Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018.

Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial-temporal heterogeneity of PM (PM2.5 and PM10) concentration in Heilongjiang Province during 2014-2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCA-GTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO2, NO2, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM.

Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018.

Objective: This study investigated the relationships between PM2.5 and 5 criteria air pollutants (SO2, NO2, PM10, CO, and O3) in Heilongjiang, China, from 2015 to 2018 using global and geographically and temporally weighted regression models. Methods: Ordinary least squares regression (OLS), linear mixed models (LMM), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) were applied to model the relationships between PM2.5 and 5 air pollutants. Results: The LMM and all GWR-based models (i.e., GWR, TWR, and GTWR) showed great advantages over OLS in terms of higher model R2 and more desirable model residuals, especially TWR and GTWR. The GWR, LMM, TWR, and GTWR improved the model explanation power by 3%, 5%, 12%, and 12%, respectively, from the R2 (0.85) of OLS. TWR yielded slightly better model performance than GTWR and reduced the root mean squared errors (RMSE) and mean absolute error (MAE) of the model residuals by 67% compared with OLS; while GWR only reduced RMSE and MAE by 15% against OLS. LMM performed slightly better than GWR by accounting for both temporal autocorrelation between observations over time and spatial heterogeneity across the 13 cities under study, which provided an alternative for modeling PM2.5. Conclusions: The traditional OLS and GWR are inadequate for describing the non-stationarity of PM2.5. The temporal dependence was more important and significant than spatial heterogeneity in our data. Our study provided evidence of spatial-temporal heterogeneity and possible solutions for modeling the relationships between PM2.5 and 5 criteria air pollutants for Heilongjiang province, China.

Effects of floodgates operation on nitrogen transformation in a lake based on structural equation modeling analysis.

Floodgates operation is one of the primary means of flood control in lake development. However, knowledge on the linkages between floodgates operation and nitrogen transformation during the flood season is limited. In this study, water samples from six sampling sites along Lake Xingkai watershed were collected before and after floodgates operation. The causal relationships between environmental factors, bacterioplankton community composition and nitrogen fractions were determined during flood season. We found that concentrations of nitrogen fractions decreased significantly when the floodgates were opened, while the concentrations of total nitrogen (TN) and NO3- increased when the floodgates had been shut for a period. Further, we proposed a possible mechanism that the influence of floodgates operation on nitrogen transformation was largely mediated through changes in dissolved organic matter, dissolved oxygen and bacterioplankton community composition as revealed by structural equation modeling (SEM). We conclude that floodgates operation has a high risk for future eutrophication of downstream watershed, although it can reduce nitrogen content temporarily. Therefore, the environmental impacts of floodgates operation should be carefully evaluated before the floodwaters were discharged into downstream watershed.

Effect of tricarboxylic acid cycle regulator on carbon retention and organic component transformation during food waste composting.

Composting is an environment friendly method to recycling organic waste. However, with the increasing concern about greenhouse gases generated in global atmosphere, it is significant to reduce the emission of carbon dioxide (CO2). This study analyzes tricarboxylic acid (TCA) cycle regulators on the effect of reducing CO2 emission, and the relationship among organic component (OC) degradation and transformation and microorganism during composting. The results showed that adding adenosine tri-phosphate (ATP) and nicotinamide adenine dinucleotide (NADH) could enhance the transformation of OC and increase the diversity of microorganism community. Malonic acid (MA) as a competitive inhibitor could decrease the emission of CO2 by inhibiting the TCA cycle. A structural equation model was established to explore effects of different OC and microorganism on humic acid (HA) concentration during composting. Furthermore, added MA provided an environmental benefit in reducing the greenhouse gas emission for manufacture sustainable products.

Assessing the environmental impact of phenanthrene in different types of land use based on the binding characteristics with dissolved organic matter.

The binding characteristics of phenanthrene with dissolved organic matter (DOM) were studied by the excitation emission matrix fluorescence spectroscopy with parallel factor analysis in four types of land use which derived from forest (F), meadow (M), cropland (C), and greenhouse (G). The results showed that the humification degree and binding characteristics of phenanthrene with DOM were distinct differences in the four soils. The binding capacities of humic-like components with phenanthrene were stronger than those of protein-like components. The log K derived from the Stern-Volmer equation significantly correlated with the humification degree of DOM (p < 0.05) in different types of land use. Besides, correlation analysis demonstrated that the potential binding index (Fk) obtained from the modified Stern-Volmer model was a more accurate parameter to describe the combination degree of DOM with phenanthrene than log K, which presented a decrease order of C > F > M > G. Therefore, the environmental impact of phenanthrene in different types of land use could be assessed deeply based on the Fk and DOM concentration.

Impact of phosphate-solubilizing bacteria inoculation methods on phosphorus transformation and long-term utilization in composting.

This study aimed to assess the effect of phosphate-solubilizing bacteria (PSB) application and inoculation methods on rock phosphate (RP) solubilization and bacterial community during composting. The results showed that PSB inoculation in different stages of composting, especially both in the beginning and cooling stages, not only improved the diversity and abundance of PSB and bacterial community, but also distinctly increased the content of potential available phosphorus. Redundancy analysis indicated that the combined inoculation of PSB in the initial stage with higher inoculation amount and in the cooling stage with lower inoculation amount was the best way to improve the inoculation effect and increase the solubilization and utilization of RP during composting. Besides, we suggested three methods to improve phosphorus transformation and long-term utilization efficiency in composts based on biological fixation of phosphates by humic substance and phosphate-accumulating organisms.

[Spatial distribution of soil total nitrogen in Liangshui National Nature Reserve based on local model].

Based on LiDAR data of Liangshui National Nature Reserve, digital elevation model (DEM) was constructed and both primary terrain attributes (slope, aspect, profile curvature, etc.) and secondary terrain attributes (wetness index, sediment transport index, relative stream power index, etc.) were extracted. According to the theory of soil formation, geographically weighted regression (GWR) was applied to predict soil total nitrogen (TN) of the area, and the predicted results were compared with those of three traditional interpolation methods including inverse distance weighting (IDW), ordinary Kriging (OK) and universal Kriging (UK). Results showed that the prediction accuracy of GWR (77.4%) was higher than that of other three interpolation methods and the accuracy of IDW (69.4%) was higher than that of OK (63.5%) and UK (60.6%). The average of TN predicted by GWR reached 4.82 g . kg-1 in the study area and TN tended to be higher in the region with higher elevation, bigger wetness index and stronger relative stream power index than in other areas. Further, TN also varied partly with various aspects and slopes. Thus, local model using terrain attributes as independent variables was effective in predicting soil attribute distribution.