[Spatio-temporal Variation in NEP in Ecological Zoning on the Loess Plateau and Its Driving Factors from 2000 to 2021].

Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m2·a)-1. The annual mean NEP for both the whole LP and each of the ecological subregions showed a significant increase trend, and the NEP of the LP increased by 6.10 g·(m2·a)-1 during the study period. The highest growth rate of the NEP was 9.04 g·(m2·a)-1, occurring in the A2 subregion of the loess sorghum gully subregions. The subregion C had the lowest growth rate of 2.74 g·(m2·a)-1. Except for the C subregion, all other ecological subregions (A1, A2, B1, B2, and D) were carbon sinks. ② On the spatial scale, the spatial distribution of annual NEP on the LP was significantly different, with the higher NEP distribution in the southeast of the LP and the lower in the northwest of the LP. The high carbon sink area was mainly distributed in the southern part of the loess sorghum gully subregions, and the carbon source area was mainly distributed in the northern part of the loess sorghum gully subregions and most of the C subregion. The high growth rate was mainly distributed in the central and the southern part of the A2 subregion and the southwest part of the B2 subregion. ③ Human activities had the greatest influence on the temporal variation in NEP in the LP and all the ecological subregions, with the correlation coefficient between human activity data and NEP being above 0.80, and the relative contribution rates of human factors was greater than 50%. The spatial distribution was greatly affected by meteorological factors, among which the precipitation and solar radiation were the main factors affecting the spatial changes in the NEP of the LP. The temporal and spatial variations in the NEP in the LP were influenced by natural and human social factors. To some extent, these results can provide a reference for the terrestrial ecosystem in the LP to reduce emissions and increase sinks and to achieve the goal of double carbon.

[Detection of Influencing Factors of Spatial Variability of Soil Respiration in Pangquangou Nature Reserve].

Soil respiration is an important process in maintaining global carbon balance. Taking the Pangquangou Nature Reserve as the research area, based on the field measurement of soil respiration (Rs) data combined with altitude (ELE), soil temperature (T), soil moisture (SWC), normalized vegetation index (NDVI), slope (slope), soil total carbon (C), total nitrogen (N), and soil bulk density (BD), we analyzed the main driving forces and interactions of Rs spatial differentiation by using the geographic detector model. The results showed that:① the spatial variation of Rs and its influencing factors in the study area was moderate. The Rs was significantly positively correlated with NDVI, T, and N (P<0.01) and negatively with ELE, slope, and SWC (P<0.01). The Rs was significantly correlated with BD(P<0.05) but not with C(P>0.05). ② The multivariate linear model composed of NDVI and T explained 64.3% of Rs spatial variation. ③ ELE, T, and NDVI were the dominant driving forces of Rs spatial differentiation in the study area, which could explain 64%, 59%, and 48% of the spatial variability. ④ The interaction of the two factors enhanced the explanatory power of Rs spatial differentiation, and the maximum interaction factors were ELE∩BD (q=0.73), and T∩slope (q=0.74), respectively. Therefore, in the process of Rs estimation, combined with topographical and environmental conditions, the interaction between multiple factors should be considered.

[Characteristics of Heavy Metal Pollution and Ecological Risk Evaluation of Indoor Dust from Urban and Rural Areas in Taiyuan City During the Heating Season].

The presence of heavy metals in indoor dust is a world-wide concern owing to its negative impact on humans. In this study, we collected indoor dust samples from urban and rural residential areas during the heating season in Taiyuan City. We then identified the concentrations of 11 heavy metals (Cd, Co, Cr, Pb, Mn, Ni, Cu, Zn, V, As, and Hg) using inductively coupled plasma-mass spectrometry. Based on the concentrations, we categorized the pollution levels of indoor dust using the geo-accumulation index and the pollution load index. We further identified the sources of heavy metals using the enrichment factor and principal component analysis. Finally, we evaluated the potential ecological risks of heavy metals via the potential ecological index. The results illustrated that ① with the exception of Co, Mn, and V, the mean concentrations of Cd, Cr, Cu, Ni, Pb, As, Zn, and Hg in indoor dust were higher than the soil background values of Shanxi Province. There was a significant difference (P<0.05) in the concentrations of Co, Cr, Cu, Mn, Ni, and Hg between the urban and rural areas. ② Overall, the pollution degree of heavy metals in indoor dust was identified as moderate in the urban area of Taiyuan City, but slight in the rural area. The indoor dust sample in the urban area was not contaminated by Co, Mn, and V. However, it was slightly polluted by As, Ni, and Hg. In addition, it was close to moderately polluted by Cd, Cr, Cu, Pb, and Zn. In the rural area, the pollution degrees of all the metals, except for Hg and V, in indoor dust were lower than those in the urban area. ③ The As, Cd, Cu, Pb, Zn, and Hg in indoor dust for both urban and rural areas might have mainly originated from anthropogenic sources. The pollution sources were mainly transportation and industry in the urban area and coal combustion and indoor smoking in the rural area. The Co, Cr, Mn, Ni, and V in indoor dust in Taiyuan City might have mainly originated from natural sources. ④ The ecological risk of heavy metal pollution in indoor dust for both the urban and rural areas of Taiyuan City was relatively high, with integrated ecological risk indexes of 359.43 and 471.02 in the urban and rural areas, respectively. In addition, Cd and Hg were the largest contributors.

[Effect of Fertilization on Soil Respiration and Its Temperature Sensitivity in a Sorghum Field].

The characteristics of soil respiration under the condition of fertilization have not been fully understood,especially for a long-term fertilization condition. In this study we measured both soil respiration using an LI-COR-6400-09 soil chamber attached to LI-COR-6400 portable photosynthesis system, and the vegetation spectrum using an ASD FieldSpec HandHeld2, in five different fertilization treatment fields. The soil respiration (Rs) and vegetation spectrum were simultaneously measured with two samples per month in the growing season in 2016 and 2017. The soil temperature at 10 cm depth (T10) and moisture (Ws) for the surface of 10 cm were also measured simultaneously. The five different fertilization treatments included no fertilization (CK), inorganic fertilizer (INF), inorganic fertilizer+organic fertilizer (INF+M), inorganic fertilizer+organic fertilizer+straw turnover (INF+M+S) and organic fertilizer+straw turnover (M+S), and all treatments had been conducted since 2011. Based on those observation data, we made an analysis of Rs and its temperature sensitivity (Q10) in the five different fertilization treatments. The results showed that no significant temporal change in Rs among the five treatments was found. No significant difference was found in Rs between the CK and INF treatments. Compared with the values of Rs in CK and INF, the Rs values in INF+M, M+S, and INF+M+S treatments increased by 28.2%-39.1%, 47.9%-76.0%, and 46.2%-50.8%, respectively. This indicated that use of organic fertilization and straw application increased Rs. Both the Ts and Ws showed 14%-96% and 6%-37% in Rs seasonal variations, respectively. Among the treatments, the correlation coefficient of the fitted equations between Rsand Ts was higher in the INF+M, INF+M+S and M+S treatment than in CK and INF, but was not between Rsand Ws. For the relationship between Rs and vegetation indexes we found that the correlation coefficients between Rs and the difference vegetation index (DVI), ratio vegetation index (RVI), and enhanced vegetation index (EVI), respectively, were higher than that of Rs and the normalized differential vegetation index (NDVI); and that the correlation coefficients between Rsand the red edge slope (Dred) and red edge area (Sred) were higher than between Rs and the red edge position (λred). This indicated that the treatments in INF+M+S increased the correlation coefficient between Rs and the spectrum characteristics index. The determination coefficient of the fitted equations including the feature spectral parameters, T10, and Wsvariables was higher than that of the equations only including both T10 and Ws variables, or a single variable of T10 or Ws. Compared with CK, the Q10 value increased by 26%, 39%, 21%, and 37% for the INF, INF+M, INF+M+S, and M+S treatments, respectively. This indicated that temperature sensitivity Q10 increased under the condition of fertilization treatments. The Shannon diversity index, bulk density, and soil organic matter were the main factors causing the difference in Rs, Q10, and R10, i.e., Rs at a temperature of 10℃, in the different treatments, which could explain the 97.6%, 78.2%, and 92.8% variations in Rs, Q10, and R10, respectively.

[Effect of the Sampling Scale and Number on the Heterogeneity of Soil Respiration in a Mixed Broadleaf-conifer Forest].

By upscaling the observed results at the plot scale, the carbon efflux from soils in a region can be estimated. Therefore, it is very important to investigate the spatial relations of soil respiration (Rs) and its environment and to evaluate the effect of the sampling scale and number on the accuracy of Rs measurement at the spatial scale. Based on field observation data for a mixed broadleaf-conifer forest in the Pangquangou Nature Reserve of the Shanxi Province, two analysis methods, that is, traditional statistics and geostatistics, were used to analyze the influence of the soil water content (Ws), soil temperature (T10), litter mass (Lw), litter moisture content (Lm), soil total carbon (C), total nitrogen (N), and ratio of C/N and sulfur (S) on the Rs heterogeneity at 4, 2, and 1 m sampling scales. The results show no significant differences between the average Rs values for the three sampling scales, but the degree of variation of Rs, which was evaluated based on the coefficient of determination, increases with increasing sampling scales, ranging from 16% to 22%. At the 4 m sampling interval, the correlations between Rs and Ws, Lw, C, and C/N are highly significant (P<0.01) and significant for N (P <0.05). At the 2 m sampling interval, Rs shows a highly negative significant correlation with T10 (P<0.01) and insignificant correlations with the other factors. At the 1 m sampling interval, significant relations between Rs and all other factors were not observed. With the decrease of the sampling interval scale, the spatial autocorrelation of Rs decreases gradually, ranging from high to weak autocorrelations.This indicates that the role the structural factors play decreases with the decrease of the sampling scale, but that of the random factors increases gradually. At the same confidence level for a certain sampling number, the estimated error in Rs decreases with decreasing sampling scale. The analysis of the effect of the sampling number at different sampling scales on the accuracy of Rs shows that the error of Rs at both the 2 and 1 m sampling scales is approximately±12% at the 95% confidence interval and±16% at the 4 m sampling scale. At the 90% confidence interval, the error of Rs at both the 2 and 1 m sampling scales is less than ±10%; at the 4 m scale, it is ±13%. Our results provide insights into how to arrange the sampling sites at the plot scale to measure the seasonal Rs.

[Effects of Microbial Diversity on Soil Carbon Mineralization].

Soil microbial community plays an important role in ecosystem functions; however, little is known about the importance of microbial diversity to the ecosystems. In this study, serial dilution of soil suspension (10-1, 10-3, and 10-5) was performed and inoculated into the sterilized soils that form the broadleaf mixed forests in Pangquangou. The change in the carbon mineralization rate and the pattern of the carbon source utilization were studied by titration, Biolog Eco, and other experimental methods. The results show that after being incubated for six weeks, carbon mineralization rate, the cumulative amount of carbon mineralization, average well color development (AWCD), and diversity index (Shannon, McIntosh, and richness index) of D1 were significantly higher than those of the D5 treatment. The cumulative amount of carbon mineralization and AWCD was strongly and inversely correlated with richness. Principal component analysis and one-way ANOVA also indicated that the patterns of carbon source utilization of microbially diverse soil were different. Therefore, the loss of microbial diversity affects the carbon mineralization rate and the pattern of carbon source utilization, leading to functional changes in terrestrial ecosystems. In the management of forest soils, the effects of soil microbial diversity on ecosystem functions should be considered.

[Interannual Variations of Soil Respiration and Its Temperature Sensitivity in an Orchard in Jinci Region of Taiyuan City].

Jinci area of Taiyuan city is a former hometown of rice, and with the cutoff of the Jinci spring the land use in the area changed fundamentally from original paddy rice to corn or orchard use. So it is very important to investigate soil respiration after land use change and to analyze the relationship between soil respiration (Rs) and soil temperature (Ts) and soil water content (SWC), and to estimate soil carbon dioxide efflux in the region. For this purpose, we measured Rs for seven years (2006 to 2012) with an interval of 1 to 3 times per month from March to December in a field originally for rice but now Chinese jujube, and analyzed seasonal, annual variations of Rs and relationships between Rs and both Ts and SWC. The results showed that the seasonal variations of Rs against day number of the year could be significantly fitted with a three-parameter Gaussian equation while there was no significant correlation between Ts and SWC. Significant exponential relationship between Rs and Ts over the season was found, but not with SWC. Interannual average estimation of soil efflux between March and December from the soil was (5.32±3.31) µmol·(m2·s)-1, and was equal to 1690.2 g·m-2 from the same period ranging from 1294 to 2006 g·m-2. No significant difference in annual efflux was found between the years. The sensitivity of Rs to Ts, Q10 value, ranged from 1.54-2.20, 1.68-2.48 and 1.82-2.46, respectively, for the Ts measurement at 5, 10 and 15 cm depths. The Rs at 10℃, R10, ranged from 2.37 to 2.81, 2.43 to 3.13 and 2.59 to 3.47µmol·(m2·s)-1, respectively, for the Ts measurement at 5, 10 and 15 cm depths. Both the Q10 and R10 increased with increasing Ts measurement depth. In comparison with the fitted one-variable of temperature model, the two-variable model combining both the Ts and SWC together could be well used to predict Rs over the season. Our research results can bear important implications for the study of CO2 efflux in the region and similar regions.

[Effects of Canopy Temperature on Carbon Dioxide Exchange of Winter Wheat in Taiyuan Basin].

We measured daily changes of carbon dioxide exchange in a winter wheat site in Taiyuan basin using chamber method for two years and analyzed relationships between environmental factors affecting carbon exchange of the wheat, including air temperature (Ta), soil temperature (Ts), canopy radiation temperature (Tc) and carbon exchange of the wheat on daily and seasonal scales. The results showed that variations of both Tc and Ta on daily and seasonal scales were consistent with a correlation coefficient of above 0.90. On daily scale, the correlation coefficients of NEE, gross primary productivity (GPP) and ecosystem respiration (Reco) against Ts on most of the measurement days were smaller than those against Ta or Tc, but the correlation coefficients for NEE, GPP and Reco against Ta and Tc showed no difference. On seasonal scale, the relationships between GPP, NEE, Reco and all the temperatures (Ta, Tc and Ts) showed a significant parabola-shape. Optimal temperature of Tc for photosynthesis was slightly lower than that of Ta, but the difference between the optimal Ta and Tc was only about 1℃. Relationship between Reco and Ta for the two-year measurement data was better than that with Ts, but correlation coefficients of Reco with Tc and Ta had nearly no difference ranging from 0.95 to 0.96. Our results could give some implications for ecosystem carbon exchange estimation with remote sensing method based on canopy temperature.

[Spatial Heterogeneity of Soil Respiration in a Planted Larch Forest in Shanxi Plateau].

Based on the data from a planted larch forest in Panquangou Natural Reserve of Shanxi Province, at three sampling scales (4, 2, and 1 m, respectively), soil respiration (Rs) and its affecting factors including soil temperature at 5 cm (T5), 10 cm (T10), and 15 cm (T15) depths, soil water content (Ws), litter mass (Lw), litter moisture (Lm), soil total carbon (C), and soil total nitrogen ( N) were determined. The spatial heterogeneities of Rs and the environmental factors were further analyzed and their intrinsic correlations were established. The results of traditional statistics showed that the spatial variations of Rs and the all measured factors were in the middle range; Rs were highly significantly positively correlated with T10, T15, and N (P < 0.01); significantly positively correlated with Lm (P < 0.05); highly significantly negatively correlated with C/N ratio (P < 0.01); and not significantly correlated with T5, Ws, Lw and C (P > 0.05). Multiple stepwise regression analysis indicated that the four factors of Lm, T10, N, and Ws together accounted for 36% of Rs heterogeneity. The results of geo-statistical analysis demonstrated that Rs was in a medium spatial autocorrelation; random and structural factors accounted for 39.5% and 60.5% of Rs heterogeneity, respectively. And the factors such as climate, landform, and soil played a leading role. The results also illustrated that the ranges for soil factors were different and the range for both Rs and T10 was 25 meters. The fractal dimension of the soil index was in the following order: Lw and C/N ratio (1.95) > N (1.91) > C (1.89) > Rs (1.78) > Lm (1.77 ) > Ws (1.69) > T10 (1.42). The spatial distribution of Rs was in consistent agreement with those of T10, Lm, C, and N; but different with those of Ws and C/N ratio. With a fixed cofidence level and certain estimated accuracy, the required sampling number of each item differed, corresponding to its spatial variation degree.

[Spatial Heterogeneity of Soil Respiration in a Larch Plantation of North China at Different Sampling Scales].

Based on observations of soil respiration rate (Rs) and both biotic and abiotic factors in Pangquangou Nature Reserve at three sampling scales (4, 2, and 1 m), we studied the spatial heterogeneity of Rs and the factors, and analyzed impacts of soil temperature at the 5, 10 and 15 cm depth (T5, T10, T15), soil moisture over the depth of 0-10 cm (Ws), and soil total nitrogen (N), soil total organic carbon (C), ratio of carbon and nitrogen (C/N), soil total sulfur (S), litter fall mass (Lw) and litter fall moisture (Lm) on the spatial heterogeneity of Rs, respectively. We also calculated the minimum sampling number of all the factors at different confidence levels and under the responding estimation accuracy. The results showed that: (1) the spatial heterogeneity of C/N at 4 m sampling scale, Ws at 2 m sampling scale and T10, T15 at 1 m sampling scale had low variability, while the spatial variation of Rs and other related factors had medium variability. Coefficients of variation of Rs, C/N and S decreased with the increase of the sampling scales, but those of N, C, Ws, T5, T10, T15, Lw and Lm showed contrary trend; (2) the spatial autocorrelation of Rs, Ws, T5, T10, T15, Lw and Lm decreased with the decrease of sampling scales but the spatial autocorrelation of C, N, C/N increased with the decrease of sampling scales, and the spatial autocorrelation of S decreased with the decrease of the sampling scales at initial stage and then increased; (3) the key factors that influenced the spatial heterogeneity of soil respiration were different at different sampling scales. Soil temperature was the key factor influencing the spatial heterogeneity of Rs at a larger scale. However, at a smaller scale, the spatial heterogeneity of Rs was influenced by C, Lw and Lm; (4) the minimum sampling number for soil respiration measurement and its influencing factors reduced greatly with the decrease of confidence level and responding estimation accuracy. The sampling numbers of Rs, C/N and S increased with the decrease of sampling scales, while those of N, C, Ws, T5, T10, T15, Lw and Lm decreased.

[Spatial heterogeneity of soil respiration in a subalpine meadow at different sampling scales].

At four different sampling scales (10, 5, 2.5 and 1.25 m) we measured soil respiration and the environmental factors affecting soil respiration in a subalpine meadow at Yundin mountain of Shanxi province. The purpose of the paper was to study the spatial heterogeneities of soil respiration and the environmental factors including soil temperature, soil moisture, total nitrogen, organic carbon, ratio of carbon and nitrogen, and total sulfur. Based on those measurements we analyzed the required sampling number at the four scales. The results showed that spatial variations of the soil respiration and environmental factors at all scales were in the middle range but for the soil temperature at 1.25 m and 2.5 m scales; and that the coefficients of variation in soil respiration and soil temperature increased with increasing sampling scale, but for total nitrogen, organic carbon, total sulfur and soil moisture the coefficients of variation decreased with increasing sampling scales. The environmental factors had different impacts on soil respiration at different sampling scales. Simple correlation analysis showed that at 10 m scale the relationship of soil respiration with total nitrogen (P < 0.01), organic carbon (P < 0.01) and soil temperature (P < 0.05) was significant, but not with total sulfur, C/N and soil moisture; at 5 m sampling scale it was highly significant with total nitrogen, organic carbon, but not with total sulfur, C/N and soil moisture and soil temperature; at 2.5 m scale it was highly significant with total nitrogen, organic carbon and soil moisture, but not with total sulfur, C/N, and soil temperature; and at the smallest sampling scale it was highly significant with total nitrogen, organic carbon and soil moisture, negatively significant with C/N, and negatively significant with soil temperature, but not with total sulfur. The required sampling numbers for 10, 5, 2.5 and 1.25 m sampling scales within ± 10% and ± 20% of its actual mean at the 95% confidence level were 28, 21, 18, 14, and 7, 5, 4, 4, respectively. The results showed a decreasing trend of required sampling number with decreasing samoline scale.

[Studies on spatial heterogeneity of soil respiration in a subalpine meadow].

We measured soil respiration (R) and the environmental factors influencing Rs including soil temperature ( T, ) , soil water content (SWC) and soil organic carbon (SOC) in a subalpine meadow in Yundin mountain of Shanxi province, and performed an analysis of the heterogeneity of Rs, T10 and SWC and SOC as well as their relationships using both traditional statistics and geostatistics methods. The results from traditional statistics showed that the measured data of R and the environmental factors exhibited a normal distribution with variation coefficients ranging between 12% and 24%. The variation of all the measured factors was in the middle range. The fact that the correlation coefficient between Rs, and SOC (r =0. 61) was larger than that between Rs, and T15(r =0. 27) and SWC (r = 0. 26) indicates that the heterogeneity of Rs was controlled mainly by SOC. The results from geostatistics analyses showed that linear model could reflect the spatial characteristics of Rs and the environmental factors. The C0/(C0 + C) values for Rs, T10, SWC and SOC were 41% , 3% , 77% , and 57% , respectively, indicating that the spatial heterogeneities of both RB and T10 were resulted mainly from structural factor, but the spatial heterogeneities of SWC and SOC were controlled mainly by random factor. The range of semi-variogram function was 53. 2 m for Rs, T, and SWC, and 52. 1 m for SOC. The fractal dimension value of Rs, T10, SWC, and SOC was 1. 85, 1. 60, 1. 96, and 1. 95, respectively, indicating that SWC had the weakest spatial dependence on scale and the most complicated spatial distribution pattern, while T10 had the simplest spatial distribution pattern. The spatial distribution of Rs showed a similar distribution character to both SWC and SOC, but had its own regularity. With the decrease of the confidence level and the estimated accuracy, the required sampling number of the Rs and the environmental factors measurements declined substantially.

[Effect of vegetation types on soil respiration characteristics on a smaller scale].

Soil respiration was measured from April 2005 to December 2007 using a LICOR-6400-09 chamber connecting a LiCor-6400 portable photosynthesis system at 3 sites with same elevation and soil texture but different vegetation types. The results indicated that seasonal trend of soil respiration showed a distinct temporal change with the higher values in summer and autumn months and the lower values in winter and spring. Annual means (March to December) of soil respiration for 3 the sampling sites were(3.58 +/- 2.50), (3.82 +/- 2.75) and (4.42 +/- 3.38) micromol x (m2 x s)(-1) (p > 0.05), respectively. Released annual amount (March to December) of CO2 efflux from 3 sites was from 854.9 to 1 297.2 g x (m2 x a)(-1) and the amount was no difference between sites and among years. The fitted exponential equations of soil respiration and soil temperature for 3 sites were all significant with the R2 from 0.61 to 0.81, and the Q10 and R10 calculated from fitted parameters of the equations ranged from 2.60 to 4.50, and from 1.70 to 3.02 micromol x (m2 x s)(-1). The relationships between soil respiration and soil water content were not significant for all 3 sites with a maximum R2 of the regression equations only 0.12 (p > 0.05). However, when the soil temperature was above 10 degrees C, the relationships between soil respiration and soil water content was significant (p < 0.05). Four combined regression equations including soil temperature and soil water content could be used to model relationships between soil respiration and both soil temperature and soil water content together, with the R2 most above 0.7, and maximum of 0.91.