Explore the vast range of titles available.

Despite the considerable cost associated with estimating household emissions from solid fuel, which are frequently undetected by air quality monitoring systems, compiling such an inventory is critical to identifying the link between indoor pollution and health effects. Therefore, this study used the UP Diliman dilution tunnel system (UPDDTS) to characterize the composition of particulate matter in the smoke and quantify the PM 2.5 emitted by traditional Philippine cooking systems, viz. , a charcoal-burning cement stove (CCP), a sawdust-burning tin-can stove (KKP), a fuelwood-burning metal-grill stove (MFP), a kerosene-burning metal stove (MKP), and a charcoalburning metal-grill stove (MCC). Forty-three sampling tests revealed that water-soluble K + (23.0 ± 1.9 µg m −3 ), Cl − (12.3 ± 1.0 µg m −3 ), and Na + (43 ± 22 µg m −3 ) contributed to the majority of the ionic mass concentrations generated by the CCP and MKP, respectively, whereas levoglucosan— a signature of biomass burning—dominated the PM 2.5 -bound monosugars emitted by the KKP (78.72 ± 6.96 µg m −3 ), MFP (0.76 ± 0.34 µg m −3 ), and MCC (10.21 ± 2.64 µg m −3 ). The abundance of the water-soluble organic carbon (WSOC) in all of the samples, except those from the MKP, depended on the surface area—and thus the facet—of the fuel. Additionally, the elemental compositions of the PM 2.5 from the CCP, KKP, and MCC mainly consisted of Pb (1.96 ± 1.04 to 76.02 ± 151.42 ng min −1 ), but those for the MFP and KKP primarily contained Cu (2.23 ± 1.18 ng min −1 ) and As (5.51 ± 1.08 ng min −1 ), respectively. The PM 2.5 emission rates exceeded the World Health Organization (WHO)’s emission rate target guideline for ventilated conditions (0.8 mg min −1 ) by 1.9 × 10 6 to 23 × 10 6 mg min −1 , and the highest PM 2.5 emission factor, 0.032 ± 0.016 kg-PM 2.5 kg-fuel −1 y −1 , which was exhibited by the MKP, surpassed values in the literature by three orders of magnitude.

We propose operational definitions and a classification framework for air quality sensor-derived data, thereby aiding users in interpreting and selecting suitable data products for their applications. We focus on differentiating independent sensor measurements (ISM) from other data products, emphasizing transparency and traceability. Recommendations are provided for manufacturers, academia, and standardization bodies to adopt these definitions, fostering data product differentiation and incentivizing the development of more robust, reliable sensor hardware.