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Abstract Light availability (LAv) dictates a variety of biological and ecological processes across a range of spatiotemporal scales. Quantifying the spatial pattern of LAv in three‐dimensional (3D) space can promote the understanding of microclimates that are critical to fine‐scale species distribution. However, there is still a lack of tools that are robust to evaluate spatiotemporal heterogeneity of LAv in forests. Here, we propose the Forest Light Analyzer python package (FLApy), an open‐source computational tool designed for the analysis of intra‐forest LAv variation across multiple spatial scales. FLApy is freely invoked by Python, facilitating the processing of LiDAR point cloud data into a 3D data container constructed by voxels, as well as traversal calculations related to the LAv regime by high performance synthetic hemispherical algorithm. Furthermore, FLApy incorporates 37 indicators, enabling users to expediently export and visualize LAv patterns and the evaluation of heterogeneity of LAv at two scales (voxel scale and 3D‐cluster scale) for a range of fine‐scale ecological study purposes. To validate the efficacy of the FLApy, we employed a simulated point cloud dataset that simulates forests (varying in canopy closure). Furthermore, to evaluate real world forest, we executed the standard workflow of FLApy utilizing drone‐derived data from three subtropical evergreen broad‐leaved forest dynamics plots within the Ailao Mountain Reserve. Our findings underscore that a series of indices derived from FLApy provide a robust characterization of light availability heterogeneity within diverse forest settings. Additionally, when juxtaposed with conventional monitoring techniques, the metrics offered by FLApy demonstrated better generality in our field assessments. FLApy offers ecologists a solution for rapid quantification of understory light 3D‐regimes across multiple scales, addressing the disparity between traditional manual approaches and the precision required for contemporary ecological studies. Moreover, FLApy provides robust support for the establishment and expansion of heterogeneity indices based on 3D micro‐environments, enhancing our understanding of the largely uncharted 3D structural patterns. Anticipated outcomes suggest that FLApy will enhance our knowledge concerning the intra‐forest climatic conditions into a 3D context, proving pivotal in the delineation of microhabitats and the development of detailed 3D‐scale species distribution models.

Abstract Beta‐diversity, or the spatio‐temporal variation in community composition, can be partitioned into turnover and nestedness components in a multidimensional framework. Forest structure, including comprehensive characteristics of vertical and horizontal complexity, strongly affects species composition and its spatial variation. However, the effects of forest structure on beta‐diversity patterns in multidimensional and multiple‐scale contexts are poorly understood. Here, we assessed beta‐diversity at local (a 20‐ha forest dynamics plot) and regional (a plot network composed of 19 1‐ha plots) scales in a Chinese subtropical evergreen broad‐leaved forest. We then evaluated the relative importance of forest structure, topography, and spatial structure on beta‐diversity and its turnover and nestedness components in taxonomic, functional, and phylogenetic dimensions at local and regional scales. We derived forest structural parameters from both unmanned aerial vehicle light detection and ranging (UAV LiDAR) data and plot inventory data. Turnover component dominated total beta‐diversity for all dimensions at the two scales. With the exception of some components (taxonomic and functional turnover at the local scale; functional nestedness at the regional scale), environmental factors (i.e., topography and forest structure) contributed more than pure spatial variation. Explanations of forest structure for beta‐diversity and its component patterns at the local scale were higher than those at the regional scale. The joint effects of spatial structure and forest structure influenced component patterns in all dimensions (except for functional turnover) to some extent at the local scale, while pure forest structure influenced taxonomic and phylogenetic nestedness patterns to some extent at the regional scale. Our results highlight the importance and scale dependence of forest structure in shaping multidimensional beta‐diversity and its component patterns. Clearly, further studies need to link forest structure directly to ecological processes (e.g., asymmetric light competition and disturbance dynamics) and explore its roles in biodiversity maintenance.

Tropical forests provide important ecosystem services and biodiversity for the world. With human activities and economic growth, tropical forests are becoming the primary sources of new plantations, making tropical regions hotspots of ecosystem service degenerations and biodiversity losses. Understanding the driving forces of ecological consequences is the key to local sustainable development and human well-being improvements. Therefore, we spatially quantified the changes in ecosystem services in tropical China in the past twenty years by remote sensing and InVEST model. The driving forces were further investigated by using Geodetector. We found that: 1) in the past twenty years, the area of evergreen broad-leaved forests, coniferous forests, and rubber forests have increased by 13.01%, 43.39%, and 101.23%, respectively. The shrublands and agricultural lands are the major contributors to these increased areas; 2) with the area increasing of forests, carbon storage and soil retention have increased by 9.40% and 0.30%, while nitrogen export and water yield have decreased by 43.30%, and 12.27%. This pattern of increase in carbon storage and soil retention was found in the highlands, while not in the lowlands, where large areas of evergreen broad-leaved forests have been converted to rubber forests; 3) NDVI was highly correlated to the carbon storage (q value = 0.201) and nitrogen export (q value = 0.109), while slope and precipitation mainly affected the soil retention (q value = 0.253) and water yield (q value = 0.073), respectively. This study provides a reference for ecological protection and management in tropical China.

There is limited information on contaminations of per- and polyfluoroalkyl substances (PFASs) in the water source affected by agricultural activities. The contamination of PFASs was investigated in the sources of agricultural groundwater and nearby surface water from an important vegetable production base supply for Beijing and Tianjin, located in North China. Σ13PFAS concentrations ranged from 0.321 to 8.285 ng/L, with an average concentration of 2.022 ng/L in the groundwater and 19.673 ng/L in the surface water. With the increase in the carbon chain length, the mean concentrations of PFASs in groundwater generally showed a decreasing trend. The dominated congeners of short-chain perfluorobutanesulfonate and perfluorooctanoate acid (PFOA) were detected in all groundwater samples, with mean concentrations of 0.944 and 0.654 ng/L. The difference was that PFOA and perfluorooctanesulfonate (PFOS) were the dominant congeners in nearby surface water, with concentrations of 7.585 and 3.421 ng/L. Thus, the concentrations of PFOA and PFOS in the surface water were about 8.5 times higher than those in the groundwater, indicating that Σ13PFAS concentrations might decrease with the water migration from the overground to the underground. In addition, significant correlations were observed between PFASs and DOC/TN related to agricultural activities, suggesting a certain relationship existed between PFAS conger distributions and organic carbon/nutrients in water. Health risk assessment indicated that the PFAS exposure caused insignificantly immediate harm to residents in the studied area. This survey provided information on the sources, spatial distribution, and potential migration characteristics of PFASs in a typical agricultural area of North China.

Emissions of agricultural machines during tillage processes played an important role in severe seasonal pollution events in agricultural areas in China and cannot be ignored. In this study, the CO, NOX, HC and PM2.5 emissions of agricultural machines during real-world tillage processes were tested using a portable emission measurement system (PEMS), and their fuel-based and tillage area-based emission factors were calculated. The CO, NOX, HC and PM2.5 emissions were influenced by emission standards, engine rated power, tillage processes and crops. Only the CO, HC and PM2.5 fuel-based emission factors were reduced from China 0 to China II. For China III agricultural machines, the fuel-based emission factors were higher during plowing and tilling than during harvesting. The tillage area-based CO, NOX, HC and PM2.5 emission factors of corn tillage process were 11.85 ± 8.30, 53.21 ± 48.80, 3.46 ± 3.14 and 1.64 ± 1.33 kg/km2, respectively. The tillage area-based CO, NOX, HC and PM2.5 emission factors of wheat tillage process were 19.69 ± 21.50, 79.98 ± 63.22, 3.90 ± 2.96 and 1.61 ± 2.43 kg/km2, respectively. The tillage area-based emission factors of China III agricultural machines during plowing and tilling were higher than those during harvesting. The fuel consumption per unit tillage area can be used to provide a reference for the interconversion of the two emission factors in future studies. By comparing the fuel-based emission factors in this study with those in the Guidelines and other studies, we observed that the CO, HC and PM2.5 emissions of agricultural machines with corresponding emission standards may be overestimated and the NOX emissions may be underestimated in areas where wheat and corn are mainly grown. Moreover, the pollutant emissions of agricultural machines were regionally different. These results could help elucidate the pollution contribution of agricultural machines in China.

A series of lockdown measures in response to the Corona Virus Disease 2019 (COVID-19) outbreak resulted in a drop in anthropogenic emissions and changes in concentrations of PM2.5 and O3. Backward trajectories analysis, cluster analysis, potential source contribution function (PSCF) and concentration weighted trajectory (CWT) technologies were conducted to reveal the characteristics and potential source areas of pollutants in Beijing before the COVID-19 outbreak (BCO period), during the outbreak (COB period) and after the outbreak (ACO period), as well as the contemporaneous period in 2019 (CCO period), which is critical for exploring the efficient control measures and making policy. The results indicated that despite the significant reduction in anthropogenic emissions during the epidemic, the PM2.5 concentrations increased by 1.0% caused by unfavorable meteorological conditions. O3 concentrations increased by 174.8% compared to that during the BCO period due to the increased temperature and inappropriate precursor reduction ratios. A considerable decrease of NO3- in PM2.5 was observed under the influence of significant reductions in vehicle emissions during the lockdown. The cluster analysis revealed that short-range transport played a significant role in the accumulation of local PM2.5 pollution, while long-range northwest airflows contributed more to O3 accumulation, and weakened as the season changed. The PSCF and CWT analysis demonstrated that potential source areas of PM2.5 were mostly located in the central and southern Hebei, the southwestern Shandong in the CCO period, and expanded to central Inner Mongolia and northern Shanxi in the COB period. These areas were highly compatible with the high emission areas of the emission inventory statistics. After the outbreak, the source areas of O3 were centered in the Beijing-Tianjin-Hebei region and Shandong province, with a radial dispersion in all directions, while they were distributed in the central Mongolia and Inner Mongolia during the other periods.

Extreme weather events are happening more frequently as a result of global climate change. Dust storms broke out in the spring of 2017 in China and drastically impacted the local air quality. In this study, a variety of data, including aerosol vertical profiles, surface particle concentration, meteorological parameters, and MODIS–derived aerosol optical depth, as well as backward trajectory analysis, were employed to analyze two dust events from April to May in Beijing. The dust plumes were mainly concentrated below 0.8 km, with peak PM10 values of 1000 μg·m−3 and 300 μg·m−3 in the two cases. The aerosols showed different vertical distribution characteristics. The pure dust in case 1 from 4 to 5 May 2017 had a longer duration (2 days) and presented a larger aerosol extinction coefficient (2.27 km−1 at 355 nm and 1.25 km−1 at 532 nm) than that of the mixed dust in case 2 on 17 April 2017 (2.01 km−1 at 355 nm and 1.33 km−1 at 532 nm). The particle depolarization ratio (PDR) remained constant (0.24 ± 0.03 in case 1) from the surface to 0.8 km in height. In contrast, the PDR profile in the mixed dust (case 2) layer was split into two regions—large values exceeding 0.15 above 0.6 km and small values of 0.11 ± 0.03 below 0.6 km. The influence of meteorological information on aerosol distribution was also investigated, and wind was predominant through the observing period. The pure dust in case 1 was mainly from Mongolia, with strong northwest winds, while the near-surface mixed pollution was caused by the combination of long-transported sand and local emission. Furthermore, lidar-derived profiles of dust mass concentrations in the two cases were presented. This study reveals the vertical characteristics of dust aerosols in the production and dissipation of localized dust events and confirms the efficacy of thorough observations with multiple approaches from the ground to space to monitor dust events in real time.

A database of real-world diesel vehicle emission factors, based on type and technology, has been developed following tests on more than 300 diesel vehicles in China using a portable emission measurement system. The database provides better understanding of diesel vehicle emissions under actual driving conditions. We found that although new regulations have reduced real-world emission levels of diesel trucks and buses significantly for most pollutants in China, NOx emissions have been inadequately controlled by the current standards, especially for diesel buses, because of bad driving conditions in the real world. We also compared the emission factors in the database with those calculated by emission factor models and used in inventory studies. The emission factors derived from COPERT (Computer Programmer to calculate Emissions from Road Transport) and MOBILE may both underestimate real emission factors, whereas the updated COPERT and PART5 (Highway Vehicle Particulate Emission Modeling Software) models may overestimate emission factors in China. Real-world measurement results and emission factors used in recent emission inventory studies are inconsistent, which has led to inaccurate estimates of emissions from diesel trucks and buses over recent years. This suggests that emission factors derived from European or US-based models will not truly represent real-world emissions in China. Therefore, it is useful and necessary to conduct systematic real-world measurements of vehicle emissions in China in order to obtain the optimum inputs for emission inventory models. [ABSTRACT FROM AUTHOR]

To alleviate air pollution and lessen the petroleum demand from the motor vehicle sector in China, natural gas vehicles (NGVs) have been rapidly developed over the last several years. However, the understanding of the real-world emissions of NGVs is very limited. In this study, the emissions from 20 compressed-natural-gas-fueled bi-fuel taxis were measured using a portable emission measurement system (PEMS) under actual driving conditions in Yichang, China. The emission characteristics of the tested vehicles were analyzed, revealing that the average CO2, CO, HC and NOx emissions from the tested compressed-natural-gas (CNG) taxis under urban driving conditions were 1.6, 4.0, 2.0 and 0.98 times those under highway road conditions, respectively. The CO, HC and NOx emissions from Euro 3 CNG vehicles were approximately 40%, 55% and 44% lower than those from Euro 2 vehicles, respectively. Compared with the values for light-duty gasoline vehicles reported in the literature, the CO2 and CO emissions from the tested CNG taxis were clearly lower; however, significant increases in the HC and NOx emissions were observed. Finally, we normalized the emissions under the actual driving cycles of the entire test route to the New European Driving Cycle (NEDC)-based emissions using a VSP modes method developed by North Carolina State University. The simulated NEDC-based CO emissions from the tested CNG taxis were better than the corresponding emissions standards, whereas the simulated NEDC-based HC and NOx emissions greatly exceeded the standards. Thus, more attention should be paid to the emissions from CNG vehicles. As for the CNG-fueled bi-fuel taxis currently in use, the department of environmental protection should strengthen their inspection and supervision to reduce the emissions from these vehicles. The results of this study will be helpful in understanding and controlling emissions from CNG-fueled bi-fuel vehicles in China. [ABSTRACT FROM AUTHOR]

On-board emission measurements were performed on 49 light-duty gasoline vehicles in seven cities of China. Vehicle-specific power mode distribution and emission characteristics were analyzed based on the data collected. The results of our study show that there were significant differences in different types of roads. The emission factors and fuel consumption rates on arterial roads and residential roads were approximately 1.4--2 times those on freeways. The carbon monoxide, hydrocarbon, and nitrogen oxides emission factors of Euro II vehicles were on average 86.2, 88.2, and 64.5% lower than those of carburetor vehicles, respectively. The new vehicle emission standards implemented in China had played an important role in reducing individual vehicle emissions. More comprehensive measures need to be considered to reduce the total amount of emissions from vehicles. [ABSTRACT FROM AUTHOR]