From surface area to smog abatement: A data-mined structure-property framework of adsorbents with pores

Urban smog is a multi-faceted pollution problem consisting of fine particles, VOCs, NO x and SO x , as well as the link between traffic and other sources, and the effects of meteorology. The problem is aggravated by high population density, fast industrialization, biomass burning, waste burning, thermal inversion and less stringent controls on emission in urban settings in South Asian cities. In this research, with the help of Data mining, the two decades' (2006–2026) studies on porous adsorbents to mitigate urban smog have been analyzed systematically. The dataset includes activated carbons, zeolites, metal-organic frameworks (MOFs), covalent organic frameworks (COFs), porous polymers, mesoporous oxides, aerogels, and composite fibrous materials. Analyzed variables consist of BET surface area, pore volume, dominant pore size, humidity indicators, functional groups, particle or fiber morphology, pollutant type, humidity, regeneration capacity and scalability evidence. VOC capture requires pore size, hydrophobicity, presence of polar adsorption sites, and water competition, as shown in the structure-property map. Pressure drops, pore structure, and electrostatic forces\ make fibrous structures less effective at removing PM 2 . 5 . BET surface area is a helpful but not adequate absolute measure of performance. Hybrid filter-adsorbent systems are more viable for mixed urban smog than individual adsorbent materials. This paper presents a research framework, dataset, coding method, and decision rules to guide selection of adsorbents for air purification in urban, industrial, textile, cement, and dense environments.