Publications by authors named "Jinkuo Lin"

With the rapid urbanization and industrialization, heavy metal contamination in urban soil and surface dust has received particular attention due to its negative effects on the eco-environment and human health. Contamination and spatio-temporal characteristics, contamination sources, and source apportionment methods, as well as the ecological and health risks of heavy metals in urban soil and surface dust were reviewed. The knowledge gaps in current research and prospects of future works were proposed.

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Analyzing cultivated land input behavior (CLIB) at the scale of rural households links with cultivated land-use efficiency (CLUE), this study examined the Yimeng Mountain area in northern China, supported by field survey data from 737 rural households. This research systematically analyzed the characteristics of CLIB of different types of rural households, measured the CLUE of different types of rural households by using a data envelopment analysis (DEA) model, and explored the influence of CLIB on CLUE based on the Tobit regression model. The results show (1) significant differences in the characteristics of the CLIB of different types of rural households in the Yimeng Mountain area.

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Using typical counties in the Yimeng Mountain area of northern China as an example, this paper analyzed the household and agricultural input characteristics of different types of peasant households using survey data from 262 farm households. The target minimization of the total absolute deviations (MOTAD) model was applied to determine the optimal combinations in the allocation of agricultural input factors and production for different types of at-risk peasant households to obtain the ideal agricultural income. The relevant results are twofold.

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In order to calculate the spatial distribution of high-resolution air-pollutant levels, the land use regression (LUR) model can be an effective method due to the comprehensive consideration of various factors. Traditional LUR models mostly use predefined buffers, which have the disadvantage of not matching high-resolution data well. In order to get a better-fitting model, a few researches have proposed new buffer selection methods.

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Dust storms have a profound impact on the atmospheric environment, global climate change, and human health, so it is of great importance to strengthen related research. The main areas of occurrence and frequency of dust storms in northwestern China were distinguished by measuring the concentration of geochemical elements in the topsoil and atmospheric dust samples, combined with the HYSPLIT backward trajectory model, MODIS true-color satellite images, and PM real-time monitoring data. On this basis, the composite fingerprints method was used to establish an end-member model between the concentration of dust storm samples and topsoil samples, and then to trace the sand and dust sources in northwest China and quantify their source contributions.

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Air pollution events occur frequently in northwest China, which results in serious detrimental effects on human health. Therefore, it is essential to understand the air pollution characteristics and assess the risks to humans. In this study, we analyzed the pollution characteristics of criteria pollutants in six key cities in northwest China from 2015 to 2018.

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Northern China is a significant source of dust source in Central Asia. Thus, high-resolution analysis of dust storms and comparison of dust sources in different regions of northern China are important to clarify the formation mechanism of East Asian dust storms and predict or even prevent such storms. Here, we analyzed spatiotemporal trends in dust storms that occurred in three main dust source regions during 1960-2007: Taklimakan Desert (western region [WR]), Badain Jaran and Tengger Deserts (middle region [MR]), and Otindag Sandy Land (eastern region [ER]).

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Heavy metals in agricultural soil receive much attention because they are easily absorbed by crop into the ecosystem. Managing the discharge of heavy metals from the source is an effective way to prevent and control heavy metals pollution. Grouped principal component analysis (GPCA) and Positive Matrix Factorization (PMF) receptor models were utilized in this study to conduct source apportionment, and the former was optimal because of the accuracy of predicting.

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With the rapid and extensive development of industry and agriculture, the soil environment inevitably becomes contaminated with heavy metals, thus creating adverse environmental conditions for flora and fauna. The traditional methods for combining field sampling with laboratory analysis of soil heavy metals are limited not only because they are time-consuming and expensive, but also because they are unable to obtain adequate information about the spatial distribution characteristics of heavy metals in soil over a large area. Three hundred and ninety-four soil samples (Gobi and farmland) were collected in an arid area in Jiuquan in Northwest China and analyzed for elements concentrations.

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Hexi Corridor is the most important base of commodity grain and producing area for cash crops. However, the rapid development of agriculture and industry has inevitably led to heavy metal contamination in the soils. Multivariate statistical analysis, GIS-based geostatistical methods and Positive Matrix Factorization (PMF) receptor modeling techniques were used to understand the levels of heavy metals and their source apportionment for agricultural soil in Hexi Corridor.

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