QCM or quartz crystal microbalance is a well-known sensor technology that generates cycles of oscillation related to mass change on the crystal’s surface. This crystal works well when it has a frequency counter and an oscillator to drive the crystal and count the oscillation, and a good airflow regulator. This study developed a measurement system for aerosol concentrations with a diameter of less than 2.5 micrometers. The system consists of QCM sensors, an oscillator, a frequency counter, and an airflow regulator. The system was tested inside an exposure chamber with a constant emission source for the different velocity speeds, namely v 1 , v 2 , v 3 , v 4 , and v 5 . The test was conducted every10seconds due to the saturated time of the QCM related to the mass loading effect of aerosol. The results show that the system can drive the QCM sensor with a frequency of 5MHz. The measurement system works well to measure aerosol concentrationafter the preload duration often seconds and every sixty seconds in which the durations are related to the optimum QCM’s response at v 1 and v 2 . The optimum performance was found to be in the laminar regime, with the sample rate of 0.6 m/s to 1.0 m/s.

Bioaerosols are the biological constituent of PM (particulate matter). Bioaerosols are produced during many activities in landfills, agricultural sectors, food preservation, and many others in daily life [1,2]. Bioaerosols can be generated from biomass burning activity, resulting in bioaerosols with a diameter <2.5 µm (fine bioaerosols) [2]. Bioaerosols commonly have many forms like fungal spores, pollen, bacteria, and even viruses. As confirmed before, bioaerosols consist of Aspergillus, Alternaria, and Cladosporium species [4,5]. Another previous study investigated bioaerosols from bacteria species in landfill areas, such as Staphylococcus aureus, Staphylococcus gordonii, Alloiococcus otitis, Kocuria rosea, Pediococcus pentosaceus, and many others [1].

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Emissions from burning biomass have become a problem in Indonesia. As found on the Indonesian island of Lombok, agricultural waste is burned for traditional industrial activities. On the other hand, biomass burning emissions contain many PMs (particulates) in different size distributions recognized to have a significant correlation to health impact. This study is conducted to predict the impact of the PM exposure on blood using a ANN (artificial neural network) model as well as a histological examination. The relationship between both methods is determined to estimate the impact of biomass burning emissions on the blood. This study used male mice as the experimental animals exposed to PM emissions (PM 0.1 , PM 2.5 , and PM 10 ) produced from the burning of various biomass (rice straw, rice husks, corn cobs, corn stalks, and tobacco) taken from Lombok Island. The sample exposure was conducted in a chamber for 100 s for ten sequence days. The blood samples were observed using a microscope with the 400 x magnification. The cell deformation was examined histologically by calculating the normal and abnormal cells. The percentage of the erythrocyte deformation was assessed using a fixed back and forth propagation ANN. The result shows that the biomass burning PM emissions have a significant impact on the erythrocyte deformation depending on the type of biomass and the particulate matter emissions. The ANN model confirms the erythrocyte deformation data obtained by the histological examination method.

The volcanic eruptions of Mt. Bromo and Mt. Raung in East Java, Indonesia, in 2015 perturbed volcanic materials and affected surface-layer air quality at surrounding locations. During the episodes, the volcanic ash from the eruptions influenced visibility, traffic accidents, flight schedules, and human health. In this research, the volcanic ash particles were collected and characterized by relying on the detail of physical observation. We performed an assessment of the volcanic ash elements to characterize the volcanic ash using two different methods which are aqua regia extracts followed by MP-AES and XRF laboratory test of bulk samples. The analysis results showed that the volcanic ash was mixed of many materials, such as Al,