Predicting the translocation of organic contaminants to plants is crucial to ensure the quality of agricultural goods and assess the risk of human exposure through the food web. In this study, the performance of a modified plant uptake model was evaluated considering a number of chemicals, such as polycyclic aromatic hydrocarbons (PAHs), organochlorine pesticides (OCPs) and polybrominated diphenyl ethers (PBDEs), with a range of physicochemical properties; different plant species (Ipomoea aquatica Forsk (swamp morning glory), Chrysanthemum coronarium L. (crown daisy), Zea mays L. (corn), Brassica rapa pekinensis (Chinese cabbage), Cucurbita moschata (pumpkin), Raphanus sativus L. (radish), Spinacia oleracea L. (spinach) and Capsicum annuum L. (pepper)); and different types of soil (paddy soil, laterite soil and black soil). The biases of predictions from a previously used partition-limited model were −76.4% to −99.9% relative to the measured concentrations. An overall transmission factor (αtf=0.39), calculated from a linear regression of the measured bioavailable fraction (Cbᵢₒ) and the total concentration in plants, was considered a crucial modification and was included in the modified model. Cbᵢₒ was found to better represent the chemical content available in soil for root uptake. The results from this study improve the accuracy of predictions for vegetation-uptake assessments by modifying the partition-limited model and then validating the modified model using comparisons between predicted data and measured values. The accuracy of the concentrations of organic contaminants in plants improved: when using the modified model, 89.5% of the predictions were within 40% of the actual value. The average bias was limited to 1.5%–30.5%. The model showed great potential to predict plant uptake using the bioavailable fraction concentration in soil.

Reduction of particulate matter (PM) has emerged as one of the most significant challenges in public health and environment protection worldwide. To address PM-related problems and effectively remove fine particulate matter (PM2.5), environmentalists proposed tree planting and afforestation as eco-friendly strategies. However, the PM removal effect of plants and its primary mechanism remains uncertain. In this study, we experimentally investigated the PM removal performance of five plant species in a closed chamber and the effects of relative humidity (RH) caused by plant evapotranspiration, as a governing parameter. On the basis of the PM removal test for various plant species, we selected Epipremnum aureum (Scindapsus) as a representative plant to identify the PM removal efficiency depending on evapotranspiration and particle type. Results showed that Scindapsus yielded a high PM removal efficiency for smoke type PM2.5 under active transpiration. We examined the correlation of PM removal and relative humidity (RH) and evaluated the increased effect of RH on PM2.5 removal by using a plant-inspired in vitro model. Based on the present results, the increase of RH due to evapotranspiration is crucial to the reduction of PM2.5 using plants.

The presence of plant leaves has been shown to lower the risks of health problems by reducing atmospheric particulate matter (PM). Leaf PM accumulation capacity will saturate in the absence of runoff. Rainfall is an effective way for PM to “wash off” into the soil and renew leaf PM accumulation. However, little is known about how PM wash-off varies with PM size and health problems caused by particulate pollution vary with PM size. This study thus used artificial rainfall with six plant species to find out how size-fractioned PM are washed off during rain processes. Total wash-off masses in fine, coarse and large fractions were 0.6–10.3 μg/cm2, 1.0–18.8 μg/cm2 and 4.5–60.1 μg/cm2 respectively. P. orientalis (cypress) and E. japonicus (evergreen broadleaved shrub) had the largest wash-off masses in each fraction during rainfall. P. cerasifera (deciduous broadleaved shrub) had the largest cumulative wash-off rates in each fraction. Rainfall intensity had more influence on wash-off masses and rates of large particles for six species and for small particles in evergreen species, but limited effect on wash-off proportions. Wash-off proportions decreased in large particles and increased in small particles along with rainfall. The results provide information for PM accumulation renewal of plants used for urban greening.

Apoplastic ascorbate (ASCapo) is an important contributor to the detoxification of ozone (O3). The objective of the study is to explore whether ASCapo is stimulated by elevated O3 concentrations. The detoxification of O3 by ASCapo was quantified in tobacco (Nicotiana L), soybean (Glycine max (L.) Merr.) and poplar (Populus L), which were exposed to charcoal-filtered air (CF) and elevated O3 treatments (E-O3). ASCapo in the three species were significantly increased by E-O3 compared with the values in the filtered treatment. For all three species, E-O3 significantly increased the malondialdehyde (MDA) content and decreased light-saturated rate of photosynthesis (Asat), suggesting that high O3 has induced injury/damage to plants. E-O3 significantly increased redox state in the apoplast (redox stateapo) for all species, whereas no effect on the apoplastic dehydroascorbate (DHAapo) was observed. In leaf tissues, E-O3 significantly enhanced reduced-ascorbate (ASC) and total ascorbate (ASC+DHA) in soybean and poplar, but significantly reduced these in tobacco, indicating different antioxidative capacity to the high O3 levels among the three species. Total antioxidant capacity in the apoplast (TACapo) was significantly increased by E-O3 in tobacco and poplar, but leaf tissue TAC was significantly enhanced only in tobacco. Leaf tissue superoxide anion (O2•-) in poplar and hydrogen peroxide (H2O2) in tobacco and soybean were significantly increased by E-O3. The diurnal variation of ASCapo, with maximum values occurring in the late morning and lower values experienced in the afternoon, appeared to play an important role in the harmful effects of O3 on tobacco, soybean and poplar.