A Review of Plants as Bioindicators of Industrial Air Pollution

Complex mixtures of gaseous pollutants, particulate matter, and potentially toxic elements are released from industrial activities and can change ecosystem functioning and human health. Conventional air-quality monitoring is still essential to quantify pollutant concentrations but is limited spatially and temporally by financial, technical, and infrastructural requirements, while plant-based biomonitoring integrates pollutant exposure over time and unveils biologically relevant responses. This review synthesizes evidence on the use of lichens, mosses, and vascular plants as bioindicators and biomonitors of industrial air pollution, focusing on plant responses to sulphur dioxide, nitrogen oxides, ozone, particulate matter, fluorides, and heavy metals, and on morphological, anatomical, physiological, biochemical, and bioaccumulation-based indicators. These include passive approaches that rely on the naturally occurring vegetation and active approaches that involve standardized exposure of selected organisms, and important assessment tools such as the Air Pollution Tolerance Index and lichen-based community indices. Recent studies show that plant biomonitoring can detect spatial pollution gradients, differentiate species-specific sensitivity and tolerance, and contribute to environmental monitoring in the vicinity of industrial facilities. Recent work shows that exposure to cement-dust can significantly alter the epidermal and stomatal morphology of plants, thus confirming the potential of locally available species for industrial biomonitoring. The review points out that plant bioindicators should be combined with instrumental monitoring instead of viewed as alternatives to it, and recommends more standardization, multi-seasonal sampling, chemical characterization of accumulated pollutants, and validation of indigenous tropical species to increase the reliability and policy relevance of plant-based monitoring, particularly in rapidly industrializing and resource-constrained regions.