Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China.

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 µm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a "V" -shaped oscillation change.

Cultivated Land Input Behavior of Different Types of Rural Households and Its Impact on Cultivated Land-Use Efficiency: A Case Study of the Yimeng Mountain Area, China.

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. Among them, the highest land, labor, and capital inputs were I part-time rural households (I PTRH), followed by full-time rural households (FTRH). In contrast, II part-time rural households (II PTRH) and non-agricultural rural households (NARH) had higher levels of non-agricultural employment; however, their input levels gradually declined. (2) The CLUE of the sample rural households was generally low and had considerable potential for improvement. Regarding the types of rural households, as the degree of part-time employment increased, the CLUE showed an inverted U-shaped trend of first increased and then decreased, namely, I PTRH > FTRH > II PTRH > NARH. This finding indicates that appropriate part-time employment could help to promote investment in agricultural production and improve the CLUE. (3) The CLIB of rural households had significant effects on CLUE; the literacy of the agricultural labor force, yield-increasing input per unit area, per capita household income, share of agricultural income, operation scale of cultivated land, effective irrigation rate of cultivated land, and soil and water conservation rate of cultivated land had positive effects on improving CLUE. Even so, there was still significant heterogeneity in the degree of influence of different rural household types. The study concluded with some policy recommendations from the perspective of different rural household types to provide references for optimizing farming inputs and improving CLUE.

Optimizing Agricultural Input and Production for Different Types of at-Risk Peasant Households: An Empirical Study of Typical Counties in the Yimeng Mountain Area of Northern China.

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. (1) The agricultural input behaviors of different types of peasant households vary significantly. The highest levels of agricultural land, labor, and yield-increasing and labor-saving inputs included I part-time peasant households (I PTPH), followed by full-time peasant households (FTPH), while the input levels of II part-time peasant households (II PTPH) and non-agricultural peasant households (NAPH) with higher levels of non-agricultural employment gradually decreased. In general, an increase in peasant households' part-time employment revealed an inverted U-shaped trend in the agricultural input level, with a trajectory of I PTPH > FTPH > II PTPH > NAPH. (2) The current agricultural inputs and production combinations of different types of peasant households have room for improvement. It is necessary to adjust agricultural inputs and optimize production combinations to obtain target incomes. Overall, all types of peasant households must streamline labor inputs and increase capital inputs, except for I PTPH, for which capital inputs should be reduced. Following optimization, economic crops gradually replace grain crops, and the optimal agricultural incomes of peasant households will be improved. The study results provide practical policy insights for reducing agricultural production risks and improving agricultural production incomes.

Using InVEST to evaluate water yield services in Shangri-La, Northwestern Yunnan, China.

Water yield is an ecosystem service that is vital to not only human life, but also sustainable development of the social economy and ecosystem. This study used annual average precipitation, potential evapotranspiration, plant available water content, soil depth, biophysical parameters, Zhang parameter, and land use/land cover (LULC) as input data for the Integrated Valuation of Ecosystem Service Tradeoffs (InVEST) model to estimate the water yield of Shangri-La City from 1974 to 2015. The spatiotemporal variations and associated factors (precipitation, evapotranspiration, LULC, and topographic factors) in water yield ecosystem services were then analyzed. The result showed that: (1) The water yield of Shangri-La City decreases from north and south to the center and showed a temporal trend from 1974 to 2015 of an initial decrease followed by an increase. Areas of higher average water yield were mainly in Hutiaoxia Town, Jinjiang Town, and Shangjiang Township. (2) Areas of importance for water yield in the study area which need to be assigned priority protection were mainly concentrated in the west of Jiantang Town, in central Xiaozhongdian Town, in central Gezan Township, in northwestern Dongwang Township, and in Hutiaoxia Town. (3) Water yield was affected by precipitation, evapotranspiration, vegetation type, and topographic factors. Water yield was positively and negatively correlated with precipitation and potential evapotranspiration, respectively. The average water yield of shrubs exceeded that of meadows and forests. Terrain factors indirectly affected the ecosystem service functions of water yield by affecting precipitation and vegetation types. The model used in this study can provide references for relevant research in similar climatic conditions.

Assessing the response of vegetation change to drought during 2009-2018 in Yunnan Province, China.

Yunnan Province in southwest China is characterized by a vast area, diverse climate types, rich ecosystem types, and unique biodiversity resources. With consideration of global climate change, there is an urgent need to evaluate the response of vegetation to drought in Yunnan. This study utilized the MOD13A3, MOD17A2, and Tropical Rainfall Measuring Mission (TRMM) 3B43 remote sensing products. The TRMM 3B43 downscaled monthly precipitation data were used to calculate the tropical rainfall condition index (TRCI) for Yunnan. The TRCI was used as a drought index, and the temporal and spatial changes in TRCI, gross primary productivity (GPP), and vegetation condition index (VCI) from 2009 to 2018 were explored. The response of vegetation to drought was evaluated under different time scales and varying land-use types. The results showed that during 2009-2018, (1) at an annual scale, the drought in Yunnan showed a weakening trend, and at a spatial scale, the drought showed a weakening trend from northwest to southeast. This weakening trend was more noticeable for cultivated land than for forest, grassland, and other land-use types. (2) GPP and VCI showed overall increasing trends at an annual scale, indicating gradual improvements in the GPP of vegetation and vegetation status, whereas the summer vegetation index showed a decreasing trend. (3) Although both the GPP and the growth state of vegetation were affected by drought, the responses of GPP and VCI to drought differed under different temporal scales and different land-use types. The responses of GPP and VCI to drought during spring were greater than those over other seasons, and the response of VCI to drought was more sensitive than that of GPP. Drought had a high impact on the GPP and vegetation growth of cultivated land and grassland with shallow root systems, whereas the impact of drought on forest was relatively stable.

[Source Identification and Spatial Distribution of Heavy Metals in Soils in Typical Areas Around the Lower Yellow River].

A total of 234 surface soil samples (0-20 cm) were collected at the nodes of a 2×2 km grid from Gaoqing County (a typical area surrounding the Lower Yellow River) and analyzed for eight heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn). This study investigated the source of the heavy metals in this area based on a correlation analysis, PCA, and ANOVA using multivariate statistical analysis. In addition, the spatial variation and distribution characteristics of the heavy metals were determined by geostatistics based on GIS. The results provided the following conclusions. ① The mean concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn exceeded the background values (BV) of the Lower Yellow River, especially for As, Cu, and Hg (1.23, 1.20 and 1.29 times the BV, respectively), indicating that there was enrichment of the heavy metals in soils at different degrees. ② The results from the multivariate analysis suggested that all eight heavy metals could be classified by two principal components (PCs). The levels of As, Cd, Cr, Cu, Ni, Pb, and Zn were dominated by human activities and the parent soil material (PC1). However, Hg originated mainly from textile printing, petrochemical engineering, and plastic processing (PC2). ③ The differences in heavy metal contents between different land use types and parent soil materials were obvious. The eight elements were highest in land related to urban construction. In addition to Hg, the remaining seven heavy metals were highest in soils in the lacustrine deposit. ④ The spatial distributions of the heavy metals in the soil were different. The high value areas of As, Cd, Cr, Cu, Ni, Pb, and Zn were mainly concentrated in the central urban and southeastern areas, while the high value areas for Hg were concentrated in the southwestern and northeastern areas. This showed that industrial emissions and agricultural production activities caused the degree of heavy metal pollution in the soils while traffic emissions aggravated the levels.

Source identification and spatial distribution of metals in soils in a typical area of the lower Yellow River, eastern China.

In this study, 234 soil samples were recently collected from Gaoqing County (a typical area of the lower Yellow River) to determine the contents of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn. Multivariate statistical analyses such as correlation analysis, principal components analysis, and one-way ANOVA were applied to identify the source of metals in the soil. Geostatistical methods were used to analyze the spatial structure and distribution of the metals. The results indicated that the mean contents of all metals exceeded the background value of the lower Yellow River, especially for As, Cu, and Hg (1.23, 1.20, and 1.29 times that of the BV, respectively), indicating that these metals were enriched in the study area to different degrees. The results derived from multivariate analysis suggested that As, Cd, Cr, Cu, Ni, Pb, and Zn were mainly controlled by the combination of human activities and soil parent material, and the human activities included industrial emissions, traffic emissions, and agricultural practices. In addition, Hg mainly originated from anthropogenic inputs, such as textile printing, plastics processing, and petrochemical engineering. The contents of metals in different types of land use and parent materials are clearly different. The mean content for eight elements in urban construction land was significantly higher than that of the other land use types; in addition to Hg, the mean content of the other elements was the highest in the lacustrine deposit. The elements of As, Cd, Cr, Cu, Ni, Pb, and Zn had similar hotspots in the urban area, indicating the significant human influence. In addition, these seven metals showed high values in the southeast lacustrine deposit area. The high-value areas of Hg were concentrated in the southwest and northeast study area, which were consistent with the spatial pattern of the industrial sites.