ABSTRACT
Microplastics are an emerging contaminant that can persist in the environment for extended periodsï¼ posing risks to ecological systems. Recentlyï¼ microplastic pollution has emerged as a major global environmental problem. In order to ensure accurate and scientific evaluation of the ecological risks associated with microplastic pollutionï¼ it is of paramount importance to improve the simplicity and reliability of microplastic identificationï¼ systematically analyze the pollution characteristics of microplastics in various environmental mediaï¼ and clarify their environmental impacts. Machine learning technology has gained widespread attention in microplastic research by learning and analyzing large volumes of data to establish result evaluation or prediction models. The use of machine learning can enhance the automation and identification efficiency of visual and spectral identification of microplasticsï¼ provide scientific support for tracing the sources of microplastic pollutionï¼ and help reveal the complex environmental effects of microplastics. This review provides a summary of the application characteristics and limitations of machine learning in the aforementioned areas by reviewing the progress made in research that employs machine learning technology in microplastic identification and environmental risk assessment. Furthermoreï¼ the findings of the review will provide suggestions and prospects for the development and application of machine learning in related areas.
ABSTRACT
Forests play an important role in regulating climate change and maintaining carbon balance. To explore the carbon storage and carbon sequestration rate of national forest parks is of great significance for carbon sequestration capacity assessment and sustainable forest management. A process-based ecosystem model (CEVSA2 model) was used to simulate the spatial distribution of carbon density, carbon storage and carbon sequestration rate of 881 national forest parks in China during 1982-2017. The results showed that the average carbon density of national forest parks was 255.18 t C·hm-2, being higher than the average carbon density of forest ecosystem in China. In 2017, the total carbon storage of national forest parks increased to 3.56 Pg C, accounting for 11.0%-12.2% of the total carbon storage in national forest ecosystems. During 1982-2017, the average carbon sequestration rate of national forest parks reached 0.45 t C·hm-2·a-1, and the carbon sequestration rate of all national forest parks was above 0.30 t C·hm-2·a-1. National forest parks in the northeast and southwest of China had the highest total carbon storage. The national forest parks in northeast of China had the highest soil organic carbon sequestration rate, while those in eastern China and central southern China had the highest biomass carbon sequestration rate. The area of national forest parks accounted for 5.8% of the total forest area of China, playing an important role in forest carbon sink management of China. Accurate assessment of the growth status, carbon sequestration potential and carbon absorption characteristics of national forest parks could provide reference for the comprehensive assessment of ecosystem service of forest parks in China.
Subject(s)
Carbon Sequestration , Carbon , Biomass , Carbon/analysis , China , Ecosystem , Forests , Soil , TreesABSTRACT
By using eddy covariance and remote sensing techniques, the relationships between winter wheat soil moisture content and farmland evapotranspiration or canopy temperature were analyzed at field scale under various environmental conditions in the North China Plain. The results showed that when the soil moisture content was below 65% of field capacity, the evaporative fraction under full canopy was low and stable during the middle part of clear days. Under clear sky condition, there was a good non-linear correlation between latent heat flux and crop canopy temperature with diurnal and seasonal patterns. The temperature difference between crop canopy and air as well as the relative evapotranspiration had a close link to the relative moisture content of 0 - 100 cm soil layer. Based on the in situ measurements of daily evapotranspiration amount (ET(d)), daily net radiation flux (Rn(d), mm), average canopy temperature (T(e), degrees C) from 13 : 30 to 14: 00, and daily maximum air temperature (T(a max), degrees C) during the field experiment, the parameters of simplified estimation model for daily evapotranspiration were established.
