Trees remove air pollutants through dry deposition processes depending upon forest structure, meteorology, and air quality that vary across space and time. Employing nationally available forest, weather, air pollution and human population data for 2010, computer simulations were performed for deciduous and evergreen trees with varying leaf area index for rural and urban areas in every county in the conterminous United States. The results populated a national database of annual air pollutant removal, concentration changes, and reductions in adverse health incidences and costs for NO2, O3, PM2.5 and SO2. The developed database enabled a first order approximation of air quality and associated human health benefits provided by trees with any forest configurations anywhere in the conterminous United States over time.Comprehensive national database of tree effects on air quality and human health in the United States was developed.

Leaves of six main tree species from the Pearl River Delta (PRD) in Southern China were collected to identify the interspecific variability, the spatial variability and the seasonal variations of polycyclic aromatic hydrocarbons' (PAHs) concentrations, and to calculate the amount of PAHs removed by leaves. PAHs concentrations in pine needles were much higher than in broad-leaves and leaves from urban/industrial areas (Baiyunshan and Heshan) exhibited two times greater concentrations than leaves from the rural area (Dinghushan). Seasonal variations of PAHs in leaves occurred with lesser concentrations in September. Leaves in PRD scavenged 3.7 ± 0.9 t PAHs y−1, accounting for about 10% of the total amount emitted in this region. This result suggests that forests play an important role in the fate of PAHs. Our results suggest that forests perform an important step for the fate of PAHs in the atmosphere.

Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM₂.₅ concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM₂.₅ concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from −56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM₂.₅ concentrations over inland areas ranging from 0.27 ppbV to −7.17 ppbV and 0.89 μg/m³ to −2.65 μg/m³, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to −2.86 ppbV and 3.41 μg/m³ to −7.47 μg/m³. The decreases in inland ozone and PM₂.₅ concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM₂.₅ formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM₂.₅ concentrations due to reduced reactions of PM₂.₅ precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM₂.₅ concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM₂.₅ inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

Urban green spaces (UGSs) are often positively associated with the health of urban residents. However, UGSs may also have adverse health effects by releasing biogenic volatile organic compounds (BVOCs) and increasing the ambient concentrations of ozone (O₃) and secondary organic aerosols in urban areas. BVOC emissions from UGSs might be underestimated because of the lack of consideration of the UGS land-use type in urban areas. As such, in this study, we used a newly released satellite dataset, Sentinel-2, with a resolution of 10 m, to derive the classification distribution of UGSs and predict the UGS emissions of BVOCs in Beijing in 2019. The results showed that the annual emissions of BVOCs from UGSs were approximately 2.9 Gg C (95% confidence interval (CI): 2.4–3.3) in the six core districts, accounting for approximately 39% of the total UGS emissions in Beijing. Compared with the results based on Sentinel-2, the BVOC emissions might be underestimated by approximately 37% (95% CI: 11–63) using the commonly used satellite dataset. UGSs produced the highest BVOC emissions in summer (from June to August), accounting for 75.2% of the annual emissions. UGSs contributed the most to the O₃ formation potential in summer, accounting for 41.5% of the total. We could attribute a considerable amount of the O₃ concentration (27.0 μg m⁻³, 95% CI: 21.4–32.6) to the UGS BVOCs produced in the core districts of Beijing in July. The new BVOC emissions dataset based on Sentinel-2 vegetation information facilitates modeling studies on the formation of surface O₃ in urban areas and assessments of the impact of UGSs on public health.

Oil pollution harms terrestrial ecosystems. There is an urgent requirement to improve on existing methods for detecting, mapping and establishing the precise extent of oil-impacted and oil-free vegetation. This is needed to quantify existing spill extents, formulate effective remediation strategies and to enable effective pipeline monitoring strategies to identify leakages at an early stage. An effective oil spill detection algorithm based on optical image spectral responses can benefit immensely from the inclusion of multi-frequency Synthetic Aperture Radar (SAR) data, especially when the effect of multi-collinearity is sufficiently reduced. This study compared the Fuzzy Forest (FF) and Random Forest (RF) methods in detecting and mapping oil-impacted vegetation from a post spill multispectral optical sentinel 2 image and multifrequency C and X Band Sentinel – 1, COSMO Skymed and TanDEM-X SAR images. FF and RF classifiers were employed to discriminate oil-spill impacted and oil-free vegetation in a study area in Nigeria. Fuzzy Forest uses specific functions for the selection and use of uncorrelated variables in the classification process to yield an improved result. This method proved an efficient variable selection technique addressing the effects of high dimensionality and multi-collinearity, as the optimization and use of different SAR and optical image variables generated more accurate results than the RF algorithm in densely vegetated areas. An Overall Accuracy (OA) of 75% was obtained for the dense (Tree Cover Area) vegetation, while cropland and grassland areas had 59.4% and 65% OA respectively. However, RF performed better in Cropland areas with OA = 75% when SAR-optical image variables were used for classification, while both methods performed equally well in Grassland areas with OA = 65%. Similarly, significant backscatter differences (P < 0.005) were observed in the C-Band backscatter sample mean of polluted and oil-free TCA, while strong linear associations existed between LAI and backscatter in grassland and TCA. This study demonstrates that SAR based monitoring of petroleum hydrocarbon impacts on vegetation is feasible and has high potential for establishing oil-impacted areas and oil pipeline monitoring.

The objective of this study was to evaluate the potential of three C4 perennial grasses (Miscanthus x giganteus, Panicum virgatum and Spartina pectinata) for biomass production on arable land unsuitable for food crop cultivation due to Pb, Cd and Zn contamination. We assessed soil properties, biomass yield, metal concentrations, and the photosynthetic performance of each species. Physico-chemical and elemental analyses were performed on soil samples before plantation establishment (2014) and after three years of cultivation (2016), when leaf area index, plant height, yield and heavy metal content of biomass were also determined. Physiological measurements (gas exchange, pigment content, chlorophyll a fluorescence) were recorded monthly between June and September on mature plants in 2016. Cultivation of investigated plants resulted in increased pH, nitrogen, and organic matter (OM) content in soil, although OM increase (13%) was significant only for S. pectinata plots. During the most productive months, maximal quantum yield values of primary photochemistry (Fv/Fm) and gas exchange parameter values reflected literature data of those plants grown on uncontaminated sites. Biomass yields of M. x giganteus (15.0 ± 0.4 t d.m. ha−1) and S. pectinata (12.6 ± 1.2 t d.m. ha−1) were also equivalent to data published from uncontaminated land. P. virgatum performed poorly (4.1 ± 0.4 t d.m. ha−1), probably due to unfavourable climatic conditions, although metal uptake in this species was the highest (3.6 times that of M. x giganteus for Pb). Yield and physiological measurements indicated that M. x giganteus and S. pectinata were unaffected by the levels of contamination and therefore offer alternatives for areas where food production is prohibited. The broad cultivatable latitudinal range of these species suggests these results are widely relevant for development of the bioeconomy. We recommend multi-location trials under diverse contaminant and environmental regimes to determine the full potential of these species.

A distributed adaptation of i-Tree Eco was used to simulate dry deposition in an urban area. This investigation focused on the effects of varying temperature, LAI, and NO2 concentration inputs on estimated NO2 dry deposition to trees in Baltimore, MD. A coupled modeling system is described, wherein WRF provided temperature and LAI fields, and CMAQ provided NO2 concentrations. A base case simulation was conducted using built-in distributed i-Tree Eco tools, and simulations using different inputs were compared against this base case. Differences in land cover classification and tree cover between the distributed i-Tree Eco and WRF resulted in changes in estimated LAI, which in turn resulted in variations in simulated NO2 dry deposition. Estimated NO2 removal decreased when CMAQ-derived concentration was applied to the distributed i-Tree Eco simulation. Discrepancies in temperature inputs did little to affect estimates of NO2 removal by dry deposition to trees in Baltimore.

Shortly after an atmospheric release, the interception of radionuclides by crop canopies represents the main uptake pathway leading to food chain contamination. The food chain models currently used in European emergency decision support systems require a large number of input parameters, which inevitably leads to high model complexity. In this study, we have established a new relationship for wet deposited radionuclides to simplify the current modelling approaches. This relationship is based on the hypothesis that the stage of plant development is the key factor governing the interception of radionuclides by crops having horizontally oriented leaves (planophile crops). The interception fraction (f) and the leaf area index normalized (fLAI) and mass normalized (fB) interception fractions were assessed for spinach (Spinacia oleracea) and radish (Raphanus sativus) at different stages of plant development and for different contamination treatments and plant densities. A database of 191 f values for Cs-137 and Th-229 was built and complemented with existing literature covering various radionuclides and crops with similar canopy structure. The overall f increased with the plant growth, while the reverse was observed for fB. The fLAI significantly decreased by doubling the contaminated rainfall deposited. Fitting a multiple linear regression to predict the f value as a function of the standing biomass (B), and the radionuclide form (anion and cation) led to a better estimation of the interception (R² = 81%) than the ECOSYS-87 model (R² = 35%). Hence, the simplified modelling approach here proposed seems to be a suitable risk assessment tool as fewer parameters will minimize the model complexity and facilitate the decision-making procedures in case of emergencies, when countermeasures need to be identified and implemented promptly.

Environmental complexity leads to differences in the spatial distribution of heavy metal pollution in soil and rice. Such spatial differences will seriously affect the safety of planted rice and can impact regional management and control. How to scientifically reveal these spatial differences is an urgent problem. In this study, the spatial mismatch relationship between Cd pollution in soil and rice grains (brown rice) was first explored by the interpolation method. To further reveal the causes of these, the specific recognition rules of the spatial relationship of Cd pollution were extracted based on a decision tree model, and the results were mapped. The results revealed a spatial mismatch in Cd pollution between the soil and rice grains in the study area, and the main results are as follows: (i) slight soil pollution and safe rice accounted for 68.88% of the area; (ii) slight soil pollution and serious rice pollution accounted for 13.39% of the area and (iii) safe soil and serious rice pollution accounted for 11.63% of the area. In addition, 11 recognition rules of Cd spatial pollution relationship between soil and rice were proposed, and the main environmental factors were determined: SOM (soil organic matter), Dis-residence (distance from residential area), soil pH and LAI (leaf area index). The average accuracy of rule recognition was 75.90%. The study reveals the spatial mismatch of heavy metal pollution in soil and crops, providing decision-making references for the spatial accurate identification and targeted prevention of heavy metal pollution spaces.

The increased bioeconomy targets for the biomass share of renewable energy production across Europe should be met using land unsuitable for food production. Miscanthus breeding programs targeted the production of plants with a diverse range of traits allowing a wider utilization of land resources for biofuel production without competing with arable crops. These traits include increasing tolerances to drought, chilling, and to metal(loid)s excess. Two novel Miscanthus hybrids, GNT41 and GNT34, were compared against Miscanthus x giganteus (Mxg) on metal-contaminated arable land in Poland. This study aimed at evaluating their yield, biomass quality and quantifying seasonal differences in photosynthetic and transpiration parameters. A secondary objective was to identify key physiological mechanisms underlying differences in metal accumulation between the investigated plants. The new hybrids produced a similar yield to Mxg (13–15 t ha−1 yr−1), had shorter shoots, higher Leaf Area Index and stem number. Based on gas exchange measurements, GNT34 exhibited isohydric (water-conserving) behavior. The stomatal response to light of the new hybrids was at least twice as fast as that of Mxg, a trait that is often associated with increased seasonal water use efficiency. This contributed to the almost 40% reduction in shoot Pb and Cd concentrations for the new hybrids as compared to Mxg. This suggested that promoting stomatal regulation in conjunction with improved water conservation may be a target for improving plants for wider use on metals contaminated land.