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Urban air quality management is critically dependent on robust air quality monitoring systems. Conventional systems, utilizing fixed, complex, and costly equipment, encounter challenges in stable data collection, transmission, and accessibility. Recent scholarly reports have highlighted the potential of low-cost sensors as an alternative to these conventional systems. This study endeavors to ascertain the optimal placement of low-cost PM2.5 monitoring sensors in Tehran. The methodology involved a two-step process. Initially, PM2.5 pollution maps of Tehran were generated utilizing data from local air quality monitoring stations and remote sensing data. Owing to its superior accuracy and comprehensive coverage, remote sensing data was employed for determining the optimal sensor placement. Subsequently, the Gaussian process model was utilized to optimize sensor locations. The initial placements were based on a scenario where a 1 × 1 km grid was defined for the initial placement of sensors. This scenario involved dividing the city into equal grids and choosing the center of each grid as the point for placing the sensor. The preliminary sensor placement considered a uniform distribution across Tehran at regular one-kilometer intervals. Following this, the Gaussian process model identified 592 optimal locations for the installation of low-cost PM2.5 monitoring sensors. The study concludes that the strategic deployment of low-cost sensors, guided by the Gaussian process model, can significantly enhance the effectiveness of urban air quality monitoring systems. It is strongly recommended that urban authorities consider the integration of low-cost sensors at the identified locations for more efficient and economical air quality monitoring.Highlights: The GP model was used to optimize the location of LCS for urban PM2.5 monitoring. PM2.5 pollution maps of Tehran city were produced using AQMS and RS data. 592 optimal locations were selected to install LCS for urban PM2.5 monitoring in Tehran city. [ABSTRACT FROM AUTHOR]

Some graphene-based adsorbents such as mesoporous graphene oxide/TiO 2 nanocomposite, thermally treated graphene nanosheets, holey graphene frameworks and 3-D graphene-based porous adsorbent have recently been introduced as promising adsorbents for post-combustion CO 2 capture while, their CO 2 adsorption kinetics and mechanisms have not sufficiently been reported by the scholars. To begin, in this study, CO 2 capture kinetics of these adsorbents are investigated by different adsorption kinetic models including pseudo-first order model, pseudo-second order model, Avrami model, and the fractal-like exponential kinetic model. Subsequently, the Eyring and Arrhenius equations are used to determine the activation parameters related to the CO 2 adsorption on these adsorbents. Furthermore, a mechanism is proposed to describe the negative activation energy assessed for CO 2 adsorption on the studied adsorbents. Eventually, the rate-limiting kinetic models including interparticle diffusion model, intraparticle diffusion model and Boyd’s film diffusion model are applied to determine CO 2 capture mechanisms on the mentioned graphene-based adsorbents. [ABSTRACT FROM AUTHOR]