[Characterization of Metal Elements in Atmospheric PM2.5 and Health Risk Assessment in Heze in Winter from 2017 to 2018].

Atmospheric PM2.5 samples were collected in Heze, Shandong Province, from a total of three sampling sites at Heze College, Huarun Pharmacy, and a wastewater treatment plant between October 15, 2017 and January 31, 2018, to determine the concentrations of 21 metal elements in PM2.5 using inductively coupled plasma mass spectrometry (ICP-MS). The degree of elemental enrichment was also discussed, the health risks and potential heavy metal ecological risks were assessed. The results showed that ρ (PM2.5) ranged from 26.7 to 284.1 µg·m-3 at the three sampling sites during the sampling period, and the concentration values did not differ significantly, all of which were at high pollution levels. The highest concentrations of K were found in the three sampling sites, accounting for 31.03%, 39.47%, and 38.43% of the total, respectively, mainly due to the high contribution of biomass burning in autumn and winter in Heze, a large agricultural city. The highest concentrations of Zn, 89.70, 84.21, and 67.68 ng·m-3, were found in the trace elements at the three sampling sites, respectively. The enrichment factor results showed that the enrichment factor values of Zn, Pb, Sn, Sb, Cd, and Se were higher than 100, among which the enrichment factors of Cd and Se were higher than 2 000 and 4 000, respectively, which were significantly influenced by anthropogenic activities and might have been related to industrial production, metal smelting, road sources, and coal combustion emissions. The health risk results showed that there was some potential non-carcinogenic risk (HQ>0.1 for children and adults) for As and a combined potential non-carcinogenic risk (HI>0.1) and some potential carcinogenic risk (CRT>1×10-6) for both children and adults at the three sampling sites. There was a more significant carcinogenic risk (CRT>1×10-4) for adults at the wastewater treatment plant, and the slightly higher carcinogenic risk for adults than that for children may have been related to the longer outdoor activity and higher PM2.5 exposure for adults. The elements with the highest potential ecological risk values were Cd, As, and Pb, with Cd exhibiting a very high potential ecological risk that should be taken seriously. All three sampling sites showed a very high combined potential ecological risk, with the intensity spatially expressed as Heze College>Huarun Pharmacy>wastewater treatment plant.

UAV hyperspectral combined with LiDAR to estimate chlorophyll content at the stand and individual tree scales.

Chlorophyll is an important indicator of vegetation health status, accurate estimation of which is important for evaluating forest carbon sink. In this study, we estimated the chlorophyll content of coniferous forests, broad-leaved forests and mixed forest stands at stand and individual tree level by unmanned air vehicle (UAV) hyperspectral data combined with light detection and ranging (LiDAR) point clouds, which improved the non-destructive estimation accuracy of forest chlorophyll. We further comprehensively analyzed the spatial distribution of chlorophyll content at different scales. A total of 36 spectral characteristic variables related to chlorophyll content were screened by correlation analysis based on the fusion of UAV hyperspectral data and LiDAR point clouds combining with the empirical data from ground plots. We constructed multiple models for chlorophyll estimation by using statistical model, including multiple stepwise regression, BP neural network, BP neural network optimized by firefly algorithm, random forest and hybrid data-driven PROSPECT mechanism model. The optimal model was selected to estimate the chlorophyll content. The horizontal and vertical distribution of chlorophyll content at the stand level and individual tree level were analyzed. The results showed that the random forest model was superior to the models constructed by multiple stepwise regression, BP neural network and BP neural network optimized by firefly algorithm for chlorophyll estimation with R2 and RMSE of 0.59-0.64 and 3.79-5.83 µg·cm-2, respectively. The accuracy of the mechanism model was the highest, with R2 and RMSE of 0.97 and 3.40 µg·cm-2. The chlorophyll contents differed across stand types, with that in broad-leaved forest (25.25-31.60 µg·cm-2) being higher than mixed forest (13.52-23.93 µg·cm-2) and coniferous forest (6.40-13.71 µg·cm-2). There were significant differences in chlorophyll contents the in vertical direction among different stands. For individual tree of different species, the chlorophyll content inside the canopy was lower than that outside the canopy in the horizontal direction. In the vertical direction, there was no difference in chlorophyll content among different layers of Pinus sylvestris var. mongolica canopy. However, significant differences were observed among the upper, middle, and lower layers of Juglans mandshurica canopy. Using the fusion of hyperspectral image and LiDAR point cloud data, the mechanism model driven by hybrid data could effectively improve the accuracy and stability of chlorophyll content estimation at different scales.