique was evaluated using various polystyrene latex particles with different sizes (125 nm, 300 nm, 700 nm, and 1000 nm); NaCl, KCl, MgCO3, CaCO3, and Al2O3 particles with different chemical compositions; an internal mixture of NaCl and KCl; and an internal mixture of NaCl, KCl, and MgCl2 with different mixing states. The results show that the SPMS can be useful for the determination of chemical characteristics and mixing states of single particles in real time. The SPMS was then applied to obtain the chemical signatures of various combustion aerosols (diesel engine exhaust, biomass burning (rice straw), coal burning, and cooking (pork)) based on their single-particle mass spectra. Elemental carbon (EC)-rich and EC-organic carbon (OC) particles were the predominant particle types identified in diesel engine exhaust, while K-rich and EC-OC-K particles were observed among rice straw burning emissions. Only one particle type (ash-rich particles) was detected among coal burning emissions. EC-rich and EC-OC particles were observed among pork burning particles. The single-particle mass spectra of the EC or OC types of particles differed among various combustion sources. The observed chemical signatures could be useful for rapidly identifying sources of atmospheric fine particles. In addition, the detected chemical signatures of the fine particles may be used to estimate their toxicity and to better understand their effects on human health.